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['Chengxi Ye', 'Yezhou Yang', 'Cornelia Fermuller', 'Yiannis Aloimonos']

1708.00631v1

We explain that the difficulties of training deep neural networks come from a syndrome of three consistency issues. This paper describes our efforts in their analysis and treatment. The first issue is the training speed inconsistency in different layers. We propose to address it with an intuitive, simple-to-implement, low footprint second-order method. The second issue is the scale inconsistency between the layer inputs and the layer residuals. We explain how second-order information provides favorable convenience in removing this roadblock. The third and most challenging issue is the inconsistency in residual propagation. Based on the fundamental theorem of linear algebra, we provide a mathematical characterization of the famous vanishing gradient problem. Thus, an important design principle for future optimization and neural network design is derived. We conclude this paper with the construction of a novel contractive neural network.

On the Importance of Consistency in Training Deep Neural Networks

http://arxiv.org/pdf/1708.00631v1

Title Importance Consistency Training Deep Neural Networks Summary explain difficulty training deep neural network come syndrome three consistency issue paper describes effort analysis treatment first issue training speed inconsistency different layer propose address intuitive simpletoimplement low footprint secondorder method second issue scale inconsistency layer input layer residual explain secondorder information provides favorable convenience removing roadblock third challenging issue inconsistency residual propagation Based fundamental theorem linear algebra provide mathematical characterization famous vanishing gradient problem Thus important design principle future optimization neural network design derived conclude paper construction novel contractive 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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['Mario Amrehn', 'Sven Gaube', 'Mathias Unberath', 'Frank Schebesch', 'Tim Horz', 'Maddalena Strumia', 'Stefan Steidl', 'Markus Kowarschik', 'Andreas Maier']

1709.03450v1

For complex segmentation tasks, fully automatic systems are inherently limited in their achievable accuracy for extracting relevant objects. Especially in cases where only few data sets need to be processed for a highly accurate result, semi-automatic segmentation techniques exhibit a clear benefit for the user. One area of application is medical image processing during an intervention for a single patient. We propose a learning-based cooperative segmentation approach which includes the computing entity as well as the user into the task. Our system builds upon a state-of-the-art fully convolutional artificial neural network (FCN) as well as an active user model for training. During the segmentation process, a user of the trained system can iteratively add additional hints in form of pictorial scribbles as seed points into the FCN system to achieve an interactive and precise segmentation result. The segmentation quality of interactive FCNs is evaluated. Iterative FCN approaches can yield superior results compared to networks without the user input channel component, due to a consistent improvement in segmentation quality after each interaction.

UI-Net: Interactive Artificial Neural Networks for Iterative Image Segmentation Based on a User Model

http://arxiv.org/pdf/1709.03450v1

Title UINet Interactive Artificial Neural Networks Iterative Image Segmentation Based User Model Summary complex segmentation task fully automatic system inherently limited achievable accuracy extracting relevant object Especially case data set need processed highly accurate result semiautomatic segmentation technique exhibit clear benefit user One area application medical image processing intervention single patient propose learningbased cooperative segmentation approach includes computing entity well user task system build upon stateoftheart fully convolutional artificial neural network FCN well active user model training segmentation process user trained system iteratively add additional hint form pictorial scribble seed point FCN system achieve interactive precise segmentation result segmentation quality interactive FCNs evaluated Iterative FCN approach yield superior result compared network without user input channel component due consistent improvement segmentation quality interaction 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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['Altaf H. Khan']

1712.05695v1

Most of the weights in a Lightweight Neural Network have a value of zero, while the remaining ones are either +1 or -1. These universal approximators require approximately 1.1 bits/weight of storage, posses a quick forward pass and achieve classification accuracies similar to conventional continuous-weight networks. Their training regimen focuses on error reduction initially, but later emphasizes discretization of weights. They ignore insignificant inputs, remove unnecessary weights, and drop unneeded hidden neurons. We have successfully tested them on the MNIST, credit card fraud, and credit card defaults data sets using networks having 2 to 16 hidden layers and up to 4.4 million weights.

Lightweight Neural Networks

http://arxiv.org/pdf/1712.05695v1

Title Lightweight Neural Networks Summary weight Lightweight Neural Network value zero remaining one either 1 1 universal approximators require approximately 11 bitsweight storage posse quick forward pas achieve classification accuracy similar conventional continuousweight network training regimen focus error reduction initially later emphasizes discretization weight ignore insignificant input remove unnecessary weight drop unneeded hidden neuron successfully tested MNIST credit card fraud credit card default data set using network 2 16 hidden layer 44 million weight 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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['Nathaniel Thomas', 'Tess Smidt', 'Steven Kearnes', 'Lusann Yang', 'Li Li', 'Kai Kohlhoff', 'Patrick Riley']

1802.08219v2

We introduce tensor field networks, which are locally equivariant to 3D rotations, translations, and permutations of points at every layer. 3D rotation equivariance removes the need for data augmentation to identify features in arbitrary orientations. Our network uses filters built from spherical harmonics; due to the mathematical consequences of this filter choice, each layer accepts as input (and guarantees as output) scalars, vectors, and higher-order tensors, in the geometric sense of these terms. We demonstrate how tensor field networks learn to model simple physics (Newtonian gravitation and moment of inertia), classify simple 3D shapes (trained on one orientation and tested on shapes in arbitrary orientations), and, given a small organic molecule with an atom removed, replace the correct element at the correct location in space.

Tensor Field Networks: Rotation- and Translation-Equivariant Neural Networks for 3D Point Clouds

http://arxiv.org/pdf/1802.08219v2

Title Tensor Field Networks Rotation TranslationEquivariant Neural Networks 3D Point Clouds Summary introduce tensor field network locally equivariant 3D rotation translation permutation point every layer 3D rotation equivariance remove need data augmentation identify feature arbitrary orientation network us filter built spherical harmonic due mathematical consequence filter choice layer accepts input guarantee output scalar vector higherorder tensor geometric sense term demonstrate tensor field network learn model simple physic Newtonian gravitation moment inertia classify simple 3D shape trained one orientation tested shape arbitrary orientation given small organic molecule atom removed replace correct element correct location 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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['Çağlar Gülçehre', 'Yoshua Bengio']

1301.4083v6

We explore the effect of introducing prior information into the intermediate level of neural networks for a learning task on which all the state-of-the-art machine learning algorithms tested failed to learn. We motivate our work from the hypothesis that humans learn such intermediate concepts from other individuals via a form of supervision or guidance using a curriculum. The experiments we have conducted provide positive evidence in favor of this hypothesis. In our experiments, a two-tiered MLP architecture is trained on a dataset with 64x64 binary inputs images, each image with three sprites. The final task is to decide whether all the sprites are the same or one of them is different. Sprites are pentomino tetris shapes and they are placed in an image with different locations using scaling and rotation transformations. The first part of the two-tiered MLP is pre-trained with intermediate-level targets being the presence of sprites at each location, while the second part takes the output of the first part as input and predicts the final task's target binary event. The two-tiered MLP architecture, with a few tens of thousand examples, was able to learn the task perfectly, whereas all other algorithms (include unsupervised pre-training, but also traditional algorithms like SVMs, decision trees and boosting) all perform no better than chance. We hypothesize that the optimization difficulty involved when the intermediate pre-training is not performed is due to the {\em composition} of two highly non-linear tasks. Our findings are also consistent with hypotheses on cultural learning inspired by the observations of optimization problems with deep learning, presumably because of effective local minima.

Knowledge Matters: Importance of Prior Information for Optimization

http://arxiv.org/pdf/1301.4083v6

Title Knowledge Matters Importance Prior Information Optimization Summary explore effect introducing prior information intermediate level neural network learning task stateoftheart machine learning algorithm tested failed learn motivate work hypothesis human learn intermediate concept individual via form supervision guidance using curriculum experiment conducted provide positive evidence favor hypothesis experiment twotiered MLP architecture trained dataset 64x64 binary input image image three sprite final task decide whether sprite one different Sprites pentomino tetri shape placed image different location using scaling rotation transformation first part twotiered MLP pretrained intermediatelevel target presence sprite location second part take output first part input predicts final task target binary event twotiered MLP architecture ten thousand example able learn task perfectly whereas algorithm include unsupervised pretraining also traditional algorithm like SVMs decision tree boosting perform better chance hypothesize optimization difficulty involved intermediate pretraining performed due em composition two highly nonlinear task finding also consistent hypothesis cultural learning inspired observation optimization problem deep learning presumably effective local minimum 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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['Kishore Konda', 'Roland Memisevic', 'David Krueger']

1402.3337v5

Regularized training of an autoencoder typically results in hidden unit biases that take on large negative values. We show that negative biases are a natural result of using a hidden layer whose responsibility is to both represent the input data and act as a selection mechanism that ensures sparsity of the representation. We then show that negative biases impede the learning of data distributions whose intrinsic dimensionality is high. We also propose a new activation function that decouples the two roles of the hidden layer and that allows us to learn representations on data with very high intrinsic dimensionality, where standard autoencoders typically fail. Since the decoupled activation function acts like an implicit regularizer, the model can be trained by minimizing the reconstruction error of training data, without requiring any additional regularization.

Zero-bias autoencoders and the benefits of co-adapting features

http://arxiv.org/pdf/1402.3337v5

Title Zerobias autoencoders benefit coadapting feature Summary Regularized training autoencoder typically result hidden unit bias take large negative value show negative bias natural result using hidden layer whose responsibility represent input data act selection mechanism ensures sparsity representation show negative bias impede learning data distribution whose intrinsic dimensionality high also propose new activation function decouples two role hidden layer allows u learn representation data high intrinsic dimensionality standard autoencoders typically fail Since decoupled activation function act like implicit regularizer model trained minimizing reconstruction error training data without requiring additional regularization 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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['Bodo Rueckauer', 'Iulia-Alexandra Lungu', 'Yuhuang Hu', 'Michael Pfeiffer']

1612.04052v1

Deep convolutional neural networks (CNNs) have shown great potential for numerous real-world machine learning applications, but performing inference in large CNNs in real-time remains a challenge. We have previously demonstrated that traditional CNNs can be converted into deep spiking neural networks (SNNs), which exhibit similar accuracy while reducing both latency and computational load as a consequence of their data-driven, event-based style of computing. Here we provide a novel theory that explains why this conversion is successful, and derive from it several new tools to convert a larger and more powerful class of deep networks into SNNs. We identify the main sources of approximation errors in previous conversion methods, and propose simple mechanisms to fix these issues. Furthermore, we develop spiking implementations of common CNN operations such as max-pooling, softmax, and batch-normalization, which allow almost loss-less conversion of arbitrary CNN architectures into the spiking domain. Empirical evaluation of different network architectures on the MNIST and CIFAR10 benchmarks leads to the best SNN results reported to date.

Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks

http://arxiv.org/pdf/1612.04052v1

Title Theory Tools Conversion Analog Spiking Convolutional Neural Networks Summary Deep convolutional neural network CNNs shown great potential numerous realworld machine learning application performing inference large CNNs realtime remains challenge previously demonstrated traditional CNNs converted deep spiking neural network SNNs exhibit similar accuracy reducing latency computational load consequence datadriven eventbased style computing provide novel theory explains conversion successful derive several new tool convert larger powerful class deep network SNNs identify main source approximation error previous conversion method propose simple mechanism fix issue Furthermore develop spiking implementation common CNN operation maxpooling softmax batchnormalization allow almost lossless conversion arbitrary CNN architecture spiking domain Empirical evaluation different network architecture MNIST CIFAR10 benchmark lead best SNN result reported date 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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['Xun Huang', 'Yixuan Li', 'Omid Poursaeed', 'John Hopcroft', 'Serge Belongie']

1612.04357v4

In this paper, we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a bottom-up discriminative network. Our model consists of a top-down stack of GANs, each learned to generate lower-level representations conditioned on higher-level representations. A representation discriminator is introduced at each feature hierarchy to encourage the representation manifold of the generator to align with that of the bottom-up discriminative network, leveraging the powerful discriminative representations to guide the generative model. In addition, we introduce a conditional loss that encourages the use of conditional information from the layer above, and a novel entropy loss that maximizes a variational lower bound on the conditional entropy of generator outputs. We first train each stack independently, and then train the whole model end-to-end. Unlike the original GAN that uses a single noise vector to represent all the variations, our SGAN decomposes variations into multiple levels and gradually resolves uncertainties in the top-down generative process. Based on visual inspection, Inception scores and visual Turing test, we demonstrate that SGAN is able to generate images of much higher quality than GANs without stacking.

Stacked Generative Adversarial Networks

http://arxiv.org/pdf/1612.04357v4

Title Stacked Generative Adversarial Networks Summary paper propose novel generative model named Stacked Generative Adversarial Networks SGAN trained invert hierarchical representation bottomup discriminative network model consists topdown stack GANs learned generate lowerlevel representation conditioned higherlevel representation representation discriminator introduced feature hierarchy encourage representation manifold generator align bottomup discriminative network leveraging powerful discriminative representation guide generative model addition introduce conditional loss encourages use conditional information layer novel entropy loss maximizes variational lower bound conditional entropy generator output first train stack independently train whole model endtoend Unlike original GAN us single noise vector represent variation SGAN decomposes variation multiple level gradually resolve uncertainty topdown generative process Based visual inspection Inception score visual Turing test demonstrate SGAN able generate image much higher quality GANs without stacking 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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['David Warde-Farley', 'Andrew Rabinovich', 'Dragomir Anguelov']

1412.6563v2

We study the problem of large scale, multi-label visual recognition with a large number of possible classes. We propose a method for augmenting a trained neural network classifier with auxiliary capacity in a manner designed to significantly improve upon an already well-performing model, while minimally impacting its computational footprint. Using the predictions of the network itself as a descriptor for assessing visual similarity, we define a partitioning of the label space into groups of visually similar entities. We then augment the network with auxilliary hidden layer pathways with connectivity only to these groups of label units. We report a significant improvement in mean average precision on a large-scale object recognition task with the augmented model, while increasing the number of multiply-adds by less than 3%.

Self-informed neural network structure learning

http://arxiv.org/pdf/1412.6563v2

Title Selfinformed neural network structure learning Summary study problem large scale multilabel visual recognition large number possible class propose method augmenting trained neural network classifier auxiliary capacity manner designed significantly improve upon already wellperforming model minimally impacting computational footprint Using prediction network descriptor assessing visual similarity define partitioning label space group visually similar entity augment network auxilliary hidden layer pathway connectivity group label unit report significant improvement mean average precision largescale object recognition task augmented model increasing number multiplyadds le 3 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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['Forest Agostinelli', 'Matthew Hoffman', 'Peter Sadowski', 'Pierre Baldi']

1412.6830v3

Artificial neural networks typically have a fixed, non-linear activation function at each neuron. We have designed a novel form of piecewise linear activation function that is learned independently for each neuron using gradient descent. With this adaptive activation function, we are able to improve upon deep neural network architectures composed of static rectified linear units, achieving state-of-the-art performance on CIFAR-10 (7.51%), CIFAR-100 (30.83%), and a benchmark from high-energy physics involving Higgs boson decay modes.

Learning Activation Functions to Improve Deep Neural Networks

http://arxiv.org/pdf/1412.6830v3

Title Learning Activation Functions Improve Deep Neural Networks Summary Artificial neural network typically fixed nonlinear activation function neuron designed novel form piecewise linear activation function learned independently neuron using gradient descent adaptive activation function able improve upon deep neural network architecture composed static rectified linear unit achieving stateoftheart performance CIFAR10 751 CIFAR100 3083 benchmark highenergy physic involving Higgs boson decay mode 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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['Antti Rasmus', 'Tapani Raiko', 'Harri Valpola']

1412.7210v4

Suitable lateral connections between encoder and decoder are shown to allow higher layers of a denoising autoencoder (dAE) to focus on invariant representations. In regular autoencoders, detailed information needs to be carried through the highest layers but lateral connections from encoder to decoder relieve this pressure. It is shown that abstract invariant features can be translated to detailed reconstructions when invariant features are allowed to modulate the strength of the lateral connection. Three dAE structures with modulated and additive lateral connections, and without lateral connections were compared in experiments using real-world images. The experiments verify that adding modulated lateral connections to the model 1) improves the accuracy of the probability model for inputs, as measured by denoising performance; 2) results in representations whose degree of invariance grows faster towards the higher layers; and 3) supports the formation of diverse invariant poolings.

Denoising autoencoder with modulated lateral connections learns invariant representations of natural images

http://arxiv.org/pdf/1412.7210v4

Title Denoising autoencoder modulated lateral connection learns invariant representation natural image Summary Suitable lateral connection encoder decoder shown allow higher layer denoising autoencoder dAE focus invariant representation regular autoencoders detailed information need carried highest layer lateral connection encoder decoder relieve pressure shown abstract invariant feature translated detailed reconstruction invariant feature allowed modulate strength lateral connection Three dAE structure modulated additive lateral connection without lateral connection compared experiment using realworld image experiment verify adding modulated lateral connection model 1 improves accuracy probability model input measured denoising performance 2 result representation whose degree invariance grows faster towards higher layer 3 support formation diverse invariant poolings 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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['Ankit B. Patel', 'Tan Nguyen', 'Richard G. Baraniuk']

1504.00641v1

A grand challenge in machine learning is the development of computational algorithms that match or outperform humans in perceptual inference tasks that are complicated by nuisance variation. For instance, visual object recognition involves the unknown object position, orientation, and scale in object recognition while speech recognition involves the unknown voice pronunciation, pitch, and speed. Recently, a new breed of deep learning algorithms have emerged for high-nuisance inference tasks that routinely yield pattern recognition systems with near- or super-human capabilities. But a fundamental question remains: Why do they work? Intuitions abound, but a coherent framework for understanding, analyzing, and synthesizing deep learning architectures has remained elusive. We answer this question by developing a new probabilistic framework for deep learning based on the Deep Rendering Model: a generative probabilistic model that explicitly captures latent nuisance variation. By relaxing the generative model to a discriminative one, we can recover two of the current leading deep learning systems, deep convolutional neural networks and random decision forests, providing insights into their successes and shortcomings, as well as a principled route to their improvement.

A Probabilistic Theory of Deep Learning

http://arxiv.org/pdf/1504.00641v1

Title Probabilistic Theory Deep Learning Summary grand challenge machine learning development computational algorithm match outperform human perceptual inference task complicated nuisance variation instance visual object recognition involves unknown object position orientation scale object recognition speech recognition involves unknown voice pronunciation pitch speed Recently new breed deep learning algorithm emerged highnuisance inference task routinely yield pattern recognition system near superhuman capability fundamental question remains work Intuitions abound coherent framework understanding analyzing synthesizing deep learning architecture remained elusive answer question developing new probabilistic framework deep learning based Deep Rendering Model generative probabilistic model explicitly capture latent nuisance variation relaxing generative model discriminative one recover two current leading deep learning system deep convolutional neural network random decision forest providing insight success shortcoming well principled route improvement 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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['Rein Houthooft', 'Filip De Turck']

1508.00451v4

Tackling pattern recognition problems in areas such as computer vision, bioinformatics, speech or text recognition is often done best by taking into account task-specific statistical relations between output variables. In structured prediction, this internal structure is used to predict multiple outputs simultaneously, leading to more accurate and coherent predictions. Structural support vector machines (SSVMs) are nonprobabilistic models that optimize a joint input-output function through margin-based learning. Because SSVMs generally disregard the interplay between unary and interaction factors during the training phase, final parameters are suboptimal. Moreover, its factors are often restricted to linear combinations of input features, limiting its generalization power. To improve prediction accuracy, this paper proposes: (i) Joint inference and learning by integration of back-propagation and loss-augmented inference in SSVM subgradient descent; (ii) Extending SSVM factors to neural networks that form highly nonlinear functions of input features. Image segmentation benchmark results demonstrate improvements over conventional SSVM training methods in terms of accuracy, highlighting the feasibility of end-to-end SSVM training with neural factors.

