Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
4,800 | 5,346 | Sequence to Sequence Learning
with Neural Networks
Ilya Sutskever
Google
ilyasu@google.com
Oriol Vinyals
Google
vinyals@google.com
Quoc V. Le
Google
qvl@google.com
Abstract
Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well wh... | 5346 |@word norm:2 sgd:2 tr:2 initial:1 configuration:1 score:21 ours:1 interestingly:1 prefix:1 document:1 outperforms:2 com:3 surprising:1 activation:1 yet:1 gpu:6 john:6 devin:2 concatenate:1 enables:1 plot:2 sont:4 progressively:1 half:1 selected:1 une:5 schluter:1 vanishing:1 short:11 core:1 provides:1 rescoring:8... |
4,801 | 5,347 | How transferable are features in deep neural
networks?
Jason Yosinski,1 Jeff Clune,2 Yoshua Bengio,3 and Hod Lipson4
1
Dept. Computer Science, Cornell University
2
Dept. Computer Science, University of Wyoming
3
Dept. Computer Science & Operations Research, University of Montreal
4
Dept. Mechanical & Aerospace Engineer... | 5347 |@word version:3 middle:5 seems:2 retraining:1 carry:1 coadaptation:1 contains:3 tuned:7 document:1 ours:1 guadarrama:1 transferability:7 com:1 surprising:5 comparing:1 activation:3 assigning:2 must:3 gpu:1 distant:3 informative:1 drop:12 plot:1 v:5 alone:1 half:10 generative:1 beginning:1 boosting:1 pascanu:1 suc... |
4,802 | 5,348 | Convolutional Kernel Networks
Julien Mairal, Piotr Koniusz, Zaid Harchaoui, and Cordelia Schmid
Inria?
firstname.lastname@inria.fr
Abstract
An important goal in visual recognition is to devise image representations that are
invariant to particular transformations. In this paper, we address this goal with a
new type of... | 5348 |@word msr:1 cnn:4 version:4 norm:6 seems:1 open:2 propagate:1 rgb:3 tried:1 p0:6 hsieh:1 sgd:2 nystr:3 tr:1 recursively:1 liblinear:2 initial:2 substitution:1 contains:1 selecting:1 rkhs:1 interestingly:2 ours:1 document:1 z2:2 activation:3 yet:2 written:1 subsequent:2 ckns:1 numerical:1 shape:14 zaid:1 plot:2 in... |
4,803 | 5,349 | Learning Deep Features for Scene Recognition
using Places Database
Bolei Zhou1 , Agata Lapedriza1,3 , Jianxiong Xiao2 , Antonio Torralba1 , and Aude Oliva1
1
Massachusetts Institute of Technology
2
Princeton University
3
Universitat Oberta de Catalunya
Abstract
Scene recognition is one of the hallmark tasks of comput... | 5349 |@word trial:8 cnn:62 proportion:1 open:2 hsieh:1 pick:2 liblinear:1 initial:1 configuration:1 contains:7 tuned:3 subjective:1 current:5 comparing:4 activation:2 scatter:2 gpu:1 candy:1 shape:1 remove:2 designed:4 gist:4 plot:3 drop:1 v:2 intelligence:1 selected:9 generative:1 discovering:1 short:1 provides:1 org:... |
4,804 | 535 | Unsupervised learning
of distributions on binary vectors
using two layer networks
David Haussler
Computer and Information Sciences
University of California Santa Cruz
Santa Cruz , CA 95064
Yoav Freund?
Computer and Information Sciences
University of California Santa Cruz
Santa Cruz, CA 95064
Abstract
We study a part... | 535 |@word middle:1 seems:1 thereby:1 moment:1 initial:2 configuration:4 tuned:1 si:5 yet:1 written:1 cruz:5 alone:2 greedy:1 selected:1 intelligence:1 unacceptably:1 ith:4 detecting:1 provides:1 severa:1 club:2 toronto:1 mathematical:2 ucsc:2 direct:1 consists:2 acti:1 combine:2 pairwise:2 huber:1 expected:1 blowup:1 ... |
4,805 | 5,350 | Learning to Discover
Efficient Mathematical Identities
Wojciech Zaremba
Dept. of Computer Science
Courant Institute
New York Unviersity
Karol Kurach
Google Zurich &
Dept. of Computer Science
University of Warsaw
Rob Fergus
Dept. of Computer Science
Courant Institute
New York Unviersity
Abstract
In this paper we expl... | 5350 |@word multitask:1 version:8 manageable:1 polynomial:7 seek:2 propagate:1 simplifying:1 citeseer:1 pick:2 cleary:1 harder:2 reduction:1 initial:1 contains:4 series:2 exclusively:1 score:5 ours:1 undiscovered:1 existing:1 current:8 com:1 surprising:1 activation:2 tackling:1 must:5 parsing:1 realize:1 numerical:5 pa... |
4,806 | 5,351 | Searching for Higgs Boson Decay Modes
with Deep Learning
Pierre Baldi
Department of Computer Science
University of California, Irvine
Irvine, CA 92617
pfbaldi@ics.uci.edu
Peter Sadowski
Department of Computer Science
University of California, Irvine
Irvine, CA 92617
peter.j.sadowski@uci.edu
Daniel Whiteson
Departmen... | 5351 |@word manageable:1 confirms:1 azimuthal:1 simulation:2 solid:1 initial:4 contains:2 denby:1 daniel:2 tuned:1 outperforms:1 existing:1 current:1 activation:1 yet:1 must:1 gpu:1 visible:1 subsequent:1 predetermined:1 shape:1 designed:1 plot:1 update:1 atlas:1 discrimination:1 aside:1 alone:2 selected:1 short:1 core... |
4,807 | 5,352 | Semi-supervised Learning with
Deep Generative Models
?
Diederik P. Kingma? , Danilo J. Rezende? , Shakir Mohamed? , Max Welling?
Machine Learning Group, Univ. of Amsterdam, {D.P.Kingma, M.Welling}@uva.nl
?
Google Deepmind, {danilor, shakir}@google.com
Abstract
The ever-increasing size of modern data sets combined wi... | 5352 |@word cnn:1 version:1 open:2 termination:1 cm2:3 simulation:1 contrastive:1 sgd:1 reduction:1 moment:3 liu:2 configuration:1 contains:1 bootstrapped:1 document:1 outperforms:1 existing:4 current:2 com:2 z2:5 activation:2 diederik:1 parsing:1 slanted:1 readily:1 atlas:2 treating:1 update:2 alone:2 generative:40 mc... |
4,808 | 5,353 | Two-Stream Convolutional Networks
for Action Recognition in Videos
Karen Simonyan
Andrew Zisserman
Visual Geometry Group, University of Oxford
{karen,az}@robots.ox.ac.uk
Abstract
We investigate architectures of discriminatively trained deep Convolutional Networks (ConvNets) for action recognition in video. The challe... | 5353 |@word multitask:2 exploitation:1 cnn:1 dalal:2 norm:3 triggs:2 open:1 confirms:1 rgb:3 sgd:1 carry:1 configuration:7 contains:7 score:10 liu:1 interestingly:2 outperforms:3 freitas:1 ullah:1 current:1 comparing:1 activation:1 dx:4 reminiscent:1 gpu:3 realistic:1 mbh:2 alone:2 selected:4 accordingly:1 provides:4 d... |
4,809 | 5,354 | Rounding-based Moves for Metric Labeling
M. Pawan Kumar
Ecole Centrale Paris & INRIA Saclay
pawan.kumar@ecp.fr
Abstract
Metric labeling is a special case of energy minimization for pairwise Markov random fields. The energy function consists of arbitrary unary potentials, and pairwise potentials that are proportional ... | 5354 |@word kohli:1 polynomial:5 flach:1 decomposition:1 pick:3 initial:2 contains:3 series:1 ecole:1 current:7 ka:3 assigning:4 dx:5 readily:1 designed:1 update:2 v:1 selected:1 nq:1 tarlow:1 characterization:1 provides:7 node:2 revisited:1 traverse:1 constructed:1 consists:7 prove:3 naor:1 x0:2 pairwise:7 td:2 solver... |
4,810 | 5,355 | Stochastic Gradient Descent, Weighted Sampling, and
the Randomized Kaczmarz algorithm
Nathan Srebro
Toyota Technological Institute at Chicago
and Dept. of Computer Science, Technion
nati@ttic.edu
Deanna Needell
Department of Mathematical Sciences
Claremont McKenna College
Claremont CA 91711
dneedell@cmc.edu
Rachel W... | 5355 |@word briefly:1 polynomial:2 norm:4 proportion:1 open:1 heuristically:1 sgd:47 moment:1 reduction:3 initial:1 kx0:4 dx:3 must:3 numerical:1 chicago:1 rward:1 enables:1 update:6 selected:1 xk:17 dissatisfying:1 steepest:1 accepting:2 iterates:8 math:2 org:3 zhang:2 mathematical:1 along:1 ik:6 prove:1 introductory:... |
4,811 | 5,356 | An Accelerated Proximal Coordinate Gradient Method
Qihang Lin
University of Iowa
Iowa City, IA, USA
qihang-lin@uiowa.edu
Zhaosong Lu
Simon Fraser University
Burnaby, BC, Canada
zhaosong@sfu.ca
Lin Xiao
Microsoft Research
Redmond, WA, USA
lin.xiao@microsoft.com
Abstract
We develop an accelerated randomized proximal c... | 5356 |@word msr:2 version:3 norm:6 hsieh:2 tr:2 reduction:2 cyclic:3 series:1 bc:1 ati:1 existing:1 ka:1 com:1 comparing:1 luo:2 numerical:2 partition:2 plot:2 update:9 zik:1 ith:1 iterates:1 zhang:5 mathematical:2 direct:1 ik:40 introductory:1 news20:2 expected:2 inspired:1 cardinality:1 totally:1 moreover:2 notation:... |
4,812 | 5,357 | Inference by Learning: Speeding-up Graphical
Model Optimization via a Coarse-to-Fine Cascade of
Pruning Classifiers
Bruno Conejo?
GPS Division, California Institute of Technology, Pasadena, CA, USA
Universite Paris-Est, Ecole des Ponts ParisTech, Marne-la-Vallee, France
bconejo@caltech.edu
Nikos Komodakis
Universite P... | 5357 |@word kohli:3 version:1 briefly:1 eliminating:1 norm:1 middle:1 proportion:1 c0:5 hu:1 decomposition:2 dramatic:1 versatile:1 series:1 contains:2 selecting:1 hereafter:4 disparity:2 ecole:2 ours:1 daniel:2 past:1 existing:1 recovered:1 enpc:2 current:8 yet:1 finest:4 refines:2 additive:1 partition:1 remove:1 prog... |
4,813 | 5,358 | Probabilistic low-rank matrix completion on finite
alphabets
?
