Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
2,900 | 3,629 | Distribution-Calibrated Hierarchical Classification
Ofer Dekel
Microsoft Research
One Microsoft Way, Redmond, WA 98052, USA
oferd@microsoft.com
Abstract
While many advances have already been made in hierarchical classification learning, we take a step back and examine how a hierarchical classification problem
should b... | 3629 |@word schurmann:1 version:2 dekel:3 open:1 tried:1 recursively:1 hasi:1 moment:1 liu:1 contains:3 loc:1 reduction:9 document:7 prefix:1 subjective:5 africa:1 existing:1 com:1 si:3 assigning:3 ddc:2 must:2 yet:1 john:1 informative:1 hofmann:1 designed:1 depict:1 leaf:6 website:2 accordingly:1 directory:1 mccallum:... |
2,901 | 363 | Modeling Time Varying Systems
Using Hidden Control Neural Architecture
Esther Levin
AT&T Bell Laboratories
Speech Research Department
Murray Hill, NJ 07974 USA
ABSTRACT
Multi-layered neural networks have recently been proposed for nonlinear prediction and system modeling. Although proven successful
for modeling time in... | 363 |@word version:2 covariance:1 fonn:2 series:7 score:1 lapedes:1 past:3 current:1 activation:1 attracted:1 must:2 fn:1 stationary:1 generative:1 selected:1 provides:2 math:1 sigmoidal:2 five:2 consists:2 inside:1 indeed:1 multi:4 actual:1 estimating:2 underlying:2 intennediate:1 argmin:3 unspecified:1 string:3 spoke... |
2,902 | 3,630 | Spatial Normalized Gamma Processes
Yee Whye Teh
Gatsby Computational Neuroscience Unit
University College London
ywteh@gatsby.ucl.ac.uk
Vinayak Rao
Gatsby Computational Neuroscience Unit
University College London
vrao@gatsby.ucl.ac.uk
Abstract
Dependent Dirichlet processes (DPs) are dependent sets of random measures... | 3630 |@word briefly:2 proportion:2 seek:2 kent:2 pick:7 tr:1 initial:1 contains:2 series:1 genetic:1 document:6 interestingly:1 ours:4 rightmost:1 existing:3 current:4 assigning:1 guez:1 additive:1 shape:2 utml:1 plot:1 update:5 v:2 stationary:1 alone:1 half:1 intelligence:2 item:1 parameterization:1 yamada:1 blei:1 lo... |
2,903 | 3,631 | Neurometric function analysis of population codes
Philipp Berens, Sebastian Gerwinn, Alexander S. Ecker and Matthias Bethge
Max Planck Institute for Biological Cybernetics
Center for Integrative Neuroscience, University of T?ubingen
Computational Vision and Neuroscience Group
Spemannstrasse 41, 72076, T?ubingen, Germa... | 3631 |@word trial:2 grey:5 integrative:1 simulation:1 covariance:7 q1:2 tr:1 united:1 tuned:1 ours:1 bradley:1 casas:1 si:4 dx:1 written:1 attracted:1 numerical:1 informative:3 shape:6 enables:1 discrimination:26 v:1 greschner:1 short:22 provides:2 philipp:1 mathematical:1 fitting:2 interscience:1 introduce:1 pairwise:... |
2,904 | 3,632 | White Functionals for Anomaly Detection in
Dynamical Systems
Marco Cuturi
ORFE - Princeton University
mcuturi@princeton.edu
Jean-Philippe Vert
Mines ParisTech, Institut Curie, INSERM U900
Jean-Philippe.Vert@mines.org
Alexandre d?Aspremont
ORFE - Princeton University
aspremon@princeton.edu
Abstract
We propose new me... | 3632 |@word norm:4 extinction:1 c0:1 seek:1 covariance:9 decomposition:3 functions2:1 carry:1 reduction:1 series:10 score:2 selecting:1 tuned:1 rkhs:4 past:2 existing:1 current:1 discretization:1 surprising:1 written:2 timestamps:1 numerical:1 predetermined:1 weyl:1 hypothesize:1 drop:1 plot:2 stationary:10 inspection:... |
2,905 | 3,633 | Semi-supervised Learning in
Gigantic Image Collections
Rob Fergus
Courant Institute, NYU,
715 Broadway,
New York, NY 10003
Yair Weiss
School of Computer Science,
Hebrew University,
91904, Jerusalem, Israel
Antonio Torralba
CSAIL, EECS, MIT,
32 Vassar St.,
Cambridge, MA 02139
fergus@cs.nyu.edu
yweiss@huji.ac.il
to... | 3633 |@word mild:1 pw:1 polynomial:2 seems:1 seek:1 propagate:3 covariance:1 solid:2 score:2 ours:1 outperforms:3 reaction:1 si:1 assigning:1 dx:3 must:3 written:2 griebel:1 numerical:2 shape:1 enables:1 designed:3 plot:1 gist:8 treating:1 v:6 cue:1 prohibitive:1 node:3 toronto:1 along:1 constructed:1 ijcv:2 fitting:1 ... |
2,906 | 3,634 | Strategy Grafting in Extensive Games
Kevin Waugh
waugh@cs.cmu.edu
Department of Computer Science
Carnegie Mellon University
Nolan Bard, Michael Bowling
{nolan,bowling}@cs.ualberta.ca
Department of Computing Science
University of Alberta
Abstract
Extensive games are often used to model the interactions of multiple ag... | 3634 |@word exploitation:2 version:1 private:6 manageable:2 stronger:1 seems:2 szafron:2 decomposition:4 contains:2 exclusively:1 selecting:1 prefix:4 past:2 existing:1 current:5 assigning:1 yet:1 must:6 mesh:3 refines:1 resent:1 partition:17 intelligence:4 fewer:3 advancement:1 item:1 inspection:1 beginning:1 caveat:1... |
2,907 | 3,635 | Asymptotic Analysis of MAP Estimation via the
Replica Method and Compressed Sensing?
Sundeep Rangan
Qualcomm Technologies
Bedminster, NJ
srangan@qualcomm.com
Alyson K. Fletcher
University of California, Berkeley
Berkeley, CA
alyson@eecs.berkeley.edu
Vivek K Goyal
Mass. Inst. of Tech.
Cambridge, MA
vgoyal@mit.edu
Ab... | 3635 |@word mild:1 trial:1 illustrating:1 version:2 achievable:1 norm:9 open:1 simulation:4 decomposition:1 p0:16 solid:1 series:1 mmse:25 multiuser:1 existing:1 com:1 dx:5 readily:1 numerical:4 additive:2 greedy:1 item:1 provides:2 characterization:1 math:1 prove:1 xpx:2 p1:4 cand:2 mechanic:2 behavior:11 actual:2 pro... |
2,908 | 3,636 | Optimal context separation of spiking haptic signals
by second-order somatosensory neurons
Romain Brasselet
CNRS - UPMC Univ Paris 6, UMR 7102
F 75005, Paris, France
romain.brasselet@upmc.fr
Roland S. Johansson
UMEA Univ, Dept Integr Medical Biology
SE-901 87 Umea, Sweden
roland.s.johansson@physiol.umu.se
Angelo Arl... | 3636 |@word trial:2 version:1 johansson:7 nd:5 r:5 pulse:1 accounting:3 initial:1 efficacy:3 selecting:1 mainen:1 past:1 reaction:1 current:2 contextual:2 yet:1 must:1 saal:1 physiol:1 plasticity:6 opin:1 remove:1 plot:2 aps:1 discrimination:38 v:1 implying:1 selected:1 nervous:2 sys:1 short:1 provides:2 revisited:2 lo... |
2,909 | 3,637 | Nonparametric Bayesian Models for Unsupervised
Event Coreference Resolution
Cosmin Adrian Bejan1 , Matthew Titsworth2 , Andrew Hickl2 , & Sanda Harabagiu1
1
Human Language Technology Research Institute, University of Texas at Dallas
2
Language Computer Corporation, Richardson, Texas
ady@hlt.utdallas.edu
Abstract
We pr... | 3637 |@word briefly:1 pw:2 proportion:1 justice:1 adrian:4 closure:1 mibp:20 tried:1 mention:51 initial:2 contains:4 series:2 score:5 selecting:1 daniel:1 tuned:1 document:39 existing:1 current:1 luo:2 john:3 fn:2 realistic:1 enables:1 generative:7 selected:5 discovering:1 intelligence:1 accordingly:1 record:1 blei:1 p... |
2,910 | 3,638 | Indian Buffet Processes with Power-law Behavior
?
Yee Whye Teh and Dilan G?orur
Gatsby Computational Neuroscience Unit, UCL
17 Queen Square, London WC1N 3AR, United Kingdom
{ywteh,dilan}@gatsby.ucl.ac.uk
Abstract
The Indian buffet process (IBP) is an exchangeable distribution over binary matrices used in Bayesian nonp... | 3638 |@word judgement:1 proportion:1 seems:1 c0:1 km:7 simulation:1 tried:9 solid:1 initial:1 configuration:1 united:1 document:20 current:1 assigning:1 reminiscent:3 must:2 analytic:1 plot:1 update:1 intelligence:3 item:2 evy:12 location:1 firstly:2 five:2 unbounded:3 beta:56 fitting:1 introduce:2 subordinators:1 expe... |
2,911 | 3,639 | Data-driven calibration of linear estimators
with minimal penalties
Sylvain Arlot ?
CNRS ; Willow Project-Team
Laboratoire d?Informatique de
l?Ecole Normale Superieure
(CNRS/ENS/INRIA UMR 8548)
23, avenue d?Italie, F-75013 Paris, France
sylvain.arlot@ens.fr
Francis Bach ?
INRIA ; Willow Project-Team
Laboratoire d?Inf... | 3639 |@word mild:1 version:1 norm:5 open:1 km:3 simulation:3 covariance:1 decomposition:2 tr:42 contains:1 series:2 selecting:4 ecole:2 rkhs:2 existing:2 ka:1 comparing:1 written:1 must:1 numerical:1 plot:2 v:1 half:2 selected:5 hfj:1 math:2 detecting:1 zhang:1 replication:2 yuan:1 consists:1 introduce:1 indeed:2 relyi... |
2,912 | 364 | VLSI Implementation of TInMANN
Matt Melton Tan Phan Doug Reeves
Electrical and Computer Engineering Dept.
