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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500 | 1,458 | Receptive field formation in natural scene
environments: comparison of single cell
learning rules
Brian S. Blais
Brown University Physics Department
Providence, Rl 02912
N.lntrator
School of Mathematical Sciences
Tel-Aviv University
Ramat-Aviv, 69978 ISRAEL
H. Shouval
Institute for Brain and Neural Systems
Brown Uni... | 1458 |@word version:4 eliminating:1 polynomial:2 norm:1 covariance:1 moment:8 contains:1 current:1 comparing:1 si:1 visible:1 realistic:1 additive:3 plasticity:1 enables:1 alone:1 half:2 unmixed:1 successive:1 sigmoidal:1 simpler:1 mathematical:2 c2:12 direct:1 become:1 qualitative:1 multimodality:1 introduce:2 manner:... |
501 | 1,459 | Intrusion Detection with Neural Networks
Jake Ryan*
Department of Computer Sciences
The University of Texas at Austin
Austin, TX 78712
Department of Electrical and Computer Engineering
The University of Texas at Austin
Austin, TX 78712
raven@cs.utexas.edu
mj@orac.ece . utexas.edu
Meng-Jang Lin
Risto Miikkulainen
... | 1459 |@word version:2 manageable:1 retraining:1 risto:2 open:1 accounting:1 detective:1 thereby:1 tr:1 interestingly:1 past:1 current:3 com:1 activation:6 must:1 planet:3 realistic:2 periodically:2 designed:1 update:1 intelligence:1 leaf:1 record:2 detecting:6 provides:2 attack:4 five:1 become:1 symposium:2 consists:3 ... |
502 | 146 | 281
PERFORMANCE OF SYNTHETIC NEURAL
NETWORK CLASSIFICATION OF NOISY
RADAR SIGNALS
S. C. Ahalt
F. D. Garber
I. Jouny
A. K . Krishnamurthy
Department of Electrical Engineering
The Ohio State University, Columbus, Ohio 43210
ABSTRACT
This study evaluates the performance of the multilayer-perceptron
and the frequency-se... | 146 |@word aircraft:7 effect:1 establish:1 true:1 indicate:1 hence:1 nd:1 objective:1 laboratory:1 simulation:2 primary:1 white:3 attractive:1 traditional:1 x5:1 during:2 self:1 rician:1 kth:2 maintained:1 distance:1 require:1 berlin:1 stepped:1 investigation:1 theoretic:2 bhanu:1 performs:3 aes:1 measured:3 length:1 m... |
503 | 1,460 | Self-similarity properties of natural images
ANTONIO TURIEL; GERMAN MATOt NESTOR PARGA t
Departamento de Fisica Te6rica. Universidad AutOnoma de Madrid
Cantoblanco, 28049 Madrid, Spain
and JEAN-PIERRE N ADAL?
Laboratoire de Physique Statistique de I'E. N.S. , Ecole Normale Superieure
24, rue Lhomond, F-75231 Paris Ced... | 1460 |@word stronger:1 dramatic:1 solid:1 moment:7 ecole:1 qth:1 must:1 numerical:2 partition:1 limp:1 plot:2 v:3 generative:1 adal:1 es:18 ith:1 compo:1 along:3 direct:1 qualitative:1 consists:2 dan:1 inside:1 insist:1 actual:1 becomes:2 spain:1 provided:1 underlying:1 mass:1 what:1 nadal:5 ail:1 developed:1 quantitat... |
504 | 1,461 | Refractoriness and Neural Precision
Michael J. Berry n and Markus Meister
Molecular and Cellular Biology Department
Harvard University
Cambridge, MA 02138
Abstract
The relationship between a neuron's refractory period and the precision of
its response to identical stimuli was investigated. We constructed a model of
a ... | 1461 |@word trial:15 solid:2 selecting:1 written:1 realistic:1 j1:5 interspike:2 plot:1 drop:2 beginning:2 psth:3 burst:1 constructed:1 become:1 qualitative:1 prove:1 combine:1 inter:4 expected:1 roughly:1 behavior:1 multi:2 actual:1 vertebrate:1 becomes:2 bounded:1 matched:1 panel:1 acoust:2 sharpening:1 nj:1 temporal... |
505 | 1,462 | Characterizing Neurons in the Primary
Auditory Cortex of the Awake Primate
U sing Reverse Correlation
R. Christopher deC harms
decharms@phy.ucsf.edu
Michael M . Merzenich
merz@phy.ucsf.edu
w. M. Keck Center for Integrative Neuroscience
University of California, San Francisco CA 94143
Abstract
While the understanding ... | 1462 |@word trial:1 wiesel:3 disk:1 open:1 cha:1 integrative:1 decomposition:1 brightness:2 thereby:1 shading:2 phy:2 selecting:1 tuned:2 yet:1 physiol:1 discernible:1 designed:3 progressively:2 discrimination:1 leaf:1 selected:3 nervous:1 tone:8 indicative:3 sutter:2 short:2 farther:1 filtered:1 characterization:3 loc... |
506 | 1,463 | Statistical Models of Conditioning
Peter Dayan*
Brain & Cognitive Sciences
E25-2IDMIT
Cambridge, MA 02139
Theresa Long
123 Hunting Cove
Williamsburg, VA 23185
Abstract
Conditioning experiments probe the ways that animals make predictions about rewards and punishments and use those predictions to control their behavi... | 1463 |@word trial:15 proportion:2 instrumental:1 nd:1 seems:1 hippocampus:1 integrative:1 confirms:1 seek:1 crucially:1 r:4 jacob:4 paid:1 dramatic:1 tr:6 ld:1 hunting:1 existing:4 reaction:1 current:1 surprising:1 nowlan:3 must:1 additive:3 chicago:2 drop:1 alone:1 generative:1 implying:1 tone:12 short:2 provides:2 di... |
507 | 1,464 | Regularisation in Sequential Learning
Algorithms
J oao FG de Freitas
Cambridge University
Engineering Department
Cambridge CB2 IPZ England
jfgf@eng.cam.ac.uk
[Corresponding author]
Mahesan Niranjan
Cambridge University
Engineering Department
Cambridge CB2 IPZ England
niranjan@eng.cam.ac.uk
Andrew H Gee
Cambridge Uni... | 1464 |@word trial:1 eliminating:2 justice:1 lwk:1 eng:4 covariance:14 thereby:1 tr:1 initial:7 series:2 outperforms:1 freitas:9 africa:1 comparing:1 jaynes:2 com:1 analysed:1 assigning:1 readily:1 fn:2 update:5 stationary:2 intelligence:1 selected:1 guess:1 provides:1 firstly:1 simpler:1 mathematical:1 multi:3 feldkamp... |
508 | 1,465 | Adaptive choice of grid and time
reinforcement learning
?
In
Stephan Pareigis
stp@numerik.uni-kiel.de
Lehrstuhl Praktische Mathematik
Christian-Albrechts-Uni versitiit Kiel
Kiel, Germany
Abstract
We propose local error estimates together with algorithms for adaptive a-posteriori grid and time refinement in reinforc... | 1465 |@word trial:2 middle:3 joh:1 polynomial:1 norm:2 open:1 simulation:1 contraction:1 initial:1 series:1 past:1 current:5 discretization:19 optim:1 urgently:1 si:2 yet:1 must:2 finest:2 refines:1 subsequent:2 numerical:10 mesh:1 christian:1 update:19 stationary:2 recherche:1 provides:1 coarse:1 math:1 kiel:4 albrech... |
509 | 1,466 | Independent Component Analysis for
identification of artifacts in
Magnetoencephalographic recordings
Ricardo Vigario 1 ; Veikko J ousmiiki2 ,
Matti Hiimiiliiinen2, Riitta Hari2, and Erkki Oja 1
1 Lab.
of Computer & Info. Science
Helsinki University of Technology
P.O. Box 2200, FIN-02015 HUT, Finland
{Ricardo.Vigario,... | 1466 |@word neurophysiology:2 norm:3 seems:1 riitta:1 covariance:2 pick:1 initial:2 denoting:1 kurt:1 yet:1 must:1 visible:1 shape:1 remove:2 plot:1 device:1 xk:8 beginning:1 ith:1 record:1 junta:1 filtered:2 location:1 mathematical:1 magnetoencephalographic:3 inside:1 expected:1 ica:7 brain:9 electroencephalography:2 ... |
510 | 1,467 | A Revolution: Belief Propagation
Graphs With Cycles
?
In
Brendan J. Frey?
