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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5,800 | Submodular Hamming Metrics Jennifer Gillenwater†, Rishabh Iyer†, Bethany Lusch∗, Rahul Kidambi†, Jeff Bilmes† † University of Washington, Dept. of EE, Seattle, U.S.A. ∗University of Washington, Dept. of Applied Math, Seattle, U.S.A. {jengi, rkiyer, herwaldt, rkidambi, bilmes}@uw.edu Abstract We show that th... | 2015 | 289 |
5,801 | Attention-Based Models for Speech Recognition Jan Chorowski University of Wrocław, Poland jan.chorowski@ii.uni.wroc.pl Dzmitry Bahdanau Jacobs University Bremen, Germany Dmitriy Serdyuk Universit´e de Montr´eal Kyunghyun Cho Universit´e de Montr´eal Yoshua Bengio Universit´e de Montr´eal CIFAR S... | 2015 | 29 |
5,802 | Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees Franc¸ois-Xavier Briol Department of Statistics University of Warwick f-x.briol@warwick.ac.uk Chris J. Oates School of Mathematical and Physical Sciences University of Technology, Sydney christopher.oates@uts.edu.au... | 2015 | 290 |
5,803 | Deep Knowledge Tracing Chris Piech∗, Jonathan Bassen∗, Jonathan Huang∗‡, Surya Ganguli∗, Mehran Sahami∗, Leonidas Guibas∗, Jascha Sohl-Dickstein∗† ∗Stanford University, †Khan Academy, ‡Google {piech,jbassen}@cs.stanford.edu, jascha@stanford.edu, Abstract Knowledge tracing—where a machine models the knowledg... | 2015 | 291 |
5,804 | Generalization in Adaptive Data Analysis and Holdout Reuse∗ Cynthia Dwork Microsoft Research Vitaly Feldman IBM Almaden Research Center† Moritz Hardt Google Research Toniann Pitassi University of Toronto Omer Reingold Samsung Research America Aaron Roth University of Pennsylvania Abstract ... | 2015 | 292 |
5,805 | Tractable Learning for Complex Probability Queries Jessa Bekker, Jesse Davis KU Leuven, Belgium {jessa.bekker,jesse.davis}@cs.kuleuven.be Arthur Choi, Adnan Darwiche, Guy Van den Broeck University of California, Los Angeles {aychoi,darwiche,guyvdb}@cs.ucla.edu Abstract Tractable learning aims to learn p... | 2015 | 293 |
5,806 | Variational Dropout and the Local Reparameterization Trick Diederik P. Kingma⇤, Tim Salimans⇥and Max Welling⇤† ⇤Machine Learning Group, University of Amsterdam ⇥Algoritmica † University of California, Irvine, and the Canadian Institute for Advanced Research (CIFAR) D.P.Kingma@uva.nl, salimans.tim@gmail.com,... | 2015 | 294 |
5,807 | Fast, Provable Algorithms for Isotonic Regression in all ℓp-norms ∗ Rasmus Kyng Dept. of Computer Science Yale University rasmus.kyng@yale.edu Anup Rao† School of Computer Science Georgia Tech arao89@gatech.edu Sushant Sachdeva Dept. of Computer Science Yale University sachdeva@cs.yale.edu A... | 2015 | 295 |
5,808 | On the Global Linear Convergence of Frank-Wolfe Optimization Variants Simon Lacoste-Julien INRIA - SIERRA project-team ´Ecole Normale Sup´erieure, Paris, France Martin Jaggi Dept. of Computer Science ETH Z¨urich, Switzerland Abstract The Frank-Wolfe (FW) optimization algorithm has lately re-gained pop... | 2015 | 296 |
5,809 | Efficient Learning by Directed Acyclic Graph For Resource Constrained Prediction Joseph Wang Department of Electrical & Computer Engineering Boston University, Boston, MA 02215 joewang@bu.edu Kirill Trapeznikov Systems & Technology Research Woburn, MA 01801 kirill.trapeznikov@ stresearch.com Ve... | 2015 | 297 |
5,810 | Finite-Time Analysis of Projected Langevin Monte Carlo S´ebastien Bubeck Microsoft Research sebubeck@microsoft.com Ronen Eldan Weizmann Institute roneneldan@gmail.com Joseph Lehec Universit´e Paris-Dauphine lehec@ceremade.dauphine.fr Abstract We analyze the projected Langevin Monte Carlo (LMC) a... | 2015 | 298 |
5,811 | A Universal Catalyst for First-Order Optimization Hongzhou Lin1, Julien Mairal1 and Zaid Harchaoui1,2 1Inria 2NYU {hongzhou.lin,julien.mairal}@inria.fr zaid.harchaoui@nyu.edu Abstract We introduce a generic scheme for accelerating first-order optimization methods in the sense of Nesterov, which builds ... | 2015 | 299 |
