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8,400 | Bayesian Optimization with Unknown Search Space Huong Ha, Santu Rana, Sunil Gupta, Thanh Nguyen, Hung Tran-The, Svetha Venkatesh Applied Artificial Intelligence Institute (A2I2) Deakin University, Geelong, Australia {huong.ha, santu.rana, sunil.gupta, thanhnt, hung.tranthe, svetha.venkatesh}@deakin.edu.au Abst... | 2019 | 1133 |
8,401 | Towards closing the gap between the theory and practice of SVRG Othmane Sebbouh LTCI, T´el´ecom Paris Institut Polytechnique de Paris othmane.sebbouh@gmail.com Nidham Gazagnadou LTCI, T´el´ecom Paris Institut Polytechnique de Paris nidham.gazagnadou@telecom-paris.fr Samy Jelassi ORFE Department ... | 2019 | 1134 |
8,402 | A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening Gecia Bravo-Hermsdorff* Princeton Neuroscience Institute Princeton University Princeton, NJ, 08544, USA geciah@princeton.edu Lee M. Gunderson* Department of Astrophysical Sciences Princeton University Princeton, NJ, 0854... | 2019 | 1135 |
8,403 | Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural Networks Gunjan Verma CCDC Army Research Laboratory Adelphi, MD 20783 gunjan.verma.civ@mail.mil Ananthram Swami CCDC Army Research Laboratory Adelphi, MD 20783 ananthram.swami.civ@mail.mil Abstra... | 2019 | 1136 |
8,404 | KerGM: Kernelized Graph Matching Zhen Zhang1, Yijian Xiang1, Lingfei Wu2, Bing Xue1, Arye Nehorai1 1Washington University in St. Louis 2IBM Research 1{zhen.zhang, yijian.xiang, xuebing, nehorai}@wustl.edu 2lwu@email.wm.edu Abstract Graph matching plays a central role in such fields as computer vision, patt... | 2019 | 1137 |
8,405 | On Human-Aligned Risk Minimization Liu Leqi Carnegie Mellon University Pittsburgh, PA 15213 leqil@cs.cmu.edu Adarsh Prasad Carnegie Mellon University Pittsburgh, PA 15213 adarshp@cs.cmu.edu Pradeep Ravikumar Carnegie Mellon Universit Pittsburgh, PA 15213 pradeepr@cs.cmu.edu Abstract The stat... | 2019 | 1138 |
8,406 | Robustness Verification of Tree-based Models Hongge Chen*,1 Huan Zhang*,2 Si Si3 Yang Li3 Duane Boning1 Cho-Jui Hsieh2,3 1Department of EECS, MIT 2Department of Computer Science, UCLA 3Google Research chenhg@mit.edu, huan@huan-zhang.com, sisidaisy@google.com liyang@google.com, boning@mtl.mit.edu, c... | 2019 | 1139 |
8,407 | Statistical-Computational Tradeoffs in High-Dimensional Single Index Models Lingxiao Wang∗ Zhuoran Yang† Zhaoran Wang‡ Abstract We study the statistical-computational tradeoffs in a high dimensional single index model Y = f(X⊤β∗) + ϵ, where f is unknown, X is a Gaussian vector and β∗is s-sparse with uni... | 2019 | 114 |
8,408 | Provable Non-linear Inductive Matrix Completion Kai Zhong Amazon kaizhong@amazon.com Zhao Song University of Washington magic.linuxkde@gmail.com Prateek Jain Microsoft prajain@microsoft.com Inderjit S. Dhillon Amazon & University of Texas at Austin isd@amazon.com Abstract Consider a standard... | 2019 | 1140 |
8,409 | STAR-CAPS: Capsule Networks with Straight-Through Attentive Routing Karim Ahmed Department of Computer Science Dartmouth College karim@cs.dartmouth.edu Lorenzo Torresani Department of Computer Science Dartmouth College LT@dartmouth.edu Abstract Capsule networks have been shown to be powerful model... | 2019 | 1141 |
8,410 | Self-attention with Functional Time Representation Learning Da Xu⇤, Chuanwei Ruan⇤, Sushant Kumar , Evren Korpeoglu , Kannan Achan Walmart Labs California, CA 94086 {Da.Xu,Chuanwei.Ruan,EKorpeoglu,SKumar4,KAchan}@walmartlabs.com Abstract Sequential modelling with self-attention has achieved cutting edge p... | 2019 | 1142 |
8,411 | Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition Xuesong Niu1,3, Hu Han1,2, Shiguang Shan1,2,3,4, Xilin Chen1,3 1 Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China 2 Peng Chen... | 2019 | 1143 |
