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6,300 | Blind Attacks on Machine Learners Alex Beatson Department of Computer Science Princeton University abeatson@princeton.edu Zhaoran Wang Department of Operations Research and Financial Engineering Princeton University zhaoran@princeton.edu Han Liu Department of Operations Research and Financial En... | 2016 | 376 |
6,301 | Learning Deep Parsimonious Representations Renjie Liao1, Alexander Schwing2, Richard S. Zemel1,3, Raquel Urtasun1 University of Toronto1 University of Illinois at Urbana-Champaign2 Canadian Institute for Advanced Research3 {rjliao, zemel, urtasun}@cs.toronto.edu, aschwing@illinois.edu Abstract In this pap... | 2016 | 377 |
6,302 | Scalable Adaptive Stochastic Optimization Using Random Projections Gabriel Krummenacher♦∗ gabriel.krummenacher@inf.ethz.ch Brian McWilliams♥∗ brian@disneyresearch.com Yannic Kilcher♦ yannic.kilcher@inf.ethz.ch Joachim M. Buhmann♦ jbuhmann@inf.ethz.ch Nicolai Meinshausen♣ meinshausen@stat.math.ethz... | 2016 | 378 |
6,303 | Graphons, mergeons, and so on! Justin Eldridge Mikhail Belkin Yusu Wang The Ohio State University {eldridge, mbelkin, yusu}@cse.ohio-state.edu Abstract In this work we develop a theory of hierarchical clustering for graphs. Our modeling assumption is that graphs are sampled from a graphon, which is a powe... | 2016 | 379 |
6,304 | Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections Xiao-Jiao Mao†, Chunhua Shen⋆, Yu-Bin Yang† †State Key Laboratory for Novel Software Technology, Nanjing University, China ⋆School of Computer Science, University of Adelaide, Australia Abstract In thi... | 2016 | 38 |
6,305 | Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings Tolga Bolukbasi1, Kai-Wei Chang2, James Zou2, Venkatesh Saligrama1,2, Adam Kalai2 1Boston University, 8 Saint Mary’s Street, Boston, MA 2Microsoft Research New England, 1 Memorial Drive, Cambridge, MA tolgab@bu.edu, kw@kwchang.n... | 2016 | 380 |
6,306 | Memory-Efficient Backpropagation Through Time Audr¯unas Gruslys Google DeepMind audrunas@google.com Rémi Munos Google DeepMind munos@google.com Ivo Danihelka Google DeepMind danihelka@google.com Marc Lanctot Google DeepMind lanctot@google.com Alex Graves Google DeepMind gravesa@google.com ... | 2016 | 381 |
6,307 | Solving Marginal MAP Problems with NP Oracles and Parity Constraints Yexiang Xue Department of Computer Science Cornell University yexiang@cs.cornell.edu Zhiyuan Li∗ Institute of Interdisciplinary Information Sciences Tsinghua University lizhiyuan13@mails.tsinghua.edu.cn Stefano Ermon Department o... | 2016 | 382 |
6,308 | Differential Privacy without Sensitivity Kentaro Minami The University of Tokyo kentaro minami@mist.i.u-tokyo.ac.jp Hiromi Arai The University of Tokyo arai@dl.itc.u-tokyo.ac.jp Issei Sato The University of Tokyo sato@k.u-tokyo.ac.jp Hiroshi Nakagawa The University of Tokyo nakagawa@dl.itc.u-tok... | 2016 | 383 |
6,309 | Adaptive Smoothed Online Multi-Task Learning Keerthiram Murugesan∗ Carnegie Mellon University kmuruges@cs.cmu.edu Hanxiao Liu∗ Carnegie Mellon University hanxiaol@cs.cmu.edu Jaime Carbonell Carnegie Mellon University jgc@cs.cmu.edu Yiming Yang Carnegie Mellon University yiming@cs.cmu.edu Abstr... | 2016 | 384 |
6,310 | Efficient and Robust Spiking Neural Circuit for Navigation Inspired by Echolocating Bats Pulkit Tandon, Yash H. Malviya Indian Institute of Technology, Bombay pulkit1495,yashmalviya94@gmail.com Bipin Rajendran New Jersey Institute of Technology bipin@njit.edu Abstract We demonstrate a spiking neural ci... | 2016 | 385 |
6,311 | Optimal Cluster Recovery in the Labeled Stochastic Block Model Se-Young Yun CNLS, Los Alamos National Lab. Los Alamos, NM 87545 syun@lanl.gov Alexandre Proutiere Automatic Control Dept., KTH Stockholm 100-44, Sweden alepro@kth.se Abstract We consider the problem of community detection or clusterin... | 2016 | 386 |
