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 |
|---|---|---|---|
4,800 | A lattice filter model of the visual pathway Karol Gregor Dmitri B. Chklovskii Janelia Farm Research Campus, HHMI 19700 Helix Drive, Ashburn, VA {gregork, mitya}@janelia.hhmi.org Abstract Early stages of visual processing are thought to decorrelate, or whiten, the incoming temporally varying signals. Motiv... | 2012 | 82 |
4,801 | Multi-scale Hyper-time Hardware Emulation of Human Motor Nervous System Based on Spiking Neurons using FPGA C. Minos Niu Department of Biomedical Engineering University of Southern California Los Angeles, CA 90089 minos.niu@sangerlab.net Sirish K. Nandyala Department of Biomedical Engineering Univer... | 2012 | 83 |
4,802 | Recognizing Activities by Attribute Dynamics Weixin Li Nuno Vasconcelos Department of Electrical and Computer Engineering University of California, San Diego La Jolla, CA 92093, United States {wel017, nvasconcelos}@ucsd.edu Abstract In this work, we consider the problem of modeling the dynamic structure... | 2012 | 84 |
4,803 | Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space Yanyan Lan Institute of Computing Technology Chinese Academy of Sciences lanyanyan@ict.ac.cn Jiafeng Guo Institute of Computing Technology Chinese Academy of Sciences guojiafeng@ict.ac.cn Xueqi Cheng Institute of... | 2012 | 85 |
4,804 | A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound Shusen Wang and Zhihua Zhang College of Computer Science & Technology Zhejiang University Hangzhou, China 310027 {wss,zhzhang}@zju.edu.cn Abstract The CUR matrix decomposition is an important extension of Nystr¨om app... | 2012 | 86 |
4,805 | Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search Arthur Guez aguez@gatsby.ucl.ac.uk David Silver d.silver@cs.ucl.ac.uk Peter Dayan dayan@gatsby.ucl.ac.uk Abstract Bayesian model-based reinforcement learning is a formally elegant approach to learning optimal behaviour under mo... | 2012 | 87 |
4,806 | Dual-Space Analysis of the Sparse Linear Model David Wipf and Yi Wu Visual Computing Group, Microsoft Research Asia davidwipf@gmail.com, jxwuyi@gmail.com Abstract Sparse linear (or generalized linear) models combine a standard likelihood function with a sparse prior on the unknown coefficients. These priors ca... | 2012 | 88 |
4,807 | Neuronal Spike Generation Mechanism as an Oversampling, Noise-shaping A-to-D Converter Dmitri B. Chklovskii Daniel Soudry Janelia Farm Research Campus Department of Electrical Engineering Howard Hughes Medical Institute ... | 2012 | 89 |
4,808 | Super-Bit Locality-Sensitive Hashing Jianqiu Ji⇤, Jianmin Li⇤, Shuicheng Yan†, Bo Zhang⇤, Qi Tian‡ ⇤State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Computer Science and Technology, Tsinghua University, Beij... | 2012 | 9 |
4,809 | Multiple Operator-valued Kernel Learning Hachem Kadri LIF - CNRS / INRIA Lille - Sequel Project Universit´e Aix-Marseille Marseille, France hachem.kadri@lif.univ-mrs.fr Alain Rakotomamonjy LITIS EA 4108 Universit´e de Rouen St Etienne du Rouvray, France alain.rakotomamony@insa-rouen.fr Francis Bac... | 2012 | 90 |
4,810 | No-Regret Algorithms for Unconstrained Online Convex Optimization Matthew Streeter Duolingo, Inc.∗ Pittsburgh, PA 15232 matt@duolingo.com H. Brendan McMahan Google, Inc. Seattle, WA 98103 mcmahan@google.com Abstract Some of the most compelling applications of online convex optimization, including ... | 2012 | 91 |
4,811 | Learning Partially Observable Models Using Temporally Abstract Decision Trees Erik Talvitie Department of Mathematics and Computer Science Franklin & Marshall College Lancaster, PA 17604 erik.talvitie@fandm.edu Abstract This paper introduces timeline trees, which are partial models of partially observab... | 2012 | 92 |
4,812 | Emergence of Object-Selective Features in Unsupervised Feature Learning Adam Coates, Andrej Karpathy, Andrew Y. Ng Computer Science Department Stanford University Stanford, CA 94305 {acoates,karpathy,ang}@cs.stanford.edu Abstract Recent work in unsupervised feature learning has focused on the goal of di... | 2012 | 93 |
