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6,800 | Context Selection for Embedding Models Li-Ping Liu∗ Tufts University Francisco J. R. Ruiz Columbia University University of Cambridge Susan Athey Stanford University David M. Blei Columbia University Abstract Word embeddings are an effective tool to analyze language. They have been recently exte... | 2017 | 313 |
6,801 | Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction Kristofer E. Bouchard∗ Alejandro F. Bujan† Farbod Roosta-Khorasani‡ Shashanka Ubaru§ Prabhat¶ Antoine M. Snijders∥ Jian-Hua Mao∥ Edward F. Chang∗∗ Michael W. Mahoney‡ Sharmodeep Bhattacharyya†† Abstract The inc... | 2017 | 314 |
6,802 | Good Semi-supervised Learning That Requires a Bad GAN Zihang Dai∗, Zhilin Yang∗, Fan Yang, William W. Cohen, Ruslan Salakhutdinov School of Computer Science Carnegie Melon University dzihang,zhiliny,fanyang1,wcohen,rsalakhu@cs.cmu.edu Abstract Semi-supervised learning methods based on generative adversari... | 2017 | 315 |
6,803 | Targeting EEG/LFP Synchrony with Neural Nets Yitong Li1, Michael Murias2, Samantha Major2, Geraldine Dawson2, Kafui Dzirasa2, Lawrence Carin1 and David E. Carlson3,4 1Department of Electrical and Computer Engineering, Duke University 2Departments of Psychiatry and Behavioral Sciences, Duke University 3Departm... | 2017 | 316 |
6,804 | Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes Anton Mallasto Department of Computer Science University of Copenhagen mallasto@di.ku.dk Aasa Feragen Department of Computer Science University of Copenhagen aasa@di.ku.dk Abstract We introduce a novel framework for st... | 2017 | 317 |
6,805 | Online Dynamic Programming Holakou Rahmanian Department of Computer Science University of California Santa Cruz Santa Cruz, CA 95060 holakou@ucsc.edu Manfred K. Warmuth Department of Computer Science University of California Santa Cruz Santa Cruz, CA 95060 manfred@ucsc.edu Abstract We consider t... | 2017 | 318 |
6,806 | Neural Discrete Representation Learning Aaron van den Oord DeepMind avdnoord@google.com Oriol Vinyals DeepMind vinyals@google.com Koray Kavukcuoglu DeepMind korayk@google.com Abstract Learning useful representations without supervision remains a key challenge in machine learning. In this paper, ... | 2017 | 319 |
6,807 | State Aware Imitation Learning Yannick Schroecker College of Computing Georgia Institute of Technology yannickschroecker@gatech.edu Charles Isbell College of Computing Georgia Institute of Technology isbell@cc.gatech.edu Abstract Imitation learning is the study of learning how to act given a set of ... | 2017 | 32 |
6,808 | Probabilistic Rule Realization and Selection Haizi Yu∗† Department of Computer Science University of Illinois at Urbana-Champaign Urbana, IL 61801 haiziyu7@illinois.edu Tianxi Li∗ Department of Statistics University of Michigan Ann Arbor, MI 48109 tianxili@umich.edu Lav R. Varshney† Department o... | 2017 | 320 |
6,809 | A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning Marco Fraccaro†∗ Simon Kamronn †∗ Ulrich Paquet‡ Ole Winther† † Technical University of Denmark ‡ DeepMind Abstract This paper takes a step towards temporal reasoning in a dynamically changing video, not in the pixel s... | 2017 | 321 |
6,810 | Stabilizing Training of Generative Adversarial Networks through Regularization Kevin Roth Department of Computer Science ETH Zürich kevin.roth@inf.ethz.ch Aurelien Lucchi Department of Computer Science ETH Zürich aurelien.lucchi@inf.ethz.ch Sebastian Nowozin Microsoft Research Cambridge, UK se... | 2017 | 322 |
6,811 | Training Deep Networks without Learning Rates Through Coin Betting Francesco Orabona∗ Department of Computer Science Stony Brook University Stony Brook, NY francesco@orabona.com Tatiana Tommasi∗ Department of Computer, Control, and Management Engineering Sapienza, Rome University, Italy tommasi@di... | 2017 | 323 |
