Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
1,900 | 2,726 | ?0-norm Minimization for Basis Selection
David Wipf and Bhaskar Rao ?
Department of Electrical and Computer Engineering
University of California, San Diego, CA 92092
dwipf@ucsd.edu, brao@ece.ucsd.edu
Abstract
Finding the sparsest, or minimum ?0 -norm, representation of a signal
given an overcomplete dictionary of basi... | 2726 |@word trial:1 determinant:1 inversion:1 norm:18 seek:1 simulation:1 decomposition:3 minus:1 accommodate:1 delgado:1 reduction:4 configuration:2 tabulate:1 outperforms:1 current:7 comparing:1 written:1 readily:1 must:11 noninformative:1 remove:2 update:3 implying:1 fewer:4 parameterization:1 urp:3 provides:2 lsm:2... |
1,901 | 2,727 | Planning for Markov Decision Processes with
Sparse Stochasticity
Maxim Likhachev
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
maxim+@cs.cmu.edu
Geoff Gordon
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
ggordon@cs.cmu.edu
Sebastian Thrun
Dept. of Computer Sc... | 2727 |@word middle:1 version:1 compression:8 seems:1 advantageous:1 open:19 termination:1 simulation:2 configuration:2 contains:3 series:1 selecting:1 current:9 si:4 yet:2 must:3 remove:2 designed:2 plot:2 update:4 v:1 greedy:5 intelligence:4 plane:1 short:1 segway:1 provides:1 location:13 accessed:1 along:3 predecesso... |
1,902 | 2,728 | Result Analysis of the NIPS 2003
Feature Selection Challenge
Isabelle Guyon
ClopiNet
Berkeley, CA 94708, USA
isabelle@clopinet.com
Steve Gunn
School of Electronics and Computer Science
University of Southampton, U.K.
s.r.gunn@ecs.soton.ac.uk
Asa Ben Hur
Department of Genome Sciences
University of Washington, USA
asa... | 2728 |@word madelon:3 repository:3 briefly:1 eliminating:2 achievable:1 elisseeff:2 concise:1 tr:1 minus:1 carry:1 reduction:1 wrapper:5 electronics:1 score:11 selecting:3 amp:1 past:1 com:4 discretization:1 surprising:1 yet:2 tackling:1 attracted:3 john:2 informative:1 kdd:2 dupont:1 remove:1 intelligence:2 selected:5... |
1,903 | 2,729 | Mistake Bounds
for Maximum Entropy Discrimination
Philip M. Long
Center for Computational Learning Systems
Columbia University
plong@cs.columbia.edu
Xinyu Wu
Department of Computer Science
National University of Singapore
wuxy@comp.nus.edu.sg
Abstract
We establish a mistake bound for an ensemble method for classifica... | 2729 |@word trial:27 version:1 polynomial:1 dekel:1 seek:1 simplifying:2 q1:1 reduction:1 past:2 bitwise:1 must:4 cruz:1 additive:1 designed:1 drop:1 update:3 discrimination:4 alone:1 warmuth:7 steepest:1 math:1 boosting:1 zhang:3 mathematical:1 direct:2 become:1 symposium:1 focs:1 prove:3 combine:1 roughly:1 p1:3 nor:... |
1,904 | 273 | 218
Bengio, De Mori and Cardin
Speaker Independent Speech Recognition with
Neural Networks and Speech Knowledge
Yoshua Bengio
Renato De Mori
Dept Computer Science Dept Computer Science
McGill University
McGill University
Montreal, Canada H3A2A7
Regis Cardin
Dept Computer Science
McGill University
ABSTRACT
We attem... | 273 |@word middle:1 compression:2 decomposition:1 idl:2 recursively:1 reduction:1 initial:4 past:3 current:2 comparing:1 activation:2 yet:1 must:2 realistic:1 enables:2 discrimination:6 v:4 half:1 intelligence:2 device:1 xk:1 short:3 coarse:1 successive:1 attack:1 burst:1 direct:3 combine:4 manner:1 expected:1 indeed:2... |
1,905 | 2,730 | Experts in a Markov Decision Process
Eyal Even-Dar
Computer Science
Tel-Aviv University
evend@post.tau.ac.il
Sham M. Kakade
Computer and Information Science
University of Pennsylvania
skakade@linc.cis.upenn.edu
Yishay Mansour ?
Computer Science
Tel-Aviv University
mansour@post.tau.ac.il
Abstract
We consider an MDP ... | 2730 |@word faculty:1 polynomial:5 stronger:1 norm:1 contraction:1 incurs:1 reduction:1 initial:4 past:1 existing:2 current:3 si:1 yet:1 must:1 treating:1 update:1 stationary:11 provides:2 c2:3 prove:5 manner:1 excellence:1 upenn:1 hardness:1 expected:4 behavior:1 frequently:2 planning:2 multi:1 examine:1 little:1 actu... |
1,906 | 2,731 | Synergies between Intrinsic and Synaptic
Plasticity in Individual Model Neurons
Jochen Triesch
Dept. of Cognitive Science, UC San Diego, La Jolla, CA, 92093-0515, USA
Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
triesch@ucsd.edu
Abstract
This paper explores the computational consequences of si... | 2731 |@word stronger:1 hyv:1 simulation:1 tried:1 solid:2 moment:12 initial:1 current:4 activation:9 plasticity:42 shape:2 plot:1 update:4 stationary:6 half:1 device:1 plane:1 core:1 record:1 draft:1 contribute:2 location:2 sigmoidal:2 zhang:1 c2:5 become:1 differential:1 consists:1 theoretically:2 ica:4 expected:4 beh... |
1,907 | 2,732 | Joint Tracking of Pose, Expression, and Texture
using Conditionally Gaussian Filters
Tim K. Marks
John Hershey
Department of Cognitive Science
University of California San Diego
La Jolla, CA 92093-0515
tkmarks@cogsci.ucsd.edu
hershey@microsoft.com
J. Cooper Roddey Javier R. Movellan
Institute for Neural Computation
U... | 2732 |@word version:1 simulation:1 covariance:4 initial:6 liu:1 contains:1 freitas:1 current:4 com:1 john:2 subsequent:1 shape:3 opin:1 generative:4 leaf:1 intelligence:1 plane:1 ith:8 provides:1 location:5 blackwellized:1 c2:1 consists:2 manner:1 expected:2 blackwellization:1 mplab:2 becomes:1 project:4 estimating:3 n... |
1,908 | 2,733 | Euclidean Embedding of Co-occurrence Data
2
Amir Globerson1 Gal Chechik2 Fernando Pereira3 Naftali Tishby1
1
School of computer Science and Engineering,
Interdisciplinary Center for Neural Computation
The Hebrew University Jerusalem, 91904, Israel
Computer Science Department, Stanford University, Stanford, CA 94305, ... | 2733 |@word cox:2 version:2 polynomial:1 norm:1 covariance:2 decomposition:1 minus:1 tr:1 reduction:3 electronics:4 document:30 ours:1 outperforms:1 current:5 dx:2 partition:3 interpretable:1 v:1 alone:1 selected:2 amir:1 inspection:1 characterization:1 provides:1 math:1 location:1 toronto:1 scholkopf:1 consists:1 intr... |
1,909 | 2,734 | Exploration-Exploitation Tradeoffs for
Experts Algorithms in Reactive Environments
Daniela Pucci de Farias
Department of Mechanical Engineering
Massachusetts Institute of Technology
Cambridge, MA 02139
pucci@mit.edu
Nimrod Megiddo
IBM Almaden Research Center
650 Harry Road, K53-B2
San Jose, CA 95120
megiddo@almaden.ib... | 2734 |@word exploitation:18 polynomial:2 achievable:5 rigged:1 instruction:1 cyclic:1 exclusively:2 past:5 reaction:1 current:7 com:1 must:2 realistic:1 designed:1 update:2 warmuth:1 capitalizes:1 vanishing:1 characterization:1 along:1 driver:4 qualitative:1 consists:2 prove:1 combine:3 symp:1 introduce:1 expected:7 in... |
1,910 | 2,735 | Learning Hyper-Features for
Visual Identification
Andras Ferencz
Erik G. Learned-Miller
Jitendra Malik
Computer Science Division, EECS
University of California at Berkeley
Berkeley, CA 94720
Abstract
We address the problem of identifying specific instances of a class (cars)
from a set of images all belonging to that c... | 2735 |@word fusiform:2 polynomial:3 seems:1 seek:1 decomposition:2 covariance:1 pick:1 brightness:1 shot:1 initial:1 score:4 selecting:1 ours:1 past:1 existing:1 outperforms:3 comparing:1 must:2 fn:1 subsequent:2 additive:1 informative:8 numerical:1 shape:2 plot:3 v:3 alone:2 half:1 selected:2 parameterization:1 xk:2 c... |
1,911 | 2,736 | Nonlinear Blind Source Separation by
Integrating Independent Component Analysis
and Slow Feature Analysis
Tobias Blaschke
Institute for Theoretical Biology
Humboldt University Berlin
Invalidenstra?e 43, D-10115 Berlin, Germany
t.blaschke@biologie.hu-berlin.de
Laurenz Wiskott
Institute for Theoretical Biology
Humboldt ... | 2736 |@word seems:1 hyv:2 hu:3 simulation:3 covariance:3 volkswagen:1 yih:1 carry:1 liu:1 interestingly:1 recovered:1 si:1 scatter:1 lang:1 written:4 remove:1 plot:1 selected:3 plane:3 short:2 provides:2 successive:3 symposium:1 consists:1 combine:3 inside:4 ica:26 moulines:1 laurenz:2 increasing:1 underlying:1 circuit... |
1,912 | 2,737 | Methods for Estimating the Computational
Power and Generalization Capability of Neural
Microcircuits
Wolfgang Maass, Robert Legenstein, Nils Bertschinger
Institute for Theoretical Computer Science
Technische Universit?at Graz
A-8010 Graz, Austria
{maass, legi, nilsb}@igi.tugraz.at
Abstract
What makes a neural microcir... | 2737 |@word middle:1 version:1 nd:1 d2:1 simulation:1 simplifying:2 carry:3 initial:1 contains:4 efficacy:4 chervonenkis:1 past:1 current:4 comparing:2 assigning:1 john:1 subsequent:1 realistic:2 informative:1 plasticity:4 interspike:1 enables:1 partition:1 wanted:1 fund:1 selected:1 device:4 ith:2 short:2 mulier:4 sim... |
1,913 | 2,738 | Supervised graph inference
Jean-Philippe Vert
Centre de G?eostatistique
Ecole des Mines de Paris
35 rue Saint-Honor?e
77300 Fontainebleau, France
Jean-Philippe.Vert@mines.org
Yoshihiro Yamanishi
Bioinformatics Center
Institute for Chemical Research
Kyoto University
Uji, Kyoto 611-0011, Japan
yoshi@kuicr.kyoto-u.ac.jp... | 2738 |@word norm:4 seems:1 decomposition:1 tr:1 recursively:1 phy:1 contains:2 score:2 loc:1 series:1 ecole:1 interestingly:1 outperforms:1 reaction:3 current:3 recovered:1 assigning:1 must:5 written:1 dashdot:1 lkv:3 chicago:1 girosi:1 remove:1 plot:1 v:15 kint:2 leaf:1 selected:5 core:1 detecting:1 node:7 location:1 ... |
1,914 | 2,739 | An Application of Boosting to
Graph Classification
Taku Kudo,
Eisaku Maeda
NTT Communication Science Laboratories.