Eric
Moulines
Institut Mines-T?el?ecom
T?el?ecom ParisTech
CNRS LTCI
Olga Klopp
CREST et MODAL?X
Universit?e Paris Ouest
Jean Lafond
Institut Mines-T?el?ecom
T?el?ecom ParisTech
CNRS LTCI
Olga.KLOPP@math.cnrs.fr
moulines@telecom-paristech.fr
jean.lafo... | 5358 |@word mild:1 version:7 norm:21 proportion:1 logit:5 seems:1 simulation:1 decomposition:2 tr:1 reduction:1 initial:2 score:1 interestingly:1 past:1 outperforms:2 recovered:1 si:1 attracted:1 john:1 fn:3 additive:2 realistic:1 numerical:2 shape:1 designed:1 update:1 juditsky:1 selected:1 item:2 xk:19 completeness:1... |
4,814 | 5,359 | Controlling privacy in recommender systems
Tommi Jaakkola
CSAIL, MIT
tommi@csail.mit.edu
Yu Xin
CSAIL, MIT
yuxin@mit.edu
Abstract
Recommender systems involve an inherent trade-off between accuracy of recommendations and the extent to which users are willing to release information about
their preferences. In this pap... | 5359 |@word private:48 norm:10 nd:12 willing:4 seek:1 guarding:1 decomposition:2 asks:1 boundedness:1 carry:2 initial:2 contains:2 selecting:1 recovered:1 current:1 protection:2 yet:1 must:2 readily:1 john:2 realistic:1 subsequent:1 numerical:1 enables:1 remove:1 update:2 alone:3 greedy:1 selected:2 device:1 item:16 xk... |
4,815 | 536 | Tangent Prop - A formalism for specifying
selected invariances in an adaptive network
Patrice Simard
AT&T Bell Laboratories
101 Crawford Corner Rd
Holmdel, NJ 07733
Yann Le Cun
AT&T Bell Laboratories
101 Crawford Corner Rd
Holmdel, NJ 07733
Bernard Victorri
Universite de Caen
Caen 14032 Cedex
France
John Denker
AT&T ... | 536 |@word version:2 middle:2 norm:1 open:1 linearized:2 solid:2 carry:1 contains:1 document:1 si:5 activation:1 must:3 john:1 designed:1 update:3 alone:1 half:1 selected:6 plane:2 provides:2 location:1 toronto:1 along:1 j3j:1 consists:2 behavior:1 ol:1 actual:2 little:1 becomes:1 what:1 nework:1 transformation:24 nj:4... |
4,816 | 5,360 | Content-based recommendations
with Poisson factorization
Laurent Charlin
Department of Computer Science
Columbia University
New York, NY 10027
lcharlin@cs.columbia.edu
Prem Gopalan
Department of Computer Science
Princeton University
Princeton, NJ 08540
pgopalan@cs.princeton.edu
David M. Blei
Departments of Statistics... | 5360 |@word repository:1 proportion:7 hu:1 simulation:1 decomposition:1 reduction:1 contains:2 uncovered:1 score:1 bibliographic:1 series:1 document:51 outperforms:4 existing:2 wd:2 comparing:1 com:2 herring:1 john:1 numerical:1 shape:7 remove:1 plot:1 interpretable:6 update:12 aside:1 generative:2 intelligence:3 websi... |
4,817 | 5,361 | Minimax-optimal Inference from Partial Rankings
Bruce Hajek
UIUC
b-hajek@illinois.edu
Sewoong Oh
UIUC
swoh@illinois.edu
Jiaming Xu
UIUC
jxu18@illinois.edu
Abstract
This paper studies the problem of rank aggregation under the Plackett-Luce model.
The goal is to infer a global ranking and related scores of the items,... | 5361 |@word norm:2 nd:1 suitably:1 logit:1 d2:6 simulation:1 bn:3 tr:1 harder:1 moment:3 liu:1 score:3 e2b:11 bradley:4 comparing:1 dx:1 must:1 numerical:3 partition:4 plot:1 mackey:1 stationary:1 fewer:1 item:60 parkes:4 provides:3 node:1 preference:16 minorization:2 dn:2 c2:2 constructed:1 prove:2 consists:2 introduc... |
4,818 | 5,362 | Efficient Optimization for Average Precision SVM
Pritish Mohapatra
IIIT Hyderabad
pritish.mohapatra@research.iiit.ac.in
C.V. Jawahar
IIIT Hyderabad
jawahar@iiit.ac.in
M. Pawan Kumar
Ecole Centrale Paris & INRIA Saclay
pawan.kumar@ecp.fr
Abstract
The accuracy of information retrieval systems is often measured using ... | 5362 |@word cnn:4 briefly:1 everingham:2 minus:1 initial:1 score:26 efficacy:2 trainval:5 ecole:1 ours:1 outperforms:1 current:1 comparing:1 si:1 activation:3 must:1 hofmann:1 update:1 greedy:4 plane:2 xk:2 provides:4 boosting:2 location:2 org:1 simpler:2 five:4 consists:6 prove:1 ijcv:2 combine:1 indeed:1 expected:1 m... |
4,819 | 5,363 | Ranking via Robust Binary Classification
Hyokun Yun
Amazon
Seattle, WA 98109
yunhyoku@amazon.com
Parameswaran Raman, S. V. N. Vishwanathan
Department of Computer Science
University of California
Santa Cruz, CA 95064
{params,vishy}@ucsc.edu
Abstract
We propose RoBiRank, a ranking algorithm that is motivated by observ... | 5363 |@word version:2 achievable:1 norm:3 seems:4 stronger:1 nd:1 liu:1 series:1 score:10 document:2 outperforms:6 existing:1 recovered:1 com:2 written:2 reminiscent:1 john:1 cruz:1 numerical:1 realistic:1 enables:2 analytic:1 asymptote:2 plot:3 designed:1 update:2 juditsky:1 rudin:1 item:22 record:2 infrastructure:1 p... |
4,820 | 5,364 | Tight Bounds for Influence in Diffusion Networks and
Application to Bond Percolation and Epidemiology
R?emi Lemonnier1,2
Kevin Scaman1
Nicolas Vayatis1
1
2
CMLA ? ENS Cachan, CNRS, France, 1000mercis, Paris, France
{lemonnier, scaman, vayatis}@cmla.ens-cachan.fr
Abstract
In this paper, we derive theoretical bounds fo... | 5364 |@word pnij:2 motoda:1 simulation:6 lakshmanan:1 solid:2 initial:6 celebrated:2 series:2 contains:1 selecting:1 configuration:2 denoting:2 janson:3 existing:4 yajun:2 virus:2 manuel:4 john:1 subsequent:1 kdd:1 plot:1 n0:27 v:1 greedy:2 selected:1 node:48 mathematical:1 along:5 direct:1 become:1 retrieving:1 qij:4 ... |
4,821 | 5,365 | Shaping Social Activity by Incentivizing Users
Mehrdad Farajtabar?
Nan Du?
Manuel Gomez-Rodriguez?
?
Isabel Valera
Hongyuan Zha?
Le Song?
?
?
Georgia Institute of Technology
MPI for Software Systems
Univ. Carlos III in Madrid?
{mehrdad,dunan}@gatech.edu
manuelgr@mpi-sws.org
{zha,lsong}@cc.gatech.edu
ivalera@tsc.uc3m.es... | 5365 |@word faculty:1 norm:3 seems:2 auu:5 consolider:1 cha:1 willing:1 simulation:1 decomposition:1 pressure:1 carry:1 initial:2 series:4 contains:1 score:1 outperforms:4 yajun:1 current:1 manuel:5 assigning:2 follower:2 vere:1 tec2009:1 john:1 realistic:1 concatenate:1 partition:2 numerical:1 shape:1 ministerio:1 kdd... |
4,822 | 5,366 | Learning Time-Varying Coverage Functions
Nan Du? , Yingyu Liang? , Maria-Florina Balcan , Le Song?
?
College of Computing, Georgia Institute of Technology
?
Department of Computer Science, Princeton University
School of Computer Science, Carnegie Mellon University
dunan@gatech.edu,yingyul@cs.princeton.edu
ninamf@cs... | 5366 |@word mild:1 faculty:1 pw:1 polynomial:3 d2:1 seek:1 decomposition:1 incurs:1 harder:1 memetracker:1 contains:1 daniel:1 tuned:2 past:2 existing:2 reaction:1 outperforms:2 manuel:2 si:29 yet:1 follower:1 must:2 attracted:1 timestamps:2 additive:1 partition:1 enables:1 treating:2 greedy:2 selected:4 website:1 item... |
4,823 | 5,367 | Online and Stochastic Gradient Methods for
Non-decomposable Loss Functions
Purushottam Kar?
Harikrishna Narasimhan?
Prateek Jain?
Microsoft Research, INDIA
?
Indian Institute of Science, Bangalore, INDIA
{t-purkar,prajain}@microsoft.com, harikrishna@csa.iisc.ernet.in
?
