Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
2,700 | 3,448 | Supervised Dictionary Learning
Julien Mairal
INRIA-Willow project
julien.mairal@inria.fr
Jean Ponce
Ecole Normale Sup?erieure
jean.ponce@ens.fr
Francis Bach
INRIA-Willow project
francis.bach@inria.fr
Guillermo Sapiro
University of Minnesota
guille@ece.umn.edu
Andrew Zisserman
University of Oxford
az@robots.ox.ac.uk... | 3448 |@word briefly:1 version:5 norm:9 open:1 decomposition:10 thereby:1 selecting:1 ecole:1 document:3 tuned:2 interestingly:1 denoting:2 ours:1 written:1 readily:1 distant:1 analytic:1 plot:1 update:3 discrimination:3 v:4 generative:18 selected:1 greedy:1 half:2 mccallum:1 core:1 blei:1 provides:3 zhang:1 five:2 alon... |
2,701 | 3,449 | Offline Handwriting Recognition with
Multidimensional Recurrent Neural Networks
Alex Graves
TU Munich, Germany
graves@in.tum.de
?
Jurgen
Schmidhuber
IDSIA, Switzerland and TU Munich, Germany
juergen@idsia.ch
Abstract
Offline handwriting recognition?the automatic transcription of images of handwritten text?is a challe... | 3449 |@word arabic:11 hu:1 thereby:1 mdlstm:11 harder:1 recursively:1 contains:3 score:2 united:1 bc:1 ours:1 document:4 past:1 current:3 blank:7 contextual:1 activation:21 must:2 wanted:1 remove:1 designed:3 intelligence:1 plane:1 desktop:1 beginning:3 l0s:1 short:3 preference:1 height:2 along:10 dn:1 install:1 npen:1... |
2,702 | 3,450 | Recursive Segmentation and Recognition Templates
for 2D Parsing
Long (Leo) Zhu
CSAIL MIT
leozhu@csail.mit.edu
Yuanhao Chen
USTC
yhchen4@ustc.edu.cn
Chenxi Lin
Microsoft Research Asia
chenxil@microsoft.com
Yuan Lin
Shanghai Jiaotong University
loirey@sjtu.edu.cn
Alan Yuille
UCLA
yuille@stat.ucla.edu
Abstract
Langu... | 3450 |@word version:2 middle:1 polynomial:6 seems:2 glue:1 plsa:1 triggs:2 grey:1 textonboost:6 harder:2 recursively:2 initial:1 configuration:5 contains:1 selecting:1 tuned:1 document:1 outperforms:3 current:3 com:1 contextual:3 must:1 parsing:29 additive:1 partition:7 happen:1 shape:5 enables:3 designed:6 gist:4 upda... |
2,703 | 3,451 | Tighter Bounds for Structured Estimation
Chuong B. Do, Quoc Le
Stanford University
{chuongdo,quocle}@cs.stanford.edu
Choon Hui Teo
Australian National University and NICTA
choonhui.teo@anu.edu.au
Olivier Chapelle, Alex Smola
Yahoo! Research
chap@yahoo-inc.com,alex@smola.org
Abstract
Large-margin structured estimati... | 3451 |@word rreg:1 version:1 briefly:1 norm:1 proportion:3 seems:1 simulation:1 eng:1 tr:1 harder:1 carry:1 liu:1 contains:1 score:2 rkhs:1 outperforms:4 existing:2 current:1 com:1 comparing:1 recovered:1 surprising:1 parsing:2 kdd:2 hofmann:1 remove:1 plot:1 selected:1 beginning:1 provides:4 recompute:1 boosting:1 pre... |
2,704 | 3,452 | Algorithms for Infinitely Many-Armed Bandits
Yizao Wang?
Department of Statistics - University of Michigan
437 West Hall, 1085 South University, Ann Arbor, MI, 48109-1107, USA
yizwang@umich.edu
Jean-Yves Audibert
Universit? Paris Est, Ecole des Ponts, ParisTech, Certis
& Willow - ENS / INRIA, Paris, France
audibert@ce... | 3452 |@word trial:2 exploitation:3 version:1 polynomial:1 open:1 r:1 tried:2 contains:1 selecting:1 ecole:1 ours:2 past:1 current:2 enpc:1 nt:1 discretization:1 enables:2 designed:2 n0:2 selected:8 fewer:1 xk:6 beginning:2 provides:2 revisited:1 location:2 teytaud:1 c2:14 symposium:1 consists:1 introduce:1 market:1 ind... |
2,705 | 3,453 | Bayesian Synchronous Grammar Induction
Phil Blunsom, Trevor Cohn, Miles Osborne
School of Informatics, University of Edinburgh
10 Crichton Street, Edinburgh, EH8 9AB, UK
{pblunsom,tcohn,miles}@inf.ed.ac.uk
Abstract
We present a novel method for inducing synchronous context free grammars
(SCFGs) from a corpus of paral... | 3453 |@word inversion:3 polynomial:1 open:1 heuristically:1 confirms:1 underperform:1 p0:10 thereby:1 substitution:2 generatively:1 series:1 score:3 efficacy:1 initialisation:1 daniel:4 current:3 assigning:3 written:1 parsing:5 must:1 john:1 subsequent:1 aside:1 generative:7 leaf:1 graehl:1 beginning:1 maximised:2 shor... |
2,706 | 3,454 | Predictive Indexing for Fast Search
Sharad Goel
Yahoo! Research
New York, NY 10018
goel@yahoo-inc.com
John Langford
Yahoo! Research
New York, NY 10018
jl@yahoo-inc.com
Alex Strehl
Yahoo! Research
New York, NY 10018
strehl@yahoo-inc.com
Abstract
We tackle the computational problem of query-conditioned search. Given ... | 3454 |@word trial:2 repository:1 version:1 compression:2 twelfth:1 vldb:1 scg:1 q1:1 it1:3 initial:1 liu:2 contains:6 score:30 document:1 past:1 existing:5 outperforms:2 com:4 mari:1 yet:1 must:1 john:1 partition:7 shape:1 ainen:2 plot:3 v:2 alone:1 fewer:1 plane:1 beginning:1 ith:1 provides:1 hyperplanes:1 along:1 sym... |
2,707 | 3,455 | Relative Performance Guarantees for
Approximate Inference in Latent Dirichlet Allocation
Indraneel Mukherjee
David M. Blei
Department of Computer Science
Princeton University
35 Olden Street
Princeton, NJ 08540
{imukherj,blei}@cs.princeton.edu
Abstract
Hierarchical probabilistic modeling of discrete data has emerged... | 3455 |@word trial:1 repository:1 kintsch:1 proportion:2 laurence:1 logmm:2 seek:1 crucially:1 simulation:3 decomposition:1 q1:1 carry:1 document:62 prefix:2 comparing:1 yet:1 additive:1 partition:1 tailoring:1 hofmann:1 plot:4 update:3 v:2 implying:2 generative:4 prohibitive:1 fewer:1 intelligence:2 discovering:1 mccal... |
2,708 | 3,456 | Human Active Learning
Rui Castro1 , Charles Kalish2 , Robert Nowak3 , Ruichen Qian4 , Timothy Rogers2 , Xiaojin Zhu4?
1
Department of Electrical Engineering
Columbia University. New York, NY 10027
Department of {2 Psychology, 3 Electrical and Computer Engineering, 4 Computer Sciences}
University of Wisconsin-Madison. ... | 3456 |@word polynomial:4 achievable:2 seems:1 scroll:1 tedious:1 instruction:1 p0:1 q1:4 paid:1 dramatic:2 harder:2 initial:2 series:1 selecting:2 offering:1 seriously:1 rightmost:1 past:2 current:6 comparing:2 discretization:1 must:1 john:1 subsequent:1 realistic:1 informative:2 shape:16 designed:1 plot:4 update:1 atl... |
2,709 | 3,457 | On the Generalization Ability of
Online Strongly Convex Programming Algorithms
Sham M. Kakade
TTI Chicago
Chicago, IL 60637
sham@tti-c.org
Ambuj Tewari
TTI Chicago
Chicago, IL 60637
tewari@tti-c.org
Abstract
This paper examines the generalization properties of online convex programming
algorithms when the loss funct... | 3457 |@word seems:1 norm:6 dekel:1 open:1 d2:1 incurs:1 moment:1 ours:2 bc:2 past:1 current:1 discretization:1 written:1 cruz:1 chicago:4 update:2 intelligence:1 org:2 zhang:5 c2:2 become:1 prove:3 eleventh:1 manner:1 examine:1 growing:1 equipped:1 solver:2 becomes:1 notation:1 bounded:4 minimizes:1 finding:1 guarantee... |
2,710 | 3,458 | Model selection and velocity estimation using novel
priors for motion patterns
Hongjing Lu
Shuang Wu
Department of Psychology
Department of Statistics
UCLA, Los Angeles, CA 90095
UCLA, Los Angeles, CA 90095
hongjing@ucla.edu
shuangw@stat.ucla.edu
Alan Yuille
Department of Statistics
UCLA
Los Angeles, CA 90095
yuille@s... | 3458 |@word neurophysiology:2 middle:4 stronger:1 proportion:1 simulation:4 contraction:3 series:1 current:1 comparing:1 mst:2 shape:1 drop:1 plot:5 v:3 discrimination:1 stationary:1 selected:4 detecting:2 math:1 mathematical:1 windowed:1 differential:3 combine:1 burr:2 introduce:2 x0:5 freeman:2 estimating:2 notation:... |
2,711 | 3,459 | Multi-Agent Filtering with Infinitely Nested Beliefs
Luke S. Zettlemoyer
MIT CSAIL
Cambridge, MA 02139
lsz@csai.mit.edu
Brian Milch?
Google Inc.
