pwc-archive/papers-with-abstracts huggingface dataset:
Learning Visual Representations for Transfer Learning by Suppressing Texture
Authors: Shlok Mishra, Anshul Shah, Ankan Bansal, Janit Anjaria, Jonghyun Choi, Abhinav Shrivastava, Abhishek Sharma, David Jacobs
Date: 2020-11-03 00:00:00
Tasks: image-classification, Image Classification, object-detection, Object Detection, Self-Supervised Learning, Transfer Learning

Recent literature has shown that features obtained from supervised training of CNNs may over-emphasize texture rather than encoding high-level information. In self-supervised learning in particular, texture as a low-level cue may provide shortcuts that prevent the network from learning higher level representations. To address these problems we propose to use classic methods based on anisotropic diffusion to augment training using images with suppressed texture. This simple method helps retain important edge information and suppress texture at the same time. We empirically show that our method achieves state-of-the-art results on object detection and image classification with eight diverse datasets in either supervised or self-supervised learning tasks such as MoCoV2 and Jigsaw. Our method is particularly effective for transfer learning tasks and we observed improved performance on five standard transfer learning datasets. The large improvements (up to 11.49\%) on the Sketch-ImageNet dataset, DTD dataset and additional visual analyses with saliency maps suggest that our approach helps in learning better representations that better transfer.
Warped Language Models for Noise Robust Language Understanding
Authors: Mahdi Namazifar, Gokhan Tur, Dilek Hakkani Tür
Date: 2020-11-03 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Natural Language Understanding, Sentence, speech-recognition, Speech Recognition, Spoken Language Understanding

Masked Language Models (MLM) are self-supervised neural networks trained to fill in the blanks in a given sentence with masked tokens. Despite the tremendous success of MLMs for various text based tasks, they are not robust for spoken language understanding, especially for spontaneous conversational speech recognition noise. In this work we introduce Warped Language Models (WLM) in which input sentences at training time go through the same modifications as in MLM, plus two additional modifications, namely inserting and dropping random tokens. These two modifications extend and contract the sentence in addition to the modifications in MLMs, hence the word "warped" in the name. The insertion and drop modification of the input text during training of WLM resemble the types of noise due to Automatic Speech Recognition (ASR) errors, and as a result WLMs are likely to be more robust to ASR noise. Through computational results we show that natural language understanding systems built on top of WLMs perform better compared to those built based on MLMs, especially in the presence of ASR errors.
Policy Transfer via Kinematic Domain Randomization and Adaptation
Authors: Ioannis Exarchos, Yifeng Jiang, Wenhao Yu, C. Karen Liu
Date: 2020-11-03 00:00:00
Tasks: Bayesian Optimization, Domain Adaptation

Transferring reinforcement learning policies trained in physics simulation to the real hardware remains a challenge, known as the "sim-to-real" gap. Domain randomization is a simple yet effective technique to address dynamics discrepancies across source and target domains, but its success generally depends on heuristics and trial-and-error. In this work we investigate the impact of randomized parameter selection on policy transferability across different types of domain discrepancies. Contrary to common practice in which kinematic parameters are carefully measured while dynamic parameters are randomized, we found that virtually randomizing kinematic parameters (e.g., link lengths) during training in simulation generally outperforms dynamic randomization. Based on this finding, we introduce a new domain adaptation algorithm that utilizes simulated kinematic parameters variation. Our algorithm, Multi-Policy Bayesian Optimization, trains an ensemble of universal policies conditioned on virtual kinematic parameters and efficiently adapts to the target environment using a limited number of target domain rollouts. We showcase our findings on a simulated quadruped robot in five different target environments covering different aspects of domain discrepancies.
RealHePoNet: a robust single-stage ConvNet for head pose estimation in the wild
Authors: Rafael Berral-Soler, Francisco J. Madrid-Cuevas, Rafael Muñoz-Salinas, Manuel J. Marín-Jiménez
Date: 2020-11-03 00:00:00
Tasks: GPU, Head Pose Estimation, Pose Estimation

