May 6, 2019

2907 words 14 mins read

Paper Group ANR 196

Piecewise Deterministic Markov Processes for Continuous-Time Monte Carlo. Generalized Root Models: Beyond Pairwise Graphical Models for Univariate Exponential Families. Sparse Graphical Representation based Discriminant Analysis for Heterogeneous Face Recognition. Temporal Feature Selection on Networked Time Series. LSTM-Based Predictions for Proac …

Piecewise Deterministic Markov Processes for Continuous-Time Monte Carlo


Title	Piecewise Deterministic Markov Processes for Continuous-Time Monte Carlo
Authors	Paul Fearnhead, Joris Bierkens, Murray Pollock, Gareth O Roberts
Abstract	Recently there have been exciting developments in Monte Carlo methods, with the development of new MCMC and sequential Monte Carlo (SMC) algorithms which are based on continuous-time, rather than discrete-time, Markov processes. This has led to some fundamentally new Monte Carlo algorithms which can be used to sample from, say, a posterior distribution. Interestingly, continuous-time algorithms seem particularly well suited to Bayesian analysis in big-data settings as they need only access a small sub-set of data points at each iteration, and yet are still guaranteed to target the true posterior distribution. Whilst continuous-time MCMC and SMC methods have been developed independently we show here that they are related by the fact that both involve simulating a piecewise deterministic Markov process. Furthermore we show that the methods developed to date are just specific cases of a potentially much wider class of continuous-time Monte Carlo algorithms. We give an informal introduction to piecewise deterministic Markov processes, covering the aspects relevant to these new Monte Carlo algorithms, with a view to making the development of new continuous-time Monte Carlo more accessible. We focus on how and why sub-sampling ideas can be used with these algorithms, and aim to give insight into how these new algorithms can be implemented, and what are some of the issues that affect their efficiency.
Tasks
Published	2016-11-23
URL	http://arxiv.org/abs/1611.07873v1
PDF	http://arxiv.org/pdf/1611.07873v1.pdf
PWC	https://paperswithcode.com/paper/piecewise-deterministic-markov-processes-for-1
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Generalized Root Models: Beyond Pairwise Graphical Models for Univariate Exponential Families


Title	Generalized Root Models: Beyond Pairwise Graphical Models for Univariate Exponential Families
Authors	David I. Inouye, Pradeep Ravikumar, Inderjit S. Dhillon
Abstract	We present a novel k-way high-dimensional graphical model called the Generalized Root Model (GRM) that explicitly models dependencies between variable sets of size k > 2—where k = 2 is the standard pairwise graphical model. This model is based on taking the k-th root of the original sufficient statistics of any univariate exponential family with positive sufficient statistics, including the Poisson and exponential distributions. As in the recent work with square root graphical (SQR) models [Inouye et al. 2016]—which was restricted to pairwise dependencies—we give the conditions of the parameters that are needed for normalization using the radial conditionals similar to the pairwise case [Inouye et al. 2016]. In particular, we show that the Poisson GRM has no restrictions on the parameters and the exponential GRM only has a restriction akin to negative definiteness. We develop a simple but general learning algorithm based on L1-regularized node-wise regressions. We also present a general way of numerically approximating the log partition function and associated derivatives of the GRM univariate node conditionals—in contrast to [Inouye et al. 2016], which only provided algorithm for estimating the exponential SQR. To illustrate GRM, we model word counts with a Poisson GRM and show the associated k-sized variable sets. We finish by discussing methods for reducing the parameter space in various situations.
Tasks
Published	2016-06-02
URL	http://arxiv.org/abs/1606.00813v1
PDF	http://arxiv.org/pdf/1606.00813v1.pdf
PWC	https://paperswithcode.com/paper/generalized-root-models-beyond-pairwise
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Sparse Graphical Representation based Discriminant Analysis for Heterogeneous Face Recognition


