July 27, 2019

3141 words 15 mins read

Paper Group ANR 626

Navigation Objects Extraction for Better Content Structure Understanding. Motion Prediction Under Multimodality with Conditional Stochastic Networks. A hybrid primal heuristic for Robust Multiperiod Network Design. On Security and Sparsity of Linear Classifiers for Adversarial Settings. Probabilistic Multigraph Modeling for Improving the Quality of …


Title	Navigation Objects Extraction for Better Content Structure Understanding
Authors	Kui Zhao, Bangpeng Li, Zilun Peng, Jiajun Bu, Can Wang
Abstract	Existing works for extracting navigation objects from webpages focus on navigation menus, so as to reveal the information architecture of the site. However, web 2.0 sites such as social networks, e-commerce portals etc. are making the understanding of the content structure in a web site increasingly difficult. Dynamic and personalized elements such as top stories, recommended list in a webpage are vital to the understanding of the dynamic nature of web 2.0 sites. To better understand the content structure in web 2.0 sites, in this paper we propose a new extraction method for navigation objects in a webpage. Our method will extract not only the static navigation menus, but also the dynamic and personalized page-specific navigation lists. Since the navigation objects in a webpage naturally come in blocks, we first cluster hyperlinks into different blocks by exploiting spatial locations of hyperlinks, the hierarchical structure of the DOM-tree and the hyperlink density. Then we identify navigation objects from those blocks using the SVM classifier with novel features such as anchor text lengths etc. Experiments on real-world data sets with webpages from various domains and styles verified the effectiveness of our method.
Tasks
Published	2017-08-26
URL	http://arxiv.org/abs/1708.07940v1
PDF	http://arxiv.org/pdf/1708.07940v1.pdf
PWC	https://paperswithcode.com/paper/navigation-objects-extraction-for-better
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Motion Prediction Under Multimodality with Conditional Stochastic Networks


Title	Motion Prediction Under Multimodality with Conditional Stochastic Networks
Authors	Katerina Fragkiadaki, Jonathan Huang, Alex Alemi, Sudheendra Vijayanarasimhan, Susanna Ricco, Rahul Sukthankar
Abstract	Given a visual history, multiple future outcomes for a video scene are equally probable, in other words, the distribution of future outcomes has multiple modes. Multimodality is notoriously hard to handle by standard regressors or classifiers: the former regress to the mean and the latter discretize a continuous high dimensional output space. In this work, we present stochastic neural network architectures that handle such multimodality through stochasticity: future trajectories of objects, body joints or frames are represented as deep, non-linear transformations of random (as opposed to deterministic) variables. Such random variables are sampled from simple Gaussian distributions whose means and variances are parametrized by the output of convolutional encoders over the visual history. We introduce novel convolutional architectures for predicting future body joint trajectories that outperform fully connected alternatives \cite{DBLP:journals/corr/WalkerDGH16}. We introduce stochastic spatial transformers through optical flow warping for predicting future frames, which outperform their deterministic equivalents \cite{DBLP:journals/corr/PatrauceanHC15}. Training stochastic networks involves an intractable marginalization over stochastic variables. We compare various training schemes that handle such marginalization through a) straightforward sampling from the prior, b) conditional variational autoencoders \cite{NIPS2015_5775,DBLP:journals/corr/WalkerDGH16}, and, c) a proposed K-best-sample loss that penalizes the best prediction under a fixed “prediction budget”. We show experimental results on object trajectory prediction, human body joint trajectory prediction and video prediction under varying future uncertainty, validating quantitatively and qualitatively our architectural choices and training schemes.
Tasks	motion prediction, Optical Flow Estimation, Trajectory Prediction, Video Prediction
Published	2017-05-05
URL	http://arxiv.org/abs/1705.02082v1
PDF	http://arxiv.org/pdf/1705.02082v1.pdf
PWC	https://paperswithcode.com/paper/motion-prediction-under-multimodality-with
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A hybrid primal heuristic for Robust Multiperiod Network Design