Human head pose estimation in images has applications in many fields such as human-computer interaction or video surveillance tasks. In this work, we address this problem, defined here as the estimation of both vertical (tilt/pitch) and horizontal (pan/yaw) angles, through the use of a single Convolutional Neural Network (ConvNet) model, trying to balance precision and inference speed in order to maximize its usability in real-world applications. Our model is trained over the combination of two datasets: 'Pointing'04' (aiming at covering a wide range of poses) and 'Annotated Facial Landmarks in the Wild' (in order to improve robustness of our model for its use on real-world images). Three different partitions of the combined dataset are defined and used for training, validation and testing purposes. As a result of this work, we have obtained a trained ConvNet model, coined RealHePoNet, that given a low-resolution grayscale input image, and without the need of using facial landmarks, is able to estimate with low error both tilt and pan angles (~4.4{\deg} average error on the test partition). Also, given its low inference time (~6 ms per head), we consider our model usable even when paired with medium-spec hardware (i.e. GTX 1060 GPU). * Code available at: https://github.com/rafabs97/headpose_final * Demo video at: https://www.youtube.com/watch?v=2UeuXh5DjAE
Automated Hyperparameter Selection for the PC Algorithm
Authors: Eric V. Strobl
Date: 2020-11-03 00:00:00

The PC algorithm infers causal relations using conditional independence tests that require a pre-specified Type I $\alpha$ level. PC is however unsupervised, so we cannot tune $\alpha$ using traditional cross-validation. We therefore propose AutoPC, a fast procedure that optimizes $\alpha$ directly for a user chosen metric. We in particular force PC to double check its output by executing a second run on the recovered graph. We choose the final output as the one which maximizes stability between the two runs. AutoPC consistently outperforms the state of the art across multiple metrics.
Unsupervised Attention Based Instance Discriminative Learning for Person Re-Identification
Authors: Kshitij Nikhal, Benjamin S. Riggan
Date: 2020-11-03 00:00:00
Tasks: Clustering, Person Re-Identification, Transfer Learning, Unsupervised Person Re-Identification

Recent advances in person re-identification have demonstrated enhanced discriminability, especially with supervised learning or transfer learning. However, since the data requirements---including the degree of data curations---are becoming increasingly complex and laborious, there is a critical need for unsupervised methods that are robust to large intra-class variations, such as changes in perspective, illumination, articulated motion, resolution, etc. Therefore, we propose an unsupervised framework for person re-identification which is trained in an end-to-end manner without any pre-training. Our proposed framework leverages a new attention mechanism that combines group convolutions to (1) enhance spatial attention at multiple scales and (2) reduce the number of trainable parameters by 59.6%. Additionally, our framework jointly optimizes the network with agglomerative clustering and instance learning to tackle hard samples. We perform extensive analysis using the Market1501 and DukeMTMC-reID datasets to demonstrate that our method consistently outperforms the state-of-the-art methods (with and without pre-trained weights).
Growth and dynamics of Econophysics: A bibliometric and network analysis
Authors: Kiran Sharma, Parul Khurana
Date: 2020-11-03 00:00:00

Digitization of publications, advancement in communication technology, and the availability of bibliographic data have made it easier for the researchers to study the growth and dynamics of any discipline. We present a study on "Econophysics" metadata extracted from the Web of Science managed by the Clarivate Analytics from 2000-2019. The study highlights the growth and dynamics of the discipline by measures of a number of publications, citations on publications, other disciplines contribution, institutions participation, country-wise spread, etc. We investigate the impact of self-citations on citations with every five-year interval. Also, we find the contribution of other disciplines by analyzing the cited references. Results emerged from micro, meso and macro-level analysis of collaborations show that the distributions among authors collaboration and affiliations of authors follow a power law. Thus, very few authors keep producing most of the papers and are from a few institutions. We find that China is leading in the production of a number of authors and a number of papers; however, shares more of national collaboration rather than international, whereas the USA shares more international collaboration. Finally, we demonstrate the evolution of the author's collaborations and affiliations networks from 2000-2019. Overall the analysis reveals the "small-world" property of the network with average path length 5. As a consequence of our analysis, this study can serve as in-depth knowledge to understand the growth and dynamics of the Econophysics network both qualitatively and quantitatively.
Intrinsic Robotic Introspection: Learning Internal States From Neuron Activations
Authors: Nikos Pitsillos, Ameya Pore, Bjorn Sand Jensen, Gerardo Aragon-Camarasa
Date: 2020-11-03 00:00:00