4,813 | CPRL – An Extension of Compressive Sensing to the Phase Retrieval Problem Henrik Ohlsson Division of Automatic Control, Department of Electrical Engineering, Link¨oping University, Sweden. Department of Electrical Engineering and Computer Sciences University of California at Berkeley, CA, USA ohlsson@eecs... | 2012 | 94 |
4,814 | Learning optimal spike-based representations Ralph Bourdoukan∗ Group for Neural Theory ´Ecole Normale Sup´erieure Paris, France ralph.bourdoukan@ens.fr David G.T. Barrett∗ Group for Neural Theory ´Ecole Normale Sup´erieure Paris, France david.barrett@ens.fr Christian K. Machens Champalimaud Neur... | 2012 | 95 |
4,815 | Collaborative Ranking With 17 Parameters Maksims N. Volkovs University of Toronto mvolkovs@cs.toronto.edu Richard S. Zemel University of Toronto zemel@cs.toronto.edu Abstract The primary application of collaborate filtering (CF) is to recommend a small set of items to a user, which entails ranking. Mos... | 2012 | 96 |
4,816 | Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models Ke Jiang, Brian Kulis Department of CSE The Ohio State University {jiangk,kulis}@cse.ohio-state.edu Michael I. Jordan Departments of EECS and Statistics University of California at Berkeley jordan@cs.berkeley.edu Abst... | 2012 | 97 |
4,817 | Deep Representations and Codes for Image Auto-Annotation Ryan Kiros Department of Computing Science University of Alberta Edmonton, AB, Canada rkiros@ualberta.ca Csaba Szepesv´ari Department of Computing Science University of Alberta Edmonton, AB, Canada szepesva@ualberta.ca Abstract The task ... | 2012 | 98 |
4,818 | Patient Risk Stratification for Hospital-Associated C. diff as a Time-Series Classification Task Jenna Wiens jwiens@mit.edu John V. Guttag guttag@mit.edu Eric Horvitz horvitz@microsoft.com Abstract A patient’s risk for adverse events is affected by temporal processes including the nature and timing of... | 2012 | 99 |
4,819 | Inferring neural population dynamics from multiple partial recordings of the same neural circuit Srinivas C. Turaga∗1,2, Lars Buesing1, Adam M. Packer2, Henry Dalgleish2, Noah Pettit2, Michael H¨ausser2 and Jakob H. Macke3,4 1Gatsby Computational Neuroscience Unit, University College London 2Wolfson Institute... | 2013 | 1 |
4,820 | A Kernel Test for Three-Variable Interactions Dino Sejdinovic, Arthur Gretton Gatsby Unit, CSML, UCL, UK {dino.sejdinovic, arthur.gretton}@gmail.com Wicher Bergsma Department of Statistics, LSE, UK w.p.bergsma@lse.ac.uk Abstract We introduce kernel nonparametric tests for Lancaster three-variable intera... | 2013 | 10 |
4,821 | Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions Yasin Abbasi-Yadkori Queensland University of Technology yasin.abbasiyadkori@qut.edu.au Peter L. Bartlett UC Berkeley and QUT bartlett@eecs.berkeley.edu Varun Kanade UC Berkeley vkanade@eecs... | 2013 | 100 |
4,822 | Improved and Generalized Upper Bounds on the Complexity of Policy Iteration Bruno Scherrer Inria, Villers-l`es-Nancy, F-54600, France Universit´e de Lorraine, LORIA, UMR 7503, Vandoeuvre-l`es-Nancy, F-54506, France bruno.scherrer@inria.fr Abstract Given a Markov Decision Process (MDP) with n states and m ... | 2013 | 101 |
4,823 | Approximate Inference in Continuous Determinantal Point Processes Raja Hafiz Affandi1, Emily B. Fox2, and Ben Taskar2 1University of Pennsylvania, rajara@wharton.upenn.edu 2University of Washington, {ebfox@stat,taskar@cs}.washington.edu Abstract Determinantal point processes (DPPs) are random point processes... | 2013 | 102 |
4,824 | Streaming Variational Bayes Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson University of California, Berkeley {tab@stat, nickboyd@eecs, wibisono@eecs, ashia@stat}.berkeley.edu Michael I. Jordan University of California, Berkeley jordan@cs.berkeley.edu Abstract We present SDA-... | 2013 | 103 |