Title	A hybrid primal heuristic for Robust Multiperiod Network Design
Authors	Fabio D’Andreagiovanni, Jonatan Krolikowski, Jonad Pulaj
Abstract	We investigate the Robust Multiperiod Network Design Problem, a generalization of the classical Capacitated Network Design Problem that additionally considers multiple design periods and provides solutions protected against traffic uncertainty. Given the intrinsic difficulty of the problem, which proves challenging even for state-of-the art commercial solvers, we propose a hybrid primal heuristic based on the combination of ant colony optimization and an exact large neighborhood search. Computational experiments on a set of realistic instances from the SNDlib show that our heuristic can find solutions of extremely good quality with low optimality gap.
Tasks
Published	2017-04-22
URL	http://arxiv.org/abs/1704.06847v1
PDF	http://arxiv.org/pdf/1704.06847v1.pdf
PWC	https://paperswithcode.com/paper/a-hybrid-primal-heuristic-for-robust
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On Security and Sparsity of Linear Classifiers for Adversarial Settings


Title	On Security and Sparsity of Linear Classifiers for Adversarial Settings
Authors	Ambra Demontis, Paolo Russu, Battista Biggio, Giorgio Fumera, Fabio Roli
Abstract	Machine-learning techniques are widely used in security-related applications, like spam and malware detection. However, in such settings, they have been shown to be vulnerable to adversarial attacks, including the deliberate manipulation of data at test time to evade detection. In this work, we focus on the vulnerability of linear classifiers to evasion attacks. This can be considered a relevant problem, as linear classifiers have been increasingly used in embedded systems and mobile devices for their low processing time and memory requirements. We exploit recent findings in robust optimization to investigate the link between regularization and security of linear classifiers, depending on the type of attack. We also analyze the relationship between the sparsity of feature weights, which is desirable for reducing processing cost, and the security of linear classifiers. We further propose a novel octagonal regularizer that allows us to achieve a proper trade-off between them. Finally, we empirically show how this regularizer can improve classifier security and sparsity in real-world application examples including spam and malware detection.
Tasks	Malware Detection
Published	2017-08-31
URL	http://arxiv.org/abs/1709.00045v1
PDF	http://arxiv.org/pdf/1709.00045v1.pdf
PWC	https://paperswithcode.com/paper/on-security-and-sparsity-of-linear
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Probabilistic Multigraph Modeling for Improving the Quality of Crowdsourced Affective Data


Title	Probabilistic Multigraph Modeling for Improving the Quality of Crowdsourced Affective Data
Authors	Jianbo Ye, Jia Li, Michelle G. Newman, Reginald B. Adams Jr., James Z. Wang
Abstract	We proposed a probabilistic approach to joint modeling of participants’ reliability and humans’ regularity in crowdsourced affective studies. Reliability measures how likely a subject will respond to a question seriously; and regularity measures how often a human will agree with other seriously-entered responses coming from a targeted population. Crowdsourcing-based studies or experiments, which rely on human self-reported affect, pose additional challenges as compared with typical crowdsourcing studies that attempt to acquire concrete non-affective labels of objects. The reliability of participants has been massively pursued for typical non-affective crowdsourcing studies, whereas the regularity of humans in an affective experiment in its own right has not been thoroughly considered. It has been often observed that different individuals exhibit different feelings on the same test question, which does not have a sole correct response in the first place. High reliability of responses from one individual thus cannot conclusively result in high consensus across individuals. Instead, globally testing consensus of a population is of interest to investigators. Built upon the agreement multigraph among tasks and workers, our probabilistic model differentiates subject regularity from population reliability. We demonstrate the method’s effectiveness for in-depth robust analysis of large-scale crowdsourced affective data, including emotion and aesthetic assessments collected by presenting visual stimuli to human subjects.
Tasks
Published	2017-01-04
URL	http://arxiv.org/abs/1701.01096v2
PDF	http://arxiv.org/pdf/1701.01096v2.pdf
PWC	https://paperswithcode.com/paper/probabilistic-multigraph-modeling-for
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Dictionary-based Tensor Canonical Polyadic Decomposition


Title	Dictionary-based Tensor Canonical Polyadic Decomposition
Authors	Jérémy E. Cohen, Nicolas Gillis
Abstract	To ensure interpretability of extracted sources in tensor decomposition, we introduce in this paper a dictionary-based tensor canonical polyadic decomposition which enforces one factor to belong exactly to a known dictionary. A new formulation of sparse coding is proposed which enables high dimensional tensors dictionary-based canonical polyadic decomposition. The benefits of using a dictionary in tensor decomposition models are explored both in terms of parameter identifiability and estimation accuracy. Performances of the proposed algorithms are evaluated on the decomposition of simulated data and the unmixing of hyperspectral images.
Tasks
Published	2017-04-03
URL	http://arxiv.org/abs/1704.00541v2
PDF	http://arxiv.org/pdf/1704.00541v2.pdf
PWC	https://paperswithcode.com/paper/dictionary-based-tensor-canonical-polyadic
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Classification and Geometry of General Perceptual Manifolds