Integrated Inference and Learning of Neural Factors in Structural Support Vector Machines

http://arxiv.org/pdf/1508.00451v4

Title Integrated Inference Learning Neural Factors Structural Support Vector Machines Summary Tackling pattern recognition problem area computer vision bioinformatics speech text recognition often done best taking account taskspecific statistical relation output variable structured prediction internal structure used predict multiple output simultaneously leading accurate coherent prediction Structural support vector machine SSVMs nonprobabilistic model optimize joint inputoutput function marginbased learning SSVMs generally disregard interplay unary interaction factor training phase final parameter suboptimal Moreover factor often restricted linear combination input feature limiting generalization power improve prediction accuracy paper proposes Joint inference learning integration backpropagation lossaugmented inference SSVM subgradient descent ii Extending SSVM factor neural network form highly nonlinear function input feature Image segmentation benchmark result demonstrate improvement conventional SSVM training method term accuracy highlighting feasibility endtoend SSVM training neural factor 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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['Patrick W. Gallagher', 'Shuai Tang', 'Zhuowen Tu']

1511.07125v1

Top-down information plays a central role in human perception, but plays relatively little role in many current state-of-the-art deep networks, such as Convolutional Neural Networks (CNNs). This work seeks to explore a path by which top-down information can have a direct impact within current deep networks. We explore this path by learning and using "generators" corresponding to the network internal effects of three types of transformation (each a restriction of a general affine transformation): rotation, scaling, and translation. We demonstrate how these learned generators can be used to transfer top-down information to novel settings, as mediated by the "feature flows" that the transformations (and the associated generators) correspond to inside the network. Specifically, we explore three aspects: 1) using generators as part of a method for synthesizing transformed images --- given a previously unseen image, produce versions of that image corresponding to one or more specified transformations, 2) "zero-shot learning" --- when provided with a feature flow corresponding to the effect of a transformation of unknown amount, leverage learned generators as part of a method by which to perform an accurate categorization of the amount of transformation, even for amounts never observed during training, and 3) (inside-CNN) "data augmentation" --- improve the classification performance of an existing network by using the learned generators to directly provide additional training "inside the CNN".

What Happened to My Dog in That Network: Unraveling Top-down Generators in Convolutional Neural Networks

http://arxiv.org/pdf/1511.07125v1

Title Happened Dog Network Unraveling Topdown Generators Convolutional Neural Networks Summary Topdown information play central role human perception play relatively little role many current stateoftheart deep network Convolutional Neural Networks CNNs work seek explore path topdown information direct impact within current deep network explore path learning using generator corresponding network internal effect three type transformation restriction general affine transformation rotation scaling translation demonstrate learned generator used transfer topdown information novel setting mediated feature flow transformation associated generator correspond inside network Specifically explore three aspect 1 using generator part method synthesizing transformed image given previously unseen image produce version image corresponding one specified transformation 2 zeroshot learning provided feature flow corresponding effect transformation unknown amount leverage learned generator part method perform accurate categorization amount transformation even amount never observed training 3 insideCNN data augmentation improve classification performance existing network using learned generator directly provide additional training inside CNN 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 Gaidon', 'Qiao Wang', 'Yohann Cabon', 'Eleonora Vig']

1605.06457v1

Modern computer vision algorithms typically require expensive data acquisition and accurate manual labeling. In this work, we instead leverage the recent progress in computer graphics to generate fully labeled, dynamic, and photo-realistic proxy virtual worlds. We propose an efficient real-to-virtual world cloning method, and validate our approach by building and publicly releasing a new video dataset, called Virtual KITTI (see http://www.xrce.xerox.com/Research-Development/Computer-Vision/Proxy-Virtual-Worlds), automatically labeled with accurate ground truth for object detection, tracking, scene and instance segmentation, depth, and optical flow. We provide quantitative experimental evidence suggesting that (i) modern deep learning algorithms pre-trained on real data behave similarly in real and virtual worlds, and (ii) pre-training on virtual data improves performance. As the gap between real and virtual worlds is small, virtual worlds enable measuring the impact of various weather and imaging conditions on recognition performance, all other things being equal. We show these factors may affect drastically otherwise high-performing deep models for tracking.

Virtual Worlds as Proxy for Multi-Object Tracking Analysis

http://arxiv.org/pdf/1605.06457v1

Title Virtual Worlds Proxy MultiObject Tracking Analysis Summary Modern computer vision algorithm typically require expensive data acquisition accurate manual labeling work instead leverage recent progress computer graphic generate fully labeled dynamic photorealistic proxy virtual world propose efficient realtovirtual world cloning method validate approach building publicly releasing new video dataset called Virtual KITTI see httpwwwxrcexeroxcomResearchDevelopmentComputerVisionProxyVirtualWorlds automatically labeled accurate ground truth object detection tracking scene instance segmentation depth optical flow provide quantitative experimental evidence suggesting modern deep learning algorithm pretrained real data behave similarly real virtual world ii pretraining virtual data improves performance gap real virtual world small virtual world enable measuring impact various weather imaging condition recognition performance thing equal show factor may affect drastically otherwise highperforming deep model tracking 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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['Jianwen Xie', 'Song-Chun Zhu', 'Ying Nian Wu']

1606.00972v2

Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal domain, and action patterns that are non-stationary in either spatial or temporal domain. We show that a spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns. The model defines a probability distribution on the video sequence, and the log probability is defined by a spatial-temporal ConvNet that consists of multiple layers of spatial-temporal filters to capture spatial-temporal patterns of different scales. The model can be learned from the training video sequences by an "analysis by synthesis" learning algorithm that iterates the following two steps. Step 1 synthesizes video sequences from the currently learned model. Step 2 then updates the model parameters based on the difference between the synthesized video sequences and the observed training sequences. We show that the learning algorithm can synthesize realistic dynamic patterns.

Synthesizing Dynamic Patterns by Spatial-Temporal Generative ConvNet

http://arxiv.org/pdf/1606.00972v2

Title Synthesizing Dynamic Patterns SpatialTemporal Generative ConvNet Summary Video sequence contain rich dynamic pattern dynamic texture pattern exhibit stationarity temporal domain action pattern nonstationary either spatial temporal domain show spatialtemporal generative ConvNet used model synthesize dynamic pattern model defines probability distribution video sequence log probability defined spatialtemporal ConvNet consists multiple layer spatialtemporal filter capture spatialtemporal pattern different scale model learned training video sequence analysis synthesis learning algorithm iterates following two step Step 1 synthesizes video sequence currently learned model Step 2 update model parameter based difference synthesized video sequence observed training sequence show learning algorithm synthesize realistic dynamic pattern 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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['Mohammad Javad Shafiee', 'Akshaya Mishra', 'Alexander Wong']

1606.04393v3

Taking inspiration from biological evolution, we explore the idea of "Can deep neural networks evolve naturally over successive generations into highly efficient deep neural networks?" by introducing the notion of synthesizing new highly efficient, yet powerful deep neural networks over successive generations via an evolutionary process from ancestor deep neural networks. The architectural traits of ancestor deep neural networks are encoded using synaptic probability models, which can be viewed as the `DNA' of these networks. New descendant networks with differing network architectures are synthesized based on these synaptic probability models from the ancestor networks and computational environmental factor models, in a random manner to mimic heredity, natural selection, and random mutation. These offspring networks are then trained into fully functional networks, like one would train a newborn, and have more efficient, more diverse network architectures than their ancestor networks, while achieving powerful modeling capabilities. Experimental results for the task of visual saliency demonstrated that the synthesized `evolved' offspring networks can achieve state-of-the-art performance while having network architectures that are significantly more efficient (with a staggering $\sim$48-fold decrease in synapses by the fourth generation) compared to the original ancestor network.

Deep Learning with Darwin: Evolutionary Synthesis of Deep Neural Networks

http://arxiv.org/pdf/1606.04393v3

Title Deep Learning Darwin Evolutionary Synthesis Deep Neural Networks Summary Taking inspiration biological evolution explore idea deep neural network evolve naturally successive generation highly efficient deep neural network introducing notion synthesizing new highly efficient yet powerful deep neural network successive generation via evolutionary process ancestor deep neural network architectural trait ancestor deep neural network encoded using synaptic probability model viewed DNA network New descendant network differing network architecture synthesized based synaptic probability model ancestor network computational environmental factor model random manner mimic heredity natural selection random mutation offspring network trained fully functional network like one would train newborn efficient diverse network architecture ancestor network achieving powerful modeling capability Experimental result task visual saliency demonstrated synthesized evolved offspring network achieve stateoftheart performance network architecture significantly efficient staggering sim48fold decrease synapsis fourth generation compared original ancestor 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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['Tian Han', 'Yang Lu', 'Song-Chun Zhu', 'Ying Nian Wu']

1606.08571v4

This paper proposes an alternating back-propagation algorithm for learning the generator network model. The model is a non-linear generalization of factor analysis. In this model, the mapping from the continuous latent factors to the observed signal is parametrized by a convolutional neural network. The alternating back-propagation algorithm iterates the following two steps: (1) Inferential back-propagation, which infers the latent factors by Langevin dynamics or gradient descent. (2) Learning back-propagation, which updates the parameters given the inferred latent factors by gradient descent. The gradient computations in both steps are powered by back-propagation, and they share most of their code in common. We show that the alternating back-propagation algorithm can learn realistic generator models of natural images, video sequences, and sounds. Moreover, it can also be used to learn from incomplete or indirect training data.

Alternating Back-Propagation for Generator Network

http://arxiv.org/pdf/1606.08571v4

Title Alternating BackPropagation Generator Network Summary paper proposes alternating backpropagation algorithm learning generator network model model nonlinear generalization factor analysis model mapping continuous latent factor observed signal parametrized convolutional neural network alternating backpropagation algorithm iterates following two step 1 Inferential backpropagation infers latent factor Langevin dynamic gradient descent 2 Learning backpropagation update parameter given inferred latent factor gradient descent gradient computation step powered backpropagation share code common show alternating backpropagation algorithm learn realistic generator model natural image video sequence sound Moreover also used learn incomplete indirect training 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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['Ilija Ilievski', 'Jiashi Feng']

1608.00218v1

Recently, several optimization methods have been successfully applied to the hyperparameter optimization of deep neural networks (DNNs). The methods work by modeling the joint distribution of hyperparameter values and corresponding error. Those methods become less practical when applied to modern DNNs whose training may take a few days and thus one cannot collect sufficient observations to accurately model the distribution. To address this challenging issue, we propose a method that learns to transfer optimal hyperparameter values for a small source dataset to hyperparameter values with comparable performance on a dataset of interest. As opposed to existing transfer learning methods, our proposed method does not use hand-designed features. Instead, it uses surrogates to model the hyperparameter-error distributions of the two datasets and trains a neural network to learn the transfer function. Extensive experiments on three CV benchmark datasets clearly demonstrate the efficiency of our method.

Hyperparameter Transfer Learning through Surrogate Alignment for Efficient Deep Neural Network Training

http://arxiv.org/pdf/1608.00218v1

Title Hyperparameter Transfer Learning Surrogate Alignment Efficient Deep Neural Network Training Summary Recently several optimization method successfully applied hyperparameter optimization deep neural network DNNs method work modeling joint distribution hyperparameter value corresponding error method become le practical applied modern DNNs whose training may take day thus one cannot collect sufficient observation accurately model distribution address challenging issue propose method learns transfer optimal hyperparameter value small source dataset hyperparameter value comparable performance dataset interest opposed existing transfer learning method proposed method use handdesigned feature Instead us surrogate model hyperparametererror distribution two datasets train neural network learn transfer function Extensive experiment three CV benchmark datasets clearly demonstrate efficiency 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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['Hao Wang', 'Dit-Yan Yeung']

1608.06884v2

While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, the subsequent tasks that involve inference, reasoning and planning require an even higher level of intelligence. The past few years have seen major advances in many perception tasks using deep learning models. For higher-level inference, however, probabilistic graphical models with their Bayesian nature are still more powerful and flexible. To achieve integrated intelligence that involves both perception and inference, it is naturally desirable to tightly integrate deep learning and Bayesian models within a principled probabilistic framework, which we call Bayesian deep learning. In this unified framework, the perception of text or images using deep learning can boost the performance of higher-level inference and in return, the feedback from the inference process is able to enhance the perception of text or images. This paper proposes a general framework for Bayesian deep learning and reviews its recent applications on recommender systems, topic models, and control. In this paper, we also discuss the relationship and differences between Bayesian deep learning and other related topics like Bayesian treatment of neural networks.

Towards Bayesian Deep Learning: A Framework and Some Existing Methods

http://arxiv.org/pdf/1608.06884v2

Title Towards Bayesian Deep Learning Framework Existing Methods Summary perception task visual object recognition text understanding play important role human intelligence subsequent task involve inference reasoning planning require even higher level intelligence past year seen major advance many perception task using deep learning model higherlevel inference however probabilistic graphical model Bayesian nature still powerful flexible achieve integrated intelligence involves perception inference naturally desirable tightly integrate deep learning Bayesian model within principled probabilistic framework call Bayesian deep learning unified framework perception text image using deep learning boost performance higherlevel inference return feedback inference process able enhance perception text image paper proposes general framework Bayesian deep learning review recent application recommender system topic model control paper also discus relationship difference Bayesian deep learning related topic like Bayesian treatment 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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['Mason McGill', 'Pietro Perona']

1703.06217v2

We propose and systematically evaluate three strategies for training dynamically-routed artificial neural networks: graphs of learned transformations through which different input signals may take different paths. Though some approaches have advantages over others, the resulting networks are often qualitatively similar. We find that, in dynamically-routed networks trained to classify images, layers and branches become specialized to process distinct categories of images. Additionally, given a fixed computational budget, dynamically-routed networks tend to perform better than comparable statically-routed networks.

Deciding How to Decide: Dynamic Routing in Artificial Neural Networks

http://arxiv.org/pdf/1703.06217v2

Title Deciding Decide Dynamic Routing Artificial Neural Networks Summary propose systematically evaluate three strategy training dynamicallyrouted artificial neural network graph learned transformation different input signal may take different path Though approach advantage others resulting network often qualitatively similar find dynamicallyrouted network trained classify image layer branch become specialized process distinct category image Additionally given fixed computational budget dynamicallyrouted network tend perform better comparable staticallyrouted 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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['Hongyang Gao', 'Hao Yuan', 'Zhengyang Wang', 'Shuiwang Ji']

1705.06820v4

Deconvolutional layers have been widely used in a variety of deep models for up-sampling, including encoder-decoder networks for semantic segmentation and deep generative models for unsupervised learning. One of the key limitations of deconvolutional operations is that they result in the so-called checkerboard problem. This is caused by the fact that no direct relationship exists among adjacent pixels on the output feature map. To address this problem, we propose the pixel deconvolutional layer (PixelDCL) to establish direct relationships among adjacent pixels on the up-sampled feature map. Our method is based on a fresh interpretation of the regular deconvolution operation. The resulting PixelDCL can be used to replace any deconvolutional layer in a plug-and-play manner without compromising the fully trainable capabilities of original models. The proposed PixelDCL may result in slight decrease in efficiency, but this can be overcome by an implementation trick. Experimental results on semantic segmentation demonstrate that PixelDCL can consider spatial features such as edges and shapes and yields more accurate segmentation outputs than deconvolutional layers. When used in image generation tasks, our PixelDCL can largely overcome the checkerboard problem suffered by regular deconvolution operations.

Pixel Deconvolutional Networks

http://arxiv.org/pdf/1705.06820v4

Title Pixel Deconvolutional Networks Summary Deconvolutional layer widely used variety deep model upsampling including encoderdecoder network semantic segmentation deep generative model unsupervised learning One key limitation deconvolutional operation result socalled checkerboard problem caused fact direct relationship exists among adjacent pixel output feature map address problem propose pixel deconvolutional layer PixelDCL establish direct relationship among adjacent pixel upsampled feature map method based fresh interpretation regular deconvolution operation resulting PixelDCL used replace deconvolutional layer plugandplay manner without compromising fully trainable capability original model proposed PixelDCL may result slight decrease efficiency overcome implementation trick Experimental result semantic segmentation demonstrate PixelDCL consider spatial feature edge shape yield accurate segmentation output deconvolutional layer used image generation task PixelDCL largely overcome checkerboard problem suffered regular deconvolution operation 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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['Stanislav Fort']

1708.02735v1

We propose a novel architecture for $k$-shot classification on the Omniglot dataset. Building on prototypical networks, we extend their architecture to what we call Gaussian prototypical networks. Prototypical networks learn a map between images and embedding vectors, and use their clustering for classification. In our model, a part of the encoder output is interpreted as a confidence region estimate about the embedding point, and expressed as a Gaussian covariance matrix. Our network then constructs a direction and class dependent distance metric on the embedding space, using uncertainties of individual data points as weights. We show that Gaussian prototypical networks are a preferred architecture over vanilla prototypical networks with an equivalent number of parameters. We report state-of-the-art performance in 1-shot and 5-shot classification both in 5-way and 20-way regime (for 5-shot 5-way, we are comparable to previous state-of-the-art) on the Omniglot dataset. We explore artificially down-sampling a fraction of images in the training set, which improves our performance even further. We therefore hypothesize that Gaussian prototypical networks might perform better in less homogeneous, noisier datasets, which are commonplace in real world applications.

Gaussian Prototypical Networks for Few-Shot Learning on Omniglot

http://arxiv.org/pdf/1708.02735v1

Title Gaussian Prototypical Networks FewShot Learning Omniglot Summary propose novel architecture kshot classification Omniglot dataset Building prototypical network extend architecture call Gaussian prototypical network Prototypical network learn map image embedding vector use clustering classification model part encoder output interpreted confidence region estimate embedding point expressed Gaussian covariance matrix network construct direction class dependent distance metric embedding space using uncertainty individual data point weight show Gaussian prototypical network preferred architecture vanilla prototypical network equivalent number parameter report stateoftheart performance 1shot 5shot classification 5way 20way regime 5shot 5way comparable previous stateoftheart Omniglot dataset explore artificially downsampling fraction image training set improves performance even therefore hypothesize Gaussian prototypical network might perform better le homogeneous noisier datasets commonplace real world application 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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['Leslie N. Smith', 'Nicholay Topin']

1708.07120v2

In this paper, we show a phenomenon, which we named "super-convergence", where residual networks can be trained using an order of magnitude fewer iterations than is used with standard training methods. The existence of super-convergence is relevant to understanding why deep networks generalize well. One of the key elements of super-convergence is training with cyclical learning rates and a large maximum learning rate. Furthermore, we present evidence that training with large learning rates improves performance by regularizing the network. In addition, we show that super-convergence provides a greater boost in performance relative to standard training when the amount of labeled training data is limited. We also derive a simplification of the Hessian Free optimization method to compute an estimate of the optimal learning rate. The architectures and code to replicate the figures in this paper are available at github.com/lnsmith54/super-convergence.

Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates

http://arxiv.org/pdf/1708.07120v2

Title SuperConvergence Fast Training Residual Networks Using Large Learning Rates Summary paper show phenomenon named superconvergence residual network trained using order magnitude fewer iteration used standard training method existence superconvergence relevant understanding deep network generalize well One key element superconvergence training cyclical learning rate large maximum learning rate Furthermore present evidence training large learning rate improves performance regularizing network addition show superconvergence provides greater boost performance relative standard training amount labeled training data limited also derive simplification Hessian Free optimization method compute estimate optimal learning rate architecture code replicate figure paper available githubcomlnsmith54superconvergence 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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['Boris Flach', 'Alexander Shekhovtsov', 'Ondrej Fikar']

1709.08524v1

Learning, taking into account full distribution of the data, referred to as generative, is not feasible with deep neural networks (DNNs) because they model only the conditional distribution of the outputs given the inputs. Current solutions are either based on joint probability models facing difficult estimation problems or learn two separate networks, mapping inputs to outputs (recognition) and vice-versa (generation). We propose an intermediate approach. First, we show that forward computation in DNNs with logistic sigmoid activations corresponds to a simplified approximate Bayesian inference in a directed probabilistic multi-layer model. This connection allows to interpret DNN as a probabilistic model of the output and all hidden units given the input. Second, we propose that in order for the recognition and generation networks to be more consistent with the joint model of the data, weights of the recognition and generator network should be related by transposition. We demonstrate in a tentative experiment that such a coupled pair can be learned generatively, modelling the full distribution of the data, and has enough capacity to perform well in both recognition and generation.

Generative learning for deep networks

http://arxiv.org/pdf/1709.08524v1

Title Generative learning deep network Summary Learning taking account full distribution data referred generative feasible deep neural network DNNs model conditional distribution output given input Current solution either based joint probability model facing difficult estimation problem learn two separate network mapping input output recognition viceversa generation propose intermediate approach First show forward computation DNNs logistic sigmoid activation corresponds simplified approximate Bayesian inference directed probabilistic multilayer model connection allows interpret DNN probabilistic model output hidden unit given input Second propose order recognition generation network consistent joint model data weight recognition generator network related transposition demonstrate tentative experiment coupled pair learned generatively modelling full distribution data enough capacity perform well recognition generation 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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['Hanxiao Liu', 'Karen Simonyan', 'Oriol Vinyals', 'Chrisantha Fernando', 'Koray Kavukcuoglu']

1711.00436v2

We explore efficient neural architecture search methods and show that a simple yet powerful evolutionary algorithm can discover new architectures with excellent performance. Our approach combines a novel hierarchical genetic representation scheme that imitates the modularized design pattern commonly adopted by human experts, and an expressive search space that supports complex topologies. Our algorithm efficiently discovers architectures that outperform a large number of manually designed models for image classification, obtaining top-1 error of 3.6% on CIFAR-10 and 20.3% when transferred to ImageNet, which is competitive with the best existing neural architecture search approaches. We also present results using random search, achieving 0.3% less top-1 accuracy on CIFAR-10 and 0.1% less on ImageNet whilst reducing the search time from 36 hours down to 1 hour.

Hierarchical Representations for Efficient Architecture Search

http://arxiv.org/pdf/1711.00436v2

Title Hierarchical Representations Efficient Architecture Search Summary explore efficient neural architecture search method show simple yet powerful evolutionary algorithm discover new architecture excellent performance approach combine novel hierarchical genetic representation scheme imitates modularized design pattern commonly adopted human expert expressive search space support complex topology algorithm efficiently discovers architecture outperform large number manually designed model image classification obtaining top1 error 36 CIFAR10 203 transferred ImageNet competitive best existing neural architecture search approach also present result using random search achieving 03 le top1 accuracy CIFAR10 01 le ImageNet whilst reducing search time 36 hour 1 hour 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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['Antreas Antoniou', 'Amos Storkey', 'Harrison Edwards']

1711.04340v3

Effective training of neural networks requires much data. In the low-data regime, parameters are underdetermined, and learnt networks generalise poorly. Data Augmentation alleviates this by using existing data more effectively. However standard data augmentation produces only limited plausible alternative data. Given there is potential to generate a much broader set of augmentations, we design and train a generative model to do data augmentation. The model, based on image conditional Generative Adversarial Networks, takes data from a source domain and learns to take any data item and generalise it to generate other within-class data items. As this generative process does not depend on the classes themselves, it can be applied to novel unseen classes of data. We show that a Data Augmentation Generative Adversarial Network (DAGAN) augments standard vanilla classifiers well. We also show a DAGAN can enhance few-shot learning systems such as Matching Networks. We demonstrate these approaches on Omniglot, on EMNIST having learnt the DAGAN on Omniglot, and VGG-Face data. In our experiments we can see over 13% increase in accuracy in the low-data regime experiments in Omniglot (from 69% to 82%), EMNIST (73.9% to 76%) and VGG-Face (4.5% to 12%); in Matching Networks for Omniglot we observe an increase of 0.5% (from 96.9% to 97.4%) and an increase of 1.8% in EMNIST (from 59.5% to 61.3%).

Data Augmentation Generative Adversarial Networks

http://arxiv.org/pdf/1711.04340v3

Title Data Augmentation Generative Adversarial Networks Summary Effective training neural network requires much data lowdata regime parameter underdetermined learnt network generalise poorly Data Augmentation alleviates using existing data effectively However standard data augmentation produce limited plausible alternative data Given potential generate much broader set augmentation design train generative model data augmentation model based image conditional Generative Adversarial Networks take data source domain learns take data item generalise generate withinclass data item generative process depend class applied novel unseen class data show Data Augmentation Generative Adversarial Network DAGAN augments standard vanilla classifier well also show DAGAN enhance fewshot learning system Matching Networks demonstrate approach Omniglot EMNIST learnt DAGAN Omniglot VGGFace data experiment see 13 increase accuracy lowdata regime experiment Omniglot 69 82 EMNIST 739 76 VGGFace 45 12 Matching Networks Omniglot observe increase 05 969 974 increase 18 EMNIST 595 613 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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['Dror Sholomon', 'Eli David', 'Nathan S. Netanyahu']

1711.08762v1

This paper introduces the first deep neural network-based estimation metric for the jigsaw puzzle problem. Given two puzzle piece edges, the neural network predicts whether or not they should be adjacent in the correct assembly of the puzzle, using nothing but the pixels of each piece. The proposed metric exhibits an extremely high precision even though no manual feature extraction is performed. When incorporated into an existing puzzle solver, the solution's accuracy increases significantly, achieving thereby a new state-of-the-art standard.

DNN-Buddies: A Deep Neural Network-Based Estimation Metric for the Jigsaw Puzzle Problem

http://arxiv.org/pdf/1711.08762v1

Title DNNBuddies Deep Neural NetworkBased Estimation Metric Jigsaw Puzzle Problem Summary paper introduces first deep neural networkbased estimation metric jigsaw puzzle problem Given two puzzle piece edge neural network predicts whether adjacent correct assembly puzzle using nothing pixel piece proposed metric exhibit extremely high precision even though manual feature extraction performed incorporated existing puzzle solver solution accuracy increase significantly achieving thereby new stateoftheart standard 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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['Eli David', 'Nathan S. Netanyahu']

1711.08763v1

In this paper we describe the problem of painter classification, and propose a novel approach based on deep convolutional autoencoder neural networks. While previous approaches relied on image processing and manual feature extraction from paintings, our approach operates on the raw pixel level, without any preprocessing or manual feature extraction. We first train a deep convolutional autoencoder on a dataset of paintings, and subsequently use it to initialize a supervised convolutional neural network for the classification phase. The proposed approach substantially outperforms previous methods, improving the previous state-of-the-art for the 3-painter classification problem from 90.44% accuracy (previous state-of-the-art) to 96.52% accuracy, i.e., a 63% reduction in error rate.

DeepPainter: Painter Classification Using Deep Convolutional Autoencoders

http://arxiv.org/pdf/1711.08763v1

Title DeepPainter Painter Classification Using Deep Convolutional Autoencoders Summary paper describe problem painter classification propose novel approach based deep convolutional autoencoder neural network previous approach relied image processing manual feature extraction painting approach operates raw pixel level without preprocessing manual feature extraction first train deep convolutional autoencoder dataset painting subsequently use initialize supervised convolutional neural network classification phase proposed approach substantially outperforms previous method improving previous stateoftheart 3painter classification problem 9044 accuracy previous stateoftheart 9652 accuracy ie 63 reduction error rate 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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['Ido Cohen', 'Eli David', 'Nathan S. Netanyahu', 'Noa Liscovitch', 'Gal Chechik']

1711.09663v1

This paper presents a novel deep learning-based method for learning a functional representation of mammalian neural images. The method uses a deep convolutional denoising autoencoder (CDAE) for generating an invariant, compact representation of in situ hybridization (ISH) images. While most existing methods for bio-imaging analysis were not developed to handle images with highly complex anatomical structures, the results presented in this paper show that functional representation extracted by CDAE can help learn features of functional gene ontology categories for their classification in a highly accurate manner. Using this CDAE representation, our method outperforms the previous state-of-the-art classification rate, by improving the average AUC from 0.92 to 0.98, i.e., achieving 75% reduction in error. The method operates on input images that were downsampled significantly with respect to the original ones to make it computationally feasible.

DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders

http://arxiv.org/pdf/1711.09663v1

Title DeepBrain Functional Representation Neural InSitu Hybridization Images Gene Ontology Classification Using Deep Convolutional Autoencoders Summary paper present novel deep learningbased method learning functional representation mammalian neural image method us deep convolutional denoising autoencoder CDAE generating invariant compact representation situ hybridization ISH image existing method bioimaging analysis developed handle image highly complex anatomical structure result presented paper show functional representation extracted CDAE help learn feature functional gene ontology category classification highly accurate manner Using CDAE representation method outperforms previous stateoftheart classification rate improving average AUC 092 098 ie achieving 75 reduction error method operates input image downsampled significantly respect original one make computationally feasible 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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['Omid Poursaeed', 'Isay Katsman', 'Bicheng Gao', 'Serge Belongie']

1712.02328v1

In this paper, we propose novel generative models for creating adversarial examples, slightly perturbed images resembling natural images but maliciously crafted to fool pre-trained models. We present trainable deep neural networks for transforming images to adversarial perturbations. Our proposed models can produce image-agnostic and image-dependent perturbations for both targeted and non-targeted attacks. We also demonstrate that similar architectures can achieve impressive results in fooling classification and semantic segmentation models, obviating the need for hand-crafting attack methods for each task. Using extensive experiments on challenging high-resolution datasets such as ImageNet and Cityscapes, we show that our perturbations achieve high fooling rates with small perturbation norms. Moreover, our attacks are considerably faster than current iterative methods at inference time.

Generative Adversarial Perturbations

http://arxiv.org/pdf/1712.02328v1

Title Generative Adversarial Perturbations Summary paper propose novel generative model creating adversarial example slightly perturbed image resembling natural image maliciously crafted fool pretrained model present trainable deep neural network transforming image adversarial perturbation proposed model produce imageagnostic imagedependent perturbation targeted nontargeted attack also demonstrate similar architecture achieve impressive result fooling classification semantic segmentation model obviating need handcrafting attack method task Using extensive experiment challenging highresolution datasets ImageNet Cityscapes show perturbation achieve high fooling rate small perturbation norm Moreover attack considerably faster current iterative method inference 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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['Logan Engstrom', 'Brandon Tran', 'Dimitris Tsipras', 'Ludwig Schmidt', 'Aleksander Madry']

1712.02779v3

We show that simple transformations, namely translations and rotations alone, are sufficient to fool neural network-based vision models on a significant fraction of inputs. This is in sharp contrast to previous work that relied on more complicated optimization approaches that are unlikely to appear outside of a truly adversarial setting. Moreover, fooling rotations and translations are easy to find and require only a few black-box queries to the target model. Overall, our findings emphasize the need for designing robust classifiers even in natural, benign contexts.

A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations

http://arxiv.org/pdf/1712.02779v3

Title Rotation Translation Suffice Fooling CNNs Simple Transformations Summary show simple transformation namely translation rotation alone sufficient fool neural networkbased vision model significant fraction input sharp contrast previous work relied complicated optimization approach unlikely appear outside truly adversarial setting Moreover fooling rotation translation easy find require blackbox query target model Overall finding emphasize need designing robust classifier even natural benign 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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['Boyang Deng', 'Junjie Yan', 'Dahua Lin']

1712.03351v1

The quest for performant networks has been a significant force that drives the advancements of deep learning in recent years. While rewarding, improving network design has never been an easy journey. The large design space combined with the tremendous cost required for network training poses a major obstacle to this endeavor. In this work, we propose a new approach to this problem, namely, predicting the performance of a network before training, based on its architecture. Specifically, we develop a unified way to encode individual layers into vectors and bring them together to form an integrated description via LSTM. Taking advantage of the recurrent network's strong expressive power, this method can reliably predict the performances of various network architectures. Our empirical studies showed that it not only achieved accurate predictions but also produced consistent rankings across datasets -- a key desideratum in performance prediction.

Peephole: Predicting Network Performance Before Training

http://arxiv.org/pdf/1712.03351v1

Title Peephole Predicting Network Performance Training Summary quest performant network significant force drive advancement deep learning recent year rewarding improving network design never easy journey large design space combined tremendous cost required network training pose major obstacle endeavor work propose new approach problem namely predicting performance network training based architecture Specifically develop unified way encode individual layer vector bring together form integrated description via LSTM Taking advantage recurrent network strong expressive power method reliably predict performance various network architecture empirical study showed achieved accurate prediction also produced consistent ranking across datasets key desideratum performance prediction 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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-0.013578121550381184, 0.010563526302576065, 0.009802348911762238, 0.024753784760832787, -0.006496364250779152, -0.04414308816194534]

['Abien Fred Agarap']

1712.03541v1

Convolutional neural networks (CNNs) are similar to "ordinary" neural networks in the sense that they are made up of hidden layers consisting of neurons with "learnable" parameters. These neurons receive inputs, performs a dot product, and then follows it with a non-linearity. The whole network expresses the mapping between raw image pixels and their class scores. Conventionally, the Softmax function is the classifier used at the last layer of this network. However, there have been studies (Alalshekmubarak and Smith, 2013; Agarap, 2017; Tang, 2013) conducted to challenge this norm. The cited studies introduce the usage of linear support vector machine (SVM) in an artificial neural network architecture. This project is yet another take on the subject, and is inspired by (Tang, 2013). Empirical data has shown that the CNN-SVM model was able to achieve a test accuracy of ~99.04% using the MNIST dataset (LeCun, Cortes, and Burges, 2010). On the other hand, the CNN-Softmax was able to achieve a test accuracy of ~99.23% using the same dataset. Both models were also tested on the recently-published Fashion-MNIST dataset (Xiao, Rasul, and Vollgraf, 2017), which is suppose to be a more difficult image classification dataset than MNIST (Zalandoresearch, 2017). This proved to be the case as CNN-SVM reached a test accuracy of ~90.72%, while the CNN-Softmax reached a test accuracy of ~91.86%. The said results may be improved if data preprocessing techniques were employed on the datasets, and if the base CNN model was a relatively more sophisticated than the one used in this study.

An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image Classification

http://arxiv.org/pdf/1712.03541v1

Title Architecture Combining Convolutional Neural Network CNN Support Vector Machine SVM Image Classification Summary Convolutional neural network CNNs similar ordinary neural network sense made hidden layer consisting neuron learnable parameter neuron receive input performs dot product follows nonlinearity whole network express mapping raw image pixel class score Conventionally Softmax function classifier used last layer network However study Alalshekmubarak Smith 2013 Agarap 2017 Tang 2013 conducted challenge norm cited study introduce usage linear support vector machine SVM artificial neural network architecture project yet another take subject inspired Tang 2013 Empirical data shown CNNSVM model able achieve test accuracy 9904 using MNIST dataset LeCun Cortes Burges 2010 hand CNNSoftmax able achieve test accuracy 9923 using dataset model also tested recentlypublished FashionMNIST dataset Xiao Rasul Vollgraf 2017 suppose difficult image classification dataset MNIST Zalandoresearch 2017 proved case CNNSVM reached test accuracy 9072 CNNSoftmax reached test accuracy 9186 said result may improved data preprocessing technique employed datasets base CNN model relatively sophisticated one used 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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['Ekaba Bisong']

1712.08314v2

Artifical Neural Networks are a particular class of learning systems modeled after biological neural functions with an interesting penchant for Hebbian learning, that is "neurons that wire together, fire together". However, unlike their natural counterparts, artificial neural networks have a close and stringent coupling between the modules of neurons in the network. This coupling or locking imposes upon the network a strict and inflexible structure that prevent layers in the network from updating their weights until a full feed-forward and backward pass has occurred. Such a constraint though may have sufficed for a while, is now no longer feasible in the era of very-large-scale machine learning, coupled with the increased desire for parallelization of the learning process across multiple computing infrastructures. To solve this problem, synthetic gradients (SG) with decoupled neural interfaces (DNI) are introduced as a viable alternative to the backpropagation algorithm. This paper performs a speed benchmark to compare the speed and accuracy capabilities of SG-DNI as opposed to a standard neural interface using multilayer perceptron MLP. SG-DNI shows good promise, in that it not only captures the learning problem, it is also over 3-fold faster due to it asynchronous learning capabilities.

Benchmarking Decoupled Neural Interfaces with Synthetic Gradients

http://arxiv.org/pdf/1712.08314v2

Title Benchmarking Decoupled Neural Interfaces Synthetic Gradients Summary Artifical Neural Networks particular class learning system modeled biological neural function interesting penchant Hebbian learning neuron wire together fire together However unlike natural counterpart artificial neural network close stringent coupling module neuron network coupling locking imposes upon network strict inflexible structure prevent layer network updating weight full feedforward backward pas occurred constraint though may sufficed longer feasible era verylargescale machine learning coupled increased desire parallelization learning process across multiple computing infrastructure solve problem synthetic gradient SG decoupled neural interface DNI introduced viable alternative backpropagation algorithm paper performs speed benchmark compare speed accuracy capability SGDNI opposed standard neural interface using multilayer perceptron MLP SGDNI show good promise capture learning problem also 3fold faster due asynchronous learning capability 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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['Amin Fehri', 'Santiago Velasco-Forero', 'Fernand Meyer']

1802.07008v1

Image segmentation is the process of partitioning an image into a set of meaningful regions according to some criteria. Hierarchical segmentation has emerged as a major trend in this regard as it favors the emergence of important regions at different scales. On the other hand, many methods allow us to have prior information on the position of structures of interest in the images. In this paper, we present a versatile hierarchical segmentation method that takes into account any prior spatial information and outputs a hierarchical segmentation that emphasizes the contours or regions of interest while preserving the important structures in the image. An application of this method to the weakly-supervised segmentation problem is presented.

Segmentation hiérarchique faiblement supervisée

http://arxiv.org/pdf/1802.07008v1

Title Segmentation hiérarchique faiblement supervisée Summary Image segmentation process partitioning image set meaningful region according criterion Hierarchical segmentation emerged major trend regard favor emergence important region different scale hand many method allow u prior information position structure interest image paper present versatile hierarchical segmentation method take account prior spatial information output hierarchical segmentation emphasizes contour region interest preserving important structure image application method weaklysupervised segmentation problem presented 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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['Mark D. McDonnell']

1802.08530v1

For fast and energy-efficient deployment of trained deep neural networks on resource-constrained embedded hardware, each learned weight parameter should ideally be represented and stored using a single bit. Error-rates usually increase when this requirement is imposed. Here, we report large improvements in error rates on multiple datasets, for deep convolutional neural networks deployed with 1-bit-per-weight. Using wide residual networks as our main baseline, our approach simplifies existing methods that binarize weights by applying the sign function in training; we apply scaling factors for each layer with constant unlearned values equal to the layer-specific standard deviations used for initialization. For CIFAR-10, CIFAR-100 and ImageNet, and models with 1-bit-per-weight requiring less than 10 MB of parameter memory, we achieve error rates of 3.9%, 18.5% and 26.0% / 8.5% (Top-1 / Top-5) respectively. We also considered MNIST, SVHN and ImageNet32, achieving 1-bit-per-weight test results of 0.27%, 1.9%, and 41.3% / 19.1% respectively. For CIFAR, our error rates halve previously reported values, and are within about 1% of our error-rates for the same network with full-precision weights. For networks that overfit, we also show significant improvements in error rate by not learning batch normalization scale and offset parameters. This applies to both full precision and 1-bit-per-weight networks. Using a warm-restart learning-rate schedule, we found that training for 1-bit-per-weight is just as fast as full-precision networks, with better accuracy than standard schedules, and achieved about 98%-99% of peak performance in just 62 training epochs for CIFAR-10/100. For full training code and trained models in MATLAB, Keras and PyTorch see https://github.com/McDonnell-Lab/1-bit-per-weight/ .

