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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5,400 | Online Optimization for Max-Norm Regularization Jie Shen Dept. of Computer Science Rutgers University Piscataway, NJ 08854 js2007@rutgers.edu Huan Xu Dept. of Mech. Engineering National Univ. of Singapore Singapore 117575 mpexuh@nus.edu.sg Ping Li Dept. of Statistics Dept. of Computer Science ... | 2014 | 299 |
5,401 | Optimal prior-dependent neural population codes under shared input noise Agnieszka Grabska-Barwi´nska Gatsby Computational Neuroscience Unit University College London agnieszka@gatsby.ucl.ac.uk Jonathan W. Pillow Princeton Neuroscience Institute Department of Psychology Princeton University pillow@p... | 2014 | 3 |
5,402 | Parallel Sampling of HDPs using Sub-Cluster Splits Jason Chang CSAIL, MIT jchang7@csail.mit.edu John W. Fisher III CSAIL, MIT fisher@csail.mit.edu Abstract We develop a sampling technique for Hierarchical Dirichlet process models. The parallel algorithm builds upon [1] by proposing large split and mer... | 2014 | 30 |
5,403 | Cone-constrained Principal Component Analysis Yash Deshpande Electrical Engineering Stanford University Andrea Montanari Electrical Engineering and Statistics Stanford University Emile Richard Electrical Engineering Stanford University Abstract Estimating a vector from noisy quadratic observations... | 2014 | 300 |
5,404 | Fast Training of Pose Detectors in the Fourier Domain Jo˜ao F. Henriques Pedro Martins Rui Caseiro Jorge Batista Institute of Systems and Robotics University of Coimbra {henriques,pedromartins,ruicaseiro,batista}@isr.uc.pt Abstract In many datasets, the samples are related by a known image transformat... | 2014 | 301 |
5,405 | Optimistic planning in Markov decision processes using a generative model Bal´azs Sz¨or´enyi INRIA Lille - Nord Europe, SequeL project, France / MTA-SZTE Research Group on Artificial Intelligence, Hungary balazs.szorenyi@inria.fr Gunnar Kedenburg INRIA Lille - Nord Europe, SequeL project, France gu... | 2014 | 302 |
5,406 | Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling Ricardo Henao, Xin Yuan and Lawrence Carin Department of Electrical and Computer Engineering Duke University, Durham, NC 27708 {r.henao,xin.yuan,lcarin}@duke.edu Abstract A new Bayesian formulation is developed for nonlinear sup... | 2014 | 303 |
5,407 | Feedback Detection for Live Predictors Stefan Wager, Nick Chamandy, Omkar Muralidharan, and Amir Najmi swager@stanford.edu, {chamandy, omuralidharan, amir}@google.com Stanford University and Google, Inc. Abstract A predictor that is deployed in a live production system may perturb the features it uses to ma... | 2014 | 304 |
5,408 | Do Convnets Learn Correspondence? Jonathan Long Ning Zhang Trevor Darrell University of California – Berkeley {jonlong, nzhang, trevor}@cs.berkeley.edu Abstract Convolutional neural nets (convnets) trained from massive labeled datasets [1] have substantially improved the state-of-the-art in image classi... | 2014 | 305 |
5,409 | Convolutional Neural Network Architectures for Matching Natural Language Sentences Baotian Hu§∗ Zhengdong Lu† Hang Li† Qingcai Chen§ §Department of Computer Science & Technology, Harbin Institute of Technology Shenzhen Graduate School, Xili, China baotianchina@gmail.com qingcai.chen@hitsz.edu.cn †... | 2014 | 306 |
5,410 | Conditional Random Field Autoencoders for Unsupervised Structured Prediction Waleed Ammar Chris Dyer Noah A. Smith School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213, USA {wammar,cdyer,nasmith}@cs.cmu.edu Abstract We introduce a framework for unsupervised learning of structur... | 2014 | 307 |
5,411 | Optimal rates for k-NN density and mode estimation Sanjoy Dasgupta University of California, San Diego, CSE dasgupta@eng.ucsd.edu Samory Kpotufe ∗ Princeton University, ORFE samory@princeton.edu Abstract We present two related contributions of independent interest: (1) high-probability finite sample ra... | 2014 | 308 |
