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7,100 | Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation Matthias Hein and Maksym Andriushchenko Department of Mathematics and Computer Science Saarland University, Saarbrücken Informatics Campus, Germany Abstract Recent work has shown that state-of-the-art classifiers are quite br... | 2017 | 584 |
7,101 | Collapsed variational Bayes for Markov jump processes Jiangwei Pan∗† Department of Computer Science Duke University panjiangwei@gmail.com Boqian Zhang∗ Department of Statistics Purdue University zhan1977@purdue.edu Vinayak Rao Department of Statistics Purdue University varao@purdue.edu Abstr... | 2017 | 585 |
7,102 | Is the Bellman residual a bad proxy? Matthieu Geist1, Bilal Piot2,3 and Olivier Pietquin 2,3 1 Université de Lorraine & CNRS, LIEC, UMR 7360, Metz, F-57070 France 2 Univ. Lille, CNRS, Centrale Lille, Inria, UMR 9189 - CRIStAL, F-59000 Lille, France 3 Now with Google DeepMind, London, United Kingdom matthieu.g... | 2017 | 586 |
7,103 | Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems Celestine D¨unner IBM Research - Zurich Switzerland cdu@zurich.ibm.com Thomas Parnell IBM Research - Zurich Switzerland tpa@zurich.ibm.com Martin Jaggi EPFL Switzerland martin.jaggi@epfl.ch Abstract We... | 2017 | 587 |
7,104 | Noise-Tolerant Interactive Learning Using Pairwise Comparisons Yichong Xu*, Hongyang Zhang*, Kyle Miller†, Aarti Singh*, and Artur Dubrawski† *Machine Learning Department, Carnegie Mellon University, USA †Auton Lab, Carnegie Mellon University, USA {yichongx, hongyanz, aarti, awd}@cs.cmu.edu, mille856@andrew... | 2017 | 588 |
7,105 | Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs Sanjiban Choudhury The Robotics Institute Carnegie Mellon University sanjiban@cmu.edu Shervin Javdani The Robotics Institute Carnegie Mellon University sjavdani@cmu.edu Siddhartha Srinivasa The Robotics Institute Carnegie Mello... | 2017 | 589 |
7,106 | Neural Variational Inference and Learning in Undirected Graphical Models Volodymyr Kuleshov Stanford University Stanford, CA 94305 kuleshov@cs.stanford.edu Stefano Ermon Stanford University Stanford, CA 94305 ermon@cs.stanford.edu Abstract Many problems in machine learning are naturally expressed ... | 2017 | 59 |
7,107 | Minimal Exploration in Structured Stochastic Bandits Richard Combes Centrale-Supelec / L2S richard.combes@supelec.fr Stefan Magureanu KTH, EE School / ACL magur@kth.se Alexandre Proutiere KTH, EE School / ACL alepro@kth.se Abstract This paper introduces and addresses a wide class of stochastic b... | 2017 | 590 |
7,108 | Learning Efficient Object Detection Models with Knowledge Distillation Guobin Chen1,2 Wongun Choi1 Xiang Yu1 Tony Han2 Manmohan Chandraker1,3 1NEC Labs America 2University of Missouri 3University of California, San Diego Abstract Despite significant accuracy improvement in convolutional neural netwo... | 2017 | 591 |
7,109 | Learning Chordal Markov Networks via Branch and Bound Kari Rantanen HIIT, Dept. Comp. Sci., University of Helsinki Antti Hyttinen HIIT, Dept. Comp. Sci., University of Helsinki Matti Järvisalo HIIT, Dept. Comp. Sci., University of Helsinki Abstract We present a new algorithmic approach for the t... | 2017 | 592 |
7,110 | Efficient Optimization for Linear Dynamical Systems with Applications to Clustering and Sparse Coding Wenbing Huang1,3, Mehrtash Harandi2, Tong Zhang2 Lijie Fan3, Fuchun Sun3, Junzhou Huang1 1 Tencent AI Lab. ; 2 Data61, CSIRO and Australian National University, Australia; 3 Department of Computer Science an... | 2017 | 593 |
7,111 | Deep Subspace Clustering Networks Pan Ji∗ University of Adelaide Tong Zhang∗ Australian National University Hongdong Li Australian National University Mathieu Salzmann EPFL - CVLab Ian Reid University of Adelaide Abstract We present a novel deep neural network architecture for unsupervised subsp... | 2017 | 594 |