http://wvw.cs.utoronto.ca/-frey
Department of Computer Science
University of Toronto
David J. C. MacKay
http://vol.ra.phy.cam.ac.uk/mackay
Department of Physics, Cavendish Laboratory
Cambridge University
Abstract
Until recently, artificial in... | 1467 |@word version:1 polynomial:1 seems:2 open:1 electronics:2 configuration:1 contains:1 phy:1 current:1 protection:1 si:1 must:1 written:1 neq:2 plot:1 intelligence:2 guess:1 signalling:1 xk:2 short:4 prespecified:1 draft:1 toronto:1 mathematical:1 along:3 direct:1 consists:2 vwv:1 manner:1 introduce:1 ra:1 increasi... |
511 | 1,468 | Learning Human-like Knowledge by Singular
Value Decomposition: A Progress Report
Thomas K. Landauer
Darrell Laham
Department of Psychology & Institute of Cognitive Science
University of Colorado at Boulder Boulder, CO 80309-0345
{landauer, dlaham}@psych.colorado.edu
Peter Foltz
Department of Psychology
New Mexico S... | 1468 |@word kintsch:7 briefly:1 cu:1 justice:1 essay:24 simulation:2 decomposition:7 moment:1 reduction:1 contains:1 score:8 document:3 past:1 contextual:1 protection:1 activation:1 yet:1 assigning:2 written:1 extensional:1 interpretable:1 discrimination:1 half:1 intelligence:1 item:2 affair:1 rehder:6 smith:2 short:3 ... |
512 | 1,469 | A Superadditive-Impairment Theory
of Optic Aphasia
Michael C. Mozer
Dept. of Computer Science
University of Colorado
Boulder; CO 80309-0430
Mark Sitton
Dept. of Computer Science
University of Colorado
Boulder; CO 80309-0430
Martha Farah
Dept. of Psychology
University of Pennsylvania
Phila., PA 19104-6196
Abstract
Ac... | 1469 |@word trial:1 proportion:3 grey:1 simulation:3 shot:1 initial:1 hereafter:3 denoting:1 seriously:1 elaborating:1 past:1 current:1 activation:1 yet:3 cottrell:1 additive:1 subsequent:2 designed:1 update:1 cue:2 tenn:2 guess:1 item:1 selected:1 ith:3 mental:1 plaut:3 location:1 five:1 lor:1 along:1 constructed:1 in... |
513 | 147 | 297
A NETWORK FOR IMAGE SEGMENTATION
USING COLOR
Anya Hurlbert and Tomaso Poggio
Center for Biological Information Processing at Whitaker College
Department of Brain and Cognitive Science
and the MIT AI Laboratory
Cambridge, MA 02139
(hur lbert@wheaties.ai.mit.edu)
ABSTRACT
We propose a parallel network of simple pro... | 147 |@word aircraft:2 loading:2 replicate:1 grey:4 brightness:4 shading:1 initial:3 disparity:1 ka:1 comparing:1 si:1 yet:2 must:2 john:1 visible:1 enables:1 sponsored:1 discrimination:1 alone:2 cue:1 half:2 intelligence:6 affair:1 filtered:2 provides:1 contribute:1 along:1 consists:1 tomaso:5 themselves:2 brain:2 enco... |
514 | 1,470 | Bach in a Box - Real-Time Harmony
Randall R. Spangler and Rodney M. Goodman*
Computation and Neural Systems
California Institute of Technology, 136-93
Pasadena, CA 91125
Jim Hawkins t
88B Milton Grove
Stoke Newington, London N16 8QY, UK
Abstract
We describe a system for learning J. S. Bach's rules of musical harmony.... | 1470 |@word version:1 briefly:1 llo:1 contains:2 accompaniment:1 existing:1 feulner:1 current:10 surprising:1 synthesizer:1 yet:1 must:6 subsequent:1 half:1 rulebase:15 provides:5 quantized:1 attack:1 simpler:1 harmonize:1 behavior:1 multi:1 decreasing:1 resolve:1 little:1 increasing:1 alto:3 riemenschneider:1 nj:1 eve... |
515 | 1,471 | A Model of Early Visual Processing
Laurent Itti, Jochen Braun, Dale K. Lee and Christof Koch
{itti, achim, jjwen, koch}Gklab.caltech.edu
Computation & Neural Systems, MSC 139-74
California Institute of Technology, Pasadena, CA 91125, U.S.A.
Abstract
We propose a model for early visual processing in primates. The
mode... | 1471 |@word h:1 trial:6 sharpens:1 seems:1 teich:1 simplifying:1 covariance:1 eng:1 solid:2 initial:1 tuned:11 bc:1 existing:2 bradley:1 neurophys:1 readily:1 mesh:1 subsequent:1 shape:2 discrimination:16 alone:2 half:1 oblique:1 num:1 provides:1 contribute:2 location:2 lx:1 node:1 sigmoidal:1 mathematical:1 along:1 be... |
516 | 1,472 | Hierarchical Non-linear Factor Analysis
and Topographic Maps
Zoubin Ghahramani and Geoffrey E. Hinton
Dept. of Computer Science, University of Toronto
Toronto, Ontario, M5S 3H5, Canada
http://www.cs.toronto.edu/neuron/
{zoubin,hinton}Ocs.toronto.edu
Abstract
We first describe a hierarchical, generative model that can... | 1472 |@word middle:2 inversion:1 nd:1 simulation:1 excited:1 arti:1 tr:1 solid:1 contains:2 disparity:12 selecting:2 favouring:1 activation:1 must:2 distant:1 visible:3 partition:5 generative:27 selected:1 discovering:1 maximised:1 sys:1 compo:1 contribute:3 toronto:5 organising:1 direct:1 become:1 incorrect:1 consists... |
517 | 1,473 | Linear concepts and hidden variables:
An empirical study
Adam J. Grove
Dan Rothe
NEC Research Institute
4 Independence Way
Princeton NJ 08540
grove@research.nj.nec.com
Department of Computer Science
University of Illinois at Urbana-Champaign
1304 W. Springfield Ave. Urbana 61801
danr@cs.uiuc.edu
Abstract
Some lear... | 1473 |@word trial:2 briefly:2 version:4 hoffgen:1 seems:6 proportion:2 tried:5 covariance:7 attainable:1 dramatic:1 past:1 current:1 com:1 surprising:3 yet:3 must:1 drop:1 designed:1 update:3 aside:2 v:1 fewer:1 parameterization:2 indicative:1 ith:1 characterization:1 simpler:1 demoted:1 along:1 direct:2 beta:2 become:... |
518 | 1,474 | Multiresolution Tangent Distance for
Affine-invariant Classification
Nuno Vasconcelos
Andrew Lippman
MIT Media Laboratory, 20 Ames St, E15-320M,
Cambridge, MA 02139, {nuno,lip }@media.mit.edu
Abstract
The ability to rely on similarity metrics invariant to image transformations is an important issue for image classif... | 1474 |@word version:7 covariance:1 decomposition:5 harder:1 shot:1 initial:3 series:2 contains:2 current:1 john:2 girosi:1 shape:1 tlp:1 designed:3 update:1 drop:1 v:1 selected:1 guess:2 plane:1 coarse:1 provides:1 ames:1 five:4 consists:1 huber:1 themselves:2 multi:3 td:8 considering:1 increasing:2 becomes:2 medium:2 ... |
519 | 1,475 | Structural Risk Minimization for
Nonparametric Time Series Prediction
Ron Meir*
Department of Electrical Engineering
Technion, Haifa 32000, Israel
rmeir@dumbo.technion.ac.il
Abstract
The problem of time series prediction is studied within the uniform convergence framework of Vapnik and Chervonenkis. The dependence in... | 1475 |@word briefly:1 version:1 norm:2 d2:1 bn:2 recapitulate:1 paid:1 thereby:1 ld:5 series:13 selecting:3 chervonenkis:3 past:3 must:2 ixil:2 j1:1 statis:1 stationary:5 pursued:1 selected:1 node:1 ron:1 complication:1 prove:1 introduce:1 expected:2 behavior:1 considering:1 increasing:2 notation:4 moreover:2 bounded:6... |
520 | 1,476 | Nonparametric Model-Based
Reinforcement Learning
Christopher G. Atkeson
College of Computing, Georgia Institute of Technology,
Atlanta, GA 30332-0280, USA
ATR Human Information Processing,
2-2 Hikaridai, Seiko-cho, Soraku-gun, 619-02 Kyoto, Japan
cga@cc.gatech.edu
http://www .cc.gatech.edu/fac/Chris.Atkeson/
Abstract... | 1476 |@word trial:11 version:1 nd:1 seek:1 initial:2 configuration:1 liu:2 uma:3 tuned:1 current:6 numerical:1 subsequent:1 intelligence:4 imitate:1 xk:16 sarcos:2 provides:1 along:5 constructed:3 direct:4 differential:6 symposium:1 introduce:1 alspector:1 behavior:2 planning:17 bellman:1 globally:1 automatically:1 act... |
521 | 1,477 | Synaptic Transmission: An
Information-Theoretic Perspective
Amit Manwani and Christof Koch
Computation and Neural Systems Program
California Institute of Technology
Pasadena, CA 91125
email: quixote@klab.caltech.edu
koch@klab.caltech.edu
Abstract
Here we analyze synaptic transmission from an infonnation-theoretic
per... | 1477 |@word trial:2 tedious:1 pulse:2 bn:1 fonn:2 shot:3 contains:1 efficacy:3 denoting:1 current:1 yet:1 additive:2 shape:1 plot:3 v:2 device:2 record:1 filtered:1 provides:2 doubly:1 falsely:1 nor:1 brain:1 terminal:1 decreasing:1 snn:2 little:2 vertebrate:1 lib:5 increasing:1 matched:2 moreover:1 what:2 minimizes:1 ... |
522 | 1,478 | Relative Loss Bounds for Multidimensional
Regression Problems
Jyrki Kivinen
Department of Computer Science
P.O. Box 26 (Teollisuuskatu 23)
FIN-00014 University of Helsinki, Finland
Manfred K. Warmuth
Department of Computer Science
University of California, Santa Cruz
Santa Cruz, CA 95064, USA
Abstract
We study on-lin... | 1478 |@word trial:3 briefly:1 eliminating:1 polynomial:1 norm:3 willing:1 initial:3 ours:1 bc:2 current:1 discretization:2 unction:1 varx:1 activation:1 must:3 realize:1 ixil:1 cruz:2 additive:9 update:13 intelligence:1 guess:1 warmuth:10 parameterization:5 realizing:1 manfred:2 wth:1 math:1 node:2 complication:1 along... |
523 | 1,479 | Unsupervised On-Line Learning of
Decision Trees for Hierarchical Data
Analysis
Marcus Held and Joachim M. Buhmann
Rheinische Friedrich-Wilhelms-U niversitat
Institut fUr Informatik III, ROmerstraBe 164
D-53117 Bonn, Germany
email: {held.jb}.cs.uni-bonn.de
WWW: http://www-dbv.cs.uni-bonn.de
Abstract
An adaptive on-lin... | 1479 |@word version:1 covariance:1 euclidian:1 recursively:1 assigning:1 yet:5 tilted:1 partition:8 j1:2 update:2 stationary:5 greedy:1 leaf:19 provides:1 contribute:1 node:16 along:1 supply:1 combine:1 introduce:1 expected:4 growing:1 actual:1 window:1 israel:1 minimizes:1 gif:1 fuzzy:1 developed:1 every:2 demonstrate... |
524 | 148 | 124
ADAPTIVE NEURAL NET PREPROCESSING
FOR SIGNAL DETECTION
IN NON-GAUSSIAN NOISE1
Richard P. Lippmann and Paul Beckman
MIT Lincoln Laboratory
Lexington, MA 02173
ABSTRACT
A nonlinearity is required before matched filtering in mInimum error
receivers when additive noise is present which is impulsive and highly
non-Gau... | 148 |@word trial:5 pulse:11 propagate:1 dramatic:1 solid:2 reduction:1 initial:2 selecting:1 lapedes:2 rightmost:1 current:1 com:1 lorentz:1 realize:1 evans:1 additive:8 shape:1 designed:1 sponsored:1 drop:1 half:1 fewer:5 selected:2 plane:1 prespecified:1 provides:1 node:18 ron:1 sigmoidal:7 mathematical:2 qualitative... |
525 | 1,480 | Ensemble Learning
for Multi-Layer Networks
David Barber?