Training wide residual networks for deployment using a single bit for each weight

http://arxiv.org/pdf/1802.08530v1

Title Training wide residual network deployment using single bit weight Summary fast energyefficient deployment trained deep neural network resourceconstrained embedded hardware learned weight parameter ideally represented stored using single bit Errorrates usually increase requirement imposed report large improvement error rate multiple datasets deep convolutional neural network deployed 1bitperweight Using wide residual network main baseline approach simplifies existing method binarize weight applying sign function training apply scaling factor layer constant unlearned value equal layerspecific standard deviation used initialization CIFAR10 CIFAR100 ImageNet model 1bitperweight requiring le 10 MB parameter memory achieve error rate 39 185 260 85 Top1 Top5 respectively also considered MNIST SVHN ImageNet32 achieving 1bitperweight test result 027 19 413 191 respectively CIFAR error rate halve previously reported value within 1 errorrates network fullprecision weight network overfit also show significant improvement error rate learning batch normalization scale offset parameter applies full precision 1bitperweight network Using warmrestart learningrate schedule found training 1bitperweight fast fullprecision network better accuracy standard schedule achieved 9899 peak performance 62 training epoch CIFAR10100 full training code trained model MATLAB Keras PyTorch see httpsgithubcomMcDonnellLab1bitperweight 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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-0.00379681633785367, 0.03255535289645195, -0.02885468304157257, -0.046987004578113556, -0.019207779318094254, -0.058313168585300446]

['Abien Fred Agarap']

1803.08375v1

We introduce the use of rectified linear units (ReLU) as the classification function in a deep neural network (DNN). Conventionally, ReLU is used as an activation function in DNNs, with Softmax function as their classification function. However, there have been several studies on using a classification function other than Softmax, and this study is an addition to those. We accomplish this by taking the activation of the penultimate layer $h_{n - 1}$ in a neural network, then multiply it by weight parameters $\theta$ to get the raw scores $o_{i}$. Afterwards, we threshold the raw scores $o_{i}$ by $0$, i.e. $f(o) = \max(0, o_{i})$, where $f(o)$ is the ReLU function. We provide class predictions $\hat{y}$ through argmax function, i.e. argmax $f(x)$.

Deep Learning using Rectified Linear Units (ReLU)

http://arxiv.org/pdf/1803.08375v1

Title Deep Learning using Rectified Linear Units ReLU Summary introduce use rectified linear unit ReLU classification function deep neural network DNN Conventionally ReLU used activation function DNNs Softmax function classification function However several study using classification function Softmax study addition accomplish taking activation penultimate layer hn 1 neural network multiply weight parameter theta get raw score oi Afterwards threshold raw score oi 0 ie fo max0 oi fo ReLU function provide class prediction haty argmax function ie argmax fx 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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['Djork-Arné Clevert', 'Andreas Mayr', 'Thomas Unterthiner', 'Sepp Hochreiter']

1502.06464v2

We propose rectified factor networks (RFNs) to efficiently construct very sparse, non-linear, high-dimensional representations of the input. RFN models identify rare and small events in the input, have a low interference between code units, have a small reconstruction error, and explain the data covariance structure. RFN learning is a generalized alternating minimization algorithm derived from the posterior regularization method which enforces non-negative and normalized posterior means. We proof convergence and correctness of the RFN learning algorithm. On benchmarks, RFNs are compared to other unsupervised methods like autoencoders, RBMs, factor analysis, ICA, and PCA. In contrast to previous sparse coding methods, RFNs yield sparser codes, capture the data's covariance structure more precisely, and have a significantly smaller reconstruction error. We test RFNs as pretraining technique for deep networks on different vision datasets, where RFNs were superior to RBMs and autoencoders. On gene expression data from two pharmaceutical drug discovery studies, RFNs detected small and rare gene modules that revealed highly relevant new biological insights which were so far missed by other unsupervised methods.

Rectified Factor Networks

http://arxiv.org/pdf/1502.06464v2

Title Rectified Factor Networks Summary propose rectified factor network RFNs efficiently construct sparse nonlinear highdimensional representation input RFN model identify rare small event input low interference code unit small reconstruction error explain data covariance structure RFN learning generalized alternating minimization algorithm derived posterior regularization method enforces nonnegative normalized posterior mean proof convergence correctness RFN learning algorithm benchmark RFNs compared unsupervised method like autoencoders RBMs factor analysis ICA PCA contrast previous sparse coding method RFNs yield sparser code capture data covariance structure precisely significantly smaller reconstruction error test RFNs pretraining technique deep network different vision datasets RFNs superior RBMs autoencoders gene expression data two pharmaceutical drug discovery study RFNs detected small rare gene module revealed highly relevant new biological insight far missed unsupervised 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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['Qi Wang', 'Joseph JaJa']

1312.1909v1

Motivated by an important insight from neural science, we propose a new framework for understanding the success of the recently proposed "maxout" networks. The framework is based on encoding information on sparse pathways and recognizing the correct pathway at inference time. Elaborating further on this insight, we propose a novel deep network architecture, called "channel-out" network, which takes a much better advantage of sparse pathway encoding. In channel-out networks, pathways are not only formed a posteriori, but they are also actively selected according to the inference outputs from the lower layers. From a mathematical perspective, channel-out networks can represent a wider class of piece-wise continuous functions, thereby endowing the network with more expressive power than that of maxout networks. We test our channel-out networks on several well-known image classification benchmarks, setting new state-of-the-art performance on CIFAR-100 and STL-10, which represent some of the "harder" image classification benchmarks.

From Maxout to Channel-Out: Encoding Information on Sparse Pathways

http://arxiv.org/pdf/1312.1909v1

Title Maxout ChannelOut Encoding Information Sparse Pathways Summary Motivated important insight neural science propose new framework understanding success recently proposed maxout network framework based encoding information sparse pathway recognizing correct pathway inference time Elaborating insight propose novel deep network architecture called channelout network take much better advantage sparse pathway encoding channelout network pathway formed posteriori also actively selected according inference output lower layer mathematical perspective channelout network represent wider class piecewise continuous function thereby endowing network expressive power maxout network test channelout network several wellknown image classification benchmark setting new stateoftheart performance CIFAR100 STL10 represent harder image classification benchmark 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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['Takashi Shinozaki', 'Yasushi Naruse']

1312.5845v7

We propose a novel learning method for multilayered neural networks which uses feedforward supervisory signal and associates classification of a new input with that of pre-trained input. The proposed method effectively uses rich input information in the earlier layer for robust leaning and revising internal representation in a multilayer neural network.

Competitive Learning with Feedforward Supervisory Signal for Pre-trained Multilayered Networks

http://arxiv.org/pdf/1312.5845v7

Title Competitive Learning Feedforward Supervisory Signal Pretrained Multilayered Networks Summary propose novel learning method multilayered neural network us feedforward supervisory signal associate classification new input pretrained input proposed method effectively us rich input information earlier layer robust leaning revising internal representation multilayer 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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['Chen-Yu Lee', 'Saining Xie', 'Patrick Gallagher', 'Zhengyou Zhang', 'Zhuowen Tu']

1409.5185v2

Our proposed deeply-supervised nets (DSN) method simultaneously minimizes classification error while making the learning process of hidden layers direct and transparent. We make an attempt to boost the classification performance by studying a new formulation in deep networks. Three aspects in convolutional neural networks (CNN) style architectures are being looked at: (1) transparency of the intermediate layers to the overall classification; (2) discriminativeness and robustness of learned features, especially in the early layers; (3) effectiveness in training due to the presence of the exploding and vanishing gradients. We introduce "companion objective" to the individual hidden layers, in addition to the overall objective at the output layer (a different strategy to layer-wise pre-training). We extend techniques from stochastic gradient methods to analyze our algorithm. The advantage of our method is evident and our experimental result on benchmark datasets shows significant performance gain over existing methods (e.g. all state-of-the-art results on MNIST, CIFAR-10, CIFAR-100, and SVHN).

Deeply-Supervised Nets

http://arxiv.org/pdf/1409.5185v2

Title DeeplySupervised Nets Summary proposed deeplysupervised net DSN method simultaneously minimizes classification error making learning process hidden layer direct transparent make attempt boost classification performance studying new formulation deep network Three aspect convolutional neural network CNN style architecture looked 1 transparency intermediate layer overall classification 2 discriminativeness robustness learned feature especially early layer 3 effectiveness training due presence exploding vanishing gradient introduce companion objective individual hidden layer addition overall objective output layer different strategy layerwise pretraining extend technique stochastic gradient method analyze algorithm advantage method evident experimental result benchmark datasets show significant performance gain existing method eg stateoftheart result MNIST CIFAR10 CIFAR100 SVHN 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', 'Ruslan Salakhutdinov', 'Nathan Srebro']

1506.02617v1

We revisit the choice of SGD for training deep neural networks by reconsidering the appropriate geometry in which to optimize the weights. We argue for a geometry invariant to rescaling of weights that does not affect the output of the network, and suggest Path-SGD, which is an approximate steepest descent method with respect to a path-wise regularizer related to max-norm regularization. Path-SGD is easy and efficient to implement and leads to empirical gains over SGD and AdaGrad.

Path-SGD: Path-Normalized Optimization in Deep Neural Networks

http://arxiv.org/pdf/1506.02617v1

Title PathSGD PathNormalized Optimization Deep Neural Networks Summary revisit choice SGD training deep neural network reconsidering appropriate geometry optimize weight argue geometry invariant rescaling weight affect output network suggest PathSGD approximate steepest descent method respect pathwise regularizer related maxnorm regularization PathSGD easy efficient implement lead empirical gain SGD AdaGrad 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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['Alan Mosca', 'George D. Magoulas']

1509.04612v2

The Resilient Propagation (Rprop) algorithm has been very popular for backpropagation training of multilayer feed-forward neural networks in various applications. The standard Rprop however encounters difficulties in the context of deep neural networks as typically happens with gradient-based learning algorithms. In this paper, we propose a modification of the Rprop that combines standard Rprop steps with a special drop out technique. We apply the method for training Deep Neural Networks as standalone components and in ensemble formulations. Results on the MNIST dataset show that the proposed modification alleviates standard Rprop's problems demonstrating improved learning speed and accuracy.

Adapting Resilient Propagation for Deep Learning

http://arxiv.org/pdf/1509.04612v2

Title Adapting Resilient Propagation Deep Learning Summary Resilient Propagation Rprop algorithm popular backpropagation training multilayer feedforward neural network various application standard Rprop however encounter difficulty context deep neural network typically happens gradientbased learning algorithm paper propose modification Rprop combine standard Rprop step special drop technique apply method training Deep Neural Networks standalone component ensemble formulation Results MNIST dataset show proposed modification alleviates standard Rprops problem demonstrating improved learning speed 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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['Nastaran Mohammadian Rad', 'Andrea Bizzego', 'Seyed Mostafa Kia', 'Giuseppe Jurman', 'Paola Venuti', 'Cesare Furlanello']

1511.01865v3

Autism Spectrum Disorders (ASDs) are often associated with specific atypical postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) have a specific visibility. While the identification and the quantification of SMM patterns remain complex, its automation would provide support to accurate tuning of the intervention in the therapy of autism. Therefore, it is essential to develop automatic SMM detection systems in a real world setting, taking care of strong inter-subject and intra-subject variability. Wireless accelerometer sensing technology can provide a valid infrastructure for real-time SMM detection, however such variability remains a problem also for machine learning methods, in particular whenever handcrafted features extracted from accelerometer signal are considered. Here, we propose to employ the deep learning paradigm in order to learn discriminating features from multi-sensor accelerometer signals. Our results provide preliminary evidence that feature learning and transfer learning embedded in the deep architecture achieve higher accurate SMM detectors in longitudinal scenarios.

Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism

http://arxiv.org/pdf/1511.01865v3

Title Convolutional Neural Network Stereotypical Motor Movement Detection Autism Summary Autism Spectrum Disorders ASDs often associated specific atypical postural motor behavior Stereotypical Motor Movements SMMs specific visibility identification quantification SMM pattern remain complex automation would provide support accurate tuning intervention therapy autism Therefore essential develop automatic SMM detection system real world setting taking care strong intersubject intrasubject variability Wireless accelerometer sensing technology provide valid infrastructure realtime SMM detection however variability remains problem also machine learning method particular whenever handcrafted feature extracted accelerometer signal considered propose employ deep learning paradigm order learn discriminating feature multisensor accelerometer signal result provide preliminary evidence feature learning transfer learning embedded deep architecture achieve higher accurate SMM detector longitudinal scenario 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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['Sasha Targ', 'Diogo Almeida', 'Kevin Lyman']

1603.08029v1

Residual networks (ResNets) have recently achieved state-of-the-art on challenging computer vision tasks. We introduce Resnet in Resnet (RiR): a deep dual-stream architecture that generalizes ResNets and standard CNNs and is easily implemented with no computational overhead. RiR consistently improves performance over ResNets, outperforms architectures with similar amounts of augmentation on CIFAR-10, and establishes a new state-of-the-art on CIFAR-100.

Resnet in Resnet: Generalizing Residual Architectures

http://arxiv.org/pdf/1603.08029v1

Title Resnet Resnet Generalizing Residual Architectures Summary Residual network ResNets recently achieved stateoftheart challenging computer vision task introduce Resnet Resnet RiR deep dualstream architecture generalizes ResNets standard CNNs easily implemented computational overhead RiR consistently improves performance ResNets outperforms architecture similar amount augmentation CIFAR10 establishes new stateoftheart CIFAR100 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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['Mohammad Javad Shafiee', 'Alexander Wong']

1609.01360v2

There has been significant recent interest towards achieving highly efficient deep neural network architectures. A promising paradigm for achieving this is the concept of evolutionary deep intelligence, which attempts to mimic biological evolution processes to synthesize highly-efficient deep neural networks over successive generations. An important aspect of evolutionary deep intelligence is the genetic encoding scheme used to mimic heredity, which can have a significant impact on the quality of offspring deep neural networks. Motivated by the neurobiological phenomenon of synaptic clustering, we introduce a new genetic encoding scheme where synaptic probability is driven towards the formation of a highly sparse set of synaptic clusters. Experimental results for the task of image classification demonstrated that the synthesized offspring networks using this synaptic cluster-driven genetic encoding scheme can achieve state-of-the-art performance while having network architectures that are not only significantly more efficient (with a ~125-fold decrease in synapses for MNIST) compared to the original ancestor network, but also tailored for GPU-accelerated machine learning applications.

Evolutionary Synthesis of Deep Neural Networks via Synaptic Cluster-driven Genetic Encoding

http://arxiv.org/pdf/1609.01360v2

Title Evolutionary Synthesis Deep Neural Networks via Synaptic Clusterdriven Genetic Encoding Summary significant recent interest towards achieving highly efficient deep neural network architecture promising paradigm achieving concept evolutionary deep intelligence attempt mimic biological evolution process synthesize highlyefficient deep neural network successive generation important aspect evolutionary deep intelligence genetic encoding scheme used mimic heredity significant impact quality offspring deep neural network Motivated neurobiological phenomenon synaptic clustering introduce new genetic encoding scheme synaptic probability driven towards formation highly sparse set synaptic cluster Experimental result task image classification demonstrated synthesized offspring network using synaptic clusterdriven genetic encoding scheme achieve stateoftheart performance network architecture significantly efficient 125fold decrease synapsis MNIST compared original ancestor network also tailored GPUaccelerated machine learning application 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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['Andrew Brock', 'Theodore Lim', 'J. M. Ritchie', 'Nick Weston']

1609.07093v3

The increasingly photorealistic sample quality of generative image models suggests their feasibility in applications beyond image generation. We present the Neural Photo Editor, an interface that leverages the power of generative neural networks to make large, semantically coherent changes to existing images. To tackle the challenge of achieving accurate reconstructions without loss of feature quality, we introduce the Introspective Adversarial Network, a novel hybridization of the VAE and GAN. Our model efficiently captures long-range dependencies through use of a computational block based on weight-shared dilated convolutions, and improves generalization performance with Orthogonal Regularization, a novel weight regularization method. We validate our contributions on CelebA, SVHN, and CIFAR-100, and produce samples and reconstructions with high visual fidelity.

Neural Photo Editing with Introspective Adversarial Networks

http://arxiv.org/pdf/1609.07093v3

Title Neural Photo Editing Introspective Adversarial Networks Summary increasingly photorealistic sample quality generative image model suggests feasibility application beyond image generation present Neural Photo Editor interface leverage power generative neural network make large semantically coherent change existing image tackle challenge achieving accurate reconstruction without loss feature quality introduce Introspective Adversarial Network novel hybridization VAE GAN model efficiently capture longrange dependency use computational block based weightshared dilated convolution improves generalization performance Orthogonal Regularization novel weight regularization method validate contribution CelebA SVHN CIFAR100 produce sample reconstruction high visual fidelity 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', 'Joseph Wang', 'Ofer Dekel', 'Venkatesh Saligrama']

1702.07811v2

We present an approach to adaptively utilize deep neural networks in order to reduce the evaluation time on new examples without loss of accuracy. Rather than attempting to redesign or approximate existing networks, we propose two schemes that adaptively utilize networks. We first pose an adaptive network evaluation scheme, where we learn a system to adaptively choose the components of a deep network to be evaluated for each example. By allowing examples correctly classified using early layers of the system to exit, we avoid the computational time associated with full evaluation of the network. We extend this to learn a network selection system that adaptively selects the network to be evaluated for each example. We show that computational time can be dramatically reduced by exploiting the fact that many examples can be correctly classified using relatively efficient networks and that complex, computationally costly networks are only necessary for a small fraction of examples. We pose a global objective for learning an adaptive early exit or network selection policy and solve it by reducing the policy learning problem to a layer-by-layer weighted binary classification problem. Empirically, these approaches yield dramatic reductions in computational cost, with up to a 2.8x speedup on state-of-the-art networks from the ImageNet image recognition challenge with minimal (<1%) loss of top5 accuracy.

Adaptive Neural Networks for Efficient Inference

http://arxiv.org/pdf/1702.07811v2

Title Adaptive Neural Networks Efficient Inference Summary present approach adaptively utilize deep neural network order reduce evaluation time new example without loss accuracy Rather attempting redesign approximate existing network propose two scheme adaptively utilize network first pose adaptive network evaluation scheme learn system adaptively choose component deep network evaluated example allowing example correctly classified using early layer system exit avoid computational time associated full evaluation network extend learn network selection system adaptively selects network evaluated example show computational time dramatically reduced exploiting fact many example correctly classified using relatively efficient network complex computationally costly network necessary small fraction example pose global objective learning adaptive early exit network selection policy solve reducing policy learning problem layerbylayer weighted binary classification problem Empirically approach yield dramatic reduction computational cost 28x speedup stateoftheart network ImageNet image recognition challenge minimal 1 loss top5 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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['Zhengyang Wang', 'Hao Yuan', 'Shuiwang Ji']

1705.06821v1

The key idea of variational auto-encoders (VAEs) resembles that of traditional auto-encoder models in which spatial information is supposed to be explicitly encoded in the latent space. However, the latent variables in VAEs are vectors, which are commonly interpreted as multiple feature maps of size 1x1. Such representations can only convey spatial information implicitly when coupled with powerful decoders. In this work, we propose spatial VAEs that use latent variables as feature maps of larger size to explicitly capture spatial information. This is achieved by allowing the latent variables to be sampled from matrix-variate normal (MVN) distributions whose parameters are computed from the encoder network. To increase dependencies among locations on latent feature maps and reduce the number of parameters, we further propose spatial VAEs via low-rank MVN distributions. Experimental results show that the proposed spatial VAEs outperform original VAEs in capturing rich structural and spatial information.