5,812 | Policy Gradient for Coherent Risk Measures Aviv Tamar UC Berkeley avivt@berkeley.edu Yinlam Chow Stanford University ychow@stanford.edu Mohammad Ghavamzadeh Adobe Research & INRIA mohammad.ghavamzadeh@inria.fr Shie Mannor Technion shie@ee.technion.ac.il Abstract Several authors have recently... | 2015 | 3 |
5,813 | Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis Jimei Yang1 Scott Reed2 Ming-Hsuan Yang1 Honglak Lee2 1University of California, Merced {jyang44, mhyang}@ucmerced.edu 2University of Michigan, Ann Arbor {reedscot, honglak}@umich.edu Abstract An important problem... | 2015 | 30 |
5,814 | Distributed Submodular Cover: Succinctly Summarizing Massive Data Baharan Mirzasoleiman ETH Zurich Amin Karbasi Yale University Ashwinkumar Badanidiyuru Google Andreas Krause ETH Zurich Abstract How can one find a subset, ideally as small as possible, that well represents a massive dataset? I.e.,... | 2015 | 300 |
5,815 | Robust PCA with compressed data Wooseok Ha University of Chicago haywse@uchicago.edu Rina Foygel Barber University of Chicago rina@uchicago.edu Abstract The robust principal component analysis (RPCA) problem seeks to separate lowrank trends from sparse outliers within a data matrix, that is, to approxim... | 2015 | 301 |
5,816 | Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution Yan Huang1 Wei Wang1 Liang Wang1,2 1Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition 2Center for Excellence in Brain Science and Intelligence Technology Institute of Auto... | 2015 | 302 |
5,817 | Regret-Based Pruning in Extensive-Form Games Noam Brown Computer Science Department Carnegie Mellon University Pittsburgh, PA 15217 noamb@cmu.edu Tuomas Sandholm Computer Science Department Carnegie Mellon University Pittsburgh, PA 15217 sandholm@cs.cmu.edu Abstract Counterfactual Regret Minimiz... | 2015 | 303 |
5,818 | Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models Theodoros Tsiligkaridis, Keith W. Forsythe Massachusetts Institute of Technology, Lincoln Laboratory Lexington, MA 02421 USA ttsili@ll.mit.edu, forsythe@ll.mit.edu Abstract We develop a sequential low-complexity inference ... | 2015 | 304 |
5,819 | Bidirectional Recurrent Neural Networks as Generative Models Mathias Berglund Aalto University, Finland Tapani Raiko Aalto University, Finland Mikko Honkala Nokia Labs, Finland Leo K¨arkk¨ainen Nokia Labs, Finland Akos Vetek Nokia Labs, Finland Juha Karhunen Aalto University, Finland Abstrac... | 2015 | 305 |
5,820 | Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing Nihar B. Shah University of California, Berkeley nihar@eecs.berkeley.edu Dengyong Zhou Microsoft Research dengyong.zhou@microsoft.com Abstract Crowdsourcing has gained immense popularity in machine learning applications for obt... | 2015 | 306 |
5,821 | Asynchronous stochastic convex optimization: the noise is in the noise and SGD don’t care Sorathan Chaturapruek1 John C. Duchi2 Chris R´e1 Departments of 1Computer Science, 2Electrical Engineering, and 2Statistics Stanford University Stanford, CA 94305 {sorathan,jduchi,chrismre}@stanford.edu Abstract ... | 2015 | 307 |
5,822 | Bounding errors of Expectation-Propagation Guillaume Dehaene University of Geneva guillaume.dehaene@gmail.com Simon Barthelmé CNRS, Gipsa-lab simon.barthelme@gipsa-lab.fr Abstract Expectation Propagation is a very popular algorithm for variational inference, but comes with few theoretical guarantees. ... | 2015 | 308 |
5,823 | On the Limitation of Spectral Methods: From the Gaussian Hidden Clique Problem to Rank-One Perturbations of Gaussian Tensors Andrea Montanari Department of Electrical Engineering and Department of Statistics. Stanford University. montanari@stanford.edu Daniel Reichman Department of Cognitive and Brain Sci... | 2015 | 309 |
5,824 | Backpropagation for Energy-Efficient Neuromorphic Computing Steve K. Esser IBM Research–Almaden 650 Harry Road, San Jose, CA 95120 sesser@us.ibm.com Rathinakumar Appuswamy IBM Research–Almaden 650 Harry Road, San Jose, CA 95120 rappusw@us.ibm.com Paul A. Merolla IBM Research–Almaden 650 Harry Roa... | 2015 | 31 |