Title	Sparse Graphical Representation based Discriminant Analysis for Heterogeneous Face Recognition
Authors	Chunlei Peng, Xinbo Gao, Nannan Wang, Jie Li
Abstract	Face images captured in heterogeneous environments, e.g., sketches generated by the artists or composite-generation software, photos taken by common cameras and infrared images captured by corresponding infrared imaging devices, usually subject to large texture (i.e., style) differences. This results in heavily degraded performance of conventional face recognition methods in comparison with the performance on images captured in homogeneous environments. In this paper, we propose a novel sparse graphical representation based discriminant analysis (SGR-DA) approach to address aforementioned face recognition in heterogeneous scenarios. An adaptive sparse graphical representation scheme is designed to represent heterogeneous face images, where a Markov networks model is constructed to generate adaptive sparse vectors. To handle the complex facial structure and further improve the discriminability, a spatial partition-based discriminant analysis framework is presented to refine the adaptive sparse vectors for face matching. We conducted experiments on six commonly used heterogeneous face datasets and experimental results illustrate that our proposed SGR-DA approach achieves superior performance in comparison with state-of-the-art methods.
Tasks	Face Recognition, Heterogeneous Face Recognition
Published	2016-07-01
URL	http://arxiv.org/abs/1607.00137v1
PDF	http://arxiv.org/pdf/1607.00137v1.pdf
PWC	https://paperswithcode.com/paper/sparse-graphical-representation-based
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Temporal Feature Selection on Networked Time Series


Title	Temporal Feature Selection on Networked Time Series
Authors	Haishuai Wang, Jia Wu, Peng Zhang, Chengqi Zhang
Abstract	This paper formulates the problem of learning discriminative features (\textit{i.e.,} segments) from networked time series data considering the linked information among time series. For example, social network users are considered to be social sensors that continuously generate social signals (tweets) represented as a time series. The discriminative segments are often referred to as \emph{shapelets} in a time series. Extracting shapelets for time series classification has been widely studied. However, existing works on shapelet selection assume that the time series are independent and identically distributed (i.i.d.). This assumption restricts their applications to social networked time series analysis, since a user’s actions can be correlated to his/her social affiliations. In this paper we propose a new Network Regularized Least Squares (NetRLS) feature selection model that combines typical time series data and user network data for analysis. Experiments on real-world networked time series Twitter and DBLP data demonstrate the performance of the proposed method. NetRLS performs better than LTS, the state-of-the-art time series feature selection approach, on real-world data.
Tasks	Feature Selection, Time Series, Time Series Analysis, Time Series Classification
Published	2016-12-20
URL	http://arxiv.org/abs/1612.06856v2
PDF	http://arxiv.org/pdf/1612.06856v2.pdf
PWC	https://paperswithcode.com/paper/temporal-feature-selection-on-networked-time
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LSTM-Based Predictions for Proactive Information Retrieval


Title	LSTM-Based Predictions for Proactive Information Retrieval
Authors	Petri Luukkonen, Markus Koskela, Patrik Floréen
Abstract	We describe a method for proactive information retrieval targeted at retrieving relevant information during a writing task. In our method, the current task and the needs of the user are estimated, and the potential next steps are unobtrusively predicted based on the user’s past actions. We focus on the task of writing, in which the user is coalescing previously collected information into a text. Our proactive system automatically recommends the user relevant background information. The proposed system incorporates text input prediction using a long short-term memory (LSTM) network. We present simulations, which show that the system is able to reach higher precision values in an exploratory search setting compared to both a baseline and a comparison system.
Tasks	Information Retrieval
Published	2016-06-20
URL	http://arxiv.org/abs/1606.06137v1
PDF	http://arxiv.org/pdf/1606.06137v1.pdf
PWC	https://paperswithcode.com/paper/lstm-based-predictions-for-proactive
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Recent advances in content based video copy detection