North Carolina State University
Raleigh, NC 27695-7911
Dave Van den Bout
Abstract
A massively parallel, all-digital, stochastic architecture - TlnMAN N - is
described which performs competitive and Kohonen types... | 364 |@word norm:1 simulation:3 carolina:1 carry:4 err:1 current:3 comparing:1 yet:1 readily:1 additive:2 update:3 selected:2 record:1 conscience:1 provides:1 consists:1 rapid:2 themselves:1 simulator:1 decreasing:1 automatically:1 becomes:1 provided:1 circuit:1 fabricated:1 tie:1 before:1 engineering:1 local:1 accumula... |
2,913 | 3,640 | Manifold Embeddings for Model-Based
Reinforcement Learning under Partial Observability
Keith Bush
School of Computer Science
McGill University
Montreal, Canada
kbush@cs.mcgill.ca
Joelle Pineau
School of Computer Science
McGill University
Montreal, Canada
jpineau@cs.mcgill.ca
Abstract
Interesting real-world datasets ... | 3640 |@word neurophysiology:1 trial:6 achievable:1 hippocampus:1 nd:2 casdagli:1 simulation:8 decomposition:1 initial:2 configuration:7 contains:2 series:4 selecting:2 efficacy:1 existing:1 current:3 discretization:1 surprising:2 yet:1 guez:1 must:2 additive:2 numerical:2 unmask:1 plot:3 succeeding:1 update:1 generativ... |
2,914 | 3,641 | Hierarchical Mixture of Classification Experts
Uncovers Interactions between Brain Regions
Bangpeng Yao1
Dirk B. Walther2
Diane M. Beck2,3?
Li Fei-Fei1?
1
Computer Science Department, Stanford University, Stanford, CA 94305
2
Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL 61801
3
Psychology D... | 3641 |@word mri:1 faculty:1 stronger:1 kriegeskorte:1 uncovers:1 attended:1 thereby:2 solid:1 carry:1 initial:2 liu:1 loc:17 series:1 denoting:1 outperforms:1 existing:4 subjective:1 current:1 comparing:1 surprising:2 luo:1 activation:1 haxby:1 update:1 fund:1 alone:1 generative:3 selected:2 leaf:1 half:1 short:1 menta... |
2,915 | 3,642 | Hierarchical Modeling of Local Image Features
through Lp-Nested Symmetric Distributions
Fabian Sinz
Max Planck Institute for Biological Cybernetics
Spemannstra?e 41
72076 T?ubingen, Germany
fabee@tuebingen.mpg.de
Eero P. Simoncelli
Center for Neural Science, and Courant Institute
of Mathematical Sciences, New York Uni... | 3642 |@word determinant:1 version:1 compression:3 norm:10 open:1 hyv:1 gradual:2 decomposition:1 covariance:1 thereby:1 cgc:23 solid:1 recursively:1 carry:1 reduction:5 contains:1 scatter:1 yet:1 reminiscent:1 must:1 distant:7 partition:9 shape:1 eichhorn:1 plot:2 depict:1 intelligence:2 leaf:7 guess:1 iso:2 ith:1 reco... |
2,916 | 3,643 | Modeling the spacing effect in sequential category
learning
Hongjing Lu
Department of Psychology & Statistics
Hongjing@ucla.edu
Matthew Weiden
Department of Psychology
mweiden@ucla.edu
Alan Yuille
Department of Statistics, Computer Science & Psychology
University of California, Los Angeles
Los Angeles, CA 90095
yuil... | 3643 |@word trial:18 sharpens:1 proportion:3 holyoak:1 willing:2 simulation:4 recursively:1 moment:2 series:1 current:2 comparing:2 contextual:1 surprising:1 must:2 exposing:1 numerical:1 subsequent:2 m1t:9 analytic:3 motor:2 plot:3 update:17 discrimination:2 item:3 fried:1 short:1 preference:2 become:1 massed:29 manne... |
2,917 | 3,644 | Whose Vote Should Count More:
Optimal Integration of Labels from Labelers of
Unknown Expertise
Jacob Whitehill, Paul Ruvolo, Tingfan Wu, Jacob Bergsma, and Javier Movellan
Machine Perception Laboratory
University of California, San Diego
La Jolla, CA, USA
{ jake, paul, ting, jbergsma, movellan }@mplab.ucsd.edu
Abstra... | 3644 |@word trial:6 version:1 proportion:5 instruction:1 essay:1 simulation:7 jacob:2 brochure:1 carry:1 liu:1 score:1 selecting:1 outperforms:2 existing:1 subjective:1 current:1 com:1 activation:2 must:4 cheap:1 drop:1 generative:1 fewer:2 half:2 selected:1 item:8 intelligence:1 inspection:1 ruvolo:1 proficient:1 core... |
2,918 | 3,645 | Bayesian estimation of orientation preference maps
Sebastian Gerwinn
MPI for Biological Cybernetics
and University of T?ubingen
Computational Vision and Neuroscience
Spemannstrasse 41, 72076 T?ubingen
sgerwinn@tuebingen.mpg.de
Jakob H. Macke
MPI for Biological Cybernetics
and University of T?ubingen
Computational Vis... | 3645 |@word trial:4 seems:1 nd:1 km:5 integrative:2 covariance:33 mammal:1 initial:1 series:2 ours:1 past:2 outperforms:3 current:1 comparing:1 written:2 john:1 evans:1 periodically:1 visibility:1 drop:1 designed:1 plot:1 v:1 stationary:1 generative:3 yokoo:1 isotropic:3 oblique:1 colored:1 filtered:1 location:20 prefe... |
2,919 | 3,646 | On the Convergence of the Concave-Convex
Procedure
Bharath K. Sriperumbudur
Department of Electrical and Computer Engineering
University of California, San Diego
La Jolla, CA 92093
bharathsv@ucsd.edu
Gert R. G. Lanckriet
Department of Electrical and Computer Engineering
University of California, San Diego
La Jolla, CA ... | 3646 |@word version:5 briefly:2 stronger:2 underline:1 open:5 closure:2 heiser:1 mention:2 initial:3 bradley:1 current:1 must:1 john:2 numerical:2 happen:1 hofmann:1 update:3 stationary:24 intelligence:2 xk:29 weierstrass:2 provides:2 iterates:1 successive:1 minorization:4 mathematical:4 along:1 constructed:1 direct:2 ... |
2,920 | 3,647 | Beyond Categories: The Visual Memex Model for
Reasoning About Object Relationships
Tomasz Malisiewicz, Alexei A. Efros
Robotics Institute
Carnegie Mellon University
{tmalisie,efros}@cs.cmu.edu
Abstract
The use of context is critical for scene understanding in computer vision, where
the recognition of an object is dri... | 3647 |@word middle:1 nd:2 cola:17 seek:1 tried:1 carolina:2 textonboost:1 pressed:1 tr:6 solid:1 holy:1 configuration:1 contains:2 score:3 liu:1 atlantic:1 current:2 contextual:11 nt:4 parsing:1 john:1 sanjiv:1 partition:1 shape:2 drop:2 plot:1 v:2 grass:3 leaf:1 device:1 item:1 selected:2 lamp:3 core:1 oblique:2 node:... |
2,921 | 3,648 | Sensitivity analysis in HMMs
with application to likelihood maximization
Pierre-Arnaud Coquelin,
Vekia, Lille, France
Romain Deguest?
