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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5,500 | Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm Deanna Needell Department of Mathematical Sciences Claremont McKenna College Claremont CA 91711 dneedell@cmc.edu Nathan Srebro Toyota Technological Institute at Chicago and Dept. of Computer Science, Technion nati@... | 2014 | 389 |
5,501 | Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology R´emi Lemonnier1,2 Kevin Scaman1 Nicolas Vayatis1 1CMLA – ENS Cachan, CNRS, France, 21000mercis, Paris, France {lemonnier, scaman, vayatis}@cmla.ens-cachan.fr Abstract In this paper, we derive theoretica... | 2014 | 39 |
5,502 | Best-Arm Identification in Linear Bandits Marta Soare Alessandro Lazaric Rémi Munos∗† INRIA Lille – Nord Europe, SequeL Team {marta.soare,alessandro.lazaric,remi.munos}@inria.fr Abstract We study the best-arm identification problem in linear bandit, where the rewards of the arms depend linearly on an unkn... | 2014 | 390 |
5,503 | Multivariate f-Divergence Estimation With Confidence Kevin R. Moon Department of EECS University of Michigan Ann Arbor, MI krmoon@umich.edu Alfred O. Hero III Department of EECS University of Michigan Ann Arbor, MI hero@eecs.umich.edu Abstract The problem of f-divergence estimation is important... | 2014 | 391 |
5,504 | Online Decision-Making in General Combinatorial Spaces Arun Rajkumar Shivani Agarwal Department of Computer Science and Automation Indian Institute of Science, Bangalore 560012, India {arun r,shivani}@csa.iisc.ernet.in Abstract We study online combinatorial decision problems, where one must make sequent... | 2014 | 392 |
5,505 | Altitude Training: Strong Bounds for Single-Layer Dropout Stefan Wager⇤, William Fithian⇤, Sida Wang†, and Percy Liang⇤,† Departments of Statistics⇤and Computer Science† Stanford University, Stanford, CA-94305, USA {swager, wfithian}@stanford.edu, {sidaw, pliang}@cs.stanford.edu Abstract Dropout training,... | 2014 | 393 |
5,506 | Probabilistic low-rank matrix completion on finite alphabets Jean Lafond Institut Mines-T´el´ecom T´el´ecom ParisTech CNRS LTCI jean.lafond@telecom-paristech.fr Olga Klopp CREST et MODAL’X Universit´e Paris Ouest Olga.KLOPP@math.cnrs.fr ´Eric Moulines Institut Mines-T´el´ecom T´el´ecom ParisTec... | 2014 | 394 |
5,507 | Tight Continuous Relaxation of the Balanced k-Cut Problem Syama Sundar Rangapuram, Pramod Kaushik Mudrakarta and Matthias Hein Department of Mathematics and Computer Science Saarland University, Saarbr¨ucken Abstract Spectral Clustering as a relaxation of the normalized/ratio cut has become one of the sta... | 2014 | 395 |
5,508 | Discrete Graph Hashing Wei Liu† Cun Mu‡ Sanjiv Kumar♯ Shih-Fu Chang‡ †IBM T. J. Watson Research Center ‡Columbia University ♯Google Research weiliu@us.ibm.com cm3052@columbia.edu sfchang@ee.columbia.edu sanjivk@google.com Abstract Hashing has emerged as a popular technique for fast nearest nei... | 2014 | 396 |
5,509 | Orbit Regularization Renato Negrinho Instituto de Telecomunicac¸˜oes Instituto Superior T´ecnico 1049–001 Lisboa, Portugal renato.negrinho@gmail.com Andr´e F. T. Martins∗ Instituto de Telecomunicac¸˜oes Instituto Superior T´ecnico 1049–001 Lisboa, Portugal atm@priberam.pt Abstract We propose a g... | 2014 | 397 |
5,510 | A Synaptical Story of Persistent Activity with Graded Lifetime in a Neural System Yuanyuan Mi, Luozheng Li State Key Laboratory of Cognitive Neuroscience & Learning, Beijing Normal University, Beijing 100875, China miyuanyuan0102@163.com, liluozheng@mail.bnu.edu.cn Dahui Wang State Key Laboratory of C... | 2014 | 398 |
5,511 | Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces H`a Quang Minh Marco San Biagio Vittorio Murino Istituto Italiano di Tecnologia Via Morego 30, Genova 16163, ITALY {minh.haquang,marco.sanbiagio,vittorio.murino}@iit.it Abstract This paper introduces a novel mathematical a... | 2014 | 399 |
