July 29, 2019

3061 words 15 mins read

Paper Group ANR 3

GoDP: Globally optimized dual pathway system for facial landmark localization in-the-wild. An Extended Framework for Marginalized Domain Adaptation. How Do People Differ? A Social Media Approach. Constructive Preference Elicitation over Hybrid Combinatorial Spaces. Language Independent Acquisition of Abbreviations. Face Recognition using Multi-Moda …

GoDP: Globally optimized dual pathway system for facial landmark localization in-the-wild


Title	GoDP: Globally optimized dual pathway system for facial landmark localization in-the-wild
Authors	Yuhang Wu, Shishir K. Shah, Ioannis A. Kakadiaris
Abstract	Facial landmark localization is a fundamental module for pose-invariant face recognition. The most common approach for facial landmark detection is cascaded regression, which is composed of two steps: feature extraction and facial shape regression. Recent methods employ deep convolutional networks to extract robust features for each step, while the whole system could be regarded as a deep cascaded regression architecture. In this work, instead of employing a deep regression network, a Globally Optimized Dual-Pathway (GoDP) deep architecture is proposed to identify the target pixels through solving a cascaded pixel labeling problem without resorting to high-level inference models or complex stacked architecture. The proposed end-to-end system relies on distance-aware softmax functions and dual-pathway proposal-refinement architecture. Results show that it outperforms the state-of-the-art cascaded regression-based methods on multiple in-the-wild face alignment databases. The model achieves 1.84 normalized mean error (NME) on the AFLW database, which outperforms 3DDFA by 61.8%. Experiments on face identification demonstrate that GoDP, coupled with DPM-headhunter, is able to improve rank-1 identification rate by 44.2% compared to Dlib toolbox on a challenging database.
Tasks	Face Alignment, Face Identification, Face Recognition, Facial Landmark Detection, Robust Face Recognition
Published	2017-04-07
URL	http://arxiv.org/abs/1704.02402v2
PDF	http://arxiv.org/pdf/1704.02402v2.pdf
PWC	https://paperswithcode.com/paper/godp-globally-optimized-dual-pathway-system
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An Extended Framework for Marginalized Domain Adaptation


Title	An Extended Framework for Marginalized Domain Adaptation
Authors	Gabriela Csurka, Boris Chidlovski, Stephane Clinchant, Sophia Michel
Abstract	We propose an extended framework for marginalized domain adaptation, aimed at addressing unsupervised, supervised and semi-supervised scenarios. We argue that the denoising principle should be extended to explicitly promote domain-invariant features as well as help the classification task. Therefore we propose to jointly learn the data auto-encoders and the target classifiers. First, in order to make the denoised features domain-invariant, we propose a domain regularization that may be either a domain prediction loss or a maximum mean discrepancy between the source and target data. The noise marginalization in this case is reduced to solving the linear matrix system $AX=B$ which has a closed-form solution. Second, in order to help the classification, we include a class regularization term. Adding this component reduces the learning problem to solving a Sylvester linear matrix equation $AX+BX=C$, for which an efficient iterative procedure exists as well. We did an extensive study to assess how these regularization terms improve the baseline performance in the three domain adaptation scenarios and present experimental results on two image and one text benchmark datasets, conventionally used for validating domain adaptation methods. We report our findings and comparison with state-of-the-art methods.
Tasks	Denoising, Domain Adaptation
Published	2017-02-20
URL	http://arxiv.org/abs/1702.05993v1
PDF	http://arxiv.org/pdf/1702.05993v1.pdf
PWC	https://paperswithcode.com/paper/an-extended-framework-for-marginalized-domain
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Title	How Do People Differ? A Social Media Approach
Authors	Vincent Wong, Yaneer Bar-Yam
Abstract	Research from a variety of fields including psychology and linguistics have found correlations and patterns in personal attributes and behavior, but efforts to understand the broader heterogeneity in human behavior have not yet integrated these approaches and perspectives with a cohesive methodology. Here we extract patterns in behavior and relate those patterns together in a high-dimensional picture. We use dimension reduction to analyze word usage in text data from the online discussion platform Reddit. We find that pronouns can be used to characterize the space of the two most prominent dimensions that capture the greatest differences in word usage, even though pronouns were not included in the determination of those dimensions. These patterns overlap with patterns of topics of discussion to reveal relationships between pronouns and topics that can describe the user population. This analysis corroborates findings from past research that have identified word use differences across populations and synthesizes them relative to one another. We believe this is a step toward understanding how differences between people are related to each other.
Tasks	Dimensionality Reduction
Published	2017-08-09
URL	http://arxiv.org/abs/1708.02900v1
PDF	http://arxiv.org/pdf/1708.02900v1.pdf
PWC	https://paperswithcode.com/paper/how-do-people-differ-a-social-media-approach
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Constructive Preference Elicitation over Hybrid Combinatorial Spaces