2-4 Hikaridai, Seika-cho, Soraku, Kyoto, Japan
{taku,maeda}@cslab.kecl.ntt.co.jp
Yuji Matsumoto
Nara Institute of Science and Technology.
8916-5 Takayama-cho, Ikoma, Nara, Japan
matsu@is.naist.jp
Abst... | 2739 |@word norm:7 reused:1 lodhi:1 termination:1 set5:1 cellphone:4 score:2 hereafter:1 selecting:1 denoting:1 document:1 outperforms:1 current:1 comparing:1 must:2 john:1 cruz:1 numerical:3 discernible:1 gv:2 short:2 boosting:38 node:3 traverse:4 org:1 supergraph:4 consists:2 redefine:1 interscience:1 introduce:2 beh... |
1,915 | 274 | On the Distribution of the Number of Local Minima
On the Distribution of the Number of Local
Minima of a Random Function on a Graph
Pierre Baldi
JPL, Caltech
Pasadena, CA 91109
1
Yosef Rinott
UCSD
La Jolla, CA 92093
Charles Stein
Stanford University
Stanford, CA 94305
INTRODUCTION
Minimization of energy or error... | 274 |@word hypercube:3 evolution:1 assigned:1 strategy:2 disordered:1 adjacent:1 game:2 samuel:1 series:1 complete:1 hold:1 consideration:1 minimizing:1 normal:7 charles:1 equilibrium:1 common:2 fe:1 volume:1 design:1 analog:1 combinatorial:2 cv:1 hamiltonian:1 mathematics:1 minimization:1 gaussian:1 ucsd:1 simpler:1 r... |
1,916 | 2,740 | Semi-supervised Learning
by Entropy Minimization
Yves Grandvalet ?
Heudiasyc, CNRS/UTC
60205 Compi`egne cedex, France
grandval@utc.fr
Yoshua Bengio
Dept. IRO, Universit?e de Montr?eal
Montreal, Qc, H3C 3J7, Canada
bengioy@iro.umontreal.ca
Abstract
We consider the semi-supervised learning problem, where a decision ru... | 2740 |@word middle:1 version:2 extinction:1 covariance:4 pick:2 substitution:1 series:5 contains:1 tuned:1 document:1 exy:1 dx:1 stemming:1 numerical:1 partition:1 informative:6 enables:2 drop:1 plot:4 designed:1 joy:1 zik:4 discrimination:2 generative:13 v:5 intelligence:1 parameterization:1 mccallum:1 parametrization... |
1,917 | 2,741 | Validity estimates for loopy Belief Propagation
on binary real-world networks
Joris Mooij
Dept. of Biophysics, Inst. for Neuroscience, Radboud Univ. Nijmegen
6525 EZ Nijmegen, the Netherlands
j.mooij@science.ru.nl
Hilbert J. Kappen
Dept. of Biophysics, Inst. for Neuroscience, Radboud Univ. Nijmegen
6525 EZ Nijmegen, t... | 2741 |@word proportionality:1 solid:1 kappen:3 initial:1 configuration:3 icis:1 outperforms:1 existing:1 paramagnetic:11 numerical:2 plot:8 implying:2 stationary:1 half:1 parameterization:1 signalling:1 fbe:2 affair:1 short:1 math:1 node:14 location:4 become:1 ik:2 qualitative:1 prove:1 consists:1 introduce:1 pairwise:... |
1,918 | 2,742 | Incremental Algorithms
for Hierarchical Classification?
Nicol`o Cesa-Bianchi
Universit`a di Milano
Milano, Italy
Claudio Gentile
Universit`a dell?Insubria
Varese, Italy
Andrea Tironi Luca Zaniboni
Universit`a di Milano
Crema, Italy
Abstract
We study the problem of hierarchical classification when labels correspondin... | 2742 |@word version:7 norm:4 yi0:5 suitably:1 dekel:1 unif:2 open:2 tried:1 pick:2 harder:2 recursively:3 pub:1 document:13 past:1 outperforms:1 current:1 com:1 surprising:1 si:5 yet:1 assigning:1 must:3 mesh:1 hofmann:1 update:6 implying:2 intelligence:1 leaf:7 item:2 mccallum:3 short:1 coarse:1 node:58 contribute:1 s... |
1,919 | 2,743 | Support Vector Classification with Input Data
Uncertainty
Jinbo Bi
Computer-Aided Diagnosis & Therapy Group
Siemens Medical Solutions, Inc.
Malvern, PA 19355
jinbo.bi@siemens.com
Tong Zhang
IBM T. J. Watson Research Center
Yorktown Heights, NY 10598
tzhang@watson.ibm.com
Abstract
This paper investigates a new learni... | 2743 |@word trial:3 middle:3 termination:1 simulation:1 seek:1 decomposition:1 covariance:1 pick:1 solid:1 configuration:3 contains:1 tuned:1 bhattacharyya:1 current:2 jinbo:2 com:2 written:1 john:1 additive:2 numerical:4 realistic:1 shape:2 treating:1 designed:1 generative:1 warmuth:1 beginning:1 hyperplanes:3 zhang:1... |
1,920 | 2,744 | Synchronization of neural networks by mutual
learning and its application to cryptography
Einat Klein
Department of Physics
Bar-Ilan University
Ramat-Gan, 52900 Israel
Rachel Mislovaty
Department of Physics
Bar-Ilan University
Ramat-Gan, 52900 Israel
Andreas Ruttor
Institut f?ur Theoretische Physik,
Universit?at W?ur... | 2744 |@word private:1 seems:1 norm:1 physik:2 simulation:5 solid:2 initial:3 series:1 genetic:1 tuned:1 current:1 yet:3 must:1 written:1 numerical:1 analytic:2 update:2 imitated:1 hypersphere:2 priel:2 attack:11 differential:1 combine:3 manner:1 introduce:1 secret:7 examine:1 mechanic:3 brain:1 increasing:1 kessler:2 m... |
1,921 | 2,745 | Beat Tracking the Graphical Model Way
Dustin Lang
Nando de Freitas
Department of Computer Science
University of British Columbia
Vancouver, BC
{dalang, nando}@cs.ubc.ca
Abstract
We present a graphical model for beat tracking in recorded music. Using
a probabilistic graphical model allows us to incorporate local infor... | 2745 |@word version:7 middle:1 instrumental:2 seek:1 pulse:2 vermaak:1 tr:1 carry:1 kappen:3 series:1 bc:1 puri:1 freitas:2 err:2 blank:1 contextual:3 discretization:1 nt:3 lang:2 si:1 must:2 klaas:1 enables:1 designed:1 plot:1 aside:1 half:9 intelligence:2 item:1 desktop:1 short:3 coarse:1 quantized:1 node:2 club:1 ma... |
1,922 | 2,746 | Common-Frame Model for Object Recognition
Pierre Moreels
Pietro Perona
California Insitute of Technology - Pasadena CA91125 - USA
pmoreels,perona@vision.caltech.edu
Abstract
A generative probabilistic model for objects in images is presented. An
object consists of a constellation of features. Feature appearance and
po... | 2746 |@word version:2 mee:4 simulation:1 configuration:2 contains:6 current:2 comparing:3 yet:1 recasting:1 informative:2 shape:3 update:1 hash:6 precaution:1 generative:2 fewer:1 greedy:1 selected:1 item:1 lr:5 filtered:1 provides:1 location:1 five:1 direct:1 pairing:1 incorrect:1 consists:4 combine:3 fitting:1 expect... |
1,923 | 2,747 | Responding to modalities with different latencies
Fredrik Bissmarck
Computational Neuroscience Labs
ATR International
Hikari-dai 2-2-2, Seika, Soraku
Kyoto 619-0288 JAPAN
xfredrik@atr.jp
Hiroyuki Nakahara
Laboratory for Mathematical Neuroscience
RIKEN Brain Science Institute
Hirosawa 2-1-1, Wako
Saitama 351-0198 JAPA... | 2747 |@word trial:6 middle:1 stronger:1 seems:1 nd:1 km:1 simulation:9 pressed:3 solid:1 carry:2 moment:2 necessity:1 initial:4 wako:1 reaction:1 contextual:2 must:1 motor:35 designed:2 update:1 alone:1 cue:1 selected:3 half:2 greedy:2 probablity:1 node:4 mathematical:1 consists:2 pathway:2 combine:3 behavioral:1 acqui... |
1,924 | 2,748 | Distributed Occlusion Reasoning for Tracking
with Nonparametric Belief Propagation
Erik B. Sudderth, Michael I. Mandel, William T. Freeman, and Alan S. Willsky
Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
esuddert@mit.edu, mim@alum.mit.edu, billf@mit.edu, willsky@mit.... | 2748 |@word middle:4 proportionality:1 simulation:4 linearized:1 propagate:1 decomposition:3 covariance:1 thereby:1 initial:1 configuration:23 contains:1 mag:1 recovered:1 discretization:3 current:2 must:2 written:2 shape:1 analytic:3 occludes:1 plot:1 update:6 occlude:1 cue:1 selected:1 generative:2 isard:4 parameteri... |
1,925 | 2,749 | Brain Inspired Reinforcement Learning
Fran?ois Rivest*
Yoshua Bengio
D?partement d?informatique et de recherche op?rationnelle
Universit? de Montr?al
CP 6128 succ. Centre Ville, Montr?al, QC H3C 3J7, Canada
francois.rivest@mail.mcgill.ca
bengioy@iro.umontreal.ca
John Kalaska
D?partement de physiologie
Universit? de ... | 2749 |@word neurophysiology:3 trial:1 version:2 middle:1 instrumental:1 hippocampus:2 norm:1 nd:1 tried:2 lobe:3 covariance:1 mammal:1 initial:3 synergistically:1 interestingly:1 existing:1 current:3 comparing:1 activation:7 must:2 readily:1 john:2 realistic:1 plasticity:1 motor:2 update:12 greedy:1 selected:2 fewer:1 ... |
1,926 | 275 | 630
Morgan and Bourfard
Generalization and Parameter Estimation
in Feedforward Nets:
Some Experiments
~. Morgan t
H. Bourlard t
International Computer Science Institute
Berkeley, CA 94704, USA
*
*Philips Research Laboratory Brussels
B-1170 Brussels, Belgium
ABSTRACT
We have done an empirical study of the relati... | 275 |@word mild:1 stronger:1 simulation:2 initial:1 necessity:1 series:1 score:5 past:1 current:1 contextual:1 surprising:1 yet:1 written:1 must:1 visible:1 hts:1 discrimination:2 half:1 fewer:1 guess:1 short:1 indefinitely:1 quantized:2 simpler:1 interdependence:1 expected:2 roughly:3 behavior:1 multi:2 decreasing:1 c... |
1,927 | 2,750 | Dynamical Synapses Give Rise to a Power-Law
Distribution of Neuronal Avalanches
Anna Levina3,4 , J. Michael Herrmann1,2 , Theo Geisel1,2,4
Bernstein Center for Computational Neuroscience Go? ttingen
Georg-August University G?ottingen, Institute for Nonlinear Dynamics
3
Graduate School Identification in Mathematical Mod... | 2750 |@word version:2 achievable:1 stronger:1 grey:2 simulation:1 thereby:1 solid:1 moment:1 efficacy:11 tuned:1 interestingly:1 current:1 activation:3 written:2 numerical:1 visible:1 nervous:1 beginning:1 meakin:1 short:1 mathematical:1 differential:1 become:1 consists:1 inside:1 inter:1 indeed:1 behavior:7 themselves... |
1,928 | 2,751 | Learning Shared Latent Structure for Image
Synthesis and Robotic Imitation
Aaron P. Shon ?
