pwc-archive/papers-with-abstracts huggingface dataset:
Do CNNs Encode Data Augmentations?
Authors: Eddie Yan, Yanping Huang
Date: 2020-02-29 00:00:00
Tasks: Data Augmentation

Data augmentations are important ingredients in the recipe for training robust neural networks, especially in computer vision. A fundamental question is whether neural network features encode data augmentation transformations. To answer this question, we introduce a systematic approach to investigate which layers of neural networks are the most predictive of augmentation transformations. Our approach uses features in pre-trained vision models with minimal additional processing to predict common properties transformed by augmentation (scale, aspect ratio, hue, saturation, contrast, and brightness). Surprisingly, neural network features not only predict data augmentation transformations, but they predict many transformations with high accuracy. After validating that neural networks encode features corresponding to augmentation transformations, we show that these features are encoded in the early layers of modern CNNs, though the augmentation signal fades in deeper layers.
Personalized Taste and Cuisine Preference Modeling via Images
Authors: Nitish Nag, Bindu Rajanna, Ramesh Jain
Date: 2020-02-26 00:00:00

With the exponential growth in the usage of social media to share live updates about life, taking pictures has become an unavoidable phenomenon. Individuals unknowingly create a unique knowledge base with these images. The food images, in particular, are of interest as they contain a plethora of information. From the image metadata and using computer vision tools, we can extract distinct insights for each user to build a personal profile. Using the underlying connection between cuisines and their inherent tastes, we attempt to develop such a profile for an individual based solely on the images of his food. Our study provides insights about an individual's inclination towards particular cuisines. Interpreting these insights can lead to the development of a more precise recommendation system. Such a system would avoid the generic approach in favor of a personalized recommendation system.
Fast Incremental and Personalized PageRank
Authors: Bahman Bahmani, Abdur Chowdhury, Ashish Goel
Date: 2010-06-15 00:00:00

In this paper, we analyze the efficiency of Monte Carlo methods for incremental computation of PageRank, personalized PageRank, and similar random walk based methods (with focus on SALSA), on large-scale dynamically evolving social networks. We assume that the graph of friendships is stored in distributed shared memory, as is the case for large social networks such as Twitter. For global PageRank, we assume that the social network has $n$ nodes, and $m$ adversarially chosen edges arrive in a random order. We show that with a reset probability of $\epsilon$, the total work needed to maintain an accurate estimate (using the Monte Carlo method) of the PageRank of every node at all times is $O(\frac{n\ln m}{\epsilon^{2}})$. This is significantly better than all known bounds for incremental PageRank. For instance, if we naively recompute the PageRanks as each edge arrives, the simple power iteration method needs $\Omega(\frac{m^2}{\ln(1/(1-\epsilon))})$ total time and the Monte Carlo method needs $O(mn/\epsilon)$ total time; both are prohibitively expensive. Furthermore, we also show that we can handle deletions equally efficiently. We then study the computation of the top $k$ personalized PageRanks starting from a seed node, assuming that personalized PageRanks follow a power-law with exponent $\alpha < 1$. We show that if we store $R>q\ln n$ random walks starting from every node for large enough constant $q$ (using the approach outlined for global PageRank), then the expected number of calls made to the distributed social network database is $O(k/(R^{(1-\alpha)/\alpha}))$. We also present experimental results from the social networking site, Twitter, verifying our assumptions and analyses. The overall result is that this algorithm is fast enough for real-time queries over a dynamic social network.
Axiom Pinpointing
Authors: Rafael Peñaloza
Date: 2020-03-18 00:00:00

Axiom pinpointing refers to the task of finding the specific axioms in an ontology which are responsible for a consequence to follow. This task has been studied, under different names, in many research areas, leading to a reformulation and reinvention of techniques. In this work, we present a general overview to axiom pinpointing, providing the basic notions, different approaches for solving it, and some variations and applications which have been considered in the literature. This should serve as a starting point for researchers interested in related problems, with an ample bibliography for delving deeper into the details.
Social Navigation with Human Empowerment driven Deep Reinforcement Learning
Authors: Tessa van der Heiden, Florian Mirus, Herke van Hoof
Date: 2020-03-18 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Robot Navigation, Social Navigation

