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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6,700 | 7,060 | Affine-Invariant Online Optimization
and the Low-rank Experts Problem
Tomer Koren
Google Brain
1600 Amphitheatre Pkwy
Mountain View, CA 94043
tkoren@google.com
Roi Livni
Princeton University
35 Olden St.
Princeton, NJ 08540
rlivni@cs.princeton.edu
Abstract
We present a new affine-invariant optimization algorithm cal... | 7060 |@word kgk:3 determinant:1 version:1 norm:17 open:1 d2:1 gradual:1 forecaster:1 tr:4 reduction:1 initial:1 ftrl:2 ours:1 erven:3 past:2 com:1 comparing:1 assigning:1 reminiscent:1 must:1 benign:1 designed:2 treating:1 update:2 fund:1 greedy:1 intelligence:1 warmuth:2 short:1 chiang:1 mannor:2 unbounded:1 gtt:27 pr... |
6,701 | 7,061 | Beyond Worst-case: A Probabilistic Analysis of Affine
Policies in Dynamic Optimization
Omar El Housni
IEOR Department
Columbia University
oe2148@columbia.edu
Vineet Goyal
IEOR Department
Columbia University
vg2277@columbia.edu
Abstract
Affine policies (or control) are widely used as a solution approach in dynamic
opt... | 7061 |@word polynomial:1 stronger:1 nd:1 linearized:1 thereby:2 contains:1 series:2 past:1 numerical:2 drop:1 generative:2 core:1 completeness:2 characterization:1 provides:1 location:1 unbounded:6 mathematical:6 along:1 ik:7 prove:2 polyhedral:2 introduce:4 theoretically:1 expected:1 p1:2 wallace:2 planning:2 mahdian:... |
6,702 | 7,062 | A Unified Approach to Interpreting Model
Predictions
Scott M. Lundberg
Paul G. Allen School of Computer Science
University of Washington
Seattle, WA 98105
slund1@cs.washington.edu
Su-In Lee
Paul G. Allen School of Computer Science
Department of Genome Sciences
University of Washington
Seattle, WA 98105
suinlee@cs.wash... | 7062 |@word version:4 eliminating:1 seems:1 replicate:1 retraining:1 stronger:1 nd:1 open:2 heuristically:6 seek:1 linearized:1 simplifying:1 recapitulate:1 profit:2 recursively:1 score:4 existing:2 current:7 com:1 comparing:1 surprising:2 activation:4 peyton:2 must:1 additive:25 enables:2 analytic:1 remove:1 designed:... |
6,703 | 7,063 | Stochastic Approximation
for Canonical Correlation Analysis
Raman Arora
Dept. of Computer Science
Johns Hopkins University
Baltimore, MD 21204
arora@cs.jhu.edu
Teodor V. Marinov
Dept. of Computer Science
Johns Hopkins University
Baltimore, MD 21204
tmarino2@jhu.edu
Poorya Mianjy
Dept. of Computer Science
Johns Hopkin... | 7063 |@word version:4 polynomial:1 norm:7 yi0:1 nd:1 seek:2 crucially:1 covariance:19 decomposition:1 sgd:1 tr:7 carry:1 plentiful:1 substitution:1 ours:3 existing:2 kmk:2 current:1 com:1 dx:7 must:1 john:3 chicago:2 numerical:3 additive:1 wx:6 drop:1 designed:1 update:14 plot:1 selected:1 kyk:3 accordingly:2 parameter... |
6,704 | 7,064 | Resurrecting the sigmoid in deep learning through
dynamical isometry: theory and practice
Jeffrey Pennington
Google Brain
Samuel S. Schoenholz
Google Brain
Surya Ganguli
Applied Physics, Stanford University and Google Brain
Abstract
It is well known that weight initialization in deep networks can have a dramatic
imp... | 7064 |@word determinant:1 version:1 inversion:1 polynomial:5 norm:2 stronger:3 d2:4 simulation:3 crucially:1 brightness:1 dramatic:4 thereby:2 tr:1 solid:4 sgd:11 carry:1 moment:7 initial:8 liu:1 daniel:1 interestingly:1 imaginary:2 activation:6 s2max:2 si:2 must:2 guez:1 john:1 diederik:1 subsequent:1 numerical:2 part... |
6,705 | 7,065 | Sample and Computationally Efficient Learning
Algorithms under S-Concave Distributions
Maria-Florina Balcan
Machine Learning Department
Carnegie Mellon University, USA
ninamf@cs.cmu.edu
Hongyang Zhang
Machine Learning Department
Carnegie Mellon University, USA
hongyanz@cs.cmu.edu
Abstract
We provide new results for ... | 7065 |@word h:3 briefly:1 polynomial:2 norm:2 c0:4 open:1 covariance:2 moment:1 reduction:1 contains:2 ours:1 existing:1 nt:2 beygelzimer:2 dx:5 must:1 realistic:1 happen:1 hongyang:1 atlas:1 progressively:1 half:2 fewer:1 greedy:1 isotropic:22 characterization:1 provides:1 mannor:1 zhang:6 dn:5 beta:2 symposium:3 prov... |
6,706 | 7,066 | Scalable Variational Inference for Dynamical Systems
Nico S. Gorbach?
Dept. of Computer Science
ETH Zurich
ngorbach@inf.ethz.ch
Stefan Bauer?
Dept. of Computer Science
ETH Zurich
bauers@inf.ethz.ch
Joachim M. Buhmann
Dept. of Computer Science
ETH Zurich
jbuhmann@inf.ethz.ch
Abstract
Gradient matching is a promising... | 7066 |@word middle:2 c0:1 km:4 closure:1 r:8 simulation:1 covariance:7 reduction:2 moment:3 series:1 initialisation:1 denoting:1 daniel:1 rightmost:1 outperforms:1 existing:5 reaction:2 recovered:1 discretization:1 current:1 yet:1 dx:3 john:1 numerical:10 additive:2 subsequent:1 realistic:1 klaas:1 designed:1 plot:21 i... |
6,707 | 7,067 | Context Selection for Embedding Models
Li-Ping Liu?
Tufts University
Francisco J. R. Ruiz
Columbia University
University of Cambridge
Susan Athey
Stanford University
David M. Blei
Columbia University
Abstract
Word embeddings are an effective tool to analyze language. They have been
recently extended to model other... | 7067 |@word trial:1 briefly:1 logit:2 open:1 contrastive:1 pick:1 yih:1 liu:2 contains:6 score:15 njk:19 outperforms:3 existing:1 xnj:23 com:1 gauvain:1 john:1 j1:1 remove:1 interpretable:1 update:2 aside:1 intelligence:2 selected:3 fewer:1 item:32 generative:1 mccallum:2 record:1 blei:7 barkan:2 provides:1 location:3 ... |
6,708 | 7,068 | Working hard to know your neighbor?s margins:
Local descriptor learning loss
Anastasiya Mishchuk1 , Dmytro Mishkin2 , Filip Radenovi?c2 , Ji?ri Matas2
1
Szkocka Research Group, Ukraine
anastasiya.mishchuk@gmail.com
2
Visual Recognition Group, CTU in Prague
{mishkdmy, filip.radenovic, matas}@cmp.felk.cvut.cz
Abstrac... | 7068 |@word cnn:5 mri:1 version:5 torsten:1 kokkinos:1 nd:1 choy:2 confirms:1 bn:7 contrastive:11 harder:1 lepetit:1 moment:1 gloss:1 configuration:1 inefficiency:1 score:1 selecting:1 daniel:2 ours:4 kurt:1 outperforms:8 current:3 com:2 comparing:2 surprising:1 michal:5 contextual:1 gmail:1 moo:1 gpu:3 luis:1 refines:... |
6,709 | 7,069 | Accelerated Stochastic Greedy Coordinate Descent by
Soft Thresholding Projection onto Simplex
Chaobing Song, Shaobo Cui, Yong Jiang, Shu-Tao Xia
Tsinghua University
{songcb16,shaobocui16}@mails.tsinghua.edu.cn
{jiangy, xiast}@sz.tsinghua.edu.cn ?
Abstract
In this paper we study the well-known greedy coordinate descent... | 7069 |@word version:2 norm:30 seems:3 nd:2 bn:1 delicately:1 sgd:3 reduction:4 necessity:1 tuned:1 existing:7 current:1 comparing:2 written:1 must:2 john:2 axk22:1 numerical:1 additive:1 cheap:1 enables:1 update:6 stationary:1 greedy:15 selected:2 intelligence:1 amir:1 xk:4 steepest:1 clarified:1 firstly:1 simpler:1 zh... |
