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['Zhengli Zhao', 'Dheeru Dua', 'Sameer Singh']

1710.11342v2

Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed. Recent work on adversarial examples, i.e. inputs with minor perturbations that result in substantially different model predictions, is helpful in evaluating the robustness of these models by exposing the adversarial scenarios where they fail. However, these malicious perturbations are often unnatural, not semantically meaningful, and not applicable to complicated domains such as language. In this paper, we propose a framework to generate natural and legible adversarial examples that lie on the data manifold, by searching in semantic space of dense and continuous data representation, utilizing the recent advances in generative adversarial networks. We present generated adversaries to demonstrate the potential of the proposed approach for black-box classifiers for a wide range of applications such as image classification, textual entailment, and machine translation. We include experiments to show that the generated adversaries are natural, legible to humans, and useful in evaluating and analyzing black-box classifiers.

Generating Natural Adversarial Examples

http://arxiv.org/pdf/1710.11342v2

Title Generating Natural Adversarial Examples Summary Due complex nature hard characterize way machine learning model misbehave exploited deployed Recent work adversarial example ie input minor perturbation result substantially different model prediction helpful evaluating robustness model exposing adversarial scenario fail However malicious perturbation often unnatural semantically meaningful applicable complicated domain language paper propose framework generate natural legible adversarial example lie data manifold searching semantic space dense continuous data representation utilizing recent advance generative adversarial network present generated adversary demonstrate potential proposed approach blackbox classifier wide range application image classification textual entailment machine translation include experiment show generated adversary natural legible human useful evaluating analyzing blackbox classifier Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Xu Sun', 'Xuancheng Ren', 'Shuming Ma', 'Bingzhen Wei', 'Wei Li', 'Houfeng Wang']

1711.06528v1

We propose a simple yet effective technique to simplify the training and the resulting model of neural networks. In back propagation, only a small subset of the full gradient is computed to update the model parameters. The gradient vectors are sparsified in such a way that only the top-$k$ elements (in terms of magnitude) are kept. As a result, only $k$ rows or columns (depending on the layout) of the weight matrix are modified, leading to a linear reduction in the computational cost. Based on the sparsified gradients, we further simplify the model by eliminating the rows or columns that are seldom updated, which will reduce the computational cost both in the training and decoding, and potentially accelerate decoding in real-world applications. Surprisingly, experimental results demonstrate that most of time we only need to update fewer than 5% of the weights at each back propagation pass. More interestingly, the accuracy of the resulting models is actually improved rather than degraded, and a detailed analysis is given. The model simplification results show that we could adaptively simplify the model which could often be reduced by around 9x, without any loss on accuracy or even with improved accuracy.

Training Simplification and Model Simplification for Deep Learning: A Minimal Effort Back Propagation Method

http://arxiv.org/pdf/1711.06528v1

Title Training Simplification Model Simplification Deep Learning Minimal Effort Back Propagation Method Summary propose simple yet effective technique simplify training resulting model neural network back propagation small subset full gradient computed update model parameter gradient vector sparsified way topk element term magnitude kept result k row column depending layout weight matrix modified leading linear reduction computational cost Based sparsified gradient simplify model eliminating row column seldom updated reduce computational cost training decoding potentially accelerate decoding realworld application Surprisingly experimental result demonstrate time need update fewer 5 weight back propagation pas interestingly accuracy resulting model actually improved rather degraded detailed analysis given model simplification result show could adaptively simplify model could often reduced around 9x without loss accuracy even improved accuracy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Abhishek Das', 'Samyak Datta', 'Georgia Gkioxari', 'Stefan Lee', 'Devi Parikh', 'Dhruv Batra']

1711.11543v2

We present a new AI task -- Embodied Question Answering (EmbodiedQA) -- where an agent is spawned at a random location in a 3D environment and asked a question ("What color is the car?"). In order to answer, the agent must first intelligently navigate to explore the environment, gather information through first-person (egocentric) vision, and then answer the question ("orange"). This challenging task requires a range of AI skills -- active perception, language understanding, goal-driven navigation, commonsense reasoning, and grounding of language into actions. In this work, we develop the environments, end-to-end-trained reinforcement learning agents, and evaluation protocols for EmbodiedQA.

Embodied Question Answering

http://arxiv.org/pdf/1711.11543v2

Title Embodied Question Answering Summary present new AI task Embodied Question Answering EmbodiedQA agent spawned random location 3D environment asked question color car order answer agent must first intelligently navigate explore environment gather information firstperson egocentric vision answer question orange challenging task requires range AI skill active perception language understanding goaldriven navigation commonsense reasoning grounding language action work develop environment endtoendtrained reinforcement learning agent evaluation protocol EmbodiedQA Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Aishwarya Agrawal', 'Dhruv Batra', 'Devi Parikh', 'Aniruddha Kembhavi']

1712.00377v1

A number of studies have found that today's Visual Question Answering (VQA) models are heavily driven by superficial correlations in the training data and lack sufficient image grounding. To encourage development of models geared towards the latter, we propose a new setting for VQA where for every question type, train and test sets have different prior distributions of answers. Specifically, we present new splits of the VQA v1 and VQA v2 datasets, which we call Visual Question Answering under Changing Priors (VQA-CP v1 and VQA-CP v2 respectively). First, we evaluate several existing VQA models under this new setting and show that their performance degrades significantly compared to the original VQA setting. Second, we propose a novel Grounded Visual Question Answering model (GVQA) that contains inductive biases and restrictions in the architecture specifically designed to prevent the model from 'cheating' by primarily relying on priors in the training data. Specifically, GVQA explicitly disentangles the recognition of visual concepts present in the image from the identification of plausible answer space for a given question, enabling the model to more robustly generalize across different distributions of answers. GVQA is built off an existing VQA model -- Stacked Attention Networks (SAN). Our experiments demonstrate that GVQA significantly outperforms SAN on both VQA-CP v1 and VQA-CP v2 datasets. Interestingly, it also outperforms more powerful VQA models such as Multimodal Compact Bilinear Pooling (MCB) in several cases. GVQA offers strengths complementary to SAN when trained and evaluated on the original VQA v1 and VQA v2 datasets. Finally, GVQA is more transparent and interpretable than existing VQA models.

Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering

http://arxiv.org/pdf/1712.00377v1

Title Dont Assume Look Answer Overcoming Priors Visual Question Answering Summary number study found today Visual Question Answering VQA model heavily driven superficial correlation training data lack sufficient image grounding encourage development model geared towards latter propose new setting VQA every question type train test set different prior distribution answer Specifically present new split VQA v1 VQA v2 datasets call Visual Question Answering Changing Priors VQACP v1 VQACP v2 respectively First evaluate several existing VQA model new setting show performance degrades significantly compared original VQA setting Second propose novel Grounded Visual Question Answering model GVQA contains inductive bias restriction architecture specifically designed prevent model cheating primarily relying prior training data Specifically GVQA explicitly disentangles recognition visual concept present image identification plausible answer space given question enabling model robustly generalize across different distribution answer GVQA built existing VQA model Stacked Attention Networks SAN experiment demonstrate GVQA significantly outperforms SAN VQACP v1 VQACP v2 datasets Interestingly also outperforms powerful VQA model Multimodal Compact Bilinear Pooling MCB several case GVQA offer strength complementary SAN trained evaluated original VQA v1 VQA v2 datasets Finally GVQA transparent interpretable existing VQA model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jin-Hwa Kim', 'Devi Parikh', 'Dhruv Batra', 'Byoung-Tak Zhang', 'Yuandong Tian']

1712.05558v1

In this work, we propose a goal-driven collaborative task that contains vision, language, and action in a virtual environment as its core components. Specifically, we develop a collaborative `Image Drawing' game between two agents, called CoDraw. Our game is grounded in a virtual world that contains movable clip art objects. Two players, Teller and Drawer, are involved. The Teller sees an abstract scene containing multiple clip arts in a semantically meaningful configuration, while the Drawer tries to reconstruct the scene on an empty canvas using available clip arts. The two players communicate via two-way communication using natural language. We collect the CoDraw dataset of ~10K dialogs consisting of 138K messages exchanged between a Teller and a Drawer from Amazon Mechanical Turk (AMT). We analyze our dataset and present three models to model the players' behaviors, including an attention model to describe and draw multiple clip arts at each round. The attention models are quantitatively compared to the other models to show how the conventional approaches work for this new task. We also present qualitative visualizations.

CoDraw: Visual Dialog for Collaborative Drawing

http://arxiv.org/pdf/1712.05558v1

Title CoDraw Visual Dialog Collaborative Drawing Summary work propose goaldriven collaborative task contains vision language action virtual environment core component Specifically develop collaborative Image Drawing game two agent called CoDraw game grounded virtual world contains movable clip art object Two player Teller Drawer involved Teller see abstract scene containing multiple clip art semantically meaningful configuration Drawer try reconstruct scene empty canvas using available clip art two player communicate via twoway communication using natural language collect CoDraw dataset 10K dialog consisting 138K message exchanged Teller Drawer Amazon Mechanical Turk AMT analyze dataset present three model model player behavior including attention model describe draw multiple clip art round attention model quantitatively compared model show conventional approach work new task also present qualitative visualization Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sang-Woo Lee', 'Yu-Jung Heo', 'Byoung-Tak Zhang']

1802.03881v1

Goal-oriented dialogue has been paid attention for its numerous applications in artificial intelligence. To solve this task, deep learning and reinforcement learning have recently been applied. However, these approaches struggle to find a competent recurrent neural questioner, owing to the complexity of learning a series of sentences. Motivated by theory of mind, we propose "Answerer in Questioner's Mind" (AQM), a novel algorithm for goal-oriented dialogue. With AQM, a questioner asks and infers based on an approximated probabilistic model of the answerer. The questioner figures out the answerer's intent via selecting a plausible question by explicitly calculating the information gain of the candidate intentions and possible answers to each question. We test our framework on two goal-oriented visual dialogue tasks: "MNIST Counting Dialog" and "GuessWhat?!." In our experiments, AQM outperforms comparative algorithms and makes human-like dialogue. We further use AQM as a tool for analyzing the mechanism of deep reinforcement learning approach and discuss the future direction of practical goal-oriented neural dialogue systems.

Answerer in Questioner's Mind for Goal-Oriented Visual Dialogue

http://arxiv.org/pdf/1802.03881v1

Title Answerer Questioners Mind GoalOriented Visual Dialogue Summary Goaloriented dialogue paid attention numerous application artificial intelligence solve task deep learning reinforcement learning recently applied However approach struggle find competent recurrent neural questioner owing complexity learning series sentence Motivated theory mind propose Answerer Questioners Mind AQM novel algorithm goaloriented dialogue AQM questioner asks infers based approximated probabilistic model answerer questioner figure answerer intent via selecting plausible question explicitly calculating information gain candidate intention possible answer question test framework two goaloriented visual dialogue task MNIST Counting Dialog GuessWhat experiment AQM outperforms comparative algorithm make humanlike dialogue use AQM tool analyzing mechanism deep reinforcement learning approach discus future direction practical goaloriented neural dialogue system Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Tolga Bolukbasi', 'Kai-Wei Chang', 'Joseph Wang', 'Venkatesh Saligrama']

1602.08761v2

We study the problem of structured prediction under test-time budget constraints. We propose a novel approach applicable to a wide range of structured prediction problems in computer vision and natural language processing. Our approach seeks to adaptively generate computationally costly features during test-time in order to reduce the computational cost of prediction while maintaining prediction performance. We show that training the adaptive feature generation system can be reduced to a series of structured learning problems, resulting in efficient training using existing structured learning algorithms. This framework provides theoretical justification for several existing heuristic approaches found in literature. We evaluate our proposed adaptive system on two structured prediction tasks, optical character recognition (OCR) and dependency parsing and show strong performance in reduction of the feature costs without degrading accuracy.

Resource Constrained Structured Prediction

http://arxiv.org/pdf/1602.08761v2

Title Resource Constrained Structured Prediction Summary study problem structured prediction testtime budget constraint propose novel approach applicable wide range structured prediction problem computer vision natural language processing approach seek adaptively generate computationally costly feature testtime order reduce computational cost prediction maintaining prediction performance show training adaptive feature generation system reduced series structured learning problem resulting efficient training using existing structured learning algorithm framework provides theoretical justification several existing heuristic approach found literature evaluate proposed adaptive system two structured prediction task optical character recognition OCR dependency parsing show strong performance reduction feature cost without degrading accuracy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hongyuan Mei', 'Mohit Bansal', 'Matthew R. Walter']

1506.04089v4

We propose a neural sequence-to-sequence model for direction following, a task that is essential to realizing effective autonomous agents. Our alignment-based encoder-decoder model with long short-term memory recurrent neural networks (LSTM-RNN) translates natural language instructions to action sequences based upon a representation of the observable world state. We introduce a multi-level aligner that empowers our model to focus on sentence "regions" salient to the current world state by using multiple abstractions of the input sentence. In contrast to existing methods, our model uses no specialized linguistic resources (e.g., parsers) or task-specific annotations (e.g., seed lexicons). It is therefore generalizable, yet still achieves the best results reported to-date on a benchmark single-sentence dataset and competitive results for the limited-training multi-sentence setting. We analyze our model through a series of ablations that elucidate the contributions of the primary components of our model.

Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences

http://arxiv.org/pdf/1506.04089v4

Title Listen Attend Walk Neural Mapping Navigational Instructions Action Sequences Summary propose neural sequencetosequence model direction following task essential realizing effective autonomous agent alignmentbased encoderdecoder model long shortterm memory recurrent neural network LSTMRNN translates natural language instruction action sequence based upon representation observable world state introduce multilevel aligner empowers model focus sentence region salient current world state using multiple abstraction input sentence contrast existing method model us specialized linguistic resource eg parser taskspecific annotation eg seed lexicon therefore generalizable yet still achieves best result reported todate benchmark singlesentence dataset competitive result limitedtraining multisentence setting analyze model series ablation elucidate contribution primary component model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Lili Mou', 'Zhengdong Lu', 'Hang Li', 'Zhi Jin']

1612.02741v4

Building neural networks to query a knowledge base (a table) with natural language is an emerging research topic in deep learning. An executor for table querying typically requires multiple steps of execution because queries may have complicated structures. In previous studies, researchers have developed either fully distributed executors or symbolic executors for table querying. A distributed executor can be trained in an end-to-end fashion, but is weak in terms of execution efficiency and explicit interpretability. A symbolic executor is efficient in execution, but is very difficult to train especially at initial stages. In this paper, we propose to couple distributed and symbolic execution for natural language queries, where the symbolic executor is pretrained with the distributed executor's intermediate execution results in a step-by-step fashion. Experiments show that our approach significantly outperforms both distributed and symbolic executors, exhibiting high accuracy, high learning efficiency, high execution efficiency, and high interpretability.

Coupling Distributed and Symbolic Execution for Natural Language Queries

http://arxiv.org/pdf/1612.02741v4

Title Coupling Distributed Symbolic Execution Natural Language Queries Summary Building neural network query knowledge base table natural language emerging research topic deep learning executor table querying typically requires multiple step execution query may complicated structure previous study researcher developed either fully distributed executor symbolic executor table querying distributed executor trained endtoend fashion weak term execution efficiency explicit interpretability symbolic executor efficient execution difficult train especially initial stage paper propose couple distributed symbolic execution natural language query symbolic executor pretrained distributed executor intermediate execution result stepbystep fashion Experiments show approach significantly outperforms distributed symbolic executor exhibiting high accuracy high learning efficiency high execution efficiency high interpretability Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Christian Napoli', 'Giuseppe Pappalardo', 'Emiliano Tramontana']

1409.8484v1

Due to the huge availability of documents in digital form, and the deception possibility raise bound to the essence of digital documents and the way they are spread, the authorship attribution problem has constantly increased its relevance. Nowadays, authorship attribution,for both information retrieval and analysis, has gained great importance in the context of security, trust and copyright preservation. This work proposes an innovative multi-agent driven machine learning technique that has been developed for authorship attribution. By means of a preprocessing for word-grouping and time-period related analysis of the common lexicon, we determine a bias reference level for the recurrence frequency of the words within analysed texts, and then train a Radial Basis Neural Networks (RBPNN)-based classifier to identify the correct author. The main advantage of the proposed approach lies in the generality of the semantic analysis, which can be applied to different contexts and lexical domains, without requiring any modification. Moreover, the proposed system is able to incorporate an external input, meant to tune the classifier, and then self-adjust by means of continuous learning reinforcement.

An agent-driven semantical identifier using radial basis neural networks and reinforcement learning

http://arxiv.org/pdf/1409.8484v1

Title agentdriven semantical identifier using radial basis neural network reinforcement learning Summary Due huge availability document digital form deception possibility raise bound essence digital document way spread authorship attribution problem constantly increased relevance Nowadays authorship attributionfor information retrieval analysis gained great importance context security trust copyright preservation work proposes innovative multiagent driven machine learning technique developed authorship attribution mean preprocessing wordgrouping timeperiod related analysis common lexicon determine bias reference level recurrence frequency word within analysed text train Radial Basis Neural Networks RBPNNbased classifier identify correct author main advantage proposed approach lie generality semantic analysis applied different context lexical domain without requiring modification Moreover proposed system able incorporate external input meant tune classifier selfadjust mean continuous learning reinforcement Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Karla Stepanova', 'Matej Hoffmann', 'Zdenek Straka', 'Frederico B. Klein', 'Angelo Cangelosi', 'Michal Vavrecka']

1706.02490v1

Humans and animals are constantly exposed to a continuous stream of sensory information from different modalities. At the same time, they form more compressed representations like concepts or symbols. In species that use language, this process is further structured by this interaction, where a mapping between the sensorimotor concepts and linguistic elements needs to be established. There is evidence that children might be learning language by simply disambiguating potential meanings based on multiple exposures to utterances in different contexts (cross-situational learning). In existing models, the mapping between modalities is usually found in a single step by directly using frequencies of referent and meaning co-occurrences. In this paper, we present an extension of this one-step mapping and introduce a newly proposed sequential mapping algorithm together with a publicly available Matlab implementation. For demonstration, we have chosen a less typical scenario: instead of learning to associate objects with their names, we focus on body representations. A humanoid robot is receiving tactile stimulations on its body, while at the same time listening to utterances of the body part names (e.g., hand, forearm and torso). With the goal at arriving at the correct "body categories", we demonstrate how a sequential mapping algorithm outperforms one-step mapping. In addition, the effect of data set size and noise in the linguistic input are studied.

Where is my forearm? Clustering of body parts from simultaneous tactile and linguistic input using sequential mapping

http://arxiv.org/pdf/1706.02490v1

Title forearm Clustering body part simultaneous tactile linguistic input using sequential mapping Summary Humans animal constantly exposed continuous stream sensory information different modality time form compressed representation like concept symbol specie use language process structured interaction mapping sensorimotor concept linguistic element need established evidence child might learning language simply disambiguating potential meaning based multiple exposure utterance different context crosssituational learning existing model mapping modality usually found single step directly using frequency referent meaning cooccurrences paper present extension onestep mapping introduce newly proposed sequential mapping algorithm together publicly available Matlab implementation demonstration chosen le typical scenario instead learning associate object name focus body representation humanoid robot receiving tactile stimulation body time listening utterance body part name eg hand forearm torso goal arriving correct body category demonstrate sequential mapping algorithm outperforms onestep mapping addition effect data set size noise linguistic input studied Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Tara N. Sainath', 'Brian Kingsbury', 'Abdel-rahman Mohamed', 'George E. Dahl', 'George Saon', 'Hagen Soltau', 'Tomas Beran', 'Aleksandr Y. Aravkin', 'Bhuvana Ramabhadran']

1309.1501v3

Deep Convolutional Neural Networks (CNNs) are more powerful than Deep Neural Networks (DNN), as they are able to better reduce spectral variation in the input signal. This has also been confirmed experimentally, with CNNs showing improvements in word error rate (WER) between 4-12% relative compared to DNNs across a variety of LVCSR tasks. In this paper, we describe different methods to further improve CNN performance. First, we conduct a deep analysis comparing limited weight sharing and full weight sharing with state-of-the-art features. Second, we apply various pooling strategies that have shown improvements in computer vision to an LVCSR speech task. Third, we introduce a method to effectively incorporate speaker adaptation, namely fMLLR, into log-mel features. Fourth, we introduce an effective strategy to use dropout during Hessian-free sequence training. We find that with these improvements, particularly with fMLLR and dropout, we are able to achieve an additional 2-3% relative improvement in WER on a 50-hour Broadcast News task over our previous best CNN baseline. On a larger 400-hour BN task, we find an additional 4-5% relative improvement over our previous best CNN baseline.