8,412 | A Primal-Dual Formulation for Deep Learning with Constraints Yatin Nandwani, Abhishek Pathak, Mausam and Parag Singla Department of Computer Science and Engineering Indian Institute of Technology Delhi {yatin.nandwani,abhishek.pathak.cs115,mausam,parags}@cse.iitd.ac.in Abstract For several problems of int... | 2019 | 1144 |
8,413 | DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections Ofir Nachum∗ Yinlam Chow∗ Bo Dai Lihong Li Google Research {ofirnachum,yinlamchow,bodai,lihong}@google.com Abstract In many real-world reinforcement learning applications, access to the environment is limited to a ... | 2019 | 1145 |
8,414 | Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks Yuan Cao Department of Computer Science University of California, Los Angeles CA 90095, USA yuancao@cs.ucla.edu Quanquan Gu Department of Computer Science University of California, Los Angeles CA 90095, USA qgu@... | 2019 | 1146 |
8,415 | Intrinsic dimension of data representations in deep neural networks Alessio Ansuini International School for Advanced Studies alessioansuini@gmail.com Alessandro Laio International School for Advanced Studies laio@sissa.it Jakob H. Macke Technical University of Munich macke@tum.de Davide Zoccolan ... | 2019 | 1147 |
8,416 | Program Synthesis and Semantic Parsing with Learned Code Idioms Richard Shin∗ UC Berkeley ricshin@berkeley.edu Miltiadis Allamanis, Marc Brockschmidt & Oleksandr Polozov Microsoft Research {miallama,mabrocks,polozov}@microsoft.com Abstract Program synthesis of general-purpose source code from natural ... | 2019 | 1148 |
8,417 | Data-driven Estimation of Sinusoid Frequencies Gautier Izacard Ecole Polytechnique gautier.izacard@polytechnique.edu Sreyas Mohan Center for Data Science New York University sm7582@nyu.edu Carlos Fernandez-Granda Courant Institute of Mathematical Sciences, and Center for Data Science New York Univ... | 2019 | 1149 |
8,418 | Probabilistic Logic Neural Networks for Reasoning Meng Qu1,2, Jian Tang1,3,4 1Mila - Quebec AI Institute 2University of Montréal 3HEC Montréal 4CIFAR AI Research Chair Abstract Knowledge graph reasoning, which aims at predicting the missing facts through reasoning with the observed facts, is critical to man... | 2019 | 115 |
8,419 | Discovering Neural Wirings Mitchell Wortsman1,2, Ali Farhadi1,2,3, Mohammad Rastegari1,3 1PRIOR @ Allen Institute for AI, 2University of Washington, 3XNOR.AI mitchnw@cs.washington.edu, {ali, mohammad}@xnor.ai Abstract The success of neural networks has driven a shift in focus from feature engineering to arc... | 2019 | 1150 |
8,420 | Locally Private Learning without Interaction Requires Separation Amit Daniely Hebrew University and Google Research Vitaly Feldman∗ Google Research Abstract We consider learning under the constraint of local differential privacy (LDP). For many learning problems known efficient algorithms in this model r... | 2019 | 1151 |
8,421 | Fixing the train-test resolution discrepancy Hugo Touvron, Andrea Vedaldi, Matthijs Douze, Herv´e J´egou Facebook AI Research Abstract Data-augmentation is key to the training of neural networks for image classification. This paper first shows that existing augmentations induce a significant discrepancy between ... | 2019 | 1152 |
8,422 | Quadratic Video Interpolation Xiangyu Xu∗† Carnegie Mellon University xuxiangyu2014@gmail.com Li Siyao∗ SenseTime Research lisiyao1@sensetime.com Wenxiu Sun SenseTime Research sunwenxiu@sensetime.com Qian Yin Beijing Normal University yinqian@bnu.edu.cn Ming-Hsuan Yang University of Californ... | 2019 | 1153 |
8,423 | Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen, Linxiao Yang, Ngai-Man Cheung Singapore University of Technology and Design (SUTD) Corresponding author: Ngai-Man Cheung <ngaiman_cheung@sutd.edu.sg> Abstract Self-supervised (SS) le... | 2019 | 1154 |
8,424 | Learning step sizes for unfolded sparse coding Pierre Ablin∗, Thomas Moreau∗, Mathurin Massias, Alexandre Gramfort Inria - CEA Université Paris-Saclay {pierre.ablin,thomas.moreau,mathurin.massias,alexandre.gramfort}@inria.fr Abstract Sparse coding is typically solved by iterative optimization techniques, su... | 2019 | 1155 |
