May 6, 2019

2917 words 14 mins read

Paper Group ANR 406

On the Exponentially Weighted Aggregate with the Laplace Prior. Automatic 3D object detection of Proteins in Fluorescent labeled microscope images with spatial statistical analysis. How effective can simple ordinal peer grading be?. Positive Definite Estimation of Large Covariance Matrix Using Generalized Nonconvex Penalties. Hardware-oriented Appr …

On the Exponentially Weighted Aggregate with the Laplace Prior


Title	On the Exponentially Weighted Aggregate with the Laplace Prior
Authors	Arnak S. Dalalyan, Edwin Grappin, Quentin Paris
Abstract	In this paper, we study the statistical behaviour of the Exponentially Weighted Aggregate (EWA) in the problem of high-dimensional regression with fixed design. Under the assumption that the underlying regression vector is sparse, it is reasonable to use the Laplace distribution as a prior. The resulting estimator and, specifically, a particular instance of it referred to as the Bayesian lasso, was already used in the statistical literature because of its computational convenience, even though no thorough mathematical analysis of its statistical properties was carried out. The present work fills this gap by establishing sharp oracle inequalities for the EWA with the Laplace prior. These inequalities show that if the temperature parameter is small, the EWA with the Laplace prior satisfies the same type of oracle inequality as the lasso estimator does, as long as the quality of estimation is measured by the prediction loss. Extensions of the proposed methodology to the problem of prediction with low-rank matrices are considered.
Tasks
Published	2016-11-25
URL	http://arxiv.org/abs/1611.08483v1
PDF	http://arxiv.org/pdf/1611.08483v1.pdf
PWC	https://paperswithcode.com/paper/on-the-exponentially-weighted-aggregate-with
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Automatic 3D object detection of Proteins in Fluorescent labeled microscope images with spatial statistical analysis


Title	Automatic 3D object detection of Proteins in Fluorescent labeled microscope images with spatial statistical analysis
Authors	Ramin Norousi, Volker J. Schmid
Abstract	Since manual object detection is very inaccurate and time consuming, some automatic object detection tools have been developed in recent years. At the moment, there is no image analysis software available which provides an automatic, objective assessment of 3D foci which is generally applicable. Complications arise from discrete foci which are very close or even come in contact to other foci, moreover they are of variable sizes and show variable signal-to-noise, and must be analyzed fully in 3D. Therefore we introduce the 3D-OSCOS (3D-Object Segmentation and Colocalization Analysis based on Spatial statistics) algorithm which is implemented as a user-friendly toolbox for interactive detection of 3D objects and visualization of labeled images.
Tasks	3D Object Detection, Object Detection, Semantic Segmentation
Published	2016-01-06
URL	http://arxiv.org/abs/1601.01216v1
PDF	http://arxiv.org/pdf/1601.01216v1.pdf
PWC	https://paperswithcode.com/paper/automatic-3d-object-detection-of-proteins-in
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How effective can simple ordinal peer grading be?


Title	How effective can simple ordinal peer grading be?
Authors	Ioannis Caragiannis, George A. Krimpas, Alexandros A. Voudouris
Abstract	Ordinal peer grading has been proposed as a simple and scalable solution for computing reliable information about student performance in massive open online courses. The idea is to outsource the grading task to the students themselves as follows. After the end of an exam, each student is asked to rank —in terms of quality— a bundle of exam papers by fellow students. An aggregation rule will then combine the individual rankings into a global one that contains all students. We define a broad class of simple aggregation rules and present a theoretical framework for assessing their effectiveness. When statistical information about the grading behaviour of students is available, the framework can be used to compute the optimal rule from this class with respect to a series of performance objectives. For example, a natural rule known as Borda is proved to be optimal when students grade correctly. In addition, we present extensive simulations and a field experiment that validate our theory and prove it to be extremely accurate in predicting the performance of aggregation rules even when only rough information about grading behaviour is available.
Tasks
Published	2016-02-25
URL	http://arxiv.org/abs/1602.07985v1
PDF	http://arxiv.org/pdf/1602.07985v1.pdf
PWC	https://paperswithcode.com/paper/how-effective-can-simple-ordinal-peer-grading
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Positive Definite Estimation of Large Covariance Matrix Using Generalized Nonconvex Penalties