Keith Grochow ? Aaron Hertzmann ? Rajesh P. N. Rao ?
?Department of Computer Science and Engineering
University of Washington
Seattle, WA 98195 USA
?Department of Computer Science
University of Toronto
Toronto, ON M5S 3G4 Canad... | 2751 |@word sgplvm:5 flexiblity:1 covariance:1 thereby:1 reduction:5 initial:1 cyclic:1 series:1 interestingly:1 rightmost:1 recovered:1 z2:1 dx:2 additive:2 informative:2 motor:2 plot:6 generative:1 discovering:1 selected:4 instantiate:1 parameterization:3 fewer:1 plane:1 imitate:2 sys:1 provides:1 parameterizations:1... |
1,929 | 2,752 | Norepinephrine and Neural Interrupts
Peter Dayan
Gatsby Computational Neuroscience Unit
University College London
17 Queen Square, London WC1N 3AR, UK
dayan@gatsby.ucl.ac.uk
Angela J. Yu
Center for Brain, Mind & Behavior
Green Hall, Princeton University
Princeton, NJ 08540, USA
ajyu@princeton.edu
Abstract
Experimenta... | 2752 |@word noradrenergic:6 exploitation:1 middle:2 briefly:1 trial:12 stronger:1 proportionality:1 rhesus:1 attended:1 dramatic:1 solid:1 reduction:1 initial:3 seriously:1 reaction:2 existing:2 timer:2 current:4 activation:6 must:1 john:1 interrupted:1 subsequent:1 happen:1 realistic:1 plasticity:1 plot:3 drop:1 discr... |
1,930 | 2,753 | Nested sampling for Potts models
Iain Murray
Gatsby Computational Neuroscience Unit
University College London
i.murray@gatsby.ucl.ac.uk
Zoubin Ghahramani
Gatsby Computational Neuroscience Unit
University College London
zoubin@gatsby.ucl.ac.uk
David J.C. MacKay
Cavendish Laboratory
University of Cambridge
mackay@mrao.... | 2753 |@word trial:1 cloned:1 simulation:1 innermost:1 pick:1 phy:2 initial:1 existing:1 current:1 si:6 dx:5 must:1 john:2 subsequent:1 partition:3 cheap:1 update:5 v:1 record:1 provides:5 node:3 symposium:1 inside:4 indeed:1 behavior:1 decreasing:1 considering:1 estimating:1 suffice:1 mass:6 developed:1 proposing:1 fin... |
1,931 | 2,754 | Recovery of Jointly Sparse Signals
from Few Random Projections
Michael B. Wakin
ECE Department
Rice University
wakin@rice.edu
Marco F. Duarte
ECE Department
Rice University
duarte@rice.edu
Dror Baron
ECE Department
Rice University
drorb@rice.edu
Shriram Sarvotham
ECE Department
Rice University
shri@rice.edu
Richar... | 2754 |@word version:1 achievable:4 compression:5 norm:1 seems:1 seek:2 sensed:1 simulation:3 decomposition:1 dramatic:1 solid:1 reduction:1 initial:2 contains:2 exclusively:1 mag:1 recovered:7 z2:1 yet:5 must:2 numerical:1 subsequent:1 informative:1 enables:3 cheap:1 remove:1 designed:1 plot:2 greedy:6 fewer:3 device:3... |
1,932 | 2,755 | From Batch to Transductive Online Learning
Sham Kakade
Toyota Technological Institute
Chicago, IL 60637
sham@tti-c.org
Adam Tauman Kalai
Toyota Technological Institute
Chicago, IL 60637
kalai@tti-c.org
Abstract
It is well-known that everything that is learnable in the difficult online
setting, where an arbitrary seq... | 2755 |@word polynomial:1 seems:1 nd:3 open:3 a02:4 pick:1 harder:2 initial:1 series:1 existing:1 current:1 surprising:1 yet:1 dx:2 must:5 john:1 chicago:2 remove:1 update:1 half:1 warmuth:1 ith:5 org:2 warmup:1 along:2 dn:2 symposium:2 consists:1 prove:1 interscience:1 manner:1 indeed:1 expected:7 provided:1 begin:3 mo... |
1,933 | 2,756 | Consistency of one-class SVM and related
algorithms
R?egis Vert
Laboratoire de Recherche en Informatique
Universit?e Paris-Sud
91405, Orsay Cedex, France
Masagroup
24 Bd de l?H?opital
75005, Paris, France
Regis.Vert@lri.fr
Jean-Philippe Vert
Geostatistics Center
Ecole des Mines de Paris - ParisTech
77300 Fontaineblea... | 2756 |@word version:4 seems:1 norm:15 open:1 decomposition:2 euclidian:1 initial:1 ecole:1 rkhs:10 denoting:1 scovel:1 dx:1 bd:1 lorentz:1 shape:1 v:1 discrimination:1 selected:1 vanishing:1 recherche:1 math:1 zhang:1 c2:2 prove:1 excellence:1 indeed:1 roughly:1 behavior:2 nor:1 sud:1 decreasing:1 provided:1 estimating... |
1,934 | 2,757 | Generalized Nonnegative Matrix
Approximations with Bregman Divergences
Inderjit S. Dhillon
Suvrit Sra
Dept. of Computer Sciences
The Univ. of Texas at Austin
Austin, TX 78712.
{inderjit,suvrit}@cs.utexas.edu
Abstract
Nonnegative matrix approximation (NNMA) is a recent technique for dimensionality reduction and data a... | 2757 |@word version:1 polynomial:1 norm:3 nd:1 open:2 seek:6 decomposition:1 reduction:2 shum:1 document:1 bc:56 past:1 ka:3 must:1 numerical:1 additive:1 analytic:2 drop:1 update:22 advancement:1 pointer:1 simpler:2 zhang:1 prove:2 ica:1 behavior:1 themselves:1 inspired:2 globally:1 encouraging:1 increasing:1 provided... |
1,935 | 2,758 | A Hierarchical Compositional System for Rapid
Object Detection
Long Zhu and Alan Yuille
Department of Statistics
University of California at Los Angeles
Los Angeles, CA 90095
{lzhu,yuille}@stat.ucla.edu
Abstract
We describe a hierarchical compositional system for detecting deformable objects in images. Objects are re... | 2758 |@word version:1 decomposition:1 harder:1 configuration:7 contains:3 parsing:1 blur:1 shape:6 enables:4 generative:3 selected:1 intelligence:1 coughlan:5 supplying:2 detecting:12 node:7 lx:1 simpler:1 consists:1 inside:1 pairwise:1 expected:1 rapid:4 roughly:1 chi:1 detects:3 decomposed:2 little:1 cpu:1 notation:1... |
1,936 | 2,759 | Analysis of Spectral Kernel Design based
Semi-supervised Learning
Tong Zhang
Yahoo! Inc.
New York City, NY 10011
Rie Kubota Ando
IBM T. J. Watson Research Center
Yorktown Heights, NY 10598
Abstract
We consider a framework for semi-supervised learning using spectral
decomposition based un-supervised kernel design. Th... | 2759 |@word norm:1 decomposition:6 pick:2 tr:2 reduction:7 initial:5 itp:1 com:1 written:1 john:1 wellbehaved:1 designed:1 treating:1 plot:1 implying:1 guess:2 node:2 simpler:1 zhang:1 height:1 constructed:1 direct:1 prove:1 consists:1 x0:8 expected:2 behavior:8 examine:1 nor:1 decomposed:1 decreasing:1 becomes:1 proje... |
1,937 | 276 | Non-Boltzmann Dynamics in Networks of Spiking Neurons
Non-Boltzmann Dynamics in Networks of
Spiking Neurons
Michael C. Crair and William Bialek
Department of Physics, and
Department of Molecular and Cell Biology
University of California at Berkeley
Berkeley, CA 94720
ABSTRACT
We study networks of spiking neurons in w... | 276 |@word hippocampus:1 nd:1 simulation:3 teich:4 existing:1 current:12 yet:1 physiol:1 subsequent:1 numerical:2 multineuron:1 realistic:4 happen:1 analytic:1 treating:1 stationary:7 alone:1 device:1 twostate:2 realism:1 short:1 math:1 complication:1 along:1 direct:2 become:2 differential:1 gustafsson:2 qualitative:2 ... |
1,938 | 2,760 | Q-Clustering
Mukund Narasimhan?