Mountain View, CA 94043
brian@google.com
Leslie Pack Kaelbling
MIT CSAIL
Cambridge, MA 02139
lpk@csail.mit.edu
Abstract
In partially observable worlds with many agents, ne... | 3459 |@word version:1 nd:1 dekel:2 open:6 p0:3 minus:1 epistemic:2 initial:2 contains:1 selecting:1 past:4 existing:2 current:9 com:1 si:3 must:15 bd:4 written:1 happen:1 predetermined:1 remove:3 drop:3 update:11 intelligence:7 prohibitive:1 amir:1 record:2 provides:2 node:1 location:10 five:1 along:2 direct:2 become:2... |
2,712 | 346 | Connection Topology and Dynamics
in Lateral Inhibition Networks
C. M. Marcus, F. R. Waugh, and R. M. Westervelt
Department of Physics and Division of Applied Sciences, Harvard University
Cambridge, MA 02138
ABSTRACT
We show analytically how the stability of two-dimensional lateral
inhibition neural networks depends o... | 346 |@word version:1 seems:1 simulation:1 ferromagnetism:1 dramatic:1 carry:1 configuration:2 current:1 recovered:1 surprising:2 written:1 must:1 physiol:1 numerical:1 informative:1 analytic:3 update:6 liapunov:1 coleman:2 reciprocal:1 math:1 sigmoidal:3 nussbaum:2 differential:2 sustained:5 expected:1 wannier:2 brain:... |
2,713 | 3,460 | Learning with Consistency between Inductive
Functions and Kernels
Haixuan Yang1,2
Irwin King1
Michael R. Lyu1
1
2
Department of Computer Science & Engineering
Department of Computer Science
The Chinese University of Hong Kong
Royal Holloway University of London
{hxyang,king,lyu}@cse.cuhk.edu.hk
haixuan@cs.rhul.ac.hk
A... | 3460 |@word kong:2 middle:2 polynomial:6 norm:1 klk:1 initial:3 necessity:1 contains:3 series:2 rkhs:2 current:1 dx:3 written:1 must:1 john:1 enables:1 v:5 nent:1 beginning:1 provides:1 cse:1 location:1 herbrich:1 zhang:1 mathematical:2 differential:1 become:1 ik:10 fitting:2 pairwise:1 expected:2 decomposed:1 consider... |
2,714 | 3,461 | An improved estimator of Variance Explained in the
presence of noise
Ralf. M. Haefner?
Laboratory for Sensorimotor Research
National Eye Institute, NIH
Bethesda, MD 20892
ralf.haefner@gmail.com
Bruce. G. Cumming
Laboratory for Sensorimotor Research
National Eye Institute, NIH
Bethesda, MD 20892
bgc@lsr.nei.nih.gov
A... | 3461 |@word illustrating:1 polynomial:4 seems:1 advantageous:1 nd:2 open:1 simulation:8 rhesus:1 accounting:5 minus:2 solid:1 series:1 disparity:9 ours:1 outperforms:1 com:2 comparing:1 surprising:1 gmail:2 readily:1 subsequent:2 numerical:2 additive:2 realistic:1 christian:1 asymptote:1 v:1 alone:1 half:1 leaf:1 fewer... |
2,715 | 3,462 | Posterior Consistency of the Silverman g-prior in
Bayesian Model Choice
Zhihua Zhang
School of Computer Science & Technology
Zhejiang University, Hangzhou, China
Michael I. Jordan
Departments of EECS and Statistics
University of California, Berkeley, CA, USA
Dit-Yan Yeung
Department of Computer Science & Engineering... | 3462 |@word kong:1 norm:1 bf:18 essay:1 seek:1 r:28 rkhs:4 ka:1 hkust:1 written:1 readily:2 noninformative:3 accordingly:1 smith:2 provides:1 zhang:1 prove:2 fitting:1 introduce:1 chi:1 decreasing:4 project:1 moreover:2 notation:1 mass:1 mcculloch:1 null:5 pursue:1 unified:1 berkeley:1 bernardo:1 k2:14 schwartz:1 yn:1 ... |
2,716 | 3,463 | Reconciling Real Scores with Binary Comparisons:
A Unified Logistic Model for Ranking
Nir Ailon
Google Research NY
111 8th Ave, 4th FL New York NY 10011 nailon@gmail.com
Abstract
The problem of ranking arises ubiquitously in almost every aspect of life, and
in particular in Machine Learning/Information Retrieval. A s... | 3463 |@word middle:1 judgement:1 seems:3 logit:8 willing:1 calculus:1 pick:1 paid:1 mention:1 tr:2 harder:1 recursively:2 reduction:2 liu:1 series:1 score:28 rightmost:3 bradley:2 com:1 comparing:1 surprising:1 beygelzimer:1 gmail:1 must:1 john:3 ronald:1 additive:2 kdd:1 interpretable:2 v:2 implying:2 generative:2 rud... |
2,717 | 3,464 | Robust Near-Isometric Matching via Structured
Learning of Graphical Models
Julian J. McAuley
NICTA/ANU
julian.mcauley
@nicta.com.au
Tib?erio S. Caetano
NICTA/ANU
tiberio.caetano
@nicta.com.au
Alexander J. Smola
Yahoo! Research?
alex@smola.org
Abstract
Models for near-rigid shape matching are typically based on dist... | 3464 |@word exploitation:1 polynomial:1 norm:3 seems:1 nd:1 proportion:1 d2:3 q1:2 incurs:1 mcauley:3 configuration:1 cyclic:1 score:1 selecting:1 tuned:1 interestingly:2 com:2 si:30 yet:2 must:2 distant:1 kdd:1 shape:34 hofmann:1 plot:2 fewer:1 smith:1 provides:1 node:3 location:1 contribute:1 org:1 misinterpreted:1 c... |
2,718 | 3,465 | Optimal Response Initiation:
Why Recent Experience Matters
Matt Jones
Dept. of Psychology &
Institute of Cognitive Science
University of Colorado
Michael C. Mozer
Dept. of Computer Science &
Institute of Cognitive Science
University of Colorado
Sachiko Kinoshita
MACCS &
Dept. of Psychology
Macquarie University
mcj@... | 3465 |@word trial:53 cingulate:1 middle:2 eliminating:1 instruction:3 simulation:15 tried:3 pressure:2 reduction:3 moment:1 series:1 cherian:2 selecting:1 offering:2 denoting:1 interestingly:1 tuned:1 past:3 existing:1 current:9 comparing:2 anterior:1 neurophys:1 lang:2 yet:1 dx:1 must:5 scatter:1 hpp:2 motor:3 drop:3 ... |
2,719 | 3,466 | Sparsity of SVMs that use the -insensitive loss
Ingo Steinwart
Information Sciences Group CCS-3
Los Alamos National Laboratory
Los Alamos, NM 87545, USA
ingo@lanl.gov
Andreas Christmann
University of Bayreuth
Department of Mathematics
D-95440 Bayreuth
Andreas.Christmann@uni-bayreuth.de
Abstract
In this paper lower ... | 3466 |@word mild:1 version:1 briefly:2 seems:2 norm:1 open:1 confirms:1 paid:2 mention:1 denoting:1 rkhs:10 scovel:1 john:1 realistic:1 shape:1 n0:3 short:2 provides:1 math:1 zhang:1 direct:1 become:1 prove:3 introduce:4 indeed:3 behavior:1 decreasing:1 gov:1 little:1 considering:2 becomes:2 begin:2 provided:1 notation... |
2,720 | 3,467 | Spike Feature Extraction Using Informative Samples
Zhi Yang, Qi Zhao and Wentai Liu
School of Engineering
University of California at Santa Cruz
1156 High Street, Santa Cruz, CA 95064
{yangzhi, zhaoqi, wentai}@soe.ucsc.edu
Abstract
This paper presents a spike feature extraction algorithm that targets real-time
spike ... | 3467 |@word compression:1 hippocampus:1 propagate:1 covariance:1 p0:2 eng:1 decomposition:1 solid:2 reduction:1 liu:5 configuration:2 contains:1 score:2 nadasdy:2 current:3 activation:1 must:1 cruz:2 distant:1 partition:1 informative:25 blur:1 shape:3 enables:1 remove:1 designed:3 alone:1 selected:1 device:1 prespecifi... |
2,721 | 3,468 | Characterizing response behavior in
multi-sensory perception with conflicting cues
Rama Natarajan1
Iain Murray1
Ladan Shams2
Richard S. Zemel1
1
Department of Computer Science, University of Toronto, Canada
{rama,murray,zemel}@cs.toronto.edu
2
Department of Psychology, University of California Los Angeles, USA
ladan@p... | 3468 |@word trial:45 faculty:2 judgement:3 proportion:4 seems:1 instruction:2 simulation:12 solid:5 contains:1 disparity:36 selecting:1 ording:2 current:1 comparing:1 yet:3 readily:1 subsequent:3 informative:1 motor:1 hypothesize:1 plot:9 alone:1 cue:21 generative:2 selected:1 implying:1 provides:1 toronto:2 location:1... |
2,722 | 3,469 | Overlaying classifiers:
a practical approach for optimal ranking
St?ephan Cl?emenc?on
Telecom Paristech (TSI) - LTCI UMR Institut Telecom/CNRS 5141
stephan.clemencon@telecom-paristech.fr
Nicolas Vayatis
ENS Cachan & UniverSud - CMLA UMR CNRS 8536
vayatis@cmla.ens-cachan.fr
Abstract
ROC curves are one of the most wide... | 3469 |@word h:3 version:2 proportion:1 norm:6 stronger:1 yi0:3 d2:4 decomposition:2 palso:1 pg:1 contains:1 interestingly:1 horvitz:1 recovered:1 discretization:1 written:2 partition:6 plot:3 v:2 provides:3 boosting:1 math:2 herbrich:1 preference:1 c2:3 tsy04:3 introduce:5 boor:1 expected:1 indeed:2 decreasing:1 equipp... |
2,723 | 347 | Signal Processing by Multiplexing and
Demultiplexing in Neurons
DavidC. Tam
Division of Neuroscience
Baylor College of Medicine
Houston, TX 77030
dtam@next-cns.neusc.bcm.tmc.edu
Abstract
Signal processing capabilities of biological neurons are
investigated. Temporally coded signals in neurons can be
multiplexed to inc... | 347 |@word version:1 hyperpolarized:5 squid:1 series:2 contains:1 current:1 thre:1 activation:1 yet:2 physiol:2 interspike:21 filtered:3 provides:1 location:2 successive:1 lx:2 direct:1 symposium:1 interaural:5 introduce:1 brain:1 detects:2 prolonged:3 window:2 circuit:1 depolarization:2 giant:1 transformation:1 tempor... |
2,724 | 3,470 | Estimating vector fields using
sparse basis field expansions
Stefan Haufe1, 2, *
Vadim V. Nikulin3, 4
Andreas Ziehe1, 2
1, 2, 4
?
