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8,600 | A Primal-Dual link between GANs and Autoencoders Hisham Husain‡,† Richard Nock†,‡,♣ Robert C. Williamson‡,† ‡The Australian National University, †Data61, ♣The University of Sydney firstname.lastname@{data61.csiro.au,anu.edu.au} Abstract Since the introduction of Generative Adversarial Networks (GANs) and ... | 2019 | 1313 |
8,601 | Transfusion: Understanding Transfer Learning for Medical Imaging Maithra Raghu∗ Cornell University and Google Brain maithrar@gmail.com Chiyuan Zhang∗ Google Brain chiyuan@google.com Jon Kleinberg† Cornell University kleinber@cs.cornell.edu Samy Bengio† Google Brain bengio@google.com Abstract... | 2019 | 1314 |
8,602 | PIDForest: Anomaly Detection via Partial Identification Parikshit Gopalan VMware Research pgopalan@vmware.com Vatsal Sharan Stanford University vsharan@stanford.edu Udi Wieder VMware Research uwieder@vmware.com Abstract We consider the problem of detecting anomalies in a large dataset. We propose... | 2019 | 1315 |
8,603 | The Randomized Midpoint Method for Log-Concave Sampling Ruoqi Shen University of Washington shenr3@cs.washington.edu Yin Tat Lee University of Washington and Microsoft Research yintat@uw.edu Abstract Sampling from log-concave distributions is a well researched problem that has many applications in s... | 2019 | 1316 |
8,604 | Face Reconstruction from Voice using Generative Adversarial Networks Yandong Wen Carnegie Mellon University Pittsburgh, PA 15213 yandongw@andrew.cmu.edu Rita Singh Carnegie Mellon University Pittsburgh, PA 15213 rsingh@cs.cmu.edu Bhiksha Raj Carnegie Mellon University Pittsburgh, PA 15213 bhik... | 2019 | 1317 |
8,605 | Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning Harm van Seijen Microsoft Research Montréal harm.vanseijen@microsoft.com Mehdi Fatemi Microsoft Research Montréal mehdi.fatemi@microsoft.com Arash Tavakoli Imperial College London a.tavakoli@imperial.ac.uk Abstr... | 2019 | 1318 |
8,606 | PRNet: Self-Supervised Learning for Partial-to-Partial Registration Yue Wang Massachusetts Institute of Technology yuewangx@mit.edu Justin Solomon Massachusetts Institute of Technology jsolomon@mit.edu Abstract We present a simple, flexible, and general framework titled Partial Registration Network (PR... | 2019 | 1319 |
8,607 | Implicit Semantic Data Augmentation for Deep Networks Yulin Wang1∗Xuran Pan1∗ Shiji Song1 Hong Zhang2 Cheng Wu1 Gao Huang1† 1Department of Automation, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology (BNRist), 2Baidu Inc., China {yulin.bh, f... | 2019 | 132 |
8,608 | Adversarial Music: Real World Audio Adversary Against Wake-word Detection System Juncheng B. Li1 junchenl@cs.cmu.edu Shuhui Qu3 shuhuiq@stanford.edu Xinjian Li1 xinjianl@cs.cmu.edu Joseph Szurley2 jszurley@bosch.com J. Zico Kolter1,2 zkolter@cs.cmu.edu Florian Metze1 fmetze@cs.cmu.edu 1Carne... | 2019 | 1320 |
8,609 | Learning to Optimize in Swarms Yue Cao, Tianlong Chen, Zhangyang Wang, Yang Shen Departments of Electrical and Computer Engineering & Computer Science and Engineering Texas A&M University, College Station, TX 77840 {cyppsp,wiwjp619,atlaswang,yshen}@tamu.edu Abstract Learning to optimize has emerged as a pow... | 2019 | 1321 |
8,610 | A Little Is Enough: Circumventing Defenses For Distributed Learning Moran Baruch 1 moran.baruch@biu.ac.il Gilad Baruch 1 gilad.baruch@biu.ac.il Yoav Goldberg 1 2 yogo@cs.biu.ac.il 1 Dept. of Computer Science, Bar Ilan University, Israel 2 The Allen Institute for Artificial Intelligence Abstract Dis... | 2019 | 1322 |
8,611 | Statistical Model Aggregation via Parameter Matching Mikhail Yurochkin1,2 mikhail.yurochkin@ibm.com Mayank Agarwal1,2 mayank.agarwal@ibm.com Soumya Ghosh1,2,3 ghoshso@us.ibm.com Kristjan Greenewald1,2 kristjan.h.greenewald@ibm.com Trong Nghia Hoang1,2 nghiaht@ibm.com IBM Research,1 MIT-IBM Watso... | 2019 | 1323 |