Nebojsa Jojic?
Jeff Bilmes?
?
Dept of Electrical Engineering, University of Washington, Seattle WA
?
Microsoft Research, Microsoft Corporation, Redmond WA
{mukundn,bilmes}@ee.washington.edu and jojic@microsoft.com
Abstract
We show that Queyranne?s algorithm for minimizing symmetric sub... | 2760 |@word trial:1 version:3 polynomial:6 seems:2 open:1 d2:1 hu:2 seek:3 pick:3 reduction:1 selecting:1 past:1 o2:1 com:1 comparing:2 surprising:1 si:8 must:1 mst:1 realistic:1 partition:24 engg:1 nebojsa:1 generative:6 fewer:1 intelligence:1 item:1 provides:1 math:1 mathematical:1 along:2 c2:8 h4:2 tirri:1 prove:2 c... |
1,939 | 2,761 | A Bayesian Framework for
Tilt Perception and Confidence
Odelia Schwartz
HHMI and Salk Institute
La Jolla, CA 92014
odelia@salk.edu
Terrence J. Sejnowski
HHMI and Salk Institute
La Jolla, CA 92014
terry@salk.edu
Peter Dayan
Gatsby, UCL
17 Queen Square, London
dayan@gatsby.ucl.ac.uk
Abstract
The misjudgement of tilt ... | 2761 |@word neurophysiology:1 version:4 proportion:1 wenderoth:1 simulation:3 tried:1 jacob:1 paid:1 solid:1 crowding:4 configuration:13 foveal:2 disparity:1 interestingly:1 subjective:1 current:1 contextual:2 surprising:1 yet:1 attracted:2 written:1 tilted:10 visible:1 entertaining:1 benign:2 shape:1 visibility:1 trea... |
1,940 | 2,762 | On the Convergence of Eigenspaces in Kernel
Principal Component Analysis
Laurent Zwald
D?epartement de Math?ematiques,
Universit?e Paris-Sud,
B?at. 425, F-91405 Orsay, France
Laurent.Zwald@math.u-psud.fr
Gilles Blanchard
Fraunhofer First (IDA),
K?ekul?estr. 7, D-12489 Berlin, Germany
blanchar@first.fhg.de
Abstract
T... | 2762 |@word h:8 version:1 norm:10 k2hk:1 open:1 covariance:12 pg:7 ld:1 moment:1 reduction:2 carry:1 series:1 epartement:1 hereafter:1 denoting:1 diagonalized:1 ida:1 bd:5 kpf:11 happen:1 alone:3 implying:1 selected:2 short:3 provides:2 math:3 arctan:1 mathematical:1 direct:1 qualitative:1 prove:3 consists:2 introduce:... |
1,941 | 2,763 | A Probabilistic Interpretation of SVMs with an
Application to Unbalanced Classification
Yves Grandvalet ?
Heudiasyc, CNRS/UTC
60205 Compi`egne cedex, France
grandval@utc.fr
Johnny Mari?ethoz Samy Bengio
IDIAP Research Institute
1920 Martigny, Switzerland
{marietho,bengio}@idiap.ch
Abstract
In this paper, we show tha... | 2763 |@word version:1 briefly:2 achievable:1 norm:1 c0:7 decomposition:1 covariance:1 p0:14 independant:1 score:17 tuned:1 recovered:1 mari:1 fn:11 kdd:1 enables:2 designed:1 v:9 alone:1 generative:1 selected:1 parameterization:1 ith:1 egne:1 provides:5 zhang:2 incorrect:1 consists:1 fitting:2 excellence:1 indeed:1 beh... |
1,942 | 2,764 | The Information-Form Data Association Filter
Brad Schumitsch, Sebastian Thrun, Gary Bradski, and Kunle Olukotun
Stanford AI Lab
Stanford University, Stanford, CA 94305
Abstract
This paper presents a new filter for online data association problems in
high-dimensional spaces. The key innovation is a representation of th... | 2764 |@word version:1 seems:1 open:1 seitz:1 simulation:2 mention:1 tr:15 liu:1 score:2 zij:7 outperforms:2 freitas:1 current:1 assigning:1 reminiscent:1 written:1 realistic:1 numerical:3 partition:4 enables:1 grumman:2 plot:1 sponsored:1 update:31 occlude:1 v:1 leaf:1 core:1 caveat:1 provides:2 location:1 along:1 zkj:... |
1,943 | 2,765 | Representing Part-Whole Relationships in
Recurrent Neural Networks
Viren Jain2 , Valentin Zhigulin1,2 , and H. Sebastian Seung1,2
1
Howard Hughes Medical Institute and
2
Brain & Cog. Sci. Dept., MIT
viren@mit.edu, valentin@mit.edu, seung@mit.edu
Abstract
There is little consensus about the computational function of to... | 2765 |@word briefly:1 middle:1 stronger:1 norm:1 simulation:5 ptot:2 initial:3 configuration:2 contains:4 interestingly:1 activation:6 must:2 numerical:1 happen:4 ith:1 detecting:1 provides:1 simpler:2 unbounded:2 mathematical:1 c2:2 become:1 prove:1 interlayer:5 behavior:5 p1:1 multi:1 brain:1 inspired:1 detects:3 rel... |
1,944 | 2,766 | Fixing two weaknesses of the Spectral Method
Kevin J. Lang
Yahoo Research
3333 Empire Ave, Burbank, CA 91504
langk@yahoo-inc.com
Abstract
We discuss two intrinsic weaknesses of the spectral graph partitioning
method, both of which have practical consequences. The first is that
spectral embeddings tend to hide the bes... | 2766 |@word economically:1 version:3 stronger:2 grey:1 thereby:1 series:4 contains:2 score:10 daniel:1 interestingly:1 current:3 com:1 surprising:1 lang:2 scatter:3 must:1 mqi:5 mesh:4 numerical:1 happen:3 partition:2 drop:2 plot:6 v:1 intelligence:1 website:1 record:2 lr:5 hypersphere:1 provides:1 math:1 node:30 lx:2 ... |
1,945 | 2,767 | Off-policy Learning with Options and
Recognizers
Richard S. Sutton
University of Alberta
Edmonton, AB, Canada
Doina Precup
McGill University
Montreal, QC, Canada
Cosmin Paduraru
University of Alberta
Edmonton, AB, Canada
Anna Koop
University of Alberta
Edmonton, AB, Canada
Satinder Singh
University of Michigan
Ann ... | 2767 |@word advantageous:1 termination:4 r:1 moment:1 initial:2 selecting:3 existing:1 current:1 must:3 partition:4 update:13 stationary:4 greedy:1 selected:7 intelligence:1 c2:2 direct:1 prove:3 introduce:3 expected:9 ingenuity:1 behavior:36 planning:1 alberta:4 td:2 little:1 considering:1 becomes:1 panel:3 lowest:2 w... |
1,946 | 2,768 | An Alternative Infinite Mixture Of Gaussian
Process Experts
Edward Meeds and Simon Osindero
Department of Computer Science
University of Toronto
Toronto, M5S 3G4
{ewm,osindero}@cs.toronto.edu
Abstract
We present an infinite mixture model in which each component comprises a multivariate Gaussian distribution over an i... | 2768 |@word inversion:1 seems:3 nd:1 simulation:2 covariance:16 jacob:1 thereby:1 tr:1 solid:2 accommodate:2 carry:2 configuration:1 wj2:1 current:1 must:2 partition:1 wanted:1 update:11 stationary:10 generative:11 yr:6 v1r:3 beginning:1 ith:1 parameterizations:1 toronto:4 location:21 simpler:1 along:3 constructed:1 ps... |
1,947 | 2,769 | Fast biped walking with a reflexive controller
and real-time policy searching
3
Tao Geng1 , Bernd Porr2 and Florentin W?org?otter1,3
1
Dept. Psychology, University of Stirling, UK.
runbot05@gmail.com
2
Dept. Electronics & Electrical Eng., University of Glasgow, UK.
b.porr@elec.gla.ac.uk
Bernstein Centre for Computati... | 2769 |@word exploitation:1 open:1 ankle:1 eng:1 simplifying:1 locomotive:1 moment:2 initial:1 electronics:1 series:1 score:2 exclusively:2 tuned:1 past:1 com:1 anterior:3 gmail:1 must:1 motor:25 designed:3 half:1 fewer:1 plane:2 gear:1 trapping:1 short:6 record:1 contribute:1 location:1 org:1 sigmoidal:1 rc:1 direct:1 ... |
1,948 | 277 | 668
Dembo, Siu and Kailath
Complexity of Finite Precision
Neural Network Classifier
Amir Dembo 1
Inform. Systems Lab.
Stanford University
Stanford, Calif. 94305
Kai-Yeung Siu
Inform. Systems Lab.
Stanford University
Stanford, Calif. 94305
Thomas Kailath
Inform. Systems Lab .
Stanford University
Stanford, Calif. 94... | 277 |@word polynomial:2 proportion:1 seems:1 open:1 simulation:1 moment:2 reduction:2 initial:1 fn:1 implying:1 fewer:1 device:2 afn:1 amir:1 dembo:4 beginning:2 ron:2 lor:1 mathematical:3 become:1 introduce:1 pairwise:1 considering:1 provided:3 moreover:2 underlying:1 circuit:3 bounded:4 what:1 affirmative:1 finding:1... |
1,949 | 2,770 | Affine Structure From Sound
Sebastian Thrun
Stanford AI Lab
Stanford University, Stanford, CA 94305
Email: thrun@stanford.edu
Abstract
We consider the problem of localizing a set of microphones together
with a set of external acoustic events (e.g., hand claps), emitted at unknown times and unknown locations. We propos... | 2770 |@word norm:1 open:1 d2:6 calculus:2 seek:2 cos2:1 simulation:1 decomposition:4 initial:1 configuration:2 series:2 selecting:1 recovered:4 current:1 yet:1 written:1 must:2 subsequent:2 shape:1 enables:1 plot:5 gist:1 aps:1 v:3 guess:2 plane:1 detecting:1 location:31 arctan:1 simpler:1 dn:6 along:2 prove:2 ijcv:1 r... |
1,950 | 2,771 | Comparing the Effects of Different Weight
Distributions on Finding Sparse Representations
David Wipf and Bhaskar Rao ?
