Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
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1,100 | 2,003 | Incremental A
S. Koenig and M. Likhachev
Georgia Institute of Technology
College of Computing
Atlanta, GA 30312-0280
skoenig, mlikhach @cc.gatech.edu
Abstract
Incremental search techniques find optimal solutions to series of similar
search tasks much faster than is possible by solving each search task
from scrat... | 2003 |@word version:9 advantageous:1 suitably:2 termination:3 d2:1 confirms:1 shot:1 initial:2 configuration:1 series:3 contains:1 document:1 outperforms:1 current:5 subsequent:2 remove:8 update:11 intelligence:4 accordingly:1 constructed:1 predecessor:4 become:1 prove:1 combine:1 planning:20 bellman:1 decreasing:3 act... |
1,101 | 2,004 | Scaling laws and local minima in Hebbian ICA
Magnus Rattray and Gleb Basalyga
Department of Computer Science, University of Manchester,
Manchester M13 9PL, UK.
magnus@cs.man.ac.uk, basalygg@cs.man.ac.uk
Abstract
We study the dynamics of a Hebbian ICA algorithm extracting a single non-Gaussian component from a high-di... | 2004 |@word version:2 polynomial:1 proportionality:1 hyv:6 gfih:1 seek:1 simulation:2 covariance:1 moment:1 initial:22 luo:1 analysed:1 attracted:1 must:4 written:1 shape:3 update:3 stationary:1 trapping:4 deflationary:2 detecting:1 jkj:3 firstly:1 differential:1 become:1 incorrect:1 ica:11 mechanic:1 decomposed:1 cons... |
1,102 | 2,005 | Audio-Visual Sound Separation Via
Hidden Markov Models
John Hershey
Department of Cognitive Science
University of California San Diego
Michael Casey
Mitsubishi Electric Research Labs
Cambridge, Massachussets
jhershey@cogsci.ucsd.edu
casey@merl.com
Abstract
It is well known that under noisy conditions we can hear sp... | 2005 |@word middle:1 manageable:1 extinction:2 open:1 mitsubishi:2 covariance:4 carry:1 series:1 contains:1 loeliger:1 envision:1 amp:1 subjective:1 existing:1 current:3 com:1 anne:1 surprising:1 must:1 john:3 subsequent:1 subcomponent:1 dupont:1 discrimination:1 stationary:2 cue:7 half:1 selected:1 devising:1 plane:1 ... |
1,103 | 2,006 | .
Information-geometric decomposition In
spike analysis
Hiroyuki Nakahara; Shun-ichi Amari
Lab. for Mathematical Neuroscience, RIKEN Brain Science Institute
2-1 Hirosawa, Wako, Saitama, 351-0198 Japan
{him, amari} @brain.riken.go.jp
Abstract
We present an information-geometric measure to systematically
investigate n... | 2006 |@word neurophysiology:1 briefly:1 nd:2 physik:1 simulation:1 decomposition:11 covariance:3 solid:1 carry:2 tlo:2 denoting:1 wako:1 si:3 yet:2 written:1 motor:2 implying:1 parameterization:1 short:1 coarse:1 provides:2 bixi:2 mathematical:1 become:1 differential:1 ik:1 behavioral:8 interdependence:1 pairwise:13 no... |
1,104 | 2,007 | Quantizing Density Estimators
Peter Meinicke
Neuroinformatics Group
University of Bielefeld
Bielefeld, Germany
pmeinick@techfak.uni-bielefeld.de
Helge Ritter
Neuroinformatics Group
University of Bielefeld
Bielefeld, Germany
helge@techfak.uni-bielefeld.de
Abstract
We suggest a nonparametric framework for unsupervised... | 2007 |@word middle:1 version:1 compression:2 meinicke:3 grey:1 covariance:1 thereby:4 tr:2 reduction:1 initial:1 contains:1 att:1 series:1 existing:1 current:3 com:1 must:3 realize:1 additive:1 zeger:1 mstep:1 remove:1 update:1 implying:1 generative:2 selected:1 yr:2 intelligence:1 mln:1 isotropic:2 parametrization:1 v... |
1,105 | 2,008 | Model Based Population Tracking and
Automatic Detection of Distribution Changes
Igor V. Cadez ?
Dept. of Information and Computer Science,
University of California,
Irvine, CA 92612
icadez@ics.uci.edu
P. S. Bradley
digiMine, Inc.
10500 NE 8th Street,
Bellevue, WA 98004-4332
paulb@digimine.com
Abstract
Probabilistic ... | 2008 |@word smirnov:1 d2:3 seek:1 bellevue:2 initial:2 contains:1 score:20 series:1 cadez:2 bradley:1 current:2 com:1 nt:3 must:5 numerical:1 additive:1 shape:2 plot:5 update:2 n0:5 device:1 website:2 inspection:1 detecting:2 insample:1 alert:1 become:1 introduce:1 pairwise:3 behavior:1 themselves:1 frequently:1 automa... |
1,106 | 2,009 | MIME: Mutual Information Minimization
and Entropy Maximization for Bayesian
Belief Propagation
Anand Rangarajan
Dept. of Computer and Information Science and Engineering
University of Florida
Gainesville, FL 32611-6120, US
anand@cise.ufl.edu
Alan L. Yuille
Smith-Kettlewell Eye Research Institute
2318 Fillmore St.
San ... | 2009 |@word mild:1 fixpoints:2 simulation:1 gainesville:1 decomposition:12 pold:7 mention:2 intriguing:1 written:1 update:17 smith:1 node:11 org:1 kettlewell:1 pairwise:12 inter:2 inspired:2 freeman:1 considering:2 begin:4 provided:6 moreover:1 xx:4 interpreted:1 pursue:1 extremum:2 transformation:2 every:1 collecting:... |
1,107 | 201 | 364
Jain and Waibel
Incremental Parsing by Modular Recurrent
Connectionist Networks
Ajay N. Jain Alex H. Waibel
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
ABSTRACT
We present a novel, modular, recurrent connectionist network architecture which learns to robustly perform incremental p... | 201 |@word briefly:1 idl:1 initial:1 substitution:2 contains:1 current:2 activation:8 lang:1 must:2 parsing:32 john:3 hypothesize:1 designed:1 plot:1 update:1 fewer:1 beginning:4 short:2 provides:2 five:1 constructed:3 direct:1 become:3 paragraph:1 manner:1 inter:1 expected:1 behavior:16 elman:2 multi:1 actual:2 affirm... |
1,108 | 2,010 | Information Geometrical Framework for
Analyzing Belief Propagation Decoder
Shiro Ikeda
Kyushu Inst. of Tech., & PRESTO, JST
Wakamatsu, Kitakyushu, Fukuoka, 808-0196 Japan
shiro@brain.kyutech.ac.jp
Toshiyuki Tanaka
Tokyo Metropolitan Univ.
Hachioji, Tokyo, 192-0397 Japan
tanaka@eei.metro-u.ac.jp
Shun-ichi Amari
RIKEN ... | 2010 |@word version:2 nd:1 r:2 mitsubishi:1 bn:5 equimarginal:3 bc:1 bhattacharyya:1 wako:1 written:1 ikeda:3 selected:1 mpm:8 mathematical:4 direct:2 manner:1 brain:2 freeman:1 window:1 tlu:1 becomes:1 project:2 minimizes:1 thorough:1 every:3 collecting:1 exactly:1 control:1 positive:1 understood:2 local:1 analyzing:3... |
1,109 | 2,011 | Orientational and geometric
determinants
place and head-
Neil Burgess & Tom Hartley
Institute of Cognitive Neuroscience & Department of Anatomy, UCL
17 Queen Square, London WCIN 3AR, UK
n. burgess@ucl.ac.uk. t.hartley@ucl.ac.uk
Abstract
We present a model of the firing of place and head-direction cells in
rat hippoca... | 2011 |@word h:1 trial:1 determinant:2 cu:1 sharpens:1 hippocampus:11 sex:1 open:1 mehta:1 grey:3 simulation:6 initial:1 bvc:12 series:1 tuned:7 ranck:2 current:4 must:2 john:1 tilted:2 physiol:2 plasticity:1 shape:8 alone:2 cue:54 short:1 cacucci:2 provides:3 location:20 preference:1 zhang:2 rc:1 along:3 become:2 quali... |
1,110 | 2,012 | Optimising Synchronisation Times for
Mobile Devices
Neil D. Lawrence
Department of Computer Science,
Regent Court, 211 Portobello Road,
Sheffield, Sl 4DP, U.K.
neil~dcs.shef . ac.uk
Antony 1. T. Rowstron Christopher M . Bishop Michael J. Taylor
Microsoft Research
7 J. J. Thomson Avenue
Cambridge, CB3 OFB, U.K.