6,312 | Relevant sparse codes with variational information bottleneck Matthew Chalk IST Austria Am Campus 1 A - 3400 Klosterneuburg, Austria Olivier Marre Institut de la Vision 17, Rue Moreau 75012, Paris, France Gasper Tkacik IST Austria Am Campus 1 A - 3400 Klosterneuburg, Austria Abstract In ma... | 2016 | 387 |
6,313 | Learning What and Where to Draw Scott Reed1,∗ reedscot@google.com Zeynep Akata2 akata@mpi-inf.mpg.de Santosh Mohan1 santoshm@umich.edu Samuel Tenka1 samtenka@umich.edu Bernt Schiele2 schiele@mpi-inf.mpg.de Honglak Lee1 honglak@umich.edu 1University of Michigan, Ann Arbor, USA 2Max Planck Ins... | 2016 | 388 |
6,314 | A Bio-inspired Redundant Sensing Architecture Anh Tuan Nguyen, Jian Xu and Zhi Yang∗ Department of Biomedical Engineering University of Minnesota Minneapolis, MN 55455 ∗yang5029@umn.edu Abstract Sensing is the process of deriving signals from the environment that allows artificial systems to interact with ... | 2016 | 389 |
6,315 | On Valid Optimal Assignment Kernels and Applications to Graph Classification Nils M. Kriege Department of Computer Science TU Dortmund, Germany nils.kriege@tu-dortmund.de Pierre-Louis Giscard Department of Computer Science University of York, UK pierre-louis.giscard@york.ac.uk Richard C. Wilson Dep... | 2016 | 39 |
6,316 | Bayesian Optimization with Robust Bayesian Neural Networks Jost Tobias Springenberg Aaron Klein Stefan Falkner Frank Hutter Department of Computer Science University of Freiburg {springj,kleinaa,sfalkner,fh}@cs.uni-freiburg.de Abstract Bayesian optimization is a prominent method for optimizing expen... | 2016 | 390 |
6,317 | Statistical Inference for Cluster Trees Jisu Kim Department of Statistics Carnegie Mellon University Pittsburgh, USA jisuk1@andrew.cmu.edu Yen-Chi Chen Department of Statistics University of Washington Seattle, USA yenchic@uw.edu Sivaraman Balakrishnan Department of Statistics Carnegie Mellon ... | 2016 | 391 |
6,318 | Combinatorial Multi-Armed Bandit with General Reward Functions Wei Chen∗ Wei Hu† Fu Li‡ Jian Li§ Yu Liu¶ Pinyan Lu∥ Abstract In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB) framework that allows a general nonlinear reward function, whose expected value may not depend... | 2016 | 392 |
6,319 | Learning Sensor Multiplexing Design through Back-propagation Ayan Chakrabarti Toyota Technological Institute at Chicago 6045 S. Kenwood Ave., Chicago, IL ayanc@ttic.edu Abstract Recent progress on many imaging and vision tasks has been driven by the use of deep feed-forward neural networks, which are tr... | 2016 | 393 |
6,320 | “Short-Dot”: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products Sanghamitra Dutta Carnegie Mellon University sanghamd@andrew.cmu.edu Viveck Cadambe Pennsylvania State University viveck@engr.psu.edu Pulkit Grover Carnegie Mellon University pgrover@andrew.cmu.edu Abstract... | 2016 | 394 |
6,321 | SEBOOST – Boosting Stochastic Learning Using Subspace Optimization Techniques Elad Richardson*1 Rom Herskovitz*1 Boris Ginsburg2 Michael Zibulevsky1 1Technion, Israel Institute of Technology 2Nvidia INC {eladrich,mzib}@cs.technion.ac.il {fornoch,boris.ginsburg}@gmail.com Abstract We present SEBOOST, a... | 2016 | 395 |
6,322 | VIME: Variational Information Maximizing Exploration Rein Houthooft§†‡, Xi Chen†‡, Yan Duan†‡, John Schulman†‡, Filip De Turck§, Pieter Abbeel†‡ † UC Berkeley, Department of Electrical Engineering and Computer Sciences § Ghent University - imec, Department of Information Technology ‡ OpenAI Abstract Scala... | 2016 | 396 |
6,323 | Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity Amit Daniely Google Brain Roy Frostig∗ Google Brain Yoram Singer Google Brain Abstract We develop a general duality between neural networks and compositional kernel Hilbert spaces. We introduc... | 2016 | 397 |
6,324 | Unsupervised Domain Adaptation with Residual Transfer Networks Mingsheng Long†, Han Zhu†, Jianmin Wang†, and Michael I. Jordan♯ †KLiss, MOE; TNList; School of Software, Tsinghua University, China ♯University of California, Berkeley, Berkeley, USA {mingsheng,jimwang}@tsinghua.edu.cn, zhuhan10@gmail.com, jordan... | 2016 | 398 |