We present an introspective framework inspired by the process of how humans perform introspection. Our working assumption is that neural network activations encode information, and building internal states from these activations can improve the performance of an actor-critic model. We perform experiments where we first train a Variational Autoencoder model to reconstruct the activations of a feature extraction network and use the latent space to improve the performance of an actor-critic when deciding which low-level robotic behaviour to execute. We show that internal states reduce the number of episodes needed by about 1300 episodes while training an actor-critic, denoting faster convergence to get a high success value while completing a robotic task.
Learning unbiased group-wise registration (LUGR) and joint segmentation: evaluation on longitudinal diffusion MRI
Authors: Bo Li, Wiro J. Niessen, Stefan Klein, M. Arfan Ikram, Meike W. Vernooij, Esther E. Bron
Date: 2020-11-03 00:00:00
Tasks: Diffusion  MRI

Analysis of longitudinal changes in imaging studies often involves both segmentation of structures of interest and registration of multiple timeframes. The accuracy of such analysis could benefit from a tailored framework that jointly optimizes both tasks to fully exploit the information available in the longitudinal data. Most learning-based registration algorithms, including joint optimization approaches, currently suffer from bias due to selection of a fixed reference frame and only support pairwise transformations. We here propose an analytical framework based on an unbiased learning strategy for group-wise registration that simultaneously registers images to the mean space of a group to obtain consistent segmentations. We evaluate the proposed method on longitudinal analysis of a white matter tract in a brain MRI dataset with 2-3 time-points for 3249 individuals, i.e., 8045 images in total. The reproducibility of the method is evaluated on test-retest data from 97 individuals. The results confirm that the implicit reference image is an average of the input image. In addition, the proposed framework leads to consistent segmentations and significantly lower processing bias than that of a pair-wise fixed-reference approach. This processing bias is even smaller than those obtained when translating segmentations by only one voxel, which can be attributed to subtle numerical instabilities and interpolation. Therefore, we postulate that the proposed mean-space learning strategy could be widely applied to learning-based registration tasks. In addition, this group-wise framework introduces a novel way for learning-based longitudinal studies by direct construction of an unbiased within-subject template and allowing reliable and efficient analysis of spatio-temporal imaging biomarkers.
Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance
Authors: Thinh T. Doan
Date: 2020-11-03 00:00:00
Tasks: Vocal Bursts Valence Prediction

Two-time-scale stochastic approximation, a generalized version of the popular stochastic approximation, has found broad applications in many areas including stochastic control, optimization, and machine learning. Despite its popularity, theoretical guarantees of this method, especially its finite-time performance, are mostly achieved for the linear case while the results for the nonlinear counterpart are very sparse. Motivated by the classic control theory for singularly perturbed systems, we study in this paper the asymptotic convergence and finite-time analysis of the nonlinear two-time-scale stochastic approximation. Under some fairly standard assumptions, we provide a formula that characterizes the rate of convergence of the main iterates to the desired solutions. In particular, we show that the method achieves a convergence in expectation at a rate $\mathcal{O}(1/k^{2/3})$, where $k$ is the number of iterations. The key idea in our analysis is to properly choose the two step sizes to characterize the coupling between the fast and slow-time-scale iterates.
Semi-supervised Facial Action Unit Intensity Estimation with Contrastive Learning
Authors: Enrique Sanchez, Adrian Bulat, Anestis Zaganidis, Georgios Tzimiropoulos
Date: 2020-11-03 00:00:00
Tasks: Contrastive Learning, Unsupervised Pre-training