Columbia University, New York City, NY 10027
pacoquelin@vekia.fr
rd2304@columbia.edu
R?mi Munos
INRIA Lille - Nord Europe, Sequel Project, France
remi.munos@inria.fr
Abstract
This ... | 3648 |@word mild:2 version:1 proportion:1 replicate:1 simulation:1 decomposition:2 eld:2 mention:3 reduction:2 initial:2 series:1 score:11 selecting:1 ecole:1 ours:1 past:1 freitas:1 discretization:1 nitesimal:6 dx:1 written:1 numerical:6 enables:2 designed:2 plot:2 resampling:4 selected:3 cult:1 xk:12 smith:1 provides... |
2,922 | 3,649 | Robust Nonparametric Regression with Metric-Space
valued Output
Matthias Hein
Department of Computer Science, Saarland University
Campus E1 1, 66123 Saarbr?ucken, Germany
hein@cs.uni-sb.de
Abstract
Motivated by recent developments in manifold-valued regression we propose a
family of nonparametric kernel-smoothing est... | 3649 |@word illustrating:1 middle:1 version:3 seems:1 norm:1 km:2 d2:1 simulation:1 decomposition:1 q1:3 moment:1 venkatasubramanian:1 series:1 contains:2 score:3 denoting:1 outperforms:2 jupp:1 expq:1 bie:1 dx:2 written:2 belmont:1 shape:2 hofmann:1 atlas:1 prohibitive:1 inam:1 provides:2 math:4 location:1 saarland:2 ... |
2,923 | 365 | Planning with an Adaptive World Model
Sebastian B. Thrun
German National Research
Center for Computer
Science (GMD)
D-5205 St. Augustin, FRG
Knut Moller
University of Bonn
Department of
Computer Science
D-5300 Bonn, FRG
Alexander Linden
German National Research
Center for Computer
Science (GMD)
D-5205 St. Augustin, ... | 365 |@word retraining:1 nd:1 grey:1 r:1 propagate:1 simulation:1 tr:3 initial:9 configuration:2 past:1 current:9 activation:3 assigning:1 yet:1 must:2 visible:1 subsequent:2 wanted:2 progressively:3 update:1 short:2 firstly:1 org:1 constructed:1 manner:1 behavior:3 elman:1 planning:31 little:1 becomes:1 moreover:2 boun... |
2,924 | 3,650 | Zero-Shot Learning with Semantic Output Codes
Dean Pomerleau
Intel Labs
Pittsburgh, PA 15213
dean.a.pomerleau@intel.com
Mark Palatucci
Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213
mpalatuc@cs.cmu.edu
Tom M. Mitchell
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA 15213
tom... | 3650 |@word trial:1 version:1 norm:1 bf:3 ankle:1 pick:1 asks:1 harder:1 shot:10 contains:2 score:1 selecting:1 hoiem:1 existing:1 com:2 protection:1 must:3 shape:1 bart:2 intelligence:2 selected:2 item:1 p7:1 short:2 provides:2 plaut:2 toronto:3 location:1 five:2 along:2 consists:1 wild:1 behavioral:1 introduce:1 g4:1... |
2,925 | 3,651 | Learning Brain Connectivity of Alzheimer's
Disease from Neuroimaging Data
Shuai Huang 1, Jing Li 1, Liang Sun 2,3, Jun Liu 2,3, Teresa Wu1, Kewei Chen 4,
Adam Fleisher 4, Eric Reiman 4, Jieping Ye 2,3
1
Industrial Engineering, 2Computer Science and Engineering, and 3 Center for Evolutionary
Functional Genomics, The Bi... | 3651 |@word mild:8 determinant:1 mri:3 hippocampus:3 norm:1 lobe:45 covariance:17 pearlson:2 liu:4 contains:2 series:1 selecting:1 genetic:1 existing:1 current:2 com:1 od:1 comparing:1 chordal:1 si:1 activation:1 evans:1 kdd:1 haxby:2 plot:7 cingulum_post_l:4 v:3 asu:2 website:1 selected:1 schapiro:1 record:1 provides:... |
2,926 | 3,652 | Semi-Supervised Learning with the Graph Laplacian:
The Limit of Infinite Unlabelled Data
Boaz Nadler
Dept. of Computer Science and Applied Mathematics
Weizmann Institute of Science
Rehovot, Israel 76100
boaz.nadler@weizmann.ac.il
Nathan Srebro
Toyota Technological Institute
Chicago, IL 60637
nati@uchicago.edu
Xueyuan... | 3652 |@word inversion:1 seems:3 norm:4 calculus:1 bn:1 carry:1 backslash:2 selecting:1 rkhs:3 interestingly:1 rightmost:1 err:1 yet:1 dx:11 must:2 written:2 numerical:5 chicago:3 informative:1 wellbehaved:1 noninformative:1 plot:4 update:1 selected:1 provides:1 location:1 constructed:2 direct:1 become:3 surprised:1 sch... |
2,927 | 3,653 | Breaking Boundaries: Active Information Acquisition
Across Learning and Diagnosis
Ashish Kapoor and Eric Horvitz
Microsoft Research
1 Microsoft Way
Redmond, WA 98052
Abstract
To date, the processes employed for active information acquisition during periods
of learning and diagnosis have been considered as separate an... | 3653 |@word middle:2 version:1 proportion:1 termination:2 seek:6 covariance:2 reduction:5 initial:1 contains:1 selecting:4 united:1 horvitz:2 existing:1 outperforms:2 current:1 past:1 written:3 must:1 partition:1 informative:5 benign:1 enables:2 remove:1 plot:1 greedy:3 selected:1 weighing:1 intelligence:1 mccallum:1 i... |
2,928 | 3,654 | FACTORIE: Probabilistic Programming
via Imperatively Defined Factor Graphs
Andrew McCallum, Karl Schultz, Sameer Singh
Department of Computer Science
University of Massachusetts Amherst
Amherst, MA 01003
{mccallum, kschultz, sameer}@cs.umass.edu
Abstract
Discriminatively trained undirected graphical models have had w... | 3654 |@word sri:1 polynomial:1 open:1 pieter:1 mention:36 tr:1 yih:1 reduction:3 necessity:1 configuration:3 contains:3 uma:2 score:15 initial:1 daniel:1 prefix:1 fa8750:1 existing:2 current:2 comparing:1 com:1 yet:3 written:1 must:4 parsing:2 john:2 distant:1 partition:1 enables:2 remove:3 designed:4 update:1 hash:1 g... |
2,929 | 3,655 | A Bayesian Model for Simultaneous Image
Clustering, Annotation and Object Segmentation
Lan Du, Lu Ren, 1 David B. Dunson and Lawrence Carin
Department of Electrical and Computer Engineering
1
Statistics Department
Duke University
Durham, NC 27708-0291, USA
{ld53, lr, lcarin}@ee.duke.edu, dunson@stats.duke.edu
Abstrac... | 3655 |@word middle:1 plsa:1 cml:10 seek:2 rgb:1 accounting:1 contains:1 score:1 tuned:1 outperforms:1 freitas:1 com:1 assigning:1 written:1 wiewiora:1 plm:1 hofmann:1 remove:2 treating:1 ainen:1 grass:4 generative:5 selected:3 website:2 half:1 parameterization:1 accordingly:1 lr:1 blei:6 provides:1 codebook:2 location:... |
2,930 | 3,656 | Functional network reorganization in motor cortex
can be explained by reward-modulated Hebbian
learning
Robert Legenstein1?, Steven M. Chase2,3,4 , Andrew B. Schwartz2,3 , Wolfgang Maass1
1
Institute for Theoretical Computer Science, Graz University of Technology, Austria
2
Department of Neurobiology, University of Pi... | 3656 |@word trial:7 briefly:1 version:5 fiete:1 stronger:3 norm:3 simulation:16 covariance:3 reduction:3 initial:2 efficacy:4 exclusively:1 tuned:2 interestingly:1 imaginary:5 current:1 comparing:2 ka:1 surprising:1 neurophys:1 si:3 activation:9 realistic:4 visible:2 subsequent:1 plasticity:12 enables:1 motor:34 drop:1... |
2,931 | 3,657 | Dirichlet-Bernoulli Alignment: A Generative Model
for Multi-Class Multi-Label Multi-Instance Corpora
Shuang-Hong Yang
College of Computing
Georgia Tech
shy@gatech.edu
Hongyuan Zha
College of Computing
Georgia Tech
zha@cc.gatech.edu
Bao-Gang Hu
NLPR & LIAMA
Chinese Academy of Sciences
hubg@nlpr.ia.ac.cn
Abstract
We ... | 3657 |@word repository:2 version:1 briefly:1 eliminating:1 advantageous:2 proportion:1 hu:2 heuristically:1 dba:54 reduction:3 contains:5 score:5 tuned:1 document:24 outperforms:1 com:1 luo:1 assigning:1 dx:1 bd:1 grain:1 kdd:1 hofmann:1 enables:1 treating:2 generative:7 discovering:1 selected:1 unacceptably:1 intellig... |
2,932 | 3,658 | Positive Semidefinite Metric Learning with Boosting
Chunhua Shen?? , Junae Kim?? , Lei Wang? , Anton van den Hengel?
?
NICTA Canberra Research Lab, Canberra, ACT 2601, Australia?
?
Australian National University, Canberra, ACT 0200, Australia
?
The University of Adelaide, Adelaide, SA 5005, Australia
Abstract
The lea... | 3658 |@word mild:1 version:1 briefly:1 polynomial:1 norm:1 d2:1 heuristically:1 decomposition:3 p0:2 tr:8 shot:1 accommodate:2 etric:40 efficacy:1 zij:1 denoting:1 tuned:1 psdboost:10 outperforms:1 existing:3 current:1 com:2 optim:1 goldberger:1 must:3 nonnegativeness:1 designed:1 drop:1 update:2 plot:1 v:5 half:1 proh... |
2,933 | 3,659 | Abstraction and relational learning
Charles Kemp & Alan Jern
Department of Psychology
Carnegie Mellon University
{ckemp,ajern}@cmu.edu
Abstract
Most models of categorization learn categories de?ned by characteristic features
but some categories are described more naturally in terms of relations. We present
a generativ... | 3659 |@word version:1 replicate:1 nd:3 holyoak:4 d2:14 arti:1 shot:10 contains:1 existing:1 comparing:2 must:6 planet:1 subsequent:1 arrayed:1 shape:1 plot:6 designed:1 generative:9 intelligence:1 imitate:1 cult:1 short:1 core:1 provides:2 preference:2 simpler:1 mathematical:1 along:20 dragged:1 combine:1 behavioral:2 ... |
2,934 | 366 | Learning to See Rotation and
Dilation with a Hebb Rule
Martin I. Sereno and Margaret E. Sereno
Cognitive Science D-015
University of California, San Diego
La Jolla, CA 92093-0115
Abstract
Previous work (M.I. Sereno, 1989; cf. M.E. Sereno, 1987) showed that a
feedforward network with area VI-like input-layer units and ... | 366 |@word suitably:1 mammal:1 shading:1 contains:2 foveal:1 tuned:1 clash:1 surprising:1 activation:2 must:2 written:1 reminiscent:1 interrupted:1 realistic:1 mst:5 shape:1 seelen:1 update:1 infant:1 half:1 beginning:1 provides:1 location:9 successive:1 height:1 along:1 constructed:3 interlayer:2 growing:1 simulator:1... |
2,935 | 3,660 | Discriminative Network Models of Schizophrenia
Guillermo A. Cecchi, Irina Rish
IBM T. J. Watson Research Center
Yorktown Heights, NY, USA
Marion Plaze
INSERM - CEA - Univ. Paris Sud
Research Unit U.797
Neuroimaging & Psychiatry
SHFJ & Neurospin, Orsay, France
Catherine Martelli
Departement de Psychiatrie
et d?Addictol... | 3660 |@word trial:2 determinant:1 exploitation:1 middle:3 norm:3 stronger:1 pearlson:1 covariance:7 decomposition:1 tr:2 reduction:1 liu:3 contains:2 series:4 selecting:2 outperforms:1 rish:1 current:1 anterior:2 comparing:1 aberrant:1 activation:77 written:1 must:2 grain:1 visible:1 informative:4 oxygenation:1 plastic... |
2,936 | 3,661 | Analysis of SVM with Indefinite Kernels
Yiming Ying? , Colin Campbell? and Mark Girolami?