5,512 | Quantized Kernel Learning for Feature Matching Danfeng Qin ETH Z¨urich Xuanli Chen TU Munich Matthieu Guillaumin ETH Z¨urich Luc Van Gool ETH Z¨urich {qind, guillaumin, vangool}@vision.ee.ethz.ch, xuanli.chen@tum.de Abstract Matching local visual features is a crucial problem in computer vision an... | 2014 | 4 |
5,513 | A Bayesian model for identifying hierarchically organised states in neural population activity Patrick Putzky1,2,3, Florian Franzen1,2,3, Giacomo Bassetto1,3, Jakob H. Macke1,3 1Max Planck Institute for Biological Cybernetics, T¨ubingen 2Graduate Training Centre of Neuroscience, University of T¨ubingen 3Berns... | 2014 | 40 |
5,514 | Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings Ian E.H. Yen Cho-Jui Hsieh Pradeep Ravikumar Inderjit Dhillon Department of Computer Science University of Texas at Austin {ianyen,cjhsieh,pradeepr,inderjit}@cs.utexas.edu Abstract State of... | 2014 | 400 |
5,515 | Sparse Bayesian structure learning with dependent relevance determination prior Anqi Wu1 Mijung Park2 Oluwasanmi Koyejo3 Jonathan W. Pillow4 1,4 Princeton Neuroscience Institute, Princeton University, {anqiw, pillow}@princeton.edu 2 The Gatsby Unit, University College London, mijung@gatsby.ucl.ac.uk 3... | 2014 | 401 |
5,516 | Deep Symmetry Networks Robert Gens Pedro Domingos Department of Computer Science and Engineering University of Washington Seattle, WA 98195-2350, U.S.A. {rcg,pedrod}@cs.washington.edu Abstract The chief difficulty in object recognition is that objects’ classes are obscured by a large number of extraneo... | 2014 | 402 |
5,517 | Covariance shrinkage for autocorrelated data Daniel Bartz Department of Computer Science TU Berlin, Berlin, Germany daniel.bartz@tu-berlin.de Klaus-Robert M¨uller TU Berlin, Berlin, Germany Korea University, Korea, Seoul klaus-robert.mueller@tu-berlin.de Abstract The accurate estimation of covarianc... | 2014 | 403 |
5,518 | Scale Adaptive Blind Deblurring Haichao Zhang Jianchao Yang Duke University, NC Adobe Research, CA hczhang1@gmail.com jiayang@adobe.com Abstract The presence of noise and small scale structures usually leads to large kernel estimation errors in blind image deblurring empirically, if not a total failure.... | 2014 | 404 |
5,519 | Parallel Feature Selection inspired by Group Testing Yingbo Zhou∗ Utkarsh Porwal∗ CSE Department SUNY at Buffalo {yingbozh, utkarshp}@buffalo.edu Ce Zhang CS Department University of Wisconsin-Madison czhang@cs.wisc.edu Hung Ngo CSE Department SUNY at Buffalo hungngo@buffalo.edu XuanLong Ngu... | 2014 | 405 |
5,520 | Streaming, Memory Limited Algorithms for Community Detection Se-Young. Yun MSR-Inria 23 Avenue d’Italie, Paris 75013 seyoung.yun@inria.fr Marc Lelarge ∗ Inria & ENS 23 Avenue d’Italie, Paris 75013 marc.lelarge@ens.fr Alexandre Proutiere † KTH, EE School / ACL Osquldasv. 10, Stockholm 100-44, Swe... | 2014 | 406 |
5,521 | Analysis of Brain States from Multi-Region LFP Time-Series Kyle Ulrich 1, David E. Carlson 1, Wenzhao Lian 1, Jana Schaich Borg 2, Kafui Dzirasa 2 and Lawrence Carin 1 1 Department of Electrical and Computer Engineering 2 Department of Psychiatry and Behavioral Sciences Duke University, Durham, NC... | 2014 | 407 |
5,522 | Clamping Variables and Approximate Inference Adrian Weller Columbia University, New York, NY 10027 adrian@cs.columbia.edu Tony Jebara Columbia University, New York, NY 10027 jebara@cs.columbia.edu Abstract It was recently proved using graph covers (Ruozzi, 2012) that the Bethe partition function is up... | 2014 | 408 |
5,523 | Neural Word Embedding as Implicit Matrix Factorization Omer Levy Department of Computer Science Bar-Ilan University omerlevy@gmail.com Yoav Goldberg Department of Computer Science Bar-Ilan University yoav.goldberg@gmail.com Abstract We analyze skip-gram with negative-sampling (SGNS), a word embedd... | 2014 | 409 |
5,524 | Deep Convolutional Neural Network for Image Deconvolution Li Xu ∗ Lenovo Research & Technology xulihk@lenovo.com Jimmy SJ. Ren Lenovo Research & Technology jimmy.sj.ren@gmail.com Ce Liu Microsoft Research celiu@microsoft.com Jiaya Jia The Chinese University of Hong Kong leojia@cse.cuhk.edu.hk ... | 2014 | 41 |