Title	Constructive Preference Elicitation over Hybrid Combinatorial Spaces
Authors	Paolo Dragone, Stefano Teso, Andrea Passerini
Abstract	Preference elicitation is the task of suggesting a highly preferred configuration to a decision maker. The preferences are typically learned by querying the user for choice feedback over pairs or sets of objects. In its constructive variant, new objects are synthesized “from scratch” by maximizing an estimate of the user utility over a combinatorial (possibly infinite) space of candidates. In the constructive setting, most existing elicitation techniques fail because they rely on exhaustive enumeration of the candidates. A previous solution explicitly designed for constructive tasks comes with no formal performance guarantees, and can be very expensive in (or unapplicable to) problems with non-Boolean attributes. We propose the Choice Perceptron, a Perceptron-like algorithm for learning user preferences from set-wise choice feedback over constructive domains and hybrid Boolean-numeric feature spaces. We provide a theoretical analysis on the attained regret that holds for a large class of query selection strategies, and devise a heuristic strategy that aims at optimizing the regret in practice. Finally, we demonstrate its effectiveness by empirical evaluation against existing competitors on constructive scenarios of increasing complexity.
Tasks
Published	2017-11-21
URL	http://arxiv.org/abs/1711.07875v2
PDF	http://arxiv.org/pdf/1711.07875v2.pdf
PWC	https://paperswithcode.com/paper/constructive-preference-elicitation-over
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Language Independent Acquisition of Abbreviations


Title	Language Independent Acquisition of Abbreviations
Authors	Michael R. Glass, Md Faisal Mahbub Chowdhury, Alfio M. Gliozzo
Abstract	This paper addresses automatic extraction of abbreviations (encompassing acronyms and initialisms) and corresponding long-form expansions from plain unstructured text. We create and are going to release a multilingual resource for abbreviations and their corresponding expansions, built automatically by exploiting Wikipedia redirect and disambiguation pages, that can be used as a benchmark for evaluation. We address a shortcoming of previous work where only the redirect pages were used, and so every abbreviation had only a single expansion, even though multiple different expansions are possible for many of the abbreviations. We also develop a principled machine learning based approach to scoring expansion candidates using different techniques such as indicators of near synonymy, topical relatedness, and surface similarity. We show improved performance over seven languages, including two with a non-Latin alphabet, relative to strong baselines.
Tasks
Published	2017-09-23
URL	http://arxiv.org/abs/1709.08074v1
PDF	http://arxiv.org/pdf/1709.08074v1.pdf
PWC	https://paperswithcode.com/paper/language-independent-acquisition-of
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Title	Face Recognition using Multi-Modal Low-Rank Dictionary Learning
Authors	Homa Foroughi, Moein Shakeri, Nilanjan Ray, Hong Zhang
Abstract	Face recognition has been widely studied due to its importance in different applications; however, most of the proposed methods fail when face images are occluded or captured under illumination and pose variations. Recently several low-rank dictionary learning methods have been proposed and achieved promising results for noisy observations. While these methods are mostly developed for single-modality scenarios, recent studies demonstrated the advantages of feature fusion from multiple inputs. We propose a multi-modal structured low-rank dictionary learning method for robust face recognition, using raw pixels of face images and their illumination invariant representation. The proposed method learns robust and discriminative representations from contaminated face images, even if there are few training samples with large intra-class variations. Extensive experiments on different datasets validate the superior performance and robustness of our method to severe illumination variations and occlusion.
Tasks	Dictionary Learning, Face Recognition, Robust Face Recognition
Published	2017-03-15
URL	http://arxiv.org/abs/1703.04853v1
PDF	http://arxiv.org/pdf/1703.04853v1.pdf
PWC	https://paperswithcode.com/paper/face-recognition-using-multi-modal-low-rank
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Group Visual Sentiment Analysis


