Paper Group ANR 1
Posterior Sampling for Reinforcement Learning Without Episodes. Maxmin convolutional neural networks for image classification. Knowledge Completion for Generics using Guided Tensor Factorization. Synthesis of Gaussian Trees with Correlation Sign Ambiguity: An Information Theoretic Approach. A Unified Framework for Sparse Non-Negative Least Squares …
Posterior Sampling for Reinforcement Learning Without Episodes
| Title | Posterior Sampling for Reinforcement Learning Without Episodes |
| Authors | Ian Osband, Benjamin Van Roy |
| Abstract | This is a brief technical note to clarify some of the issues with applying the application of the algorithm posterior sampling for reinforcement learning (PSRL) in environments without fixed episodes. In particular, this paper aims to: - Review some of results which have been proven for finite horizon MDPs (Osband et al 2013, 2014a, 2014b, 2016) and also for MDPs with finite ergodic structure (Gopalan et al 2014). - Review similar results for optimistic algorithms in infinite horizon problems (Jaksch et al 2010, Bartlett and Tewari 2009, Abbasi-Yadkori and Szepesvari 2011), with particular attention to the dynamic episode growth. - Highlight the delicate technical issue which has led to a fault in the proof of the lazy-PSRL algorithm (Abbasi-Yadkori and Szepesvari 2015). We present an explicit counterexample to this style of argument. Therefore, we suggest that the Theorem 2 in (Abbasi-Yadkori and Szepesvari 2015) be instead considered a conjecture, as it has no rigorous proof. - Present pragmatic approaches to apply PSRL in infinite horizon problems. We conjecture that, under some additional assumptions, it will be possible to obtain bounds $O( \sqrt{T} )$ even without episodic reset. We hope that this note serves to clarify existing results in the field of reinforcement learning and provides interesting motivation for future work. |
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| Published | 2016-08-09 |
| URL | http://arxiv.org/abs/1608.02731v1 |
| http://arxiv.org/pdf/1608.02731v1.pdf | |
| PWC | https://paperswithcode.com/paper/posterior-sampling-for-reinforcement-learning-1 |
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Maxmin convolutional neural networks for image classification
| Title | Maxmin convolutional neural networks for image classification |
| Authors | Michael Blot, Matthieu Cord, Nicolas Thome |
| Abstract | Convolutional neural networks (CNN) are widely used in computer vision, especially in image classification. However, the way in which information and invariance properties are encoded through in deep CNN architectures is still an open question. In this paper, we propose to modify the standard convo- lutional block of CNN in order to transfer more information layer after layer while keeping some invariance within the net- work. Our main idea is to exploit both positive and negative high scores obtained in the convolution maps. This behav- ior is obtained by modifying the traditional activation func- tion step before pooling. We are doubling the maps with spe- cific activations functions, called MaxMin strategy, in order to achieve our pipeline. Extensive experiments on two classical datasets, MNIST and CIFAR-10, show that our deep MaxMin convolutional net outperforms standard CNN. |
| Tasks | Image Classification |
| Published | 2016-10-25 |
| URL | http://arxiv.org/abs/1610.07882v1 |
| http://arxiv.org/pdf/1610.07882v1.pdf | |
| PWC | https://paperswithcode.com/paper/maxmin-convolutional-neural-networks-for |
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Knowledge Completion for Generics using Guided Tensor Factorization
| Title | Knowledge Completion for Generics using Guided Tensor Factorization |
| Authors | Hanie Sedghi, Ashish Sabharwal |
| Abstract | Given a knowledge base or KB containing (noisy) facts about common nouns or generics, such as “all trees produce oxygen” or “some animals live in forests”, we consider the problem of inferring additional such facts at a precision similar to that of the starting KB. Such KBs capture general knowledge about the world, and are crucial for various applications such as question answering. Different from commonly studied named entity KBs such as Freebase, generics KBs involve quantification, have more complex underlying regularities, tend to be more incomplete, and violate the commonly used locally closed world assumption (LCWA). We show that existing KB completion methods struggle with this new task, and present the first approach that is successful. Our results demonstrate that external information, such as relation schemas and entity taxonomies, if used appropriately, can be a surprisingly powerful tool in this setting. First, our simple yet effective knowledge guided tensor factorization approach achieves state-of-the-art results on two generics KBs (80% precise) for science, doubling their size at 74%-86% precision. Second, our novel taxonomy guided, submodular, active learning method for collecting annotations about rare entities (e.g., oriole, a bird) is 6x more effective at inferring further new facts about them than multiple active learning baselines. |