{antr,c... | 2012 |@word proceeded:2 briefly:2 middle:3 seek:3 weekday:6 thereby:1 reduction:1 series:1 selecting:1 initialisation:2 com:2 yet:1 written:5 must:4 realistic:1 plot:2 update:12 v:1 stationary:2 selected:2 device:11 maximised:1 data2:1 firstly:1 simpler:1 five:3 along:2 constructed:1 saturday:3 symposium:2 consists:1 r... |
1,111 | 2,013 | Grouping with Bias
Stella X. Yu
Robotics Institute
Carnegie Mellon University
Center for the Neural Basis of Cognition
Pittsburgh, PA 15213-3890
Jianbo Shi
Robotics Institute
Carnegie Mellon University
5000 Forbes Ave
Pittsburgh, PA 15213-3890
stella. yu@es. emu. edu
jshi@es.emu.edu
Abstract
With the optimization ... | 2013 |@word cox:1 version:1 eliminating:1 glue:1 open:1 seek:2 propagate:1 pick:1 configuration:2 contains:4 pbx:1 segmentaion:1 discretization:1 ka:1 written:2 subsequent:1 partition:3 wx:1 hofmann:1 shape:1 discrimination:2 v_:1 generative:6 cue:3 selected:2 half:1 amir:1 accordingly:1 intelligence:2 supplying:1 node... |
1,112 | 2,014 | A New Discriminative Kernel From Probabilistic Models
K. Tsuda,*tM. Kawanabe,* G. Ratsch,?*S . Sonnenburg,* and K.-R. Muller*+
t AIST CBRC, 2-41-6, Aomi, Koto-ku , Tokyo, 135-0064, Japan
*Fraunhofer FIRST, Kekulestr. 7, 12489 Berlin , Germany
? Australian National U ni versi ty,
Research School for Information Science... | 2014 |@word msr:1 nd:1 tr:1 contains:1 score:1 att:1 loc:1 outperforms:2 erms:1 yet:1 import:1 must:1 written:2 realistic:1 wx:7 enables:1 generative:1 ial:4 ith:1 smith:1 compo:1 ire:2 provides:1 detecting:1 istical:1 constructed:2 become:1 consists:1 manner:1 theoretically:1 hresholding:1 notation:1 bounded:2 moreove... |
1,113 | 2,015 | Geometrical Singularities in the
Neuromanifold of Multilayer Perceptrons
Shun-ichi Amari, Hyeyoung Park, and Tomoko Ozeki
Brain Science Institute, RIKEN
Hirosawa 2-1, Wako, Saitama, 351-0198, Japan
{amari, hypark, tomoko} @brain.riken.go.jp
Abstract
Singularities are ubiquitous in the parameter space of hierarchical
... | 2015 |@word effect:2 toda:1 normalized:1 version:1 true:4 polynomial:1 proportion:1 differ:1 y2:3 hence:4 classical:1 merged:1 symmetric:1 occurs:1 modifying:1 simulation:1 stochastic:1 eg:3 covariance:1 wid:3 gradient:3 subspace:1 shun:1 xid:4 ja:1 mapped:1 ld:1 reduction:1 criterion:2 fix:1 generalization:11 manifold... |
1,114 | 2,016 | ALGONQUIN - Learning dynamic noise
models from noisy speech for robust
speech recognition
Brendan J. Freyl, Trausti T. Kristjansson l , Li Deng2 , Alex Acero 2
1
Probabilistic and Statistical Inference Group, University of Toronto
http://www.psi.toronto.edu
2 Speech Technology Group , Microsoft Research
Abstract
A c... | 2016 |@word version:13 proportion:3 nd:4 kristjansson:2 accounting:1 covariance:10 decomposition:1 reduction:3 series:7 contains:2 current:2 com:1 si:1 yet:1 additive:3 partition:1 remove:2 moreno:4 drop:1 update:4 stationary:1 dissertation:1 toronto:2 org:1 simpler:1 windowed:1 consists:2 roughly:1 automatically:1 beg... |
1,115 | 2,017 | Stabilizing Value Function
with the
Xin Wang
Department of Computer Science
Oregon State University
Corvallis, OR, 97331
wangxi@cs. orst. edu
Thomas G Dietterich
Department of Computer Science
Oregon State University
Corvallis, OR, 97331
tgd@cs. orst. edu
Abstract
We address the problem of non-convergence of online ... | 2017 |@word trial:1 sex:1 termination:1 tried:2 tr:1 recursively:1 initial:1 contains:1 fragment:1 tuned:1 current:5 ginsberg:2 si:14 yet:1 subsequent:1 eleven:1 plot:1 update:2 v:1 alone:1 greedy:7 leaf:3 fewer:1 selected:1 plane:5 provides:1 coarse:1 node:6 zhang:5 five:1 rollout:1 along:8 constructed:1 sii:1 direct:... |
1,116 | 2,018 | The Fidelity of Local Ordinal Encoding
Javid Sadr, Sayan Mukherjee, Keith Thoresz, Pawan Sinha
Center for Biological and Computational Learning
Department of Brain and Cognitive Sciences, MIT
Cambridge, Massachusetts, 02142 USA
{sadr,sayan,thorek,sinha}@ai.mit.edu
Abstract
A key question in neuroscience is how to enc... | 2018 |@word neurophysiology:1 seems:3 norm:2 open:1 jacob:1 brightness:7 thereby:1 foveal:1 series:2 denoting:1 document:1 rkhs:2 must:1 john:1 subsequent:1 update:4 v:1 devising:1 postnatal:1 provides:1 detecting:1 contribute:1 quantized:1 math:1 herbrich:1 five:1 ipb:1 qualitative:1 vide:1 introduce:1 pairwise:1 hube... |
1,117 | 2,019 | Learning Body Pose via Specialized Maps
Romer Rosales
Department of Computer Science
Boston University, Boston, MA 02215
rrosales@cs.bu.edu
Stan Sclaroff
Department of Computer Science
Boston University, Boston, MA 02215
sclaroff@cs.bu.edu
Abstract
A nonlinear supervised learning model, the Specialized Mappings
Arch... | 2019 |@word h:6 illustrating:1 middle:1 johansson:1 hu:1 covariance:2 jacob:1 thereby:1 harder:1 moment:1 configuration:2 series:2 cyclic:1 must:1 john:1 subsequent:1 numerical:1 visibility:1 designed:1 update:3 cue:1 isard:1 plane:2 short:1 argm:1 location:2 simpler:2 height:3 along:1 consists:6 fitting:1 introduce:1 ... |
1,118 | 202 | 490
Bell
Learning in higher-order' artificial dendritic trees'
Tony Bell
Artificial Intelligence Laboratory
Vrije Universiteit Brussel
Pleinlaan 2, B-1050 Brussels, BELGIUM
(tony@arti.vub.ac.be)
ABSTRACT
If neurons sum up their inputs in a non-linear way, as some simula-
tions suggest, how is this distributed fine... | 202 |@word determinant:1 version:1 polynomial:5 nd:2 simulation:2 propagate:1 tried:1 covariance:1 arti:2 fonn:2 minus:1 mlk:1 moment:2 reduction:1 initial:1 hereafter:2 denoting:1 unction:2 lang:3 yet:1 activation:1 must:2 john:1 plasticity:1 shape:2 enables:1 concert:1 intelligence:1 leaf:2 half:2 fewer:1 ficial:1 ya... |
1,119 | 2,020 | On Discriminative vs. Generative
classifiers: A comparison of logistic
regression and naive Bayes
Andrew Y. Ng
Michael I. Jordan
Computer Science Division
C.S. Div. & Dept. of Stat.
University of California, Berkeley University of California, Berkeley
Berkeley, CA 94720
Berkeley, CA 94720
Abstract
We compare discrimi... | 2020 |@word repository:2 version:4 polynomial:1 seems:3 stronger:2 covariance:4 pick:3 solid:1 offering:1 must:2 john:1 informative:1 j1:1 plot:1 v:4 aside:1 generative:24 implying:1 fewer:1 plane:1 mccallum:1 record:1 provides:1 hyperplanes:1 ipi:1 direct:1 pairing:1 theoretically:1 expected:2 indeed:5 behavior:1 roug... |
1,120 | 2,021 | Group Redundancy Measures Reveal
Redundancy Reduction in the Auditory
Pathway
Gal Chechik
Amir Globerson
Naftali Tishby
School of Computer Science and Engineering
and The Interdisciplinary Center for Neural Computation
Hebrew University of Jerusalem , Israel
ggal@cs.huji.ac.il
Michael J. Anderson
Eric D. Young
Departm... | 2021 |@word r:1 jacob:1 carry:2 reduction:8 series:1 contains:1 tuned:1 interestingly:2 optican:1 current:2 comparing:1 must:3 john:1 informative:1 stationary:1 half:1 intelligence:1 amir:1 xk:2 short:3 provides:2 contribute:2 revisited:1 five:1 mathematical:1 along:6 direct:2 become:2 pathway:10 dan:1 pairwise:1 expec... |
1,121 | 2,022 | Learning Lateral Interactions for
Feature Binding and Sensory Segmentation
Heiko Wersing
HONDA R&D Europe GmbH
Carl-Legien-Str.30, 63073 Offenbach/Main, Germany
heiko.wersing@hre-ftr.f.rd.honda.co.jp
Abstract
We present a new approach to the supervised learning of lateral interactions for the competitive layer model ... | 2022 |@word cylindrical:1 faculty:1 briefly:1 heuristically:1 tried:1 decomposition:1 carry:1 reduction:1 contains:3 contextual:1 discretization:2 activation:1 written:1 must:1 additive:1 shape:1 hofmann:2 plot:3 half:1 selected:1 intelligence:2 desktop:1 provides:1 honda:2 constructed:1 direct:1 consists:4 manner:1 in... |
1,122 | 2,023 | Pranking with Ranking
Koby Crammer and Yoram Singer
School of Computer Science & Engineering
The Hebrew University, Jerusalem 91904, Israel
{kobics,singer}@cs.huji.ac.il
Abstract
We discuss the problem of ranking instances. In our framework
each instance is associated with a rank or a rating, which is an
integer from... | 2023 |@word middle:3 polynomial:1 norm:4 bn:1 carry:1 contains:1 outperforms:1 past:2 current:1 com:1 nt:24 must:1 realistic:1 plot:5 update:13 yr:10 item:2 short:1 record:1 eskin:1 ire:1 boosting:1 herbrich:3 hyperplanes:1 preference:1 mathematical:2 along:2 prove:5 introduce:1 nor:1 considering:1 totally:3 increasing... |
1,123 | 2,024 | Incorporating Invariances in Nonlinear
Support Vector Machines
Olivier Chapelle
Bernhard Scholkopf
olivier.chapelle@lip6.fr
LIP6, Paris, France
Biowulf Technologies
bernhard.schoelkopf@tuebingen.mpg.de
Max-Planck-Institute, Tiibingen, Germany
Biowulf Technologies
Abstract
The choice of an SVM kernel corresponds to... | 2024 |@word version:1 polynomial:1 tried:2 covariance:3 decomposition:1 past:1 comparing:1 si:1 dx:2 john:2 girosi:1 plot:2 aside:1 record:1 hyperplanes:1 become:1 scholkopf:1 consists:2 introduce:3 indeed:2 mpg:1 globally:1 automatically:1 encouraging:1 decoste:1 provided:1 notation:1 what:3 transformation:8 different... |
1,124 | 2,025 | A Model of the Phonological Loop:
Generalization and Binding
Randall C. O'Reilly
Department of Psychology
University of Colorado Boulder
345 UCB
Boulder, CO 80309
Rodolfo Soto
Department of Psychology
University of Colorado Boulder
345 UCB
Boulder, CO 80309
oreilly@psych.colorado.edu
Abstract
We present a neural ne... | 2025 |@word trial:1 version:3 hippocampus:6 ences:1 integrative:1 gradual:1 accounting:1 initial:2 series:1 interestingly:1 existing:2 current:10 emory:1 activation:10 conjunctive:7 must:3 realistic:1 enables:1 motor:2 update:4 cue:2 device:1 item:22 complementing:1 updatable:2 short:1 colored:1 provides:3 coarse:3 mur... |
1,125 | 2,026 | Modeling Temporal Structure in Classical
Conditioning
Aaron C. Courville 1 ,3 and David S. Touretzk y 2,3
1 Robotics Institute, 2Computer Science Department
3Center for the Neural Basis of Cognition
Carnegie Mellon University, Pittsburgh, PA 15213-3891
{ aarone, dst} @es.emu.edu
Abstract
The Temporal Coding Hypothes... | 2026 |@word mild:1 trial:9 version:5 briefly:1 simulation:5 t_:1 accounting:1 covariance:1 initial:2 series:1 past:1 existing:2 current:7 surprising:1 must:2 afl:2 update:7 pursued:1 selected:1 tone:13 huo:2 smith:1 short:2 i1d:5 lx:1 klx:1 become:1 beta:1 viable:1 pairing:1 surprised:1 ik:3 fitting:1 headed:2 g4:1 acq... |
1,126 | 2,027 | TAP Gibbs Free Energy, Belief Propagation and
Sparsity
Lehel Csat?o and Manfred Opper
Neural Computing Research Group
School of Engineering and Applied Science
Aston University, Birmingham B4 7ET, UK.