Christopher M. Bishopt
Neural Computing Research Group
Department of Applied Mathematics and Computer Science
Aston University, Birmingham B4 7ET, U.K.
http://www.ncrg.aston.ac.uk/
Abstract
Bayesian treatments of learning in neural networks are typically
base... | 1480 |@word version:1 wla:2 grooteplein:1 simulation:2 r:1 covariance:21 pressed:1 tr:1 initial:1 contains:1 current:1 com:2 z2:1 activation:1 trc:1 written:1 numerical:2 additive:1 partition:1 analytic:2 alone:1 intelligence:1 leaf:1 plane:1 isotropic:2 tpresent:1 node:1 location:1 sigmoidal:1 simpler:2 symposium:1 co... |
526 | 1,481 | The Error Coding and Substitution PaCTs
GARETH JAMES
and
TREVOR HASTIE
Department of Statistics, Stanford University
Abstract
A new class of plug in classification techniques have recently been developed in the statistics and machine learning literature. A plug in classification technique (PaCT) is a method that ta... | 1481 |@word kong:1 repository:1 simulation:1 covariance:1 simplifying:1 fonn:3 ld:1 reduction:5 substitution:21 contains:1 zij:4 past:3 z2:1 assigning:1 designed:3 plot:1 drop:1 tenn:1 fewer:1 intelligence:1 ith:4 provides:1 ipi:1 direct:1 prove:1 behavior:1 examine:1 ecoc:23 curse:1 pf:4 lib:1 provided:3 classifies:1 ... |
527 | 1,482 | A Principle for Unsupervised
Hierarchical Decomposition of Visual Scenes
Michael C. Mozer
Dept. of Computer Science
University of Colorado
Boulder, CO 80309-0430
ABSTRACT
Structure in a visual scene can be described at many levels of granularity. At a coarse level, the scene is composed of objects; at a finer level,
e... | 1482 |@word version:3 briefly:1 stronger:2 seems:1 simulation:7 propagate:1 decomposition:12 recursively:3 carry:1 initial:1 configuration:12 cyclic:1 fragment:3 contains:1 selecting:1 denoting:1 current:2 nowlan:2 written:1 readily:1 must:2 alone:6 intelligence:1 leaf:2 selected:2 nervous:1 devising:1 plane:1 coarse:2... |
528 | 1,483 | The Bias-Variance Tradeoff and the Randomized
GACV
Grace Wahba, Xiwu Lin and Fangyu Gao
Dong Xiang
Dept of Statistics
Univ of Wisconsin
1210 W Dayton Street
Madison, WI 53706
wahba,xiwu,fgao@stat.wisc.edu
SAS Institute, Inc.
SAS Campus Drive
Cary, NC 27513
sasdxx@unx.sas.com
Ronald Klein, MD and Barbara Klein, MD
... | 1483 |@word polynomial:1 replicate:2 logit:1 open:2 simulation:5 pressure:1 tr:7 ld:1 initial:1 liu:2 series:3 unx:1 efficacy:1 selecting:2 inefficiency:2 com:1 fn:5 additive:2 ronald:1 numerical:3 chicago:2 girosi:2 designed:1 plot:4 v:1 d5i:1 intelligence:1 selected:1 beaver:3 ith:1 compo:1 provides:2 attack:1 sigmoi... |
529 | 1,484 | The Bias-Variance Tradeoff and the Randomized
GACV
Grace Wahba, Xiwu Lin and Fangyu Gao
Dong Xiang
Dept of Statistics
Univ of Wisconsin
1210 W Dayton Street
Madison, WI 53706
wahba,xiwu,fgao@stat.wisc.edu
SAS Institute, Inc.
SAS Campus Drive
Cary, NC 27513
sasdxx@unx.sas.com
Ronald Klein, MD and Barbara Klein, MD
... | 1484 |@word middle:1 polynomial:1 replicate:2 logit:1 open:2 grey:1 simulation:5 pressure:1 tr:7 ld:1 initial:1 liu:2 series:3 unx:1 efficacy:1 selecting:5 inefficiency:2 contains:1 initialisation:7 bhattacharyya:3 outperforms:1 current:3 com:1 comparing:3 must:2 ronald:1 fn:5 numerical:3 additive:2 chicago:2 girosi:2 ... |
530 | 1,485 | Dynamically Adapting Kernels in Support
Vector Machines
N ello Cristianini
Dept. of Engineering Mathematics
University of Bristol, UK
nello.cristianini@bristol.ac.uk
Colin Campbell
Dept. of Engineering Mathematics
University of Bristol, UK
c.campbell@bristol.ac.uk
John Shawe-Taylor
Dept. of Computer Science
Royal Hol... | 1485 |@word norm:1 calculus:1 simulation:2 solid:2 reduction:1 united:1 pub:1 ka:1 yet:1 written:1 john:2 belmont:1 numerical:1 predetermined:1 confirming:1 cheap:1 designed:1 plot:3 beginning:1 maximised:1 provides:2 postal:1 hyperplanes:1 ofo:2 prove:2 rapid:1 automatically:3 little:5 enumeration:1 lll:1 provided:2 m... |
531 | 1,486 | Evidence for a Forward Dynamics Model
in Human Adaptive Motor Control
Nikhil Bhushan and Reza Shadmehr
Dept. of Biomedical Engineering
Johns Hopkins University, Baltimore, MD 21205
Email: nbhushan@bme.jhu.edu, reza@bme.jhu.edu
Abstract
Based on computational principles, the concept of an internal
model for adaptive c... | 1486 |@word longterm:1 middle:2 simulation:12 tried:1 eng:1 moment:1 initial:3 ivaldi:3 current:4 activation:3 yet:1 written:1 john:1 motor:10 nervous:1 sudden:2 provides:4 rc:1 along:1 incorrect:1 behavioral:1 expected:5 rapid:1 behavior:10 examine:2 mechanic:1 brain:1 torque:1 td:2 little:1 jm:1 actual:3 inappropriat... |
532 | 1,487 | A Reinforcement Learning Algorithm
in Partially Observable Environments
Using Short-Term Memory
Nobuo Suematsu and Akira Hayashi
Faculty of Computer Sciences
Hiroshima City University
3-4-1 Ozuka-higashi, Asaminami-ku, Hiroshima 731-3194 Japan
{suematsu,akira} @im.hiroshima-cu.ac.jp
Abstract
We describe a Reinforceme... | 1487 |@word trial:2 cu:1 version:1 faculty:1 wla:4 solid:2 dedi:2 initial:1 selecting:2 ala:2 past:1 err:2 nt:3 dx:4 realistic:1 designed:1 update:1 intelligence:1 leaf:10 selected:1 warmuth:2 accordingly:2 mccallum:4 short:11 utile:3 provides:7 node:5 ron:2 location:2 compose:1 introduce:1 expected:3 hardness:1 isi:1 ... |
533 | 1,488 | A Model for Associative Multiplication
G. Bjorn Christianson*
Department of Psychology
McMaster University
Hamilton,Ont. L8S 4Kl
bjorn@caltech.edu
Suzanna Becker
Department of Psychology
McMaster University
Hamilton, Onto L8S 4Kl
becker@mcmaster.ca
Abstract
Despite the fact that mental arithmetic is based on only a ... | 1488 |@word trial:2 instruction:1 simulation:1 accounting:1 pressed:1 current:3 blank:1 surprising:1 activation:1 must:1 cottrell:2 numerical:1 plot:1 intelligence:1 mental:1 coarse:10 five:1 rc:1 along:2 consists:2 fitting:1 expected:1 nor:1 multi:1 brain:1 ont:1 little:1 increasing:1 provided:1 sokol:1 monkey:1 nj:2 ... |
534 | 1,489 | A N euromorphic Monaural Sound
Localizer
John G. Harris, Chiang-Jung Pu, and Jose C. Principe
Department of Electrical & Computer Engineering
University of Florida
Gainesville, FL 32611
Abstract
We describe the first single microphone sound localization system
and its inspiration from theories of human monaural sound ... | 1489 |@word version:1 middle:1 sex:1 d2:2 pulse:19 simulation:4 gainesville:2 azimuthal:1 pick:1 solid:1 amp:1 dx:2 john:1 physiol:1 shape:1 remove:1 designed:2 drop:1 cue:4 half:1 nervous:1 plane:4 smith:1 record:1 chiang:2 compo:1 provides:4 detecting:5 location:6 lbo:1 along:1 direct:7 become:1 viable:1 interaural:3... |
535 | 149 | 57
Self Organizing Neural Networks for the
Identification Problem
Manoel Fernando Tenorio
VVei-Tsih Lee
School of Electrical Engineering
Purdue University
School of Electrical Engineering
Purdue University
VV. Lafayette, UN. 47907
VV. Lafayette, UN. 47907
lwt@ed.ecn.purdue.edu
tenoriQ@ee.ecn.purdue.edu
ABSTRACT
T... | 149 |@word version:4 polynomial:4 nd:1 jacob:1 tr:1 accommodate:1 recursively:2 initial:1 configuration:2 series:10 lapedes:4 current:2 si:3 dx:1 tenn:3 selected:1 accepting:1 provides:2 node:22 simpler:1 constructed:2 differential:2 ood:1 manner:1 behavior:2 nor:1 simulator:1 terminal:4 actual:1 lll:1 linearity:1 deve... |
536 | 1,490 | Very Fast EM-based Mixture Model
Clustering using Multiresolution kd-trees
Andrew W. Moore
Robotics Inst.i t. ut.e, Carnegie tl1plloll University
Pittsburgh , PA 15:21:3. a\\'l11'9.'cs.cll1u.eciu
Abstract
Clust ering is impor ta nt in m any fi elds including m a nufac tlll'ing ,
bio l og~' , fin a nce , a nd astronom... | 1490 |@word rising:1 nd:23 bf:1 willing:1 tat:3 covariance:1 cla:3 eld:1 tr:1 etric:1 score:1 imat:1 current:1 nt:9 od:6 si:1 yet:1 ust:1 must:1 tot:1 john:1 ints:1 depict:1 fewer:1 leaf:4 yr:1 ria:2 sys:2 ith:1 short:1 erat:1 record:2 lr:1 num:1 provides:1 node:17 location:1 ional:1 lx:2 zhang:1 ra:3 behavior:1 dist:2... |
537 | 1,491 | Kernel peA and De-Noising in Feature Spaces
Sebastian Mika, Bernhard Scholkopf, Alex Smola
Klaus-Robert Muller, Matthias Scholz, Gunnar Riitsch
GMD FIRST, Rudower Chaussee 5, 12489 Berlin, Germany
{mika, bs, smola, klaus, scholz, raetsch} @first.gmd.de
Abstract
Kernel PCA as a nonlinear feature extractor has proven p... | 1491 |@word version:2 briefly:1 polynomial:1 compression:2 open:1 covariance:1 q1:2 solid:1 contains:1 selecting:1 riitsch:1 activation:1 must:2 written:1 attracted:1 oldenbourg:1 additive:2 numerical:1 eleven:2 gv:2 drop:1 half:3 selected:1 parameterization:2 accordingly:1 plane:1 xk:7 provides:1 zhang:1 five:2 along:... |
538 | 1,492 | Viewing Classifier Systems
as Model Free Learning in POMDPs
Akira Hayashi and Nobuo Suematsu
Faculty of Information Sciences
Hiroshima City University
3-4-1 Ozuka-higashi, Asaminami-ku, Hiroshima, 731-3194 Japan
{akira,suematsu }@im.hiroshima-cu.ac.jp
Abstract
Classifier systems are now viewed disappointing because o... | 1492 |@word cu:1 faculty:1 seems:2 suitably:1 simulation:1 profit:1 carry:1 initial:2 series:1 contains:3 selecting:1 genetic:12 ramsey:1 current:5 must:1 update:3 v:6 intelligence:1 selected:1 p7:1 mccallum:5 iso:1 record:2 utile:1 consists:2 combine:1 introduce:1 alm:1 expected:1 behavior:1 aliasing:4 food:3 actual:1... |
539 | 1,493 | Multi-electrode spike sorting
by clustering transfer functions
Dmitry Rinberg
Hanan Davidowitz
N aftali Tishby*
NEe Research Institute
4 Independence Way
Princeton, N J 08540
E-mail: {dima,hanan, tishby }<Dreseareh. nj . nee . com
Categories: spike sorting, population coding, signal processing.
