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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7,000 | Integration Methods and Optimization Algorithms Damien Scieur INRIA, ENS, PSL Research University, Paris France damien.scieur@inria.fr Vincent Roulet INRIA, ENS, PSL Research University, Paris France vincent.roulet@inria.fr Francis Bach INRIA, ENS, PSL Research University, Paris France fra... | 2017 | 494 |
7,001 | The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings Krzysztof Choromanski ∗ Google Brain Robotics kchoro@google.com Mark Rowland ∗ University of Cambridge mr504@cam.ac.uk Adrian Weller University of Cambridge and Alan Turing Institute aw665@cam.ac.uk Abstract We examine a c... | 2017 | 495 |
7,002 | A KL-LUCB Bandit Algorithm for Large-Scale Crowdsourcing Ervin Tánczos∗and Robert Nowak† University of Wisconsin-Madison tanczos@wisc.edu, rdnowak@wisc.edu Bob Mankoff Former Cartoon Editor of the New Yorker bmankoff@hearst.com Abstract This paper focuses on best-arm identification in multi-armed bandi... | 2017 | 496 |
7,003 | Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes Jianshu Chen⇤, Chong Wang†, Lin Xiao⇤, Ji He‡, Lihong Li† and Li Deng‡ ⇤Microsoft Research, Redmond, WA, USA {jianshuc,lin.xiao}@microsoft.com †Google Inc., Kirkland, WA, USA⇤ {chongw,lihong}@google.com ‡Citadel LLC, Seattle/Chi... | 2017 | 497 |
7,004 | Streaming Weak Submodularity: Interpreting Neural Networks on the Fly Ethan R. Elenberg Department of Electrical and Computer Engineering The University of Texas at Austin elenberg@utexas.edu Alexandros G. Dimakis Department of Electrical and Computer Engineering The University of Texas at Austin ... | 2017 | 498 |
7,005 | Decomposable Submodular Function Minimization Discrete and Continuous Alina Ene∗ Huy L. Nguy˜ên† László A. Végh‡ Abstract This paper investigates connections between discrete and continuous approaches for decomposable submodular function minimization. We provide improved running time estimates for the s... | 2017 | 499 |
7,006 | Inverse Filtering for Hidden Markov Models Robert Mattila Department of Automatic Control KTH Royal Institute of Technology rmattila@kth.se Cristian R. Rojas Department of Automatic Control KTH Royal Institute of Technology crro@kth.se Vikram Krishnamurthy Cornell Tech Cornell University vikramk... | 2017 | 5 |
7,007 | Safe Adaptive Importance Sampling Sebastian U. Stich EPFL sebastian.stich@epfl.ch Anant Raj Max Planck Institute for Intelligent Systems anant.raj@tuebingen.mpg.de Martin Jaggi EPFL martin.jaggi@epfl.ch Abstract Importance sampling has become an indispensable strategy to speed up optimization algo... | 2017 | 50 |
7,008 | Learning Affinity via Spatial Propagation Networks Sifei Liu UC Merced, NVIDIA Shalini De Mello NVIDIA Jinwei Gu NVIDIA Guangyu Zhong Dalian University of Technology Ming-Hsuan Yang UC Merced, NVIDIA Jan Kautz NVIDIA Abstract In this paper, we propose spatial propagation networks for learning... | 2017 | 500 |
7,009 | Gated Recurrent Convolution Neural Network for OCR Jianfeng Wang∗ Beijing University of Posts and Telecommunications Beijing 100876, China jianfengwang1991@gmail.com Xiaolin Hu Tsinghua National Laboratory for Information Science and Technology (TNList) Department of Computer Science and Technology Ce... | 2017 | 501 |
7,010 | Multi-view Matrix Factorization for Linear Dynamical System Estimation Mahdi Karami, Martha White, Dale Schuurmans, Csaba Szepesvári Department of Computer Science University of Alberta Edmonton, AB, Canada {karami1, whitem, daes, szepesva}@ualberta.ca Abstract We consider maximum likelihood estimation ... | 2017 | 502 |
7,011 | Policy Gradient With Value Function Approximation For Collective Multiagent Planning Duc Thien Nguyen Akshat Kumar Hoong Chuin Lau School of Information Systems Singapore Management University 80 Stamford Road, Singapore 178902 {dtnguyen.2014,akshatkumar,hclau}@smu.edu.sg Abstract Decentralized (PO)... | 2017 | 503 |