6,710 | 707 | A Knowledge-Based Model of Geometry Learning
Geoffrey Towell
Siemens Corporate Research
755 College Road East
Princeton, NJ 08540
Richard Lehrer
Educational Psychology
University of Wisconsin
1025 West Johnson St.
Madison, WI 53706
towe ll@ learning. siemens. com
lehrer@vms.macc. wisc.edu
Abstract
We propose a mod... | 707 |@word trial:1 version:2 briefly:3 instruction:18 solid:1 initial:17 series:1 contains:2 att:13 bc:16 interestingly:1 quadrilateral:1 existing:1 com:1 adj:1 comparing:1 surprising:2 yet:1 must:3 shape:11 discrimination:2 selected:1 item:1 short:3 math:1 constructed:1 become:2 pairing:2 consists:1 manner:1 expected:... |
6,711 | 7,070 | Multi-Task Learning for Contextual Bandits
Aniket Anand Deshmukh
Department of EECS
University of Michigan Ann Arbor
Ann Arbor, MI 48105
aniketde@umich.edu
Urun Dogan
Microsoft Research
Cambridge CB1 2FB, UK
urun.dogan@skype.net
Clayton Scott
Department of EECS
University of Michigan Ann Arbor
Ann Arbor, MI 48105
cl... | 7070 |@word trial:6 exploitation:3 version:3 k2hk:2 seek:1 citeseer:1 tr:2 rkhs:2 ours:1 past:3 ka:1 contextual:21 nt:5 yet:1 chu:2 must:1 interpretable:1 update:2 greedy:1 selected:7 website:2 intelligence:1 zhang:1 five:1 mathematical:1 along:1 consists:1 khk:2 introduce:1 x0:2 inter:2 ra:10 indeed:1 expected:2 multi... |
6,712 | 7,071 | Learning to Prune Deep Neural Networks via
Layer-wise Optimal Brain Surgeon
Xin Dong
Nanyang Technological University, Singapore
n1503521a@e.ntu.edu.sg
Shangyu Chen
Nanyang Technological University, Singapore
schen025@e.ntu.edu.sg
Sinno Jialin Pan
Nanyang Technological University, Singapore
sinnopan@ntu.edu.sg
Abst... | 7071 |@word version:1 briefly:1 compression:19 norm:7 seems:1 retraining:28 middle:1 heuristically:1 hu:1 shuicheng:1 tried:1 propagate:1 pick:1 sgd:1 mention:2 recursively:1 initial:1 liu:3 series:3 score:2 selecting:2 zij:4 document:1 interestingly:1 past:1 existing:3 err:8 freitas:1 com:1 z2:3 activation:7 written:1... |
6,713 | 7,072 | Accelerated First-order Methods for Geodesically
Convex Optimization on Riemannian Manifolds
Yuanyuan Liu1 , Fanhua Shang1?, James Cheng1 , Hong Cheng2 , Licheng Jiao3
1
Dept. of Computer Science and Engineering, The Chinese University of Hong Kong
2
Dept. of Systems Engineering and Engineering Management,
The Chinese... | 7072 |@word kong:4 kgk:2 norm:3 open:1 hu:1 calculus:1 eng:1 tr:2 sepulchre:2 reduction:4 liu:1 series:1 tuned:1 ours:5 outperforms:1 current:1 optim:4 must:1 numerical:2 cheap:1 designed:1 update:5 intelligence:1 selected:1 kyk:3 xk:68 provides:1 math:1 cse:1 simpler:2 zhang:5 along:1 direct:1 differential:3 stronglyc... |
6,714 | 7,073 | Selective Classification for Deep Neural Networks
Yonatan Geifman
Computer Science Department
Technion ? Israel Institute of Technology
yonatan.g@cs.technion.ac.il
Ran El-Yaniv
Computer Science Department
Technion ? Israel Institute of Technology
rani@cs.technion.ac.il
Abstract
Selective classification techniques (a... | 7073 |@word rani:1 achievable:1 open:3 pg:2 pick:1 sgd:1 incurs:1 minus:1 solid:2 initial:1 liu:1 luigi:1 existing:1 activation:4 yet:1 must:2 realize:1 numerical:3 analytic:1 plot:1 half:2 selected:1 zmax:3 ith:2 ire:1 boosting:2 simpler:2 zhang:1 driver:1 viable:1 ijcv:1 expected:1 zmin:3 behavior:2 xz:1 multi:1 equi... |
6,715 | 7,074 | Minimax Estimation of Bandable Precision Matrices
Addison J. Hu?
Department of Statistics and Data Science
Yale University
New Haven, CT 06520
addison.hu@yale.edu
Sahand N. Negahban
Department of Statistics and Data Science
Yale University
New Haven, CT 06520
sahand.negahban@yale.edu
Abstract
The inverse covariance m... | 7074 |@word trial:2 mri:1 version:4 inversion:8 norm:30 hu:3 cleanly:1 simulation:2 confirms:1 bn:1 covariance:29 decomposition:1 initial:1 liu:2 series:8 tapering:12 past:2 existing:1 molenaar:1 com:1 luo:1 numerical:3 subsequent:1 confirming:1 plot:4 progressively:1 update:5 fewer:1 beginning:1 ith:1 regressive:1 pro... |
6,716 | 7,075 | Monte-Carlo Tree Search by Best Arm Identification
Emilie Kaufmann
CNRS & Univ. Lille, UMR 9189 (CRIStAL), Inria SequeL
Lille, France
emilie.kaufmann@univ-lille1.fr
Wouter M. Koolen
Centrum Wiskunde & Informatica,
Science Park 123, 1098 XG Amsterdam, The Netherlands
wmkoolen@cwi.nl
Abstract
Recent advances in bandit ... | 7075 |@word middle:1 version:2 proportion:2 replicate:2 aske:1 simulation:1 tried:1 r:2 kalyanakrishnan:2 shot:2 recursively:3 reduction:1 bai:38 selecting:1 tuned:1 ours:1 past:1 existing:1 current:1 comparing:1 yet:1 intriguing:1 reminiscent:2 guez:1 john:1 designed:1 update:1 v:6 alone:1 intelligence:3 leaf:46 guess... |
6,717 | 7,076 | Group Additive Structure Identification for Kernel
Nonparametric Regression
Pan Chao
Department of Statistics
Purdue University
West Lafayette, IN 47906
panchao25@gmail.com
Michael Zhu
Department of Statistics, Purdue University
West Lafayette, IN 47906
Center for Statistical Science
Department of Industrial Engineer... | 7076 |@word middle:3 polynomial:1 norm:1 unif:2 closure:1 hu:2 simulation:5 citeseer:2 concise:1 accommodate:1 reduction:1 contains:3 tuned:2 rkhs:11 existing:1 com:1 comparing:1 gmail:1 universality:1 written:1 must:2 additive:93 partition:2 plot:3 drop:1 greedy:1 selected:4 kandasamy:1 xk:1 beginning:1 firstly:1 arct... |
6,718 | 7,077 | Fast, Sample-Ef?cient Algorithms for
Structured Phase Retrieval
Gauri jagatap
Electrical and Computer Engineering
Iowa State University
Chinmay Hegde
Electrical and Computer Engineering
Iowa State University
Abstract
We consider the problem of recovering a signal x? ? Rn , from magnitude-only
measurements, yi = |ai ... | 7077 |@word trial:2 version:2 phasemax:2 polynomial:1 norm:3 nd:1 suitably:1 earnest:1 r:2 simulation:1 covariance:1 dirksen:1 pick:1 thereby:1 bahmani:1 carry:1 marchesini:1 shechtman:2 reduction:1 contains:2 initial:17 mag:1 woodruff:1 ours:1 mixon:1 past:1 existing:4 current:1 whp:4 recovered:1 numerical:2 cant:2 pr... |
6,719 | 7,078 | Hash Embeddings for Efficient Word Representations
Dan Svenstrup
Department for Applied Mathematics and Computer Science
Technical University of Denmark (DTU)
2800 Lyngby, Denmark
dsve@dtu.dk
Jonas Meinertz Hansen
FindZebra
Copenhagen, Denmark
jonas@findzebra.com
Ole Winther
Department for Applied Mathematics and Co... | 7078 |@word multitask:1 kulis:2 cnn:2 judgement:1 compression:1 norm:1 pw:8 seems:1 nd:1 open:1 d2:1 reduction:6 initial:1 bai:2 selecting:1 document:4 subjective:1 existing:1 com:2 activation:2 written:1 bd:1 concatenate:1 remove:1 treating:1 drop:2 hash:100 v:1 pursued:1 selected:2 generative:1 codebook:2 barrault:1 ... |
6,720 | 7,079 | Online Learning for Multivariate Hawkes Processes
Yingxiang Yang?
Jalal Etesami?
Niao He?
Negar Kiyavash??
University of Illinois at Urbana-Champaign
Urbana, IL 61801
{yyang172,etesami2,niaohe,kiyavash} @illinois.edu
Abstract
We develop a nonparametric and online learning algorithm that estimates the
triggering funct... | 7079 |@word trial:1 version:3 rising:1 polynomial:2 seems:1 open:1 simulation:1 elisseeff:1 tr:4 memetracker:3 series:3 score:3 tuned:1 rkhs:5 interestingly:1 outperforms:1 existing:2 discretization:7 comparing:2 written:1 readily:1 numerical:3 partition:1 visible:1 predetermined:1 shape:1 plot:2 update:5 stationary:1 ... |
6,721 | 708 | An Object-Oriented Framework for the
Simulation of Neural Nets
A. Linden
Th. Sudbrak
Ch. Tietz
F. Weber
German National Research Center for Computer Science
D-5205 Sankt Augustin 1, Germany
Abstract
The field of software simulators for neural networks has been expanding very rapidly in the last years but their importa... | 708 |@word ia2:1 version:1 briefly:1 reused:2 simulation:12 propagate:1 rol:1 tr:1 moment:1 configuration:4 contains:3 existing:2 current:2 nt:3 activation:3 yet:1 must:2 enables:1 designed:3 selfsupervised:1 update:2 spec:1 deadlock:2 directory:1 short:1 supplying:1 provides:2 node:2 accessed:1 mathematical:2 construc... |
6,722 | 7,080 | Maximum Margin Interval Trees
Alexandre Drouin
D?partement d?informatique et de g?nie logiciel
Universit? Laval, Qu?bec, Canada
alexandre.drouin.8@ulaval.ca
Toby Dylan Hocking
McGill Genome Center
McGill University, Montr?al, Canada
toby.hocking@r-project.org
Fran?ois Laviolette
D?partement d?informatique et de g?ni... | 7080 |@word repository:2 version:1 middle:1 cox:1 proportion:2 triazine:2 open:1 seek:1 simulation:1 git:4 recursively:1 initial:1 configuration:1 contains:3 series:1 selecting:1 lichman:3 ours:1 interestingly:1 rightmost:2 outperforms:2 existing:3 current:1 recovered:1 si:6 yet:1 aft:1 must:2 numerical:1 partition:2 j... |
6,723 | 7,081 | DropoutNet: Addressing Cold Start
in Recommender Systems
Maksims Volkovs
layer6.ai
maks@layer6.ai
Guangwei Yu
layer6.ai
guang@layer6.ai
Tomi Poutanen
layer6.ai
tomi@layer6.ai
Abstract
Latent models have become the default choice for recommender systems due to
their performance and scalability. However, research in ... | 7081 |@word version:4 wmf:24 norm:2 hu:1 reduction:2 contains:4 score:5 selecting:1 document:1 outperforms:2 existing:9 current:1 com:2 activation:8 gpu:1 citeulike:6 devin:1 subsequent:1 christian:1 remove:2 drop:1 update:4 generative:2 selected:6 device:1 item:70 intelligence:1 core:1 blei:4 provides:1 location:3 pre... |
6,724 | 7,082 | A simple neural network module
for relational reasoning
Adam Santoro?
adamsantoro@google.com
Mateusz Malinowski
mateuszm@google.com
David Raposo?
draposo@google.com
Razvan Pascanu
razp@google.com
David G.T. Barrett
barrettdavid@google.com
Peter Battaglia
peterbattaglia@google.com
Timothy Lillicrap
DeepMind
London, ... | 7082 |@word cnn:16 version:10 advantageous:1 d2:1 propagate:1 rgb:1 mengye:1 asks:1 thereby:1 versatile:2 catastrophically:1 configuration:2 contains:1 murder:1 united:1 exclusively:1 jimenez:1 daniel:1 reynolds:1 o2:1 outperforms:1 current:1 com:7 comparing:1 culprit:1 must:8 parsing:2 john:1 shape:9 remove:2 hypothes... |
6,725 | 7,083 | Q-LDA: Uncovering Latent Patterns in Text-based
Sequential Decision Processes
Jianshu Chen? , Chong Wang? , Lin Xiao? , Ji He? , Lihong Li? and Li Deng?