Improvements to deep convolutional neural networks for LVCSR

http://arxiv.org/pdf/1309.1501v3

Title Improvements deep convolutional neural network LVCSR Summary Deep Convolutional Neural Networks CNNs powerful Deep Neural Networks DNN able better reduce spectral variation input signal also confirmed experimentally CNNs showing improvement word error rate WER 412 relative compared DNNs across variety LVCSR task paper describe different method improve CNN performance First conduct deep analysis comparing limited weight sharing full weight sharing stateoftheart feature Second apply various pooling strategy shown improvement computer vision LVCSR speech task Third introduce method effectively incorporate speaker adaptation namely fMLLR logmel feature Fourth introduce effective strategy use dropout Hessianfree sequence training find improvement particularly fMLLR dropout able achieve additional 23 relative improvement WER 50hour Broadcast News task previous best CNN baseline larger 400hour BN task find additional 45 relative improvement previous best CNN baseline Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hao Wang', 'Naiyan Wang', 'Dit-Yan Yeung']

1409.2944v2

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recent advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art.

Collaborative Deep Learning for Recommender Systems

http://arxiv.org/pdf/1409.2944v2

Title Collaborative Deep Learning Recommender Systems Summary Collaborative filtering CF successful approach commonly used many recommender system Conventional CFbased method use rating given item user sole source information learning make recommendation However rating often sparse many application causing CFbased method degrade significantly recommendation performance address sparsity problem auxiliary information item content information may utilized Collaborative topic regression CTR appealing recent method taking approach tightly couple two component learn two different source information Nevertheless latent representation learned CTR may effective auxiliary information sparse address problem generalize recent advance deep learning iid input noniid CFbased input propose paper hierarchical Bayesian model called collaborative deep learning CDL jointly performs deep representation learning content information collaborative filtering rating feedback matrix Extensive experiment three realworld datasets different domain show CDL significantly advance state art Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Leila Arras', 'Franziska Horn', 'Grégoire Montavon', 'Klaus-Robert Müller', 'Wojciech Samek']

1606.07298v1

Layer-wise relevance propagation (LRP) is a recently proposed technique for explaining predictions of complex non-linear classifiers in terms of input variables. In this paper, we apply LRP for the first time to natural language processing (NLP). More precisely, we use it to explain the predictions of a convolutional neural network (CNN) trained on a topic categorization task. Our analysis highlights which words are relevant for a specific prediction of the CNN. We compare our technique to standard sensitivity analysis, both qualitatively and quantitatively, using a "word deleting" perturbation experiment, a PCA analysis, and various visualizations. All experiments validate the suitability of LRP for explaining the CNN predictions, which is also in line with results reported in recent image classification studies.

Explaining Predictions of Non-Linear Classifiers in NLP

http://arxiv.org/pdf/1606.07298v1

Title Explaining Predictions NonLinear Classifiers NLP Summary Layerwise relevance propagation LRP recently proposed technique explaining prediction complex nonlinear classifier term input variable paper apply LRP first time natural language processing NLP precisely use explain prediction convolutional neural network CNN trained topic categorization task analysis highlight word relevant specific prediction CNN compare technique standard sensitivity analysis qualitatively quantitatively using word deleting perturbation experiment PCA analysis various visualization experiment validate suitability LRP explaining CNN prediction also line result reported recent image classification study Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Vasily Pestun', 'Yiannis Vlassopoulos']

1710.10248v2

We propose a new statistical model suitable for machine learning of systems with long distance correlations such as natural languages. The model is based on directed acyclic graph decorated by multi-linear tensor maps in the vertices and vector spaces in the edges, called tensor network. Such tensor networks have been previously employed for effective numerical computation of the renormalization group flow on the space of effective quantum field theories and lattice models of statistical mechanics. We provide explicit algebro-geometric analysis of the parameter moduli space for tree graphs, discuss model properties and applications such as statistical translation.

Tensor network language model

http://arxiv.org/pdf/1710.10248v2

Title Tensor network language model Summary propose new statistical model suitable machine learning system long distance correlation natural language model based directed acyclic graph decorated multilinear tensor map vertex vector space edge called tensor network tensor network previously employed effective numerical computation renormalization group flow space effective quantum field theory lattice model statistical mechanic provide explicit algebrogeometric analysis parameter modulus space tree graph discus model property application statistical translation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Vasily Pestun', 'John Terilla', 'Yiannis Vlassopoulos']

1711.01416v1

We propose a statistical model for natural language that begins by considering language as a monoid, then representing it in complex matrices with a compatible translation invariant probability measure. We interpret the probability measure as arising via the Born rule from a translation invariant matrix product state.

Language as a matrix product state

http://arxiv.org/pdf/1711.01416v1

Title Language matrix product state Summary propose statistical model natural language begin considering language monoid representing complex matrix compatible translation invariant probability measure interpret probability measure arising via Born rule translation invariant matrix product state Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Tara N. Sainath', 'Lior Horesh', 'Brian Kingsbury', 'Aleksandr Y. Aravkin', 'Bhuvana Ramabhadran']

1309.1508v3

Hessian-free training has become a popular parallel second or- der optimization technique for Deep Neural Network training. This study aims at speeding up Hessian-free training, both by means of decreasing the amount of data used for training, as well as through reduction of the number of Krylov subspace solver iterations used for implicit estimation of the Hessian. In this paper, we develop an L-BFGS based preconditioning scheme that avoids the need to access the Hessian explicitly. Since L-BFGS cannot be regarded as a fixed-point iteration, we further propose the employment of flexible Krylov subspace solvers that retain the desired theoretical convergence guarantees of their conventional counterparts. Second, we propose a new sampling algorithm, which geometrically increases the amount of data utilized for gradient and Krylov subspace iteration calculations. On a 50-hr English Broadcast News task, we find that these methodologies provide roughly a 1.5x speed-up, whereas, on a 300-hr Switchboard task, these techniques provide over a 2.3x speedup, with no loss in WER. These results suggest that even further speed-up is expected, as problems scale and complexity grows.

Accelerating Hessian-free optimization for deep neural networks by implicit preconditioning and sampling

http://arxiv.org/pdf/1309.1508v3

Title Accelerating Hessianfree optimization deep neural network implicit preconditioning sampling Summary Hessianfree training become popular parallel second der optimization technique Deep Neural Network training study aim speeding Hessianfree training mean decreasing amount data used training well reduction number Krylov subspace solver iteration used implicit estimation Hessian paper develop LBFGS based preconditioning scheme avoids need access Hessian explicitly Since LBFGS cannot regarded fixedpoint iteration propose employment flexible Krylov subspace solver retain desired theoretical convergence guarantee conventional counterpart Second propose new sampling algorithm geometrically increase amount data utilized gradient Krylov subspace iteration calculation 50hr English Broadcast News task find methodology provide roughly 15x speedup whereas 300hr Switchboard task technique provide 23x speedup loss WER result suggest even speedup expected problem scale complexity grows Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Roberto Camacho Barranco', 'Laura M. Rodriguez', 'Rebecca Urbina', 'M. Shahriar Hossain']

1606.07496v1

While textual reviews have become prominent in many recommendation-based systems, automated frameworks to provide relevant visual cues against text reviews where pictures are not available is a new form of task confronted by data mining and machine learning researchers. Suggestions of pictures that are relevant to the content of a review could significantly benefit the users by increasing the effectiveness of a review. We propose a deep learning-based framework to automatically: (1) tag the images available in a review dataset, (2) generate a caption for each image that does not have one, and (3) enhance each review by recommending relevant images that might not be uploaded by the corresponding reviewer. We evaluate the proposed framework using the Yelp Challenge Dataset. While a subset of the images in this particular dataset are correctly captioned, the majority of the pictures do not have any associated text. Moreover, there is no mapping between reviews and images. Each image has a corresponding business-tag where the picture was taken, though. The overall data setting and unavailability of crucial pieces required for a mapping make the problem of recommending images for reviews a major challenge. Qualitative and quantitative evaluations indicate that our proposed framework provides high quality enhancements through automatic captioning, tagging, and recommendation for mapping reviews and images.

Is a Picture Worth Ten Thousand Words in a Review Dataset?

http://arxiv.org/pdf/1606.07496v1

Title Picture Worth Ten Thousand Words Review Dataset Summary textual review become prominent many recommendationbased system automated framework provide relevant visual cue text review picture available new form task confronted data mining machine learning researcher Suggestions picture relevant content review could significantly benefit user increasing effectiveness review propose deep learningbased framework automatically 1 tag image available review dataset 2 generate caption image one 3 enhance review recommending relevant image might uploaded corresponding reviewer evaluate proposed framework using Yelp Challenge Dataset subset image particular dataset correctly captioned majority picture associated text Moreover mapping review image image corresponding businesstag picture taken though overall data setting unavailability crucial piece required mapping make problem recommending image review major challenge Qualitative quantitative evaluation indicate proposed framework provides high quality enhancement automatic captioning tagging recommendation mapping review image Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Matthias Scholz']

1204.0684v1

Linear principal component analysis (PCA) can be extended to a nonlinear PCA by using artificial neural networks. But the benefit of curved components requires a careful control of the model complexity. Moreover, standard techniques for model selection, including cross-validation and more generally the use of an independent test set, fail when applied to nonlinear PCA because of its inherent unsupervised characteristics. This paper presents a new approach for validating the complexity of nonlinear PCA models by using the error in missing data estimation as a criterion for model selection. It is motivated by the idea that only the model of optimal complexity is able to predict missing values with the highest accuracy. While standard test set validation usually favours over-fitted nonlinear PCA models, the proposed model validation approach correctly selects the optimal model complexity.

Validation of nonlinear PCA

http://arxiv.org/pdf/1204.0684v1

Title Validation nonlinear PCA Summary Linear principal component analysis PCA extended nonlinear PCA using artificial neural network benefit curved component requires careful control model complexity Moreover standard technique model selection including crossvalidation generally use independent test set fail applied nonlinear PCA inherent unsupervised characteristic paper present new approach validating complexity nonlinear PCA model using error missing data estimation criterion model selection motivated idea model optimal complexity able predict missing value highest accuracy standard test set validation usually favour overfitted nonlinear PCA model proposed model validation approach correctly selects optimal model complexity Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ethan Fetaya', 'Ohad Shamir', 'Shimon Ullman']

1406.2602v1

We consider the problem of learning from a similarity matrix (such as spectral clustering and lowd imensional embedding), when computing pairwise similarities are costly, and only a limited number of entries can be observed. We provide a theoretical analysis using standard notions of graph approximation, significantly generalizing previous results (which focused on spectral clustering with two clusters). We also propose a new algorithmic approach based on adaptive sampling, which experimentally matches or improves on previous methods, while being considerably more general and computationally cheaper.

Graph Approximation and Clustering on a Budget

http://arxiv.org/pdf/1406.2602v1

Title Graph Approximation Clustering Budget Summary consider problem learning similarity matrix spectral clustering lowd imensional embedding computing pairwise similarity costly limited number entry observed provide theoretical analysis using standard notion graph approximation significantly generalizing previous result focused spectral clustering two cluster also propose new algorithmic approach based adaptive sampling experimentally match improves previous method considerably general computationally cheaper Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Shai Shalev-Shwartz', 'Yonatan Wexler', 'Amnon Shashua']

1109.0820v1

Multiclass prediction is the problem of classifying an object into a relevant target class. We consider the problem of learning a multiclass predictor that uses only few features, and in particular, the number of used features should increase sub-linearly with the number of possible classes. This implies that features should be shared by several classes. We describe and analyze the ShareBoost algorithm for learning a multiclass predictor that uses few shared features. We prove that ShareBoost efficiently finds a predictor that uses few shared features (if such a predictor exists) and that it has a small generalization error. We also describe how to use ShareBoost for learning a non-linear predictor that has a fast evaluation time. In a series of experiments with natural data sets we demonstrate the benefits of ShareBoost and evaluate its success relatively to other state-of-the-art approaches.

ShareBoost: Efficient Multiclass Learning with Feature Sharing

http://arxiv.org/pdf/1109.0820v1

Title ShareBoost Efficient Multiclass Learning Feature Sharing Summary Multiclass prediction problem classifying object relevant target class consider problem learning multiclass predictor us feature particular number used feature increase sublinearly number possible class implies feature shared several class describe analyze ShareBoost algorithm learning multiclass predictor us shared feature prove ShareBoost efficiently find predictor us shared feature predictor exists small generalization error also describe use ShareBoost learning nonlinear predictor fast evaluation time series experiment natural data set demonstrate benefit ShareBoost evaluate success relatively stateoftheart approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Nan Lin', 'Junhai Jiang', 'Shicheng Guo', 'Momiao Xiong']

1408.0204v1

Due to advances in sensors, growing large and complex medical image data have the ability to visualize the pathological change in the cellular or even the molecular level or anatomical changes in tissues and organs. As a consequence, the medical images have the potential to enhance diagnosis of disease, prediction of clinical outcomes, characterization of disease progression, management of health care and development of treatments, but also pose great methodological and computational challenges for representation and selection of features in image cluster analysis. To address these challenges, we first extend one dimensional functional principal component analysis to the two dimensional functional principle component analyses (2DFPCA) to fully capture space variation of image signals. Image signals contain a large number of redundant and irrelevant features which provide no additional or no useful information for cluster analysis. Widely used methods for removing redundant and irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on how to select penalty parameters and a threshold for selecting features. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attention in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image cluster analysis. The proposed method is applied to ovarian and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis.

Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis

http://arxiv.org/pdf/1408.0204v1

Title Functional Principal Component Analysis Randomized Sparse Clustering Algorithm Medical Image Analysis Summary Due advance sensor growing large complex medical image data ability visualize pathological change cellular even molecular level anatomical change tissue organ consequence medical image potential enhance diagnosis disease prediction clinical outcome characterization disease progression management health care development treatment also pose great methodological computational challenge representation selection feature image cluster analysis address challenge first extend one dimensional functional principal component analysis two dimensional functional principle component analysis 2DFPCA fully capture space variation image signal Image signal contain large number redundant irrelevant feature provide additional useful information cluster analysis Widely used method removing redundant irrelevant feature sparse clustering algorithm using lassotype penalty select feature However accuracy clustering using lassotype penalty depends select penalty parameter threshold selecting feature practice difficult determine Recently randomized algorithm received great deal attention big data analysis paper present randomized algorithm accurate feature selection image cluster analysis proposed method applied ovarian kidney cancer histology image data TCGA database result demonstrate randomized feature selection method coupled functional principal component analysis substantially outperforms current sparse clustering algorithm image cluster analysis Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Liwen Zhang', 'Subhransu Maji', 'Ryota Tomioka']

1503.01521v3

Similarity between objects is multi-faceted and it can be easier for human annotators to measure it when the focus is on a specific aspect. We consider the problem of mapping objects into view-specific embeddings where the distance between them is consistent with the similarity comparisons of the form "from the t-th view, object A is more similar to B than to C". Our framework jointly learns view-specific embeddings exploiting correlations between views. Experiments on a number of datasets, including one of multi-view crowdsourced comparison on bird images, show the proposed method achieves lower triplet generalization error when compared to both learning embeddings independently for each view and all views pooled into one view. Our method can also be used to learn multiple measures of similarity over input features taking class labels into account and compares favorably to existing approaches for multi-task metric learning on the ISOLET dataset.

Jointly Learning Multiple Measures of Similarities from Triplet Comparisons

http://arxiv.org/pdf/1503.01521v3

Title Jointly Learning Multiple Measures Similarities Triplet Comparisons Summary Similarity object multifaceted easier human annotator measure focus specific aspect consider problem mapping object viewspecific embeddings distance consistent similarity comparison form tth view object similar B C framework jointly learns viewspecific embeddings exploiting correlation view Experiments number datasets including one multiview crowdsourced comparison bird image show proposed method achieves lower triplet generalization error compared learning embeddings independently view view pooled one view method also used learn multiple measure similarity input feature taking class label account compare favorably existing approach multitask metric learning ISOLET dataset Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Andreas C. Damianou', 'Michalis K. Titsias', 'Neil D. Lawrence']

1409.2287v1

The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dimensionality reduction that has been widely applied. However, the current approach for training GP-LVMs is based on maximum likelihood, where the latent projection variables are maximized over rather than integrated out. In this paper we present a Bayesian method for training GP-LVMs by introducing a non-standard variational inference framework that allows to approximately integrate out the latent variables and subsequently train a GP-LVM by maximizing an analytic lower bound on the exact marginal likelihood. We apply this method for learning a GP-LVM from iid observations and for learning non-linear dynamical systems where the observations are temporally correlated. We show that a benefit of the variational Bayesian procedure is its robustness to overfitting and its ability to automatically select the dimensionality of the nonlinear latent space. The resulting framework is generic, flexible and easy to extend for other purposes, such as Gaussian process regression with uncertain inputs and semi-supervised Gaussian processes. We demonstrate our method on synthetic data and standard machine learning benchmarks, as well as challenging real world datasets, including high resolution video data.

Variational Inference for Uncertainty on the Inputs of Gaussian Process Models

http://arxiv.org/pdf/1409.2287v1

Title Variational Inference Uncertainty Inputs Gaussian Process Models Summary Gaussian process latent variable model GPLVM provides flexible approach nonlinear dimensionality reduction widely applied However current approach training GPLVMs based maximum likelihood latent projection variable maximized rather integrated paper present Bayesian method training GPLVMs introducing nonstandard variational inference framework allows approximately integrate latent variable subsequently train GPLVM maximizing analytic lower bound exact marginal likelihood apply method learning GPLVM iid observation learning nonlinear dynamical system observation temporally correlated show benefit variational Bayesian procedure robustness overfitting ability automatically select dimensionality nonlinear latent space resulting framework generic flexible easy extend purpose Gaussian process regression uncertain input semisupervised Gaussian process demonstrate method synthetic data standard machine learning benchmark well challenging real world datasets including high resolution video data Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mehdi Mirza', 'Simon Osindero']

1411.1784v1

Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags which are not part of training labels.

Conditional Generative Adversarial Nets

http://arxiv.org/pdf/1411.1784v1

Title Conditional Generative Adversarial Nets Summary Generative Adversarial Nets 8 recently introduced novel way train generative model work introduce conditional version generative adversarial net constructed simply feeding data wish condition generator discriminator show model generate MNIST digit conditioned class label also illustrate model could used learn multimodal model provide preliminary example application image tagging demonstrate approach generate descriptive tag part training label Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Krzysztof Chalupka', 'Pietro Perona', 'Frederick Eberhardt']

1412.2309v2

We provide a rigorous definition of the visual cause of a behavior that is broadly applicable to the visually driven behavior in humans, animals, neurons, robots and other perceiving systems. Our framework generalizes standard accounts of causal learning to settings in which the causal variables need to be constructed from micro-variables. We prove the Causal Coarsening Theorem, which allows us to gain causal knowledge from observational data with minimal experimental effort. The theorem provides a connection to standard inference techniques in machine learning that identify features of an image that correlate with, but may not cause, the target behavior. Finally, we propose an active learning scheme to learn a manipulator function that performs optimal manipulations on the image to automatically identify the visual cause of a target behavior. We illustrate our inference and learning algorithms in experiments based on both synthetic and real data.

Visual Causal Feature Learning

http://arxiv.org/pdf/1412.2309v2

Title Visual Causal Feature Learning Summary provide rigorous definition visual cause behavior broadly applicable visually driven behavior human animal neuron robot perceiving system framework generalizes standard account causal learning setting causal variable need constructed microvariables prove Causal Coarsening Theorem allows u gain causal knowledge observational data minimal experimental effort theorem provides connection standard inference technique machine learning identify feature image correlate may cause target behavior Finally propose active learning scheme learn manipulator function performs optimal manipulation image automatically identify visual cause target behavior illustrate inference learning algorithm experiment based synthetic real data Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Behnam Neyshabur', 'Ryota Tomioka', 'Nathan Srebro']

1412.6614v4

We present experiments demonstrating that some other form of capacity control, different from network size, plays a central role in learning multilayer feed-forward networks. We argue, partially through analogy to matrix factorization, that this is an inductive bias that can help shed light on deep learning.