?Department of Engineering Mathematics, University of Bristol,
Bristol BS8 1TR, United Kingdom
?Department of Computer Science, University of Glasgow,
S.A.W. Building, G12 8QQ, United Kingdom
Abstract
The recent introduction of ... | 3661 |@word repository:2 kondor:1 version:3 norm:4 decomposition:5 covariance:2 q1:29 tr:4 score:1 united:2 tuned:1 com:2 attracted:2 written:1 partition:1 analytic:3 plot:1 intelligence:2 plane:3 realizing:1 characterization:1 herbrich:1 zhang:1 mathematical:1 laub:1 prove:3 introductory:1 introduce:2 blast:1 pairwise... |
2,937 | 3,662 | Variational Inference for the
Nested Chinese Restaurant Process
Chong Wang
Computer Science Department
Princeton University
David M. Blei
Computer Science Department
Princeton University
chongw@cs.princeton.edu
blei@cs.princeton.edu
Abstract
The nested Chinese restaurant process (nCRP) is a powerful nonparametric
B... | 3662 |@word cox:1 polynomial:1 compression:2 proportion:6 loading:1 decomposition:1 tr:4 accommodate:1 contains:7 wcn:5 series:1 genetic:1 document:6 ecole:1 csn:2 current:2 written:2 must:1 partition:3 update:1 bart:1 stationary:1 generative:1 discovering:1 selected:1 item:1 greedy:2 leaf:1 fewer:1 blei:5 provides:2 n... |
2,938 | 3,663 | Structural inference affects depth perception in the
context of potential occlusion
Ian H. Stevenson and Konrad P. K?ording
Department of Physical Medicine and Rehabilitation
Northwestern University
Chicago, IL 60611
i-stevenson@northwestern.edu
Abstract
In many domains, humans appear to combine perceptual cues in a ... | 3663 |@word neurophysiology:1 trial:9 approved:1 accounting:1 irb:1 solid:1 shading:2 disparity:19 ording:3 past:1 subjective:1 current:1 nt:1 refresh:1 chicago:1 shape:3 analytic:2 motor:1 visibility:1 designed:1 cue:48 selected:1 generative:1 nervous:2 beginning:1 location:1 ladendorf:1 along:1 constructed:1 direct:2... |
2,939 | 3,664 | Efficient Recovery of Jointly Sparse Vectors
Liang Sun, Jun Liu, Jianhui Chen, Jieping Ye
School of Computing, Informatics, and Decision Systems Engineering
Arizona State University
Tempe, AZ 85287
{sun.liang,j.liu,jianhui.chen,jieping.ye}asu.edu
Abstract
We consider the reconstruction of sparse signals in the multip... | 3664 |@word neurophysiology:1 polynomial:1 norm:15 hu:11 simulation:4 p0:3 tr:6 delgado:1 liu:2 series:1 interestingly:2 ati:2 past:2 existing:7 outperforms:1 ka:4 must:1 greedy:2 asu:1 fewer:1 accordingly:1 xk:1 huo:1 ith:3 core:1 location:1 mathematical:1 dn:4 constructed:1 become:1 kak22:4 consists:3 prove:1 p1:4 ex... |
2,940 | 3,665 | A Neural Implementation of the Kalman Filter
Leif H. Finkel
Department of Bioengineering
University of Pennsylvania
Philadelphia, PA 19103
Robert C. Wilson
Department of Psychology
Princeton University
Princeton, NJ 08540
rcw2@princeton.edu
Abstract
Recent experimental evidence suggests that the brain is capable of ... | 3665 |@word neurophysiology:1 cu:1 version:1 briefly:1 proportion:1 seems:1 open:1 simulation:5 crucially:1 gradual:1 covariance:2 wjf:2 configuration:1 series:1 interestingly:2 ranck:2 current:6 z2:1 erms:1 activation:4 yet:1 intriguing:1 must:2 written:1 shape:3 analytic:1 remove:1 plot:6 update:4 v:1 alone:1 cue:6 h... |
2,941 | 3,666 | Distribution Matching for Transduction
Novi Quadrianto
RSISE, ANU & SML, NICTA
Canberra, ACT, Australia
novi.quad@gmail.com
James Petterson
RSISE, ANU & SML, NICTA
Canberra, ACT, Australia
james.petterson@nicta.com.au
Alex J. Smola
Yahoo! Research
Santa Clara, CA, USA
alex@smola.org
Abstract
Many transductive infer... | 3666 |@word rreg:1 repository:3 polynomial:1 norm:2 smirnov:1 yi0:2 achievable:1 additively:1 p0:7 pick:2 sgd:1 moment:1 reduction:1 initial:1 contains:1 score:9 exclusively:2 tuned:1 ours:2 document:1 past:1 existing:3 outperforms:3 current:1 com:2 comparing:1 surprising:1 clara:1 gmail:1 written:1 numerical:1 update:... |
2,942 | 3,667 | Learning to Hash with Binary Reconstructive
Embeddings
Brian Kulis and Trevor Darrell
UC Berkeley EECS and ICSI
Berkeley, CA
{kulis,trevor}@eecs.berkeley.edu
Abstract
Fast retrieval methods are increasingly critical for many large-scale analysis tasks,
and there have been several recent methods that attempt to learn ... | 3667 |@word kulis:3 repository:1 nkb:1 norm:5 underperform:1 vldb:1 seitz:1 covariance:1 recursively:1 reduction:1 configuration:1 contains:1 selecting:1 outperforms:4 existing:9 comparing:2 surprising:1 beygelzimer:1 must:2 readily:1 written:1 partition:1 plot:2 gist:6 update:19 hash:58 generative:2 selected:2 fewer:1... |
2,943 | 3,668 | A Sparse Non-Parametric Approach for Single
Channel Separation of Known Sounds
Paris Smaragdis
Adobe Systems Inc.
paris@adobe.com
Madhusudana Shashanka
Mars Inc.
shashanka@alum.bu.edu
Bhiksha Raj
Carnegie Mellon University
bhiksha@cs.cmu.edu
Abstract
In this paper we present an algorithm for separating mixed sounds... | 3668 |@word middle:2 stronger:1 simulation:2 kristjansson:1 decomposition:4 thereby:2 series:1 bc:1 outperforms:3 recovered:1 com:1 written:1 transcendental:1 realistic:3 additive:1 remove:1 plot:14 update:1 alone:1 half:1 desktop:1 cursory:1 short:1 gribonval:2 characterization:5 provides:3 complication:1 direct:1 com... |
2,944 | 3,669 | Large Scale Nonparametric Bayesian Inference:
Data Parallelisation in the Indian Buffet Process
Finale Doshi-Velez?
University of Cambridge
Cambridge, CB21PZ, UK
finale@alum.mit.edu
David Knowles?
University of Cambridge
Cambridge, CB21PZ, UK
dak33@cam.ac.uk
Shakir Mohamed?
University of Cambridge
Cambridge, CB21PZ, ... | 3669 |@word middle:1 proportion:1 hairiness:7 open:1 indiscriminate:1 twelfth:1 simulation:2 tried:2 propagate:1 eng:1 covariance:2 solid:2 initial:1 configuration:1 series:1 existing:1 current:3 comparing:1 chu:1 must:2 readily:2 partition:1 shape:1 enables:1 plot:3 exploded:1 update:1 polyphonic:1 resampling:5 genera... |
2,945 | 367 | Relaxation Networks for Large Supervised Learning Problems
Joshua Alspector Robert B. Allen Anthony Jayakumar
Torsten Zeppenfeld and Ronny Meir
Bellcore
Morristown, NJ 07962-1910
Abstract
Feedback connections are required so that the teacher signal on the output
neurons can modify weights during supervised learning. ... | 367 |@word torsten:1 version:2 sharpens:1 simulation:9 pg:2 electronics:1 contains:1 current:3 surprising:1 activation:4 si:1 yet:1 chu:1 plot:1 designed:2 update:3 accordingly:1 steepest:1 node:1 accessed:1 along:1 direct:2 supply:2 replication:8 microchip:3 xji:1 roughly:2 alspector:6 decreasing:1 actual:2 linearity:... |
2,946 | 3,670 | Multi-step Linear Dyna-style Planning
Hengshuai Yao
Department of Computing Science
University of Alberta
Edmonton, AB, Canada T6G2E8
Shalabh Bhatnagar
Department of Computer Science
and Automation
Indian Institute of Science
Bangalore, India 560012
Dongcui Diao
School of Economics and Management
South China Normal ... | 3670 |@word version:1 inversion:2 advantageous:2 hu:1 tried:1 initial:2 contains:2 interestingly:1 past:2 existing:5 imaginary:4 current:2 enables:1 cheap:1 update:2 fund:1 stationary:2 greedy:13 selected:3 intelligence:1 beginning:1 ith:1 iterates:1 location:1 along:1 become:1 manner:3 introduce:2 forgetting:1 expecte... |
2,947 | 3,671 | Subject independent EEG-based BCI decoding
Siamac Fazli
Cristian Grozea
M?arton Dan?oczy
Florin Popescu
Benjamin Blankertz
Klaus-Robert M?uller
Abstract
In the quest to make Brain Computer Interfacing (BCI) more usable, dry electrodes have emerged that get rid of the initial 30 minutes required for placing an
electro... | 3671 |@word trial:19 version:1 r13:1 heuristically:1 covariance:1 eng:4 decomposition:1 cleary:1 initial:2 series:2 score:1 exclusively:2 tuned:4 interestingly:3 existing:1 comparing:1 activation:1 must:1 subsequent:1 enables:1 motor:5 remove:1 designed:1 discrimination:1 v:3 cue:3 selected:5 half:1 device:1 xk:4 sys:1... |
2,948 | 3,672 | Replacing supervised classification learning by
Slow Feature Analysis in spiking neural networks
Stefan Klampfl, Wolfgang Maass
Institute for Theoretical Computer Science
Graz University of Technology
A-8010 Graz, Austria
{klampfl,maass}@igi.tugraz.at
Abstract
It is open how neurons in the brain are able to learn wit... | 3672 |@word trial:1 version:3 compression:1 cochleagram:2 open:1 simulation:5 tried:1 covariance:10 p0:1 thereby:2 solid:1 ld:4 moment:1 initial:2 series:26 liquid:1 current:1 scatter:3 written:1 realistic:1 subsequent:1 plasticity:1 shape:2 enables:1 plot:6 fund:1 discrimination:2 stationary:1 selected:2 filtered:3 pr... |
2,949 | 3,673 | Multi-label Prediction via Sparse Infinite CCA
Piyush Rai and Hal Daum?e III
School of Computing, University of Utah
{piyush,hal}@cs.utah.edu
Abstract
Canonical Correlation Analysis (CCA) is a useful technique for modeling dependencies between two (or more) sets of variables. Building upon the recently suggested prob... | 3673 |@word multitask:9 repository:1 wiesel:1 seems:1 twelfth:1 open:1 d2:8 seek:1 covariance:6 decomposition:1 thereby:2 reduction:20 uncovered:1 efficacy:1 score:2 selecting:1 document:1 past:1 existing:5 comparing:1 readily:1 wx:8 kdd:1 treating:1 interpretable:1 rd2:2 zik:1 alone:3 generative:2 discovering:2 greedy... |
2,950 | 3,674 | Unsupervised feature learning for audio classification
using convolutional deep belief networks
Honglak Lee
Yan Largman
Peter Pham
Computer Science Department
Stanford University
Stanford, CA 94305
Andrew Y. Ng
Abstract
In recent years, deep learning approaches have gained significant interest as a
way of building ... | 3674 |@word trial:6 middle:1 briefly:1 crbms:3 contrastive:2 mammal:1 harder:1 series:1 score:1 selecting:1 reynolds:3 comparing:1 activation:4 si:1 written:1 visible:8 informative:1 shape:1 moreno:2 treating:1 interpretable:1 generative:2 greedy:3 selected:9 inspection:1 smith:1 core:1 provides:1 location:1 five:11 be... |
2,951 | 3,675 | Efficient and Accurate `p-Norm
Multiple Kernel Learning
Marius Kloft
University of California
Berkeley, USA
Pavel Laskov
Universit?at T?ubingen
T?ubingen, Germany
Ulf Brefeld
Yahoo! Research
Barcelona, Spain
?