7,112 | Robust Estimation of Neural Signals in Calcium Imaging Hakan Inan 1 inanh@stanford.edu Murat A. Erdogdu 2,3 erdogdu@cs.toronto.edu Mark J. Schnitzer 1,4 mschnitz@stanford.edu 1Stanford University 2Microsoft Research 3Vector Institute 4Howard Hughes Medical Institute Abstract Calcium imaging is a pro... | 2017 | 595 |
7,113 | Fast-Slow Recurrent Neural Networks Asier Mujika Department of Computer Science ETH Zürich, Switzerland asierm@ethz.ch Florian Meier Department of Computer Science ETH Zürich, Switzerland meierflo@inf.ethz.ch Angelika Steger Department of Computer Science ETH Zürich, Switzerland steger@inf.ethz.... | 2017 | 596 |
7,114 | PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs Yunbo Wang School of Software Tsinghua University wangyb15@mails.tsinghua.edu.cn Mingsheng Long∗ School of Software Tsinghua University mingsheng@tsinghua.edu.cn Jianmin Wang School of Software Tsinghua Univers... | 2017 | 597 |
7,115 | Dual Discriminator Generative Adversarial Nets Tu Dinh Nguyen, Trung Le, Hung Vu, Dinh Phung Deakin University, Geelong, Australia Centre for Pattern Recognition and Data Analytics {tu.nguyen, trung.l, hungv, dinh.phung}@deakin.edu.au Abstract We propose in this paper a novel approach to tackle the problem ... | 2017 | 598 |
7,116 | Beyond Parity: Fairness Objectives for Collaborative Filtering Sirui Yao Department of Computer Science Virginia Tech Blacksburg, VA 24061 ysirui@vt.edu Bert Huang Department of Computer Science Virginia Tech Blacksburg, VA 24061 bhuang@vt.edu Abstract We study fairness in collaborative-filteri... | 2017 | 599 |
7,117 | On clustering network-valued data Soumendu Sundar Mukherjee Department of Statistics University of California, Berkeley Berkeley, California 94720, USA soumendu@berkeley.edu Purnamrita Sarkar Department of Statistics and Data Sciences University of Texas, Austin Austin, Texas 78712, USA purna.sarkar... | 2017 | 6 |
7,118 | Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification Bikash Joshi Univ. Grenoble Alps, LIG Grenoble, France bikash.joshi@imag.fr Massih-Reza Amini Univ. Grenoble Alps, LIG Grenoble, France massih-reza.amini@imag.fr Ioannis Partalas Expedia EWE Geneva, S... | 2017 | 60 |
7,119 | Multitask Spectral Learning of Weighted Automata Guillaume Rabusseau ∗ McGill University Borja Balle † Amazon Research Cambridge Joelle Pineau‡ McGill University Abstract We consider the problem of estimating multiple related functions computed by weighted automata (WFA). We first present a natural not... | 2017 | 600 |
7,120 | A simple neural network module for relational reasoning Adam Santoro∗ adamsantoro@google.com David Raposo∗ draposo@google.com David G.T. Barrett barrettdavid@google.com Mateusz Malinowski mateuszm@google.com Razvan Pascanu razp@google.com Peter Battaglia peterbattaglia@google.com Timothy Lil... | 2017 | 601 |
7,121 | Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks Arash Vahdat D-Wave Systems Inc. Burnaby, BC, Canada avahdat@dwavesys.com Abstract Collecting large training datasets, annotated with high-quality labels, is costly and time-consuming. This paper proposes a novel frame... | 2017 | 602 |
7,122 | Stochastic Mirror Descent in Variationally Coherent Optimization Problems Zhengyuan Zhou Stanford University zyzhou@stanford.edu Panayotis Mertikopoulos Univ. Grenoble Alpes, CNRS, Inria, LIG panayotis.mertikopoulos@imag.fr Nicholas Bambos Stanford University bambos@stanford.edu Stephen Boyd Sta... | 2017 | 603 |
7,123 | Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication Qian Yu∗, Mohammad Ali Maddah-Ali†, and A. Salman Avestimehr∗ ∗Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA † Nokia Bell Labs, Holmdel, NJ, USA Abstract We consider a large-s... | 2017 | 604 |
7,124 | From Bayesian Sparsity to Gated Recurrent Nets Hao He Massachusetts Institute of Technology haohe@mit.edu Bo Xin Microsoft Research, Beijing, China jimxinbo@gmail.com Satoshi Ikehata National Institute of Informatics satoshi.ikehata@gmail.com David Wipf Microsoft Research, Beijing, China davidwi... | 2017 | 605 |