5,412 | Coresets for k-Segmentation of Streaming Data Guy Rosman ∗† CSAIL, MIT 32 Vassar St., 02139, Cambridge, MA USA rosman@csail.mit.edu Mikhail Volkov † CSAIL, MIT 32 Vassar St., 02139, Cambridge, MA USA mikhail@csail.mit.edu Danny Feldman † CSAIL, MIT 32 Vassar St., 02139, Cambridge, MA USA d... | 2014 | 309 |
5,413 | Recursive Inversion Models for Permutations Christopher Meek Microsoft Research Redmond, Washington 98052 meek@microsoft.com Marina Meil˘a University of Washington Seattle, Washington 98195 mmp@stat.washington.edu Abstract We develop a new exponential family probabilistic model for permutations that... | 2014 | 31 |
5,414 | Message Passing Inference for Large Scale Graphical Models with High Order Potentials Jian Zhang ETH Zurich jizhang@ethz.ch Alexander G. Schwing University of Toronto aschwing@cs.toronto.edu Raquel Urtasun University of Toronto urtasun@cs.toronto.edu Abstract To keep up with the Big Data challen... | 2014 | 310 |
5,415 | Weighted importance sampling for off-policy learning with linear function approximation A. Rupam Mahmood, Hado van Hasselt, Richard S. Sutton Reinforcement Learning and Artificial Intelligence Laboratory University of Alberta Edmonton, Alberta, Canada T6G 1S2 {ashique,vanhasse,sutton}@cs.ualberta.ca Abstra... | 2014 | 311 |
5,416 | Incremental Clustering: The Case for Extra Clusters Margareta Ackerman Florida State University 600 W College Ave, Tallahassee, FL 32306 mackerman@fsu.edu Sanjoy Dasgupta UC San Diego 9500 Gilman Dr, La Jolla, CA 92093 dasgupta@eng.ucsd.edu Abstract The explosion in the amount of data available for ... | 2014 | 312 |
5,417 | Positive Curvature and Hamiltonian Monte Carlo Christof Seiler Simon Rubinstein-Salzedo∗ Susan Holmes Department of Statistics Stanford University {cseiler,simonr}@stanford.edu, susan@stat.stanford.edu Abstract The Jacobi metric introduced in mathematical physics can be used to analyze Hamiltonian Mon... | 2014 | 313 |
5,418 | Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit Karin C. Knudson Department of Mathematics The University of Texas at Austin kknudson@math.utexas.edu Jacob L. Yates Department of Neuroscience The University of Texas at Austin jlyates@utexas.edu Alex... | 2014 | 314 |
5,419 | Zeta Hull Pursuits: Learning Nonconvex Data Hulls Yuanjun Xiong† Wei Liu‡ Deli Zhao♯ Xiaoou Tang† †Information Engineering Department, The Chinese University of Hong Kong, Hong Kong ‡IBM T. J. Watson Research Center, Yorktown Heights, New York, USA ♯Advanced Algorithm Research Group, HTC, Beijing, China... | 2014 | 315 |
5,420 | Near-Optimal-Sample Estimators for Spherical Gaussian Mixtures Jayadev Acharya∗ MIT jayadev@mit.edu Ashkan Jafarpour, Alon Orlitsky, Ananda Theertha Suresh UC San Diego {ashkan, alon, asuresh}@ucsd.edu Abstract Many important distributions are high dimensional, and often they can be modeled as Gauss... | 2014 | 316 |
5,421 | Provable Tensor Factorization with Missing Data Prateek Jain Microsoft Research Bangalore, India prajain@microsoft.com Sewoong Oh Dept. of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign Urbana, IL 61801 swoh@illinois.edu Abstract We study the problem of lo... | 2014 | 317 |
5,422 | Learning Generative Models with Visual Attention Yichuan Tang, Nitish Srivastava, Ruslan Salakhutdinov Department of Computer Science University of Toronto Toronto, Ontario, Canada {tang,nitish,rsalakhu}@cs.toronto.edu Abstract Attention has long been proposed by psychologists to be important for efficient... | 2014 | 318 |
5,423 | Bandit Convex Optimization: Towards Tight Bounds Elad Hazan Technion—Israel Institute of Technology Haifa 32000, Israel ehazan@ie.technion.ac.il Kfir Y. Levy Technion—Israel Institute of Technology Haifa 32000, Israel kfiryl@tx.technion.ac.il Abstract Bandit Convex Optimization (BCO) is a fundamental... | 2014 | 319 |
5,424 | Active Learning and Best-Response Dynamics Maria-Florina Balcan Carnegie Mellon ninamf@cs.cmu.edu Christopher Berlind Georgia Tech cberlind@gatech.edu Avrim Blum Carnegie Mellon avrim@cs.cmu.edu Emma Cohen Georgia Tech ecohen@gatech.edu Kaushik Patnaik Georgia Tech kpatnaik3@gatech.edu L... | 2014 | 32 |