6,325 | Stochastic Gradient MCMC with Stale Gradients Changyou Chen† Nan Ding‡ Chunyuan Li† Yizhe Zhang† Lawrence Carin† †Dept. of Electrical and Computer Engineering, Duke University, Durham, NC, USA ‡Google Inc., Venice, CA, USA †{cc448,cl319,yz196,lcarin}@duke.edu; ‡dingnan@google.com Abstract Stochastic... | 2016 | 399 |
6,326 | Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics Wei-Shou Hsu and Pascal Poupart David R. Cheriton School of Computer Science University of Waterloo Wateroo, ON N2L 3G1 {wwhsu,ppoupart}@uwaterloo.ca Abstract Latent Dirichlet Allocation (LDA) is a very popular model for to... | 2016 | 4 |
6,327 | Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models Tomoharu Iwata NTT Communication Science Laboratories iwata.tomoharu@lab.ntt.co.jp Makoto Yamada Kyoto University makoto.m.yamada@ieee.org Abstract We propose probabilistic latent variable models for multi-view anomaly detect... | 2016 | 40 |
6,328 | Efficient Nonparametric Smoothness Estimation Shashank Singh Carnegie Mellon University sss1@andrew.cmu.edu Simon S. Du Carnegie Mellon University ssdu@cs.cmu.edu Barnabás Póczos Carnegie Mellon University bapoczos@cs.cmu.edu Abstract Sobolev quantities (norms, inner products, and distances) of pro... | 2016 | 400 |
6,329 | Adversarial Multiclass Classification: A Risk Minimization Perspective Rizal Fathony Anqi Liu Kaiser Asif Brian D. Ziebart Department of Computer Science University of Illinois at Chicago Chicago, IL 60607 {rfatho2, aliu33, kasif2, bziebart}@uic.edu Abstract Recently proposed adversarial classificat... | 2016 | 401 |
6,330 | Long-term causal effects via behavioral game theory Panagiotis (Panos) Toulis Econometrics & Statistics, Booth School University of Chicago Chicago, IL, 60637 panos.toulis@chicagobooth.edu David C. Parkes Department of Computer Science Harvard University Cambridge, MA, 02138 parkes@eecs.harvard.edu ... | 2016 | 402 |
6,331 | Sampling for Bayesian Program Learning Kevin Ellis Brain and Cognitive Sciences MIT ellisk@mit.edu Armando Solar-Lezama CSAIL MIT asolar@csail.mit.edu Joshua B. Tenenbaum Brain and Cognitive Sciences MIT jbt@mit.edu Abstract Towards learning programs from data, we introduce the problem of sa... | 2016 | 403 |
6,332 | Unifying Count-Based Exploration and Intrinsic Motivation Marc G. Bellemare bellemare@google.com Sriram Srinivasan srsrinivasan@google.com Georg Ostrovski ostrovski@google.com Tom Schaul schaul@google.com David Saxton saxton@google.com Google DeepMind London, United Kingdom R´emi Munos munos... | 2016 | 404 |
6,333 | Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices Kirthevasan Kandasamy⇤ Carnegie Mellon University Pittsburgh, PA 15213 kandasamy@cs.cmu.edu Maruan Al-Shedivat⇤ Carnegie Mellon University Pittsburgh, PA 15213 alshedivat@cs.cmu.edu Eric P. Xing Carnegie ... | 2016 | 405 |
6,334 | Observational-Interventional Priors for Dose-Response Learning Ricardo Silva Department of Statistical Science and Centre for Computational Statistics and Machine Learning University College London ricardo@stats.ucl.ac.uk Abstract Controlled interventions provide the most direct source of information for ... | 2016 | 406 |
6,335 | Improved Error Bounds for Tree Representations of Metric Spaces Samir Chowdhury Department of Mathematics The Ohio State University Columbus, OH 43210 chowdhury.57@osu.edu Facundo Mémoli Department of Mathematics Department of Computer Science and Engineering The Ohio State University Columbus, OH... | 2016 | 407 |
6,336 | A Bayesian method for reducing bias in neural representational similarity analysis Ming Bo Cai Princeton Neuroscience Institute Princeton University Princeton, NJ 08544 mcai@princeton.edu Nicolas W. Schuck Princeton Neuroscience Institute Princeton University Princeton, NJ 08544 nschuck@princeton.... | 2016 | 408 |
6,337 | Multistage Campaigning in Social Networks Mehrdad Farajtabar∗ Xiaojing Ye⋄ Sahar Harati† Le Song∗ Hongyuan Zha∗ Georgia Institute of Technology∗ Georgia State University⋄ Emory University† mehrdad@gatech.edu xye@gsu.edu sahar.harati@emory.edu {lsong,zha}@cc.gatech.edu Abstract We consider th... | 2016 | 409 |