5,825 | Convergence Rates of Active Learning for Maximum Likelihood Estimation Kamalika Chaudhuri ⇤ Sham M. Kakade † Praneeth Netrapalli ‡ Sujay Sanghavi § Abstract An active learner is given a class of models, a large set of unlabeled examples, and the ability to interactively query labels of a subset of these... | 2015 | 310 |
5,826 | Distributionally Robust Logistic Regression Soroosh Shafieezadeh-Abadeh Peyman Mohajerin Esfahani Daniel Kuhn ´Ecole Polytechnique F´ed´erale de Lausanne, CH-1015 Lausanne, Switzerland {soroosh.shafiee,peyman.mohajerin,daniel.kuhn} @epfl.ch Abstract This paper proposes a distributionally robust approach to... | 2015 | 311 |
5,827 | Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing Thomas Goldstein⇤ Department of Computer Science University of Maryland College Park, MD Min Li† School of Economics and Management Southeast University Nanjing, China Xiaoming Yuan‡ Department of Mathematics Ho... | 2015 | 312 |
5,828 | Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring Junpei Komiyama The University of Tokyo junpei@komiyama.info Junya Honda The University of Tokyo honda@stat.t.u-tokyo.ac.jp Hiroshi Nakagawa The University of Tokyo nakagawa@dl.itc.u-tokyo.ac.jp Abstract Partial mon... | 2015 | 313 |
5,829 | Online Prediction at the Limit of Zero Temperature Mark Herbster Stephen Pasteris Department of Computer Science University College London London WC1E 6BT, England, UK {m.herbster,s.pasteris}@cs.ucl.ac.uk Shaona Ghosh ECS University of Southampton Southampton, UK SO17 1BJ ghosh.shaona@gmail.com ... | 2015 | 314 |
5,830 | Scalable Semi-Supervised Aggregation of Classifiers Akshay Balsubramani UC San Diego abalsubr@cs.ucsd.edu Yoav Freund UC San Diego yfreund@cs.ucsd.edu Abstract We present and empirically evaluate an efficient algorithm that learns to aggregate the predictions of an ensemble of binary classifiers. The algor... | 2015 | 315 |
5,831 | Deep Convolutional Inverse Graphics Network Tejas D. Kulkarni*1, William F. Whitney*2, Pushmeet Kohli3, Joshua B. Tenenbaum4 1,2,4Massachusetts Institute of Technology, Cambridge, USA 3Microsoft Research, Cambridge, UK 1tejask@mit.edu 2wwhitney@mit.edu 3pkohli@microsoft.com 4jbt@mit.edu * First two au... | 2015 | 316 |
5,832 | Smooth and Strong: MAP Inference with Linear Convergence Ofer Meshi TTI Chicago Mehrdad Mahdavi TTI Chicago Alexander G. Schwing University of Toronto Abstract Maximum a-posteriori (MAP) inference is an important task for many applications. Although the standard formulation gives rise to a hard combin... | 2015 | 317 |
5,833 | Efficient Compressive Phase Retrieval with Constrained Sensing Vectors Sohail Bahmani, Justin Romberg School of Electrical and Computer Engineering. Georgia Institute of Technology Atlanta, GA 30332 {sohail.bahmani,jrom}@ece.gatech.edu Abstract We propose a robust and efficient approach to the problem o... | 2015 | 318 |
5,834 | Convolutional Spike-triggered Covariance Analysis for Neural Subunit Models Anqi Wu1 Il Memming Park2 Jonathan W. Pillow1 1 Princeton Neuroscience Institute, Princeton University {anqiw, pillow}@princeton.edu 2 Department of Neurobiology and Behavior, Stony Brook University memming.park@stonybrook.edu ... | 2015 | 319 |
5,835 | Efficient and Robust Automated Machine Learning Matthias Feurer Aaron Klein Katharina Eggensperger Jost Tobias Springenberg Manuel Blum Frank Hutter Department of Computer Science University of Freiburg, Germany {feurerm,kleinaa,eggenspk,springj,mblum,fh}@cs.uni-freiburg.de Abstract The success of ... | 2015 | 32 |
5,836 | Recovering Communities in the General Stochastic Block Model Without Knowing the Parameters Emmanuel Abbe Department of Electrical Engineering and PACM Princeton University Princeton, NJ 08540 eabbe@princeton.edu Colin Sandon Department of Mathematics Princeton University Princeton, NJ 08540 sando... | 2015 | 320 |