Abstract
A new para... | 1493 |@word neurophysiology:2 tr:2 papoulis:1 carry:1 imaginary:2 current:2 com:1 si:1 shape:10 treating:1 plot:1 stationary:1 cue:1 leaf:1 nervous:1 plane:1 short:1 record:1 lr:1 detecting:2 contribute:1 become:2 prove:1 presumed:1 themselves:1 multi:5 brain:1 detects:2 overwhelming:1 window:2 trv:1 provided:1 medium:... |
540 | 1,494 | Where does the population vector of motor
cortical cells point during reaching movements?
Pierre Baraduc*
pbaraduc@snv.jussieu.fr
Emmanuel Guigon
guigon@ccr.jussieu.fr
Yves Burnod
ybteam@ccr.jussieu.fr
INSERM U483, Universite Pierre et Marie Curie
9 quai St Bernard, 75252 Paris cedex 05, France
Abstract
Visually-gu... | 1494 |@word norm:1 simulation:1 contraction:1 solid:1 initial:1 configuration:3 tuned:8 nt:4 activation:1 must:1 motor:29 designed:1 accordingly:1 provides:2 contribute:1 successive:1 combine:1 ray:1 inside:1 guenther:1 behavior:3 nor:1 planning:1 brain:3 inspired:1 anisotropy:3 actual:7 elbow:1 provided:1 underlying:1... |
541 | 1,495 | A High Performance k-NN Classifier Using a
Binary Correlation Matrix Memory
Ping Zhou
zhoup@cs.york.ac.uk
Jim Austin
austin@cs.york.ac.uk
John Kennedy
johnk@cs.york.ac.uk
Advanced Computer Architecture Group
Department of Computer Science
University of York, York YOW 500, UK
Abstract
This paper presents a novel and... | 1495 |@word version:4 achievable:1 shot:1 configuration:1 contains:4 current:4 written:1 must:1 john:1 numerical:2 partition:2 cheap:1 designed:1 hts:1 v:1 half:1 device:11 core:1 ron:1 along:1 become:1 consists:2 multi:2 considering:1 mpj:1 project:4 provided:1 matched:2 developed:3 willshaw:2 classifier:21 uk:5 contr... |
542 | 1,496 | Coordinate Transformation Learning of
Hand Position Feedback Controller by
U sing Change of Position Error Norm
Eimei Oyama*
Mechanical Eng. Lab.
Namiki 1-2, Tsukuba Science City
Ibaraki 305-8564 Japan
Susumu Tachi
The University of Tokyo
Hongo 7-3-1, Bunkyo-ku
Tokyo 113-0033 Japan
Abstract
In order to grasp an obje... | 1496 |@word trial:9 norm:10 tat:1 simulation:4 eng:1 covariance:1 electronics:1 configuration:5 initial:2 existing:2 yet:1 dx:1 must:1 numerical:3 motor:3 update:1 infant:3 nervous:1 plane:1 steepest:2 realizing:1 provides:1 simpler:1 five:1 direct:2 guenther:1 expected:4 dist:1 xti:1 solver:9 considering:1 becomes:3 t... |
543 | 1,497 | Finite-dimensional approximation of
Gaussian processes
Giancarlo Ferrari Trecate
Dipartimento di Informatica e Sistemistica, Universita di Pavia,
Via Ferrata 1, 27100 Pavia, Italy
ferrari@conpro.unipv.it
Christopher K. I. Williams
Department of Artificial Intelligence, University of Edinburgh,
5 Forrest Hill, Edinburg... | 1497 |@word inversion:2 bn:1 covariance:7 attainable:1 tr:7 solid:1 phy:1 series:1 outperforms:1 dx:2 numerical:1 stationary:2 intelligence:1 xk:2 ith:1 lr:2 manfred:1 draft:1 characterization:1 location:1 dn:4 constructed:1 anchorage:1 introduce:1 ra:1 expected:2 growing:1 decreasing:1 pbr:14 little:1 considering:2 in... |
544 | 1,498 | Making Templates Rotationally Invariant:
An Application to Rotated Digit Recognition
Shurneet Baluja
baluja@cs.cmu.edu
Justsystem Pittsburgh Research Center &
School of Computer Science, Carnegie Mellon University
Abstract
This paper describes a simple and efficient method to make template-based
object classification ... | 1498 |@word version:1 briefly:1 proportion:1 tried:3 tr:3 reduction:1 configuration:1 contains:7 existing:1 contextual:1 comparing:1 activation:2 must:1 written:1 designed:1 discrimination:2 alone:2 intelligence:1 plane:6 smith:1 provides:1 draft:1 location:1 successive:1 incorrect:1 consists:2 manner:1 uja:1 expected:... |
545 | 1,499 | VLSI Implementation of Motion Centroid
Localization for Autonomous Navigation
Ralph Etienne-Cummings
Dept. of ECE,
Johns Hopkins University,
Baltimore, MD
Viktor Gruev
Dept. of ECE,
Johns Hopkins University,
Baltimore, MD
Mohammed Abdel Ghani
Dept. ofEE,
S. Illinois University,
Carbondale, IL
Abstract
A circuit for... | 1499 |@word version:1 inversion:1 compression:1 cco:1 instruction:1 r:3 pulse:1 cml:1 brightness:1 lightweight:1 contains:1 offering:1 detc:1 cleared:1 current:3 yet:1 follower:2 must:3 john:2 realize:3 s21:1 motor:5 designed:1 cue:1 vmin:1 slowing:1 plane:10 realizing:1 smith:1 provides:8 node:1 location:6 five:2 wind... |
546 | 15 | 270
Correlational Strength and Computational Algebra
of Synaptic Connections Between Neurons
Eberhard E. Fetz
Department of Physiology & Biophysics,
University of Washington, Seattle, WA 98195
ABSTRACT
Intracellular recordings in spinal cord motoneurons and cerebral
cortex neurons have provided new evidence on the cor... | 15 |@word middle:3 rising:1 proportion:1 closure:1 simulation:1 t_:1 reduction:1 phy:1 yet:1 physiol:4 additive:2 subsequent:2 confirming:1 interspike:3 shape:6 nervous:1 reciprocal:1 completeness:1 provides:1 cheney:1 successive:1 height:1 mathematical:1 correlograms:5 direct:1 undetectable:1 gustafsson:2 sustained:2 ... |
547 | 150 | 256
AN INFORMATION THEORETIC APPROACH TO
RULE-BASED CONNECTIONIST EXPERT SYSTEMS
Rodney M. Goodman, John W. Miller
Department of Electrical Engineering
C altech 116-81
Pasadena, CA 91125
Padhraic Smyth
Communication Systems Research
Jet Propulsion Laboratories 238-420
4800 Oak Grove Drive
Pasadena, CA 91109
Abstract... | 150 |@word agressive:2 eng:1 initial:3 xiy:3 interestingly:1 current:2 activation:8 xiyi:10 si:6 must:2 john:2 chicago:1 numerical:2 informative:2 cheap:1 motor:1 sponsored:1 fund:16 discrimination:1 selected:1 device:1 item:1 smith:1 quantized:2 node:12 preference:2 firstly:1 oak:1 sigmoidal:1 five:1 direct:1 become:1... |
548 | 1,500 | Outcomes of the Equivalence of Adaptive Ridge
with Least Absolute Shrinkage
Yves Grandvalet
Stephane Canu
Heudiasyc, UMR CNRS 6599, Universite de Technologie de Compiegne,
BP 20.529, 60205 Compiegne cedex, France
Yves.Grandvalet@hds.utc.fr
Abstract
Adaptive Ridge is a special form of Ridge regression, balancing the
q... | 1500 |@word trial:1 version:1 norm:1 simplifying:1 solid:1 harder:1 initial:1 configuration:2 series:2 current:1 unction:1 nowlan:1 additive:9 numerical:1 girosi:2 interpretable:1 update:1 parameterization:1 provides:1 node:2 five:2 backfitting:1 fitting:4 expected:1 utc:1 automatically:2 becomes:1 underlying:1 lineari... |
549 | 1,501 | Learning curves for Gaussian processes
Peter Sollich *
Department of Physics, University of Edinburgh
Edinburgh EH9 3JZ, U.K. Email: P.Sollich<Oed.ac . uk
Abstract
I consider the problem of calculating learning curves (i.e., average
generalization performance) of Gaussian processes used for regression. A simple expre... | 1501 |@word version:4 nd:1 suitably:1 simulation:5 decomposition:4 covariance:21 tr:17 solid:2 existing:7 comparing:1 dx:1 written:1 readily:1 visible:1 periodically:1 numerical:1 realistic:1 shape:3 drop:1 interpretable:1 update:2 treating:2 plot:2 stationary:5 alone:1 manfred:1 lx:4 dn:1 along:1 become:5 differential... |
550 | 1,502 | Adding Constrained Discontinuities to Gaussian
Process Models of Wind Fields
Ian T. Nabney
Christopher K. I. Williams t
Neural Computing Research Group
Aston University, BIRMINGHAM, B4 7ET, UK
d.comford@aston.ac.uk
Dan Cornford*
Abstract
Gaussian Processes provide good prior models for spatial data, but can
be too sm... | 1502 |@word polynomial:1 seems:1 km:3 d2:6 simulation:2 dz1:1 covariance:11 tr:1 initial:3 o2:1 atlantic:1 com:1 z2:7 yet:1 john:1 realistic:3 remove:2 plot:1 stationary:2 generative:10 plane:2 inspection:1 scotland:1 location:1 toronto:1 accessed:1 along:16 mla:1 retrieving:2 dan:1 gpl:2 introduce:1 p1:2 scatterometer... |
551 | 1,503 | Learning from Dyadic Data
Thomas Hofmann?, Jan Puzicha+, Michael I. Jordan?