6,338 | Optimal Architectures in a Solvable Model of Deep Networks Jonathan Kadmon The Racah Institute of Physics and ELSC The Hebrew University, Israel jonathan.kadmon@mail.huji.ac.il Haim Sompolinsky The Racah Institute of Physics and ELSC The Hebrew University, Israel and Center for Brain Science Harva... | 2016 | 41 |
6,339 | Conditional Image Generation with PixelCNN Decoders Aäron van den Oord Google DeepMind avdnoord@google.com Nal Kalchbrenner Google DeepMind nalk@google.com Oriol Vinyals Google DeepMind vinyals@google.com Lasse Espeholt Google DeepMind espeholt@google.com Alex Graves Google DeepMind grav... | 2016 | 410 |
6,340 | Global Optimality of Local Search for Low Rank Matrix Recovery Srinadh Bhojanapalli srinadh@ttic.edu Behnam Neyshabur bneyshabur@ttic.edu Nathan Srebro nati@ttic.edu Toyota Technological Institute at Chicago Abstract We show that there are no spurious local minima in the non-convex factorized para... | 2016 | 411 |
6,341 | Tensor Switching Networks Chuan-Yung Tsai∗, Andrew Saxe∗, David Cox Center for Brain Science, Harvard University, Cambridge, MA 02138 {chuanyungtsai,asaxe,davidcox}@fas.harvard.edu Abstract We present a novel neural network algorithm, the Tensor Switching (TS) network, which generalizes the Rectified Linear ... | 2016 | 412 |
6,342 | Optimistic Bandit Convex Optimization Mehryar Mohri Courant Institute and Google 251 Mercer Street New York, NY 10012 mohri@cims.nyu.edu Scott Yang Courant Institute 251 Mercer Street New York, NY 10012 yangs@cims.nyu.edu Abstract We introduce the general and powerful scheme of predicting inform... | 2016 | 413 |
6,343 | Interpretable Nonlinear Dynamic Modeling of Neural Trajectories Yuan Zhao and Il Memming Park Department of Neurobiology and Behavior Department of Applied Mathematics and Statistics Institute for Advanced Computational Science Stony Brook University, NY 11794 {yuan.zhao, memming.park}@stonybrook.edu Ab... | 2016 | 414 |
6,344 | Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu Dilin Wang Department of Computer Science Dartmouth College Hanover, NH 03755 {qiang.liu, dilin.wang.gr}@dartmouth.edu Abstract We propose a general purpose variational inference algorithm that forms a natural... | 2016 | 415 |
6,345 | Learning in Games: Robustness of Fast Convergence Dylan J. Foster⇤ Zhiyuan Li† Thodoris Lykouris⇤ Karthik Sridharan⇤ Éva Tardos⇤ Abstract We show that learning algorithms satisfying a low approximate regret property experience fast convergence to approximate optimality in a large class of repeated gam... | 2016 | 416 |
6,346 | Causal Bandits: Learning Good Interventions via Causal Inference Finnian Lattimore Australian National University and Data61/NICTA finn.lattimore@gmail.com Tor Lattimore Indiana University, Bloomington tor.lattimore@gmail.com Mark D. Reid Australian National University and Data61/NICTA mark.reid@anu... | 2016 | 417 |
6,347 | Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning Taiji Suzuki∗, Heishiro Kanagawa† ∗,†Department of Mathematical and Computing Science, Tokyo Institute of Technology ∗PRESTO, Japan Science and Technology Agency ∗Center for Advanced Integrated Intelligence Research, RIKEN s-t... | 2016 | 418 |
6,348 | Universal Correspondence Network Christopher B. Choy Stanford University chrischoy@ai.stanford.edu JunYoung Gwak Stanford University jgwak@ai.stanford.edu Silvio Savarese Stanford University ssilvio@stanford.edu Manmohan Chandraker NEC Laboratories America, Inc. manu@nec-labs.com Abstract We... | 2016 | 419 |
6,349 | Efficient state-space modularization for planning: theory, behavioral and neural signatures Daniel McNamee, Daniel Wolpert, Máté Lengyel Computational and Biological Learning Lab Department of Engineering University of Cambridge Cambridge CB2 1PZ, United Kingdom {d.mcnamee|wolpert|m.lengyel}@eng.cam.ac.uk ... | 2016 | 42 |