5,837 | On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants Sashank J. Reddi Carnegie Mellon University sjakkamr@cs.cmu.edu Ahmed Hefny Carnegie Mellon University ahefny@cs.cmu.edu Suvrit Sra Massachusetts Institute of Technology suvrit@mit.edu Barnab´as P´oczos Carnegie M... | 2015 | 321 |
5,838 | Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning Jiajun Wu∗ EECS, MIT jiajunwu@mit.edu Ilker Yildirim∗ BCS MIT, The Rockefeller University ilkery@mit.edu Joseph J. Lim EECS, MIT lim@csail.mit.edu William T. Freeman EECS, MIT billf@mit.edu Josh... | 2015 | 322 |
5,839 | Optimal Rates for Random Fourier Features Bharath K. Sriperumbudur∗ Department of Statistics Pennsylvania State University University Park, PA 16802, USA bks18@psu.edu Zolt´an Szab´o∗ Gatsby Unit, CSML, UCL Sainsbury Wellcome Centre, 25 Howland Street London - W1T 4JG, UK zoltan.szabo@gatsby.ucl.ac.... | 2015 | 323 |
5,840 | Deep learning with Elastic Averaging SGD Sixin Zhang Courant Institute, NYU zsx@cims.nyu.edu Anna Choromanska Courant Institute, NYU achoroma@cims.nyu.edu Yann LeCun Center for Data Science, NYU & Facebook AI Research yann@cims.nyu.edu Abstract We study the problem of stochastic optimization for d... | 2015 | 324 |
5,841 | Online F-Measure Optimization R´obert Busa-Fekete Department of Computer Science University of Paderborn, Germany busarobi@upb.de Bal´azs Sz¨or´enyi Technion, Haifa, Israel / MTA-SZTE Research Group on Artificial Intelligence, Hungary szorenyibalazs@gmail.com Krzysztof Dembczy´nski Institute of Com... | 2015 | 325 |
5,842 | Bayesian Manifold Learning: The Locally Linear Latent Variable Model Mijung Park, Wittawat Jitkrittum, Ahmad Qamar∗, Zolt´an Szab´o, Lars Buesing†, Maneesh Sahani Gatsby Computational Neuroscience Unit University College London {mijung, wittawat, zoltan.szabo}@gatsby.ucl.ac.uk atqamar@gmail.com, lbuesin... | 2015 | 326 |
5,843 | Smooth Interactive Submodular Set Cover Bryan He Stanford University bryanhe@stanford.edu Yisong Yue California Institute of Technology yyue@caltech.edu Abstract Interactive submodular set cover is an interactive variant of submodular set cover over a hypothesis class of submodular functions, where th... | 2015 | 327 |
5,844 | Column Selection via Adaptive Sampling Saurabh Paul Global Risk Sciences, Paypal Inc. saupaul@paypal.com Malik Magdon-Ismail CS Dept., Rensselaer Polytechnic Institute magdon@cs.rpi.edu Petros Drineas CS Dept., Rensselaer Polytechnic Institute drinep@cs.rpi.edu Abstract Selecting a good column (or... | 2015 | 328 |
5,845 | Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation Marijn F. Stollenga*123, Wonmin Byeon*1245, Marcus Liwicki4, and Juergen Schmidhuber123 *Shared first authors, both Authors contribruted equally to this work. Corresponding authors: marijn@idsia.ch, wonmin.byeon@df... | 2015 | 329 |
5,846 | Time-Sensitive Recommendation From Recurrent User Activities Nan Du⋄, Yichen Wang⋄, Niao He∗, Le Song⋄ ⋄College of Computing, Georgia Tech ∗H. Milton Stewart School of Industrial & System Engineering, Georgia Tech dunan@gatech.edu, yichen.wang@gatech.edu, nhe6@gatech.edu lsong@cc.gatech.edu Abstract By ... | 2015 | 33 |
5,847 | Discriminative Robust Transformation Learning Jiaji Huang Qiang Qiu Guillermo Sapiro Robert Calderbank Department of Electrical Engineering, Duke University Durham, NC 27708 {jiaji.huang,qiang.qiu,guillermo.sapiro,robert.calderbank}@duke.edu Abstract This paper proposes a framework for learning featur... | 2015 | 330 |
5,848 | Deep Poisson Factor Modeling Ricardo Henao, Zhe Gan, James Lu and Lawrence Carin Department of Electrical and Computer Engineering Duke University, Durham, NC 27708 {r.henao,zhe.gan,james.lu,lcarin}@duke.edu Abstract We propose a new deep architecture for topic modeling, based on Poisson Factor Analysis (PF... | 2015 | 331 |
5,849 | Max-Margin Majority Voting for Learning from Crowds Tian Tian, Jun Zhu Department of Computer Science & Technology; Center for Bio-Inspired Computing Research Tsinghua National Lab for Information Science & Technology State Key Lab of Intelligent Technology & Systems; Tsinghua University, Beijing 100084, Chin... | 2015 | 332 |