5,525 | Constrained convex minimization via model-based excessive gap Quoc Tran-Dinh and Volkan Cevher Laboratory for Information and Inference Systems (LIONS) ´Ecole Polytechnique F´ed´erale de Lausanne (EPFL), CH1015-Lausanne, Switzerland {quoc.trandinh, volkan.cevher}@epfl.ch Abstract We introduce a model-base... | 2014 | 410 |
5,526 | A Filtering Approach to Stochastic Variational Inference Neil M.T. Houlsby ∗ Google Research Zurich, Switzerland neilhoulsby@google.com David M. Blei Department of Statistics Department of Computer Science Colombia University david.blei@colombia.edu Abstract Stochastic variational inference (SVI... | 2014 | 411 |
5,527 | Attentional Neural Network: Feature Selection Using Cognitive Feedback Qian Wang Department of Biomedical Engineering Tsinghua University Beijing, China 100084 qianwang.thu@gmail.com Jiaxing Zhang Microsoft Research Asia 5 Danning Road, Haidian District Beijing, China 100080 jiaxz@microsoft.com ... | 2014 | 42 |
5,528 | The Blinded Bandit: Learning with Adaptive Feedback Ofer Dekel Microsoft Research oferd@microsoft.com Elad Hazan Technion ehazan@ie.technion.ac.il Tomer Koren Technion tomerk@technion.ac.il Abstract We study an online learning setting where the player is temporarily deprived of feedback each t... | 2014 | 43 |
5,529 | Zero-Shot Recognition with Unreliable Attributes Dinesh Jayaraman University of Texas at Austin Austin, TX 78701 dineshj@cs.utexas.edu Kristen Grauman University of Texas at Austin Austin, TX 78701 grauman@cs.utexas.edu Abstract In principle, zero-shot learning makes it possible to train a recogniti... | 2014 | 44 |
5,530 | Communication Efficient Distributed Machine Learning with the Parameter Server Mu Li∗†, David G. Andersen∗, Alexander Smola∗‡, and Kai Yu† ∗Carnegie Mellon University †Baidu ‡Google {muli, dga}@cs.cmu.edu, alex@smola.org, yukai@baidu.com Abstract This paper describes a third-generation parameter server f... | 2014 | 45 |
5,531 | Beyond the Birkhoff Polytope: Convex Relaxations for Vector Permutation Problems Cong Han Lim Department of Computer Sciences University of Wisconsin - Madison Madison, WI 53706 conghan@cs.wisc.edu Stephen J. Wright Department of Computer Sciences University of Wisconsin - Madison Madison, WI 53706 ... | 2014 | 46 |
5,532 | Deconvolution of High Dimensional Mixtures via Boosting, with Application to Diffusion-Weighted MRI of Human Brain Charles Y. Zheng Department of Statistics Stanford University Stanford, CA 94305 snarles@stanford.edu Franco Pestilli Department of Psychological and Brain Sciences Indiana University, ... | 2014 | 47 |
5,533 | On Iterative Hard Thresholding Methods for High-dimensional M-Estimation Prateek Jain∗ Ambuj Tewari† Purushottam Kar∗ ∗Microsoft Research, INDIA †University of Michigan, Ann Arbor, USA {prajain,t-purkar}@microsoft.com, tewaria@umich.edu Abstract The use of M-estimators in generalized linear regression... | 2014 | 48 |
5,534 | Bayesian Sampling Using Stochastic Gradient Thermostats Nan Ding∗ Google Inc. dingnan@google.com Youhan Fang∗ Purdue University yfang@cs.purdue.edu Ryan Babbush Google Inc. babbush@google.com Changyou Chen Duke University cchangyou@gmail.com Robert D. Skeel Purdue University skeel@cs.pur... | 2014 | 49 |
5,535 | QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models Cho-Jui Hsieh, Inderjit S. Dhillon, Pradeep Ravikumar University of Texas at Austin Austin, TX 78712 USA {cjhsieh,inderjit,pradeepr}@cs.utexas.edu Stephen Becker University of Colorado at Boulder Boulder, CO 80309 USA stephe... | 2014 | 5 |
5,536 | Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors Lingqiao Liu1, Chunhua Shen1,2, Lei Wang3, Anton van den Hengel1,2, Chao Wang3 1 School of Computer Science, University of Adelaide, Australia 2 ARC Centre of Excellence for Robotic Vision 3 School of Computer Science and Software ... | 2014 | 50 |