| Tasks | Active Learning, Question Answering |
| Published | 2016-12-12 |
| URL | http://arxiv.org/abs/1612.03871v3 |
| http://arxiv.org/pdf/1612.03871v3.pdf | |
| PWC | https://paperswithcode.com/paper/knowledge-completion-for-generics-using |
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Synthesis of Gaussian Trees with Correlation Sign Ambiguity: An Information Theoretic Approach
| Title | Synthesis of Gaussian Trees with Correlation Sign Ambiguity: An Information Theoretic Approach |
| Authors | Ali Moharrer, Shuangqing Wei, George T. Amariucai, Jing Deng |
| Abstract | In latent Gaussian trees the pairwise correlation signs between the variables are intrinsically unrecoverable. Such information is vital since it completely determines the direction in which two variables are associated. In this work, we resort to information theoretical approaches to achieve two fundamental goals: First, we quantify the amount of information loss due to unrecoverable sign information. Second, we show the importance of such information in determining the maximum achievable rate region, in which the observed output vector can be synthesized, given its probability density function. In particular, we model the graphical model as a communication channel and propose a new layered encoding framework to synthesize observed data using upper layer Gaussian inputs and independent Bernoulli correlation sign inputs from each layer. We find the achievable rate region for the rate tuples of multi-layer latent Gaussian messages to synthesize the desired observables. |
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| Published | 2016-01-24 |
| URL | http://arxiv.org/abs/1601.06403v5 |
| http://arxiv.org/pdf/1601.06403v5.pdf | |
| PWC | https://paperswithcode.com/paper/synthesis-of-gaussian-trees-with-correlation |
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A Unified Framework for Sparse Non-Negative Least Squares using Multiplicative Updates and the Non-Negative Matrix Factorization Problem
| Title | A Unified Framework for Sparse Non-Negative Least Squares using Multiplicative Updates and the Non-Negative Matrix Factorization Problem |
| Authors | Igor Fedorov, Alican Nalci, Ritwik Giri, Bhaskar D. Rao, Truong Q. Nguyen, Harinath Garudadri |
| Abstract | We study the sparse non-negative least squares (S-NNLS) problem. S-NNLS occurs naturally in a wide variety of applications where an unknown, non-negative quantity must be recovered from linear measurements. We present a unified framework for S-NNLS based on a rectified power exponential scale mixture prior on the sparse codes. We show that the proposed framework encompasses a large class of S-NNLS algorithms and provide a computationally efficient inference procedure based on multiplicative update rules. Such update rules are convenient for solving large sets of S-NNLS problems simultaneously, which is required in contexts like sparse non-negative matrix factorization (S-NMF). We provide theoretical justification for the proposed approach by showing that the local minima of the objective function being optimized are sparse and the S-NNLS algorithms presented are guaranteed to converge to a set of stationary points of the objective function. We then extend our framework to S-NMF, showing that our framework leads to many well known S-NMF algorithms under specific choices of prior and providing a guarantee that a popular subclass of the proposed algorithms converges to a set of stationary points of the objective function. Finally, we study the performance of the proposed approaches on synthetic and real-world data. |
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| Published | 2016-04-07 |
| URL | http://arxiv.org/abs/1604.02181v6 |
| http://arxiv.org/pdf/1604.02181v6.pdf | |
| PWC | https://paperswithcode.com/paper/a-unified-framework-for-sparse-non-negative |
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Prediction of Video Popularity in the Absence of Reliable Data from Video Hosting Services: Utility of Traces Left by Users on the Web
| Title | Prediction of Video Popularity in the Absence of Reliable Data from Video Hosting Services: Utility of Traces Left by Users on the Web |
| Authors | Alexey Drutsa, Gleb Gusev, Pavel Serdyukov |
| Abstract | With the growth of user-generated content, we observe the constant rise of the number of companies, such as search engines, content aggregators, etc., that operate with tremendous amounts of web content not being the services hosting it. Thus, aiming to locate the most important content and promote it to the users, they face the need of estimating the current and predicting the future content popularity. In this paper, we approach the problem of video popularity prediction not from the side of a video hosting service, as done in all previous studies, but from the side of an operating company, which provides a popular video search service that aggregates content from different video hosting websites. We investigate video popularity prediction based on features from three primary sources available for a typical operating company: first, the content hosting provider may deliver its data via its API, second, the operating company makes use of its own search and browsing logs, third, the company crawls information about embeds of a video and links to a video page from publicly available resources on the Web. We show that video popularity prediction based on the embed and link data coupled with the internal search and browsing data significantly improves video popularity prediction based only on the data provided by the video hosting and can even adequately replace the API data in the cases when it is partly or completely unavailable. |