? Center for Biological and Computational Learning, M .I.T
Cambridge , MA , {hofmann , jordan}@ai.mit.edu
+ Institut fi.ir Informatik III , Universitat Bonn, Germany, jan@cs.uni-bonn.de
Abstract
Dyadzc data refers to a domain with two finite ... | 1503 |@word version:2 bigram:4 proportion:1 nd:2 heuristically:1 r:1 tat:1 jacob:3 loc:1 document:6 past:1 outperforms:1 thre:1 skipping:2 si:3 yet:2 written:1 visible:1 subsequent:2 partition:1 hofmann:13 update:3 intelligence:2 parameterization:1 xk:1 node:6 location:1 preference:3 x128:1 five:1 direct:1 become:1 ect... |
552 | 1,504 | Example Based Image Synthesis of Articulated
Figures
Trevor Darrell
Interval Research. 1801C Page Mill Road. Palo Alto CA 94304
trevor@interval.com, http://www.interval.com/-trevor/
Abstract
We present a method for learning complex appearance mappings. such
as occur with images of articulated objects. Traditional int... | 1504 |@word cu:1 norm:1 open:2 willing:1 seitz:1 dramatic:1 initial:3 configuration:7 contains:1 disparity:1 offering:1 existing:1 current:2 com:2 recovered:1 must:2 distant:1 subsequent:1 realistic:6 partition:1 shape:23 girosi:1 alone:2 selected:2 leaf:1 plane:1 location:4 along:3 constructed:1 direct:1 inside:1 nor:... |
553 | 1,505 | Call-based Fraud Detection in Mobile
Communication Networks using a Hierarchical
Regime-Switching Model
Jaakko Hollmen
Helsinki University of Technology
Lab. of Computer and Information Science
P.O. Box 5400, 02015 HUT, Finland
laakko.Hollmen@hut.fi
Volker Tresp
Siemens AG, Corporate Technology
Dept. Information and ... | 1505 |@word verrelst:1 subscriber:4 profit:1 solid:1 recursively:1 initial:1 series:6 tuned:1 past:2 current:4 distant:1 realistic:1 numerical:1 subsequent:2 discrimination:3 generative:4 inconvenience:1 provides:1 detecting:3 attack:1 unacceptable:1 differential:1 consists:1 behavioral:1 expected:1 behavior:10 termina... |
554 | 1,506 | Applications of multi-resolution neural
networks to mammography
Clay D. Spence and Paul Sajda
Sarnoff Corporation
CN5300
Princeton, NJ 08543-5300
{cspence, psajda }@sarnoff.com
Abstract
We have previously presented a coarse-to-fine hierarchical pyramid/neural network (HPNN) architecture which combines multiscale imag... | 1506 |@word version:1 propagate:1 leow:1 reduction:3 initial:2 iple:1 imaginary:1 current:1 com:1 contextual:2 cad:21 must:2 written:1 john:2 visible:1 chicago:12 blur:1 benign:2 shape:1 designed:2 alone:1 half:2 cue:1 intelligence:1 beginning:1 filtered:1 coarse:34 detecting:8 location:4 zhang:1 five:4 constructed:2 d... |
555 | 1,507 | Lazy Learning Meets
the Recursive Least Squares Algorithm
Mauro Birattari, Gianluca Bontempi, and Hugues Bersini
Iridia - Universite Libre de Bruxelles
Bruxelles, Belgium
{mbiro, gbonte, bersini} @ulb.ac.be
Abstract
Lazy learning is a memory-based technique that, once a query is received, extracts a prediction interp... | 1507 |@word repository:3 polynomial:4 covariance:1 kent:1 recursively:2 moment:1 contains:1 selecting:1 comparing:1 com:1 wx:3 update:1 intelligence:2 selected:4 cubist:9 ith:4 lr:4 lx:1 five:1 consists:3 combine:1 introduce:1 homoscedasticity:2 themselves:1 frequently:1 growing:1 mpg:3 globally:3 cpu:3 hugues:1 provid... |
556 | 1,508 | Markov processes on curves for
automatic speech recognition
Lawrence Saul and Mazin Rahim
AT&T Labs - Research
Shannon Laboratory
180 Park Ave E-171
Florham Park, NJ 07932
{lsaul,rnazin}Gresearch.att.com
Abstract
We investigate a probabilistic framework for automatic speech
recognition based on the intrinsic geometri... | 1508 |@word determinant:2 proportion:2 aske:1 covariance:1 solid:1 att:1 united:1 subword:1 com:1 comparing:1 realize:1 partition:1 plot:1 update:1 stationary:2 selected:1 short:4 traverse:1 mathematical:1 along:10 windowed:1 differential:2 consists:1 introduce:1 roughly:1 frequently:1 decreasing:1 moreover:1 spoken:1 ... |
557 | 1,509 | Experimental Results on Learning Stochastic
Memoryless Policies for Partially Observable
Markov Decision Processes
John K. Williams
Department of Mathematics
University of Colorado
Boulder, CO 80309-0395
jkwillia@euclid.colorado.edu
Satinder Singh
AT &T Labs-Research
180 Park Avenue
Florham Park, NJ 07932
baveja@rese... | 1509 |@word version:3 twelfth:1 simulation:1 seek:1 thereby:1 solid:2 recursively:1 reduction:1 initial:2 att:1 efficacy:1 selecting:1 past:4 current:5 com:1 assigning:1 written:1 must:1 john:1 remove:1 drop:1 update:1 stationary:3 half:1 intelligence:2 yr:1 es:1 simpler:1 admission:1 along:1 constructed:1 differential... |
558 | 151 | 703
WINNER-TAKE-ALL
NETWORKS OF O(N) COMPLEXITY
J. Lazzaro, S. Ryckebusch, M.A. Mahowald, and C. A. Mead
California Institute of Technology
Pasadena, CA 91125
ABSTRACT
We have designed, fabricated, and tested a series of compact CMOS
integrated circuits that realize the winner-take-all function. These
analog, continu... | 151 |@word tr:1 solid:3 carry:1 series:2 document:1 current:26 ida:1 must:7 john:2 realize:3 distant:3 confirming:1 designed:3 plot:2 sponsored:1 device:2 realizing:2 ofo:4 mathematical:1 supply:2 mask:1 behavior:5 decreasing:1 provided:1 circuit:69 medium:3 ringing:1 fabricated:3 temporal:1 ti:2 platt:1 t1:1 service:1... |
559 | 1,510 | The Effect of Correlations on the Fisher
Information of Population Codes
Hyoungsoo Yoon
hyoung@fiz.huji.ac.il
Haim Sompolinsky
hairn@fiz.huji.ac.il
Racah Institute of Physics and Center for Neural Computation
Hebrew University, Jerusalem 91904, Israel
Abstract
We study the effect of correlated noise on the accuracy... | 1510 |@word crucially:1 covariance:2 reduction:2 contains:1 united:1 tuned:2 must:1 numerical:1 shape:1 motor:5 fund:1 discrimination:1 half:2 plane:1 inspection:1 smith:3 short:1 height:1 manner:1 pairwise:2 presumed:1 expected:1 behavior:5 frequently:1 little:1 zohary:2 increasing:2 becomes:5 moreover:1 israel:3 subs... |
560 | 1,511 | Learning multi-class dynamics
A. Blake, B. North and M. Isard
Department of Engineering Science, University of Oxford, Oxford OXl 3P J, UK.
Web: http://www.robots.ox.ac.uk/ ... vdg/
Abstract
Standard techniques (eg. Yule-Walker) are available for learning
Auto-Regressive process models of simple, directly observable,... | 1511 |@word briefly:1 version:1 grey:3 bn:1 harder:1 carry:4 moment:2 initial:1 series:3 selecting:1 current:3 shape:2 treating:1 update:1 isard:6 accordingly:1 smith:1 short:1 bwt:1 regressive:3 successive:1 constructed:1 direct:1 incorrect:1 autocorrelation:1 expected:4 themselves:1 multi:14 ry:1 actual:1 becomes:1 n... |
561 | 1,512 | Replicator Equations, Maximal Cliques,
and Graph Isomorphism
Marcello Pelillo
Dipartimento di Informatica
Universita Ca' Foscari di Venezia
Via Torino 155, 30172 Venezia Mestre, Italy
E-mail: pelillo@dsi.unive.it
Abstract
We present a new energy-minimization framework for the graph
isomorphism problem which is based ... | 1512 |@word version:2 polynomial:4 seems:1 proportion:1 initial:1 ours:1 hearn:1 current:1 optim:1 yet:1 attracted:1 readily:1 stationary:2 short:1 transposition:1 completeness:1 characterization:2 math:5 bijection:1 successive:1 mathematical:2 along:1 expected:7 indeed:1 themselves:1 frequently:1 mechanic:1 freeman:1 ... |
562 | 1,513 | Convergence Rates of Algorithms for
Visual Search: Detecting Visual Contours
A.L. Yuille
Smith-Kettlewell Inst .