Klaus-Robert Muller
Guido Nolte2
1
TU Berlin, Dept. of Computer Science, Machine Learning Laboratory, Berlin, Germany
2
Fraunhofer Institute FIRST (IDA), Berlin, Germany
3
Charit?e Uni... | 3470 |@word trial:3 mri:1 version:2 norm:9 stronger:1 proportionality:1 pulse:2 linearized:1 bn:1 decomposition:2 eng:1 tr:6 outlook:1 ivaldi:1 selecting:2 unintended:1 bc:3 interestingly:2 franklin:1 outperforms:1 current:23 ida:1 comparing:1 optim:1 activation:1 assigning:1 written:2 evans:1 numerical:1 realistic:3 t... |
2,725 | 3,471 | Convergence and Rate of Convergence of A
Manifold-Based Dimension Reduction Algorithm
Andrew K. Smith, Xiaoming Huo
School of Industrial and Systems Engineering
Georgia Institute of Technology
Atlanta, GA 30332
andrewsmith81@gmail.com, huo@gatech.edu
Hongyuan Zha
College of Computing
Georgia Institute of Technology
A... | 3471 |@word version:2 norm:1 seems:1 nd:2 tedious:1 open:3 simulation:2 mitsubishi:1 bn:2 covariance:1 decomposition:3 simplifying:1 carry:2 reduction:12 ours:1 ati:1 existing:3 recovered:2 com:1 si:12 gmail:1 attracted:1 must:3 numerical:3 v:1 selected:1 xk:3 huo:4 parametrization:4 smith:4 ith:2 math:1 zhang:4 along:... |
2,726 | 3,472 | Cascaded Classification Models:
Combining Models for Holistic Scene Understanding
Geremy Heitz
Stephen Gould
Department of Electrical Engineering
Stanford University, Stanford, CA 94305
Ashutosh Saxena
Daphne Koller
Department of Computer Science
Stanford University, Stanford, CA 94305
{gaheitz,sgould}@stanford.edu
... | 3472 |@word middle:2 version:2 briefly:1 dalal:3 proportion:1 heterogeneously:1 triggs:3 everingham:1 open:1 seek:2 rgb:1 git:1 configuration:1 contains:4 series:1 score:3 hoiem:3 document:1 ours:2 interestingly:1 fa8750:1 existing:1 contextual:3 com:1 tackling:1 assigning:2 must:1 parsing:2 subsequent:2 numerical:2 di... |
2,727 | 3,473 | QUIC-SVD: Fast SVD Using Cosine Trees
Michael P. Holmes, Alexander G. Gray and Charles Lee Isbell, Jr.
College of Computing
Georgia Tech
Atlanta, GA 30327
{mph, agray, isbell}@cc.gatech.edu
Abstract
The Singular Value Decomposition is a key operation in many machine learning
methods. Its computational cost, however, ... | 3473 |@word madelon:11 version:4 eliminating:1 seems:1 norm:1 km:1 seek:1 decomposition:4 mention:1 minus:1 carry:1 reduction:2 contains:1 series:1 mag:1 past:2 current:1 comparing:1 si:1 yet:2 ctn:6 numerical:1 additive:1 enables:1 remove:1 designed:1 extrapolating:1 update:1 v:2 guess:1 ith:2 transposition:1 provides... |
2,728 | 3,474 | Temporal Dynamics of Cognitive Control
Michael C. Mozer
Department of Computer Science and
Institute of Cognitive Science
University of Colorado
Boulder, CO 80309
mozer@colorado.edu
Jeremy R. Reynolds
Department of Psychology
University of Denver
Denver, CO 80208
jeremy.reynolds@psy.du.edu
Abstract
Cognitive control... | 3474 |@word trial:38 mri:1 middle:2 inversion:1 loading:1 seems:1 instruction:11 grey:1 integrative:2 simulation:8 propagate:1 essay:1 accounting:1 initial:1 hereafter:1 practiced:2 denoting:1 interestingly:1 reynolds:2 past:1 current:12 anterior:2 crippled:1 surprising:1 activation:1 must:7 subsequent:5 cpds:5 shape:1... |
2,729 | 3,475 | Online Prediction on Large Diameter Graphs
Mark Herbster, Guy Lever, Massimiliano Pontil
Department of Computer Science
University College London
Gower Street, London WC1E 6BT, England, UK
{m.herbster, g.lever, m.pontil}@cs.ucl.ac.uk
Abstract
We continue our study of online prediction of the labelling of a graph. We ... | 3475 |@word trial:15 norm:10 vi1:6 nd:1 d2:1 incurs:2 contains:1 existing:1 current:5 tackling:1 yet:1 dx:1 subsequent:1 partition:1 frievald:1 enables:2 designed:1 congestion:1 leaf:4 pelckmans:1 hamiltonian:2 provides:1 multiset:2 node:2 math:2 firstly:1 mathematical:2 along:3 c2:7 direct:1 symposium:1 descendant:1 p... |
2,730 | 3,476 | Effects of Stimulus Type and of Error-Correcting
Code Design on BCI Speller Performance
Jeremy Hill1
Jason Farquhar2
Felix Bie?mann1,3
Suzanne Martens1
Bernhard Sch?olkopf1
1
Max Planck Institute for Biological Cybernetics
{firstname.lastname}@tuebingen.mpg.de
2
NICI, Radboud University, Nijmegen, The Netherland... | 3476 |@word neurophysiology:2 trial:6 illustrating:1 middle:2 version:2 achievable:1 proportion:2 stronger:1 seems:1 grey:2 covariance:1 eng:2 pick:1 initial:2 series:3 denoting:1 rightmost:1 outperforms:1 current:3 bie:1 must:2 alphanumeric:1 enables:1 plot:2 olkopf1:1 v:1 alone:2 generative:1 selected:3 greedy:1 cue:... |
2,731 | 3,477 | Linear Classification and Selective Sampling
Under Low Noise Conditions
Giovanni Cavallanti
DSI, Universit`a degli Studi di Milano, Italy
cavallanti@dsi.unimi.it
Nicol`o Cesa-Bianchi
DSI, Universit`a degli Studi di Milano, Italy
cesa-bianchi@dsi.unimi.it
Claudio Gentile
DICOM, Universit`a dell?Insubria, Italy
claudio... | 3477 |@word nificantly:1 version:6 advantageous:1 seems:3 norm:5 suitably:1 d2:2 covariance:3 thereby:1 harder:2 carry:1 initial:2 selecting:1 document:4 task1:1 current:11 recovered:1 nt:15 comparing:1 beygelzimer:1 scovel:1 issuing:1 realize:1 fn:1 realistic:1 informative:1 atlas:3 ainen:2 update:5 designed:1 v:1 plo... |
2,732 | 3,478 | Clustering via LP-based Stabilities
Nikos Komodakis
University of Crete
komod@csd.uoc.gr
Nikos Paragios
Ecole Centrale de Paris
INRIA Saclay Ile-de-France
nikos.paragios@ecp.fr
Georgios Tziritas
University of Crete
tziritas@csd.uoc.gr
Abstract
A novel center-based clustering algorithm is proposed in this paper. We ... | 3478 |@word version:1 briefly:1 stronger:1 seek:1 tried:1 paid:1 mention:2 solid:1 hereafter:3 selecting:1 ecole:1 ours:2 interestingly:1 outperforms:1 existing:1 current:10 hpp:2 assigning:1 must:2 update:9 half:1 selected:2 accordingly:1 plane:1 core:2 colored:1 provides:2 detecting:1 accessed:1 along:1 become:1 prov... |
2,733 | 3,479 | MAS: a multiplicative approximation scheme for
probabilistic inference
Christopher Meek
Microsoft Research
Redmond, WA 98052
meek@microsoft.com
Ydo Wexler
Microsoft Research
Redmond, WA 98052
ydow@microsoft.com
Abstract
We propose a multiplicative approximation scheme (MAS) for inference problems
in graphical models... | 3479 |@word repository:1 middle:1 norm:4 simulation:1 r:3 wexler:5 decomposition:41 bn:1 incurs:1 harder:1 initial:2 contains:1 denoting:1 bc:3 existing:3 current:2 com:3 comparing:2 rish:1 yet:2 written:1 dechter:6 partition:4 analytic:1 remove:1 siepel:2 selected:1 xk:2 num:1 provides:2 node:8 five:1 unbounded:1 phyl... |
2,734 | 348 | Generalization Dynamics in
LMS Trained Linear Networks
Yves Chauvin?
Psychology Department
Stanford University
Stanford, CA 94305
Abstract
For a simple linear case, a mathematical analysis of the training and generalization (validation) performance of networks trained by gradient descent
on a Least Mean Square cost f... | 348 |@word nd:1 simulation:9 covariance:5 solid:1 veigend:1 initial:6 wcn:1 realistic:2 numerical:1 provides:1 simpler:1 mathematical:1 direct:1 become:4 weave:1 baldi:1 con0:1 indeed:1 behavior:1 decreasing:4 ote:1 considering:2 becomes:2 provided:4 linearity:1 alto:1 kaufman:2 substantially:1 eigenvector:2 suite:1 de... |
2,735 | 3,480 | Spectral Clustering with Perturbed Data
Ling Huang
Intel Research
Donghui Yan
UC Berkeley
Michael I. Jordan
UC Berkeley
Nina Taft
Intel Research
ling.huang@intel.com
dhyan@stat.berkeley.edu
jordan@cs.berkeley.edu
nina.taft@intel.com
Abstract
Spectral clustering is useful for a wide-ranging set of applications ... | 3480 |@word repository:2 briefly:1 version:4 compression:3 proportion:1 norm:1 d2:1 vldb:2 simulation:1 seek:1 invoking:1 nystr:2 recursively:1 moment:9 reduction:1 contains:1 existing:2 com:2 yet:2 numerical:1 partition:1 treating:1 plot:1 intelligence:2 device:1 short:1 cormode:1 characterization:1 provides:1 brandt:... |
2,736 | 3,481 | Nonparametric sparse hierarchical models
describe V1 fMRI responses to natural images
Pradeep Ravikumar, Vincent Q. Vu and Bin Yu
Department of Statistics
University of California, Berkeley
Berkeley, CA 94720-3860
Thomas Naselaris, Kendrick N. Kay and Jack L. Gallant
Department of Psychology
University of California, ... | 3481 |@word briefly:1 version:1 mri:1 norm:2 valois:2 series:1 contains:1 liu:1 tuned:1 current:1 blank:1 comparing:1 activation:5 must:1 john:1 additive:15 oxygenation:1 shape:1 interpretable:1 stationary:1 alone:1 selected:1 xk:1 provides:5 boosting:2 contribute:2 location:8 successive:3 simpler:1 direct:1 consists:7... |
2,737 | 3,482 | Using matrices to model symbolic relationships
Ilya Sutskever and Geoffrey Hinton
University of Toronto
{ilya, hinton}@cs.utoronto.ca
Abstract
We describe a way of learning matrix representations of objects and relationships.