Abstract
Modern applications in sensitive domai... | 5367 |@word mild:2 repository:2 version:3 polynomial:1 proportion:1 advantageous:1 nd:1 dekel:1 seek:1 crucially:2 mention:1 harder:1 ftrl:7 contains:1 score:1 uma:1 offering:1 interestingly:1 bhattacharyya:1 existing:2 com:1 define1:1 readily:1 john:1 belmont:1 additive:1 j1:1 kdd:8 drop:1 update:7 zik:1 intelligence:... |
4,824 | 5,368 | Optimistic planning in Markov decision processes
using a generative model
Bal?azs Sz?or?enyi
INRIA Lille - Nord Europe,
SequeL project, France /
MTA-SZTE Research Group on
Arti?cial Intelligence, Hungary
balazs.szorenyi@inria.fr
Gunnar Kedenburg
INRIA Lille - Nord Europe,
SequeL project, France
gunnar.kedenburg@inria.... | 5368 |@word version:2 polynomial:3 proportion:2 nd:1 reused:1 open:3 termination:1 simulation:1 arti:5 initial:5 contains:3 ours:1 mishra:1 current:4 comparing:1 yet:1 john:1 shlomo:1 cant:1 camacho:1 generative:12 intelligence:5 leaf:7 accordingly:2 beginning:1 recherche:1 provides:2 mannor:1 node:41 contribute:3 teyt... |
4,825 | 5,369 | Conditional Swap Regret and
Conditional Correlated Equilibrium
Mehryar Mohri
Courant Institute and Google
251 Mercer Street
New York, NY 10012
Scott Yang
Courant Institute
251 Mercer Street
New York, NY 10012
mohri@cims?nyu?edu
yangs@cims?nyu?edu
Abstract
We introduce a natural extension of the notion of swap regr... | 5369 |@word inversion:1 bigram:11 stronger:2 dekel:2 decomposition:1 prokhorov:1 incurs:2 thereby:1 series:1 denoting:2 past:3 current:1 com:1 fn:1 numerical:1 subsequent:1 j1:11 update:1 aside:1 stationary:4 selected:1 warmuth:1 manfred:1 provides:2 completeness:1 authority:2 mathematical:1 along:1 c2:1 direct:1 ik:2 ... |
4,826 | 537 | Adaptive Synchronization of
Neural and Physical Oscillators
Kenji Doya
University of California, San Diego
La Jolla, CA 92093-0322, USA
Shuji Yoshizawa
University of Tokyo
Bunkyo-ku, Tokyo 113, Japan
Abstract
Animal locomotion patterns are controlled by recurrent neural networks
called central pattern generators (CP... | 537 |@word mr2:2 hippocampus:1 vi1:1 simulation:1 jacob:2 covariance:2 solid:1 moment:1 configuration:2 cyclic:1 tlo:1 genetic:1 yet:3 must:6 realize:1 motor:3 pacemaker:1 accordingly:1 cpg:14 lor:1 gio:1 pathway:1 multi:1 vertebrate:3 moreover:1 underlying:1 mass:1 ttl:2 cm:5 kg:2 selverston:1 nj:1 ti:3 oscillates:1 e... |
4,827 | 5,370 | Efficient Partial Monitoring with Prior Information
Hastagiri P Vanchinathan
Dept. of Computer Science
ETH Z?urich, Switzerland
hastagiri@inf.ethz.ch
G?abor Bart?ok
Dept. of Computer Science
ETH Z?urich, Switzerland
bartok@inf.ethz.ch
Andreas Krause
Dept. of Computer Science
ETH Z?urich, Switzerland
krausea@ethz.ch
... | 5370 |@word private:2 faculty:1 version:8 norm:2 willing:1 forecaster:1 covariance:6 p0:10 decomposition:7 pick:1 harder:1 reduction:1 initial:2 selecting:2 outperforms:2 past:2 existing:4 current:4 comparing:1 com:1 si:12 subsequent:1 happen:1 informative:1 benign:3 realistic:1 enables:1 christian:1 designed:3 plot:1 ... |
4,828 | 5,371 | Nonparametric Bayesian inference on multivariate
exponential families
William Vega-Brown, Marek Doniec, and Nicholas Roy
Massachusetts Institute of Technology
Cambridge, MA 02139
{wrvb, doniec, nickroy}@csail.mit.edu
Abstract
We develop a model by choosing the maximum entropy distribution from the
set of models satis... | 5371 |@word covariance:23 citeseer:1 dramatic:1 initial:1 series:3 past:1 outperforms:2 must:3 readily:1 additive:2 enables:1 generative:6 ith:1 caveat:1 provides:2 node:1 along:1 yuan:2 prove:1 doubly:1 fitting:1 x0:5 expected:1 nor:1 inappropriate:1 provided:3 cleveland:2 underlying:2 notation:1 bounded:2 moreover:3 ... |
4,829 | 5,372 | Fast Kernel Learning for Multidimensional Pattern
Extrapolation
Andrew Gordon Wilson?
CMU
Elad Gilboa?
WUSTL
Arye Nehorai
WUSTL
John P. Cunningham
Columbia
Abstract
The ability to automatically discover patterns and perform extrapolation is an essential quality of intelligent systems. Kernel methods, such as Gauss... | 5372 |@word determinant:1 briefly:1 repository:1 km:9 covariance:3 decomposition:1 inpainting:13 nystr:1 recursively:1 moment:1 initial:1 contains:2 initialisation:8 outperforms:2 existing:2 imaginary:5 recovered:1 surprising:1 luo:1 must:3 john:2 numerical:2 remove:1 extrapolating:3 update:1 stationary:5 alone:1 prohi... |
4,830 | 5,373 | Mind the Nuisance: Gaussian Process
Classification using Privileged Noise
Daniel Hern?andez-Lobato
Universidad Aut?onoma de Madrid
Madrid, Spain
Viktoriia Sharmanska
IST Austria
Klosterneuburg, Austria
daniel.hernandez@uam.es
vsharman@ist.ac.at
Kristian Kersting
TU Dortmund
Dortmund, Germany
Christoph H. Lampert
... | 5373 |@word trial:1 seal:1 crucially:1 tried:1 deems:1 solid:2 shot:1 reduction:3 united:1 daniel:2 genetic:1 document:2 ours:1 outperforms:2 current:1 com:1 surprising:1 analysed:1 activation:2 riihim:1 attracted:2 must:1 numerical:2 informative:2 shape:1 kyb:1 dupont:1 treating:1 interpretable:2 update:2 n0:2 intelli... |
4,831 | 5,374 | Automated Variational Inference
for Gaussian Process Models
Edwin V. Bonilla
The University of New South Wales
e.bonilla@unsw.edu.au
Trung V. Nguyen
ANU & NICTA
VanTrung.Nguyen@nicta.com.au
Abstract
We develop an automated variational method for approximate inference in Gaussian process (GP) models whose posteriors a... | 5374 |@word cox:6 middle:4 repository:1 tedious:1 simulation:1 covariance:20 decomposition:1 q1:4 tr:2 edric:1 reduction:2 series:1 lichman:1 ours:1 existing:3 elliptical:3 com:1 lgcp:2 anne:1 si:1 yet:2 must:1 readily:1 fn:19 numerical:1 confirming:1 shape:1 christian:1 plot:11 designed:1 intelligence:1 devising:1 ntr... |
4,832 | 5,375 | Variational Gaussian Process State-Space Models
Roger Frigola, Yutian Chen and Carl E. Rasmussen
Department of Engineering
University of Cambridge
{rf342,yc373,cer54}@cam.ac.uk
Abstract
State-space models have been successfully used for more than fifty years in different areas of science and engineering. We present a... | 5375 |@word aircraft:1 hippocampus:1 simulation:1 crucially:1 covariance:7 xtest:1 tr:4 reduction:1 initial:2 series:14 att:2 contains:1 rightmost:2 past:2 existing:1 arkk:1 yet:1 dx:1 attracted:1 readily:1 kiebel:1 john:1 additive:3 plot:1 update:1 intelligence:2 manfred:1 regressive:2 blei:1 provides:1 location:2 ssm... |
4,833 | 5,376 | Gaussian Process Volatility Model
Jos?e Miguel Hern?andez Lobato
Cambridge University
jmh233@cam.ac.uk
Yue Wu
Cambridge University
wu5@post.harvard.edu
Zoubin Ghahramani
Cambridge University
zoubin@eng.cam.ac.uk
Abstract
The prediction of time-changing variances is an important task in the modeling of
financial dat... | 5376 |@word version:1 middle:5 inversion:1 simulation:2 propagate:2 eng:1 covariance:10 moment:1 initial:5 configuration:2 series:24 contains:1 liu:1 liquid:1 amp:1 past:5 existing:3 outperforms:1 current:3 recovered:1 com:1 freitas:1 afl:1 distant:1 happen:1 informative:2 numerical:1 enables:1 analytic:1 designed:2 pl... |
4,834 | 5,377 | Bandit Convex Optimization: Towards Tight Bounds
Kfir Y. Levy
Technion?Israel Institute of Technology
Haifa 32000, Israel
kfiryl@tx.technion.ac.il
Elad Hazan
Technion?Israel Institute of Technology
Haifa 32000, Israel
ehazan@ie.technion.ac.il
Abstract
Bandit Convex Optimization (BCO) is a fundamental framework for d... | 5377 |@word exploitation:3 version:13 polynomial:2 norm:5 dekel:1 open:2 bn:5 jacob:1 attainable:3 ftrl:1 interestingly:1 past:1 dikin:5 yet:1 predetermined:1 enables:2 update:2 devising:2 advancement:1 provides:1 along:1 differential:2 supply:1 stronglyconvex:2 inside:1 introduce:1 expected:5 multi:2 inspired:1 spheri... |
4,835 | 5,378 | Stochastic Multi-Armed-Bandit Problem
with Non-stationary Rewards
Yonatan Gur
Stanford University
Stanford, CA
ygur@stanford.edu
Omar Besbes
Columbia University
New York, NY
ob2105@columbia.edu
Assaf Zeevi
Columbia University
New York, NY
assaf@gsb.columbia.edu
Abstract
In a multi-armed bandit (MAB) problem a gambl... | 5378 |@word trial:2 exploitation:5 achievable:13 leighton:1 polynomial:1 suitably:1 open:1 decomposition:1 paid:1 series:1 efficacy:1 selecting:3 tuned:5 denoting:1 past:5 existing:2 current:1 surprising:1 yet:5 must:5 john:1 partition:1 j1:4 treating:1 designed:1 update:1 stationary:32 selected:2 fewer:1 beginning:3 c... |
4,836 | 5,379 | Extreme bandits
Alexandra Carpentier
Statistical Laboratory, CMS
University of Cambridge, UK
Michal Valko
SequeL team
INRIA Lille - Nord Europe, France
a.carpentier@statslab.cam.ac.uk
michal.valko@inria.fr
Abstract
In many areas of medicine, security, and life sciences, we want to allocate limited resources to diff... | 5379 |@word mild:1 version:2 middle:2 stronger:2 d2:2 seek:1 simulation:1 attainable:1 concise:1 reduction:1 initial:1 liu:1 series:1 selecting:1 daniel:2 offering:1 ours:1 interestingly:1 outperforms:1 scovel:1 contextual:1 michal:2 discretization:1 must:1 john:4 ronald:1 enables:1 remove:1 update:1 intelligence:3 sel... |
4,837 | 538 | Iterative Construction of
Sparse Polynomial Approximations
Terence D. Sanger
Massachusetts Institute
of Technology
Room E25-534
Cambridge, MA 02139
tds@ai.mit.edu
Richard S. Sutton
GTE Laboratories
Incorporated
40 Sylvan Road
Waltham, MA 02254
sutton@gte.com
Christopher J. Matheus
GTE Laboratories
Incorporated
40 Sy... | 538 |@word version:4 polynomial:47 norm:2 nd:1 dekker:1 united:1 existing:2 current:1 com:2 z2:6 si:1 must:1 ikeda:2 chicago:1 drop:2 succeeding:1 alone:1 record:1 weierstrass:1 draft:1 ire:1 contribute:1 node:2 successive:2 sigmoidal:3 direct:1 consists:1 expected:1 themselves:1 growing:2 simulator:1 brain:1 conv:1 pr... |
4,838 | 5,380 | Discovering, Learning and Exploiting Relevance
Mihaela van der Schaar
Electrical Engineering Department
University of California Los Angeles
mihaela@ee.ucla.edu
Cem Tekin
Electrical Engineering Department
University of California Los Angeles
cmtkn@ucla.edu
Abstract
In this paper we consider the problem of learning o... | 5380 |@word exploitation:22 d2:4 decomposition:1 p0:10 euclidian:1 minus:1 reduction:2 rind:1 initial:1 selecting:1 past:5 current:2 contextual:15 comparing:1 discretization:2 mihaela:2 chu:1 written:2 must:2 numerical:1 partition:6 treating:1 update:3 intelligence:3 discovering:1 selected:6 greedy:1 beginning:1 provid... |
4,839 | 5,381 | Online combinatorial optimization with stochastic
decision sets and adversarial losses
Gergely Neu
Michal Valko
SequeL team, INRIA Lille ? Nord Europe, France
{gergely.neu,michal.valko}@inria.fr
Abstract
Most work on sequential learning assumes a fixed set of actions that are available
all the time. However, in practi... | 5381 |@word exploitation:3 version:1 middle:4 stronger:1 seems:2 tedious:2 d2:1 additively:1 crucially:2 forecaster:3 pick:5 reduction:1 initial:4 pt0:1 current:1 michal:2 nt:8 surprising:1 yet:2 cheap:1 plot:1 update:1 bart:4 resampling:1 leaf:1 item:2 warmuth:1 beginning:1 provides:2 completeness:1 node:1 simpler:2 m... |
4,840 | 5,382 | Multilabel Structured Output Learning with Random
Spanning Trees of Max-Margin Markov Networks
Hongyu Su
Helsinki Institute for Information Technology
Dept of Information and Computer Science
Aalto University, Finland
hongyu.su@aalto.fi
Mario Marchand
D?epartement d?informatique et g?enie logiciel
Universit?e Laval
Q... | 5382 |@word multitask:1 polynomial:6 norm:19 seems:1 yv0:1 decomposition:1 q1:1 didate:1 ytn:1 epartement:1 contains:1 score:13 outperforms:1 current:1 nt:2 forbidding:2 written:3 dx:1 john:5 realistic:2 eleven:1 drop:1 plot:1 selected:1 amir:1 xk:12 steepest:1 provides:1 iterates:1 node:3 theodoros:1 org:1 daphne:1 fi... |
4,841 | 5,383 | Metric Learning for Temporal Sequence Alignment
Damien Garreau ? ?