7,125 | Compatible Reward Inverse Reinforcement Learning Alberto Maria Metelli DEIB Politecnico di Milano, Italy albertomaria.metelli@polimi.it Matteo Pirotta SequeL Team Inria Lille, France matteo.pirotta@inria.fr Marcello Restelli DEIB Politecnico di Milano, Italy marcello.restelli@polimi.it Abstrac... | 2017 | 606 |
7,126 | Consistent Robust Regression Kush Bhatia∗ University of California, Berkeley kushbhatia@berkeley.edu Prateek Jain Microsoft Research, India prajain@microsoft.com Parameswaran Kamalaruban† EPFL, Switzerland kamalaruban.parameswaran@epfl.ch Purushottam Kar Indian Institute of Technology, Kanpur pu... | 2017 | 607 |
7,127 | Scalable Variational Inference for Dynamical Systems Nico S. Gorbach∗ Dept. of Computer Science ETH Zurich ngorbach@inf.ethz.ch Stefan Bauer∗ Dept. of Computer Science ETH Zurich bauers@inf.ethz.ch Joachim M. Buhmann Dept. of Computer Science ETH Zurich jbuhmann@inf.ethz.ch Abstract Gradient... | 2017 | 608 |
7,128 | Learning multiple visual domains with residual adapters Sylvestre-Alvise Rebuffi1 Hakan Bilen1,2 Andrea Vedaldi1 1 Visual Geometry Group University of Oxford {srebuffi,hbilen,vedaldi}@robots.ox.ac.uk 2 School of Informatics University of Edinburgh Abstract There is a growing interest in learning da... | 2017 | 609 |
7,129 | Learning Linear Dynamical Systems via Spectral Filtering Elad Hazan, Karan Singh, Cyril Zhang Department of Computer Science Princeton University Princeton, NJ 08544 {ehazan,karans,cyril.zhang}@cs.princeton.edu Abstract We present an efficient and practical algorithm for the online prediction of discre... | 2017 | 61 |
7,130 | Incorporating Side Information by Adaptive Convolution Di Kang Debarun Dhar Antoni B. Chan Department of Computer Science City University of Hong Kong {dkang5-c, ddhar2-c}@my.cityu.edu.hk, abchan@cityu.edu.hk Abstract Computer vision tasks often have side information available that is helpful to sol... | 2017 | 610 |
7,131 | Hierarchical Clustering Beyond the Worst-Case Vincent Cohen-Addad University of Copenhagen vcohenad@gmail.com Varun Kanade University of Oxford Alan Turing Institute varunk@cs.ox.ac.uk Frederik Mallmann-Trenn MIT mallmann@mit.edu Abstract Hiererachical clustering, that is computing a recursive p... | 2017 | 611 |
7,132 | Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference Geoffrey Roeder University of Toronto roeder@cs.toronto.edu Yuhuai Wu University of Toronto ywu@cs.toronto.edu David Duvenaud University of Toronto duvenaud@cs.toronto.edu Abstract We propose a simple and ... | 2017 | 612 |
7,133 | Multiplicative Weights Update with Constant Step-Size in Congestion Games: Convergence, Limit Cycles and Chaos Gerasimos Palaiopanos∗ SUTD Singapore gerasimosath@yahoo.com Ioannis Panageas† MIT Cambridge, MA 02139 ioannis@csail.mit.edu Georgios Piliouras‡ SUTD Singapore georgios@sutd.edu.sg ... | 2017 | 613 |
7,134 | QMDP-Net: Deep Learning for Planning under Partial Observability Peter Karkus1,2 David Hsu1,2 Wee Sun Lee2 1NUS Graduate School for Integrative Sciences and Engineering 2School of Computing National University of Singapore {karkus, dyhsu, leews}@comp.nus.edu.sg Abstract This paper introduces the QMD... | 2017 | 614 |
7,135 | Deep Supervised Discrete Hashing Qi Li Zhenan Sun Ran He Tieniu Tan Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition CAS Center for Excellence in Brain Science and Intelligence Technology Institute of Automation, Chinese Academy of Sciences {qli,zn... | 2017 | 615 |
7,136 | Approximation Algorithms for ℓ0-Low Rank Approximation Karl Bringmann1 kbringma@mpi-inf.mpg.de Pavel Kolev1∗ pkolev@mpi-inf.mpg.de David P. Woodruff2 dwoodruf@cs.cmu.edu 1 Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany 2 Department of Computer Science, Carnegie... | 2017 | 616 |
7,137 | ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization Yi Xu†, Mingrui Liu†, 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-... | 2017 | 617 |