6,350 | Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games Sougata Chaudhuri Department of Statistics University of Michigan Ann Arbor sougata@umich.edu Ambuj Tewari Department of Statistics and Department of EECS University of Michigan Ann Arbor tewaria@umich.edu... | 2016 | 420 |
6,351 | Showing versus Doing: Teaching by Demonstration Mark K Ho Department of Cognitive, Linguistic, and Psychological Sciences Brown University Providence, RI 02912 mark_ho@brown.edu Michael L. Littman Department of Computer Science Brown University Providence, RI 02912 mlittman@cs.brown.edu James MacG... | 2016 | 421 |
6,352 | Learning Transferrable Representations for Unsupervised Domain Adaptation Ozan Sener1, Hyun Oh Song1, Ashutosh Saxena2, Silvio Savarese1 Stanford University1 Brain of Things2 {ozan,hsong,asaxena,ssilvio}@cs.stanford.edu Abstract Supervised learning with large scale labelled datasets and deep layered... | 2016 | 422 |
6,353 | On Robustness of Kernel Clustering Bowei Yan Department of Statistics and Data Sciences University of Texas at Austin Purnamrita Sarkar Department of Statistics and Data Sciences University of Texas at Austin Abstract Clustering is an important unsupervised learning problem in machine learning and sta... | 2016 | 423 |
6,354 | Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than O(1/ϵ) Yi Xu†∗, Yan Yan‡∗, Qihang Lin♮, Tianbao Yang u† † Department of Computer Science, University of Iowa, Iowa City, IA 52242 ‡ QCIS, University of Technology Sydney, NSW 2007, Australia ♮Department of Management Sciences, University of... | 2016 | 424 |
6,355 | Fast Algorithms for Robust PCA via Gradient Descent Xinyang Yi∗ Dohyung Park∗ Yudong Chen† Constantine Caramanis∗ ∗The University of Texas at Austin †Cornell University ∗{yixy,dhpark,constantine}@utexas.edu †yudong.chen@cornell.edu Abstract We consider the problem of Robust PCA in the fully and pa... | 2016 | 425 |
6,356 | Dimensionality Reduction of Massive Sparse Datasets Using Coresets Dan Feldman University of Haifa Haifa, Israel dannyf.post@gmail.com Mikhail Volkov CSAIL, MIT Cambridge, MA, USA mikhail@csail.mit.edu Daniela Rus CSAIL, MIT Cambridge, MA, USA rus@csail.mit.edu Abstract In this paper we pr... | 2016 | 426 |
6,357 | Doubly Convolutional Neural Networks Shuangfei Zhai Binghamton University Vestal, NY 13902, USA szhai2@binghamton.edu Yu Cheng IBM T.J. Watson Research Center Yorktown Heights, NY 10598, USA chengyu@us.ibm.com Weining Lu Tsinghua University Beijing 10084, China luwn14@mails.tsinghua.edu.cn Zho... | 2016 | 427 |
6,358 | Brains on Beats Umut Güçlü Radboud University, Donders Institute for Brain, Cognition and Behaviour Nijmegen, the Netherlands u.guclu@donders.ru.nl Jordy Thielen Radboud University, Donders Institute for Brain, Cognition and Behaviour Nijmegen, the Netherlands j.thielen@psych.ru.nl Michael Hanke∗ ... | 2016 | 428 |
6,359 | Local Minimax Complexity of Stochastic Convex Optimization Yuancheng Zhu Wharton Statistics Department University of Pennsylvania Sabyasachi Chatterjee Department of Statistics University of Chicago John Duchi Department of Statistics Department of Electrical Engineering Stanford University John... | 2016 | 429 |
6,360 | A Communication-Efficient Parallel Algorithm for Decision Tree Qi Meng1,∗, Guolin Ke2,∗, Taifeng Wang2, Wei Chen2, Qiwei Ye2, Zhi-Ming Ma3, Tie-Yan Liu2 1Peking University 2Microsoft Research 3Chinese Academy of Mathematics and Systems Science 1qimeng13@pku.edu.cn; 2{Guolin.Ke, taifengw, wche, qiwye, tie-y... | 2016 | 43 |
6,361 | Kronecker Determinantal Point Processes Zelda Mariet Massachusetts Institute of Technology Cambridge, MA 02139 zelda@csail.mit.edu Suvrit Sra Massachusetts Institute of Technology Cambridge, MA 02139 suvrit@mit.edu Abstract Determinantal Point Processes (DPPs) are probabilistic models over all subse... | 2016 | 430 |
6,362 | Normalized Spectral Map Synchronization Yanyao Shen UT Austin Austin, TX 78712 shenyanyao@utexas.edu Qixing Huang TTI Chicago and UT Austin Austin, TX 78712 huangqx@cs.utexas.edu Nathan Srebro TTI Chicago Chicago, IL 60637 nati@ttic.edu Sujay Sanghavi UT Austin Austin, TX 78712 sanghavi@... | 2016 | 431 |