ENS
damien.garreau@ens.fr
R?emi Lajugie ? ?
INRIA
remi.lajugie@inria.fr
Sylvain Arlot ?
CNRS
sylvain.arlot@ens.fr
Francis Bach ?
INRIA
francis.bach@inria.fr
Abstract
In this paper, we propose to learn a Mahalanobis distance to perform alignment
of ... | 5383 |@word kohli:1 version:1 norm:2 km:1 grey:2 hu:1 tr:30 mcauley:1 epartement:1 series:8 score:7 hoiem:1 ecole:1 must:2 john:1 realistic:1 partition:2 hofmann:1 polyphonic:2 v:1 generative:1 plane:2 beginning:1 parametrization:1 completeness:2 detecting:1 five:2 mathematical:1 along:1 welldefined:1 prove:1 consists:... |
4,842 | 5,384 | Proximal Quasi-Newton for Computationally
Intensive `1-regularized M -estimators
Kai Zhong 1
Ian E.H. Yen 2
Inderjit S. Dhillon 2
Pradeep Ravikumar 2
2
Institute for Computational Engineering & Sciences
Department of Computer Science
University of Texas at Austin
zhongkai@ices.utexas.edu, {ianyen,inderjit,pradeepr}@cs.... | 5384 |@word pw:5 bigram:2 norm:1 owlqn:2 nd:1 termination:2 simulation:1 tried:1 covariance:2 hsieh:4 sgd:11 liblinear:1 initial:1 contains:1 series:1 tuned:2 task1:1 current:8 com:1 luo:1 si:1 attracted:2 bd:1 written:1 hou:1 numerical:2 partition:1 plot:1 update:24 progressively:2 intelligence:1 leaf:2 accordingly:1 ... |
4,843 | 5,385 | Discriminative Metric Learning by
Neighborhood Gerrymandering
Shubhendu Trivedi, David McAllester, Gregory Shakhnarovich
Toyota Technological Institute
Chicago, IL - 60637
{shubhendu,mcallester,greg}@ttic.edu
Abstract
We formulate the problem of metric learning for k nearest neighbor classification
as a large margin ... | 5385 |@word h:2 kulis:2 version:1 briefly:2 norm:2 replicate:1 decomposition:1 sgd:3 harder:1 offending:3 score:9 tuned:2 ours:4 imposter:1 existing:2 current:1 beygelzimer:1 goldberger:1 yet:1 reminiscent:1 must:1 written:2 chicago:1 partition:3 update:6 hash:1 pursued:1 selected:1 greedy:1 item:1 oldest:1 farther:3 t... |
4,844 | 5,386 | Fundamental Limits of Online and Distributed
Algorithms for Statistical Learning and Estimation
Ohad Shamir
Weizmann Institute of Science
ohad.shamir@weizmann.ac.il
Abstract
Many machine learning approaches are characterized by information constraints
on how they interact with the training data. These include memory ... | 5386 |@word stronger:3 dekel:1 open:5 d2:7 seek:8 crucially:1 nemirovsky:1 covariance:10 contraction:1 attainable:2 pick:1 paid:1 nystr:1 moment:1 reduction:1 contains:1 woodruff:2 ours:1 interestingly:2 current:1 contextual:1 parameter1:1 yet:2 must:2 import:1 john:2 numerical:1 realistic:2 analytic:1 ligett:1 update:... |
4,845 | 5,387 | Optimal rates for k-NN density and mode estimation
Sanjoy Dasgupta
University of California, San Diego, CSE
dasgupta@eng.ucsd.edu
Samory Kpotufe ?
Princeton University, ORFE
samory@princeton.edu
Abstract
We present two related contributions of independent interest: (1) high-probability
finite sample rates for k-NN d... | 5387 |@word mild:2 version:2 achievable:2 eng:1 pick:7 concise:1 initial:1 liu:1 contains:1 chervonenkis:1 document:1 interestingly:1 kx0:4 steiner:1 recovered:1 must:1 fn:16 chicago:1 subsequent:1 update:1 intelligence:3 xk:4 core:1 provides:1 cse:1 simpler:1 mathematical:3 along:1 direct:3 prove:3 ray:1 x0:88 ascend:... |
4,846 | 5,388 | Learning on graphs using Orthonormal
Representation is Statistically Consistent
Chiranjib Bhattacharyya
Department of CSA
Indian Institute of Science
Bangalore, 560012, INDIA
chiru@csa.iisc.ernet.in
Rakesh S
Department of Electrical Engineering
Indian Institute of Science
Bangalore, 560012, INDIA
rakeshsmysore@gmail.... | 5388 |@word h:2 repository:1 stronger:1 c0:2 open:1 hu:3 tr:1 ld:1 contains:1 chervonenkis:1 bhattacharyya:2 outperforms:1 existing:3 com:1 comparing:1 gmail:1 designed:1 drop:1 ith:1 nips14:2 provides:1 characterization:1 node:20 attack:1 zhang:3 dn:4 c2:2 prove:2 introduce:1 lov:11 expected:2 sdp:1 multi:2 little:1 c... |
4,847 | 5,389 | Optimal prior-dependent neural population codes
under shared input noise
?
Agnieszka Grabska-Barwinska
Gatsby Computational Neuroscience Unit
University College London
agnieszka@gatsby.ucl.ac.uk
Jonathan W. Pillow
Princeton Neuroscience Institute
Department of Psychology
Princeton University
pillow@princeton.edu
Abst... | 5389 |@word h:1 trial:1 illustrating:1 simulation:2 covariance:2 prominence:1 solid:1 configuration:1 valois:1 mainen:1 tuned:1 outperforms:1 existing:2 si:1 must:1 numerical:2 additive:1 realistic:2 blur:1 shape:2 analytic:1 motor:1 moreno:1 plot:5 discrimination:3 half:1 fewer:1 selected:1 steepest:2 short:3 manfred:... |
4,848 | 539 | Nonlinear Pattern Separation in Single Hippocampal
Neurons with Active Dendritic Membrane
Anthony M. Zador t
t
Brenda J. Claiborne?