San Francisco, CA 94115
James M. Coughlan
Smith-Kettlewell Inst.
San Francisco, CA 94115
Abstract
This paper formulates the problem of visual search as Bayesian
inference and defines a Bayesian ensemble o... | 1513 |@word middle:1 version:1 briefly:1 eliminating:2 nd:2 simplifying:1 pg:2 initial:1 substitution:1 subjective:1 comparing:1 must:2 shape:1 remove:1 intelligence:2 smith:2 coughlan:19 detecting:6 mathematical:1 along:2 kettlewell:2 prove:1 combine:1 interscience:1 indeed:1 expected:9 examine:1 becomes:2 provided:5 ... |
563 | 1,514 | Coding time-varying signals using sparse,
shift-invariant representations
Terrence J. Sejnowski
terryCsalk.edu
Michael S. Lewicki*
lewickiCsalk.edu
Howard Hughes Medical Institute
Computational Neurobiology Laboratory
The Salk Institute
10010 N. Torrey Pines Rd.
La Jolla, CA 92037
Abstract
A common way to represent ... | 1514 |@word decomposition:2 initial:8 series:9 selecting:2 existing:1 si:1 must:1 additive:4 plot:2 generative:1 fewer:1 leaf:1 ith:1 short:2 provides:1 contribute:1 location:2 attack:1 zhang:2 constructed:3 direct:1 fitting:5 coifman:2 roughly:1 themselves:2 freeman:1 underlying:5 notation:1 finding:3 extremum:1 tempo... |
564 | 1,515 | Almost Linear VC Dimension Bounds for
Piecewise Polynomial Networks
Peter L. Bartlett
Department of System Engineering
Australian National University
Canberra, ACT 0200
Australia
Peter.Bartlett@anu.edu.au
Vitaly Maiorov
Department of Mathematics
Technion, Haifa 32000
Israel
Ron Meir
Department of Electrical Engineer... | 1515 |@word behw89:2 briefly:1 polynomial:31 bn:1 mention:1 recursively:1 initial:1 selecting:1 chervonenkis:1 recovered:1 activation:18 si:2 realistic:1 partition:8 implying:1 fewer:1 warmuth:1 lr:1 node:1 ron:1 sigmoidal:1 unbounded:1 c2:4 constructed:2 prove:1 behavior:1 multi:2 jm:1 cardinality:1 begin:1 provided:1... |
565 | 1,516 | Probabilistic Image Sensor Fusion
Ravi K. Sharma1 , Todd K. Leen 2 and Misha Pavel 1
1 Department
of Electrical and Computer Engineering
2Department of Computer Science and Engineering
Oregon Graduate Institute of Science and Technology
P.O. Box 91000 , Portland , OR 97291-1000
Email: {ravi,pavel} @ece.ogi.edu, tleen... | 1516 |@word aircraft:3 f32:1 loading:1 proportionality:1 covariance:13 pavel:7 invoking:1 contains:1 efficacy:1 selecting:1 envision:1 must:2 additive:2 visible:11 visibility:2 depict:1 v:1 selected:1 device:1 ith:1 compo:1 provides:2 cse:1 location:3 successive:1 ames:1 ect:1 combine:2 actual:1 increasing:1 provided:1... |
566 | 1,517 | On the optimality of incremental neural network
algorithms
Ron Meir*
Department of Electrical Engineering
Technion, Haifa 32000, Israel
rmeir@dumbo.technion.ac.il
Vitaly Maiorov t
Department of Mathematics
Technion, Haifa 32000, Israel
maiorov@tx.technion.ac.il
Abstract
We study the approximation of functions by two... | 1517 |@word version:1 polynomial:1 norm:15 tedious:1 open:2 closure:4 iki:2 thereby:1 moment:1 pub:1 comparing:1 od:1 activation:2 must:1 numerical:1 statis:2 ilii:2 juditsky:1 implying:1 greedy:11 warmuth:1 node:2 ron:1 forfeiting:1 sigmoidal:2 simpler:1 along:1 symposium:1 boor:1 indeed:1 expected:1 surge:1 dist:2 mu... |
567 | 1,518 | Contrast adaptation in simple cells by changing
the transmitter release probability
Peter Adorjan
Klaus Obennayer
Dept. of Computer Science, FR2-1, Technical University Berlin
Franklinstrasse 28/2910587 Berlin, Germany
{adp, oby} @cs.tu-berlin.de http://www.ni.cs.tu-berlin.de
Abstract
The contrast response function (... | 1518 |@word vogt:1 simulation:3 mammal:1 solid:4 moment:1 initial:1 series:2 current:6 od:1 surprising:1 activation:1 physiol:1 additive:1 subsequent:1 interspike:1 plasticity:5 analytic:1 christian:1 piepenbrock:1 stationary:1 half:1 short:3 ire:1 fitting:1 shapley:1 wannier:1 expected:1 rapid:2 integrator:3 brain:1 f... |
568 | 1,519 | Controlling the Complexity of HMM Systems by
Regularization
Christoph Neukirchen, Gerhard Rigoll
Department of Computer Science
Gerhard-Mercator-University Duisburg
47057 Duisburg, Germany
email: {chn.rigoll}@fb9-ti.uni-duisburg.de
Abstract
This paper introduces a method for regularization ofHMM systems that
avoids p... | 1519 |@word stronger:1 simplifying:1 initial:1 series:1 selecting:1 outperforms:1 nowlan:4 gauvain:2 lang:2 must:5 written:1 partition:3 drop:1 v:2 intelligence:1 selected:2 quantizer:2 plaut:2 codebook:2 along:2 constructed:1 direct:1 become:1 consists:2 fitting:1 iogb:2 brain:1 decomposed:1 subvectors:1 increasing:1 ... |
569 | 152 | 436
SIMULATION AND MEASUREMENT OF
THE ELECTRIC FIELDS GENERATED
BY WEAKLY ELECTRIC FISH
Brian Rasnow 1, Christopher Assad2, Mark E. Nelson3 and James M. Bow~
Divisions of Physics1,Elecbical Engineerini, and Biolo~
Caltech, Pasadena, 91125
ABSTRACT
The weakly electric fish, Gnathonemus peters;;, explores its environme... | 152 |@word simulation:18 electrosensory:4 decomposition:1 electroreceptors:3 fmite:6 electronics:1 configuration:2 existing:1 current:8 discretization:2 activation:1 yet:1 assigning:1 must:1 readily:2 j1:1 midway:1 shape:1 analytic:3 motor:2 drop:1 designed:1 discrimination:4 alone:1 selected:1 nervous:2 accordingly:1 ... |
570 | 1,520 | Exploiting generative models
discriminative classifiers
Tommi S. Jaakkola*
MIT Artificial Intelligence Laboratorio
545 Technology Square
Cambridge, MA 02139
?
In
David Haussler
Department of Computer Science
University of California
Santa Cruz, CA 95064
Abstract
Generative probability models such as hidden ~larkov... | 1520 |@word middle:1 version:3 advantageous:1 nd:3 suitably:3 covariance:2 solid:2 phy:1 series:1 score:6 fragment:2 current:2 must:1 written:3 cruz:1 treating:1 discrimination:1 alone:4 generative:26 intelligence:1 parameterization:1 plane:1 steepest:1 accepting:1 provides:3 clarified:1 ional:1 simpler:1 along:1 consi... |
571 | 1,521 | SMEM Algorithm for Mixture Models
N aonori U eda Ryohei Nakano
{ueda, nakano }@cslab.kecl.ntt.co.jp
NTT Communication Science Laboratories
Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237 Japan
Zoubin Ghahramani Geoffrey E. Hinton
zoubin@gatsby.uc1.ac.uk g.hinton@ucl.ac.uk
Gatsby Computational Neuroscience Unit, Unive... | 1521 |@word eliminating:1 seems:1 loading:1 dekker:1 open:1 tried:1 covariance:3 decomposition:1 thereby:1 tr:1 xlw:1 initial:4 configuration:1 selecting:1 amp:1 outperforms:1 current:1 comparing:1 written:1 additive:1 wx:2 update:1 generative:1 location:2 lx:4 successive:2 toronto:1 direct:1 ryohei:1 fitting:1 introdu... |
572 | 1,522 | An entropic estimator for structure discovery
Matthew Brand
Mitsubishi Electric Research Laboratories, 201 Broadway, Cambridge MA 02139
brand@merl.com
Abstract
We introduce a novel framework for simultaneous structure and parameter learning in
hidden-variable conditional probability models, based on an en tropic prio... | 1522 |@word trial:3 repository:2 compression:1 stronger:2 retraining:1 extinction:3 open:1 seek:1 mitsubishi:1 covariance:1 concise:2 thereby:2 tr:2 accommodate:1 initial:4 configuration:1 series:2 complexit:1 tuned:2 reynolds:2 current:1 com:1 contextual:1 chordal:1 numerical:1 partition:1 informative:1 subsequent:1 s... |
573 | 1,523 | Optimizing Classifiers for Imbalanced
Training Sets
Grigoris Karakoulas
Global Analytics Group
Canadian Imperial Bank of Commerce
161 Bay St., BCE-ll,
Toronto ON, Canada M5J 2S8
Email: karakoulOcibc.ca
John Shawe-Taylor
Department of Computer Science
Royal Holloway, University of London
Egham, TW20 OEX
England
Email:... | 1523 |@word repository:2 polynomial:2 seek:1 simplifying:1 initial:4 suppressing:1 current:3 john:3 analytic:1 remove:2 update:1 lr:1 boosting:5 toronto:1 hyperplanes:1 beta:1 symposium:1 focs:1 eleventh:1 introduce:2 li3:1 expected:2 examine:1 bounded:4 notation:2 kubat:1 developed:1 voting:1 xd:5 fat:10 classifier:2 ... |
574 | 1,525 | Batch and On-line Parameter Estimation of
Gaussian Mixtures Based on the Joint Entropy
Yoram Singer
AT&T Labs
singer@research.att.com
Manfred K. Warmuth
University of California, Santa Cruz
