index int64 0 20.3k | text stringlengths 0 1.3M | year stringdate 1987-01-01 00:00:00 2024-01-01 00:00:00 | No stringlengths 1 4 |
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5,700 | Sample Complexity of Learning Mahalanobis Distance Metrics Nakul Verma Janelia Research Campus, HHMI verman@janelia.hhmi.org Kristin Branson Janelia Research Campus, HHMI bransonk@janelia.hhmi.org Abstract Metric learning seeks a transformation of the feature space that enhances prediction quality for... | 2015 | 199 |
5,701 | Associative Memory via a Sparse Recovery Model Arya Mazumdar Department of ECE University of Minnesota Twin Cities arya@umn.edu Ankit Singh Rawat⇤ Computer Science Department Carnegie Mellon University asrawat@andrew.cmu.edu Abstract An associative memory is a structure learned from a dataset M of v... | 2015 | 2 |
5,702 | Online Gradient Boosting Alina Beygelzimer Yahoo Labs New York, NY 10036 beygel@yahoo-inc.com Elad Hazan Princeton University Princeton, NJ 08540 ehazan@cs.princeton.edu Satyen Kale Yahoo Labs New York, NY 10036 satyen@yahoo-inc.com Haipeng Luo Princeton University Princeton, NJ 08540 ha... | 2015 | 20 |
5,703 | Sample Efficient Path Integral Control under Uncertainty Yunpeng Pan, Evangelos A. Theodorou, and Michail Kontitsis Autonomous Control and Decision Systems Laboratory Institute for Robotics and Intelligent Machines School of Aerospace Engineering Georgia Institute of Technology, Atlanta, GA 30332 {ypan37,e... | 2015 | 200 |
5,704 | Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction Been Kim Julie Shah Massachusetts Institute of Technology Cambridge, MA 02139 {beenkim, julie a shah}@csail.mit.edu Finale Doshi-Velez Harvard University Cambridge, MA 02138 finale@seas.harvard.edu Abstract We ... | 2015 | 201 |
5,705 | Regularization Path of Cross-Validation Error Lower Bounds Atsushi Shibagaki, Yoshiki Suzuki, Masayuki Karasuyama, and Ichiro Takeuchi Nagoya Institute of Technology Nagoya, 466-8555, Japan {shibagaki.a.mllab.nit,suzuki.mllab.nit}@gmail.com {karasuyama,takeuchi.ichiro}@nitech.ac.jp Abstract Careful tuni... | 2015 | 202 |
5,706 | Reflection, Refraction, and Hamiltonian Monte Carlo Hadi Mohasel Afshar Research School of Computer Science Australian National University Canberra, ACT 0200 hadi.afshar@anu.edu.au Justin Domke National ICT Australia (NICTA) & Australian National University Canberra, ACT 0200 Justin.Domke@nicta.com.a... | 2015 | 203 |
5,707 | Exploring Models and Data for Image Question Answering Mengye Ren1, Ryan Kiros1, Richard S. Zemel1,2 University of Toronto1 Canadian Institute for Advanced Research2 {mren, rkiros, zemel}@cs.toronto.edu Abstract This work aims to address the problem of image-based question-answering (QA) with new models... | 2015 | 204 |
5,708 | Learning Structured Densities via Infinite Dimensional Exponential Families Siqi Sun TTI Chicago siqi.sun@ttic.edu Mladen Kolar University of Chicago mkolar@chicagobooth.edu Jinbo Xu TTI Chicago jinbo.xu@gmail.com Abstract Learning the structure of a probabilistic graphical models is a well studi... | 2015 | 205 |
5,709 | Streaming Min-Max Hypergraph Partitioning Dan Alistarh Microsoft Research Cambridge, United Kingdom dan.alistarh@microsoft.com Jennifer Iglesias∗ Carnegie Mellon University Pittsburgh, PA jiglesia@andrew.cmu.edu Milan Vojnovic Microsoft Research Cambridge, United Kingdom milanv@microsoft.com A... | 2015 | 206 |
5,710 | Principal Differences Analysis: Interpretable Characterization of Differences between Distributions Jonas Mueller CSAIL, MIT jonasmueller@csail.mit.edu Tommi Jaakkola CSAIL, MIT tommi@csail.mit.edu Abstract We introduce principal differences analysis (PDA) for analyzing differences between high-dimens... | 2015 | 207 |
5,711 | An Active Learning Framework using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching Xiao Li UC Berkeley xiaoli@berkeley.edu Kannan Ramchandran∗ UC Berkeley kannanr@berkeley.edu Abstract Let f : {−1, 1}n →R be an n-variate polynomial consisting of 2n monomials, in which only s ≪2n coeffic... | 2015 | 208 |