5,537 | Efficient learning by implicit exploration in bandit problems with side observations Tom´aˇs Koc´ak Gergely Neu Michal Valko R´emi Munos∗ SequeL team, INRIA Lille – Nord Europe, France {tomas.kocak,gergely.neu,michal.valko,remi.munos}@inria.fr Abstract We consider online learning problems under a a par... | 2014 | 51 |
5,538 | An Integer Polynomial Programming Based Framework for Lifted MAP Inference Somdeb Sarkhel, Deepak Venugopal Computer Science Department The University of Texas at Dallas {sxs104721,dxv021000}@utdallas.edu Parag Singla Department of CSE I.I.T. Delhi parags@cse.iitd.ac.in Vibhav Gogate Computer Scie... | 2014 | 52 |
5,539 | Hardness of parameter estimation in graphical models Guy Bresler1 David Gamarnik2 Devavrat Shah1 Laboratory for Information and Decision Systems Department of EECS1 and Sloan School of Management2 Massachusetts Institute of Technology {gbresler,gamarnik,devavrat}@mit.edu Abstract We consider the pro... | 2014 | 53 |
5,540 | On the Information Theoretic Limits of Learning Ising Models Karthikeyan Shanmugam1∗, Rashish Tandon2†, Alexandros G. Dimakis1‡, Pradeep Ravikumar2⋆ 1Department of Electrical and Computer Engineering, 2Department of Computer Science The University of Texas at Austin, USA ∗karthiksh@utexas.edu, †rashish@cs.ute... | 2014 | 54 |
5,541 | Projecting Markov Random Field Parameters for Fast Mixing Xianghang Liu NICTA, The University of New South Wales xianghang.liu@nicta.com.au Justin Domke NICTA, The Australian National University justin.domke@nicta.com.au Abstract Markov chain Monte Carlo (MCMC) algorithms are simple and extremely powe... | 2014 | 55 |
5,542 | A Boosting Framework on Grounds of Online Learning Tofigh Naghibi, Beat Pfister Computer Engineering and Networks Laboratory ETH Zurich, Switzerland naghibi@tik.ee.ethz.ch, pfister@tik.ee.ethz.ch Abstract By exploiting the duality between boosting and online learning, we present a boosting framework which... | 2014 | 56 |
5,543 | Near–Optimal Density Estimation in Near–Linear Time Using Variable–Width Histograms Siu-On Chan Microsoft Research sochan@gmail.com Ilias Diakonikolas University of Edinburgh ilias.d@ed.ac.uk Rocco A. Servedio Columbia University rocco@cs.columbia.edu Xiaorui Sun Columbia University xiaoruisun... | 2014 | 57 |
5,544 | Efficient Minimax Signal Detection on Graphs Jing Qian Division of Systems Engineering Boston University Brookline, MA 02446 jingq@bu.edu Venkatesh Saligrama Department of Electrical and Computer Engineering Boston University Boston, MA 02215 srv@bu.edu Abstract Several problems such as network i... | 2014 | 58 |
5,545 | Magnitude-sensitive preference formation Nisheeth Srivastava∗ Department of Psychology University of San Diego La Jolla, CA 92093 nisheeths@gmail.com Edward Vul Department of Psychology University of San Diego La Jolla, CA 92093 edwardvul@gmail.com Paul R Schrater Dept of Psychology University... | 2014 | 59 |
5,546 | On a Theory of Nonparametric Pairwise Similarity for Clustering: Connecting Clustering to Classification Yingzhen Yang1 Feng Liang1 Shuicheng Yan2 Zhangyang Wang1 Thomas S. Huang1 1 University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA {yyang58,liangf,zwang119,t-huang1}@illinois.edu 2 National Un... | 2014 | 6 |
5,547 | Accelerated Mini-batch Randomized Block Coordinate Descent Method Tuo Zhao†§⇤ Mo Yu‡⇤Yiming Wang† Raman Arora† Han Liu§ †Johns Hopkins University ‡Harbin Institute of Technology §Princeton University {tour,myu25,freewym,arora}@jhu.edu,hanliu@princeton.edu Abstract We consider regularized empirical... | 2014 | 60 |
5,548 | Multiscale Fields of Patterns Pedro F. Felzenszwalb Brown University Providence, RI 02906 pff@brown.edu John G. Oberlin Brown University Providence, RI 02906 john oberlin@brown.edu Abstract We describe a framework for defining high-order image models that can be used in a variety of applications. T... | 2014 | 61 |
5,549 | “How hard is my MDP?” The distribution-norm to the rescue Odalric-Ambrym Maillard The Technion, Haifa, Israel odalric-ambrym.maillard@ens-cachan.org Timothy A. Mann The Technion, Haifa, Israel mann.timothy@gmail.com Shie Mannor The Technion, Haifa, Israel shie@ee.technion.ac.il Abstract In Reinf... | 2014 | 62 |