5,425 | A Latent Source Model for Online Collaborative Filtering Guy Bresler George H. Chen Devavrat Shah Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge, MA 02139 {gbresler,georgehc,devavrat}@mit.edu Abstract Despite the prevalence of collaborative ... | 2014 | 320 |
5,426 | Self-Paced Learning with Diversity Lu Jiang1, Deyu Meng1,2, Shoou-I Yu1, Zhenzhong Lan1, Shiguang Shan1,3, Alexander G. Hauptmann1 1School of Computer Science, Carnegie Mellon University 2School of Mathematics and Statistics, Xi’an Jiaotong University 3Institute of Computing Technology, Chinese Academy of Sci... | 2014 | 321 |
5,427 | Inference by Learning: Speeding-up Graphical Model Optimization via a Coarse-to-Fine Cascade of Pruning Classifiers Bruno Conejo∗ GPS Division, California Institute of Technology, Pasadena, CA, USA Universite Paris-Est, Ecole des Ponts ParisTech, Marne-la-Vallee, France bconejo@caltech.edu Nikos Komodakis ... | 2014 | 322 |
5,428 | Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs David I. Inouye Pradeep Ravikumar Inderjit S. Dhillon Department of Computer Science University of Texas at Austin {dinouye,pradeepr,inderjit}@cs.utexas.edu Abstract We develop a fast algorithm for the Admixture of P... | 2014 | 323 |
5,429 | Kernel Mean Estimation via Spectral Filtering Krikamol Muandet MPI-IS, T¨ubingen krikamol@tue.mpg.de Bharath Sriperumbudur Dept. of Statistics, PSU bks18@psu.edu Bernhard Sch¨olkopf MPI-IS, T¨ubingen bs@tue.mpg.de Abstract The problem of estimating the kernel mean in a reproducing kernel Hilbert s... | 2014 | 324 |
5,430 | A Dual Algorithm for Olfactory Computation in the Locust Brain Sina Tootoonian M´at´e Lengyel st582@eng.cam.ac.uk m.lengyel@eng.cam.ac.uk Computational & Biological Learning Laboratory Department of Engineering, University of Cambridge Trumpington Street, Cambridge CB2 1PZ, United Kingdom Abstract W... | 2014 | 325 |
5,431 | Online combinatorial optimization with stochastic decision sets and adversarial losses Gergely Neu Michal Valko SequeL team, INRIA Lille – Nord Europe, France {gergely.neu,michal.valko}@inria.fr Abstract Most work on sequential learning assumes a fixed set of actions that are available all the time. Howe... | 2014 | 326 |
5,432 | Learning Multiple Tasks in Parallel with a Shared Annotator Haim Cohen Department of Electrical Engeneering The Technion – Israel Institute of Technology Haifa, 32000 Israel hcohen@tx.technion.ac.il Koby Crammer Department of Electrical Engeneering The Technion – Israel Institute of Technology Haifa... | 2014 | 327 |
5,433 | A Complete Variational Tracker Ryan Turner Northrop Grumman Corp. ryan.turner@ngc.com Steven Bottone Northrop Grumman Corp. steven.bottone@ngc.com Bhargav Avasarala Northrop Grumman Corp. bhargav.avasarala@ngc.com Abstract We introduce a novel probabilistic tracking algorithm that incorporates com... | 2014 | 328 |
5,434 | Low-dimensional models of neural population activity in sensory cortical circuits Evan Archer1,2, Urs K¨oster3, Jonathan Pillow4, Jakob H. Macke1,2 1Max Planck Institute for Biological Cybernetics, T¨ubingen 2Bernstein Center for Computational Neuroscience, T¨ubingen 3Redwood Center for Theoretical Neuroscien... | 2014 | 329 |
5,435 | Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks Mario Marchand D´epartement d’informatique et g´enie logiciel Universit´e Laval Qu´ebec (QC), Canada mario.marchand@ift.ulaval.ca Hongyu Su Helsinki Institute for Information Technology Dept of Information a... | 2014 | 33 |
5,436 | Spectral k-Support Norm Regularization Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos Department of Computer Science University College London {a.mcdonald,m.pontil,d.stamos}@cs.ucl.ac.uk Abstract The k-support norm has successfully been applied to sparse vector prediction problems. We observe th... | 2014 | 330 |