5,712 | Efficient Thompson Sampling for Online Matrix-Factorization Recommendation Jaya Kawale, Hung Bui, Branislav Kveton Adobe Research San Jose, CA {kawale, hubui, kveton}@adobe.com Long Tran Thanh University of Southampton Southampton, UK ltt08r@ecs.soton.ac.uk Sanjay Chawla Qatar Computing Research In... | 2015 | 209 |
5,713 | Optimal Ridge Detection using Coverage Risk Yen-Chi Chen Department of Statistics Carnegie Mellon University yenchic@andrew.cmu.edu Christopher R. Genovese Department of Statistics Carnegie Mellon University genovese@stat.cmu.edu Shirley Ho Department of Physics Carnegie Mellon University shirle... | 2015 | 21 |
5,714 | Structured Transforms for Small-Footprint Deep Learning Vikas Sindhwani Tara N. Sainath Sanjiv Kumar Google, New York {sindhwani, tsainath, sanjivk}@google.com Abstract We consider the task of building compact deep learning pipelines suitable for deployment on storage and power constrained mobile device... | 2015 | 210 |
5,715 | Linear Multi-Resource Allocation with Semi-Bandit Feedback Tor Lattimore Department of Computing Science University of Alberta, Canada tor.lattimore@gmail.com Koby Crammer Department of Electrical Engineering The Technion, Israel koby@ee.technion.ac.il Csaba Szepesv´ari Department of Computing Sci... | 2015 | 211 |
5,716 | On the Optimality of Classifier Chain for Multi-label Classification Weiwei Liu Ivor W. Tsang∗ Centre for Quantum Computation and Intelligent Systems University of Technology, Sydney liuweiwei863@gmail.com, ivor.tsang@uts.edu.au Abstract To capture the interdependencies between labels in multi-label class... | 2015 | 212 |
5,717 | Consistent Multilabel Classification Oluwasanmi Koyejo⇤ Department of Psychology, Stanford University sanmi@stanford.edu Nagarajan Natarajan⇤ Department of Computer Science, University of Texas at Austin naga86@cs.utexas.edu Pradeep Ravikumar Department of Computer Science, University of Texas at A... | 2015 | 213 |
5,718 | A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks Cengiz Pehlevan Simons Center for Data Analysis Simons Foundation New York, NY 10010 cpehlevan@simonsfoundation.org Dmitri B. Chklovskii Simons Center for Data Analysis Simons Foundation New York, NY 10010 dchklovskii@simon... | 2015 | 214 |
5,719 | Hidden Technical Debt in Machine Learning Systems D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips {dsculley,gholt,dgg,edavydov,toddphillips}@google.com Google, Inc. Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Franc¸ois Crespo, Dan Dennison {ebner,vchaudhary,mwyoung,jfcrespo,dennis... | 2015 | 215 |
5,720 | NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning Kevin Jamieson UC Berkeley kjamieson@berkeley.edu Lalit Jain, Chris Fernandez, Nick Glattard, Robert Nowak University of Wisconsin - Madison {ljain,crfernandez,glattard,rdnowak}@wisc.edu Abstract Active learning ... | 2015 | 216 |
5,721 | A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA James Voss The Ohio State University vossj@cse.ohio-state.edu Mikhail Belkin The Ohio State University mbelkin@cse.ohio-state.edu Luis Rademacher The Ohio State University lrademac@cse.ohio-state.edu Abstract Independent Component An... | 2015 | 217 |
5,722 | Learning Structured Output Representation using Deep Conditional Generative Models Kihyuk Sohn∗† Xinchen Yan† Honglak Lee† ∗NEC Laboratories America, Inc. † University of Michigan, Ann Arbor ksohn@nec-labs.com, {xcyan,honglak}@umich.edu Abstract Supervised deep learning has been successfully applied t... | 2015 | 218 |
5,723 | Estimating Mixture Models via Mixtures of Polynomials Sida I. Wang Arun Tejasvi Chaganty Percy Liang Computer Science Department, Stanford University, Stanford, CA, 94305 {sidaw,chaganty,pliang}@cs.stanford.edu Abstract Mixture modeling is a general technique for making any simple model more expressive ... | 2015 | 219 |
5,724 | A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding Yuval Harel, Ron Meir Department of Electrical Engineering Technion – Israel Institute of Technology Technion City, Haifa, Israel {yharel@tx,rmeir@ee}.technion.ac.il Manfred Opper Department of Artificial Intelli... | 2015 | 22 |