?
Microsoft Research, Redmond, WA, USA
{jianshuc,lin.xiao}@microsoft.com
?
Google Inc., Kirkland, WA, USA?
{chongw,lihong}@google.com
?
Citadel LLC, Seattle/Chicago, U... | 7083 |@word version:1 middle:1 proportion:15 triggs:1 adrian:1 pieter:1 tat:25 seek:1 sgd:2 shot:1 recursively:2 moment:1 initial:2 celebrated:1 selecting:1 document:6 interestingly:2 outperforms:1 freitas:1 silvescu:1 current:3 com:4 ka:4 surprising:1 mari:1 activation:1 guez:1 readily:1 john:6 chicago:1 periodically:... |
6,726 | 7,084 | Online Reinforcement Learning in Stochastic Games
Yi-Te Hong
Institute of Information Science
Academia Sinica, Taiwan
ted0504@iis.sinica.edu.tw
Chen-Yu Wei
Institute of Information Science
Academia Sinica, Taiwan
bahh723@iis.sinica.edu.tw
Chi-Jen Lu
Institute of Information Science
Academia Sinica, Taiwan
cjlu@iis.s... | 7084 |@word h:1 exploitation:2 version:6 eliminating:1 polynomial:3 stronger:1 open:3 d2:2 prasad:1 decomposition:2 attainable:1 pick:4 initial:10 contains:1 selecting:4 ours:3 bilal:1 past:1 imaginary:1 current:1 written:1 must:1 import:1 ronald:3 academia:3 happen:1 benign:4 garud:1 update:1 stationary:31 intelligenc... |
6,727 | 7,085 | Position-based Multiple-play Bandit Problem with
Unknown Position Bias
Junpei Komiyama
The University of Tokyo
junpei@komiyama.info
Junya Honda
The University of Tokyo / RIKEN
honda@stat.t.u-tokyo.ac.jp
Akiko Takeda
The Institute of Statistical Mathematics / RIKEN
atakeda@ism.ac.jp
Abstract
Motivated by online adver... | 7085 |@word exploitation:1 version:3 polynomial:1 norm:2 nd:1 simulation:2 decomposition:1 necessity:1 liu:1 contains:1 series:1 united:1 ramsey:1 existing:4 current:2 com:2 yet:2 numerical:1 kdd:2 plot:1 designed:1 sponsored:1 bart:2 half:1 fewer:1 greedy:1 plane:5 akiko:1 beginning:1 completeness:1 characterization:1... |
6,728 | 7,086 | Active Exploration for Learning
Symbolic Representations
Garrett Andersen
PROWLER.io
Cambridge, United Kingdom
garrett@prowler.io
George Konidaris
Department of Computer Science
Brown University
gdk@cs.brown.edu
Abstract
We introduce an online active exploration algorithm for data-efficiently learning
an abstract sy... | 7086 |@word sri:1 polynomial:1 nd:1 open:2 termination:5 simulation:2 contraction:1 reduction:1 contains:1 score:2 united:1 selecting:1 outperforms:2 past:1 o2:6 current:5 yet:1 must:2 partition:15 thrust:1 christian:1 remove:1 succeeding:1 update:6 smdp:1 stationary:1 greedy:21 intelligence:7 leaf:2 amir:1 node:1 loca... |
6,729 | 7,087 | Clone MCMC: Parallel High-Dimensional Gaussian
Gibbs Sampling
Andrei-Cristian B?arbos
IMS Laboratory
Univ. Bordeaux - CNRS - BINP
andbarbos@u-bordeaux.fr
Fran?ois Caron
Department of Statistics
University of Oxford
caron@stats.ox.ac.uk
Jean-Fran?ois Giovannelli
IMS Laboratory
Univ. Bordeaux - CNRS - BINP
Giova@ims-b... | 7087 |@word version:2 inversion:1 polynomial:1 norm:1 bekkerman:1 frigessi:1 d2:2 cloned:1 simulation:3 grey:3 covariance:9 decomposition:3 inpainting:3 initial:1 substitution:2 contains:1 series:2 outperforms:1 written:1 gpu:1 john:1 partition:1 plot:2 interpretable:1 update:7 stationary:7 prohibitive:1 selected:2 ton... |
6,730 | 7,088 | Fair Clustering Through Fairlets
Flavio Chierichetti
Dipartimento di Informatica
Sapienza University
Rome, Italy
Ravi Kumar
Google Research
1600 Amphitheater Parkway
Mountain View, CA 94043
Silvio Lattanzi
Google Research
76 9th Ave
New York, NY 10011
Sergei Vassilvitskii
Google Research
76 9th Ave
New York, NY 100... | 7088 |@word repository:2 version:4 polynomial:2 stronger:1 cortez:1 open:1 decomposition:42 pick:1 harder:1 reduction:4 venkatasubramanian:1 contains:6 lichman:2 score:1 bc:1 interestingly:1 sendhil:2 bilal:1 existing:1 contextual:1 manuel:1 b1c:1 sergei:1 must:2 applicant:1 portuguese:1 r1c:2 john:1 partition:7 kdd:4 ... |
6,731 | 7,089 | Polynomial time algorithms for dual volume sampling
Chengtao Li
MIT
ctli@mit.edu
Stefanie Jegelka
MIT
stefje@csail.mit.edu
Suvrit Sra
MIT
suvrit@mit.edu
Abstract
We study dual volume sampling, a method for selecting k columns from an n ? m
short and wide matrix (n ? k ? m) such that the probability of selection is p... | 7089 |@word determinant:5 version:4 inversion:1 polynomial:34 norm:2 nd:2 open:2 unif:3 decomposition:3 pg:1 pick:1 thereby:1 reduction:2 initial:1 substitution:1 siebel:1 score:2 selecting:7 cort:1 ka:12 current:3 incidence:2 repelling:1 z2:1 si:1 yet:2 reminiscent:1 must:1 determinantal:7 partition:3 enables:1 remove... |
6,732 | 709 | The Computation of Stereo Disparity for
Transparent and for Opaque Surfaces
Suthep Madarasmi
Computer Science Department
University of Minnesota
Minneapolis, MN 55455
Daniel Kersten
Department of Psychology
University of Minnesota
Ting-Chuen Pong
Computer Science Department
University of Minnesota
Abstract
The clas... | 709 |@word simulation:4 thereby:1 reduction:1 configuration:1 disparity:33 daniel:1 current:1 written:1 must:1 distant:1 visible:1 shape:2 plot:1 v:4 half:1 plane:4 dfl:1 incorrect:2 consists:1 fitting:1 idr:1 multi:3 inspired:1 decreasing:1 resolve:1 provided:1 matched:1 lowest:1 minimizes:1 gurations:1 ag:2 scaled:1 ... |
6,733 | 7,090 | Hindsight Experience Replay
Marcin Andrychowicz? , Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong,
Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel? , Wojciech Zaremba?
OpenAI
Abstract
Dealing with sparse rewards is one of the biggest challenges in Reinforcement
Learning (RL). We present a novel technique c... | 7090 |@word multitask:1 trial:2 cnn:1 version:5 polynomial:1 retraining:1 nd:2 pieter:1 confirms:3 simulation:4 tried:4 pick:8 shot:1 initial:8 necessity:1 bootstrapped:2 reynolds:1 existing:1 current:10 com:1 comparing:2 discretization:1 activation:1 guez:1 john:1 devin:3 informative:2 motor:2 plot:4 reproducible:1 su... |
6,734 | 7,091 | Stochastic and Adversarial Online Learning without
Hyperparameters
Ashok Cutkosky
Department of Computer Science
Stanford University
ashokc@cs.stanford.edu
Kwabena Boahen
Department of Bioengineering
Stanford University
boahen@stanford.edu
Abstract
Most online optimization algorithms focus on one of two things: perf... | 7091 |@word kgk:2 version:2 norm:2 open:2 decomposition:1 jacob:1 boundedness:1 ftrl:2 inefficiency:1 erven:2 must:1 remove:1 update:9 amir:1 prove:4 introduce:1 expected:7 themselves:1 little:2 increasing:1 notation:5 bounded:4 hitherto:1 kg:3 kind:1 argmin:3 degrading:1 suppresses:2 guarantee:2 pseudo:2 every:1 exact... |
6,735 | 7,092 | Teaching Machines to Describe Images via Natural
Language Feedback
Huan Ling1 , Sanja Fidler1,2
University of Toronto1 , Vector Institute2
{linghuan,fidler}@cs.toronto.edu
Abstract
Robots will eventually be part of every household. It is thus critical to enable
algorithms to learn from and be guided by non-expert use... | 7092 |@word briefly:1 stronger:2 pieter:1 tried:2 bn:3 paid:1 mention:2 versatile:1 jacqueline:1 initial:1 liu:1 contains:4 score:1 att:4 ours:2 interestingly:3 outperforms:2 bradley:2 guadarrama:1 current:2 sosa:1 comparing:1 com:1 assigning:1 yet:1 diederik:1 guez:1 written:4 parsing:1 ronald:1 visible:1 realistic:1 ... |
6,736 | 7,093 | Perturbative Black Box Variational Inference
Robert Bamler?
Disney Research
Pittsburgh, USA
Cheng Zhang?
Disney Research
Pittsburgh, USA
Manfred Opper
TU Berlin
Berlin, Germany
Stephan Mandt?