[csatol,opperm]@aston.ac.uk
Ole Winther
Center for Biological Sequence Analysis, BioCentrum
Technical University of De... | 2027 |@word version:2 simulation:4 crucially:1 covariance:6 eng:1 thereby:1 outlook:1 moment:8 series:1 contains:1 current:1 com:1 must:2 written:2 numerical:1 additive:1 lkv:1 remove:1 update:6 manfred:1 provides:2 recompute:1 node:6 math:1 org:1 specialize:1 shorthand:1 consists:1 ica:1 freeman:1 actual:1 increasing:... |
1,127 | 2,028 | Learning Discriminative Feature Transforms
to Low Dimensions in Low Dimensions
Kari Torkkola
Motorola Labs, 7700 South River Parkway, MD ML28, Tempe AZ 85284, USA
Kari.Torkkola@motorola.com http://members.home.net/torkkola
Abstract
The marriage of Renyi entropy with Parzen density estimation has been
shown to be a via... | 2028 |@word advantageous:1 retraining:2 nd:1 tedious:1 covariance:2 minus:1 reduction:2 wrapper:1 configuration:1 substitution:1 selecting:1 bhattacharyya:2 existing:2 current:1 com:1 comparing:3 must:1 drop:1 discrimination:1 half:1 intelligence:2 guess:1 accordingly:1 inconvenience:1 beginning:1 haykin:1 compo:1 simp... |
1,128 | 2,029 | Hyperbolic Self-Organizing Maps for Semantic
Navigation
J?org Ontrup
Neuroinformatics Group
Faculty of Technology
Bielefeld University
D-33501 Bielefeld, Germany
jontrup@techfak.uni-bielefeld.de
Helge Ritter
Neuroinformatics Group
Faculty of Technology
Bielefeld University
D-33501 Bielefeld, Germany
helge@techfak.uni... | 2029 |@word deformed:1 faculty:2 proportion:1 suitably:2 disk:4 lodhi:1 euclidian:1 carry:1 contains:3 att:1 series:1 document:42 discretization:1 com:2 must:3 john:2 grain:3 numerical:1 distant:1 shape:1 leipzig:1 selected:2 tesselations:7 plane:13 inspection:2 isotropic:1 provides:4 boosting:2 node:39 toronto:2 attac... |
1,129 | 203 | 622
Atlas, Cole, Connor, EI-Sharkawi, Marks, Muthusamy and Barnard
Performance Comparisons Between
Backpropagation Networks and Classification Trees
on Three Real-World Applications
Ronald Cole
Dept. of CS&E
Oregon Graduate Institute
Beaverton. Oregon 97006
Les Atlas
Dept. of EE. Fr-10
University of Washington
Seat... | 203 |@word proceeded:1 trial:1 version:1 norm:1 weekday:1 rivera:1 initial:2 contains:1 series:1 dff:2 past:1 current:1 comparing:1 yet:1 must:1 belmont:1 ronald:1 happen:1 partition:1 remove:1 atlas:6 designed:2 treating:1 alone:1 half:1 selected:3 indicative:1 plane:3 provides:1 node:1 successive:3 five:1 along:1 con... |
1,130 | 2,030 | Classifying Single Trial EEG:
Towards Brain Computer Interfacing
Benjamin Blankertz1?, Gabriel Curio2 and Klaus-Robert M?ller1,3
1 Fraunhofer-FIRST.IDA, Kekul?str. 7, 12489 Berlin, Germany
2 Neurophysics Group, Dept. of Neurology, Klinikum Benjamin Franklin,
Freie Universit?t Berlin, Hindenburgdamm 30, 12203 Berlin, G... | 2030 |@word blankertz1:1 trial:27 neurophysiology:1 briefly:1 norm:2 loading:1 seems:1 duda:1 nd:1 simulation:1 eng:4 pressed:1 cp2:1 contains:1 series:1 chervonenkis:1 tuned:1 franklin:1 past:1 existing:1 reaction:1 current:1 ida:1 lang:3 issuing:1 tetraplegic:1 shape:1 enables:1 motor:20 designed:1 v:4 alone:1 pursue... |
1,131 | 2,031 | Correlation Codes in Neuronal Populations
Maoz Shamir and Haim Sompolinsky
Racah Institute of Physics and Center for Neural Computation,
The Hebrew University
of
Jerusalem,
Jerusalem
91904, Israel
Abstract
Population codes often rely on the tuning of the mean respons... | 2031 |@word open:2 simulation:2 solid:1 ulus:1 contains:1 hereafter:1 tuned:3 comparing:1 must:1 written:1 numerical:5 motor:1 discrimination:7 nervous:1 smith:1 provides:1 simpler:1 become:1 consists:1 indeed:1 behavior:4 zohary:1 provided:1 estimating:1 notation:1 underlying:2 linearity:1 lowest:1 israel:2 sut:3 mini... |
1,132 | 2,032 | Stochastic Mixed-Signal VLSI Architecture for
High-Dimensional Kernel Machines
Roman Genov and Gert Cauwenberghs
Department of Electrical and Computer Engineering
Johns Hopkins University, Baltimore, MD 21218
roman,gert @jhu.edu
Abstract
A mixed-signal paradigm is presented for high-resolution parallel innerproduct... | 2032 |@word compression:1 norm:1 nd:1 r:2 accounting:1 pick:1 solid:2 reduction:1 contains:1 series:1 outperforms:1 written:1 refresh:1 john:2 grain:2 informative:1 drop:1 msb:2 selected:3 device:2 plane:2 nent:1 core:2 compo:1 chiang:1 quantizer:2 quantized:2 node:2 location:2 provides:2 deactivating:1 simpler:1 along... |
1,133 | 2,033 | Grouping and dimensionality reduction by
locally linear embedding
Marzia Polito
Division of Physics, Mathematics and Astronomy
California Institute of Technology
Pasadena, CA, 91125
polito @caltech.edu
Pietro Perona
Division of Engeneering and Applied Mathematics
California Institute of Technology
Pasadena, CA, 91125... | 2033 |@word middle:2 norm:1 nd:4 covariance:4 pick:1 yih:1 tr:1 carry:1 reduction:2 contains:1 rightmost:1 written:1 finest:1 visible:1 numerical:4 partition:6 shape:1 designed:1 davi:2 fewer:1 parametrization:2 provides:2 along:5 direct:1 consists:1 prove:1 behavior:1 globally:1 automatically:3 estimating:1 moreover:1... |
1,134 | 2,034 | Receptive field structure of flow detectors
for heading perception
Jaap A. B e intema
Dept. Zoology & Neurobiology
Ruhr University Bochum, Germany, 44780
beintema@neurobiologie.ruhr-uni-bochum.de
Albert V. van den Berg
Dept. of Neuro-ethology, Helmholtz Institute,
Utrecht University, The Netherlands
a. v. vandenberg@b... | 2034 |@word neurophysiology:3 version:2 proportion:1 seems:1 nd:4 ruhr:4 sensed:1 contraction:1 mammal:1 extrastriate:1 contains:3 tuned:13 interestingly:2 current:1 analysed:1 yet:2 mst:11 medial:1 stationary:1 implying:1 selected:2 plane:4 location:12 five:1 mathematical:1 along:9 constructed:2 direct:1 become:1 fixa... |
1,135 | 2,035 | A Bayesian Network for Real-Time
Musical Accompaniment
Christopher Raphael
Department of Mathematics and Statistics,
University of Massachusetts at Amherst,
Amherst, MA 01003-4515,
raphael~math.umass.edu
Abstract
We describe a computer system that provides a real-time musical accompaniment for a live soloist in a pie... | 2035 |@word version:1 middle:1 compression:1 nd:1 retraining:1 gradual:1 bn:2 covariance:3 initial:5 series:2 uma:2 score:8 accompaniment:55 ours:1 past:4 surprising:1 must:4 ctn:1 treating:1 update:5 intelligence:2 slowing:1 accordingly:1 beginning:1 provides:1 math:2 location:1 constructed:1 become:1 manner:2 automat... |
1,136 | 2,036 | Minimax Probability Machine
Gert R.G. Lanckriet*
Department of EECS
University of California, Berkeley
Berkeley, CA 94720-1770
gert@eecs. berkeley.edu
Laurent EI Ghaoui
Department of EECS
University of California, Berkeley
Berkeley, CA 94720-1770
elghaoui@eecs.berkeley.edu
Chiranjib Bhattacharyya
Department of EECS
... | 2036 |@word repository:1 pw:1 polynomial:1 d2:5 seek:1 nemirovsky:1 covariance:7 thereby:2 carry:1 moment:3 tuned:1 bhattacharyya:1 existing:1 current:2 z2:4 must:1 written:1 partition:2 update:1 tsa:3 discrimination:1 generative:1 mln:1 provides:1 math:1 mathematical:1 direct:1 become:2 ik:1 indeed:2 increasing:2 beco... |
1,137 | 2,037 | Escaping the Convex Hull with
Extrapolated Vector Machines.