6,363 | Error Analysis of Generalized Nyström Kernel Regression Hong Chen Computer Science and Engineering University of Texas at Arlington Arlington, TX, 76019 chenh@mail.hzau.edu.cn Haifeng Xia Mathematics and Statistics Huazhong Agricultural University Wuhan 430070,China haifeng.xia0910@gmail.com Wei... | 2016 | 432 |
6,364 | Regularized Nonlinear Acceleration Damien Scieur INRIA & D.I., UMR 8548, École Normale Supérieure, Paris, France. damien.scieur@inria.fr Alexandre d’Aspremont CNRS & D.I., UMR 8548, École Normale Supérieure, Paris, France. aspremon@di.ens.fr Francis Bach INRIA & D.I., UMR 8548, École Normale Supér... | 2016 | 433 |
6,365 | Pairwise Choice Markov Chains Stephen Ragain Management Science & Engineering Stanford University Stanford, CA 94305 sragain@stanford.edu Johan Ugander Management Science & Engineering Stanford University Stanford, CA 94305 jugander@stanford.edu Abstract As datasets capturing human choices grow ... | 2016 | 434 |
6,366 | Stochastic Variational Deep Kernel Learning Andrew Gordon Wilson* Cornell University Zhiting Hu* CMU Ruslan Salakhutdinov CMU Eric P. Xing CMU Abstract Deep kernel learning combines the non-parametric flexibility of kernel methods with the inductive biases of deep learning architectures. We propose... | 2016 | 435 |
6,367 | Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning Gang Niu1 Marthinus C. du Plessis1 Tomoya Sakai1 Yao Ma3 Masashi Sugiyama2,1 1The University of Tokyo, Japan 2RIKEN, Japan 3Boston University, USA { gang@ms., christo@ms., sakai@ms., yao@ms., sugi@ }k.u-tokyo.a... | 2016 | 436 |
6,368 | A Non-generative Framework and Convex Relaxations for Unsupervised Learning Elad Hazan Princeton University 35 Olden Street 08540 ehazan@cs.princeton.edu. Tengyu Ma Princeton University 35 Olden Street, NJ 08540 tengyu@cs.princeton.edu. Abstract We give a novel formal theoretical framework for uns... | 2016 | 437 |
6,369 | DISCO Nets: DISsimilarity COefficient Networks Diane Bouchacourt University of Oxford diane@robots.ox.ac.uk M. Pawan Kumar University of Oxford pawan@robots.ox.ac.uk Sebastian Nowozin Microsoft Research Cambridge sebastian.nowozin@microsoft.com Abstract We present a new type of probabilistic model ... | 2016 | 438 |
6,370 | Learning to Poke by Poking: Experiential Learning of Intuitive Physics Pulkit Agrawal∗ Ashvin Nair∗ Pieter Abbeel Jitendra Malik Sergey Levine Berkeley Artificial Intelligence Research Laboratory (BAIR) University of California Berkeley {pulkitag,anair17,pabbeel,malik,svlevine}@berkeley.edu Abstract ... | 2016 | 439 |
6,371 | Supervised Word Mover’s Distance Gao Huang∗, Chuan Guo∗ Cornell University {gh349,cg563}@cornell.edu Matt J. Kusner† Alan Turing Institute, University of Warwick mkusner@turing.ac.uk Yu Sun, Kilian Q. Weinberger Cornell University {ys646,kqw4}@cornell.edu Fei Sha University of California, Los Ange... | 2016 | 44 |
6,372 | Value Iteration Networks Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, and Pieter Abbeel Dept. of Electrical Engineering and Computer Sciences, UC Berkeley Abstract We introduce the value iteration network (VIN): a fully differentiable neural network with a ‘planning module’ embedded within. VINs can learn ... | 2016 | 440 |
6,373 | Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images Junhua Mao1 Jiajing Xu2 Yushi Jing2 Alan Yuille1,3 1University of California, Los Angeles 2Pinterest Inc. 3Johns Hopkins University mjhustc@ucla.edu, {jiajing,jing}@pinterest.com, alan.l.yuille@gmail.com Abs... | 2016 | 441 |
6,374 | Simple and Efficient Weighted Minwise Hashing Anshumali Shrivastava Department of Computer Science Rice University Houston, TX, 77005 anshumali@rice.edu Abstract Weighted minwise hashing (WMH) is one of the fundamental subroutine, required by many celebrated approximation algorithms, commonly adopted in ... | 2016 | 442 |