manfred@cse.ucsc.edu
Abstract
We describe a new iterative method for parameter estimation of Gaussian mixtures. The new method ... | 1525 |@word version:1 proportion:1 duda:1 tedious:1 si8:4 seek:1 covariance:9 b39:1 tr:2 tci1:1 att:1 offering:1 current:2 com:1 od:1 dx:6 herring:1 cruz:1 additive:1 ota:1 update:38 joy:1 half:2 tenn:2 warmuth:12 ith:1 smith:1 manfred:2 cse:1 ucsc:1 become:1 consists:1 behavior:3 themselves:1 isi:2 becomes:1 estimatin... |
575 | 1,526 | Recurrent Cortical Amplification Produces
Complex Cell Responses
Frances S. Chance~ Sacha B. Nelson~ and L. F. Abbott
Volen Center and Department of Biology
Brandeis University
Waltham, MA 02454
Abstract
Cortical amplification has been proposed as a mechanism for enhancing
the selectivity of neurons in the primary vi... | 1526 |@word middle:1 wiesel:4 tr:3 tuned:3 current:1 physiol:6 realistic:1 feedfoward:1 short:1 tolhurst:2 preference:1 kix:1 five:1 along:1 direct:3 differential:2 pathway:2 shapley:4 manner:1 indeed:1 roughly:1 behavior:4 brain:3 provided:1 notation:1 circuit:2 panel:4 eigenvector:1 monkey:1 kimura:1 temporal:4 toyam... |
576 | 1,527 | Facial Memory is Kernel Density Estimation
(Almost)
Matthew N. Dailey
Garrison W. Cottrell
Department of Computer Science and Engineering
U.C. San Diego
La Jolla, CA 92093-0114
{mdailey,gary}@cs.ucsd.edu
Thomas A. Busey
Department of Psychology
Indiana University
Bloomington, IN 47405
busey@indiana.edu
Abstract
We... | 1527 |@word version:3 inversion:9 achievable:1 seems:2 vogt:1 covariance:2 efficacy:1 interestingly:1 outperforms:1 surprising:1 yet:1 dx:1 must:3 cottrell:5 hypothesize:1 xex:3 discrimination:2 xk:1 caveat:2 provides:1 draft:1 contribute:1 location:2 gillund:3 positing:1 five:2 height:4 constructed:1 fitting:4 introdu... |
577 | 1,528 | Orientation, Scale, and Discontinuity as
Emergent Properties of Illusory Contour
Shape
Karvel K. Thornber
NEC Research Institute
4 Independence Way
Princeton, NJ 08540
Lance R. Williams
Dept. of Computer Science
University of New Mexico
Albuquerque, NM 87131
Abstract
A recent neural model of illusory contour formati... | 1528 |@word closure:2 confirms:2 jacob:7 minus:1 solid:4 initial:1 contains:1 past:1 si:8 yet:2 must:1 ixil:1 numerical:2 shape:17 analytic:1 plot:3 drop:1 v:4 alone:4 half:1 selected:1 accordingly:1 plane:2 beginning:1 short:1 location:1 consists:4 x0:1 nor:1 frequently:1 aliasing:1 increasing:1 becomes:1 begin:4 what... |
578 | 1,529 | Stationarity and Stability of
Autoregressive Neural Network Processes
Friedrich Leisch\ Adrian Trapletti 2 & Kurt Hornik l
1 Institut fur Statistik
Technische UniversiUit Wien
Wiedner Hauptstrafie 8-10 / 1071
A-1040 Wien, Austria
firstname.lastname@ci.tuwien.ac.at
2
Institut fiir Unternehmensfiihrung
Wirtschaftsuni... | 1529 |@word version:1 polynomial:2 stronger:1 adrian:2 tat:1 covariance:2 moment:1 series:59 kurt:1 dx:1 willinger:1 additive:3 happen:1 stationary:43 accordingly:1 beginning:1 short:1 lr:5 unbounded:2 mathematical:1 mandelbrot:3 xtl:3 combine:1 leland:2 behavior:4 growing:2 multi:1 tuwien:1 notation:1 bounded:5 null:2... |
579 | 153 | 594
Range Image Restoration
using Mean Field Annealing
Wesley E. Snyder
Griff L. Bilbro
Center for Communications and Signal Processing
North Carolina State University
Raleigh, NC
Abstract
A new optimization strategy, Mean Field Annealing, is presented.
Its application to MAP restoration of noisy range images is der... | 153 |@word norm:1 grey:1 simulation:1 carolina:3 tr:1 necessity:1 initial:3 existing:1 current:3 z2:1 must:2 numerical:1 realistic:1 shape:3 analytic:1 update:1 steepest:1 hamiltonian:2 simpler:2 mathematical:1 along:2 direct:1 fitting:1 polyhedral:1 manner:1 expected:1 fez:1 lll:1 increasing:1 becomes:1 begin:1 underl... |
580 | 1,530 | A Precise Characterization of the Class of
Languages Recognized by Neural Nets under
Gaussian and other Common Noise Distributions
Wolfgang Maass*
Inst. for Theoretical Computer Science,
Technische Universitat Graz
Klosterwiesgasse 3212,
A-80lO Graz, Austria
email: maass@igi.tu-graz.ac.at
Eduardo D. Sontag
Oep. of Ma... | 1530 |@word version:2 rno:2 pick:4 recursively:1 doeblin:7 carry:1 initial:2 irnxn:1 orponen:5 blank:1 activation:5 assigning:1 readily:1 realistic:1 benign:1 nur:1 accepting:5 v211:1 characterization:4 provides:1 math:2 sigmoidal:7 mathematical:1 lku:5 symposium:1 prove:3 consists:1 manner:1 introduce:2 coa:2 utc:1 co... |
581 | 1,531 | Finite-Sample Convergence Rates for
Q-Learning and Indirect Algorithms
Michael Kearns and Satinder Singh
AT&T Labs
180 Park Avenue
Florham Park , NJ 07932
{mkearns,baveja}@research.att.com
Abstract
In this paper, we address two issues of long-standing interest in the reinforcement learning literature. First, what kind... | 1531 |@word trial:3 version:3 briefly:2 polynomial:3 tried:1 contraction:1 initial:2 mkearns:1 att:1 current:1 com:1 surprising:1 yet:1 must:8 remove:1 update:3 maxv:1 stationary:5 greedy:3 guess:1 footing:1 successive:1 direct:9 become:2 prove:1 fth:2 introduce:2 indeed:1 expected:7 rapid:3 p1:6 examine:1 roughly:1 di... |
582 | 1,532 | Mean field methods for classification with
Gaussian processes
Manfred Opper
Neural Computing Research Group
Division of Electronic Engineering and Computer Science
Aston University Birmingham B4 7ET, UK.
opperm~aston.ac.uk
Ole Winther
Theoretical Physics II, Lund University, S6lvegatan 14 A
S-223 62 Lund, Sweden
CONN... | 1532 |@word simulation:4 covariance:4 tr:1 initial:1 phy:1 pub:1 mag:1 imaginary:3 reaction:1 comparing:1 dx:2 written:1 must:4 partition:2 guess:1 manfred:1 math:1 toronto:1 ron:2 simpler:4 along:1 direct:1 become:1 specialize:1 introduce:1 ra:1 mechanic:2 ol:1 automatically:1 becomes:2 estimating:1 lowest:1 ail:1 ons... |
583 | 1,533 | Modeling Surround Suppression in VI Neurons
with a Statistically-Derived Normalization Model
Eero P. Simoncelli
Center for Neural Science, and
Courant Institute of Mathematical Sciences
New York University
eero.simoncelli@nyu.edu
Odelia Schwartz
Center for Neural Science
New York University
odelia@cns.nyu.edu
Abstra... | 1533 |@word dividing:1 effect:1 hypothesized:1 concept:2 approximating:1 classical:2 adequately:1 symmetric:1 filter:1 receptive:5 primary:2 decomposition:9 adjacent:1 diagonal:1 bialek:1 exhibit:1 uniquely:1 noted:1 steady:1 recursively:1 evident:1 ridge:1 demonstrate:1 image:11 relationship:1 recently:1 must:1 early:... |
584 | 1,534 | Analog VLSI Cellular Implementation of the
Boundary Contour System
Gert Cauwenberghs and James Waskiewicz
Department of Electrical and Computer Engineering
Johns Hopkins University
3400 North Charles Street
Baltimore, MD 21218-2686
E-mail: {gert, davros }@bach. ece. jhu. edu
Abstract
We present an analog VLSI cellula... | 1534 |@word version:1 bf:2 propagate:1 excited:1 incurs:1 solid:2 reduction:1 contains:3 selecting:2 optically:1 tuned:1 current:11 luo:1 john:1 partition:1 designed:1 plane:9 short:1 schaik:1 provides:1 quantized:1 node:2 location:2 detecting:1 along:1 direct:2 become:1 viable:1 consists:1 resistive:5 combine:2 acquir... |
585 | 1,535 | DTs: Dynamic Trees
Christopher K. I. Williams
Nicholas J. Adams
Institute for Adaptive and Neural Computation
Division of Informatics, 5 Forrest Hill
Edinburgh, EHI 2QL, UK.
http://www.anc.ed.ac . uk/
ckiw~dai.ed.ac.uk
nicka~dai.ed.ac.uk
Abstract
In this paper we introduce a new class of image models, which we
call ... | 1535 |@word proportion:1 crucially:1 shading:1 configuration:4 current:2 comparing:2 readily:1 visible:1 designed:1 plot:5 v:1 stationary:1 discrimination:1 leaf:5 fewer:1 accepting:1 node:38 location:2 successive:2 preference:1 five:3 mathematical:1 become:1 consists:1 inside:1 introduce:2 roughly:1 multi:1 tsbn:16 qu... |
586 | 1,536 | Divisive Normalization, Line Attractor
Networks and Ideal Observers
Sophie Deneve l Alexandre Pougetl, and P.E. Latham 2
Institute for Computational and Cognitive Sciences,
Georgetown University, Washington, DC 20007-2197
2Dpt of Neurobiology, UCLA, Los Angeles, CA 90095-1763, U.S.A.