5,725 | Online Learning with Gaussian Payoffs and Side Observations Yifan Wu1 Andr´as Gy¨orgy2 Csaba Szepesv´ari1 1Dept. of Computing Science University of Alberta {ywu12,szepesva}@ualberta.ca 2Dept. of Electrical and Electronic Engineering Imperial College London a.gyorgy@imperial.ac.uk Abstract We con... | 2015 | 220 |
5,726 | Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families Heiko Strathmann∗Dino Sejdinovic+ Samuel Livingstoneo Zoltan Szabo∗Arthur Gretton∗ ∗Gatsby Unit University College London +Department of Statistics University of Oxford oSchool of Mathematics University of Bristol Abstract... | 2015 | 221 |
5,727 | Approximating Sparse PCA from Incomplete Data Abhisek Kundu ∗ Petros Drineas † Malik Magdon-Ismail ‡ Abstract We study how well one can recover sparse principal components of a data matrix using a sketch formed from a few of its elements. We show that for a wide class of optimization problems, if the sketch... | 2015 | 222 |
5,728 | Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices Martin Slawski Ping Li Department of Statistics & Biostatistics Department of Computer Science Rutgers University Piscataway, NJ 08854, USA {martin.slawski@rutgers.edu, pingli@stat.rutgers.edu} Matthias H... | 2015 | 223 |
5,729 | Learning visual biases from human imagination Carl Vondrick Hamed Pirsiavash† Aude Oliva Antonio Torralba Massachusetts Institute of Technology †University of Maryland, Baltimore County {vondrick,oliva,torralba}@mit.edu hpirsiav@umbc.edu Abstract Although the human visual system can recognize many c... | 2015 | 224 |
5,730 | End-To-End Memory Networks Sainbayar Sukhbaatar Dept. of Computer Science Courant Institute, New York University sainbar@cs.nyu.edu Arthur Szlam Jason Weston Rob Fergus Facebook AI Research New York {aszlam,jase,robfergus}@fb.com Abstract We introduce a neural network with a recurrent attention ... | 2015 | 225 |
5,731 | Fast Distributed k-Center Clustering with Outliers on Massive Data Gustavo Malkomes, Matt J. Kusner, Wenlin Chen Department of Computer Science and Engineering Washington University in St. Louis St. Louis, MO 63130 {luizgustavo,mkusner,wenlinchen}@wustl.edu Kilian Q. Weinberger Department of Computer Sc... | 2015 | 226 |
5,732 | BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions Dominik Rothenh¨ausler⇤ Seminar f¨ur Statistik ETH Z¨urich, Switzerland rothenhaeusler@stat.math.ethz.ch Christina Heinze⇤ Seminar f¨ur Statistik ETH Z¨urich, Switzerland heinze@stat.math.ethz.ch Jonas Peters Max Planck Inst... | 2015 | 227 |
5,733 | Lifelong Learning with Non-i.i.d. Tasks Anastasia Pentina IST Austria Klosterneuburg, Austria apentina@ist.ac.at Christoph H. Lampert IST Austria Klosterneuburg, Austria chl@ist.ac.at Abstract In this work we aim at extending the theoretical foundations of lifelong learning. Previous work analyzin... | 2015 | 228 |
5,734 | Regularized EM Algorithms: A Unified Framework and Statistical Guarantees Xinyang Yi Dept. of Electrical and Computer Engineering The University of Texas at Austin yixy@utexas.edu Constantine Caramanis Dept. of Electrical and Computer Engineering The University of Texas at Austin constantine@utexas.edu... | 2015 | 229 |
5,735 | Barrier Frank-Wolfe for Marginal Inference Rahul G. Krishnan Courant Institute New York University Simon Lacoste-Julien INRIA - Sierra Project-Team ´Ecole Normale Sup´erieure, Paris David Sontag Courant Institute New York University Abstract We introduce a globally-convergent algorithm for optimiz... | 2015 | 23 |
5,736 | Beyond Convexity: Stochastic Quasi-Convex Optimization Elad Hazan Princeton University ehazan@cs.princeton.edu Kfir Y. Levy Technion kfiryl@tx.technion.ac.il Shai Shalev-Shwartz The Hebrew University shais@cs.huji.ac.il Abstract Stochastic convex optimization is a basic and well studied primitive... | 2015 | 230 |