6,375 | Cooperative Inverse Reinforcement Learning Dylan Hadfield-Menell∗ Anca Dragan Pieter Abbeel Stuart Russell Electrical Engineering and Computer Science University of California at Berkeley Berkeley, CA 94709 Abstract For an autonomous system to be helpful to humans and to pose no unwarranted risks, it... | 2016 | 443 |
6,376 | Safe and efficient off-policy reinforcement learning R´emi Munos munos@google.com Google DeepMind Thomas Stepleton stepleton@google.com Google DeepMind Anna Harutyunyan anna.harutyunyan@vub.ac.be Vrije Universiteit Brussel Marc G. Bellemare bellemare@google.com Google DeepMind Abstract In thi... | 2016 | 444 |
6,377 | LightRNN: Memory and Computation-Efficient Recurrent Neural Networks Xiang Li1 Tao Qin2 Jian Yang1 Tie-Yan Liu2 1Nanjing University of Science and Technology 2Microsoft Research Asia 1implusdream@gmail.com 1csjyang@njust.edu.cn 2{taoqin, tie-yan.liu}@microsoft.com Abstract Recurrent neural networks (... | 2016 | 445 |
6,378 | Deep Learning Games Dale Schuurmans∗ Google daes@ualberta.ca Martin Zinkevich Google martinz@google.com Abstract We investigate a reduction of supervised learning to game playing that reveals new connections and learning methods. For convex one-layer problems, we demonstrate an equivalence between g... | 2016 | 446 |
6,379 | Strategic Attentive Writer for Learning Macro-Actions Alexander (Sasha) Vezhnevets, Volodymyr Mnih, John Agapiou, Simon Osindero, Alex Graves, Oriol Vinyals, Koray Kavukcuoglu Google DeepMind {vezhnick,vmnih,jagapiou,osindero,gravesa,vinyals,korayk}@google.com Abstract We present a novel deep recurrent ne... | 2016 | 447 |
6,380 | Clustering with Bregman Divergences: an Asymptotic Analysis Chaoyue Liu, Mikhail Belkin Department of Computer Science & Engineering The Ohio State University liu.2656@osu.edu, mbelkin@cse.ohio-state.edu Abstract Clustering, in particular k-means clustering, is a central topic in data analysis. Clusteri... | 2016 | 448 |
6,381 | Swapout: Learning an ensemble of deep architectures Saurabh Singh, Derek Hoiem, David Forsyth Department of Computer Science University of Illinois, Urbana-Champaign {ss1, dhoiem, daf}@illinois.edu Abstract We describe Swapout, a new stochastic training method, that outperforms ResNets of identical networ... | 2016 | 449 |
6,382 | Fast and accurate spike sorting of high-channel count probes with KiloSort Marius Pachitariu1, Nick Steinmetz1, Shabnam Kadir1 Matteo Carandini1 and Kenneth Harris1 1 UCL, UK {ucgtmpa, }@ucl.ac.uk Abstract New silicon technology is enabling large-scale electrophysiological recordings in vivo from hundreds... | 2016 | 45 |
6,383 | Stochastic Online AUC Maximization Yiming Ying†, Longyin Wen‡, Siwei Lyu‡ †Department of Mathematics and Statistics SUNY at Albany, Albany, NY, 12222, USA ‡Department of Computer Science SUNY at Albany, Albany, NY, 12222, USA Abstract Area under ROC (AUC) is a metric which is widely used for measuring the... | 2016 | 450 |
6,384 | Optimizing Affinity-Based Binary Hashing Using Auxiliary Coordinates Ramin Raziperchikolaei EECS, University of California, Merced rraziperchikolaei@ucmerced.edu Miguel ´A. Carreira-Perpi˜n´an EECS, University of California, Merced mcarreira-perpinan@ucmerced.edu Abstract In supervised binary hashing, ... | 2016 | 451 |
6,385 | Sample Complexity of Automated Mechanism Design Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 {ninamf,sandholm,vitercik}@cs.cmu.edu Abstract The design of revenue-maximizing combinatorial auctions, i.e. multi-item auctions ... | 2016 | 452 |
6,386 | LazySVD: Even Faster SVD Decomposition Yet Without Agonizing Pain∗ Zeyuan Allen-Zhu zeyuan@csail.mit.edu Institute for Advanced Study & Princeton University Yuanzhi Li yuanzhil@cs.princeton.edu Princeton University Abstract We study k-SVD that is to obtain the first k singular vectors of a matrix A. ... | 2016 | 453 |
6,387 | A Probabilistic Framework for Deep Learning Ankit B. Patel Baylor College of Medicine, Rice University ankitp@bcm.edu,abp4@rice.edu Tan Nguyen Rice University mn15@rice.edu Richard G. Baraniuk Rice University richb@rice.edu Abstract We develop a probabilistic framework for deep learning based on t... | 2016 | 454 |
