July 28, 2019

2755 words 13 mins read

Paper Group ANR 434

On the estimation of correlation in a binary sequence model. Joint Structured Learning and Predictions under Logical Constraints in Conditional Random Fields. Snapshot Difference Imaging using Time-of-Flight Sensors. Generating Query Suggestions to Support Task-Based Search. Depth Creates No Bad Local Minima. Asymptotic Allocation Rules for a Class …

On the estimation of correlation in a binary sequence model


Title	On the estimation of correlation in a binary sequence model
Authors	Haolei Weng, Yang Feng
Abstract	We consider a binary sequence generated by thresholding a hidden continuous sequence. The hidden variables are assumed to have a compound symmetry covariance structure with a single parameter characterizing the common correlation. We study the parameter estimation problem under such one-parameter models. We demonstrate that maximizing the likelihood function does not yield consistent estimates for the correlation. We then formally prove the nonestimability of the parameter by deriving a non-vanishing minimax lower bound. This counter-intuitive phenomenon provides an interesting insight that one-bit information of each latent variable is not sufficient to consistently recover their common correlation. On the other hand, we further show that trinary data generated from the hidden variables can consistently estimate the correlation with parametric convergence rate. Thus we reveal a phase transition phenomenon regarding the discretization of latent continuous variables while preserving the estimability of the correlation. Numerical experiments are performed to validate the conclusions.
Tasks
Published	2017-12-27
URL	https://arxiv.org/abs/1712.09694v2
PDF	https://arxiv.org/pdf/1712.09694v2.pdf
PWC	https://paperswithcode.com/paper/a-note-on-estimation-in-a-simple-probit-model
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Joint Structured Learning and Predictions under Logical Constraints in Conditional Random Fields


Title	Joint Structured Learning and Predictions under Logical Constraints in Conditional Random Fields
Authors	Jean-Luc Meunier
Abstract	This paper is concerned with structured machine learning, in a supervised machine learning context. It discusses how to make joint structured learning on interdependent objects of different nature, as well as how to enforce logical con-straints when predicting labels. We explain how this need arose in a Document Understanding task. We then discuss a general extension to Conditional Random Field (CRF) for this purpose and present the contributed open source implementation on top of the open source PyStruct library. We evaluate its performance on a publicly available dataset.
Tasks
Published	2017-08-25
URL	http://arxiv.org/abs/1708.07644v1
PDF	http://arxiv.org/pdf/1708.07644v1.pdf
PWC	https://paperswithcode.com/paper/joint-structured-learning-and-predictions
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Snapshot Difference Imaging using Time-of-Flight Sensors


Title	Snapshot Difference Imaging using Time-of-Flight Sensors
Authors	Clara Callenberg, Felix Heide, Gordon Wetzstein, Matthias Hullin
Abstract	Computational photography encompasses a diversity of imaging techniques, but one of the core operations performed by many of them is to compute image differences. An intuitive approach to computing such differences is to capture several images sequentially and then process them jointly. Usually, this approach leads to artifacts when recording dynamic scenes. In this paper, we introduce a snapshot difference imaging approach that is directly implemented in the sensor hardware of emerging time-of-flight cameras. With a variety of examples, we demonstrate that the proposed snapshot difference imaging technique is useful for direct-global illumination separation, for direct imaging of spatial and temporal image gradients, for direct depth edge imaging, and more.
Tasks
Published	2017-05-19
URL	http://arxiv.org/abs/1705.07108v1
PDF	http://arxiv.org/pdf/1705.07108v1.pdf
PWC	https://paperswithcode.com/paper/snapshot-difference-imaging-using-time-of
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Generating Query Suggestions to Support Task-Based Search


Title	Generating Query Suggestions to Support Task-Based Search
Authors	Darío Garigliotti, Krisztian Balog
Abstract	We address the problem of generating query suggestions to support users in completing their underlying tasks (which motivated them to search in the first place). Given an initial query, these query suggestions should provide a coverage of possible subtasks the user might be looking for. We propose a probabilistic modeling framework that obtains keyphrases from multiple sources and generates query suggestions from these keyphrases. Using the test suites of the TREC Tasks track, we evaluate and analyze each component of our model.
Tasks
Published	2017-08-28
URL	http://arxiv.org/abs/1708.08289v1
PDF	http://arxiv.org/pdf/1708.08289v1.pdf
PWC	https://paperswithcode.com/paper/generating-query-suggestions-to-support-task
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Depth Creates No Bad Local Minima