5,737 | Learning From Small Samples: An Analysis of Simple Decision Heuristics ¨Ozg¨ur S¸ims¸ek and Marcus Buckmann Center for Adaptive Behavior and Cognition Max Planck Institute for Human Development Lentzeallee 94, 14195 Berlin, Germany {ozgur, buckmann}@mpib-berlin.mpg.de Abstract Simple decision heuristics... | 2015 | 231 |
5,738 | Deep Temporal Sigmoid Belief Networks for Sequence Modeling Zhe Gan, Chunyuan Li, Ricardo Henao, David Carlson and Lawrence Carin Department of Electrical and Computer Engineering Duke University, Durham, NC 27708 {zhe.gan, chunyuan.li, r.henao, david.carlson, lcarin}@duke.edu Abstract Deep dynamic genera... | 2015 | 232 |
5,739 | Subsampled Power Iteration: a Unified Algorithm for Block Models and Planted CSP’s Vitaly Feldman IBM Research - Almaden vitaly@post.harvard.edu Will Perkins University of Birmingham w.f.perkins@bham.ac.uk Santosh Vempala Georgia Tech vempala@cc.gatech.edu Abstract We present an algorithm for rec... | 2015 | 233 |
5,740 | Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels Felipe Tobar ftobar@dim.uchile.cl Center for Mathematical Modeling Universidad de Chile Thang D. Bui tdb40@cam.ac.uk Department of Engineering University of Cambridge Richard E. Turner ret26@cam.ac.uk Department ... | 2015 | 234 |
5,741 | Improved Iteration Complexity Bounds of Cyclic Block Coordinate Descent for Convex Problems Ruoyu Sun∗, Mingyi Hong† ‡ Abstract The iteration complexity of the block-coordinate descent (BCD) type algorithm has been under extensive investigation. It was recently shown that for convex problems the classical c... | 2015 | 235 |
5,742 | Community Detection via Measure Space Embedding Mark Kozdoba The Technion, Haifa, Israel markk@tx.technion.ac.il Shie Mannor The Technion, Haifa, Israel shie@ee.technion.ac.il Abstract We present a new algorithm for community detection. The algorithm uses random walks to embed the graph in a space of me... | 2015 | 236 |
5,743 | Color Constancy by Learning to Predict Chromaticity from Luminance Ayan Chakrabarti Toyota Technological Institute at Chicago 6045 S. Kenwood Ave., Chicago, IL 60637 ayanc@ttic.edu Abstract Color constancy is the recovery of true surface color from observed color, and requires estimating the chromaticit... | 2015 | 237 |
5,744 | Sample Complexity Bounds for Iterative Stochastic Policy Optimization Marin Kobilarov Department of Mechanical Engineering Johns Hopkins University Baltimore, MD 21218 marin@jhu.edu Abstract This paper is concerned with robustness analysis of decision making under uncertainty. We consider a class of ite... | 2015 | 238 |
5,745 | Copeland Dueling Bandits Masrour Zoghi Informatics Institute University of Amsterdam, Netherlands m.zoghi@uva.nl Zohar Karnin Yahoo Labs New York, NY zkarnin@yahoo-inc.com Shimon Whiteson Department of Computer Science University of Oxford, UK shimon.whiteson@cs.ox.ac.uk Maarten de Rijke Inf... | 2015 | 239 |
5,746 | Combinatorial Bandits Revisited Richard Combes∗ M. Sadegh Talebi† Alexandre Proutiere† Marc Lelarge‡ ∗Centrale-Supelec, L2S, Gif-sur-Yvette, FRANCE † Department of Automatic Control, KTH, Stockholm, SWEDEN ‡ INRIA & ENS, Paris, FRANCE richard.combes@supelec.fr,{mstms,alepro}@kth.se,marc.lelarge@ens.fr ... | 2015 | 24 |
5,747 | Taming the Wild: A Unified Analysis of HOGWILD!-Style Algorithms Christopher De Sa, Ce Zhang, Kunle Olukotun, and Christopher R´e cdesa@stanford.edu, czhang@cs.wisc.edu, kunle@stanford.edu, chrismre@stanford.edu Departments of Electrical Engineering and Computer Science Stanford University, Stanford, CA 9430... | 2015 | 240 |
5,748 | High-dimensional neural spike train analysis with generalized count linear dynamical systems Yuanjun Gao Department of Statistics Columbia University New York, NY 10027 yg2312@columbia.edu Lars Buesing Department of Statistics Columbia University New York, NY 10027 lars@stat.columbia.edu Krishna... | 2015 | 241 |
5,749 | Neural Adaptive Sequential Monte Carlo Shixiang Gu†‡ Zoubin Ghahramani† Richard E. Turner† † University of Cambridge, Department of Engineering, Cambridge UK ‡ MPI for Intelligent Systems, T¨ubingen, Germany sg717@cam.ac.uk, zoubin@eng.cam.ac.uk, ret26@cam.ac.uk Abstract Sequential Monte Carlo (SMC), or... | 2015 | 242 |
