Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
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6,400 | 679 | Filter Selection Model for Generating
Visual Motion Signals
Steven J. Nowlan?
CNL, The Salk Institute
P.O. Box 85800, San Diego, CA
92186-5800
Terrence J. Sejnowski
CNL, The Salk Institute
P.O. Box 85800, San Diego, CA
92186-5800
Abstract
Neurons in area MT of primate visual cortex encode the velocity
of moving obje... | 679 |@word middle:1 tr:1 shading:2 extrastriate:1 born:2 contains:3 selecting:3 tuned:4 current:3 neurophys:1 nowlan:7 must:2 realistic:1 motor:1 designed:1 mounting:1 alone:1 stationary:1 selected:3 cue:1 intelligence:2 indicative:1 plane:1 filtered:1 provides:1 location:16 toronto:1 simpler:1 rc:1 along:1 combine:2 p... |
6,401 | 6,790 | Batch Renormalization: Towards Reducing
Minibatch Dependence in Batch-Normalized Models
Sergey Ioffe
Google
sioffe@google.com
Abstract
Batch Normalization is quite effective at accelerating and improving the training
of deep models. However, its effectiveness diminishes when the training minibatches are small, or do ... | 6790 |@word version:1 norm:1 nd:1 r:1 crucially:1 bn:1 jacob:1 p0:3 sgd:1 moment:1 reduction:1 initial:1 contains:3 selecting:1 existing:1 current:2 com:1 activation:19 goldberger:1 diederik:2 numerical:1 christian:2 hypothesize:1 remove:1 drop:3 update:7 v:1 alone:3 half:4 selected:1 generative:1 alec:1 beginning:1 it... |
6,402 | 6,791 | Generating steganographic images via adversarial
training
Jamie Hayes
University College London
j.hayes@cs.ucl.ac.uk
George Danezis
University College London
The Alan Turing Institute
g.danezis@ucl.ac.uk
Abstract
Adversarial training has proved to be competitive against supervised learning
methods on computer vision... | 6791 |@word trial:2 unaltered:2 norm:1 c0:2 open:1 seek:2 rgb:1 eng:1 carry:2 initial:1 liu:1 contains:1 score:1 selecting:1 daniel:1 groundwork:1 outperforms:1 current:1 luo:1 activation:2 assigning:1 yet:1 diederik:1 readily:1 gpu:1 devin:2 realistic:2 visible:2 concatenate:1 designed:2 aside:1 generative:4 discoveri... |
6,403 | 6,792 | Near-linear time approximation algorithms for
optimal transport via Sinkhorn iteration
Jason Altschuler
MIT
jasonalt@mit.edu
Jonathan Weed
MIT
jweed@mit.edu
Philippe Rigollet
MIT
rigollet@mit.edu
Abstract
Computing optimal transport distances such as the earth mover?s distance is a
fundamental problem in machine le... | 6792 |@word determinant:1 version:1 achievable:1 polynomial:2 norm:2 stronger:1 c0:3 villani:1 open:1 adrian:1 simulation:1 jacob:1 thereby:1 carry:1 reduction:1 contains:1 efficacy:1 pprox:2 outperforms:6 err:2 current:1 comparing:1 ka:2 com:1 john:1 mesh:1 numerical:2 interpretable:2 update:8 juditsky:1 greedy:3 proh... |
6,404 | 6,793 | PixelGAN Autoencoders
Alireza Makhzani, Brendan Frey
University of Toronto
{makhzani,frey}@psi.toronto.edu
Abstract
In this paper, we describe the ?PixelGAN autoencoder?, a generative autoencoder
in which the generative path is a convolutional autoregressive neural network on
pixels (PixelCNN) that is conditioned on a... | 6793 |@word illustrating:1 middle:1 cha:1 pieter:2 d2:7 decomposition:21 paid:1 thereby:1 carry:1 liu:1 jimenez:2 daniel:1 hyunsoo:1 maosong:1 outperforms:2 steiner:1 z2:2 assigning:1 dx:2 diederik:5 gpu:1 yet:1 john:2 tilted:1 devin:1 enables:1 christian:1 designed:1 plot:1 update:1 interpretable:1 v:8 generative:32 a... |
6,405 | 6,794 | Consistent Multitask Learning with
Nonlinear Output Relations
Carlo Ciliberto ?,1
Alessandro Rudi ?,? ,2
Lorenzo Rosasco 3,4,5
Massimiliano Pontil 1,5
{c.ciliberto,m.pontil}@ucl.ac.uk alessandro.rudi@inria.fr lrosasco@mit.edu
1
Department of Computer Science, University College London, London, UK.
2
INRIA - Sierra ... | 6794 |@word multitask:20 trial:4 version:2 norm:2 paredes:1 c0:1 hector:1 dekel:1 jacob:1 score:3 exclusively:1 document:6 interestingly:6 romera:1 outperforms:3 current:2 recovered:1 nt:17 must:1 written:1 john:1 realistic:1 hofmann:2 jawanpuria:2 designed:1 mtfl:1 n0:4 mackey:1 implying:1 intelligence:1 discovering:1... |
6,406 | 6,795 | Alternating minimization for dictionary learning with
random initialization
Niladri S. Chatterji
UC Berkeley
niladri.chatterji@berkeley.edu
Peter L. Bartlett
UC Berkeley
peter@berkeley.edu
Abstract
We present theoretical guarantees for an alternating minimization algorithm for
the dictionary learning/sparse coding pr... | 6795 |@word instrumental:1 norm:25 seems:1 open:1 r:1 decomposition:3 invoking:1 pick:1 boundedness:2 initial:12 series:1 ka:6 written:1 subsequent:1 update:8 generative:4 xk:10 huo:1 ith:3 gribonval:2 provides:1 complication:1 mcdiarmid:2 kelner:1 mathematical:2 c2:1 symposium:3 ik:4 focs:1 prove:6 excellence:1 theore... |
6,407 | 6,796 | Learning ReLUs via Gradient Descent
Mahdi Soltanolkotabi
Ming Hsieh Department of Electrical Engineering
University of Southern California
Los Angeles, CA
soltanol@usc.edu
Abstract
In this paper we study the problem of learning Rectified Linear Units (ReLUs)
which are functions of the form x ? max(0, ?w, x?) with w ?... | 6796 |@word multitask:1 trial:4 version:4 polynomial:2 norm:2 stronger:1 nd:2 simulation:1 bn:2 hsieh:1 decomposition:1 carry:1 initial:1 configuration:1 contains:1 denoting:3 ours:1 ganti:1 surprising:1 si:1 activation:1 bd:8 mesh:3 numerical:10 confirming:1 plot:2 interpretable:1 update:5 n0:10 depict:3 fewer:1 yr:1 ... |
6,408 | 6,797 | Stabilizing Training of Generative Adversarial
Networks through Regularization
Kevin Roth
Department of Computer Science
ETH Z?rich
Aurelien Lucchi
Department of Computer Science
ETH Z?rich
kevin.roth@inf.ethz.ch
aurelien.lucchi@inf.ethz.ch
Sebastian Nowozin
Microsoft Research
Cambridge, UK
sebastian.Nowozin@micros... | 6797 |@word norm:6 logit:1 open:3 heuristically:1 jacob:1 thereby:3 tr:1 solid:1 ipm:1 moment:1 initial:5 liu:2 rkhs:1 com:2 luo:2 activation:2 yet:2 dx:3 diederik:2 readily:1 john:1 realize:1 fn:2 hofmann:2 analytic:3 christian:1 yinda:1 update:6 v:4 kilcher:1 generative:22 leaf:1 alec:1 parametrization:1 short:1 inde... |
6,409 | 6,798 | Expectation Propagation with Stochastic Kinetic
Model in Complex Interaction Systems
Le Fang, Fan Yang, Wen Dong, Tong Guan, and Chunming Qiao
Department of Computer Science and Engineering
University at Buffalo
{lefang, fyang24, wendong, tongguan, qiao}@buffalo.edu
Abstract
Technological breakthroughs allow us to co... | 6798 |@word briefly:1 proportion:1 open:1 adrian:1 simulation:6 propagate:1 covariance:1 minus:3 recursively:2 moment:2 initial:1 series:4 daniel:1 past:1 reaction:4 freitas:1 current:4 attracted:1 written:1 john:1 realistic:3 partition:3 sdes:2 gv:2 designed:1 drop:2 update:4 plot:2 resampling:1 congestion:1 stationar... |
6,410 | 6,799 | Data-Efficient Reinforcement Learning in
Continuous State-Action Gaussian-POMDPs
Rowan Thomas McAllister
Department of Engineering
Cambridge University
Cambridge, CB2 1PZ
rtm26@cam.ac.uk
Carl Edward Rasmussen
Department of Engineering
University of Cambridge
Cambridge, CB2 1PZ
cer54@cam.ac.uk
Abstract
We present a da... | 6799 |@word trial:4 version:2 advantageous:1 d2:4 grey:1 simulation:2 covariance:2 eng:1 thereby:1 moment:12 initial:4 series:1 score:1 initialisation:1 rowan:1 outperforms:1 comparing:3 yet:7 must:2 realistic:2 additive:3 shape:1 enables:2 analytic:5 remove:1 plot:2 drop:1 update:3 v:1 intelligence:1 fewer:2 advanceme... |
6,411 | 68 | 367
SCHEMA
OT ILl ZING
A
I'OR
NETWORK
MOTOR
MODEL
CONTROL
01'
THE
CEREBELLUM
James C. Houk, Ph.D.
Northwestern University Medical School, Chicago, Illinois
60201
ABSTRACT
This paper outlines a schema for movement control
based on two stages of signal processing. The higher stage
is a neural network model that... | 68 |@word noradrenergic:6 seems:2 instrumental:1 advantageous:2 d2:1 simulation:1 propagate:1 accounting:1 pg:4 innervating:1 reduction:1 vigorously:1 initial:1 efficacy:1 karger:1 past:1 yet:1 intriguing:1 physiol:2 subsequent:1 chicago:1 plasticity:1 shape:1 motor:47 math:1 relayed:1 burst:8 consists:1 sustained:4 pa... |
6,412 | 680 | Information Theoretic Analysis of
Connection Structure from Spike Trains
Satoru Shiono?