6,388 | Without-Replacement Sampling for Stochastic Gradient Methods Ohad Shamir Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot, Israel ohad.shamir@weizmann.ac.il Abstract Stochastic gradient methods for machine learning and optimization problems are usually analy... | 2016 | 455 |
6,389 | Learning to Communicate with Deep Multi-Agent Reinforcement Learning Jakob N. Foerster1,† jakob.foerster@cs.ox.ac.uk Yannis M. Assael1,† yannis.assael@cs.ox.ac.uk Nando de Freitas1,2,3 nandodefreitas@google.com Shimon Whiteson1 shimon.whiteson@cs.ox.ac.uk 1University of Oxford, United Kingdom 2Can... | 2016 | 456 |
6,390 | Understanding the Effective Receptive Field in Deep Convolutional Neural Networks Wenjie Luo∗ Yujia Li∗ Raquel Urtasun Richard Zemel Department of Computer Science University of Toronto {wenjie, yujiali, urtasun, zemel}@cs.toronto.edu Abstract We study characteristics of receptive fields of units in ... | 2016 | 457 |
6,391 | Barzilai-Borwein Step Size for Stochastic Gradient Descent Conghui Tan The Chinese University of Hong Kong chtan@se.cuhk.edu.hk Shiqian Ma The Chinese University of Hong Kong sqma@se.cuhk.edu.hk Yu-Hong Dai Chinese Academy of Sciences, Beijing, China dyh@lsec.cc.ac.cn Yuqiu Qian The University o... | 2016 | 458 |
6,392 | The Power of Optimization from Samples Eric Balkanski Harvard University ericbalkanski@g.harvard.edu Aviad Rubinstein University of California, Berkeley aviad@eecs.berkeley.edu Yaron Singer Harvard University yaron@seas.harvard.edu Abstract We consider the problem of optimization from samples of m... | 2016 | 459 |
6,393 | Learning brain regions via large-scale online structured sparse dictionary-learning Elvis Dohmatob, Arthur Mensch, Gael Varoquaux, Bertrand Thirion firstname.lastname@inria.fr Parietal Team, INRIA / CEA, Neurospin, Université Paris-Saclay, France Abstract We propose a multivariate online dictionary-learning... | 2016 | 46 |
6,394 | New Liftable Classes for First-Order Probabilistic Inference Seyed Mehran Kazemi The University of British Columbia smkazemi@cs.ubc.ca Angelika Kimmig KU Leuven angelika.kimmig@cs.kuleuven.be Guy Van den Broeck University of California, Los Angeles guyvdb@cs.ucla.edu David Poole The University o... | 2016 | 460 |
6,395 | Optimal Tagging with Markov Chain Optimization Nir Rosenfeld School of Computer Science and Engineering Hebrew University of Jerusalem nir.rosenfeld@mail.huji.ac.il Amir Globerson The Blavatnik School of Computer Science Tel Aviv University gamir@post.tau.ac.il Abstract Many information systems use ... | 2016 | 461 |
6,396 | Fast and Flexible Monotonic Functions with Ensembles of Lattices K. Canini, A. Cotter, M. R. Gupta, M. Milani Fard, J. Pfeifer Google Inc. 1600 Amphitheatre Parkway, Mountain View, CA 94043 {canini,acotter,mayagupta,janpf,mmilanifard}@google.com Abstract For many machine learning problems, there are some ... | 2016 | 462 |
6,397 | A scaled Bregman theorem with applications Richard Nock†,‡,§ Aditya Krishna Menon†,‡ Cheng Soon Ong†,‡ †Data61, ‡the Australian National University and §the University of Sydney {richard.nock, aditya.menon, chengsoon.ong}@data61.csiro.au Abstract Bregman divergences play a central role in the design and a... | 2016 | 463 |
6,398 | The Product Cut Xavier Bresson Nanyang Technological University Singapore xavier.bresson@ntu.edu.sg Thomas Laurent Loyola Marymount University Los Angeles tlaurent@lmu.edu Arthur Szlam Facebook AI Research New York aszlam@fb.com James H. von Brecht California State University, Long Beach L... | 2016 | 464 |
6,399 | Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs Shahin Jabbari, Ryan Rogers, Aaron Roth, Zhiwei Steven Wu University of Pennsylvania {jabbari@cis, ryrogers@sas, aaroth@cis, wuzhiwei@cis}.upenn.edu Abstract We define and study the problem of predicting the solution to a linear... | 2016 | 465 |
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