6,812 | Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis Jian Zhao1,2∗† Lin Xiong3 Karlekar Jayashree3 Jianshu Li1 Fang Zhao1 Zhecan Wang4† Sugiri Pranata3 Shengmei Shen3 Shuicheng Yan1,5 Jiashi Feng1 1National University of Singapore 2National University of Defense Tec... | 2017 | 324 |
6,813 | Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation Christian Borgs Jennifer Chayes Christina E. Lee borgs@microsoft.com jchayes@microsoft.com celee@mit.edu Microsoft Research New England One Memorial Drive, Cambridge MA, 02142 Devavrat Shah devavrat@mit.edu M... | 2017 | 325 |
6,814 | Positive-Unlabeled Learning with Non-Negative Risk Estimator Ryuichi Kiryo1,2 Gang Niu1,2 Marthinus C. du Plessis Masashi Sugiyama2,1 1The University of Tokyo, 7-3-1 Hongo, Tokyo 113-0033, Japan 2RIKEN, 1-4-1 Nihonbashi, Tokyo 103-0027, Japan { kiryo@ms., gang@ms., sugi@ }k.u-tokyo.ac.jp Abstract Fr... | 2017 | 326 |
6,815 | Gradient descent GAN optimization is locally stable Vaishnavh Nagarajan Computer Science Department Carnegie-Mellon University Pittsburgh, PA 15213 vaishnavh@cs.cmu.edu J. Zico Kolter Computer Science Department Carnegie-Mellon University Pittsburgh, PA 15213 zkolter@cs.cmu.edu Abstract Despite ... | 2017 | 327 |
6,816 | Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers Cong Fang Feng Cheng Zhouchen Lin∗ Key Laboratory of Machine Perception (MOE), School of EECS, Peking University, P. R. China Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, P. R. China fangcong@pku... | 2017 | 328 |
6,817 | Group Sparse Additive Machine Hong Chen1, Xiaoqian Wang1, Cheng Deng2, Heng Huang1∗ 1 Department of Electrical and Computer Engineering, University of Pittsburgh, USA 2 School of Electronic Engineering, Xidian University, China chenh@mail.hzau.edu.cn,xqwang1991@gmail.com chdeng@mail.xidian.edu.cn,heng.huang@p... | 2017 | 329 |
6,818 | Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter Yi Xu†, Qihang Lin‡, Tianbao Yang† †Department of Computer Science, The University of Iowa, Iowa City, IA 52242, USA ‡Department of Management Sciences, The University of Iowa, Iowa City, IA 52242, USA {yi-xu, qihang-lin, tianbao... | 2017 | 33 |
6,819 | PixelGAN Autoencoders Alireza Makhzani, Brendan Frey University of Toronto {makhzani,frey}@psi.toronto.edu Abstract In this paper, we describe the “PixelGAN autoencoder”, a generative autoencoder in which the generative path is a convolutional autoregressive neural network on pixels (PixelCNN) that is con... | 2017 | 330 |
6,820 | Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models Rishit Sheth and Roni Khardon Department of Computer Science, Tufts University Medford, MA, 02155, USA rishit.sheth@tufts.edu | roni@cs.tufts.edu Abstract Bayesian models are established as one of the main successf... | 2017 | 331 |
6,821 | Online control of the false discovery rate with decaying memory Aaditya Ramdas Fanny Yang Martin J. Wainwright Michael I. Jordan University of California, Berkeley {aramdas, fanny-yang, wainwrig, jordan} @berkeley.edu Abstract In the online multiple testing problem, p-values corresponding to different... | 2017 | 332 |
6,822 | Safe and Nested Subgame Solving for Imperfect-Information Games Noam Brown Computer Science Department Carnegie Mellon University Pittsburgh, PA 15217 noamb@cs.cmu.edu Tuomas Sandholm Computer Science Department Carnegie Mellon University Pittsburgh, PA 15217 sandholm@cs.cmu.edu Abstract In im... | 2017 | 333 |
6,823 | A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent Ben London blondon@amazon.com Amazon AI Abstract We study the generalization error of randomized learning algorithms—focusing on stochastic gradient descent (SGD)—using a novel combination of PAC-Bayes and alg... | 2017 | 334 |
6,824 | Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning El Mahdi El Mhamdi EPFL, Switzerland elmahdi.elmhamdi@epfl.ch Rachid Guerraoui EPFL, Switzerland rachid.guerraoui@epfl.ch Hadrien Hendrikx∗ ´Ecole Polytechnique, France hadrien.hendrikx@gmail.com Alexandre Maurer ... | 2017 | 335 |