5,850 | Competitive Distribution Estimation: Why is Good-Turing Good Alon Orlitsky UC San Diego alon@ucsd.edu Ananda Theertha Suresh UC San Diego asuresh@ucsd.edu Abstract Estimating distributions over large alphabets is a fundamental machine-learning tenet. Yet no method is known to estimate all distributi... | 2015 | 333 |
5,851 | Embedding Inference for Structured Multilabel Prediction Farzaneh Mirzazadeh Siamak Ravanbakhsh University of Alberta {mirzazad,mravanba}@ualberta.ca Nan Ding Google dingnan@google.com Dale Schuurmans University of Alberta daes@ualberta.ca Abstract A key bottleneck in structured output predict... | 2015 | 334 |
5,852 | Spectral Learning of Large Structured HMMs for Comparative Epigenomics Chicheng Zhang UC San Diego chz038@eng.ucsd.edu Jimin Song Rutgers University song@dls.rutgers.edu Kevin C Chen Rutgers University kcchen@dls.rutgers.edu Kamalika Chaudhuri UC San Diego kamalika@eng.ucsd.edu Abstract We... | 2015 | 335 |
5,853 | Deeply Learning the Messages in Message Passing Inference Guosheng Lin, Chunhua Shen, Ian Reid, Anton van den Hengel The University of Adelaide, Australia; and Australian Centre for Robotic Vision E-mail: {guosheng.lin,chunhua.shen,ian.reid,anton.vandenhengel}@adelaide.edu.au Abstract Deep structured output... | 2015 | 336 |
5,854 | Bayesian Active Model Selection with an Application to Automated Audiometry Jacob R. Gardner CS, Cornell University Ithaca, NY 14850 jrg365@cornell.edu Gustavo Malkomes CSE, WUSTL St. Louis, MO 63130 luizgustavo@wustl.edu Roman Garnett CSE, WUSTL St. Louis, MO 63130 garnett@wustl.edu Kilian ... | 2015 | 337 |
5,855 | Collaboratively Learning Preferences from Ordinal Data Sewoong Oh , Kiran K. Thekumparampil University of Illinois at Urbana-Champaign {swoh,thekump2}@illinois.edu Jiaming Xu The Wharton School, UPenn jiamingx@wharton.upenn.edu Abstract In personalized recommendation systems, it is important to predic... | 2015 | 338 |
5,856 | Shepard Convolutional Neural Networks Jimmy SJ. Ren∗ SenseTime Group Limited rensijie@sensetime.com Li Xu SenseTime Group Limited xuli@sensetime.com Qiong Yan SenseTime Group Limited yanqiong@sensetime.com Wenxiu Sun SenseTime Group Limited sunwenxiu@sensetime.com Abstract Deep learning has ... | 2015 | 339 |
5,857 | Local Expectation Gradients for Black Box Variational Inference Michalis K. Titsias Athens University of Economics and Business mtitsias@aueb.gr Miguel L´azaro-Gredilla Vicarious miguel@vicarious.com Abstract We introduce local expectation gradients which is a general purpose stochastic variational ... | 2015 | 34 |
5,858 | Learning Wake-Sleep Recurrent Attention Models Jimmy Ba University of Toronto jimmy@psi.toronto.edu Roger Grosse University of Toronto rgrosse@cs.toronto.edu Ruslan Salakhutdinov University of Toronto rsalskhu@cs.toronto.edu Brendan Frey University of Toronto frey@psi.toronto.edu Abstract De... | 2015 | 340 |
5,859 | Matrix Manifold Optimization for Gaussian Mixtures Reshad Hosseini School of ECE College of Engineering University of Tehran, Tehran, Iran reshad.hosseini@ut.ac.ir Suvrit Sra Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge, MA. suvrit@mit.edu Abstrac... | 2015 | 341 |
5,860 | Minimum Weight Perfect Matching via Blossom Belief Propagation Sungsoo Ahn∗ Sejun Park∗ Michael Chertkov† Jinwoo Shin∗ ∗School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea †Theoretical Division and Center for Nonlinear Studies, Los Alamos National Labor... | 2015 | 342 |
5,861 | Human Memory Search as Initial-Visit Emitting Random Walk Kwang-Sung Jun∗, Xiaojin Zhu†, Timothy Rogers‡ ∗Wisconsin Institute for Discovery, †Department of Computer Sciences, ‡Department of Psychology University of Wisconsin-Madison kjun@discovery.wisc.edu, jerryzhu@cs.wisc.edu, ttrogers@wisc.edu Zhuoran Ya... | 2015 | 343 |
5,862 | Bounding the Cost of Search-Based Lifted Inference David Smith University of Texas At Dallas 800 W Campbell Rd, Richardson, TX 75080 dbs014200@utdallas.edu Vibhav Gogate University of Texas At Dallas 800 W Campbell Rd, Richardson, TX 75080 vibhav.gogate@utdallas.edu Abstract Recently, there has been... | 2015 | 344 |