Mobile robot navigation has seen extensive research in the last decades. The aspect of collaboration with robots and humans sharing workspaces will become increasingly important in the future. Therefore, the next generation of mobile robots needs to be socially-compliant to be accepted by their human collaborators. However, a formal definition of compliance is not straightforward. On the other hand, empowerment has been used by artificial agents to learn complicated and generalized actions and also has been shown to be a good model for biological behaviors. In this paper, we go beyond the approach of classical \acf{RL} and provide our agent with intrinsic motivation using empowerment. In contrast to self-empowerment, a robot employing our approach strives for the empowerment of people in its environment, so they are not disturbed by the robot's presence and motion. In our experiments, we show that our approach has a positive influence on humans, as it minimizes its distance to humans and thus decreases human travel time while moving efficiently towards its own goal. An interactive user-study shows that our method is considered more social than other state-of-the-art approaches by the participants.
SneakySnake: A Fast and Accurate Universal Genome Pre-Alignment Filter for CPUs, GPUs, and FPGAs
Authors: Mohammed Alser, Taha Shahroodi, Juan Gomez-Luna, Can Alkan, Onur Mutlu
Date: 2019-10-20 00:00:00
Tasks: CPU, GPU

Motivation: We introduce SneakySnake, a highly parallel and highly accurate pre-alignment filter that remarkably reduces the need for computationally costly sequence alignment. The key idea of SneakySnake is to reduce the approximate string matching (ASM) problem to the single net routing (SNR) problem in VLSI chip layout. In the SNR problem, we are interested in finding the optimal path that connects two terminals with the least routing cost on a special grid layout that contains obstacles. The SneakySnake algorithm quickly solves the SNR problem and uses the found optimal path to decide whether or not performing sequence alignment is necessary. Reducing the ASM problem into SNR also makes SneakySnake efficient to implement on CPUs, GPUs, and FPGAs. Results: SneakySnake significantly improves the accuracy of pre-alignment filtering by up to four orders of magnitude compared to the state-of-the-art pre-alignment filters, Shouji, GateKeeper, and SHD. For short sequences, SneakySnake accelerates Edlib (state-of-the-art implementation of Myers's bit-vector algorithm) and Parasail (state-of-the-art sequence aligner with a configurable scoring function), by up to 37.7x and 43.9x (>12x on average), respectively, with its CPU implementation, and by up to 413x and 689x (>400x on average), respectively, with FPGA and GPU acceleration. For long sequences, the CPU implementation of SneakySnake accelerates Parasail and KSW2 (sequence aligner of minimap2) by up to 979x (276.9x on average) and 91.7x (31.7x on average), respectively. As SneakySnake does not replace sequence alignment, users can still obtain all capabilities (e.g., configurable scoring functions) of the aligner of their choice, unlike existing acceleration efforts that sacrifice some aligner capabilities. Availability: https://github.com/CMU-SAFARI/SneakySnake
Better Mixing via Deep Representations
Authors: Yoshua Bengio, Grégoire Mesnil, Yann Dauphin, Salah Rifai
Date: 2012-07-18 00:00:00

It has previously been hypothesized, and supported with some experimental evidence, that deeper representations, when well trained, tend to do a better job at disentangling the underlying factors of variation. We study the following related conjecture: better representations, in the sense of better disentangling, can be exploited to produce faster-mixing Markov chains. Consequently, mixing would be more efficient at higher levels of representation. To better understand why and how this is happening, we propose a secondary conjecture: the higher-level samples fill more uniformly the space they occupy and the high-density manifolds tend to unfold when represented at higher levels. The paper discusses these hypotheses and tests them experimentally through visualization and measurements of mixing and interpolating between samples.
Neural Network Tracking of Moving Objects with Unknown Equations of Motion
Authors: Boaz Fish, Ben Zion Bobrovsky
Date: 2020-03-13 00:00:00