6,825 | Toward Multimodal Image-to-Image Translation Jun-Yan Zhu UC Berkeley Richard Zhang UC Berkeley Deepak Pathak UC Berkeley Trevor Darrell UC Berkeley Alexei A. Efros UC Berkeley Oliver Wang Adobe Research Eli Shechtman Adobe Research Abstract Many image-to-image translation problems are am... | 2017 | 336 |
6,826 | The Marginal Value of Adaptive Gradient Methods in Machine Learning Ashia C. Wilson], Rebecca Roelofs], Mitchell Stern], Nathan Srebro†, and Benjamin Recht] {ashia,roelofs,mitchell}@berkeley.edu, nati@ttic.edu, brecht@berkeley.edu ]University of California, Berkeley †Toyota Technological Institute at Chicago ... | 2017 | 337 |
6,827 | Mean Field Residual Networks: On the Edge of Chaos Greg Yang⇤ Microsoft Research AI gregyang@microsoft.com Samuel S. Schoenholz Google Brain schsam@google.com Abstract We study randomly initialized residual networks using mean field theory and the theory of difference equations. Classical feedforward n... | 2017 | 338 |
6,828 | Non-Convex Finite-Sum Optimization Via SCSG Methods Lihua Lei UC Berkeley lihua.lei@berkeley.edu Cheng Ju UC Berkeley cju@berkeley.edu Jianbo Chen UC Berkeley jianbochen@berkeley.edu Michael I. Jordan UC Berkeley jordan@stat.berkeley.edu Abstract We develop a class of algorithms, as varian... | 2017 | 339 |
6,829 | Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data Wei-Ning Hsu, Yu Zhang, and James Glass Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139, USA {wnhsu,yzhang87,glass}@csail.mit.edu Abstract We pres... | 2017 | 34 |
6,830 | First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization Aryan Mokhtari University of Pennsylvania aryanm@seas.upenn.edu Alejandro Ribeiro University of Pennsylvania aribeiro@seas.upenn.edu Abstract This paper studies empirical risk minimization (ERM) problems for l... | 2017 | 340 |
6,831 | Doubly Stochastic Variational Inference for Deep Gaussian Processes Hugh Salimbeni Imperial College London and PROWLER.io hrs13@ic.ac.uk Marc Peter Deisenroth Imperial College London and PROWLER.io m.deisenroth@imperial.ac.uk Abstract Gaussian processes (GPs) are a good choice for function approximati... | 2017 | 341 |
6,832 | From Parity to Preference-based Notions of Fairness in Classification Muhammad Bilal Zafar MPI-SWS mzafar@mpi-sws.org Isabel Valera MPI-IS isabel.valera@tue.mpg.de Manuel Gomez Rodriguez MPI-SWS manuelgr@mpi-sws.org Krishna P. Gummadi MPI-SWS gummadi@mpi-sws.org Adrian Weller University of ... | 2017 | 342 |
6,833 | Nonparametric Online Regression while Learning the Metric Ilja Kuzborskij EPFL Switzerland ilja.kuzborskij@gmail.com Nicol`o Cesa-Bianchi Dipartimento di Informatica Universit`a degli Studi di Milano Milano 20135, Italy nicolo.cesa-bianchi@unimi.it Abstract We study algorithms for online nonpara... | 2017 | 343 |
6,834 | Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure Alberto Bietti Inria∗ alberto.bietti@inria.fr Julien Mairal Inria∗ julien.mairal@inria.fr Abstract Stochastic optimization algorithms with variance reduction have proven successful for minimizing large fini... | 2017 | 344 |
6,835 | Working hard to know your neighbor’s margins: Local descriptor learning loss Anastasiya Mishchuk1, Dmytro Mishkin2, Filip Radenovi´c2, Jiˇri Matas2 1 Szkocka Research Group, Ukraine anastasiya.mishchuk@gmail.com 2 Visual Recognition Group, CTU in Prague {mishkdmy, filip.radenovic, matas}@cmp.felk.cvut.cz ... | 2017 | 345 |
6,836 | Hiding Images in Plain Sight: Deep Steganography Shumeet Baluja Google Research Google, Inc. shumeet@google.com Abstract Steganography is the practice of concealing a secret message within another, ordinary, message. Commonly, steganography is used to unobtrusively hide a small message within the nois... | 2017 | 346 |
6,837 | Lookahead Bayesian Optimization with Inequality Constraints Remi R. Lam Massachusetts Institute of Technology Cambridge, MA rlam@mit.edu Karen E. Willcox Massachusetts Institute of Technology Cambridge, MA kwillcox@mit.edu Abstract We consider the task of optimizing an objective function subject t... | 2017 | 347 |