8,612 | Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement Chao Yang1∗, Xiaojian Ma12∗, Wenbing Huang1∗, Fuchun Sun1, Huaping Liu1, Junzhou Huang3, Chuang Gan4 1 Beijing National Research Center for Information Science and Technology (BNRist), State Key Lab on Intelligent Technology and ... | 2019 | 1324 |
8,613 | Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees Muhammad Osama˚ muhammad.osama@it.uu.se Dave Zachariah˚ dave.zachariah@it.uu.se Peter Stoica˚ peter.stoica@it.uu.se *Division of System and Control, Department of Information Technology, Uppsala University Abstract... | 2019 | 1325 |
8,614 | STREETS: A Novel Camera Network Dataset for Traffic Flow Corey Snyder University of Illinois cesnyde2@illinois.edu Minh N. Do University of Illinois minhdo@illinois.edu Abstract In this paper, we introduce STREETS, a novel traffic flow dataset from publicly available web cameras in the suburbs of Chicago... | 2019 | 1326 |
8,615 | A Meta-Analysis of Overfitting in Machine Learning Rebecca Roelofs∗ UC Berkeley roelofs@berkeley.edu Sara Fridovich-Keil∗ UC Berkeley sfk@berkeley.edu John Miller UC Berkeley miller_john@berkeley.edu Vaishaal Shankar UC Berkeley vaishaal@berkeley.edu Moritz Hardt UC Berkeley hardt@berkeley.... | 2019 | 1327 |
8,616 | Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions Peng Chen, Keyi Wu, Joshua Chen, Thomas O’Leary-Roseberry, Omar Ghattas Oden Institute for Computational Engineering and Sciences The University of Texas at Austin Austin, TX 78712. {peng, keyi, joshua, to... | 2019 | 1328 |
8,617 | From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction Hidenori Tanaka1,2,†, Aran Nayebi3, Niru Maheswaranathan3,5, Lane McIntosh3, Stephen A. Baccus4, and Surya Ganguli2,5,† 1Physics & Informatics Laboratories, NTT Research, Inc., East Palo Alto, CA, USA 2Departm... | 2019 | 1329 |
8,618 | q-means: A quantum algorithm for unsupervised machine learning Iordanis Kerenidis CNRS, IRIF, Université Paris Diderot, Paris, France jkeren@irif.fr Jonas Landman CNRS, IRIF, Universiteé Paris Diderot, Paris, France Ecole Polytechnique, Palaiseau, France. landman@irif.fr Alessandro Luongo CNRS, IRIF, ... | 2019 | 133 |
8,619 | Abstract Reasoning with Distracting Features Kecheng Zheng University of Science and Technology of China zkcys001@mail.ustc.edu.cn Zheng-jun Zha∗ University of Science and Technology of China zhazj@ustc.edu.cn Wei Wei Google Research wewei@google.com Abstract Abstraction reasoning is a long-st... | 2019 | 1330 |
8,620 | Deep Scale-spaces: Equivariance Over Scale Daniel E. Worrall∗ AMLAB, Philips Lab University of Amsterdam d.e.worrall@uva.nl Max Welling AMLAB, Philips Lab University of Amsterdam m.welling@uva.nl Abstract We introduce deep scale-spaces (DSS), a generalization of convolutional neural networks, exploi... | 2019 | 1331 |
8,621 | Differentially private anonymized histograms Ananda Theertha Suresh Google Research, New York theertha@google.com Abstract For a dataset of label-count pairs, an anonymized histogram is the multiset of counts. Anonymized histograms appear in various potentially sensitive contexts such as password-frequenc... | 2019 | 1332 |
8,622 | Generalized Sliced Wasserstein Distances Soheil Kolouri1∗, Kimia Nadjahi2∗, Umut ¸Sim¸sekli2,3, Roland Badeau2, Gustavo K. Rohde4 1: HRL Laboratories, LLC., Malibu, CA, USA, 90265 2: LTCI, Télécom Paris, Institut Polytechnique de Paris, France 3: Department of Statistics, University of Oxford, UK 4: Universit... | 2019 | 1333 |
8,623 | Outlier-robust estimation of a sparse linear model using ℓ1-penalized Huber’s M-estimator Arnak S. Dalalyan ENSAE Paristech-CREST arnak.dalalyan@ensae.fr Philip Thompson ENSAE Paristech-CREST philipthomp@gmail.com Abstract We study the problem of estimating a p-dimensional s-sparse vector in a linear ... | 2019 | 1334 |
8,624 | Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference Cole L. Hurwitz School of Informatics University of Edinburgh, United Kingdom cole.hurwitz@ed.ac.uk Kai Xu School of Informatics University of Edinburgh, United Kingdom kai.xu@ed.ac.uk Akash Srivasta... | 2019 | 1335 |