Patrick Haffner
AT&T Labs-Research, 200 Laurel Ave, Middletown, NJ 07748
haffner@research.att.com
Abstract
Maximum margin classifiers such as Support Vector Machines
(SVMs) critically depends upon the convex hulls of the training
samples of each class, as th... | 2037 |@word trial:1 polynomial:5 stronger:1 tried:1 decomposition:1 concise:1 harder:1 configuration:2 contains:4 att:1 disparity:1 document:1 bhattacharyya:1 err:10 com:1 nt:2 surprising:1 yet:1 must:1 john:1 numerical:1 happen:1 reproducible:1 extrapolating:1 treating:1 v:3 leaf:1 fewer:1 jkj:1 ron:1 location:1 hyper... |
1,138 | 2,038 | Spike timing and the coding of naturalistic
sounds in a central auditory area of songbirds
Brian D. Wright,
Kamal Sen,
William Bialek
and Allison J. Doupe
Sloan?Swartz
Center
for
Theoretical
Neurobiology
Departments of Physiology and Psychiatry
University
of California at San Francisco, ... | 2038 |@word trial:3 version:1 stronger:1 open:2 gradual:1 pressure:1 carry:2 initial:1 synergistically:1 contains:1 series:1 liu:2 phy:1 comparing:1 tackling:1 must:2 physiol:1 informative:1 remove:1 reproducible:1 v:1 discrimination:1 half:2 ajd:1 tone:1 rebrik:1 record:3 provides:2 math:1 psth:3 attack:1 burst:1 cons... |
1,139 | 2,039 | ADynamic HMM for On-line
Segmentation of Sequential Data
Jens Kohlmorgen*
Fraunhofer FIRST.IDA
Kekulestr. 7
12489 Berlin, Germany
Steven Lemm
Fraunhofer FIRST.IDA
Kekulestr. 7
12489 Berlin, Germany
jek@first?fraunhofer.de
lemm @first?fraunhofer.de
Abstract
We propose a novel method for the analysis of sequential d... | 2039 |@word middle:2 version:1 norm:1 termination:1 thereby:1 tr:1 initial:2 series:11 denoting:1 past:3 existing:1 current:1 ida:2 recovered:1 si:1 scatter:1 dx:2 must:7 readily:1 written:1 numerical:1 visible:1 remove:1 plot:1 update:10 v:2 stationary:8 mackey:3 selected:1 oldest:1 short:2 provides:1 lx:1 along:1 dif... |
1,140 | 204 | 308
Donnett and Smithers
Neuronal Group Selection Theory:
A Grounding in Robotics
Jim Donnett and Tim Smithers
Department of Artificial Intelligence
University of Edinburgh
5 Forrest Hill
Edinburgh EH12QL
SCOTLAND
ABSTRACT
In this paper, we discuss a current attempt at applying the organizational principle Edelman c... | 204 |@word simulation:3 seek:1 paid:1 reentrant:1 initial:4 configuration:3 efficacy:1 ours:2 precluding:1 current:2 yet:1 must:8 tenet:1 distant:2 predetermined:1 eleven:3 shape:3 motor:9 intelligence:4 selected:2 device:3 nervous:2 signalling:2 scotland:1 location:1 launching:1 burst:4 constructed:2 differential:1 dr... |
1,141 | 2,040 | PAC Generalization Bounds for Co-training
Sanjoy Dasgupta
AT&T Labs?Research
dasgupta@research.att.com
Michael L. Littman
AT&T Labs?Research
mlittman@research.att.com
David McAllester
AT&T Labs?Research
dmac@research.att.com
Abstract
The rule-based bootstrapping introduced by Yarowsky, and its cotraining variant by... | 2040 |@word pick:3 carry:1 initial:1 configuration:5 contains:1 att:3 selecting:1 prefix:3 current:1 com:3 yet:2 must:7 written:1 remove:1 update:1 alone:1 greedy:9 pursued:1 guess:3 selected:1 provides:1 boosting:1 location:1 incorrect:1 prove:1 consists:2 combine:1 manner:1 introduce:2 growing:1 inspired:1 globally:1... |
1,142 | 2,041 | Grammatical Bigrams
Mark A. Paskin
Computer Science Division
University of California, Berkeley
Berkeley, CA 94720
paskin@cs.berkeley.edu
Abstract
Unsupervised learning algorithms have been derived for several statistical models of English grammar, but their computational complexity makes applying them to large data ... | 2041 |@word determinant:1 middle:2 manageable:1 bigram:21 stronger:1 version:1 efficacy:1 selecting:1 daniel:1 current:1 must:2 parsing:16 john:1 realize:1 ronald:1 realistic:1 deniz:1 informative:1 designed:1 drop:2 intelligence:1 fewer:1 selected:2 parameterization:1 simpler:2 five:3 ironically:1 tagger:1 direct:1 in... |
1,143 | 2,042 | Boosting and Maximum Likelihood for
Exponential Models
Guy Lebanon
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
John Lafferty
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
lebanon@cs.cmu.edu
lafferty@cs.cmu.edu
Abstract
We derive an equivalence between Ada... | 2042 |@word version:1 pw:3 closure:1 incurs:1 moment:1 com:1 must:1 john:1 additive:2 enables:1 plot:1 interpretable:1 update:3 v_:7 discrimination:1 intelligence:1 selected:1 yr:12 warmuth:2 boosting:34 direct:1 become:1 psfrag:6 vad:2 introduce:1 expected:2 multi:1 little:1 increasing:1 becomes:4 ua:3 moreover:2 nota... |
1,144 | 2,043 | Efficient Resources Allocation
for Markov Decision Processes
Remi Munos
CMAP, Ecole Polytechnique, 91128 Palaiseau, France
http://www.cmap.polytechnique.fr/....munos
remi.munos@polytechnique.fr
Abstract
It is desirable that a complex decision-making problem in an uncertain world be adequately modeled by a Markov Decis... | 2043 |@word version:1 norm:1 proportion:1 km:1 contraction:1 decomposition:1 moment:1 initial:4 ecole:2 discretization:3 dx:1 john:1 cheap:1 enables:1 lue:1 designed:1 intelligence:1 accordingly:1 xk:9 provides:2 lx:1 successive:1 direct:1 differential:2 prove:1 interscience:1 ressources:1 introduce:3 indeed:1 expected... |
1,145 | 2,044 | Speech Recognition using SVMs
Nathan Smith
Cambridge University
Engineering Dept
Cambridge, CB2 1PZ, U.K.
ndsl 002@eng.cam.ac.uk
Mark Gales
Cambridge University
Engineering Dept
Cambridge, CB2 1PZ, U.K.
mjfg@eng.cam.ac.uk
Abstract
An important issue in applying SVMs to speech recognition is the
ability to classify v... | 2044 |@word version:2 polynomial:3 nd:3 ajj:4 eng:4 covariance:3 tr:2 initial:2 series:2 score:112 selecting:1 outperforms:1 o2:1 recovered:1 comparing:1 must:1 readily:1 jkl:1 distant:1 speakerindependent:1 discrimination:2 generative:30 selected:4 half:1 smith:4 compo:1 num:1 location:2 hyperplanes:1 simpler:1 constr... |
1,146 | 2,045 | Orientation-Selective aVLSI Spiking Neurons
Shih-Chii Liu, J?org Kramer, Giacomo Indiveri,
Tobias Delbruck,
?
and Rodney Douglas
Institute of Neuroinformatics
University of Zurich and ETH Zurich
Winterthurerstrasse 190
CH-8057 Zurich, Switzerland
Abstract
We describe a programmable multi-chip VLSI neuronal system tha... | 2045 |@word wiesel:2 open:1 simulation:7 pulse:8 solid:2 initial:1 liu:3 contains:1 configuration:1 tuned:5 current:5 router:1 follower:1 physiol:1 subsequent:1 plot:2 half:1 selected:5 accordingly:1 provides:2 location:1 org:1 along:1 m7:1 driver:1 transceiver:13 consists:2 manner:1 behavior:1 isi:3 multi:23 integrato... |
1,147 | 2,046 | Multi Dimensional ICA to Separate
Correlated Sources
Roland Vollgraf, Klaus Obermayer
Department of Electrical Engineering and Computer Science
Technical University of Berlin Germany
{ vro, oby} @cs.tu-berlin.de
Abstract
We present a new method for the blind separation of sources, which
do not fulfill the independence... | 2046 |@word advantageous:1 norm:15 nd:1 decomposition:1 outlook:1 carry:7 moment:1 series:1 selecting:1 recovered:1 si:4 numerical:1 wx:3 v:1 stationary:1 accordingly:1 lr:2 detecting:1 provides:3 inside:1 expected:1 ica:20 nor:1 examine:1 multi:1 little:1 increasing:1 provided:2 project:2 estimating:1 what:1 transform... |
1,148 | 2,047 | The Intelligent Surfer:
Probabilistic Combination of Link and
Content Information in PageRank
Matthew Richardson
Pedro Domingos
Department of Computer Science and Engineering
University of Washington
Box 352350
Seattle, WA 98195-2350, USA
{mattr, pedrod}@cs.washington.edu
Abstract
The PageRank algorithm, used in the ... | 2047 |@word repository:2 faculty:1 version:2 disk:2 willing:1 q1:1 accommodate:1 initial:2 contains:2 score:8 document:17 outperforms:1 current:1 com:2 must:4 john:1 shakespeare:1 hofmann:2 remove:1 treating:1 alone:3 fewer:1 selected:1 item:1 indefinitely:2 authority:1 node:7 lexicon:2 predecessor:1 symposium:1 chakra... |