Title	Depth Creates No Bad Local Minima
Authors	Haihao Lu, Kenji Kawaguchi
Abstract	In deep learning, \textit{depth}, as well as \textit{nonlinearity}, create non-convex loss surfaces. Then, does depth alone create bad local minima? In this paper, we prove that without nonlinearity, depth alone does not create bad local minima, although it induces non-convex loss surface. Using this insight, we greatly simplify a recently proposed proof to show that all of the local minima of feedforward deep linear neural networks are global minima. Our theoretical results generalize previous results with fewer assumptions, and this analysis provides a method to show similar results beyond square loss in deep linear models.
Tasks
Published	2017-02-27
URL	http://arxiv.org/abs/1702.08580v2
PDF	http://arxiv.org/pdf/1702.08580v2.pdf
PWC	https://paperswithcode.com/paper/depth-creates-no-bad-local-minima
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Asymptotic Allocation Rules for a Class of Dynamic Multi-armed Bandit Problems


Title	Asymptotic Allocation Rules for a Class of Dynamic Multi-armed Bandit Problems
Authors	T. W. U. Madhushani, D. H. S. Maithripala, N. E. Leonard
Abstract	This paper presents a class of Dynamic Multi-Armed Bandit problems where the reward can be modeled as the noisy output of a time varying linear stochastic dynamic system that satisfies some boundedness constraints. The class allows many seemingly different problems with time varying option characteristics to be considered in a single framework. It also opens up the possibility of considering many new problems of practical importance. For instance it affords the simultaneous consideration of temporal option unavailabilities and the depen- dencies between options with time varying option characteristics in a seamless manner. We show that, for this class of problems, the combination of any Upper Confidence Bound type algorithm with any efficient reward estimator for the expected reward ensures the logarithmic bounding of the expected cumulative regret. We demonstrate the versatility of the approach by the explicit consideration of a new example of practical interest.
Tasks
Published	2017-10-02
URL	http://arxiv.org/abs/1710.00450v2
PDF	http://arxiv.org/pdf/1710.00450v2.pdf
PWC	https://paperswithcode.com/paper/asymptotic-allocation-rules-for-a-class-of
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SOFAR: large-scale association network learning


Title	SOFAR: large-scale association network learning
Authors	Yoshimasa Uematsu, Yingying Fan, Kun Chen, Jinchi Lv, Wei Lin
Abstract	Many modern big data applications feature large scale in both numbers of responses and predictors. Better statistical efficiency and scientific insights can be enabled by understanding the large-scale response-predictor association network structures via layers of sparse latent factors ranked by importance. Yet sparsity and orthogonality have been two largely incompatible goals. To accommodate both features, in this paper we suggest the method of sparse orthogonal factor regression (SOFAR) via the sparse singular value decomposition with orthogonality constrained optimization to learn the underlying association networks, with broad applications to both unsupervised and supervised learning tasks such as biclustering with sparse singular value decomposition, sparse principal component analysis, sparse factor analysis, and spare vector autoregression analysis. Exploiting the framework of convexity-assisted nonconvex optimization, we derive nonasymptotic error bounds for the suggested procedure characterizing the theoretical advantages. The statistical guarantees are powered by an efficient SOFAR algorithm with convergence property. Both computational and theoretical advantages of our procedure are demonstrated with several simulation and real data examples.
Tasks
Published	2017-04-26
URL	http://arxiv.org/abs/1704.08349v1
PDF	http://arxiv.org/pdf/1704.08349v1.pdf
PWC	https://paperswithcode.com/paper/sofar-large-scale-association-network
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Recurrent computations for visual pattern completion


Title	Recurrent computations for visual pattern completion
Authors	Hanlin Tang, Martin Schrimpf, Bill Lotter, Charlotte Moerman, Ana Paredes, Josue Ortega Caro, Walter Hardesty, David Cox, Gabriel Kreiman
Abstract	Making inferences from partial information constitutes a critical aspect of cognition. During visual perception, pattern completion enables recognition of poorly visible or occluded objects. We combined psychophysics, physiology and computational models to test the hypothesis that pattern completion is implemented by recurrent computations and present three pieces of evidence that are consistent with this hypothesis. First, subjects robustly recognized objects even when rendered <15% visible, but recognition was largely impaired when processing was interrupted by backward masking. Second, invasive physiological responses along the human ventral cortex exhibited visually selective responses to partially visible objects that were delayed compared to whole objects, suggesting the need for additional computations. These physiological delays were correlated with the effects of backward masking. Third, state-of-the-art feed-forward computational architectures were not robust to partial visibility. However, recognition performance was recovered when the model was augmented with attractor-based recurrent connectivity. These results provide a strong argument of plausibility for the role of recurrent computations in making visual inferences from partial information.
Tasks
Published	2017-06-07
URL	http://arxiv.org/abs/1706.02240v2
PDF	http://arxiv.org/pdf/1706.02240v2.pdf
PWC	https://paperswithcode.com/paper/recurrent-computations-for-visual-pattern
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Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach


Title	Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach
Authors	Slobodan Mitrović, Ilija Bogunovic, Ashkan Norouzi-Fard, Jakub Tarnawski, Volkan Cevher
Abstract	We study the classical problem of maximizing a monotone submodular function subject to a cardinality constraint k, with two additional twists: (i) elements arrive in a streaming fashion, and (ii) m items from the algorithm’s memory are removed after the stream is finished. We develop a robust submodular algorithm STAR-T. It is based on a novel partitioning structure and an exponentially decreasing thresholding rule. STAR-T makes one pass over the data and retains a short but robust summary. We show that after the removal of any m elements from the obtained summary, a simple greedy algorithm STAR-T-GREEDY that runs on the remaining elements achieves a constant-factor approximation guarantee. In two different data summarization tasks, we demonstrate that it matches or outperforms existing greedy and streaming methods, even if they are allowed the benefit of knowing the removed subset in advance.
Tasks	Data Summarization
Published	2017-11-07
URL	http://arxiv.org/abs/1711.02598v1
PDF	http://arxiv.org/pdf/1711.02598v1.pdf
PWC	https://paperswithcode.com/paper/streaming-robust-submodular-maximization-a
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Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction Mention Extraction


Title	Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction Mention Extraction
Authors	Shashank Gupta, Sachin Pawar, Nitin Ramrakhiyani, Girish Palshikar, Vasudeva Varma
Abstract	Social media is an useful platform to share health-related information due to its vast reach. This makes it a good candidate for public-health monitoring tasks, specifically for pharmacovigilance. We study the problem of extraction of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from twitter. Medical information extraction from social media is challenging, mainly due to short and highly information nature of text, as compared to more technical and formal medical reports. Current methods in ADR mention extraction relies on supervised learning methods, which suffers from labeled data scarcity problem. The State-of-the-art method uses deep neural networks, specifically a class of Recurrent Neural Network (RNN) which are Long-Short-Term-Memory networks (LSTMs) \cite{hochreiter1997long}. Deep neural networks, due to their large number of free parameters relies heavily on large annotated corpora for learning the end task. But in real-world, it is hard to get large labeled data, mainly due to heavy cost associated with manual annotation. Towards this end, we propose a novel semi-supervised learning based RNN model, which can leverage unlabeled data also present in abundance on social media. Through experiments we demonstrate the effectiveness of our method, achieving state-of-the-art performance in ADR mention extraction.
Tasks
Published	2017-09-06
URL	http://arxiv.org/abs/1709.01687v1
PDF	http://arxiv.org/pdf/1709.01687v1.pdf
PWC	https://paperswithcode.com/paper/semi-supervised-recurrent-neural-network-for
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Least square ellipsoid fitting using iterative orthogonal transformations


Title	Least square ellipsoid fitting using iterative orthogonal transformations
Authors	Amit Reza, Anand S. Sengupta
Abstract	We describe a generalised method for ellipsoid fitting against a minimum set of data points. The proposed method is numerically stable and applies to a wide range of ellipsoidal shapes, including highly elongated and arbitrarily oriented ellipsoids. This new method also provides for the retrieval of rotational angle and length of semi-axes of the fitted ellipsoids accurately. We demonstrate the efficacy of this algorithm on simulated data sets and also indicate its potential use in gravitational wave data analysis.
Tasks
Published	2017-04-17
URL	http://arxiv.org/abs/1704.04877v3
PDF	http://arxiv.org/pdf/1704.04877v3.pdf
PWC	https://paperswithcode.com/paper/least-square-ellipsoid-fitting-using
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FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go


Title	FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go
Authors	Chang-Shing Lee, Mei-Hui Wang, Sheng-Chi Yang, Pi-Hsia Hung, Su-Wei Lin, Nan Shuo, Naoyuki Kubota, Chun-Hsun Chou, Ping-Chiang Chou, Chia-Hsiu Kao
Abstract	In this paper, we demonstrate the application of Fuzzy Markup Language (FML) to construct an FML-based Dynamic Assessment Agent (FDAA), and we present an FML-based Human-Machine Cooperative System (FHMCS) for the game of Go. The proposed FDAA comprises an intelligent decision-making and learning mechanism, an intelligent game bot, a proximal development agent, and an intelligent agent. The intelligent game bot is based on the open-source code of Facebook Darkforest, and it features a representational state transfer application programming interface mechanism. The proximal development agent contains a dynamic assessment mechanism, a GoSocket mechanism, and an FML engine with a fuzzy knowledge base and rule base. The intelligent agent contains a GoSocket engine and a summarization agent that is based on the estimated win rate, real-time simulation number, and matching degree of predicted moves. Additionally, the FML for player performance evaluation and linguistic descriptions for game results commentary are presented. We experimentally verify and validate the performance of the FDAA and variants of the FHMCS by testing five games in 2016 and 60 games of Google Master Go, a new version of the AlphaGo program, in January 2017. The experimental results demonstrate that the proposed FDAA can work effectively for Go applications.
Tasks	Decision Making, Game of Go
Published	2017-07-16
URL	http://arxiv.org/abs/1707.04828v1
PDF	http://arxiv.org/pdf/1707.04828v1.pdf
PWC	https://paperswithcode.com/paper/fml-based-dynamic-assessment-agent-for-human
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Forest-based methods and ensemble model output statistics for rainfall ensemble forecasting