Disney Research
Pittsburgh, USA
firstname.lastname@{disneyresearch.com, tu-berlin.de}
Abstract
Black box variational infere... | 7093 |@word repository:2 briefly:1 version:2 polynomial:2 covariance:1 thereby:1 harder:1 carry:1 kappen:2 ndez:1 contains:3 series:2 tuned:4 document:1 outperforms:1 com:1 perturbative:9 additive:1 numerical:2 enables:2 analytic:7 designed:1 implying:2 generative:5 stationary:1 half:3 fewer:1 manfred:1 blei:8 num:1 pr... |
6,737 | 7,094 | GibbsNet: Iterative Adversarial Inference for Deep
Graphical Models
Alex Lamb
R Devon Hjelm
Yaroslav Ganin
Aaron Courville
Joseph Paul Cohen
Yoshua Bengio
Abstract
Directed latent variable models that formulate the joint distribution as p(x, z) =
p(z)p(x | z) have the advantage of fast and exact sampling. Howeve... | 7094 |@word illustrating:1 version:1 stronger:1 gradual:1 propagate:1 decomposition:1 jacob:1 inpainting:21 solid:2 liu:2 series:1 score:8 ours:1 outperforms:1 existing:1 luo:1 yet:2 reminiscent:1 must:2 realistic:2 partition:1 visible:6 informative:1 stationary:7 generative:24 fewer:1 half:2 intelligence:1 isotropic:1... |
6,738 | 7,095 | PointNet++: Deep Hierarchical Feature Learning on
Point Sets in a Metric Space
Charles R. Qi
Li Yi Hao Su Leonidas J. Guibas
Stanford University
Abstract
Few prior works study deep learning on point sets. PointNet [20] is a pioneer in this
direction. However, by design PointNet does not capture local structures induc... | 7095 |@word cnn:6 version:1 repository:1 stronger:1 propagate:2 rgb:3 concise:1 recursively:4 moment:2 reduction:1 liu:1 contains:3 score:1 ours:15 document:2 outperforms:3 contextual:1 comparing:2 cad:1 yet:1 intriguing:1 finest:1 pioneer:1 mesh:1 distant:1 partition:4 informative:1 shape:24 remove:1 drop:3 succeeding... |
6,739 | 7,096 | Regularizing Deep Neural Networks by Noise:
Its Interpretation and Optimization
Hyeonwoo Noh
Tackgeun You
Jonghwan Mun
Bohyung Han
Dept. of Computer Science and Engineering, POSTECH, Korea
{shgusdngogo,tackgeun.you,choco1916,bhhan}@postech.ac.kr
Abstract
Overfitting is one of the most critical challenges in deep neur... | 7096 |@word trial:1 cnn:5 version:1 stronger:1 open:3 hu:1 sgd:7 incurs:2 harder:1 liu:2 score:1 selecting:2 outperforms:1 existing:1 com:3 activation:14 neuraltalk2:1 enables:2 designed:1 drop:2 generative:1 krikun:1 num:2 provides:1 toronto:1 zhang:3 constructed:1 differential:1 consists:1 fitting:3 wild:1 privacy:1 ... |
6,740 | 7,097 | Learning Graph Representations with Embedding
Propagation
Alberto Garc?a-Dur?n
NEC Labs Europe
Heidelberg, Germany
alberto.duran@neclab.eu
Mathias Niepert
NEC Labs Europe
Heidelberg, Germany
mathias.niepert@neclab.eu
Abstract
We propose Embedding Propagation (E P), an unsupervised learning framework for
graph-struct... | 7097 |@word multitask:1 repository:1 compression:1 retraining:1 nd:2 duran:2 d2:1 decomposition:1 hsieh:1 citeseer:9 tr:6 reduction:2 liblinear:2 liu:2 wrapper:1 fragment:2 score:8 tuned:1 document:3 franklin:1 outperforms:5 existing:9 current:9 nt:1 reminiscent:1 gpu:2 remove:1 plot:1 update:8 maxv:2 v:2 bickson:1 int... |
6,741 | 7,098 | Efficient Modeling of Latent Information in
Supervised Learning using Gaussian Processes
Zhenwen Dai ??
zhenwend@amazon.com
Mauricio A. ?lvarez ?
mauricio.alvarez@sheffield.ac.uk
Neil D. Lawrence ??
lawrennd@amazon.com
Abstract
Often in machine learning, data are collected as a combination of multiple conditions, e.... | 7098 |@word illustrating:1 version:1 nd:3 humidity:1 cleanly:1 d2:2 covariance:20 decomposition:8 tr:16 reduction:1 moment:1 initial:4 contains:1 series:1 outperforms:2 existing:2 com:3 dx:1 john:1 fn:2 partition:4 enables:4 motor:1 plot:6 intelligence:1 fewer:1 sawade:1 geospatial:1 location:4 firstly:1 org:1 daphne:1... |
6,742 | 7,099 | A-NICE-MC: Adversarial Training for MCMC
Jiaming Song
Stanford University
tsong@cs.stanford.edu
Shengjia Zhao
Stanford University
zhaosj12@cs.stanford.edu
Stefano Ermon
Stanford University
ermon@cs.stanford.edu
Abstract
Existing Markov Chain Monte Carlo (MCMC) methods are either based on generalpurpose and domain-ag... | 7099 |@word cnn:1 determinant:1 seems:1 simulation:4 tried:1 thereby:1 ld:3 moment:1 initial:8 contains:1 score:4 series:1 daniel:1 bootstrapped:1 suppressing:1 outperforms:1 existing:3 freitas:2 com:1 si:1 yet:3 devin:1 additive:1 partition:1 informative:1 distant:1 analytic:7 plot:1 update:3 resampling:2 stationary:8... |
6,743 | 71 | 62
Centric Models of the Orientation Map in Primary Visual Cortex
William Baxter
Department of Computer Science, S.U.N.Y. at Buffalo, NY 14620
Bruce Dow
Department of Physiology, S.U.N.Y. at Buffalo, NY 14620
Abstract
In the visual cortex of the monkey the horizontal organization of the preferred
orientations of orien... | 71 |@word version:3 wiesel:13 stronger:1 seems:1 simulation:8 synergistically:1 contains:1 series:2 hereafter:1 foveal:1 daniel:1 tuned:1 interestingly:1 yet:2 must:3 grain:1 physiol:3 realistic:4 arrayed:1 depict:1 iso:3 short:1 record:2 compo:6 location:4 preference:4 along:2 consists:1 lj2:2 introduce:1 manner:2 exp... |
6,744 | 710 | Context-Dependent Multiple
Distribution Phonetic Modeling with
MLPs
Michael Cohen
SRI International
Menlo Park. CA 94025
Horacio Franco
Nelson Morgan
SRl International
IntI. Computer Science Inst.
Berkeley, CA 94704
Victor Abrash
SRI International
David Rumelhart
Stanford University
Stanford, CA 94305
Abstract
A... | 710 |@word middle:2 version:1 sri:5 retraining:1 semicontinuous:1 r:1 thereby:1 feb91:2 reduction:3 initial:7 current:1 comparing:1 activation:1 must:2 discrimination:1 fewer:1 provides:1 sigmoidal:1 simpler:3 five:2 combine:2 theoretically:1 ica:2 rapid:1 window:1 increasing:1 provided:3 estimating:1 string:3 biphone:... |
6,745 | 7,100 | Excess Risk Bounds for the Bayes Risk using
Variational Inference in Latent Gaussian Models
Rishit Sheth and Roni Khardon
Department of Computer Science, Tufts University
Medford, MA, 02155, USA
rishit.sheth@tufts.edu | roni@cs.tufts.edu
Abstract
Bayesian models are established as one of the main successful paradigms ... | 7100 |@word mild:1 trial:1 repository:1 version:1 briefly:2 compression:1 stronger:1 achievable:1 nd:3 covariance:1 pick:2 tr:1 lichman:1 jimenez:1 denoting:3 document:3 existing:1 current:1 comparing:1 yet:1 must:2 john:1 generative:2 accordingly:1 blei:2 provides:2 characterization:1 ron:1 herbrich:1 zhang:1 wierstra... |
6,746 | 7,101 | Real-Time Bidding with Side Information
Arthur Flajolet
MIT, ORC
flajolet@mit.edu
Patrick Jaillet
MIT, EECS, LIDS, ORC
jaillet@mit.edu
Abstract
We consider the problem of repeated bidding in online advertising auctions when
some side information (e.g. browser cookies) is available ahead of submitting a bid
in the fo... | 7101 |@word mild:2 exploitation:2 achievable:1 norm:2 open:1 d2:1 simulation:1 incurs:5 harder:1 carry:2 reduction:1 initial:1 selecting:1 ours:1 past:6 current:3 contextual:37 chu:3 must:2 realize:1 additive:4 enables:2 visibility:1 designed:1 sponsored:2 progressively:3 update:8 stationary:1 half:1 intelligence:2 ite... |
6,747 | 7,102 | Saliency-based Sequential Image Attention with
Multiset Prediction
Sean Welleck
New York University
wellecks@nyu.edu
Jialin Mao
New York University
jialin.mao@nyu.edu
Kyunghyun Cho
New York University
kyunghyun.cho@nyu.edu
Zheng Zhang
New York University
zz@nyu.edu
Abstract
Humans process visual scenes selectively ... | 7102 |@word cnn:2 version:2 illustrating:1 open:1 pieter:2 attended:3 pick:1 thereby:1 reduction:2 initial:5 liu:1 series:2 exclusively:1 score:3 selecting:1 foveal:2 tuned:3 suppressing:2 past:2 existing:1 current:2 blank:1 kowler:1 anne:1 luo:1 activation:27 si:2 hou:1 john:1 ronald:1 subsequent:2 remove:1 progressiv... |
6,748 | 7,103 | Variational Inference for Gaussian Process Models
with Linear Complexity
Ching-An Cheng
Institute for Robotics and Intelligent Machines
Georgia Institute of Technology
Atlanta, GA 30332
cacheng@gatech.edu
Byron Boots
Institute for Robotics and Intelligent Machines
Georgia Institute of Technology
Atlanta, GA 30332
bbo... | 7103 |@word briefly:1 inversion:2 seems:1 nd:3 heuristically:1 pieter:1 seek:1 covariance:23 tr:1 initial:1 contains:3 rkhs:11 outperforms:4 existing:2 current:3 comparing:1 surprising:1 diederik:1 john:1 numerical:1 shape:1 enables:1 christian:1 update:3 v:2 greedy:1 selected:4 intelligence:4 parametrization:7 short:2... |
6,749 | 7,104 | K-Medoids for K-Means Seeding
James Newling
Idiap Research Institue and
?
Ecole
polytechnique f?ed?erale de Lausanne
james.newling@idiap.ch
Franc?ois Fleuret
Idiap Research Institue and
?
Ecole
polytechnique f?ed?erale de Lausanne
francois.fleuret@idiap.ch
Abstract
We show experimentally that the algorithm clarans o... | 7104 |@word repository:1 briefly:2 compression:1 bf:12 open:1 km:35 vldb:1 simulation:2 scg:1 git:1 motoda:1 cla:3 reduction:3 initial:5 liu:1 contains:1 series:3 selecting:1 kingravi:1 initialisation:1 ecole:2 outperforms:1 bradley:3 current:1 com:2 comparing:1 clara:1 john:1 distant:1 partition:3 kdd:1 enables:1 seed... |
6,750 | 7,105 | Identifying Outlier Arms in Multi-Armed Bandit ?
Honglei Zhuang1?