In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning

http://arxiv.org/pdf/1412.6614v4

Title Search Real Inductive Bias Role Implicit Regularization Deep Learning Summary present experiment demonstrating form capacity control different network size play central role learning multilayer feedforward network argue partially analogy matrix factorization inductive bias help shed light deep learning Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Muhammad Ghifary', 'W. Bastiaan Kleijn', 'Mengjie Zhang', 'David Balduzzi']

1508.07680v1

The problem of domain generalization is to take knowledge acquired from a number of related domains where training data is available, and to then successfully apply it to previously unseen domains. We propose a new feature learning algorithm, Multi-Task Autoencoder (MTAE), that provides good generalization performance for cross-domain object recognition. Our algorithm extends the standard denoising autoencoder framework by substituting artificially induced corruption with naturally occurring inter-domain variability in the appearance of objects. Instead of reconstructing images from noisy versions, MTAE learns to transform the original image into analogs in multiple related domains. It thereby learns features that are robust to variations across domains. The learnt features are then used as inputs to a classifier. We evaluated the performance of the algorithm on benchmark image recognition datasets, where the task is to learn features from multiple datasets and to then predict the image label from unseen datasets. We found that (denoising) MTAE outperforms alternative autoencoder-based models as well as the current state-of-the-art algorithms for domain generalization.

Domain Generalization for Object Recognition with Multi-task Autoencoders

http://arxiv.org/pdf/1508.07680v1

Title Domain Generalization Object Recognition Multitask Autoencoders Summary problem domain generalization take knowledge acquired number related domain training data available successfully apply previously unseen domain propose new feature learning algorithm MultiTask Autoencoder MTAE provides good generalization performance crossdomain object recognition algorithm extends standard denoising autoencoder framework substituting artificially induced corruption naturally occurring interdomain variability appearance object Instead reconstructing image noisy version MTAE learns transform original image analog multiple related domain thereby learns feature robust variation across domain learnt feature used input classifier evaluated performance algorithm benchmark image recognition datasets task learn feature multiple datasets predict image label unseen datasets found denoising MTAE outperforms alternative autoencoderbased model well current stateoftheart algorithm domain generalization Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['John-Alexander M. Assael', 'Niklas Wahlström', 'Thomas B. Schön', 'Marc Peter Deisenroth']

1510.02173v2

Data-efficient reinforcement learning (RL) in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. We consider a particularly important instance of this challenge, the pixels-to-torques problem, where an RL agent learns a closed-loop control policy ("torques") from pixel information only. We introduce a data-efficient, model-based reinforcement learning algorithm that learns such a closed-loop policy directly from pixel information. The key ingredient is a deep dynamical model for learning a low-dimensional feature embedding of images jointly with a predictive model in this low-dimensional feature space. Joint learning is crucial for long-term predictions, which lie at the core of the adaptive nonlinear model predictive control strategy that we use for closed-loop control. Compared to state-of-the-art RL methods for continuous states and actions, our approach learns quickly, scales to high-dimensional state spaces, is lightweight and an important step toward fully autonomous end-to-end learning from pixels to torques.

Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models

http://arxiv.org/pdf/1510.02173v2

Title DataEfficient Learning Feedback Policies Image Pixels using Deep Dynamical Models Summary Dataefficient reinforcement learning RL continuous stateaction space using highdimensional observation remains key challenge developing fully autonomous system consider particularly important instance challenge pixelstotorques problem RL agent learns closedloop control policy torque pixel information introduce dataefficient modelbased reinforcement learning algorithm learns closedloop policy directly pixel information key ingredient deep dynamical model learning lowdimensional feature embedding image jointly predictive model lowdimensional feature space Joint learning crucial longterm prediction lie core adaptive nonlinear model predictive control strategy use closedloop control Compared stateoftheart RL method continuous state action approach learns quickly scale highdimensional state space lightweight important step toward fully autonomous endtoend learning pixel torque Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Muhammad Ghifary', 'David Balduzzi', 'W. Bastiaan Kleijn', 'Mengjie Zhang']

1510.04373v2

This paper addresses classification tasks on a particular target domain in which labeled training data are only available from source domains different from (but related to) the target. Two closely related frameworks, domain adaptation and domain generalization, are concerned with such tasks, where the only difference between those frameworks is the availability of the unlabeled target data: domain adaptation can leverage unlabeled target information, while domain generalization cannot. We propose Scatter Component Analyis (SCA), a fast representation learning algorithm that can be applied to both domain adaptation and domain generalization. SCA is based on a simple geometrical measure, i.e., scatter, which operates on reproducing kernel Hilbert space. SCA finds a representation that trades between maximizing the separability of classes, minimizing the mismatch between domains, and maximizing the separability of data; each of which is quantified through scatter. The optimization problem of SCA can be reduced to a generalized eigenvalue problem, which results in a fast and exact solution. Comprehensive experiments on benchmark cross-domain object recognition datasets verify that SCA performs much faster than several state-of-the-art algorithms and also provides state-of-the-art classification accuracy in both domain adaptation and domain generalization. We also show that scatter can be used to establish a theoretical generalization bound in the case of domain adaptation.

Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization

http://arxiv.org/pdf/1510.04373v2

Title Scatter Component Analysis Unified Framework Domain Adaptation Domain Generalization Summary paper address classification task particular target domain labeled training data available source domain different related target Two closely related framework domain adaptation domain generalization concerned task difference framework availability unlabeled target data domain adaptation leverage unlabeled target information domain generalization cannot propose Scatter Component Analyis SCA fast representation learning algorithm applied domain adaptation domain generalization SCA based simple geometrical measure ie scatter operates reproducing kernel Hilbert space SCA find representation trade maximizing separability class minimizing mismatch domain maximizing separability data quantified scatter optimization problem SCA reduced generalized eigenvalue problem result fast exact solution Comprehensive experiment benchmark crossdomain object recognition datasets verify SCA performs much faster several stateoftheart algorithm also provides stateoftheart classification accuracy domain adaptation domain generalization also show scatter used establish theoretical generalization bound case domain adaptation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Zhao Kang', 'Chong Peng', 'Qiang Cheng']

1510.08971v1

Matrix rank minimization problem is in general NP-hard. The nuclear norm is used to substitute the rank function in many recent studies. Nevertheless, the nuclear norm approximation adds all singular values together and the approximation error may depend heavily on the magnitudes of singular values. This might restrict its capability in dealing with many practical problems. In this paper, an arctangent function is used as a tighter approximation to the rank function. We use it on the challenging subspace clustering problem. For this nonconvex minimization problem, we develop an effective optimization procedure based on a type of augmented Lagrange multipliers (ALM) method. Extensive experiments on face clustering and motion segmentation show that the proposed method is effective for rank approximation.

Robust Subspace Clustering via Tighter Rank Approximation

http://arxiv.org/pdf/1510.08971v1

Title Robust Subspace Clustering via Tighter Rank Approximation Summary Matrix rank minimization problem general NPhard nuclear norm used substitute rank function many recent study Nevertheless nuclear norm approximation add singular value together approximation error may depend heavily magnitude singular value might restrict capability dealing many practical problem paper arctangent function used tighter approximation rank function use challenging subspace clustering problem nonconvex minimization problem develop effective optimization procedure based type augmented Lagrange multiplier ALM method Extensive experiment face clustering motion segmentation show proposed method effective rank approximation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Amogh Gudi']

1512.00743v2

The human face constantly conveys information, both consciously and subconsciously. However, as basic as it is for humans to visually interpret this information, it is quite a big challenge for machines. Conventional semantic facial feature recognition and analysis techniques are already in use and are based on physiological heuristics, but they suffer from lack of robustness and high computation time. This thesis aims to explore ways for machines to learn to interpret semantic information available in faces in an automated manner without requiring manual design of feature detectors, using the approach of Deep Learning. This thesis provides a study of the effects of various factors and hyper-parameters of deep neural networks in the process of determining an optimal network configuration for the task of semantic facial feature recognition. This thesis explores the effectiveness of the system to recognize the various semantic features (like emotions, age, gender, ethnicity etc.) present in faces. Furthermore, the relation between the effect of high-level concepts on low level features is explored through an analysis of the similarities in low-level descriptors of different semantic features. This thesis also demonstrates a novel idea of using a deep network to generate 3-D Active Appearance Models of faces from real-world 2-D images. For a more detailed report on this work, please see [arXiv:1512.00743v1].

Recognizing Semantic Features in Faces using Deep Learning

http://arxiv.org/pdf/1512.00743v2

Title Recognizing Semantic Features Faces using Deep Learning Summary human face constantly conveys information consciously subconsciously However basic human visually interpret information quite big challenge machine Conventional semantic facial feature recognition analysis technique already use based physiological heuristic suffer lack robustness high computation time thesis aim explore way machine learn interpret semantic information available face automated manner without requiring manual design feature detector using approach Deep Learning thesis provides study effect various factor hyperparameters deep neural network process determining optimal network configuration task semantic facial feature recognition thesis explores effectiveness system recognize various semantic feature like emotion age gender ethnicity etc present face Furthermore relation effect highlevel concept low level feature explored analysis similarity lowlevel descriptor different semantic feature thesis also demonstrates novel idea using deep network generate 3D Active Appearance Models face realworld 2D image detailed report work please see arXiv151200743v1 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Muhammad Ghifary', 'W. Bastiaan Kleijn', 'Mengjie Zhang', 'David Balduzzi', 'Wen Li']

1607.03516v2

In this paper, we propose a novel unsupervised domain adaptation algorithm based on deep learning for visual object recognition. Specifically, we design a new model called Deep Reconstruction-Classification Network (DRCN), which jointly learns a shared encoding representation for two tasks: i) supervised classification of labeled source data, and ii) unsupervised reconstruction of unlabeled target data.In this way, the learnt representation not only preserves discriminability, but also encodes useful information from the target domain. Our new DRCN model can be optimized by using backpropagation similarly as the standard neural networks. We evaluate the performance of DRCN on a series of cross-domain object recognition tasks, where DRCN provides a considerable improvement (up to ~8% in accuracy) over the prior state-of-the-art algorithms. Interestingly, we also observe that the reconstruction pipeline of DRCN transforms images from the source domain into images whose appearance resembles the target dataset. This suggests that DRCN's performance is due to constructing a single composite representation that encodes information about both the structure of target images and the classification of source images. Finally, we provide a formal analysis to justify the algorithm's objective in domain adaptation context.

Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation

http://arxiv.org/pdf/1607.03516v2

Title Deep ReconstructionClassification Networks Unsupervised Domain Adaptation Summary paper propose novel unsupervised domain adaptation algorithm based deep learning visual object recognition Specifically design new model called Deep ReconstructionClassification Network DRCN jointly learns shared encoding representation two task supervised classification labeled source data ii unsupervised reconstruction unlabeled target dataIn way learnt representation preserve discriminability also encodes useful information target domain new DRCN model optimized using backpropagation similarly standard neural network evaluate performance DRCN series crossdomain object recognition task DRCN provides considerable improvement 8 accuracy prior stateoftheart algorithm Interestingly also observe reconstruction pipeline DRCN transforms image source domain image whose appearance resembles target dataset suggests DRCNs performance due constructing single composite representation encodes information structure target image classification source image Finally provide formal analysis justify algorithm objective domain adaptation context Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Po-Hsuan Chen', 'Xia Zhu', 'Hejia Zhang', 'Javier S. Turek', 'Janice Chen', 'Theodore L. Willke', 'Uri Hasson', 'Peter J. Ramadge']

1608.04846v1

Finding the most effective way to aggregate multi-subject fMRI data is a long-standing and challenging problem. It is of increasing interest in contemporary fMRI studies of human cognition due to the scarcity of data per subject and the variability of brain anatomy and functional response across subjects. Recent work on latent factor models shows promising results in this task but this approach does not preserve spatial locality in the brain. We examine two ways to combine the ideas of a factor model and a searchlight based analysis to aggregate multi-subject fMRI data while preserving spatial locality. We first do this directly by combining a recent factor method known as a shared response model with searchlight analysis. Then we design a multi-view convolutional autoencoder for the same task. Both approaches preserve spatial locality and have competitive or better performance compared with standard searchlight analysis and the shared response model applied across the whole brain. We also report a system design to handle the computational challenge of training the convolutional autoencoder.

A Convolutional Autoencoder for Multi-Subject fMRI Data Aggregation

http://arxiv.org/pdf/1608.04846v1

Title Convolutional Autoencoder MultiSubject fMRI Data Aggregation Summary Finding effective way aggregate multisubject fMRI data longstanding challenging problem increasing interest contemporary fMRI study human cognition due scarcity data per subject variability brain anatomy functional response across subject Recent work latent factor model show promising result task approach preserve spatial locality brain examine two way combine idea factor model searchlight based analysis aggregate multisubject fMRI data preserving spatial locality first directly combining recent factor method known shared response model searchlight analysis design multiview convolutional autoencoder task approach preserve spatial locality competitive better performance compared standard searchlight analysis shared response model applied across whole brain also report system design handle computational challenge training convolutional autoencoder Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yun Wang', 'Xu Chen', 'Peter J. Ramadge']

1608.06010v2

One way to solve lasso problems when the dictionary does not fit into available memory is to first screen the dictionary to remove unneeded features. Prior research has shown that sequential screening methods offer the greatest promise in this endeavor. Most existing work on sequential screening targets the context of tuning parameter selection, where one screens and solves a sequence of $N$ lasso problems with a fixed grid of geometrically spaced regularization parameters. In contrast, we focus on the scenario where a target regularization parameter has already been chosen via cross-validated model selection, and we then need to solve many lasso instances using this fixed value. In this context, we propose and explore a feedback controlled sequential screening scheme. Feedback is used at each iteration to select the next problem to be solved. This allows the sequence of problems to be adapted to the instance presented and the number of intermediate problems to be automatically selected. We demonstrate our feedback scheme using several datasets including a dictionary of approximate size 100,000 by 300,000.

Feedback-Controlled Sequential Lasso Screening

http://arxiv.org/pdf/1608.06010v2

Title FeedbackControlled Sequential Lasso Screening Summary One way solve lasso problem dictionary fit available memory first screen dictionary remove unneeded feature Prior research shown sequential screening method offer greatest promise endeavor existing work sequential screening target context tuning parameter selection one screen solves sequence N lasso problem fixed grid geometrically spaced regularization parameter contrast focus scenario target regularization parameter already chosen via crossvalidated model selection need solve many lasso instance using fixed value context propose explore feedback controlled sequential screening scheme Feedback used iteration select next problem solved allows sequence problem adapted instance presented number intermediate problem automatically selected demonstrate feedback scheme using several datasets including dictionary approximate size 100000 300000 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yun Wang', 'Peter J. Ramadge']

1608.06014v2

Recently dictionary screening has been proposed as an effective way to improve the computational efficiency of solving the lasso problem, which is one of the most commonly used method for learning sparse representations. To address today's ever increasing large dataset, effective screening relies on a tight region bound on the solution to the dual lasso. Typical region bounds are in the form of an intersection of a sphere and multiple half spaces. One way to tighten the region bound is using more half spaces, which however, adds to the overhead of solving the high dimensional optimization problem in lasso screening. This paper reveals the interesting property that the optimization problem only depends on the projection of features onto the subspace spanned by the normals of the half spaces. This property converts an optimization problem in high dimension to much lower dimension, and thus sheds light on reducing the computation overhead of lasso screening based on tighter region bounds.

The Symmetry of a Simple Optimization Problem in Lasso Screening

http://arxiv.org/pdf/1608.06014v2

Title Symmetry Simple Optimization Problem Lasso Screening Summary Recently dictionary screening proposed effective way improve computational efficiency solving lasso problem one commonly used method learning sparse representation address today ever increasing large dataset effective screening relies tight region bound solution dual lasso Typical region bound form intersection sphere multiple half space One way tighten region bound using half space however add overhead solving high dimensional optimization problem lasso screening paper reveals interesting property optimization problem depends projection feature onto subspace spanned normal half space property convert optimization problem high dimension much lower dimension thus shed light reducing computation overhead lasso screening based tighter region bound Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Maxime Bucher', 'Stéphane Herbin', 'Frédéric Jurie']

1608.07441v1

Zero-Shot learning has been shown to be an efficient strategy for domain adaptation. In this context, this paper builds on the recent work of Bucher et al. [1], which proposed an approach to solve Zero-Shot classification problems (ZSC) by introducing a novel metric learning based objective function. This objective function allows to learn an optimal embedding of the attributes jointly with a measure of similarity between images and attributes. This paper extends their approach by proposing several schemes to control the generation of the negative pairs, resulting in a significant improvement of the performance and giving above state-of-the-art results on three challenging ZSC datasets.

Hard Negative Mining for Metric Learning Based Zero-Shot Classification

http://arxiv.org/pdf/1608.07441v1

Title Hard Negative Mining Metric Learning Based ZeroShot Classification Summary ZeroShot learning shown efficient strategy domain adaptation context paper build recent work Bucher et al 1 proposed approach solve ZeroShot classification problem ZSC introducing novel metric learning based objective function objective function allows learn optimal embedding attribute jointly measure similarity image attribute paper extends approach proposing several scheme control generation negative pair resulting significant improvement performance giving stateoftheart result three challenging ZSC datasets Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Xiang Xiang', 'Trac D. Tran']

1609.07042v4

In this paper, we deal with two challenges for measuring the similarity of the subject identities in practical video-based face recognition - the variation of the head pose in uncontrolled environments and the computational expense of processing videos. Since the frame-wise feature mean is unable to characterize the pose diversity among frames, we define and preserve the overall pose diversity and closeness in a video. Then, identity will be the only source of variation across videos since the pose varies even within a single video. Instead of simply using all the frames, we select those faces whose pose point is closest to the centroid of the K-means cluster containing that pose point. Then, we represent a video as a bag of frame-wise deep face features while the number of features has been reduced from hundreds to K. Since the video representation can well represent the identity, now we measure the subject similarity between two videos as the max correlation among all possible pairs in the two bags of features. On the official 5,000 video-pairs of the YouTube Face dataset for face verification, our algorithm achieves a comparable performance with VGG-face that averages over deep features of all frames. Other vision tasks can also benefit from the generic idea of employing geometric cues to improve the descriptiveness of deep features.

Pose-Selective Max Pooling for Measuring Similarity

http://arxiv.org/pdf/1609.07042v4

Title PoseSelective Max Pooling Measuring Similarity Summary paper deal two challenge measuring similarity subject identity practical videobased face recognition variation head pose uncontrolled environment computational expense processing video Since framewise feature mean unable characterize pose diversity among frame define preserve overall pose diversity closeness video identity source variation across video since pose varies even within single video Instead simply using frame select face whose pose point closest centroid Kmeans cluster containing pose point represent video bag framewise deep face feature number feature reduced hundred K Since video representation well represent identity measure subject similarity two video max correlation among possible pair two bag feature official 5000 videopairs YouTube Face dataset face verification algorithm achieves comparable performance VGGface average deep feature frame vision task also benefit generic idea employing geometric cue improve descriptiveness deep feature Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Shehroz S. Khan', 'Babak Taati']

1610.03761v3

A fall is an abnormal activity that occurs rarely, so it is hard to collect real data for falls. It is, therefore, difficult to use supervised learning methods to automatically detect falls. Another challenge in using machine learning methods to automatically detect falls is the choice of engineered features. In this paper, we propose to use an ensemble of autoencoders to extract features from different channels of wearable sensor data trained only on normal activities. We show that the traditional approach of choosing a threshold as the maximum of the reconstruction error on the training normal data is not the right way to identify unseen falls. We propose two methods for automatic tightening of reconstruction error from only the normal activities for better identification of unseen falls. We present our results on two activity recognition datasets and show the efficacy of our proposed method against traditional autoencoder models and two standard one-class classification methods.

Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble of Autoencoders

http://arxiv.org/pdf/1610.03761v3

Title Detecting Unseen Falls Wearable Devices using Channelwise Ensemble Autoencoders Summary fall abnormal activity occurs rarely hard collect real data fall therefore difficult use supervised learning method automatically detect fall Another challenge using machine learning method automatically detect fall choice engineered feature paper propose use ensemble autoencoders extract feature different channel wearable sensor data trained normal activity show traditional approach choosing threshold maximum reconstruction error training normal data right way identify unseen fall propose two method automatic tightening reconstruction error normal activity better identification unseen fall present result two activity recognition datasets show efficacy proposed method traditional autoencoder model two standard oneclass classification method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jure Sokolic', 'Raja Giryes', 'Guillermo Sapiro', 'Miguel R. D. Rodrigues']

1610.04574v3

This paper studies the generalization error of invariant classifiers. In particular, we consider the common scenario where the classification task is invariant to certain transformations of the input, and that the classifier is constructed (or learned) to be invariant to these transformations. Our approach relies on factoring the input space into a product of a base space and a set of transformations. We show that whereas the generalization error of a non-invariant classifier is proportional to the complexity of the input space, the generalization error of an invariant classifier is proportional to the complexity of the base space. We also derive a set of sufficient conditions on the geometry of the base space and the set of transformations that ensure that the complexity of the base space is much smaller than the complexity of the input space. Our analysis applies to general classifiers such as convolutional neural networks. We demonstrate the implications of the developed theory for such classifiers with experiments on the MNIST and CIFAR-10 datasets.

Generalization Error of Invariant Classifiers

http://arxiv.org/pdf/1610.04574v3

Title Generalization Error Invariant Classifiers Summary paper study generalization error invariant classifier particular consider common scenario classification task invariant certain transformation input classifier constructed learned invariant transformation approach relies factoring input space product base space set transformation show whereas generalization error noninvariant classifier proportional complexity input space generalization error invariant classifier proportional complexity base space also derive set sufficient condition geometry base space set transformation ensure complexity base space much smaller complexity input space analysis applies general classifier convolutional neural network demonstrate implication developed theory classifier experiment MNIST CIFAR10 datasets Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Seyed-Mohsen Moosavi-Dezfooli', 'Alhussein Fawzi', 'Omar Fawzi', 'Pascal Frossard']

1610.08401v3

Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be misclassified with high probability. We propose a systematic algorithm for computing universal perturbations, and show that state-of-the-art deep neural networks are highly vulnerable to such perturbations, albeit being quasi-imperceptible to the human eye. We further empirically analyze these universal perturbations and show, in particular, that they generalize very well across neural networks. The surprising existence of universal perturbations reveals important geometric correlations among the high-dimensional decision boundary of classifiers. It further outlines potential security breaches with the existence of single directions in the input space that adversaries can possibly exploit to break a classifier on most natural images.

Universal adversarial perturbations

http://arxiv.org/pdf/1610.08401v3

Title Universal adversarial perturbation Summary Given stateoftheart deep neural network classifier show existence universal imageagnostic small perturbation vector cause natural image misclassified high probability propose systematic algorithm computing universal perturbation show stateoftheart deep neural network highly vulnerable perturbation albeit quasiimperceptible human eye empirically analyze universal perturbation show particular generalize well across neural network surprising existence universal perturbation reveals important geometric correlation among highdimensional decision boundary classifier outline potential security breach existence single direction input space adversary possibly exploit break classifier natural image Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Xiang Xiang', 'Trac D. Tran']

1701.03102v1

Limited annotated data available for the recognition of facial expression and action units embarrasses the training of deep networks, which can learn disentangled invariant features. However, a linear model with just several parameters normally is not demanding in terms of training data. In this paper, we propose an elegant linear model to untangle confounding factors in challenging realistic multichannel signals such as 2D face videos. The simple yet powerful model does not rely on huge training data and is natural for recognizing facial actions without explicitly disentangling the identity. Base on well-understood intuitive linear models such as Sparse Representation based Classification (SRC), previous attempts require a prepossessing of explicit decoupling which is practically inexact. Instead, we exploit the low-rank property across frames to subtract the underlying neutral faces which are modeled jointly with sparse representation on the action components with group sparsity enforced. On the extended Cohn-Kanade dataset (CK+), our one-shot automatic method on raw face videos performs as competitive as SRC applied on manually prepared action components and performs even better than SRC in terms of true positive rate. We apply the model to the even more challenging task of facial action unit recognition, verified on the MPI Face Video Database (MPI-VDB) achieving a decent performance. All the programs and data have been made publicly available.

Linear Disentangled Representation Learning for Facial Actions

http://arxiv.org/pdf/1701.03102v1

Title Linear Disentangled Representation Learning Facial Actions Summary Limited annotated data available recognition facial expression action unit embarrasses training deep network learn disentangled invariant feature However linear model several parameter normally demanding term training data paper propose elegant linear model untangle confounding factor challenging realistic multichannel signal 2D face video simple yet powerful model rely huge training data natural recognizing facial action without explicitly disentangling identity Base wellunderstood intuitive linear model Sparse Representation based Classification SRC previous attempt require prepossessing explicit decoupling practically inexact Instead exploit lowrank property across frame subtract underlying neutral face modeled jointly sparse representation action component group sparsity enforced extended CohnKanade dataset CK oneshot automatic method raw face video performs competitive SRC applied manually prepared action component performs even better SRC term true positive rate apply model even challenging task facial action unit recognition verified MPI Face Video Database MPIVDB achieving decent performance program data made publicly available Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jan Hendrik Metzen', 'Tim Genewein', 'Volker Fischer', 'Bastian Bischoff']

1702.04267v2

Machine learning and deep learning in particular has advanced tremendously on perceptual tasks in recent years. However, it remains vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system while being quasi-imperceptible to a human. In this work, we propose to augment deep neural networks with a small "detector" subnetwork which is trained on the binary classification task of distinguishing genuine data from data containing adversarial perturbations. Our method is orthogonal to prior work on addressing adversarial perturbations, which has mostly focused on making the classification network itself more robust. We show empirically that adversarial perturbations can be detected surprisingly well even though they are quasi-imperceptible to humans. Moreover, while the detectors have been trained to detect only a specific adversary, they generalize to similar and weaker adversaries. In addition, we propose an adversarial attack that fools both the classifier and the detector and a novel training procedure for the detector that counteracts this attack.

On Detecting Adversarial Perturbations

http://arxiv.org/pdf/1702.04267v2

Title Detecting Adversarial Perturbations Summary Machine learning deep learning particular advanced tremendously perceptual task recent year However remains vulnerable adversarial perturbation input crafted specifically fool system quasiimperceptible human work propose augment deep neural network small detector subnetwork trained binary classification task distinguishing genuine data data containing adversarial perturbation method orthogonal prior work addressing adversarial perturbation mostly focused making classification network robust show empirically adversarial perturbation detected surprisingly well even though quasiimperceptible human Moreover detector trained detect specific adversary generalize similar weaker adversary addition propose adversarial attack fool classifier detector novel training procedure detector counteracts attack Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Zhiming Zhou', 'Han Cai', 'Shu Rong', 'Yuxuan Song', 'Kan Ren', 'Weinan Zhang', 'Yong Yu', 'Jun Wang']

1703.02000v7

Class labels have been empirically shown useful in improving the sample quality of generative adversarial nets (GANs). In this paper, we mathematically study the properties of the current variants of GANs that make use of class label information. With class aware gradient and cross-entropy decomposition, we reveal how class labels and associated losses influence GAN's training. Based on that, we propose Activation Maximization Generative Adversarial Networks (AM-GAN) as an advanced solution. Comprehensive experiments have been conducted to validate our analysis and evaluate the effectiveness of our solution, where AM-GAN outperforms other strong baselines and achieves state-of-the-art Inception Score (8.91) on CIFAR-10. In addition, we demonstrate that, with the Inception ImageNet classifier, Inception Score mainly tracks the diversity of the generator, and there is, however, no reliable evidence that it can reflect the true sample quality. We thus propose a new metric, called AM Score, to provide more accurate estimation on the sample quality. Our proposed model also outperforms the baseline methods in the new metric.

Activation Maximization Generative Adversarial Nets

http://arxiv.org/pdf/1703.02000v7

Title Activation Maximization Generative Adversarial Nets Summary Class label empirically shown useful improving sample quality generative adversarial net GANs paper mathematically study property current variant GANs make use class label information class aware gradient crossentropy decomposition reveal class label associated loss influence GANs training Based propose Activation Maximization Generative Adversarial Networks AMGAN advanced solution Comprehensive experiment conducted validate analysis evaluate effectiveness solution AMGAN outperforms strong baseline achieves stateoftheart Inception Score 891 CIFAR10 addition demonstrate Inception ImageNet classifier Inception Score mainly track diversity generator however reliable evidence reflect true sample quality thus propose new metric called Score provide accurate estimation sample quality proposed model also outperforms baseline method new metric Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ruth Fong', 'Andrea Vedaldi']

1704.03296v3

As machine learning algorithms are increasingly applied to high impact yet high risk tasks, such as medical diagnosis or autonomous driving, it is critical that researchers can explain how such algorithms arrived at their predictions. In recent years, a number of image saliency methods have been developed to summarize where highly complex neural networks "look" in an image for evidence for their predictions. However, these techniques are limited by their heuristic nature and architectural constraints. In this paper, we make two main contributions: First, we propose a general framework for learning different kinds of explanations for any black box algorithm. Second, we specialise the framework to find the part of an image most responsible for a classifier decision. Unlike previous works, our method is model-agnostic and testable because it is grounded in explicit and interpretable image perturbations.

Interpretable Explanations of Black Boxes by Meaningful Perturbation

http://arxiv.org/pdf/1704.03296v3

Title Interpretable Explanations Black Boxes Meaningful Perturbation Summary machine learning algorithm increasingly applied high impact yet high risk task medical diagnosis autonomous driving critical researcher explain algorithm arrived prediction recent year number image saliency method developed summarize highly complex neural network look image evidence prediction However technique limited heuristic nature architectural constraint paper make two main contribution First propose general framework learning different kind explanation black box algorithm Second specialise framework find part image responsible classifier decision Unlike previous work method modelagnostic testable grounded explicit interpretable image perturbation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hong Zhao']

1704.06885v1

Though the deep learning is pushing the machine learning to a new stage, basic theories of machine learning are still limited. The principle of learning, the role of the a prior knowledge, the role of neuron bias, and the basis for choosing neural transfer function and cost function, etc., are still far from clear. In this paper, we present a general theoretical framework for machine learning. We classify the prior knowledge into common and problem-dependent parts, and consider that the aim of learning is to maximally incorporate them. The principle we suggested for maximizing the former is the design risk minimization principle, while the neural transfer function, the cost function, as well as pretreatment of samples, are endowed with the role for maximizing the latter. The role of the neuron bias is explained from a different angle. We develop a Monte Carlo algorithm to establish the input-output responses, and we control the input-output sensitivity of a learning machine by controlling that of individual neurons. Applications of function approaching and smoothing, pattern recognition and classification, are provided to illustrate how to train general learning machines based on our theory and algorithm. Our method may in addition induce new applications, such as the transductive inference.

A General Theory for Training Learning Machine

http://arxiv.org/pdf/1704.06885v1

Title General Theory Training Learning Machine Summary Though deep learning pushing machine learning new stage basic theory machine learning still limited principle learning role prior knowledge role neuron bias basis choosing neural transfer function cost function etc still far clear paper present general theoretical framework machine learning classify prior knowledge common problemdependent part consider aim learning maximally incorporate principle suggested maximizing former design risk minimization principle neural transfer function cost function well pretreatment sample endowed role maximizing latter role neuron bias explained different angle develop Monte Carlo algorithm establish inputoutput response control inputoutput sensitivity learning machine controlling individual neuron Applications function approaching smoothing pattern recognition classification provided illustrate train general learning machine based theory algorithm method may addition induce new application transductive inference Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yotam Hechtlinger', 'Purvasha Chakravarti', 'Jining Qin']

1704.08165v1

This paper introduces a generalization of Convolutional Neural Networks (CNNs) from low-dimensional grid data, such as images, to graph-structured data. We propose a novel spatial convolution utilizing a random walk to uncover the relations within the input, analogous to the way the standard convolution uses the spatial neighborhood of a pixel on the grid. The convolution has an intuitive interpretation, is efficient and scalable and can also be used on data with varying graph structure. Furthermore, this generalization can be applied to many standard regression or classification problems, by learning the the underlying graph. We empirically demonstrate the performance of the proposed CNN on MNIST, and challenge the state-of-the-art on Merck molecular activity data set.

A Generalization of Convolutional Neural Networks to Graph-Structured Data

http://arxiv.org/pdf/1704.08165v1

Title Generalization Convolutional Neural Networks GraphStructured Data Summary paper introduces generalization Convolutional Neural Networks CNNs lowdimensional grid data image graphstructured data propose novel spatial convolution utilizing random walk uncover relation within input analogous way standard convolution us spatial neighborhood pixel grid convolution intuitive interpretation efficient scalable also used data varying graph structure Furthermore generalization applied many standard regression classification problem learning underlying graph empirically demonstrate performance proposed CNN MNIST challenge stateoftheart Merck molecular activity data set Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Matthias Hein', 'Maksym Andriushchenko']

1705.08475v2

Recent work has shown that state-of-the-art classifiers are quite brittle, in the sense that a small adversarial change of an originally with high confidence correctly classified input leads to a wrong classification again with high confidence. This raises concerns that such classifiers are vulnerable to attacks and calls into question their usage in safety-critical systems. We show in this paper for the first time formal guarantees on the robustness of a classifier by giving instance-specific lower bounds on the norm of the input manipulation required to change the classifier decision. Based on this analysis we propose the Cross-Lipschitz regularization functional. We show that using this form of regularization in kernel methods resp. neural networks improves the robustness of the classifier without any loss in prediction performance.

Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation

http://arxiv.org/pdf/1705.08475v2

Title Formal Guarantees Robustness Classifier Adversarial Manipulation Summary Recent work shown stateoftheart classifier quite brittle sense small adversarial change originally high confidence correctly classified input lead wrong classification high confidence raise concern classifier vulnerable attack call question usage safetycritical system show paper first time formal guarantee robustness classifier giving instancespecific lower bound norm input manipulation required change classifier decision Based analysis propose CrossLipschitz regularization functional show using form regularization kernel method resp neural network improves robustness classifier without loss prediction performance Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Alhussein Fawzi', 'Seyed-Mohsen Moosavi-Dezfooli', 'Pascal Frossard', 'Stefano Soatto']

1705.09552v1

The goal of this paper is to analyze the geometric properties of deep neural network classifiers in the input space. We specifically study the topology of classification regions created by deep networks, as well as their associated decision boundary. Through a systematic empirical investigation, we show that state-of-the-art deep nets learn connected classification regions, and that the decision boundary in the vicinity of datapoints is flat along most directions. We further draw an essential connection between two seemingly unrelated properties of deep networks: their sensitivity to additive perturbations in the inputs, and the curvature of their decision boundary. The directions where the decision boundary is curved in fact remarkably characterize the directions to which the classifier is the most vulnerable. We finally leverage a fundamental asymmetry in the curvature of the decision boundary of deep nets, and propose a method to discriminate between original images, and images perturbed with small adversarial examples. We show the effectiveness of this purely geometric approach for detecting small adversarial perturbations in images, and for recovering the labels of perturbed images.

Classification regions of deep neural networks

http://arxiv.org/pdf/1705.09552v1

Title Classification region deep neural network Summary goal paper analyze geometric property deep neural network classifier input space specifically study topology classification region created deep network well associated decision boundary systematic empirical investigation show stateoftheart deep net learn connected classification region decision boundary vicinity datapoints flat along direction draw essential connection two seemingly unrelated property deep network sensitivity additive perturbation input curvature decision boundary direction decision boundary curved fact remarkably characterize direction classifier vulnerable finally leverage fundamental asymmetry curvature decision boundary deep net propose method discriminate original image image perturbed small adversarial example show effectiveness purely geometric approach detecting small adversarial perturbation image recovering label perturbed image Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Seyed-Mohsen Moosavi-Dezfooli', 'Alhussein Fawzi', 'Omar Fawzi', 'Pascal Frossard', 'Stefano Soatto']

1705.09554v1

Deep networks have recently been shown to be vulnerable to universal perturbations: there exist very small image-agnostic perturbations that cause most natural images to be misclassified by such classifiers. In this paper, we propose the first quantitative analysis of the robustness of classifiers to universal perturbations, and draw a formal link between the robustness to universal perturbations, and the geometry of the decision boundary. Specifically, we establish theoretical bounds on the robustness of classifiers under two decision boundary models (flat and curved models). We show in particular that the robustness of deep networks to universal perturbations is driven by a key property of their curvature: there exists shared directions along which the decision boundary of deep networks is systematically positively curved. Under such conditions, we prove the existence of small universal perturbations. Our analysis further provides a novel geometric method for computing universal perturbations, in addition to explaining their properties.

Analysis of universal adversarial perturbations

http://arxiv.org/pdf/1705.09554v1

Title Analysis universal adversarial perturbation Summary Deep network recently shown vulnerable universal perturbation exist small imageagnostic perturbation cause natural image misclassified classifier paper propose first quantitative analysis robustness classifier universal perturbation draw formal link robustness universal perturbation geometry decision boundary Specifically establish theoretical bound robustness classifier two decision boundary model flat curved model show particular robustness deep network universal perturbation driven key property curvature exists shared direction along decision boundary deep network systematically positively curved condition prove existence small universal perturbation analysis provides novel geometric method computing universal perturbation addition explaining property Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yunus Saatchi', 'Andrew Gordon Wilson']

1705.09558v3

Generative adversarial networks (GANs) can implicitly learn rich distributions over images, audio, and data which are hard to model with an explicit likelihood. We present a practical Bayesian formulation for unsupervised and semi-supervised learning with GANs. Within this framework, we use stochastic gradient Hamiltonian Monte Carlo to marginalize the weights of the generator and discriminator networks. The resulting approach is straightforward and obtains good performance without any standard interventions such as feature matching, or mini-batch discrimination. By exploring an expressive posterior over the parameters of the generator, the Bayesian GAN avoids mode-collapse, produces interpretable and diverse candidate samples, and provides state-of-the-art quantitative results for semi-supervised learning on benchmarks including SVHN, CelebA, and CIFAR-10, outperforming DCGAN, Wasserstein GANs, and DCGAN ensembles.

Bayesian GAN

http://arxiv.org/pdf/1705.09558v3

Title Bayesian GAN Summary Generative adversarial network GANs implicitly learn rich distribution image audio data hard model explicit likelihood present practical Bayesian formulation unsupervised semisupervised learning GANs Within framework use stochastic gradient Hamiltonian Monte Carlo marginalize weight generator discriminator network resulting approach straightforward obtains good performance without standard intervention feature matching minibatch discrimination exploring expressive posterior parameter generator Bayesian GAN avoids modecollapse produce interpretable diverse candidate sample provides stateoftheart quantitative result semisupervised learning benchmark including SVHN CelebA CIFAR10 outperforming DCGAN Wasserstein GANs DCGAN ensemble Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Emily Denton', 'Vighnesh Birodkar']

1705.10915v1

We present a new model DrNET that learns disentangled image representations from video. Our approach leverages the temporal coherence of video and a novel adversarial loss to learn a representation that factorizes each frame into a stationary part and a temporally varying component. The disentangled representation can be used for a range of tasks. For example, applying a standard LSTM to the time-vary components enables prediction of future frames. We evaluate our approach on a range of synthetic and real videos, demonstrating the ability to coherently generate hundreds of steps into the future.

Unsupervised Learning of Disentangled Representations from Video

http://arxiv.org/pdf/1705.10915v1

Title Unsupervised Learning Disentangled Representations Video Summary present new model DrNET learns disentangled image representation video approach leverage temporal coherence video novel adversarial loss learn representation factorizes frame stationary part temporally varying component disentangled representation used range task example applying standard LSTM timevary component enables prediction future frame evaluate approach range synthetic real video demonstrating ability coherently generate hundred step future Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yujia Li', 'Alexander Schwing', 'Kuan-Chieh Wang', 'Richard Zemel']

1706.06216v1

Generative adversarial nets (GANs) are a promising technique for modeling a distribution from samples. It is however well known that GAN training suffers from instability due to the nature of its maximin formulation. In this paper, we explore ways to tackle the instability problem by dualizing the discriminator. We start from linear discriminators in which case conjugate duality provides a mechanism to reformulate the saddle point objective into a maximization problem, such that both the generator and the discriminator of this 'dualing GAN' act in concert. We then demonstrate how to extend this intuition to non-linear formulations. For GANs with linear discriminators our approach is able to remove the instability in training, while for GANs with nonlinear discriminators our approach provides an alternative to the commonly used GAN training algorithm.