5,863 | Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications Kai Wei1 Rishabh Iyer1 Shengjie Wang2 Wenruo Bai1 Jeff Bilmes1 1 Department of Electrical Engineering, University of Washington 2 Department of Computer Science, University of Washington {kaiwei, rkiyer, wangsj... | 2015 | 345 |
5,864 | Gradient Estimation Using Stochastic Computation Graphs John Schulman1,2 joschu@eecs.berkeley.edu Nicolas Heess1 heess@google.com Theophane Weber1 theophane@google.com Pieter Abbeel2 pabbeel@eecs.berkeley.edu 1 Google DeepMind 2 University of California, Berkeley, EECS Department Abstract In a... | 2015 | 346 |
5,865 | Rectified Factor Networks Djork-Arn´e Clevert, Andreas Mayr, Thomas Unterthiner and Sepp Hochreiter Institute of Bioinformatics, Johannes Kepler University, Linz, Austria {okko,mayr,unterthiner,hochreit}@bioinf.jku.at Abstract We propose rectified factor networks (RFNs) to efficiently construct very sparse, no... | 2015 | 347 |
5,866 | Adaptive Stochastic Optimization: From Sets to Paths Zhan Wei Lim David Hsu Wee Sun Lee Department of Computer Science, National University of Singapore {limzhanw,dyhsu,leews}@comp.nus.edu.sg Abstract Adaptive stochastic optimization (ASO) optimizes an objective function adaptively under uncertainty. It p... | 2015 | 348 |
5,867 | A Universal Primal-Dual Convex Optimization Framework Alp Yurtsever: Quoc Tran-Dinh; Volkan Cevher: : Laboratory for Information and Inference Systems, EPFL, Switzerland {alp.yurtsever, volkan.cevher}@epfl.ch ; Department of Statistics and Operations Research, UNC, USA quoctd@email.unc.edu Abstract We... | 2015 | 349 |
5,868 | Training Restricted Boltzmann Machines via the Thouless-Anderson-Palmer Free Energy Marylou Gabri´e Eric W. Tramel Florent Krzakala Laboratoire de Physique Statistique, UMR 8550 CNRS ´Ecole Normale Sup´erieure & Universit´e Pierre et Marie Curie 75005 Paris, France {marylou.gabrie, eric.tramel}@lps.ens.... | 2015 | 35 |
5,869 | Adversarial Prediction Games for Multivariate Losses Hong Wang Wei Xing Kaiser Asif Brian D. Ziebart Department of Computer Science University of Illinois at Chicago Chicago, IL 60607 {hwang27, wxing3, kasif2, bziebart}@uic.edu Abstract Multivariate loss functions are used to assess performance in m... | 2015 | 350 |
5,870 | Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning Shakir Mohamed and Danilo J. Rezende Google DeepMind, London {shakir, danilor}@google.com Abstract The mutual information is a core statistical quantity that has applications in all areas of machine learning, whether thi... | 2015 | 351 |
5,871 | Deep Visual Analogy-Making Scott Reed Yi Zhang Yuting Zhang Honglak Lee University of Michigan, Ann Arbor, MI 48109, USA {reedscot,yeezhang,yutingzh,honglak}@umich.edu Abstract In addition to identifying the content within a single image, relating images and generating related images are critical task... | 2015 | 352 |
5,872 | Rate-Agnostic (Causal) Structure Learning Sergey Plis The Mind Research Network, Albuquerque, NM s.m.plis@gmail.com David Danks Carnegie-Mellon University Pittsburgh, PA ddanks@cmu.edu Cynthia Freeman The Mind Research Network, CS Dept., University of New Mexico Albuquerque, NM cynthiaw2004@gm... | 2015 | 353 |
5,873 | Structured Estimation with Atomic Norms: General Bounds and Applications Sheng Chen Arindam Banerjee Dept. of Computer Science & Engg., University of Minnesota, Twin Cities {shengc,banerjee}@cs.umn.edu Abstract For structured estimation problems with atomic norms, recent advances in the literature express... | 2015 | 354 |
5,874 | Logarithmic Time Online Multiclass prediction Anna Choromanska Courant Institute of Mathematical Sciences New York, NY, USA achoroma@cims.nyu.edu John Langford Microsoft Research New York, NY, USA jcl@microsoft.com Abstract We study the problem of multiclass classification with an extremely large num... | 2015 | 355 |