6,838 | Online Learning with Transductive Regret Mehryar Mohri Courant Institute and Google Research New York, NY mohri@cims.nyu.edu Scott Yang⇤ D. E. Shaw & Co. New York, NY yangs@cims.nyu.edu Abstract We study online learning with the general notion of transductive regret, that is regret with modificatio... | 2017 | 348 |
6,839 | Pixels to Graphs by Associative Embedding Alejandro Newell Jia Deng Computer Science and Engineering University of Michigan, Ann Arbor {alnewell, jiadeng}@umich.edu Abstract Graphs are a useful abstraction of image content. Not only can graphs represent details about individual objects in a scene but th... | 2017 | 349 |
6,840 | Recurrent Ladder Networks Isabeau Prémont-Schwarz, Alexander Ilin, Tele Hotloo Hao, Antti Rasmus, Rinu Boney, Harri Valpola The Curious AI Company {isabeau,alexilin,hotloo,antti,rinu,harri}@cai.fi Abstract We propose a recurrent extension of the Ladder networks [22] whose structure is motivated by the inf... | 2017 | 35 |
6,841 | Accelerated Stochastic Greedy Coordinate Descent by Soft Thresholding Projection onto Simplex Chaobing Song, Shaobo Cui, Yong Jiang, Shu-Tao Xia Tsinghua University {songcb16,cuishaobo16}@mails.tsinghua.edu.cn {jiangy, xiast}@sz.tsinghua.edu.cn ∗ Abstract In this paper we study the well-known greedy coord... | 2017 | 350 |
6,842 | Reinforcement Learning under Model Mismatch Aurko Roy1, Huan Xu2, and Sebastian Pokutta2 1Google ∗, Email: aurkor@google.com 2ISyE, Georgia Institute of Technology, Atlanta, GA, USA. Email: huan.xu@isye.gatech.edu 2ISyE, Georgia Institute of Technology, Atlanta, GA, USA. Email: sebastian.pokutta@isye.gatech... | 2017 | 351 |
6,843 | Concrete Dropout Yarin Gal yarin.gal@eng.cam.ac.uk University of Cambridge and Alan Turing Institute, London Jiri Hron jh2084@cam.ac.uk University of Cambridge Alex Kendall agk34@cam.ac.uk University of Cambridge Abstract Dropout is used as a practical tool to obtain uncertainty estimates in lar... | 2017 | 352 |
6,844 | Multiresolution Kernel Approximation for Gaussian Process Regression Yi Ding∗, Risi Kondor∗†, Jonathan Eskreis-Winkler† ∗Department of Computer Science, †Department of Statistics The University of Chicago, Chicago, IL, 60637 {dingy,risi,eskreiswinkler}@uchicago.edu Abstract Gaussian process regression gen... | 2017 | 353 |
6,845 | Near Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem Yasin Abbasi-Yadkori Adobe Research Peter L. Bartlett UC Berkeley Victor Gabillon Queensland University of Technology Abstract We study minimax strategies for the online prediction problem with expert advice. It has been co... | 2017 | 354 |
6,846 | Learned D-AMP: Principled Neural Network Based Compressive Image Recovery Christopher A. Metzler Rice University chris.metzler@rice.edu Ali Mousavi Rice University ali.mousavi@rice.edu Richard G. Baraniuk Rice University richb@rice.edu Abstract Compressive image recovery is a challenging problem... | 2017 | 355 |
6,847 | Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks Ahmed M. Alaa Electrical Engineering Department University of California, Los Angeles ahmedmalaa@ucla.edu Mihaela van der Schaar Department of Engineering Science University of Oxford mihaela.vanderschaar@eng.ox.ac.uk Abst... | 2017 | 356 |
6,848 | Unsupervised Transformation Learning via Convex Relaxations Tatsunori B. Hashimoto John C. Duchi Percy Liang Stanford University Stanford, CA 94305 {thashim,jduchi,pliang}@cs.stanford.edu Abstract Our goal is to extract meaningful transformations from raw images, such as varying the thickness of lin... | 2017 | 357 |
6,849 | Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations Eirikur Agustsson ETH Zurich aeirikur@vision.ee.ethz.ch Fabian Mentzer ETH Zurich mentzerf@vision.ee.ethz.ch Michael Tschannen ETH Zurich michaelt@nari.ee.ethz.ch Lukas Cavigelli ETH Zurich cavigelli@iis.ee.e... | 2017 | 358 |