Depts. of Psychology and Cellular
& Molecular Physiology
Yale University
New Haven, CT 06511
zador@yale.edu
Thomas H. Brown
t
?Division of Life Sciences
University of Texas
San Antoni... | 539 |@word cu:2 briefly:1 seems:1 pulse:1 simulation:10 fonn:1 fortuitous:1 solid:3 series:3 mainen:3 longitudinal:4 current:14 neurophys:1 surprising:1 activation:2 reminiscent:1 physiol:2 subsequent:1 realistic:2 informative:1 plasticity:1 arrayed:1 discrimination:1 alone:1 signalling:2 une:2 math:2 sigmoidal:4 simpl... |
4,849 | 5,390 | Optimal Neural Codes for Control and Estimation
Alex Susemihl1 , Manfred Opper
Methods of Artificial Intelligence
Technische Universit?at Berlin
1
Current affiliation: Google
Ron Meir
Department of Electrical Engineering
Technion - Haifa
Abstract
Agents acting in the natural world aim at selecting appropriate action... | 5390 |@word determinant:3 version:1 achievable:1 seems:1 pillar:1 grey:1 seek:2 simulation:1 gradual:1 covariance:10 p0:3 meansquare:1 q1:2 tr:12 reduction:1 initial:3 selecting:2 interestingly:1 mmse:11 rightmost:2 current:2 nt:3 si:1 dx:2 must:5 written:1 pioneer:1 readily:2 tilted:2 numerical:1 partition:1 shlomo:1 ... |
4,850 | 5,391 | The Large Margin Mechanism
for Differentially Private Maximization
Kamalika Chaudhuri
UC San Diego
La Jolla, CA
kamalika@cs.ucsd.edu
Daniel Hsu
Columbia University
New York, NY
djhsu@cs.columbia.edu
Shuang Song
UC San Diego
La Jolla, CA
shs037@eng.ucsd.edu
Abstract
A basic problem in the design of privacy-preservin... | 5391 |@word private:80 version:2 stronger:1 nd:1 c0:5 bun:1 vldb:4 prasad:2 eng:1 decomposition:3 pick:3 selecting:1 daniel:3 reaction:1 err:15 current:1 z2:1 must:2 john:1 ronald:1 subsequent:2 informative:1 kdd:2 remove:1 ligett:1 v:1 discovering:1 item:26 smith:6 core:1 short:1 record:3 provides:7 boosting:1 revisit... |
4,851 | 5,392 | Extremal Mechanisms for Local Differential Privacy
Peter Kairouz1
Sewoong Oh2
Pramod Viswanath1
1
Department of Electrical & Computer Engineering
2
Department of Industrial & Enterprise Systems Engineering
University of Illinois Urbana-Champaign
Urbana, IL 61801, USA
{kairouz2,swoh,pramodv}@illinois.edu
Abstract
Loca... | 5392 |@word private:26 version:2 illustrating:1 achievable:4 stronger:2 eliminating:1 nd:1 sheffet:1 p0:43 celebrated:1 contains:2 exclusively:1 ktv:2 existing:2 current:1 comparing:1 protection:1 si:4 must:1 john:1 ligett:1 discrimination:1 smith:1 core:1 institution:1 provides:2 characterization:1 kairouz:2 firstly:1... |
4,852 | 5,393 | Reputation-based Worker Filtering in Crowdsourcing
Srikanth Jagabathula1 Lakshminarayanan Subramanian2,3 Ashwin Venkataraman2,3
1
Department of IOMS, NYU Stern School of Business
Department of Computer Science, New York University
3
CTED, New York University Abu Dhabi
sjagabat@stern.nyu.edu {lakshmi,ashwin}@cs.nyu.edu
... | 5393 |@word version:5 briefly:1 pw:1 achievable:2 eliminating:1 nd:1 d2:1 simulation:2 pick:1 profit:1 reduction:1 contains:1 score:11 karger:1 document:1 outperforms:1 existing:11 subjective:1 com:1 assigning:1 must:1 cheap:1 remove:1 designed:3 v:1 fewer:2 guess:1 complementing:1 ruvolo:1 filtered:8 provides:9 detect... |
4,853 | 5,394 | Feedback Detection for Live Predictors
Stefan Wager, Nick Chamandy, Omkar Muralidharan, and Amir Najmi
swager@stanford.edu, {chamandy, omuralidharan, amir}@google.com
Stanford University and Google, Inc.
Abstract
A predictor that is deployed in a live production system may perturb the features
it uses to make predicti... | 5394 |@word trial:1 version:1 polynomial:1 instrumental:1 replicate:1 bf:4 additively:1 confirms:1 simulation:4 propagate:1 seek:1 covariance:1 asks:1 solid:1 carry:1 reduction:1 tuned:1 past:1 bradley:2 current:2 com:1 yet:1 must:2 realistic:2 additive:7 happen:1 predetermined:1 shape:2 girosi:1 half:3 discovering:1 a... |
4,854 | 5,395 | DFacTo: Distributed Factorization of Tensors
S. V. N. Vishwanathan
Statistics and Computer Science
Purdue University
West Lafayette IN 47907
vishy@stat.purdue.edu
Joon Hee Choi
Electrical and Computer Engineering
Purdue University
West Lafayette IN 47907
choi240@purdue.edu
Abstract
We present a technique for signific... | 5395 |@word version:6 norm:1 open:3 decomposition:2 mcauley:1 contains:6 exclusively:1 daniel:2 document:1 outperforms:1 existing:3 com:7 nell:16 written:3 must:2 john:1 stemming:1 numerical:1 kdd:1 acar:2 designed:1 update:4 n0:2 intelligence:1 selected:1 fewer:1 item:6 core:1 filtered:1 alexandros:1 completeness:1 ge... |
4,855 | 5,396 | Distributed Power-law Graph Computing:
Theoretical and Empirical Analysis
Ling Yan
Dept. of Comp. Sci. and Eng.
Shanghai Jiao Tong University
800 Dongchuan Road
Shanghai 200240, China
yling0718@sjtu.edu.cn
Cong Xie
Dept. of Comp. Sci. and Eng.
Shanghai Jiao Tong University
800 Dongchuan Road
Shanghai 200240, China
xc... | 5396 |@word arabic:1 briefly:2 compression:1 vldb:1 hu:1 eng:3 contains:1 score:2 daniel:1 franklin:1 existing:6 ka:3 com:1 gmail:1 boldi:1 attracted:3 danny:3 partition:3 kdd:1 fund:1 bickson:3 hash:30 greedy:1 fewer:1 intelligence:1 core:1 provides:1 node:1 firstly:1 org:1 zhang:2 mathematical:1 symposium:3 replicati... |
4,856 | 5,397 | Scalable Nonlinear Learning with
Adaptive Polynomial Expansions
Alina Beygelzimer
Yahoo! Labs
beygel@yahoo-inc.com
Alekh Agarwal
Microsoft Research
alekha@microsoft.com
Daniel Hsu
Columbia University
djhsu@cs.columbia.edu
John Langford
Microsoft Research
jcl@microsoft.com
Matus Telgarsky?
Rutgers University
mtelgar... | 5397 |@word repository:2 version:2 polynomial:23 stronger:1 seems:1 bigram:5 disk:1 open:1 d2:1 tried:1 git:1 pick:5 paid:1 nystr:2 harder:1 recursively:1 reduction:2 nomao:2 initial:3 daniel:1 tuned:1 outperforms:1 existing:3 err:3 current:7 com:4 comparing:1 beygelzimer:2 john:1 partition:1 enables:1 designed:1 plot:... |
4,857 | 5,398 | Orbit Regularization
Andr?e F. T. Martins?
Instituto de Telecomunicac?o? es
Instituto Superior T?ecnico
1049?001 Lisboa, Portugal
atm@priberam.pt
Renato Negrinho
Instituto de Telecomunicac?o? es
Instituto Superior T?ecnico
1049?001 Lisboa, Portugal
renato.negrinho@gmail.com
Abstract
We propose a general framework fo... | 5398 |@word trial:1 norm:45 turlach:1 hu:4 closure:1 simulation:3 decomposition:1 pick:1 concise:1 contains:1 kpv:1 series:2 hardy:2 existing:1 current:3 com:1 olkin:1 gmail:1 must:2 written:1 john:1 numerical:2 happen:1 shape:1 gv:24 designed:1 plot:3 maxv:1 half:1 selected:1 characterization:3 provides:1 revisited:1 ... |
4,858 | 5,399 | Covariance shrinkage for autocorrelated data
Daniel Bartz
Department of Computer Science
TU Berlin, Berlin, Germany
daniel.bartz@tu-berlin.de
?
Klaus-Robert Muller
TU Berlin, Berlin, Germany
Korea University, Korea, Seoul
klaus-robert.mueller@tu-berlin.de
Abstract
The accurate estimation of covariance matrices is es... | 5399 |@word trial:17 middle:6 inversion:1 wiesel:1 stronger:3 norm:1 simulation:10 covariance:32 tr:4 harder:2 prial:4 moment:7 reduction:2 bai:1 series:4 daniel:4 bc:18 mmse:1 outperforms:5 comparing:1 analysed:1 yet:3 visible:2 j1:1 analytic:9 motor:4 christian:1 drop:1 ainen:1 resampling:2 generative:2 yi1:1 math:1 ... |
4,859 | 54 | 860
A METHOD FOR THE DESIGN OF STABLE LATERAL INHIBITION
NETWORKS THAT IS ROBUST IN THE PRESENCE
OF CIRCUIT PARASITICS
J.L. WYATT, Jr and D.L. STANDLEY
Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Cambridge, Massachusetts 02139
ABSTRACT
In the analog VLSI implementati... | 54 |@word neurophysiology:1 version:3 polynomial:1 stronger:1 linearized:2 initial:4 contains:2 series:1 imaginary:2 current:3 luo:1 must:2 readily:1 distant:1 subsequent:1 analytic:1 designed:1 plot:3 progressively:1 half:11 plane:14 mathematical:1 c2:1 ik:1 resistive:11 sustained:1 behavior:1 terminal:5 vertebrate:1 ... |
4,860 | 540 | Learning to Segment Images
Using Dynamic Feature Binding
Michael C. Moser
Dept. of Compo Science &
Inst. of Cognitive Science
University of Colorado
Boulder, CO 80309-0430
Richard S. Zemel
Dept. of Compo Science
University of Toronto
Toronto, Ontario
Canada M5S lA4
Marlene Behrmann
Dept. of Psychology &
Faculty of M... | 540 |@word trial:2 faculty:1 simulation:4 decomposition:3 pick:1 initial:3 configuration:7 contains:7 selecting:1 current:4 activation:9 must:1 readily:1 predetermined:1 shape:3 succeeding:1 update:2 intelligence:1 leaf:1 nervous:1 indicative:1 plane:1 compo:2 coarse:1 toronto:4 location:4 five:1 mathematical:1 constru... |
4,861 | 5,400 | A Dual Algorithm for Olfactory Computation in the
Locust Brain
Sina Tootoonian
st582@eng.cam.ac.uk
M?at?e Lengyel
m.lengyel@eng.cam.ac.uk
Computational & Biological Learning Laboratory
Department of Engineering, University of Cambridge
Trumpington Street, Cambridge CB2 1PZ, United Kingdom
Abstract