7,012 | Stochastic Submodular Maximization: The Case of Coverage Functions Mohammad Reza Karimi Department of Computer Science ETH Zurich mkarimi@ethz.ch Mario Lucic Department of Computer Science ETH Zurich lucic@inf.ethz.ch Hamed Hassani Department of Electrical and Systems Engineering University of P... | 2017 | 504 |
7,013 | Stochastic Approximation for Canonical Correlation Analysis Raman Arora Dept. of Computer Science Johns Hopkins University Baltimore, MD 21204 arora@cs.jhu.edu Teodor V. Marinov Dept. of Computer Science Johns Hopkins University Baltimore, MD 21204 tmarino2@jhu.edu Poorya Mianjy Dept. of Compu... | 2017 | 505 |
7,014 | Linear regression without correspondence Daniel Hsu Columbia University New York, NY djhsu@cs.columbia.edu Kevin Shi Columbia University New York, NY kshi@cs.columbia.edu Xiaorui Sun Microsoft Research Redmond, WA xiaoruisun@cs.columbia.edu Abstract This article considers algorithmic and sta... | 2017 | 506 |
7,015 | Structured Generative Adversarial Networks 1Zhijie Deng∗, 2,3Hao Zhang∗, 2Xiaodan Liang, 2Luona Yang, 1,2Shizhen Xu, 1Jun Zhu†, 3Eric P. Xing 1Tsinghua University, 2Carnegie Mellon University, 3Petuum Inc. {dzj17,xsz12}@mails.tsinghua.edu.cn, {hao,xiaodan1,luonay1}@cs.cmu.edu, dcszj@mail.tsinghua.edu.cn, epxi... | 2017 | 507 |
7,016 | Dynamic-Depth Context Tree Weighting João V. Messias∗ Morpheus Labs Oxford, UK jmessias@morpheuslabs.co.uk Shimon Whiteson University of Oxford Oxford, UK shimon.whiteson@cs.ox.ac.uk Abstract Reinforcement learning (RL) in partially observable settings is challenging because the agent’s observations... | 2017 | 508 |
7,017 | Fast, Sample-Efficient Algorithms for Structured Phase Retrieval Gauri jagatap Electrical and Computer Engineering Iowa State University Chinmay Hegde Electrical and Computer Engineering Iowa State University Abstract We consider the problem of recovering a signal x∗∈Rn, from magnitude-only measureme... | 2017 | 509 |
7,018 | Introspective Classification with Convolutional Nets Long Jin UC San Diego longjin@ucsd.edu Justin Lazarow UC San Diego jlazarow@ucsd.edu Zhuowen Tu UC San Diego ztu@ucsd.edu Abstract We propose introspective convolutional networks (ICN) that emphasize the importance of having convolutional neural ... | 2017 | 51 |
7,019 | Hierarchical Methods of Moments Matteo Ruffini ⇤ Universitat Politècnica de Catalunya Guillaume Rabusseau † McGill University Borja Balle ‡ Amazon Research Cambridge Abstract Spectral methods of moments provide a powerful tool for learning the parameters of latent variable models. Despite their the... | 2017 | 510 |
7,020 | A New Alternating Direction Method for Linear Programming Sinong Wang Department of ECE The Ohio State University wang.7691@osu.edu Ness Shroff Department of ECE and CSE The Ohio State University shroff.11@osu.edu Abstract It is well known that, for a linear program (LP) with constraint matrix A ∈... | 2017 | 511 |
7,021 | Near Optimal Sketching of Low-Rank Tensor Regression Jarvis Haupt1 jdhaupt@umn.edu Xingguo Li1,2 lixx1661@umn.edu David P. Woodruff 3 dwoodruf@cs.cmu.edu ⇤ 1 University of Minnesota 2Georgia Tech 3Carnegie Mellon University Abstract We study the least squares regression problem min ⇥2Rp1⇥···⇥p... | 2017 | 512 |
7,022 | Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models Sergey Ioffe Google sioffe@google.com Abstract Batch Normalization is quite effective at accelerating and improving the training of deep models. However, its effectiveness diminishes when the training minibatches are s... | 2017 | 513 |
7,023 | Position-based Multiple-play Bandit Problem with Unknown Position Bias Junpei Komiyama The University of Tokyo junpei@komiyama.info Junya Honda The University of Tokyo / RIKEN honda@stat.t.u-tokyo.ac.jp Akiko Takeda The Institute of Statistical Mathematics / RIKEN atakeda@ism.ac.jp Abstract Moti... | 2017 | 514 |