6,850 | Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM Katrina Ligett Caltech and Hebrew University Seth Neel University of Pennsylvania Aaron Roth University of Pennsylvania Bo Waggoner University of Pennsylvania Zhiwei Steven Wu Microsoft Research Abstract Tradit... | 2017 | 359 |
6,851 | Distral: Robust Multitask Reinforcement Learning Yee Whye Teh, Victor Bapst, Wojciech Marian Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, Razvan Pascanu DeepMind London, UK Abstract Most deep reinforcement learning algorithms are data inefficient in complex and rich environments, l... | 2017 | 36 |
6,852 | Triple Generative Adversarial Nets Chongxuan Li, Kun Xu, Jun Zhu∗, Bo Zhang Dept. of Comp. Sci. & Tech., TNList Lab, State Key Lab of Intell. Tech. & Sys., Center for Bio-Inspired Computing Research, Tsinghua University, Beijing, 100084, China {licx14, xu-k16}@mails.tsinghua.edu.cn, {dcszj, dcszb}@mail.tsingh... | 2017 | 360 |
6,853 | Deep Learning with Topological Signatures Christoph Hofer Department of Computer Science University of Salzburg, Austria chofer@cosy.sbg.ac.at Roland Kwitt Department of Computer Science University of Salzburg, Austria Roland.Kwitt@sbg.ac.at Marc Niethammer UNC Chapel Hill, NC, USA mn@cs.unc.edu ... | 2017 | 361 |
6,854 | Revenue Optimization with Approximate Bid Predictions Andr´es Mu˜noz Medina Google Research 76 9th Ave New York, NY 10011 Sergei Vassilvitskii Google Research 76 9th Ave New York, NY 10011 Abstract In the context of advertising auctions, finding good reserve prices is a notoriously challenging le... | 2017 | 362 |
6,855 | Mapping distinct timescales of functional interactions among brain networks Mali Sundaresan1 s.malisundar@gmail.com Arshed Nabeel2 arshed@iisc.ac.in Devarajan Sridharan1,2∗ sridhar@iisc.ac.in 1Center for Neuroscience, Indian Institute of Science, Bangalore 2Department of Computer Science and Automatio... | 2017 | 363 |
6,856 | Improved Training of Wasserstein GANs Ishaan Gulrajani1⇤, Faruk Ahmed1, Martin Arjovsky2, Vincent Dumoulin1, Aaron Courville1,3 1 Montreal Institute for Learning Algorithms 2 Courant Institute of Mathematical Sciences 3 CIFAR Fellow igul222@gmail.com {faruk.ahmed,vincent.dumoulin,aaron.courville}@umontreal.... | 2017 | 364 |
6,857 | Adaptive stimulus selection for optimizing neural population responses Benjamin R. Cowley1,2, Ryan C. Williamson1,2,5, Katerina Acar2,6, Matthew A. Smith∗,2,7, Byron M. Yu∗,2,3,4 1Machine Learning Dept., 2Center for Neural Basis of Cognition, 3Dept. of Electrical and Computer Engineering, 4Dept. of Biomedical... | 2017 | 365 |
6,858 | Matrix Norm Estimation from a Few Entries Ashish Khetan Department of ISE University of Illinois Urbana-Champaign khetan2@illinois.edu Sewoong Oh Department of ISE University of Illinois Urbana-Champaign swoh@illinois.edu Abstract Singular values of a data in a matrix form provide insights on the st... | 2017 | 366 |
6,859 | On the Power of Truncated SVD for General High-rank Matrix Estimation Problems Simon S. Du Carnegie Mellon University ssdu@cs.cmu.edu Yining Wang Carnegie Mellon University yiningwa@cs.cmu.edu Aarti Singh Carnegie Mellon University aartisingh@cmu.edu Abstract We show that given an estimate A th... | 2017 | 367 |
6,860 | TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning Wei Wen1, Cong Xu2, Feng Yan3, Chunpeng Wu1, Yandan Wang4, Yiran Chen1, Hai Li1 1Duke University, 2Hewlett Packard Labs, 3University of Nevada – Reno, 4University of Pittsburgh 1{wei.wen, chunpeng.wu, yiran.chen, hai.li}@duke.edu ... | 2017 | 368 |
6,861 | GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium Martin Heusel Hubert Ramsauer Thomas Unterthiner Bernhard Nessler Sepp Hochreiter LIT AI Lab & Institute of Bioinformatics, Johannes Kepler University Linz A-4040 Linz, Austria {mhe,ramsauer,unterthiner,nessler,hochrei... | 2017 | 369 |
6,862 | Real-Time Bidding with Side Information Arthur Flajolet MIT, ORC flajolet@mit.edu Patrick Jaillet MIT, EECS, LIDS, ORC jaillet@mit.edu Abstract We consider the problem of repeated bidding in online advertising auctions when some side information (e.g. browser cookies) is available ahead of submitting ... | 2017 | 37 |