8,625 | Prior-Free Dynamic Auctions with Low Regret Buyers Yuan Deng Duke University ericdy@cs.duke.edu Jon Schneider Google Research jschnei@google.com Balasubramanian Sivan Google Research balusivan@google.com Abstract We study the problem of how to repeatedly sell to a buyer running a no-regret, me... | 2019 | 1336 |
8,626 | When Does Label Smoothing Help? Rafael Müller∗, Simon Kornblith, Geoffrey Hinton Google Brain Toronto rafaelmuller@google.com Abstract The generalization and learning speed of a multi-class neural network can often be significantly improved by using soft targets that are a weighted average of the hard ta... | 2019 | 1337 |
8,627 | A General Framework for Symmetric Property Estimation Moses Charikar Stanford University moses@cs.stanford.edu Kirankumar Shiragur Stanford University shiragur@stanford.edu Aaron Sidford Stanford University sidford@stanford.edu Abstract In this paper we provide a general framework for estimating... | 2019 | 1338 |
8,628 | Deep Generative Video Compression Jun Han∗ Dartmouth College junhan@cs.dartmouth.edu Salvator Lombardo∗ Disney Research LA salvator.d.lombardo@disney.com Christopher Schroers DisneyResearch|Studios christopher.schroers@disney.com Stephan Mandt University of California, Irvine mandt@uci.edu Abs... | 2019 | 1339 |
8,629 | RUDDER: Return Decomposition for Delayed Rewards Jose A. Arjona-Medina∗ Michael Gillhofer∗ Michael Widrich∗ Thomas Unterthiner Johannes Brandstetter Sepp Hochreiter† LIT AI Lab Institute for Machine Learning Johannes Kepler University Linz, Austria †also at Institute of Advanced Research in Artific... | 2019 | 134 |
8,630 | CondConv: Conditionally Parameterized Convolutions for Efficient Inference Brandon Yang∗ Google Brain bcyang@google.com Gabriel Bender Google Brain gbender@google.com Quoc V. Le Google Brain qvl@google.com Jiquan Ngiam Google Brain jngiam@google.com Abstract Convolutional layers are one of ... | 2019 | 1340 |
8,631 | Towards a Zero-One Law for Column Subset Selection Zhao Song∗ University of Washington magic.linuxkde@gmail.com David P. Woodruff∗ Carnegie Mellon University dwoodruf@cs.cmu.edu Peilin Zhong∗ Columbia University pz2225@columbia.edu Abstract There are a number of approximation algorithms for NP-h... | 2019 | 1341 |
8,632 | Neural Attribution for Semantic Bug-Localization in Student Programs Rahul Gupta1 Aditya Kanade1,2 Shirish Shevade1 1Department of Computer Science and Automation, Indian Institute of Science, Bangalore, KA 560012, India 2Google Brain, CA, USA {rahulg, kanade, shirish}@iisc.ac.in Abstract Providing ... | 2019 | 1342 |
8,633 | Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning Igor Colin Ludovic Dos Santos Kevin Scaman Huawei Noah’s Ark Lab Abstract We investigate the theoretical limits of pipeline parallel learning of deep learning architectures, a distributed setup in which the c... | 2019 | 1343 |
8,634 | DPPNET: Approximating Determinantal Point Processes with Deep Networks Zelda Mariet ∗ Massachusetts Institute of Technology Cambridge, Massachusetts 02139, USA zelda@csail.mit.edu Yaniv Ovadia & Jasper Snoek Google Brain Cambridge, Massachusetts 02139, USA {yovadia, jsnoek}@google.com Abstract Det... | 2019 | 1344 |
8,635 | Nonzero-sum Adversarial Hypothesis Testing Games Sarath Yasodharan Department of Electrical Communication Engineering Indian Institute of Science Bangalore 560 012, India sarath@iisc.ac.in Patrick Loiseau Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG & MPI-SWS 700 avenue Centrale Domaine Univer... | 2019 | 1345 |
8,636 | Global Sparse Momentum SGD for Pruning Very Deep Neural Networks Xiaohan Ding 1 Guiguang Ding 1 Xiangxin Zhou 2 Yuchen Guo 1, 3 Jungong Han 4 Ji Liu 5 1 Beijing National Research Center for Information Science and Technology (BNRist); School of Software, Tsinghua University, Beijing, China 2 Departm... | 2019 | 1346 |
8,637 | Thompson Sampling with Approximate Inference My Phan College of Information and Computer Science University of Massachusetts Amherst, MA myphan@cs.umass.edu Yasin Abbasi-Yadkori VinAI Hanoi, Vietnam yasin.abbasi@gmail.com Justin Domke College of Information and Computer Science University of Mas... | 2019 | 1347 |