Klaus-Robert Muller
Technische Universit?at Berlin
Berlin, Germany
S?oren Sonnenburg
Technische Universit?a... | 3675 |@word version:1 norm:48 km:8 simulation:1 decomposition:1 pavel:1 thereby:1 tr:1 kwm:1 moment:1 reduction:1 substitution:1 contains:1 initial:2 selecting:1 wrapper:3 interestingly:1 outperforms:1 existing:3 recovered:1 wherefore:1 written:1 subsequent:1 numerical:1 informative:1 remove:1 update:1 v:2 plane:6 olhe... |
2,952 | 3,676 | Potential-Based Agnostic Boosting
Varun Kanade
Harvard University
vkanade@fas.harvard.edu
Adam Tauman Kalai
Microsoft Research
adum@microsoft.com
Abstract
We prove strong noise-tolerance properties of a potential-based boosting algorithm, similar to MadaBoost (Domingo and Watanabe, 2000) and SmoothBoost
(Servedio, 2... | 3676 |@word repository:2 version:1 polynomial:3 stronger:2 nd:2 suitably:1 twelfth:1 bylander:1 hu:2 km:1 pick:2 err:9 current:5 com:1 nt:1 bradley:1 must:1 additive:3 realistic:1 happen:1 enables:1 drop:1 intelligence:2 leaf:1 boosting:56 simpler:1 c2:1 constructed:1 symposium:3 focs:1 prove:3 symp:1 manner:1 multi:1 ... |
2,953 | 3,677 | L1-Penalized Robust Estimation for a Class of Inverse
Problems Arising in Multiview Geometry
Arnak S. Dalalyan and Renaud Keriven
IMAGINE/LabIGM,
Universit?e Paris Est - Ecole des Ponts ParisTech,
Marne-la-Vall?ee, France
dalalyan,keriven@imagine.enpc.fr
Abstract
We propose a new approach to the problem of robust est... | 3677 |@word mild:1 illustrating:1 norm:22 proportion:1 c0:6 proportionality:1 covariance:1 simplifying:1 decomposition:1 initial:1 substitution:1 contains:1 hereafter:2 ecole:1 seriously:1 interestingly:1 kahl:6 existing:2 ka:1 enpc:1 optim:1 scatter:1 written:2 numerical:3 visible:1 realistic:1 shape:1 plot:2 juditsky... |
2,954 | 3,678 | Learning Bregman Distance Functions and Its
Application for Semi-Supervised Clustering
?
Lei Wu?] , Rong Jin? , Steven C.H. Hoi? , Jianke Zhu\ , and Nenghai Yu]
School of Computer Engineering, Nanyang Technological University, Singapore
?
Department of Computer Science & Engineering, Michigan State University
\
Compu... | 3678 |@word kulis:1 repository:1 version:1 seems:1 euclidian:1 initial:2 liu:4 efficacy:1 score:3 outperforms:4 existing:4 current:1 comparing:3 com:1 contextual:1 goldberger:1 si:1 must:2 written:1 partition:1 kdd:1 update:3 stationary:1 intelligence:1 xk:1 boosting:3 location:1 mathematical:1 differential:1 consists:... |
2,955 | 3,679 | Toward Provably Correct Feature Selection in
Arbitrary Domains
Dimitris Margaritis
Department of Computer Science
Iowa State University
Ames, IA 50010, USA
dmarg@cs.iastate.edu
Abstract
In this paper we address the problem of provably correct feature selection in arbitrary domains. An optimal solution to the problem ... | 3679 |@word repository:1 middle:3 version:6 motoda:2 decomposition:13 contraction:12 invoking:1 paid:1 elisseeff:2 moment:1 wrapper:3 liu:4 contains:4 series:1 selecting:3 john:3 interrupted:2 subsequent:1 partition:1 plot:3 update:1 v:3 intelligence:4 selected:2 monk:2 provides:1 consulting:1 completeness:1 ames:1 dap... |
2,956 | 368 | A four neuron circuit accounts for change sensitive
inhibition in salamander retina
Jeffrey L. Teeters
Lawrence Livennore Lab
PO Box 808, L-426
Livennore CA 94550
Frank H. Eeckman
Lawrence Livennore Lab
PO Box 808, L-270
Livennore CA 94550
Frank S. Werblin
UC-Berkeley
Room 145, LSA
Berkeley CA 94720
Abstract
In sala... | 368 |@word simulation:6 reduction:2 current:8 yet:2 physiol:2 hyperpolarizing:1 stationary:6 height:1 constructed:1 direct:2 sustained:3 pathway:1 manner:1 roughly:1 brain:1 terminal:8 vertebrate:1 underlying:1 circuit:15 depolarization:1 temporal:2 berkeley:2 hypothetical:1 bipolar:22 control:1 unit:1 underlie:1 lsa:1... |
2,957 | 3,680 | Unsupervised Detection of Regions of Interest
Using Iterative Link Analysis
Gunhee Kim
School of Computer Science
Carnegie Mellon University
gunhee@cs.cmu.edu
Antonio Torralba
Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology
torralba@csail.mit.edu
Abstract
This paper prop... | 3680 |@word advantageous:1 reused:1 open:2 widom:1 pick:1 initial:7 liu:1 score:3 shum:1 ours:10 rightmost:1 shape:1 drop:3 plot:1 update:1 discrimination:1 intelligence:7 selected:16 guess:1 discovering:1 beginning:1 harvesting:1 detecting:1 provides:1 node:5 location:1 org:1 five:3 become:1 consists:3 inside:1 manner... |
2,958 | 3,681 | Sparse Estimation Using General Likelihoods and
Non-Factorial Priors
David Wipf and Srikantan Nagarajan, ?
Biomagnetic Imaging Lab, UC San Francisco
{david.wipf, sri}@mrsc.ucsf.edu
Abstract
Finding maximally sparse representations from overcomplete feature dictionaries
frequently involves minimizing a cost function co... | 3681 |@word trial:3 briefly:1 sri:1 manageable:1 norm:13 calculus:1 willing:1 simulation:4 seek:1 covariance:1 simplifying:1 harder:1 contains:1 efficacy:1 selecting:1 tuned:1 existing:2 current:1 yet:1 dx:1 must:3 readily:1 assigning:1 subsequent:2 cheap:1 mrsc:1 remove:1 designed:1 update:14 plot:1 stationary:1 greed... |
2,959 | 3,682 | Particle-based Variational Inference
for Continuous Systems
Alexander T. Ihler
Dept. of Computer Science
Univ. of California, Irvine
ihler@ics.uci.edu
Andrew J. Frank
Dept. of Computer Science
Univ. of California, Irvine
ajfrank@ics.uci.edu
Padhraic Smyth
Dept. of Computer Science
Univ. of California, Irvine
smyth@i... | 3682 |@word trial:2 version:2 manageable:1 underst:1 nd:1 open:1 confirms:1 simulation:1 mxt:2 carry:2 configuration:5 series:2 loeliger:1 current:4 discretization:7 surprising:1 yet:1 chu:1 must:1 numerical:1 partition:7 pseudomarginals:4 enables:1 remove:1 plot:1 interpretable:1 resampling:2 half:1 prohibitive:1 assu... |
2,960 | 3,683 | Skill Discovery in Continuous Reinforcement
Learning Domains using Skill Chaining
Andrew Barto
Computer Science Department
University of Massachusetts Amherst
Amherst MA 01003 USA
barto@cs.umass.edu
George Konidaris
Computer Science Department
University of Massachusetts Amherst
Amherst MA 01003 USA
gdk@cs.umass.edu
... | 3683 |@word trial:1 proceeded:1 polynomial:1 seems:1 nd:3 termination:6 simulation:1 decomposition:1 incurs:2 thereby:2 initial:8 lightweight:2 uma:3 lqr:2 ours:1 existing:1 pickett:1 assigning:1 must:5 john:1 designed:2 treating:1 update:4 plot:1 stationary:1 intelligence:4 selected:1 fewer:1 betweenness:1 discovering... |
2,961 | 3,684 | Manifold Regularization for SIR with Rate Root-n
Convergence
Wei Bian
School of Computer Engineering
Nanyang Technological University
Singapore, 639798
weibian@pmail.ntu.edu.sg
Dacheng Tao
School of Computer Engineering
Nanyang Technological University
Singapore, 639798
dctao@ntu.edu.sg
Abstract
In this paper, we st... | 3684 |@word mild:1 d2:1 seek:1 git:1 covariance:3 decomposition:4 euclidian:2 ld:1 moment:1 reduction:13 liu:1 contains:1 existing:2 current:1 enables:2 xdx:2 guess:1 item:1 cook:1 xk:3 smith:1 record:1 five:1 along:3 c2:1 constructed:2 edelman:1 prove:6 pairwise:1 discretized:1 xti:15 considering:1 deem:1 project:2 xx... |
2,962 | 3,685 | Sharing Features among Dynamical Systems
with Beta Processes
Emily B. Fox
Electrical Engineering & Computer Science, Massachusetts Institute of Technology
ebfox@mit.edu
Erik B. Sudderth
Computer Science, Brown University
sudderth@cs.brown.edu
Michael I. Jordan
Electrical Engineering & Computer Science and Statistics, U... | 3685 |@word trial:2 frigessi:1 ankle:1 km:1 seek:1 covariance:2 pavlovi:2 recursively:1 liu:1 series:19 njk:3 selecting:1 existing:4 current:3 comparing:1 subsequent:2 partition:1 informative:1 j1:1 motor:1 plot:2 update:7 n0:3 resampling:4 generative:3 discovering:2 instantiate:2 fewer:1 selected:2 intelligence:3 ith:... |
2,963 | 3,686 | Directed Regression
Yi-hao Kao
Stanford University
Stanford, CA 94305
yihaokao@stanford.edu
Benjamin Van Roy
Stanford University
Stanford, CA 94305
bvr@stanford.edu
Xiang Yan
Stanford University
Stanford, CA 94305
xyan@stanford.edu
Abstract