5,875 | Copula variational inference Dustin Tran Harvard University David M. Blei Columbia University Edoardo M. Airoldi Harvard University Abstract We develop a general variational inference method that preserves dependency among the latent variables. Our method uses copulas to augment the families of dist... | 2015 | 356 |
5,876 | Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze–like Environments Dane Corneil Laboratory of Computational Neuroscience ´Ecole Polytechnique F´ed´erale de Lausanne CH-1015 Lausanne, Switzerland dane.corneil@epfl.ch Wulfram Gerstner Laboratory of Computational Neuroscience ´Ec... | 2015 | 357 |
5,877 | Exactness of Approximate MAP Inference in Continuous MRFs Nicholas Ruozzi Department of Computer Science University of Texas at Dallas Richardson, TX 75080 Abstract Computing the MAP assignment in graphical models is generally intractable. As a result, for discrete graphical models, the MAP problem is o... | 2015 | 358 |
5,878 | Explore no more: Improved high-probability regret bounds for non-stochastic bandits Gergely Neu∗ SequeL team INRIA Lille – Nord Europe gergely.neu@gmail.com Abstract This work addresses the problem of regret minimization in non-stochastic multiarmed bandit problems, focusing on performance guarantees that... | 2015 | 359 |
5,879 | High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality⇤ Zhaoran Wang Princeton University Quanquan Gu University of Virginia Yang Ning Princeton University Han Liu Princeton University Abstract We provide a general theory of the expectation-maximization (EM) algorithm for... | 2015 | 36 |
5,880 | Subspace Clustering with Irrelevant Features via Robust Dantzig Selector Chao Qu Department of Mechanical Engineering National University of Singapore A0117143@u.nus.edu Huan Xu Department of Mechanical Engineering National University of Singapore mpexuh@nus.edu.sg Abstract This paper considers th... | 2015 | 360 |
5,881 | Variational Consensus Monte Carlo Maxim Rabinovich, Elaine Angelino, and Michael I. Jordan Computer Science Division University of California, Berkeley {rabinovich, elaine, jordan}@eecs.berkeley.edu Abstract Practitioners of Bayesian statistics have long depended on Markov chain Monte Carlo (MCMC) to obta... | 2015 | 361 |
5,882 | Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks Samy Bengio, Oriol Vinyals, Navdeep Jaitly, Noam Shazeer Google Research Mountain View, CA, USA {bengio,vinyals,ndjaitly,noam}@google.com Abstract Recurrent Neural Networks can be trained to produce sequences of tokens given some ... | 2015 | 362 |
5,883 | Path-SGD: Path-Normalized Optimization in Deep Neural Networks Behnam Neyshabur Toyota Technological Institute at Chicago bneyshabur@ttic.edu Ruslan Salakhutdinov Departments of Statistics and Computer Science University of Toronto rsalakhu@cs.toronto.edu Nathan Srebro Toyota Technological Institute... | 2015 | 363 |
5,884 | Orthogonal NMF through Subspace Exploration Megasthenis Asteris The University of Texas at Austin megas@utexas.edu Dimitris Papailiopoulos University of California, Berkeley dimitrisp@berkeley.edu Alexandros G. Dimakis The University of Texas at Austin dimakis@austin.utexas.edu Abstract Orthogonal... | 2015 | 364 |
5,885 | M-Statistic for Kernel Change-Point Detection Shuang Li, Yao Xie H. Milton Stewart School of Industrial and Systems Engineering Georgian Institute of Technology sli370@gatech.edu yao.xie@isye.gatech.edu Hanjun Dai, Le Song Computational Science and Engineering College of Computing Georgia Institute ... | 2015 | 365 |
5,886 | Active Learning from Weak and Strong Labelers Chicheng Zhang UC San Diego chichengzhang@ucsd.edu Kamalika Chaudhuri UC San Diego kamalika@eng.ucsd.edu Abstract An active learner is given a hypothesis class, a large set of unlabeled examples and the ability to interactively query labels to an oracle of... | 2015 | 366 |
5,887 | Sum-of-Squares Lower Bounds for Sparse PCA Tengyu Ma∗1 and Avi Wigderson†2 1Department of Computer Science, Princeton University 2School of Mathematics, Institute for Advanced Study Abstract This paper establishes a statistical versus computational trade-off for solving a basic high-dimensional machine lear... | 2015 | 367 |