Title	Positive Definite Estimation of Large Covariance Matrix Using Generalized Nonconvex Penalties
Authors	Fei Wen, Yuan Yang, Peilin Liu, Robert C. Qiu
Abstract	This work addresses the issue of large covariance matrix estimation in high-dimensional statistical analysis. Recently, improved iterative algorithms with positive-definite guarantee have been developed. However, these algorithms cannot be directly extended to use a nonconvex penalty for sparsity inducing. Generally, a nonconvex penalty has the capability of ameliorating the bias problem of the popular convex lasso penalty, and thus is more advantageous. In this work, we propose a class of positive-definite covariance estimators using generalized nonconvex penalties. We develop a first-order algorithm based on the alternating direction method framework to solve the nonconvex optimization problem efficiently. The convergence of this algorithm has been proved. Further, the statistical properties of the new estimators have been analyzed for generalized nonconvex penalties. Moreover, extension of this algorithm to covariance estimation from sketched measurements has been considered. The performances of the new estimators have been demonstrated by both a simulation study and a gene clustering example for tumor tissues. Code for the proposed estimators is available at https://github.com/FWen/Nonconvex-PDLCE.git.
Tasks
Published	2016-04-15
URL	http://arxiv.org/abs/1604.04348v3
PDF	http://arxiv.org/pdf/1604.04348v3.pdf
PWC	https://paperswithcode.com/paper/positive-definite-estimation-of-large
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Hardware-oriented Approximation of Convolutional Neural Networks


Title	Hardware-oriented Approximation of Convolutional Neural Networks
Authors	Philipp Gysel, Mohammad Motamedi, Soheil Ghiasi
Abstract	High computational complexity hinders the widespread usage of Convolutional Neural Networks (CNNs), especially in mobile devices. Hardware accelerators are arguably the most promising approach for reducing both execution time and power consumption. One of the most important steps in accelerator development is hardware-oriented model approximation. In this paper we present Ristretto, a model approximation framework that analyzes a given CNN with respect to numerical resolution used in representing weights and outputs of convolutional and fully connected layers. Ristretto can condense models by using fixed point arithmetic and representation instead of floating point. Moreover, Ristretto fine-tunes the resulting fixed point network. Given a maximum error tolerance of 1%, Ristretto can successfully condense CaffeNet and SqueezeNet to 8-bit. The code for Ristretto is available.
Tasks
Published	2016-04-11
URL	http://arxiv.org/abs/1604.03168v3
PDF	http://arxiv.org/pdf/1604.03168v3.pdf
PWC	https://paperswithcode.com/paper/hardware-oriented-approximation-of
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Query-Focused Opinion Summarization for User-Generated Content


Title	Query-Focused Opinion Summarization for User-Generated Content
Authors	Lu Wang, Hema Raghavan, Claire Cardie, Vittorio Castelli
Abstract	We present a submodular function-based framework for query-focused opinion summarization. Within our framework, relevance ordering produced by a statistical ranker, and information coverage with respect to topic distribution and diverse viewpoints are both encoded as submodular functions. Dispersion functions are utilized to minimize the redundancy. We are the first to evaluate different metrics of text similarity for submodularity-based summarization methods. By experimenting on community QA and blog summarization, we show that our system outperforms state-of-the-art approaches in both automatic evaluation and human evaluation. A human evaluation task is conducted on Amazon Mechanical Turk with scale, and shows that our systems are able to generate summaries of high overall quality and information diversity.
Tasks
Published	2016-06-17
URL	http://arxiv.org/abs/1606.05702v1
PDF	http://arxiv.org/pdf/1606.05702v1.pdf
PWC	https://paperswithcode.com/paper/query-focused-opinion-summarization-for-user
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Item Popularity Prediction in E-commerce Using Image Quality Feature Vectors