5,437 | Learning Optimal Commitment to Overcome Insecurity Avrim Blum Carnegie Mellon University avrim@cs.cmu.edu Nika Haghtalab Carnegie Mellon University nika@cmu.edu Ariel D. Procaccia Carnegie Mellon University arielpro@cs.cmu.edu Abstract Game-theoretic algorithms for physical security have made an i... | 2014 | 331 |
5,438 | A Drifting-Games Analysis for Online Learning and Applications to Boosting Haipeng Luo Department of Computer Science Princeton University Princeton, NJ 08540 haipengl@cs.princeton.edu Robert E. Schapire⇤ Department of Computer Science Princeton University Princeton, NJ 08540 schapire@cs.princeton... | 2014 | 332 |
5,439 | Dynamic Rank Factor Model for Text Streams Shaobo Han∗, Lin Du∗, Esther Salazar and Lawrence Carin Duke University, Durham, NC 27708 {shaobo.han, lin.du, esther.salazar, lcarin}@duke.edu Abstract We propose a semi-parametric and dynamic rank factor model for topic modeling, capable of (i) discovering topic pr... | 2014 | 333 |
5,440 | Consistency of weighted majority votes Daniel Berend Computer Science Department and Mathematics Department Ben Gurion University Beer Sheva, Israel berend@cs.bgu.ac.il Aryeh Kontorovich Computer Science Department Ben Gurion University Beer Sheva, Israel karyeh@cs.bgu.ac.il Abstract We revisit from a s... | 2014 | 334 |
5,441 | Modeling Deep Temporal Dependencies with Recurrent “Grammar Cells” Vincent Michalski Goethe University Frankfurt, Germany vmichals@rz.uni-frankfurt.de Roland Memisevic University of Montreal, Canada roland.memisevic@umontreal.ca Kishore Konda Goethe University Frankfurt, Germany konda.kishorereddy@g... | 2014 | 335 |
5,442 | Augur: Data-Parallel Probabilistic Modeling Jean-Baptiste Tristan1, Daniel Huang2, Joseph Tassarotti3, Adam Pocock1, Stephen J. Green1, Guy L. Steele, Jr1 1Oracle Labs {jean.baptiste.tristan, adam.pocock, stephen.x.green, guy.steele}@oracle.com 2Harvard University dehuang@fas.harvard.edu 3Carnegie Mellon Un... | 2014 | 336 |
5,443 | Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning Siamak Ravanbakhsh Department of Computing Science University of Alberta Edmonton, AB T6G 2E8 mravanba@ualberta.ca Reihaneh Rabbany Department of Computing Science University of Alberta Edmonton, AB T6G 2E8 rabbanyk... | 2014 | 337 |
5,444 | Mondrian Forests: Efficient Online Random Forests Balaji Lakshminarayanan Gatsby Unit University College London Daniel M. Roy Department of Engineering University of Cambridge Yee Whye Teh Department of Statistics University of Oxford Abstract Ensembles of randomized decision trees, usually referre... | 2014 | 338 |
5,445 | A Probabilistic Framework for Multimodal Retrieval using Integrative Indian Buffet Process Bahadir Ozdemir Department of Computer Science University of Maryland College Park, MD 20742 USA ozdemir@cs.umd.edu Larry S. Davis Institute for Advanced Computer Studies University of Maryland College Park, M... | 2014 | 339 |
5,446 | Identifying and attacking the saddle point problem in high-dimensional non-convex optimization Yann N. Dauphin Razvan Pascanu Caglar Gulcehre Kyunghyun Cho Universit´e de Montr´eal dauphiya@iro.umontreal.ca, r.pascanu@gmail.com, gulcehrc@iro.umontreal.ca, kyunghyun.cho@umontreal.ca Surya Ganguli Stanford ... | 2014 | 34 |
5,447 | A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input Mateusz Malinowski Mario Fritz Max Planck Institute for Informatics Saarbr¨ucken, Germany {mmalinow,mfritz}@mpi-inf.mpg.de Abstract We propose a method for automatically answering questions about images by br... | 2014 | 340 |
5,448 | Semi-supervised Learning with Deep Generative Models Diederik P. Kingma∗, Danilo J. Rezende†, Shakir Mohamed†, Max Welling∗ ∗Machine Learning Group, Univ. of Amsterdam, {D.P.Kingma, M.Welling}@uva.nl †Google Deepmind, {danilor, shakir}@google.com Abstract The ever-increasing size of modern data sets combine... | 2014 | 341 |
5,449 | Biclustering Using Message Passing Luke O’Connor Bioinformatics and Integrative Genomics Harvard University Cambridge, MA 02138 loconnor@g.harvard.edu Soheil Feizi Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge, MA 02139 sfeizi@mit.edu Abstract Biclus... | 2014 | 342 |