1,149 | 2,048 | Motivated Reinforcement Learning
Peter Dayan
Gatsby Computational Neuroscience Unit
17 Queen Square, London, England, WClN 3AR.
dayan@gatsby.ucl.ac.uk
Abstract
The standard reinforcement learning view of the involvement
of neuromodulatory systems in instrumental conditioning includes a rather straightforward concepti... | 2048 |@word nificantly:1 version:4 instrumental:29 extinction:2 twelfth:1 cleanly:1 willing:1 r:5 integrative:1 decomposition:1 paid:1 tr:1 solid:3 accommodate:1 substitution:5 att:1 reaction:1 current:2 si:2 yet:1 subsequent:1 chicago:2 plasticity:1 christian:1 update:1 discrimination:1 half:2 selected:1 intelligence:... |
1,150 | 2,049 | The Noisy Euclidean Traveling Salesman
Problem and Learning
Mikio L. Braun, Joachim M. Buhmann
braunm@cs.uni-bonn.de, jb@cs.uni-bonn.de
Institute for Computer Science, Dept. III,
University of Bonn
R6merstraBe 164, 53117 Bonn, Germany
Abstract
We consider noisy Euclidean traveling salesman problems in the
plane, whi... | 2049 |@word briefly:1 polynomial:1 norm:1 simulation:1 selecting:1 si:1 reminiscent:1 readily:1 must:3 grain:1 shape:1 treating:1 update:1 plane:3 beginning:1 realizing:1 coarse:3 provides:1 location:4 district:1 simpler:1 constructed:1 c2:2 consists:2 prove:3 advocate:1 paragraph:1 hardness:1 expected:6 planning:1 mec... |
1,151 | 205 | 516
Grossman
The CHIR Algorithm for Feed Forward
Networks with Binary Weights
Tal Grossman
Department of Electronics
Weizmann Institute of Science
Rehovot 76100 Israel
ABSTRACT
A new learning algorithm, Learning by Choice of Internal Represetations (CHIR), was recently introduced. Whereas many algorithms reduce the... | 205 |@word briefly:1 version:9 seems:1 electronics:1 cyclic:1 initial:3 existing:1 current:7 nowlan:1 si:3 must:1 realize:1 happen:2 treating:1 update:2 guess:2 accordingly:1 plaut:1 ron:1 wijsj:1 consists:1 manner:2 indeed:1 aborted:1 multi:1 increasing:1 becomes:1 totally:1 moreover:1 israel:1 what:2 kind:1 nadal:1 d... |
1,152 | 2,050 | Linear Time Inference in Hierarchical HMMs
Kevin P. Murphy and Mark A. Paskin
Computer Science Department
University of California
Berkeley, CA 94720-1776
murphyk,paskin @cs.berkeley.edu
Abstract
The hierarchical hidden Markov model (HHMM) is a generalization of
the hidden Markov model (HMM) that models sequences w... | 2050 |@word middle:2 version:5 glue:1 hu:1 multitasked:1 thereby:4 solid:1 recursively:1 initial:1 configuration:1 contains:1 current:1 yet:1 must:5 parsing:1 cpds:5 enables:1 designed:1 v:3 alone:1 generative:1 leaf:1 fewer:1 greedy:1 intelligence:2 selected:1 math:1 node:30 simpler:3 height:1 blackwellized:1 become:1... |
1,153 | 2,051 | A General Greedy Approximation Algorithm
with Applications
Tong Zhang
IBM T.J. Watson Research Center
Yorktown Heights, NY 10598
tzhang@watson.ibm.com
Abstract
Greedy approximation algorithms have been frequently used to obtain
sparse solutions to learning problems. In this paper, we present a general
greedy algorith... | 2051 |@word concept:2 skip:1 implies:6 true:1 verify:1 regularization:1 strong:1 closely:3 quantity:3 hull:2 illustrated:1 attractive:1 gradient:2 mention:1 razor:1 argued:1 yorktown:1 sci:1 originated:1 fix:1 generalization:4 generalized:1 proposition:3 theoretic:1 induction:1 com:1 considered:2 hall:1 relationship:3 ... |
1,154 | 2,052 | Variance Reduction Techniques for Gradient
Estimates in Reinforcement Learning
Evan Greensmith
Australian National University
evan@csl.anu.edu.au
Peter L. Bartlett?
BIOwulf Technologies
Peter.Bartlett@anu.edu.au
Jonathan Baxter?
WhizBang! Labs, East
jbaxter@whizbang.com
Abstract
We consider the use of two additive ... | 2052 |@word briefly:1 version:1 norm:6 simulation:1 covariance:1 reduction:3 initial:2 score:1 selecting:3 current:1 com:1 comparing:1 analysed:1 readily:1 evans:1 additive:2 plot:5 stationary:4 intelligence:2 xk:2 ith:1 short:1 consists:1 x0:7 indeed:2 expected:6 discounted:5 decreasing:1 csl:1 considering:1 becomes:1... |
1,155 | 2,053 | Rates of Convergence of Performance Gradient
Estimates Using Function Approximation and
Bias in Reinforcement Learning
Gregory Z. Grudic
University of Colorado, Boulder
grudic@cs.colorado.edu
Lyle H. Ungar
University of Pennsylvania
ungar@cis.upenn.edu
Abstract
We address two open theoretical questions in Policy Gra... | 2053 |@word build:2 implemented:2 c:1 predicted:3 implies:1 indicate:1 unbiased:1 establish:1 question:7 open:6 mdp:2 km:1 fa:8 simulation:1 stochastic:1 attractive:1 pg:6 mcallester:1 during:1 gradient:35 require:2 reinforce:1 simulated:1 ungar:3 generalization:1 efficacy:1 degrade:2 denoting:1 complete:2 toward:2 ass... |
1,156 | 2,054 | Adaptive N earest Neighbor Classification
using Support Vector Machines
Carlotta Domeniconi, Dimitrios Gunopulos
Dept. of Computer Science, University of California, Riverside, CA 92521
{ carlotta, dg} @cs.ucr.edu
Abstract
The nearest neighbor technique is a simple and appealing method
to address classification prob... | 2054 |@word repository:1 proportion:1 duda:1 nd:2 covariance:1 thereby:3 solid:1 initial:1 contains:1 efficacy:1 denoting:1 si:2 assigning:1 john:1 cruz:1 informative:1 girosi:1 remove:1 plot:2 alone:3 greedy:2 intelligence:1 isotropic:1 xk:2 farther:1 provides:2 cse:1 location:2 five:1 adamenn:5 along:13 become:1 cons... |
1,157 | 2,055 | Infinite Mixtures of Gaussian Process Experts
Carl Edward Rasmussen and Zoubin Ghahramani
Gatsby Computational Neuroscience Unit
University College London
17 Queen Square, London WC1N 3AR, England
edward,zoubin@gatsby.ucl.ac.uk
http://www.gatsby.ucl.ac.uk
Abstract
We present an extension to the Mixture of Experts (ME... | 2055 |@word middle:1 inversion:3 advantageous:1 proportion:1 seems:1 simulation:3 gradual:1 covariance:18 jacob:2 thereby:1 nystr:1 initial:2 configuration:1 selecting:1 bc:1 current:2 comparing:1 nowlan:1 assigning:2 scatter:1 must:1 readily:1 realistic:1 shape:1 plot:10 interpretable:1 update:3 progressively:1 statio... |
1,158 | 2,056 | Associative memory in realistic neuronal
networks
P.E. Latham*
Department of Neurobiology
University of California at Los Angeles
Los Angeles, CA 90095
pel@ucla.edu
Abstract
Almost two decades ago , Hopfield [1] showed that networks of
highly reduced model neurons can exhibit multiple attracting fixed
points, thus pr... | 2056 |@word open:3 simulation:5 solid:7 reduction:1 moment:1 exclusively:1 neurobio:1 z2:1 current:3 must:5 written:3 physiol:1 realistic:7 webster:1 plot:2 nervous:2 plane:2 short:1 location:1 sigmoidal:1 along:1 constructed:1 become:1 persistent:1 behavior:4 brain:2 decreasing:1 goldman:1 encouraging:1 increasing:3 b... |
1,159 | 2,057 | Matching Free Trees with Replicator Equations
Marcello Pelillo
Dipartimento di Informatica
Universit`a Ca? Foscari di Venezia
Via Torino 155, 30172 Venezia Mestre, Italy
E-mail: pelillo@dsi.unive.it
Abstract
Motivated by our recent work on rooted tree matching, in this paper we
provide a solution to the problem of ma... | 2057 |@word version:2 polynomial:1 proportion:1 stronger:1 replicate:1 hu:1 confirms:1 simulation:1 thereby:2 gnm:2 solid:1 initial:1 liu:1 series:1 denoting:1 optim:1 intriguing:1 must:2 readily:2 shape:2 plot:1 stationary:2 transposition:1 characterization:1 math:1 node:34 bijection:1 successive:1 mathematical:1 alon... |
1,160 | 2,058 | K-Local Hyperplane and Convex Distance
Nearest Neighbor Algorithms
Pascal Vincent and Yoshua Bengio
Dept. IRO, Universit?e de Montr?eal
C.P. 6128, Montreal, Qc, H3C 3J7, Canada
vincentp,bengioy @iro.umontreal.ca
http://www.iro.umontreal.ca/ vincentp
Abstract
Guided by an initial idea of building a complex (non l... | 2058 |@word version:1 middle:1 seems:1 nonsensical:1 covariance:1 dramatic:1 mention:3 reduction:2 myles:1 initial:4 tuned:1 outperforms:1 yet:1 written:1 visible:1 partition:1 shape:1 wanted:1 remove:1 discrimination:2 intelligence:2 fewer:2 short:3 hyperplanes:1 zhang:1 along:1 direct:1 qualitative:1 prove:1 consists... |
1,161 | 2,059 | Kernel Logistic Regression and the Import
Vector Machine
Trevor Hastie
Department of Statistics
Stanford University
Stanford, CA 94305
hastie@stat.stanford.edu
Ji Zhu
Department of Statistics
Stanford University
Stanford, CA 94305
jzhu@stat.stanford.edu
Abstract
The support vector machine (SVM) is known for its good ... | 2059 |@word version:1 briefly:2 middle:1 seems:1 norm:1 logit:2 nd:1 simulation:5 decomposition:1 usee:1 solid:1 initial:1 score:2 selecting:3 rkhs:2 terion:1 current:1 od:1 import:21 written:1 john:1 additive:1 shape:1 update:1 greedy:3 prohibitive:1 provides:3 math:1 contribute:2 along:2 qualitative:1 fitting:1 combi... |
1,162 | 206 | 590
Atiya and Abu-Mostafa
A Method for the Associative Storage
of Analog Vectors
Amir Atiya (*) and Yaser Abu-Mostafa (**)
(*) Department of Electrical Engineering
(**) Departments of Electrical Engineering and Computer Science
California Institute Technology
Pasadena, Ca 91125
ABSTRACT
A method for storing analog v... | 206 |@word uj:2 graded:2 version:2 hence:2 correct:3 simulation:3 costly:1 diagonal:2 sci:2 capacity:1 initial:1 unstable:1 franklin:2 enforcing:1 around:3 considered:1 hall:1 ratio:2 attracted:1 bd:1 equilibrium:16 aul:3 sigmoid:2 numerical:1 visible:9 mostafa:6 physical:1 mostly:1 a2:1 negative:1 analog:11 proc:2 des... |
1,163 | 2,060 | The 9 Factor: Relating Distributions on
Features to Distributions on Images
James M. Coughlan and A. L. Yuille
Smith-Kettlewell Eye Research Institute,
2318 Fillmore Street ,
San Francisco, CA 94115, USA.