7,138 | A Learning Error Analysis for Structured Prediction with Approximate Inference Yuanbin Wu1, 2, Man Lan1, 2, Shiliang Sun1, Qi Zhang3, Xuanjing Huang3 1School of Computer Science and Software Engineering, East China Normal University 2Shanghai Key Laboratory of Multidimensional Information Processing 3School o... | 2017 | 618 |
7,139 | Simple Strategies for Recovering Inner Products from Coarsely Quantized Random Projections Ping Li Baidu Research, and Rutgers University pingli98@gmail.com Martin Slawski Department of Statistics George Mason University mslawsk3@gmu.edu Abstract Random projections have been increasingly adopted f... | 2017 | 619 |
7,140 | Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes Zhenwen Dai ∗‡ zhenwend@amazon.com Mauricio A. Álvarez † mauricio.alvarez@sheffield.ac.uk Neil D. Lawrence †‡ lawrennd@amazon.com Abstract Often in machine learning, data are collected as a combination of multiple ... | 2017 | 62 |
7,141 | Trimmed Density Ratio Estimation Song Liu∗ University of Bristol song.liu@bristol.ac.uk Akiko Takeda The Institute of Statistical Mathematics, AIP, RIKEN, atakeda@ism.ac.jp Taiji Suzuki University of Tokyo, Sakigake (PRESTO), JST, AIP, RIKEN, taiji@mist.i.u-tokyo.ac.jp Kenji Fukumizu The Ins... | 2017 | 620 |
7,142 | Adaptive Batch Size for Safe Policy Gradients Matteo Papini DEIB Politecnico di Milano, Italy matteo.papini@polimi.it Matteo Pirotta SequeL Team Inria Lille, France matteo.pirotta@inria.fr Marcello Restelli DEIB Politecnico di Milano, Italy marcello.restelli@polimi.it Abstract Policy gradien... | 2017 | 621 |
7,143 | Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting Rebecca E. Morrison MIT rmorriso@mit.edu Ricardo Baptista MIT rsb@mit.edu Youssef Marzouk MIT ymarz@mit.edu Abstract We present an algorithm to identify sparse dependence structure in continuous and no... | 2017 | 622 |
7,144 | REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models George Tucker1,⇤, Andriy Mnih2, Chris J. Maddison2,3, Dieterich Lawson1,*, Jascha Sohl-Dickstein1 1Google Brain, 2DeepMind, 3University of Oxford {gjt, amnih, dieterichl, jaschasd}@google.com cmaddis@stats.ox.ac.uk Abstra... | 2017 | 623 |
7,145 | Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues Noga Alon Tel Aviv University, Israel and Microsoft Research nogaa@tau.ac.il Moshe Babaioff Microsoft Research moshe@microsoft.com Yannai A. Gonczarowski The Hebrew University of Jerusalem, Israel and Microsoft Research yann... | 2017 | 624 |
7,146 | Tensor Biclustering Soheil Feizi Stanford University sfeizi@stanford.edu Hamid Javadi Stanford University hrhakim@stanford.edu David Tse Stanford University dntse@stanford.edu Abstract Consider a dataset where data is collected on multiple features of multiple individuals over multiple times. This... | 2017 | 625 |
7,147 | On the Model Shrinkage Effect of Gamma Process Edge Partition Models Iku Ohama⋆‡ Issei Sato† Takuya Kida‡ Hiroki Arimura‡ ⋆Panasonic Corp., Japan †The Univ. of Tokyo, Japan ‡Hokkaido Univ., Japan ohama.iku@jp.panasonic.com sato@k.u-tokyo.ac.jp {kida,arim}@ist.hokudai.ac.jp Abstract The edge par... | 2017 | 626 |
7,148 | Estimating Mutual Information for Discrete-Continuous Mixtures Weihao Gao Department of ECE Coordinated Science Laboratory University of Illinois at Urbana-Champaign wgao9@illinois.edu Sreeram Kannan Department of Electrical Engineering University of Washington ksreeram@uw.edu Sewoong Oh Departm... | 2017 | 627 |
7,149 | Reconstructing perceived faces from brain activations with deep adversarial neural decoding Ya˘gmur Güçlütürk*, Umut Güçlü*, Katja Seeliger, Sander Bosch, Rob van Lier, Marcel van Gerven, Radboud University, Donders Institute for Brain, Cognition and Behaviour Nijmegen, the Netherlands {y.gucluturk, u.guc... | 2017 | 628 |
7,150 | An Inner-loop Free Solution to Inverse Problems using Deep Neural Networks Kai Fai∗ Duke University kai.fan@stat.duke.edu Qi Wei∗ Duke University qi.wei@duke.edu Lawrence Carin Duke University lcarin@duke.edu Katherine Heller Duke University kheller@stat.duke.edu Abstract We propose a new ... | 2017 | 629 |