Chi Wang2
Yifan Wang3
1
University of Illinois at Urbana-Champaign
2
Microsoft Research, Redmond
3
Tsinghua University
hzhuang3@illinois.edu
wang.chi@microsoft.com
yifan-wa16@mails.tsinghua.edu.cn
Abstract
We study a novel problem lying at the intersect... | 7105 |@word exploitation:2 averagely:1 nscta:1 open:1 termination:6 heuristically:1 confirms:1 kalyanakrishnan:1 pick:2 reduction:6 configuration:7 zimek:2 liu:2 document:1 existing:4 nrr:6 current:4 com:1 comparing:2 contextual:1 tackling:1 yet:1 must:1 chu:1 distant:2 kdd:4 remove:1 plot:2 sponsored:1 fund:1 update:5... |
6,751 | 7,106 | Online Learning with Transductive Regret
Mehryar Mohri
Courant Institute and Google Research
New York, NY
mohri@cims.nyu.edu
Scott Yang?
D. E. Shaw & Co.
New York, NY
yangs@cims.nyu.edu
Abstract
We study online learning with the general notion of transductive regret, that is
regret with modification rules applying to... | 7106 |@word mild:3 illustrating:2 km:1 simulation:6 citeseer:1 q1:2 pick:1 incurs:2 thereby:1 accommodate:3 initial:5 efficacy:1 selecting:1 past:1 existing:8 current:2 must:2 written:1 additive:1 plot:1 update:1 stationary:6 selected:1 warmuth:6 wfst:9 accepting:2 iterates:1 org:1 mathematical:1 along:3 transducer:17 ... |
6,752 | 7,107 | Riemannian approach to batch normalization
Minhyung Cho
Jaehyung Lee
Applied Research Korea, Gracenote Inc.
mhyung.cho@gmail.com
jaehyung.lee@kaist.ac.kr
Abstract
Batch Normalization (BN) has proven to be an effective algorithm for deep neural
network training by normalizing the input to each neuron and reducing the ... | 7107 |@word illustrating:1 briefly:2 version:5 norm:6 nd:2 proportionality:1 bn:39 covariance:1 sgd:23 euclidian:2 tr:5 sepulchre:2 recursively:1 moment:1 initial:8 configuration:1 contains:2 liu:1 selecting:1 hereafter:1 daniel:2 rippel:1 ours:1 outperforms:2 mishra:1 com:2 activation:6 gmail:1 scatter:1 written:1 mus... |
6,753 | 7,108 | Self-supervised Learning of Motion Capture
Hsiao-Yu Fish Tung 1 , Hsiao-Wei Tung 2 , Ersin Yumer 3 , Katerina Fragkiadaki 1
1
Carnegie Mellon University, Machine Learning Department
2
University of Pittsburgh, Department of Electrical and Computer Engineering
3
Adobe Research
{htung, katef}@cs.cmu.edu, hst11@pitt.edu,... | 7108 |@word cnn:4 middle:1 version:1 tedious:3 open:2 simulation:1 rgb:4 reduction:1 configuration:1 contains:5 batista:1 animated:1 outperforms:3 existing:1 steiner:1 current:2 com:1 si:2 activation:1 readily:1 mesh:48 realistic:2 visible:5 refines:1 additive:1 shape:13 devin:1 romero:3 visibility:6 selfsupervised:1 u... |
6,754 | 7,109 | Triangle Generative Adversarial Networks
Zhe Gan? , Liqun Chen? , Weiyao Wang, Yunchen Pu, Yizhe Zhang,
Hao Liu, Chunyuan Li, Lawrence Carin
Duke University
zhe.gan@duke.edu
Abstract
A Triangle Generative Adversarial Network (?-GAN) is developed for semisupervised cross-domain joint distribution matching, where the t... | 7109 |@word mild:1 changyou:1 nd:6 open:3 cha:1 d2:18 pg:4 citeseer:1 lantao:1 liu:6 ours:1 hyunsoo:1 document:1 trustworthy:1 luo:1 readily:3 realistic:4 christian:1 designed:4 jenson:1 grass:3 generative:26 alec:2 provides:5 philipp:1 gx:16 zhang:8 junbo:1 yuan:1 consists:7 combine:1 wild:1 aitken:1 roughly:1 multi:5... |
6,755 | 7,110 | PRUNE: Preserving Proximity and Global Ranking
for Network Embedding
Yi-An Lai ??
National Taiwan University
b99202031@ntu.edu.tw
Chin-Chi Hsu ??
Academia Sinica
chinchi@iis.sinica.edu.tw
Mi-Yen Yeh ?
Academia Sinica
miyen@iis.sinica.edu.tw
Wen-Hao Chen ?
National Taiwan University
b02902023@ntu.edu.tw
Shou-De Lin... | 7110 |@word nd:3 hu:1 d2:1 propagate:1 decomposition:1 sgd:4 asks:1 solid:2 reduction:1 liu:6 contains:1 score:7 selecting:1 icis:1 maosong:2 outperforms:7 existing:4 com:1 z2:1 activation:8 yet:1 si:2 import:1 diederik:1 academia:2 informative:1 kdd:5 designed:3 drop:2 update:2 treating:1 implying:1 generative:2 websi... |
6,756 | 7,111 | Bayesian Optimization with Gradients
Jian Wu 1
Matthias Poloczek 2 Andrew Gordon Wilson 1
1
Cornell University, 2 University of Arizona
Peter I. Frazier 1
Abstract
Bayesian optimization has been successful at global optimization of expensiveto-evaluate multimodal objective functions. However, unlike most optimizati... | 7111 |@word mild:2 aircraft:1 exploitation:2 kohli:1 d2:3 hu:1 seek:2 simulation:1 covariance:2 citeseer:1 pick:3 minus:1 moment:1 ndez:1 contains:1 liu:1 selecting:2 interestingly:1 outperforms:3 freitas:2 current:1 discretization:10 com:2 di2:2 lang:1 must:3 readily:1 written:1 john:1 determinantal:1 numerical:1 addi... |
6,757 | 7,112 | Scalable trust-region method for deep reinforcement
learning using Kronecker-factored approximation
Yuhuai Wu?
University of Toronto
Vector Institute
ywu@cs.toronto.edu
Elman Mansimov?
New York University
mansimov@cs.nyu.edu
Roger Grosse
University of Toronto
Vector Institute
rgrosse@cs.toronto.edu
Shun Liao
Univer... | 7112 |@word version:2 norm:17 nd:1 simulation:1 covariance:1 sgd:5 inefficiency:1 series:1 score:2 bootstrapped:1 outperforms:1 freitas:1 current:5 com:5 activation:3 guez:1 john:1 numerical:1 update:29 v:1 intelligence:1 spaceinvaders:1 website:1 parameterization:3 steepest:3 provides:3 toronto:7 wierstra:2 constructe... |
6,758 | 7,113 | R?nyi Differential Privacy Mechanisms for Posterior
Sampling
Joseph Geumlek
University of California, San Diego
jgeumlek@cs.ucsd.edu
Shuang Song
University of California, San Diego
shs037@eng.ucsd.edu
Kamalika Chaudhuri
University of California, San Diego
kamalika@cs.ucsd.edu
Abstract
With the newly proposed privac... | 7113 |@word mild:1 trial:3 private:12 version:1 faculty:1 achievable:5 stronger:3 proportion:1 logit:1 norm:1 polynomial:1 bun:2 nd:2 eng:1 covariance:5 pset:6 efficacy:1 offering:1 existing:7 mishra:1 comparing:1 com:1 yet:1 must:2 written:6 numerical:1 partition:4 gv:2 asymptote:2 designed:1 plot:5 v:4 mitrokotsa:1 i... |
6,759 | 7,114 | Online Learning with a Hint
Ofer Dekel
Microsoft Research
oferd@microsoft.com
Nika Haghtalab
Computer Science Department
Carnegie Mellon University
nika@cmu.edu
Arthur Flajolet
Operations Research Center
Massachusetts Institute of Technology
flajolet@mit.edu
Patrick Jaillet
EECS, LIDS, ORC
Massachusetts Institute of... | 7114 |@word achievable:2 polynomial:2 stronger:1 norm:2 dekel:1 c0:1 gradual:1 jacob:1 pick:3 incurs:3 kxkk:2 reduction:4 com:1 discretization:1 yet:1 additive:1 subsequent:1 designed:1 ints:1 update:2 instantiate:2 beginning:2 ith:1 farther:1 chiang:2 provides:2 boosting:3 location:1 become:1 symposium:1 prove:5 combi... |
6,760 | 7,115 | Identification of Gaussian Process State Space Models
Stefanos Eleftheriadis? , Thomas F.W. Nicholson? , Marc P. Deisenroth?? , James Hensman?
?
PROWLER.io, ? Imperial College London
{stefanos, tom, marc, james}@prowler.io
Abstract
The Gaussian process state space model (GPSSM) is a non-linear dynamical system, where ... | 7115 |@word trial:1 faculty:1 middle:1 advantageous:1 seems:2 hu:1 simulation:2 nicholson:1 propagate:1 covariance:3 pressure:1 recursively:1 moment:1 initial:4 inefficiency:3 series:7 contains:1 efficacy:1 liquid:1 past:1 outperforms:1 current:1 recovered:1 activation:2 yet:1 diederik:2 additive:2 enables:1 analytic:1... |
6,761 | 7,116 | Robust Imitation of Diverse Behaviors
Ziyu Wang?, Josh Merel? , Scott Reed, Greg Wayne, Nando de Freitas, Nicolas Heess
DeepMind
ziyu,jsmerel,reedscot,gregwayne,nandodefreitas,heess@google.com
Abstract
Deep generative models have recently shown great promise in imitation learning
for motor control. Given enough data, ... | 7116 |@word illustrating:1 briefly:1 version:1 pw:1 simulation:1 jacob:1 dramatic:1 versatile:1 shot:6 carry:1 reduction:3 initial:2 plentiful:1 inefficiency:1 configuration:1 contains:1 ndez:1 tuned:1 bc:12 ours:1 animated:1 freitas:1 existing:1 current:1 com:1 yet:2 mesh:1 realistic:1 enables:2 motor:2 remove:1 plot:... |
6,762 | 7,117 | Can Decentralized Algorithms Outperform
Centralized Algorithms? A Case Study for
Decentralized Parallel Stochastic Gradient Descent
Xiangru Lian? , Ce Zhang? , Huan Zhang+ , Cho-Jui Hsieh+ , Wei Zhang# , and Ji Liu?\
?