In this paper we present a Neural Network design that can be used to track the location of a moving object within a given range based on the object's noisy coordinates measurement. A function commonly performed by the KLMn filter, our goal is to show that our method outperforms the Kalman filter in certain scenarios.
A Deep Learning based Feature Selection Method with Multi Level Feature Identification and Extraction using Convolutional Neural Network
Authors: Anil K.R, Gladston Raj S
Date: 2017-12-01 00:00:00
Proceeding: JOURNAL FOR ADVANCED RESEARCH IN APPLIED SCIENCES 2017 12
Tasks: Diagnostic, feature selection, General Classification

: Increasing popularity of feature selection in bioinformatics has led to the development of novel algorithms
using neural networks. The objectives of the adaptation of neural networks architectures are proposed on efficient and
optimal model for feature classification and selection. A competitive end unique approach in feature selection is adopted
here using a convolutional neural network (CNN). Deep learning approach on feature selection is the novel idea which can
contribute to the evolvement of identification process, diagnostic methods etc. The experimental work has given good result
of ranking the attributes by building a CNN model. The traditional concept of CNN for classification has transformed to a
modern approach for feature selection. Handling millions of data with multiple class identities can only be classified with a
multi layers network. The CNN models are trained in completely supervised way with a batch gradient back propagation.
The parameters are tuned and optimized to get better build type.
Multiple birth least squares support vector machine for multi-class classification
Authors: Su-Gen Chen1, 2•Xiao-Jun Wu1
Date: 2016-06-10 00:00:00
Proceeding: journal 2016 6
Tasks: Binary Classification, General Classification, Multi-class Classification

Least squares twin support vector machine (LSTSVM) was initially designed for binary classification. However, practical problems often require the discrimina-tion more than two categories. To tackle multi-class classi-fication problem, a novel algorithm, called multiple birth least squares support vector machine (MBLSSVM), is proposed. Our MBLSSVM solves K quadratic programming problems (QPPs) to obtain K hyperplanes, each problem is similar to binary LSTSVM. Comparison against the Multi-LSTSVM, Multi-TWSVM, MBSVM and our MBLSSVM on both UCI datasets and ORL, YALE face datasets illustrates the effectiveness of the proposed method.
Multiple birth support vector machine for multi-class classification
Authors: Zhi-Xia Yang•Yuan-Hai Shao•Xiang-Sun Zhang
Date: 2012-08-11 00:00:00
Proceeding: journal 2012 8
Tasks: Classification, General Classification, Multi-class Classification

For multi-class classification problem, a novel algorithm, called as multiple birth support vector machine (MBSVM), is proposed, which can be considered as an extension of twin support vector machine. Our MBSVM has been compared with the several typical support vector machines. From theoretical point of view, it has been shown that its computational complexity is remarkably
low, especially when the class number K is large. Based on our MBSVM, the dual problems of MBSVM are equivalent to symmetric mixed linear complementarity problems to which successive overrelaxation (SOR) can be directly applied. We establish our SOR algorithm for MBSVM. The SOR algorithm handles one data point at a time, so it can process large dataset that need no reside in memory. From practical point of view, its accuracy has been validated by the preliminary numerical experiments.
Mathematical Formulae in Wikimedia Projects 2020
Authors: Moritz Schubotz, André Greiner-Petter, Norman Meuschke, Olaf Teschke, Bela Gipp
Date: 2020-03-20 00:00:00

This poster summarizes our contributions to Wikimedia's processing pipeline for mathematical formulae. We describe how we have supported the transition from rendering formulae as course-grained PNG images in 2001 to providing modern semantically enriched language-independent MathML formulae in 2020. Additionally, we describe our plans to improve the accessibility and discoverability of mathematical knowledge in Wikimedia projects further.
Quantum noise protects quantum classifiers against adversaries
Authors: Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, DaCheng Tao, Nana Liu
Date: 2020-03-20 00:00:00
Tasks: Classification, General Classification