We study the early... | 5400 |@word trial:5 private:1 norm:1 lobe:13 eng:2 excited:1 reduction:1 configuration:1 contains:2 united:1 outperforms:2 recovered:1 current:2 analysed:1 activation:9 mushroom:9 must:2 axk22:3 numerical:1 additive:1 plasticity:2 plot:1 update:5 v:2 generative:4 fewer:1 patterning:1 half:1 isotropic:1 reciprocal:4 fee... |
4,862 | 5,401 | Online Optimization for Max-Norm Regularization
Jie Shen
Dept. of Computer Science
Rutgers University
Piscataway, NJ 08854
Huan Xu
Dept. of Mech. Engineering
National Univ. of Singapore
Singapore 117575
Ping Li
Dept. of Statistics
Dept. of Computer Science
Rutgers University
js2007@rutgers.edu
mpexuh@nus.edu.sg
p... | 5401 |@word mild:1 briefly:1 norm:70 nd:1 shuicheng:2 simulation:3 decomposition:8 tr:5 iii1360971:1 harder:1 initial:1 celebrated:1 contains:1 liu:1 mpexuh:1 interestingly:1 outperforms:2 yet:1 attracted:1 pcp:6 john:1 fn:2 additive:1 update:1 stationary:7 implying:1 intelligence:1 item:2 vanishing:1 fa9550:1 lr:4 pro... |
4,863 | 5,402 | Finding a sparse vector in a subspace:
Linear sparsity using alternating directions
Qing Qu, Ju Sun, and John Wright
{qq2105, js4038, jw2966}@columbia.edu
Dept. of Electrical Engineering, Columbia University, New York City, NY, USA, 10027
Abstract
We consider the problem of recovering the sparsest vector in a subspac... | 5402 |@word version:1 briefly:1 polynomial:2 seems:4 norm:4 open:1 simulation:3 decomposition:2 jacob:1 q1:15 carry:1 initial:1 contains:2 selecting:1 interestingly:1 kx0:2 recovered:3 surprising:1 must:1 john:1 numerical:2 subsequent:1 partition:2 benign:1 realistic:1 succeeding:1 stationary:3 intelligence:1 selected:... |
4,864 | 5,403 | Compressive Sensing of Signals from a GMM with
Sparse Precision Matrices
1
1
2
1
Jianbo Yang
Xuejun Liao
Minhua Chen
Lawrence Carin
1
Department of Electrical and Computer Engineering, Duke University
2
Department of Statistics & Department of Computer Science, University of Chicago
{jianbo.yang;xjliao;lcarin@duke@du... | 5403 |@word middle:1 norm:3 c0:2 km:2 covariance:10 attainable:1 tr:5 blade:2 reduction:1 liu:1 series:1 efficacy:1 tuned:1 mmse:1 outperforms:3 com:3 od:1 gmail:1 written:2 dct:2 chicago:1 partition:1 analytic:1 designed:1 plot:1 update:1 discrimination:1 selected:1 website:2 accordingly:1 provides:3 iterates:1 node:5... |
4,865 | 5,404 | On the Relationship Between LFP & Spiking Data
David E. Carlson1 , Jana Schaich Borg2 , Kafui Dzirasa2 , and Lawrence Carin1
1
Department of Electrical and Computer Engineering
2
Department of Psychiatry and Behavioral Sciences
Duke University
Duham, NC 27701
{david.carlson, jana.borg, kafui.dzirasa, lcarin}@duke.edu
... | 5404 |@word neurophysiology:1 version:3 middle:12 hippocampus:17 c0:2 seek:1 bn:1 splitmerge:1 pick:1 dramatic:1 acknowlegements:1 tr:1 accommodate:1 reduction:1 moment:1 series:5 genetic:1 outperforms:1 recovered:3 comparing:1 ka:1 shape:10 motor:3 drop:1 plot:2 update:1 medial:2 designed:1 stationary:1 beginning:1 sm... |
4,866 | 5,405 | A Synaptical Story of Persistent Activity with
Graded Lifetime in a Neural System
Yuanyuan Mi,
Luozheng Li
State Key Laboratory of Cognitive Neuroscience & Learning,
Beijing Normal University, Beijing 100875, China
miyuanyuan0102@163.com, liluozheng@mail.bnu.edu.cn
Dahui Wang
State Key Laboratory of Cognitive Neurosci... | 5405 |@word d2:2 simulation:5 colby:1 carry:2 kappen:1 moment:3 initial:2 efficacy:2 idg:1 denoting:2 interestingly:1 current:2 com:1 si:1 yet:2 dx:2 written:1 attracted:1 realize:2 plasticity:4 enables:2 remove:1 plot:1 fund:1 medial:1 stationary:1 shut:1 ith:1 vanishing:2 short:9 core:1 funahashi:1 node:1 location:2 ... |
4,867 | 5,406 | Sparse PCA via Covariance Thresholding
Andrea Montanari
Electrical Engineering and Statistics
Stanford University
montanari@stanford.edu
Yash Deshpande
Electrical Engineering
Stanford University
yashd@stanford.edu
Abstract
In sparse principal component analysis we are given noisy observations of a lowrank matrix of ... | 5406 |@word mild:1 briefly:2 eliminating:1 polynomial:3 norm:7 version:3 suitably:1 open:1 confirms:1 seek:1 simulation:4 covariance:35 decomposition:3 arous:2 moment:2 hereafter:1 denoting:2 recovered:5 nicolai:1 karoui:1 perturbative:2 attracted:2 realistic:1 additive:1 numerical:1 remove:1 drop:1 plot:1 half:1 greed... |
4,868 | 5,407 | Low Rank Approximation Lower Bounds in
Row-Update Streams
David P. Woodruff
IBM Research Almaden
dpwoodru@us.ibm.com
Abstract
We study low-rank approximation in the streaming model in which the rows of
an n ? d matrix A are presented one at a time in an arbitrary order. At the end
of the stream, the streaming algorit... | 5407 |@word determinant:1 version:5 cu:1 seems:1 norm:6 nd:3 tedious:1 r:7 decomposition:2 reduction:1 contains:1 woodruff:9 fa8750:1 ka:12 com:1 nt:1 comparing:2 must:3 written:1 john:1 numerical:3 additive:1 partition:3 kdd:2 succeeding:1 update:8 prohibitive:1 item:1 cormode:1 provides:3 complication:1 bijection:2 k... |
4,869 | 5,408 | Tight convex relaxations for sparse matrix
factorization
Emile Richard
Electrical Engineering
Stanford University
Guillaume Obozinski
Universit?e Paris-Est
Ecole des Ponts - ParisTech
Jean-Philippe Vert
MINES ParisTech
Institut Curie
Abstract
Based on a new atomic norm, we propose a new convex formulation for sparse... | 5408 |@word multitask:1 version:1 briefly:1 polynomial:3 norm:89 stronger:1 eliminating:1 zkf:1 confirms:1 simulation:1 decomposition:6 covariance:10 tr:1 selecting:1 ecole:1 denoting:1 rightmost:1 outperforms:2 current:2 must:1 numerical:4 additive:2 shape:1 designed:1 interpretable:1 plot:1 mackey:1 recherche:1 provi... |
4,870 | 5,409 | Robust Tensor Decomposition with Gross Corruption
Huan Gui? Jiawei Han
Department of Computer Science
University of Illinois
at Urbana-Champaign
Urbana, IL 61801
{huangui2,hanj}@illinois.edu
Quanquan Gu?
Department of Operations Research
and Financial Engineering
Princeton University
Princeton, NJ 08544
qgu@princeton... | 5409 |@word version:2 polynomial:1 norm:38 vi1:2 nscta:1 decomposition:37 mention:1 initial:1 liu:1 recovered:5 chu:1 additive:1 underly:1 numerical:3 sponsored:1 update:1 v:1 selected:1 core:2 provides:4 location:1 zhang:3 mathematical:1 lathauwer:1 ik:14 prove:1 theoretically:1 behavior:2 cand:5 uiuc:1 multi:4 cardin... |
4,871 | 541 | Network activity determines
spatio-temporal integration in single cells
Ojvind Bernander, Christof Koch *
Computation and Neural Systems Program,
California Institut.e of Technology,
Pasadena, Ca 91125, USA.
Rodney J. Douglas
Anatomical Neuropharmacology Unit,
Dept. Pharmacology,
Oxford, UK.
Abstract
Single nerve cel... | 541 |@word version:1 pulse:3 simulation:2 solid:3 disparity:1 tuned:1 current:11 activation:5 physiol:1 hyperpolarizing:1 plot:1 v:1 shut:1 compo:1 leakiness:1 provides:1 location:1 five:1 burst:1 symp:1 expected:1 indeed:1 spine:1 behavior:2 simulator:1 ol:1 brain:1 morphology:1 automatically:1 increasing:3 becomes:1 ... |
4,872 | 5,410 | PEWA: Patch-based Exponentially Weighted
Aggregation for image denoising
Charles Kervrann
Inria Rennes - Bretagne Atlantique
Serpico Project-Team
Campus Universitaire de Beaulieu, 35 042 Rennes Cedex, France
charles.kervrann@inria.fr
Abstract
Patch-based methods have been widely used for noise reduction in recent yea... | 5410 |@word aircraft:2 version:7 blu:1 nd:1 disk:1 simulation:2 covariance:1 reduction:1 initial:1 mmse:1 current:4 dx:2 written:1 must:1 fn:8 dct:17 additive:1 shape:1 drop:1 stationary:1 selected:2 website:1 accordingly:1 core:2 awg:1 boosting:1 location:4 preference:2 org:2 mathematical:2 along:1 become:1 consists:1... |
4,873 | 5,411 | A Multi-World Approach to Question Answering
about Real-World Scenes based on Uncertain Input
Mateusz Malinowski
Mario Fritz
Max Planck Institute for Informatics
Saarbr?ucken, Germany
{mmalinow,mfritz}@mpi-inf.mpg.de
Abstract
We propose a method for automatically answering questions about images by
bringing together ... | 5411 |@word kohli:1 kong:1 illustrating:1 middle:1 seems:2 nd:2 open:4 d2:1 vldb:1 seek:1 rgb:1 q1:1 shot:1 loc:2 contains:1 score:11 series:1 hoiem:1 fragment:1 miklau:1 current:4 guadarrama:2 si:3 yet:3 wherefore:1 reminiscent:1 parsing:4 readily:1 realize:1 wup:13 realistic:1 visible:1 informative:1 shape:2 plot:1 d... |
4,874 | 5,412 | Quantized Kernel Learning for Feature Matching
Danfeng Qin
ETH Z?urich
Xuanli Chen
TU Munich
Matthieu Guillaumin
ETH Z?urich
Luc Van Gool
ETH Z?urich
{qind, guillaumin, vangool}@vision.ee.ethz.ch, xuanli.chen@tum.de
Abstract
Matching local visual features is a crucial problem in computer vision and its
accuracy g... | 5412 |@word exploitation:1 version:1 compression:5 norm:1 stronger:1 nd:11 seitz:1 bn:1 accounting:1 decomposition:1 q1:3 pick:1 egou:2 mention:1 lepetit:2 electronics:1 initial:1 contains:2 series:1 interestingly:1 outperforms:1 existing:2 current:1 comparing:1 luo:1 yet:4 must:2 readily:2 john:1 additive:9 sanjiv:1 i... |
4,875 | 5,413 | Diverse Sequential Subset Selection for
Supervised Video Summarization
Boqing Gong?