5,550 | Spectral Learning of Mixture of Hidden Markov Models Y. Cem S¨ubakan♭, Johannes Traa♯, Paris Smaragdis♭,♯,♮ ♭Department of Computer Science, University of Illinois at Urbana-Champaign ♯Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign ♮Adobe Systems, Inc. {subakan... | 2014 | 63 |
5,551 | Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation Emily Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun and Rob Fergus Dept. of Computer Science, Courant Institute, New York University {denton, zaremba, bruna, lecun, fergus} @cs.nyu.edu Abstract We present techniques for spe... | 2014 | 64 |
5,552 | Tree-structured Gaussian Process Approximations Thang Bui tdb40@cam.ac.uk Richard Turner ret26@cam.ac.uk Computational and Biological Learning Lab, Department of Engineering University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, UK Abstract Gaussian process regression can be accelerated by con... | 2014 | 65 |
5,553 | Optimal decision-making with time-varying evidence reliability Jan Drugowitsch1 Rub´en Moreno-Bote2 Alexandre Pouget1 1D´ept. des Neurosciences Fondamentales Universit´e de Gen`eve CH-1211 Gen`eve 4, Switzerland jdrugo@gmail.com, alexandre.pouget@unige.ch 2Research Unit, Parc Sanitari Sant Joan de... | 2014 | 66 |
5,554 | An Autoencoder Approach to Learning Bilingual Word Representations Sarath Chandar A P1 ∗, Stanislas Lauly2 ∗, Hugo Larochelle2, Mitesh M Khapra3, Balaraman Ravindran1, Vikas Raykar3, Amrita Saha3 1Indian Institute of Technology Madras, 2Universit´e de Sherbrooke, 3IBM Research India apsarathchandar@gmail.com,... | 2014 | 67 |
5,555 | Testing Unfaithful Gaussian Graphical Models De Wen Soh Department of Electrical Engineering Yale University 17 Hillhouse Ave, New Haven, CT 06511 dewen.soh@yale.edu Sekhar Tatikonda Department of Electrical Engineering Yale University 17 Hillhouse Ave, New Haven, CT 06511 sekhar.tatikonda@yale.edu ... | 2014 | 68 |
5,556 | Deep Recursive Neural Networks for Compositionality in Language Ozan ˙Irsoy Department of Computer Science Cornell University Ithaca, NY 14853 oirsoy@cs.cornell.edu Claire Cardie Department of Computer Science Cornell University Ithaca, NY 14853 cardie@cs.cornell.edu Abstract Recursive neural ... | 2014 | 69 |
5,557 | Predictive Entropy Search for Efficient Global Optimization of Black-box Functions Jos´e Miguel Hern´andez-Lobato jmh233@cam.ac.uk University of Cambridge Matthew W. Hoffman mwh30@cam.ac.uk University of Cambridge Zoubin Ghahramani zoubin@eng.cam.ac.uk University of Cambridge Abstract We propose ... | 2014 | 7 |
5,558 | Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation Mingjun Zhong, Nigel Goddard, Charles Sutton School of Informatics University of Edinburgh United Kingdom {mzhong,nigel.goddard,csutton}@inf.ed.ac.uk Abstract Blind source separation problems are difficult ... | 2014 | 70 |
5,559 | Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data Florian Stimberg Computer Science, TU Berlin Florian.Stimberg@tu-berlin.de Andreas Ruttor Computer Science, TU Berlin Andreas.Ruttor@tu-berlin.de Manfred Opper Computer Science, TU Berlin Manfred.Opper@tu-be... | 2014 | 71 |
5,560 | Low-Rank Time-Frequency Synthesis C´edric F´evotte Laboratoire Lagrange (CNRS, OCA & Universit´e de Nice) Nice, France cfevotte@unice.fr Matthieu Kowalski∗ Laboratoire des Signaux et Syst`emes (CNRS, Sup´elec & Universit´e Paris-Sud) Gif-sur-Yvette, France kowalski@lss.supelec.fr Abstract Many s... | 2014 | 72 |
5,561 | Spike Frequency Adaptation Implements Anticipative Tracking in Continuous Attractor Neural Networks Yuanyuan Mi State Key Laboratory of Cognitive Neuroscience & Learning, Beijing Normal University,Beijing 100875,China miyuanyuan0102@bnu.edu.cn C. C. Alan Fung, K. Y. Michael Wong Department of Physics, T... | 2014 | 73 |