5,750 | Supervised Learning for Dynamical System Learning Ahmed Hefny ∗ Carnegie Mellon University Pittsburgh, PA 15213 ahefny@cs.cmu.edu Carlton Downey † Carnegie Mellon University Pittsburgh, PA 15213 cmdowney@cs.cmu.edu Geoffrey J. Gordon ‡ Carnegie Mellon University Pittsburgh, PA 15213 ggordon@cs.c... | 2015 | 243 |
5,751 | A Complete Recipe for Stochastic Gradient MCMC Yi-An Ma, Tianqi Chen, and Emily B. Fox University of Washington {yianma@u,tqchen@cs,ebfox@stat}.washington.edu Abstract Many recent Markov chain Monte Carlo (MCMC) samplers leverage continuous dynamics to define a transition kernel that efficiently explores a targ... | 2015 | 244 |
5,752 | Segregated Graphs and Marginals of Chain Graph Models Ilya Shpitser Department of Computer Science Johns Hopkins University ilyas@cs.jhu.edu Abstract Bayesian networks are a popular representation of asymmetric (for example causal) relationships between random variables. Markov random fields (MRFs) are... | 2015 | 245 |
5,753 | Rethinking LDA: Moment Matching for Discrete ICA Anastasia Podosinnikova Francis Bach Simon Lacoste-Julien INRIA - ´Ecole normale sup´erieure Paris Abstract We consider moment matching techniques for estimation in latent Dirichlet allocation (LDA). By drawing explicit links between LDA and discrete versions... | 2015 | 246 |
5,754 | Max-Margin Deep Generative Models Chongxuan Li†, Jun Zhu†, Tianlin Shi‡, Bo Zhang† †Dept. of Comp. Sci. & Tech., State Key Lab of Intell. Tech. & Sys., TNList Lab, Center for Bio-Inspired Computing Research, Tsinghua University, Beijing, 100084, China ‡Dept. of Comp. Sci., Stanford University, Stanford, CA 9430... | 2015 | 247 |
5,755 | Convolutional Neural Networks with Intra-layer Recurrent Connections for Scene Labeling Ming Liang Xiaolin Hu Bo Zhang Tsinghua National Laboratory for Information Science and Technology (TNList) Department of Computer Science and Technology Center for Brain-Inspired Computing Research (CBICR) Tsinghua ... | 2015 | 248 |
5,756 | Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability Xia Qu Epic Systems Verona, WI 53593 quxiapisces@gmail.com Prashant Doshi THINC Lab, Dept. of Computer Science University of Georgia, Athens, GA 30622 pdoshi@cs.uga.edu Abstract This paper provides the... | 2015 | 249 |
5,757 | Efficient and Parsimonious Agnostic Active Learning Tzu-Kuo Huang Microsoft Research, NYC tkhuang@microsoft.com Alekh Agarwal Microsoft Research, NYC alekha@microsoft.com Daniel Hsu Columbia University djhsu@cs.columbia.edu John Langford Microsoft Research, NYC jcl@microsoft.com Robert E. Schap... | 2015 | 25 |
5,758 | Expectation Particle Belief Propagation Thibaut Lienart, Yee Whye Teh, Arnaud Doucet Department of Statistics University of Oxford Oxford, UK {lienart,teh,doucet}@stats.ox.ac.uk Abstract We propose an original particle-based implementation of the Loopy Belief Propagation (LPB) algorithm for pairwise Marko... | 2015 | 250 |
5,759 | Secure Multi-party Differential Privacy Peter Kairouz1 Sewoong Oh2 Pramod Viswanath1 1Department of Electrical & Computer Engineering 2Department of Industrial & Enterprise Systems Engineering University of Illinois Urbana-Champaign Urbana, IL 61801, USA {kairouz2,swoh,pramodv}@illinois.edu Abstract ... | 2015 | 251 |
5,760 | Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images Manuel Watter∗ Jost Tobias Springenberg∗ Joschka Boedecker University of Freiburg, Germany {watterm,springj,jboedeck}@cs.uni-freiburg.de Martin Riedmiller Google DeepMind London, UK riedmiller@google.com Abstract ... | 2015 | 252 |
5,761 | Newton-Stein Method: A Second Order Method for GLMs via Stein’s Lemma Murat A. Erdogdu Department of Statistics Stanford University erdogdu@stanford.edu Abstract We consider the problem of efficiently computing the maximum likelihood estimator in Generalized Linear Models (GLMs) when the number of observat... | 2015 | 253 |