4,825 | One-shot learning by inverting a compositional causal process Brenden M. Lake Dept. of Brain and Cognitive Sciences MIT brenden@mit.edu Ruslan Salakhutdinov Dept. of Statistics and Computer Science University of Toronto rsalakhu@cs.toronto.edu Joshua B. Tenenbaum Dept. of Brain and Cognitive Scien... | 2013 | 104 |
4,826 | Large Scale Distributed Sparse Precision Estimation Huahua Wang, Arindam Banerjee Dept. of Computer Science & Engg, University of Minnesota, Twin Cities {huwang,banerjee}@cs.umn.edu Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon Dept. of Computer Science, University of Texas, Austin {cjhsieh,pradeepr... | 2013 | 105 |
4,827 | Online Variational Approximations to non-Exponential Family Change Point Models: With Application to Radar Tracking Ryan Turner Northrop Grumman Corp. ryan.turner@ngc.com Steven Bottone Northrop Grumman Corp. steven.bottone@ngc.com Clay Stanek Northrop Grumman Corp. clay.stanek@ngc.com Abstract ... | 2013 | 106 |
4,828 | RNADE: The real-valued neural autoregressive density-estimator Benigno Uria and Iain Murray School of Informatics University of Edinburgh {b.uria,i.murray}@ed.ac.uk Hugo Larochelle D´epartement d’informatique Universit´e de Sherbrooke hugo.larochelle@usherbrooke.ca Abstract We introduce RNADE, a n... | 2013 | 107 |
4,829 | Estimating the Unseen: Improved Estimators for Entropy and other Properties Gregory Valiant ∗ Stanford University Stanford, CA 94305 valiant@stanford.edu Paul Valiant † Brown University Providence, RI 02912 pvaliant@gmail.com Abstract Recently, Valiant and Valiant [1, 2] showed that a class of d... | 2013 | 108 |
4,830 | Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture Trevor Campbell MIT Cambridge, MA 02139 tdjc@mit.edu Miao Liu Duke University Durham, NC 27708 miao.liu@duke.edu Brian Kulis Ohio State University Columbus, OH 43210 kulis@cse.ohio-state.edu Jonathan P. How MIT ... | 2013 | 109 |
4,831 | Accelerated Mini-Batch Stochastic Dual Coordinate Ascent Shai Shalev-Shwartz School of Computer Science and Engineering Hebrew University, Jerusalem, Israel Tong Zhang Department of Statistics Rutgers University, NJ, USA Abstract Stochastic dual coordinate ascent (SDCA) is an effective technique for s... | 2013 | 11 |
4,832 | Parametric Task Learning Ichiro Takeuchi Nagoya Institute of Technology Nagoya, 466-8555, Japan takeuchi.ichiro@nitech.ac.jp Tatsuya Hongo Nagoya Institute of Technology Nagoya, 466-8555, Japan hongo.mllab.nit@gmail.com Masashi Sugiyama Tokyo Institute of Technology Tokyo, 152-8552, Japan sugi@c... | 2013 | 110 |
4,833 | Generalized Denoising Auto-Encoders as Generative Models Yoshua Bengio, Li Yao, Guillaume Alain, and Pascal Vincent D´epartement d’informatique et recherche op´erationnelle, Universit´e de Montr´eal Abstract Recent work has shown how denoising and contractive autoencoders implicitly capture the structure of... | 2013 | 111 |
4,834 | Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation John C. Duchi1 Michael I. Jordan1,2 Martin J. Wainwright1,2 1Department of Electrical Engineering and Computer Science 2Department of Statistics University of California, Berkeley {jduchi,jordan,wainwrig}@eecs.berkeley.edu Abstra... | 2013 | 112 |
4,835 | Reward Mapping for Transfer in Long-Lived Agents Xiaoxiao Guo Computer Science and Eng. University of Michigan guoxiao@umich.edu Satinder Singh Computer Science and Eng. University of Michigan baveja@umich.edu Richard Lewis Department of Psychology University of Michigan rickl@umich.edu Abstra... | 2013 | 113 |
4,836 | Distributed Exploration in Multi-Armed Bandits Eshcar Hillel Yahoo Labs, Haifa eshcar@yahoo-inc.com Zohar Karnin Yahoo Labs, Haifa zkarnin@yahoo-inc.com Tomer Koren∗ Technion — Israel Inst. of Technology tomerk@technion.ac.il Ronny Lempel Yahoo Labs, Haifa rlempel@yahoo-inc.com Oren Somekh Y... | 2013 | 114 |
4,837 | It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals Barbara Rakitsch Machine Learning and Computational Biology Research Group Max Planck Institutes T¨ubingen, Germany rakitsch@tuebingen.mpg.de Christoph Lippert Microsoft Research Los Angeles, USA lippert... | 2013 | 115 |