The goal of learning is to allow multiplication of matrices to represent symbolic
relationsh... | 3482 |@word niece:2 version:7 proportion:2 norm:2 holyoak:1 tried:2 pick:1 minus:3 initial:3 contains:2 series:1 subjective:1 existing:1 yet:1 must:2 written:4 happen:1 enables:1 alone:2 half:1 fewer:1 selected:2 ck2:2 toronto:2 five:2 penelope:2 direct:1 become:2 incorrect:2 consists:2 emma:1 acquired:1 pairwise:1 ra:... |
2,738 | 3,483 | MCBoost: Multiple Classifier Boosting for Perceptual
Co-clustering of Images and Visual Features
Tae-Kyun Kim?
Sidney Sussex College
University of Cambridge
Cambridge CB2 3HU, UK
tkk22@cam.ac.uk
Roberto Cipolla
Department of Engineering
University of Cambridge
Cambridge CB2 1PZ, UK
cipolla@cam.ac.uk
Abstract
We prese... | 3483 |@word middle:2 briefly:1 dalal:1 triggs:1 hu:1 eng:1 jacob:1 moment:1 initial:6 configuration:1 contains:1 score:3 initialisation:10 outperforms:1 existing:2 assigning:1 must:1 visible:3 partition:3 shape:1 update:2 discrimination:3 cue:2 selected:4 prohibitive:1 half:6 intelligence:3 provides:1 boosting:32 contr... |
2,739 | 3,484 | Mind the Duality Gap:
Logarithmic regret algorithms for online optimization
Sham M. Kakade
Toyota Technological Institute at Chicago
sham@tti-c.org
Shai Shalev-Shwartz
Toyota Technological Institute at Chicago
shai@tti-c.org
Abstract
We describe a primal-dual framework for the design and analysis of online
strongly c... | 3484 |@word norm:17 dekel:1 forecaster:1 simplifying:1 configuration:1 current:1 written:1 must:1 chicago:2 update:26 prohibitive:1 warmuth:2 provides:2 org:2 warmup:2 mathematical:1 direct:1 become:2 differential:2 shorthand:1 consists:1 prove:1 manner:1 indeed:1 roughly:1 growing:2 decreasing:1 decomposed:1 increasin... |
2,740 | 3,485 | On the Efficient Minimization of Classification
Calibrated Surrogates
Richard Nock
C EREGMIA ? Univ. Antilles-Guyane
97275 Schoelcher Cedex, Martinique, France
rnock@martinique.univ-ag.fr
Frank Nielsen
L IX - Ecole Polytechnique
91128 Palaiseau Cedex, France
nielsen@lix.polytechnique.fr
Abstract
Bartlett et al (2006... | 3485 |@word repository:1 seems:1 norm:1 logit:6 proportion:1 tedious:1 wla:9 stronger:1 d2:1 open:1 p0:1 pick:4 carry:1 contains:4 score:1 hereafter:1 ecole:1 current:2 dx:1 written:3 readily:1 additive:2 shape:1 analytic:1 wanted:1 asymptote:2 plot:1 depict:1 update:2 half:1 greedy:2 selected:1 warmuth:2 accordingly:1... |
2,741 | 3,486 | Privacy-preserving logistic regression
Kamalika Chaudhuri
Information Theory and Applications
University of California, San Diego
kamalika@soe.ucsd.edu
Claire Monteleoni?
Center for Computational Learning Systems
Columbia University
cmontel@ccls.columbia.edu
Abstract
This paper addresses the important tradeoff betwe... | 3486 |@word private:14 version:3 norm:7 stronger:1 open:1 d2:5 simulation:4 pick:4 incurs:1 initial:1 contains:1 daniel:1 tuned:2 ours:1 outperforms:1 mishra:1 remove:1 ligett:1 smith:3 record:1 lr:3 pointer:1 hypersphere:2 provides:4 kasiviswanathan:1 attack:3 five:4 differential:19 symposium:2 focs:1 prove:3 ex2:1 pr... |
2,742 | 3,487 | Sparse Signal Recovery Using Markov Random Fields
Volkan Cevher
Rice University
volkan@rice.edu
Marco F. Duarte
Rice University
duarte@rice.edu
Chinmay Hegde
Rice University
chinmay@rice.edu
Richard G. Baraniuk
Rice University
richb@rice.edu
Abstract
Compressive Sensing (CS) combines sampling and compression into ... | 3487 |@word briefly:1 polynomial:2 compression:6 stronger:1 norm:3 simulation:2 accounting:1 reduction:1 initial:2 offering:1 outperforms:1 current:6 recovered:1 si:31 starring:1 must:4 written:2 conforming:1 dct:1 numerical:7 partition:1 additive:1 enables:1 v:5 greedy:6 fewer:6 selected:1 lamp:39 leadership:1 fa9550:... |
2,743 | 3,488 | Semi-supervised Learning with Weakly-Related
Unlabeled Data: Towards Better Text Categorization
Liu Yang
Machine Learning Dept.
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213
liuy@cs.cmu.edu
Rong Jin
Dept. of Computer Sci. and Eng.
3115 Engineering Building
Michigan State University
East Lansing, ... | 3488 |@word trial:1 faculty:3 briefly:1 norm:3 d2:1 confirms:1 seek:1 eng:1 dramatic:1 tr:4 carry:1 reduction:1 liu:1 score:1 document:35 outperforms:2 recovered:1 comparing:1 si:3 r01gm079688:1 grain:2 chicago:1 informative:3 analytic:1 gv:1 kyb:1 hypothesize:1 generative:2 selected:4 intelligence:1 ith:3 short:2 prov... |
2,744 | 3,489 | Rademacher Complexity Bounds
for Non-I.I.D. Processes
Mehryar Mohri
Courant Institute of Mathematical Sciences
and Google Research
251 Mercer Street
New York, NY 10012
Afshin Rostamizadeh
Department of Computer Science
Courant Institute of Mathematical Sciences
251 Mercer Street
New York, NY 10012
mohri@cims.nyu.edu
... | 3489 |@word mr2:1 briefly:1 advantageous:1 stronger:1 r:8 tr:3 series:3 contains:1 selecting:1 past:1 existing:3 scovel:1 z2:2 must:3 written:1 stationary:20 leaf:1 ith:1 boosting:2 mcdiarmid:11 mathematical:2 constructed:2 consists:2 expected:1 behavior:1 decreasing:1 actual:1 es0:1 considering:1 bounded:10 linearity:... |
2,745 | 349 | Dynamics of Generalization in Linear Perceptrons
Anders Krogh
Niels Bohr Institute
Blegdamsvej 17
DK-2100 Copenhagen, Denmark
John A. Hertz
NORDITA
Blegdamsvej 17
DK-2100 Copenhagen, Denmark
Abstract
We study the evolution of the generalization ability of a simple linear perceptron with N inputs which learns to imit... | 349 |@word deformed:1 briefly:1 eliminating:1 thereby:1 solid:1 initial:4 imaginary:1 current:1 john:1 additive:1 succeeding:1 imitate:2 slowing:3 inspection:1 ith:1 ron:1 simpler:1 become:1 qualitative:1 frequently:1 examine:1 spherical:1 little:1 increasing:1 becomes:2 what:2 kind:1 pseudo:1 growth:1 exactly:1 schwar... |
2,746 | 3,490 | Learning the Semantic Correlation: An
Alternative Way to Gain from Unlabeled Text
Yi Zhang
Machine Learning Department
Carnegie Mellon University
yizhang1@cs.cmu.edu
Jeff Schneider
The Robotics Institute
Carnegie Mellon University
schneide@cs.cmu.edu
Artur Dubrawski
The Robotics Institute
Carnegie Mellon University
... | 3490 |@word version:2 norm:1 tried:2 covariance:16 contains:1 document:35 outperforms:1 z2:2 comparing:2 dx:2 written:2 must:1 informative:10 hofmann:1 treating:1 resampling:1 v:1 generative:4 selected:2 mccallum:1 ith:1 blei:1 provides:1 org:1 zhang:2 constructed:1 scholkopf:1 introduce:1 indeed:1 ica:1 dist:1 decreas... |
2,747 | 3,491 | Measures of Clustering Quality: A Working Set of
Axioms for Clustering
Margareta Ackerman and Shai Ben-David
School of Computer Science
University of Waterloo, Canada
Abstract
Aiming towards the development of a general clustering theory, we discuss abstract axiomatization for clustering. In this respect, we follow u... | 3491 |@word version:1 polynomial:5 seems:2 contains:1 existing:1 comparing:3 yet:1 readily:2 additive:11 partition:5 predetermined:1 hofmann:1 enables:1 generative:3 selected:1 accordingly:1 completeness:5 coarse:2 lx:3 preference:3 c2:2 prove:1 consists:2 advocate:1 introduce:5 indeed:1 begin:1 notation:1 underlying:4... |
2,748 | 3,492 | Translated Learning: Transfer Learning across
Different Feature Spaces
?
Wenyuan Dai, ? Yuqiang Chen, ? Gui-Rong Xue, ? Qiang Yang and ? Yong Yu
?
Shanghai Jiao Tong University
Shanghai 200240, China
{dwyak,yuqiangchen,grxue,yyu}@apex.sjtu.edu.cn
?