8,638 | Quantum Wasserstein GANs Shouvanik Chakrabarti1,2,4,⇤, Yiming Huang3,1,5,⇤, Tongyang Li1,2,4 Soheil Feizi2,4, Xiaodi Wu1,2,4 1 Joint Center for Quantum Information and Computer Science, University of Maryland 2 Department of Computer Science, University of Maryland 3 School of Information and Software Enginee... | 2019 | 1348 |
8,639 | Deep Learning without Weight Transport Mohamed Akrout University of Toronto, Triage Collin Wilson University of Toronto Peter C. Humphreys DeepMind Timothy Lillicrap DeepMind, University College London Douglas Tweed University of Toronto, York University Abstract Current algorithms for deep lear... | 2019 | 1349 |
8,640 | Learning-Based Low-Rank Approximations Piotr Indyk CSAIL, MIT indyk@mit.edu Ali Vakilian∗ University of Wisconsin - Madison vakilian@wisc.edu Yang Yuan∗ Tsinghua University yuanyang@tsinghua.edu.cn Abstract We introduce a “learning-based” algorithm for the low-rank decomposition problem: given an ... | 2019 | 135 |
8,641 | Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks Gauthier Gidel Mila & DIRO Universit´e de Montr´eal Francis Bach INRIA & ´Ecole Normale Sup´erieure PSL Research University, Paris Simon Lacoste-Julien∗ Mila & DIRO Universit´e de Montr´eal Abstract When optimizing o... | 2019 | 1350 |
8,642 | Generative models for graph-based protein design John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi Jaakkola Computer Science and Artificial Intelligence Lab, MIT {ingraham, vgarg, regina, tommi}@csail.mit.edu Abstract Engineered proteins offer the potential to solve many problems in biomedicine, energy, a... | 2019 | 1351 |
8,643 | Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Wenrui Zhang University of California, Santa Barbara Santa Barbara, CA 93106 wenruizhang@ucsb.edu Peng Li University of California, Santa Barbara Santa Barbara, CA 93106 lip@ucsb.edu Abstract Spiking neural netwo... | 2019 | 1352 |
8,644 | Fully Parameterized Quantile Function for Distributional Reinforcement Learning Derek Yang∗ UC San Diego dyang1206@gmail.com Li Zhao Microsoft Research lizo@microsoft.com Zichuan Lin Tsinghua University linzc16@mails.tsinghua.edu.cn Tao Qin Microsoft Research taoqin@microsoft.com Jiang Bian ... | 2019 | 1353 |
8,645 | Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain Activity Aria Y. Wang Carnegie Mellon University ariawang@cmu.edu Michael J. Tarr Carnegie Mellon University michaeltarr@cmu.edu Leila Wehbe Carnegie Mellon University lwehbe@cmu.edu Abstract Convolutional neur... | 2019 | 1354 |
8,646 | Adaptive Gradient-Based Meta-Learning Methods Mikhail Khodak Carnegie Mellon University khodak@cmu.edu Maria-Florina Balcan Carnegie Mellon University ninamf@cs.cmu.edu Ameet Talwalkar Carnegie Mellon University & Determined AI talwalkar@cmu.edu Abstract We build a theoretical framework for desi... | 2019 | 1355 |
8,647 | Compositional generalization through meta sequence-to-sequence learning Brenden M. Lake New York University Facebook AI Reasearch brenden@nyu.edu Abstract People can learn a new concept and use it compositionally, understanding how to “blicket twice” after learning how to “blicket.” In contrast, powerfu... | 2019 | 1356 |
8,648 | Meta-Learning Representations for Continual Learning Khurram Javed, Martha White Department of Computing Science University of Alberta T6G 1P8 kjaved@ualberta.ca, whitem@ualberta.ca Abstract A continual learning agent should be able to build on top of existing knowledge to learn on new data quickly wh... | 2019 | 1357 |
8,649 | Massively Scalable Sinkhorn Distances via the Nyström Method Jason Altschuler MIT jasonalt@mit.edu Francis Bach INRIA - ENS - PSL francis.bach@inria.fr Alessandro Rudi INRIA - ENS - PSL alessandro.rudi@inria.fr Jonathan Niles-Weed NYU jnw@cims.nyu.edu Abstract The Sinkhorn “distance,” a va... | 2019 | 1358 |