Noise in quantum information processing is often viewed as a disruptive and difficult-to-avoid feature, especially in near-term quantum technologies. However, noise has often played beneficial roles, from enhancing weak signals in stochastic resonance to protecting the privacy of data in differential privacy. It is then natural to ask, can we harness the power of quantum noise that is beneficial to quantum computing? An important current direction for quantum computing is its application to machine learning, such as classification problems. One outstanding problem in machine learning for classification is its sensitivity to adversarial examples. These are small, undetectable perturbations from the original data where the perturbed data is completely misclassified in otherwise extremely accurate classifiers. They can also be considered as `worst-case' perturbations by unknown noise sources. We show that by taking advantage of depolarisation noise in quantum circuits for classification, a robustness bound against adversaries can be derived where the robustness improves with increasing noise. This robustness property is intimately connected with an important security concept called differential privacy which can be extended to quantum differential privacy. For the protection of quantum data, this is the first quantum protocol that can be used against the most general adversaries. Furthermore, we show how the robustness in the classical case can be sensitive to the details of the classification model, but in the quantum case the details of classification model are absent, thus also providing a potential quantum advantage for classical data that is independent of quantum speedups. This opens the opportunity to explore other ways in which quantum noise can be used in our favour, as well as identifying other ways quantum algorithms can be helpful that is independent of quantum speedups.
Comments on Sejnowski's "The unreasonable effectiveness of deep learning in artificial intelligence" [arXiv:2002.04806]
Authors: Leslie S. Smith
Date: 2020-03-20 00:00:00

Terry Sejnowski's 2020 paper [arXiv:2002.04806] is entitled "The unreasonable effectiveness of deep learning in artificial intelligence". However, the paper doesn't attempt to answer the implied question of why Deep Convolutional Neural Networks (DCNNs) can approximate so many of the mappings that they have been trained to model. While there are detailed mathematical analyses, this short paper attempts to look at the issue differently, considering the way that these networks are used, the subset of these functions that can be achieved by training (starting from some location in the original function space), as well as the functions to which these networks will actually be applied.
Explainable Object-induced Action Decision for Autonomous Vehicles
Authors: Yiran Xu, Xiaoyin Yang, Lihang Gong, Hsuan-Chu Lin, Tz-Ying Wu, Yunsheng Li, Nuno Vasconcelos
Date: 2020-03-20 00:00:00
Proceeding: CVPR 2020 6
Tasks: Autonomous Driving, Autonomous Vehicles, Object, Scene Understanding

A new paradigm is proposed for autonomous driving. The new paradigm lies between the end-to-end and pipelined approaches, and is inspired by how humans solve the problem. While it relies on scene understanding, the latter only considers objects that could originate hazard. These are denoted as action-inducing, since changes in their state should trigger vehicle actions. They also define a set of explanations for these actions, which should be produced jointly with the latter. An extension of the BDD100K dataset, annotated for a set of 4 actions and 21 explanations, is proposed. A new multi-task formulation of the problem, which optimizes the accuracy of both action commands and explanations, is then introduced. A CNN architecture is finally proposed to solve this problem, by combining reasoning about action inducing objects and global scene context. Experimental results show that the requirement of explanations improves the recognition of action-inducing objects, which in turn leads to better action predictions.
RGB-Topography and X-rays Image Registration for Idiopathic Scoliosis Children Patient Follow-up
Authors: Insaf Setitra, Noureddine Aouaa, Abdelkrim Meziane, Afef Benrabia, Houria Kaced, Hanene Belabassi, Sara Ait Ziane, Nadia Henda Zenati, Oualid Djekkoune
Date: 2020-03-20 00:00:00
Tasks: Image Registration