4,838 | Projecting Ising Model Parameters for Fast Mixing Justin Domke NICTA, The Australian National University justin.domke@nicta.com.au Xianghang Liu NICTA, The University of New South Wales xianghang.liu@nicta.com.au Abstract Inference in general Ising models is difficult, due to high treewidth making treeba... | 2013 | 116 |
4,839 | Low-rank matrix reconstruction and clustering via approximate message passing Ryosuke Matsushita NTT DATA Mathematical Systems Inc. 1F Shinanomachi Rengakan, 35, Shinanomachi, Shinjuku-ku, Tokyo, 160-0016, Japan matsur8@gmail.com Toshiyuki Tanaka Department of Systems Science, Graduate School of Inf... | 2013 | 117 |
4,840 | Inverse Density as an Inverse Problem: the Fredholm Equation Approach Qichao Que, Mikhail Belkin Department of Computer Science and Engineering The Ohio State University {que,mbelkin}@cse.ohio-state.edu Abstract We address the problem of estimating the ratio q p where p is a density function and q is ... | 2013 | 118 |
4,841 | Modeling Overlapping Communities with Node Popularities Prem Gopalan1, Chong Wang2, and David M. Blei1 1Department of Computer Science, Princeton University, {pgopalan,blei}@cs.princeton.edu 2Machine Learning Department, Carnegie Mellon University, {chongw}@cs.cmu.edu Abstract We develop a probabilistic app... | 2013 | 119 |
4,842 | A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks Junming Yin School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 junmingy@cs.cmu.edu Qirong Ho School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 qho@cs.cmu.edu Eric ... | 2013 | 12 |
4,843 | Reflection methods for user-friendly submodular optimization Stefanie Jegelka UC Berkeley Berkeley, CA, USA Francis Bach INRIA - ENS Paris, France Suvrit Sra MPI for Intelligent Systems T¨ubingen, Germany Abstract Recently, it has become evident that submodularity naturally captures widely occu... | 2013 | 120 |
4,844 | Compressive Feature Learning Hristo S. Paskov Department of Computer Science Stanford University hpaskov@cs.stanford.edu Robert West Department of Computer Science Stanford University west@cs.stanford.edu John C. Mitchell Department of Computer Science Stanford University mitchell@cs.stanford.ed... | 2013 | 121 |
4,845 | Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions Eftychios A. Pnevmatikakis and Liam Paninski Department of Statistics, Center for Theoretical Neuroscience Grossman Center for the Statistics of Mind, Columbia University, New York, NY {eftychios, liam}@stat.col... | 2013 | 122 |
4,846 | Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms Adrien Todeschini INRIA - IMB - Univ. Bordeaux 33405 Talence, France Adrien.Todeschini@inria.fr Franc¸ois Caron Univ. Oxford, Dept. of Statistics Oxford, OX1 3TG, UK Caron@stats.ox.ac.uk Marie Chavent Univ. B... | 2013 | 123 |
4,847 | Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering Shinichi Nakajima Nikon Corporation Tokyo, 140-8601 Japan nakajima.s@nikon.co.jp Akiko Takeda The University of Tokyo Tokyo, 113-8685 Japan takeda@mist.i.u-tokyo.ac.jp S. Derin Babacan Google Inc. ... | 2013 | 124 |
4,848 | Reservoir Boosting : Between Online and Offline Ensemble Learning Leonidas Lefakis Idiap Research Institute Martigny, Switzerland leonidas.lefakis@idiap.ch Franc¸ois Fleuret Idiap Research Institute Martigny, Switzerland francois.fleuret@idiap.ch Abstract We propose to train an ensemble with the he... | 2013 | 125 |
4,849 | Faster Ridge Regression via the Subsampled Randomized Hadamard Transform Yichao Lu1 Paramveer S. Dhillon2 Dean Foster1 Lyle Ungar2 1Statistics (Wharton School), 2Computer & Information Science University of Pennsylvania, Philadelphia, PA, U.S.A {dhillon|ungar}@cis.upenn.edu foster@wharton.upenn.edu, y... | 2013 | 126 |