Department of Electrical and Computer Engineering
University of California, San Diego, CA 92093
dwipf@ucsd.edu, brao@ece.ucsd.edu
Abstract
Given a redundant dictionary of basis vectors (or atoms), our... | 2771 |@word trial:2 determinant:1 seems:2 norm:11 stronger:1 suitably:1 simulation:1 covariance:6 thereby:1 born:1 interestingly:1 outperforms:1 existing:1 current:3 comparing:4 recovered:1 si:2 yet:3 must:6 readily:1 noninformative:1 plot:5 update:4 greedy:2 fewer:1 selected:3 parameterization:1 urp:5 gribonval:1 diss... |
1,951 | 2,772 | Describing Visual Scenes using
Transformed Dirichlet Processes
Erik B. Sudderth, Antonio Torralba, William T. Freeman, and Alan S. Willsky
Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
esuddert@mit.edu, torralba@csail.mit.edu, billf@mit.edu, willsky@mit.edu
Abstract
Mo... | 2772 |@word seems:1 proportion:4 reused:4 covariance:5 decomposition:1 thereby:1 solid:2 shot:1 moment:1 contains:1 interestingly:1 existing:3 elliptical:2 contextual:4 comparing:1 yet:1 must:1 parsing:3 partition:2 shape:2 resampling:1 generative:9 fewer:1 selected:1 cue:1 parameterization:1 tdp:35 intelligence:1 disc... |
1,952 | 2,773 | Silicon Growth Cones Map Silicon Retina
Brian Taba and Kwabena Boahen?
Department of Bioengineering
University of Pennsylvania
Philadelphia, PA 19104
{btaba,boahen}@seas.upenn.edu
Abstract
We demonstrate the first fully hardware implementation of retinotopic
self-organization, from photon transduction to neural map f... | 2773 |@word compression:1 achievable:1 c0:4 open:1 instruction:2 grey:10 simulation:1 eng:1 postsynaptically:2 excited:1 gertler:1 initial:1 contains:1 tuned:1 existing:1 coactive:1 z2:2 neurophys:1 activation:1 must:1 readily:1 plasticity:1 shape:1 displace:1 designed:2 update:12 cue:2 selected:2 half:3 device:1 plane... |
1,953 | 2,774 | Learning Multiple Related Tasks using Latent
Independent Component Analysis
Jian Zhang?, Zoubin Ghahramani??, Yiming Yang?
? School of Computer Science
? Gatsby Computational Neuroscience Unit
Cargenie Mellon University
University College London
Pittsburgh, PA 15213
London WC1N 3AR, UK
{jian.zhang, zoubin, yiming}@cs.... | 2774 |@word multitask:1 inversion:1 nd:1 seek:1 covariance:5 simplifying:1 tr:1 shot:1 moment:2 series:1 score:1 document:5 existing:2 written:1 stemming:1 happen:1 remove:4 designed:1 treating:4 update:1 unidentifiability:3 generative:4 intelligence:2 item:1 simpler:1 zhang:3 stopwords:1 c2:1 specialize:1 indeed:1 exp... |
1,954 | 2,775 | Data-Driven Online to Batch Conversions
Ofer Dekel and Yoram Singer
School of Computer Science and Engineering
The Hebrew University, Jerusalem 91904, Israel
{oferd,singer}@cs.huji.ac.il
Abstract
Online learning algorithms are typically fast, memory efficient, and simple to implement. However, many common learning pro... | 2775 |@word h:7 version:2 compression:2 dekel:3 r:1 simplifying:1 incurs:2 moment:1 contains:1 selecting:1 outperforms:3 existing:3 current:1 must:3 predetermined:1 enables:1 remove:1 plot:3 update:7 half:2 warmuth:4 beginning:2 mathematical:1 along:1 c2:2 direct:2 h4:1 constructed:1 incorrect:1 consists:1 manner:1 the... |
1,955 | 2,776 | Modeling Neural Population Spiking Activity
with Gibbs Distributions
Frank Wood, Stefan Roth, and Michael J. Black
Department of Computer Science
Brown University
Providence, RI 02912
{fwood,roth,black}@cs.brown.edu
Abstract
Probabilistic modeling of correlated neural population firing activity is
central to understan... | 2776 |@word neurophysiology:1 nd:1 seek:1 covariance:4 p0:1 contrastive:9 liu:1 selecting:1 shum:1 outperforms:1 past:3 current:2 discretization:1 surprising:1 must:1 subsequent:1 partition:5 motor:7 plot:1 selected:1 manipulandum:3 guess:1 plane:1 xk:3 revisited:1 five:1 along:1 dn:1 direct:1 consists:3 combine:1 beha... |
1,956 | 2,777 | Stimulus Evoked Independent Factor Analysis of
MEG Data with Large Background Activity
S.S. Nagarajan
Biomagnetic Imaging Laboratory
Department of Radiology
University of California, San Francisco
San Francisco, CA 94122
sri@radiology.ucsf.edu
H.T. Attias
Golden Metallic, Inc.
P.O. Box 475608
San Francisco, CA 94147
... | 2777 |@word trial:13 version:3 sri:1 middle:3 loading:1 mri:2 suitably:1 bun:5 proportionality:1 simulation:4 covariance:6 eng:2 tr:1 reduction:1 series:2 mosher:1 denoting:1 suppressing:2 interestingly:1 outperforms:4 existing:1 current:1 com:1 neurophys:1 activation:2 si:1 must:5 mrsc:1 designed:1 plot:1 n0:10 tone:2... |
1,957 | 2,778 | From Weighted Classification to Policy Search
D. Blatt
Department of Electrical Engineering
and Computer Science
University of Michigan
Ann Arbor, MI 48109-2122
dblatt@eecs.umich.edu
A. O. Hero
Department of Electrical Engineering
and Computer Science
University of Michigan
Ann Arbor, MI 48109-2122
hero@eecs.umich.ed... | 2778 |@word recursively:1 reduction:10 initial:17 cyclic:1 past:2 current:1 beygelzimer:1 si:1 assigning:1 written:1 must:1 john:2 plot:1 update:3 stationary:6 generative:3 leaf:2 node:1 simpler:1 si1:15 along:3 constructed:2 manner:2 expected:1 planning:1 multi:1 decreasing:1 automatically:1 overwhelming:1 considering... |
1,958 | 2,779 | Generalization Error Bounds for Aggregation by
Mirror Descent with Averaging
Anatoli Juditsky
Laboratoire de Mod?elisation et Calcul - Universit?e Grenoble I
B.P. 53, 38041 Grenoble, France
anatoli.iouditski@imag.fr
Alexander Nazin
Institute of Control Sciences - Russian Academy of Science
65, Profsoyuznaya str., GSP-... | 2779 |@word version:2 norm:5 logit:1 crucially:1 boundedness:2 initial:5 contains:1 denoting:1 ours:2 existing:1 scovel:1 optim:2 numerical:1 additive:1 update:6 juditsky:4 warmuth:2 compelled:1 positron:1 provides:1 boosting:3 zhang:3 direct:4 differential:1 consists:1 prove:1 interscience:1 manner:1 kiwiel:1 introduc... |
1,959 | 2,780 | Spectral Bounds for Sparse PCA:
Exact and Greedy Algorithms
Baback Moghaddam
MERL
Cambridge MA, USA
baback@merl.com
Yair Weiss
Hebrew University
Jerusalem, Israel
yweiss@cs.huji.ac.il
Shai Avidan
MERL
Cambridge MA, USA
avidan@merl.com
Abstract
Sparse PCA seeks approximate sparse ?eigenvectors? whose projections
cap... | 2780 |@word trial:2 determinant:1 version:1 repository:1 interleave:1 loading:14 norm:6 instrumental:1 termination:1 seek:2 covariance:8 decomposition:3 pick:2 concise:1 mention:1 solid:1 reduction:1 initial:1 contains:1 dspca:28 selecting:1 bc:1 ala:1 interestingly:1 past:1 com:2 comparing:1 yet:2 must:2 readily:1 joh... |
1,960 | 2,781 | Analyzing Coupled Brain Sources:
Distinguishing True from Spurious Interaction
1,2
?
Guido Nolte1 , Andreas Ziehe3 , Frank Meinecke1 and Klaus-Robert Muller
1
2
Fraunhofer FIRST.IDA, Kekul?estr. 7, 12489 Berlin, Germany
Dept. of CS, University of Potsdam, August-Bebel-Strasse 89, 14482 Potsdam, Germany
3
TU Berlin, ... | 2781 |@word neurophysiology:1 version:2 middle:2 tedious:1 decomposition:1 covariance:3 bai:1 configuration:1 initial:3 existing:2 imaginary:8 diagonalized:5 ida:1 mari:1 si:1 activation:1 must:2 stemming:1 synchronicity:1 subsequent:5 plot:1 guess:3 complementing:1 vanishing:3 short:1 contribute:3 location:1 construct... |
1,961 | 2,782 | Temporally changing synaptic plasticity
4
Minija Tamosiunaite1,2 , Bernd Porr3 , and Florentin W?org?otter1,4
1
Department of Psychology, University of Stirling
Stirling FK9 4LA, Scotland
2
Department of Informatics, Vytautas Magnus University
Kaunas, Lithuania
3
Department of Electronics & Electrical Engineering, Un... | 2782 |@word stronger:3 seems:2 pulse:13 simulation:1 q1:3 moment:2 electronics:1 efficacy:1 selecting:1 current:1 readily:1 physiol:2 happen:1 plasticity:36 shape:5 plot:3 drop:1 depict:1 designed:1 aps:1 isotropic:1 beginning:1 scotland:2 smith:1 node:1 complication:1 location:2 org:4 simpler:1 along:1 differential:4 ... |
1,962 | 2,783 | Gaussian Process Dynamical Models
Jack M. Wang, David J. Fleet, Aaron Hertzmann
Department of Computer Science
University of Toronto, Toronto, ON M5S 3G4
{jmwang,hertzman}@dgp.toronto.edu, fleet@cs.toronto.edu
Abstract
This paper introduces Gaussian Process Dynamical Models (GPDM) for
nonlinear time series analysis. ... | 2783 |@word sgplvm:3 nd:1 nonsensical:1 simulation:1 xout:7 thereby:1 mention:1 tr:4 pavlovi:1 initial:2 configuration:1 series:8 animated:1 past:1 existing:1 current:1 wd:1 com:1 must:2 written:1 concatenate:1 additive:1 shape:2 remove:1 plot:1 depict:2 generative:1 accordingly:1 isotropic:3 smith:3 short:1 regressive... |
1,963 | 2,784 | Factorial Switching Kalman Filters for
Condition Monitoring in Neonatal Intensive
Care
Christopher K. I. Williams and John Quinn
School of Informatics, University of Edinburgh
Edinburgh EH1 2QL, UK
c.k.i.williams@ed.ac.uk
john.quinn@ed.ac.uk
Neil McIntosh
Simpson Centre for Reproductive
Health, Edinburgh EH16 4SB, UK
... | 2784 |@word humidity:4 open:4 pulse:1 covariance:1 pressure:11 series:4 contains:2 bc:1 o2:3 reaction:1 freitas:1 current:1 comparing:1 john:2 predetermined:1 drop:5 interpretable:1 update:4 plot:1 infant:7 sys:3 tcp:10 core:7 record:1 provides:1 detecting:1 node:1 firstly:2 predecessor:1 consists:1 fitting:1 inside:1 ... |
1,964 | 2,785 | Learning to Control an Octopus Arm with
Gaussian Process Temporal Difference Methods
Yaakov Engel?