Hong Kong University of Science and Technology
Kowloon, Hong Kong
q... | 3492 |@word multitask:1 kong:3 c0:11 open:2 mammal:2 hunting:1 tuned:1 document:20 outperforms:1 existing:1 com:2 stemmed:1 ust:1 informative:1 v:14 directory:3 codebook:1 cse:1 preference:2 org:2 five:1 constructed:1 combine:1 tagging:1 expected:2 indeed:1 themselves:1 multi:11 muslea:1 little:1 considering:1 totally:... |
2,749 | 3,493 | Nonrigid Structure from Motion in Trajectory Space
Ijaz Akhter
LUMS School of Science and Engineering
Lahore, Pakistan
akhter@lums.edu.pk
Yaser Sheikh
Carnegie Mellon University
Pittsburgh, PA, USA
yaser@cs.cmu.edu
Sohaib Khan
LUMS School of Science and Engineering
Lahore, Pakistan
sohaib@lums.edu.pk
Takeo Kanade
C... | 3493 |@word compression:2 norm:1 zelnik:2 simulation:1 r:1 q1:1 reduction:2 efficacy:1 ours:1 existing:1 recovered:1 michal:1 marquardt:1 takeo:1 dct:9 subsequent:1 numerical:1 shape:42 remove:1 plot:1 v:1 alone:1 plane:1 ith:1 flexing:1 location:3 x1p:1 constructed:1 consists:1 ijcv:4 overhead:1 manner:1 multi:1 decom... |
2,750 | 3,494 | Gaussian-process factor analysis for low-dimensional
single-trial analysis of neural population activity
Byron M. Yu1,2,4 , John P. Cunningham1 , Gopal Santhanam1 ,
Stephen I. Ryu1,3 , Krishna V. Shenoy1,2
1
Department of Electrical Engineering, 2 Neurosciences Program,
3
Department of Neurosurgery, Stanford University... | 3494 |@word trial:21 sgf:1 middle:1 briefly:2 seek:2 rhesus:1 lobe:1 covariance:19 decomposition:1 thereby:1 solid:6 briggman:2 reduction:12 series:6 reaction:1 current:1 comparing:2 ka:1 readily:1 john:1 informative:2 shape:3 motor:5 opin:2 update:1 stationary:7 cue:2 half:1 intelligence:2 sys:2 ith:2 smith:1 regressi... |
2,751 | 3,495 | Weighted Sums of Random Kitchen Sinks: Replacing
minimization with randomization in learning
Paper #858
Abstract
Randomized neural networks are immortalized in this AI Koan:
In the days when Sussman was a novice, Minsky once came to him as he sat
hacking at the PDP-6.
?What are you doing?? asked Minsky. ?I am trainin... | 3495 |@word version:1 norm:3 triggs:1 seek:1 tried:1 attainable:3 thereby:1 boundedness:1 moment:1 contains:1 series:1 tuned:1 existing:1 wd:2 surprising:1 yet:1 additive:1 shape:1 girosi:3 plot:4 intelligence:1 greedy:4 denison:1 shut:1 xk:8 ith:2 provides:1 boosting:2 sigmoidal:2 mcdiarmid:2 zhang:1 mathematical:1 co... |
2,752 | 3,496 | Influence of graph construction on graph-based
clustering measures
Markus Maier
Ulrike von Luxburg
Max Planck Institute for Biological Cybernetics, T?ubingen, Germany
Matthias Hein
Saarland University, Saarbr?ucken, Germany
Abstract
Graph clustering methods such as spectral clustering are defined for general
weighted... | 3496 |@word repository:2 middle:1 suitably:2 open:1 tried:2 decomposition:1 solid:2 reduction:1 pandora:1 denoting:1 ours:1 current:1 comparing:1 ida:1 yet:5 dx:9 assigning:1 visible:1 partition:4 informative:4 kyb:1 plot:5 intelligence:2 node:1 location:1 hyperplanes:8 mcdiarmid:2 saarland:1 mathematical:3 constructed... |
2,753 | 3,497 | Accelerating Bayesian Inference over Nonlinear
Differential Equations with Gaussian Processes
Ben Calderhead
Dept. of Computing Sci.
University of Glasgow
bc@dcs.gla.ac.uk
Mark Girolami
Dept. of Computing Sci.
University of Glasgow
girolami@dcs.gla.ac.uk
Neil D. Lawrence
School of Computer Sci.
University of Manches... | 3497 |@word determinant:2 version:1 seems:2 suitably:1 squid:1 simulation:1 covariance:9 decomposition:1 pg:1 p0:5 incurs:1 dramatic:2 solid:1 initial:12 series:4 denoting:1 bc:1 tuned:1 genetic:1 freitas:1 current:4 mayraz:1 dx:2 must:2 dde:3 written:1 fn:10 numerical:6 partition:1 additive:1 informative:1 tarantola:1... |
2,754 | 3,498 | Predicting the Geometry of Metal Binding Sites from
Protein Sequence
Paolo Frasconi
Universit`a degli Studi di Firenze
Via di S. Marta 3, 50139 Firenze, Italy
p-f@dsi.unifi.it
Andrea Passerini
Universit`a degli Studi di Trento
Via Sommarive, 14, 38100 Povo, Italy
passerini@disi.unitn.it
Abstract
Metal binding is imp... | 3498 |@word version:2 polynomial:1 norm:1 advantageous:1 yi0:8 nd:2 km:2 seek:2 r:7 elisseeff:1 pick:3 necessity:1 contains:1 score:2 terminus:1 interestingly:1 current:5 surprising:1 yet:1 must:1 parsing:1 john:1 subsequent:2 additive:2 hofmann:2 update:3 alone:5 greedy:19 generative:7 core:1 characterization:3 certif... |
2,755 | 3,499 | Clustered Multi-Task Learning:
a Convex Formulation
Laurent Jacob
Mines ParisTech ? CBIO
INSERM U900, Institut Curie
35, rue Saint Honor?e, 77300 Fontainebleau, France
laurent.jacob@mines-paristech.fr
Francis Bach
INRIA ? Willow Project
Ecole Normale Sup?erieure,
45, rue d?Ulm, 75230 Paris, France
francis.bach@mines.o... | 3499 |@word multitask:3 version:2 norm:34 stronger:1 km:1 simulation:1 tried:1 jacob:3 covariance:1 tr:3 contains:2 series:1 ecole:1 denoting:2 outperforms:2 recovered:1 comparing:1 written:2 partition:7 kdd:1 girosi:1 designed:3 e65:1 selected:1 fewer:1 beginning:1 short:2 node:1 preference:2 successive:2 org:1 simple... |
2,756 | 35 | 804
INTRODUCTION TO A SYSTEM FOR IMPLEMENTING NEURAL NET
CONNECTIONS ON SIMD ARCHITECTURES
Sherryl Tomboulian
Institute for Computer Applications in Science and Engineering
NASA Langley Research Center, Hampton VA 23665
ABSTRACT
Neural networks have attracted much interest recently, and using parallel
architectures to... | 35 |@word trial:5 leighton:1 instruction:9 calculus:1 simulation:4 overwritten:1 propagate:2 versatile:1 exclusively:1 existing:2 current:4 router:5 attracted:1 must:16 realize:1 mesh:3 realistic:1 analytic:1 designed:1 intelligence:1 device:2 nervous:1 realizing:2 dissertation:1 provides:1 ire:1 location:1 successive:... |
2,757 | 350 | Simple Spin Models
for the Development of Ocular Dominance
Columns and Iso-Orientation Patches
J.D. Cowan & A.E. Friedman
Department of Mathematics. Committee on
Neurobiology. and Brain Research Institute.
The University of Chicago. 5734 S. Univ. Ave .?
Chicago. Illinois 60637
Abstract
Simple classical spin models we... | 350 |@word soc:2 classical:4 evolution:1 exhibiting:1 alternating:1 stryker:2 disordered:1 gradient:2 material:1 thank:1 shading:2 simulated:4 initial:1 configuration:2 ao:1 avec:1 evident:1 singularity:2 swindale:6 stretch:2 length:2 around:1 considered:1 relationship:1 intercalated:1 exp:2 blasdel:2 chicago:3 tor:1 j... |
2,758 | 3,500 | ICA based on a Smooth Estimation of the Differential
Entropy
Lev Faivishevsky
School of Engineering, Bar-Ilan University
levtemp@gmail.com
Jacob Goldberger
School of Engineering, Bar-Ilan University
goldbej@eng.biu.ac.il
Abstract
In this paper we introduce the MeanNN approach for estimation of main information theor... | 3500 |@word version:2 norm:1 jacob:1 eng:1 mention:3 uma:1 denoting:1 com:1 wd:1 goldberger:1 gmail:1 dx:5 written:1 readily:2 grassberger:2 numerical:6 enables:1 joy:1 plane:2 parametrization:2 provides:1 trinomial:1 mathematical:1 differential:15 symposium:1 interscience:1 inside:1 symp:1 manner:1 introduce:1 x0:1 pa... |
2,759 | 3,501 | Fitted Q-iteration by Advantage Weighted Regression
Gerhard Neumann
Institute for Theoretical Computer Science
Graz University of Technology
A-8010 Graz, Austria
gerhard@igi.tu-graz.ac.at
Jan Peters
Max Planck Institute for Biological Cybernetics
D-72076 T?bingen, Germany
mail@jan-peters.net
Abstract
Recently, fitte... | 3501 |@word trial:2 version:3 simulation:1 carry:1 initial:4 current:3 discretization:1 si:33 yet:1 subsequent:1 numerical:1 additive:1 motor:3 plot:1 update:2 fund:1 greedy:14 intelligence:1 beginning:1 coarse:1 mannor:1 c2:13 become:2 qualitative:1 consists:1 combine:1 introduce:1 forgetting:1 expected:1 behavior:3 f... |
2,760 | 3,502 | Structured Ranking Learning using
Cumulative Distribution Networks
Jim C. Huang
Probabilistic and Statistical Inference Group
University of Toronto
Toronto, ON, Canada M5S 3G4
jim@psi.toronto.edu
Brendan J. Frey
Probabilistic and Statistical Inference Group
University of Toronto
Toronto, ON, Canada M5S 3G4
frey@psi.to... | 3502 |@word norm:1 accounting:2 liu:3 series:2 score:9 document:12 current:2 z2:3 si:2 must:3 readily:1 numerical:1 partition:1 listmle:8 plot:1 update:2 intelligence:1 selected:1 item:2 xk:2 renshaw:1 provides:3 node:41 toronto:6 preference:41 sigmoidal:1 zhang:1 along:2 consists:7 interdependence:1 g4:2 pairwise:19 i... |
2,761 | 3,503 | Simple Local Models for Complex Dynamical Systems
Erik Talvitie
Computer Science and Engineering
University of Michigan
etalviti@umich.edu
Satinder Singh
Computer Science and Engineering
University of Michigan
baveja@umich.edu
Abstract
We present a novel mathematical formalism for the idea of a ?local model? of an
u... | 3503 |@word trial:4 illustrating:1 version:2 briefly:1 manageable:1 seems:1 proportion:2 decomposition:1 homomorphism:1 solid:1 moment:1 configuration:1 contains:1 series:1 prefix:4 o2:2 past:2 current:3 nt:2 yet:1 written:2 must:7 happen:3 partition:6 treating:1 update:8 implying:1 intelligence:7 leaf:1 selected:2 tal... |
2,762 | 3,504 | MDPs with Non-Deterministic Policies
Mahdi Milani Fard
School of Computer Science
McGill University
Montreal, Canada
mmilan1@cs.mcgill.ca
Joelle Pineau
School of Computer Science
McGill University
Montreal, Canada
jpineau@cs.mcgill.ca
Abstract
Markov Decision Processes (MDPs) have been extensively studied and used in... | 3504 |@word trial:2 complying:1 seems:1 seek:1 pick:1 minus:1 initial:1 contains:1 score:2 prescriptive:1 mmilan1:1 current:2 comparing:1 yet:1 john:1 numerical:1 additive:1 remove:1 designed:1 maxv:1 intelligence:1 vmin:3 selected:1 bup:5 provides:3 mannor:1 preference:3 along:3 augmentable:15 constructed:2 qualitativ... |