4,850 | Convex Relaxations for Permutation Problems Fajwel Fogel C.M.A.P., ´Ecole Polytechnique, Palaiseau, France fogel@cmap.polytechnique.fr Rodolphe Jenatton CRITEO, Paris & C.M.A.P., ´Ecole Polytechnique, Palaiseau, France jenatton@cmap.polytechnique.fr Francis Bach INRIA, SIERRA Project-Team & D.I., ... | 2013 | 127 |
4,851 | Online Learning of Dynamic Parameters in Social Networks Shahin Shahrampour 1 Alexander Rakhlin 2 Ali Jadbabaie 1 1Department of Electrical and Systems Engineering, 2Department of Statistics University of Pennsylvania Philadelphia, PA 19104 USA 1{shahin,jadbabai}@seas.upenn.edu 2rakhlin@wharton.upenn.... | 2013 | 128 |
4,852 | Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests Yacine Jernite, Yoni Halpern, David Sontag Courant Institute of Mathematical Sciences New York University {halpern, jernite, dsontag}@cs.nyu.edu Abstract We give a polynomial-time algorithm for provably learning the structure and pa... | 2013 | 129 |
4,853 | Multi-Prediction Deep Boltzmann Machines Ian J. Goodfellow, Mehdi Mirza, Aaron Courville, Yoshua Bengio D´epartement d’informatique et de recherche op´erationnelle Universit´e de Montr´eal Montr´eal, QC H3C 3J7 {goodfeli,mirzamom,courvila}@iro.umontreal.ca, Yoshua.Bengio@umontreal.ca Abstract We introdu... | 2013 | 13 |
4,854 | Gaussian Process Conditional Copulas with Applications to Financial Time Series Jos´e Miguel Hern´andez-Lobato Engineering Department University of Cambridge jmh233@cam.ac.uk James Robert Lloyd Engineering Department University of Cambridge jrl44@cam.ac.uk Daniel Hern´andez-Lobato Computer Science... | 2013 | 130 |
4,855 | Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty Haichao Zhang†‡ and David Wipf § † School of Computer Science, Northwestern Polytechnical University, Xi’an, China ‡ Department of Electrical and Computer Engineering, Duke University, USA § Visual Computing Group, Microsoft Research A... | 2013 | 131 |
4,856 | Online Learning in Episodic Markovian Decision Processes by Relative Entropy Policy Search Alexander Zimin Institute of Science and Technology Austria alexander.zimin@ist.ac.at Gergely Neu INRIA Lille – Nord Europe gergely.neu@gmail.com Abstract We study the problem of online learning in finite episodi... | 2013 | 132 |
4,857 | Bayesian inference for low rank spatiotemporal neural receptive fields Mijung Park Electrical and Computer Engineering The University of Texas at Austin mjpark@mail.utexas.edu Jonathan W. Pillow Center for Perceptual Systems The University of Texas at Austin pillow@mail.utexas.edu Abstract The rece... | 2013 | 133 |
4,858 | Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation Bogdan Savchynskyy1 J¨org Kappes2 Paul Swoboda2 Christoph Schn¨orr1,2 1Heidelberg Collaboratory for Image Processing, Heidelberg University, Germany bogdan.savchynskyy@iwr.uni-heidelberg.de 2Image and Pattern Analysi... | 2013 | 134 |
4,859 | Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion Franz J. Kir´aly⇤ Department of Statistical Science and Centre for Inverse Problems University College London f.kiraly@ucl.ac.uk Louis Theran† Institute of Mathematics Discrete Geometry Group Freie Univ... | 2013 | 135 |
4,860 | Decision Jungles: Compact and Rich Models for Classification Jamie Shotton Toby Sharp Pushmeet Kohli Sebastian Nowozin John Winn Antonio Criminisi Microsoft Research Abstract Randomized decision trees and forests have a rich history in machine learning and have seen considerable success in applicat... | 2013 | 136 |
4,861 | Bayesian Estimation of Latently-grouped Parameters in Undirected Graphical Models Jie Liu Dept of CS, University of Wisconsin Madison, WI 53706 jieliu@cs.wisc.edu David Page Dept of BMI, University of Wisconsin Madison, WI 53706 page@biostat.wisc.edu Abstract In large-scale applications of undirec... | 2013 | 137 |
4,862 | (More) Efficient Reinforcement Learning via Posterior Sampling Osband, Ian Stanford University Stanford, CA 94305 iosband@stanford.edu Van Roy, Benjamin Stanford University Stanford, CA 94305 bvr@stanford.edu Russo, Daniel Stanford University Stanford, CA 94305 djrusso@stanford.edu Abstract ... | 2013 | 138 |