AICML, Dept. of Computing Science
University of Alberta
Edmonton, Canada
yaki@cs.ualberta.ca
Peter Szabo and Dmitry Volkinshtein
Dept. of Electrical Engineering
Technion Institute of Technology
Haifa, Israel
peter.z.sza... | 2785 |@word neurophysiology:1 trial:4 version:1 polynomial:1 nd:2 open:2 simulation:7 gptd:26 contraction:3 decomposition:1 covariance:1 hochner:3 pressure:1 mention:1 solid:2 versatile:1 moment:2 initial:10 configuration:3 series:1 seriously:1 longitudinal:3 quadrilateral:1 current:4 com:2 activation:5 gmail:2 yet:3 t... |
1,965 | 2,786 | Oblivious Equilibrium: A Mean Field
Approximation for Large-Scale Dynamic Games
Gabriel Y. Weintraub, Lanier Benkard, and Benjamin Van Roy
Stanford University
{gweintra,lanierb,bvr}@stanford.edu
Abstract
We propose a mean-field approximation that dramatically reduces the
computational complexity of solving stochastic... | 2786 |@word mild:1 manageable:1 logit:1 open:1 simulation:3 fifteen:1 profit:14 initial:1 hereafter:1 current:2 yet:1 must:2 numerical:3 drop:1 stationary:2 merger:1 benkard:2 institution:1 provides:1 five:1 unbounded:1 ik:2 prove:1 shorthand:1 consists:1 introduce:1 expected:11 market:7 behavior:3 roughly:1 growing:1 ... |
1,966 | 2,787 | Subsequence Kernels for Relation Extraction
Razvan C. Bunescu
Department of Computer Sciences
University of Texas at Austin
1 University Station C0500
Austin, TX 78712
razvan@cs.utexas.edu
Raymond J. Mooney
Department of Computer Sciences
University of Texas at Austin
1 University Station C0500
Austin, TX 78712
moone... | 2787 |@word faculty:1 version:5 nd:1 lodhi:1 mention:3 yih:1 contains:4 exclusively:1 bibliographic:1 document:5 outperforms:3 current:1 activation:1 si:1 must:1 parsing:3 john:1 written:1 j1:2 v:2 greedy:1 intelligence:2 beginning:1 pointer:1 contribute:2 location:1 five:2 retrieving:1 shorthand:1 consists:5 ray:1 tag... |
1,967 | 2,788 | Hot Coupling: A Particle Approach to Inference
and Normalization on Pairwise Undirected
Graphs of Arbitrary Topology
Firas Hamze
Nando de Freitas
Department of Computer Science
University of British Columbia
Abstract
This paper presents a new sampling algorithm for approximating functions of variables representable as... | 2788 |@word version:1 polynomial:1 seems:1 simulation:4 bn:3 q1:2 recursively:1 xv1:4 carry:1 reduction:1 celebrated:2 initial:3 freitas:4 err:1 current:2 john:1 realize:1 fn:19 subsequent:1 partition:15 analytic:2 drop:1 plot:1 update:2 resampling:5 selected:1 menendez:1 xk:2 isotropic:1 mccallum:1 normalising:1 node:... |
1,968 | 2,789 | Large scale networks fingerprinting and
visualization using the k-core decomposition
J. Ignacio Alvarez-Hamelin?
LPT (UMR du CNRS 8627),
Universit?e de Paris-Sud,
91405 ORSAY Cedex France
Ignacio.Alvarez-Hamelin@lri.fr
Luca Dall?Asta
LPT (UMR du CNRS 8627),
Universit?e de Paris-Sud,
91405 ORSAY Cedex France
Luca.Dall... | 2789 |@word disk:2 open:1 simulation:1 decomposition:22 innermost:2 recursively:3 reduction:1 necessity:1 configuration:1 series:3 initial:1 tuned:2 assigning:2 router:2 visible:1 partition:1 subsequent:1 remove:1 treating:1 progressively:1 half:1 core:56 granting:1 colored:2 infrastructure:1 characterization:1 provide... |
1,969 | 279 | Note on Development or Modularity in Simple Cortical Models
Note on Development of Modularity
in Simple Cortical Models
Alex Chernjavskyl
Neuroscience Graduate Program
Section of Molecular Neurobiology
Howard Hughes Medical Institute
Yale University
John Moody2
Yale Computer Science
PO Box 2158 Yale Station
New Have... | 279 |@word neurophysiology:1 version:1 simulation:7 covariance:2 thereby:1 solid:5 initial:1 daniel:1 reaction:3 activation:4 john:6 ronald:1 additive:12 realistic:2 plasticity:1 shape:1 enables:2 hypothesize:1 remove:1 etwork:1 stationary:1 nervous:4 ith:1 smith:1 short:2 math:1 sigmoidal:4 edelman:1 olfactory:2 theor... |
1,970 | 2,790 | Convergence and Consistency of
Regularized Boosting Algorithms with
Stationary ?-Mixing Observations
Aur?elie C. Lozano
Department of Electrical Engineering
Princeton University
Princeton, NJ 08544
alozano@princeton.edu
Sanjeev R. Kulkarni
Department of Electrical Engineering
Princeton University
Princeton, NJ 08544
... | 2790 |@word version:1 norm:3 logit:1 nd:1 distribue:1 c0:1 bn:55 contraction:2 pick:1 series:6 seriously:1 past:2 z2:1 fn:9 additive:2 enables:1 stationary:14 greedy:1 xk:2 boosting:19 mcdiarmid:2 zhang:2 mathematical:1 c2:5 become:1 incorrect:1 prove:2 consists:1 combine:1 fitting:1 expected:1 behavior:1 examine:1 aut... |
1,971 | 2,791 | Gaussian Processes for Multiuser Detection in
CDMA receivers
Juan Jos?e Murillo-Fuentes, Sebastian Caro
Dept. Signal Theory and Communications
University of Seville
{murillo,scaro}@us.es
Fernando P?erez-Cruz
Gatsby Computational Neuroscience
University College London
fernando@gatsby.ucl.ac.uk
Abstract
In this paper w... | 2791 |@word trial:1 version:1 achievable:1 bn:1 covariance:2 contains:4 hereafter:1 mmse:22 multiuser:11 outperforms:1 recovered:1 nt:3 must:1 readily:2 cruz:2 designed:2 v:3 selected:1 ith:2 short:6 provides:1 direct:1 inter:1 inspired:1 unpredictable:1 xx:1 notation:1 matched:3 lowest:1 interpreted:2 minimizes:2 deve... |
1,972 | 2,792 | Robust Fisher Discriminant Analysis
Seung-Jean Kim
Alessandro Magnani
Stephen P. Boyd
Information Systems Laboratory
Electrical Engineering Department, Stanford University
Stanford, CA 94305-9510
sjkim@stanford.edu
alem@stanford.edu
boyd@stanford.edu
Abstract
Fisher linear discriminant analysis (LDA) can be sensit... | 2792 |@word establish:2 repository:1 briefly:1 quotient:2 implies:1 norm:2 involves:1 objective:1 symmetric:1 laboratory:1 d2:3 nonzero:1 covariance:22 diagonal:1 tr:3 solid:1 mapped:2 hx:1 mlrepository:1 d4:1 suffices:1 criterion:2 athena:1 alleviate:2 nx:8 proposition:3 kuf:3 demonstrate:1 discriminant:45 bhattachary... |
1,973 | 2,793 | A Bayes Rule for Density Matrices
Manfred K. Warmuth?