7,024 | Deep Voice 2: Multi-Speaker Neural Text-to-Speech Sercan Ö. Arık⇤ sercanarik@baidu.com Gregory Diamos⇤ gregdiamos@baidu.com Andrew Gibiansky⇤ gibianskyandrew@baidu.com John Miller⇤ millerjohn@baidu.com Kainan Peng⇤ pengkainan@baidu.com Wei Ping⇤ pingwei01@baidu.com Jonathan Raiman⇤ jonathanr... | 2017 | 515 |
7,025 | Eigen-Distortions of Hierarchical Representations Alexander Berardino Center for Neural Science New York University agb313@nyu.edu Johannes Ballé Center for Neural Science New York University∗ johannes.balle@nyu.edu Valero Laparra Image Processing Laboratory Universitat de València valero.laparr... | 2017 | 516 |
7,026 | Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon Xin Dong Nanyang Technological University, Singapore n1503521a@e.ntu.edu.sg Shangyu Chen Nanyang Technological University, Singapore schen025@e.ntu.edu.sg Sinno Jialin Pan Nanyang Technological University, Singapore sinnopan... | 2017 | 517 |
7,027 | Deliberation Networks: Sequence Generation Beyond One-Pass Decoding ∗ 1Yingce Xia, 2Fei Tian, 3Lijun Wu, 1Jianxin Lin, 2Tao Qin, 1Nenghai Yu, 2Tie-Yan Liu 1University of Science and Technology of China, Hefei, China 2Microsoft Research, Beijing, China 3Sun Yat-sen University, Guangzhou, China 1yingce.xia@gm... | 2017 | 518 |
7,028 | Do Deep Neural Networks Suffer from Crowding? Anna Volokitin†♮ Gemma Roig†‡ι Tomaso Poggio†‡ voanna@vision.ee.ethz.ch gemmar@mit.edu tp@csail.mit.edu †Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA ‡Istituto Italiano di Tecnologia at Massachusetts Institute o... | 2017 | 519 |
7,029 | Hybrid Reward Architecture for Reinforcement Learning Harm van Seijen1 harm.vanseijen@microsoft.com Mehdi Fatemi1 mehdi.fatemi@microsoft.com Joshua Romoff12 joshua.romoff@mail.mcgill.ca Romain Laroche1 romain.laroche@microsoft.com Tavian Barnes1 tavian.barnes@microsoft.com Jeffrey Tsang1 tsang... | 2017 | 52 |
7,030 | Non-Stationary Spectral Kernels Sami Remes sami.remes@aalto.fi Markus Heinonen markus.o.heinonen@aalto.fi Samuel Kaski samuel.kaski@aalto.fi Helsinki Institute for Information Technology HIIT Department of Computer Science, Aalto University Abstract We propose non-stationary spectral kernels for Gau... | 2017 | 520 |
7,031 | Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations Marcel Nonnenmacher1, Srinivas C. Turaga2 and Jakob H. Macke1∗ 1research center caesar, an associate of the Max Planck Society, Bonn, Germany 2HHMI Janelia Research Campus, Ashburn,... | 2017 | 521 |
7,032 | Minimizing a Submodular Function from Samples Eric Balkanski Harvard University ericbalkanski@g.harvard.edu Yaron Singer Harvard University yaron@seas.harvard.edu Abstract In this paper we consider the problem of minimizing a submodular function from training data. Submodular functions can be efficient... | 2017 | 522 |
7,033 | A graph-theoretic approach to multitasking Noga Alon∗ Tel-Aviv University Daniel Reichman† UC Berkeley Igor Shinkar∗ UC Berkeley Tal Wagner∗ MIT Sebastian Musslick Princeton University Jonathan D. Cohen ‡ Princeton University Thomas L. Griffiths UC Berkeley Biswadip Dey Princeton Universi... | 2017 | 523 |
7,034 | Adversarial Surrogate Losses for Ordinal Regression Rizal Fathony Mohammad Bashiri Brian D. Ziebart Department of Computer Science University of Illinois at Chicago Chicago, IL 60607 {rfatho2, mbashi4, bziebart}@uic.edu Abstract Ordinal regression seeks class label predictions when the penalty incurre... | 2017 | 524 |
7,035 | Self-Supervised Intrinsic Image Decomposition Michael Janner MIT janner@mit.edu Jiajun Wu MIT jiajunwu@mit.edu Tejas D. Kulkarni DeepMind tejasdkulkarni@gmail.com Ilker Yildirim MIT ilkery@mit.edu Joshua B. Tenenbaum MIT jbt@mit.edu Abstract Intrinsic decomposition from a single image ... | 2017 | 525 |
7,036 | On-the-fly Operation Batching in Dynamic Computation Graphs Graham Neubig⇤ Language Technologies Institute Carnegie Mellon University gneubig@cs.cmu.edu Yoav Goldberg⇤ Computer Science Department Bar-Ilan University yogo@cs.biu.ac.il Chris Dyer DeepMind cdyer@google.com Abstract Dynamic neura... | 2017 | 526 |