Title	Item Popularity Prediction in E-commerce Using Image Quality Feature Vectors
Authors	Stephen Zakrewsky, Kamelia Aryafar, Ali Shokoufandeh
Abstract	Online retail is a visual experience- Shoppers often use images as first order information to decide if an item matches their personal style. Image characteristics such as color, simplicity, scene composition, texture, style, aesthetics and overall quality play a crucial role in making a purchase decision, clicking on or liking a product listing. In this paper we use a set of image features that indicate quality to predict product listing popularity on a major e-commerce website, Etsy. We first define listing popularity through search clicks, favoriting and purchase activity. Next, we infer listing quality from the pixel-level information of listed images as quality features. We then compare our findings to text-only models for popularity prediction. Our initial results indicate that a combined image and text modeling of product listings outperforms text-only models in popularity prediction.
Tasks
Published	2016-05-12
URL	http://arxiv.org/abs/1605.03663v1
PDF	http://arxiv.org/pdf/1605.03663v1.pdf
PWC	https://paperswithcode.com/paper/item-popularity-prediction-in-e-commerce
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A Model Explanation System: Latest Updates and Extensions


Title	A Model Explanation System: Latest Updates and Extensions
Authors	Ryan Turner
Abstract	We propose a general model explanation system (MES) for “explaining” the output of black box classifiers. This paper describes extensions to Turner (2015), which is referred to frequently in the text. We use the motivating example of a classifier trained to detect fraud in a credit card transaction history. The key aspect is that we provide explanations applicable to a single prediction, rather than provide an interpretable set of parameters. We focus on explaining positive predictions (alerts). However, the presented methodology is symmetrically applicable to negative predictions.
Tasks
Published	2016-06-30
URL	http://arxiv.org/abs/1606.09517v1
PDF	http://arxiv.org/pdf/1606.09517v1.pdf
PWC	https://paperswithcode.com/paper/a-model-explanation-system-latest-updates-and
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Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots


Title	Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots
Authors	Lei Tai, Ming Liu
Abstract	Exploration in an unknown environment is the core functionality for mobile robots. Learning-based exploration methods, including convolutional neural networks, provide excellent strategies without human-designed logic for the feature extraction. But the conventional supervised learning algorithms cost lots of efforts on the labeling work of datasets inevitably. Scenes not included in the training set are mostly unrecognized either. We propose a deep reinforcement learning method for the exploration of mobile robots in an indoor environment with the depth information from an RGB-D sensor only. Based on the Deep Q-Network framework, the raw depth image is taken as the only input to estimate the Q values corresponding to all moving commands. The training of the network weights is end-to-end. In arbitrarily constructed simulation environments, we show that the robot can be quickly adapted to unfamiliar scenes without any man-made labeling. Besides, through analysis of receptive fields of feature representations, deep reinforcement learning motivates the convolutional networks to estimate the traversability of the scenes. The test results are compared with the exploration strategies separately based on deep learning or reinforcement learning. Even trained only in the simulated environment, experimental results in real-world environment demonstrate that the cognitive ability of robot controller is dramatically improved compared with the supervised method. We believe it is the first time that raw sensor information is used to build cognitive exploration strategy for mobile robots through end-to-end deep reinforcement learning.
Tasks
Published	2016-10-06
URL	http://arxiv.org/abs/1610.01733v1
PDF	http://arxiv.org/pdf/1610.01733v1.pdf
PWC	https://paperswithcode.com/paper/towards-cognitive-exploration-through-deep
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Towards Understanding Sparse Filtering: A Theoretical Perspective