8,425 | Efficient Graph Generation with Graph Recurrent Attention Networks Renjie Liao1,2,3, Yujia Li4, Yang Song5, Shenlong Wang1,2,3, William L. Hamilton6,7, David Duvenaud1,3, Raquel Urtasun1,2,3, Richard Zemel1,3,8 University of Toronto1, Uber ATG Toronto2, Vector Institute3, DeepMind4, Stanford University5, McGil... | 2019 | 1156 |
8,426 | Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks Vineet Kosaraju1∗ Amir Sadeghian1,2∗Roberto Martín-Martín1 Ian Reid3 S. Hamid Rezatofighi1,3 Silvio Savarese1 1Stanford University 2 Aibee Inc 3 University of Adelaide vineetk@stanford.edu Abstract Pred... | 2019 | 1157 |
8,427 | Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds Bo Yang 1 Jianan Wang 2 Ronald Clark 3 Qingyong Hu 1 Sen Wang 4 Andrew Markham 1 Niki Trigoni 1 1University of Oxford 2DeepMind 3Imperial College London 4Heriot-Watt University firstname.lastname@cs.ox.ac.uk Abstract... | 2019 | 1158 |
8,428 | Re-examination of the Role of Latent Variables in Sequence Modeling Guokun Lai⇤1, Zihang Dai⇤1, Yiming Yang1, Shinjae Yoo2 1Carnegie Mellon University, 2Brookhaven National Laboratory 1{guokun,dzihang,yiming}@cs.cmu.edu, 2sjyoo@bnl.gov Abstract With latent variables, stochastic recurrent models have achieve... | 2019 | 1159 |
8,429 | Joint-task Self-supervised Learning for Temporal Correspondence Xueting Li1⇤, Sifei Liu2⇤, Shalini De Mello2, Xiaolong Wang3, Jan Kautz2, Ming-Hsuan Yang1 1University of California, Merced, 2NVIDIA, 3 Carnegie Mellon University Abstract This paper proposes to learn reliable dense correspondence from videos in... | 2019 | 116 |
8,430 | Consistency-based Semi-supervised Learning for Object Detection Jisoo Jeong∗, Seungeui Lee∗, Jeesoo Kim & Nojun Kwak Department of Transdisciplinary Studies Graduate School of Convergence Science and Technology Seoul National University Seoul, Korea {soo3553, seungeui.lee, kimjiss0305, nojunk}@snu.ac.kr ... | 2019 | 1160 |
8,431 | Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration Kwang-Sung Jun The University of Arizona˚ kjun@cs.arizona.edu Ashok Cutkosky Google Research ashok@cutkosky.com Francesco Orabona Boston University francesco@orabona.com Abstract In this paper, we consider the... | 2019 | 1161 |
8,432 | Bandits with Feedback Graphs and Switching Costs 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 Mehryar Mohri Google Research & Courant... | 2019 | 1162 |
8,433 | Exact Combinatorial Optimization with Graph Convolutional Neural Networks Maxime Gasse Mila, Polytechnique Montréal maxime.gasse@polymtl.ca Didier Chételat Polytechnique Montréal didier.chetelat@polymtl.ca Nicola Ferroni University of Bologna n.ferroni@specialvideo.it Laurent Charlin Mila, HEC M... | 2019 | 1163 |
8,434 | Comparing Unsupervised Word Translation Methods Step by Step Mareike Hartmann Department of Computer Science University of Copenhagen Copenhagen, Denmark hartmann@di.ku.dk Yova Kementchedjhieva Department of Computer Science University of Copenhagen Copenhagen, Denmark yova@di.ku.dk Anders Søgaa... | 2019 | 1164 |
8,435 | Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge Tingting Qiao1,2∗ Jing Zhang2∗ Duanqing Xu1† Dacheng Tao2 1College of Computer Science and Technology, Zhejiang University, China 2UBTECH Sydney AI Centre, School of Computer Science, Faculty of Engineering The University of Sydney... | 2019 | 1165 |
8,436 | Compiler Auto-Vectorization with Imitation Learning Charith Mendis MIT CSAIL charithm@mit.edu Cambridge Yang MIT CSAIL camyang@csail.mit.edu Yewen Pu MIT CSAIL yewenpu@mit.edu Saman Amarasinghe MIT CSAIL saman@csail.mit.edu Michael Carbin MIT CSAIL mcarbin@csail.mit.edu Abstract Modern... | 2019 | 1166 |
8,437 | Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations Debraj Basu ⇤ Adobe Inc. dbasu@adobe.com Deepesh Data UCLA deepeshdata@ucla.edu Can Karakus ⇤ Amazon Inc. cakarak@amazon.com Suhas Diggavi UCLA suhasdiggavi@ucla.edu Abstract Communication bottlenec... | 2019 | 1167 |