Title	Group Visual Sentiment Analysis
Authors	Zeshan Hussain, Tariq Patanam, Hardie Cate
Abstract	In this paper, we introduce a framework for classifying images according to high-level sentiment. We subdivide the task into three primary problems: emotion classification on faces, human pose estimation, and 3D estimation and clustering of groups of people. We introduce novel algorithms for matching body parts to a common individual and clustering people in images based on physical location and orientation. Our results outperform several baseline approaches.
Tasks	Emotion Classification, Pose Estimation, Sentiment Analysis
Published	2017-01-07
URL	http://arxiv.org/abs/1701.01885v1
PDF	http://arxiv.org/pdf/1701.01885v1.pdf
PWC	https://paperswithcode.com/paper/group-visual-sentiment-analysis
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Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization


Title	Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization
Authors	Ahmet Alacaoglu, Quoc Tran-Dinh, Olivier Fercoq, Volkan Cevher
Abstract	We propose a new randomized coordinate descent method for a convex optimization template with broad applications. Our analysis relies on a novel combination of four ideas applied to the primal-dual gap function: smoothing, acceleration, homotopy, and coordinate descent with non-uniform sampling. As a result, our method features the first convergence rate guarantees among the coordinate descent methods, that are the best-known under a variety of common structure assumptions on the template. We provide numerical evidence to support the theoretical results with a comparison to state-of-the-art algorithms.
Tasks
Published	2017-11-09
URL	http://arxiv.org/abs/1711.03439v1
PDF	http://arxiv.org/pdf/1711.03439v1.pdf
PWC	https://paperswithcode.com/paper/smooth-primal-dual-coordinate-descent
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Parametric Adversarial Divergences are Good Task Losses for Generative Modeling


Title	Parametric Adversarial Divergences are Good Task Losses for Generative Modeling
Authors	Gabriel Huang, Hugo Berard, Ahmed Touati, Gauthier Gidel, Pascal Vincent, Simon Lacoste-Julien
Abstract	Generative modeling of high dimensional data like images is a notoriously difficult and ill-defined problem. In particular, how to evaluate a learned generative model is unclear. In this position paper, we argue that adversarial learning, pioneered with generative adversarial networks (GANs), provides an interesting framework to implicitly define more meaningful task losses for generative modeling tasks, such as for generating “visually realistic” images. We refer to those task losses as parametric adversarial divergences and we give two main reasons why we think parametric divergences are good learning objectives for generative modeling. Additionally, we unify the processes of choosing a good structured loss (in structured prediction) and choosing a discriminator architecture (in generative modeling) using statistical decision theory; we are then able to formalize and quantify the intuition that “weaker” losses are easier to learn from, in a specific setting. Finally, we propose two new challenging tasks to evaluate parametric and nonparametric divergences: a qualitative task of generating very high-resolution digits, and a quantitative task of learning data that satisfies high-level algebraic constraints. We use two common divergences to train a generator and show that the parametric divergence outperforms the nonparametric divergence on both the qualitative and the quantitative task.
Tasks	Structured Prediction
Published	2017-08-08
URL	http://arxiv.org/abs/1708.02511v3
PDF	http://arxiv.org/pdf/1708.02511v3.pdf
PWC	https://paperswithcode.com/paper/parametric-adversarial-divergences-are-good
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Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes


Title	Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes
Authors	Steve Hanneke
Abstract	This work initiates a general study of learning and generalization without the i.i.d. assumption, starting from first principles. While the standard approach to statistical learning theory is based on assumptions chosen largely for their convenience (e.g., i.i.d. or stationary ergodic), in this work we are interested in developing a theory of learning based only on the most fundamental and natural assumptions implicit in the requirements of the learning problem itself. We specifically study universally consistent function learning, where the objective is to obtain low long-run average loss for any target function, when the data follow a given stochastic process. We are then interested in the question of whether there exist learning rules guaranteed to be universally consistent given only the assumption that universally consistent learning is possible for the given data process. The reasoning that motivates this criterion emanates from a kind of optimist’s decision theory, and so we refer to such learning rules as being optimistically universal. We study this question in three natural learning settings: inductive, self-adaptive, and online. Remarkably, as our strongest positive result, we find that optimistically universal learning rules do indeed exist in the self-adaptive learning setting. Establishing this fact requires us to develop new approaches to the design of learning algorithms. Along the way, we also identify concise characterizations of the family of processes under which universally consistent learning is possible in the inductive and self-adaptive settings. We additionally pose a number of enticing open problems, particularly for the online learning setting.
Tasks
Published	2017-06-05
URL	http://arxiv.org/abs/1706.01418v1
PDF	http://arxiv.org/pdf/1706.01418v1.pdf
PWC	https://paperswithcode.com/paper/learning-whenever-learning-is-possible
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Neurology-as-a-Service for the Developing World


Title	Neurology-as-a-Service for the Developing World
Authors	Tejas Dharamsi, Payel Das, Tejaswini Pedapati, Gregory Bramble, Vinod Muthusamy, Horst Samulowitz, Kush R. Varshney, Yuvaraj Rajamanickam, John Thomas, Justin Dauwels
Abstract	Electroencephalography (EEG) is an extensively-used and well-studied technique in the field of medical diagnostics and treatment for brain disorders, including epilepsy, migraines, and tumors. The analysis and interpretation of EEGs require physicians to have specialized training, which is not common even among most doctors in the developed world, let alone the developing world where physician shortages plague society. This problem can be addressed by teleEEG that uses remote EEG analysis by experts or by local computer processing of EEGs. However, both of these options are prohibitively expensive and the second option requires abundant computing resources and infrastructure, which is another concern in developing countries where there are resource constraints on capital and computing infrastructure. In this work, we present a cloud-based deep neural network approach to provide decision support for non-specialist physicians in EEG analysis and interpretation. Named `neurology-as-a-service,’ the approach requires almost no manual intervention in feature engineering and in the selection of an optimal architecture and hyperparameters of the neural network. In this study, we deploy a pipeline that includes moving EEG data to the cloud and getting optimal models for various classification tasks. Our initial prototype has been tested only in developed world environments to-date, but our intention is to test it in developing world environments in future work. We demonstrate the performance of our proposed approach using the BCI2000 EEG MMI dataset, on which our service attains 63.4% accuracy for the task of classifying real vs. imaginary activity performed by the subject, which is significantly higher than what is obtained with a shallow approach such as support vector machines. \|
Tasks	EEG, Feature Engineering
Published	2017-11-16
URL	http://arxiv.org/abs/1711.06195v2
PDF	http://arxiv.org/pdf/1711.06195v2.pdf
PWC	https://paperswithcode.com/paper/neurology-as-a-service-for-the-developing
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Framework

Simulated Annealing with Levy Distribution for Fast Matrix Factorization-Based Collaborative Filtering


Title	Simulated Annealing with Levy Distribution for Fast Matrix Factorization-Based Collaborative Filtering
Authors	Mostafa A. Shehata, Mohammad Nassef, Amr A. Badr
Abstract	Matrix factorization is one of the best approaches for collaborative filtering, because of its high accuracy in presenting users and items latent factors. The main disadvantages of matrix factorization are its complexity, and being very hard to be parallelized, specially with very large matrices. In this paper, we introduce a new method for collaborative filtering based on Matrix Factorization by combining simulated annealing with levy distribution. By using this method, good solutions are achieved in acceptable time with low computations, compared to other methods like stochastic gradient descent, alternating least squares, and weighted non-negative matrix factorization.
Tasks
Published	2017-08-09
URL	http://arxiv.org/abs/1708.02867v1
PDF	http://arxiv.org/pdf/1708.02867v1.pdf
PWC	https://paperswithcode.com/paper/simulated-annealing-with-levy-distribution
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Framework