Title	Towards Understanding Sparse Filtering: A Theoretical Perspective
Authors	Fabio Massimo Zennaro, Ke Chen
Abstract	In this paper we present a theoretical analysis to understand sparse filtering, a recent and effective algorithm for unsupervised learning. The aim of this research is not to show whether or how well sparse filtering works, but to understand why and when sparse filtering does work. We provide a thorough theoretical analysis of sparse filtering and its properties, and further offer an experimental validation of the main outcomes of our theoretical analysis. We show that sparse filtering works by explicitly maximizing the entropy of the learned representation through the maximization of the proxy of sparsity, and by implicitly preserving mutual information between original and learned representations through the constraint of preserving a structure of the data, specifically the structure defined by relations of neighborhoodness under the cosine distance. Furthermore, we empirically validate our theoretical results with artificial and real data sets, and we apply our theoretical understanding to explain the success of sparse filtering on real-world problems. Our work provides a strong theoretical basis for understanding sparse filtering: it highlights assumptions and conditions for success behind this feature distribution learning algorithm, and provides insights for developing new feature distribution learning algorithms.
Tasks
Published	2016-03-29
URL	http://arxiv.org/abs/1603.08831v3
PDF	http://arxiv.org/pdf/1603.08831v3.pdf
PWC	https://paperswithcode.com/paper/towards-understanding-sparse-filtering-a
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Active Search for Sparse Signals with Region Sensing


Title	Active Search for Sparse Signals with Region Sensing
Authors	Yifei Ma, Roman Garnett, Jeff Schneider
Abstract	Autonomous systems can be used to search for sparse signals in a large space; e.g., aerial robots can be deployed to localize threats, detect gas leaks, or respond to distress calls. Intuitively, search algorithms may increase efficiency by collecting aggregate measurements summarizing large contiguous regions. However, most existing search methods either ignore the possibility of such region observations (e.g., Bayesian optimization and multi-armed bandits) or make strong assumptions about the sensing mechanism that allow each measurement to arbitrarily encode all signals in the entire environment (e.g., compressive sensing). We propose an algorithm that actively collects data to search for sparse signals using only noisy measurements of the average values on rectangular regions (including single points), based on the greedy maximization of information gain. We analyze our algorithm in 1d and show that it requires $\tilde{O}(\frac{n}{\mu^2}+k^2)$ measurements to recover all of $k$ signal locations with small Bayes error, where $\mu$ and $n$ are the signal strength and the size of the search space, respectively. We also show that active designs can be fundamentally more efficient than passive designs with region sensing, contrasting with the results of Arias-Castro, Candes, and Davenport (2013). We demonstrate the empirical performance of our algorithm on a search problem using satellite image data and in high dimensions.
Tasks	Compressive Sensing, Multi-Armed Bandits
Published	2016-12-02
URL	http://arxiv.org/abs/1612.00583v1
PDF	http://arxiv.org/pdf/1612.00583v1.pdf
PWC	https://paperswithcode.com/paper/active-search-for-sparse-signals-with-region
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Fast Learning of Clusters and Topics via Sparse Posteriors


Title	Fast Learning of Clusters and Topics via Sparse Posteriors
Authors	Michael C. Hughes, Erik B. Sudderth
Abstract	Mixture models and topic models generate each observation from a single cluster, but standard variational posteriors for each observation assign positive probability to all possible clusters. This requires dense storage and runtime costs that scale with the total number of clusters, even though typically only a few clusters have significant posterior mass for any data point. We propose a constrained family of sparse variational distributions that allow at most $L$ non-zero entries, where the tunable threshold $L$ trades off speed for accuracy. Previous sparse approximations have used hard assignments ($L=1$), but we find that moderate values of $L>1$ provide superior performance. Our approach easily integrates with stochastic or incremental optimization algorithms to scale to millions of examples. Experiments training mixture models of image patches and topic models for news articles show that our approach produces better-quality models in far less time than baseline methods.
Tasks	Topic Models
Published	2016-09-23
URL	http://arxiv.org/abs/1609.07521v1
PDF	http://arxiv.org/pdf/1609.07521v1.pdf
PWC	https://paperswithcode.com/paper/fast-learning-of-clusters-and-topics-via
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Exploiting Multi-typed Treebanks for Parsing with Deep Multi-task Learning