University of Rochester, ? ETH Zurich
+
University of California, Davis, # IBM T. J. Watson Research ... | 7117 |@word cnn:1 version:2 norm:3 johansson:1 dekel:5 open:1 d2:4 gradual:1 linearized:1 hsieh:4 sgd:30 boundedness:2 accommodate:1 initial:1 liu:8 configuration:7 series:1 envision:1 outperforms:4 existing:4 current:1 com:5 gmail:2 yet:1 gpu:9 fn:2 devin:1 partition:1 designed:1 treating:1 update:5 juditsky:1 sundara... |
6,763 | 7,118 | Local Aggregative Games
Vikas K. Garg
CSAIL, MIT
vgarg@csail.mit.edu
Tommi Jaakkola
CSAIL, MIT
tommi@csail.mit.edu
Aggregative games provide a rich abstraction to model strategic multi-agent interactions. We introduce
local aggregative games, where the payoff of each player is a function of its own action and the
ag... | 7118 |@word norm:1 justice:10 suitably:1 c0:1 bf:5 bn:2 decomposition:4 pg:9 accommodate:2 initial:2 configuration:7 contains:1 efficacy:2 ka:5 contextual:1 recovered:5 issuing:1 chu:1 fn:4 hofmann:1 designed:1 plot:1 cue:1 prohibitive:1 nq:1 parameterization:2 plane:2 mccallum:1 ith:1 hinged:1 short:1 provides:4 node:... |
6,764 | 7,119 | A Sample Complexity Measure with Applications to
Learning Optimal Auctions
Vasilis Syrgkanis
Microsoft Research
vasy@microsoft.com
Abstract
We introduce a new sample complexity measure, which we refer to as split-sample
growth rate. For any hypothesis H and for any sample S of size m, the splitsample growth rate ??H ... | 7119 |@word h:45 private:2 compression:1 open:1 r:18 pick:5 thereby:1 series:1 ironing:1 existing:1 com:1 si:1 additive:7 remove:1 half:2 item:28 yannai:1 alexandros:1 mcdiarmid:1 simpler:1 symposium:2 focs:1 manner:1 introduce:1 expected:10 multi:1 increasing:1 abound:1 bounded:7 moreover:7 maximizes:1 prophet:1 goncz... |
6,765 | 712 | Reinforcement Learning Applied to
Linear Quadratic Regulation
Steven J. Bradtke
Computer Science Department
University of Massachusetts
Amherst, MA 01003
bradtke@cs.umass.edu
Abstract
Recent research on reinforcement learning has focused on algorithms based on the principles of Dynamic Programming (DP).
One of the mo... | 712 |@word polynomial:1 norm:2 suitably:2 hu:3 jacob:2 initial:3 uma:1 lqr:22 existing:1 current:2 z2:1 yet:1 written:3 must:1 john:1 numerical:1 championship:1 update:5 selected:1 lx:1 manner:1 nor:1 planning:1 brain:1 discretized:1 discounted:1 pitfall:2 td:3 panel:2 what:1 argmin:2 developed:2 nj:1 temporal:3 rememb... |
6,766 | 7,120 | Thinking Fast and Slow
with Deep Learning and Tree Search
Thomas Anthony1, , Zheng Tian1 , and David Barber1,2
1
University College London
2
Alan Turing Institute
thomas.anthony.14@ucl.ac.uk
Abstract
Sequential decision making problems, such as structured prediction, robotic control,
and game playing, require a combi... | 7120 |@word multitask:1 cnn:1 version:8 stronger:11 simulation:11 thereby:1 reduction:1 initial:3 selecting:2 initialisation:1 daniel:1 outperforms:6 existing:1 current:4 si:3 yet:1 must:1 gpu:2 evans:1 dive:1 analytic:2 cheap:1 update:2 greedy:1 fewer:1 selected:2 half:1 imitate:2 leaf:1 beginning:1 short:1 node:9 pen... |
6,767 | 7,121 | EEG-GRAPH: A Factor-Graph-Based Model for
Capturing Spatial, Temporal, and Observational
Relationships in Electroencephalograms
Yogatheesan Varatharajah ?
Benjamin Brinkmann?
Min Jin Chong?
Krishnakant Saboo?
Gregory Worrell?
Brent Berry?
Ravishankar Iyer?
Abstract
This paper presents a probabilistic-graphical m... | 7121 |@word neurophysiology:3 trial:2 illustrating:1 faculty:1 polynomial:1 approved:1 d2:1 hu:2 lobe:2 accommodate:1 liu:2 contains:4 series:1 efficacy:1 lightweight:4 score:4 loeliger:1 phuong:1 outperforms:5 existing:2 horvitz:1 current:5 must:1 partition:3 analytic:1 remove:2 treating:1 v:1 alone:4 intelligence:5 a... |
6,768 | 7,122 | Improving the Expected Improvement Algorithm
Chao Qin
Columbia Business School
New York, NY 10027
cqin22@gsb.columbia.edu
Diego Klabjan
Northwestern University
Evanston, IL 60208
d-klabjan@northwestern.edu
Daniel Russo
Columbia Business School
New York, NY 10027
djr2174@gsb.columbia.edu
Abstract
The expected improv... | 7122 |@word trial:2 version:2 proportion:26 stronger:1 simulation:6 kalyanakrishnan:1 soare:1 moment:1 bai:3 selecting:1 daniel:2 offering:1 outperforms:4 existing:1 freitas:1 current:5 comparing:1 surprising:2 dx:4 must:1 readily:1 john:1 numerical:2 enables:1 cis:1 designed:3 update:3 greedy:3 half:1 beginning:1 vani... |
6,769 | 7,123 | Hybrid Reward Architecture for
Reinforcement Learning
Harm van Seijen1
harm.vanseijen@microsoft.com
Mehdi Fatemi1
mehdi.fatemi@microsoft.com
Joshua Romoff12
joshua.romoff@mail.mcgill.ca
Romain Laroche1
romain.laroche@microsoft.com
Tavian Barnes1
tavian.barnes@microsoft.com
Jeffrey Tsang1
tsang.jeffrey@microsoft.c... | 7123 |@word h:1 version:2 decomposition:4 diuk:3 carry:1 contains:1 yidqn:2 current:4 com:5 tackling:1 enables:1 update:4 greedy:2 selected:1 node:3 location:9 simpler:2 along:1 consists:2 combine:1 gvfs:4 notably:1 expected:3 behavior:1 multi:2 terminal:5 discounted:2 decomposed:2 agnostic:1 atari:4 substantially:1 pr... |
6,770 | 7,124 | Approximate Supermodularity Bounds for
Experimental Design
Luiz F. O. Chamon and Alejandro Ribeiro
Electrical and Systems Engineering
University of Pennsylvania
{luizf,aribeiro}@seas.upenn.edu
Abstract
This work provides performance guarantees for the greedy solution of experimental design problems. In particular, it... | 7124 |@word determinant:5 version:7 polynomial:2 norm:1 open:1 seek:2 simulation:2 covariance:16 euclidian:1 tr:4 reduction:1 initial:1 celebrated:1 contains:2 liu:1 selecting:8 denoting:1 interestingly:1 recovered:1 comparing:1 must:3 readily:1 additive:2 numerical:2 informative:1 designed:1 plot:2 greedy:41 selected:... |
6,771 | 7,125 | Maximizing Subset Accuracy with Recurrent Neural
Networks in Multi-label Classification
Jinseok Nam1 , Eneldo Loza Menc?a1 , Hyunwoo J. Kim2 , and Johannes F?rnkranz1
2
1
Knowledge Engineering Group, TU Darmstadt
Department of Computer Sciences, University of Wisconsin-Madison
Abstract
Multi-label classification is t... | 7125 |@word h:2 cnn:1 version:1 pcc:24 tadepalli:1 confirms:1 ylp:3 grk:1 bioasq:13 thereby:1 initial:2 configuration:2 contains:2 score:4 selecting:1 liu:1 tuned:1 document:23 past:1 outperforms:1 lichtenberg:1 current:1 com:1 nt:3 comparing:1 anne:1 guadarrama:1 tackling:1 written:1 gpu:1 fn:3 partition:6 cis:1 hypot... |
6,772 | 7,126 | AdaGAN: Boosting Generative Models
Ilya Tolstikhin
MPI for Intelligent Systems
T?bingen, Germany
ilya@tue.mpg.de
Olivier Bousquet
Google Brain
Z?rich, Switzerland
obousquet@google.com
Sylvain Gelly
Google Brain
Z?rich, Switzerland
sylvaingelly@google.com
Carl-Johann Simon-Gabriel
MPI for Intelligent Systems
T?bingen... | 7126 |@word middle:1 inversion:2 seems:1 heuristically:1 tried:3 jacob:1 pg:40 pick:1 harder:3 initial:1 contains:2 ours:1 interestingly:2 outperforms:1 existing:1 current:9 com:3 comparing:1 assigning:1 reminiscent:1 written:1 must:1 additive:4 visible:1 shape:1 remove:1 reproducible:1 adagan:21 update:5 interpretable... |
6,773 | 7,127 | Straggler Mitigation in Distributed Optimization
Through Data Encoding
Can Karakus
UCLA
Los Angeles, CA
karakus@ucla.edu
Yifan Sun
Technicolor Research
Los Altos, CA
Yifan.Sun@technicolor.com
Suhas Diggavi
UCLA
Los Angeles, CA
suhasdiggavi@ucla.edu
Wotao Yin
UCLA
Los Angeles, CA
wotaoyin@math.ucla.edu
Abstract
Slo... | 7127 |@word trial:1 determinant:1 version:1 norm:2 hu:1 thereby:1 yea:1 reduction:2 initial:3 exclusively:1 mixon:1 existing:2 steiner:2 recovered:2 com:1 current:2 si:9 yet:1 must:2 readily:2 numerical:2 partition:3 enables:1 drop:2 designed:1 update:9 fewer:1 ksm:1 ith:1 short:3 core:1 mitigation:4 infrastructure:1 m... |
6,774 | 7,128 | Multi-View Decision Processes:
The Helper-AI Problem
Christos Dimitrakakis
Chalmers University of Technology & University of Lille
???????????????????????????????
David C. Parkes
Harvard University
???????????????????????
Goran Radanovic
Harvard University
????????????????????????
Paul Tylkin
Harvard University
????... | 7128 |@word version:1 middle:1 polynomial:3 achievable:4 norm:1 nd:4 heuristically:1 pieter:1 seek:2 simulation:4 q1:1 arti:6 eld:2 harder:1 boundedness:1 shot:1 reduction:1 electronics:1 cyclic:2 hereafter:1 past:1 current:3 surprising:1 follower:4 ust:7 must:3 ronald:1 sorg:2 chicago:1 informative:1 designed:1 plot:4... |
6,775 | 7,129 | A Greedy Approach for
Budgeted Maximum Inner Product Search
Hsiang-Fu Yu?