Satoshi Yamada
Cen tral Research Laboratory
Mi tsu bishi Electric Corporation
Amagasaki, Hyogo 661, Japan
Central Research Laboratory
Mitsu bishi Electric Corporation
Amagasaki, Hyogo 661, Japan
Michio Nakashima
Kenji Matsumot... | 680 |@word effect:1 especially:2 kenji:1 briefly:1 true:1 quantity:6 laboratory:3 spike:18 simulation:3 aertsen:2 inferior:1 kutta:1 simulated:4 coincides:1 capacity:7 m:5 me:1 hereafter:1 investigation:1 proposition:1 theoretic:16 complete:1 theor:1 optican:2 mathematically:1 presynaptic:1 existing:1 code:1 koch:2 con... |
6,413 | 6,800 | Compatible Reward Inverse Reinforcement Learning
Alberto Maria Metelli
DEIB
Politecnico di Milano, Italy
Matteo Pirotta
SequeL Team
Inria Lille, France
Marcello Restelli
DEIB
Politecnico di Milano, Italy
albertomaria.metelli@polimi.it
matteo.pirotta@inria.fr
marcello.restelli@polimi.it
Abstract
Inverse Reinforce... | 6800 |@word norm:1 replicate:2 pieter:2 r:1 decomposition:1 crite:1 pick:1 nystr:1 tr:5 necessity:1 initial:2 series:4 bc:11 bilal:3 outperforms:2 existing:1 recovered:14 transferability:1 current:4 ka:1 michal:1 assigning:1 diederik:1 must:1 written:1 john:1 ronald:2 chicago:1 numerical:2 informative:2 weyl:1 lqg:2 co... |
6,414 | 6,801 | First-Order Adaptive Sample Size Methods to
Reduce Complexity of Empirical Risk Minimization
Aryan Mokhtari
University of Pennsylvania
aryanm@seas.upenn.edu
Alejandro Ribeiro
University of Pennsylvania
aribeiro@seas.upenn.edu
Abstract
This paper studies empirical risk minimization (ERM) problems for large-scale
datas... | 6801 |@word version:5 norm:1 stronger:1 nd:1 seems:1 reduction:3 initial:6 celebrated:1 contains:6 united:2 ecole:1 existing:1 current:1 numerical:3 hofmann:2 zaid:1 designed:1 plot:4 update:15 v:2 half:1 prohibitive:1 iterates:8 provides:2 tahoe:2 simpler:1 zhang:3 mathematical:1 direct:1 become:1 fitting:1 combine:1 ... |
6,415 | 6,802 | Hiding Images in Plain Sight:
Deep Steganography
Shumeet Baluja
Google Research
Google, Inc.
shumeet@google.com
Abstract
Steganography is the practice of concealing a secret message within another,
ordinary, message. Commonly, steganography is used to unobtrusively hide a small
message within the noisy regions of a l... | 6802 |@word middle:2 unaltered:1 compression:1 briefly:1 nd:2 c0:3 retraining:1 open:2 reused:1 willing:1 hyv:1 seek:1 r:1 rgb:3 decomposition:2 thereby:3 substitution:2 contains:1 selecting:1 interestingly:1 rightmost:1 existing:1 recovered:2 com:2 current:1 surprising:1 diederik:1 must:6 readily:2 john:1 dct:3 visibl... |
6,416 | 6,803 | Neural Program Meta-Induction
Jacob Devlin?
Google
jacobdevlin@google.com
Rudy Bunel?
University of Oxford
rudy@robots.ox.ac.uk
Rishabh Singh
Microsoft Research
risin@microsoft.com
Pushmeet Kohli?
DeepMind
pushmeet@google.com
Matthew Hausknecht
Microsoft Research
mahauskn@microsoft.com
Abstract
Most recently prop... | 6803 |@word kohli:2 cnn:4 armand:2 seems:1 pieter:1 confirms:1 jacob:3 sgd:7 thereby:1 shot:8 series:1 efficacy:1 score:1 daniel:1 outperforms:3 existing:4 past:5 current:1 com:4 o2:1 freitas:1 written:2 must:2 john:2 uria:1 treating:1 v:5 sukhbaatar:1 spec:1 fewer:2 selected:1 kushman:1 ivo:2 smith:2 tarlow:1 node:1 o... |
6,417 | 6,804 | Bayesian Dyadic Trees and Histograms for Regression
St?phanie van der Pas
Mathematical Institute
Leiden University
Leiden, The Netherlands
svdpas@math.leidenuniv.nl
Veronika Ro?ckov?
Booth School of Business
University of Chicago
Chicago, IL, 60637
Veronika.Rockova@ChicagoBooth.edu
Abstract
Many machine learning too... | 6804 |@word faculty:1 version:3 norm:2 proportion:1 nd:1 suitably:1 bn:4 contraction:4 thereby:3 carry:1 reduction:1 liu:1 series:2 selecting:1 com:1 luo:1 assigning:1 dx:1 fn:6 chicago:3 partition:52 belmont:1 additive:2 opin:1 designed:1 fund:1 aside:2 bart:3 generative:2 leaf:3 selected:1 denison:1 ith:1 smith:1 rec... |
6,418 | 6,805 | A graph-theoretic approach to multitasking
Noga Alon?
Tel-Aviv University
Sebastian Musslick
Princeton University
Daniel Reichman?
UC Berkeley
Jonathan D. Cohen ?
Princeton University
Igor Shinkar?
UC Berkeley
Thomas L. Griffiths
UC Berkeley
Tal Wagner?
MIT
Biswadip Dey
Princeton University
Kayhan Ozcimder
Prince... | 6805 |@word multitask:2 worsens:1 version:9 stronger:1 nd:1 open:1 d2:1 simulation:1 multitasked:6 shading:2 carry:2 contains:11 exclusively:1 ce2:1 series:2 daniel:3 interestingly:2 current:1 comparing:1 must:3 john:3 numerical:1 ramamohan:1 remove:1 v:4 vanishing:1 record:1 math:1 node:22 pascanu:1 mathematical:1 alo... |
6,419 | 6,806 | 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
purushot@cse.iitk.ac.i... | 6806 |@word trial:1 faculty:1 version:2 polynomial:3 seems:2 norm:2 nd:4 tedious:1 open:1 unif:1 seek:1 sensed:1 crucially:1 thereby:1 catastrophically:1 reduction:3 offering:1 tuned:1 past:1 existing:6 current:2 com:1 yet:1 john:1 realize:1 plot:1 update:1 v:1 half:2 leaf:1 intelligence:1 isotropic:1 core:1 coarse:5 p... |
6,420 | 6,807 | Natural Value Approximators:
Learning when to Trust Past Estimates
Zhongwen Xu
DeepMind
zhongwen@google.com
Andre Barreto
DeepMind
andrebarreto@google.com
Joseph Modayil
DeepMind
modayil@google.com
David Silver
DeepMind
davidsilver@google.com
Hado van Hasselt
DeepMind
hado@google.com
Tom Schaul
DeepMind
schaul@googl... | 6807 |@word version:1 proportion:2 bptt:2 propagate:1 solid:1 recursively:1 reduction:1 moment:1 initial:3 score:4 document:1 past:7 existing:1 hasselt:5 current:5 com:6 comparing:2 surprising:1 freitas:1 yet:2 diederik:1 must:1 guez:1 john:1 subsequent:2 shape:2 hypothesize:2 drop:4 plot:4 update:11 alone:1 greedy:2 i... |
6,421 | 6,808 | Bandits Dueling on Partially Ordered Sets
Julien Audiffren
CMLA
ENS Paris-Saclay, CNRS
Universit?e Paris-Saclay, France
julien.audiffren@gmail.com
Liva Ralaivola
Lab. Informatique Fondamentale de Marseille
CNRS, Aix Marseille University
Institut Universitaire de France
F-13288 Marseille Cedex 9, France
liva.ralaivola@... | 6808 |@word katja:1 determinant:1 version:4 briefly:1 c0:5 simulation:1 prominence:1 pick:1 mention:1 carry:1 initial:1 liu:2 uncovered:1 contains:4 selecting:1 score:3 initialisation:2 offering:1 ours:1 daniel:1 envision:1 subjective:1 existing:1 recovered:1 com:1 comparing:4 current:1 contextual:2 gmail:1 liva:2 yet:... |
6,422 | 6,809 | Elementary Symmetric Polynomials for Optimal
Experimental Design
Zelda Mariet
Massachusetts Institute of Technology
Cambridge, MA 02139
zelda@csail.mit.edu
Suvrit Sra
Massachusetts Institute of Technology
Cambridge, MA 02139
suvrit@mit.edu
Abstract
We revisit the classical problem of optimal experimental design (OED... | 6809 |@word mild:1 determinant:4 version:2 repository:1 polynomial:18 seems:1 stronger:1 chromium:2 open:2 calculus:1 hu:1 seek:2 confirms:1 tr:6 hager:1 initial:1 series:1 selecting:3 outperforms:1 optim:1 bie:1 written:1 must:2 determinantal:1 john:1 n0:2 greedy:24 item:2 plane:1 smith:1 provides:4 math:4 simpler:2 m... |
6,423 | 681 | A Parallel Gradient Descent Method for Learning
in Analog VLSI Neural Networks
J. Alspector
R. Meir'" B. Yuhas A. Jayakumar
Bellcore
Morristown, NJ 07962-1910
D. Lippet
Abstract
Typical methods for gradient descent in neural network learning involve
calculation of derivatives based on a detailed knowledge of the ne... | 681 |@word version:1 pw:1 seems:3 replicate:1 simulation:5 tried:2 electronics:1 activation:4 perturbative:4 reminiscent:1 chu:1 refresh:3 additive:1 shape:1 plot:1 update:1 alone:1 device:2 dembo:6 core:1 tpresent:1 mathematical:1 loll:1 replication:6 qualitative:1 yuhas:5 roughly:1 behavior:1 alspector:9 actual:1 pf:... |
6,424 | 6,810 | Emergence of Language with Multi-agent Games:
Learning to Communicate with Sequences of Symbols
Serhii Havrylov
ILCC, School of Informatics
University of Edinburgh
s.havrylov@inf.ed.ac.uk
Ivan Titov
ILCC, School of Informatics
University of Edinburgh
ILLC, University of Amsterdam
ititov@inf.ed.ac.uk
Abstract
Learnin... | 6810 |@word cnn:1 version:1 armand:1 compression:1 seems:1 instruction:2 pieter:1 simulation:3 prasad:1 dramatic:1 mention:1 solid:1 harder:2 configuration:2 contains:1 score:9 selecting:1 tuned:2 interestingly:3 prefix:1 outperforms:1 freitas:1 icn:1 surprising:2 diederik:2 reminiscent:1 written:1 parsing:2 john:1 ron... |
6,425 | 6,811 | Training Deep Networks without Learning Rates
Through Coin Betting
Francesco Orabona?