8,438 | Fast Sparse Group Lasso Yasutoshi Ida1,3 Yasuhiro Fujiwara2 Hisashi Kashima3,4 1NTT Software Innovation Center 2NTT Communication Science Laboratories 3Kyoto University 4RIKEN AIP yasutoshi.ida@ieee.org yasuhiro.fujiwara.kh@hco.ntt.co.jp kashima@i.kyoto-u.ac.jp Abstract Sparse Group Lasso is a m... | 2019 | 1168 |
8,439 | Deep Random Splines for Point Process Intensity Estimation of Neural Population Data Gabriel Loaiza-Ganem Department of Statistics Columbia University gl2480@columbia.edu Sean M. Perkins Department of Biomedical Engineering Columbia University sp3222@columbia.edu Karen E. Schroeder Department of N... | 2019 | 1169 |
8,440 | Learning Sparse Distributions using Iterative Hard Thresholding Jacky Y. Zhang Department of Computer Science University of Illinois at Urbana-Champaign yiboz@illinois.edu Rajiv Khanna Department of Statistics University of California at Berkeley rajivak@berkeley.edu Anastasios Kyrillidis Departme... | 2019 | 117 |
8,441 | Fast Decomposable Submodular Function Minimization using Constrained Total Variation K S Sesh Kumar Data Science Institute Imperial College London, UK s.karri@imperial.ac.uk Francis Bach INRIA and Ecole normale superieure PSL Research University, Paris France. francis.bach@inria.fr Thomas Pock Ins... | 2019 | 1170 |
8,442 | Deep Signature Transforms Patric Bonnier1,∗ Patrick Kidger1,2,∗ Imanol Perez Arribas1,2,∗ Cristopher Salvi1,2,∗ Terry Lyons1,2 1 Mathematical Institute, University of Oxford 2 The Alan Turing Institute, British Library {bonnier, kidger, perez, salvi, tlyons}@maths.ox.ac.uk Abstract The signature is ... | 2019 | 1171 |
8,443 | ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies Bao Wang Department of Mathematics University of California, Los Angeles wangbaonj@gmail.com Binjie Yuan School of Aerospace Tsinghua University ybj14@mail.tsinghua.edu.cn Zuoqiang Shi Department of Mathematics... | 2019 | 1172 |
8,444 | Guided Meta-Policy Search Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, Chelsea Finn Department of Electrical Engineering and Computer Science University of California, Berkeley {russellm, cbfinn}@berkeley.edu {abhigupta, pabbeel, svlevine}@eecs.berkeley.edu rdkralev@gmail.co... | 2019 | 1173 |
8,445 | Learning elementary structures for 3D shape generation and matching Theo Deprelle1∗, Thibault Groueix1, Matthew Fisher2, Vladimir G. Kim2, Bryan C. Russell2, Mathieu Aubry1 1LIGM (UMR 8049), École des Ponts, UPE, 2Adobe Research Abstract We propose to represent shapes as the deformation and combination of l... | 2019 | 1174 |
8,446 | Cross-Modal Learning with Adversarial Samples Chao Li1,2 Cheng Deng1,∗ Shangqian Gao2 De Xie1 Wei Liu3,∗ 1School of Electronic Engineering, Xidian University, Xi’an, Shaanxi, China 2Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA 3Tencent AI Lab, China {chaolee.xd, c... | 2019 | 1175 |
8,447 | Learning Disentangled Representation for Robust Person Re-identification Chanho Eom Bumsub Ham∗ School of Electrical and Electronic Engineering, Yonsei University cheom@yonsei.ac.kr bumsub.ham@yonsei.ac.kr ∗Corresponding author Abstract We address the problem of person re-identification (reID), that is,... | 2019 | 1176 |
8,448 | On Testing for Biases in Peer Review Ivan Stelmakh, Nihar B. Shah and Aarti Singh School of Computer Science Carnegie Mellon University {stiv,nihars,aarti}@cs.cmu.edu Abstract We consider the issue of biases in scholarly research, specifically, in peer review. There is a long standing debate on whether exp... | 2019 | 1177 |
8,449 | Learning Deterministic Weighted Automata with Queries and Counterexamples Gail Weiss Technion sgailw@cs.technion.ac.il Yoav Goldberg Bar Ilan University Allen Institute for AI yogo@cs.biu.ac.il Eran Yahav Technion yahave@cs.technion.ac.il Abstract We present an algorithm for extraction of a pr... | 2019 | 1178 |