Amazon Inc.
rofuyu@cs.utexas.edu
Cho-Jui Hsieh
University of California, Davis
chohsieh@ucdavis.edu
Qi Lei
The University of Texas at Austin
leiqi@ices.utexas.edu
Inderjit S. Dhillon
The University of Texas at Austin
inderjit@cs.utexas.edu
Ab... | 7129 |@word version:1 nd:1 grey:1 hsieh:1 tr:3 reduction:6 contains:5 score:1 selecting:1 outperforms:1 existing:7 current:10 must:1 partition:2 j1:4 kdd:2 enables:2 drop:1 designed:4 hash:3 chohsieh:1 greedy:46 half:1 selected:4 item:16 leaf:1 intelligence:1 beginning:1 core:3 pointer:3 iterates:2 node:1 preference:2 ... |
6,776 | 713 | Adaptive Stimulus Representations:
A Computational Theory of
Hippocampal-Region Function
Mark A. Gluck
Catherine E. Myers
Center for Molecular and Behavioral Neuroscience
Rutgers University. Newark. NJ 07102
g IlIck@pOl ?/OI?.I'lI(gers.edll
mycrs@p(/\ -Iol'.rl/(gers.edll
Abstract
We present a theory of cortico-hip... | 713 |@word trial:10 version:1 compression:6 hippocampus:10 extinction:1 simulation:1 fonn:2 schmaltz:2 solid:1 harder:1 initial:1 series:1 current:4 contextual:6 activation:5 buckingham:2 must:7 readily:1 subsequent:3 partition:2 informative:1 discrimination:13 alone:2 cue:7 instantiate:1 intelligence:1 tone:4 record:1... |
6,777 | 7,130 | SVD-Softmax: Fast Softmax Approximation on Large
Vocabulary Neural Networks
Kyuhong Shim, Minjae Lee, Iksoo Choi, Yoonho Boo, Wonyong Sung
Department of Electrical and Computer Engineering
Seoul National University, Seoul, Korea
skhu20@snu.ac.kr, {mjlee, ischoi, yhboo}@dsp.snu.ac.kr, wysung@snu.ac.kr
Abstract
We prop... | 7130 |@word h:4 luk:1 armand:1 norm:1 open:2 d2:1 carolina:1 decomposition:5 jacob:2 contrastive:1 citeseer:2 sgd:3 reduction:1 initial:3 liu:1 contains:2 score:3 blackout:2 rightmost:1 gauvain:1 yet:1 must:1 gpu:10 parsing:1 john:1 predetermined:1 christian:2 cis:1 drop:1 plot:1 update:1 bart:1 alone:1 half:1 selected... |
6,778 | 7,131 | Plan, Attend, Generate:
Planning for Sequence-to-Sequence Models
Francis Dutil?
University of Montreal (MILA)
frdutil@gmail.com
Caglar Gulcehre?
University of Montreal (MILA)
ca9lar@gmail.com
Adam Trischler
Microsoft Research Maluuba
adam.trischler@microsoft.com
Yoshua Bengio
University of Montreal (MILA)
yoshua.um... | 7131 |@word norm:3 open:1 bachman:1 minus:1 initial:1 score:2 united:1 daniel:1 document:10 subword:1 outperforms:3 past:1 existing:5 current:8 com:7 luo:2 activation:2 gmail:3 guez:1 reminiscent:1 must:2 john:1 ronald:1 hofmann:1 enables:1 update:13 bart:2 generative:1 fewer:6 ith:2 aja:1 pointer:2 regressive:1 tarlow... |
6,779 | 7,132 | Task-based End-to-end Model Learning
in Stochastic Optimization
Priya L. Donti
Dept. of Computer Science
Dept. of Engr. & Public Policy
Carnegie Mellon University
Pittsburgh, PA 15213
pdonti@cs.cmu.edu
Brandon Amos
Dept. of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
bamos@cs.cmu.edu
J. Zico Kolt... | 7132 |@word version:2 c0:3 pieter:3 jingdong:1 weekday:1 dramatic:1 profit:1 ld:2 initial:1 cherian:1 daniel:1 tuned:4 genetic:1 interestingly:1 outperforms:6 past:10 existing:1 current:1 com:1 babenko:1 yet:1 diederik:2 must:6 written:4 neq:2 john:1 cruz:1 realistic:1 subsequent:2 periodically:1 christian:1 update:4 s... |
6,780 | 7,133 | ALICE: Towards Understanding Adversarial
Learning for Joint Distribution Matching
Chunyuan Li1 , Hao Liu2 , Changyou Chen3 , Yunchen Pu1 , Liqun Chen1 ,
Ricardo Henao1 and Lawrence Carin1
1
Duke University 2 Nanjing University 3 University at Buffalo
cl319@duke.edu
Abstract
We investigate the non-identifiability issu... | 7133 |@word trial:2 changyou:1 cha:1 simulation:1 seek:2 covariance:1 shot:1 configuration:4 liu:2 score:2 selecting:1 z2:7 com:1 luo:1 written:1 readily:2 realistic:5 shape:2 enables:1 visibility:1 interpretable:1 v:1 generative:13 discovering:1 accordingly:2 indicative:1 isotropic:1 colored:1 provides:2 philipp:1 zha... |
6,781 | 7,134 | Finite sample analysis of the GTD Policy Evaluation
Algorithms in Markov Setting
Yue Wang ?
School of Science
Beijing Jiaotong University
11271012@bjtu.edu.cn
Wei Chen
Microsoft Research
wche@microsoft.com
Zhi-Ming Ma
Academy of Mathematics and Systems Science
Chinese Academy of Sciences
mazm@amt.ac.cn
Yuting Liu
S... | 7134 |@word briefly:1 norm:3 johansson:1 nd:1 d2:5 simulation:3 decomposition:5 citeseer:1 sgd:1 initial:1 liu:17 o2:6 err:5 current:3 com:2 si:8 guez:1 john:2 realistic:6 wiewiora:1 update:7 juditsky:1 stationary:6 fewer:1 kyk:1 dissertation:1 aja:1 yuting:1 firstly:2 k2m:1 mathematical:1 wierstra:1 prove:1 combine:1 ... |
6,782 | 7,135 | On the Complexity of Learning Neural Networks
Le Song
Georgia Institute of Technology
Atlanta, GA 30332
lsong@cc.gatech.edu
Santosh Vempala
Georgia Institute of Technology
Atlanta, GA 30332
vempala@gatech.edu
John Wilmes
Georgia Institute of Technology
Atlanta, GA 30332
wilmesj@gatech.edu
Bo Xie
Georgia Institute o... | 7135 |@word mild:1 version:5 briefly:1 polynomial:9 stronger:2 norm:2 softsign:2 c0:4 decomposition:1 covariance:5 sgd:1 moment:1 kurt:1 existing:2 current:4 comparing:1 varx:2 activation:22 merrick:1 dx:1 must:2 john:2 ronald:1 realistic:1 hanie:1 informative:1 benign:4 drop:1 update:3 v:1 fewer:2 cy0:2 amir:1 isotrop... |
6,783 | 7,136 | Hierarchical Implicit Models and
Likelihood-Free Variational Inference
Dustin Tran
Columbia University
Rajesh Ranganath
Princeton University
David M. Blei
Columbia University
Abstract
Implicit probabilistic models are a flexible class of models defined by a simulation process for data. They form the basis for theori... | 7136 |@word open:2 proportionality:1 lezaun:1 simulation:6 thereby:1 harder:1 initial:4 ndez:4 series:5 score:2 liu:1 tuned:2 fa8750:1 existing:1 activation:2 john:1 exposing:1 additive:1 concatenate:1 enables:1 christian:1 drop:1 interpretable:2 update:3 generative:17 intelligence:1 blei:10 provides:2 toronto:1 gx:2 u... |
6,784 | 7,137 | Semi-supervised Learning with GANs: Manifold
Invariance with Improved Inference
Abhishek Kumar?
IBM Research AI
Yorktown Heights, NY
abhishk@us.ibm.com
Prasanna Sattigeri?
IBM Research AI
Yorktown Heights, NY
psattig@us.ibm.com
P. Thomas Fletcher
University of Utah
Salt Lake City, UT
fletcher@sci.utah.edu
Abstract
... | 7137 |@word middle:2 version:2 norm:6 seems:1 logit:2 open:4 calculus:1 linearized:1 jacob:2 covariance:1 pg:8 sgd:2 series:1 jimenez:1 outperforms:1 existing:3 current:8 com:2 comparing:1 assigning:1 diederik:3 john:1 realistic:5 happen:1 shape:1 enables:2 christian:1 remove:1 plot:4 v:1 discrimination:2 generative:16... |
6,785 | 7,138 | Approximation and Convergence Properties of
Generative Adversarial Learning
Shuang Liu
University of California, San Diego
shuangliu@ucsd.edu
Olivier Bousquet
Google Brain
obousquet@google.com
Kamalika Chaudhuri
University of California, San Diego
kamalika@cs.ucsd.edu
Abstract
Generative adversarial networks (GAN) ... | 7138 |@word mild:1 norm:1 villani:1 stronger:12 mimick:1 ipm:1 moment:7 liu:1 contains:2 series:1 past:1 existing:2 current:1 com:1 comparing:1 activation:1 attracted:1 happen:1 metrizes:2 enables:1 generative:11 rudin:1 xk:1 characterization:1 zhang:1 direct:1 inside:1 hellinger:1 x0:10 theoretically:1 indeed:1 nor:1 ... |
6,786 | 7,139 | From Bayesian Sparsity to Gated Recurrent Nets
Hao He
Massachusetts Institute of Technology
haohe@mit.edu
Satoshi Ikehata
National Institute of Informatics
satoshi.ikehata@gmail.com
Bo Xin
Microsoft Research, Beijing, China
jimxinbo@gmail.com
David Wipf
Microsoft Research, Beijing, China
davidwipf@gmail.com
Abstract
... | 7139 |@word mild:1 trial:2 version:3 norm:10 seems:1 suitably:1 open:1 sgd:1 harder:1 initial:2 series:1 contains:2 mosher:1 tuned:1 ours:4 interestingly:1 envision:1 outperforms:2 existing:4 freitas:1 recovered:1 current:2 com:3 activation:4 yet:2 dx:1 gmail:3 must:3 readily:1 concatenate:1 happen:1 partition:1 shape:... |
6,787 | 714 | Feudal Reinforcement Learning
Peter Dayan
CNL
The Salk Institute
PO Box 85800
San Diego CA 92186-5800, USA
Geoffrey E Hinton
Department of Computer Science
University of Toronto
6 Kings College Road, Toronto,
Canada M5S 1A4
dayan~helmholtz.sdsc.edu
hinton~ai.toronto.edu
Abstract
One way to speed up reinforcement l... | 714 |@word instruction:1 r:1 jacob:2 decomposition:2 pick:1 dramatic:1 inefficiency:1 selecting:1 punishes:1 current:3 com:1 nowlan:2 must:2 finest:1 grain:4 happen:2 shape:1 selected:1 fewer:1 accordingly:1 coarse:1 detecting:1 toronto:3 successive:1 location:3 ron:1 instructs:1 five:1 direct:1 become:1 consists:1 man... |
6,788 | 7,140 | Min-Max Propagation
Christopher Srinivasa
University of Toronto
Borealis AI
christopher.srinivasa
@gmail.com
Inmar Givoni
University of
Toronto
inmar.givoni
@gmail.com
Siamak Ravanbakhsh
University of
British
Columbia
siamakx@cs.ubc.ca
Brendan J. Frey
University of Toronto
Vector Institute
Deep Genomics
frey@psi.to... | 7140 |@word version:3 middle:1 polynomial:1 reused:1 termination:2 seek:2 decomposition:1 reduction:10 initial:2 configuration:3 contains:1 selecting:4 loeliger:1 outperforms:1 ka:1 com:2 current:3 si:2 gmail:2 partition:1 j1:9 enables:2 siamak:1 drop:1 update:15 designed:1 v:1 half:4 leaf:4 greedy:2 xk:12 tarlow:2 nod... |
6,789 | 7,141 | What Uncertainties Do We Need in Bayesian Deep
Learning for Computer Vision?