Department of Computer Science
Stony Brook University
Stony Brook, NY
francesco@orabona.com
Tatiana Tommasi?
Department of Computer, Control, and
Management Engineering
Sapienza, Rome University, Italy
tommasi@dis.uniroma1.it
Abstr... | 6811 |@word cnn:3 version:2 briefly:1 nchen:1 norm:3 seems:1 rgb:1 sgd:4 reduction:3 initial:10 contains:2 tuned:4 denoting:2 document:1 outperforms:2 existing:1 past:2 current:2 com:3 comparing:1 steiner:1 guadarrama:1 activation:1 yet:3 stony:4 attracted:1 realize:1 devin:1 numerical:1 shape:1 plot:2 update:14 v:3 is... |
6,426 | 6,812 | Pixels to Graphs by Associative Embedding
Alejandro Newell
Jia Deng
Computer Science and Engineering
University of Michigan, Ann Arbor
{alnewell, jiadeng}@umich.edu
Abstract
Graphs are a useful abstraction of image content. Not only can graphs represent
details about individual objects in a scene but they can capture... | 6812 |@word h:1 cnn:3 version:2 manageable:1 proportion:1 open:1 hu:1 choy:1 tat:1 jacob:1 pick:1 accommodate:2 reduction:1 initial:1 liu:3 score:3 exclusively:2 existing:1 comparing:1 activation:4 assigning:1 must:13 john:1 devin:1 happen:1 informative:1 shape:1 hourglass:7 drop:1 update:1 rpn:6 v:5 sponsored:1 cue:2 ... |
6,427 | 6,813 | Runtime Neural Pruning
Ji Lin?
Department of Automation
Tsinghua University
lin-j14@mails.tsinghua.edu.cn
Jiwen Lu
Department of Automation
Tsinghua University
lujiwen@tsinghua.edu.cn
Yongming Rao?
Department of Automation
Tsinghua University
raoyongming95@gmail.com
Jie Zhou
Department of Automation
Tsinghua Universi... | 6813 |@word cnn:19 compression:1 nd:1 dekel:1 km:1 hu:1 pieter:1 sgd:2 mention:1 harder:1 f0k:1 reduction:2 initial:1 liu:2 series:1 tuned:2 ours:5 humanlevel:1 document:1 rightmost:1 outperforms:2 existing:2 current:4 com:1 comparing:1 amjad:1 guadarrama:1 activation:3 gmail:1 written:1 gpu:6 john:4 ronan:1 christian:... |
6,428 | 6,814 | Eigenvalue Decay Implies Polynomial-Time
Learnability for Neural Networks
Surbhi Goel ?
Department of Computer Science
University of Texas at Austin
surbhi@cs.utexas.edu
Adam Klivans ?
Department of Computer Science
University of Texas at Austin
klivans@cs.utexas.edu
Abstract
We consider the problem of learning func... | 6814 |@word mild:1 h:6 version:9 polynomial:34 seems:2 stronger:2 norm:11 suitably:1 compression:31 open:2 c0:2 decomposition:1 sgd:1 asks:1 nystr:12 tr:1 arous:1 moment:1 reduction:1 score:1 daniel:3 denoting:1 rkhs:7 suppressing:1 woodruff:1 existing:1 current:2 jaz:1 activation:8 tackling:1 dx:6 must:1 john:2 realiz... |
6,429 | 6,815 | MMD GAN: Towards Deeper Understanding of
Moment Matching Network
Chun-Liang Li1,? Wei-Cheng Chang1,? Yu Cheng2 Yiming Yang1 Barnab?s P?czos1
1
Carnegie Mellon University, 2 AI Foundations, IBM Research
{chunlial,wchang2,yiming,bapoczos}@cs.cmu.edu chengyu@us.ibm.com
(? denotes equal contribution)
Abstract
Generative m... | 6815 |@word mild:2 polynomial:1 seems:1 stronger:2 norm:1 open:1 hu:1 decomposition:1 covariance:1 concise:2 boundedness:1 reduction:2 moment:21 minmax:1 liu:1 score:8 necessity:1 daniel:1 rkhs:1 interestingly:1 rog:1 document:1 outperforms:1 existing:2 com:3 comparing:1 luo:1 diederik:1 gpu:2 shape:1 krikamol:1 plot:2... |
6,430 | 6,816 | 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
Abstract
D... | 6816 |@word determinant:2 version:1 briefly:1 eliminating:1 middle:1 underperform:1 additively:1 tried:1 bn:2 incurs:2 sgd:3 mengye:1 thereby:1 solid:2 recursively:1 reduction:1 liu:1 series:2 ours:1 envision:1 com:1 surprising:1 activation:57 must:5 gpu:7 parsing:1 devin:2 subsequent:2 distant:1 partition:2 additive:1... |
6,431 | 6,817 | Fast Rates for Bandit Optimization with
Upper-Confidence Frank-Wolfe
Quentin Berthet ?
University of Cambridge
q.berthet@statslab.cam.ac.uk
Vianney Perchet ?
ENS Paris-Saclay & Criteo Research, Paris
vianney.perchet@normalesup.org
Abstract
We consider the problem of bandit optimization, inspired by stochastic optimi... | 6817 |@word exploitation:2 illustrating:1 norm:2 proportion:3 gaspard:1 approachability:1 covariance:1 p0:1 pick:2 mention:1 klk:2 cobb:2 denoting:1 interestingly:2 past:2 existing:1 recovered:1 contextual:1 surprising:1 yet:1 written:1 must:1 remove:1 update:2 intelligence:1 indicative:1 xk:1 caveat:1 provides:1 math:... |
6,432 | 6,818 | Zap Q-Learning
Adithya M. Devraj
Sean P. Meyn
Department of Electrical and Computer Engineering,
University of Florida,
Gainesville, FL 32608.
adithyamdevraj@ufl.edu, meyn@ece.ufl.edu
Abstract
The Zap Q-learning algorithm introduced in this paper is an improvement of
Watkins? original algorithm and recent competitors... | 6818 |@word trial:1 version:6 inversion:1 polynomial:2 stronger:1 seems:1 open:1 mehta:1 simulation:3 gainesville:1 covariance:17 bn:20 q1:6 automat:2 dramatic:2 recursively:1 carry:1 kappen:1 reduction:1 liu:1 contains:3 denoting:3 optim:2 readily:1 fn:2 numerical:6 cheap:1 zap:38 designed:2 plot:2 update:6 juditsky:2... |
6,433 | 6,819 | 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 distributions are h... | 6819 |@word deformed:5 determinant:1 briefly:1 vanhatalo:1 covariance:1 p0:2 moment:11 celebrated:1 contains:3 outperforms:1 z2:2 expq:6 dx:3 numerical:1 partition:2 enables:2 update:5 intelligence:1 record:1 provides:2 mathematical:4 ect:3 yuan:1 issei:1 combine:1 jpmjcr1403:1 expected:3 behavior:1 pf:1 z13:1 moreover... |
6,434 | 682 | Synaptic Weight Noise During MLP
Learning Enhances Fault-Tolerance,
Generalisation and Learning Trajectory
Alan F. Murray
Dept. of Electrical Engineering
Edinburgh University
Scotland
Peter J. Edwards
Dept. of Electrical Engjneering
Edinburgh University
Scotland
Abstract
We analyse the effects of analog noise on the... | 682 |@word version:2 proportion:1 nd:1 simulation:15 pressure:1 reduction:1 electronics:1 contains:1 existing:1 scatter:1 tot:1 remove:1 plot:1 update:3 selected:1 scotland:2 node:10 location:1 mathematical:3 along:1 sii:1 become:1 manner:1 themselves:1 multi:1 actual:1 considering:3 provided:1 underlying:1 circuit:1 m... |
6,435 | 6,820 | Few-Shot Learning Through an Information
Retrieval Lens
Eleni Triantafillou
University of Toronto
Vector Institute
Richard Zemel
University of Toronto
Vector Institute
Raquel Urtasun
University of Toronto
Vector Institute
Uber ATG
Abstract
Few-shot learning refers to understanding new concepts from only a few exampl... | 6820 |@word mild:1 trial:1 version:1 proportion:1 pieter:1 jacob:1 contrastive:1 harder:1 shot:76 contains:1 score:6 selecting:1 offering:1 ours:7 outperforms:1 current:1 activation:1 yet:1 goldberger:1 diederik:1 john:1 realistic:1 happen:1 informative:2 hofmann:1 christian:1 designed:3 update:20 pursued:1 selected:1 ... |
6,436 | 6,821 | Formal Guarantees on the Robustness of a
Classifier against Adversarial Manipulation
Matthias Hein and Maksym Andriushchenko
Department of Mathematics and Computer Science
Saarland University, Saarbr?cken Informatics Campus, Germany
Abstract
Recent work has shown that state-of-the-art classifiers are quite brittle,
i... | 6821 |@word moosavi:2 middle:1 achievable:1 norm:15 open:1 hu:3 r:4 simplifying:1 sgd:1 outlook:1 liu:2 interestingly:1 existing:1 steiner:1 current:2 wd:1 surprising:1 activation:5 yet:1 intriguing:1 written:1 devin:1 subsequent:1 kdd:2 v:1 generative:2 leaf:1 isard:1 intelligence:1 discovering:1 yamada:1 lr:3 provide... |
6,437 | 6,822 | Associative Embedding: End-to-End Learning for
Joint Detection and Grouping
Alejandro Newell
Computer Science and Engineering
University of Michigan
Ann Arbor, MI
Zhiao Huang*
Institute for Interdisciplinary Information Sciences
Tsinghua University
Beijing, China
alnewell@umich.edu
hza14@mails.tsinghua.edu.cn
Jia ... | 6822 |@word cnn:1 version:1 kokkinos:1 stronger:1 adrian:1 ankle:2 hu:1 seek:1 decomposition:1 accommodate:1 initial:1 series:1 score:10 contains:1 iqbal:2 document:1 outperforms:1 steiner:1 current:2 comparing:1 activation:3 chu:1 must:1 parsing:4 takeo:1 john:1 devin:1 visible:1 concatenate:1 partition:1 shape:1 enab... |
6,438 | 6,823 | Practical Locally Private Heavy Hitters
Raef Bassily?