8,450 | Making the Cut: A Bandit-based Approach to Tiered Interviewing Candice Schumann? Zhi Lang? Jeffrey S. Foster† John P. Dickerson? ?University of Maryland †Tufts University {schumann,zlang}@cs.umd.edu, jfoster@cs.tufts.edu, john@cs.umd.edu Abstract Given a huge set of applicants, how should a firm allo... | 2019 | 1179 |
8,451 | On Distributed Averaging for Stochastic k-PCA Aditya Bhaskara School of Computing University of Utah bhaskara@cs.utah.edu Maheshakya Wijewardena School of Computing University of Utah pmaheshakya4@gmail.com Abstract In the stochastic k-PCA problem, we are given i.i.d. samples from an unknown distr... | 2019 | 118 |
8,452 | Manifold-regression to predict from MEG/EEG brain signals without source modeling David Sabbagh ∗†‡, Pierre Ablin, Gaël Varoquaux, Alexandre Gramfort, Denis A. Engemann § Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France Abstract Magnetoencephalography and electroencephalography (M/EEG) can reveal... | 2019 | 1180 |
8,453 | Reflection Separation using a Pair of Unpolarized and Polarized Images Youwei Lyu1♯† Zhaopeng Cui2♯ Si Li1∗ Marc Pollefeys2 Boxin Shi3,4∗ 1Beijing University of Posts and Telecommunications 2Department of Computer Science, ETH Zürich 3National Engineering Laboratory for Video Technology, Peking Univers... | 2019 | 1181 |
8,454 | Co-Generation with GANs using AIS based HMC Tiantian Fang University of Illinois at Urbana-Champaign tf6@illinois.edu Alexander G. Schwing University of Illinois at Urbana-Champaign aschwing@illinois.edu Abstract Inferring the most likely configuration for a subset of variables of a joint distribution ... | 2019 | 1182 |
8,455 | Sim2real transfer learning for 3D human pose estimation: motion to the rescue Carl Doersch∗ Andrew Zisserman∗† ∗Deepmind, London † VGG, Department of Engineering Science, University of Oxford Abstract Synthetic visual data can provide practically infinite diversity and rich labels, while avoiding ethical... | 2019 | 1183 |
8,456 | Dimension-Free Bounds for Low-Precision Training Zheng Li IIIS, Tsinghua University lzlz19971997@gmail.com Christopher De Sa Cornell University cdesa@cs.cornell.edu Abstract Low-precision training is a promising way of decreasing the time and energy cost of training machine learning models. Previous w... | 2019 | 1184 |
8,457 | Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds Nathan Kallus Cornell University New York, NY kallus@cornell.edu Angela Zhou Cornell University New York, NY az434@cornell.edu Abstract Personalized interventions in social services, education, and healthcare lever... | 2019 | 1185 |
8,458 | Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution Thang Vu, Hyunjun Jang, Trung X. Pham, Chang D. Yoo Department of Electrical Engineering Korea Advanced Institute of Science and Technology {thangvubk,wiseholi,trungpx,cd_yoo}@kaist.ac.kr Abstract This paper con... | 2019 | 1186 |
8,459 | Variational Bayesian Optimal Experimental Design Adam Foster†∗Martin Jankowiak‡ Eli Bingham‡ Paul Horsfall‡ Yee Whye Teh† Tom Rainforth† Noah Goodman‡§ †Department of Statistics, University of Oxford, Oxford, UK ‡Uber AI Labs, Uber Technologies Inc., San Francisco, CA, USA §Stanford University, Stanfo... | 2019 | 1187 |
8,460 | Flexible Modeling of Diversity with Strongly Log-Concave Distributions Joshua Robinson Massachusetts Institute of Technology joshrob@mit.edu Suvrit Sra Massachusetts Institute of Technology suvrit@mit.edu Stefanie Jegelka Massachusetts Institute of Technology stefje@csail.mit.edu Abstract Strong... | 2019 | 1188 |
8,461 | Neural Machine Translation with Soft Prototype Yiren Wang1,∗, Yingce Xia2,†, Fei Tian3, Fei Gao4, Tao Qin2, ChengXiang Zhai1, Tie-Yan Liu2 1University of Illinois at Urbana-Champaign, 2Microsoft Research, 3Facebook, 4Institute of Computing Technology, Chinese Academy of Sciences 1{yiren, czhai}@illinois.edu 2... | 2019 | 1189 |