Tel. (415) 345-2146/2144. Fax. (415) 345-8455.
Email: coughlan@ski.org.yuille@ski.org
Abstract
We describe the g-... | 2060 |@word briefly:1 seems:1 simulation:2 simplifying:1 solid:1 carry:1 initial:1 liu:2 selecting:1 reaction:1 nt:2 erms:1 dx:1 partition:1 informative:1 shape:1 enables:4 update:3 v:1 cue:1 leaf:1 coughlan:8 smith:1 quantized:1 lx:2 org:2 mathematical:1 direct:1 become:1 kettlewell:1 consists:1 combinational:1 theore... |
1,164 | 2,061 | Small-World Phenomena and the
Dynamics of Information
Jon Kleinberg
Department of Computer Science
Cornell University
Ithaca NY 14853
1
Introduction
The problem of searching for information in networks like the World Wide Web can
be approached in a variety of ways, ranging from centralized indexing schemes to
decen... | 2061 |@word faculty:1 version:4 polynomial:2 leighton:1 nd:2 suitably:1 instruction:1 vldb:1 seek:1 crucially:1 initial:1 contains:3 karger:1 document:1 current:3 com:3 yet:1 crawling:3 must:6 john:1 subsequent:1 hofmann:1 greedy:1 leaf:13 sys:1 short:5 math:1 node:63 location:3 traverse:1 simpler:1 zhang:2 height:4 be... |
1,165 | 2,062 | Estimating Car Insurance Premia:
a Case Study in High-Dimensional Data
Inference
Nicolas Chapados, Yoshua Bengio, Pascal Vincent, Joumana
Ghosn, Charles Dugas, Ichiro Takeuchi, Linyan Meng
University of Montreal, dept. IRQ, CP 6128, Succ. Centre-Ville, Montreal, Qc, Canada, H3C3J7
{chapadosJbengioy,vincentp,ghosnJduga... | 2062 |@word version:2 norm:1 proportion:2 seek:1 tried:2 jacob:2 ronchetti:1 thereby:1 profit:1 minus:2 carry:1 tuned:2 past:2 existing:1 ka:3 current:3 discretization:1 comparing:1 nowlan:1 activation:4 must:2 john:3 numerical:2 partition:1 shape:2 designed:1 interpretable:1 greedy:1 selected:1 beginning:1 argm:1 stah... |
1,166 | 2,063 | Thomas L . Griffiths & Joshua B. Tenenbaum
Department of Psychology
Stanford University, Stanford, CA 94305
{gruffydd,jbt}?psych. stanford. edu
Abstract
Estimating the parameters of sparse multinomial distributions is
an important component of many statistical learning tasks. Recent
approaches have used uncertainty ov... | 2063 |@word faculty:1 compression:9 proportion:3 essay:1 accounting:1 simplifying:1 tr:2 cleary:1 contains:1 document:1 outperforms:1 ka:2 com:1 informative:1 christian:3 remove:1 mccallum:1 sys:3 ith:1 gure:1 provides:3 along:1 direct:1 become:1 qualitative:1 consists:5 expected:3 rapid:1 behavior:2 abscissa:1 ol:1 te... |
1,167 | 2,064 | Learning from Infinite Data
in Finite Time
Pedro Domingos
Geoff H ulten
Department of Computer Science and Engineering
University of Washington
Seattle, WA 98185-2350, U.S.A.
{pedrod, ghulten} @cs.washington.edu
Abstract
We propose the following general method for scaling learning
algorithms to arbitrarily large data ... | 2064 |@word msr:2 faculty:1 version:1 proportionality:1 willing:1 km:1 covariance:2 tr:2 ld:1 moment:1 initial:1 inefficiency:1 series:4 past:1 current:2 ixj:1 moo:9 must:1 subsequent:1 motor:1 v:1 greedy:2 selected:1 affair:1 ith:1 zhang:1 along:1 become:1 consists:2 theoretically:1 ol:1 spherical:1 company:1 lll:1 be... |
1,168 | 2,065 | Probabilistic Abstraction Hierarchies
Eran Segal
Computer Science Dept.
Stanford University
eran@cs.stanford.edu
Daphne Koller
Computer Science Dept.
Stanford University
koller@cs.stanford.edu
Dirk Ormoneit
Computer Science Dept.
Stanford University
ormoneit@cs.stanford.edu
Abstract
Many domains are naturally organi... | 2065 |@word hierachy:1 sgf:1 termination:1 simplifying:1 thereby:1 initial:2 configuration:1 series:1 score:4 selecting:2 denoting:1 document:4 imaginary:1 steiner:3 current:1 com:1 stemmed:1 must:2 mst:5 additive:1 numerical:1 realistic:1 hofmann:2 designed:1 update:1 generative:1 discovering:1 leaf:28 item:3 paramete... |
1,169 | 2,066 | Rao-Blackwellised Particle Filtering
Data Augmentation
.
VIa
Christophe Andrieu
N ando de Freitas
Arnaud Doucet
Statistics Group
University of Bristol
University Walk
Bristol BS8 1TW, UK
Computer Science
UC Berkeley
387 Soda Hall, Berkeley
CA 94720-1776, USA
EE Engineering
University of Melbourne
Parkville, Vic... | 2066 |@word mild:1 version:1 briefly:1 simulation:3 covariance:1 tr:2 klk:1 carry:1 ld:1 reduction:1 initial:1 series:1 daniel:1 past:1 freitas:9 nt:2 bd:1 must:1 numerical:2 enables:1 plot:1 hts:1 update:2 isard:2 intelligence:1 es:1 marine:2 location:2 attack:1 sigmoidal:1 welg:1 become:1 ik:1 consists:1 combine:2 in... |
1,170 | 2,067 | Perceptual Metamers
in Stereoscopic Vision
Benjamin T. Backus*
Department of Psychology
University of Pennsylvania
Philadelphia, PA 19104-6196
backus@psych.upenn.edu
Abstract
Theories of cue combination suggest the possibility of constructing
visual stimuli that evoke different patterns of neural activity in
sensory ... | 2067 |@word trial:3 exploitation:1 briefly:2 version:3 middle:1 seems:1 open:1 reduction:2 disparity:27 mag:1 practiced:2 past:1 must:5 slanted:5 reminiscent:1 physiol:1 visible:1 remove:1 drop:1 fund:1 discrimination:1 alone:3 cue:15 fewer:1 nervous:1 short:1 metamerization:3 colored:1 draft:1 location:2 become:2 dipl... |
1,171 | 2,068 | Learning hierarchical structures with
Linear Relational Embedding
Alberto Paccanaro
Geoffrey E. Hinton
Gatsby Computational Neuroscience Unit
UCL, 17 Queen Square, London, UK
alberto,hinton @gatsby.ucl.ac.uk
Abstract
We present Linear Relational Embedding (LRE), a new method of learning a distributed representation... | 2068 |@word niece:1 version:3 seems:1 norm:1 grey:1 initial:1 configuration:1 must:4 written:1 update:1 intelligence:1 leaf:15 beginning:1 node:11 location:2 toronto:1 lawyer:2 penelope:1 height:1 c2:11 become:2 supply:1 consists:4 prove:1 emma:2 love:2 annoy:5 nor:1 terminal:7 inspired:1 spherical:2 decomposed:1 actua... |
1,172 | 2,069 | Thin Junction Trees
Francis R. Bach
Computer Science Division
University of California
Berkeley, CA 94720
fbach@cs.berkeley.edu
Michael I. Jordan
Computer Science and Statistics
University of California
Berkeley, CA 94720
jordan@cs.berkeley.edu
Abstract
We present an algorithm that induces a class of models with thi... | 2069 |@word middle:2 polynomial:2 achievable:1 proportion:1 tried:1 decomposition:1 pick:1 minus:1 liu:6 contains:1 att:1 selecting:2 rightmost:1 current:3 blank:2 com:1 mayraz:1 written:1 readily:2 enables:2 remove:2 plot:3 treating:1 update:5 v:2 greedy:2 selected:3 generative:6 leaf:1 half:2 provides:1 math:2 node:3... |
1,173 | 207 | 524
Fablman and Lebiere
The Cascade-Correlation Learning Architecture
Scott E. Fahlman and Christian Lebiere
School of Computer Science
Carnegie-Mellon University
Pittsburgh, PA 15213
ABSTRACT
Cascade-Correlation is a new architecture and supervised learning algorithm for artificial neural networks. Instead of just ... | 207 |@word trial:4 briefly:1 version:1 eliminating:1 simulation:2 propagate:3 quickprop:9 covariance:1 tried:1 pick:1 dramatic:1 mention:1 reduction:1 initial:1 score:1 ours:1 existing:6 comparing:2 activation:9 yet:4 lang:7 must:2 merrick:2 happen:1 shape:1 christian:1 asymptote:1 sponsored:1 update:1 progressively:2 ... |
1,174 | 2,070 | Latent Dirichlet Allocation
David M. Blei, Andrew Y. Ng and Michael I. Jordan
University of California, Berkeley
Berkeley, CA 94720
Abstract
We propose a generative model for text and other collections of discrete data that generalizes or improves on several previous models
including naive Bayes/unigram, mixture of un... | 2070 |@word version:1 bigram:1 proportion:1 nd:2 innermost:1 profit:1 thereby:1 ld:1 reduction:2 document:58 current:1 virus:1 written:1 readily:2 numerical:1 hofmann:5 designed:1 update:1 fund:1 generative:9 selected:1 intelligence:1 plane:1 mccallum:1 ith:1 randolph:1 blei:1 provides:1 node:3 location:1 idi:1 monday:... |
1,175 | 2,071 | Distribution of Mutual Information
Marcus Hutter
IDSIA, Galleria 2, CH-6928 Manno-Lugano, Switzerland
marcus@idsia.ch
http://www.idsia.ch/- marcus
Abstract
The mutual information of two random variables z and J with joint
probabilities {7rij} is commonly used in learning Bayesian nets as