Spatial Variational Auto-Encoding via Matrix-Variate Normal Distributions

http://arxiv.org/pdf/1705.06821v1

Title Spatial Variational AutoEncoding via MatrixVariate Normal Distributions Summary key idea variational autoencoders VAEs resembles traditional autoencoder model spatial information supposed explicitly encoded latent space However latent variable VAEs vector commonly interpreted multiple feature map size 1x1 representation convey spatial information implicitly coupled powerful decoder work propose spatial VAEs use latent variable feature map larger size explicitly capture spatial information achieved allowing latent variable sampled matrixvariate normal MVN distribution whose parameter computed encoder network increase dependency among location latent feature map reduce number parameter propose spatial VAEs via lowrank MVN distribution Experimental result show proposed spatial VAEs outperform original VAEs capturing rich structural spatial information 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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['Jun Li', 'Yongjun Chen', 'Lei Cai', 'Ian Davidson', 'Shuiwang Ji']

1705.08881v2

The key idea of current deep learning methods for dense prediction is to apply a model on a regular patch centered on each pixel to make pixel-wise predictions. These methods are limited in the sense that the patches are determined by network architecture instead of learned from data. In this work, we propose the dense transformer networks, which can learn the shapes and sizes of patches from data. The dense transformer networks employ an encoder-decoder architecture, and a pair of dense transformer modules are inserted into each of the encoder and decoder paths. The novelty of this work is that we provide technical solutions for learning the shapes and sizes of patches from data and efficiently restoring the spatial correspondence required for dense prediction. The proposed dense transformer modules are differentiable, thus the entire network can be trained. We apply the proposed networks on natural and biological image segmentation tasks and show superior performance is achieved in comparison to baseline methods.

Dense Transformer Networks

http://arxiv.org/pdf/1705.08881v2

Title Dense Transformer Networks Summary key idea current deep learning method dense prediction apply model regular patch centered pixel make pixelwise prediction method limited sense patch determined network architecture instead learned data work propose dense transformer network learn shape size patch data dense transformer network employ encoderdecoder architecture pair dense transformer module inserted encoder decoder path novelty work provide technical solution learning shape size patch data efficiently restoring spatial correspondence required dense prediction proposed dense transformer module differentiable thus entire network trained apply proposed network natural biological image segmentation task show superior performance achieved comparison baseline 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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['Saikat Chatterjee', 'Alireza M. Javid', 'Mostafa Sadeghi', 'Partha P. Mitra', 'Mikael Skoglund']

1710.08177v1

We develop an algorithm for systematic design of a large artificial neural network using a progression property. We find that some non-linear functions, such as the rectifier linear unit and its derivatives, hold the property. The systematic design addresses the choice of network size and regularization of parameters. The number of nodes and layers in network increases in progression with the objective of consistently reducing an appropriate cost. Each layer is optimized at a time, where appropriate parameters are learned using convex optimization. Regularization parameters for convex optimization do not need a significant manual effort for tuning. We also use random instances for some weight matrices, and that helps to reduce the number of parameters we learn. The developed network is expected to show good generalization power due to appropriate regularization and use of random weights in the layers. This expectation is verified by extensive experiments for classification and regression problems, using standard databases.

Progressive Learning for Systematic Design of Large Neural Networks

http://arxiv.org/pdf/1710.08177v1

Title Progressive Learning Systematic Design Large Neural Networks Summary develop algorithm systematic design large artificial neural network using progression property find nonlinear function rectifier linear unit derivative hold property systematic design address choice network size regularization parameter number node layer network increase progression objective consistently reducing appropriate cost layer optimized time appropriate parameter learned using convex optimization Regularization parameter convex optimization need significant manual effort tuning also use random instance weight matrix help reduce number parameter learn developed network expected show good generalization power due appropriate regularization use random weight layer expectation verified extensive experiment classification regression problem using standard 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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['Shibani Santurkar', 'Ludwig Schmidt', 'Aleksander Mądry']

1711.00970v3

A fundamental, and still largely unanswered, question in the context of Generative Adversarial Networks (GANs) is whether GANs are actually able to capture the key characteristics of the datasets they are trained on. The current approaches to examining this issue require significant human supervision, such as visual inspection of sampled images, and often offer only fairly limited scalability. In this paper, we propose new techniques that employ a classification-based perspective to evaluate synthetic GAN distributions and their capability to accurately reflect the essential properties of the training data. These techniques require only minimal human supervision and can easily be scaled and adapted to evaluate a variety of state-of-the-art GANs on large, popular datasets. Our analysis indicates that GANs have significant problems in reproducing the more distributional properties of the training dataset. In particular, when seen through the lens of classification, the diversity of GAN data is orders of magnitude less than that of the original data.

A Classification-Based Perspective on GAN Distributions

http://arxiv.org/pdf/1711.00970v3

Title ClassificationBased Perspective GAN Distributions Summary fundamental still largely unanswered question context Generative Adversarial Networks GANs whether GANs actually able capture key characteristic datasets trained current approach examining issue require significant human supervision visual inspection sampled image often offer fairly limited scalability paper propose new technique employ classificationbased perspective evaluate synthetic GAN distribution capability accurately reflect essential property training data technique require minimal human supervision easily scaled adapted evaluate variety stateoftheart GANs large popular datasets analysis indicates GANs significant problem reproducing distributional property training dataset particular seen lens classification diversity GAN data order magnitude le original 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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['Ethan Perez', 'Harm de Vries', 'Florian Strub', 'Vincent Dumoulin', 'Aaron Courville']

1707.03017v5

Achieving artificial visual reasoning - the ability to answer image-related questions which require a multi-step, high-level process - is an important step towards artificial general intelligence. This multi-modal task requires learning a question-dependent, structured reasoning process over images from language. Standard deep learning approaches tend to exploit biases in the data rather than learn this underlying structure, while leading methods learn to visually reason successfully but are hand-crafted for reasoning. We show that a general-purpose, Conditional Batch Normalization approach achieves state-of-the-art results on the CLEVR Visual Reasoning benchmark with a 2.4% error rate. We outperform the next best end-to-end method (4.5%) and even methods that use extra supervision (3.1%). We probe our model to shed light on how it reasons, showing it has learned a question-dependent, multi-step process. Previous work has operated under the assumption that visual reasoning calls for a specialized architecture, but we show that a general architecture with proper conditioning can learn to visually reason effectively.

Learning Visual Reasoning Without Strong Priors

http://arxiv.org/pdf/1707.03017v5

Title Learning Visual Reasoning Without Strong Priors Summary Achieving artificial visual reasoning ability answer imagerelated question require multistep highlevel process important step towards artificial general intelligence multimodal task requires learning questiondependent structured reasoning process image language Standard deep learning approach tend exploit bias data rather learn underlying structure leading method learn visually reason successfully handcrafted reasoning show generalpurpose Conditional Batch Normalization approach achieves stateoftheart result CLEVR Visual Reasoning benchmark 24 error rate outperform next best endtoend method 45 even method use extra supervision 31 probe model shed light reason showing learned questiondependent multistep process Previous work operated assumption visual reasoning call specialized architecture show general architecture proper conditioning learn visually reason effectively 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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['Jieyu Zhao', 'Tianlu Wang', 'Mark Yatskar', 'Vicente Ordonez', 'Kai-Wei Chang']

1707.09457v1

Language is increasingly being used to define rich visual recognition problems with supporting image collections sourced from the web. Structured prediction models are used in these tasks to take advantage of correlations between co-occurring labels and visual input but risk inadvertently encoding social biases found in web corpora. In this work, we study data and models associated with multilabel object classification and visual semantic role labeling. We find that (a) datasets for these tasks contain significant gender bias and (b) models trained on these datasets further amplify existing bias. For example, the activity cooking is over 33% more likely to involve females than males in a training set, and a trained model further amplifies the disparity to 68% at test time. We propose to inject corpus-level constraints for calibrating existing structured prediction models and design an algorithm based on Lagrangian relaxation for collective inference. Our method results in almost no performance loss for the underlying recognition task but decreases the magnitude of bias amplification by 47.5% and 40.5% for multilabel classification and visual semantic role labeling, respectively.

Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

http://arxiv.org/pdf/1707.09457v1

Title Men Also Like Shopping Reducing Gender Bias Amplification using Corpuslevel Constraints Summary Language increasingly used define rich visual recognition problem supporting image collection sourced web Structured prediction model used task take advantage correlation cooccurring label visual input risk inadvertently encoding social bias found web corpus work study data model associated multilabel object classification visual semantic role labeling find datasets task contain significant gender bias b model trained datasets amplify existing bias example activity cooking 33 likely involve female male training set trained model amplifies disparity 68 test time propose inject corpuslevel constraint calibrating existing structured prediction model design algorithm based Lagrangian relaxation collective inference method result almost performance loss underlying recognition task decrease magnitude bias amplification 475 405 multilabel classification visual semantic role labeling respectively 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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['Guillem Collell', 'Luc Van Gool', 'Marie-Francine Moens']

1711.06821v2

Spatial understanding is a fundamental problem with wide-reaching real-world applications. The representation of spatial knowledge is often modeled with spatial templates, i.e., regions of acceptability of two objects under an explicit spatial relationship (e.g., "on", "below", etc.). In contrast with prior work that restricts spatial templates to explicit spatial prepositions (e.g., "glass on table"), here we extend this concept to implicit spatial language, i.e., those relationships (generally actions) for which the spatial arrangement of the objects is only implicitly implied (e.g., "man riding horse"). In contrast with explicit relationships, predicting spatial arrangements from implicit spatial language requires significant common sense spatial understanding. Here, we introduce the task of predicting spatial templates for two objects under a relationship, which can be seen as a spatial question-answering task with a (2D) continuous output ("where is the man w.r.t. a horse when the man is walking the horse?"). We present two simple neural-based models that leverage annotated images and structured text to learn this task. The good performance of these models reveals that spatial locations are to a large extent predictable from implicit spatial language. Crucially, the models attain similar performance in a challenging generalized setting, where the object-relation-object combinations (e.g.,"man walking dog") have never been seen before. Next, we go one step further by presenting the models with unseen objects (e.g., "dog"). In this scenario, we show that leveraging word embeddings enables the models to output accurate spatial predictions, proving that the models acquire solid common sense spatial knowledge allowing for such generalization.

Acquiring Common Sense Spatial Knowledge through Implicit Spatial Templates

http://arxiv.org/pdf/1711.06821v2

Title Acquiring Common Sense Spatial Knowledge Implicit Spatial Templates Summary Spatial understanding fundamental problem widereaching realworld application representation spatial knowledge often modeled spatial template ie region acceptability two object explicit spatial relationship eg etc contrast prior work restricts spatial template explicit spatial preposition eg glass table extend concept implicit spatial language ie relationship generally action spatial arrangement object implicitly implied eg man riding horse contrast explicit relationship predicting spatial arrangement implicit spatial language requires significant common sense spatial understanding introduce task predicting spatial template two object relationship seen spatial questionanswering task 2D continuous output man wrt horse man walking horse present two simple neuralbased model leverage annotated image structured text learn task good performance model reveals spatial location large extent predictable implicit spatial language Crucially model attain similar performance challenging generalized setting objectrelationobject combination egman walking dog never seen Next go one step presenting model unseen object eg dog scenario show leveraging word embeddings enables model output accurate spatial prediction proving model acquire solid common sense spatial knowledge allowing 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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['Ethan Perez', 'Florian Strub', 'Harm de Vries', 'Vincent Dumoulin', 'Aaron Courville']

1709.07871v2

We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network computation via a simple, feature-wise affine transformation based on conditioning information. We show that FiLM layers are highly effective for visual reasoning - answering image-related questions which require a multi-step, high-level process - a task which has proven difficult for standard deep learning methods that do not explicitly model reasoning. Specifically, we show on visual reasoning tasks that FiLM layers 1) halve state-of-the-art error for the CLEVR benchmark, 2) modulate features in a coherent manner, 3) are robust to ablations and architectural modifications, and 4) generalize well to challenging, new data from few examples or even zero-shot.

FiLM: Visual Reasoning with a General Conditioning Layer

http://arxiv.org/pdf/1709.07871v2

Title FiLM Visual Reasoning General Conditioning Layer Summary introduce generalpurpose conditioning method neural network called FiLM Featurewise Linear Modulation FiLM layer influence neural network computation via simple featurewise affine transformation based conditioning information show FiLM layer highly effective visual reasoning answering imagerelated question require multistep highlevel process task proven difficult standard deep learning method explicitly model reasoning Specifically show visual reasoning task FiLM layer 1 halve stateoftheart error CLEVR benchmark 2 modulate feature coherent manner 3 robust ablation architectural modification 4 generalize well challenging new data example even zeroshot 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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['Ivan Titov', 'Ehsan Khoddam']

1412.2812v1

We introduce a new approach to unsupervised estimation of feature-rich semantic role labeling models. Our model consists of two components: (1) an encoding component: a semantic role labeling model which predicts roles given a rich set of syntactic and lexical features; (2) a reconstruction component: a tensor factorization model which relies on roles to predict argument fillers. When the components are estimated jointly to minimize errors in argument reconstruction, the induced roles largely correspond to roles defined in annotated resources. Our method performs on par with most accurate role induction methods on English and German, even though, unlike these previous approaches, we do not incorporate any prior linguistic knowledge about the languages.

Unsupervised Induction of Semantic Roles within a Reconstruction-Error Minimization Framework

http://arxiv.org/pdf/1412.2812v1

Title Unsupervised Induction Semantic Roles within ReconstructionError Minimization Framework Summary introduce new approach unsupervised estimation featurerich semantic role labeling model model consists two component 1 encoding component semantic role labeling model predicts role given rich set syntactic lexical feature 2 reconstruction component tensor factorization model relies role predict argument filler component estimated jointly minimize error argument reconstruction induced role largely correspond role defined annotated resource method performs par accurate role induction method English German even though unlike previous approach incorporate prior linguistic knowledge language 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', 'James Zou', 'Venkatesh Saligrama', 'Adam Kalai']

1607.06520v1

The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. We show that even word embeddings trained on Google News articles exhibit female/male gender stereotypes to a disturbing extent. This raises concerns because their widespread use, as we describe, often tends to amplify these biases. Geometrically, gender bias is first shown to be captured by a direction in the word embedding. Second, gender neutral words are shown to be linearly separable from gender definition words in the word embedding. Using these properties, we provide a methodology for modifying an embedding to remove gender stereotypes, such as the association between between the words receptionist and female, while maintaining desired associations such as between the words queen and female. We define metrics to quantify both direct and indirect gender biases in embeddings, and develop algorithms to "debias" the embedding. Using crowd-worker evaluation as well as standard benchmarks, we empirically demonstrate that our algorithms significantly reduce gender bias in embeddings while preserving the its useful properties such as the ability to cluster related concepts and to solve analogy tasks. The resulting embeddings can be used in applications without amplifying gender bias.

Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

http://arxiv.org/pdf/1607.06520v1

Title Man Computer Programmer Woman Homemaker Debiasing Word Embeddings Summary blind application machine learning run risk amplifying bias present data danger facing u word embedding popular framework represent text data vector used many machine learning natural language processing task show even word embeddings trained Google News article exhibit femalemale gender stereotype disturbing extent raise concern widespread use describe often tends amplify bias Geometrically gender bias first shown captured direction word embedding Second gender neutral word shown linearly separable gender definition word word embedding Using property provide methodology modifying embedding remove gender stereotype association word receptionist female maintaining desired association word queen female define metric quantify direct indirect gender bias embeddings develop algorithm debias embedding Using crowdworker evaluation well standard benchmark empirically demonstrate algorithm significantly reduce gender bias embeddings preserving useful property ability cluster related concept solve analogy task resulting embeddings used application without amplifying gender bias 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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['Adji B. Dieng', 'Chong Wang', 'Jianfeng Gao', 'John Paisley']

1611.01702v2

In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based language model designed to directly capture the global semantic meaning relating words in a document via latent topics. Because of their sequential nature, RNNs are good at capturing the local structure of a word sequence - both semantic and syntactic - but might face difficulty remembering long-range dependencies. Intuitively, these long-range dependencies are of semantic nature. In contrast, latent topic models are able to capture the global underlying semantic structure of a document but do not account for word ordering. The proposed TopicRNN model integrates the merits of RNNs and latent topic models: it captures local (syntactic) dependencies using an RNN and global (semantic) dependencies using latent topics. Unlike previous work on contextual RNN language modeling, our model is learned end-to-end. Empirical results on word prediction show that TopicRNN outperforms existing contextual RNN baselines. In addition, TopicRNN can be used as an unsupervised feature extractor for documents. We do this for sentiment analysis on the IMDB movie review dataset and report an error rate of $6.28\%$. This is comparable to the state-of-the-art $5.91\%$ resulting from a semi-supervised approach. Finally, TopicRNN also yields sensible topics, making it a useful alternative to document models such as latent Dirichlet allocation.

TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency

http://arxiv.org/pdf/1611.01702v2

Title TopicRNN Recurrent Neural Network LongRange Semantic Dependency Summary paper propose TopicRNN recurrent neural network RNNbased language model designed directly capture global semantic meaning relating word document via latent topic sequential nature RNNs good capturing local structure word sequence semantic syntactic might face difficulty remembering longrange dependency Intuitively longrange dependency semantic nature contrast latent topic model able capture global underlying semantic structure document account word ordering proposed TopicRNN model integrates merit RNNs latent topic model capture local syntactic dependency using RNN global semantic dependency using latent topic Unlike previous work contextual RNN language modeling model learned endtoend Empirical result word prediction show TopicRNN outperforms existing contextual RNN baseline addition TopicRNN used unsupervised feature extractor document sentiment analysis IMDB movie review dataset report error rate 628 comparable stateoftheart 591 resulting semisupervised approach Finally TopicRNN also yield sensible topic making useful alternative document model latent Dirichlet allocation 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', 'John Winn', 'Ryota Tomioka']

1611.02266v2

We propose the Gaussian attention model for content-based neural memory access. With the proposed attention model, a neural network has the additional degree of freedom to control the focus of its attention from a laser sharp attention to a broad attention. It is applicable whenever we can assume that the distance in the latent space reflects some notion of semantics. We use the proposed attention model as a scoring function for the embedding of a knowledge base into a continuous vector space and then train a model that performs question answering about the entities in the knowledge base. The proposed attention model can handle both the propagation of uncertainty when following a series of relations and also the conjunction of conditions in a natural way. On a dataset of soccer players who participated in the FIFA World Cup 2014, we demonstrate that our model can handle both path queries and conjunctive queries well.

Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering

http://arxiv.org/pdf/1611.02266v2

Title Gaussian Attention Model Application Knowledge Base Embedding Question Answering Summary propose Gaussian attention model contentbased neural memory access proposed attention model neural network additional degree freedom control focus attention laser sharp attention broad attention applicable whenever assume distance latent space reflects notion semantics use proposed attention model scoring function embedding knowledge base continuous vector space train model performs question answering entity knowledge base proposed attention model handle propagation uncertainty following series relation also conjunction condition natural way dataset soccer player participated FIFA World Cup 2014 demonstrate model handle path query conjunctive query well 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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['Yacine Jernite', 'Edouard Grave', 'Armand Joulin', 'Tomas Mikolov']

1611.06188v2

Recurrent neural networks (RNNs) have been used extensively and with increasing success to model various types of sequential data. Much of this progress has been achieved through devising recurrent units and architectures with the flexibility to capture complex statistics in the data, such as long range dependency or localized attention phenomena. However, while many sequential data (such as video, speech or language) can have highly variable information flow, most recurrent models still consume input features at a constant rate and perform a constant number of computations per time step, which can be detrimental to both speed and model capacity. In this paper, we explore a modification to existing recurrent units which allows them to learn to vary the amount of computation they perform at each step, without prior knowledge of the sequence's time structure. We show experimentally that not only do our models require fewer operations, they also lead to better performance overall on evaluation tasks.