7,037 | Fitting Low-Rank Tensors in Constant Time Kohei Hayashi∗ National Institute of Advanced Industrial Science and Technology RIKEN AIP hayashi.kohei@gmail.com Yuichi Yoshida† National Institute of Informatics yyoshida@nii.ac.jp Abstract In this paper, we develop an algorithm that approximates the residua... | 2017 | 527 |
7,038 | Random Projection Filter Bank for Time Series Data Amir-massoud Farahmand Mitsubishi Electric Research Laboratories (MERL) Cambridge, MA, USA farahmand@merl.com Sepideh Pourazarm Mitsubishi Electric Research Laboratories (MERL) Cambridge, MA, USA sepid@bu.edu Daniel Nikovski Mitsubishi Electric Rese... | 2017 | 528 |
7,039 | Dynamic Revenue Sharing∗ Santiago Balseiro Columbia University New York City, NY srb2155@columbia.edu Max Lin Google New York City, NY whlin@google.com Vahab Mirrokni Google New York City, NY mirrokni@google.com Renato Paes Leme Google New York City, NY renatoppl@google.com Song Zuo† ... | 2017 | 529 |
7,040 | When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness Chris Russell∗ The Alan Turing Institute and University of Surrey crussell@turing.ac.uk Matt J. Kusner∗ The Alan Turing Institute and University of Warwick mkusner@turing.ac.uk Joshua R. Loftus† New York University ... | 2017 | 53 |
7,041 | Prototypical Networks for Few-shot Learning Jake Snell University of Toronto∗ Vector Institute Kevin Swersky Twitter Richard Zemel University of Toronto Vector Institute Canadian Institute for Advanced Research Abstract We propose Prototypical Networks for the problem of few-shot classification, wh... | 2017 | 530 |
7,042 | Unsupervised learning of object frames by dense equivariant image labelling James Thewlis1 Hakan Bilen2 Andrea Vedaldi1 1 Visual Geometry Group University of Oxford {jdt,vedaldi}@robots.ox.ac.uk 2 School of Informatics University of Edinburgh hbilen@ed.ac.uk Abstract One of the key challenges of... | 2017 | 531 |
7,043 | Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays Cesar F. Caiafa∗ Department of Psychological and Brain Sciences Indiana University (47405) Bloomington, IN, USA IAR - CCT La Plata, CONICET / CIC-PBA (1894) V. Elisa, ARGENTINA ccaiafa@gmail.com O... | 2017 | 532 |
7,044 | Random Permutation Online Isotonic Regression Wojciech Kotłowski Pozna´n University of Technology Poland wkotlowski@cs.put.poznan.pl Wouter M. Koolen Centrum Wiskunde & Informatica Amsterdam, The Netherlands wmkoolen@cwi.nl Alan Malek MIT Cambridge, MA amalek@mit.edu Abstract We revisit isot... | 2017 | 533 |
7,045 | PRUNE: Preserving Proximity and Global Ranking for Network Embedding Yi-An Lai ∗‡ National Taiwan University b99202031@ntu.edu.tw Chin-Chi Hsu †‡ Academia Sinica chinchi@iis.sinica.edu.tw Wen-Hao Chen ∗ National Taiwan University b02902023@ntu.edu.tw Mi-Yen Yeh † Academia Sinica miyen@iis.sini... | 2017 | 534 |
7,046 | Online to Offline Conversions, Universality and Adaptive Minibatch Sizes Kfir Y. Levy Department of Computer Science, ETH Zürich. yehuda.levy@inf.ethz.ch Abstract We present an approach towards convex optimization that relies on a novel scheme which converts adaptive online algorithms into offline methods. I... | 2017 | 535 |
7,047 | Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network Wengong Jin† Connor W. Coley‡ Regina Barzilay† Tommi Jaakkola† †Computer Science and Artificial Intelligence Lab, MIT ‡Department of Chemical Engineering, MIT †{wengong,regina,tommi}@csail.mit.edu, ‡ccoley@mit.edu Abstract The predic... | 2017 | 536 |
7,048 | Inferring Generative Model Structure with Static Analysis Paroma Varma1, Bryan He2, Payal Bajaj2, Nishith Khandwala2, Imon Banerjee3, Daniel Rubin3,4, Christopher Ré2 1Electrical Engineering, 2Computer Science, 3Biomedical Data Science, 4Radiology Stanford University {paroma,bryanhe,pabajaj,nishith,imonb,ru... | 2017 | 537 |
7,049 | Influence Maximization with ε-Almost Submodular Threshold Functions Qiang Li∗†, Wei Chen‡, Xiaoming Sun∗†, Jialin Zhang∗† ∗CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences †University of Chinese Academy of Sciences ‡Microsoft Research {liqia... | 2017 | 538 |