When used to guide decisions, linear regression analysis typically involves... | 3686 |@word trial:6 version:1 incurs:2 selecting:2 offering:1 past:1 current:1 comparing:1 subsequent:2 designed:1 plot:3 generative:10 selected:8 xk:12 preference:1 ik:1 prove:2 combine:1 fitting:3 sacrifice:2 indeed:1 expected:8 behavior:1 multi:1 increasing:2 becomes:2 provided:2 xx:3 underlying:1 what:1 pto:1 argmi... |
2,964 | 3,687 | Non-stationary continuous dynamic
Bayesian networks
Marco Grzegorczyk
Department of Statistics, TU Dortmund University, 44221 Dortmund, Germany
grzegorczyk@statistik.tu-dortmund.de
Dirk Husmeier
Biomathematics & Statistics Scotland (BioSS)
JCMB, The King?s Buildings, Edinburgh EH93JZ, United Kingdom
dirk@bioss.ac.uk
... | 3687 |@word mild:1 version:1 advantageous:1 stronger:1 giudici:1 km:2 grey:1 simulation:5 bn:8 b39:1 pg:2 thereby:3 shading:1 biomathematics:1 phosphorylation:1 reduction:1 configuration:2 series:23 score:9 united:1 contains:2 genetic:3 past:1 outperforms:2 affymetrix:1 current:3 discretization:2 imoto:1 plcg:1 realist... |
2,965 | 3,688 | Lower bounds on minimax rates for nonparametric
regression with additive sparsity and smoothness
Garvesh Raskutti1 , Martin J. Wainwright1,2 , Bin Yu1,2
1
UC Berkeley Department of Statistics
2
UC Berkeley Department of Electrical Engineering and Computer Science
Abstract
We study minimax rates for estimating high-di... | 3688 |@word version:2 polynomial:5 achievable:1 norm:13 additively:1 decomposition:4 thereby:1 harder:2 reduction:1 liu:1 series:1 rkhs:1 past:2 existing:3 additive:19 drop:1 device:1 accordingly:1 wahrsch:1 provides:1 math:1 minskii:1 zhang:1 constructed:1 symposium:1 yuan:3 prove:2 yu1:1 fitting:2 pairwise:1 p1:1 gro... |
2,966 | 3,689 | Information-theoretic lower bounds on the oracle
complexity of convex optimization
Alekh Agarwal
Computer Science Division
UC Berkeley
alekh@cs.berkeley.edu
Peter Bartlett
Computer Science Division
Department of Statistics
UC Berkeley
bartlett@cs.berkeley.edu
Pradeep Ravikumar
Department of Computer Sciences
UT Aust... | 3689 |@word msr:1 version:1 norm:4 d2:2 paid:1 pick:1 moment:3 reduction:1 series:1 contains:2 hereafter:1 selecting:1 past:1 wainwrig:1 recovered:1 written:1 must:2 john:1 belmont:1 realistic:1 designed:1 accordingly:1 beginning:1 wahrsch:1 characterization:1 provides:1 minskii:1 simpler:2 consists:2 prove:2 introduct... |
2,967 | 369 | Translating Locative Prepositions
Paul W. Munro and Mary Tabasko
Department of Information Science
University of Pittsburgh
Pittsburgh, PA 15260
ABSTRACT
A network was trained by back propagation to map locative expressions
of the form "noun-preposition-noun" to a semantic representation, as in
Cosic and Munro (1988)... | 369 |@word simulation:5 accounting:1 eng:6 tr:1 contains:1 current:1 comparing:1 contextual:1 activation:1 must:1 grass:1 selected:1 plane:1 five:1 along:1 become:1 incorrect:1 consists:2 elman:2 inspired:1 actual:1 inappropriate:1 provided:1 matched:1 lowest:1 finding:1 transformation:1 temporal:2 sky:1 every:2 sr1:1 ... |
2,968 | 3,690 | Learning from Multiple Partially Observed Views ?
an Application to Multilingual Text Categorization
Massih R. Amini
Interactive Language Technologies Group
National Research Council Canada
Nicolas Usunier
Laboratoire d?Informatique de Paris 6
Universit?e Pierre et Marie Curie, France
Massih-Reza.Amini@cnrc-nrc.gc.c... | 3690 |@word achievable:3 proportion:1 advantageous:1 uncovers:1 reduction:1 initial:2 contains:1 document:23 outperforms:1 existing:1 lang:1 yet:1 written:2 alphanumeric:1 kdd:1 v:2 filtered:1 provides:1 boosting:1 prove:1 manner:1 introduce:2 excellence:1 expected:3 roughly:1 multi:33 muslea:1 automatically:1 becomes:... |
2,969 | 3,691 | Group Sparse Coding
Samy Bengio
Google
Mountain View, CA
bengio@google.com
Fernando Pereira
Google
Mountain View, CA
pereira@google.com
Yoram Singer
Google
Mountain View, CA
singer@google.com
Dennis Strelow
Google
Mountain View, CA
strelow@google.com
Abstract
Bag-of-words document representations are often used in... | 3691 |@word version:1 norm:17 everingham:1 seek:1 pick:1 lepetit:1 initial:2 denoting:1 document:10 genetic:1 interestingly:1 past:1 com:4 must:3 john:1 j1:1 shape:1 remove:1 plot:2 discrimination:1 v:2 selected:3 provides:1 boosting:1 contribute:1 location:1 org:1 accessed:1 constructed:1 c2:2 consists:1 expected:2 in... |
2,970 | 3,692 | Learning Non-Linear Combinations of Kernels
Corinna Cortes
Google Research
76 Ninth Ave
New York, NY 10011
corinna@google.com
Mehryar Mohri
Courant Institute and Google
251 Mercer Street
New York, NY 10012
mohri@cims.nyu.edu
Afshin Rostamizadeh
Courant Institute and Google
251 Mercer Street
New York, NY 10012
rostam... | 3692 |@word repository:2 bigram:4 polynomial:15 seems:1 norm:13 advantageous:2 nd:1 blender:1 tr:4 solid:2 boundedness:1 minus:1 reduction:1 electronics:3 wrapper:1 contains:1 rkhs:1 com:2 written:1 must:2 plot:2 stationary:6 intelligence:1 selected:2 yr:1 fewer:1 readability:1 simpler:5 constructed:1 direct:1 become:1... |
2,971 | 3,693 | Asymptotically Optimal Regularization
in Smooth Parametric Models
Percy Liang
University of California, Berkeley
Francis Bach
?
INRIA - Ecole
Normale Sup?erieure, France
pliang@cs.berkeley.edu
francis.bach@ens.fr
Guillaume Bouchard
Xerox Research Centre Europe, France
Michael I. Jordan
University of California, Be... | 3693 |@word multitask:2 norm:1 triggs:1 d2:2 jacob:1 elisseeff:1 tr:21 fortuitous:1 carry:1 moment:1 reduction:5 substitution:1 ecole:1 existing:1 com:1 surprising:1 must:1 reminiscent:1 subsequent:1 dydx:1 xrce:1 remove:1 interpretable:2 n0:4 stationary:1 generative:24 intelligence:1 advancement:1 xk:1 mccallum:2 vani... |
2,972 | 3,694 | Lattice Regression
Maya R. Gupta
Department of Electrical Engineering
University of Washington
Seattle, WA 98195
gupta@ee.washington.edu
Eric K. Garcia
Department of Electrical Engineering
University of Washington
Seattle, WA 98195
garciaer@ee.washington.edu
Abstract
We present a new empirical risk minimization fram... | 3694 |@word version:1 achievable:1 polynomial:2 printer:22 confirms:1 simulation:4 rgb:9 covariance:1 tr:4 contains:2 series:1 united:1 interestingly:1 outperforms:2 comparing:3 surprising:2 si:4 must:2 plot:1 update:1 alone:2 intelligence:1 device:9 inspection:1 cursory:1 lr:6 geospatial:5 coarse:2 characterization:4 ... |
2,973 | 3,695 | Noise Characterization, Modeling, and Reduction for
In Vivo Neural Recording
1
Zhi Yang1 , Qi Zhao2 , Edward Keefer3,4 , and Wentai Liu1
University of California at Santa Cruz, 2 California Institute of Technology
3
UT Southwestern Medical Center, 4 Plexon Inc
yangzhi@soe.ucsc.edu
Abstract
Studying signal and noise ... | 3695 |@word trial:3 cox:2 hippocampus:1 pulse:1 simulation:1 r:2 cos2:1 eng:2 decomposition:1 pick:1 reduction:4 electronics:5 liu:3 series:5 score:2 denoting:1 subjective:1 nadasdy:1 current:5 activation:2 cruz:1 distant:4 realistic:1 informative:1 romero:2 webster:1 drop:1 designed:1 plot:3 n0:2 v:3 stationary:1 sele... |
2,974 | 3,696 | Boosting with Spatial Regularization
Zhen James Xiang1
Yongxin Taylor Xi1
Uri Hasson2
Peter J. Ramadge1
1: Department of Electrical Engineering, Princeton University, Princeton NJ, USA
2: Department of Psychology, and Neuroscience Institute, Princeton University, Princeton NJ, USA
{zxiang, yxi, hasson, ramadge} @ ... | 3696 |@word trial:1 repository:1 version:1 briefly:3 fusiform:1 kriegeskorte:1 blu:2 d2:2 tried:1 covariance:1 incurs:1 tr:1 extrastriate:1 cyclic:1 contains:2 score:3 selecting:1 current:2 activation:2 yet:1 written:1 informative:3 j1:3 haxby:3 remove:1 plot:2 interpretable:2 fund:1 greedy:5 selected:8 pursued:1 harma... |
2,975 | 3,697 | Thresholding Procedures for High Dimensional
Variable Selection and Statistical Estimation
Shuheng Zhou
Seminar f?ur Statistik
ETH Z?urich
CH-8092, Switzerland
Abstract
Given n noisy samples with p dimensions, where n ? p, we show that the multistep thresholding procedure can accurately estimate a sparse vector ? ? Rp... | 3697 |@word briefly:3 version:1 stronger:3 norm:4 seems:1 suitably:1 c0:8 open:1 simulation:4 covariance:1 p0:1 decomposition:1 initial:9 selecting:1 current:1 must:2 enables:1 v:1 greedy:1 fewer:1 selected:1 parametrization:1 persistency:1 c22:1 c2:1 supply:2 prove:2 inside:1 shuheng:1 indeed:4 expected:1 roughly:1 ca... |