2,763 | 3,505 | Understanding Brain Connectivity Patterns during
Motor Imagery for Brain-Computer Interfacing
Moritz Grosse-Wentrup
Max Planck Institute for Biological Cybernetics
Spemannstr. 38
72076 T?ubingen, Germany
moritzgw@ieee.org
Abstract
EEG connectivity measures could provide a new type of feature space for inferring
a sub... | 3505 |@word neurophysiology:2 trial:12 determinant:1 middle:1 inversion:1 mri:1 stronger:1 underline:1 tedious:1 cincotti:1 open:1 fatourechi:1 covariance:9 eng:2 minus:1 sychronization:1 reduction:6 series:7 contains:1 interestingly:2 past:1 imaginary:1 current:2 si:11 assigning:1 lang:1 numerical:1 enables:1 motor:50... |
2,764 | 3,506 | Natural Image Denoising with
Convolutional Networks
Viren Jain1
Brain & Cognitive Sciences
Massachusetts Institute of Technology
H. Sebastian Seung1,2
Howard Hughes Medical Institute
Massachusetts Institute of Technology
1
2
Abstract
We present an approach to low-level vision that combines two main ideas: the
use o... | 3506 |@word trial:1 tried:1 rgb:1 decomposition:1 inpainting:1 briggman:1 initial:1 liu:2 tuned:1 abundantly:1 surprising:1 yet:1 must:1 gpu:1 numerical:1 visible:5 realistic:1 partition:3 designed:3 drop:1 update:4 generative:2 greedy:2 fewer:1 intelligence:1 ith:1 feedfoward:1 core:1 provides:3 location:2 firstly:1 f... |
2,765 | 3,507 | Playing Pinball with non-invasive BCI
Michael W. Tangermann
Machine Learning Laboratory
Berlin Institute of Technology
Berlin, Germany
Matthias Krauledat
Machine Learning Laboratory
Berlin Institute of Technology
Berlin, Germany
schroedm@cs.tu-berlin.de
kraulem@cs.tu-berlin.de
Konrad Grzeska
Machine Learning Labor... | 3507 |@word neurophysiology:1 trial:10 cox:1 middle:2 mri:1 briefly:1 stronger:1 nd:1 c0:2 r13:1 decomposition:1 covariance:1 eng:4 shot:13 moment:1 necessity:1 contains:1 score:5 series:1 tuned:1 rightmost:1 subjective:1 reaction:2 current:1 activation:1 must:2 realize:1 chicago:1 motor:11 flipper:2 plot:1 discriminat... |
2,766 | 3,508 | Learning to use Working Memory in Partially
Observable Environments through
Dopaminergic Reinforcement
Michael T. Todd, Yael Niv, Jonathan D. Cohen
Department of Psychology & Princeton Neuroscience Institute
Princeton University, Princeton, NJ 08544
{mttodd,yael,jdc}@princeton.edu
Abstract
Working memory is a central ... | 3508 |@word trial:5 exploitation:1 repository:1 eliminating:1 simulation:4 gradual:1 concise:1 minus:1 series:1 past:2 reaction:1 current:16 must:6 reminiscent:3 distant:2 shape:3 motor:11 update:11 v:4 alone:1 device:3 rts:2 accordingly:1 mccallum:3 short:2 meuleau:2 utile:3 mental:1 provides:1 node:2 preference:4 pos... |
2,767 | 3,509 | Designing neurophysiology experiments to optimally
constrain receptive field models along parametric
submanifolds.
Jeremy Lewi ?
School of Bioengineering
Georgia Institute of Technology
jeremy@lewi.us
Robert Butera
School of Electrical and Computer Engineering
Georgia Institute of Technology
rbutera@ece.gatech.edu
Da... | 3509 |@word neurophysiology:9 trial:18 illustrating:1 middle:1 version:1 stronger:1 simulation:1 covariance:4 decomposition:2 pick:3 recursively:1 reduction:1 initial:1 series:1 past:1 existing:3 outperforms:1 comparing:1 must:1 written:1 numerical:1 happen:1 realistic:2 informative:1 shape:2 plot:4 update:1 fewer:1 pr... |
2,768 | 351 | Connectionist Implementation of a Theory of Generalization
Roger N. Shepard
Sheila Kannappan
Department of Psychology
Stanford University
Stanford, CA 94305-2130
Department of Physics
Harvard University
Cambridge, MA 02138
Abstract
Empirically, generalization between a training and a test stimulus falls off in
clo... | 351 |@word proceeded:1 proportion:2 consequential:12 simulation:7 accounting:1 fonn:2 initial:3 contains:1 genetic:1 current:2 activation:18 si:4 yet:1 must:2 subsequent:1 shape:6 asymptote:1 drop:2 discrimination:10 intelligence:1 tone:1 accordingly:1 slowing:1 beginning:1 mental:1 location:5 successive:2 five:1 heigh... |
2,769 | 3,510 | On the Complexity of Linear Prediction:
Risk Bounds, Margin Bounds, and Regularization
Sham M. Kakade
TTI Chicago
Chicago, IL 60637
sham@tti-c.org
Karthik Sridharan
TTI Chicago
Chicago, IL 60637
karthik@tti-c.org
Ambuj Tewari
TTI Chicago
Chicago, IL 60637
tewari@tti-c.org
Abstract
This work characterizes the genera... | 3510 |@word polynomial:2 norm:21 twelfth:1 d2:3 seek:2 interestingly:5 nt:1 si:7 reminiscent:1 chicago:6 remove:1 update:6 v:1 intelligence:1 warmuth:1 completeness:1 provides:8 boosting:4 ron:1 org:3 zhang:11 along:1 direct:4 symposium:1 prove:1 expected:3 themselves:1 examine:1 little:1 considering:1 provided:8 begin... |
2,770 | 3,511 | Signal-to-Noise Ratio Analysis
of Policy Gradient Algorithms
John W. Roberts and Russ Tedrake
Computer Science and
Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139
Abstract
Policy gradient (PG) reinforcement learning algorithms have strong (local) convergence guarantees, bu... | 3511 |@word trial:7 proportion:1 simulation:2 covariance:3 simplifying:1 pg:9 concise:1 thereby:1 reduction:5 initial:3 series:1 surprising:1 written:2 must:1 john:1 numerical:1 additive:4 shape:2 plot:1 update:38 v:1 intelligence:2 fewer:1 isotropic:1 beginning:1 meuleau:2 jwi:16 zhang:4 five:1 along:1 direct:1 become... |
2,771 | 3,512 | Counting Solution Clusters in Graph Coloring
Problems Using Belief Propagation
Lukas Kroc
Ashish Sabharwal
Bart Selman
Department of Computer Science
Cornell University, Ithaca NY 14853-7501, U.S.A.
{kroc,sabhar,selman}@cs.cornell.edu ?
Abstract
We show that an important and computationally challenging solution spac... | 3512 |@word trial:1 version:1 briefly:1 polynomial:3 closure:1 seek:1 tried:1 decomposition:1 citeseer:1 mention:1 harder:1 nonexistent:1 substitution:1 configuration:2 initial:1 interestingly:1 fa8750:1 written:1 must:3 visible:1 partition:6 happen:1 analytic:1 plot:2 update:1 bart:1 stationary:2 v:6 leaf:4 fewer:1 fa... |
2,772 | 3,513 | Dependence of Orientation Tuning on Recurrent
Excitation and Inhibition in a Network Model of V1
Klaus Wimmer1 * , Marcel Stimberg1 * , Robert Martin1 , Lars Schwabe2 , Jorge Mari?o3 ,
James Schummers4 , David C. Lyon5 , Mriganka Sur4 , and Klaus Obermayer1
1
Bernstein Center for Computational Neuroscience and Technis... | 3513 |@word wiesel:1 stronger:1 seems:2 nd:1 simulation:3 teich:1 accounting:1 solid:1 exclusively:1 mainen:1 tuned:3 current:16 mari:3 comparing:2 contextual:1 yet:1 mst:1 physiol:1 numerical:1 shape:1 motor:1 opin:2 half:1 isotropic:1 parametrization:1 iso:6 short:1 provides:2 location:20 preference:9 consists:1 sust... |
2,773 | 3,514 | From Online to Batch Learning with
Cutoff-Averaging
Anonymous Author(s)
Affiliation
Address
email
Abstract
We present cutoff averaging, a technique for converting any conservative online
learning algorithm into a batch learning algorithm. Most online-to-batch conversion techniques work well with certain types of onli... | 3514 |@word version:2 dekel:2 instruction:1 simplifying:1 contains:2 document:1 prefix:4 outperforms:2 current:1 must:2 happen:2 shawetaylor:1 plot:2 update:4 aside:1 v:20 half:1 beginning:1 short:3 completeness:1 zhang:1 constructed:5 become:1 prove:2 combine:1 manner:1 finitehorizon:1 indeed:1 market:1 expected:3 beh... |
2,774 | 3,515 | Transfer Learning by Distribution Matching
for Targeted Advertising
Steffen Bickel, Christoph Sawade, and Tobias Scheffer
University of Potsdam, Germany
{bickel, sawade, scheffer}@cs.uni-potsdam.de
Abstract
We address the problem of learning classifiers for several related tasks that may
differ in their joint distrib... | 3515 |@word polynomial:1 proportion:4 seek:2 accounting:1 thereby:2 solid:1 tuned:7 outperforms:4 current:3 yet:1 enables:1 resampling:11 discrimination:1 sawade:2 leaf:1 accessed:1 direct:1 shorthand:1 introduce:1 expected:8 market:1 vtt:1 behavior:1 planning:1 multi:7 steffen:1 becomes:1 estimating:3 underlying:1 sur... |
2,775 | 3,516 | Load and Attentional Bayes
Peter Dayan
Gatsby Computational Neuroscience Unit, UCL
London, England, WC1N 3AR
dayan@gatsby.ucl.ac.uk
Abstract
Selective attention is a most intensively studied psychological phenomenon, rife
with theoretical suggestions and schisms. A critical idea is that of limited capacity,
the alloc... | 3516 |@word trial:1 determinant:1 cingulate:1 version:5 eliminating:1 seems:2 compression:1 nd:2 attended:2 extrastriate:1 necessity:2 inefficiency:1 suppressing:1 reynolds:2 past:1 existing:1 reaction:4 current:3 comparing:1 anterior:1 contextual:1 subsequent:1 realistic:1 informative:2 permeated:1 plot:2 generative:1... |
2,776 | 3,517 | Temporal Difference Based Actor Critic Learning Convergence and Neural Implementation
Dotan Di Castro, Dmitry Volkinshtein and Ron Meir
Department of Electrical Engineering
Technion, Haifa 32000, Israel
{dot@tx},{dmitryv@tx},{rmeir@ee}.technion.ac.il
Abstract
Actor-critic algorithms for reinforcement learning are ach... | 3517 |@word version:1 seems:1 instrumental:1 closure:1 simulation:4 solid:1 boundedness:1 delgado:1 initial:3 contains:1 efficacy:1 renewed:1 interestingly:1 envision:1 bc:1 reynolds:1 current:1 optim:1 bd:1 realistic:2 plasticity:8 motor:2 update:12 v:1 stationary:4 intelligence:1 selected:1 xk:3 beginning:1 short:1 p... |
2,777 | 3,518 | The Infinite Factorial Hidden Markov Model
Jurgen Van Gael?