4,863 | Forgetful Bayes and myopic planning: Human learning and decision-making in a bandit setting Shunan Zhang Department of Cognitive Science University of California, San Diego La Jolla, CA 92093 s6zhang@ucsd.edu Angela J. Yu Department of Cognitive Science University of California, San Diego La Jolla, ... | 2013 | 139 |
4,864 | Learning and using language via recursive pragmatic reasoning about other agents Nathaniel J. Smith∗ University of Edinburgh Noah D. Goodman Stanford University Michael C. Frank Stanford University Abstract Language users are remarkably good at making inferences about speakers’ intentions in context, ... | 2013 | 14 |
4,865 | Stochastic Optimization of PCA with Capped MSG Raman Arora TTI-Chicago Chicago, IL USA arora@ttic.edu Andrew Cotter TTI-Chicago Chicago, IL USA cotter@ttic.edu Nathan Srebro Technion, Haifa, Israel and TTI-Chicago nati@ttic.edu Abstract We study PCA as a stochastic optimization problem and p... | 2013 | 140 |
4,866 | Embed and Project: Discrete Sampling with Universal Hashing Stefano Ermon, Carla P. Gomes Dept. of Computer Science Cornell University Ithaca NY 14853, U.S.A. Ashish Sabharwal IBM Watson Research Ctr. Yorktown Heights NY 10598, U.S.A. Bart Selman Dept. of Computer Science Cornell University It... | 2013 | 141 |
4,867 | Fisher-Optimal Neural Population Codes for High-Dimensional Diffeomorphic Stimulus Representations Zhuo Wang Department of Mathematics University of Pennsylvania Philadelphia, PA 19104 wangzhuo@sas.upenn.edu Alan A. Stocker Department of Psychology University of Pennsylvania Philadelphia, PA 19104... | 2013 | 142 |
4,868 | Near-Optimal Entrywise Sampling for Data Matrices Dimitris Achlioptas UC Santa Cruz optas@cs.ucsc.edu Zohar Karnin Yahoo Labs zkarnin@ymail.com Edo Liberty Yahoo Labs edo.liberty@ymail.com Abstract We consider the problem of selecting non-zero entries of a matrix A in order to produce a sparse s... | 2013 | 143 |
4,869 | A Comparative Framework for Preconditioned Lasso Algorithms Fabian L. Wauthier Statistics and WTCHG University of Oxford flw@stats.ox.ac.uk Nebojsa Jojic Microsoft Research, Redmond jojic@microsoft.com Michael I. Jordan Computer Science Division University of California, Berkeley jordan@cs.berke... | 2013 | 144 |
4,870 | Universal models for binary spike patterns using centered Dirichlet processes Il Memming Park123, Evan Archer24, Kenneth Latimer12, Jonathan W. Pillow1234 1. Institue for Neuroscience, 2. Center for Perceptual Systems, 3. Department of Psychology 4. Division of Statistics & Scientific Computation The Universit... | 2013 | 145 |
4,871 | What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach Zhenwen Dai University of Sheffield, UK, and FIAS, Goethe-University Frankfurt, Germany z.dai@sheffield.ac.uk Georgios Exarchakis Redwood Center for Theoretical Neuroscience, The University of California, Be... | 2013 | 146 |
4,872 | Correlations strike back (again): the case of associative memory retrieval Cristina Savin1 cs664@cam.ac.uk Peter Dayan2 dayan@gatsby.ucl.ac.uk M´at´e Lengyel1 m.lengyel@eng.cam.ac.uk 1Computational & Biological Learning Lab, Dept. Engineering, University of Cambridge, UK 2Gatsby Computational Neurosci... | 2013 | 147 |
4,873 | Understanding Dropout Pierre Baldi Department of Computer Science University of California, Irvine Irvine, CA 92697 pfbaldi@uci.edu Peter Sadowski Department of Computer Science University of California, Irvine Irvine, CA 92697 pjsadows@ics.uci.edu Abstract Dropout is a relatively new algorithm ... | 2013 | 148 |
4,874 | Efficient Supervised Sparse Analysis and Synthesis Operators Pablo Sprechmann Duke University pablo.sprechmann@duke.edu Roee Litman Tel Aviv University roeelitman@post.tau.ac.il Tal Ben Yakar Tel Aviv University talby10@gmail.com Alex Bronstein Tel Aviv University bron@eng.tau.ac.il Guillermo... | 2013 | 149 |