Variable Computation in Recurrent Neural Networks

http://arxiv.org/pdf/1611.06188v2

Title Variable Computation Recurrent Neural Networks Summary Recurrent neural network RNNs used extensively increasing success model various type sequential data Much progress achieved devising recurrent unit architecture flexibility capture complex statistic data long range dependency localized attention phenomenon However many sequential data video speech language highly variable information flow recurrent model still consume input feature constant rate perform constant number computation per time step detrimental speed model capacity paper explore modification existing recurrent unit allows learn vary amount computation perform step without prior knowledge sequence time structure show experimentally model require fewer operation also lead better performance overall evaluation task 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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['Mostafa Dehghani', 'Aliaksei Severyn', 'Sascha Rothe', 'Jaap Kamps']

1711.11383v1

In this paper, we propose a method for training neural networks when we have a large set of data with weak labels and a small amount of data with true labels. In our proposed model, we train two neural networks: a target network, the learner and a confidence network, the meta-learner. The target network is optimized to perform a given task and is trained using a large set of unlabeled data that are weakly annotated. We propose to control the magnitude of the gradient updates to the target network using the scores provided by the second confidence network, which is trained on a small amount of supervised data. Thus we avoid that the weight updates computed from noisy labels harm the quality of the target network model.

Learning to Learn from Weak Supervision by Full Supervision

http://arxiv.org/pdf/1711.11383v1

Title Learning Learn Weak Supervision Full Supervision Summary paper propose method training neural network large set data weak label small amount data true label proposed model train two neural network target network learner confidence network metalearner target network optimized perform given task trained using large set unlabeled data weakly annotated propose control magnitude gradient update target network using score provided second confidence network trained small amount supervised data Thus avoid weight update computed noisy label harm quality target network 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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['Garrett B. Goh', 'Nathan O. Hodas', 'Charles Siegel', 'Abhinav Vishnu']

1712.02034v2

Chemical databases store information in text representations, and the SMILES format is a universal standard used in many cheminformatics software. Encoded in each SMILES string is structural information that can be used to predict complex chemical properties. In this work, we develop SMILES2vec, a deep RNN that automatically learns features from SMILES to predict chemical properties, without the need for additional explicit feature engineering. Using Bayesian optimization methods to tune the network architecture, we show that an optimized SMILES2vec model can serve as a general-purpose neural network for predicting distinct chemical properties including toxicity, activity, solubility and solvation energy, while also outperforming contemporary MLP neural networks that uses engineered features. Furthermore, we demonstrate proof-of-concept of interpretability by developing an explanation mask that localizes on the most important characters used in making a prediction. When tested on the solubility dataset, it identified specific parts of a chemical that is consistent with established first-principles knowledge with an accuracy of 88%. Our work demonstrates that neural networks can learn technically accurate chemical concept and provide state-of-the-art accuracy, making interpretable deep neural networks a useful tool of relevance to the chemical industry.

SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties

http://arxiv.org/pdf/1712.02034v2

Title SMILES2Vec Interpretable GeneralPurpose Deep Neural Network Predicting Chemical Properties Summary Chemical database store information text representation SMILES format universal standard used many cheminformatics software Encoded SMILES string structural information used predict complex chemical property work develop SMILES2vec deep RNN automatically learns feature SMILES predict chemical property without need additional explicit feature engineering Using Bayesian optimization method tune network architecture show optimized SMILES2vec model serve generalpurpose neural network predicting distinct chemical property including toxicity activity solubility solvation energy also outperforming contemporary MLP neural network us engineered feature Furthermore demonstrate proofofconcept interpretability developing explanation mask localizes important character used making prediction tested solubility dataset identified specific part chemical consistent established firstprinciples knowledge accuracy 88 work demonstrates neural network learn technically accurate chemical concept provide stateoftheart accuracy making interpretable deep neural network useful tool relevance chemical industry 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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['Gellért Weisz', 'Paweł Budzianowski', 'Pei-Hao Su', 'Milica Gašić']

1802.03753v1

In spoken dialogue systems, we aim to deploy artificial intelligence to build automated dialogue agents that can converse with humans. A part of this effort is the policy optimisation task, which attempts to find a policy describing how to respond to humans, in the form of a function taking the current state of the dialogue and returning the response of the system. In this paper, we investigate deep reinforcement learning approaches to solve this problem. Particular attention is given to actor-critic methods, off-policy reinforcement learning with experience replay, and various methods aimed at reducing the bias and variance of estimators. When combined, these methods result in the previously proposed ACER algorithm that gave competitive results in gaming environments. These environments however are fully observable and have a relatively small action set so in this paper we examine the application of ACER to dialogue policy optimisation. We show that this method beats the current state-of-the-art in deep learning approaches for spoken dialogue systems. This not only leads to a more sample efficient algorithm that can train faster, but also allows us to apply the algorithm in more difficult environments than before. We thus experiment with learning in a very large action space, which has two orders of magnitude more actions than previously considered. We find that ACER trains significantly faster than the current state-of-the-art.

Sample Efficient Deep Reinforcement Learning for Dialogue Systems with Large Action Spaces

http://arxiv.org/pdf/1802.03753v1

Title Sample Efficient Deep Reinforcement Learning Dialogue Systems Large Action Spaces Summary spoken dialogue system aim deploy artificial intelligence build automated dialogue agent converse human part effort policy optimisation task attempt find policy describing respond human form function taking current state dialogue returning response system paper investigate deep reinforcement learning approach solve problem Particular attention given actorcritic method offpolicy reinforcement learning experience replay various method aimed reducing bias variance estimator combined method result previously proposed ACER algorithm gave competitive result gaming environment environment however fully observable relatively small action set paper examine application ACER dialogue policy optimisation show method beat current stateoftheart deep learning approach spoken dialogue system lead sample efficient algorithm train faster also allows u apply algorithm difficult environment thus experiment learning large action space two order magnitude action previously considered find ACER train significantly faster current stateoftheart 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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['M. Andrecut']

1802.09914v1

In this paper we explore the "vector semantics" problem from the perspective of "almost orthogonal" property of high-dimensional random vectors. We show that this intriguing property can be used to "memorize" random vectors by simply adding them, and we provide an efficient probabilistic solution to the set membership problem. Also, we discuss several applications to word context vector embeddings, document sentences similarity, and spam filtering.

High-Dimensional Vector Semantics

http://arxiv.org/pdf/1802.09914v1

Title HighDimensional Vector Semantics Summary paper explore vector semantics problem perspective almost orthogonal property highdimensional random vector show intriguing property used memorize random vector simply adding provide efficient probabilistic solution set membership problem Also discus several application word context vector embeddings document sentence similarity spam filtering 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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['Ashutosh Modi', 'Ivan Titov']

1312.5198v4

Induction of common sense knowledge about prototypical sequences of events has recently received much attention. Instead of inducing this knowledge in the form of graphs, as in much of the previous work, in our method, distributed representations of event realizations are computed based on distributed representations of predicates and their arguments, and then these representations are used to predict prototypical event orderings. The parameters of the compositional process for computing the event representations and the ranking component of the model are jointly estimated from texts. We show that this approach results in a substantial boost in ordering performance with respect to previous methods.

Learning Semantic Script Knowledge with Event Embeddings

http://arxiv.org/pdf/1312.5198v4

Title Learning Semantic Script Knowledge Event Embeddings Summary Induction common sense knowledge prototypical sequence event recently received much attention Instead inducing knowledge form graph much previous work method distributed representation event realization computed based distributed representation predicate argument representation used predict prototypical event ordering parameter compositional process computing event representation ranking component model jointly estimated text show approach result substantial boost ordering performance respect previous 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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['Andrew S. Lan', 'Divyanshu Vats', 'Andrew E. Waters', 'Richard G. Baraniuk']

1501.04346v1

While computer and communication technologies have provided effective means to scale up many aspects of education, the submission and grading of assessments such as homework assignments and tests remains a weak link. In this paper, we study the problem of automatically grading the kinds of open response mathematical questions that figure prominently in STEM (science, technology, engineering, and mathematics) courses. Our data-driven framework for mathematical language processing (MLP) leverages solution data from a large number of learners to evaluate the correctness of their solutions, assign partial-credit scores, and provide feedback to each learner on the likely locations of any errors. MLP takes inspiration from the success of natural language processing for text data and comprises three main steps. First, we convert each solution to an open response mathematical question into a series of numerical features. Second, we cluster the features from several solutions to uncover the structures of correct, partially correct, and incorrect solutions. We develop two different clustering approaches, one that leverages generic clustering algorithms and one based on Bayesian nonparametrics. Third, we automatically grade the remaining (potentially large number of) solutions based on their assigned cluster and one instructor-provided grade per cluster. As a bonus, we can track the cluster assignment of each step of a multistep solution and determine when it departs from a cluster of correct solutions, which enables us to indicate the likely locations of errors to learners. We test and validate MLP on real-world MOOC data to demonstrate how it can substantially reduce the human effort required in large-scale educational platforms.

Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions

http://arxiv.org/pdf/1501.04346v1

Title Mathematical Language Processing Automatic Grading Feedback Open Response Mathematical Questions Summary computer communication technology provided effective mean scale many aspect education submission grading assessment homework assignment test remains weak link paper study problem automatically grading kind open response mathematical question figure prominently STEM science technology engineering mathematics course datadriven framework mathematical language processing MLP leverage solution data large number learner evaluate correctness solution assign partialcredit score provide feedback learner likely location error MLP take inspiration success natural language processing text data comprises three main step First convert solution open response mathematical question series numerical feature Second cluster feature several solution uncover structure correct partially correct incorrect solution develop two different clustering approach one leverage generic clustering algorithm one based Bayesian nonparametrics Third automatically grade remaining potentially large number solution based assigned cluster one instructorprovided grade per cluster bonus track cluster assignment step multistep solution determine departs cluster correct solution enables u indicate likely location error learner test validate MLP realworld MOOC data demonstrate substantially reduce human effort required largescale educational platform 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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['Tadahiro Taniguchi', 'Ryo Nakashima', 'Shogo Nagasaka']

1506.06646v2

Human infants can discover words directly from unsegmented speech signals without any explicitly labeled data. In this paper, we develop a novel machine learning method called nonparametric Bayesian double articulation analyzer (NPB-DAA) that can directly acquire language and acoustic models from observed continuous speech signals. For this purpose, we propose an integrative generative model that combines a language model and an acoustic model into a single generative model called the "hierarchical Dirichlet process hidden language model" (HDP-HLM). The HDP-HLM is obtained by extending the hierarchical Dirichlet process hidden semi-Markov model (HDP-HSMM) proposed by Johnson et al. An inference procedure for the HDP-HLM is derived using the blocked Gibbs sampler originally proposed for the HDP-HSMM. This procedure enables the simultaneous and direct inference of language and acoustic models from continuous speech signals. Based on the HDP-HLM and its inference procedure, we developed a novel double articulation analyzer. By assuming HDP-HLM as a generative model of observed time series data, and by inferring latent variables of the model, the method can analyze latent double articulation structure, i.e., hierarchically organized latent words and phonemes, of the data in an unsupervised manner. The novel unsupervised double articulation analyzer is called NPB-DAA. The NPB-DAA can automatically estimate double articulation structure embedded in speech signals. We also carried out two evaluation experiments using synthetic data and actual human continuous speech signals representing Japanese vowel sequences. In the word acquisition and phoneme categorization tasks, the NPB-DAA outperformed a conventional double articulation analyzer (DAA) and baseline automatic speech recognition system whose acoustic model was trained in a supervised manner.

Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals

http://arxiv.org/pdf/1506.06646v2

Title Nonparametric Bayesian Double Articulation Analyzer Direct Language Acquisition Continuous Speech Signals Summary Human infant discover word directly unsegmented speech signal without explicitly labeled data paper develop novel machine learning method called nonparametric Bayesian double articulation analyzer NPBDAA directly acquire language acoustic model observed continuous speech signal purpose propose integrative generative model combine language model acoustic model single generative model called hierarchical Dirichlet process hidden language model HDPHLM HDPHLM obtained extending hierarchical Dirichlet process hidden semiMarkov model HDPHSMM proposed Johnson et al inference procedure HDPHLM derived using blocked Gibbs sampler originally proposed HDPHSMM procedure enables simultaneous direct inference language acoustic model continuous speech signal Based HDPHLM inference procedure developed novel double articulation analyzer assuming HDPHLM generative model observed time series data inferring latent variable model method analyze latent double articulation structure ie hierarchically organized latent word phoneme data unsupervised manner novel unsupervised double articulation analyzer called NPBDAA NPBDAA automatically estimate double articulation structure embedded speech signal also carried two evaluation experiment using synthetic data actual human continuous speech signal representing Japanese vowel sequence word acquisition phoneme categorization task NPBDAA outperformed conventional double articulation analyzer DAA baseline automatic speech recognition system whose acoustic model trained supervised manner 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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['Zhiting Hu', 'Xuezhe Ma', 'Zhengzhong Liu', 'Eduard Hovy', 'Eric Xing']

1603.06318v4

Combining deep neural networks with structured logic rules is desirable to harness flexibility and reduce uninterpretability of the neural models. We propose a general framework capable of enhancing various types of neural networks (e.g., CNNs and RNNs) with declarative first-order logic rules. Specifically, we develop an iterative distillation method that transfers the structured information of logic rules into the weights of neural networks. We deploy the framework on a CNN for sentiment analysis, and an RNN for named entity recognition. With a few highly intuitive rules, we obtain substantial improvements and achieve state-of-the-art or comparable results to previous best-performing systems.

Harnessing Deep Neural Networks with Logic Rules

http://arxiv.org/pdf/1603.06318v4

Title Harnessing Deep Neural Networks Logic Rules Summary Combining deep neural network structured logic rule desirable harness flexibility reduce uninterpretability neural model propose general framework capable enhancing various type neural network eg CNNs RNNs declarative firstorder logic rule Specifically develop iterative distillation method transfer structured information logic rule weight neural network deploy framework CNN sentiment analysis RNN named entity recognition highly intuitive rule obtain substantial improvement achieve stateoftheart comparable result previous bestperforming 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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['Zhiting Hu', 'Zichao Yang', 'Xiaodan Liang', 'Ruslan Salakhutdinov', 'Eric P. Xing']

1703.00955v3

Generic generation and manipulation of text is challenging and has limited success compared to recent deep generative modeling in visual domain. This paper aims at generating plausible natural language sentences, whose attributes are dynamically controlled by learning disentangled latent representations with designated semantics. We propose a new neural generative model which combines variational auto-encoders and holistic attribute discriminators for effective imposition of semantic structures. With differentiable approximation to discrete text samples, explicit constraints on independent attribute controls, and efficient collaborative learning of generator and discriminators, our model learns highly interpretable representations from even only word annotations, and produces realistic sentences with desired attributes. Quantitative evaluation validates the accuracy of sentence and attribute generation.

Toward Controlled Generation of Text

http://arxiv.org/pdf/1703.00955v3

Title Toward Controlled Generation Text Summary Generic generation manipulation text challenging limited success compared recent deep generative modeling visual domain paper aim generating plausible natural language sentence whose attribute dynamically controlled learning disentangled latent representation designated semantics propose new neural generative model combine variational autoencoders holistic attribute discriminator effective imposition semantic structure differentiable approximation discrete text sample explicit constraint independent attribute control efficient collaborative learning generator discriminator model learns highly interpretable representation even word annotation produce realistic sentence desired attribute Quantitative evaluation validates accuracy sentence attribute generation 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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['Lianhui Qin', 'Zhisong Zhang', 'Hai Zhao', 'Zhiting Hu', 'Eric P. Xing']

1704.00217v1

Implicit discourse relation classification is of great challenge due to the lack of connectives as strong linguistic cues, which motivates the use of annotated implicit connectives to improve the recognition. We propose a feature imitation framework in which an implicit relation network is driven to learn from another neural network with access to connectives, and thus encouraged to extract similarly salient features for accurate classification. We develop an adversarial model to enable an adaptive imitation scheme through competition between the implicit network and a rival feature discriminator. Our method effectively transfers discriminability of connectives to the implicit features, and achieves state-of-the-art performance on the PDTB benchmark.

Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification

http://arxiv.org/pdf/1704.00217v1

Title Adversarial Connectiveexploiting Networks Implicit Discourse Relation Classification Summary Implicit discourse relation classification great challenge due lack connective strong linguistic cue motivates use annotated implicit connective improve recognition propose feature imitation framework implicit relation network driven learn another neural network access connective thus encouraged extract similarly salient feature accurate classification develop adversarial model enable adaptive imitation scheme competition implicit network rival feature discriminator method effectively transfer discriminability connective implicit feature achieves stateoftheart performance PDTB benchmark 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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['Maxim Rabinovich', 'Mitchell Stern', 'Dan Klein']

1704.07535v1

Tasks like code generation and semantic parsing require mapping unstructured (or partially structured) inputs to well-formed, executable outputs. We introduce abstract syntax networks, a modeling framework for these problems. The outputs are represented as abstract syntax trees (ASTs) and constructed by a decoder with a dynamically-determined modular structure paralleling the structure of the output tree. On the benchmark Hearthstone dataset for code generation, our model obtains 79.2 BLEU and 22.7% exact match accuracy, compared to previous state-of-the-art values of 67.1 and 6.1%. Furthermore, we perform competitively on the Atis, Jobs, and Geo semantic parsing datasets with no task-specific engineering.

Abstract Syntax Networks for Code Generation and Semantic Parsing

http://arxiv.org/pdf/1704.07535v1

Title Abstract Syntax Networks Code Generation Semantic Parsing Summary Tasks like code generation semantic parsing require mapping unstructured partially structured input wellformed executable output introduce abstract syntax network modeling framework problem output represented abstract syntax tree ASTs constructed decoder dynamicallydetermined modular structure paralleling structure output tree benchmark Hearthstone dataset code generation model obtains 792 BLEU 227 exact match accuracy compared previous stateoftheart value 671 61 Furthermore perform competitively Atis Jobs Geo semantic parsing datasets taskspecific engineering 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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['Ben Athiwaratkun', 'Andrew Gordon Wilson']

1704.08424v1

Word embeddings provide point representations of words containing useful semantic information. We introduce multimodal word distributions formed from Gaussian mixtures, for multiple word meanings, entailment, and rich uncertainty information. To learn these distributions, we propose an energy-based max-margin objective. We show that the resulting approach captures uniquely expressive semantic information, and outperforms alternatives, such as word2vec skip-grams, and Gaussian embeddings, on benchmark datasets such as word similarity and entailment.

Multimodal Word Distributions

http://arxiv.org/pdf/1704.08424v1

Title Multimodal Word Distributions Summary Word embeddings provide point representation word containing useful semantic information introduce multimodal word distribution formed Gaussian mixture multiple word meaning entailment rich uncertainty information learn distribution propose energybased maxmargin objective show resulting approach capture uniquely expressive semantic information outperforms alternative word2vec skipgrams Gaussian embeddings benchmark datasets word similarity entailment 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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['Brent Harrison', 'Upol Ehsan', 'Mark O. Riedl']

1707.08616v2

In this work we present a technique to use natural language to help reinforcement learning generalize to unseen environments. This technique uses neural machine translation, specifically the use of encoder-decoder networks, to learn associations between natural language behavior descriptions and state-action information. We then use this learned model to guide agent exploration using a modified version of policy shaping to make it more effective at learning in unseen environments. We evaluate this technique using the popular arcade game, Frogger, under ideal and non-ideal conditions. This evaluation shows that our modified policy shaping algorithm improves over a Q-learning agent as well as a baseline version of policy shaping.