7,050 | Improved Dynamic Regret for Non-degenerate Functions Lijun Zhang∗, Tianbao Yang†, Jinfeng Yi‡, Rong Jin§, Zhi-Hua Zhou∗ ∗National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China †Department of Computer Science, The University of Iowa, Iowa City, USA ‡AI Foundations Lab, IBM Th... | 2017 | 539 |
7,051 | Dualing GANs Yujia Li1∗ Alexander Schwing3 Kuan-Chieh Wang1,2 Richard Zemel1,2 1Department of Computer Science, University of Toronto 2Vector Institute 3Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign {yujiali, wangkua1, zemel}@cs.toronto.edu aschwing@illi... | 2017 | 54 |
7,052 | AdaGAN: Boosting Generative Models Ilya Tolstikhin MPI for Intelligent Systems Tübingen, Germany ilya@tue.mpg.de Sylvain Gelly Google Brain Zürich, Switzerland sylvaingelly@google.com Olivier Bousquet Google Brain Zürich, Switzerland obousquet@google.com Carl-Johann Simon-Gabriel MPI for Int... | 2017 | 540 |
7,053 | Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences Kinjal Basu, Ankan Saha, Shaunak Chatterjee LinkedIn Corporation Mountain View, CA 94043 {kbasu, asaha, shchatte}@linkedin.com Abstract We consider the problem of solving a large-scale Quadratically Constrained Quadrat... | 2017 | 541 |
7,054 | Graph Matching via Multiplicative Update Algorithm Bo Jiang School of Computer Science and Technology Anhui University, China jiangbo@ahu.edu.cn Jin Tang School of Computer Science and Technology Anhui University, China tj@ahu.edu.cn Chris Ding CSE Department, University of Texas at Arlingto... | 2017 | 542 |
7,055 | Neural Expectation Maximization Klaus Greff∗ IDSIA klaus@idsia.ch Sjoerd van Steenkiste∗ IDSIA sjoerd@idsia.ch Jürgen Schmidhuber IDSIA juergen@idsia.ch Abstract Many real world tasks such as reasoning and physical interaction require identification and manipulation of conceptual entities. A first s... | 2017 | 543 |
7,056 | Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs Saurabh Verma Department of Computer Science University of Minnesota, Twin Cities verma@cs.umn.edu Zhi-Li Zhang Department of Computer Science University of Minnesota, Twin Cities zhang@cs.umn.edu Abstract For the purpose of l... | 2017 | 544 |
7,057 | Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems Le Fang, Fan Yang, Wen Dong, Tong Guan, and Chunming Qiao Department of Computer Science and Engineering University at Buffalo {lefang, fyang24, wendong, tongguan, qiao}@buffalo.edu Abstract Technological breakthroughs al... | 2017 | 545 |
7,058 | Welfare Guarantees from Data Darrell Hoy University of Maryland darrell.hoy@gmail.com Denis Nekipelov University of Virginia denis@virginia.edu Vasilis Syrgkanis Microsoft Research vasy@microsoft.com Abstract Analysis of efficiency of outcomes in game theoretic settings has been a main item of st... | 2017 | 546 |
7,059 | Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference Abhishek Kumar∗ IBM Research AI Yorktown Heights, NY abhishk@us.ibm.com Prasanna Sattigeri∗ IBM Research AI Yorktown Heights, NY psattig@us.ibm.com P. Thomas Fletcher University of Utah Salt Lake City, UT fletcher@... | 2017 | 547 |
7,060 | Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples Moustapha Cisse Facebook AI Research moustaphacisse@fb.com Yossi Adi* Bar-Ilan University, Israel yossiadidrum@gmail.com Natalia Neverova* Facebook AI Research nneverova@fb.com Joseph Keshet Bar-Ilan ... | 2017 | 548 |
7,061 | Clustering Stable Instances of Euclidean k-means Abhratanu Dutta∗ Northwestern University adutta@u.northwestern.edu Aravindan Vijayaraghavan∗ Northwestern University aravindv@northwestern.edu Alex Wang† Carnegie Mellon University alexwang@u.northwestern.edu Abstract The Euclidean k-means problem i... | 2017 | 549 |