4,875 | Reinforcement Learning in Robust Markov Decision Processes Shiau Hong Lim Department of Mechanical Engineering National University of Singapore Singapore mpelsh@nus.edu.sg Huan Xu Department of Mechanical Engineering National University of Singapore Singapore mpexuh@nus.edu.sg Shie Mannor Depa... | 2013 | 15 |
4,876 | The Pareto Regret Frontier Wouter M. Koolen Queensland University of Technology wouter.koolen@qut.edu.au Abstract Performance guarantees for online learning algorithms typically take the form of regret bounds, which express that the cumulative loss overhead compared to the best expert in hindsight is smal... | 2013 | 150 |
4,877 | Approximate Dynamic Programming Finally Performs Well in the Game of Tetris Victor Gabillon INRIA Lille - Nord Europe, Team SequeL, FRANCE victor.gabillon@inria.fr Mohammad Ghavamzadeh∗ INRIA Lille - Team SequeL & Adobe Research mohammad.ghavamzadeh@inria.fr Bruno Scherrer INRIA Nancy - Grand Est,... | 2013 | 151 |
4,878 | Learning Feature Selection Dependencies in Multi-task Learning Daniel Hern´andez-Lobato Computer Science Department Universidad Aut´onoma de Madrid daniel.hernandez@uam.es Jos´e Miguel Hern´andez-Lobato Department of Engineering University of Cambridge jmh233@cam.ac.uk Abstract A probabilistic mod... | 2013 | 152 |
4,879 | Dimension-Free Exponentiated Gradient Francesco Orabona Toyota Technological Institute at Chicago Chicago, USA francesco@orabona.com Abstract I present a new online learning algorithm that extends the exponentiated gradient framework to infinite dimensional spaces. My analysis shows that the algorithm is ... | 2013 | 153 |
4,880 | Memory Limited, Streaming PCA Ioannis Mitliagkas Dept. of Electrical and Computer Engineering The University of Texas at Austin ioannis@utexas.edu Constantine Caramanis Dept. of Electrical and Computer Engineering The University of Texas at Austin constantine@utexas.edu Prateek Jain Microsoft Resear... | 2013 | 154 |
4,881 | Σ-Optimality for Active Learning on Gaussian Random Fields Yifei Ma Machine Learning Department Carnegie Mellon University yifeim@cs.cmu.edu Roman Garnett Computer Science Department University of Bonn rgarnett@uni-bonn.de Jeff Schneider Robotics Institute Carnegie Mellon University schneide@c... | 2013 | 155 |
4,882 | Recurrent linear models of simultaneously-recorded neural populations Marius Pachitariu, Biljana Petreska, Maneesh Sahani Gatsby Computational Neuroscience Unit University College London, UK {marius,biljana,maneesh}@gatsby.ucl.ac.uk Abstract Population neural recordings with long-range temporal structure ... | 2013 | 156 |
4,883 | On the Complexity and Approximation of Binary Evidence in Lifted Inference Guy Van den Broeck and Adnan Darwiche Computer Science Department University of California, Los Angeles {guyvdb,darwiche}@cs.ucla.edu Abstract Lifted inference algorithms exploit symmetries in probabilistic models to speed up inf... | 2013 | 157 |
4,884 | Pass-Efficient Unsupervised Feature Selection Crystal Maung Department of Computer Science The University of Texas at Dallas Crystal.Maung@gmail.com Haim Schweitzer Department of Computer Science The University of Texas at Dallas HSchweitzer@utdallas.edu Abstract The goal of unsupervised feature sele... | 2013 | 158 |
4,885 | Adaptive dropout for training deep neural networks Lei Jimmy Ba Brendan Frey Department of Electrical and Computer Engineering University of Toronto jimmy, frey@psi.utoronto.ca Abstract Recently, it was shown that deep neural networks can perform very well if the activities of hidden units are regulariz... | 2013 | 159 |
4,886 | Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel Tai Qin Department of Statistics University of Wisconsin-Madison Madison, WI qin@stat.wisc.edu Karl Rohe Department of Statistics University of Wisconsin-Madison Madison, WI karlrohe@stat.wisc.edu Abstract Spectra... | 2013 | 16 |
4,887 | On the Representational Efficiency of Restricted Boltzmann Machines James Martens∗ Arkadev Chattopadhyay+ Toniann Pitassi∗ Richard Zemel∗ ∗Department of Computer Science +School of Technology & Computer Science University of Toronto Tata Institute of Fundamental Research {jmartens,toni,zemel}@cs.toro... | 2013 | 160 |