5,562 | Learning to Optimize via Information-Directed Sampling Daniel Russo Stanford University Stanford, CA 94305 djrusso@stanford.edu Benjamin Van Roy Stanford University Stanford, CA 94305 bvr@stanford.edu Abstract We propose information-directed sampling – a new algorithm for online optimization probl... | 2014 | 74 |
5,563 | Distributed Estimation, Information Loss and Exponential Families Qiang Liu Alexander Ihler Department of Computer Science, University of California, Irvine qliu1@uci.edu ihler@ics.uci.edu Abstract Distributed learning of probabilistic models from multiple data repositories with minimum communication ... | 2014 | 75 |
5,564 | A⇤Sampling Chris J. Maddison Dept. of Computer Science University of Toronto cmaddis@cs.toronto.edu Daniel Tarlow, Tom Minka Microsoft Research {dtarlow,minka}@microsoft.com Abstract The problem of drawing samples from a discrete distribution can be converted into a discrete optimization problem [1,... | 2014 | 76 |
5,565 | Consistent Binary Classification with Generalized Performance Metrics Oluwasanmi Koyejo⇤ Department of Psychology, Stanford University sanmi@stanford.edu Nagarajan Natarajan⇤ Department of Computer Science, University of Texas at Austin naga86@cs.utexas.edu Pradeep Ravikumar Department of Computer ... | 2014 | 77 |
5,566 | Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS) Anshumali Shrivastava Department of Computer Science Computing and Information Science Cornell University Ithaca, NY 14853, USA anshu@cs.cornell.edu Ping Li Department of Statistics and Biostatistics Department of Computer ... | 2014 | 78 |
5,567 | Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data Karthika Mohan and Judea Pearl Cognitive Systems Laboratory Computer Science Department University of California, Los Angeles, CA 90024 {karthika,judea}@cs.ucla.edu Abstract We address the problem of deciding whether a ca... | 2014 | 79 |
5,568 | Discriminative Unsupervised Feature Learning with Convolutional Neural Networks Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller and Thomas Brox Department of Computer Science University of Freiburg 79110, Freiburg im Breisgau, Germany {dosovits,springj,riedmiller,brox}@cs.uni-freiburg.de A... | 2014 | 8 |
5,569 | Restricted Boltzmann machines modeling human choice Takayuki Osogami IBM Research - Tokyo osogami@jp.ibm.com Makoto Otsuka IBM Research - Tokyo motsuka@ucla.edu Abstract We extend the multinomial logit model to represent some of the empirical phenomena that are frequently observed in the choices made ... | 2014 | 80 |
5,570 | On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures Harikrishna Narasimhan†, Rohit Vaish†, Shivani Agarwal Department of Computer Science and Automation Indian Institute of Science, Bangalore – 560012, India {harikrishna, rohit.vaish, shivani}@csa.iisc.ernet.in Abs... | 2014 | 81 |
5,571 | Clustering from Labels and Time-Varying Graphs Shiau Hong Lim National University of Singapore mpelsh@nus.edu.sg Yudong Chen EECS, University of California, Berkeley yudong.chen@eecs.berkeley.edu Huan Xu National University of Singapore mpexuh@nus.edu.sg Abstract We present a general framework for... | 2014 | 82 |
5,572 | Unsupervised learning of an efficient short-term memory network Pietro Vertechi Wieland Brendel ∗ Christian K. Machens Champalimaud Neuroscience Programme Champalimaud Centre for the Unknown Lisbon, Portugal first.last@neuro.fchampalimaud.org Abstract Learning in recurrent neural networks has been a ... | 2014 | 83 |
5,573 | Projective dictionary pair learning for pattern classification Shuhang Gu1, Lei Zhang1, Wangmeng Zuo2, Xiangchu Feng3 1Dept. of Computing, The Hong Kong Polytechnic University, Hong Kong, China 2School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China 3Dept. of Applied Mathemati... | 2014 | 84 |
5,574 | Analysis of Learning from Positive and Unlabeled Data Marthinus C. du Plessis The University of Tokyo Tokyo, 113-0033, Japan christo@ms.k.u-tokyo.ac.jp Gang Niu Baidu Inc. Beijing, 100085, China niugang@baidu.com Masashi Sugiyama The University of Tokyo Tokyo, 113-0033, Japan sugi@k.u-tokyo.ac... | 2014 | 85 |