5,450 | Stochastic Proximal Gradient Descent with Acceleration Techniques Atsushi Nitanda NTT DATA Mathematical Systems Inc. 1F Shinanomachi Rengakan, 35, Shinanomachi, Shinjuku-ku, Tokyo, 160-0016, Japan nitanda@msi.co.jp Abstract Proximal gradient descent (PGD) and stochastic proximal gradient descent (SP... | 2014 | 343 |
5,451 | DFacTo: Distributed Factorization of Tensors Joon Hee Choi Electrical and Computer Engineering Purdue University West Lafayette IN 47907 choi240@purdue.edu S. V. N. Vishwanathan Statistics and Computer Science Purdue University West Lafayette IN 47907 vishy@stat.purdue.edu Abstract We present a ... | 2014 | 344 |
5,452 | Robust Classification Under Sample Selection Bias Anqi Liu Department of Computer Science University of Illinois at Chicago Chicago, IL 60607 aliu33@uic.edu Brian D. Ziebart Department of Computer Science University of Illinois at Chicago Chicago, IL 60607 bziebart@uic.edu Abstract In many import... | 2014 | 345 |
5,453 | Deep Joint Task Learning for Generic Object Extraction Xiaolong Wang1,4, Liliang Zhang1, Liang Lin1,3∗, Zhujin Liang1, Wangmeng Zuo2 1Sun Yat-sen University, Guangzhou 510006, China 2School of Computer Science and Technology, Harbin Institute of Technology, China 3SYSU-CMU Shunde International Joint Research ... | 2014 | 346 |
5,454 | Automatic Discovery of Cognitive Skills to Improve the Prediction of Student Learning Robert V. Lindsey, Mohammad Khajah, Michael C. Mozer Department of Computer Science and Institute of Cognitive Science University of Colorado, Boulder Abstract To master a discipline such as algebra or physics, students mu... | 2014 | 347 |
5,455 | General Table Completion using a Bayesian Nonparametric Model Isabel Valera Department of Signal Processing and Communications University Carlos III in Madrid ivalera@tsc.uc3m.es Zoubin Ghahramani Department of Engineering University of Cambridge zoubin@eng.cam.ac.uk Abstract Even though heterog... | 2014 | 348 |
5,456 | A Representation Theory for Ranking Functions Harsh Pareek, Pradeep Ravikumar Department of Computer Science University of Texas at Austin {harshp,pradeepr}@cs.utexas.edu Abstract This paper presents a representation theory for permutation-valued functions, which in their general form can also be called l... | 2014 | 349 |
5,457 | PEWA: Patch-based Exponentially Weighted Aggregation for image denoising Charles Kervrann Inria Rennes - Bretagne Atlantique Serpico Project-Team Campus Universitaire de Beaulieu, 35 042 Rennes Cedex, France charles.kervrann@inria.fr Abstract Patch-based methods have been widely used for noise reduction... | 2014 | 35 |
5,458 | Multivariate Regression with Calibration⇤ Han Liu Department of Operations Research and Financial Engineering Princeton University Lie Wang Department of Mathematics Massachusetts Institute of Technology Tuo Zhao† Department of Computer Science Johns Hopkins University Abstract We propose a new me... | 2014 | 350 |
5,459 | Blossom Tree Graphical Models Zhe Liu Department of Statistics University of Chicago John Lafferty Department of Statistics Department of Computer Science University of Chicago Abstract We combine the ideas behind trees and Gaussian graphical models to form a new nonparametric family of graphical mo... | 2014 | 351 |
5,460 | Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices Austin R. Benson ICME Stanford University Stanford, CA arbenson@stanford.edu Jason D. Lee ICME Stanford University Stanford, CA jdl17@stanford.edu Bartek Rajwa Bindley Biosciences Center Purdu... | 2014 | 352 |
5,461 | Rates of convergence for nearest neighbor classification Kamalika Chaudhuri Computer Science and Engineering University of California, San Diego kamalika@cs.ucsd.edu Sanjoy Dasgupta Computer Science and Engineering University of California, San Diego dasgupta@cs.ucsd.edu Abstract We analyze the beh... | 2014 | 353 |