4,888 | Robust Spatial Filtering with Beta Divergence Wojciech Samek1,4 Duncan Blythe1,4 Klaus-Robert M¨uller1,2 Motoaki Kawanabe3 1Machine Learning Group, Berlin Institute of Technology (TU Berlin), Berlin, German 2Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea 3ATR Brain Informati... | 2013 | 161 |
4,889 | DeViSE: A Deep Visual-Semantic Embedding Model Andrea Frome*, Greg S. Corrado*, Jonathon Shlens*, Samy Bengio Jeffrey Dean, Marc’Aurelio Ranzato, Tomas Mikolov * These authors contributed equally. {afrome, gcorrado, shlens, bengio, jeff, ranzato†, tmikolov}@google.com Google, Inc. Mountain View, CA, USA A... | 2013 | 162 |
4,890 | Symbolic Opportunistic Policy Iteration for Factored-Action MDPs Aswin Raghavana Roni Khardonb Alan Ferna Prasad Tadepallia a School of EECS, Oregon State University, Corvallis, OR, USA {nadamuna,afern,tadepall}@eecs.orst.edu b Department of Computer Science, Tufts University, Medford, MA, USA roni@cs.tufts... | 2013 | 163 |
4,891 | Least Informative Dimensions Fabian H. Sinz Department for Neuroethology Eberhard Karls University T¨ubingen fabee@epagoge.de Anna St¨ockl Department for Functional Zoology Lund University, Sweden Anna.Stockl@biol.lu.se Jan Grewe Department for Neuroethology Eberhard Karls University T¨ubingen j... | 2013 | 164 |
4,892 | A memory frontier for complex synapses Subhaneil Lahiri and Surya Ganguli Department of Applied Physics, Stanford University, Stanford CA sulahiri@stanford.edu, sganguli@stanford.edu Abstract An incredible gulf separates theoretical models of synapses, often described solely by a single scalar value denotin... | 2013 | 165 |
4,893 | Data-driven Distributionally Robust Polynomial Optimization Martin Mevissen IBM Research—Ireland martmevi@ie.ibm.com Emanuele Ragnoli IBM Research—Ireland eragnoli@ie.ibm.com Jia Yuan Yu IBM Research—Ireland jy@osore.ca Abstract We consider robust optimization for polynomial optimization problem... | 2013 | 166 |
4,894 | Learning Stochastic Inverses Andreas Stuhlm¨uller Brain and Cognitive Sciences MIT Jessica Taylor Department of Computer Science Stanford University Noah D. Goodman Department of Psychology Stanford University Abstract We describe a class of algorithms for amortized inference in Bayesian networks.... | 2013 | 167 |
4,895 | Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs Yann N. Dauphin, Yoshua Bengio D´epartement d’informatique et de recherche op´erationnelle Universit´e de Montr´eal Montr´eal, QC H3C 3J7 dauphiya@iro.umontreal.ca, Yoshua.Bengio@umontreal.ca Abstract Sparse high-dimensional data vec... | 2013 | 168 |
4,896 | Distributed k-Means and k-Median Clustering on General Topologies Maria Florina Balcan, Steven Ehrlich, Yingyu Liang School of Computer Science Georgia Institute of Technology Atlanta, GA 30332 {ninamf,sehrlich}@cc.gatech.edu,yliang39@gatech.edu Abstract This paper provides new algorithms for distribute... | 2013 | 169 |
4,897 | A Novel Two-Step Method for Cross Language Representation Learning Min Xiao and Yuhong Guo Department of Computer and Information Sciences Temple University, Philadelphia, PA 19122, USA {minxiao, yuhong}@temple.edu Abstract Cross language text classification is an important learning task in natural languag... | 2013 | 17 |
4,898 | Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) Francis Bach INRIA - Sierra Project-team Ecole Normale Sup´erieure, Paris, France francis.bach@ens.fr Eric Moulines LTCI Telecom ParisTech, Paris, France eric.moulines@enst.fr Abstract We consider the stochastic appro... | 2013 | 170 |
4,899 | Predicting Parameters in Deep Learning Misha Denil1 Babak Shakibi2 Laurent Dinh3 Marc’Aurelio Ranzato4 Nando de Freitas1,2 1University of Oxford, United Kingdom 2University of British Columbia, Canada 3Universit´e de Montr´eal, Canada 4Facebook Inc., USA {misha.denil,nando.de.freitas}@cs.ox.ac.uk ... | 2013 | 171 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.