Dualing GANs

http://arxiv.org/pdf/1706.06216v1

Title Dualing GANs Summary Generative adversarial net GANs promising technique modeling distribution sample however well known GAN training suffers instability due nature maximin formulation paper explore way tackle instability problem dualizing discriminator start linear discriminator case conjugate duality provides mechanism reformulate saddle point objective maximization problem generator discriminator dualing GAN act concert demonstrate extend intuition nonlinear formulation GANs linear discriminator approach able remove instability training GANs nonlinear discriminator approach provides alternative commonly used GAN training algorithm Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Eunhee Kang', 'Jaejun Yoo', 'Jong Chul Ye']

1707.09938v2

Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT are computationally expensive. To address this problem, we recently proposed the world-first deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the texture were not fully recovered. To cope with this problem, here we propose a deep residual learning approach in directional wavelet domain. The proposed method is motivated by an observation that a deep convolutional neural network can be interpreted as a multilayer convolutional framelets expansion using non-local basis convolved with data-driven local basis. We further extend the idea to derive a deep convolutional framelet expansion by combining global redundant transforms and signal boosting from multiple signal representations. Extensive experimental results confirm that the proposed network has significantly improved performance and preserves the detail texture of the original images

Wavelet Residual Network for Low-Dose CT via Deep Convolutional Framelets

http://arxiv.org/pdf/1707.09938v2

Title Wavelet Residual Network LowDose CT via Deep Convolutional Framelets Summary Model based iterative reconstruction MBIR algorithm lowdose Xray CT computationally expensive address problem recently proposed worldfirst deep convolutional neural network CNN lowdose Xray CT second place 2016 AAPM LowDose CT Grand Challenge However texture fully recovered cope problem propose deep residual learning approach directional wavelet domain proposed method motivated observation deep convolutional neural network interpreted multilayer convolutional framelets expansion using nonlocal basis convolved datadriven local basis extend idea derive deep convolutional framelet expansion combining global redundant transforms signal boosting multiple signal representation Extensive experimental result confirm proposed network significantly improved performance preserve detail texture original image Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Chuhang Zou', 'Ersin Yumer', 'Jimei Yang', 'Duygu Ceylan', 'Derek Hoiem']

1708.01648v1

The success of various applications including robotics, digital content creation, and visualization demand a structured and abstract representation of the 3D world from limited sensor data. Inspired by the nature of human perception of 3D shapes as a collection of simple parts, we explore such an abstract shape representation based on primitives. Given a single depth image of an object, we present 3D-PRNN, a generative recurrent neural network that synthesizes multiple plausible shapes composed of a set of primitives. Our generative model encodes symmetry characteristics of common man-made objects, preserves long-range structural coherence, and describes objects of varying complexity with a compact representation. We also propose a method based on Gaussian Fields to generate a large scale dataset of primitive-based shape representations to train our network. We evaluate our approach on a wide range of examples and show that it outperforms nearest-neighbor based shape retrieval methods and is on-par with voxel-based generative models while using a significantly reduced parameter space.

3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks

http://arxiv.org/pdf/1708.01648v1

Title 3DPRNN Generating Shape Primitives Recurrent Neural Networks Summary success various application including robotics digital content creation visualization demand structured abstract representation 3D world limited sensor data Inspired nature human perception 3D shape collection simple part explore abstract shape representation based primitive Given single depth image object present 3DPRNN generative recurrent neural network synthesizes multiple plausible shape composed set primitive generative model encodes symmetry characteristic common manmade object preserve longrange structural coherence describes object varying complexity compact representation also propose method based Gaussian Fields generate large scale dataset primitivebased shape representation train network evaluate approach wide range example show outperforms nearestneighbor based shape retrieval method onpar voxelbased generative model using significantly reduced parameter space Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Zhiming Zhou', 'Weinan Zhang', 'Jun Wang']

1708.01729v2

In this article, we mathematically study several GAN related topics, including Inception score, label smoothing, gradient vanishing and the -log(D(x)) alternative. --- An advanced version is included in arXiv:1703.02000 "Activation Maximization Generative Adversarial Nets". Please refer Section 6 in 1703.02000 for detailed analysis on Inception Score, and refer its appendix for the discussions on Label Smoothing, Gradient Vanishing and -log(D(x)) Alternative. ---

Inception Score, Label Smoothing, Gradient Vanishing and -log(D(x)) Alternative

http://arxiv.org/pdf/1708.01729v2

Title Inception Score Label Smoothing Gradient Vanishing logDx Alternative Summary article mathematically study several GAN related topic including Inception score label smoothing gradient vanishing logDx alternative advanced version included arXiv170302000 Activation Maximization Generative Adversarial Nets Please refer Section 6 170302000 detailed analysis Inception Score refer appendix discussion Label Smoothing Gradient Vanishing logDx Alternative Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Kai Arulkumaran', 'Marc Peter Deisenroth', 'Miles Brundage', 'Anil Anthony Bharath']

1708.05866v2

Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world. Currently, deep learning is enabling reinforcement learning to scale to problems that were previously intractable, such as learning to play video games directly from pixels. Deep reinforcement learning algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of reinforcement learning, then progress to the main streams of value-based and policy-based methods. Our survey will cover central algorithms in deep reinforcement learning, including the deep $Q$-network, trust region policy optimisation, and asynchronous advantage actor-critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via reinforcement learning. To conclude, we describe several current areas of research within the field.

A Brief Survey of Deep Reinforcement Learning

http://arxiv.org/pdf/1708.05866v2

Title Brief Survey Deep Reinforcement Learning Summary Deep reinforcement learning poised revolutionise field AI represents step towards building autonomous system higher level understanding visual world Currently deep learning enabling reinforcement learning scale problem previously intractable learning play video game directly pixel Deep reinforcement learning algorithm also applied robotics allowing control policy robot learned directly camera input real world survey begin introduction general field reinforcement learning progress main stream valuebased policybased method survey cover central algorithm deep reinforcement learning including deep Qnetwork trust region policy optimisation asynchronous advantage actorcritic parallel highlight unique advantage deep neural network focusing visual understanding via reinforcement learning conclude describe several current area research within field Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Caiwen Ding', 'Siyu Liao', 'Yanzhi Wang', 'Zhe Li', 'Ning Liu', 'Youwei Zhuo', 'Chao Wang', 'Xuehai Qian', 'Yu Bai', 'Geng Yuan', 'Xiaolong Ma', 'Yipeng Zhang', 'Jian Tang', 'Qinru Qiu', 'Xue Lin', 'Bo Yuan']

1708.08917v1

Large-scale deep neural networks (DNNs) are both compute and memory intensive. As the size of DNNs continues to grow, it is critical to improve the energy efficiency and performance while maintaining accuracy. For DNNs, the model size is an important factor affecting performance, scalability and energy efficiency. Weight pruning achieves good compression ratios but suffers from three drawbacks: 1) the irregular network structure after pruning; 2) the increased training complexity; and 3) the lack of rigorous guarantee of compression ratio and inference accuracy. To overcome these limitations, this paper proposes CirCNN, a principled approach to represent weights and process neural networks using block-circulant matrices. CirCNN utilizes the Fast Fourier Transform (FFT)-based fast multiplication, simultaneously reducing the computational complexity (both in inference and training) from O(n2) to O(nlogn) and the storage complexity from O(n2) to O(n), with negligible accuracy loss. Compared to other approaches, CirCNN is distinct due to its mathematical rigor: it can converge to the same effectiveness as DNNs without compression. The CirCNN architecture, a universal DNN inference engine that can be implemented on various hardware/software platforms with configurable network architecture. To demonstrate the performance and energy efficiency, we test CirCNN in FPGA, ASIC and embedded processors. Our results show that CirCNN architecture achieves very high energy efficiency and performance with a small hardware footprint. Based on the FPGA implementation and ASIC synthesis results, CirCNN achieves 6-102X energy efficiency improvements compared with the best state-of-the-art results.

CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices

http://arxiv.org/pdf/1708.08917v1

Title CirCNN Accelerating Compressing Deep Neural Networks Using BlockCirculantWeight Matrices Summary Largescale deep neural network DNNs compute memory intensive size DNNs continues grow critical improve energy efficiency performance maintaining accuracy DNNs model size important factor affecting performance scalability energy efficiency Weight pruning achieves good compression ratio suffers three drawback 1 irregular network structure pruning 2 increased training complexity 3 lack rigorous guarantee compression ratio inference accuracy overcome limitation paper proposes CirCNN principled approach represent weight process neural network using blockcirculant matrix CirCNN utilizes Fast Fourier Transform FFTbased fast multiplication simultaneously reducing computational complexity inference training On2 Onlogn storage complexity On2 negligible accuracy loss Compared approach CirCNN distinct due mathematical rigor converge effectiveness DNNs without compression CirCNN architecture universal DNN inference engine implemented various hardwaresoftware platform configurable network architecture demonstrate performance energy efficiency test CirCNN FPGA ASIC embedded processor result show CirCNN architecture achieves high energy efficiency performance small hardware footprint Based FPGA implementation ASIC synthesis result CirCNN achieves 6102X energy efficiency improvement compared best stateoftheart result Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Cătălina Cangea', 'Petar Veličković', 'Pietro Liò']

1709.00572v1

We propose two multimodal deep learning architectures that allow for cross-modal dataflow (XFlow) between the feature extractors, thereby extracting more interpretable features and obtaining a better representation than through unimodal learning, for the same amount of training data. These models can usefully exploit correlations between audio and visual data, which have a different dimensionality and are therefore nontrivially exchangeable. Our work improves on existing multimodal deep learning metholodogies in two essential ways: (1) it presents a novel method for performing cross-modality (before features are learned from individual modalities) and (2) extends the previously proposed cross-connections, which only transfer information between streams that process compatible data. Both cross-modal architectures outperformed their baselines (by up to 7.5%) when evaluated on the AVletters dataset.

XFlow: 1D-2D Cross-modal Deep Neural Networks for Audiovisual Classification

http://arxiv.org/pdf/1709.00572v1

Title XFlow 1D2D Crossmodal Deep Neural Networks Audiovisual Classification Summary propose two multimodal deep learning architecture allow crossmodal dataflow XFlow feature extractor thereby extracting interpretable feature obtaining better representation unimodal learning amount training data model usefully exploit correlation audio visual data different dimensionality therefore nontrivially exchangeable work improves existing multimodal deep learning metholodogies two essential way 1 present novel method performing crossmodality feature learned individual modality 2 extends previously proposed crossconnections transfer information stream process compatible data crossmodal architecture outperformed baseline 75 evaluated AVletters dataset Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Kun ho Kim', 'Oisin Mac Aodha', 'Pietro Perona']

1710.01691v2

Low dimensional embeddings that capture the main variations of interest in collections of data are important for many applications. One way to construct these embeddings is to acquire estimates of similarity from the crowd. However, similarity is a multi-dimensional concept that varies from individual to individual. Existing models for learning embeddings from the crowd typically make simplifying assumptions such as all individuals estimate similarity using the same criteria, the list of criteria is known in advance, or that the crowd workers are not influenced by the data that they see. To overcome these limitations we introduce Context Embedding Networks (CENs). In addition to learning interpretable embeddings from images, CENs also model worker biases for different attributes along with the visual context i.e. the visual attributes highlighted by a set of images. Experiments on two noisy crowd annotated datasets show that modeling both worker bias and visual context results in more interpretable embeddings compared to existing approaches.

Context Embedding Networks

http://arxiv.org/pdf/1710.01691v2

Title Context Embedding Networks Summary Low dimensional embeddings capture main variation interest collection data important many application One way construct embeddings acquire estimate similarity crowd However similarity multidimensional concept varies individual individual Existing model learning embeddings crowd typically make simplifying assumption individual estimate similarity using criterion list criterion known advance crowd worker influenced data see overcome limitation introduce Context Embedding Networks CENs addition learning interpretable embeddings image CENs also model worker bias different attribute along visual context ie visual attribute highlighted set image Experiments two noisy crowd annotated datasets show modeling worker bias visual context result interpretable embeddings compared existing approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Garrett B. Goh', 'Charles Siegel', 'Abhinav Vishnu', 'Nathan O. Hodas', 'Nathan Baker']

1710.02238v2

The meteoric rise of deep learning models in computer vision research, having achieved human-level accuracy in image recognition tasks is firm evidence of the impact of representation learning of deep neural networks. In the chemistry domain, recent advances have also led to the development of similar CNN models, such as Chemception, that is trained to predict chemical properties using images of molecular drawings. In this work, we investigate the effects of systematically removing and adding localized domain-specific information to the image channels of the training data. By augmenting images with only 3 additional basic information, and without introducing any architectural changes, we demonstrate that an augmented Chemception (AugChemception) outperforms the original model in the prediction of toxicity, activity, and solvation free energy. Then, by altering the information content in the images, and examining the resulting model's performance, we also identify two distinct learning patterns in predicting toxicity/activity as compared to solvation free energy. These patterns suggest that Chemception is learning about its tasks in the manner that is consistent with established knowledge. Thus, our work demonstrates that advanced chemical knowledge is not a pre-requisite for deep learning models to accurately predict complex chemical properties.

How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?

http://arxiv.org/pdf/1710.02238v2

Title Much Chemistry Deep Neural Network Need Know Make Accurate Predictions Summary meteoric rise deep learning model computer vision research achieved humanlevel accuracy image recognition task firm evidence impact representation learning deep neural network chemistry domain recent advance also led development similar CNN model Chemception trained predict chemical property using image molecular drawing work investigate effect systematically removing adding localized domainspecific information image channel training data augmenting image 3 additional basic information without introducing architectural change demonstrate augmented Chemception AugChemception outperforms original model prediction toxicity activity solvation free energy altering information content image examining resulting model performance also identify two distinct learning pattern predicting toxicityactivity compared solvation free energy pattern suggest Chemception learning task manner consistent established knowledge Thus work demonstrates advanced chemical knowledge prerequisite deep learning model accurately predict complex chemical property Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Abhishek Kumar', 'Prasanna Sattigeri', 'Avinash Balakrishnan']

1711.00848v2

Disentangled representations, where the higher level data generative factors are reflected in disjoint latent dimensions, offer several benefits such as ease of deriving invariant representations, transferability to other tasks, interpretability, etc. We consider the problem of unsupervised learning of disentangled representations from large pool of unlabeled observations, and propose a variational inference based approach to infer disentangled latent factors. We introduce a regularizer on the expectation of the approximate posterior over observed data that encourages the disentanglement. We evaluate the proposed approach using several quantitative metrics and empirically observe significant gains over existing methods in terms of both disentanglement and data likelihood (reconstruction quality).

Variational Inference of Disentangled Latent Concepts from Unlabeled Observations

http://arxiv.org/pdf/1711.00848v2

Title Variational Inference Disentangled Latent Concepts Unlabeled Observations Summary Disentangled representation higher level data generative factor reflected disjoint latent dimension offer several benefit ease deriving invariant representation transferability task interpretability etc consider problem unsupervised learning disentangled representation large pool unlabeled observation propose variational inference based approach infer disentangled latent factor introduce regularizer expectation approximate posterior observed data encourages disentanglement evaluate proposed approach using several quantitative metric empirically observe significant gain existing method term disentanglement data likelihood reconstruction quality Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Stanisław Jastrzębski', 'Zachary Kenton', 'Devansh Arpit', 'Nicolas Ballas', 'Asja Fischer', 'Yoshua Bengio', 'Amos Storkey']

1711.04623v1

We study the properties of the endpoint of stochastic gradient descent (SGD). By approximating SGD as a stochastic differential equation (SDE) we consider the Boltzmann-Gibbs equilibrium distribution of that SDE under the assumption of isotropic variance in loss gradients. Through this analysis, we find that three factors - learning rate, batch size and the variance of the loss gradients - control the trade-off between the depth and width of the minima found by SGD, with wider minima favoured by a higher ratio of learning rate to batch size. We have direct control over the learning rate and batch size, while the variance is determined by the choice of model architecture, model parameterization and dataset. In the equilibrium distribution only the ratio of learning rate to batch size appears, implying that the equilibrium distribution is invariant under a simultaneous rescaling of learning rate and batch size by the same amount. We then explore experimentally how learning rate and batch size affect SGD from two perspectives: the endpoint of SGD and the dynamics that lead up to it. For the endpoint, the experiments suggest the endpoint of SGD is invariant under simultaneous rescaling of batch size and learning rate, and also that a higher ratio leads to flatter minima, both findings are consistent with our theoretical analysis. We note experimentally that the dynamics also seem to be invariant under the same rescaling of learning rate and batch size, which we explore showing that one can exchange batch size and learning rate for cyclical learning rate schedule. Next, we illustrate how noise affects memorization, showing that high noise levels lead to better generalization. Finally, we find experimentally that the invariance under simultaneous rescaling of learning rate and batch size breaks down if the learning rate gets too large or the batch size gets too small.

Three Factors Influencing Minima in SGD

http://arxiv.org/pdf/1711.04623v1

Title Three Factors Influencing Minima SGD Summary study property endpoint stochastic gradient descent SGD approximating SGD stochastic differential equation SDE consider BoltzmannGibbs equilibrium distribution SDE assumption isotropic variance loss gradient analysis find three factor learning rate batch size variance loss gradient control tradeoff depth width minimum found SGD wider minimum favoured higher ratio learning rate batch size direct control learning rate batch size variance determined choice model architecture model parameterization dataset equilibrium distribution ratio learning rate batch size appears implying equilibrium distribution invariant simultaneous rescaling learning rate batch size amount explore experimentally learning rate batch size affect SGD two perspective endpoint SGD dynamic lead endpoint experiment suggest endpoint SGD invariant simultaneous rescaling batch size learning rate also higher ratio lead flatter minimum finding consistent theoretical analysis note experimentally dynamic also seem invariant rescaling learning rate batch size explore showing one exchange batch size learning rate cyclical learning rate schedule Next illustrate noise affect memorization showing high noise level lead better generalization Finally find experimentally invariance simultaneous rescaling learning rate batch size break learning rate get large batch size get small Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Paweł Liskowski', 'Wojciech Jaśkowski', 'Krzysztof Krawiec']

1711.06583v1

Achieving superhuman playing level by AlphaGo corroborated the capabilities of convolutional neural architectures (CNNs) for capturing complex spatial patterns. This result was to a great extent due to several analogies between Go board states and 2D images CNNs have been designed for, in particular translational invariance and a relatively large board. In this paper, we verify whether CNN-based move predictors prove effective for Othello, a game with significantly different characteristics, including a much smaller board size and complete lack of translational invariance. We compare several CNN architectures and board encodings, augment them with state-of-the-art extensions, train on an extensive database of experts' moves, and examine them with respect to move prediction accuracy and playing strength. The empirical evaluation confirms high capabilities of neural move predictors and suggests a strong correlation between prediction accuracy and playing strength. The best CNNs not only surpass all other 1-ply Othello players proposed to date but defeat (2-ply) Edax, the best open-source Othello player.

Learning to Play Othello with Deep Neural Networks

http://arxiv.org/pdf/1711.06583v1

Title Learning Play Othello Deep Neural Networks Summary Achieving superhuman playing level AlphaGo corroborated capability convolutional neural architecture CNNs capturing complex spatial pattern result great extent due several analogy Go board state 2D image CNNs designed particular translational invariance relatively large board paper verify whether CNNbased move predictor prove effective Othello game significantly different characteristic including much smaller board size complete lack translational invariance compare several CNN architecture board encoding augment stateoftheart extension train extensive database expert move examine respect move prediction accuracy playing strength empirical evaluation confirms high capability neural move predictor suggests strong correlation prediction accuracy playing strength best CNNs surpass 1ply Othello player proposed date defeat 2ply Edax best opensource Othello player Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jaejun Yoo', 'Sohail Sabir', 'Duchang Heo', 'Kee Hyun Kim', 'Abdul Wahab', 'Yoonseok Choi', 'Seul-I Lee', 'Eun Young Chae', 'Hak Hee Kim', 'Young Min Bae', 'Young-wook Choi', 'Seungryong Cho', 'Jong Chul Ye']

1712.00912v1

Can artificial intelligence (AI) learn complicated non-linear physics? Here we propose a novel deep learning approach that learns non-linear photon scattering physics and obtains accurate 3D distribution of optical anomalies. In contrast to the traditional black-box deep learning approaches to inverse problems, our deep network learns to invert the Lippmann-Schwinger integral equation which describes the essential physics of photon migration of diffuse near-infrared (NIR) photons in turbid media. As an example for clinical relevance, we applied the method to our prototype diffuse optical tomography (DOT). We show that our deep neural network, trained with only simulation data, can accurately recover the location of anomalies within biomimetic phantoms and live animals without the use of an exogenous contrast agent.