well as in many other fields... | 2071 |@word version:1 briefly:1 extinction:1 simulation:2 covariance:7 tr:1 moment:8 contains:1 series:1 ours:1 z2:1 john:1 numerical:4 informative:6 noninformative:1 drop:3 half:1 ivo:1 inspection:1 dover:1 short:1 node:1 mathematical:1 multinomially:1 incorrect:1 advocate:1 fitting:1 encouraging:1 estimating:1 bounde... |
1,176 | 2,072 | Sampling Techniques for Kernel Methods
Dimitris Achlioptas
Microsoft Research
optas@microsoft.com
Frank McSherry
University of Washington
mcsherry@cs.washington.edu
Bernhard Sch?olkopf
Biowulf Technologies NY
bs@conclu.de
Abstract
We propose randomized techniques for speeding up Kernel Principal
Component Analysis ... | 2072 |@word trial:2 polynomial:1 seems:1 norm:4 stronger:1 lodhi:1 covariance:1 decomposition:1 simplifying:1 pick:1 offering:1 diagonalized:1 com:1 yet:2 must:1 readily:5 numerical:2 additive:1 girosi:1 progressively:1 greedy:1 instantiate:1 fewer:2 tolle:1 coarse:1 simpler:2 mathematical:1 symposium:1 prove:3 special... |
1,177 | 2,073 | A Natural Policy Gradient
Sham Kakade
Gatsby Computational Neuroscience Unit
17 Queen Square, London, UK WC1N 3AR
http: //www.gatsby.ucl.ac.uk
sham @gatsby.ucl.ac.uk
Abstract
We provide a natural gradient method that represents the steepest
descent direction based on the underlying structure of the parameter space. A... | 2073 |@word version:1 q7f:3 bf:1 simulation:4 seek:1 tried:1 solid:2 reduction:1 initial:4 substitution:1 score:1 must:1 informative:1 lqg:1 asymptote:1 plot:3 drop:2 update:3 v:4 stationary:6 greedy:14 alone:1 parameterization:3 plane:1 steepest:5 provides:3 sigmoidal:2 height:5 direct:2 become:1 overhead:2 manner:1 e... |
1,178 | 2,074 | Contextual Modulation of Target Saliency
Antonio Torralba
Dept. of Brain and Cognitive Sciences
MIT, Cambridge, MA 02139
torralba@ai. mit. edu
Abstract
The most popular algorithms for object detection require the use of
exhaustive spatial and scale search procedures. In such approaches,
an object is defined by means ... | 2074 |@word decomposition:2 covariance:1 attended:1 reduction:2 configuration:1 efficacy:1 selecting:1 exclusively:1 tuned:2 current:1 contextual:37 nt:2 pcp:1 parsing:1 shape:4 cue:3 selected:6 intelligence:1 compo:1 coarse:1 provides:9 contribute:1 location:11 detecting:1 height:1 mathematical:1 become:1 incorrect:1 ... |
1,179 | 2,075 | Modeling the Modulatory Effect of
Attention on Human Spatial Vision
Laurent Itti
Computer Science Department, Hedco Neuroscience Building HNB-30A,
University of Southern California, Los Angeles, CA 90089-2520, U.S.A.
J oehen Braun
nstitute of Neuroscience and School of Computing,
University of Plymouth, Plymouth Devon... | 2075 |@word trial:1 cu:3 middle:1 sharpens:2 disk:1 simulation:2 attended:26 configuration:1 tuned:6 bc:1 reynolds:1 existing:1 activation:1 yet:2 happen:1 informative:6 plot:1 fund:1 mounting:1 discrimination:16 alone:1 selected:1 mental:1 location:3 preference:1 successive:1 sigmoidal:1 simpler:1 five:4 overhead:1 be... |
1,180 | 2,076 | Exact differential equation population
dynamics for Integrate-and-Fire neurons
Julian Eggert *
HONDA R&D Europe (Deutschland) GmbH
Future Technology Research
Carl-Legien-StraBe 30
63073 Offenbach/Main, Germany
julian. eggert@hre-ftr.f.rd.honda.co.jp
Berthold Bauml
Institut fur Robotik und Mechatronik
Deutsches Zentrum... | 2076 |@word norm:1 underline:1 simulation:15 solid:2 initial:2 selecting:1 past:4 current:4 dx:3 written:2 must:1 realistic:3 subsequent:2 numerical:2 enables:3 cheap:1 update:2 stationary:1 accordingly:1 beginning:1 short:1 honda:2 simpler:1 mathematical:2 rc:2 differential:10 rapid:1 ry:3 brain:1 automatically:1 actu... |
1,181 | 2,077 | Constructing Distributed Representations
Using Additive Clustering
Wheeler Ruml
Division of Engineering and Applied Sciences
Harvard University
33 Oxford Street, Cambridge, MA 02138
ruml@eecs.harvard.edu
Abstract
If the promise of computational modeling is to be fully realized in higherlevel cognitive domains such as... | 2077 |@word version:4 tedious:1 pbil:3 decomposition:1 initial:2 configuration:2 contains:1 selecting:1 daniel:1 tuned:1 genetic:1 existing:2 current:4 recovered:1 activation:1 assigning:1 yet:1 must:1 reminiscent:1 cottrell:5 additive:16 predetermined:1 pursued:1 fewer:2 leaf:1 assurance:1 obsolete:1 mental:1 recomput... |
1,182 | 2,078 | A Generalization of Principal Component
Analysis to the Exponential Family
Michael Collins
Sanjoy Dasgupta
Robert E. Schapire
AT&T Labs Research
180 Park Avenue, Florham Park, NJ 07932
mcollins, dasgupta, schapire @research.att.com
Abstract
Principal component analysis (PCA) is a commonly applied technique
for ... | 2078 |@word version:2 seems:2 nd:2 open:2 pg:5 reduction:4 initial:2 series:1 att:1 daniel:1 terion:1 com:1 reminiscent:2 written:6 must:2 numerical:1 hofmann:4 afield:1 drop:1 update:4 v:1 stationary:6 warmuth:3 oldest:2 reciprocal:1 farther:1 manfred:2 vxw:1 prove:1 shorthand:1 manner:4 expected:1 roughly:1 nor:1 ina... |
1,183 | 2,079 | Improvisation and Learning
Judy A. Franklin
Computer Science Department
Smith College
Northampton, MA 01063
jfranklin@cs.smith.edu
Abstract
This article presents a 2-phase computational learning model and application. As a demonstration, a system has been built, called CHIME for
Computer Human Interacting Musical Ent... | 2079 |@word nd:1 pg:1 solid:7 initial:2 substitution:2 series:1 score:1 contains:1 accompaniment:1 franklin:3 must:1 john:3 enables:1 update:1 half:6 tone:7 monk:1 beginning:1 smith:3 provides:2 firstly:1 resolve:1 little:3 precursor:1 notation:1 benbrahim:2 rmax:1 contrasting:1 nj:1 temporal:1 exactly:1 control:2 unit... |
1,184 | 208 | 550
Ackley and Littman
Generalization and scaling in reinforcement
learning
David H. Ackley
Michael L. Littman
Cognitive Science Research Group
Bellcore
Morristown, NJ 07960
ABSTRACT
In associative reinforcement learning, an environment generates input
vectors, a learning system generates possible output vectors, an... | 208 |@word trial:1 middle:5 seems:1 stronger:1 advantageous:1 simulation:7 propagate:3 bn:1 pick:4 tr:2 genetic:1 designed:3 update:3 v:1 alone:1 discovering:1 dissertation:2 provides:2 location:1 casp:3 five:1 direct:1 supply:1 profound:1 pairing:2 acquired:1 expected:1 globally:1 automatically:1 increasing:1 begin:1 ... |
1,185 | 2,080 | The Emergence of Multiple Movement Units in
the Presence of Noise and Feedback Delay
Michael Kositsky
Andrew G. Barto
Department of Computer Science
University of Massachusetts
Amherst, MA 01003-4610
kositsky,barto @cs.umass.edu
Abstract
Tangential hand velocity profiles of rapid human arm movements often appear as... | 2080 |@word trial:2 version:1 pulse:2 simulation:9 solid:2 carry:1 initial:8 uma:1 longitudinal:1 existing:1 current:2 activation:15 subsequent:1 berthier:1 motor:14 plot:1 designed:1 infant:4 selected:4 nervous:6 beginning:1 smith:1 provides:2 preference:1 simpler:1 mathematical:1 novak:1 asanuma:1 behavioral:1 lenner... |
1,186 | 2,081 | Fragment completion in humans and machines
David Jacobs
NEC Research Institute
4 Independence Way, Princeton, NJ 08540
dwj@research.nj.nec.com
Archisman Rudra
CS Department at NYU
251 Mercer St., New York, NY 10012
archi@cs.nyu.edu
Bas Rokers
Psychology Department at UCLA
PO Box 951563, Los Angeles, CA 90095
rokers@... | 2081 |@word trial:1 determinant:1 bigram:23 seems:1 norm:1 simulation:1 jacob:4 accounting:1 harder:1 liu:1 contains:2 fragment:59 reaction:1 current:1 com:1 blank:1 comparing:1 contextual:1 activation:3 must:2 fn:1 shape:2 enables:1 hypothesize:1 plot:1 drop:2 update:2 cue:43 selected:6 item:13 beginning:4 short:1 ite... |
1,187 | 2,082 | Global Coordination of Local Linear Models
Sam Roweis , Lawrence K. Saul , and Geoffrey E. Hinton
Department of Computer Science, University of Toronto
Department of Computer and Information Science, University of Pennsylvania
Abstract
High dimensional data that lies on or near a low dimensional manifold can be ... | 2082 |@word proceeded:1 loading:1 seems:1 open:1 covariance:3 simplifying:1 pressure:2 tr:1 reduction:4 initial:1 contains:1 document:1 must:2 reminiscent:1 cottrell:1 shape:2 wanted:1 remove:1 treating:1 designed:1 update:4 interpretable:1 plot:1 implying:1 pursued:1 generative:4 item:1 parameterization:2 plane:2 awry... |