5,575 | Learning with Fredholm Kernels Qichao Que Mikhail Belkin Yusu Wang Department of Computer Science and Engineering The Ohio State University Columbus, OH 43210 {que,mbelkin,yusu}@cse.ohio-state.edu Abstract In this paper we propose a framework for supervised and semi-supervised learning based on refo... | 2014 | 86 |
5,576 | How transferable are features in deep neural networks? Jason Yosinski,1 Jeff Clune,2 Yoshua Bengio,3 and Hod Lipson4 1 Dept. Computer Science, Cornell University 2 Dept. Computer Science, University of Wyoming 3 Dept. Computer Science & Operations Research, University of Montreal 4 Dept. Mechanical & Ae... | 2014 | 87 |
5,577 | Concavity of reweighted Kikuchi approximation Po-Ling Loh Department of Statistics The Wharton School University of Pennsylvania loh@wharton.upenn.edu Andre Wibisono Computer Science Division University of California, Berkeley wibisono@berkeley.edu Abstract We analyze a reweighted version of the K... | 2014 | 88 |
5,578 | The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification Been Kim, Cynthia Rudin and Julie Shah Massachusetts Institute of Technology Cambridge, Massachusetts 02139 {beenkim, rudin, julie a shah}@csail.mit.edu Abstract We present the Bayesian Case Model (BCM), ... | 2014 | 89 |
5,579 | Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights Daniel Soudry1, Itay Hubara2, Ron Meir2 (1) Department of Statistics, Columbia University (2) Department of Electrical Engineering, Technion, Israel Institute of Technology daniel.soudry@gm... | 2014 | 9 |
5,580 | Advances in Learning Bayesian Networks of Bounded Treewidth Siqi Nie Rensselaer Polytechnic Institute Troy, NY, USA nies@rpi.edu Denis D. Mau´a University of S˜ao Paulo S˜ao Paulo, Brazil denis.maua@usp.br Cassio P. de Campos Queen’s University Belfast Belfast, UK c.decampos@qub.ac.uk Qiang ... | 2014 | 90 |
5,581 | Learning From Weakly Supervised Data by The Expectation Loss SVM (e-SVM) algorithm Jun Zhu Department of Statistics University of California, Los Angeles jzh@ucla.edu Junhua Mao Department of Statistics University of California, Los Angeles mjhustc@ucla.edu Alan Yuille Department of Statistics U... | 2014 | 91 |
5,582 | On the Computational Efficiency of Training Neural Networks Roi Livni The Hebrew University roi.livni@mail.huji.ac.il Shai Shalev-Shwartz The Hebrew University shais@cs.huji.ac.il Ohad Shamir Weizmann Institute of Science ohad.shamir@weizmann.ac.il Abstract It is well-known that neural networks a... | 2014 | 92 |
5,583 | Scaling-up Importance Sampling for Markov Logic Networks Deepak Venugopal Department of Computer Science University of Texas at Dallas dxv021000@utdallas.edu Vibhav Gogate Department of Computer Science University of Texas at Dallas vgogate@hlt.utdallas.edu Abstract Markov Logic Networks (MLNs) ar... | 2014 | 93 |
5,584 | Unsupervised Transcription of Piano Music Taylor Berg-Kirkpatrick Jacob Andreas Dan Klein Computer Science Division University of California, Berkeley {tberg,jda,klein}@cs.berkeley.edu Abstract We present a new probabilistic model for transcribing piano music from audio to a symbolic form. Our model r... | 2014 | 94 |
5,585 | Multi-Scale Spectral Decomposition of Massive Graphs Si Si⇤ Department of Computer Science University of Texas at Austin ssi@cs.utexas.edu Donghyuk Shin⇤ Department of Computer Science University of Texas at Austin dshin@cs.utexas.edu Inderjit S. Dhillon Department of Computer Science University... | 2014 | 95 |
5,586 | Dimensionality Reduction with Subspace Structure Preservation Devansh Arpit Department of Computer Science SUNY Buffalo Buffalo, NY 14260 devansha@buffalo.edu Ifeoma Nwogu Department of Computer Science SUNY Buffalo Buffalo, NY 14260 inwogu@buffalo.edu Venu Govindaraju Department of Computer S... | 2014 | 96 |