7,151 | Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks Wei-Sheng Lai1 Jia-Bin Huang2 Ming-Hsuan Yang1,3 1University of California, Merced 2Virginia Tech 3Nvidia Research 1{wlai24|mhyang}@ucmerced.edu 2jbhuang@vt.edu Abstract Convolutional neural networks (CNNs) have recentl... | 2017 | 63 |
7,152 | A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control Fanny Yang Dept. of EECS, U.C. Berkeley fanny-yang@berkeley.edu Aaditya Ramdas Dept. of EECS and Statistics, U.C. Berkeley ramdas@berkeley.edu Kevin Jamieson Allen School of CSE, U. of Washington jamieson@cs.washington.edu Mart... | 2017 | 630 |
7,153 | Interactive Submodular Bandit Lin Chen1,2, Andreas Krause3, Amin Karbasi1,2 1 Department of Electrical Engineering, 2 Yale Institute for Network Science, Yale University 3 Department of Computer Science, ETH Zürich {lin.chen, amin.karbasi}@yale.edu, krausea@ethz.ch Abstract In many machine learning applicat... | 2017 | 631 |
7,154 | Hash Embeddings for Efficient Word Representations Dan Svenstrup Department for Applied Mathematics and Computer Science Technical University of Denmark (DTU) 2800 Lyngby, Denmark dsve@dtu.dk Jonas Meinertz Hansen FindZebra Copenhagen, Denmark jonas@findzebra.com Ole Winther Department for Applied ... | 2017 | 632 |
7,155 | Learning Low-Dimensional Metrics Lalit Jain ⇤ University of Michigan Ann Arbor, MI 48109 lalitj@umich.edu Blake Mason ⇤ University of Wisconsin Madison, WI 53706 bmason3@wisc.edu Robert Nowak University of Wisconsin Madison, WI 53706 rdnowak@wisc.edu Abstract This paper investigates the theo... | 2017 | 633 |
7,156 | Unsupervised Sequence Classification using Sequential Output Statistics Yu Liu †, Jianshu Chen ⇤, and Li Deng† ⇤Microsoft Research, Redmond, WA 98052, USA⇤ jianshuc@microsoft.com † Citadel LLC, Seattle/Chicago, USA† Li.Deng@citadel.com Abstract We consider learning a sequence classifier without labeled da... | 2017 | 634 |
7,157 | Deep Sets Manzil Zaheer1,2, Satwik Kottur1, Siamak Ravanbhakhsh1, Barnabás Póczos1, Ruslan Salakhutdinov1, Alexander J Smola1,2 1 Carnegie Mellon University 2 Amazon Web Services {manzilz,skottur,mravanba,bapoczos,rsalakhu,smola}@cs.cmu.edu Abstract We study the problem of designing models for machine lea... | 2017 | 635 |
7,158 | Optimal Shrinkage of Singular Values Under Random Data Contamination Danny Barash School of Computer Science and Engineering Hebrew University Jerusalem, Israel danny.barash@mail.huji.ac.il Matan Gavish School of Computer Science and Engineering Hebrew University Jerusalem, Israel gavish@cs.huji.a... | 2017 | 636 |
7,159 | Learning Mixture of Gaussians with Streaming Data Aditi Raghunathan Stanford University aditir@stanford.edu Prateek Jain Microsoft Research, India prajain@microsoft.com Ravishankar Krishnaswamy Microsoft Research, India rakri@microsoft.com Abstract In this paper, we study the problem of learning a... | 2017 | 637 |
7,160 | Learning to Compose Domain-Specific Transformations for Data Augmentation Alexander J. Ratner∗, Henry R. Ehrenberg∗, Zeshan Hussain, Jared Dunnmon, Christopher Ré Stanford University {ajratner,henryre,zeshanmh,jdunnmon,chrismre}@cs.stanford.edu Abstract Data augmentation is a ubiquitous technique for incre... | 2017 | 638 |
7,161 | Preventing Gradient Explosions in Gated Recurrent Units Sekitoshi Kanai, Yasuhiro Fujiwara, Sotetsu Iwamura NTT Software Innovation Center 3-9-11, Midori-cho, Musashino-shi, Tokyo {kanai.sekitoshi, fujiwara.yasuhiro, iwamura.sotetsu}@lab.ntt.co.jp Abstract A gated recurrent unit (GRU) is a successful recu... | 2017 | 639 |
7,162 | Phase Transitions in the Pooled Data Problem Jonathan Scarlett and Volkan Cevher Laboratory for Information and Inference Systems (LIONS) École Polytechnique Fédérale de Lausanne (EPFL) {jonathan.scarlett,volkan.cevher}@epfl.ch Abstract In this paper, we study the pooled data problem of identifying the label... | 2017 | 64 |