Title	Classification and Geometry of General Perceptual Manifolds
Authors	SueYeon Chung, Daniel D. Lee, Haim Sompolinsky
Abstract	Perceptual manifolds arise when a neural population responds to an ensemble of sensory signals associated with different physical features (e.g., orientation, pose, scale, location, and intensity) of the same perceptual object. Object recognition and discrimination requires classifying the manifolds in a manner that is insensitive to variability within a manifold. How neuronal systems give rise to invariant object classification and recognition is a fundamental problem in brain theory as well as in machine learning. Here we study the ability of a readout network to classify objects from their perceptual manifold representations. We develop a statistical mechanical theory for the linear classification of manifolds with arbitrary geometry revealing a remarkable relation to the mathematics of conic decomposition. Novel geometrical measures of manifold radius and manifold dimension are introduced which can explain the classification capacity for manifolds of various geometries. The general theory is demonstrated on a number of representative manifolds, including L2 ellipsoids prototypical of strictly convex manifolds, L1 balls representing polytopes consisting of finite sample points, and orientation manifolds which arise from neurons tuned to respond to a continuous angle variable, such as object orientation. The effects of label sparsity on the classification capacity of manifolds are elucidated, revealing a scaling relation between label sparsity and manifold radius. Theoretical predictions are corroborated by numerical simulations using recently developed algorithms to compute maximum margin solutions for manifold dichotomies. Our theory and its extensions provide a powerful and rich framework for applying statistical mechanics of linear classification to data arising from neuronal responses to object stimuli, as well as to artificial deep networks trained for object recognition tasks.
Tasks	Object Classification, Object Recognition
Published	2017-10-17
URL	http://arxiv.org/abs/1710.06487v3
PDF	http://arxiv.org/pdf/1710.06487v3.pdf
PWC	https://paperswithcode.com/paper/classification-and-geometry-of-general
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Inference on Auctions with Weak Assumptions on Information


Title	Inference on Auctions with Weak Assumptions on Information
Authors	Vasilis Syrgkanis, Elie Tamer, Juba Ziani
Abstract	Given a sample of bids from independent auctions, this paper examines the question of inference on auction fundamentals (e.g. valuation distributions, welfare measures) under weak assumptions on information structure. The question is important as it allows us to learn about the valuation distribution in a robust way, i.e., without assuming that a particular information structure holds across observations. We leverage the recent contributions of \cite{Bergemann2013} in the robust mechanism design literature that exploit the link between Bayesian Correlated Equilibria and Bayesian Nash Equilibria in incomplete information games to construct an econometrics framework for learning about auction fundamentals using observed data on bids. We showcase our construction of identified sets in private value and common value auctions. Our approach for constructing these sets inherits the computational simplicity of solving for correlated equilibria: checking whether a particular valuation distribution belongs to the identified set is as simple as determining whether a {\it linear} program is feasible. A similar linear program can be used to construct the identified set on various welfare measures and counterfactual objects. For inference and to summarize statistical uncertainty, we propose novel finite sample methods using tail inequalities that are used to construct confidence regions on sets. We also highlight methods based on Bayesian bootstrap and subsampling. A set of Monte Carlo experiments show adequate finite sample properties of our inference procedures. We illustrate our methods using data from OCS auctions.
Tasks
Published	2017-10-10
URL	http://arxiv.org/abs/1710.03830v2
PDF	http://arxiv.org/pdf/1710.03830v2.pdf
PWC	https://paperswithcode.com/paper/inference-on-auctions-with-weak-assumptions
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Framework