Department of Engineering
University of Cambridge, UK
jv279@cam.ac.uk
Yee Whye Teh
Gatsby Unit
University College London, UK
ywteh@gatsby.ucl.ac.uk
Zoubin Ghahramani
Department of Engineering
University of Cambridge, UK
zoubin@eng.cam.ac.uk
Abstract
We intr... | 3518 |@word version:2 open:1 mibp:11 tried:1 eng:2 asks:2 initial:3 series:8 interestingly:1 past:1 current:1 recovered:3 yet:1 must:2 readily:1 moreno:1 designed:1 generative:3 discovering:1 website:1 selected:1 intelligence:1 inspection:1 inversegamma:1 record:1 provides:2 successive:1 unbounded:1 beta:8 consists:1 c... |
2,778 | 3,519 | Sequential effects: Superstition or rational behavior?
Angela J. Yu
Department of Cognitive Science
University of California, San Diego
ajyu@ucsd.edu
Jonathan D. Cohen
Department of Psychology
Princeton University
jdc@princeton.edu
Abstract
In a variety of behavioral tasks, subjects exhibit an automatic and apparent... | 3519 |@word trial:42 version:1 interleave:1 seems:4 nd:3 instruction:1 p0:9 pick:1 initial:3 born:1 contains:2 ours:1 past:15 reaction:4 current:1 comparing:1 hpp:1 assigning:1 written:1 readily:2 subsequent:2 additive:1 confirming:1 lengthen:1 motor:1 hypothesize:1 designed:1 plot:2 progressively:2 update:1 stationary... |
2,779 | 352 | Kohonen Networks and Clustering: Comparative
Performance in Color Clustering
Wesley Snyder
Department of Radiology
Bowman Gray School of
Medicine
Wake Forest University
Winston-Salem, NC 27103
Daniel Nissman, David Van den Bout,
and Grift BUbro
Center for Communications and Signal Processing
North Carolina State Unive... | 352 |@word sri:1 compression:1 carolina:1 rgb:6 solid:1 initial:6 configuration:1 daniel:1 subjective:1 current:1 comparing:1 com:2 assigning:2 must:1 reproducible:1 update:3 v:1 tenn:1 tone:1 colored:1 conscience:6 provides:1 quantizer:1 codebook:5 x128:1 bowman:1 along:1 direct:1 become:1 symposium:1 manner:1 roughly... |
2,780 | 3,520 | An Extended Level Method for
Efficient Multiple Kernel Learning
Zenglin Xu?
Rong Jin?
Irwin King?
Michael R. Lyu?
?
Dept. of Computer Science & Engineering
Dept. of Computer Science & Engineering
The Chinese University of Hong Kong
Michigan State University
Shatin, N.T., Hong Kong
East Lansing, MI, 48824
{zlxu, king, ... | 3520 |@word kong:3 repository:1 version:1 polynomial:1 advantageous:1 nd:1 km:1 p0:2 elisseeff:1 solid:1 initial:1 series:1 denoting:1 past:4 existing:1 current:8 comparing:2 com:1 surprising:1 john:1 r01gm079688:1 numerical:1 designed:5 plot:2 update:12 selected:2 plane:23 ith:1 boosting:1 cse:3 hyperplanes:1 five:2 m... |
2,781 | 3,521 | Reducing statistical dependencies in natural signals
using radial Gaussianization
Siwei Lyu
Computer Science Department
University at Albany, SUNY
Albany, NY 12222
lsw@cs.albany.edu
Eero P. Simoncelli
Center for Neural Science
New York University
New York, NY 10003
eero@cns.nyu.edu
Abstract
We consider the problem o... | 3521 |@word worsens:1 middle:1 version:3 eliminating:2 seems:1 norm:1 compression:1 nd:1 hyv:1 covariance:3 decomposition:1 solid:1 reduction:15 liu:1 selecting:1 past:1 elliptical:1 comparing:1 scatter:1 dx:1 grassberger:1 numerical:1 distant:3 shape:2 hofmann:1 remove:3 plot:5 selected:2 xk:3 isotropic:1 filtered:6 p... |
2,782 | 3,522 | Extracting State Transition Dynamics from Multiple
Spike Trains with Correlated Poisson HMM
Kentaro Katahira1,2 , Jun Nishikawa2 , Kazuo Okanoya2 and Masato Okada1,2
1
Graduate School of Frontier Sciences The University of Tokyo
Kashiwa, Chiba 277-8561, Japan
2
RIKEN Brain Science Institute
Wako, Saitama 351-0198, Jap... | 3522 |@word trial:9 nd:4 covariance:1 moment:1 initial:2 wako:1 motor:1 treating:4 plot:2 stationary:10 implying:1 selected:17 fewer:1 intelligence:1 xk:1 short:2 transposition:1 psth:2 constructed:2 ik:2 fitting:4 introduce:3 pairwise:3 inter:1 behavior:1 brain:1 automatically:1 xti:1 window:11 estimating:2 moreover:1... |
2,783 | 3,523 | Biasing Approximate Dynamic Programming with a
Lower Discount Factor
Marek Petrik
Department of Computer Science
University of Massachusetts Amherst
Amherst, MA 01003
petrik@cs.umass.edu
Bruno Scherrer
LORIA Campus Scientifique B.P. 239
54506 Vandoeuvre-les-Nancy, France
bruno.scherrer@loria.fr
Abstract
Most algorith... | 3523 |@word illustrating:1 norm:1 contraction:1 solid:1 initial:2 uma:1 score:2 tuned:1 existing:4 current:1 surprising:1 must:1 john:2 ronald:1 enables:1 remove:1 drop:1 greedy:4 fa9550:1 five:1 qualitative:1 prove:1 interscience:1 expected:2 rapid:1 p1:2 bellman:9 discounted:5 decomposed:1 decreasing:2 actual:1 jm:8 ... |
2,784 | 3,524 | Asynchronous Distributed Learning of Topic Models
Arthur Asuncion, Padhraic Smyth, Max Welling
Department of Computer Science
University of California, Irvine
{asuncion,smyth,welling}@ics.uci.edu
Abstract
Distributed learning is a problem of fundamental interest in machine learning and
cognitive science. In this pape... | 3524 |@word middle:5 version:2 briefly:1 open:1 simulation:2 propagate:2 nks:1 xtest:1 pick:1 contains:3 njk:3 zij:10 denoting:1 document:27 current:1 com:2 comparing:1 must:3 realistic:1 remove:2 plot:4 update:1 v:1 newest:1 intelligence:1 generative:1 device:1 half:2 mccallum:2 colored:1 blei:2 provides:1 node:2 five... |
2,785 | 3,525 | Finding Latent Causes in Causal Networks:
an Efficient Approach Based on Markov Blankets
1
Jean-Philippe Pellet 1 ,2
jep@zurich . ibm . com
Pattern Recognition and Machine Learning Group
Swiss Federal Institute of Technology Zurich
8092 Zurich, Switzerland
Andre Elisseeff2
ae l@ zurich.ibm .c om
2 Data Analytics Gro... | 3525 |@word covariance:1 elisseeff:2 initial:1 series:4 contains:1 mag:15 interestingly:1 current:1 com:1 artijiciallntelligence:1 must:3 remove:5 drop:1 unshielded:1 alone:1 greedy:1 fewer:3 prohibitive:1 discovering:2 provides:1 node:16 district:8 constructed:1 direct:7 become:1 descendant:1 prove:2 jly:1 introduce:1... |
2,786 | 3,526 | Multi-stage Convex Relaxation for Learning with
Sparse Regularization
Tong Zhang
Statistics Department
Rutgers University, NJ
tzhang@stat.rutgers.edu
Abstract
We study learning formulations with non-convex regularizaton that are natural for
sparse linear models. There are two approaches to this problem:
? Heuristic m... | 3526 |@word repository:1 version:1 norm:5 simulation:3 pick:1 initial:4 contains:2 current:1 wd:3 nicolai:1 yet:2 attracted:1 numerical:5 partition:1 remove:1 reproducible:2 treating:1 selected:2 boosting:1 complication:1 simpler:1 zhang:2 along:1 direct:1 become:1 prove:1 theoretically:2 peng:1 expected:2 behavior:3 m... |
2,787 | 3,527 | A Massively Parallel Digital
Learning Processor
Hans Peter Graf
hpg@nec-labs.com
Srihari Cadambi
cadambi@nec-labs.com
Igor Durdanovic
igord@nec-labs.com
Venkata Jakkula
Murugan Sankardadass
Eric Cosatto
Srimat Chakradhar
Jakkula@nec-labs.com murugs@nec-labs.com cosatto@nec-labs.com chak@nec-labs.com
NEC Laboratories... | 3527 |@word cnn:7 version:1 polynomial:1 compression:2 achievable:1 nd:1 underline:1 instruction:3 simulation:3 configuration:3 contains:2 score:2 ati:1 com:7 yet:6 chu:1 must:4 gpu:6 designed:2 update:3 sundaram:1 alone:1 half:2 ctu:1 plane:2 core:13 rch:2 provides:1 gx:2 rc:1 dn:1 burst:1 become:2 symposium:1 sustain... |
2,788 | 3,528 | Tracking Changing Stimuli in Continuous Attractor
Neural Networks
C. C. Alan Fung, K. Y. Michael Wong
Department of Physics, The Hong Kong University of Science and Technology,
Clear Water Bay, Hong Kong, China
alanfung@ust.hk, phkywong@ust.hk
Si Wu
Department of Informatics, University of Sussex, Brighton, United King... | 3528 |@word neurophysiology:1 kong:3 polynomial:1 coombes:1 simulation:11 linearized:1 bn:4 excited:1 initial:3 united:1 denoting:1 interestingly:1 reaction:10 current:1 z2:2 hkust:2 si:1 perturbative:4 ust:2 shape:9 analytic:1 v:1 stationary:13 implying:1 sys:1 core:1 sudden:1 mental:1 completeness:1 contribute:1 zhan... |
2,789 | 3,529 | Modeling human function learning
with Gaussian processes
Thomas L. Griffiths Christopher G. Lucas Joseph J. Williams
Department of Psychology
University of California, Berkeley
Berkeley, CA 94720-1650
{tom griffiths,clucas,joseph williams}@berkeley.edu
Michael L. Kalish
Institute of Cognitive Science
University of Lou... | 3529 |@word trial:1 version:1 briefly:1 polynomial:4 seek:1 simulation:1 covariance:8 accounting:1 tr:1 initial:1 cyclic:1 series:1 selecting:1 lqr:2 existing:1 current:4 activation:5 assigning:1 readily:1 additive:1 subsequent:1 xb1:1 extrapolating:1 stationary:1 instantiate:1 parameterization:1 xk:1 smith:1 fa9550:1 ... |
2,790 | 353 | Self-organization of Hebbian Synapses
in Hippocampal Neurons
Thomas H. Brown,t Zachary F. Mainen,t Anthony M. Zador,t and Brenda J. Claiborne?