Title	Exploiting Multi-typed Treebanks for Parsing with Deep Multi-task Learning
Authors	Jiang Guo, Wanxiang Che, Haifeng Wang, Ting Liu
Abstract	Various treebanks have been released for dependency parsing. Despite that treebanks may belong to different languages or have different annotation schemes, they contain syntactic knowledge that is potential to benefit each other. This paper presents an universal framework for exploiting these multi-typed treebanks to improve parsing with deep multi-task learning. We consider two kinds of treebanks as source: the multilingual universal treebanks and the monolingual heterogeneous treebanks. Multiple treebanks are trained jointly and interacted with multi-level parameter sharing. Experiments on several benchmark datasets in various languages demonstrate that our approach can make effective use of arbitrary source treebanks to improve target parsing models.
Tasks	Dependency Parsing, Multi-Task Learning
Published	2016-06-03
URL	http://arxiv.org/abs/1606.01161v1
PDF	http://arxiv.org/pdf/1606.01161v1.pdf
PWC	https://paperswithcode.com/paper/exploiting-multi-typed-treebanks-for-parsing
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Probabilistic Linear Multistep Methods


Title	Probabilistic Linear Multistep Methods
Authors	Onur Teymur, Konstantinos Zygalakis, Ben Calderhead
Abstract	We present a derivation and theoretical investigation of the Adams-Bashforth and Adams-Moulton family of linear multistep methods for solving ordinary differential equations, starting from a Gaussian process (GP) framework. In the limit, this formulation coincides with the classical deterministic methods, which have been used as higher-order initial value problem solvers for over a century. Furthermore, the natural probabilistic framework provided by the GP formulation allows us to derive probabilistic versions of these methods, in the spirit of a number of other probabilistic ODE solvers presented in the recent literature. In contrast to higher-order Runge-Kutta methods, which require multiple intermediate function evaluations per step, Adams family methods make use of previous function evaluations, so that increased accuracy arising from a higher-order multistep approach comes at very little additional computational cost. We show that through a careful choice of covariance function for the GP, the posterior mean and standard deviation over the numerical solution can be made to exactly coincide with the value given by the deterministic method and its local truncation error respectively. We provide a rigorous proof of the convergence of these new methods, as well as an empirical investigation (up to fifth order) demonstrating their convergence rates in practice.
Tasks
Published	2016-10-26
URL	http://arxiv.org/abs/1610.08417v1
PDF	http://arxiv.org/pdf/1610.08417v1.pdf
PWC	https://paperswithcode.com/paper/probabilistic-linear-multistep-methods
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Simple Search Algorithms on Semantic Networks Learned from Language Use


Title	Simple Search Algorithms on Semantic Networks Learned from Language Use
Authors	Aida Nematzadeh, Filip Miscevic, Suzanne Stevenson
Abstract	Recent empirical and modeling research has focused on the semantic fluency task because it is informative about semantic memory. An interesting interplay arises between the richness of representations in semantic memory and the complexity of algorithms required to process it. It has remained an open question whether representations of words and their relations learned from language use can enable a simple search algorithm to mimic the observed behavior in the fluency task. Here we show that it is plausible to learn rich representations from naturalistic data for which a very simple search algorithm (a random walk) can replicate the human patterns. We suggest that explicitly structuring knowledge about words into a semantic network plays a crucial role in modeling human behavior in memory search and retrieval; moreover, this is the case across a range of semantic information sources.
Tasks
Published	2016-02-10
URL	http://arxiv.org/abs/1602.03265v2
PDF	http://arxiv.org/pdf/1602.03265v2.pdf
PWC	https://paperswithcode.com/paper/simple-search-algorithms-on-semantic-networks
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