5,462 | Inferring synaptic conductances from spike trains under a biophysically inspired point process model Kenneth W. Latimer The Institute for Neuroscience The University of Texas at Austin latimerk@utexas.edu E. J. Chichilnisky Department of Neurosurgery Hansen Experimental Physics Laboratory Stanford Uni... | 2014 | 354 |
5,463 | Scalable Inference for Neuronal Connectivity from Calcium Imaging Alyson K. Fletcher Sundeep Rangan Abstract Fluorescent calcium imaging provides a potentially powerful tool for inferring connectivity in neural circuits with up to thousands of neurons. However, a key challenge in using calcium imaging for... | 2014 | 355 |
5,464 | Minimax-optimal Inference from Partial Rankings Bruce Hajek UIUC b-hajek@illinois.edu Sewoong Oh UIUC swoh@illinois.edu Jiaming Xu UIUC jxu18@illinois.edu Abstract This paper studies the problem of rank aggregation under the Plackett-Luce model. The goal is to infer a global ranking and related ... | 2014 | 356 |
5,465 | Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks Yanping Huang University of Washington huangyp@cs.uw.edu Rajesh P.N. Rao University of Washington rao@cs.uw.edu Abstract We propose a spiking network model capable of performing both approximate inference and learni... | 2014 | 357 |
5,466 | Simple MAP Inference via Low-Rank Relaxations Roy Frostig⇤, Sida I. Wang,⇤ Percy Liang, Christopher D. Manning Computer Science Department, Stanford University, Stanford, CA, 94305 {rf,sidaw,pliang}@cs.stanford.edu, manning@stanford.edu Abstract We focus on the problem of maximum a posteriori (MAP) infe... | 2014 | 358 |
5,467 | Fast Prediction for Large-Scale Kernel Machines Cho-Jui Hsieh, Si Si, and Inderjit S. Dhillon Department of Computer Science University of Texas at Austin Austin, TX 78712 USA {cjhsieh,ssi,inderjit}@cs.utexas.edu Abstract Kernel machines such as kernel SVM and kernel ridge regression usually construct hig... | 2014 | 359 |
5,468 | A Safe Screening Rule for Sparse Logistic Regression Jie Wang Arizona State University Tempe, AZ 85287 jie.wang.ustc@asu.edu Jiayu Zhou Arizona State University Tempe, AZ 85287 jiayu.zhou@asu.edu Jun Liu SAS Institute Inc. Cary, NC 27513 jun.liu@sas.com Peter Wonka Arizona State University ... | 2014 | 36 |
5,469 | Median Selection Subset Aggregation for Parallel Inference Xiangyu Wang Dept. of Statistical Science Duke University xw56@stat.duke.edu Peichao Peng Statistics Department University of Pennsylvania ppeichao@yahoo.com David B. Dunson Dept. of Statistical Science Duke University dunson@stat.duke... | 2014 | 360 |
5,470 | Design Principles of the Hippocampal Cognitive Map Kimberly L. Stachenfeld1, Matthew M. Botvinick1, and Samuel J. Gershman2 1Princeton Neuroscience Institute and Department of Psychology, Princeton University 2Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology kls4@princeton.edu, ... | 2014 | 361 |
5,471 | Smoothed Gradients for Stochastic Variational Inference Stephan Mandt Department of Physics Princeton University smandt@princeton.edu David Blei Department of Computer Science Department of Statistics Columbia University david.blei@columbia.edu Abstract Stochastic variational inference (SVI) let... | 2014 | 362 |
5,472 | A Multiplicative Model for Learning Distributed Text-Based Attribute Representations Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov University of Toronto Canadian Institute for Advanced Research {rkiros, zemel, rsalakhu}@cs.toronto.edu Abstract In this paper we propose a general framework for learning... | 2014 | 363 |
5,473 | Feedforward Learning of Mixture Models Matthew Lawlor∗ Applied Math Yale University New Haven, CT 06520 mflawlor@gmail.com Steven W. Zucker Computer Science Yale University New Haven, CT 06520 zucker@cs.yale.edu Abstract We develop a biologically-plausible learning rule that provably converges t... | 2014 | 364 |
5,474 | Searching for Higgs Boson Decay Modes with Deep Learning Peter Sadowski Department of Computer Science University of California, Irvine Irvine, CA 92617 peter.j.sadowski@uci.edu Pierre Baldi Department of Computer Science University of California, Irvine Irvine, CA 92617 pfbaldi@ics.uci.edu Dani... | 2014 | 365 |