Guiding Reinforcement Learning Exploration Using Natural Language

http://arxiv.org/pdf/1707.08616v2

Title Guiding Reinforcement Learning Exploration Using Natural Language Summary work present technique use natural language help reinforcement learning generalize unseen environment technique us neural machine translation specifically use encoderdecoder network learn association natural language behavior description stateaction information use learned model guide agent exploration using modified version policy shaping make effective learning unseen environment evaluate technique using popular arcade game Frogger ideal nonideal condition evaluation show modified policy shaping algorithm improves Qlearning agent well baseline version policy shaping 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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['Mo Yu', 'Xiaoxiao Guo', 'Jinfeng Yi', 'Shiyu Chang', 'Saloni Potdar', 'Gerald Tesauro', 'Haoyu Wang', 'Bowen Zhou']

1708.07918v1

We investigate task clustering for deep-learning based multi-task and few-shot learning in a many-task setting. We propose a new method to measure task similarities with cross-task transfer performance matrix for the deep learning scenario. Although this matrix provides us critical information regarding similarity between tasks, its asymmetric property and unreliable performance scores can affect conventional clustering methods adversely. Additionally, the uncertain task-pairs, i.e., the ones with extremely asymmetric transfer scores, may collectively mislead clustering algorithms to output an inaccurate task-partition. To overcome these limitations, we propose a novel task-clustering algorithm by using the matrix completion technique. The proposed algorithm constructs a partially-observed similarity matrix based on the certainty of cluster membership of the task-pairs. We then use a matrix completion algorithm to complete the similarity matrix. Our theoretical analysis shows that under mild constraints, the proposed algorithm will perfectly recover the underlying "true" similarity matrix with a high probability. Our results show that the new task clustering method can discover task clusters for training flexible and superior neural network models in a multi-task learning setup for sentiment classification and dialog intent classification tasks. Our task clustering approach also extends metric-based few-shot learning methods to adapt multiple metrics, which demonstrates empirical advantages when the tasks are diverse.

Robust Task Clustering for Deep Many-Task Learning

http://arxiv.org/pdf/1708.07918v1

Title Robust Task Clustering Deep ManyTask Learning Summary investigate task clustering deeplearning based multitask fewshot learning manytask setting propose new method measure task similarity crosstask transfer performance matrix deep learning scenario Although matrix provides u critical information regarding similarity task asymmetric property unreliable performance score affect conventional clustering method adversely Additionally uncertain taskpairs ie one extremely asymmetric transfer score may collectively mislead clustering algorithm output inaccurate taskpartition overcome limitation propose novel taskclustering algorithm using matrix completion technique proposed algorithm construct partiallyobserved similarity matrix based certainty cluster membership taskpairs use matrix completion algorithm complete similarity matrix theoretical analysis show mild constraint proposed algorithm perfectly recover underlying true similarity matrix high probability result show new task clustering method discover task cluster training flexible superior neural network model multitask learning setup sentiment classification dialog intent classification task task clustering approach also extends metricbased fewshot learning method adapt multiple metric demonstrates empirical advantage task diverse 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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['Gino Brunner', 'Yuyi Wang', 'Roger Wattenhofer', 'Michael Weigelt']

1801.06024v1

We train multi-task autoencoders on linguistic tasks and analyze the learned hidden sentence representations. The representations change significantly when translation and part-of-speech decoders are added. The more decoders a model employs, the better it clusters sentences according to their syntactic similarity, as the representation space becomes less entangled. We explore the structure of the representation space by interpolating between sentences, which yields interesting pseudo-English sentences, many of which have recognizable syntactic structure. Lastly, we point out an interesting property of our models: The difference-vector between two sentences can be added to change a third sentence with similar features in a meaningful way.

Natural Language Multitasking: Analyzing and Improving Syntactic Saliency of Hidden Representations

http://arxiv.org/pdf/1801.06024v1

Title Natural Language Multitasking Analyzing Improving Syntactic Saliency Hidden Representations Summary train multitask autoencoders linguistic task analyze learned hidden sentence representation representation change significantly translation partofspeech decoder added decoder model employ better cluster sentence according syntactic similarity representation space becomes le entangled explore structure representation space interpolating sentence yield interesting pseudoEnglish sentence many recognizable syntactic structure Lastly point interesting property model differencevector two sentence added change third sentence similar feature meaningful way 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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['Minghai Chen', 'Sen Wang', 'Paul Pu Liang', 'Tadas Baltrušaitis', 'Amir Zadeh', 'Louis-Philippe Morency']

1802.00924v1

With the increasing popularity of video sharing websites such as YouTube and Facebook, multimodal sentiment analysis has received increasing attention from the scientific community. Contrary to previous works in multimodal sentiment analysis which focus on holistic information in speech segments such as bag of words representations and average facial expression intensity, we develop a novel deep architecture for multimodal sentiment analysis that performs modality fusion at the word level. In this paper, we propose the Gated Multimodal Embedding LSTM with Temporal Attention (GME-LSTM(A)) model that is composed of 2 modules. The Gated Multimodal Embedding alleviates the difficulties of fusion when there are noisy modalities. The LSTM with Temporal Attention performs word level fusion at a finer fusion resolution between input modalities and attends to the most important time steps. As a result, the GME-LSTM(A) is able to better model the multimodal structure of speech through time and perform better sentiment comprehension. We demonstrate the effectiveness of this approach on the publicly-available Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis (CMU-MOSI) dataset by achieving state-of-the-art sentiment classification and regression results. Qualitative analysis on our model emphasizes the importance of the Temporal Attention Layer in sentiment prediction because the additional acoustic and visual modalities are noisy. We also demonstrate the effectiveness of the Gated Multimodal Embedding in selectively filtering these noisy modalities out. Our results and analysis open new areas in the study of sentiment analysis in human communication and provide new models for multimodal fusion.

Multimodal Sentiment Analysis with Word-Level Fusion and Reinforcement Learning

http://arxiv.org/pdf/1802.00924v1

Title Multimodal Sentiment Analysis WordLevel Fusion Reinforcement Learning Summary increasing popularity video sharing website YouTube Facebook multimodal sentiment analysis received increasing attention scientific community Contrary previous work multimodal sentiment analysis focus holistic information speech segment bag word representation average facial expression intensity develop novel deep architecture multimodal sentiment analysis performs modality fusion word level paper propose Gated Multimodal Embedding LSTM Temporal Attention GMELSTMA model composed 2 module Gated Multimodal Embedding alleviates difficulty fusion noisy modality LSTM Temporal Attention performs word level fusion finer fusion resolution input modality attends important time step result GMELSTMA able better model multimodal structure speech time perform better sentiment comprehension demonstrate effectiveness approach publiclyavailable Multimodal Corpus Sentiment Intensity Subjectivity Analysis CMUMOSI dataset achieving stateoftheart sentiment classification regression result Qualitative analysis model emphasizes importance Temporal Attention Layer sentiment prediction additional acoustic visual modality noisy also demonstrate effectiveness Gated Multimodal Embedding selectively filtering noisy modality result analysis open new area study sentiment analysis human communication provide new model multimodal fusion 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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['Ed Collins', 'Isabelle Augenstein', 'Sebastian Riedel']

1706.03946v1

Automatic summarisation is a popular approach to reduce a document to its main arguments. Recent research in the area has focused on neural approaches to summarisation, which can be very data-hungry. However, few large datasets exist and none for the traditionally popular domain of scientific publications, which opens up challenging research avenues centered on encoding large, complex documents. In this paper, we introduce a new dataset for summarisation of computer science publications by exploiting a large resource of author provided summaries and show straightforward ways of extending it further. We develop models on the dataset making use of both neural sentence encoding and traditionally used summarisation features and show that models which encode sentences as well as their local and global context perform best, significantly outperforming well-established baseline methods.

A Supervised Approach to Extractive Summarisation of Scientific Papers

http://arxiv.org/pdf/1706.03946v1

Title Supervised Approach Extractive Summarisation Scientific Papers Summary Automatic summarisation popular approach reduce document main argument Recent research area focused neural approach summarisation datahungry However large datasets exist none traditionally popular domain scientific publication open challenging research avenue centered encoding large complex document paper introduce new dataset summarisation computer science publication exploiting large resource author provided summary show straightforward way extending develop model dataset making use neural sentence encoding traditionally used summarisation feature show model encode sentence well local global context perform best significantly outperforming wellestablished baseline 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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['Jacob Devlin', 'Hao Cheng', 'Hao Fang', 'Saurabh Gupta', 'Li Deng', 'Xiaodong He', 'Geoffrey Zweig', 'Margaret Mitchell']

1505.01809v3

Two recent approaches have achieved state-of-the-art results in image captioning. The first uses a pipelined process where a set of candidate words is generated by a convolutional neural network (CNN) trained on images, and then a maximum entropy (ME) language model is used to arrange these words into a coherent sentence. The second uses the penultimate activation layer of the CNN as input to a recurrent neural network (RNN) that then generates the caption sequence. In this paper, we compare the merits of these different language modeling approaches for the first time by using the same state-of-the-art CNN as input. We examine issues in the different approaches, including linguistic irregularities, caption repetition, and data set overlap. By combining key aspects of the ME and RNN methods, we achieve a new record performance over previously published results on the benchmark COCO dataset. However, the gains we see in BLEU do not translate to human judgments.

Language Models for Image Captioning: The Quirks and What Works

http://arxiv.org/pdf/1505.01809v3

Title Language Models Image Captioning Quirks Works Summary Two recent approach achieved stateoftheart result image captioning first us pipelined process set candidate word generated convolutional neural network CNN trained image maximum entropy language model used arrange word coherent sentence second us penultimate activation layer CNN input recurrent neural network RNN generates caption sequence paper compare merit different language modeling approach first time using stateoftheart CNN input examine issue different approach including linguistic irregularity caption repetition data set overlap combining key aspect RNN method achieve new record performance previously published result benchmark COCO dataset However gain see BLEU translate human judgment 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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['Mengye Ren', 'Ryan Kiros', 'Richard Zemel']

1505.02074v4

This work aims to address the problem of image-based question-answering (QA) with new models and datasets. In our work, we propose to use neural networks and visual semantic embeddings, without intermediate stages such as object detection and image segmentation, to predict answers to simple questions about images. Our model performs 1.8 times better than the only published results on an existing image QA dataset. We also present a question generation algorithm that converts image descriptions, which are widely available, into QA form. We used this algorithm to produce an order-of-magnitude larger dataset, with more evenly distributed answers. A suite of baseline results on this new dataset are also presented.

Exploring Models and Data for Image Question Answering

http://arxiv.org/pdf/1505.02074v4

Title Exploring Models Data Image Question Answering Summary work aim address problem imagebased questionanswering QA new model datasets work propose use neural network visual semantic embeddings without intermediate stage object detection image segmentation predict answer simple question image model performs 18 time better published result existing image QA dataset also present question generation algorithm convert image description widely available QA form used algorithm produce orderofmagnitude larger dataset evenly distributed answer suite baseline result new dataset also presented 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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['Yash Goyal', 'Tejas Khot', 'Douglas Summers-Stay', 'Dhruv Batra', 'Devi Parikh']

1612.00837v3

Problems at the intersection of vision and language are of significant importance both as challenging research questions and for the rich set of applications they enable. However, inherent structure in our world and bias in our language tend to be a simpler signal for learning than visual modalities, resulting in models that ignore visual information, leading to an inflated sense of their capability. We propose to counter these language priors for the task of Visual Question Answering (VQA) and make vision (the V in VQA) matter! Specifically, we balance the popular VQA dataset by collecting complementary images such that every question in our balanced dataset is associated with not just a single image, but rather a pair of similar images that result in two different answers to the question. Our dataset is by construction more balanced than the original VQA dataset and has approximately twice the number of image-question pairs. Our complete balanced dataset is publicly available at www.visualqa.org as part of the 2nd iteration of the Visual Question Answering Dataset and Challenge (VQA v2.0). We further benchmark a number of state-of-art VQA models on our balanced dataset. All models perform significantly worse on our balanced dataset, suggesting that these models have indeed learned to exploit language priors. This finding provides the first concrete empirical evidence for what seems to be a qualitative sense among practitioners. Finally, our data collection protocol for identifying complementary images enables us to develop a novel interpretable model, which in addition to providing an answer to the given (image, question) pair, also provides a counter-example based explanation. Specifically, it identifies an image that is similar to the original image, but it believes has a different answer to the same question. This can help in building trust for machines among their users.

Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering

http://arxiv.org/pdf/1612.00837v3

Title Making V VQA Matter Elevating Role Image Understanding Visual Question Answering Summary Problems intersection vision language significant importance challenging research question rich set application enable However inherent structure world bias language tend simpler signal learning visual modality resulting model ignore visual information leading inflated sense capability propose counter language prior task Visual Question Answering VQA make vision V VQA matter Specifically balance popular VQA dataset collecting complementary image every question balanced dataset associated single image rather pair similar image result two different answer question dataset construction balanced original VQA dataset approximately twice number imagequestion pair complete balanced dataset publicly available wwwvisualqaorg part 2nd iteration Visual Question Answering Dataset Challenge VQA v20 benchmark number stateofart VQA model balanced dataset model perform significantly worse balanced dataset suggesting model indeed learned exploit language prior finding provides first concrete empirical evidence seems qualitative sense among practitioner Finally data collection protocol identifying complementary image enables u develop novel interpretable model addition providing answer given image question pair also provides counterexample based explanation Specifically identifies image similar original image belief different answer question help building trust machine among user 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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['Mateusz Malinowski', 'Mario Fritz']

1410.0210v4

We propose a method for automatically answering questions about images by bringing together recent advances from natural language processing and computer vision. We combine discrete reasoning with uncertain predictions by a multi-world approach that represents uncertainty about the perceived world in a bayesian framework. Our approach can handle human questions of high complexity about realistic scenes and replies with range of answer like counts, object classes, instances and lists of them. The system is directly trained from question-answer pairs. We establish a first benchmark for this task that can be seen as a modern attempt at a visual turing test.

A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input

http://arxiv.org/pdf/1410.0210v4

Title MultiWorld Approach Question Answering RealWorld Scenes based Uncertain Input Summary propose method automatically answering question image bringing together recent advance natural language processing computer vision combine discrete reasoning uncertain prediction multiworld approach represents uncertainty perceived world bayesian framework approach handle human question high complexity realistic scene reply range answer like count object class instance list system directly trained questionanswer pair establish first benchmark task seen modern attempt visual turing test 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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['Mateusz Malinowski', 'Mario Fritz']

1501.03302v2

Progress in language and image understanding by machines has sparkled the interest of the research community in more open-ended, holistic tasks, and refueled an old AI dream of building intelligent machines. We discuss a few prominent challenges that characterize such holistic tasks and argue for "question answering about images" as a particular appealing instance of such a holistic task. In particular, we point out that it is a version of a Turing Test that is likely to be more robust to over-interpretations and contrast it with tasks like grounding and generation of descriptions. Finally, we discuss tools to measure progress in this field.

Hard to Cheat: A Turing Test based on Answering Questions about Images

http://arxiv.org/pdf/1501.03302v2

Title Hard Cheat Turing Test based Answering Questions Images Summary Progress language image understanding machine sparkled interest research community openended holistic task refueled old AI dream building intelligent machine discus prominent challenge characterize holistic task argue question answering image particular appealing instance holistic task particular point version Turing Test likely robust overinterpretations contrast task like grounding generation description Finally discus tool measure progress 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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['Aishwarya Agrawal', 'Dhruv Batra', 'Devi Parikh']

1606.07356v2

Recently, a number of deep-learning based models have been proposed for the task of Visual Question Answering (VQA). The performance of most models is clustered around 60-70%. In this paper we propose systematic methods to analyze the behavior of these models as a first step towards recognizing their strengths and weaknesses, and identifying the most fruitful directions for progress. We analyze two models, one each from two major classes of VQA models -- with-attention and without-attention and show the similarities and differences in the behavior of these models. We also analyze the winning entry of the VQA Challenge 2016. Our behavior analysis reveals that despite recent progress, today's VQA models are "myopic" (tend to fail on sufficiently novel instances), often "jump to conclusions" (converge on a predicted answer after 'listening' to just half the question), and are "stubborn" (do not change their answers across images).

Analyzing the Behavior of Visual Question Answering Models

http://arxiv.org/pdf/1606.07356v2

Title Analyzing Behavior Visual Question Answering Models Summary Recently number deeplearning based model proposed task Visual Question Answering VQA performance model clustered around 6070 paper propose systematic method analyze behavior model first step towards recognizing strength weakness identifying fruitful direction progress analyze two model one two major class VQA model withattention withoutattention show similarity difference behavior model also analyze winning entry VQA Challenge 2016 behavior analysis reveals despite recent progress today VQA model myopic tend fail sufficiently novel instance often jump conclusion converge predicted answer listening half question stubborn change answer across 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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['Harsh Agrawal', 'Arjun Chandrasekaran', 'Dhruv Batra', 'Devi Parikh', 'Mohit Bansal']

1606.07493v5

Temporal common sense has applications in AI tasks such as QA, multi-document summarization, and human-AI communication. We propose the task of sequencing -- given a jumbled set of aligned image-caption pairs that belong to a story, the task is to sort them such that the output sequence forms a coherent story. We present multiple approaches, via unary (position) and pairwise (order) predictions, and their ensemble-based combinations, achieving strong results on this task. We use both text-based and image-based features, which depict complementary improvements. Using qualitative examples, we demonstrate that our models have learnt interesting aspects of temporal common sense.

Sort Story: Sorting Jumbled Images and Captions into Stories

http://arxiv.org/pdf/1606.07493v5

Title Sort Story Sorting Jumbled Images Captions Stories Summary Temporal common sense application AI task QA multidocument summarization humanAI communication propose task sequencing given jumbled set aligned imagecaption pair belong story task sort output sequence form coherent story present multiple approach via unary position pairwise order prediction ensemblebased combination achieving strong result task use textbased imagebased feature depict complementary improvement Using qualitative example demonstrate model learnt interesting aspect temporal common sense 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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['Ashkan Mokarian', 'Mateusz Malinowski', 'Mario Fritz']

1608.02717v1

We present Mean Box Pooling, a novel visual representation that pools over CNN representations of a large number, highly overlapping object proposals. We show that such representation together with nCCA, a successful multimodal embedding technique, achieves state-of-the-art performance on the Visual Madlibs task. Moreover, inspired by the nCCA's objective function, we extend classical CNN+LSTM approach to train the network by directly maximizing the similarity between the internal representation of the deep learning architecture and candidate answers. Again, such approach achieves a significant improvement over the prior work that also uses CNN+LSTM approach on Visual Madlibs.