Deep Learning Can Reverse Photon Migration for Diffuse Optical Tomography

http://arxiv.org/pdf/1712.00912v1

Title Deep Learning Reverse Photon Migration Diffuse Optical Tomography Summary artificial intelligence AI learn complicated nonlinear physic propose novel deep learning approach learns nonlinear photon scattering physic obtains accurate 3D distribution optical anomaly contrast traditional blackbox deep learning approach inverse problem deep network learns invert LippmannSchwinger integral equation describes essential physic photon migration diffuse nearinfrared NIR photon turbid medium example clinical relevance applied method prototype diffuse optical tomography DOT show deep neural network trained simulation data accurately recover location anomaly within biomimetic phantom live animal without use exogenous contrast agent Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Garrett B. Goh', 'Charles Siegel', 'Abhinav Vishnu', 'Nathan O. Hodas']

1712.02734v2

With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks. However, in chemistry, data is inherently small and fragmented. In this work, we develop an approach of using rule-based knowledge for training ChemNet, a transferable and generalizable deep neural network for chemical property prediction that learns in a weak-supervised manner from large unlabeled chemical databases. When coupled with transfer learning approaches to predict other smaller datasets for chemical properties that it was not originally trained on, we show that ChemNet's accuracy outperforms contemporary DNN models that were trained using conventional supervised learning. Furthermore, we demonstrate that the ChemNet pre-training approach is equally effective on both CNN (Chemception) and RNN (SMILES2vec) models, indicating that this approach is network architecture agnostic and is effective across multiple data modalities. Our results indicate a pre-trained ChemNet that incorporates chemistry domain knowledge, enables the development of generalizable neural networks for more accurate prediction of novel chemical properties.

Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction

http://arxiv.org/pdf/1712.02734v2

Title Using RuleBased Labels Weak Supervised Learning ChemNet Transferable Chemical Property Prediction Summary access large datasets deep neural network DNN achieved humanlevel accuracy image speech recognition task However chemistry data inherently small fragmented work develop approach using rulebased knowledge training ChemNet transferable generalizable deep neural network chemical property prediction learns weaksupervised manner large unlabeled chemical database coupled transfer learning approach predict smaller datasets chemical property originally trained show ChemNets accuracy outperforms contemporary DNN model trained using conventional supervised learning Furthermore demonstrate ChemNet pretraining approach equally effective CNN Chemception RNN SMILES2vec model indicating approach network architecture agnostic effective across multiple data modality result indicate pretrained ChemNet incorporates chemistry domain knowledge enables development generalizable neural network accurate prediction novel chemical property Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yeo Hun Yoon', 'Shujaat Khan', 'Jaeyoung Huh', 'Jong Chul Ye']

1712.06096v2

In portable, three dimensional, and ultra-fast ultrasound (US) imaging systems, there is an increasing need to reconstruct high quality images from a limited number of RF data from receiver (Rx) or scan-line (SC) sub-sampling. However, due to the severe side lobe artifacts from RF sub-sampling, the standard beam-former often produces blurry images with less contrast that are not suitable for diagnostic purpose. To address this problem, some researchers have studied compressed sensing (CS) to exploit the sparsity of the image or RF data in some domains. However, the existing CS approaches require either hardware changes or computationally expensive algorithms. To overcome these limitations, here we propose a novel deep learning approach that directly interpolates the missing RF data by utilizing redundancy in the Rx-SC plane. In particular, the network design principle derives from a novel interpretation of the deep neural network as a cascaded convolution framelets that learns the data-driven bases for Hankel matrix decomposition. Our extensive experimental results from sub-sampled RF data from a real US system confirmed that the proposed method can effectively reduce the data rate without sacrificing the image quality.

Deep Learning in RF Sub-sampled B-mode Ultrasound Imaging

http://arxiv.org/pdf/1712.06096v2

Title Deep Learning RF Subsampled Bmode Ultrasound Imaging Summary portable three dimensional ultrafast ultrasound US imaging system increasing need reconstruct high quality image limited number RF data receiver Rx scanline SC subsampling However due severe side lobe artifact RF subsampling standard beamformer often produce blurry image le contrast suitable diagnostic purpose address problem researcher studied compressed sensing CS exploit sparsity image RF data domain However existing CS approach require either hardware change computationally expensive algorithm overcome limitation propose novel deep learning approach directly interpolates missing RF data utilizing redundancy RxSC plane particular network design principle derives novel interpretation deep neural network cascaded convolution framelets learns datadriven base Hankel matrix decomposition extensive experimental result subsampled RF data real US system confirmed proposed method effectively reduce data rate without sacrificing image quality Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yoseob Han', 'Jawook Gu', 'Jong Chul Ye']

1712.10248v2

Interior tomography for the region-of-interest (ROI) imaging has advantages of using a small detector and reducing X-ray radiation dose. However, standard analytic reconstruction suffers from severe cupping artifacts due to existence of null space in the truncated Radon transform. Existing penalized reconstruction methods may address this problem but they require extensive computations due to the iterative reconstruction. Inspired by the recent deep learning approaches to low-dose and sparse view CT, here we propose a deep learning architecture that removes null space signals from the FBP reconstruction. Experimental results have shown that the proposed method provides near-perfect reconstruction with about 7-10 dB improvement in PSNR over existing methods in spite of significantly reduced run-time complexity.

Deep Learning Interior Tomography for Region-of-Interest Reconstruction

http://arxiv.org/pdf/1712.10248v2

Title Deep Learning Interior Tomography RegionofInterest Reconstruction Summary Interior tomography regionofinterest ROI imaging advantage using small detector reducing Xray radiation dose However standard analytic reconstruction suffers severe cupping artifact due existence null space truncated Radon transform Existing penalized reconstruction method may address problem require extensive computation due iterative reconstruction Inspired recent deep learning approach lowdose sparse view CT propose deep learning architecture remove null space signal FBP reconstruction Experimental result shown proposed method provides nearperfect reconstruction 710 dB improvement PSNR existing method spite significantly reduced runtime complexity Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yoseob Han', 'Jingu Kang', 'Jong Chul Ye']

1801.01258v1

For homeland and transportation security applications, 2D X-ray explosive detection system (EDS) have been widely used, but they have limitations in recognizing 3D shape of the hidden objects. Among various types of 3D computed tomography (CT) systems to address this issue, this paper is interested in a stationary CT using fixed X-ray sources and detectors. However, due to the limited number of projection views, analytic reconstruction algorithms produce severe streaking artifacts. Inspired by recent success of deep learning approach for sparse view CT reconstruction, here we propose a novel image and sinogram domain deep learning architecture for 3D reconstruction from very sparse view measurement. The algorithm has been tested with the real data from a prototype 9-view dual energy stationary CT EDS carry-on baggage scanner developed by GEMSS Medical Systems, Korea, which confirms the superior reconstruction performance over the existing approaches.

Deep Learning Reconstruction for 9-View Dual Energy CT Baggage Scanner

http://arxiv.org/pdf/1801.01258v1

Title Deep Learning Reconstruction 9View Dual Energy CT Baggage Scanner Summary homeland transportation security application 2D Xray explosive detection system EDS widely used limitation recognizing 3D shape hidden object Among various type 3D computed tomography CT system address issue paper interested stationary CT using fixed Xray source detector However due limited number projection view analytic reconstruction algorithm produce severe streaking artifact Inspired recent success deep learning approach sparse view CT reconstruction propose novel image sinogram domain deep learning architecture 3D reconstruction sparse view measurement algorithm tested real data prototype 9view dual energy stationary CT EDS carryon baggage scanner developed GEMSS Medical Systems Korea confirms superior reconstruction performance existing approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jayanta K Dutta', 'Jiayi Liu', 'Unmesh Kurup', 'Mohak Shah']

1801.08577v1

Deep learning has shown promising results on many machine learning tasks but DL models are often complex networks with large number of neurons and layers, and recently, complex layer structures known as building blocks. Finding the best deep model requires a combination of finding both the right architecture and the correct set of parameters appropriate for that architecture. In addition, this complexity (in terms of layer types, number of neurons, and number of layers) also present problems with generalization since larger networks are easier to overfit to the data. In this paper, we propose a search framework for finding effective architectural building blocks for convolutional neural networks (CNN). Our approach is much faster at finding models that are close to state-of-the-art in performance. In addition, the models discovered by our approach are also smaller than models discovered by similar techniques. We achieve these twin advantages by designing our search space in such a way that it searches over a reduced set of state-of-the-art building blocks for CNNs including residual block, inception block, inception-residual block, ResNeXt block and many others. We apply this technique to generate models for multiple image datasets and show that these models achieve performance comparable to state-of-the-art (and even surpassing the state-of-the-art in one case). We also show that learned models are transferable between datasets.

Effective Building Block Design for Deep Convolutional Neural Networks using Search

http://arxiv.org/pdf/1801.08577v1

Title Effective Building Block Design Deep Convolutional Neural Networks using Search Summary Deep learning shown promising result many machine learning task DL model often complex network large number neuron layer recently complex layer structure known building block Finding best deep model requires combination finding right architecture correct set parameter appropriate architecture addition complexity term layer type number neuron number layer also present problem generalization since larger network easier overfit data paper propose search framework finding effective architectural building block convolutional neural network CNN approach much faster finding model close stateoftheart performance addition model discovered approach also smaller model discovered similar technique achieve twin advantage designing search space way search reduced set stateoftheart building block CNNs including residual block inception block inceptionresidual block ResNeXt block many others apply technique generate model multiple image datasets show model achieve performance comparable stateoftheart even surpassing stateoftheart one case also show learned model transferable datasets Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Haque Ishfaq', 'Assaf Hoogi', 'Daniel Rubin']

1802.04403v1

Deep metric learning has been demonstrated to be highly effective in learning semantic representation and encoding information that can be used to measure data similarity, by relying on the embedding learned from metric learning. At the same time, variational autoencoder (VAE) has widely been used to approximate inference and proved to have a good performance for directed probabilistic models. However, for traditional VAE, the data label or feature information are intractable. Similarly, traditional representation learning approaches fail to represent many salient aspects of the data. In this project, we propose a novel integrated framework to learn latent embedding in VAE by incorporating deep metric learning. The features are learned by optimizing a triplet loss on the mean vectors of VAE in conjunction with standard evidence lower bound (ELBO) of VAE. This approach, which we call Triplet based Variational Autoencoder (TVAE), allows us to capture more fine-grained information in the latent embedding. Our model is tested on MNIST data set and achieves a high triplet accuracy of 95.60% while the traditional VAE (Kingma & Welling, 2013) achieves triplet accuracy of 75.08%.

TVAE: Triplet-Based Variational Autoencoder using Metric Learning

http://arxiv.org/pdf/1802.04403v1

Title TVAE TripletBased Variational Autoencoder using Metric Learning Summary Deep metric learning demonstrated highly effective learning semantic representation encoding information used measure data similarity relying embedding learned metric learning time variational autoencoder VAE widely used approximate inference proved good performance directed probabilistic model However traditional VAE data label feature information intractable Similarly traditional representation learning approach fail represent many salient aspect data project propose novel integrated framework learn latent embedding VAE incorporating deep metric learning feature learned optimizing triplet loss mean vector VAE conjunction standard evidence lower bound ELBO VAE approach call Triplet based Variational Autoencoder TVAE allows u capture finegrained information latent embedding model tested MNIST data set achieves high triplet accuracy 9560 traditional VAE Kingma Welling 2013 achieves triplet accuracy 7508 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Nick Haber', 'Damian Mrowca', 'Li Fei-Fei', 'Daniel L. K. Yamins']

1802.07442v1

Infants are experts at playing, with an amazing ability to generate novel structured behaviors in unstructured environments that lack clear extrinsic reward signals. We seek to mathematically formalize these abilities using a neural network that implements curiosity-driven intrinsic motivation. Using a simple but ecologically naturalistic simulated environment in which an agent can move and interact with objects it sees, we propose a "world-model" network that learns to predict the dynamic consequences of the agent's actions. Simultaneously, we train a separate explicit "self-model" that allows the agent to track the error map of its own world-model, and then uses the self-model to adversarially challenge the developing world-model. We demonstrate that this policy causes the agent to explore novel and informative interactions with its environment, leading to the generation of a spectrum of complex behaviors, including ego-motion prediction, object attention, and object gathering. Moreover, the world-model that the agent learns supports improved performance on object dynamics prediction, detection, localization and recognition tasks. Taken together, our results are initial steps toward creating flexible autonomous agents that self-supervise in complex novel physical environments.

Learning to Play with Intrinsically-Motivated Self-Aware Agents

http://arxiv.org/pdf/1802.07442v1

Title Learning Play IntrinsicallyMotivated SelfAware Agents Summary Infants expert playing amazing ability generate novel structured behavior unstructured environment lack clear extrinsic reward signal seek mathematically formalize ability using neural network implement curiositydriven intrinsic motivation Using simple ecologically naturalistic simulated environment agent move interact object see propose worldmodel network learns predict dynamic consequence agent action Simultaneously train separate explicit selfmodel allows agent track error map worldmodel us selfmodel adversarially challenge developing worldmodel demonstrate policy cause agent explore novel informative interaction environment leading generation spectrum complex behavior including egomotion prediction object attention object gathering Moreover worldmodel agent learns support improved performance object dynamic prediction detection localization recognition task Taken together result initial step toward creating flexible autonomous agent selfsupervise complex novel physical environment Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Nick Haber', 'Damian Mrowca', 'Li Fei-Fei', 'Daniel L. K. Yamins']

1802.07461v1

Infants are experts at playing, with an amazing ability to generate novel structured behaviors in unstructured environments that lack clear extrinsic reward signals. We seek to replicate some of these abilities with a neural network that implements curiosity-driven intrinsic motivation. Using a simple but ecologically naturalistic simulated environment in which the agent can move and interact with objects it sees, the agent learns a world model predicting the dynamic consequences of its actions. Simultaneously, the agent learns to take actions that adversarially challenge the developing world model, pushing the agent to explore novel and informative interactions with its environment. We demonstrate that this policy leads to the self-supervised emergence of a spectrum of complex behaviors, including ego motion prediction, object attention, and object gathering. Moreover, the world model that the agent learns supports improved performance on object dynamics prediction and localization tasks. Our results are a proof-of-principle that computational models of intrinsic motivation might account for key features of developmental visuomotor learning in infants.

Emergence of Structured Behaviors from Curiosity-Based Intrinsic Motivation

http://arxiv.org/pdf/1802.07461v1

Title Emergence Structured Behaviors CuriosityBased Intrinsic Motivation Summary Infants expert playing amazing ability generate novel structured behavior unstructured environment lack clear extrinsic reward signal seek replicate ability neural network implement curiositydriven intrinsic motivation Using simple ecologically naturalistic simulated environment agent move interact object see agent learns world model predicting dynamic consequence action Simultaneously agent learns take action adversarially challenge developing world model pushing agent explore novel informative interaction environment demonstrate policy lead selfsupervised emergence spectrum complex behavior including ego motion prediction object attention object gathering Moreover world model agent learns support improved performance object dynamic prediction localization task result proofofprinciple computational model intrinsic motivation might account key feature developmental visuomotor learning infant Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Emily Denton', 'Rob Fergus']

1802.07687v2

Generating video frames that accurately predict future world states is challenging. Existing approaches either fail to capture the full distribution of outcomes, or yield blurry generations, or both. In this paper we introduce an unsupervised video generation model that learns a prior model of uncertainty in a given environment. Video frames are generated by drawing samples from this prior and combining them with a deterministic estimate of the future frame. The approach is simple and easily trained end-to-end on a variety of datasets. Sample generations are both varied and sharp, even many frames into the future, and compare favorably to those from existing approaches.

Stochastic Video Generation with a Learned Prior

http://arxiv.org/pdf/1802.07687v2

Title Stochastic Video Generation Learned Prior Summary Generating video frame accurately predict future world state challenging Existing approach either fail capture full distribution outcome yield blurry generation paper introduce unsupervised video generation model learns prior model uncertainty given environment Video frame generated drawing sample prior combining deterministic estimate future frame approach simple easily trained endtoend variety datasets Sample generation varied sharp even many frame future compare favorably existing approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Weifeng Ge', 'Sibei Yang', 'Yizhou Yu']

1802.09129v1

Supervised object detection and semantic segmentation require object or even pixel level annotations. When there exist image level labels only, it is challenging for weakly supervised algorithms to achieve accurate predictions. The accuracy achieved by top weakly supervised algorithms is still significantly lower than their fully supervised counterparts. In this paper, we propose a novel weakly supervised curriculum learning pipeline for multi-label object recognition, detection and semantic segmentation. In this pipeline, we first obtain intermediate object localization and pixel labeling results for the training images, and then use such results to train task-specific deep networks in a fully supervised manner. The entire process consists of four stages, including object localization in the training images, filtering and fusing object instances, pixel labeling for the training images, and task-specific network training. To obtain clean object instances in the training images, we propose a novel algorithm for filtering, fusing and classifying object instances collected from multiple solution mechanisms. In this algorithm, we incorporate both metric learning and density-based clustering to filter detected object instances. Experiments show that our weakly supervised pipeline achieves state-of-the-art results in multi-label image classification as well as weakly supervised object detection and very competitive results in weakly supervised semantic segmentation on MS-COCO, PASCAL VOC 2007 and PASCAL VOC 2012.

Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning

http://arxiv.org/pdf/1802.09129v1

Title MultiEvidence Filtering Fusion MultiLabel Classification Object Detection Semantic Segmentation Based Weakly Supervised Learning Summary Supervised object detection semantic segmentation require object even pixel level annotation exist image level label challenging weakly supervised algorithm achieve accurate prediction accuracy achieved top weakly supervised algorithm still significantly lower fully supervised counterpart paper propose novel weakly supervised curriculum learning pipeline multilabel object recognition detection semantic segmentation pipeline first obtain intermediate object localization pixel labeling result training image use result train taskspecific deep network fully supervised manner entire process consists four stage including object localization training image filtering fusing object instance pixel labeling training image taskspecific network training obtain clean object instance training image propose novel algorithm filtering fusing classifying object instance collected multiple solution mechanism algorithm incorporate metric learning densitybased clustering filter detected object instance Experiments show weakly supervised pipeline achieves stateoftheart result multilabel image classification well weakly supervised object detection competitive result weakly supervised semantic segmentation MSCOCO PASCAL VOC 2007 PASCAL VOC 2012 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Quynh Nguyen', 'Mahesh Mukkamala', 'Matthias Hein']

1803.00094v1

In the recent literature the important role of depth in deep learning has been emphasized. In this paper we argue that sufficient width of a feedforward network is equally important by answering the simple question under which conditions the decision regions of a neural network are connected. It turns out that for a class of activation functions including leaky ReLU, neural networks having a pyramidal structure, that is no layer has more hidden units than the input dimension, produce necessarily connected decision regions. This implies that a sufficiently wide layer is necessary to produce disconnected decision regions. We discuss the implications of this result for the construction of neural networks, in particular the relation to the problem of adversarial manipulation of classifiers.

Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions

http://arxiv.org/pdf/1803.00094v1

Title Neural Networks Wide Enough Learn Disconnected Decision Regions Summary recent literature important role depth deep learning emphasized paper argue sufficient width feedforward network equally important answering simple question condition decision region neural network connected turn class activation function including leaky ReLU neural network pyramidal structure layer hidden unit input dimension produce necessarily connected decision region implies sufficiently wide layer necessary produce disconnected decision region discus implication result construction neural network particular relation problem adversarial manipulation classifier Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Chengliang Yang', 'Anand Rangarajan', 'Sanjay Ranka']

1803.02544v2

We develop three efficient approaches for generating visual explanations from 3D convolutional neural networks (3D-CNNs) for Alzheimer's disease classification. One approach conducts sensitivity analysis on hierarchical 3D image segmentation, and the other two visualize network activations on a spatial map. Visual checks and a quantitative localization benchmark indicate that all approaches identify important brain parts for Alzheimer's disease diagnosis. Comparative analysis show that the sensitivity analysis based approach has difficulty handling loosely distributed cerebral cortex, and approaches based on visualization of activations are constrained by the resolution of the convolutional layer. The complementarity of these methods improves the understanding of 3D-CNNs in Alzheimer's disease classification from different perspectives.

Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification

http://arxiv.org/pdf/1803.02544v2

Title Visual Explanations Deep 3D Convolutional Neural Networks Alzheimers Disease Classification Summary develop three efficient approach generating visual explanation 3D convolutional neural network 3DCNNs Alzheimers disease classification One approach conduct sensitivity analysis hierarchical 3D image segmentation two visualize network activation spatial map Visual check quantitative localization benchmark indicate approach identify important brain part Alzheimers disease diagnosis Comparative analysis show sensitivity analysis based approach difficulty handling loosely distributed cerebral cortex approach based visualization activation constrained resolution convolutional layer complementarity method improves understanding 3DCNNs Alzheimers disease classification different perspective Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Pavel Izmailov', 'Dmitrii Podoprikhin', 'Timur Garipov', 'Dmitry Vetrov', 'Andrew Gordon Wilson']

1803.05407v1

Deep neural networks are typically trained by optimizing a loss function with an SGD variant, in conjunction with a decaying learning rate, until convergence. We show that simple averaging of multiple points along the trajectory of SGD, with a cyclical or constant learning rate, leads to better generalization than conventional training. We also show that this Stochastic Weight Averaging (SWA) procedure finds much broader optima than SGD, and approximates the recent Fast Geometric Ensembling (FGE) approach with a single model. Using SWA we achieve notable improvement in test accuracy over conventional SGD training on a range of state-of-the-art residual networks, PyramidNets, DenseNets, and Shake-Shake networks on CIFAR-10, CIFAR-100, and ImageNet. In short, SWA is extremely easy to implement, improves generalization, and has almost no computational overhead.

Averaging Weights Leads to Wider Optima and Better Generalization

http://arxiv.org/pdf/1803.05407v1

Title Averaging Weights Leads Wider Optima Better Generalization Summary Deep neural network typically trained optimizing loss function SGD variant conjunction decaying learning rate convergence show simple averaging multiple point along trajectory SGD cyclical constant learning rate lead better generalization conventional training also show Stochastic Weight Averaging SWA procedure find much broader optimum SGD approximates recent Fast Geometric Ensembling FGE approach single model Using SWA achieve notable improvement test accuracy conventional SGD training range stateoftheart residual network PyramidNets DenseNets ShakeShake network CIFAR10 CIFAR100 ImageNet short SWA extremely easy implement improves generalization almost computational overhead Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Abdulrahman Oladipupo Ibraheem']

1412.6749v1

By drawing on ideas from optimisation theory, artificial neural networks (ANN), graph embeddings and sparse representations, I develop a novel technique, termed SENNS (Sparse Extraction Neural NetworkS), aimed at addressing the feature extraction problem. The proposed method uses (preferably deep) ANNs for projecting input attribute vectors to an output space wherein pairwise distances are maximized for vectors belonging to different classes, but minimized for those belonging to the same class, while simultaneously enforcing sparsity on the ANN outputs. The vectors that result from the projection can then be used as features in any classifier of choice. Mathematically, I formulate the proposed method as the minimisation of an objective function which can be interpreted, in the ANN output space, as a negative factor of the sum of the squares of the pair-wise distances between output vectors belonging to different classes, added to a positive factor of the sum of squares of the pair-wise distances between output vectors belonging to the same classes, plus sparsity and weight decay terms. To derive an algorithm for minimizing the objective function via gradient descent, I use the multi-variate version of the chain rule to obtain the partial derivatives of the function with respect to ANN weights and biases, and find that each of the required partial derivatives can be expressed as a sum of six terms. As it turns out, four of those six terms can be computed using the standard back propagation algorithm; the fifth can be computed via a slight modification of the standard backpropagation algorithm; while the sixth one can be computed via simple arithmetic. Finally, I propose experiments on the ARABASE Arabic corpora of digits and letters, the CMU PIE database of faces, the MNIST digits database, and other standard machine learning databases.

SENNS: Sparse Extraction Neural NetworkS for Feature Extraction

http://arxiv.org/pdf/1412.6749v1

Title SENNS Sparse Extraction Neural NetworkS Feature Extraction Summary drawing idea optimisation theory artificial neural network ANN graph embeddings sparse representation develop novel technique termed SENNS Sparse Extraction Neural NetworkS aimed addressing feature extraction problem proposed method us preferably deep ANNs projecting input attribute vector output space wherein pairwise distance maximized vector belonging different class minimized belonging class simultaneously enforcing sparsity ANN output vector result projection used feature classifier choice Mathematically formulate proposed method minimisation objective function interpreted ANN output space negative factor sum square pairwise distance output vector belonging different class added positive factor sum square pairwise distance output vector belonging class plus sparsity weight decay term derive algorithm minimizing objective function via gradient descent use multivariate version chain rule obtain partial derivative function respect ANN weight bias find required partial derivative expressed sum six term turn four six term computed using standard back propagation algorithm fifth computed via slight modification standard backpropagation algorithm sixth one computed via simple arithmetic Finally propose experiment ARABASE Arabic corpus digit letter CMU PIE database face MNIST digit database standard machine learning database Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Dougal J. Sutherland', 'Hsiao-Yu Tung', 'Heiko Strathmann', 'Soumyajit De', 'Aaditya Ramdas', 'Alex Smola', 'Arthur Gretton']

1611.04488v4

We propose a method to optimize the representation and distinguishability of samples from two probability distributions, by maximizing the estimated power of a statistical test based on the maximum mean discrepancy (MMD). This optimized MMD is applied to the setting of unsupervised learning by generative adversarial networks (GAN), in which a model attempts to generate realistic samples, and a discriminator attempts to tell these apart from data samples. In this context, the MMD may be used in two roles: first, as a discriminator, either directly on the samples, or on features of the samples. Second, the MMD can be used to evaluate the performance of a generative model, by testing the model's samples against a reference data set. In the latter role, the optimized MMD is particularly helpful, as it gives an interpretable indication of how the model and data distributions differ, even in cases where individual model samples are not easily distinguished either by eye or by classifier.

Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy

http://arxiv.org/pdf/1611.04488v4

Title Generative Models Model Criticism via Optimized Maximum Mean Discrepancy Summary propose method optimize representation distinguishability sample two probability distribution maximizing estimated power statistical test based maximum mean discrepancy MMD optimized MMD applied setting unsupervised learning generative adversarial network GAN model attempt generate realistic sample discriminator attempt tell apart data sample context MMD may used two role first discriminator either directly sample feature sample Second MMD used evaluate performance generative model testing model sample reference data set latter role optimized MMD particularly helpful give interpretable indication model data distribution differ even case individual model sample easily distinguished either eye classifier Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jiequn Han', 'Weinan E']

1611.07422v1

Many real world stochastic control problems suffer from the "curse of dimensionality". To overcome this difficulty, we develop a deep learning approach that directly solves high-dimensional stochastic control problems based on Monte-Carlo sampling. We approximate the time-dependent controls as feedforward neural networks and stack these networks together through model dynamics. The objective function for the control problem plays the role of the loss function for the deep neural network. We test this approach using examples from the areas of optimal trading and energy storage. Our results suggest that the algorithm presented here achieves satisfactory accuracy and at the same time, can handle rather high dimensional problems.

Deep Learning Approximation for Stochastic Control Problems

http://arxiv.org/pdf/1611.07422v1

Title Deep Learning Approximation Stochastic Control Problems Summary Many real world stochastic control problem suffer curse dimensionality overcome difficulty develop deep learning approach directly solves highdimensional stochastic control problem based MonteCarlo sampling approximate timedependent control feedforward neural network stack network together model dynamic objective function control problem play role loss function deep neural network test approach using example area optimal trading energy storage result suggest algorithm presented achieves satisfactory accuracy time handle rather high dimensional problem Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Marwin H. S. Segler', 'Thierry Kogej', 'Christian Tyrchan', 'Mark P. Waller']

1701.01329v1

In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model. In order to enrich libraries with molecules active towards a given biological target, we propose to fine-tune the model with small sets of molecules, which are known to be active against that target. Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test molecules that medicinal chemists designed, whereas against Plasmodium falciparum (Malaria) it reproduced 28% of 1240 test molecules. When coupled with a scoring function, our model can perform the complete de novo drug design cycle to generate large sets of novel molecules for drug discovery.

Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks

http://arxiv.org/pdf/1701.01329v1

Title Generating Focussed Molecule Libraries Drug Discovery Recurrent Neural Networks Summary de novo drug design computational strategy used generate novel molecule good affinity desired biological target work show recurrent neural network trained generative model molecular structure similar statistical language model natural language processing demonstrate property generated molecule correlate well property molecule used train model order enrich library molecule active towards given biological target propose finetune model small set molecule known active target Staphylococcus aureus model reproduced 14 6051 holdout test molecule medicinal chemist designed whereas Plasmodium falciparum Malaria reproduced 28 1240 test molecule coupled scoring function model perform complete de novo drug design cycle generate large set novel molecule drug discovery Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Matthias Plappert', 'Rein Houthooft', 'Prafulla Dhariwal', 'Szymon Sidor', 'Richard Y. Chen', 'Xi Chen', 'Tamim Asfour', 'Pieter Abbeel', 'Marcin Andrychowicz']

1706.01905v2

Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent's parameters, which can lead to more consistent exploration and a richer set of behaviors. Methods such as evolutionary strategies use parameter perturbations, but discard all temporal structure in the process and require significantly more samples. Combining parameter noise with traditional RL methods allows to combine the best of both worlds. We demonstrate that both off- and on-policy methods benefit from this approach through experimental comparison of DQN, DDPG, and TRPO on high-dimensional discrete action environments as well as continuous control tasks. Our results show that RL with parameter noise learns more efficiently than traditional RL with action space noise and evolutionary strategies individually.

Parameter Space Noise for Exploration

http://arxiv.org/pdf/1706.01905v2

Title Parameter Space Noise Exploration Summary Deep reinforcement learning RL method generally engage exploratory behavior noise injection action space alternative add noise directly agent parameter lead consistent exploration richer set behavior Methods evolutionary strategy use parameter perturbation discard temporal structure process require significantly sample Combining parameter noise traditional RL method allows combine best world demonstrate onpolicy method benefit approach experimental comparison DQN DDPG TRPO highdimensional discrete action environment well continuous control task result show RL parameter noise learns efficiently traditional RL action space noise evolutionary strategy individually Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['El Mahdi El Mhamdi', 'Rachid Guerraoui', 'Sebastien Rouault']

1707.08167v2

With the development of neural networks based machine learning and their usage in mission critical applications, voices are rising against the \textit{black box} aspect of neural networks as it becomes crucial to understand their limits and capabilities. With the rise of neuromorphic hardware, it is even more critical to understand how a neural network, as a distributed system, tolerates the failures of its computing nodes, neurons, and its communication channels, synapses. Experimentally assessing the robustness of neural networks involves the quixotic venture of testing all the possible failures, on all the possible inputs, which ultimately hits a combinatorial explosion for the first, and the impossibility to gather all the possible inputs for the second. In this paper, we prove an upper bound on the expected error of the output when a subset of neurons crashes. This bound involves dependencies on the network parameters that can be seen as being too pessimistic in the average case. It involves a polynomial dependency on the Lipschitz coefficient of the neurons activation function, and an exponential dependency on the depth of the layer where a failure occurs. We back up our theoretical results with experiments illustrating the extent to which our prediction matches the dependencies between the network parameters and robustness. Our results show that the robustness of neural networks to the average crash can be estimated without the need to neither test the network on all failure configurations, nor access the training set used to train the network, both of which are practically impossible requirements.

On The Robustness of a Neural Network

http://arxiv.org/pdf/1707.08167v2

Title Robustness Neural Network Summary development neural network based machine learning usage mission critical application voice rising textitblack box aspect neural network becomes crucial understand limit capability rise neuromorphic hardware even critical understand neural network distributed system tolerates failure computing node neuron communication channel synapsis Experimentally assessing robustness neural network involves quixotic venture testing possible failure possible input ultimately hit combinatorial explosion first impossibility gather possible input second paper prove upper bound expected error output subset neuron crash bound involves dependency network parameter seen pessimistic average case involves polynomial dependency Lipschitz coefficient neuron activation function exponential dependency depth layer failure occurs back theoretical result experiment illustrating extent prediction match dependency network parameter robustness result show robustness neural network average crash estimated without need neither test network failure configuration access training set used train network practically impossible requirement Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jiaxin Shi', 'Jianfei Chen', 'Jun Zhu', 'Shengyang Sun', 'Yucen Luo', 'Yihong Gu', 'Yuhao Zhou']

1709.05870v1

In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike existing deep learning libraries, which are mainly designed for deterministic neural networks and supervised tasks, ZhuSuan is featured for its deep root into Bayesian inference, thus supporting various kinds of probabilistic models, including both the traditional hierarchical Bayesian models and recent deep generative models. We use running examples to illustrate the probabilistic programming on ZhuSuan, including Bayesian logistic regression, variational auto-encoders, deep sigmoid belief networks and Bayesian recurrent neural networks.

ZhuSuan: A Library for Bayesian Deep Learning

http://arxiv.org/pdf/1709.05870v1

Title ZhuSuan Library Bayesian Deep Learning Summary paper introduce ZhuSuan python probabilistic programming library Bayesian deep learning conjoins complimentary advantage Bayesian method deep learning ZhuSuan built upon Tensorflow Unlike existing deep learning library mainly designed deterministic neural network supervised task ZhuSuan featured deep root Bayesian inference thus supporting various kind probabilistic model including traditional hierarchical Bayesian model recent deep generative model use running example illustrate probabilistic programming ZhuSuan including Bayesian logistic regression variational autoencoders deep sigmoid belief network Bayesian recurrent neural network Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Konstantinos Chatzilygeroudis', 'Jean-Baptiste Mouret']

1709.06917v2

The most data-efficient algorithms for reinforcement learning in robotics are model-based policy search algorithms, which alternate between learning a dynamical model of the robot and optimizing a policy to maximize the expected return given the model and its uncertainties. Among the few proposed approaches, the recently introduced Black-DROPS algorithm exploits a black-box optimization algorithm to achieve both high data-efficiency and good computation times when several cores are used; nevertheless, like all model-based policy search approaches, Black-DROPS does not scale to high dimensional state/action spaces. In this paper, we introduce a new model learning procedure in Black-DROPS that leverages parameterized black-box priors to (1) scale up to high-dimensional systems, and (2) be robust to large inaccuracies of the prior information. We demonstrate the effectiveness of our approach with the "pendubot" swing-up task in simulation and with a physical hexapod robot (48D state space, 18D action space) that has to walk forward as fast as possible. The results show that our new algorithm is more data-efficient than previous model-based policy search algorithms (with and without priors) and that it can allow a physical 6-legged robot to learn new gaits in only 16 to 30 seconds of interaction time.

Using Parameterized Black-Box Priors to Scale Up Model-Based Policy Search for Robotics

http://arxiv.org/pdf/1709.06917v2

Title Using Parameterized BlackBox Priors Scale ModelBased Policy Search Robotics Summary dataefficient algorithm reinforcement learning robotics modelbased policy search algorithm alternate learning dynamical model robot optimizing policy maximize expected return given model uncertainty Among proposed approach recently introduced BlackDROPS algorithm exploit blackbox optimization algorithm achieve high dataefficiency good computation time several core used nevertheless like modelbased policy search approach BlackDROPS scale high dimensional stateaction space paper introduce new model learning procedure BlackDROPS leverage parameterized blackbox prior 1 scale highdimensional system 2 robust large inaccuracy prior information demonstrate effectiveness approach pendubot swingup task simulation physical hexapod robot 48D state space 18D action space walk forward fast possible result show new algorithm dataefficient previous modelbased policy search algorithm without prior allow physical 6legged robot learn new gait 16 30 second interaction time Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Rémi Pautrat', 'Konstantinos Chatzilygeroudis', 'Jean-Baptiste Mouret']

1709.06919v2

One of the most interesting features of Bayesian optimization for direct policy search is that it can leverage priors (e.g., from simulation or from previous tasks) to accelerate learning on a robot. In this paper, we are interested in situations for which several priors exist but we do not know in advance which one fits best the current situation. We tackle this problem by introducing a novel acquisition function, called Most Likely Expected Improvement (MLEI), that combines the likelihood of the priors and the expected improvement. We evaluate this new acquisition function on a transfer learning task for a 5-DOF planar arm and on a possibly damaged, 6-legged robot that has to learn to walk on flat ground and on stairs, with priors corresponding to different stairs and different kinds of damages. Our results show that MLEI effectively identifies and exploits the priors, even when there is no obvious match between the current situations and the priors.

Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy Search

http://arxiv.org/pdf/1709.06919v2

Title Bayesian Optimization Automatic Prior Selection DataEfficient Direct Policy Search Summary One interesting feature Bayesian optimization direct policy search leverage prior eg simulation previous task accelerate learning robot paper interested situation several prior exist know advance one fit best current situation tackle problem introducing novel acquisition function called Likely Expected Improvement MLEI combine likelihood prior expected improvement evaluate new acquisition function transfer learning task 5DOF planar arm possibly damaged 6legged robot learn walk flat ground stair prior corresponding different stair different kind damage result show MLEI effectively identifies exploit prior even obvious match current situation prior Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Thiago Serra', 'Christian Tjandraatmadja', 'Srikumar Ramalingam']

1711.02114v2

In this paper, we study the representational power of deep neural networks (DNN) that belong to the family of piecewise-linear (PWL) functions, based on PWL activation units such as rectifier or maxout. We investigate the complexity of such networks by studying the number of linear regions of the PWL function. Typically, a PWL function from a DNN can be seen as a large family of linear functions acting on millions of such regions. We directly build upon the work of Montufar et al. (2014), Montufar (2017) and Raghu et al. (2017) by refining the upper and lower bounds on the number of linear regions for rectified and maxout networks. In addition to achieving tighter bounds, we also develop a novel method to perform exact enumeration or counting of the number of linear regions with a mixed-integer linear formulation that maps the input space to output. We use this new capability to visualize how the number of linear regions change while training DNNs.

Bounding and Counting Linear Regions of Deep Neural Networks

http://arxiv.org/pdf/1711.02114v2

Title Bounding Counting Linear Regions Deep Neural Networks Summary paper study representational power deep neural network DNN belong family piecewiselinear PWL function based PWL activation unit rectifier maxout investigate complexity network studying number linear region PWL function Typically PWL function DNN seen large family linear function acting million region directly build upon work Montufar et al 2014 Montufar 2017 Raghu et al 2017 refining upper lower bound number linear region rectified maxout network addition achieving tighter bound also develop novel method perform exact enumeration counting number linear region mixedinteger linear formulation map input space output use new capability visualize number linear region change training DNNs Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Guillaume Bellec', 'David Kappel', 'Wolfgang Maass', 'Robert Legenstein']

1711.05136v4

Neuromorphic hardware tends to pose limits on the connectivity of deep networks that one can run on them. But also generic hardware and software implementations of deep learning run more efficiently for sparse networks. Several methods exist for pruning connections of a neural network after it was trained without connectivity constraints. We present an algorithm, DEEP R, that enables us to train directly a sparsely connected neural network. DEEP R automatically rewires the network during supervised training so that connections are there where they are most needed for the task, while its total number is all the time strictly bounded. We demonstrate that DEEP R can be used to train very sparse feedforward and recurrent neural networks on standard benchmark tasks with just a minor loss in performance. DEEP R is based on a rigorous theoretical foundation that views rewiring as stochastic sampling of network configurations from a posterior.