2,976 | 3,698 | Compressed Least-Squares Regression
Odalric-Ambrym Maillard and R?emi Munos
SequeL Project, INRIA Lille - Nord Europe, France
{odalric.maillard, remi.munos}@inria.fr
Abstract
We consider the problem of learning, from K data, a regression function in a linear space of high dimension N using projections onto a random s... | 3698 |@word mild:1 version:3 polynomial:1 compression:1 norm:11 seems:1 c0:5 km:1 decomposition:4 pressed:1 jafarpour:1 moment:2 reduction:2 initial:16 series:1 interestingly:1 past:1 chazelle:1 john:2 fn:43 numerical:6 enables:1 remove:1 plot:1 xk:14 parametrization:1 vanishing:2 short:1 persistency:1 provides:7 math:... |
2,977 | 3,699 | Nonlinear directed acyclic structure learning
with weakly additive noise models
Peter Spirtes
Arthur Gretton
Robert E. Tillman
Carnegie Mellon University Carnegie Mellon University, Carnegie Mellon University
Pittsburgh, PA
MPI for Biological Cybernetics
Pittsburgh, PA
ps7z@andrew.cmu.edu
Pittsburgh, PA
rtillman@cmu.e... | 3699 |@word version:1 polynomial:1 norm:1 proportion:2 nd:1 hyv:4 simulation:1 covariance:2 tr:4 series:1 score:3 halchenko:1 rkhs:1 fa8750:1 ramsey:2 current:3 com:1 comparing:1 gmail:1 must:6 written:1 w911nf0810242:1 additive:66 partition:1 remove:1 treating:1 update:1 v:1 greedy:3 instantiate:1 fewer:1 tillman:1 in... |
2,978 | 37 | 709
TIME-SEQUENTIAL SELF-ORGANIZATION OF HIERARCHICAL
NEURAL NETWORKS
Ronald H. Silverman
Cornell University Medical College, New York, NY 10021
Andrew S. Noetzel
polytechnic University, Brooklyn, NY 11201
ABSTRACT
Self-organization of multi-layered networks can be realized
by time-sequential organization of successiv... | 37 |@word middle:1 selforganization:3 instruction:1 simulation:4 gradual:1 excited:2 shading:2 initial:4 series:1 lowermost:3 adj:1 si:1 must:2 ronald:1 subsequent:1 concert:2 positionally:1 provides:4 contribute:1 successive:7 five:2 along:1 become:1 pathway:1 manner:3 themselves:2 multi:3 increasing:2 becomes:1 provi... |
2,979 | 370 | Exploiting Syllable Structure
in a Connectionist Phonology Model
David S. Touretzky Deirdre W. Wheeler
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213-3890
Abstract
In a previous paper (Touretzky & Wheeler, 1990a) we showed how adding a
clustering operation to a connectionist phonology mode... | 370 |@word aircraft:1 middle:1 pressure:1 thereby:1 autosegmental:1 initial:2 rightmost:1 existing:2 current:1 clements:2 activation:1 yet:1 must:7 parsing:2 chicago:1 sponsored:1 v:1 generative:1 fewer:1 half:2 short:2 htu:1 dissertation:1 mental:3 provides:1 draft:1 contribute:1 constructed:1 become:1 introductory:1 ... |
2,980 | 3,700 | Reading Tea Leaves: How Humans Interpret Topic Models
Jonathan Chang ?
Facebook
1601 S California Ave.
Palo Alto, CA 94304
jonchang@facebook.com
Jordan Boyd-Graber ?
Institute for Advanced Computer Studies
University of Maryland
jbg@umiacs.umd.edu
Sean Gerrish, Chong Wang, David M. Blei
Department of Computer Scienc... | 3700 |@word trial:1 kintsch:1 proportion:11 laurence:1 earnest:1 km:1 seek:1 decomposition:4 covariance:1 paid:1 minus:1 tlo:2 score:1 series:1 document:68 past:1 existing:1 current:1 com:2 comparing:2 wd:5 surprising:2 scatter:1 intriguing:1 must:3 readily:1 john:1 kdd:1 shape:1 hofmann:1 cheap:1 remove:1 treating:1 i... |
2,981 | 3,701 | Occlusive Components Analysis
?
J?org Lucke
Frankfurt Institute for Advanced Studies
Goethe-University Frankfurt, Germany
luecke@fias.uni-frankfurt.de
Richard Turner
Gatsby Computational Neuroscience Unit, UCL
17 Queen Square, London WC1N 3AR, UK
turner@gatsby.ucl.ac.uk
Maneesh Sahani
Gatsby Computational Neuroscien... | 3701 |@word trial:15 briefly:2 version:4 advantageous:1 ucke:2 grey:2 hyv:1 rgb:3 harder:1 initial:4 substitution:1 contains:3 recovered:2 current:2 assigning:1 written:2 numerical:4 realistic:3 distant:4 occludes:2 shape:1 enables:1 remove:3 update:4 occlude:3 generative:8 leaf:1 advancement:1 greedy:1 plane:1 colored... |
2,982 | 3,702 | Periodic Step-Size Adaptation for
Single-Pass On-line Learning
Chun-Nan Hsu1,2,? , Yu-Ming Chang1 , Han-Shen Huang1 and Yuh-Jye Lee3
1
Institute of Information Science, Academia Sinica, Taipei 115, Taiwan
2
USC/Information Sciences Institute, Marina del Rey, CA 90292, USA
3
Department of Computer Science and Informati... | 3702 |@word kong:2 cnn:2 version:1 disk:2 open:1 tried:1 decomposition:1 pick:1 sgd:39 mention:1 liblinear:5 initial:3 score:16 bc:1 document:1 com:2 yet:1 parsing:2 hou:3 periodically:3 academia:1 numerical:3 kdd:1 designed:1 update:16 v:2 stationary:1 selected:5 amir:1 provides:2 node:1 org:2 burr:1 introduce:1 theor... |
2,983 | 3,703 | Regularized Distance Metric Learning:
Theory and Algorithm
Rong Jin1
Shijun Wang2
Yang Zhou1
1
Dept. of Computer Science & Engineering, Michigan State University, East Lansing, MI 48824
2
Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD 20892
rongjin@cse.msu.edu wangshi@cc.nih.gov zhouyang@m... | 3703 |@word kulis:1 repository:1 compression:1 advantageous:2 norm:5 open:1 grey:2 bn:2 covariance:1 pavel:1 elisseeff:1 tr:5 reduction:1 liu:3 efficacy:3 att:3 tuned:1 ka:1 contextual:1 luo:1 si:1 comn:1 intelligence:1 selected:5 xk:1 cse:1 mcdiarmid:5 dn:3 become:1 consists:1 introduce:1 lansing:1 pairwise:1 tagging:... |
2,984 | 3,704 | Robust Principal Component Analysis:
Exact Recovery of Corrupted Low-Rank Matrices by
Convex Optimization
John Wright?, Yigang Peng, Yi Ma
Visual Computing Group
Microsoft Research Asia
{jowrig,v-yipe,mayi}@microsoft.com
Arvind Ganesh, Shankar Rao
Coordinated Science Laboratory
University of Illinois at Urbana-Champai... | 3704 |@word version:1 briefly:1 polynomial:3 proportion:4 norm:17 c0:2 open:1 simulation:6 seek:2 decomposition:5 jacob:1 brightness:2 reduction:2 efficacy:2 existing:5 recovered:11 com:1 ka:2 toh:1 john:1 additive:1 numerical:1 remove:3 plot:1 implying:1 plane:1 vanishing:4 detecting:1 math:2 org:1 mathematical:2 beco... |
2,985 | 3,705 | An Online Algorithm for
Large Scale Image Similarity Learning
Gal Chechik
Google
Mountain View, CA
gal@google.com
Varun Sharma
Google
Bengalooru, Karnataka, India
vasharma@google.com
Uri Shalit
ICNC, The Hebrew University
Israel
uri.shalit@mail.huji.ac.il
Samy Bengio
Google
Mountain View, CA
bengio@google.com
Abst... | 3705 |@word kulis:3 repository:1 version:1 judgement:1 norm:3 nd:1 dekel:1 decomposition:1 tr:1 reduction:3 initial:1 contains:1 score:5 selecting:2 document:1 outperforms:1 existing:1 err:1 current:5 com:4 comparing:1 numerical:1 partition:1 subsequent:1 shape:1 designed:1 update:5 intelligence:1 prohibitive:1 selecte... |
2,986 | 3,706 | Heterogeneous Multitask Learning with Joint
Sparsity Constraints
Xiaolin Yang
Department of Statistics
Carnegie Mellon University
Pittsburgh, PA 15213
xyang@stat.cmu.edu
Seyoung Kim
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA 15213
sssykim@cs.cmu.edu
Eric P. Xing
Machine Learning Department... | 3706 |@word multitask:25 norm:13 nd:1 open:1 km:7 simulation:1 pg:3 contains:1 series:2 selecting:3 genetic:6 outperforms:1 realistic:1 treating:1 plot:1 update:3 fvc:2 ith:3 steepest:1 smith:1 lr:6 detecting:3 zhang:3 five:6 along:1 yuan:1 consists:1 fitting:2 combine:1 introduce:2 indeed:1 themselves:1 multi:5 automa... |
2,987 | 3,707 | Fast Image Deconvolution
using Hyper-Laplacian Priors
Dilip Krishnan,
Dept. of Computer Science,
Courant Institute,
New York University
dilip@cs.nyu.edu
Rob Fergus,
Dept. of Computer Science,
Courant Institute,
New York University
fergus@cs.nyu.edu
Abstract
The heavy-tailed distribution of gradients in natural scene... | 3707 |@word version:2 mri:1 briefly:1 polynomial:12 norm:7 seek:2 pick:4 tapering:1 series:2 score:1 selecting:1 t7:7 ours:11 reynolds:1 existing:4 imaginary:5 current:2 recovered:1 comparing:2 com:2 must:8 numerical:1 realistic:1 blur:5 analytic:16 half:4 rudin:1 xk:1 core:1 wolfram:2 record:1 successive:1 zhang:1 alo... |
2,988 | 3,708 | Ranking Measures and Loss Functions
in Learning to Rank
Wei Chen?