Computer Science Department
University of California at Santa Cruz
manfred@cse.ucsc.edu
Abstract
The classical Bayes rule computes the posterior model probability
from the prior probability and the data likelihood. We generalize
this rule to the case when the prio... | 2793 |@word trial:1 torsten:1 middle:1 version:2 calculus:1 covariance:14 tr:31 solid:1 minus:1 moment:1 initial:1 si:2 must:2 cruz:1 additive:1 visible:1 happen:2 plot:2 update:9 depict:1 leaf:1 warmuth:5 ith:1 manfred:2 cse:1 along:6 ucsc:1 ik:1 combine:1 hermitian:2 expected:12 nor:1 eurocolt:1 decomposed:1 becomes:... |
1,974 | 2,794 | Structured Prediction via the Extragradient
Method
Ben Taskar
Computer Science
UC Berkeley, Berkeley, CA 94720
taskar@cs.berkeley.edu
Simon Lacoste-Julien
Computer Science
UC Berkeley, Berkeley, CA 94720
slacoste@cs.berkeley.edu
Michael I. Jordan
Computer Science and Statistics
UC Berkeley, Berkeley, CA 94720
jordan@... | 2794 |@word illustrating:1 version:2 pw:2 polynomial:3 yi0:18 seek:1 pick:1 reduction:3 contains:2 score:5 selecting:1 percep:2 assigning:1 written:2 fn:3 partition:1 hofmann:1 cheap:1 eleven:1 shape:3 plot:1 generative:1 mccallum:1 problemspecific:1 detecting:1 math:1 node:18 lx:1 five:1 dn:1 viable:1 specialize:1 con... |
1,975 | 2,795 | Distance Metric Learning for Large Margin
Nearest Neighbor Classification
Kilian Q. Weinberger, John Blitzer and Lawrence K. Saul
Department of Computer and Information Science, University of Pennsylvania
Levine Hall, 3330 Walnut Street, Philadelphia, PA 19104
{kilianw, blitzer, lsaul}@cis.upenn.edu
Abstract
We show ... | 2795 |@word repository:1 version:1 middle:2 polynomial:1 norm:1 briefly:1 seek:1 crucially:1 covariance:1 set5:1 pavel:1 incurs:1 thereby:1 reduction:1 contains:1 att:1 com:2 goldberger:2 intriguing:1 john:1 shape:2 designed:1 update:1 intelligence:3 oldest:1 mccallum:2 banff:1 incorrect:1 consists:1 pairwise:4 peng:1 ... |
1,976 | 2,796 | Efficient Unsupervised Learning for Localization
and Detection in Object Categories
Nicolas Loeff, Himanshu Arora
ECE Department
University of Illinois at
Urbana-Champaign
Alexander Sorokin, David Forsyth
Computer Science Department
University of Illinois at
Urbana-Champaign
{loeff,harora1}@uiuc.edu
{sorokin2,daf}@... | 2796 |@word determinant:1 version:1 manageable:1 covariance:5 dramatic:1 tr:1 reduction:1 configuration:4 ours:1 current:1 yet:1 visible:3 shape:1 enables:1 gv:7 remove:1 plot:4 update:5 v:2 generative:5 cue:1 boosting:2 location:19 simpler:1 ijcv:2 inside:1 introduce:1 mask:1 hardness:1 rapid:1 uiuc:2 detects:1 little... |
1,977 | 2,797 | Products of ?Edge-perts?
Peter Gehler
Max Planck Institute for Biological Cybernetics
Spemannstra?e 38, 72076 T?ubingen, Germany
pgehler@tuebingen.mpg.de
Max Welling
Department of Computer Science
University of California Irvine
welling@ics.uci.edu
Abstract
Images represent an important and abundant source of data. ... | 2797 |@word compression:2 seems:3 decomposition:3 covariance:6 simplifying:1 incurs:1 mention:1 solid:1 moment:6 bc:1 outperforms:2 elliptical:1 comparing:2 predetermined:1 shape:2 kyb:1 plot:1 drop:1 rrt:1 generative:3 selected:1 implying:1 discovering:1 intelligence:1 indicative:1 iso:1 filtered:1 location:1 simpler:... |
1,978 | 2,798 | Worst-Case Bounds for Gaussian Process Models
Sham M. Kakade
University of Pennsylvania
Matthias W. Seeger
UC Berkeley
Dean P. Foster
University of Pennsylvania
Abstract
We present a competitive analysis of some non-parametric Bayesian algorithms in a worst-case online learning setting, where no probabilistic
assump... | 2798 |@word version:4 polynomial:1 norm:3 stronger:1 c0:1 d2:1 covariance:3 thereby:1 tr:2 contains:2 rkhs:9 interestingly:1 past:1 must:2 stine:3 additive:1 implying:1 warmuth:6 isotropic:1 sys:1 manfred:2 unbounded:1 shtarkov:5 constructed:1 consists:1 pmap:5 manner:1 x0:4 behavior:1 examine:1 actual:1 provided:1 bou... |
1,979 | 2,799 | Identifying Distributed Object Representations
in Human Extrastriate Visual Cortex
Rory Sayres
Department of Neuroscience
Stanford University
Stanford, CA 94305
sayres@stanford.edu
David Ress
Department of Neuroscience
Brown University
Providence, RI 02912
ress@brown.edu
Kalanit Grill-Spector
Departments of Neuroscie... | 2799 |@word trial:17 mri:1 version:1 proportion:2 solid:1 reduction:1 extrastriate:4 foveal:1 series:1 contains:2 selecting:1 subjective:1 current:1 anterior:2 activation:1 yet:2 readily:1 informative:2 oxygenation:1 analytic:1 haxby:2 progressively:1 discrimination:4 half:4 selected:2 guess:5 pursued:1 ith:1 oblique:1... |
1,980 | 28 | 262
ON TROPISTIC PROCESSING AND ITS APPLICATIONS
Manuel F. Fernandez
General Electric Advanced Technology Laboratories
Syracuse, New York 13221
ABSTRACT
The interaction of a set of tropisms is sufficient in many
cases to explain the seemingly complex behavioral responses
exhibited by varied classes of biological syste... | 28 |@word aircraft:2 version:1 briefly:1 suitably:1 d2:1 simulation:3 attainable:1 profit:1 tr:2 series:1 past:2 reaction:6 elliptical:1 od:1 manuel:1 attracted:1 john:1 refines:1 designed:1 update:4 depict:1 v:1 intelligence:1 selected:1 advancement:1 plane:1 steepest:1 provides:1 sigmoidal:1 prove:1 behavioral:3 insi... |
1,981 | 280 | Generalized Hopfield Networks and Nonlinear Optimization
Generalized Hopfield Networks
and
Nonlinear Optimization
Gintaras v. Reklaitis
Dept. of Chemical Eng.
Purdue University
W. Lafayette, IN. 47907
Athanasios G. Tsirukis 1
Dept. of Chemical Eng.
Purdue University
W. Lafayette, IN. 47907
Manoel F. Tenorio
Dept of... | 280 |@word version:1 inversion:2 tedious:1 d2:1 eng:3 initial:4 series:1 exclusively:1 t7:1 existing:1 marquardt:1 activation:2 dx:3 must:1 numerical:1 shape:1 designed:2 depict:1 update:1 fewer:1 xk:1 steepest:2 chua:2 successive:3 differential:3 become:1 consists:1 interscience:1 manner:2 ravindran:1 rapid:1 behavior... |
1,982 | 2,800 | Convex Neural Networks
Yoshua Bengio, Nicolas Le Roux, Pascal Vincent, Olivier Delalleau, Patrice Marcotte
Dept. IRO, Universit?e de Montr?eal
P.O. Box 6128, Downtown Branch, Montreal, H3C 3J7, Qc, Canada
{bengioy,lerouxni,vincentp,delallea,marcotte}@iro.umontreal.ca
Abstract
Convexity has recently received a lot of a... | 2800 |@word version:1 polynomial:1 seems:1 norm:2 nd:1 termination:1 decomposition:1 initial:1 necessity:1 selecting:2 denoting:1 current:1 com:1 comparing:1 activation:1 yet:1 must:4 reminiscent:1 written:1 additive:3 remove:1 discrimination:1 greedy:4 selected:3 steepest:1 core:1 characterization:1 boosting:15 mathem... |
1,983 | 2,801 | Rate Distortion Codes in Sensor Networks:
A System-level Analysis
Tatsuto Murayama and Peter Davis
NTT Communication Science Laboratories
Nippon Telegraph and Telephone Corporation
?Keihanna Science City?, Kyoto 619-0237, Japan
{murayama,davis}@cslab.kecl.ntt.co.jp
Abstract
This paper provides a system-level analysis... | 2801 |@word trial:1 seems:2 carry:2 series:1 hereafter:1 outperforms:1 com:1 dx:1 must:1 belmont:1 numerical:1 additive:2 informative:1 partition:2 predetermined:1 implying:1 selected:2 device:2 accordingly:1 xk:2 hamiltonian:2 vanishing:2 provides:4 firstly:1 zhang:2 mathematical:1 qualitative:1 introduce:2 market:1 e... |
1,984 | 2,802 | Optimal cue selection strategy
Vidhya Navalpakkam
Department of Computer Science
USC, Los Angeles
navalpak@usc.edu
Laurent Itti
Department of Computer Science
USC, Los Angeles
itti@usc.edu
Abstract
Survival in the natural world demands the selection of relevant visual
cues to rapidly and reliably guide attention tow... | 2802 |@word trial:14 middle:1 briefly:2 nd:2 proportionality:1 simulation:2 rayner:1 pavel:1 thereby:1 configuration:6 tuned:12 suppressing:1 existing:1 nt:2 si:5 activation:2 must:2 najemnik:1 subsequent:1 additive:1 plot:2 designed:1 fund:1 cue:34 item:5 maximised:2 ith:5 steepest:4 farther:1 cognit:1 detecting:2 boo... |
1,985 | 2,803 | An aVLSI cricket ear model
Andr? van Schaik*
The University of Sydney
NSW 2006, AUSTRALIA
andre@ee.usyd.edu.au
Richard Reeve+
University of Edinburgh
Edinburgh, UK
richardr@inf.ed.ac.uk
Craig Jin*
craig@ee.usyd.edu.au
Tara Hamilton*
tara@ee.usyd.edu.au
Abstract
Female crickets can locate males by phonotaxis to the ... | 2803 |@word version:2 inversion:1 c0:2 simulation:4 out1:1 nsw:1 solid:1 recursively:1 carry:3 reduction:1 initial:1 configuration:2 necessity:1 tuned:2 existing:1 current:22 torben:1 attracted:1 physiol:2 ota:2 motor:1 designed:3 drop:1 medial:1 plot:1 half:1 tone:2 schaik:4 node:1 simpler:1 along:3 direct:1 different... |
1,986 | 2,804 | Learning Rankings via Convex Hull Separation
Glenn Fung, R?omer Rosales, Balaji Krishnapuram
Computer Aided Diagnosis, Siemens Medical Solutions USA, Malvern, PA 19355
{glenn.fung, romer.rosales, balaji.krishnapuram}@siemens.com
Abstract
We propose efficient algorithms for learning ranking functions from order constr... | 2804 |@word version:1 norm:1 triazine:1 incurs:1 contains:1 denoting:1 document:2 interestingly:1 rkhs:1 current:3 com:1 comparing:1 chu:1 written:1 informative:2 kdd:1 hofmann:2 designed:1 prohibitive:1 xk:4 ith:1 boosting:2 preference:3 herbrich:3 five:1 mathematical:1 along:1 direct:1 become:3 inside:2 inter:1 indee... |
1,987 | 2,805 | The Role of Top-down and Bottom-up Processes
in Guiding Eye Movements during Visual Search
Gregory J. Zelinsky?? , Wei Zhang? , Bing Yu? , Xin Chen?? , Dimitris Samaras?