Kobbi Nissim?
Uri Stemmer?
Abhradeep Thakurta?
Abstract
We present new practical local differentially private heavy hitters algorithms
achieving optimal or near-optimal worst-case error ? TreeHist and Bitstogram.
?
?
In both algorithms, server running time is O... | 6823 |@word private:20 briefly:2 version:7 repository:2 achievable:1 compression:1 nd:1 bun:1 crucially:1 prasad:1 pihur:2 harder:1 reduction:1 contains:4 efficacy:2 selecting:1 ours:1 prefix:13 mishra:1 current:1 comparing:2 com:1 yet:1 must:1 attracted:1 cruz:1 fn:1 partition:1 plot:2 korolova:1 maxv:2 hash:18 v:3 le... |
6,439 | 6,824 | Large-Scale Quadratically Constrained Quadratic
Program via Low-Discrepancy Sequences
Kinjal Basu, Ankan Saha, Shaunak Chatterjee
LinkedIn Corporation
Mountain View, CA 94043
{kbasu, asaha, shchatte}@linkedin.com
Abstract
We consider the problem of solving a large-scale Quadratically Constrained
Quadratic Program. Su... | 6824 |@word cylindrical:2 briefly:1 version:1 polynomial:2 middle:1 simulation:4 p0:2 pick:1 t2n:1 configuration:3 contains:1 series:1 tabulate:1 existing:3 current:1 com:1 comparing:2 chu:2 written:1 numerical:2 shape:1 plot:3 update:1 v:1 item:2 plane:7 xk:2 provides:1 location:1 hyperplanes:1 c6:2 simpler:1 zhang:2 ... |
6,440 | 6,825 | Inhomogeneous Hypergraph Clustering with
Applications
Pan Li
Department ECE
UIUC
panli2@illinois.edu
Olgica Milenkovic
Department ECE
UIUC
milenkov@illinois.edu
Abstract
Hypergraph partitioning is an important problem in machine learning, computer
vision and network analytics. A widely used method for hypergraph par... | 6825 |@word kohli:1 milenkovic:2 version:1 inversion:1 middle:1 hu:5 grey:1 zelnik:1 decomposition:1 sheffet:1 thereby:1 shot:1 liu:1 contains:1 exclusively:4 pub:1 ours:1 outperforms:2 reaction:5 recovered:1 com:2 si:4 yet:1 attracted:1 subsequent:1 partition:39 recasting:1 realistic:1 numerical:1 remove:1 v:2 half:1 ... |
6,441 | 6,826 | Differentiable Learning of Logical Rules for
Knowledge Base Reasoning
Fan Yang
Zhilin Yang
William W. Cohen
School of Computer Science
Carnegie Mellon University
{fanyang1,zhiliny,wcohen}@cs.cmu.edu
Abstract
We study the problem of learning probabilistic first-order logical rules for knowledge base reasoning. This l... | 6826 |@word illustrating:1 version:1 achievable:1 interleave:2 norm:1 retraining:1 nd:1 duran:1 iki:3 mrk:7 jacob:1 yih:1 blade:6 contains:12 score:5 series:1 ours:1 document:1 kurt:1 reynolds:1 outperforms:2 past:1 existing:1 recovered:1 current:1 comparing:1 com:2 diederik:1 john:1 evans:1 numerical:1 otero:1 enables... |
6,442 | 6,827 | Deep Multi-task Gaussian Processes for
Survival Analysis with Competing Risks
Ahmed M. Alaa
Electrical Engineering Department
University of California, Los Angeles
ahmedmalaa@ucla.edu
Mihaela van der Schaar
Department of Engineering Science
University of Oxford
mihaela.vanderschaar@eng.ox.ac.uk
Abstract
Designing ... | 6827 |@word cox:13 version:1 seems:1 prognostic:2 steck:1 eng:1 k7:1 pick:1 thereby:1 rivera:1 moment:2 contains:2 score:1 united:1 t7:1 outperforms:3 existing:2 incidence:5 mihaela:2 must:1 written:3 realistic:1 informative:1 enables:1 update:1 depict:2 mounting:1 auerbach:1 generative:1 leaf:2 record:3 blei:1 provide... |
6,443 | 6,828 | Masked Autoregressive Flow for Density Estimation
George Papamakarios
University of Edinburgh
g.papamakarios@ed.ac.uk
Theo Pavlakou
University of Edinburgh
theo.pavlakou@ed.ac.uk
Iain Murray
University of Edinburgh
i.murray@ed.ac.uk
Abstract
Autoregressive models are among the best performing neural density estimat... | 6828 |@word kohli:1 determinant:3 version:9 eliminating:1 repository:2 logit:3 suitably:1 nd:6 open:2 simulation:2 contains:1 lichman:1 rippel:1 outperforms:4 existing:1 com:2 comparing:2 rnade:4 laparra:1 scatter:2 yet:1 must:1 readily:1 gpu:1 uria:8 enables:3 designed:2 drop:2 update:1 plot:2 interpretable:1 generati... |
6,444 | 6,829 | Non-Convex Finite-Sum Optimization
Via SCSG Methods
Lihua Lei
UC Berkeley
lihua.lei@berkeley.edu
Cheng Ju
UC Berkeley
cju@berkeley.edu
Jianbo Chen
UC Berkeley
jianbochen@berkeley.edu
Michael I. Jordan
UC Berkeley
jordan@stat.berkeley.edu
Abstract
We develop a class of algorithms, as variants of the stochastically c... | 6829 |@word cnn:8 version:6 achievable:1 norm:2 heuristically:1 pick:1 incurs:1 sgd:37 carry:1 reduction:13 initial:1 tuned:2 ours:1 interestingly:1 suppressing:1 document:1 outperforms:5 existing:4 xnj:1 current:1 comparing:2 surprising:1 naman:1 diederik:1 written:1 readily:1 gpu:2 john:5 subsequent:1 plot:1 update:7... |
6,445 | 683 | Learning Control Under Extreme
Uncertainty
Vijaykumar Gullapalli
Computer Science Department
University of Massachusetts
Amherst, MA 01003
Abstract
A peg-in-hole insertion task is used as an example to illustrate
the utility of direct associative reinforcement learning methods for
learning control under real-world co... | 683 |@word trial:6 cylindrical:1 version:3 instruction:1 simulation:2 sensed:18 moment:4 configuration:2 denoting:1 realistic:1 progressively:1 intelligence:1 gear:2 prespecified:1 el1:1 characterization:1 five:2 direct:9 symposium:1 grupen:2 combine:1 expected:1 behavior:5 planning:6 decreasing:1 automatically:1 actua... |
6,446 | 6,830 | 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 non-Gaussian probability... | 6830 |@word illustrating:1 version:1 polynomial:4 norm:1 stronger:1 villani:1 open:1 covariance:6 decomposition:1 deems:1 liu:4 contains:1 score:1 nonparanormal:2 current:1 recovered:8 z2:16 chordal:1 si:2 yet:1 goldberger:1 must:4 realistic:1 subsequent:3 numerical:2 remove:1 drop:1 plot:1 acar:1 greedy:1 fewer:3 half... |
6,447 | 6,831 | An Inner-loop Free Solution to Inverse Problems
using Deep Neural Networks
Kai Fai?
Duke University
kai.fan@stat.duke.edu
Lawrence Carin
Duke University
lcarin@duke.edu
Qi Wei?
Duke University
qi.wei@duke.edu
Katherine Heller
Duke University
kheller@stat.duke.edu
Abstract
We propose a new method that uses deep learn... | 6831 |@word mild:1 middle:4 version:3 inversion:21 mri:1 faculty:1 open:1 hu:1 simulation:1 mention:1 shot:2 ld:2 initial:1 liu:1 series:1 daniel:2 tuned:1 document:1 existing:3 michal:1 luo:1 activation:1 chu:1 gpu:1 axk22:1 tarantola:1 periodically:1 concatenate:1 hajnal:1 enables:1 remove:1 update:24 generative:4 xk... |
6,448 | 6,832 | OnACID: Online Analysis of Calcium Imaging Data
in Real Time
Andrea Giovannucci?1
Anne K. Churchland?
Johannes Friedrich??1
Dmitri Chklovskii?