Chinese Academy of sciences
chenwei@amss.ac.cn
Tie-Yan Liu
Microsoft Research Asia
tyliu@micorsoft.com
Zhiming Ma
Chinese Academy of sciences
mazm@amt.ac.cn
Yanyan Lan
Chinese Academy of sciences
lanyanyan@amss.ac.cn
Hang Li
Microsoft Research Asia
... | 3708 |@word mcrank:2 nd:1 tried:1 decomposition:2 liu:7 document:2 existing:6 com:3 written:1 kdd:1 listmle:16 hypothesize:1 remove:4 ainen:1 selected:1 accordingly:1 lr:2 renshaw:1 boosting:4 preference:1 herbrich:1 zhang:4 constructed:1 become:2 incorrect:1 prove:6 introduce:3 pairwise:29 multi:6 discounted:1 decreas... |
2,989 | 3,709 | Fast Graph Laplacian Regularized Kernel Learning
via Semidefinite?Quadratic?Linear Programming
Xiao-Ming Wu
Dept. of IE
The Chinese University of Hong Kong
wxm007@ie.cuhk.edu.hk
Anthony Man-Cho So
Dept. of SE&EM
The Chinese University of Hong Kong
manchoso@se.cuhk.edu.hk
Zhenguo Li
Dept. of IE
The Chinese University... | 3709 |@word kong:5 cu:2 version:1 briefly:2 kulis:2 norm:1 nd:1 seek:2 crucially:1 thereby:1 tr:4 reduction:10 liu:4 contains:1 series:1 existing:1 surprising:2 toh:5 tackling:2 pcp:12 must:1 john:1 treating:1 designed:1 plot:1 v:1 implying:1 intelligence:2 dissertation:1 colored:5 hypersphere:3 math:1 toronto:1 org:1 ... |
2,990 | 371 | Associative Memory in a Network of 'biological'
Neurons
\Vulfram Gerstner ?
Department of Physics
University of California
Ber keley, CA 94720
Abstract
The Hopfield network (Hopfield, 1982,1984) provides a simple model of an
associative memory in a neuronal structure. This model, however, is based
on highly artificia... | 371 |@word physik:2 mention:1 carry:1 initial:2 efficacy:2 current:5 si:1 must:1 realistic:5 interspike:1 shape:2 analytic:2 plot:2 overshooting:1 stationary:2 ith:1 short:2 provides:1 unbiological:1 burst:2 constructed:1 indeed:2 behavior:3 themselves:1 brain:2 pf:2 linearity:2 what:1 berkeley:2 quantitative:1 ti:2 un... |
2,991 | 3,710 | Randomized Pruning: Efficiently Calculating
Expectations in Large Dynamic Programs
Alexandre Bouchard-C?ot?e1
bouchard@cs.berkeley.edu
Slav Petrov2,?
slav@google.com
1
Computer Science Division
University of California at Berkeley
Berkeley, CA 94720
Dan Klein1
klein@cs.berkeley.edu
2
Google Research
76 Ninth Ave
N... | 3710 |@word version:1 inversion:1 polynomial:1 advantageous:4 proportionality:1 simulation:2 decomposition:4 pick:3 thereby:2 versatile:1 reduction:1 configuration:8 contains:1 score:10 charniak:1 ours:1 outperforms:3 existing:1 current:9 com:1 comparing:1 skipping:2 yet:3 dx:2 reminiscent:1 parsing:38 subsequent:1 rea... |
2,992 | 3,711 | Perceptual Multistability as Markov Chain Monte
Carlo Inference
Samuel J. Gershman
Department of Psychology and Neuroscience Institute
Princeton University
Princeton, NJ 08540
sjgershm@princeton.edu
Edward Vul & Joshua B. Tenenbaum
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambrid... | 3711 |@word illustrating:1 seems:1 open:1 gradual:1 simulation:5 jacob:1 reduction:1 initial:5 configuration:3 liu:2 existing:1 current:6 contextual:7 surprising:1 reali:1 must:2 optometry:1 tilted:3 distant:1 subsequent:1 shape:2 designed:1 implying:1 cue:3 fewer:1 selected:1 stationary:2 intelligence:1 reciprocal:1 s... |
2,993 | 3,712 | Speeding up Magnetic Resonance Image Acquisition
by Bayesian Multi-Slice Adaptive Compressed
Sensing
Matthias W. Seeger
Saarland University and Max Planck Institute for Informatics
Campus E1.4, 66123 Saarbr?ucken, Germany
mseeger@mmci.uni-saarland.de
Abstract
We show how to sequentially optimize magnetic resonance im... | 3712 |@word economically:1 trialand:1 mri:25 briefly:2 norm:2 inversion:2 middle:3 eliminating:1 nd:1 km:8 calculus:1 pulse:3 covariance:13 decomposition:2 thereby:1 solid:1 harder:1 moment:1 reduction:3 initial:2 score:6 mseeger:1 denoting:1 favouring:1 comparing:2 nt:1 anterior:1 skipping:1 yet:1 written:1 john:1 fn:... |
2,994 | 3,713 | Monte Carlo Sampling for Regret Minimization in
Extensive Games
Kevin Waugh
School of Computer Science
Carnegie Mellon University
Pittsburgh PA 15213-3891
waugh@cs.cmu.edu
Marc Lanctot
Department of Computing Science
University of Alberta
Edmonton, Alberta, Canada T6G 2E8
lanctot@ualberta.ca
Martin Zinkevich
Yahoo! Re... | 3713 |@word version:2 maz:1 seems:1 approachability:1 rigged:1 q1:2 dramatic:1 reduction:3 initial:1 contains:4 selecting:2 prefix:8 past:1 current:6 com:1 clara:1 yet:1 must:2 john:1 partition:5 designed:1 update:3 greedy:1 short:1 core:1 iterates:1 node:15 revisited:1 traverse:2 location:1 tr09:2 mathematical:1 along... |
2,995 | 3,714 | A Game-Theoretic Approach to
Hypergraph Clustering
Samuel Rota Bul`o
Marcello Pelillo
University of Venice, Italy
{srotabul,pelillo}@dsi.unive.it
Abstract
Hypergraph clustering refers to the process of extracting maximally coherent
groups from a set of objects using high-order (rather than pairwise) similarities.
Tra... | 3714 |@word collinearity:1 version:2 polynomial:7 proportion:1 extinction:1 vldb:1 zelnik:1 hu:2 pressure:1 asks:1 thereby:1 mention:2 ours:1 soules:1 si:1 guez:2 partition:8 s21:1 remove:1 drop:5 selected:4 xk:16 es:25 ith:2 underestimating:1 provides:4 characterization:1 math:5 ron:1 five:2 mathematical:1 along:1 con... |
2,996 | 3,715 | Structured output regression for detection with
partial truncation
Andrea Vedaldi
Andrew Zisserman
Department of Engineering
University of Oxford
Oxford, UK
{vedaldi,az}@robots.ox.ac.uk
Abstract
We develop a structured output model for object category detection that explicitly
accounts for alignment, multiple aspects... | 3715 |@word dalal:2 everingham:1 triggs:2 p0:8 pick:1 solid:1 initial:2 liu:1 contains:3 score:5 seriously:1 assigning:1 must:1 visible:2 partition:1 hofmann:1 remove:1 designed:1 gist:1 alone:1 selected:2 guess:1 plane:9 maximised:1 detecting:3 coarse:2 recompute:1 location:2 successive:2 provides:1 deactivating:2 org... |
2,997 | 3,716 | Time-Varying Dynamic Bayesian Networks
Le Song, Mladen Kolar and Eric P. Xing
School of Computer Science, Carnegie Mellon University
{lesong, mkolar, epxing}@cs.cmu.edu
Abstract
Directed graphical models such as Bayesian networks are a favored formalism
for modeling the dependency structures in complex multivariate s... | 3716 |@word trial:2 version:1 norm:3 seems:1 simulation:1 tried:1 bn:2 decomposition:2 eng:1 moment:1 configuration:1 series:22 contains:1 score:5 ati:8 past:1 existing:1 current:3 recovered:1 surprising:1 activation:1 yet:1 assigning:1 realistic:2 partition:1 periodically:2 motor:6 designed:2 treating:1 update:1 plot:... |
2,998 | 3,717 | Learning in Markov Random Fields using
Tempered Transitions
Ruslan Salakhutdinov
Brain and Cognitive Sciences and CSAIL
Massachusetts Institute of Technology
rsalakhu@mit.edu
Abstract
Markov random fields (MRF?s), or undirected graphical models, provide a powerful framework for modeling complex dependencies among ran... | 3717 |@word proceeded:1 middle:2 c0:1 decomposition:1 p0:3 contrastive:4 q1:2 tr:1 moment:1 configuration:2 contains:5 liu:1 existing:2 current:9 discretization:2 visible:7 partition:6 enables:2 utml:1 treating:1 update:30 aside:2 stationary:1 generative:4 leaf:2 intelligence:3 plane:1 xk:4 geyer:1 provides:3 math:1 to... |
2,999 | 3,718 | Segmenting Scenes by Matching Image Composites
Bryan C. Russell1 Alexei A. Efros2,1 Josef Sivic1 William T. Freeman3 Andrew Zisserman4,1
1
INRIA?
2
3
Carnegie Mellon University
CSAIL MIT
4
University of Oxford
Abstract
In this paper, we investigate how, given an image, similar images sharing the same
global des... | 3718 |@word plsa:1 grey:2 seek:1 rgb:1 brightness:1 thereby:1 inpainting:1 shechtman:1 liu:3 contains:1 score:9 ecole:1 warmer:1 colburn:1 recovered:5 must:2 parsing:2 partition:1 informative:1 hofmann:1 shape:3 plot:1 gist:5 cue:4 half:1 intelligence:4 realism:1 blei:1 detecting:2 coarse:1 quantized:1 location:6 along... |
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