8,650 | Deep Multimodal Multilinear Fusion with High-order Polynomial Pooling Ming Hou1,∗, Jiajia Tang2,1,∗, Jianhai Zhang2, Wanzeng Kong2, Qibin Zhao1,† 1 Tensor Learning Unit, Center for Advanced Intelligence Project, RIKEN, Japan 2 College of Computer Science, Hangzhou Dianzi University, China ming.hou@riken.jp, h... | 2019 | 1359 |
8,651 | Convergence Guarantees for Adaptive Bayesian Quadrature Methods Motonobu Kanagawa†,#∗and Philipp Hennig# †EURECOM, Sophia Antipolis, France #University of Tübingen and Max Planck Institute for Intelligent Systems, Tübingen, Germany motonobu.kanagawa@eurecom.fr & philipp.hennig@uni-tuebingen.de Abstract Ad... | 2019 | 136 |
8,652 | A Composable Specification Language for Reinforcement Learning Tasks Kishor Jothimurugan, Rajeev Alur, Osbert Bastani University of Pennsylvania {kishor,alur,obastani}@cis.upenn.edu Abstract Reinforcement learning is a promising approach for learning control policies for robot tasks. However, specifying co... | 2019 | 1360 |
8,653 | Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer Wenzheng Chen1,2,3 Jun Gao1,2,3,∗ Huan Ling1,2,3,∗ Edward J. Smith1,4,∗ Jaakko Lehtinen1,5 Alec Jacobson2 Sanja Fidler1,2,3 NVIDIA1 University of Toronto2 Vector Institute3 McGill University4 Aalto University5 ... | 2019 | 1361 |
8,654 | On the Utility of Learning about Humans for Human-AI Coordination Micah Carroll UC Berkeley mdc@berkeley.edu Rohin Shah UC Berkeley rohinmshah@berkeley.edu Mark K. Ho Princeton University mho@princeton.edu Thomas L. Griffiths Princeton University Sanjit A. Seshia UC Berkeley Pieter Abbeel ... | 2019 | 1362 |
8,655 | FastSpeech: Fast, Robust and Controllable Text to Speech Yi Ren∗ Zhejiang University rayeren@zju.edu.cn Yangjun Ruan∗ Zhejiang University ruanyj3107@zju.edu.cn Xu Tan Microsoft Research xuta@microsoft.com Tao Qin Microsoft Research taoqin@microsoft.com Sheng Zhao Microsoft STC Asia Sheng... | 2019 | 1363 |
8,656 | Maximum Expected Hitting Cost of a Markov Decision Process and Informativeness of Rewards Falcon Z. Dai Toyota Technological Institute at Chicago Chicago, IL, USA 60637 dai@ttic.edu Matthew R. Walter Toyota Technological Institute at Chicago Chicago, IL, USA 60637 mwalter@ttic.edu Abstract We prop... | 2019 | 1364 |
8,657 | Park: An Open Platform for Learning-Augmented Computer Systems Hongzi Mao, Parimarjan Negi, Akshay Narayan, Hanrui Wang, Jiacheng Yang, Haonan Wang, Ryan Marcus, Ravichandra Addanki, Mehrdad Khani, Songtao He, Vikram Nathan, Frank Cangialosi, Shaileshh Bojja Venkatakrishnan, Wei-Hung Weng, Song Han, Tim Krask... | 2019 | 1365 |
8,658 | Adaptive Influence Maximization with Myopic Feedback Binghui Peng∗ Columbia University bp2601@columbia.edu Wei Chen Microsoft Research weic@microsoft.com Abstract We study the adaptive influence maximization problem with myopic feedback under the independent cascade model: one sequentially selects k n... | 2019 | 1366 |
8,659 | Compression with Flows via Local Bits-Back Coding Jonathan Ho UC Berkeley jonathanho@berkeley.edu Evan Lohn UC Berkeley evan.lohn@berkeley.edu Pieter Abbeel UC Berkeley, covariant.ai pabbeel@cs.berkeley.edu Abstract Likelihood-based generative models are the backbones of lossless compression due ... | 2019 | 1367 |
8,660 | On Adversarial Mixup Resynthesis Christopher Beckham1,3, Sina Honari1,3, Vikas Verma1,6,†, Alex Lamb1,2, Farnoosh Ghadiri1,3, R Devon Hjelm1,2,5, Yoshua Bengio1,2,∗& Christopher Pal1,3,4,‡,∗ 1Mila - Québec Artificial Intelligence Institute, Montréal, Canada 2Université de Montréal, Canada 3Polytechnique Montré... | 2019 | 1368 |
8,661 | High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks Ruben Villegas1,4 Arkanath Pathak3 Harini Kannan2 Dumitru Erhan2 Quoc V. Le2 Honglak Lee2 1 University of Michigan 2 Google Research 3 Google 4 Adobe Research Abstract Predicting future video frames is extremely cha... | 2019 | 1369 |
8,662 | A First-Order Algorithmic Framework for Wasserstein Distributionally Robust Logistic Regression Jiajin Li, Sen Huang, Anthony Man-Cho So Department of Systems Engineering & Engineering Management The Chinese University of Hong Kong Shatin, N. T., Hong Kong {jjli,hsen,manchoso}@se.cuhk.edu.hk Abstract Wa... | 2019 | 137 |