5,475 | Decoupled Variational Gaussian Inference Mohammad Emtiyaz Khan Ecole Polytechnique F´ed´erale de Lausanne (EPFL), Switzerland emtiyaz@gmail.com Abstract Variational Gaussian (VG) inference methods that optimize a lower bound to the marginal likelihood are a popular approach for Bayesian inference. A difficul... | 2014 | 366 |
5,476 | On Multiplicative Multitask Feature Learning Xin Wang†, Jinbo Bi†, Shipeng Yu‡, Jiangwen Sun† †Dept. of Computer Science & Engineering ‡ Health Services Innovation Center University of Connecticut Siemens Healthcare Storrs, CT 06269 Malvern, PA 19355 wangxin,jinbo,javon@engr.uconn.edu shipeng.yu@sieme... | 2014 | 367 |
5,477 | Learning Distributed Representations for Structured Output Prediction Vivek Srikumar∗ University of Utah svivek@cs.utah.edu Christopher D. Manning Stanford University manning@cs.stanford.edu Abstract In recent years, distributed representations of inputs have led to performance gains in many applica... | 2014 | 368 |
5,478 | Large-scale L-BFGS using MapReduce Weizhu Chen, Zhenghao Wang, Jingren Zhou Microsoft {wzchen,zhwang,jrzhou}@microsoft.com Abstract L-BFGS has been applied as an effective parameter estimation method for various machine learning algorithms since 1980s. With an increasing demand to deal with massive instan... | 2014 | 369 |
5,479 | Weakly-supervised Discovery of Visual Pattern Configurations Hyun Oh Song Yong Jae Lee* Stefanie Jegelka Trevor Darrell University of California, Berkeley *University of California, Davis Abstract The prominence of weakly labeled data gives rise to a growing demand for object detection methods that can... | 2014 | 37 |
5,480 | Sparse PCA via Covariance Thresholding Yash Deshpande Electrical Engineering Stanford University yashd@stanford.edu Andrea Montanari Electrical Engineering and Statistics Stanford University montanari@stanford.edu Abstract In sparse principal component analysis we are given noisy observations of a l... | 2014 | 370 |
5,481 | Randomized Experimental Design for Causal Graph Discovery Huining Hu School of Computer Science, McGill University. huining.hu@mail.mcgill.ca Zhentao Li LIENS, ´Ecole Normale Sup´erieure zhentao.li@ens.fr Adrian Vetta Department of Mathematics and Statistics and School of Computer Science, McGill Univ... | 2014 | 371 |
5,482 | Combinatorial Pure Exploration of Multi-Armed Bandits Shouyuan Chen1⇤ Tian Lin2 Irwin King1 Michael R. Lyu1 Wei Chen3 1The Chinese University of Hong Kong 2Tsinghua University 3Microsoft Research Asia 1{sychen,king,lyu}@cse.cuhk.edu.hk 2lint10@mails.tsinghua.edu.cn 3weic@microsoft.com Abstract... | 2014 | 372 |
5,483 | Deep Learning Face Representation by Joint Identification-Verification Yi Sun1 Yuheng Chen2 Xiaogang Wang3,4 Xiaoou Tang1,4 1Department of Information Engineering, The Chinese University of Hong Kong 2SenseTime Group 3Department of Electronic Engineering, The Chinese University of Hong Kong 4Shenzhen In... | 2014 | 373 |
5,484 | Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation Jonathan Tompson, Arjun Jain, Yann LeCun, Christoph Bregler New York University {tompson, ajain, yann, bregler}@cs.nyu.edu Abstract This paper proposes a new hybrid architecture that consists of a deep Convolutional Ne... | 2014 | 374 |
5,485 | Permutation Diffusion Maps (PDM) with Application to the Image Association Problem in Computer Vision Deepti Pachauri†, Risi Kondor§, Gautam Sargur†, Vikas Singh‡† †Dept. of Computer Sciences, University of Wisconsin–Madison ‡Dept. of Biostatistics & Medical Informatics, University of Wisconsin–Madison §Dept.... | 2014 | 375 |
5,486 | Structure learning of antiferromagnetic Ising 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 In this paper we in... | 2014 | 376 |
5,487 | Clustered factor analysis of multineuronal spike data Lars Buesing1, Timothy A. Machado1,2, John P. Cunningham1 and Liam Paninski1 1 Department of Statistics, Center for Theoretical Neuroscience & Grossman Center for the Statistics of Mind 2 Howard Hughes Medical Institute & Department of Neuroscience Columbi... | 2014 | 377 |