5,762 | Is Approval Voting Optimal Given Approval Votes? Ariel D. Procaccia Computer Science Department Carnegie Mellon University arielpro@cs.cmu.edu Nisarg Shah Computer Science Department Carnegie Mellon University nkshah@cs.cmu.edu Abstract Some crowdsourcing platforms ask workers to express their opini... | 2015 | 254 |
5,763 | Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference Edward Meeds Informatics Institute University of Amsterdam tmeeds@gmail.com Max Welling∗ Informatics Institute University of Amsterdam welling.max@gmail.com Abstract We describe an embarrassingly parallel, any... | 2015 | 255 |
5,764 | Basis Refinement Strategies for Linear Value Function Approximation in MDPs Gheorghe Comanici School of Computer Science McGill University Montreal, Canada gcoman@cs.mcgill.ca Doina Precup School of Computer Science McGill University Montreal, Canada dprecup@cs.mcgill.ca Prakash Panangaden Scho... | 2015 | 256 |
5,765 | Stop Wasting My Gradients: Practical SVRG Reza Babanezhad1, Mohamed Osama Ahmed1, Alim Virani2, Mark Schmidt1 Department of Computer Science University of British Columbia 1{rezababa, moahmed, schmidtm}@cs.ubc.ca,2alim.virani@gmail.com Jakub Koneˇcn´y School of Mathematics University of Edinburgh kubo.k... | 2015 | 257 |
5,766 | Saliency, Scale and Information: Towards a Unifying Theory Shafin Rahman Department of Computer Science University of Manitoba shafin109@gmail.com Neil D.B. Bruce Department of Computer Science University of Manitoba bruce@cs.umanitoba.ca Abstract In this paper we present a definition for visual sal... | 2015 | 258 |
5,767 | Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression Yu-Ying Liu, Shuang Li, Fuxin Li, Le Song, and James M. Rehg College of Computing Georgia Institute of Technology Atlanta, GA Abstract The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling di... | 2015 | 259 |
5,768 | Policy Evaluation Using the Ω-Return Philip S. Thomas University of Massachusetts Amherst Carnegie Mellon University Scott Niekum University of Texas at Austin Georgios Theocharous Adobe Research George Konidaris Duke University Abstract We propose the Ω-return as an alternative to the λ-return cu... | 2015 | 26 |
5,769 | Efficient Output Kernel Learning for Multiple Tasks Pratik Jawanpuria1, Maksim Lapin2, Matthias Hein1 and Bernt Schiele2 1Saarland University, Saarbr¨ucken, Germany 2Max Planck Institute for Informatics, Saarbr¨ucken, Germany Abstract The paradigm of multi-task learning is that one can achieve better generaliz... | 2015 | 260 |
5,770 | Texture Synthesis Using Convolutional Neural Networks Leon A. Gatys Centre for Integrative Neuroscience, University of T¨ubingen, Germany Bernstein Center for Computational Neuroscience, T¨ubingen, Germany Graduate School of Neural Information Processing, University of T¨ubingen, Germany leon.gatys@bethgela... | 2015 | 261 |
5,771 | Hessian-free Optimization for Learning Deep Multidimensional Recurrent Neural Networks Minhyung Cho Chandra Shekhar Dhir Jaehyung Lee Applied Research Korea, Gracenote Inc. {mhyung.cho,shekhardhir}@gmail.com jaehyung.lee@kaist.ac.kr Abstract Multidimensional recurrent neural networks (MDRNNs) have sho... | 2015 | 262 |
5,772 | Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation Alaa Saade1 and Florent Krzakala1,2 1 Laboratoire de Physique Statistique, CNRS & École Normale Supérieure, Paris, France. 2Sorbonne Universités, Université Pierre et Marie Curie Paris 06, F-75005, Paris, France Lenka Zdeborová ... | 2015 | 263 |
5,773 | Large-scale probabilistic predictors with and without guarantees of validity Vladimir Vovk∗, Ivan Petej∗, and Valentina Fedorova† ∗Department of Computer Science, Royal Holloway, University of London, UK †Yandex, Moscow, Russia {volodya.vovk,ivan.petej,alushaf}@gmail.com Abstract This paper studies theore... | 2015 | 264 |
5,774 | Learning with a Wasserstein Loss Charlie Frogner⇤ Chiyuan Zhang⇤ Center for Brains, Minds and Machines Massachusetts Institute of Technology frogner@mit.edu, chiyuan@mit.edu Hossein Mobahi CSAIL Massachusetts Institute of Technology hmobahi@csail.mit.edu Mauricio Araya-Polo Shell International E &... | 2015 | 265 |