8,462 | Learning dynamic polynomial proofs Alhussein Fawzi DeepMind afawzi@google.com Mateusz Malinowski DeepMind mateuszm@google.com Hamza Fawzi University of Cambridge hf323@cam.ac.uk Omar Fawzi ENS Lyon omar.fawzi@ens-lyon.fr Abstract Polynomial inequalities lie at the heart of many mathematical ... | 2019 | 119 |
8,463 | Unsupervised Curricula for Visual Meta-Reinforcement Learning Allan Jabriα Kyle Hsuβ,† Benjamin Eysenbachγ Abhishek Guptaα Sergey Levineα Chelsea Finnδ Abstract In principle, meta-reinforcement learning algorithms leverage experience across many tasks to learn fast reinforcement learning (RL) strategies tha... | 2019 | 1190 |
8,464 | Improved Regret Bounds for Bandit Combinatorial Optimization∗ Shinji Ito† NEC Corporation, The University of Tokyo i-shinji@nec.com Daisuke Hatano RIKEN AIP daisuke.hatano@riken.jp Hanna Sumita Tokyo Metropolitan University sumita@tmu.ac.jp Kei Takemura NEC Corporation kei_takemura@nec.com T... | 2019 | 1191 |
8,465 | Doubly-Robust Lasso Bandit Gi-Soo Kim Department of Statistics Seoul National University gisoo1989@snu.ac.kr Myunghee Cho Paik Department of Statistics Seoul National University myungheechopaik@snu.ac.kr Abstract Contextual multi-armed bandit algorithms are widely used in sequential decision tasks... | 2019 | 1192 |
8,466 | Recurrent Kernel Networks Dexiong Chen Inria∗ dexiong.chen@inria.fr Laurent Jacob CNRS† laurent.jacob@univ-lyon1.fr Julien Mairal Inria∗ julien.mairal@inria.fr Abstract Substring kernels are classical tools for representing biological sequences or text. However, when large amounts of annotated d... | 2019 | 1193 |
8,467 | Thinning for Accelerating the Learning of Point Processes Tianbo Li, Yiping Ke School of Computer Science and Engineering Nanyang Technological University, Singapore tianbo001@e.ntu.edu.sg, ypke@ntu.edu.sg Abstract This paper discusses one of the most fundamental issues about point processes that wh... | 2019 | 1194 |
8,468 | A Universally Optimal Multistage Accelerated Stochastic Gradient Method Necdet Serhat Aybat∗ Pennsylvania State University University Park, PA, USA nsa10@psu.edu Alireza Fallah∗ Massachusetts Institute of Technology Cambridge, MA, USA afallah@mit.edu Mert Gürbüzbalaban∗ Rutgers University Piscat... | 2019 | 1195 |
8,469 | Ask not what AI can do, but what AI should do: Towards a framework of task delegability Brian Lubars University of Colorado Boulder brian.lubars@colorado.edu Chenhao Tan University of Colorado Boulder chenhao.tan@colorado.edu Abstract While artificial intelligence (AI) holds promise for addressing soci... | 2019 | 1196 |
8,470 | Offline Contextual Bandits with High Probability Fairness Guarantees Blossom Metevier1 Stephen Giguere1 Sarah Brockman1 Ari Kobren1 Yuriy Brun1 Emma Brunskill2 Philip S. Thomas1 1College of Information and Computer Sciences 2Computer Science Department University of Massachusetts Amherst Stanford... | 2019 | 1197 |
8,471 | Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting Aditya Grover1, Jiaming Song1, Alekh Agarwal2, Kenneth Tran2, Ashish Kapoor2, Eric Horvitz2, Stefano Ermon1 1Stanford University, 2Microsoft Research, Redmond Abstract A learned generative model often produces biased sta... | 2019 | 1198 |
8,472 | LCA: Loss Change Allocation for Neural Network Training Janice Lan Uber AI janlan@uber.com Rosanne Liu Uber AI rosanne@uber.com Hattie Zhou Uber hattie@uber.com Jason Yosinski Uber AI yosinski@uber.com Abstract Neural networks enjoy widespread use, but many aspects of their training, repre... | 2019 | 1199 |
8,473 | Improved Precision and Recall Metric for Assessing Generative Models Tuomas Kynkäänniemi∗ Aalto University NVIDIA tuomas.kynkaanniemi@aalto.fi Tero Karras NVIDIA tkarras@nvidia.com Samuli Laine NVIDIA slaine@nvidia.com Jaakko Lehtinen Aalto University NVIDIA jlehtinen@nvidia.com Timo Ail... | 2019 | 12 |