Matthew Kaufman?
Liam Paninski?
Eftychios A. Pnevmatikakis?2
? Flatiron Institute, New York, NY 10010
? Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724
?
Columbia... | 6832 |@word neurophysiology:1 version:1 middle:4 villani:1 nd:1 bf:1 open:2 hu:1 grey:1 pengcheng:1 thereby:1 schnitzer:1 reduction:1 initial:2 deisseroth:4 series:1 score:6 optically:1 contains:1 daniel:7 genetic:1 reynolds:1 schuck:1 existing:5 luigi:1 current:4 outperforms:6 rish:1 anne:1 manuel:1 com:1 yet:1 assign... |
6,449 | 6,833 | Collaborative PAC Learning
Avrim Blum
Toyota Technological Institute at Chicago
Chicago, IL 60637
avrim@ttic.edu
Ariel D. Procaccia
Computer Science Department
Carnegie Mellon University
Pittsburgh, PA 15213
arielpro@cs.cmu.edu
Nika Haghtalab
Computer Science Department
Carnegie Mellon University
Pittsburgh, PA 1521... | 6833 |@word multitask:2 seems:1 norm:1 nd:2 dekel:1 simulation:1 asks:1 incurs:2 thereby:1 tr:4 carry:1 reduction:1 series:2 contains:2 interestingly:2 existing:1 must:5 john:1 subsequent:1 chicago:2 remove:1 update:1 n0:5 half:1 intelligence:2 item:2 accordingly:1 xk:3 record:1 completeness:1 boosting:3 location:2 rc:... |
6,450 | 6,834 | Fast Black-box Variational Inference
through Stochastic Trust-Region Optimization
Jeffrey Regier
jregier@cs.berkeley.edu
Michael I. Jordan
jordan@cs.berkeley.edu
Jon McAuliffe
jon@stat.berkeley.edu
Abstract
We introduce TrustVI, a fast second-order algorithm for black-box variational
inference based on trust-region ... | 6834 |@word mild:1 kgk:2 repository:1 trial:3 inversion:1 norm:8 nd:1 open:1 instruction:1 simulation:2 tried:1 p0:2 pick:3 sgd:3 carry:1 initial:2 exclusively:1 selecting:1 jimenez:1 bradley:1 current:2 com:1 diederik:1 must:3 john:1 numerical:8 realistic:1 subsequent:1 visible:1 analytic:3 christian:1 update:1 statio... |
6,451 | 6,835 | Scalable Demand-Aware Recommendation
Jinfeng Yi1?, Cho-Jui Hsieh2 , Kush R. Varshney3 , Lijun Zhang4 , Yao Li2
1
AI Foundations Lab, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA
2
University of California, Davis, CA, USA
3
IBM Research AI, Yorktown Heights, NY, USA
4
National Key Laboratory for Nove... | 6835 |@word adomavicius:1 version:1 norm:8 open:2 hu:2 seek:1 decomposition:1 hsieh:3 bellevue:1 liu:2 contains:2 score:1 interestingly:1 yaoli:1 outperforms:3 existing:4 steiner:1 recovered:2 com:3 comparing:4 bradley:1 must:2 written:2 john:1 numerical:1 j1:1 update:5 v:3 chohsieh:1 stationary:1 selected:1 fewer:1 it... |
6,452 | 6,836 | SGD Learns the Conjugate Kernel Class of the
Network
Amit Daniely
Hebrew University and Google Research
amit.daniely@mail.huji.ac.il
Abstract
We show that the standard stochastic gradient decent (SGD) algorithm is guaranteed to learn,
in polynomial time, a function that is competitive with the best function in the con... | 6836 |@word h:1 version:1 polynomial:28 norm:6 seems:1 nd:3 hu:1 covariance:1 arti:1 eld:1 sgd:29 recursively:1 ld:13 initial:6 contains:2 pt0:1 ours:1 com:1 activation:22 dx:1 nt1:1 ronald:1 update:1 maxv:1 generative:1 intelligence:1 complementing:1 ith:1 provides:1 node:16 gautam:1 zhang:2 symposium:2 replication:4 ... |
6,453 | 6,837 | 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.cmu.... | 6837 |@word version:3 dekel:1 open:1 covariance:1 citeseer:1 mention:2 harder:1 reduction:2 contains:1 score:3 ours:5 fa8750:1 err:9 bradley:1 comparing:1 beygelzimer:2 si:3 must:1 hongyang:1 update:1 isotropic:2 beginning:1 preference:5 zhang:10 along:1 c2:11 direct:2 kvk2:1 prove:2 combine:4 x0:11 pairwise:18 lov:1 h... |
6,454 | 6,838 | Analyzing Hidden Representations in End-to-End
Automatic Speech Recognition Systems
Yonatan Belinkov and James Glass
Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139
{belinkov, glass}@mit.edu
Abstract
Neural networks have become ubiquitous in automatic s... | 6838 |@word multitask:1 version:1 nd:2 initial:3 ndez:1 series:1 score:2 qatar:1 panayotov:1 interestingly:2 blank:17 comparing:2 com:2 surprising:1 contextual:1 activation:2 grapheme:1 attracted:1 dx:1 subsequent:1 chrupa:3 informative:1 shape:2 remove:1 drop:8 plot:2 v:2 alone:1 intelligence:1 fewer:1 selected:1 shor... |
6,455 | 6,839 | 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 University, Rep. of Korea
f... | 6839 |@word kulis:1 determinant:3 repository:1 eliminating:1 turlach:1 covariance:5 decomposition:2 tr:2 reduction:3 configuration:2 series:3 nohyung:1 daniel:1 comparing:1 goldberger:1 yet:1 written:1 john:1 distant:2 eleven:1 shape:3 plot:1 update:1 generative:7 selected:1 intelligence:4 isotropic:2 haykin:1 hypersph... |
6,456 | 684 | A Neural Model of Descending Gain
Control in the Electrosensory System
Mark E. Nelson
Beckman Institute
University of Illinois
405 N. Mathews
Urbana, IL 61801
Abstract
In the electrosensory system of weakly electric fish, descending
pathways to a first-order sensory nucleus have been shown to influence the gain of it... | 684 |@word eex:3 open:1 electrosensory:19 lobe:7 electroreceptors:4 excited:1 cytology:1 current:6 activation:1 yet:4 toh:1 must:3 tot:7 physiol:5 subsequent:1 shape:1 nervous:3 compo:7 provides:1 characterization:1 location:1 ron:2 rc:3 direct:1 resistive:1 pathway:28 presumed:1 behavior:1 themselves:1 morphology:1 br... |
6,457 | 6,840 | Information Theoretic Properties of Markov Random
Fields, and their Algorithmic Applications
Linus Hamilton?
Frederic Koehler ?
Ankur Moitra ?
Abstract
Markov random fields are a popular model for high-dimensional probability distributions. Over the years, many mathematical, statistical and algorithmic problems
on ... | 6840 |@word cu:4 version:1 polynomial:3 seems:1 open:1 pieter:1 seek:1 decomposition:2 pick:1 configuration:3 liu:2 contains:3 selecting:1 daniel:1 surprising:2 written:1 must:1 john:1 partition:1 remove:1 n0:1 intelligence:4 greedy:5 guess:5 parameterization:1 hyuk:1 junta:1 node:35 gautam:1 daphne:2 narayana:1 mathem... |
6,458 | 6,841 | Fitting Low-Rank Tensors in Constant Time
Kohei Hayashi?
National Institute of Advanced Industrial Science and Technology
RIKEN AIP
hayashi.kohei@gmail.com
Yuichi Yoshida?
National Institute of Informatics
yyoshida@nii.ac.jp
Abstract
In this paper, we develop an algorithm that approximates the residual error of
Tucke... | 6841 |@word kgk:1 repository:1 version:3 trial:1 norm:12 seems:1 disk:1 r:4 crucially:1 decomposition:28 pick:1 series:1 nii:1 outperforms:1 existing:1 com:3 gmail:1 dx:4 must:2 acar:2 selected:2 xk:10 core:6 bijection:4 gx:1 mathematical:2 along:1 constructed:1 lathauwer:1 ik:14 focs:1 consists:3 prove:1 shorthand:1 f... |
6,459 | 6,842 | 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,znsun,rhe,tnt}@nlpr... | 6842 |@word multitask:1 kulis:2 cnn:20 norm:2 vldb:1 propagate:2 tr:1 accommodate:1 cyclic:3 contains:3 liu:5 ours:5 outperforms:6 existing:1 current:2 activation:1 attracted:1 designed:1 gist:1 update:2 drop:1 hash:55 intelligence:1 selected:1 zhang:7 five:1 rc:2 constructed:2 direct:1 consists:3 pairwise:13 excellenc... |
6,460 | 6,843 | Using Options and Covariance Testing for Long
Horizon Off-Policy Policy Evaluation
Zhaohan Daniel Guo
Carnegie Mellon University
Pittsburgh, PA 15213
zguo@cs.cmu.edu
Philip S. Thomas
University of Massachusetts Amherst
Amherst, MA 01003
pthomas@cs.umass.edu
Emma Brunskill
Stanford University
Stanford, CA 94305
ebrun... | 6843 |@word trial:1 faculty:1 termination:2 simulation:2 crucially:1 tried:1 covariance:11 decomposition:3 pick:3 reduction:6 configuration:1 contains:1 uma:1 exclusively:1 hereafter:1 daniel:2 series:1 o2:2 current:2 comparing:2 realize:1 partition:4 motor:1 drop:6 stationary:10 greedy:5 leaf:1 half:1 intelligence:4 b... |
6,461 | 6,844 | How regularization affects the critical points in linear
networks
Amirhossein Taghvaei?