6,863 | A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Paul G. Allen School of Computer Science University of Washington Seattle, WA 98105 slund1@cs.washington.edu Su-In Lee Paul G. Allen School of Computer Science Department of Genome Sciences University of Washington Seattle, WA 9... | 2017 | 370 |
6,864 | Nonbacktracking Bounds on the Influence in Independent Cascade Models Emmanuel Abbe1 2 Sanjeev Kulkarni2 Eun Jee Lee1 1Program in Applied and Computational Mathematics 2The Department of Electrical Engineering Princeton University {eabbe, kulkarni, ejlee}@princeton.edu Abstract This paper develops upper an... | 2017 | 371 |
6,865 | Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls∗ Zeyuan Allen-Zhu Microsoft Research, Redmond zeyuan@csail.mit.edu Elad Hazan Princeton University ehazan@cs.princeton.edu Wei Hu Princeton University huwei@cs.princeton.edu Yuanzhi Li Princeton University yuanzhil@cs.pri... | 2017 | 372 |
6,866 | Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach Roel Dobbe∗ Electrical Engineering and Computer Science University of California, Berkeley Berkeley, CA 94720 dobbe@eecs.berkeley.edu David Fridovich-Keil∗ Electrical Engineering and Computer Science University of ... | 2017 | 373 |
6,867 | Neural system identification for large populations separating “what” and “where” David A. Klindt * 1-3, Alexander S. Ecker * 1,2,4,6, Thomas Euler 1-3, Matthias Bethge 1,2,4-6 * Authors contributed equally 1 Centre for Integrative Neuroscience, University of Tübingen, Germany 2 Bernstein Center for Computation... | 2017 | 374 |
6,868 | Learning Active Learning from Data Ksenia Konyushkova⇤ CVLab, EPFL Lausanne, Switzerland ksenia.konyushkova@epfl.ch Sznitman Raphael ARTORG Center, University of Bern Bern, Switzerland raphael.sznitman@artorg.unibe.ch Pascal Fua CVLab, EPFL Lausanne, Switzerland pascal.fua@epfl.ch Abstract I... | 2017 | 375 |
6,869 | Controllable Invariance through Adversarial Feature Learning Qizhe Xie, Zihang Dai, Yulun Du, Eduard Hovy, Graham Neubig Language Technologies Institute Carnegie Mellon University {qizhex, dzihang, yulund, hovy, gneubig}@cs.cmu.edu Abstract Learning meaningful representations that maintain the content nec... | 2017 | 376 |
6,870 | Visual Interaction Networks: Learning a Physics Simulator from Video Nicholas Watters, Andrea Tacchetti, Théophane Weber Razvan Pascanu, Peter Battaglia, Daniel Zoran DeepMind London, United Kingdom {nwatters, atacchet, theophane, razp, peterbattaglia, danielzoran}@google.com Abstract From just a glan... | 2017 | 377 |
6,871 | Repeated Inverse Reinforcement Learning Kareem Amin∗ Google Research New York, NY 10011 kamin@google.com Nan Jiang∗ Satinder Singh Computer Science & Engineering, University of Michigan, Ann Arbor, MI 48104 {nanjiang,baveja}@umich.edu Abstract We introduce a novel repeated Inverse Reinforcement Le... | 2017 | 378 |
6,872 | Inference in Graphical Models via Semidefinite Programming Hierarchies Murat A. Erdogdu Microsoft Research erdogdu@cs.toronto.edu Yash Deshpande MIT and Microsoft Research yash@mit.edu Andrea Montanari Stanford University montanari@stanford.edu Abstract Maximum A posteriori Probability (MAP) infe... | 2017 | 379 |
6,873 | Learning Spherical Convolution for Fast Features from 360° Imagery Yu-Chuan Su Kristen Grauman The University of Texas at Austin Abstract While 360° cameras offer tremendous new possibilities in vision, graphics, and augmented reality, the spherical images they produce make core feature extraction non-tri... | 2017 | 38 |
6,874 | Gauging Variational Inference Sungsoo Ahn∗ Michael Chertkov† Jinwoo Shin∗ ∗School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea †1 Theoretical Division, T-4 & Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545, USA, †2Skolko... | 2017 | 380 |