A Deterministic Global Optimization Method for Variational Inference


Title	A Deterministic Global Optimization Method for Variational Inference
Authors	Hachem Saddiki, Andrew C. Trapp, Patrick Flaherty
Abstract	Variational inference methods for latent variable statistical models have gained popularity because they are relatively fast, can handle large data sets, and have deterministic convergence guarantees. However, in practice it is unclear whether the fixed point identified by the variational inference algorithm is a local or a global optimum. Here, we propose a method for constructing iterative optimization algorithms for variational inference problems that are guaranteed to converge to the $\epsilon$-global variational lower bound on the log-likelihood. We derive inference algorithms for two variational approximations to a standard Bayesian Gaussian mixture model (BGMM). We present a minimal data set for empirically testing convergence and show that a variational inference algorithm frequently converges to a local optimum while our algorithm always converges to the globally optimal variational lower bound. We characterize the loss incurred by choosing a non-optimal variational approximation distribution suggesting that selection of the approximating variational distribution deserves as much attention as the selection of the original statistical model for a given data set.
Tasks
Published	2017-03-21
URL	http://arxiv.org/abs/1703.07169v1
PDF	http://arxiv.org/pdf/1703.07169v1.pdf
PWC	https://paperswithcode.com/paper/a-deterministic-global-optimization-method
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Monte Carlo Action Programming


Title	Monte Carlo Action Programming
Authors	Lenz Belzner
Abstract	This paper proposes Monte Carlo Action Programming, a programming language framework for autonomous systems that act in large probabilistic state spaces with high branching factors. It comprises formal syntax and semantics of a nondeterministic action programming language. The language is interpreted stochastically via Monte Carlo Tree Search. Effectiveness of the approach is shown empirically.
Tasks
Published	2017-02-25
URL	http://arxiv.org/abs/1702.08441v1
PDF	http://arxiv.org/pdf/1702.08441v1.pdf
PWC	https://paperswithcode.com/paper/monte-carlo-action-programming
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Wasserstein Distributional Robustness and Regularization in Statistical Learning


Title	Wasserstein Distributional Robustness and Regularization in Statistical Learning
Authors	Rui Gao, Xi Chen, Anton J. Kleywegt
Abstract	A central question in statistical learning is to design algorithms that not only perform well on training data, but also generalize to new and unseen data. In this paper, we tackle this question by formulating a distributionally robust stochastic optimization (DRSO) problem, which seeks a solution that minimizes the worst-case expected loss over a family of distributions that are close to the empirical distribution in Wasserstein distances. We establish a connection between such Wasserstein DRSO and regularization. More precisely, we identify a broad class of loss functions, for which the Wasserstein DRSO is asymptotically equivalent to a regularization problem with a gradient-norm penalty. Such relation provides new interpretations for problems involving regularization, including a great number of statistical learning problems and discrete choice models (e.g. multinomial logit). The connection suggests a principled way to regularize high-dimensional, non-convex problems. This is demonstrated through the training of Wasserstein generative adversarial networks in deep learning.
Tasks	Stochastic Optimization
Published	2017-12-17
URL	http://arxiv.org/abs/1712.06050v2
PDF	http://arxiv.org/pdf/1712.06050v2.pdf
PWC	https://paperswithcode.com/paper/wasserstein-distributional-robustness-and
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Framework

Dialectometric analysis of language variation in Twitter


Title	Dialectometric analysis of language variation in Twitter
Authors	Gonzalo Donoso, David Sanchez
Abstract	In the last few years, microblogging platforms such as Twitter have given rise to a deluge of textual data that can be used for the analysis of informal communication between millions of individuals. In this work, we propose an information-theoretic approach to geographic language variation using a corpus based on Twitter. We test our models with tens of concepts and their associated keywords detected in Spanish tweets geolocated in Spain. We employ dialectometric measures (cosine similarity and Jensen-Shannon divergence) to quantify the linguistic distance on the lexical level between cells created in a uniform grid over the map. This can be done for a single concept or in the general case taking into account an average of the considered variants. The latter permits an analysis of the dialects that naturally emerge from the data. Interestingly, our results reveal the existence of two dialect macrovarieties. The first group includes a region-specific speech spoken in small towns and rural areas whereas the second cluster encompasses cities that tend to use a more uniform variety. Since the results obtained with the two different metrics qualitatively agree, our work suggests that social media corpora can be efficiently used for dialectometric analyses.
Tasks
Published	2017-02-22
URL	http://arxiv.org/abs/1702.06777v1
PDF	http://arxiv.org/pdf/1702.06777v1.pdf
PWC	https://paperswithcode.com/paper/dialectometric-analysis-of-language-variation
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Robust non-local means filter for ultrasound image denoising