Title	Forest-based methods and ensemble model output statistics for rainfall ensemble forecasting
Authors	Maxime Taillardat, Anne-Laure Fougères, Philippe Naveau, Olivier Mestre
Abstract	Rainfall ensemble forecasts have to be skillful for both low precipitation and extreme events. We present statistical post-processing methods based on Quantile Regression Forests (QRF) and Gradient Forests (GF) with a parametric extension for heavy-tailed distributions. Our goal is to improve ensemble quality for all types of precipitation events, heavy-tailed included, subject to a good overall performance. Our hybrid proposed methods are applied to daily 51-h forecasts of 6-h accumulated precipitation from 2012 to 2015 over France using the M{'e}t{'e}o-France ensemble prediction system called PEARP. They provide calibrated pre-dictive distributions and compete favourably with state-of-the-art methods like Analogs method or Ensemble Model Output Statistics. In particular, hybrid forest-based procedures appear to bring an added value to the forecast of heavy rainfall.
Tasks
Published	2017-11-29
URL	http://arxiv.org/abs/1711.10937v1
PDF	http://arxiv.org/pdf/1711.10937v1.pdf
PWC	https://paperswithcode.com/paper/forest-based-methods-and-ensemble-model
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Regression Phalanxes


Title	Regression Phalanxes
Authors	Hongyang Zhang, William J. Welch, Ruben H. Zamar
Abstract	Tomal et al. (2015) introduced the notion of “phalanxes” in the context of rare-class detection in two-class classification problems. A phalanx is a subset of features that work well for classification tasks. In this paper, we propose a different class of phalanxes for application in regression settings. We define a “Regression Phalanx” - a subset of features that work well together for prediction. We propose a novel algorithm which automatically chooses Regression Phalanxes from high-dimensional data sets using hierarchical clustering and builds a prediction model for each phalanx for further ensembling. Through extensive simulation studies and several real-life applications in various areas (including drug discovery, chemical analysis of spectra data, microarray analysis and climate projections) we show that an ensemble of Regression Phalanxes improves prediction accuracy when combined with effective prediction methods like Lasso or Random Forests.
Tasks	Drug Discovery
Published	2017-07-03
URL	http://arxiv.org/abs/1707.00727v1
PDF	http://arxiv.org/pdf/1707.00727v1.pdf
PWC	https://paperswithcode.com/paper/regression-phalanxes
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Mapping the world population one building at a time


Title	Mapping the world population one building at a time
Authors	Tobias G. Tiecke, Xianming Liu, Amy Zhang, Andreas Gros, Nan Li, Gregory Yetman, Talip Kilic, Siobhan Murray, Brian Blankespoor, Espen B. Prydz, Hai-Anh H. Dang
Abstract	High resolution datasets of population density which accurately map sparsely-distributed human populations do not exist at a global scale. Typically, population data is obtained using censuses and statistical modeling. More recently, methods using remotely-sensed data have emerged, capable of effectively identifying urbanized areas. Obtaining high accuracy in estimation of population distribution in rural areas remains a very challenging task due to the simultaneous requirements of sufficient sensitivity and resolution to detect very sparse populations through remote sensing as well as reliable performance at a global scale. Here, we present a computer vision method based on machine learning to create population maps from satellite imagery at a global scale, with a spatial sensitivity corresponding to individual buildings and suitable for global deployment. By combining this settlement data with census data, we create population maps with ~30 meter resolution for 18 countries. We validate our method, and find that the building identification has an average precision and recall of 0.95 and 0.91, respectively and that the population estimates have a standard error of a factor ~2 or less. Based on our data, we analyze 29 percent of the world population, and show that 99 percent lives within 36 km of the nearest urban cluster. The resulting high-resolution population datasets have applications in infrastructure planning, vaccination campaign planning, disaster response efforts and risk analysis such as high accuracy flood risk analysis.
Tasks
Published	2017-12-15
URL	http://arxiv.org/abs/1712.05839v1
PDF	http://arxiv.org/pdf/1712.05839v1.pdf
PWC	https://paperswithcode.com/paper/mapping-the-world-population-one-building-at
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