8,663 | Variational Bayes under Model Misspecification Yixin Wang Columbia University David M. Blei Columbia University Abstract Variational Bayes (VB) is a scalable alternative to Markov chain Monte Carlo (MCMC) for Bayesian posterior inference. Though popular, VB comes with few theoretical guarantees, most of ... | 2019 | 1370 |
8,664 | Certifying Geometric Robustness of Neural Networks Mislav Balunovi´c, Maximilian Baader, Gagandeep Singh, Timon Gehr, Martin Vechev Department of Computer Science ETH Zurich {mislav.balunovic, mbaader, gsingh, timon.gehr, martin.vechev}@inf.ethz.ch Abstract The use of neural networks in safety-critical comp... | 2019 | 1371 |
8,665 | Constrained deep neural network architecture search for IoT devices accounting for hardware calibration Florian Scheidegger1,2 Luca Benini1,3 Costas Bekas2 Cristiano Malossi2 1 ETH Zürich, Rämistrasse 101, 8092 Zürich, Switzerland 2 IBM Research - Zürich, Säumerstrasse 4, 8803 Rüschlikon, Switzerland 3 ... | 2019 | 1372 |
8,666 | MAVEN: Multi-Agent Variational Exploration Anuj Mahajan⇤† Tabish Rashid† Mikayel Samvelyan‡ Shimon Whiteson† Abstract Centralised training with decentralised execution is an important setting for cooperative deep multi-agent reinforcement learning due to communication constraints during execution and comp... | 2019 | 1373 |
8,667 | The continuous Bernoulli: fixing a pervasive error in variational autoencoders Gabriel Loaiza-Ganem Department of Statistics Columbia University gl2480@columbia.edu John P. Cunningham Department of Statistics Columbia University jpc2181@columbia.edu Abstract Variational autoencoders (VAE) have quic... | 2019 | 1374 |
8,668 | Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters Alberto Maria Metelli∗ DEIB Politecnico di Milano Milan, Italy albertomaria.metelli@polimi.it Amarildo Likmeta∗ DEIB Politecnico di Milano Milan, Italy amarildo.likmeta@polimi.it Marcello Restelli DEIB Politecnico ... | 2019 | 1375 |
8,669 | DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters Asiri Wijesinghe Research School of Computer Science The Australian National University asiri.wijesinghe@anu.edu.au Qing Wang Research School of Computer Science The Australian National University qing.wang@anu.edu.au Abstract We propos... | 2019 | 1376 |
8,670 | Multiclass Learning from Contradictions Sauptik Dhar LG Electronics Santa Clara, CA 95054 sauptik.dhar@lge.com Vladimir Cherkassky University of Minnesota Minneapolis, MN 55455 cherk001@umn.edu Mohak Shah LG Electronics Santa Clara, CA 95054 mohak.shah@lge.com Abstract We introduce the notio... | 2019 | 1377 |
8,671 | Multi-relational Poincaré Graph Embeddings Ivana Balaževi´c1 Carl Allen1 Timothy Hospedales1,2 1 School of Informatics, University of Edinburgh, UK 2 Samsung AI Centre, Cambridge, UK {ivana.balazevic, carl.allen, t.hospedales}@ed.ac.uk Abstract Hyperbolic embeddings have recently gained attention in mac... | 2019 | 1378 |
8,672 | Verified Uncertainty Calibration Ananya Kumar, Percy Liang, Tengyu Ma Department of Computer Science Stanford University Abstract Applications such as weather forecasting and personalized medicine demand models that output calibrated probability estimates—those representative of the true likelihood of a pred... | 2019 | 1379 |
8,673 | Theoretical Analysis of Adversarial Learning: A Minimax Approach Zhuozhuo Tu1, Jingwei Zhang2,1, Dacheng Tao1 1UBTECH Sydney AI Centre, School of Computer Science, The University of Sydney, Australia 2Department of Computer Science and Engineering, HKUST, Hong Kong zhtu3055@uni.sydney.edu.au, jzhangey@cse.ust... | 2019 | 138 |
8,674 | Episodic Memory in Lifelong Language Learning Cyprien de Masson d’Autume, Sebastian Ruder, Lingpeng Kong, Dani Yogatama DeepMind London, United Kingdom {cyprien,ruder,lingpenk,dyogatama}@google.com Abstract We introduce a lifelong language learning setup where a model needs to learn from a stream of text ... | 2019 | 1380 |