Title	Robust non-local means filter for ultrasound image denoising
Authors	Hamid Reza Shahdoosti
Abstract	This paper introduces a new approach to non-local means image denoising. Instead of using all pixels located in the search window for estimating the value of a pixel, we identify the highly corrupted pixels and assign less weight to these pixels. This method is called robust non-local means. Numerical and subjective evaluations using ultrasound images show good performances of the proposed denoising method in recovering the shape of edges and important detailed components, in comparison to traditional ultrasound image denoising methods
Tasks	Denoising, Image Denoising
Published	2017-09-26
URL	http://arxiv.org/abs/1710.01245v1
PDF	http://arxiv.org/pdf/1710.01245v1.pdf
PWC	https://paperswithcode.com/paper/robust-non-local-means-filter-for-ultrasound
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Poverty Mapping Using Convolutional Neural Networks Trained on High and Medium Resolution Satellite Images, With an Application in Mexico


Title	Poverty Mapping Using Convolutional Neural Networks Trained on High and Medium Resolution Satellite Images, With an Application in Mexico
Authors	Boris Babenko, Jonathan Hersh, David Newhouse, Anusha Ramakrishnan, Tom Swartz
Abstract	Mapping the spatial distribution of poverty in developing countries remains an important and costly challenge. These “poverty maps” are key inputs for poverty targeting, public goods provision, political accountability, and impact evaluation, that are all the more important given the geographic dispersion of the remaining bottom billion severely poor individuals. In this paper we train Convolutional Neural Networks (CNNs) to estimate poverty directly from high and medium resolution satellite images. We use both Planet and Digital Globe imagery with spatial resolutions of 3-5 sq. m. and 50 sq. cm. respectively, covering all 2 million sq. km. of Mexico. Benchmark poverty estimates come from the 2014 MCS-ENIGH combined with the 2015 Intercensus and are used to estimate poverty rates for 2,456 Mexican municipalities. CNNs are trained using the 896 municipalities in the 2014 MCS-ENIGH. We experiment with several architectures (GoogleNet, VGG) and use GoogleNet as a final architecture where weights are fine-tuned from ImageNet. We find that 1) the best models, which incorporate satellite-estimated land use as a predictor, explain approximately 57% of the variation in poverty in a validation sample of 10 percent of MCS-ENIGH municipalities; 2) Across all MCS-ENIGH municipalities explanatory power reduces to 44% in a CNN prediction and landcover model; 3) Predicted poverty from the CNN predictions alone explains 47% of the variation in poverty in the validation sample, and 37% over all MCS-ENIGH municipalities; 4) In urban areas we see slight improvements from using Digital Globe versus Planet imagery, which explain 61% and 54% of poverty variation respectively. We conclude that CNNs can be trained end-to-end on satellite imagery to estimate poverty, although there is much work to be done to understand how the training process influences out of sample validation.
Tasks
Published	2017-11-16
URL	http://arxiv.org/abs/1711.06323v1
PDF	http://arxiv.org/pdf/1711.06323v1.pdf
PWC	https://paperswithcode.com/paper/poverty-mapping-using-convolutional-neural
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Population-based Respiratory 4D Motion Atlas Construction and its Application for VR Simulations of Liver Punctures


Title	Population-based Respiratory 4D Motion Atlas Construction and its Application for VR Simulations of Liver Punctures
Authors	Andre Mastmeyer, Matthias Wilms, Heinz Handels
Abstract	Virtual reality (VR) training simulators of liver needle insertion in the hepatic area of breathing virtual patients currently need 4D data acquisitions as a prerequisite. Here, first a population-based breathing virtual patient 4D atlas can be built and second the requirement of a dose-relevant or expensive acquisition of a 4D data set for a new static 3D patient can be mitigated by warping the mean atlas motion. The breakthrough contribution of this work is the construction and reuse of population-based learned 4D motion models.
Tasks
Published	2017-12-05
URL	http://arxiv.org/abs/1712.01893v2
PDF	http://arxiv.org/pdf/1712.01893v2.pdf
PWC	https://paperswithcode.com/paper/population-based-respiratory-4d-motion-atlas
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