Dept. of Psychology? , Dept. of Computer Science?
State University of New York at Stony Brook
Stony Brook, NY 11794
Gregory.Zelinsky@stonybrook.edu? ,... | 2805 |@word h:2 trial:2 version:4 eliminating:1 briefly:1 proportion:4 nd:1 open:1 instruction:1 termination:1 simulation:1 decomposition:1 dramatic:1 thereby:2 fortuitous:1 moment:1 initial:1 foveal:6 offering:1 existing:2 current:14 comparing:2 surprising:1 yet:1 stony:2 visible:1 realistic:1 zap:1 remove:2 record:1 ... |
1,988 | 2,806 | The Forgetron:
A Kernel-Based Perceptron on a Fixed Budget
Ofer Dekel Shai Shalev-Shwartz Yoram Singer
School of Computer Science & Engineering
The Hebrew University, Jerusalem 91904, Israel
{oferd,shais,singer}@cs.huji.ac.il
Abstract
The Perceptron algorithm, despite its simplicity, often performs well on
online cla... | 2806 |@word kgk:3 version:1 manageable:1 polynomial:1 norm:8 stronger:1 seems:1 dekel:2 open:1 closure:1 q1:1 rkhs:1 ala:1 outperforms:1 current:2 kft:7 must:1 remove:4 plot:1 update:11 device:1 unacceptably:1 xk:1 oldest:6 eminent:1 along:1 direct:1 prove:4 consists:1 redefine:1 expected:1 indeed:2 rapid:1 themselves:... |
1,989 | 2,807 | Ideal Observers for Detecting Motion:
Correspondence Noise
Hongjing Lu
Department of Psychology, UCLA
Los Angeles, CA 90095
hongjing@psych.ucla.edu
Alan Yuille
Department of Statistics, UCLA
Los Angeles, CA 90095
yuille@stat.ucla.edu
Abstract
We derive a Bayesian Ideal Observer (BIO) for detecting motion and
solving... | 2807 |@word trial:1 proportion:2 open:2 seek:1 harder:1 configuration:1 liu:1 rightmost:1 comparing:1 surprising:1 enables:3 plot:2 designed:1 discrimination:8 alone:1 generative:1 detecting:2 firstly:1 simpler:1 combine:1 burr:2 swets:2 roughly:2 nor:1 wallace:1 decreasing:1 little:1 window:1 estimating:1 underlying:1... |
1,990 | 2,808 | Rodeo: Sparse Nonparametric Regression in
High Dimensions
John Lafferty
School of Computer Science
Carnegie Mellon University
Larry Wasserman
Department of Statistics
Carnegie Mellon University
Abstract
We present a method for nonparametric regression that performs bandwidth selection and variable selection simultan... | 2808 |@word trial:1 version:11 polynomial:1 norm:1 sex:1 simulation:1 bn:5 pset:1 pressure:1 tr:2 carry:1 reduction:1 moment:1 initial:2 series:1 xnj:3 current:2 dx:1 written:1 john:1 additive:1 wx:13 remove:1 plot:2 juditsky:1 greedy:7 selected:2 fewer:1 cook:1 xk:1 boosting:2 zhang:2 rc:2 along:3 constructed:1 fittin... |
1,991 | 2,809 | Augmented Rescorla-Wagner and Maximum
Likelihood estimation.
Alan Yuille
Department of Statistics
University of California at Los Angeles
Los Angeles, CA 90095
yuille@stat.ucla.edu
Abstract
We show that linear generalizations of Rescorla-Wagner can perform
Maximum Likelihood estimation of the parameters of all generat... | 2809 |@word determinant:4 briefly:1 holyoak:1 covariance:1 current:1 conjunctive:1 written:1 realize:2 remove:1 drop:1 update:11 discrimination:1 generative:18 steepest:1 record:1 ire:1 simpler:1 mathematical:1 c2:101 direct:3 h4:2 consists:2 prove:1 expected:1 v1t:8 becomes:1 provided:9 distri:1 notation:1 moreover:2 ... |
1,992 | 281 | An Efficient Implementation of the Back-propagation Algorithm
A n Efficient Implementation of
the Back-propagation Algorithm on
the Connection Machine CM-2
Xiru Zhang!
Michael Mckenna
Jill P. Mesirov
David L. Waltz
Thinking Machines Corporation
245 First Street, Cambridge, MA 02142-1214
ABSTRACT
In this paper, we... | 281 |@word cu:1 proportion:1 cm2:2 instruction:1 simulation:3 propagate:2 recursively:1 initial:1 contains:2 rightmost:1 recovered:2 od:1 activation:3 additive:1 girosi:1 remove:1 update:20 alone:1 intelligence:1 beginning:1 short:1 lr:1 provides:1 draft:1 node:42 llii:1 zhang:6 mathematical:1 interprocessor:1 become:1... |
1,993 | 2,810 | The Curse of Highly Variable Functions for
Local Kernel Machines
Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux
Dept. IRO, Universit?e de Montr?eal
P.O. Box 6128, Downtown Branch, Montreal, H3C 3J7, Qc, Canada
{bengioy,delallea,lerouxni}@iro.umontreal.ca
Abstract
We present a series of theoretical arguments support... | 2810 |@word duda:2 confirms:1 epartement:1 reduction:2 series:1 bc:1 written:4 must:6 bd:6 j1:2 shape:1 intelligence:3 plane:2 werwatz:1 short:2 core:1 recherche:1 node:2 sperlich:1 mathematical:1 along:5 c2:2 become:1 prove:2 indeed:2 expected:3 nor:1 relying:1 little:1 curse:5 window:1 considering:1 becomes:1 begin:1... |
1,994 | 2,811 | Inference with Minimal Communication:
a Decision-Theoretic Variational Approach
O. Patrick Kreidl and Alan S. Willsky
Department of Electrical Engineering and Computer Science
MIT Laboratory for Information and Decision Systems
Cambridge, MA 02139
{opk,willsky}@mit.edu
Abstract
Given a directed graphical model with b... | 2811 |@word version:1 achievable:1 termination:3 bn:9 u11:1 mention:1 carry:2 reduction:2 initial:2 cyclic:1 existing:1 recovered:1 b01:1 must:2 john:1 stemming:1 fn:4 subsequent:2 additive:2 shape:2 update:2 n0:2 implying:1 parameterization:6 mpm:11 accepting:1 iterates:1 node:39 successive:2 firstly:2 unbounded:1 mat... |
1,995 | 2,812 | Gradient Flow Independent Component
Analysis in Micropower VLSI
Abdullah Celik, Milutin Stanacevic and Gert Cauwenberghs
Johns Hopkins University, Baltimore, MD 21218
{acelik,miki,gert}@jhu.edu
Abstract
We present micropower mixed-signal VLSI hardware for real-time blind
separation and localization of acoustic source... | 2812 |@word briefly:1 inversion:1 pressure:2 solid:1 reduction:1 configuration:2 contains:1 existing:1 current:2 recovered:1 incidence:1 john:1 additive:1 datapath:1 wx:1 shape:1 designed:1 update:13 cue:1 intelligence:1 plane:1 sys:1 ith:1 provides:2 quantized:1 node:1 characterization:1 outerproduct:2 along:2 c2:3 di... |
1,996 | 2,813 | On the Accuracy of Bounded Rationality:
How Far from Optimal Is Fast and Frugal?
Michael Schmitt
Ludwig-Marum-Gymnasium
Schlossgartenstra?e 11
76327 Pfinztal, Germany
mschmittm@googlemail.com
Laura Martignon
Institut f?ur Mathematik und Informatik
P?adagogische Hochschule Ludwigsburg
Reuteallee 46, 71634 Ludwigsburg,... | 2813 |@word version:1 achievable:1 polynomial:17 open:1 checkable:1 simulation:2 bn:1 dieckmann:3 attainable:1 harder:1 reduction:3 contains:1 com:1 comparing:2 yet:1 must:3 ecis:1 numerical:1 j1:2 remove:1 cue:82 greedy:9 selected:1 dawes:1 mental:1 provides:1 constructed:2 symposium:1 incorrect:35 consists:4 prove:2 ... |
1,997 | 2,814 | Cue Integration for Figure/Ground Labeling
Xiaofeng Ren, Charless C. Fowlkes and Jitendra Malik
Computer Science Division, University of California, Berkeley, CA 94720
{xren,fowlkes,malik}@cs.berkeley.edu
Abstract
We present a model of edge and region grouping using a conditional
random field built over a scale-invar... | 2814 |@word h:3 collinearity:1 closure:3 brightness:5 recursively:1 carry:1 configuration:3 suppressing:1 existing:1 contextual:1 must:1 parsing:1 john:1 mesh:1 blur:5 shape:11 plot:1 cue:39 half:3 intelligence:1 mccallum:1 record:1 provides:2 quantized:1 boosting:1 location:5 along:1 combine:1 introduce:1 x0:4 pairwis... |
1,998 | 2,815 | Bayesian models of human action understanding
Chris L. Baker, Joshua B. Tenenbaum & Rebecca R. Saxe
{clbaker,jbt,saxe}@mit.edu
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Abstract
We present a Bayesian framework for explaining how people reason
about and predict the actions of an... | 2815 |@word trial:1 illustrating:1 middle:2 version:3 inversion:1 nd:1 simulation:1 seek:1 minus:1 initial:1 configuration:1 series:1 charniak:1 preverbal:5 ording:1 animated:1 current:2 must:5 realize:1 belmont:1 subsequent:2 shape:1 treating:1 designed:1 infant:26 generative:2 intelligence:1 cue:1 core:2 colored:1 me... |
1,999 | 2,816 | Sequence and Tree Kernels
with Statistical Feature Mining
Jun Suzuki and Hideki Isozaki
NTT Communication Science Laboratories, NTT Corp.
2-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto,619-0237 Japan
{jun, isozaki}@cslab.kecl.ntt.co.jp
Abstract
This paper proposes a new approach to feature selection based on a statistic... | 2816 |@word cu:6 briefly:3 eliminating:1 advantageous:1 lodhi:1 bn:1 decomposition:1 tr:1 recursively:1 contains:2 hereafter:1 prefix:7 subjective:1 written:1 parsing:1 cruz:1 kdd:1 remove:1 v:1 selected:4 t2j:5 pointer:5 provides:1 node:8 constructed:1 become:2 fitting:3 symp:1 introduce:3 presumed:1 themselves:1 seik... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.