This paper tackles the challenging problem of estimating the intensity of Facial Action Units with few labeled images. Contrary to previous works, our method does not require to manually select key frames, and produces state-of-the-art results with as little as $2\%$ of annotated frames, which are \textit{randomly chosen}. To this end, we propose a semi-supervised learning approach where a spatio-temporal model combining a feature extractor and a temporal module are learned in two stages. The first stage uses datasets of unlabeled videos to learn a strong spatio-temporal representation of facial behavior dynamics based on contrastive learning. To our knowledge we are the first to build upon this framework for modeling facial behavior in an unsupervised manner. The second stage uses another dataset of randomly chosen labeled frames to train a regressor on top of our spatio-temporal model for estimating the AU intensity. We show that although backpropagation through time is applied only with respect to the output of the network for extremely sparse and randomly chosen labeled frames, our model can be effectively trained to estimate AU intensity accurately, thanks to the unsupervised pre-training of the first stage. We experimentally validate that our method outperforms existing methods when working with as little as $2\%$ of randomly chosen data for both DISFA and BP4D datasets, without a careful choice of labeled frames, a time-consuming task still required in previous approaches.
DeL-haTE: A Deep Learning Tunable Ensemble for Hate Speech Detection
Authors: Joshua Melton, Arunkumar Bagavathi, Siddharth Krishnan
Date: 2020-11-03 00:00:00
Tasks: Hate Speech Detection, Transfer Learning

Online hate speech on social media has become a fast-growing problem in recent times. Nefarious groups have developed large content delivery networks across several main-stream (Twitter and Facebook) and fringe (Gab, 4chan, 8chan, etc.) outlets to deliver cascades of hate messages directed both at individuals and communities. Thus addressing these issues has become a top priority for large-scale social media outlets. Three key challenges in automated detection and classification of hateful content are the lack of clearly labeled data, evolving vocabulary and lexicon - hashtags, emojis, etc. - and the lack of baseline models for fringe outlets such as Gab. In this work, we propose a novel framework with three major contributions. (a) We engineer an ensemble of deep learning models that combines the strengths of state-of-the-art approaches, (b) we incorporate a tuning factor into this framework that leverages transfer learning to conduct automated hate speech classification on unlabeled datasets, like Gab, and (c) we develop a weak supervised learning methodology that allows our framework to train on unlabeled data. Our ensemble models achieve an 83% hate recall on the HON dataset, surpassing the performance of the state-of-the-art deep models. We demonstrate that weak supervised training in combination with classifier tuning significantly increases model performance on unlabeled data from Gab, achieving a hate recall of 67%.
Decoupling entrainment from consistency using deep neural networks
Authors: Andreas Weise, Rivka Levitan
Date: 2020-11-03 00:00:00

Human interlocutors tend to engage in adaptive behavior known as entrainment to become more similar to each other. Isolating the effect of consistency, i.e., speakers adhering to their individual styles, is a critical part of the analysis of entrainment. We propose to treat speakers' initial vocal features as confounds for the prediction of subsequent outputs. Using two existing neural approaches to deconfounding, we define new measures of entrainment that control for consistency. These successfully discriminate real interactions from fake ones. Interestingly, our stricter methods correlate with social variables in opposite direction from previous measures that do not account for consistency. These results demonstrate the advantages of using neural networks to model entrainment, and raise questions regarding how to interpret prior associations of conversation quality with entrainment measures that do not account for consistency.
A Study of Policy Gradient on a Class of Exactly Solvable Models
Authors: Gavin McCracken, Colin Daniels, Rosie Zhao, Anna Brandenberger, Prakash Panangaden, Doina Precup
Date: 2020-11-03 00:00:00
Tasks: Policy Gradient Methods