5,775 | Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks Emily Denton∗ Dept. of Computer Science Courant Institute New York University Soumith Chintala∗ Arthur Szlam Rob Fergus Facebook AI Research New York Abstract In this paper we introduce a generative parametric model c... | 2015 | 266 |
5,776 | Estimating Jaccard Index with Missing Observations: A Matrix Calibration Approach Wenye Li Macao Polytechnic Institute Macao SAR, China wyli@ipm.edu.mo Abstract The Jaccard index is a standard statistics for comparing the pairwise similarity between data samples. This paper investigates the problem of est... | 2015 | 267 |
5,777 | On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs Wei Cao1 Jian Li1 Yufei Tao2 Zhize Li1 1Tsinghua University 2Chinese University of Hong Kong 1{cao-w13@mails, lijian83@mail, zz-li14@mails}.tsinghua.edu.cn 2taoyf@cse.cuhk.edu.hk Abstract This paper discusses how to efficiently c... | 2015 | 268 |
5,778 | Black-box optimization of noisy functions with unknown smoothness Jean-Bastien Grill Michal Valko SequeL team, INRIA Lille - Nord Europe, France jean-bastien.grill@inria.fr michal.valko@inria.fr R´emi Munos Google DeepMind, UK∗ munos@google.com Abstract We study the problem of black-box optimizati... | 2015 | 269 |
5,779 | Bayesian Optimization with Exponential Convergence Kenji Kawaguchi MIT Cambridge, MA, 02139 kawaguch@mit.edu Leslie Pack Kaelbling MIT Cambridge, MA, 02139 lpk@csail.mit.edu Tom´as Lozano-P´erez MIT Cambridge, MA, 02139 tlp@csail.mit.edu Abstract This paper presents a Bayesian optimization m... | 2015 | 27 |
5,780 | Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding Rie Johnson RJ Research Consulting Tarrytown, NY, USA riejohnson@gmail.com Tong Zhang∗ Baidu Inc., Beijing, China Rutgers University, Piscataway, NJ, USA tzhang@stat.rutgers.edu Abstract This paper presents ... | 2015 | 270 |
5,781 | Fast Rates for Exp-concave Empirical Risk Minimization Tomer Koren Technion Haifa 32000, Israel tomerk@technion.ac.il Kfir Y. Levy Technion Haifa 32000, Israel kfiryl@tx.technion.ac.il Abstract We consider Empirical Risk Minimization (ERM) in the context of stochastic optimization with exp-concave ... | 2015 | 271 |
5,782 | Learning both Weights and Connections for Efficient Neural Networks Song Han Stanford University songhan@stanford.edu Jeff Pool NVIDIA jpool@nvidia.com John Tran NVIDIA johntran@nvidia.com William J. Dally Stanford University NVIDIA dally@stanford.edu Abstract Neural networks are both com... | 2015 | 272 |
5,783 | Bayesian Dark Knowledge Anoop Korattikara, Vivek Rathod, Kevin Murphy Google Research {kbanoop, rathodv, kpmurphy}@google.com Max Welling University of Amsterdam m.welling@uva.nl Abstract We consider the problem of Bayesian parameter estimation for deep neural networks, which is important in problem set... | 2015 | 273 |
5,784 | On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators Changyou Chen† Nan Ding‡ Lawrence Carin† †Dept. of Electrical and Computer Engineering, Duke University, Durham, NC, USA ‡Google Inc., Venice, CA, USA cchangyou@gmail.com; dingnan@google.com; lcarin@duke.edu Abstract ... | 2015 | 274 |
5,785 | Teaching Machines to Read and Comprehend Karl Moritz Hermann† Tom´aˇs Koˇcisk´y†‡ Edward Grefenstette† Lasse Espeholt† Will Kay† Mustafa Suleyman† Phil Blunsom†‡ †Google DeepMind ‡University of Oxford {kmh,tkocisky,etg,lespeholt,wkay,mustafasul,pblunsom}@google.com Abstract Teaching machines to ... | 2015 | 275 |
5,786 | Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring David Kappel1 Stefan Habenschuss1 Robert Legenstein Wolfgang Maass Institute for Theoretical Computer Science Graz University of Technology A-8010 Graz, Austria [kappel, habenschuss, legi, maass]@igi.tugraz.at Abstract ... | 2015 | 276 |
5,787 | ß´¬»®²¿¬·²¹ Ó·²·³·¦¿¬·±² º±® λ¹®»·±² Ю±¾´»³ ©·¬¸ Ê»½¬±®óª¿´«»¼ Ñ«¬°«¬ Ю¿¬»»µ Ö¿·² Ó·½®±±º¬ λ»¿®½¸ô ×ÒÜ×ß °®¿¶¿·²à³·½®±±º¬ò½±³ ß³¾«¶ Ì»©¿®· ˲·ª»®·¬§ ±º Ó·½¸·¹¿²ô ß²² ß®¾±®ô ËÍß ¬»©¿®·¿à«³·½¸ò»¼« ß¾¬®¿½¬ ײ ®»¹®»·±² °®±¾´»³ ·²ª±´ª·²¹ ª»½¬±®óª¿´«»¼ ±«¬°«¬ ø±® »¯«·ª¿´»²¬´§ô ³«´¬·°´» ®»... | 2015 | 277 |