Title	Recent advances in content based video copy detection
Authors	Sanket Shinde, Girija Chiddarwar
Abstract	With the immense number of videos being uploaded to the video sharing sites, issue of copyright infringement arises with uploading of illicit copies or transformed versions of original video. Thus safeguarding copyright of digital media has become matter of concern. To address this concern, it is obliged to have a video copy detection system which is sufficiently robust to detect these transformed videos with ability to pinpoint location of copied segments. This paper outlines recent advancement in content based video copy detection, mainly focusing on different visual features employed by video copy detection systems. Finally we evaluate performance of existing video copy detection systems.
Tasks
Published	2016-10-28
URL	http://arxiv.org/abs/1610.09087v1
PDF	http://arxiv.org/pdf/1610.09087v1.pdf
PWC	https://paperswithcode.com/paper/recent-advances-in-content-based-video-copy
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Single-Molecule Protein Identification by Sub-Nanopore Sensors


Title	Single-Molecule Protein Identification by Sub-Nanopore Sensors
Authors	Mikhail Kolmogorov, Eamonn Kennedy, Zhuxin Dong, Gregory Timp, Pavel Pevzner
Abstract	Recent advances in top-down mass spectrometry enabled identification of intact proteins, but this technology still faces challenges. For example, top-down mass spectrometry suffers from a lack of sensitivity since the ion counts for a single fragmentation event are often low. In contrast, nanopore technology is exquisitely sensitive to single intact molecules, but it has only been successfully applied to DNA sequencing, so far. Here, we explore the potential of sub-nanopores for single-molecule protein identification (SMPI) and describe an algorithm for identification of the electrical current blockade signal (nanospectrum) resulting from the translocation of a denaturated, linearly charged protein through a sub-nanopore. The analysis of identification p-values suggests that the current technology is already sufficient for matching nanospectra against small protein databases, e.g., protein identification in bacterial proteomes.
Tasks
Published	2016-04-08
URL	http://arxiv.org/abs/1604.02270v2
PDF	http://arxiv.org/pdf/1604.02270v2.pdf
PWC	https://paperswithcode.com/paper/single-molecule-protein-identification-by-sub
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Automatic Identification of Retinal Arteries and Veins in Fundus Images using Local Binary Patterns


Title	Automatic Identification of Retinal Arteries and Veins in Fundus Images using Local Binary Patterns
Authors	Nima Hatami, Michael Goldbaum
Abstract	Artery and vein (AV) classification of retinal images is a key to necessary tasks, such as automated measurement of arteriolar-to-venular diameter ratio (AVR). This paper comprehensively reviews the state-of-the art in AV classification methods. To improve on previous methods, a new Local Bi- nary Pattern-based method (LBP) is proposed. Beside its simplicity, LBP is robust against low contrast and low quality fundus images; and it helps the process by including additional AV texture and shape information. Experimental results compare the performance of the new method with the state-of-the art; and also methods with different feature extraction and classification schemas.
Tasks
Published	2016-05-03
URL	http://arxiv.org/abs/1605.00763v1
PDF	http://arxiv.org/pdf/1605.00763v1.pdf
PWC	https://paperswithcode.com/paper/automatic-identification-of-retinal-arteries
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Mode Regularized Generative Adversarial Networks


Title	Mode Regularized Generative Adversarial Networks
Authors	Tong Che, Yanran Li, Athul Paul Jacob, Yoshua Bengio, Wenjie Li
Abstract	Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes. We argue that these bad behaviors of GANs are due to the very particular functional shape of the trained discriminators in high dimensional spaces, which can easily make training stuck or push probability mass in the wrong direction, towards that of higher concentration than that of the data generating distribution. We introduce several ways of regularizing the objective, which can dramatically stabilize the training of GAN models. We also show that our regularizers can help the fair distribution of probability mass across the modes of the data generating distribution, during the early phases of training and thus providing a unified solution to the missing modes problem.
Tasks
Published	2016-12-07
URL	http://arxiv.org/abs/1612.02136v5
PDF	http://arxiv.org/pdf/1612.02136v5.pdf
PWC	https://paperswithcode.com/paper/mode-regularized-generative-adversarial
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On the Emergence of Shortest Paths by Reinforced Random Walks