Coordinated Science Laboratory
University of Illinois at Urbana-Champaign
Urbana, IL, 61801
taghvae2@illinois.edu
Jin W. Kim
Coordinated Science Laboratory
University of Illinois at Urbana-Champaign
Urbana, IL, 61801
jkim684@illino... | 6844 |@word mild:1 briefly:1 stronger:1 mehta:1 open:2 decomposition:1 p0:3 tr:11 arous:2 reduction:1 moment:1 initial:1 renewed:1 numerical:9 update:2 v:1 plane:1 dembo:1 steepest:2 hamiltonian:4 characterization:6 provides:2 pascanu:1 simpler:1 zhang:2 mathematical:1 along:2 differential:1 qualitative:3 yuan:1 baldi:... |
6,462 | 6,845 | Fisher GAN
Youssef Mroueh? , Tom Sercu?
mroueh@us.ibm.com, tom.sercu1@ibm.com
? Equal Contribution
AI Foundations, IBM Research AI
IBM T.J Watson Research Center
Abstract
Generative Adversarial Networks (GANs) are powerful models for learning complex distributions. Stable training of GANs has been addressed in many re... | 6845 |@word h:3 norm:4 nd:1 c0:2 bn:6 covariance:5 bachman:1 sgd:2 boundedness:2 ipm:53 ld:1 moment:7 liu:2 score:13 daniel:1 ours:3 interestingly:1 rkhs:2 com:3 comparing:1 varx:3 luo:1 amjad:1 diederik:1 dx:3 written:4 gpu:1 john:1 numerical:1 chicago:1 enables:1 christian:1 hypothesize:1 plot:4 yinda:1 update:4 gene... |
6,463 | 6,846 | Information-theoretic analysis of generalization
capability of learning algorithms
Aolin Xu
Maxim Raginsky
{aolinxu2,maxim}@illinois.edu ?
Abstract
We derive upper bounds on the generalization error of a learning algorithm in
terms of the mutual information between its input and output. The bounds provide
an informati... | 6846 |@word private:1 version:1 pw:31 polynomial:1 stronger:1 elisseeff:2 pick:3 denoting:1 interestingly:1 existing:1 written:1 intelligence:1 selected:1 ith:1 smith:1 provides:2 boosting:1 quantized:1 codebook:1 preference:4 org:1 simpler:1 zhang:2 unbounded:3 dn:2 shatter:1 differential:6 symposium:3 pwj:1 focs:1 fi... |
6,464 | 6,847 | Sparse Approximate Conic Hulls
Gregory Van Buskirk, Benjamin Raichel, and Nicholas Ruozzi
Department of Computer Science
University of Texas at Dallas
Richardson, TX 75080
{greg.vanbuskirk, benjamin.raichel, nicholas.ruozzi}@utdallas.edu
Abstract
We consider the problem of computing a restricted nonnegative matrix fa... | 6847 |@word cu:2 version:18 repository:1 polynomial:4 norm:3 seems:1 open:2 seek:3 decomposition:6 p0:2 simplifying:1 carry:1 reduction:7 liu:1 contains:2 bc:5 skd:2 hottopixx:1 document:2 existing:1 current:4 surprising:1 si:2 must:3 additive:1 plot:1 alone:1 greedy:16 selected:7 fewer:1 implying:1 intelligence:3 xk:4... |
6,465 | 6,848 | Rigorous Dynamics and Consistent Estimation in
Arbitrarily Conditioned Linear Systems
Alyson K. Fletcher
Dept. Statistics
UC Los Angeles
akfletcher@ucla.edu
Mojtaba Sahraee-Ardakan
Dept. EE,
UC Los Angeles
msahraee@ucla.edu
Sundeep Rangan
Dept. ECE,
NYU
srangan@nyu.edu
Philip Schniter
Dept. ECE,
The Ohio State Univ... | 6848 |@word briefly:1 simulation:4 decomposition:1 p0:3 tr:3 recursively:1 moment:1 initial:8 selecting:1 tuned:1 amp:13 mmse:2 past:1 z2:2 discretization:1 comparing:1 must:1 written:4 numerical:4 additive:1 analytic:1 shamai:1 update:7 aside:1 intelligence:1 selected:1 provides:5 characterization:2 along:3 ik:15 comb... |
6,466 | 6,849 | Toward Goal-Driven Neural Network Models for the
Rodent Whisker-Trigeminal System
Chengxu Zhuang
Department of Psychology
Stanford University
Stanford, CA 94305
chengxuz@stanford.edu
Mitra Hartmann
Departments of Biomedical Engineering
and Mechanical Engineering
Northwestern University
Evanston, IL 60208
hartmann@nor... | 6849 |@word neurophysiology:1 trial:1 cox:1 version:2 repository:1 cnn:1 kriegeskorte:2 middle:2 nd:1 houweling:1 loading:2 open:1 integrative:1 simulation:2 seek:3 evaluating:1 pavel:1 citeseer:1 moment:1 initial:7 configuration:1 series:2 contains:1 united:1 nonlinearly:1 daniel:7 past:1 existing:1 reaction:1 current... |
6,467 | 685 | Statistical Modeling of Cell-Assemblies
Activities in Associative Cortex of
Behaving Monkeys
Itay Gat and Naftali Tishby
Institute of Computer Science and
Center for Neural Computation
Hebrew University, Jerusalem 91904, Israel *
Abstract
So far there has been no general method for relating extracellular
electrophysi... | 685 |@word trial:9 nd:1 rhesus:2 contains:1 recovered:1 yet:1 treating:1 drop:2 ilii:1 discrimination:3 v:1 poritz:1 selected:2 short:1 provides:1 characterization:3 location:2 profound:1 fixation:2 behavioral:9 introduce:1 manner:2 pairwise:4 hardness:1 behavior:3 multi:10 brain:2 little:1 encouraging:1 window:5 estim... |
6,468 | 6,850 | Accuracy First: Selecting a Differential Privacy Level
for Accuracy-Constrained ERM
Katrina Ligett
Caltech and Hebrew University
Seth Neel
University of Pennsylvania
Bo Waggoner
University of Pennsylvania
Aaron Roth
University of Pennsylvania
Zhiwei Steven Wu
Microsoft Research
Abstract
Traditional approaches to ... | 6850 |@word trial:2 private:68 version:12 achievable:1 stronger:2 norm:3 bun:1 open:1 gradual:2 covariance:11 pick:1 incurs:4 pihur:1 shot:1 reduction:20 necessity:2 selecting:1 daniel:1 interestingly:1 prefix:7 outperforms:1 existing:3 com:1 written:1 john:1 subsequent:2 kdd:5 plot:2 ligett:1 update:2 v:5 selected:1 i... |
6,469 | 6,851 | EX2: Exploration with Exemplar Models for Deep
Reinforcement Learning
Justin Fu?
John D. Co-Reyes?
Sergey Levine
University of California Berkeley
{justinfu,jcoreyes,svlevine}@eecs.berkeley.edu
Abstract
Deep reinforcement learning algorithms have been shown to learn complex tasks
using highly general policy classes. ... | 6851 |@word version:2 polynomial:4 open:1 proportionality:1 pieter:6 seek:2 tried:1 simulation:1 thereby:1 carry:1 initial:1 generatively:1 contains:2 score:3 tuned:1 bootstrapped:2 ours:2 outperforms:1 current:4 com:1 nuttapong:1 tackling:1 dx:14 must:2 john:6 ronald:1 realistic:1 informative:1 designed:1 generative:2... |
6,470 | 6,852 | 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 notion of relate... | 6852 |@word multitask:31 mr2:1 norm:2 proportion:1 tedious:1 d2:15 hu:1 decomposition:2 moment:1 initial:3 liu:1 series:1 contains:1 selecting:1 daniel:1 offering:1 prefix:7 outperforms:2 recovered:2 tackling:1 liva:1 numerical:1 v:1 intelligence:1 selected:1 xk:3 ith:2 math:1 contribute:1 denis:2 theodoros:1 zhang:2 a... |
6,471 | 6,853 | Multi-way Interacting Regression via Factorization
Machines
XuanLong Nguyen
Department of Statistics
University of Michigan
xuanlong@umich.edu
Mikhail Yurochkin
Department of Statistics
University of Michigan
moonfolk@umich.edu
Nikolaos Vasiloglou
LogicBlox
nikolaos.vasiloglou@logicblox.com
Abstract
We propose a Bay... | 6853 |@word version:1 briefly:1 polynomial:3 seems:1 proportion:4 faculty:1 logit:1 norm:1 simulation:2 accounting:2 pick:1 carry:2 series:2 selecting:1 zij:2 daniel:1 existing:3 current:1 com:2 incidence:3 recovered:3 must:1 john:1 realistic:1 treating:1 plot:1 update:2 half:1 fewer:1 selected:3 ffm:19 trung:1 beginni... |
6,472 | 6,854 | Predicting Organic Reaction Outcomes with
Weisfeiler-Lehman Network
Wengong Jin? Connor W. Coley? Regina Barzilay? Tommi Jaakkola?
?
Computer Science and Artificial Intelligence Lab, MIT
?
Department of Chemical Engineering, MIT
?
{wengong,regina,tommi}@csail.mit.edu, ? ccoley@mit.edu
Abstract
The prediction of organi... | 6854 |@word h:1 cu:5 version:1 eliminating:1 determinant:2 proportion:1 advantageous:1 hu:1 p0:5 invoking:1 pick:1 initial:1 configuration:5 cristina:1 score:12 liu:1 outperforms:5 reaction:124 existing:3 current:3 comparing:1 contextual:1 clara:1 diederik:1 dx:1 written:1 must:1 enables:1 designed:1 hash:1 intelligenc... |
6,473 | 6,855 | Practical Data-Dependent Metric Compression with
Provable Guarantees
Piotr Indyk?
MIT
Ilya Razenshteyn?
MIT
Tal Wagner?