1,188 | 2,083 | The Method of Quantum Clustering
David Horn and Assaf Gottlieb
School of Physics and Astronomy
Raymond and Beverly Sackler Faculty of Exact Sciences
Tel Aviv University, Tel Aviv 69978, Israel
Abstract
We propose a novel clustering method that is an extension of ideas inherent to scale-space clustering and support-ve... | 2083 |@word repository:2 faculty:1 seems:1 duda:1 nd:4 decomposition:1 covariance:1 contains:1 uncovered:1 paramagnetic:1 numerical:1 shape:1 analytic:1 enables:1 plot:2 intelligence:1 discovering:1 accordingly:2 hamiltonian:1 core:1 provides:1 location:6 five:1 differential:1 prove:1 consists:1 assaf:2 interscience:1 ... |
1,189 | 2,084 | Intransitive Likelihood-Ratio Classifiers
Jeff Bilmes
and
Gang Ji
Department of Electrical Engineering
University of Washington
Seattle, WA 98195-2500
bilmes,gji @ee.washington.edu
Marina Meil?a
Department of Statistics
University of Washington
Seattle, WA 98195-4322
mmp@stat.washington.edu
Abstract
In this work, w... | 2084 |@word trial:3 middle:1 proportion:1 duda:1 tried:1 covariance:3 pick:1 dramatic:1 thereby:2 recursively:1 reduction:1 initial:2 contains:1 score:3 comparing:2 must:1 john:1 confirming:1 plot:1 drop:1 discrimination:1 nynex:1 alone:1 fewer:1 steal:2 record:2 num:1 detecting:1 mathematical:1 along:1 constructed:1 b... |
1,190 | 2,085 | Multiplicative Updates for Classification
by Mixture Models
Lawrence K. Saul and Daniel D. Lee
Department
of Computer and Information Science
Department of Electrical Engineering
University of Pennsylvania, Philadelphia, PA 19104
Abstract
We investigate a learning algorithm for the classification of nonnegative d... | 2085 |@word erate:1 suitably:1 tedious:1 dekker:1 linearized:1 covariance:3 contrastive:1 minus:1 versatile:1 daniel:1 document:1 recovered:1 comparing:1 must:3 attracted:1 additive:1 cheap:1 plot:2 update:29 stationary:2 generative:8 discovering:1 half:1 selected:1 intelligence:1 warmuth:1 mathematical:1 prove:1 combi... |
1,191 | 2,086 | Estimating the Reliability of leA
Projections
F. Meinecke l ,2, A. Ziehe l , M. Kawanabe l and K.-R. Miiller l ,2*
1 Fraunhofer FIRST.IDA, Kekuh~str. 7, 12489 Berlin, Germany
2University of Potsdam, Am Neuen Palais 10, 14469 Potsdam, Germany
{meinecke,ziehe,nabe,klaus}?first.fhg.de
Abstract
When applying unsupervised... | 2086 |@word underline:2 nd:1 open:1 tried:1 covariance:3 decomposition:1 solid:1 series:7 selecting:3 r5t:1 interestingly:1 amp:6 ida:1 comparing:2 assigning:1 ij1:1 wx:2 resampling:21 v:1 parameterization:1 plane:1 inspection:1 five:1 along:2 become:1 supply:1 ziemke:1 combine:1 expected:2 ica:17 brain:1 ptb:1 inspire... |
1,192 | 2,087 | Face Recognition Using Kernel Methods
Ming-Hsuan Yang
Honda Fundamental Research Labs
Mountain View, CA 94041
myang@hra.com
Abstract
Principal Component Analysis and Fisher Linear Discriminant
methods have demonstrated their success in face detection, recognition, and tracking. The representation in these subspace me... | 2087 |@word polynomial:2 r:1 seek:1 covariance:4 xkn:1 carry:1 reduction:1 moment:1 contains:2 denoting:2 riitsch:1 ka:1 com:1 negentropy:1 scatter:2 must:1 informative:1 v:1 xk:2 provides:1 honda:1 constructed:1 fld:6 interscience:1 acquired:1 ica:12 compensating:1 ming:1 increasing:1 becomes:1 provided:1 project:5 ma... |
1,193 | 2,088 | 1
Bayesian morphometry of hippocampal cells
suggests same-cell somatodendritic repulsion
Giorgio A. Ascoli *
Alexei Samsonovich
Krasnow Institute for Advanced Study at George Mason University
Fairfax, VA 22030-4444
ascoli@gmu.edu
asamsono@gmu.edu
Abstract
Visual inspection of neurons suggests that dendritic orientati... | 2088 |@word cylindrical:1 stronger:1 hippocampus:2 open:1 simulation:4 bn:1 accounting:1 dramatic:2 initial:5 genetic:1 interestingly:1 current:2 od:2 yet:1 must:1 stemming:2 numerical:2 subsequent:1 realistic:2 shape:7 inspection:3 plane:2 postnatal:1 short:2 node:4 location:6 along:3 qualitative:1 expected:1 indeed:2... |
1,194 | 2,089 | Convolution Kernels for Natural Language
Michael Collins
AT&T Labs?Research
180 Park Avenue, New Jersey, NJ 07932
mcollins@research.att.com
Nigel Duffy
Department of Computer Science
University of California at Santa Cruz
nigeduff@cse.ucsc.edu
Abstract
We describe the application of kernel methods to Natural Languag... | 2089 |@word version:2 polynomial:3 lodhi:2 recursively:1 carry:1 reduction:3 contains:2 att:1 fragment:11 score:20 charniak:1 existing:1 com:1 must:1 parsing:19 cruz:3 generative:1 selected:1 leaf:1 reranking:1 short:1 num:1 characterization:1 boosting:2 cse:1 node:7 provides:1 preference:1 ucsc:2 constructed:1 scholko... |
1,195 | 209 | Unsupervised Learning in Neurodynamics
Unsupervised Learning in Neurodynamics Using
the Phase Velocity Field Approach
Michail Zak
Nikzad Toornarian
Center for Space Microelectronics Technology
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, CA 91109
ABSTRACT
A new concept for unsupervised lea... | 209 |@word dividing:1 uj:1 concept:4 implies:1 hence:2 assigned:4 objective:1 laboratory:2 simulation:2 illustrated:1 imbedded:2 sgn:2 during:1 subspace:3 distance:1 carry:1 assign:1 initial:2 propulsion:2 vo:3 toward:2 reason:2 gravitational:2 practically:1 sufficiently:1 considered:3 ic:1 activation:1 geometrical:1 i... |
1,196 | 2,090 | A Variational Approach to Learning Curves
D?orthe Malzahn
Manfred Opper
Neural Computing Research Group
School of Engineering and Applied Science
Aston University, Birmingham B4 7ET, United Kingdom.
[malzahnd,opperm]@aston.ac.uk
Abstract
We combine the replica approach from statistical physics with a variational appr... | 2090 |@word trial:1 version:2 simulation:5 covariance:1 thereby:1 outlook:1 solid:1 moment:1 contains:1 united:1 partition:4 enables:1 hamiltonian:3 manfred:1 characterization:1 simpler:2 become:1 specialize:1 combine:1 expected:2 mechanic:3 increasing:2 becomes:1 notation:3 panel:4 medium:2 z:2 orland:1 act:1 exactly:... |
1,197 | 2,091 | Fast and Robust Classification using Asymmetric
AdaBoost and a Detector Cascade
Paul Viola and Michael Jones
Mistubishi Electric Research Lab
Cambridge, MA
viola@merl.com and mjones@merl.com
Abstract
This paper develops a new approach for extremely fast detection in domains where the distribution of positive and negat... | 2091 |@word briefly:1 reduction:2 initial:4 wrapper:1 pfleger:1 selecting:1 current:1 com:2 comparing:2 yet:3 must:1 john:1 subsequent:3 designed:1 greedy:3 selected:6 record:1 provides:2 parameterizations:1 boosting:18 location:2 constructed:2 direct:1 absorbs:1 introduce:1 acquired:1 detects:1 eurocolt:1 automaticall... |
1,198 | 2,092 | On Spectral Clustering:
Analysis and an algorithm
Andrew Y. Ng
CS Division
U.C. Berkeley
ang@cs.berkeley.edu
Michael I. Jordan
CS Div. & Dept. of Stat.
U.C. Berkeley
jordan@cs.berkeley.edu
Yair Weiss
School of CS & Engr.
The Hebrew Univ.
yweiss@cs.huji.ac.il
Abstract
Despite many empirical successes of spectral clu... | 2092 |@word version:1 briefly:1 seems:1 nd:1 cleanly:1 llo:1 simplifying:1 pick:2 atrix:1 recursively:1 eigensolvers:1 series:1 ours:2 si:19 intriguing:1 must:1 mesh:1 partition:6 benign:1 treating:1 rrt:1 generative:1 half:1 guess:1 xk:4 math:1 node:1 mathematical:1 symposium:2 scholkopf:1 shorthand:1 consists:2 prove... |
1,199 | 2,093 | Sequential noise compensation by
sequential Monte Carlo method
Kaisheng Yao and Satoshi Nakamura
ATR Spoken Language Translation Research Laboratories
2-2-2, Hikaridai Seika-cho, Souraku-gun, Kyoto, 619-0288, Japan
E-mail: {kaisheng.yao, satoshi.nakamura}@slt.atr.co.jp
Abstract
We present a sequential Monte Carlo met... | 2093 |@word kristjansson:1 decomposition:1 solid:2 reduction:1 initial:1 liu:1 series:1 mmse:4 current:1 dct:2 subsequent:1 additive:4 remove:1 resampling:2 stationary:23 accordingly:2 five:1 mathematical:1 constructed:2 become:1 seika:1 xz:1 compensating:1 window:1 increasing:2 underlying:1 linearity:1 formidable:1 ev... |
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