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| Published | 2016-11-28 |
| URL | http://arxiv.org/abs/1611.09083v1 |
| http://arxiv.org/pdf/1611.09083v1.pdf | |
| PWC | https://paperswithcode.com/paper/prediction-of-video-popularity-in-the-absence |
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mlr Tutorial
| Title | mlr Tutorial |
| Authors | Julia Schiffner, Bernd Bischl, Michel Lang, Jakob Richter, Zachary M. Jones, Philipp Probst, Florian Pfisterer, Mason Gallo, Dominik Kirchhoff, Tobias Kühn, Janek Thomas, Lars Kotthoff |
| Abstract | This document provides and in-depth introduction to the mlr framework for machine learning experiments in R. |
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| Published | 2016-09-18 |
| URL | http://arxiv.org/abs/1609.06146v1 |
| http://arxiv.org/pdf/1609.06146v1.pdf | |
| PWC | https://paperswithcode.com/paper/mlr-tutorial |
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The Information-Collecting Vehicle Routing Problem: Stochastic Optimization for Emergency Storm Response
| Title | The Information-Collecting Vehicle Routing Problem: Stochastic Optimization for Emergency Storm Response |
| Authors | Lina Al-Kanj, Warren B. Powell, Belgacem Bouzaiene-Ayari |
| Abstract | Utilities face the challenge of responding to power outages due to storms and ice damage, but most power grids are not equipped with sensors to pinpoint the precise location of the faults causing the outage. Instead, utilities have to depend primarily on phone calls (trouble calls) from customers who have lost power to guide the dispatching of utility trucks. In this paper, we develop a policy that routes a utility truck to restore outages in the power grid as quickly as possible, using phone calls to create beliefs about outages, but also using utility trucks as a mechanism for collecting additional information. This means that routing decisions change not only the physical state of the truck (as it moves from one location to another) and the grid (as the truck performs repairs), but also our belief about the network, creating the first stochastic vehicle routing problem that explicitly models information collection and belief modeling. We address the problem of managing a single utility truck, which we start by formulating as a sequential stochastic optimization model which captures our belief about the state of the grid. We propose a stochastic lookahead policy, and use Monte Carlo tree search (MCTS) to produce a practical policy that is asymptotically optimal. Simulation results show that the developed policy restores the power grid much faster compared to standard industry heuristics. |
| Tasks | Stochastic Optimization |
| Published | 2016-05-18 |
| URL | http://arxiv.org/abs/1605.05711v1 |
| http://arxiv.org/pdf/1605.05711v1.pdf | |
| PWC | https://paperswithcode.com/paper/the-information-collecting-vehicle-routing |
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Stochastic Variance Reduced Riemannian Eigensolver
| Title | Stochastic Variance Reduced Riemannian Eigensolver |
| Authors | Zhiqiang Xu, Yiping Ke |
| Abstract | We study the stochastic Riemannian gradient algorithm for matrix eigen-decomposition. The state-of-the-art stochastic Riemannian algorithm requires the learning rate to decay to zero and thus suffers from slow convergence and sub-optimal solutions. In this paper, we address this issue by deploying the variance reduction (VR) technique of stochastic gradient descent (SGD). The technique was originally developed to solve convex problems in the Euclidean space. We generalize it to Riemannian manifolds and realize it to solve the non-convex eigen-decomposition problem. We are the first to propose and analyze the generalization of SVRG to Riemannian manifolds. Specifically, we propose the general variance reduction form, SVRRG, in the framework of the stochastic Riemannian gradient optimization. It’s then specialized to the problem with eigensolvers and induces the SVRRG-EIGS algorithm. We provide a novel and elegant theoretical analysis on this algorithm. The theory shows that a fixed learning rate can be used in the Riemannian setting with an exponential global convergence rate guaranteed. The theoretical results make a significant improvement over existing studies, with the effectiveness empirically verified. |
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| Published | 2016-05-26 |
| URL | http://arxiv.org/abs/1605.08233v2 |
| http://arxiv.org/pdf/1605.08233v2.pdf | |
| PWC | https://paperswithcode.com/paper/stochastic-variance-reduced-riemannian |
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Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles
| Title | Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles |
| Authors | Stefan Lee, Senthil Purushwalkam, Michael Cogswell, Viresh Ranjan, David Crandall, Dhruv Batra |
| Abstract | Many practical perception systems exist within larger processes that include interactions with users or additional components capable of evaluating the quality of predicted solutions. In these contexts, it is beneficial to provide these oracle mechanisms with multiple highly likely hypotheses rather than a single prediction. In this work, we pose the task of producing multiple outputs as a learning problem over an ensemble of deep networks – introducing a novel stochastic gradient descent based approach to minimize the loss with respect to an oracle. Our method is simple to implement, agnostic to both architecture and loss function, and parameter-free. Our approach achieves lower oracle error compared to existing methods on a wide range of tasks and deep architectures. We also show qualitatively that the diverse solutions produced often provide interpretable representations of task ambiguity. |
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| Published | 2016-06-24 |
| URL | http://arxiv.org/abs/1606.07839v3 |
| http://arxiv.org/pdf/1606.07839v3.pdf | |
| PWC | https://paperswithcode.com/paper/stochastic-multiple-choice-learning-for |
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Robust Dialog State Tracking for Large Ontologies
| Title | Robust Dialog State Tracking for Large Ontologies |
| Authors | Franck Dernoncourt, Ji Young Lee, Trung H. Bui, Hung H. Bui |
| Abstract | The Dialog State Tracking Challenge 4 (DSTC 4) differentiates itself from the previous three editions as follows: the number of slot-value pairs present in the ontology is much larger, no spoken language understanding output is given, and utterances are labeled at the subdialog level. This paper describes a novel dialog state tracking method designed to work robustly under these conditions, using elaborate string matching, coreference resolution tailored for dialogs and a few other improvements. The method can correctly identify many values that are not explicitly present in the utterance. On the final evaluation, our method came in first among 7 competing teams and 24 entries. The F1-score achieved by our method was 9 and 7 percentage points higher than that of the runner-up for the utterance-level evaluation and for the subdialog-level evaluation, respectively. |
| Tasks | Coreference Resolution, Spoken Language Understanding |
| Published | 2016-05-07 |
| URL | http://arxiv.org/abs/1605.02130v1 |
| http://arxiv.org/pdf/1605.02130v1.pdf | |
| PWC | https://paperswithcode.com/paper/robust-dialog-state-tracking-for-large |
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Robust Semi-Supervised Graph Classifier Learning with Negative Edge Weights
| Title | Robust Semi-Supervised Graph Classifier Learning with Negative Edge Weights |
| Authors | Gene Cheung, Weng-Tai Su, Yu Mao, Chia-Wen Lin |
| Abstract | In a semi-supervised learning scenario, (possibly noisy) partially observed labels are used as input to train a classifier, in order to assign labels to unclassified samples. In this paper, we study this classifier learning problem from a graph signal processing (GSP) perspective. Specifically, by viewing a binary classifier as a piecewise constant graph-signal in a high-dimensional feature space, we cast classifier learning as a signal restoration problem via a classical maximum a posteriori (MAP) formulation. Unlike previous graph-signal restoration works, we consider in addition edges with negative weights that signify anti-correlation between samples. One unfortunate consequence is that the graph Laplacian matrix $\mathbf{L}$ can be indefinite, and previously proposed graph-signal smoothness prior $\mathbf{x}^T \mathbf{L} \mathbf{x}$ for candidate signal $\mathbf{x}$ can lead to pathological solutions. In response, we derive an optimal perturbation matrix $\boldsymbol{\Delta}$ - based on a fast lower-bound computation of the minimum eigenvalue of $\mathbf{L}$ via a novel application of the Haynsworth inertia additivity formula—so that $\mathbf{L} + \boldsymbol{\Delta}$ is positive semi-definite, resulting in a stable signal prior. Further, instead of forcing a hard binary decision for each sample, we define the notion of generalized smoothness on graph that promotes ambiguity in the classifier signal. Finally, we propose an algorithm based on iterative reweighted least squares (IRLS) that solves the posed MAP problem efficiently. Extensive simulation results show that our proposed algorithm outperforms both SVM variants and graph-based classifiers using positive-edge graphs noticeably. |
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| Published | 2016-11-15 |
| URL | http://arxiv.org/abs/1611.04924v2 |
| http://arxiv.org/pdf/1611.04924v2.pdf | |
| PWC | https://paperswithcode.com/paper/robust-semi-supervised-graph-classifier |
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Scalable Dynamic Topic Modeling with Clustered Latent Dirichlet Allocation (CLDA)