MIT
Abstract
We introduce a new distance-preserving compact representation of multidimensional point-sets. Given n points in a d-dimensional space where each
coordinate is represented using B bits ... | 6855 |@word multitask:1 version:2 compression:13 seems:1 nd:3 bn:1 reduction:1 liu:1 series:2 contains:2 tuned:1 document:1 mishra:1 recovered:1 current:1 written:1 gpu:1 exposing:1 concatenate:2 partition:1 razenshteyn:1 remove:2 designed:1 plot:3 v:1 hash:1 half:2 leaf:6 intelligence:2 short:5 provides:1 node:14 loca... |
6,474 | 6,856 | REBAR: Low-variance, unbiased gradient estimates
for discrete latent variable models
George Tucker1,?, Andriy Mnih2 , Chris J. Maddison2,3 ,
Dieterich Lawson1,* , Jascha Sohl-Dickstein1
1
Google Brain, 2 DeepMind, 3 University of Oxford
{gjt, amnih, dieterichl, jaschasd}@google.com
cmaddis@stats.ox.ac.uk
Abstract
Lear... | 6856 |@word trial:6 version:3 middle:1 seek:2 bn:3 sgd:1 moment:1 reduction:8 configuration:1 score:2 jimenez:1 daniel:1 tuned:2 denoting:2 ours:1 com:2 activation:1 diederik:2 must:2 john:1 ronald:1 hypothesize:1 designed:1 plot:5 update:1 alone:1 generative:12 half:3 intelligence:1 beginning:1 ith:1 tarlow:1 blei:3 c... |
6,475 | 6,857 | Nonlinear random matrix theory for deep learning
Jeffrey Pennington
Google Brain
jpennin@google.com
Pratik Worah
Google Research
pworah@google.com
Abstract
Neural network configurations with random weights play an important role in the
analysis of deep learning. They define the initial loss landscape and are closely
... | 6857 |@word inversion:1 polynomial:6 eliminating:1 nd:1 open:1 confirms:2 simulation:4 propagate:4 covariance:9 simplifying:1 thereby:1 tr:11 solid:1 arous:1 moment:16 initial:3 configuration:2 series:1 contains:1 daniel:1 past:1 existing:2 recovered:2 com:2 z2:2 surprising:3 karoui:2 activation:20 universality:1 intri... |
6,476 | 6,858 | Parallel Streaming Wasserstein Barycenters
Matthew Staib
MIT CSAIL
mstaib@mit.edu
Sebastian Claici
MIT CSAIL
sclaici@mit.edu
Justin Solomon
MIT CSAIL
jsolomon@mit.edu
Stefanie Jegelka
MIT CSAIL
stefje@mit.edu
Abstract
Efficiently aggregating data from different sources is a challenging problem, particularly when sa... | 6858 |@word mild:1 repository:1 version:2 norm:1 advantageous:2 villani:2 unif:1 adrian:1 gradual:1 jacob:1 asks:1 reduction:2 initial:1 moment:1 series:1 selecting:3 united:1 recovered:1 current:2 optim:2 discretization:3 com:2 si:6 yet:1 dx:1 chu:1 must:8 attracted:1 written:1 mesh:6 readily:1 john:4 recasting:1 nume... |
6,477 | 6,859 | ELF: An Extensive, Lightweight and Flexible
Research Platform for Real-time Strategy Games
Yuandong Tian1
Qucheng Gong1
Wenling Shang2
Yuxin Wu1
C. Lawrence Zitnick1
1
2
Facebook AI Research
Oculus
1
2
{yuandong, qucheng, yuxinwu, zitnick}@fb.com
wendy.shang@oculus.com
Abstract
In this paper, we propose ELF, an... | 6859 |@word katja:1 repository:1 version:1 cnn:1 stronger:2 johansson:1 open:5 adrian:1 pieter:1 simulation:9 bn:2 pick:1 initial:3 wrapper:2 lightweight:6 score:1 configuration:2 contains:5 daniel:1 interestingly:1 existing:10 current:7 com:9 yet:1 issuing:1 guez:1 gpu:5 written:3 john:2 realistic:3 cant:1 hofmann:1 c... |
6,478 | 686 | Self-Organizing Rules for Robust
Principal Component Analysis
Lei Xu l ,2"'and Alan Yuille l
1. Division of Applied Sciences, Harvard University, Cambridge, MA 02138
2. Dept. of Mathematics, Peking University, Beijing, P.R.China
Abstract
In the presence of outliers, the existing self-organizing rules for
Principal Co... | 686 |@word mild:1 version:5 compression:1 nd:1 paid:1 tr:2 contains:3 denoting:1 existing:6 current:1 xiyi:2 moo:1 partition:1 j1:2 plane:3 prespecified:1 math:2 along:1 unacceptable:1 fitting:1 deteriorate:1 brain:1 little:1 jm:1 totally:1 becomes:1 mass:1 minimizes:1 eigenvector:1 akl:1 developed:1 finding:3 act:1 ta... |
6,479 | 6,860 | 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 of mode col... | 6860 |@word version:5 middle:1 stronger:1 seems:2 d2:41 semicontinuous:2 seek:2 confirms:1 rgb:1 covariance:1 jacob:1 pg:30 shot:1 harder:1 configuration:1 contains:2 score:27 united:1 ours:1 outperforms:2 steiner:1 com:2 activation:4 yet:1 dx:4 diederik:1 devin:1 realistic:1 numerical:2 partition:1 shape:1 christian:1... |
6,480 | 6,861 | Dynamic Revenue Sharing?
Santiago Balseiro
Columbia University
New York City, NY
srb2155@columbia.edu
Max Lin
Google
New York City, NY
whlin@google.com
Vahab Mirrokni
Google
New York City, NY
mirrokni@google.com
Song Zuo?
Tsinghua University
Beijing, China
songzuo.z@gmail.com
Renato Paes Leme
Google
New York City,... | 6861 |@word version:10 advantageous:1 stronger:1 c0:2 bf:2 willing:2 confirms:1 r:5 simulation:3 simplifying:1 paid:4 profit:51 mention:1 prefix:8 past:2 com:6 discretization:1 gmail:1 must:1 sergei:1 exposing:1 john:1 partition:1 implying:2 greedy:1 stationary:1 item:12 granting:1 record:3 provides:1 mathematical:2 dr... |
6,481 | 6,862 | Decomposition-Invariant Conditional Gradient for
General Polytopes with Line Search
Mohammad Ali Bashiri
Xinhua Zhang
Department of Computer Science, University of Illinois at Chicago
Chicago, Illinois 60661
{mbashi4,zhangx}@uic.edu
Abstract
Frank-Wolfe (FW) algorithms with linear convergence rates have recently achi... | 6862 |@word h:38 msr:1 repository:2 version:4 pw:4 norm:6 seems:1 advantageous:1 c0:8 d2:9 heuristically:1 decomposition:15 hsieh:1 prokhorov:1 q1:1 delicately:2 incurs:1 pick:3 tr:1 reduction:1 initial:1 liu:1 exclusively:1 lichman:1 denoting:1 outperforms:2 existing:1 current:3 surprising:1 yet:1 written:4 must:7 chi... |
6,482 | 6,863 | VAIN: Attentional Multi-agent Predictive Modeling
Yedid Hoshen
Facebook AI Research, NYC
yedidh@fb.com
Abstract
Multi-agent predictive modeling is an essential step for understanding physical,
social and team-play systems. Recently, Interaction Networks (INs) were proposed
for the task of modeling multi-agent physica... | 6863 |@word cnn:1 repository:1 illustrating:2 fcns:3 advantageous:2 stronger:4 nd:1 simulation:3 jingdong:1 brightness:2 initial:2 configuration:1 contains:1 selecting:1 jimenez:1 daniel:1 ours:4 outperforms:5 current:3 com:5 activation:2 diederik:1 guez:1 parmar:1 gpu:2 john:1 additive:7 informative:2 cheap:1 designed... |
6,483 | 6,864 | An Empirical Bayes Approach to Optimizing
Machine Learning Algorithms
James McInerney
Spotify Research
45 W 18th St, 7th Floor
New York, NY 10011
jamesm@spotify.com
Abstract
There is rapidly growing interest in using Bayesian optimization to tune model and
inference hyperparameters for machine learning algorithms tha... | 6864 |@word trial:1 exploitation:2 simulation:3 simplifying:1 covariance:2 xtest:1 contraction:1 necessity:1 configuration:1 selecting:3 punishes:1 bootstrapped:1 document:9 outperforms:3 existing:1 freitas:1 current:3 com:3 comparing:2 surprising:2 recovered:1 past:1 readily:1 informative:1 predetermined:1 pertinent:1... |
6,484 | 6,865 | 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 Buffalo
Buffalo, NY ... | 6865 |@word private:32 achievable:1 norm:18 open:1 calculus:1 pg:1 pick:2 sgd:5 nsw:1 moment:2 necessity:1 reduction:3 initial:3 ours:1 existing:1 z2:1 comparing:1 chu:1 bd:2 ust:2 chicago:1 interpretable:1 intelligence:1 accordingly:1 xk:6 smith:4 ojasiewicz:2 boosting:1 revisited:1 kasiviswanathan:2 attack:1 zhang:6 ... |
6,485 | 6,866 | Variational Inference via
? Upper Bound Minimization
Adji B. Dieng
Columbia University
Dustin Tran
Columbia University
John Paisley
Columbia University
Rajesh Ranganath
Princeton University
David M. Blei
Columbia University
Abstract
Variational inference (VI) is widely used as an efficient alternative to Markov
c... | 6866 |@word madelon:1 cox:9 version:1 open:1 seek:2 covariance:1 shot:11 reduction:1 moment:1 ndez:3 score:1 fa8750:1 outperforms:2 existing:3 must:2 john:3 tilted:1 partition:2 enables:3 christian:2 plot:7 concert:2 update:1 aside:1 generative:3 beginning:1 hamiltonian:1 blei:6 provides:6 location:4 wierstra:1 viable:... |
6,486 | 6,867 | On Quadratic Convergence of DC Proximal Newton
Algorithm in Nonconvex Sparse Learning
Xingguo Li1,4 Lin F. Yang2? Jason Ge2 Jarvis Haupt1 Tong Zhang3 Tuo Zhao4?