6,875 | Teaching Machines to Describe Images via Natural Language Feedback Huan Ling1, Sanja Fidler1,2 University of Toronto1, Vector Institute2 {linghuan,fidler}@cs.toronto.edu Abstract Robots will eventually be part of every household. It is thus critical to enable algorithms to learn from and be guided by non-... | 2017 | 381 |
6,876 | Associative Embedding: End-to-End Learning for Joint Detection and Grouping Alejandro Newell Computer Science and Engineering University of Michigan Ann Arbor, MI alnewell@umich.edu Zhiao Huang* Institute for Interdisciplinary Information Sciences Tsinghua University Beijing, China hza14@mails.tsi... | 2017 | 382 |
6,877 | Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications Linus Hamilton∗ Frederic Koehler † Ankur Moitra ‡ Abstract Markov random fields are a popular model for high-dimensional probability distributions. Over the years, many mathematical, statistical and algorithmic probl... | 2017 | 383 |
6,878 | Subset Selection and Summarization in Sequential Data Ehsan Elhamifar Computer and Information Science College Northeastern University Boston, MA 02115 eelhami@ccs.neu.edu M. Clara De Paolis Kaluza Computer and Information Science College Northeastern University Boston, MA 02115 clara@ccs.neu.edu ... | 2017 | 384 |
6,879 | Z-Forcing: Training Stochastic Recurrent Networks Anirudh Goyal MILA, Université de Montréal Alessandro Sordoni Microsoft Maluuba Marc-Alexandre Côté Microsoft Maluuba Nan Rosemary Ke MILA, Polytechnique Montréal Yoshua Bengio MILA, Université de Montréal Abstract Many efforts have been devoted ... | 2017 | 385 |
6,880 | Regret Minimization in MDPs with Options without Prior Knowledge Ronan Fruit Sequel Team - Inria Lille ronan.fruit@inria.fr Matteo Pirotta Sequel Team - Inria Lille matteo.pirotta@inria.fr Alessandro Lazaric Sequel Team - Inria Lille alessandro.lazaric@inria.fr Emma Brunskill Stanford University... | 2017 | 386 |
6,881 | Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity Asish Ghoshal and Jean Honorio Department of Computer Science, Purdue University, West Lafayette, IN - 47906 {aghoshal, jhonorio}@purdue.edu Abstract Learning the directed acyclic graph (DAG) structure of a Bayesian net... | 2017 | 387 |
6,882 | Learning Neural Representations of Human Cognition across Many fMRI Studies Arthur Mensch∗ Inria arthur.mensch@m4x.org Julien Mairal† Inria julien.mairal@inria.fr Danilo Bzdok Department of Psychiatry, RWTH danilo.bzdok@rwth-aachen.de Bertrand Thirion∗ Inria bertrand.thirion@inria.fr Gaël Va... | 2017 | 388 |
6,883 | Conic Scan-and-Cover algorithms for nonparametric topic modeling Mikhail Yurochkin Department of Statistics University of Michigan moonfolk@umich.edu Aritra Guha Department of Statistics University of Michigan aritra@umich.edu XuanLong Nguyen Department of Statistics University of Michigan xua... | 2017 | 389 |
6,884 | Approximate Supermodularity Bounds for Experimental Design Luiz F. O. Chamon and Alejandro Ribeiro Electrical and Systems Engineering University of Pennsylvania {luizf,aribeiro}@seas.upenn.edu Abstract This work provides performance guarantees for the greedy solution of experimental design problems. In pa... | 2017 | 39 |
6,885 | Online Learning for Multivariate Hawkes Processes Yingxiang Yang∗ Jalal Etesami† Niao He† Negar Kiyavash∗† University of Illinois at Urbana-Champaign Urbana, IL 61801 {yyang172,etesami2,niaohe,kiyavash} @illinois.edu Abstract We develop a nonparametric and online learning algorithm that estimates the ... | 2017 | 390 |
6,886 | An Empirical Study on The Properties of Random Bases for Kernel Methods Maximilian Alber, Pieter-Jan Kindermans, Kristof T. Schütt Technische Universität Berlin maximilian.alber@tu-berlin.de Klaus-Robert Müller Technische Universität Berlin Korea University Max Planck Institut für Informatik Fei Sha ... | 2017 | 391 |
6,887 | Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions Aryeh Kontorovich Department of Computer Science Ben-Gurion University of the Negev karyeh@cs.bgu.ac.il Sivan Sabato Department of Computer Science Ben-Gurion University of the Negev sabatos@bgu.ac.il Roi Weiss Departm... | 2017 | 392 |