8,474 | Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control Sai Qian Zhang Harvard University Qi Zhang Amazon Inc. Jieyu Lin University of Toronto Abstract Multi-agent reinforcement learning (MARL) has recently received considerable attention due to its applicability to a w... | 2019 | 120 |
8,475 | Adaptive Cross-Modal Few-shot Learning Chen Xing∗ College of Computer Science, Nankai University, Tianjin, China Element AI, Montreal, Canada Negar Rostamzadeh Element AI, Montreal, Canada Boris N. Oreshkin Element AI, Montreal, Canada Pedro O. Pinheiro Element AI, Montreal, Canada Abstract Metr... | 2019 | 1200 |
8,476 | Polynomial Cost of Adaptation for X-Armed Bandits Hédi Hadiji Laboratoire de Mathématiques d’Orsay Université Paris-Sud, Orsay, France hedi.hadiji@math.u-psud.fr Abstract In the context of stochastic continuum-armed bandits, we present an algorithm that adapts to the unknown smoothness of the objective fu... | 2019 | 1201 |
8,477 | Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians Axel Brando ∗ BBVA Data & Analytics Universitat de Barcelona Jose A. Rodríguez-Serrano† BBVA Data & Analytics Jordi Vitrià‡ Universitat de Barcelona Alberto Rubio BBVA Data & Analytics Abstract In regressi... | 2019 | 1202 |
8,478 | GNNExplainer: Generating Explanations for Graph Neural Networks Rex Ying† Dylan Bourgeois†,‡ Jiaxuan You† Marinka Zitnik† Jure Leskovec† †Department of Computer Science, Stanford University ‡Robust.AI {rexying, dtsbourg, jiaxuan, marinka, jure}@cs.stanford.edu Abstract Graph Neural Networks (GNNs)... | 2019 | 1203 |
8,479 | Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm Assumption Wei Ma∗ George H. Chen∗ Carnegie Mellon University Pittsburgh, PA 15213 {weima,georgechen}@cmu.edu Abstract Matrix completion is often applied to data with entries ... | 2019 | 1204 |
8,480 | Unsupervised Learning of Object Structure and Dynamics from Videos Matthias Minderer∗ Chen Sun Ruben Villegas Forrester Cole Kevin Murphy Honglak Lee Google Research {mjlm, chensun, rubville, fcole, kpmurphy, honglak}@google.com Abstract Extracting and predicting object structure and dynamics from... | 2019 | 1205 |
8,481 | Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data Dominik Linzner1 Michael Schmidt1 Heinz Koeppl1,2 1Department of Electrical Engineering and Information Technology 2Department of Biology Technische Universität Darmstadt {dominik.linzner, michael.schmidt, heinz.koeppl... | 2019 | 1206 |
8,482 | Cross-channel Communication Networks Jianwei Yang1 Zhile Ren1 Chuang Gan3 Hongyuan Zhu4 Devi Parikh1,2 1Georgia Institute of Technology, 2Facebook AI Research, 3 MIT-IBM Watson AI Lab, 4Institute for Infocomm Research, A*Star, Singapore Abstract Convolutional neural networks (CNNs) process input data by... | 2019 | 1207 |
8,483 | Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training Haichao Zhang∗ Jianyu Wang Horizon Robotics Baidu Research hczhang1@gmail.com wjyouch@gmail.com Abstract We introduce a feature scattering-based adversarial training approach for improving model robustness agains... | 2019 | 1208 |
8,484 | Identifying Causal Effects via Context-specific Independence Relations Santtu Tikka Department of Mathematics and Statistics University of Jyvaskyla, Finland santtu.tikka@jyu.fi Antti Hyttinen HIIT, Department of Computer Science University of Helsinki, Finland antti.hyttinen@helsinki.fi Juha Karvane... | 2019 | 1209 |
8,485 | Global Convergence of Gradient Descent for Deep Linear Residual Networks Lei Wu∗ Qingcan Wang∗ Chao Ma Program in Applied and Computational Mathematics Princeton University Princeton, NJ 08544, USA {leiwu,qingcanw,chaom}@princeton.edu Abstract We analyze the global convergence of gradient descent fo... | 2019 | 121 |
8,486 | Differentiable Ranks and Sorting using Optimal Transport Marco Cuturi Olivier Teboul Jean-Philippe Vert Google Research, Brain Team {cuturi,oliviert,jpvert}@google.com Abstract Sorting is used pervasively in machine learning, either to define elementary algorithms, such as k-nearest neighbors (k-NN) rule... | 2019 | 1210 |