Children diagnosed with a scoliosis pathology are exposed during their follow up to ionic radiations in each X-rays diagnosis. This exposure can have negative effects on the patient's health and cause diseases in the adult age. In order to reduce X-rays scanning, recent systems provide diagnosis of scoliosis patients using solely RGB images. The output of such systems is a set of augmented images and scoliosis related angles. These angles, however, confuse the physicians due to their large number. Moreover, the lack of X-rays scans makes it impossible for the physician to compare RGB and X-rays images, and decide whether to reduce X-rays exposure or not. In this work, we exploit both RGB images of scoliosis captured during clinical diagnosis, and X-rays hard copies provided by patients in order to register both images and give a rich comparison of diagnoses. The work consists in, first, establishing the monomodal (RGB topography of the back) and multimodal (RGB and Xrays) image database, then registering images based on patient landmarks, and finally blending registered images for a visual analysis and follow up by the physician. The proposed registration is based on a rigid transformation that preserves the topology of the patient's back. Parameters of the rigid transformation are estimated using a proposed angle minimization of Cervical vertebra 7, and Posterior Superior Iliac Spine landmarks of a source and target diagnoses. Experiments conducted on the constructed database show a better monomodal and multimodal registration using our proposed method compared to registration using an Equation System Solving based registration.
Deep Constrained Q-learning
Authors: Gabriel Kalweit, Maria Huegle, Moritz Werling, Joschka Boedecker
Date: 2020-03-20 00:00:00
Tasks: Autonomous Driving, Decision Making, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In many real world applications, reinforcement learning agents have to optimize multiple objectives while following certain rules or satisfying a list of constraints. Classical methods based on reward shaping, i.e. a weighted combination of different objectives in the reward signal, or Lagrangian methods, including constraints in the loss function, have no guarantees that the agent satisfies the constraints at all points in time and can lead to undesired behavior. When a discrete policy is extracted from an action-value function, safe actions can be ensured by restricting the action space at maximization, but can lead to sub-optimal solutions among feasible alternatives. In this work, we propose Constrained Q-learning, a novel off-policy reinforcement learning framework restricting the action space directly in the Q-update to learn the optimal Q-function for the induced constrained MDP and the corresponding safe policy. In addition to single-step constraints referring only to the next action, we introduce a formulation for approximate multi-step constraints under the current target policy based on truncated value-functions. We analyze the advantages of Constrained Q-learning in the tabular case and compare Constrained DQN to reward shaping and Lagrangian methods in the application of high-level decision making in autonomous driving, considering constraints for safety, keeping right and comfort. We train our agent in the open-source simulator SUMO and on the real HighD data set.
Comprehensive Instructional Video Analysis: The COIN Dataset and Performance Evaluation
Authors: Yansong Tang, Jiwen Lu, Jie zhou
Date: 2020-03-20 00:00:00
Tasks: Action Detection

Thanks to the substantial and explosively inscreased instructional videos on the Internet, novices are able to acquire knowledge for completing various tasks. Over the past decade, growing efforts have been devoted to investigating the problem on instructional video analysis. However, the most existing datasets in this area have limitations in diversity and scale, which makes them far from many real-world applications where more diverse activities occur. To address this, we present a large-scale dataset named as "COIN" for COmprehensive INstructional video analysis. Organized with a hierarchical structure, the COIN dataset contains 11,827 videos of 180 tasks in 12 domains (e.g., vehicles, gadgets, etc.) related to our daily life. With a new developed toolbox, all the videos are annotated efficiently with a series of step labels and the corresponding temporal boundaries. In order to provide a benchmark for instructional video analysis, we evaluate plenty of approaches on the COIN dataset under five different settings. Furthermore, we exploit two important characteristics (i.e., task-consistency and ordering-dependency) for localizing important steps in instructional videos. Accordingly, we propose two simple yet effective methods, which can be easily plugged into conventional proposal-based action detection models. We believe the introduction of the COIN dataset will promote the future in-depth research on instructional video analysis for the community. Our dataset, annotation toolbox and source code are available at http://coin-dataset.github.io.