Title	On the Emergence of Shortest Paths by Reinforced Random Walks
Authors	Daniel R. Figueiredo, Michele Garetto
Abstract	The co-evolution between network structure and functional performance is a fundamental and challenging problem whose complexity emerges from the intrinsic interdependent nature of structure and function. Within this context, we investigate the interplay between the efficiency of network navigation (i.e., path lengths) and network structure (i.e., edge weights). We propose a simple and tractable model based on iterative biased random walks where edge weights increase over time as function of the traversed path length. Under mild assumptions, we prove that biased random walks will eventually only traverse shortest paths in their journey towards the destination. We further characterize the transient regime proving that the probability to traverse non-shortest paths decays according to a power-law. We also highlight various properties in this dynamic, such as the trade-off between exploration and convergence, and preservation of initial network plasticity. We believe the proposed model and results can be of interest to various domains where biased random walks and decentralized navigation have been applied.
Tasks
Published	2016-05-09
URL	http://arxiv.org/abs/1605.02619v1
PDF	http://arxiv.org/pdf/1605.02619v1.pdf
PWC	https://paperswithcode.com/paper/on-the-emergence-of-shortest-paths-by
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Multiple Human Tracking in RGB-D Data: A Survey


Title	Multiple Human Tracking in RGB-D Data: A Survey
Authors	Massimo Camplani, Adeline Paiement, Majid Mirmehdi, Dima Damen, Sion Hannuna, Tilo Burghardt, Lili Tao
Abstract	Multiple human tracking (MHT) is a fundamental task in many computer vision applications. Appearance-based approaches, primarily formulated on RGB data, are constrained and affected by problems arising from occlusions and/or illumination variations. In recent years, the arrival of cheap RGB-Depth (RGB-D) devices has {led} to many new approaches to MHT, and many of these integrate color and depth cues to improve each and every stage of the process. In this survey, we present the common processing pipeline of these methods and review their methodology based (a) on how they implement this pipeline and (b) on what role depth plays within each stage of it. We identify and introduce existing, publicly available, benchmark datasets and software resources that fuse color and depth data for MHT. Finally, we present a brief comparative evaluation of the performance of those works that have applied their methods to these datasets.
Tasks
Published	2016-06-14
URL	http://arxiv.org/abs/1606.04450v1
PDF	http://arxiv.org/pdf/1606.04450v1.pdf
PWC	https://paperswithcode.com/paper/multiple-human-tracking-in-rgb-d-data-a
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Adversarial Message Passing For Graphical Models


Title	Adversarial Message Passing For Graphical Models
Authors	Theofanis Karaletsos
Abstract	Bayesian inference on structured models typically relies on the ability to infer posterior distributions of underlying hidden variables. However, inference in implicit models or complex posterior distributions is hard. A popular tool for learning implicit models are generative adversarial networks (GANs) which learn parameters of generators by fooling discriminators. Typically, GANs are considered to be models themselves and are not understood in the context of inference. Current techniques rely on inefficient global discrimination of joint distributions to perform learning, or only consider discriminating a single output variable. We overcome these limitations by treating GANs as a basis for likelihood-free inference in generative models and generalize them to Bayesian posterior inference over factor graphs. We propose local learning rules based on message passing minimizing a global divergence criterion involving cooperating local adversaries used to sidestep explicit likelihood evaluations. This allows us to compose models and yields a unified inference and learning framework for adversarial learning. Our framework treats model specification and inference separately and facilitates richly structured models within the family of Directed Acyclic Graphs, including components such as intractable likelihoods, non-differentiable models, simulators and generally cumbersome models. A key result of our treatment is the insight that Bayesian inference on structured models can be performed only with sampling and discrimination when using nonparametric variational families, without access to explicit distributions. As a side-result, we discuss the link to likelihood maximization. These approaches hold promise to be useful in the toolbox of probabilistic modelers and enrich the gamut of current probabilistic programming applications.
Tasks	Bayesian Inference, Probabilistic Programming
Published	2016-12-15
URL	http://arxiv.org/abs/1612.05048v1
PDF	http://arxiv.org/pdf/1612.05048v1.pdf
PWC	https://paperswithcode.com/paper/adversarial-message-passing-for-graphical
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Rule Extraction Algorithm for Deep Neural Networks: A Review