8,487 | Ordered Memory Yikang Shen∗ Mila/Universit´e de Montr´eal and Microsoft Research Montr´eal, Canada Shawn Tan∗ Mila/Universit´e de Montr´eal Montr´eal, Canada Arian Hosseini∗ Mila/Universit´e de Montr´eal and Microsoft Research Montr´eal, Canada Zhouhan Lin Mila/Universit´e de Montr´eal Montr... | 2019 | 1211 |
8,488 | Approximating the Permanent by Sampling from Adaptive Partitions Jonathan Kuck1, Tri Dao1, Hamid Rezatofighi1, Ashish Sabharwal2, and Stefano Ermon1 1Stanford University 2Allen Institute for Artificial Intelligence {kuck,trid,hamidrt,ermon}@stanford.edu, ashishs@allenai.org Abstract Computing the permanent ... | 2019 | 1212 |
8,489 | Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics Niru Maheswaranathan∗ Google Brain, Google Inc. Mountain View, CA nirum@google.com Alex H. Williams∗ Stanford University Stanford, CA ahwillia@stanford.edu Matthew D. Golub Stanford University Stan... | 2019 | 1213 |
8,490 | Quaternion Knowledge Graph Embeddings Shuai Zhang†∗, Yi Tayψ∗, Lina Yao†, Qi Liuφ † University of New South Wales ψNanyang Technological University, φUniversity of Oxford Abstract In this work, we move beyond the traditional complex-valued representations, introducing more expressive hypercomplex representa... | 2019 | 1214 |
8,491 | Initialization of ReLUs for Dynamical Isometry Rebekka Burkholz Department of Biostatistics Harvard T.H. Chan School of Public Health 655 Huntington Avenue, Boston, MA 02115 rburkholz@hsph.harvard.edu Alina Dubatovka Department of Computer Science ETH Zurich Universitätstrasse 6, 8092 Zurich alina.d... | 2019 | 1215 |
8,492 | On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset Muhammad Waleed Gondal1⇤† Manuel Wüthrich1⇤ Ðor ¯de Miladinovi´c2 Francesco Locatello12 Martin Breidt3 Valentin Volchkov1 Joel Akpo1 Olivier Bachem4 Bernhard Schölkopf1 Stefan Bauer1† 1Max Planck... | 2019 | 1216 |
8,493 | Subquadratic High-Dimensional Hierarchical Clustering Amir Abboud IBM Research amir.abboud@gmail.com Vincent Cohen-Addad CNRS & Sorbonne Universit´e vcohenad@gmail.com Hussein Houdrouge ´Ecole Polytechnique hussein.houdrouge@polytechnique.edu Abstract We consider the widely-used average-linkage,... | 2019 | 1217 |
8,494 | PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization Thijs Vogels EPFL Lausanne, Switzerland thijs.vogels@epfl.ch Sai Praneeth Karimireddy EPFL Lausanne, Switzerland sai.karimrieddy@epfl.ch Martin Jaggi EPFL Lausanne, Switzerland martin.jaggi@epfl.ch Abstract We s... | 2019 | 1218 |
8,495 | 2019 | 1219 | |
8,496 | Dying Experts: Efficient Algorithms with Optimal Regret Bounds Hamid Shayestehmanesh∗ Department of Computer Science University of Victoria Sajjad Azami∗ Department of Computer Science University of Victoria Nishant A. Mehta Department of Computer Science University of Victoria {hamidshayestehmanes... | 2019 | 122 |
8,497 | Multilabel reductions: what is my loss optimising? Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, and Sanjiv Kumar Google Research New York, NY 10011 {adityakmenon, sashank, ankitsrawat, sanjivk}@google.com Abstract Multilabel classification is a challenging problem arising in applications rangin... | 2019 | 1220 |
8,498 | A Similarity-preserving Neural Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit Yanis Bahroun † Anirvan M. Sengupta †‡ Dmitri B. Chklovskii†∗ †Flatiron Institute ‡Rutgers University ∗NYU Langone Medical Center {ybahroun,dchklovskii}@flatironinstit... | 2019 | 1221 |
8,499 | CNN2: Viewpoint Generalization via a Binocular Vision Wei-Da Chen Department of Computer Science National Tsing-Hua University Taiwan, R.O.C. wdchen@datalab.cs.nthu.edu.tw Shan-Hung Wu Department of Computer Science National Tsing-Hua University Taiwan, R.O.C. shwu@cs.nthu.edu.tw Abstract The ... | 2019 | 1222 |
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