Department of Computer Science
University of Southern California
Los Angeles, CA 90089
boqinggo@usc.edu
Wei-Lun Chao?
Department of Computer Science
University of Southern California
Los Angeles, CA 90089
weilunc@usc.edu
Kristen Grau... | 5413 |@word determinant:1 briefly:1 open:4 underperform:1 seitz:1 decomposition:5 shot:1 liu:2 contains:1 score:8 selecting:7 document:21 ours:2 past:3 existing:7 outperforms:1 contextual:5 comparing:1 luo:2 yet:3 must:1 determinantal:15 john:1 distant:4 partition:1 informative:2 enables:1 remove:1 treating:1 plot:2 de... |
4,876 | 5,414 | Grouping-Based Low-Rank Trajectory Completion
and 3D Reconstruction
Marta Salas
Universidad de Zaragoza,
Zaragoza, Spain
msalasg@unizar.es
Katerina Fragkiadaki
EECS, University of California,
Berkeley, CA 94720
katef@berkeley.edu
Jitendra Malik
EECS, University of California,
Berkeley, CA 94720
malik@eecs.berkeley.ed... | 5414 |@word mild:4 achievable:1 norm:12 nd:1 open:1 decomposition:6 covariance:2 tr:1 harder:1 contains:6 series:1 ours:8 rightmost:1 existing:2 bradley:1 current:3 recovered:4 discretization:1 toh:1 tackling:1 written:1 gpu:1 realistic:4 occl:4 visible:1 ministerio:1 shape:53 depict:2 sundaram:1 zpf:1 cue:1 intelligen... |
4,877 | 5,415 | Learning Mixtures of Submodular Functions for
Image Collection Summarization
Rishabh Iyer
Department of Electrical Engineering
University of Washington
rkiyer@u.washington.edu
Sebastian Tschiatschek
Department of Electrical Engineering
Graz University of Technology
tschiatschek@tugraz.at
Haochen Wei
LinkedIn & Depart... | 5415 |@word briefly:1 achievable:1 advantageous:1 norm:5 seems:2 nd:1 semidifferential:1 open:1 instruction:2 km:2 seitz:1 r:6 rgb:2 egou:1 mention:1 initial:1 contains:2 disparity:1 score:25 loc:3 selecting:2 offering:1 document:9 interestingly:2 outperforms:3 subjective:1 past:1 current:1 com:1 si:8 gmail:1 activatio... |
4,878 | 5,416 | Deep Learning Face Representation by Joint
Identification-Verification
Yi Sun1
Yuheng Chen2
Xiaogang Wang3,4
Xiaoou Tang1,4
Department of Information Engineering, The Chinese University of Hong Kong
2
SenseTime Group
3
Department of Electronic Engineering, The Chinese University of Hong Kong
4
Shenzhen Institutes of Ad... | 5416 |@word kong:2 middle:1 compression:1 norm:11 hu:1 propagate:1 rgb:1 reduction:3 contains:3 score:1 document:1 existing:1 com:1 wd:1 comparing:1 luo:2 gmail:1 scatter:5 must:1 gpu:1 designed:2 update:1 v:1 alone:1 greedy:2 selected:6 accordingly:2 short:1 provides:2 revisited:1 zhang:1 constructed:1 become:3 wild:2... |
4,879 | 5,417 | Fast Training of Pose Detectors in the Fourier Domain
Jo?ao F. Henriques
Pedro Martins
Rui Caseiro
Jorge Batista
Institute of Systems and Robotics
University of Coimbra
{henriques,pedromartins,ruicaseiro,batista}@isr.uc.pt
Abstract
In many datasets, the samples are related by a known image transformation, such
as ro... | 5417 |@word version:3 dalal:2 inversion:2 norm:4 seems:2 triggs:2 open:1 rgb:1 covariance:1 decomposition:2 q1:1 mention:2 reduction:1 cyclic:17 contains:5 series:3 liu:1 batista:5 renewed:1 diagonalized:1 yet:1 must:4 concatenate:1 blur:1 alone:1 half:2 plane:6 core:3 short:1 paulin:1 transposition:1 traverse:1 simple... |
4,880 | 5,418 | LSDA: Large Scale Detection through Adaptation
Judy Hoffman , Sergio Guadarrama , Eric Tzeng , Ronghang Hu? , Jeff Donahue ,
EECS, UC Berkeley, ? EE, Tsinghua University
{jhoffman, sguada, tzeng, jdonahue}@eecs.berkeley.edu
hrh11@mails.tsinghua.edu.cn
Ross Girshick , Trevor Darrell , Kate Saenko4
EECS, UC Be... | 5418 |@word kulis:1 cnn:18 version:3 dalal:1 achievable:1 retraining:1 everingham:1 triggs:1 open:1 hu:1 seek:1 contains:2 uma:1 efficacy:1 score:9 loc:3 tuned:1 ours:2 guadarrama:2 activation:3 yet:2 mushroom:2 enables:1 remove:1 update:1 half:3 leaf:2 fewer:1 intelligence:1 core:1 filtered:1 boosting:1 node:3 philipp... |
4,881 | 5,419 | Local Decorrelation for Improved Pedestrian Detection
Woonhyun Nam?
StradVision, Inc.
woonhyun.nam@stradvision.com
Piotr Doll?ar
Microsoft Research
Joon Hee Han
POSTECH, Republic of Korea
pdollar@microsoft.com
joonhan@postech.ac.kr
Abstract
Even with the advent of more sophisticated, data-hungry methods, boosted ... | 5419 |@word version:3 briefly:2 dalal:1 triggs:1 covariance:15 decomposition:1 decorrelate:2 dramatic:2 reduction:5 necessity:1 initial:1 series:3 score:1 renewed:1 interestingly:1 past:2 outperforms:3 existing:1 current:2 com:2 contextual:1 yet:1 scatter:2 must:1 dct:5 subsequent:1 additive:1 wx:2 remove:2 update:1 st... |
4,882 | 542 | Learning Global Direct Inverse Kinematics
Kenneth Kreutz-Delgado t
Electrical & Computer Eng.
UC San Diego
La Jolla, CA 92093-0407
David DeMers?
Computer Science & Eng.
UC San Diego
La Jolla, CA 92093-0114
Abstract
We introduce and demonstrate a bootstrap method for construction of an inverse function for the robot ... | 542 |@word achievable:1 proportion:1 open:2 seek:1 eng:2 paid:1 thereby:2 delgado:6 configuration:21 bootstrapped:1 activation:5 assigning:1 yet:1 must:1 john:1 cottrell:1 numerical:1 partition:5 thrust:1 burdick:4 motor:1 half:1 selected:2 parameterization:1 location:2 along:3 direct:10 differential:4 consists:1 manip... |
4,883 | 5,420 | Do Convnets Learn Correspondence?
Jonathan Long
Ning Zhang
Trevor Darrell
University of California ? Berkeley
{jonlong, nzhang, trevor}@cs.berkeley.edu
Abstract
Convolutional neural nets (convnets) trained from massive labeled datasets [1]
have substantially improved the state-of-the-art in image classification [2] a... | 5420 |@word cnn:2 version:2 everingham:1 open:2 jacob:1 pick:1 carry:2 initial:1 liu:2 contains:2 score:5 deepens:1 ours:1 document:1 outperforms:1 existing:1 guadarrama:1 com:1 activation:4 intriguing:1 written:1 visible:1 concatenate:1 shape:1 plot:3 v:2 cue:1 selected:1 record:1 colored:1 coarse:3 provides:1 potted:... |
4,884 | 5,421 | Deep Learning for Real-Time Atari Game Play
Using Offline Monte-Carlo Tree Search Planning
Satinder Singh
Computer Science and Eng.
University of Michigan
baveja@umich.edu
Xiaoxiao Guo
Computer Science and Eng.
University of Michigan
guoxiao@umich.edu
Honglak Lee
Computer Science and Eng.
University of Michigan
hongl... | 5421 |@word trial:1 cnn:34 version:3 eliminating:1 exploitation:2 middle:2 illustrating:1 reused:1 termination:1 simulation:1 tried:1 rgb:1 eng:4 pick:1 reduction:1 initial:3 score:10 selecting:1 hereafter:1 genetic:1 document:1 past:1 existing:1 outperforms:5 current:7 comparing:3 skipping:1 informative:1 confirming:1... |
4,885 | 5,422 | On the Number of Linear Regions of
Deep Neural Networks
Guido Mont?ufar
Max Planck Institute for Mathematics in the Sciences
montufar@mis.mpg.de
Razvan Pascanu
Universit?e de Montr?eal
pascanur@iro.umontreal.ca
Yoshua Bengio
Universit?e de Montr?eal, CIFAR Fellow
yoshua.bengio@umontreal.ca
Kyunghyun Cho
Universit?e... | 5422 |@word polynomial:1 replicate:1 open:2 km:3 attainable:2 tr:1 outlook:1 solid:1 recursively:3 contains:1 interestingly:1 elaborating:1 existing:1 current:1 activation:33 visible:1 partition:6 wx:8 enables:2 utml:1 drop:1 n0:30 v:2 half:1 parametrization:1 provides:1 pascanu:11 math:1 toronto:2 hyperplanes:11 sigmo... |
4,886 | 5,423 | Generative Adversarial Nets
Ian J. Goodfellow?, Jean Pouget-Abadie?, Mehdi Mirza, Bing Xu, David Warde-Farley,
Sherjil Ozair?, Aaron Courville, Yoshua Bengio?
D?epartement d?informatique et de recherche op?erationnelle
Universit?e de Montr?eal
Montr?eal, QC H3C 3J7
Abstract
We propose a new framework for estimating ge... | 5423 |@word version:1 eliminating:1 stronger:1 seek:2 covariance:1 contrastive:2 pg:45 tr:1 solid:1 epartement:1 ecole:1 document:1 deconvolutional:1 rightmost:1 existing:1 com:1 activation:3 yet:2 intriguing:2 assigning:1 must:4 dx:2 gpu:1 visible:1 numerical:1 partition:1 utml:1 designed:1 update:6 generative:44 inte... |
4,887 | 5,424 | Deep Symmetry Networks
Robert Gens
Pedro Domingos
Department of Computer Science and Engineering
University of Washington
Seattle, WA 98195-2350, U.S.A.