Deep Rewiring: Training very sparse deep networks

http://arxiv.org/pdf/1711.05136v4

Title Deep Rewiring Training sparse deep network Summary Neuromorphic hardware tends pose limit connectivity deep network one run also generic hardware software implementation deep learning run efficiently sparse network Several method exist pruning connection neural network trained without connectivity constraint present algorithm DEEP R enables u train directly sparsely connected neural network DEEP R automatically rewires network supervised training connection needed task total number time strictly bounded demonstrate DEEP R used train sparse feedforward recurrent neural network standard benchmark task minor loss performance DEEP R based rigorous theoretical foundation view rewiring stochastic sampling network configuration posterior Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Artit Wangperawong', 'Kettip Kriangchaivech', 'Austin Lanari', 'Supui Lam', 'Panthong Wangperawong']

1801.03143v1

To compare entities of differing types and structural components, the artificial neural network paradigm was used to cross-compare structural components between heterogeneous documents. Trainable weighted structural components were input into machine-learned activation functions of the neurons. The model was used for matching news articles and videos, where the inputs and activation functions respectively consisted of term vectors and cosine similarity measures between the weighted structural components. The model was tested with different weights, achieving as high as 59.2% accuracy for matching videos to news articles. A mobile application user interface for recommending related videos for news articles was developed to demonstrate consumer value, including its potential usefulness for cross-selling products from unrelated categories.

Comparing heterogeneous entities using artificial neural networks of trainable weighted structural components and machine-learned activation functions

http://arxiv.org/pdf/1801.03143v1

Title Comparing heterogeneous entity using artificial neural network trainable weighted structural component machinelearned activation function Summary compare entity differing type structural component artificial neural network paradigm used crosscompare structural component heterogeneous document Trainable weighted structural component input machinelearned activation function neuron model used matching news article video input activation function respectively consisted term vector cosine similarity measure weighted structural component model tested different weight achieving high 592 accuracy matching video news article mobile application user interface recommending related video news article developed demonstrate consumer value including potential usefulness crossselling product unrelated category Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Adrien Baranes', 'Pierre-Yves Oudeyer']

1301.4862v1

We introduce the Self-Adaptive Goal Generation - Robust Intelligent Adaptive Curiosity (SAGG-RIAC) architecture as an intrinsi- cally motivated goal exploration mechanism which allows active learning of inverse models in high-dimensional redundant robots. This allows a robot to efficiently and actively learn distributions of parameterized motor skills/policies that solve a corresponding distribution of parameterized tasks/goals. The architecture makes the robot sample actively novel parameterized tasks in the task space, based on a measure of competence progress, each of which triggers low-level goal-directed learning of the motor policy pa- rameters that allow to solve it. For both learning and generalization, the system leverages regression techniques which allow to infer the motor policy parameters corresponding to a given novel parameterized task, and based on the previously learnt correspondences between policy and task parameters. We present experiments with high-dimensional continuous sensorimotor spaces in three different robotic setups: 1) learning the inverse kinematics in a highly-redundant robotic arm, 2) learning omnidirectional locomotion with motor primitives in a quadruped robot, 3) an arm learning to control a fishing rod with a flexible wire. We show that 1) exploration in the task space can be a lot faster than exploration in the actuator space for learning inverse models in redundant robots; 2) selecting goals maximizing competence progress creates developmental trajectories driving the robot to progressively focus on tasks of increasing complexity and is statistically significantly more efficient than selecting tasks randomly, as well as more efficient than different standard active motor babbling methods; 3) this architecture allows the robot to actively discover which parts of its task space it can learn to reach and which part it cannot.

Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in Robots

http://arxiv.org/pdf/1301.4862v1

Title Active Learning Inverse Models Intrinsically Motivated Goal Exploration Robots Summary introduce SelfAdaptive Goal Generation Robust Intelligent Adaptive Curiosity SAGGRIAC architecture intrinsi cally motivated goal exploration mechanism allows active learning inverse model highdimensional redundant robot allows robot efficiently actively learn distribution parameterized motor skillspolicies solve corresponding distribution parameterized tasksgoals architecture make robot sample actively novel parameterized task task space based measure competence progress trigger lowlevel goaldirected learning motor policy pa rameters allow solve learning generalization system leverage regression technique allow infer motor policy parameter corresponding given novel parameterized task based previously learnt correspondence policy task parameter present experiment highdimensional continuous sensorimotor space three different robotic setup 1 learning inverse kinematics highlyredundant robotic arm 2 learning omnidirectional locomotion motor primitive quadruped robot 3 arm learning control fishing rod flexible wire show 1 exploration task space lot faster exploration actuator space learning inverse model redundant robot 2 selecting goal maximizing competence progress creates developmental trajectory driving robot progressively focus task increasing complexity statistically significantly efficient selecting task randomly well efficient different standard active motor babbling method 3 architecture allows robot actively discover part task space learn reach part cannot Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Peter Ondruska', 'Julie Dequaire', 'Dominic Zeng Wang', 'Ingmar Posner']

1604.05091v2

In this work we present a novel end-to-end framework for tracking and classifying a robot's surroundings in complex, dynamic and only partially observable real-world environments. The approach deploys a recurrent neural network to filter an input stream of raw laser measurements in order to directly infer object locations, along with their identity in both visible and occluded areas. To achieve this we first train the network using unsupervised Deep Tracking, a recently proposed theoretical framework for end-to-end space occupancy prediction. We show that by learning to track on a large amount of unsupervised data, the network creates a rich internal representation of its environment which we in turn exploit through the principle of inductive transfer of knowledge to perform the task of it's semantic classification. As a result, we show that only a small amount of labelled data suffices to steer the network towards mastering this additional task. Furthermore we propose a novel recurrent neural network architecture specifically tailored to tracking and semantic classification in real-world robotics applications. We demonstrate the tracking and classification performance of the method on real-world data collected at a busy road junction. Our evaluation shows that the proposed end-to-end framework compares favourably to a state-of-the-art, model-free tracking solution and that it outperforms a conventional one-shot training scheme for semantic classification.

End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks

http://arxiv.org/pdf/1604.05091v2

Title EndtoEnd Tracking Semantic Segmentation Using Recurrent Neural Networks Summary work present novel endtoend framework tracking classifying robot surroundings complex dynamic partially observable realworld environment approach deploys recurrent neural network filter input stream raw laser measurement order directly infer object location along identity visible occluded area achieve first train network using unsupervised Deep Tracking recently proposed theoretical framework endtoend space occupancy prediction show learning track large amount unsupervised data network creates rich internal representation environment turn exploit principle inductive transfer knowledge perform task semantic classification result show small amount labelled data suffices steer network towards mastering additional task Furthermore propose novel recurrent neural network architecture specifically tailored tracking semantic classification realworld robotics application demonstrate tracking classification performance method realworld data collected busy road junction evaluation show proposed endtoend framework compare favourably stateoftheart modelfree tracking solution outperforms conventional oneshot training scheme semantic classification Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Peter Ondruska', 'Ingmar Posner']

1602.00991v2

This paper presents to the best of our knowledge the first end-to-end object tracking approach which directly maps from raw sensor input to object tracks in sensor space without requiring any feature engineering or system identification in the form of plant or sensor models. Specifically, our system accepts a stream of raw sensor data at one end and, in real-time, produces an estimate of the entire environment state at the output including even occluded objects. We achieve this by framing the problem as a deep learning task and exploit sequence models in the form of recurrent neural networks to learn a mapping from sensor measurements to object tracks. In particular, we propose a learning method based on a form of input dropout which allows learning in an unsupervised manner, only based on raw, occluded sensor data without access to ground-truth annotations. We demonstrate our approach using a synthetic dataset designed to mimic the task of tracking objects in 2D laser data -- as commonly encountered in robotics applications -- and show that it learns to track many dynamic objects despite occlusions and the presence of sensor noise.

Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks

http://arxiv.org/pdf/1602.00991v2

Title Deep Tracking Seeing Beyond Seeing Using Recurrent Neural Networks Summary paper present best knowledge first endtoend object tracking approach directly map raw sensor input object track sensor space without requiring feature engineering system identification form plant sensor model Specifically system accepts stream raw sensor data one end realtime produce estimate entire environment state output including even occluded object achieve framing problem deep learning task exploit sequence model form recurrent neural network learn mapping sensor measurement object track particular propose learning method based form input dropout allows learning unsupervised manner based raw occluded sensor data without access groundtruth annotation demonstrate approach using synthetic dataset designed mimic task tracking object 2D laser data commonly encountered robotics application show learns track many dynamic object despite occlusion presence sensor noise Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['William Lotter', 'Gabriel Kreiman', 'David Cox']

1605.08104v5

While great strides have been made in using deep learning algorithms to solve supervised learning tasks, the problem of unsupervised learning - leveraging unlabeled examples to learn about the structure of a domain - remains a difficult unsolved challenge. Here, we explore prediction of future frames in a video sequence as an unsupervised learning rule for learning about the structure of the visual world. We describe a predictive neural network ("PredNet") architecture that is inspired by the concept of "predictive coding" from the neuroscience literature. These networks learn to predict future frames in a video sequence, with each layer in the network making local predictions and only forwarding deviations from those predictions to subsequent network layers. We show that these networks are able to robustly learn to predict the movement of synthetic (rendered) objects, and that in doing so, the networks learn internal representations that are useful for decoding latent object parameters (e.g. pose) that support object recognition with fewer training views. We also show that these networks can scale to complex natural image streams (car-mounted camera videos), capturing key aspects of both egocentric movement and the movement of objects in the visual scene, and the representation learned in this setting is useful for estimating the steering angle. Altogether, these results suggest that prediction represents a powerful framework for unsupervised learning, allowing for implicit learning of object and scene structure.

Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning

http://arxiv.org/pdf/1605.08104v5

Title Deep Predictive Coding Networks Video Prediction Unsupervised Learning Summary great stride made using deep learning algorithm solve supervised learning task problem unsupervised learning leveraging unlabeled example learn structure domain remains difficult unsolved challenge explore prediction future frame video sequence unsupervised learning rule learning structure visual world describe predictive neural network PredNet architecture inspired concept predictive coding neuroscience literature network learn predict future frame video sequence layer network making local prediction forwarding deviation prediction subsequent network layer show network able robustly learn predict movement synthetic rendered object network learn internal representation useful decoding latent object parameter eg pose support object recognition fewer training view also show network scale complex natural image stream carmounted camera video capturing key aspect egocentric movement movement object visual scene representation learned setting useful estimating steering angle Altogether result suggest prediction represents powerful framework unsupervised learning allowing implicit learning object scene structure Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Martin Engelcke', 'Dushyant Rao', 'Dominic Zeng Wang', 'Chi Hay Tong', 'Ingmar Posner']

1609.06666v2

This paper proposes a computationally efficient approach to detecting objects natively in 3D point clouds using convolutional neural networks (CNNs). In particular, this is achieved by leveraging a feature-centric voting scheme to implement novel convolutional layers which explicitly exploit the sparsity encountered in the input. To this end, we examine the trade-off between accuracy and speed for different architectures and additionally propose to use an L1 penalty on the filter activations to further encourage sparsity in the intermediate representations. To the best of our knowledge, this is the first work to propose sparse convolutional layers and L1 regularisation for efficient large-scale processing of 3D data. We demonstrate the efficacy of our approach on the KITTI object detection benchmark and show that Vote3Deep models with as few as three layers outperform the previous state of the art in both laser and laser-vision based approaches by margins of up to 40% while remaining highly competitive in terms of processing time.

Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks

http://arxiv.org/pdf/1609.06666v2

Title Vote3Deep Fast Object Detection 3D Point Clouds Using Efficient Convolutional Neural Networks Summary paper proposes computationally efficient approach detecting object natively 3D point cloud using convolutional neural network CNNs particular achieved leveraging featurecentric voting scheme implement novel convolutional layer explicitly exploit sparsity encountered input end examine tradeoff accuracy speed different architecture additionally propose use L1 penalty filter activation encourage sparsity intermediate representation best knowledge first work propose sparse convolutional layer L1 regularisation efficient largescale processing 3D data demonstrate efficacy approach KITTI object detection benchmark show Vote3Deep model three layer outperform previous state art laser laservision based approach margin 40 remaining highly competitive term processing time Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Naveen Kodali', 'Jacob Abernethy', 'James Hays', 'Zsolt Kira']

1705.07215v5

We propose studying GAN training dynamics as regret minimization, which is in contrast to the popular view that there is consistent minimization of a divergence between real and generated distributions. We analyze the convergence of GAN training from this new point of view to understand why mode collapse happens. We hypothesize the existence of undesirable local equilibria in this non-convex game to be responsible for mode collapse. We observe that these local equilibria often exhibit sharp gradients of the discriminator function around some real data points. We demonstrate that these degenerate local equilibria can be avoided with a gradient penalty scheme called DRAGAN. We show that DRAGAN enables faster training, achieves improved stability with fewer mode collapses, and leads to generator networks with better modeling performance across a variety of architectures and objective functions.

On Convergence and Stability of GANs

http://arxiv.org/pdf/1705.07215v5

Title Convergence Stability GANs Summary propose studying GAN training dynamic regret minimization contrast popular view consistent minimization divergence real generated distribution analyze convergence GAN training new point view understand mode collapse happens hypothesize existence undesirable local equilibrium nonconvex game responsible mode collapse observe local equilibrium often exhibit sharp gradient discriminator function around real data point demonstrate degenerate local equilibrium avoided gradient penalty scheme called DRAGAN show DRAGAN enables faster training achieves improved stability fewer mode collapse lead generator network better modeling performance across variety architecture objective function Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['YuXuan Liu', 'Abhishek Gupta', 'Pieter Abbeel', 'Sergey Levine']

1707.03374v1

Imitation learning is an effective approach for autonomous systems to acquire control policies when an explicit reward function is unavailable, using supervision provided as demonstrations from an expert, typically a human operator. However, standard imitation learning methods assume that the agent receives examples of observation-action tuples that could be provided, for instance, to a supervised learning algorithm. This stands in contrast to how humans and animals imitate: we observe another person performing some behavior and then figure out which actions will realize that behavior, compensating for changes in viewpoint, surroundings, and embodiment. We term this kind of imitation learning as imitation-from-observation and propose an imitation learning method based on video prediction with context translation and deep reinforcement learning. This lifts the assumption in imitation learning that the demonstration should consist of observations and actions in the same environment, and enables a variety of interesting applications, including learning robotic skills that involve tool use simply by observing videos of human tool use. Our experimental results show that our approach can perform imitation-from-observation for a variety of real-world robotic tasks modeled on common household chores, acquiring skills such as sweeping from videos of a human demonstrator. Videos can be found at https://sites.google.com/site/imitationfromobservation

Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation

http://arxiv.org/pdf/1707.03374v1

Title Imitation Observation Learning Imitate Behaviors Raw Video via Context Translation Summary Imitation learning effective approach autonomous system acquire control policy explicit reward function unavailable using supervision provided demonstration expert typically human operator However standard imitation learning method assume agent receives example observationaction tuples could provided instance supervised learning algorithm stand contrast human animal imitate observe another person performing behavior figure action realize behavior compensating change viewpoint surroundings embodiment term kind imitation learning imitationfromobservation propose imitation learning method based video prediction context translation deep reinforcement learning lift assumption imitation learning demonstration consist observation action environment enables variety interesting application including learning robotic skill involve tool use simply observing video human tool use experimental result show approach perform imitationfromobservation variety realworld robotic task modeled common household chore acquiring skill sweeping video human demonstrator Videos found httpssitesgooglecomsiteimitationfromobservation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Vamsi K. Ithapu', 'Sathya Ravi', 'Vikas Singh']

1506.03412v3

Unsupervised pretraining and dropout have been well studied, especially with respect to regularization and output consistency. However, our understanding about the explicit convergence rates of the parameter estimates, and their dependence on the learning (like denoising and dropout rate) and structural (like depth and layer lengths) aspects of the network is less mature. An interesting question in this context is to ask if the network structure could "guide" the choices of such learning parameters. In this work, we explore these gaps between network structure, the learning mechanisms and their interaction with parameter convergence rates. We present a way to address these issues based on the backpropagation convergence rates for general nonconvex objectives using first-order information. We then incorporate two learning mechanisms into this general framework -- denoising autoencoder and dropout, and subsequently derive the convergence rates of deep networks. Building upon these bounds, we provide insights into the choices of learning parameters and network sizes that achieve certain levels of convergence accuracy. The results derived here support existing empirical observations, and we also conduct a set of experiments to evaluate them.

Convergence rates for pretraining and dropout: Guiding learning parameters using network structure

http://arxiv.org/pdf/1506.03412v3

Title Convergence rate pretraining dropout Guiding learning parameter using network structure Summary Unsupervised pretraining dropout well studied especially respect regularization output consistency However understanding explicit convergence rate parameter estimate dependence learning like denoising dropout rate structural like depth layer length aspect network le mature interesting question context ask network structure could guide choice learning parameter work explore gap network structure learning mechanism interaction parameter convergence rate present way address issue based backpropagation convergence rate general nonconvex objective using firstorder information incorporate two learning mechanism general framework denoising autoencoder dropout subsequently derive convergence rate deep network Building upon bound provide insight choice learning parameter network size achieve certain level convergence accuracy result derived support existing empirical observation also conduct set experiment evaluate Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Zhuolin Jiang', 'Yaming Wang', 'Larry Davis', 'Walt Andrews', 'Viktor Rozgic']

1602.01168v2

Deep Convolutional Neural Networks (CNN) enforces supervised information only at the output layer, and hidden layers are trained by back propagating the prediction error from the output layer without explicit supervision. We propose a supervised feature learning approach, Label Consistent Neural Network, which enforces direct supervision in late hidden layers. We associate each neuron in a hidden layer with a particular class label and encourage it to be activated for input signals from the same class. More specifically, we introduce a label consistency regularization called "discriminative representation error" loss for late hidden layers and combine it with classification error loss to build our overall objective function. This label consistency constraint alleviates the common problem of gradient vanishing and tends to faster convergence; it also makes the features derived from late hidden layers discriminative enough for classification even using a simple $k$-NN classifier, since input signals from the same class will have very similar representations. Experimental results demonstrate that our approach achieves state-of-the-art performances on several public benchmarks for action and object category recognition.

Learning Discriminative Features via Label Consistent Neural Network

http://arxiv.org/pdf/1602.01168v2

Title Learning Discriminative Features via Label Consistent Neural Network Summary Deep Convolutional Neural Networks CNN enforces supervised information output layer hidden layer trained back propagating prediction error output layer without explicit supervision propose supervised feature learning approach Label Consistent Neural Network enforces direct supervision late hidden layer associate neuron hidden layer particular class label encourage activated input signal class specifically introduce label consistency regularization called discriminative representation error loss late hidden layer combine classification error loss build overall objective function label consistency constraint alleviates common problem gradient vanishing tends faster convergence also make feature derived late hidden layer discriminative enough classification even using simple kNN classifier since input signal class similar representation Experimental result demonstrate approach achieves stateoftheart performance several public benchmark action object category recognition Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hamid Dadkhahi', 'Marco F. Duarte', 'Benjamin Marlin']

1606.08282v3

This paper proposes an out-of-sample extension framework for a global manifold learning algorithm (Isomap) that uses temporal information in out-of-sample points in order to make the embedding more robust to noise and artifacts. Given a set of noise-free training data and its embedding, the proposed framework extends the embedding for a noisy time series. This is achieved by adding a spatio-temporal compactness term to the optimization objective of the embedding. To the best of our knowledge, this is the first method for out-of-sample extension of manifold embeddings that leverages timing information available for the extension set. Experimental results demonstrate that our out-of-sample extension algorithm renders a more robust and accurate embedding of sequentially ordered image data in the presence of various noise and artifacts when compared to other timing-aware embeddings. Additionally, we show that an out-of-sample extension framework based on the proposed algorithm outperforms the state of the art in eye-gaze estimation.

Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series

http://arxiv.org/pdf/1606.08282v3

Title OutofSample Extension Dimensionality Reduction Noisy Time Series Summary paper proposes outofsample extension framework global manifold learning algorithm Isomap us temporal information outofsample point order make embedding robust noise artifact Given set noisefree training data embedding proposed framework extends embedding noisy time series achieved adding spatiotemporal compactness term optimization objective embedding best knowledge first method outofsample extension manifold embeddings leverage timing information available extension set Experimental result demonstrate outofsample extension algorithm render robust accurate embedding sequentially ordered image data presence various noise artifact compared timingaware embeddings Additionally show outofsample extension framework based proposed algorithm outperforms state art eyegaze estimation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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