5,587 | Ranking via Robust Binary Classification Hyokun Yun Amazon Seattle, WA 98109 yunhyoku@amazon.com Parameswaran Raman, S. V. N. Vishwanathan Department of Computer Science University of California Santa Cruz, CA 95064 {params,vishy}@ucsc.edu Abstract We propose RoBiRank, a ranking algorithm that is m... | 2014 | 97 |
5,588 | Learning the Learning Rate for Prediction with Expert Advice Wouter M. Koolen Queensland University of Technology and UC Berkeley wouter.koolen@qut.edu.au Tim van Erven Leiden University, the Netherlands tim@timvanerven.nl Peter D. Gr¨unwald Leiden University and Centrum Wiskunde & Informatica, the Ne... | 2014 | 98 |
5,589 | Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling Mingyuan Zhou IROM Department, McCombs School of Business The University of Texas at Austin, Austin, TX 78712, USA mingyuan.zhou@mccombs.utexas.edu Abstract The beta-negative binomial process (BNBP), an integer... | 2014 | 99 |
5,590 | Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling Zheng Qu Department of Mathematics The University of Hong Kong Hong Kong zhengqu@maths.hku.hk Peter Richt´arik School of Mathematics The University of Edinburgh EH9 3FD, United Kingdom peter.richtarik@ed.ac.uk Tong Zhang Departm... | 2015 | 1 |
5,591 | Parallel Recursive Best-First AND/OR Search for Exact MAP Inference in Graphical Models Akihiro Kishimoto IBM Research, Ireland akihirok@ie.ibm.com Radu Marinescu IBM Research, Ireland radu.marinescu@ie.ibm.com Adi Botea IBM Research, Ireland adibotea@ie.ibm.com Abstract The paper presents and e... | 2015 | 10 |
5,592 | Streaming, Distributed Variational Inference for Bayesian Nonparametrics Trevor Campbell1 Julian Straub2 John W. Fisher III2 Jonathan P. How1 1LIDS, 2CSAIL, MIT {tdjc@ , jstraub@csail. , fisher@csail. , jhow@}mit.edu Abstract This paper presents a methodology for creating streaming, distributed infere... | 2015 | 100 |
5,593 | Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models Juho Lee and Seungjin Choi Department of Computer Science and Engineering Pohang University of Science and Technology 77 Cheongam-ro, Nam-gu, Pohang 37673, Korea {stonecold,seungjin}@postech.ac.kr Abstract Normalized random measures... | 2015 | 101 |
5,594 | The Self-Normalized Estimator for Counterfactual Learning Adith Swaminathan Department of Computer Science Cornell University adith@cs.cornell.edu Thorsten Joachims Department of Computer Science Cornell University tj@cs.cornell.edu Abstract This paper identifies a severe problem of the counterfact... | 2015 | 102 |
5,595 | Information-theoretic lower bounds for convex optimization with erroneous oracles Yaron Singer Harvard University Cambridge, MA 02138 yaron@seas.harvard.edu Jan Vondr´ak IBM Almaden Research Center San Jose, CA 95120 jvondrak@us.ibm.com Abstract We consider the problem of optimizing convex and con... | 2015 | 103 |
5,596 | A Nonconvex Optimization Framework for Low Rank Matrix Estimation⇤ Tuo Zhao Johns Hopkins University Zhaoran Wang Han Liu Princeton University Abstract We study the estimation of low rank matrices via nonconvex optimization. Compared with convex relaxation, nonconvex optimization exhibits superior empir... | 2015 | 104 |
5,597 | Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection Kisuk Lee, Aleksandar Zlateski Massachusetts Institute of Technology {kisuklee,zlateski}@mit.edu Ashwin Vishwanathan, H. Sebastian Seung Princeton University {ashwinv,sseung}@princeton.edu Abstract Efforts to automate t... | 2015 | 105 |
5,598 | Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms Yunwen Lei Department of Mathematics City University of Hong Kong yunwelei@cityu.edu.hk ¨Ur¨un Dogan Microsoft Research Cambridge CB1 2FB, UK udogan@microsoft.com Alexander Binder ISTD Pillar Singapore Univer... | 2015 | 106 |
5,599 | Scalable Inference for Gaussian Process Models with Black-Box Likelihoods Amir Dezfouli The University of New South Wales akdezfuli@gmail.com Edwin V. Bonilla The University of New South Wales e.bonilla@unsw.edu.au Abstract We propose a sparse method for scalable automated variational inference (AVI) ... | 2015 | 107 |
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