The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task


Title	The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task
Authors	Amr Sharaf, Shi Feng, Khanh Nguyen, Kianté Brantley, Hal Daumé III
Abstract	We describe the University of Maryland machine translation systems submitted to the WMT17 German-English Bandit Learning Task. The task is to adapt a translation system to a new domain, using only bandit feedback: the system receives a German sentence to translate, produces an English sentence, and only gets a scalar score as feedback. Targeting these two challenges (adaptation and bandit learning), we built a standard neural machine translation system and extended it in two ways: (1) robust reinforcement learning techniques to learn effectively from the bandit feedback, and (2) domain adaptation using data selection from a large corpus of parallel data.
Tasks	Domain Adaptation, Machine Translation
Published	2017-08-03
URL	http://arxiv.org/abs/1708.01318v2
PDF	http://arxiv.org/pdf/1708.01318v2.pdf
PWC	https://paperswithcode.com/paper/the-umd-neural-machine-translation-systems-at
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Algorithm and Hardware Design of Discrete-Time Spiking Neural Networks Based on Back Propagation with Binary Activations


Title	Algorithm and Hardware Design of Discrete-Time Spiking Neural Networks Based on Back Propagation with Binary Activations
Authors	Shihui Yin, Shreyas K. Venkataramanaiah, Gregory K. Chen, Ram Krishnamurthy, Yu Cao, Chaitali Chakrabarti, Jae-sun Seo
Abstract	We present a new back propagation based training algorithm for discrete-time spiking neural networks (SNN). Inspired by recent deep learning algorithms on binarized neural networks, binary activation with a straight-through gradient estimator is used to model the leaky integrate-fire spiking neuron, overcoming the difficulty in training SNNs using back propagation. Two SNN training algorithms are proposed: (1) SNN with discontinuous integration, which is suitable for rate-coded input spikes, and (2) SNN with continuous integration, which is more general and can handle input spikes with temporal information. Neuromorphic hardware designed in 40nm CMOS exploits the spike sparsity and demonstrates high classification accuracy (>98% on MNIST) and low energy (48.4-773 nJ/image).
Tasks
Published	2017-09-19
URL	http://arxiv.org/abs/1709.06206v1
PDF	http://arxiv.org/pdf/1709.06206v1.pdf
PWC	https://paperswithcode.com/paper/algorithm-and-hardware-design-of-discrete
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Generic 3D Representation via Pose Estimation and Matching


Title	Generic 3D Representation via Pose Estimation and Matching
Authors	Amir R. Zamir, Tilman Wekel, Pulkit Argrawal, Colin Weil, Jitendra Malik, Silvio Savarese
Abstract	Though a large body of computer vision research has investigated developing generic semantic representations, efforts towards developing a similar representation for 3D has been limited. In this paper, we learn a generic 3D representation through solving a set of foundational proxy 3D tasks: object-centric camera pose estimation and wide baseline feature matching. Our method is based upon the premise that by providing supervision over a set of carefully selected foundational tasks, generalization to novel tasks and abstraction capabilities can be achieved. We empirically show that the internal representation of a multi-task ConvNet trained to solve the above core problems generalizes to novel 3D tasks (e.g., scene layout estimation, object pose estimation, surface normal estimation) without the need for fine-tuning and shows traits of abstraction abilities (e.g., cross-modality pose estimation). In the context of the core supervised tasks, we demonstrate our representation achieves state-of-the-art wide baseline feature matching results without requiring apriori rectification (unlike SIFT and the majority of learned features). We also show 6DOF camera pose estimation given a pair local image patches. The accuracy of both supervised tasks come comparable to humans. Finally, we contribute a large-scale dataset composed of object-centric street view scenes along with point correspondences and camera pose information, and conclude with a discussion on the learned representation and open research questions.
Tasks	Pose Estimation
Published	2017-10-23
URL	http://arxiv.org/abs/1710.08247v1
PDF	http://arxiv.org/pdf/1710.08247v1.pdf
PWC	https://paperswithcode.com/paper/generic-3d-representation-via-pose-estimation
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