1
University of Minnesota 2 Princeton University 3 Tencent AI Lab 4 Georgia Tech
Abstract
We propose a DC proximal Newton algorithm for solving nonconvex regu... | 6867 |@word mild:1 madelon:2 trial:1 polynomial:1 norm:1 nd:2 c0:2 r:3 covariance:1 jacob:1 hsieh:1 wrapper:1 liu:10 contains:2 series:2 initial:2 tuned:1 outperforms:2 existing:4 luo:1 john:3 numerical:3 additive:2 realistic:1 plot:1 update:5 selected:1 harmany:1 amir:1 runze:1 core:1 characterization:2 c6:3 zhang:8 m... |
6,487 | 6,868 | #Exploration: A Study of Count-Based Exploration
for Deep Reinforcement Learning
Haoran Tang1? , Rein Houthooft34? , Davis Foote2 , Adam Stooke2 , Xi Chen2? ,
Yan Duan2? , John Schulman4 , Filip De Turck3 , Pieter Abbeel 2?
1
UC Berkeley, Department of Mathematics
2
UC Berkeley, Department of Electrical Engineering an... | 6868 |@word trial:1 exploitation:1 version:2 dalal:1 polynomial:3 compression:1 nd:1 triggs:1 pieter:5 simulation:1 propagate:1 rgb:1 pressure:1 pick:1 solid:3 lepetit:1 necessity:1 initial:1 contains:1 score:1 typology:1 jimenez:1 bootstrapped:3 deconvolutional:1 outperforms:2 existing:1 hasselt:2 current:2 com:2 disc... |
6,488 | 6,869 | An Empirical Study on The Properties of
Random Bases for Kernel Methods
Maximilian Alber, Pieter-Jan Kindermans, Kristof T. Sch?tt
Technische Universit?t Berlin
maximilian.alber@tu-berlin.de
Klaus-Robert M?ller
Technische Universit?t Berlin
Korea University
Max Planck Institut f?r Informatik
Fei Sha
University of Sout... | 6869 |@word trial:1 advantageous:1 norm:1 open:1 pieter:1 heuristically:1 seek:1 hu:2 orf:1 elisseeff:1 nystr:6 solid:1 electronics:1 liu:1 series:1 selecting:1 daniel:1 tuned:1 dubourg:1 existing:1 kx0:1 current:1 ka:1 comparing:1 com:1 jaz:1 activation:2 yet:1 freitas:1 john:2 sanjiv:2 numerical:1 informative:1 kdd:1... |
6,489 | 687 | Holographic Recurrent Networks
Tony A. Plate
Department of Computer Science
University of Toronto
Toronto, M5S lA4 Canada
Abstract
Holographic Recurrent Networks (HRNs) are recurrent networks
which incorporate associative memory techniques for storing sequential structure. HRNs can be easily and quickly trained using... | 687 |@word version:1 tried:1 tr:2 initial:1 contains:1 interestingly:1 current:1 activation:12 must:4 john:1 subsequent:1 predetermined:1 designed:2 update:1 generative:7 intelligence:2 item:3 beginning:1 short:1 provides:1 node:1 toronto:3 location:1 successive:1 instructs:1 lor:1 along:2 acti:1 expected:2 roughly:1 e... |
6,490 | 6,870 | 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) based on a relations... | 6870 |@word trial:1 briefly:1 version:3 eliminating:1 faculty:1 nd:1 simulation:1 dramatic:1 mention:1 harder:1 recursively:3 kappen:4 reduction:2 inefficiency:1 series:1 renewed:1 bootstrapped:1 outperforms:3 freitas:2 current:3 com:2 recovered:1 contextual:1 si:38 yet:1 bello:1 must:3 written:1 chu:1 seeding:1 plot:3... |
6,491 | 6,871 | Premise Selection for Theorem Proving
by Deep Graph Embedding
Mingzhe Wang? Yihe Tang? Jian Wang Jia Deng
University of Michigan, Ann Arbor
Abstract
We propose a deep learning-based approach to the problem of premise selection:
selecting mathematical statements relevant for proving a given conjecture. We
represent a ... | 6871 |@word cnn:2 version:2 laurence:1 nd:2 calculus:1 heiser:1 tat:2 bn:5 citeseer:1 kutzkov:1 harder:1 recursively:2 initial:6 configuration:7 contains:4 fragment:1 selecting:2 daniel:4 interestingly:1 existing:3 current:2 comparing:1 si:3 yet:1 parsing:4 john:4 ashesh:1 realistic:1 christian:5 designed:1 drop:3 upda... |
6,492 | 6,872 | A Bayesian Data Augmentation Approach for
Learning Deep Models
Toan Tran1 , Trung Pham1 , Gustavo Carneiro1 , Lyle Palmer2 and Ian Reid1
1
School of Computer Science, 2 School of Public Health
The University of Adelaide, Australia
{toan.m.tran, trung.pham, gustavo.carneiro,
lyle.palmer, ian.reid} @adelaide.edu.au
Abs... | 6872 |@word multitask:1 nd:1 contrastive:2 citeseer:1 sgd:3 series:1 denoting:1 document:4 ours:12 deconvolutional:1 subjective:1 existing:2 outperforms:1 current:4 com:2 realistic:3 additive:2 analytic:1 enables:1 designed:1 interpretable:1 update:1 stationary:1 generative:26 selected:1 intelligence:2 trung:2 accordin... |
6,493 | 6,873 | Principles of Riemannian Geometry
in Neural Networks
Michael Hauser
Department of Mechanical Engineering
Pennsylvania State University
State College, PA 16801
mzh190@psu.edu
Asok Ray
Department of Mechanical Engineering
Pennsylvania State University
State College, PA 16801
axr2@psu.edu
Abstract
This study deals with ... | 6873 |@word version:2 norm:1 d2:1 closure:2 p0:2 yih:1 recursively:1 reduction:1 document:1 interestingly:1 deconvolutional:1 past:1 comparing:1 activation:4 dx:1 written:2 gpu:1 ronald:1 numerical:2 partition:1 shape:1 designed:3 implying:1 device:1 parameterization:3 accordingly:1 ivo:1 beginning:2 short:2 feedfoward... |
6,494 | 6,874 | 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, namely warm-start tr... | 6874 |@word version:2 bigram:1 norm:2 confirms:1 r:1 pg:8 pick:2 concise:1 recursively:1 carry:1 reduction:7 initial:1 inefficiency:1 configuration:3 contains:4 zij:9 score:19 liu:1 series:1 document:2 outperforms:1 existing:1 guadarrama:1 com:3 comparing:1 guez:1 evans:1 numerical:2 partition:1 ronald:1 christian:1 re... |
6,495 | 6,875 | Online Dynamic Programming
Holakou Rahmanian
Department of Computer Science
University of California Santa Cruz
Santa Cruz, CA 95060
holakou@ucsc.edu
Manfred K. Warmuth
Department of Computer Science
University of California Santa Cruz
Santa Cruz, CA 95060
manfred@ucsc.edu
Abstract
We consider the problem of repeated... | 6875 |@word trial:28 version:2 briefly:2 polynomial:8 seems:2 middle:1 norm:1 additively:1 crucially:1 decomposition:1 pick:1 incurs:1 kijima:2 carry:1 initial:2 cyclic:1 series:2 selecting:1 past:1 existing:1 current:5 recovered:1 tackling:1 must:1 parsing:1 cruz:4 ronald:2 additive:1 partition:1 remove:1 update:2 gre... |
6,496 | 6,876 | 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 with structured ... | 6876 |@word multitask:1 trial:1 exploitation:1 version:1 achievable:4 norm:22 seems:1 suitably:2 c0:1 hu:1 confirms:2 covariance:28 jacob:1 invoking:1 paid:1 tr:14 initial:2 liu:3 series:2 past:1 existing:3 outperforms:1 comparing:2 luo:2 assigning:1 subsequent:1 kdd:1 plot:2 update:3 resampling:10 intelligence:1 selec... |
6,497 | 6,877 | Convolutional Gaussian Processes
Mark van der Wilk
Department of Engineering
University of Cambridge, UK
mv310@cam.ac.uk
Carl Edward Rasmussen
Department of Engineering
University of Cambridge, UK
cer54@cam.ac.uk
James Hensman
prowler.io
Cambridge, UK
james@prowler.io
Abstract
We present a practical way of introduci... | 6877 |@word illustrating:1 middle:2 version:2 seems:1 nd:1 hu:1 rgb:1 covariance:13 p0:1 eng:1 reduction:2 ndez:3 contains:1 score:1 daniel:2 denoting:2 kuf:1 ours:1 document:1 kurt:1 rkhs:1 existing:4 blank:1 com:1 diederik:1 dx:3 reminiscent:1 gpu:2 must:2 universality:1 additive:16 zaid:1 drop:2 v:2 intelligence:6 d... |
6,498 | 6,878 | Estimation of the covariance structure of heavy-tailed
distributions
Stanislav Minsker
Department of Mathematics
University of Southern California
Los Angeles, CA 90007
minsker@usc.edu
Xiaohan Wei
Department of Electrical Engineering
University of Southern California
Los Angeles, CA 90007
xiaohanw@usc.edu
Abstract
We... | 6878 |@word determinant:1 version:1 norm:10 johansson:1 stronger:1 c0:2 d2:3 km:2 covariance:38 decomposition:2 mention:1 tr:8 reduction:1 moment:5 liu:3 contains:1 series:1 giulini:2 past:1 existing:3 elliptical:5 comparing:1 ka:1 aberrant:1 scatter:3 bd:2 saal:3 written:1 numerical:1 shape:1 rd2:1 xk:3 provides:1 com... |
6,499 | 6,879 | 6879 |@word |
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