8,675 | MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization Shangyu Chen Nanyang Technological University, Singapore schen025@e.ntu.edu.sg Wenya Wang Nanyang Technological University, Singapore wangwy@ntu.edu.sg Sinno Jialin Pan Nanyang Technological University, Singapore ... | 2019 | 1381 |
8,676 | Normalization Helps Training of Quantized LSTM Lu Hou1, Jinhua Zhu2, James T. Kwok1, Fei Gao3, Tao Qin3, Tie-yan Liu3 1Hong Kong University of Science and Technology, Hong Kong {lhouab,jamesk}@cse.ust.hk 2University of Science and Technology of China, Hefei, China teslazhu@mail.ustc.edu.cn 3Microsoft Resear... | 2019 | 1382 |
8,677 | Differentially Private Bayesian Linear Regression Garrett Bernstein University of Massachusetts Amherst gbernstein@cs.umass.edu Daniel Sheldon University of Massachusetts Amherst sheldon@cs.umass.edu Abstract Linear regression is an important tool across many fields that work with sensitive human-sourc... | 2019 | 1383 |
8,678 | Wasserstein Dependency Measure for Representation Learning Sherjil Ozair Mila, Université de Montréal Corey Lynch Google Brain Yoshua Bengio Mila, Université de Montréal Aäron van den Oord Deepmind Sergey Levine Google Brain Pierre Sermanet Google Brain Abstract Mutual information maximiza... | 2019 | 1384 |
8,679 | Multi-Agent Common Knowledge Reinforcement Learning Christian A. Schroeder de Witt⇤† Jakob N. Foerster⇤† Gregory Farquhar† Philip H. S. Torr† Wendelin Böhmer† Shimon Whiteson† Abstract Cooperative multi-agent reinforcement learning often requires decentralised policies, which severely limit the agents... | 2019 | 1385 |
8,680 | Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections Boris Muzellec CREST, ENSAE boris.muzellec@ensae.fr Marco Cuturi Google Brain and CREST, ENSAE cuturi@google.com Abstract Computing optimal transport (OT) between measures in high dimensions is doomed by the curse of... | 2019 | 1386 |
8,681 | The Broad Optimality of Profile Maximum Likelihood Yi Hao Dept. of Electrical and Computer Engineering University of California, San Diego yih179@ucsd.edu Alon Orlitsky Dept. of Electrical and Computer Engineering University of California, San Diego alon@ucsd.edu Abstract We study three fundamental s... | 2019 | 1387 |
8,682 | Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers Guang-He Lee1, Yang Yuan1,2, Shiyu Chang3, Tommi S. Jaakkola1 1MIT Computer Science and Artificial Intelligence Lab 2Institute for Interdisciplinary Information Sciences, Tsinghua University 3MIT-IBM Watson AI Lab {guanghe, yangyuan... | 2019 | 1388 |
8,683 | Exact sampling of determinantal point processes with sublinear time preprocessing Michał Derezi´nski⇤ Department of Statistics University of California, Berkeley mderezin@berkeley.edu Daniele Calandriello⇤ LCSL Istituto Italiano di Tecnologia, Italy daniele.calandriello@iit.it Michal Valko DeepMin... | 2019 | 1389 |
8,684 | Compositional De-Attention Networks †Yi Tay∗, ♯Luu Anh Tuan∗, ♮Aston Zhang, ♣Shuohang Wang, ♭Siu Cheung Hui †,♭Nanyang Technological University, Singapore ♯MIT CSAIL, ♮Amazon AI ♣Microsoft Dynamics 365 AI Research ytay017@gmail.com Abstract Attentional models are distinctly characterized by their ability ... | 2019 | 139 |
8,685 | Neural Diffusion Distance for Image Segmentation Jian Sun and Zongben Xu School of Mathematics and Statistics Xi’an Jiaotong University, P. R. China {jiansun,zbxu}@xjtu.edu.cn Abstract Diffusion distance is a spectral method for measuring distance among nodes on graph considering global data structure. In... | 2019 | 1390 |
8,686 | Experience Replay for Continual Learning David Rolnick University of Pennsylvania Philadelphia, PA USA drolnick@seas.upenn.edu Arun Ahuja DeepMind London, UK arahuja@google.com Jonathan Schwarz DeepMind London, UK schwarzjn@google.com Timothy P. Lillicrap DeepMind London, UK countzero@go... | 2019 | 1391 |
8,687 | Efficient online learning with Kernels for adversarial large scale problems Rémi Jézéquel Pierre Gaillard Alessandro Rudi INRIA - Département d’Informatique de l’École Normale Supérieure PSL Research University, Paris, France {remi.jezequel,pierre.gaillard,alessandro.rudi}@inria.fr Abstract We are inte... | 2019 | 1392 |