Mean Box Pooling: A Rich Image Representation and Output Embedding for the Visual Madlibs Task

http://arxiv.org/pdf/1608.02717v1

Title Mean Box Pooling Rich Image Representation Output Embedding Visual Madlibs Task Summary present Mean Box Pooling novel visual representation pool CNN representation large number highly overlapping object proposal show representation together nCCA successful multimodal embedding technique achieves stateoftheart performance Visual Madlibs task Moreover inspired nCCAs objective function extend classical CNNLSTM approach train network directly maximizing similarity internal representation deep learning architecture candidate answer approach achieves significant improvement prior work also us CNNLSTM approach Visual Madlibs 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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['Yuval Atzmon', 'Jonathan Berant', 'Vahid Kezami', 'Amir Globerson', 'Gal Chechik']

1608.07639v1

Recurrent neural networks have recently been used for learning to describe images using natural language. However, it has been observed that these models generalize poorly to scenes that were not observed during training, possibly depending too strongly on the statistics of the text in the training data. Here we propose to describe images using short structured representations, aiming to capture the crux of a description. These structured representations allow us to tease-out and evaluate separately two types of generalization: standard generalization to new images with similar scenes, and generalization to new combinations of known entities. We compare two learning approaches on the MS-COCO dataset: a state-of-the-art recurrent network based on an LSTM (Show, Attend and Tell), and a simple structured prediction model on top of a deep network. We find that the structured model generalizes to new compositions substantially better than the LSTM, ~7 times the accuracy of predicting structured representations. By providing a concrete method to quantify generalization for unseen combinations, we argue that structured representations and compositional splits are a useful benchmark for image captioning, and advocate compositional models that capture linguistic and visual structure.

Learning to generalize to new compositions in image understanding

http://arxiv.org/pdf/1608.07639v1

Title Learning generalize new composition image understanding Summary Recurrent neural network recently used learning describe image using natural language However observed model generalize poorly scene observed training possibly depending strongly statistic text training data propose describe image using short structured representation aiming capture crux description structured representation allow u teaseout evaluate separately two type generalization standard generalization new image similar scene generalization new combination known entity compare two learning approach MSCOCO dataset stateoftheart recurrent network based LSTM Show Attend Tell simple structured prediction model top deep network find structured model generalizes new composition substantially better LSTM 7 time accuracy predicting structured representation providing concrete method quantify generalization unseen combination argue structured representation compositional split useful benchmark image captioning advocate compositional model capture linguistic visual 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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['C. Lawrence Zitnick', 'Aishwarya Agrawal', 'Stanislaw Antol', 'Margaret Mitchell', 'Dhruv Batra', 'Devi Parikh']

1608.08716v1

As machines have become more intelligent, there has been a renewed interest in methods for measuring their intelligence. A common approach is to propose tasks for which a human excels, but one which machines find difficult. However, an ideal task should also be easy to evaluate and not be easily gameable. We begin with a case study exploring the recently popular task of image captioning and its limitations as a task for measuring machine intelligence. An alternative and more promising task is Visual Question Answering that tests a machine's ability to reason about language and vision. We describe a dataset unprecedented in size created for the task that contains over 760,000 human generated questions about images. Using around 10 million human generated answers, machines may be easily evaluated.

Measuring Machine Intelligence Through Visual Question Answering

http://arxiv.org/pdf/1608.08716v1

Title Measuring Machine Intelligence Visual Question Answering Summary machine become intelligent renewed interest method measuring intelligence common approach propose task human excels one machine find difficult However ideal task also easy evaluate easily gameable begin case study exploring recently popular task image captioning limitation task measuring machine intelligence alternative promising task Visual Question Answering test machine ability reason language vision describe dataset unprecedented size created task contains 760000 human generated question image Using around 10 million human generated answer machine may easily evaluated 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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['Yash Goyal', 'Akrit Mohapatra', 'Devi Parikh', 'Dhruv Batra']

1608.08974v2

Deep neural networks have shown striking progress and obtained state-of-the-art results in many AI research fields in the recent years. However, it is often unsatisfying to not know why they predict what they do. In this paper, we address the problem of interpreting Visual Question Answering (VQA) models. Specifically, we are interested in finding what part of the input (pixels in images or words in questions) the VQA model focuses on while answering the question. To tackle this problem, we use two visualization techniques -- guided backpropagation and occlusion -- to find important words in the question and important regions in the image. We then present qualitative and quantitative analyses of these importance maps. We found that even without explicit attention mechanisms, VQA models may sometimes be implicitly attending to relevant regions in the image, and often to appropriate words in the question.

Towards Transparent AI Systems: Interpreting Visual Question Answering Models

http://arxiv.org/pdf/1608.08974v2

Title Towards Transparent AI Systems Interpreting Visual Question Answering Models Summary Deep neural network shown striking progress obtained stateoftheart result many AI research field recent year However often unsatisfying know predict paper address problem interpreting Visual Question Answering VQA model Specifically interested finding part input pixel image word question VQA model focus answering question tackle problem use two visualization technique guided backpropagation occlusion find important word question important region image present qualitative quantitative analysis importance map found even without explicit attention mechanism VQA model may sometimes implicitly attending relevant region image often appropriate word question 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', 'Satwik Kottur', 'Khushi Gupta', 'Avi Singh', 'Deshraj Yadav', 'José M. F. Moura', 'Devi Parikh', 'Dhruv Batra']

1611.08669v5

We introduce the task of Visual Dialog, which requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content. Specifically, given an image, a dialog history, and a question about the image, the agent has to ground the question in image, infer context from history, and answer the question accurately. Visual Dialog is disentangled enough from a specific downstream task so as to serve as a general test of machine intelligence, while being grounded in vision enough to allow objective evaluation of individual responses and benchmark progress. We develop a novel two-person chat data-collection protocol to curate a large-scale Visual Dialog dataset (VisDial). VisDial v0.9 has been released and contains 1 dialog with 10 question-answer pairs on ~120k images from COCO, with a total of ~1.2M dialog question-answer pairs. We introduce a family of neural encoder-decoder models for Visual Dialog with 3 encoders -- Late Fusion, Hierarchical Recurrent Encoder and Memory Network -- and 2 decoders (generative and discriminative), which outperform a number of sophisticated baselines. We propose a retrieval-based evaluation protocol for Visual Dialog where the AI agent is asked to sort a set of candidate answers and evaluated on metrics such as mean-reciprocal-rank of human response. We quantify gap between machine and human performance on the Visual Dialog task via human studies. Putting it all together, we demonstrate the first 'visual chatbot'! Our dataset, code, trained models and visual chatbot are available on https://visualdialog.org

Visual Dialog

http://arxiv.org/pdf/1611.08669v5

Title Visual Dialog Summary introduce task Visual Dialog requires AI agent hold meaningful dialog human natural conversational language visual content Specifically given image dialog history question image agent ground question image infer context history answer question accurately Visual Dialog disentangled enough specific downstream task serve general test machine intelligence grounded vision enough allow objective evaluation individual response benchmark progress develop novel twoperson chat datacollection protocol curate largescale Visual Dialog dataset VisDial VisDial v09 released contains 1 dialog 10 questionanswer pair 120k image COCO total 12M dialog questionanswer pair introduce family neural encoderdecoder model Visual Dialog 3 encoders Late Fusion Hierarchical Recurrent Encoder Memory Network 2 decoder generative discriminative outperform number sophisticated baseline propose retrievalbased evaluation protocol Visual Dialog AI agent asked sort set candidate answer evaluated metric meanreciprocalrank human response quantify gap machine human performance Visual Dialog task via human study Putting together demonstrate first visual chatbot dataset code trained model visual chatbot available httpsvisualdialogorg 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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['Abhinav Thanda', 'Shankar M Venkatesan']

1701.02477v1

Multi-task learning (MTL) involves the simultaneous training of two or more related tasks over shared representations. In this work, we apply MTL to audio-visual automatic speech recognition(AV-ASR). Our primary task is to learn a mapping between audio-visual fused features and frame labels obtained from acoustic GMM/HMM model. This is combined with an auxiliary task which maps visual features to frame labels obtained from a separate visual GMM/HMM model. The MTL model is tested at various levels of babble noise and the results are compared with a base-line hybrid DNN-HMM AV-ASR model. Our results indicate that MTL is especially useful at higher level of noise. Compared to base-line, upto 7\% relative improvement in WER is reported at -3 SNR dB

Multi-task Learning Of Deep Neural Networks For Audio Visual Automatic Speech Recognition

http://arxiv.org/pdf/1701.02477v1

Title Multitask Learning Deep Neural Networks Audio Visual Automatic Speech Recognition Summary Multitask learning MTL involves simultaneous training two related task shared representation work apply MTL audiovisual automatic speech recognitionAVASR primary task learn mapping audiovisual fused feature frame label obtained acoustic GMMHMM model combined auxiliary task map visual feature frame label obtained separate visual GMMHMM model MTL model tested various level babble noise result compared baseline hybrid DNNHMM AVASR model result indicate MTL especially useful higher level noise Compared baseline upto 7 relative improvement WER reported 3 SNR dB 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', 'Satwik Kottur', 'José M. F. Moura', 'Stefan Lee', 'Dhruv Batra']

1703.06585v2

We introduce the first goal-driven training for visual question answering and dialog agents. Specifically, we pose a cooperative 'image guessing' game between two agents -- Qbot and Abot -- who communicate in natural language dialog so that Qbot can select an unseen image from a lineup of images. We use deep reinforcement learning (RL) to learn the policies of these agents end-to-end -- from pixels to multi-agent multi-round dialog to game reward. We demonstrate two experimental results. First, as a 'sanity check' demonstration of pure RL (from scratch), we show results on a synthetic world, where the agents communicate in ungrounded vocabulary, i.e., symbols with no pre-specified meanings (X, Y, Z). We find that two bots invent their own communication protocol and start using certain symbols to ask/answer about certain visual attributes (shape/color/style). Thus, we demonstrate the emergence of grounded language and communication among 'visual' dialog agents with no human supervision. Second, we conduct large-scale real-image experiments on the VisDial dataset, where we pretrain with supervised dialog data and show that the RL 'fine-tuned' agents significantly outperform SL agents. Interestingly, the RL Qbot learns to ask questions that Abot is good at, ultimately resulting in more informative dialog and a better team.

Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning

http://arxiv.org/pdf/1703.06585v2

Title Learning Cooperative Visual Dialog Agents Deep Reinforcement Learning Summary introduce first goaldriven training visual question answering dialog agent Specifically pose cooperative image guessing game two agent Qbot Abot communicate natural language dialog Qbot select unseen image lineup image use deep reinforcement learning RL learn policy agent endtoend pixel multiagent multiround dialog game reward demonstrate two experimental result First sanity check demonstration pure RL scratch show result synthetic world agent communicate ungrounded vocabulary ie symbol prespecified meaning X Z find two bot invent communication protocol start using certain symbol askanswer certain visual attribute shapecolorstyle Thus demonstrate emergence grounded language communication among visual dialog agent human supervision Second conduct largescale realimage experiment VisDial dataset pretrain supervised dialog data show RL finetuned agent significantly outperform SL agent Interestingly RL Qbot learns ask question Abot good ultimately resulting informative dialog better team 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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['Wei-Lun Chao', 'Hexiang Hu', 'Fei Sha']

1704.07121v1

Visual question answering (QA) has attracted a lot of attention lately, seen essentially as a form of (visual) Turing test that artificial intelligence should strive to achieve. In this paper, we study a crucial component of this task: how can we design good datasets for the task? We focus on the design of multiple-choice based datasets where the learner has to select the right answer from a set of candidate ones including the target (i.e. the correct one) and the decoys (i.e. the incorrect ones). Through careful analysis of the results attained by state-of-the-art learning models and human annotators on existing datasets, we show the design of the decoy answers has a significant impact on how and what the learning models learn from the datasets. In particular, the resulting learner can ignore the visual information, the question, or the both while still doing well on the task. Inspired by this, we propose automatic procedures to remedy such design deficiencies. We apply the procedures to re-construct decoy answers for two popular visual QA datasets as well as to create a new visual QA dataset from the Visual Genome project, resulting in the largest dataset for this task. Extensive empirical studies show that the design deficiencies have been alleviated in the remedied datasets and the performance on them is likely a more faithful indicator of the difference among learning models. The datasets are released and publicly available via http://www.teds.usc.edu/website_vqa/.

Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets

http://arxiv.org/pdf/1704.07121v1

Title Negative Constructively Lessons Learnt Creating Better Visual Question Answering Datasets Summary Visual question answering QA attracted lot attention lately seen essentially form visual Turing test artificial intelligence strive achieve paper study crucial component task design good datasets task focus design multiplechoice based datasets learner select right answer set candidate one including target ie correct one decoy ie incorrect one careful analysis result attained stateoftheart learning model human annotator existing datasets show design decoy answer significant impact learning model learn datasets particular resulting learner ignore visual information question still well task Inspired propose automatic procedure remedy design deficiency apply procedure reconstruct decoy answer two popular visual QA datasets well create new visual QA dataset Visual Genome project resulting largest dataset task Extensive empirical study show design deficiency alleviated remedied datasets performance likely faithful indicator difference among learning model datasets released publicly available via httpwwwtedsusceduwebsitevqa 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', 'Aniruddha Kembhavi', 'Dhruv Batra', 'Devi Parikh']

1704.08243v1

Visual Question Answering (VQA) has received a lot of attention over the past couple of years. A number of deep learning models have been proposed for this task. However, it has been shown that these models are heavily driven by superficial correlations in the training data and lack compositionality -- the ability to answer questions about unseen compositions of seen concepts. This compositionality is desirable and central to intelligence. In this paper, we propose a new setting for Visual Question Answering where the test question-answer pairs are compositionally novel compared to training question-answer pairs. To facilitate developing models under this setting, we present a new compositional split of the VQA v1.0 dataset, which we call Compositional VQA (C-VQA). We analyze the distribution of questions and answers in the C-VQA splits. Finally, we evaluate several existing VQA models under this new setting and show that the performances of these models degrade by a significant amount compared to the original VQA setting.

C-VQA: A Compositional Split of the Visual Question Answering (VQA) v1.0 Dataset

http://arxiv.org/pdf/1704.08243v1

Title CVQA Compositional Split Visual Question Answering VQA v10 Dataset Summary Visual Question Answering VQA received lot attention past couple year number deep learning model proposed task However shown model heavily driven superficial correlation training data lack compositionality ability answer question unseen composition seen concept compositionality desirable central intelligence paper propose new setting Visual Question Answering test questionanswer pair compositionally novel compared training questionanswer pair facilitate developing model setting present new compositional split VQA v10 dataset call Compositional VQA CVQA analyze distribution question answer CVQA split Finally evaluate several existing VQA model new setting show performance model degrade significant amount compared original VQA setting 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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['Alexander Kuhnle', 'Ann Copestake']

1706.01322v1

We discuss problems with the standard approaches to evaluation for tasks like visual question answering, and argue that artificial data can be used to address these as a complement to current practice. We demonstrate that with the help of existing 'deep' linguistic processing technology we are able to create challenging abstract datasets, which enable us to investigate the language understanding abilities of multimodal deep learning models in detail.

Deep learning evaluation using deep linguistic processing

http://arxiv.org/pdf/1706.01322v1

Title Deep learning evaluation using deep linguistic processing Summary discus problem standard approach evaluation task like visual question answering argue artificial data used address complement current practice demonstrate help existing deep linguistic processing technology able create challenging abstract datasets enable u investigate language understanding ability multimodal deep learning model detail 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', 'Houfeng Wang']

1706.06197v4

We propose a simple yet effective technique for neural network learning. The forward propagation is computed as usual. 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 ($k$ divided by the vector dimension) in the computational cost. Surprisingly, experimental results demonstrate that we can update only 1--4\% of the weights at each back propagation pass. This does not result in a larger number of training iterations. More interestingly, the accuracy of the resulting models is actually improved rather than degraded, and a detailed analysis is given. The code is available at https://github.com/jklj077/meProp

meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting

http://arxiv.org/pdf/1706.06197v4

Title meProp Sparsified Back Propagation Accelerated Deep Learning Reduced Overfitting Summary propose simple yet effective technique neural network learning forward propagation computed usual 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 k divided vector dimension computational cost Surprisingly experimental result demonstrate update 14 weight back propagation pas result larger number training iteration interestingly accuracy resulting model actually improved rather degraded detailed analysis given code available httpsgithubcomjklj077meProp 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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['Suranjana Samanta', 'Sameep Mehta']

1707.02812v1

Adversarial samples are strategically modified samples, which are crafted with the purpose of fooling a classifier at hand. An attacker introduces specially crafted adversarial samples to a deployed classifier, which are being mis-classified by the classifier. However, the samples are perceived to be drawn from entirely different classes and thus it becomes hard to detect the adversarial samples. Most of the prior works have been focused on synthesizing adversarial samples in the image domain. In this paper, we propose a new method of crafting adversarial text samples by modification of the original samples. Modifications of the original text samples are done by deleting or replacing the important or salient words in the text or by introducing new words in the text sample. Our algorithm works best for the datasets which have sub-categories within each of the classes of examples. While crafting adversarial samples, one of the key constraint is to generate meaningful sentences which can at pass off as legitimate from language (English) viewpoint. Experimental results on IMDB movie review dataset for sentiment analysis and Twitter dataset for gender detection show the efficiency of our proposed method.

Towards Crafting Text Adversarial Samples

http://arxiv.org/pdf/1707.02812v1

Title Towards Crafting Text Adversarial Samples Summary Adversarial sample strategically modified sample crafted purpose fooling classifier hand attacker introduces specially crafted adversarial sample deployed classifier misclassified classifier However sample perceived drawn entirely different class thus becomes hard detect adversarial sample prior work focused synthesizing adversarial sample image domain paper propose new method crafting adversarial text sample modification original sample Modifications original text sample done deleting replacing important salient word text introducing new word text sample algorithm work best datasets subcategories within class example crafting adversarial sample one key constraint generate meaningful sentence pas legitimate language English viewpoint Experimental result IMDB movie review dataset sentiment analysis Twitter dataset gender detection show efficiency proposed 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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['Ramakanth Pasunuru', 'Mohit Bansal']

1708.02300v1

Sequence-to-sequence models have shown promising improvements on the temporal task of video captioning, but they optimize word-level cross-entropy loss during training. First, using policy gradient and mixed-loss methods for reinforcement learning, we directly optimize sentence-level task-based metrics (as rewards), achieving significant improvements over the baseline, based on both automatic metrics and human evaluation on multiple datasets. Next, we propose a novel entailment-enhanced reward (CIDEnt) that corrects phrase-matching based metrics (such as CIDEr) to only allow for logically-implied partial matches and avoid contradictions, achieving further significant improvements over the CIDEr-reward model. Overall, our CIDEnt-reward model achieves the new state-of-the-art on the MSR-VTT dataset.

Reinforced Video Captioning with Entailment Rewards

http://arxiv.org/pdf/1708.02300v1

Title Reinforced Video Captioning Entailment Rewards Summary Sequencetosequence model shown promising improvement temporal task video captioning optimize wordlevel crossentropy loss training First using policy gradient mixedloss method reinforcement learning directly optimize sentencelevel taskbased metric reward achieving significant improvement baseline based automatic metric human evaluation multiple datasets Next propose novel entailmentenhanced reward CIDEnt corrects phrasematching based metric CIDEr allow logicallyimplied partial match avoid contradiction achieving significant improvement CIDErreward model Overall CIDEntreward model achieves new stateoftheart MSRVTT 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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['Licheng Yu', 'Mohit Bansal', 'Tamara L. Berg']

1708.02977v1

We address the problem of end-to-end visual storytelling. Given a photo album, our model first selects the most representative (summary) photos, and then composes a natural language story for the album. For this task, we make use of the Visual Storytelling dataset and a model composed of three hierarchically-attentive Recurrent Neural Nets (RNNs) to: encode the album photos, select representative (summary) photos, and compose the story. Automatic and human evaluations show our model achieves better performance on selection, generation, and retrieval than baselines.

Hierarchically-Attentive RNN for Album Summarization and Storytelling

http://arxiv.org/pdf/1708.02977v1

Title HierarchicallyAttentive RNN Album Summarization Storytelling Summary address problem endtoend visual storytelling Given photo album model first selects representative summary photo composes natural language story album task make use Visual Storytelling dataset model composed three hierarchicallyattentive Recurrent Neural Nets RNNs encode album photo select representative summary photo compose story Automatic human evaluation show model achieves better performance selection generation retrieval 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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