5,888 | Where are they looking? Adri`a Recasens∗ Aditya Khosla∗ Carl Vondrick Antonio Torralba Massachusetts Institute of Technology {recasens, khosla, vondrick, torralba}@csail.mit.edu (* - indicates equal contribution) Abstract Humans have the remarkable ability to follow the gaze of other people to identif... | 2015 | 368 |
5,889 | Softstar: Heuristic-Guided Probabilistic Inference Mathew Monfort Computer Science Department University of Illinois at Chicago Chicago, IL 60607 mmonfo2@uic.edu Brenden M. Lake Center for Data Science New York University New York, NY 10003 brenden@nyu.edu Brian D. Ziebart Computer Science Depar... | 2015 | 369 |
5,890 | Learning Continuous Control Policies by Stochastic Value Gradients Nicolas Heess⇤, Greg Wayne⇤, David Silver, Timothy Lillicrap, Yuval Tassa, Tom Erez Google DeepMind {heess, gregwayne, davidsilver, countzero, tassa, etom}@google.com ⇤These authors contributed equally. Abstract We present a unified framewo... | 2015 | 37 |
5,891 | Fast Bidirectional Probability Estimation in Markov Models Siddhartha Banerjee ∗ sbanerjee@cornell.edu Peter Lofgren† plofgren@cs.stanford.edu Abstract We develop a new bidirectional algorithm for estimating Markov chain multi-step transition probabilities: given a Markov chain, we want to estimate the ... | 2015 | 370 |
5,892 | Learning to Linearize Under Uncertainty Ross Goroshin∗1 Michael Mathieu∗1 Yann LeCun1,2 1Dept. of Computer Science, Courant Institute of Mathematical Science, New York, NY 2Facebook AI Research, New York, NY {goroshin,mathieu,yann}@cs.nyu.edu Abstract Training deep feature hierarchies to solve supervised le... | 2015 | 371 |
5,893 | Variance Reduced Stochastic Gradient Descent with Neighbors Thomas Hofmann Department of Computer Science ETH Zurich, Switzerland Aurelien Lucchi Department of Computer Science ETH Zurich, Switzerland Simon Lacoste-Julien INRIA - Sierra Project-Team ´Ecole Normale Sup´erieure, Paris, France Brian ... | 2015 | 372 |
5,894 | On Elicitation Complexity Rafael Frongillo University of Colorado, Boulder raf@colorado.edu Ian A. Kash Microsoft Research iankash@microsoft.com Abstract Elicitation is the study of statistics or properties which are computable via empirical risk minimization. While several recent papers have approached... | 2015 | 373 |
5,895 | Learning with Relaxed Supervision Jacob Steinhardt Stanford University jsteinhardt@cs.stanford.edu Percy Liang Stanford University pliang@cs.stanford.edu Abstract For weakly-supervised problems with deterministic constraints between the latent variables and observed output, learning necessitates perfo... | 2015 | 374 |
5,896 | Matrix Completion Under Monotonic Single Index Models Ravi Ganti Wisconsin Institutes for Discovery UW-Madison gantimahapat@wisc.edu Laura Balzano Electrical Engineering and Computer Sciences University of Michigan Ann Arbor girasole@umich.edu Rebecca Willett Department of Electrical and Computer ... | 2015 | 375 |
5,897 | Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric Vivien Seguy Graduate School of Informatics Kyoto University vivien.seguy@iip.ist.i.kyoto-u.ac.jp Marco Cuturi Graduate School of Informatics Kyoto University mcuturi@i.kyoto-u.ac.jp Abstract Given a family of ... | 2015 | 376 |
5,898 | HONOR: Hybrid Optimization for NOn-convex Regularized problems Pinghua Gong Univeristy of Michigan, Ann Arbor, MI 48109 gongp@umich.edu Jieping Ye Univeristy of Michigan, Ann Arbor, MI 48109 jpye@umich.edu Abstract Recent years have witnessed the superiority of non-convex sparse learning formulations ... | 2015 | 377 |
5,899 | The Poisson Gamma Belief Network Mingyuan Zhou McCombs School of Business The University of Texas at Austin Austin, TX 78712, USA Yulai Cong National Laboratory of RSP Xidian University Xi’an, Shaanxi, China Bo Chen National Laboratory of RSP Xidian University Xi’an, Shaanxi, China Abstract ... | 2015 | 378 |
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