| Title | Scalable Dynamic Topic Modeling with Clustered Latent Dirichlet Allocation (CLDA) |
| Authors | Chris Gropp, Alexander Herzog, Ilya Safro, Paul W. Wilson, Amy W. Apon |
| Abstract | Topic modeling, a method for extracting the underlying themes from a collection of documents, is an increasingly important component of the design of intelligent systems enabling the sense-making of highly dynamic and diverse streams of text data. Traditional methods such as Dynamic Topic Modeling (DTM) do not lend themselves well to direct parallelization because of dependencies from one time step to another. In this paper, we introduce and empirically analyze Clustered Latent Dirichlet Allocation (CLDA), a method for extracting dynamic latent topics from a collection of documents. Our approach is based on data decomposition in which the data is partitioned into segments, followed by topic modeling on the individual segments. The resulting local models are then combined into a global solution using clustering. The decomposition and resulting parallelization leads to very fast runtime even on very large datasets. Our approach furthermore provides insight into how the composition of topics changes over time and can also be applied using other data partitioning strategies over any discrete features of the data, such as geographic features or classes of users. In this paper CLDA is applied successfully to seventeen years of NIPS conference papers (2,484 documents and 3,280,697 words), seventeen years of computer science journal abstracts (533,560 documents and 32,551,540 words), and to forty years of the PubMed corpus (4,025,978 documents and 273,853,980 words). |
| Tasks | |
| Published | 2016-10-25 |
| URL | https://arxiv.org/abs/1610.07703v3 |
| https://arxiv.org/pdf/1610.07703v3.pdf | |
| PWC | https://paperswithcode.com/paper/scalable-dynamic-topic-modeling-with |
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Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network
| Title | Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network |
| Authors | Xibin Song, Yuchao Dai, Xueying Qin |
| Abstract | Depth image super-resolution is an extremely challenging task due to the information loss in sub-sampling. Deep convolutional neural network have been widely applied to color image super-resolution. Quite surprisingly, this success has not been matched to depth super-resolution. This is mainly due to the inherent difference between color and depth images. In this paper, we bridge up the gap and extend the success of deep convolutional neural network to depth super-resolution. The proposed deep depth super-resolution method learns the mapping from a low-resolution depth image to a high resolution one in an end-to-end style. Furthermore, to better regularize the learned depth map, we propose to exploit the depth field statistics and the local correlation between depth image and color image. These priors are integrated in an energy minimization formulation, where the deep neural network learns the unary term, the depth field statistics works as global model constraint and the color-depth correlation is utilized to enforce the local structure in depth images. Extensive experiments on various depth super-resolution benchmark datasets show that our method outperforms the state-of-the-art depth image super-resolution methods with a margin. |
| Tasks | Image Super-Resolution, Super-Resolution |
| Published | 2016-07-07 |
| URL | http://arxiv.org/abs/1607.01977v1 |
| http://arxiv.org/pdf/1607.01977v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-depth-super-resolution-learning-depth |
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Using Kernel Methods and Model Selection for Prediction of Preterm Birth
| Title | Using Kernel Methods and Model Selection for Prediction of Preterm Birth |
| Authors | Ilia Vovsha, Ansaf Salleb-Aouissi, Anita Raja, Thomas Koch, Alex Rybchuk, Axinia Radeva, Ashwath Rajan, Yiwen Huang, Hatim Diab, Ashish Tomar, Ronald Wapner |
| Abstract | We describe an application of machine learning to the problem of predicting preterm birth. We conduct a secondary analysis on a clinical trial dataset collected by the National In- stitute of Child Health and Human Development (NICHD) while focusing our attention on predicting different classes of preterm birth. We compare three approaches for deriving predictive models: a support vector machine (SVM) approach with linear and non-linear kernels, logistic regression with different model selection along with a model based on decision rules prescribed by physician experts for prediction of preterm birth. Our approach highlights the pre-processing methods applied to handle the inherent dynamics, noise and gaps in the data and describe techniques used to handle skewed class distributions. Empirical experiments demonstrate significant improvement in predicting preterm birth compared to past work. |
| Tasks | Model Selection |
| Published | 2016-07-27 |
| URL | http://arxiv.org/abs/1607.07959v2 |
| http://arxiv.org/pdf/1607.07959v2.pdf | |
| PWC | https://paperswithcode.com/paper/using-kernel-methods-and-model-selection-for |
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