5,488 | Mode Estimation for High Dimensional Discrete Tree Graphical Models Chao Chen Department of Computer Science Rutgers, The State University of New Jersey Piscataway, NJ 08854-8019 chao.chen.cchen@gmail.com Han Liu Department of Operations Research and Financial Engineering Princeton University, Princ... | 2014 | 378 |
5,489 | Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature Tom Gunter, Michael A. Osborne Engineering Science University of Oxford {tgunter,mosb}@robots.ox.ac.uk Roman Garnett Knowledge Discovery and Machine Learning University of Bonn rgarnett@uni-bonn.de Philipp Hennig MPI for ... | 2014 | 379 |
5,490 | Deep Networks with Internal Selective Attention through Feedback Connections Marijn F. Stollenga∗, Jonathan Masci∗, Faustino Gomez, Juergen Schmidhuber IDSIA, USI-SUPSI Manno-Lugano, Switzerland {marijn,jonathan,tino,juergen}@idsia.ch Abstract Traditional convolutional neural networks (CNN) are stationary... | 2014 | 38 |
5,491 | Do Deep Nets Really Need to be Deep? Lei Jimmy Ba University of Toronto jimmy@psi.utoronto.ca Rich Caruana Microsoft Research rcaruana@microsoft.com Abstract Currently, deep neural networks are the state of the art on problems such as speech recognition and computer vision. In this paper we empiricall... | 2014 | 380 |
5,492 | A Unified Semantic Embedding: Relating Taxonomies and Attributes Sung Ju Hwang∗ Disney Research Pittsburgh, PA sungju.hwang@disneyresearch.com Leonid Sigal Disney Research Pittsburgh, PA lsigal@disneyresearch.com Abstract We propose a method that learns a discriminative yet semantic space for objec... | 2014 | 381 |
5,493 | Parallel Double Greedy Submodular Maximization Xinghao Pan1 Stefanie Jegelka1 Joseph Gonzalez1 Joseph Bradley1 Michael I. Jordan1,2 1Department of Electrical Engineering and Computer Science, and 2Department of Statistics University of California, Berkeley, Berkeley, CA USA 94720 {xinghao,stefje,jegonzal,joseph... | 2014 | 382 |
5,494 | Efficient Optimization for Average Precision SVM Pritish Mohapatra IIIT Hyderabad pritish.mohapatra@research.iiit.ac.in C.V. Jawahar IIIT Hyderabad jawahar@iiit.ac.in M. Pawan Kumar Ecole Centrale Paris & INRIA Saclay pawan.kumar@ecp.fr Abstract The accuracy of information retrieval systems is ofte... | 2014 | 383 |
5,495 | SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives Aaron Defazio Ambiata ∗ Australian National University, Canberra Francis Bach INRIA - Sierra Project-Team ´Ecole Normale Sup´erieure, Paris, France Simon Lacoste-Julien INRIA - Sierra Project-Team ´E... | 2014 | 384 |
5,496 | A Differential Equation for Modeling Nesterov’s Accelerated Gradient Method: Theory and Insights Weijie Su1 Stephen Boyd2 Emmanuel J. Cand`es1,3 1Department of Statistics, Stanford University, Stanford, CA 94305 2Department of Electrical Engineering, Stanford University, Stanford, CA 94305 3Department of ... | 2014 | 385 |
5,497 | Sparse PCA with Oracle Property Quanquan Gu Department of Operations Research and Financial Engineering Princeton University Princeton, NJ 08544, USA qgu@princeton.edu Zhaoran Wang Department of Operations Research and Financial Engineering Princeton University Princeton, NJ 08544, USA zhaoran@p... | 2014 | 386 |
5,498 | SerialRank: Spectral Ranking using Seriation Fajwel Fogel C.M.A.P., ´Ecole Polytechnique, Palaiseau, France fogel@cmap.polytechnique.fr Alexandre d’Aspremont CNRS & D.I., ´Ecole Normale Sup´erieure Paris, France aspremon@ens.fr Milan Vojnovic Microsoft Research, Cambridge, UK milanv@microsoft.co... | 2014 | 387 |
5,499 | Pre-training of Recurrent Neural Networks via Linear Autoencoders Luca Pasa, Alessandro Sperduti Department of Mathematics University of Padova, Italy {pasa,sperduti}@math.unipd.it Abstract We propose a pre-training technique for recurrent neural networks based on linear autoencoder networks for sequenc... | 2014 | 388 |
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