t Department of Psychology
? Division of Life Sciences
Yale University
University of Texas
New Haven, cr 06511
San Antonio, TX 78285
ABSTRACT
We are exploring the signif... | 353 |@word trial:6 selforganization:1 version:2 jlf:1 advantageous:1 hippocampus:1 simulation:16 fonn:2 solid:2 initial:3 exclusively:1 hereafter:1 mainen:8 efficacy:1 tuned:2 current:3 activation:1 realistic:1 plasticity:1 tenn:1 selected:2 beginning:2 ial:1 colored:1 location:1 sigmoidal:1 ofo:1 become:1 differential... |
2,791 | 3,530 | The Conjoint Effect of Divisive Normalization and
Orientation Selectivity on Redundancy Reduction in
Natural Images
Matthias Bethge
MPI for Biological Cybernetics
72076 T?ubingen, Germany
mbethge@tuebingen.mpg.de
Fabian Sinz
MPI for Biological Cybernetics
72076 T?ubingen, Germany
fabee@tuebingen.mpg.de
Abstract
Band... | 3530 |@word determinant:1 middle:2 compression:1 advantageous:1 norm:10 hyv:4 covariance:1 decomposition:1 carry:1 reduction:28 initial:1 contains:1 score:1 series:2 denoting:1 interestingly:1 existing:1 must:2 written:2 john:1 shape:12 eichhorn:1 remove:1 plot:4 v:2 alone:1 intelligence:1 leaf:1 isotropic:1 iso:1 smit... |
2,792 | 3,531 | Dynamic Visual Attention: Searching for coding
length increments
Xiaodi Hou1,2 and Liqing Zhang1 ?
Department of Computer Science and Engineering, Shanghai Jiao Tong University
No. 800 Dongchuan Road, 200240, China
2
Department of Computation and Neural Systems, California Institute of Technology
MC 136-93, Pasadena, ... | 3531 |@word advantageous:1 seek:1 simulation:1 rgb:1 attended:2 thereby:1 shot:1 initial:1 series:1 contains:2 selecting:1 efficacy:1 foveal:3 reynolds:1 past:1 existing:1 current:1 contextual:1 comparing:2 activation:1 yet:1 dx:1 attracted:1 extraclassical:1 john:1 najemnik:1 mst:1 partition:1 wx:1 informative:1 perti... |
2,793 | 3,532 | Bayesian Exponential Family PCA
Shakir Mohamed
Katherine Heller
Zoubin Ghahramani
Department of Engineering, University of Cambridge
Cambridge, CB2 1PZ, UK
{sm694,kah60,zoubin}@eng.cam.ac.uk
Abstract
Principal Components Analysis (PCA) has become established as one of the
key tools for dimensionality reduction when d... | 3532 |@word determinant:1 repository:1 loading:3 proportion:1 seek:1 simulation:1 eng:1 covariance:2 tr:1 reduction:5 initial:3 configuration:2 score:5 selecting:1 existing:2 freitas:1 comparing:4 written:1 must:4 shape:1 plot:2 generative:2 half:1 sutter:1 hamiltonian:2 toronto:1 become:1 incorrect:1 shorthand:1 consi... |
2,794 | 3,533 | A ?Shape Aware? Model for semi-supervised
Learning of Objects and its Context
Abhinav Gupta1 , Jianbo Shi2 and Larry S. Davis1
Dept. of Computer Science, Univ. of Maryland, College Park
2
Dept. of Computer and Information Sciences, Univ. of Pennsylvania
agupta@cs.umd.edu, jshi@cis.upenn.edu, lsd@cs.umd.edu
1
Abstract... | 3533 |@word nd:2 retraining:1 plsa:1 selecting:1 document:8 outperforms:1 contextual:4 od:2 com:1 informative:4 hofmann:1 shape:46 cue:3 generative:3 selected:4 blei:2 provides:5 iterates:2 location:22 become:1 ijcv:1 combine:3 manner:1 falsely:1 upenn:1 expected:1 inspired:1 freeman:3 resolve:1 zhi:1 provided:4 discov... |
2,795 | 3,534 | Relative Margin Machines
Pannagadatta K Shivaswamy and Tony Jebara
Department of Computer Science, Columbia University, New York, NY
pks2103,jebara@cs.columbia.edu
Abstract
In classification problems, Support Vector Machines maximize the margin
of separation between two classes. While the paradigm has been successful... | 3534 |@word repository:2 briefly:1 inversion:2 polynomial:2 seems:2 version:2 seek:1 tried:1 covariance:2 pick:1 carry:1 exclusively:2 denoting:1 bc:2 tuned:1 interestingly:1 document:1 outperforms:1 recovered:1 comparing:1 scatter:1 readily:1 shape:1 remove:1 plot:3 v:1 alone:1 greedy:1 selected:1 fewer:1 prohibitive:... |
2,796 | 3,535 | On Computational Power and the Order-Chaos
Phase Transition in Reservoir Computing
Benjamin Schrauwen
Electronics and Information Systems Department
Ghent University
B-9000 Ghent, Belgium
benjamin.schrauwen@ugent.be
?
Lars Busing,
Robert Legenstein
Institute for Theoretical Computer Science
Graz University of Technol... | 3535 |@word trial:2 sharpens:1 seems:2 norm:1 busing:1 gradual:2 simulation:2 decomposition:1 solid:3 harder:1 incarnation:1 shot:1 initial:8 electronics:1 series:2 liquid:2 interestingly:1 past:2 comparing:3 written:1 numerical:1 additive:1 plot:9 drop:1 fund:1 implying:1 device:2 short:2 haykin:1 provides:1 quantized... |
2,797 | 3,536 | Implicit Mixtures of Restricted Boltzmann Machines
Vinod Nair and Geoffrey Hinton
Department of Computer Science, University of Toronto
10 King?s College Road, Toronto, M5S 3G5 Canada
{vnair,hinton}@cs.toronto.edu
Abstract
We present a mixture model whose components are Restricted Boltzmann Machines (RBMs). This poss... | 3536 |@word middle:1 proportion:11 willing:1 tried:4 covariance:1 contrastive:5 pick:3 tr:1 initial:2 contains:2 series:1 comparing:1 com:1 surprising:1 activation:5 assigning:1 yet:1 numerical:1 happen:1 visible:18 partition:6 remove:1 plot:1 treating:1 update:3 aside:1 half:1 selected:3 discovering:1 plane:1 ith:2 to... |
2,798 | 3,537 | Clusters and Coarse Partitions in LP Relaxations
David Sontag
CSAIL, MIT
dsontag@csail.mit.edu
Amir Globerson
School of Computer Science and Engineering
The Hebrew University
gamir@cs.huji.ac.il
Tommi Jaakkola
CSAIL, MIT
tommi@csail.mit.edu
Abstract
We propose a new class of consistency constraints for Linear Progra... | 3537 |@word determinant:1 version:4 c0:5 open:1 seek:1 tried:1 recursively:1 initial:1 configuration:4 contains:1 ours:1 current:4 z2:1 yet:1 must:2 finest:1 written:1 subsequent:2 partition:22 remove:1 designed:1 update:5 progressively:1 greedy:2 fewer:1 intelligence:1 amir:1 xk:3 beginning:1 coarse:19 provides:1 node... |
2,799 | 3,538 | Differentiable Sparse Coding
David M. Bradley
Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213
dbradley@cs.cmu.edu
J. Andrew Bagnell
Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213
dbagnell@ri.cmu.edu
Abstract
Prior work has shown that features which appear to be biologically pla... | 3538 |@word version:1 duda:1 advantageous:1 nd:1 norm:4 calculus:1 decomposition:1 lpp:2 citeseer:1 pick:1 sgd:3 contains:2 score:1 selecting:1 document:5 existing:3 bradley:2 com:1 egd:4 must:1 written:1 periodically:1 additive:1 update:3 stationary:1 generative:10 greedy:2 website:1 warmuth:1 xk:1 mccallum:1 ith:3 do... |
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