Title	Rule Extraction Algorithm for Deep Neural Networks: A Review
Authors	Tameru Hailesilassie
Abstract	Despite the highest classification accuracy in wide varieties of application areas, artificial neural network has one disadvantage. The way this Network comes to a decision is not easily comprehensible. The lack of explanation ability reduces the acceptability of neural network in data mining and decision system. This drawback is the reason why researchers have proposed many rule extraction algorithms to solve the problem. Recently, Deep Neural Network (DNN) is achieving a profound result over the standard neural network for classification and recognition problems. It is a hot machine learning area proven both useful and innovative. This paper has thoroughly reviewed various rule extraction algorithms, considering the classification scheme: decompositional, pedagogical, and eclectics. It also presents the evaluation of these algorithms based on the neural network structure with which the algorithm is intended to work. The main contribution of this review is to show that there is a limited study of rule extraction algorithm from DNN.
Tasks
Published	2016-09-16
URL	http://arxiv.org/abs/1610.05267v1
PDF	http://arxiv.org/pdf/1610.05267v1.pdf
PWC	https://paperswithcode.com/paper/rule-extraction-algorithm-for-deep-neural
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Edge Detection for Pattern Recognition: A Survey


Title	Edge Detection for Pattern Recognition: A Survey
Authors	Alex Pappachen James
Abstract	This review provides an overview of the literature on the edge detection methods for pattern recognition that inspire from the understanding of human vision. We note that edge detection is one of the most fundamental process within the low level vision and provides the basis for the higher level visual intelligence in primates. The recognition of the patterns within the images relate closely to the spatiotemporal processes of edge formations, and its implementation needs a crossdisciplanry approach in neuroscience, computing and pattern recognition. In this review, the edge detectors are grouped in as edge features, gradients and sketch models, and some example applications are provided for reference. We note a significant increase in the amount of published research in the last decade that utilizes edge features in a wide range of problems in computer vision and image understanding having a direct implication to pattern recognition with images.
Tasks	Edge Detection
Published	2016-02-15
URL	http://arxiv.org/abs/1602.04593v1
PDF	http://arxiv.org/pdf/1602.04593v1.pdf
PWC	https://paperswithcode.com/paper/edge-detection-for-pattern-recognition-a
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Integrating Distributional Lexical Contrast into Word Embeddings for Antonym-Synonym Distinction


Title	Integrating Distributional Lexical Contrast into Word Embeddings for Antonym-Synonym Distinction
Authors	Kim Anh Nguyen, Sabine Schulte im Walde, Ngoc Thang Vu
Abstract	We propose a novel vector representation that integrates lexical contrast into distributional vectors and strengthens the most salient features for determining degrees of word similarity. The improved vectors significantly outperform standard models and distinguish antonyms from synonyms with an average precision of 0.66-0.76 across word classes (adjectives, nouns, verbs). Moreover, we integrate the lexical contrast vectors into the objective function of a skip-gram model. The novel embedding outperforms state-of-the-art models on predicting word similarities in SimLex-999, and on distinguishing antonyms from synonyms.
Tasks	Word Embeddings
Published	2016-05-25
URL	http://arxiv.org/abs/1605.07766v1
PDF	http://arxiv.org/pdf/1605.07766v1.pdf
PWC	https://paperswithcode.com/paper/integrating-distributional-lexical-contrast
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