Alex Kendall
University of Cambridge
agk34@cam.ac.uk
Yarin Gal
University of Cambridge
yg279@cam.ac.uk
Abstract
There are two major types of uncertainty one can model. Aleatoric uncertainty
captures noise inherent in the observations. On th... | 7141 |@word kohli:1 illustrating:1 economically:1 kokkinos:1 logit:4 c0:3 nd:1 seek:1 rgb:1 citeseer:1 egou:1 harder:1 epistemic:75 liu:5 contains:1 score:2 efficacy:2 hoiem:1 daniel:1 denoting:1 salzmann:1 outperforms:1 existing:4 comparing:2 michal:1 si:5 gpu:2 john:1 devin:1 distant:1 romero:1 analytic:1 christian:2... |
6,790 | 7,142 | Gradient descent GAN optimization is locally stable
Vaishnavh Nagarajan
Computer Science Department
Carnegie-Mellon University
Pittsburgh, PA 15213
vaishnavh@cs.cmu.edu
J. Zico Kolter
Computer Science Department
Carnegie-Mellon University
Pittsburgh, PA 15213
zkolter@cs.cmu.edu
Abstract
Despite the growing prominence... | 7142 |@word briefly:2 version:1 stronger:1 norm:3 seems:2 calculus:1 fairer:1 linearized:1 crucially:1 prominence:3 jacob:1 pg:1 moment:1 contains:1 daniel:1 offering:1 precluding:1 interestingly:1 com:1 dx:1 written:2 must:4 realistic:2 kdd:11 hofmann:1 christian:1 shape:1 plot:3 update:36 v:1 generative:14 alec:2 par... |
6,791 | 7,143 | Toward Robustness against Label Noise in
Training Deep Discriminative Neural Networks
Arash Vahdat
D-Wave Systems Inc.
Burnaby, BC, Canada
avahdat@dwavesys.com
Abstract
Collecting large training datasets, annotated with high-quality labels, is costly
and time-consuming. This paper proposes a novel framework for train... | 7143 |@word h:2 cnn:14 paredes:1 hu:1 seek:1 propagate:2 contrastive:1 configuration:3 contains:2 score:1 daniel:1 bc:1 romera:1 outperforms:2 existing:1 current:2 com:1 recovered:1 jinbo:1 yet:1 diederik:1 written:1 must:1 john:2 partition:3 analytic:2 christian:1 seeding:1 designed:1 update:4 v:1 sukhbaatar:1 generat... |
6,792 | 7,144 | Dualing GANs
Yujia Li1?
Alexander Schwing3
Kuan-Chieh Wang1,2
Richard Zemel1,2
1
2
Department of Computer Science, University of Toronto
Vector Institute
3
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
{yujiali, wangkua1, zemel}@cs.toronto.edu
aschwing@illinois.edu
Abst... | 7144 |@word illustrating:1 middle:1 pw:3 norm:2 seems:1 tried:2 bn:1 carry:1 moment:10 contains:1 score:24 daniel:1 offering:1 tuned:2 interestingly:1 document:1 current:1 culprit:1 activation:1 assigning:1 intriguing:1 realistic:2 concatenate:1 informative:2 cheap:1 analytic:1 remove:2 concert:1 update:8 interpretable... |
6,793 | 7,145 | Deep Learning for Precipitation Nowcasting:
A Benchmark and A New Model
Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-Yan Yeung
Department of Computer Science and Engineering
Hong Kong University of Science and Technology
{xshiab,zgaoag,lelausen,hwangaz,dyyeung}@cse.ust.hk
Wai-kin Wong, Wang-chun Woo
Hong Kon... | 7145 |@word kong:9 cnn:29 middle:1 proportion:2 norm:2 stronger:2 d2:1 km:3 shuicheng:1 tried:2 forecaster:1 k7:1 configuration:2 contains:5 score:17 outperforms:4 existing:2 current:3 activation:1 yet:2 issuing:1 ust:1 parsing:1 written:1 john:2 fn:5 numerical:1 timestamps:1 ashesh:1 ronan:1 shape:1 designed:3 plot:1 ... |
6,794 | 7,146 | Do Deep Neural Networks Suffer from Crowding?
Anna Volokitin?\
Gemma Roig???
Tomaso Poggio??
voanna@vision.ee.ethz.ch
gemmar@mit.edu
tp@csail.mit.edu
?
Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA
?
Istituto Italiano di Tecnologia at Massachusetts Institute of Technology,... | 7146 |@word cnn:1 norm:4 grey:1 shot:1 carry:1 crowding:39 configuration:25 contains:1 liu:1 selecting:2 foveal:1 series:1 document:1 interestingly:2 com:1 luo:1 yet:5 shape:1 plot:6 progressively:2 half:2 fewer:1 selected:2 intelligence:1 plane:1 beginning:2 provides:1 contribute:1 location:8 zhang:1 accessed:1 height... |
6,795 | 7,147 | Learning from Complementary Labels
Takashi Ishida1,2,3 Gang Niu2,3 Weihua Hu2,3 Masashi Sugiyama3,2
1
Sumitomo Mitsui Asset Management, Tokyo, Japan
2
The University of Tokyo, Tokyo, Japan
3
RIKEN, Tokyo, Japan
{ishida@ms., gang@ms., hu@ms., sugi@}k.u-tokyo.ac.jp
Abstract
Collecting labeled data is costly and thus a c... | 7147 |@word trial:6 private:1 version:2 kulis:1 open:1 hu:1 contraction:1 incurs:2 score:1 selecting:3 luo:1 activation:1 goldberger:1 dx:3 must:1 written:1 john:1 informative:2 kdd:1 enables:1 intelligence:1 website:2 mccallum:1 yamada:1 contribute:1 denis:1 mcdiarmid:1 zhang:1 five:2 direct:2 differential:1 incorrect... |
6,796 | 7,148 | Online control of the false discovery rate with
decaying memory
Aaditya Ramdas
Fanny Yang Martin J. Wainwright Michael I. Jordan
University of California, Berkeley
{aramdas, fanny-yang, wainwrig, jordan} @berkeley.edu
Abstract
In the online multiple testing problem, p-values corresponding to different null
hypotheses... | 7148 |@word trial:2 briefly:1 version:1 proportion:7 simulation:5 paid:2 accommodate:1 carry:2 moment:1 initial:5 series:5 contains:1 ours:1 past:15 wainwrig:1 existing:3 current:3 assigning:1 must:17 reminiscent:1 john:1 stine:5 subsequent:1 numerical:2 designed:2 drop:1 update:6 implying:1 stationary:1 affair:1 short... |
6,797 | 7,149 | Learning from uncertain curves:
The 2-Wasserstein metric for Gaussian processes
Anton Mallasto
Department of Computer Science
University of Copenhagen
mallasto@di.ku.dk
Aasa Feragen
Department of Computer Science
University of Copenhagen
aasa@di.ku.dk
Abstract
We introduce a novel framework for statistical analysis ... | 7149 |@word h:1 version:1 mri:3 norm:6 villani:1 grey:1 d2:3 closure:3 crucially:1 iki:1 covariance:39 bn:6 pick:2 tr:8 carry:1 series:2 salzmann:1 longitudinal:1 existing:1 com:1 comparing:1 olkin:1 protection:1 fn:1 subsequent:1 confirming:1 cheap:1 wanted:1 webster:1 plot:1 auerbach:1 wassermann:1 ith:1 vanishing:1 ... |
6,798 | 715 | A Boundary Hunting Radial Basis Function
Classifier Which Allocates Centers
Constructively
Eric I. Chang and Richard P. Lippmann
MIT Lincoln Laboratory
Lexington, MA02173-0073, USA
Abstract
A new boundary hunting radial basis function (BH-RBF) classifier
which allocates RBF centers constructively near class boundaries... | 715 |@word trial:1 dekker:1 tried:2 covariance:2 barney:2 initial:4 hunting:7 contains:1 score:1 selecting:1 tuned:1 assigning:2 must:1 designed:2 discrimination:4 fewer:3 selected:4 short:1 provides:5 node:7 five:3 consists:1 theoretically:1 frequently:1 multi:1 automatically:1 actual:2 considering:1 linda:1 kaufman:2... |
6,799 | 7,150 | Discriminative State-Space Models
Vitaly Kuznetsov
Google Research
New York, NY 10011, USA
vitaly@cims.nyu.edu
Mehryar Mohri
Courant Institute and Google Research
New York, NY 10011, USA
mohri@cims.nyu.edu
Abstract
We introduce and analyze Discriminative State-Space Models for forecasting nonstationary time series. W... | 7150 |@word mild:2 version:1 norm:2 d2:1 r:3 seek:1 pg:1 q1:1 harder:1 series:30 contains:1 united:1 denoting:1 existing:4 z2:1 subcomponents:1 luo:2 subsequent:1 enables:1 stationary:6 generative:7 selected:1 provides:3 boosting:1 node:1 mathematical:1 along:2 adk:1 consists:3 shorthand:1 prove:1 combine:1 manner:1 in... |
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