Policy gradient methods are extensively used in reinforcement learning as a way to optimize expected return. In this paper, we explore the evolution of the policy parameters, for a special class of exactly solvable POMDPs, as a continuous-state Markov chain, whose transition probabilities are determined by the gradient of the distribution of the policy's value. Our approach relies heavily on random walk theory, specifically on affine Weyl groups. We construct a class of novel partially observable environments with controllable exploration difficulty, in which the value distribution, and hence the policy parameter evolution, can be derived analytically. Using these environments, we analyze the probabilistic convergence of policy gradient to different local maxima of the value function. To our knowledge, this is the first approach developed to analytically compute the landscape of policy gradient in POMDPs for a class of such environments, leading to interesting insights into the difficulty of this problem.
Analytical aspects of non-differentiable neural networks
Authors: Gian Paolo Leonardi, Matteo Spallanzani
Date: 2020-11-03 00:00:00
Tasks: Quantization

Research in computational deep learning has directed considerable efforts towards hardware-oriented optimisations for deep neural networks, via the simplification of the activation functions, or the quantization of both activations and weights. The resulting non-differentiability (or even discontinuity) of the networks poses some challenging problems, especially in connection with the learning process. In this paper, we address several questions regarding both the expressivity of quantized neural networks and approximation techniques for non-differentiable networks. First, we answer in the affirmative the question of whether QNNs have the same expressivity as DNNs in terms of approximation of Lipschitz functions in the $L^{\infty}$ norm. Then, considering a continuous but not necessarily differentiable network, we describe a layer-wise stochastic regularisation technique to produce differentiable approximations, and we show how this approach to regularisation provides elegant quantitative estimates. Finally, we consider networks defined by means of Heaviside-type activation functions, and prove for them a pointwise approximation result by means of smooth networks under suitable assumptions on the regularised activations.
Finding Friends and Flipping Frenemies: Automatic Paraphrase Dataset Augmentation Using Graph Theory
Authors: Hannah Chen, Yangfeng Ji, David Evans
Date: 2020-11-03 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Sentence

Most NLP datasets are manually labeled, so suffer from inconsistent labeling or limited size. We propose methods for automatically improving datasets by viewing them as graphs with expected semantic properties. We construct a paraphrase graph from the provided sentence pair labels, and create an augmented dataset by directly inferring labels from the original sentence pairs using a transitivity property. We use structural balance theory to identify likely mislabelings in the graph, and flip their labels. We evaluate our methods on paraphrase models trained using these datasets starting from a pretrained BERT model, and find that the automatically-enhanced training sets result in more accurate models.
Robust hypothesis testing and distribution estimation in Hellinger distance
Authors: Ananda Theertha Suresh
Date: 2020-11-03 00:00:00
Tasks: Two-sample testing

We propose a simple robust hypothesis test that has the same sample complexity as that of the optimal Neyman-Pearson test up to constants, but robust to distribution perturbations under Hellinger distance. We discuss the applicability of such a robust test for estimating distributions in Hellinger distance. We empirically demonstrate the power of the test on canonical distributions.
Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks
Authors: Denis Emelin, Ivan Titov, Rico Sennrich
Date: 2020-11-03 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Adversarial Attack, Machine Translation, NMT, Translation, Word Sense Disambiguation

Word sense disambiguation is a well-known source of translation errors in NMT. We posit that some of the incorrect disambiguation choices are due to models' over-reliance on dataset artifacts found in training data, specifically superficial word co-occurrences, rather than a deeper understanding of the source text. We introduce a method for the prediction of disambiguation errors based on statistical data properties, demonstrating its effectiveness across several domains and model types. Moreover, we develop a simple adversarial attack strategy that minimally perturbs sentences in order to elicit disambiguation errors to further probe the robustness of translation models. Our findings indicate that disambiguation robustness varies substantially between domains and that different models trained on the same data are vulnerable to different attacks.