5,788 | Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks Kevin Scaman1 R´emi Lemonnier1,2 Nicolas Vayatis1 1CMLA, ENS Cachan, CNRS, Universit´e Paris- Saclay, France, 21000mercis, Paris, France {scaman, lemonnier, vayatis}@cmla.ens-cachan.fr Abstract The paper studies tra... | 2015 | 278 |
5,789 | Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width Christopher De Sa, Ce Zhang, Kunle Olukotun, and Christopher R´e cdesa@stanford.edu, czhang@cs.wisc.edu, kunle@stanford.edu, chrismre@stanford.edu Departments of Electrical Engineering and Computer Science Stanford University, ... | 2015 | 279 |
5,790 | Statistical Model Criticism using Kernel Two Sample Tests James Robert Lloyd Department of Engineering University of Cambridge Zoubin Ghahramani Department of Engineering University of Cambridge Abstract We propose an exploratory approach to statistical model criticism using maximum mean discrepancy... | 2015 | 28 |
5,791 | A Reduced-Dimension fMRI Shared Response Model Po-Hsuan Chen1, Janice Chen2, Yaara Yeshurun2, Uri Hasson2, James V. Haxby3, Peter J. Ramadge1 1Department of Electrical Engineering, Princeton University 2Princeton Neuroscience Institute and Department of Psychology, Princeton University 3Department of Psycholo... | 2015 | 280 |
5,792 | Semi-Proximal Mirror-Prox for Nonsmooth Composite Minimization Niao He Georgia Institute of Technology nhe6@gatech.edu Zaid Harchaoui NYU, Inria firstname.lastname@nyu.edu Abstract We propose a new first-order optimization algorithm to solve high-dimensional non-smooth composite minimization problems... | 2015 | 281 |
5,793 | Subset Selection by Pareto Optimization Chao Qian Yang Yu Zhi-Hua Zhou National Key Laboratory for Novel Software Technology, Nanjing University Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023, China {qianc,yuy,zhouzh}@lamda.nju.edu.cn Abstract Selecti... | 2015 | 282 |
5,794 | Parallel Correlation Clustering on Big Graphs Xinghao Pan↵,✏, Dimitris Papailiopoulos↵,✏, Samet Oymak↵,✏, Benjamin Recht↵,✏,σ, Kannan Ramchandran✏, and Michael I. Jordan↵,✏,σ ↵AMPLab, ✏EECS at UC Berkeley, σStatistics at UC Berkeley Abstract Given a similarity graph between items, correlation clustering (CC) ... | 2015 | 283 |
5,795 | Fast Two-Sample Testing with Analytic Representations of Probability Measures Kacper Chwialkowski Gatsby Computational Neuroscience Unit, UCL kacper.chwialkowski@gmail.com Aaditya Ramdas Dept. of EECS and Statistics, UC Berkeley aramdas@cs.berkeley.edu Dino Sejdinovic Dept of Statistics, University of... | 2015 | 284 |
5,796 | A Recurrent Latent Variable Model for Sequential Data Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron Courville, Yoshua Bengio∗ Department of Computer Science and Operations Research Universit´e de Montr´eal ∗CIFAR Senior Fellow {firstname.lastname}@umontreal.ca Abstract In this paper... | 2015 | 285 |
5,797 | Unsupervised Learning by Program Synthesis Kevin Ellis Department of Brain and Cognitive Sciences Massachusetts Institute of Technology ellisk@mit.edu Armando Solar-Lezama MIT CSAIL Massachusetts Institute of Technology asolar@csail.mit.edu Joshua B. Tenenbaum Department of Brain and Cognitive Scien... | 2015 | 286 |
5,798 | Learning Causal Graphs with Small Interventions Karthikeyan Shanmugam1, Murat Kocaoglu2, Alexandros G. Dimakis3, Sriram Vishwanath4 Department of Electrical and Computer Engineering The University of Texas at Austin, USA 1karthiksh@utexas.edu,2mkocaoglu@utexas.edu, 3dimakis@austin.utexas.edu,4sriram@ece.utexa... | 2015 | 287 |
5,799 | Learning to Transduce with Unbounded Memory Edward Grefenstette Google DeepMind etg@google.com Karl Moritz Hermann Google DeepMind kmh@google.com Mustafa Suleyman Google DeepMind mustafasul@google.com Phil Blunsom Google DeepMind and Oxford University pblunsom@google.com Abstract Recently, s... | 2015 | 288 |
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