7,062 | A Universal Analysis of Large-Scale Regularized Least Squares Solutions Ashkan Panahi Department of Electrical and Computer Engineering North Carolina State University Raleigh, NC 27606 apanahi@ncsu.edu Babak Hassibi Department of Electrical Engineering California Institute of Technology Pasadena, C... | 2017 | 55 |
7,063 | Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin Ritambhara Singh, Jack Lanchantin, Arshdeep Sekhon, Yanjun Qi Department of Computer Science University of Virginia yanjun@virginia.edu Abstract The past decade has seen a revolution in genomic technologies that enabled ... | 2017 | 550 |
7,064 | Deep Reinforcement Learning from Human Preferences Paul F Christiano OpenAI paul@openai.com Jan Leike DeepMind leike@google.com Tom B Brown Google Brain⇤ tombbrown@google.com Miljan Martic DeepMind miljanm@google.com Shane Legg DeepMind legg@google.com Dario Amodei OpenAI damodei@o... | 2017 | 551 |
7,065 | Subset Selection under Noise Chao Qian1 Jing-Cheng Shi2 Yang Yu2 Ke Tang3,1 Zhi-Hua Zhou2 1Anhui Province Key Lab of Big Data Analysis and Application, USTC, China 2National Key Lab for Novel Software Technology, Nanjing University, China 3Shenzhen Key Lab of Computational Intelligence, SUSTech, China ... | 2017 | 552 |
7,066 | PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space Charles R. Qi Li Yi Hao Su Leonidas J. Guibas Stanford University Abstract Few prior works study deep learning on point sets. PointNet [20] is a pioneer in this direction. However, by design PointNet does not capture local ... | 2017 | 553 |
7,067 | Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search Benjamin Moseley∗ Carnegie Mellon University Pittsburgh, PA 15213, USA moseleyb@andrew.cmu.edu Joshua R. Wang† Department of Computer Science Stanford University 353 Serra Mall, Stanford, CA 94305, U... | 2017 | 554 |
7,068 | Thinking Fast and Slow with Deep Learning and Tree Search Thomas Anthony1, , Zheng Tian1, and David Barber1,2 1University College London 2Alan Turing Institute thomas.anthony.14@ucl.ac.uk Abstract Sequential decision making problems, such as structured prediction, robotic control, and game playing, re... | 2017 | 555 |
7,069 | Learning Combinatorial Optimization Algorithms over Graphs Hanjun Dai†⇤, Elias B. Khalil†⇤, Yuyu Zhang†, Bistra Dilkina†, Le Song†§ † College of Computing, Georgia Institute of Technology § Ant Financial {hanjun.dai, elias.khalil, yuyu.zhang, bdilkina, lsong}@cc.gatech.edu Abstract The design of good heuris... | 2017 | 556 |
7,070 | Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice Jeffrey Pennington Google Brain Samuel S. Schoenholz Google Brain Surya Ganguli Applied Physics, Stanford University and Google Brain Abstract It is well known that weight initialization in deep networks can have... | 2017 | 557 |
7,071 | Adaptive Classification for Prediction Under a Budget Feng Nan Systems Engineering Boston University Boston, MA 02215 fnan@bu.edu Venkatesh Saligrama Electrical Engineering Boston University Boston, MA 02215 srv@bu.edu Abstract We propose a novel adaptive approximation approach for test-time reso... | 2017 | 558 |
7,072 | Online Convex Optimization with Stochastic Constraints Hao Yu, Michael J. Neely, Xiaohan Wei Department of Electrical Engineering, University of Southern California⇤ {yuhao,mjneely,xiaohanw}@usc.edu Abstract This paper considers online convex optimization (OCO) with stochastic constraints, which gener... | 2017 | 559 |
7,073 | Diffusion Approximations for Online Principal Component Estimation and Global Convergence Chris Junchi Li Mengdi Wang Han Liu Princeton University Department of Operations Research and Financial Engineering, Princeton, NJ 08544 {junchil,mengdiw,hanliu}@princeton.edu Tong Zhang Tencent AI Lab Shennan... | 2017 | 56 |
7,074 | Structured Bayesian Pruning via Log-Normal Multiplicative Noise Kirill Neklyudov 1,2 k.necludov@gmail.com Dmitry Molchanov 1,3 dmolchanov@hse.ru Arsenii Ashukha 1,2 aashukha@hse.ru Dmitry Vetrov 1,2 dvetrov@hse.ru 1National Research University Higher School of Economics 2Yandex 3Skolkovo Institu... | 2017 | 560 |