{rcg,pedrod}@cs.washington.edu
Abstract
The chief difficulty in object recognition is that objects? classes are obscured by
a large number of extraneous sources of v... | 5424 |@word collinearity:1 version:2 kondor:2 polynomial:1 closure:1 grey:1 decomposition:1 p0:10 covariance:2 reduction:1 contains:3 united:1 bc:2 document:1 fa8750:1 current:1 comparing:1 blank:1 surprising:1 yet:1 intriguing:1 must:1 gpu:1 determinantal:2 realistic:2 partition:1 wx:1 shape:4 remove:2 designed:1 upda... |
4,888 | 5,425 | A Multiplicative Model for Learning Distributed
Text-Based Attribute Representations
Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov
University of Toronto
Canadian Institute for Advanced Research
{rkiros, zemel, rsalakhu}@cs.toronto.edu
Abstract
In this paper we propose a general framework for learning distributed... | 5425 |@word multitask:1 proceeded:1 version:1 middle:2 norm:3 duran:1 willing:1 decomposition:2 contrastive:1 shot:1 accommodate:1 initial:4 contains:4 score:1 document:12 interestingly:3 outperforms:1 existing:1 current:3 written:1 lauly:1 ronan:1 shlomo:1 kdd:1 plot:2 joy:2 v:3 ial:1 contribute:1 toronto:2 node:1 phi... |
4,889 | 5,426 | Sparse Polynomial Learning and Graph Sketching
Murat Kocaoglu1? , Karthikeyan Shanmugam1? , Alexandros G.Dimakis1? , Adam Klivans2?
1
Department of Electrical and Computer Engineering, 2 Department of Computer Science
The University of Texas at Austin, USA
?
mkocaoglu@utexas.edu, ? karthiksh@utexas.edu
?
dimakis@austi... | 5426 |@word trial:1 polynomial:45 nd:3 kf2:1 open:2 simulation:2 pick:1 thereby:1 configuration:1 contains:4 ecole:1 ka:1 comparing:1 recovered:1 com:1 si:15 written:1 must:1 fn:2 subsequent:1 additive:1 plot:1 v:4 intelligence:1 selected:2 core:1 short:2 junta:3 record:1 alexandros:1 filtered:2 caveat:1 dissertation:1... |
4,890 | 5,427 | A Residual Bootstrap for High-Dimensional
Regression with Near Low-Rank Designs
Miles E. Lopes
Department of Statistics
University of California, Berkeley
Berkeley, CA 94720
mlopes@stat.berkeley.edu
Abstract
We study the residual bootstrap (RB) method in the context of high-dimensional
linear regression. Specifically... | 5427 |@word collinearity:1 briefly:1 version:2 stronger:1 norm:2 open:1 d2:2 simulation:1 bn:6 decomposition:4 covariance:1 moment:5 liu:1 series:1 score:2 hereafter:1 selecting:1 denoting:1 groundwork:1 current:1 attracted:1 written:1 fn:4 numerical:1 n0:2 resampling:1 beginning:1 ith:3 smith:1 core:1 grfp:1 provides:... |
4,891 | 5,428 | Fast and Robust Least Squares Estimation in
Corrupted Linear Models
Brian McWilliams?
Gabriel Krummenacher? Mario Lucic Joachim M. Buhmann
Department of Computer Science
ETH Z?urich, Switzerland
{mcbrian,gabriel.krummenacher,lucic,jbuhmann}@inf.ethz.ch
Abstract
Subsampling methods have been recently proposed to speed... | 5428 |@word collinearity:1 briefly:2 version:1 proportion:3 norm:5 simulation:1 crucially:1 covariance:5 sgd:3 recursively:1 reduction:5 series:1 score:17 selecting:1 woodruff:1 daniel:1 outperforms:1 current:2 comparing:1 analysed:1 si:9 must:1 dct:1 additive:2 realistic:2 wx:1 remove:1 alone:3 implying:1 selected:1 c... |
4,892 | 5,429 | Fast Multivariate Spatio-temporal Analysis
via Low Rank Tensor Learning
Mohammad Taha Bahadori?
Dept. of Electrical Engineering
Univ. of Southern California
Los Angeles, CA 90089
mohammab@usc.edu
Qi (Rose) Yu?
Dept. of Computer Science
Univ. of Southern California
Los Angeles, CA 90089
qiyu@usc.edu
Yan Liu
Dept. of ... | 5429 |@word multitask:6 version:1 norm:8 paredes:1 confirms:1 simulation:1 seek:2 bn:3 covariance:8 decomposition:8 pick:1 concise:1 tr:2 bai:1 liu:2 series:10 efficacy:1 contains:3 romera:1 past:1 existing:4 current:1 com:1 comparing:1 chu:1 written:1 john:1 timestamps:1 numerical:1 informative:1 kdd:1 enables:1 desig... |
4,893 | 543 | Recognition of Manipulated Objects
by Motor Learning
Hiroaki Gomi
Mitsuo Kawato
ATR Auditory and Visual Perception Research Laboratories,
Inui-dani, Sanpei-dani, Seika-cho, Soraku-gun, Kyoto 619-02, Japan
Abstract
We present two neural network controller learning schemes based on feedbackerror-learning and modular ar... | 543 |@word simulation:10 jacob:5 decomposition:2 tr:1 configuration:3 contains:1 tuned:1 current:1 comparing:1 nowlan:4 realize:1 informative:1 motor:26 alone:1 cue:4 selected:7 sys:1 steepest:1 provides:1 toronto:1 location:1 five:1 skilled:1 direct:1 ect:1 introduce:1 acquired:3 seika:1 multi:1 automatically:1 actual... |
4,894 | 5,430 | Provable Non-convex Robust PCA
Praneeth Netrapalli 1? U N Niranjan2?
1
Sujay Sanghavi3
Animashree Anandkumar2
Prateek Jain4
Microsoft Research, Cambridge MA. 2 The University of California at Irvine.
3
The University of Texas at Austin. 4 Microsoft Research, India.
Abstract
We propose a new method for robust PCA ?... | 5430 |@word faculty:1 polynomial:1 norm:6 seems:1 open:1 km:3 decomposition:11 covariance:1 contraction:3 minming:1 incurs:1 harder:1 klk:2 carry:1 initial:4 series:1 selecting:1 daniel:1 interestingly:1 outperforms:1 existing:3 past:1 current:1 ksk1:1 luo:1 jns13:2 john:1 additive:2 subsequent:1 blur:2 enables:1 remov... |
4,895 | 5,431 | Spectral Methods Meet EM: A Provably Optimal
Algorithm for Crowdsourcing
Yuchen Zhang?
Xi Chen]
Dengyong Zhou?
Michael I. Jordan?
?
University of California, Berkeley, Berkeley, CA 94720
{yuczhang,jordan}@berkeley.edu
]
New York University, New York, NY 10012
xichen@nyu.edu
?
Microsoft Research, 1 Microsoft Way, ... | 5431 |@word mild:1 version:2 norm:1 nd:1 c0:1 decomposition:5 contrastive:1 moment:14 initial:5 liu:3 series:1 karger:5 zij:18 denoting:1 bc:3 document:1 outperforms:1 existing:3 recovered:1 com:1 current:1 comparing:2 yet:1 assigning:4 written:1 refines:1 partition:1 cheap:1 plot:2 update:5 stationary:1 generative:3 g... |
4,896 | 5,432 | Unsupervised Transcription of Piano Music
Taylor Berg-Kirkpatrick Jacob Andreas Dan Klein
Computer Science Division
University of California, Berkeley
{tberg,jda,klein}@cs.berkeley.edu
Abstract
We present a new probabilistic model for transcribing piano music from audio to
a symbolic form. Our model reflects the proc... | 5432 |@word rising:1 d2:1 jacob:1 decomposition:1 pressed:3 series:3 score:9 daniel:1 outperforms:3 past:1 existing:2 activation:31 synthesizer:3 must:2 readily:1 additive:1 informative:1 shape:4 christian:1 drop:1 update:11 polyphonic:11 n0:4 generative:4 half:1 tillman:1 parameterization:4 warmuth:1 inspection:1 shor... |
4,897 | 5,433 | Combinatorial Pure Exploration of
Multi-Armed Bandits
Shouyuan Chen1? Tian Lin2
Irwin King1
Michael R. Lyu1
Wei Chen3
2
3
The Chinese University of Hong Kong
Tsinghua University
Microsoft Research Asia
1
2
{sychen,king,lyu}@cse.cuhk.edu.hk
lint10@mails.tsinghua.edu.cn 3 weic@microsoft.com
1
Abstract
We study the combi... | 5433 |@word cpe:24 kong:2 exploitation:1 version:1 kalyanakrishnan:3 series:3 contains:4 ours:1 existing:2 current:2 com:1 nt:1 must:1 john:1 partition:2 benign:3 analytic:1 cis:1 designed:1 update:3 fund:1 beginning:2 oneto:1 characterization:1 mannor:4 cse:1 successive:3 five:1 unbounded:1 become:1 yuan:1 prove:2 int... |
4,898 | 5,434 | From Stochastic Mixability to Fast Rates
Robert C. Williamson
Research School of Computer Science
Australian National University and NICTA
bob.williamson@anu.edu.au
Nishant A. Mehta
Research School of Computer Science
Australian National University
nishant.mehta@anu.edu.au
Abstract
Empirical risk minimization (ERM) ... | 5434 |@word mild:1 version:5 inversion:1 polynomial:1 norm:1 nd:1 mehta:2 open:5 d2:4 forecaster:1 jacob:1 pick:2 minus:1 moment:13 initial:1 ecole:1 erven:6 yet:2 must:1 discrimination:1 leaf:1 beginning:1 core:1 provides:1 characterization:1 simpler:1 mathematical:1 direct:5 consists:1 excellence:1 indeed:1 roughly:1... |
4,899 | 5,435 | Beyond Disagreement-based Agnostic Active
Learning
Chicheng Zhang
University of California, San Diego
9500 Gilman Drive, La Jolla, CA 92093
chichengzhang@ucsd.edu
Kamalika Chaudhuri
University of California, San Diego
9500 Gilman Drive, La Jolla, CA 92093
kamalika@cs.ucsd.edu
Abstract
We study agnostic active learnin... | 5435 |@word exploitation:1 version:7 nd:5 c0:3 open:1 reduction:1 contains:1 ours:2 past:1 err:8 beygelzimer:3 dx:4 bd:4 written:1 additive:1 informative:1 atlas:1 ainen:1 update:1 greedy:1 isotropic:1 characterization:1 provides:5 complication:1 coarse:1 allerton:1 zhang:3 manner:1 expected:4 roughly:1 frequently:1 in... |
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