6,888 | Causal Effect Inference with Deep Latent-Variable Models Christos Louizos University of Amsterdam TNO Intelligent Imaging c.louizos@uva.nl Uri Shalit New York University CIMS uas1@nyu.edu Joris Mooij University of Amsterdam j.m.mooij@uva.nl David Sontag Massachusetts Institute of Technology ... | 2017 | 393 |
6,889 | Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach Emmanouil A. Platanios Carnegie Mellon University Pittsburgh, PA e.a.platanios@cs.cmu.edu Hoifung Poon Microsoft Research Redmond, WA hoifung@microsoft.com Tom M. Mitchell Carnegie Mellon University Pittsburgh, PA tom.mitc... | 2017 | 394 |
6,890 | A Decomposition of Forecast Error in Prediction Markets Miroslav Dudík Microsoft Research, New York, NY mdudik@microsoft.com Sébastien Lahaie Google, New York, NY slahaie@google.com Ryan Rogers University of Pennsylvania, Philadelphia, PA rrogers386@gmail.com Jennifer Wortman Vaughan Microsoft R... | 2017 | 395 |
6,891 | Ranking Data with Continuous Labels through Oriented Recursive Partitions Stephan Cl´emenc¸on Mastane Achab LTCI, T´el´ecom ParisTech, Universit´e Paris-Saclay 75013 Paris, France first.last@telecom-paristech.fr Abstract We formulate a supervised learning problem, referred to as continuous ranking, wh... | 2017 | 396 |
6,892 | Scalable Log Determinants for Gaussian Process Kernel Learning Kun Dong 1, David Eriksson 1, Hannes Nickisch 2, David Bindel 1, Andrew Gordon Wilson 1 1 Cornell University, 2 Phillips Research Hamburg Abstract For applications as varied as Bayesian neural networks, determinantal point processes, elliptical gr... | 2017 | 397 |
6,893 | Fair Clustering Through Fairlets Flavio Chierichetti Dipartimento di Informatica Sapienza University Rome, Italy Ravi Kumar Google Research 1600 Amphitheater Parkway Mountain View, CA 94043 Silvio Lattanzi Google Research 76 9th Ave New York, NY 10011 Sergei Vassilvitskii Google Research 7... | 2017 | 398 |
6,894 | A Linear-Time Kernel Goodness-of-Fit Test Wittawat Jitkrittum Gatsby Unit, UCL wittawatj@gmail.com Wenkai Xu Gatsby Unit, UCL wenkaix@gatsby.ucl.ac.uk Zoltán Szabó∗ CMAP, École Polytechnique zoltan.szabo@polytechnique.edu Kenji Fukumizu The Institute of Statistical Mathematics fukumizu@ism.ac.jp... | 2017 | 399 |
6,895 | Learning to Inpaint for Image Compression Mohammad Haris Baig∗ Department of Computer Science Dartmouth College Hanover, NH Vladlen Koltun Intel Labs Santa Clara, CA Lorenzo Torresani Dartmouth College Hanover, NH Abstract We study the design of deep architectures for lossy image compression. We... | 2017 | 4 |
6,896 | Differentiable Learning of Logical Rules for Knowledge Base Reasoning Fan Yang Zhilin Yang William W. Cohen School of Computer Science Carnegie Mellon University {fanyang1,zhiliny,wcohen}@cs.cmu.edu Abstract We study the problem of learning probabilistic first-order logical rules for knowledge base rea... | 2017 | 40 |
6,897 | Rotting Bandits Nir Levine Electrical Engineering Department The Technion Haifa 32000, Israel levin.nir1@gmail.com Koby Crammer Electrical Engineering Department The Technion Haifa 32000, Israel koby@ee.technion.ac.il Shie Mannor Electrical Engineering Department The Technion Haifa 32000, Is... | 2017 | 400 |
6,898 | Scalable Planning with Tensorflow for Hybrid Nonlinear Domains Ga Wu Buser Say Scott Sanner Department of Mechanical & Industrial Engineering, University of Toronto, Canada email: {wuga,bsay,ssanner}@mie.utoronto.ca Abstract Given recent deep learning results that demonstrate the ability to effectively o... | 2017 | 401 |
6,899 | Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models Chris. J. Oates1,5, Steven Niederer2, Angela Lee2, François-Xavier Briol3, Mark Girolami4,5 1Newcastle University, 2King’s College London, 3University of Warwick, 4Imperial College London, 5Alan Turing Institute Abstract... | 2017 | 402 |
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