7,163 | Streaming Sparse Gaussian Process Approximations Thang D. Bui∗ Cuong V. Nguyen∗ Richard E. Turner Department of Engineering, University of Cambridge, UK {tdb40,vcn22,ret26}@cam.ac.uk Abstract Sparse pseudo-point approximations for Gaussian process (GP) models provide a suite of methods that support depl... | 2017 | 640 |
7,164 | Differentially Private Empirical Risk Minimization Revisited: Faster and More General∗ Di Wang Dept. of Computer Science and Engineering State University of New York at Buffalo Buffalo, NY 14260 dwang45@buffalo.edu Minwei Ye Dept. of Computer Science and Engineering State University of New York at Buf... | 2017 | 641 |
7,165 | Unbounded cache model for online language modeling with open vocabulary Edouard Grave Facebook AI Research egrave@fb.com Moustapha Cisse Facebook AI Research moustaphacisse@fb.com Armand Joulin Facebook AI Research ajoulin@fb.com Abstract Recently, continuous cache models were proposed as extens... | 2017 | 642 |
7,166 | Shape and Material from Sound Zhoutong Zhang MIT Qiujia Li University of Cambridge Zhengjia Huang ShanghaiTech University Jiajun Wu MIT Joshua B. Tenenbaum MIT William T. Freeman MIT, Google Research Abstract Hearing an object falling onto the ground, humans can recover rich information in... | 2017 | 643 |
7,167 | On the Consistency of Quick Shift Heinrich Jiang Google Inc. 1600 Amphitheatre Parkway, Mountain View, CA 94043 heinrich.jiang@gmail.com Abstract Quick Shift is a popular mode-seeking and clustering algorithm. We present finite sample statistical consistency guarantees for Quick Shift on mode and cluster ... | 2017 | 644 |
7,168 | Wasserstein Learning of Deep Generative Point Process Models Shuai Xiao∗†, Mehrdad Farajtabar∗⋄Xiaojing Ye‡, Junchi Yan§, Le Song⋄¶, Hongyuan Zha⋄ †Shanghai Jiao Tong University ⋄College of Computing, Georgia Institute of Technology ‡School of Mathematics, Georgia State University §IBM Research – China ¶A... | 2017 | 645 |
7,169 | Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent Peva Blanchard EPFL, Switzerland peva.blanchard@epfl.ch El Mahdi El Mhamdi∗ EPFL, Switzerland elmahdi.elmhamdi@epfl.ch Rachid Guerraoui EPFL, Switzerland rachid.guerraoui@epfl.ch Julien Stainer EPFL, Switzerland julien.stai... | 2017 | 646 |
7,170 | Protein Interface Prediction using Graph Convolutional Networks Alex Fout† Department of Computer Science Colorado State University Fort Collins, CO 80525 fout@colostate.edu Jonathon Byrd† Department of Computer Science Colorado State University Fort Collins, CO 80525 jonbyrd@colostate.edu Basir... | 2017 | 647 |
7,171 | Convergence rates of a partition based Bayesian multivariate density estimation method Linxi Liu ∗ Department of Statistics Columbia University ll3098@columbia.edu Dangna Li ICME Stanford University dangna@stanford.edu Wing Hung Wong Department of Statistics Stanford University whwong@stanford... | 2017 | 648 |
7,172 | Avoiding Discrimination through Causal Reasoning Niki Kilbertus†‡ nkilbertus@tue.mpg.de Mateo Rojas-Carulla†‡ mrojas@tue.mpg.de Giambattista Parascandolo†§ gparascandolo@tue.mpg.de Moritz Hardt∗ hardt@berkeley.edu Dominik Janzing† janzing@tue.mpg.de Bernhard Sch¨olkopf† bs@tue.mpg.de †Max Plan... | 2017 | 649 |
7,173 | Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning Christoph Dann Machine Learning Department Carnegie-Mellon University cdann@cdann.net Tor Lattimore∗ tor.lattimore@gmail.com Emma Brunskill Computer Science Department Stanford University ebrun@cs.stanford.edu Abstra... | 2017 | 65 |
7,174 | Alternating Estimation for Structured High-Dimensional Multi-Response Models Sheng Chen Arindam Banerjee Dept. of Computer Science & Engineering University of Minnesota, Twin Cities {shengc,banerjee}@cs.umn.edu Abstract We consider the problem of learning high-dimensional multi-response linear models wi... | 2017 | 650 |