8,688 | KNG: The K-Norm Gradient Mechanism Matthew Reimherr ∗ Department of Statistics Pennsylvania State University State College, PA 16802 mreimherr@psu.edu Jordan Awan Department of Statistics Pennsylvania State University State College, PA 16802 awan@psu.edu Abstract This paper presents a new mechan... | 2019 | 1393 |
8,689 | On the Downstream Performance of Compressed Word Embeddings Avner May Jian Zhang Tri Dao Christopher Ré Department of Computer Science, Stanford University {avnermay, zjian, trid, chrismre}@cs.stanford.edu Abstract Compressing word embeddings is important for deploying NLP models in memoryconstrained ... | 2019 | 1394 |
8,690 | Primal-Dual Block Generalized Frank-Wolfe Qi Lei†∗, Jiacheng Zhuo†∗, Constantine Caramanis†, Inderjit S. Dhillon†‡, and Alexandros G. Dimakis† † UT Austin ‡ Amazon {leiqi@oden., jzhuo@, constantine@, inderjit@cs., dimakis@austin.}utexas.edu Abstract We propose a generalized variant of Frank-Wolfe algori... | 2019 | 1395 |
8,691 | Nonparametric Density Estimation and Convergence of GANs under Besov IPM Losses Ananya Uppal Department of Mathematical Sciences Carnegie Mellon University auppal@andrew.cmu.edu Shashank Singh∗ Barnabás Póczos Machine Learning Department Carnegie Mellon University {sss1,bapoczos}@cs.cmu.edu Abstra... | 2019 | 1396 |
8,692 | Blended Matching Pursuit Cyrille W. Combettes Georgia Institute of Technology Atlanta, GA, USA cyrille@gatech.edu Sebastian Pokutta Zuse Institute Berlin and TU Berlin Berlin, Germany pokutta@zib.de Abstract Matching pursuit algorithms are an important class of algorithms in signal processing and ma... | 2019 | 1397 |
8,693 | Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models Aditya Gangrade Boston University gangrade@bu.edu Praveen Venkatesh Carnegie Mellon University vpraveen@cmu.edu Bobak Nazer Boston University bobak@bu.edu Venkatesh Saligrama Boston University srv@bu.edu A... | 2019 | 1398 |
8,694 | Who is Afraid of Big Bad Minima? Analysis of Gradient-Flow in a Spiked Matrix-Tensor Model Stefano Sarao Mannelli†, Giulio Biroli‡, Chiara Cammarota∗, Florent Krzakala‡, and Lenka ZdeborovᆠAbstract Gradient-based algorithms are effective for many machine learning tasks, but despite ample recent effort and... | 2019 | 1399 |
8,695 | Zero-Shot Semantic Segmentation Maxime Bucher valeo.ai maxime.bucher@valeo.com Tuan-Hung Vu valeo.ai tuan-hung.vu@valeo.com Matthieu Cord Sorbonne Université valeo.ai matthieu.cord@lip6.fr Patrick Pérez valeo.ai patrick.perez@valeo.com Abstract Semantic segmentation models are limited in t... | 2019 | 14 |
8,696 | Robust Attribution Regularization Jiefeng Chen ⇤1 Xi Wu ⇤2 Vaibhav Rastogi †2 Yingyu Liang 1 Somesh Jha 1,3 1 University of Wisconsin-Madison 2 Google 3 XaiPient Abstract An emerging problem in trustworthy machine learning is to train models that produce robust interpretations for their predictions.... | 2019 | 140 |
8,697 | Online Convex Matrix Factorization with Representative Regions Abhishek Agarwal ∗ Electrical and Computer Engineering University of Illinois Urbana-Champaign abhiag@illinois.edu Jianhao Peng ∗ Electrical and Computer Engineering University of Illinois Urbana-Champaign jianhao2@illinois.edu Olgica Mi... | 2019 | 1400 |
8,698 | Differential Privacy Has Disparate Impact on Model Accuracy Eugene Bagdasaryan Cornell Tech eugene@cs.cornell.edu Omid Poursaeed∗ Cornell Tech op63@cornell.edu Vitaly Shmatikov Cornell Tech shmat@cs.cornell.edu Abstract Differential privacy (DP) is a popular mechanism for training machine learni... | 2019 | 1401 |
8,699 | Fair Algorithms for Clustering Suman K. Bera UC Santa Cruz Santa Cruz, CA 95064 sbera@ucsc.edu Deeparnab Chakrabarty Dartmouth College Hanover, NH 03755 deeparnab@dartmouth.edu Nicolas J. Flores Dartmouth College Hanover, NH 03755 nicolasflores.19@dartmouth.edu Maryam Negahbani Dartmouth Col... | 2019 | 1402 |
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