7,075 | Clustering with Noisy Queries Arya Mazumdar and Barna Saha College of Information and Computer Sciences University of Massachusetts Amherst Amherst, MA 01003 {arya,barna}@cs.umass.edu Abstract In this paper, we provide a rigorous theoretical study of clustering with noisy queries. Given a set of n eleme... | 2017 | 561 |
7,076 | Compression-aware Training of Deep Networks Jose M. Alvarez Toyota Research Institute Los Altos, CA 94022 jose.alvarez@tri.global Mathieu Salzmann EPFL - CVLab Lausanne, Switzerland mathieu.salzmann@epfl.ch Abstract In recent years, great progress has been made in a variety of application domains ... | 2017 | 562 |
7,077 | Maxing and Ranking with Few Assumptions Moein Falahatgar Yi Hao Alon Orlitsky Venkatadheeraj Pichapati Vaishakh Ravindrakumar University of California, San Deigo {moein,yih179,alon,dheerajpv7,vaishakhr}@ucsd.edu Abstract PAC maximum selection (maxing) and ranking of n elements via random pairwise comparison... | 2017 | 563 |
7,078 | Subspace Clustering via Tangent Cones Amin Jalali Wisconsin Institute for Discovery University of Wisconsin Madison, WI 53715 amin.jalali@wisc.edu Rebecca Willett Department of Electrical and Computer Engineering University of Wisconsin Madison, WI 53706 willett@discovery.wisc.edu Abstract Given... | 2017 | 564 |
7,079 | DropoutNet: Addressing Cold Start in Recommender Systems Maksims Volkovs layer6.ai maks@layer6.ai Guangwei Yu layer6.ai guang@layer6.ai Tomi Poutanen layer6.ai tomi@layer6.ai Abstract Latent models have become the default choice for recommender systems due to their performance and scalability.... | 2017 | 565 |
7,080 | Unsupervised Image-to-Image Translation Networks Ming-Yu Liu, Thomas Breuel, Jan Kautz NVIDIA {mingyul,tbreuel,jkautz}@nvidia.com Abstract Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in ind... | 2017 | 566 |
7,081 | SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability Maithra Raghu,1,2 Justin Gilmer,1 Jason Yosinski,3 & Jascha Sohl-Dickstein1 1Google Brain 2Cornell University 3Uber AI Labs maithrar@gmail•com, gilmer@google•com, yosinski@uber•com, jaschasd@google•com Abst... | 2017 | 567 |
7,082 | Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe Quentin Berthet ∗ University of Cambridge q.berthet@statslab.cam.ac.uk Vianney Perchet † ENS Paris-Saclay & Criteo Research, Paris vianney.perchet@normalesup.org Abstract We consider the problem of bandit optimization, inspired by sto... | 2017 | 568 |
7,083 | Identifying Outlier Arms in Multi-Armed Bandit ∗ Honglei Zhuang1† Chi Wang2 Yifan Wang3 1University of Illinois at Urbana-Champaign 2Microsoft Research, Redmond 3Tsinghua University hzhuang3@illinois.edu wang.chi@microsoft.com yifan-wa16@mails.tsinghua.edu.cn Abstract We study a novel problem lyin... | 2017 | 569 |
7,084 | k-Support and Ordered Weighted Sparsity for Overlapping Groups: Hardness and Algorithms Cong Han Lim University of Wisconsin-Madison clim9@wisc.edu Stephen J. Wright University of Wisconsin-Madison swright@cs.wisc.edu Abstract The k-support and OWL norms generalize the ℓ1 norm, providing better predic... | 2017 | 57 |
7,085 | Discovering Potential Correlations via Hypercontractivity Hyeji Kim1⇤ Weihao Gao1⇤ Sreeram Kannan2† Sewoong Oh1‡ Pramod Viswanath1⇤ University of Illinois at Urbana Champaign1 and University of Washington2 {hyejikim,wgao9}@illinois.edu,ksreeram@uw.edu,{swoh,pramodv}@illinois.edu Abstract Discovering... | 2017 | 570 |
7,086 | A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering Hongteng Xu∗ School of ECE Georgia Institute of Technology hongtengxu313@gmail.com Hongyuan Zha College of Computing Georgia Institute of Technology zha@cc.gatech.edu Abstract How to cluster event sequences generated via d... | 2017 | 571 |
7,087 | Efficient Approximation Algorithms for String Kernel Based Sequence Classification Muhammad Farhan Department of Computer Science School of Science and Engineering Lahore University of Management Sciences Lahore, Pakistan 14030031@lums.edu.pk Juvaria Tariq Department of Mathematics School of Science a... | 2017 | 572 |