7,175 | Multimodal Learning and Reasoning for Visual Question Answering Ilija Ilievski Integrative Sciences and Engineering National University of Singapore ilija.ilievski@u.nus.edu Jiashi Feng Electrical and Computer Engineering National University of Singapore elefjia@nus.edu.sg Abstract Reasoning about... | 2017 | 651 |
7,176 | Generative Local Metric Learning for Kernel Regression Yung-Kyun Noh Seoul National University, Rep. of Korea nohyung@snu.ac.kr Masashi Sugiyama RIKEN / The University of Tokyo, Japan sugi@k.u-tokyo.ac.jp Kee-Eung Kim KAIST, Rep. of Korea kekim@cs.kaist.ac.kr Frank C. Park Seoul National Univers... | 2017 | 652 |
7,177 | Overcoming Catastrophic Forgetting by Incremental Moment Matching Sang-Woo Lee1, Jin-Hwa Kim1, Jaehyun Jun1, Jung-Woo Ha2, and Byoung-Tak Zhang1,3 Seoul National University1 Clova AI Research, NAVER Corp2 Surromind Robotics3 {slee,jhkim,jhjun}@bi.snu.ac.kr jungwoo.ha@navercorp.com btzhang@bi.snu.ac.kr A... | 2017 | 653 |
7,178 | Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent Xiangru Lian†, Ce Zhang∗, Huan Zhang+, Cho-Jui Hsieh+, Wei Zhang#, and Ji Liu†♮ †University of Rochester, ∗ETH Zurich +University of California, Davis, #IBM T. J. Watson Research... | 2017 | 654 |
7,179 | Gradient Descent Can Take Exponential Time to Escape Saddle Points Simon S. Du Carnegie Mellon University ssdu@cs.cmu.edu Chi Jin University of California, Berkeley chijin@berkeley.edu Jason D. Lee University of Southern California jasonlee@marshall.usc.edu Michael I. Jordan University of Califo... | 2017 | 655 |
7,180 | Dual Path Networks Yunpeng Chen1, Jianan Li1,2, Huaxin Xiao1,3, Xiaojie Jin1, Shuicheng Yan4,1, Jiashi Feng1 1National University of Singapore 2Beijing Institute of Technology 3National University of Defense Technology 4Qihoo 360 AI Institute Abstract In this work, we present a simple, highly efficient and... | 2017 | 656 |
7,181 | Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit Laurence Aitchison University of Cambridge Cambridge, CB2 1PZ, UK laurence.aitchison@gmail.com Lloyd Russell University College London London, WC1E 6BT, UK llerussell@gmail.com Ada... | 2017 | 657 |
7,182 | Universal consistency and minimax rates for online Mondrian Forests Jaouad Mourtada Centre de Mathématiques Appliquées École Polytechnique, Palaiseau, France jaouad.mourtada@polytechnique.edu Stéphane Gaïffas Centre de Mathématiques Appliquées École Polytechnique,Palaiseau, France stéphane.gaiffas@pol... | 2017 | 658 |
7,183 | Gradient Episodic Memory for Continual Learning David Lopez-Paz and Marc’Aurelio Ranzato Facebook Artificial Intelligence Research {dlp,ranzato}@fb.com Abstract One major obstacle towards AI is the poor ability of models to solve new problems quicker, and without forgetting previously acquired knowledge. To be... | 2017 | 659 |
7,184 | Stein Variational Gradient Descent as Gradient Flow Qiang Liu Department of Computer Science Dartmouth College Hanover, NH 03755 qiang.liu@dartmouth.edu Abstract Stein variational gradient descent (SVGD) is a deterministic sampling algorithm that iteratively transports a set of particles to approximate ... | 2017 | 66 |
7,185 | Variational Inference for Gaussian Process Models with Linear Complexity Ching-An Cheng Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta, GA 30332 cacheng@gatech.edu Byron Boots Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atl... | 2017 | 660 |
7,186 | The Reversible Residual Network: Backpropagation Without Storing Activations Aidan N. Gomez∗1, Mengye Ren∗1,2,3, Raquel Urtasun1,2,3, Roger B. Grosse1,2 University of Toronto1 Vector Institute for Artificial Intelligence2 Uber Advanced Technologies Group3 {aidan, mren, urtasun, rgrosse}@cs.toronto.edu Abst... | 2017 | 661 |
7,187 | Character-Level Language Modeling with Recurrent Highway Hypernetworks Joseph Suarez Stanford University joseph15@stanford.edu Abstract We present extensive experimental and theoretical support for the efficacy of recurrent highway networks (RHNs) and recurrent hypernetworks complimentary to the original w... | 2017 | 662 |
7,188 | Parametric Simplex Method for Sparse Learning Haotian Pang‡ Robert Vanderbei‡ Han Liu?‡ Tuo Zhao⇧ ‡Princeton University ?Tencent AI Lab ‡Northwestern University ⇧Georgia Tech⇤ Abstract High dimensional sparse learning has imposed a great computational challenge to large scale data analysis. In this ... | 2017 | 663 |
7,189 | Filtering Variational Objectives Chris J. Maddison1,3,*, Dieterich Lawson,2,* George Tucker2,* Nicolas Heess1, Mohammad Norouzi2, Andriy Mnih1, Arnaud Doucet3, Yee Whye Teh1 1DeepMind, 2Google Brain, 3University of Oxford {cmaddis, dieterichl, gjt}@google.com Abstract When used as a surrogate objective for ... | 2017 | 664 |
7,190 | Cold-Start Reinforcement Learning with Softmax Policy Gradient Nan Ding Google Inc. Venice, CA 90291 dingnan@google.com Radu Soricut Google Inc. Venice, CA 90291 rsoricut@google.com Abstract Policy-gradient approaches to reinforcement learning have two common and undesirable overhead procedures, n... | 2017 | 665 |
7,191 | Bridging the Gap Between Value and Policy Based Reinforcement Learning Ofir Nachum1 Mohammad Norouzi Kelvin Xu1 Dale Schuurmans {ofirnachum,mnorouzi,kelvinxx}@google.com, daes@ualberta.ca Google Brain Abstract We establish a new connection between value and policy based reinforcement learning (RL) ba... | 2017 | 666 |
7,192 | Asynchronous Coordinate Descent under More Realistic Assumption∗ Tao Sun National University of Defense Technology Changsha, Hunan 410073, China nudtsuntao@163.com Robert Hannah University of California, Los Angeles Los Angeles, CA 90095, USA RobertHannah89@math.ucla.edu Wotao Yin University of Ca... | 2017 | 667 |
7,193 | EEG-GRAPH: A Factor-Graph-Based Model for Capturing Spatial, Temporal, and Observational Relationships in Electroencephalograms Yogatheesan Varatharajah ∗ Min Jin Chong∗ Krishnakant Saboo∗ Brent Berry† Benjamin Brinkmann† Gregory Worrell† Ravishankar Iyer∗ Abstract This paper presents a probabilis... | 2017 | 668 |
7,194 | Natural Value Approximators: Learning when to Trust Past Estimates Zhongwen Xu DeepMind zhongwen@google.com Joseph Modayil DeepMind modayil@google.com Hado van Hasselt DeepMind hado@google.com Andre Barreto DeepMind andrebarreto@google.com David Silver DeepMind davidsilver@google.com T... | 2017 | 669 |
7,195 | Expectation Propagation for t-Exponential Family Using q-Algebra Futoshi Futami The University of Tokyo, RIKEN futami@ms.k.u-tokyo.ac.jp Issei Sato The University of Tokyo, RIKEN sato@k.u-tokyo.ac.jp Masashi Sugiyama RIKEN, The University of Tokyo sugi@k.u-tokyo.ac.jp Abstract Exponential family... | 2017 | 67 |
7,196 | Active Exploration for Learning Symbolic Representations Garrett Andersen PROWLER.io Cambridge, United Kingdom garrett@prowler.io George Konidaris Department of Computer Science Brown University gdk@cs.brown.edu Abstract We introduce an online active exploration algorithm for data-efficiently learn... | 2017 | 670 |
7,197 | Balancing information exposure in social networks Kiran Garimella Aalto University & HIIT Helsinki, Finland kiran.garimella@aalto.fi Aristides Gionis Aalto University & HIIT Helsinki, Finland aristides.gionis@aalto.fi Nikos Parotsidis University of Rome Tor Vergata Rome, Italy nikos.parotsidis@u... | 2017 | 671 |
7,198 | Nonlinear Acceleration of Stochastic Algorithms Damien Scieur INRIA, ENS, PSL Research University, Paris France damien.scieur@inria.fr Francis Bach INRIA, ENS, PSL Research University, Paris France francis.bach@inria.fr Alexandre d’Aspremont CNRS, ENS, PSL Research University, Paris France ... | 2017 | 672 |
7,199 | Multi-way Interacting Regression via Factorization Machines Mikhail Yurochkin Department of Statistics University of Michigan moonfolk@umich.edu XuanLong Nguyen Department of Statistics University of Michigan xuanlong@umich.edu Nikolaos Vasiloglou LogicBlox nikolaos.vasiloglou@logicblox.com Ab... | 2017 | 673 |
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