7,088 | Multi-output Polynomial Networks and Factorization Machines Mathieu Blondel NTT Communication Science Laboratories Kyoto, Japan mathieu@mblondel.org Vlad Niculae∗ Cornell University Ithaca, NY vlad@cs.cornell.edu Takuma Otsuka NTT Communication Science Laboratories Kyoto, Japan otsuka.takuma@l... | 2017 | 573 |
7,089 | Tractability in Structured Probability Spaces Arthur Choi University of California Los Angeles, CA 90095 aychoi@cs.ucla.edu Yujia Shen University of California Los Angeles, CA 90095 yujias@cs.ucla.edu Adnan Darwiche University of California Los Angeles, CA 90095 darwiche@cs.ucla.edu Abstract ... | 2017 | 574 |
7,090 | Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search Luigi Acerbi∗ Center for Neural Science New York University luigi.acerbi@nyu.edu Wei Ji Ma Center for Neural Science & Dept. of Psychology New York University weijima@nyu.edu Abstract Computational models in fiel... | 2017 | 575 |
7,091 | Multi-Information Source Optimization Matthias Poloczek Department of Systems and Industrial Engineering University of Arizona Tucson, AZ 85721 poloczek@email.arizona.edu Jialei Wang Chief Analytics Office IBM Armonk, NY 10504 jw865@cornell.edu Peter I. Frazier School of Operations Research and I... | 2017 | 576 |
7,092 | Differentially private Bayesian learning on distributed data Mikko Heikkilä1 mikko.a.heikkila@helsinki.fi Eemil Lagerspetz2 eemil.lagerspetz@helsinki.fi Samuel Kaski3 samuel.kaski@aalto.fi Kana Shimizu4 shimizu.kana.g@gmail.com Sasu Tarkoma2 sasu.tarkoma@helsinki.fi Antti Honkela1,5 antti.honk... | 2017 | 577 |
7,093 | MMD GAN: Towards Deeper Understanding of Moment Matching Network Chun-Liang Li1,⇤ Wei-Cheng Chang1,⇤ Yu Cheng2 Yiming Yang1 Barnabás Póczos1 1 Carnegie Mellon University, 2AI Foundations, IBM Research {chunlial,wchang2,yiming,bapoczos}@cs.cmu.edu chengyu@us.ibm.com (⇤denotes equal contribution) ... | 2017 | 578 |
7,094 | Convergence of Gradient EM on Multi-component Mixture of Gaussians Bowei Yan University of Texas at Austin boweiy@utexas.edu Mingzhang Yin University of Texas at Austin mzyin@utexas.edu Purnamrita Sarkar University of Texas at Austin purna.sarkar@austin.utexas.edu Abstract In this paper, we stud... | 2017 | 579 |
7,095 | Learning to Model the Tail Yu-Xiong Wang Deva Ramanan Martial Hebert Robotics Institute, Carnegie Mellon University {yuxiongw,dramanan,hebert}@cs.cmu.edu Abstract We describe an approach to learning from long-tailed, imbalanced datasets that are prevalent in real-world settings. Here, the challenge is t... | 2017 | 58 |
7,096 | Bayesian Dyadic Trees and Histograms for Regression Stéphanie van der Pas Mathematical Institute Leiden University Leiden, The Netherlands svdpas@math.leidenuniv.nl Veronika Roˇcková Booth School of Business University of Chicago Chicago, IL, 60637 Veronika.Rockova@ChicagoBooth.edu Abstract Many... | 2017 | 580 |
7,097 | Efficient and Flexible Inference for Stochastic Systems Stefan Bauer∗ Department of Computer Science ETH Zurich bauers@inf.ethz.ch Nico S. Gorbach∗ Department of Computer Science ETH Zurich ngorbach@inf.ethz.ch Ðor ¯de Miladinovi´c Department of Computer Science ETH Zurich djordjem@inf.ethz.ch ... | 2017 | 581 |
7,098 | Learning ReLUs via Gradient Descent Mahdi Soltanolkotabi Ming Hsieh Department of Electrical Engineering University of Southern California Los Angeles, CA soltanol@usc.edu Abstract In this paper we study the problem of learning Rectified Linear Units (ReLUs) which are functions of the form x ↦max(0,⟨w,x⟩... | 2017 | 582 |
7,099 | Learning Graph Representations with Embedding Propagation Alberto García-Durán NEC Labs Europe Heidelberg, Germany alberto.duran@neclab.eu Mathias Niepert NEC Labs Europe Heidelberg, Germany mathias.niepert@neclab.eu Abstract We propose Embedding Propagation (EP), an unsupervised learning framewor... | 2017 | 583 |
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