1 Georgetown
Abstract
Gain control... | 1536 |@word trial:1 cos2:2 simulation:11 covariance:3 initial:6 nally:1 perturbative:1 written:2 must:1 realistic:1 j1:2 discrimination:1 alone:1 filtered:1 provides:1 zhang:1 mathematical:1 direct:1 consists:2 prove:1 behavior:1 actual:1 jm:1 becomes:1 notation:1 what:2 kg:1 eigenvector:1 quantitative:1 every:2 multid... |
587 | 1,537 | Maximum Conditional Likelihood via
Bound Maximization and the CEM
Algorithm
Tony J ebara and Alex Pentland
Vision and Modeling, MIT Media Laboratory, Cambridge MA
http://www.rnedia.rnit.edu/ ~ jebara
{ jebara,sandy }~rnedia.rnit.edu
Abstract
We present the CEM (Conditional Expectation Maximi::ation) algorithm as an e... | 1537 |@word proportion:3 tried:1 covariance:10 b39:1 jacob:1 concise:1 ld:1 initial:1 current:2 must:1 update:14 v:1 yi1:1 location:1 simpler:1 direct:1 introduce:2 becomes:2 estimating:2 bounded:2 xx:4 maximizes:2 medium:2 what:1 finding:1 transformation:1 xd:1 growth:2 biometrika:1 rm:2 unit:1 attend:1 local:2 oxford... |
588 | 1,538 | Robot Docking using Mixtures of Gaussians
Matthew Williamson*
Roderick Murray-Smith t
Volker Hansen t
Abstract
This paper applies the Mixture of Gaussians probabilistic model, combined with Expectation Maximization optimization to the task of summarizing three dimensional range data for a mobile robot. This provide... | 1538 |@word duda:2 pulse:1 covariance:8 pick:1 ld:1 initial:3 series:2 com:1 od:2 trc:1 written:2 readily:2 john:1 visible:2 visibility:5 plot:1 update:10 parameterization:6 plane:14 ith:1 smith:5 oblique:1 provides:2 location:3 preference:1 mathematical:1 shiny:1 become:1 fitting:5 ray:1 indeed:1 expected:4 behavior:2... |
589 | 1,539 | Fisher Scoring and a Mixture of Modes
Approach for Approximate Inference and
Learning in Nonlinear State Space Models
Thomas Briegel and Volker Tresp
Siemens AG, Corporate Technology
Dept. Information and Communications
Otto-Hahn-Ring 6,81730 Munich, Germany
{Thomas.Briegel, Volker.Tresp} @mchp.siemens.de
Abstract
We... | 1539 |@word dtk:1 version:1 seems:1 covariance:14 decomposition:1 recursively:2 initial:4 series:10 score:1 past:1 current:3 must:1 subsequent:1 hofmann:2 gv:8 update:4 stationary:1 pursued:1 yr:1 metrika:1 regressive:1 provides:1 height:2 welg:1 ik:2 consists:1 introduce:1 xt_l:3 expected:7 multi:2 feldkamp:2 ag:1 uno... |
590 | 154 | 81
WHAT SIZE NET GIVES VALID
GENERALIZATION?*
Eric B. Baum
Department of Physics
Princeton University
Princeton NJ 08540
David Haussler
Computer and Information Science
University of California
Santa Cruz, CA 95064
ABSTRACT
We address the question of when a network can be expected to
generalize from m random trainin... | 154 |@word briefly:1 version:1 polynomial:1 stronger:1 maz:1 duda:1 open:2 initial:1 chervonenkis:4 ketch:1 nt:1 si:1 yet:1 cruz:4 fn:1 partition:1 offunctions:1 designed:1 sponsored:1 v:2 half:2 fewer:5 guess:2 intelligence:2 warmuth:2 plane:2 completeness:1 provides:1 node:36 ron:1 draft:1 math:2 simpler:1 shatter:1 ... |
591 | 1,540 | General-purpose localization of textured
?
?
Image
regions
Rutb Rosenboltz?
XeroxPARC
3333 Coyote Hill Rd.
Palo Alto, CA 94304
Abstract
We suggest a working definition of texture: Texture is stuff that is
more compactly represented by its statistics than by specifying the
configuration of its parts. This definition s... | 1540 |@word compression:1 seems:1 nd:1 tried:1 eng:1 configuration:1 current:1 com:1 surprising:1 must:2 visible:1 visibility:2 remove:1 grass:2 cue:6 item:3 filtered:1 detecting:1 coarse:1 characterization:1 ames:1 simpler:1 pun:1 unacceptable:1 fitting:2 roughly:1 distractor:1 little:1 window:7 alto:1 medium:1 what:2... |
592 | 1,541 | Active Noise Canceling using Analog NeuroChip with On-Chip Learning Capability
Jung-Wook Cho and Soo-Young Lee
Computation and Neural Systems Laboratory
Department of Electrical Engineering
Korea Advanced Institute of Science and Technology
373-1 Kusong-dong, Yusong-gu, Taejon 305-701, Korea
sylee@ee.kaist.ac.kr
Abstr... | 1541 |@word middle:1 advantageous:1 donham:1 simulation:6 solid:1 harder:1 reduction:2 electronics:1 nrr:1 current:4 cad:1 activation:3 must:1 refresh:2 wx:1 designed:3 update:3 device:1 short:1 quantized:2 node:1 anchorage:1 constructed:1 c2:1 differential:2 consists:3 expected:1 alspector:1 multi:4 actual:3 pf:1 beco... |
593 | 1,542 | Bayesian modeling of human concept learning
Joshua B. Tenenbaum
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology, Cambridge, MA 02139
jbt@psyche.mit.edu
Abstract
I consider the problem of learning concepts from small numbers of positive examples, a feat which humans perform routinely b... | 1542 |@word trial:5 version:1 sharpens:1 selecting:1 offering:1 existing:2 glh:1 blank:1 unction:1 assigning:1 must:1 realistic:2 confirming:1 dilemna:1 plot:1 generative:1 intelligence:2 guess:2 plane:1 core:1 short:2 cjx:1 provides:2 detecting:1 math:1 location:2 along:9 become:3 qualitative:1 consists:2 behavioral:1... |
594 | 1,543 | Learning Mixture Hierarchies
Nuno Vasconcelos
Andrew Lippman
MIT Media Laboratory, 20 Ames St, EI5-320M, Cambridge, MA 02139,
{nuno,lip} @media.mit.edu,
http://www.media.mit.edwnuno
Abstract
The hierarchical representation of data has various applications in domains such as data mining, machine vision, or informati... | 1543 |@word middle:1 proportion:1 covariance:3 xilzij:5 fonn:1 harder:1 shot:1 initial:3 zij:4 ida:1 od:1 si:1 dx:1 subsequent:1 partition:1 j1:8 plot:4 update:1 v:2 beginning:1 ith:2 provides:1 characterization:2 coarse:1 ames:1 node:2 simpler:1 along:1 direct:1 become:4 consists:3 combine:1 compose:1 introduce:1 mann... |
595 | 1,544 | Exploratory Data Analysis Using Radial Basis
Function Latent Variable Models
Alan D. Marrs and Andrew R. Webb
DERA
St Andrews Road, Malvern
Worcestershire U.K. WR14 3PS
{marrs,webb}@signal.dera.gov.uk
@British Crown Copyright 1998
Abstract
Two developments of nonlinear latent variable models based on radial
basis func... | 1544 |@word version:2 briefly:1 norm:1 nd:1 seek:1 covariance:6 tiw:2 tr:1 reduction:5 initial:1 denoting:1 existing:1 current:2 written:3 realistic:1 plot:2 update:1 resampling:7 discrimination:1 generative:3 fewer:1 plane:2 maximised:1 provides:1 revisited:1 along:1 manner:1 introduce:1 gov:1 lll:2 provided:2 notatio... |
596 | 1,545 | Independent Component Analysis of
Intracellular Calcium Spike Data
Klaus Prank, Julia Borger, Alexander von zur Miihlen,
Georg Brabant, Christof Schoil
Department of Clinical Endocrinology
Medical School Hannover
D-30625 Hannover
Germany
Abstract
Calcium (Ca2 +)is an ubiquitous intracellular messenger which regulate... | 1545 |@word norm:3 nd:1 pancreatic:1 contraction:1 covariance:2 initial:1 contains:1 series:2 activation:2 si:4 yet:1 remove:1 intelligence:1 coarse:1 provides:2 tahoe:1 five:4 mathematical:3 differential:1 symposium:1 sustained:1 manner:1 pharmacologically:1 ica:13 rapid:1 secretory:1 proliferation:1 resolve:2 prolong... |
597 | 1,546 | Vertex Identification in High Energy
Physics Experiments
Gideon Dror*
Department of Computer Science
The Academic College of Tel-Aviv-Yaffo, Tel Aviv 64044 , Israel
Halina Abramowicz t David Horn t
School of Physics and Astronomy
Raymond and Beverly Sackler Faculty of Exact Sciences
Tel-Aviv University, Tel Aviv 69978... | 1546 |@word cylindrical:1 faculty:1 wiesel:1 duda:1 open:1 cm2:1 simulation:1 cml:1 pulse:1 denby:1 selecting:1 denoting:1 current:1 activation:2 scatter:1 readily:1 physiol:1 shape:1 v:1 selected:1 device:1 plane:7 kbytes:1 filtered:1 location:11 five:2 along:1 abramowicz:5 indeed:1 nor:1 ry:1 simulator:1 brain:1 insp... |
598 | 1,547 | Attentional Modulation of Human Pattern
Discrimination Psychophysics Reproduced
by a Quantitative Model
Laurent Itti, Jochen Braun, Dale K. Lee and Christof Koch
{itti, achim, jjwen, koch}Oklab.caltech.edu
Computation & Neural Systems, MSC 139-74
California Institute of Technology, Pasadena, CA 91125, U.S.A.
Abstract... | 1547 |@word trial:2 grey:1 simulation:1 attended:18 tuned:6 bc:1 interestingly:1 tilted:1 mst:1 happen:1 discrimination:17 alone:1 half:1 provides:1 location:6 successive:1 sigmoidal:2 zhang:1 five:1 transducer:1 consists:2 fitting:7 overhead:1 behavioral:2 inside:1 manner:2 mask:4 indeed:1 brain:2 compensating:1 inspi... |
599 | 1,548 | Familiarity Discrimination of Radar
Pulses
Eric Grangerl, Stephen Grossberg 2
Mark A. RUbin2 , William W. Streilein 2
1 Department
of Electrical and Computer Engineering
Ecole Polytechnique de Montreal
Montreal, Qc. H3C 3A 7 CAN ADA
2Department of Cognitive and Neural Systems, Boston University
Boston, MA 02215 USA
... | 1548 |@word aircraft:4 compression:1 norm:1 retraining:1 open:1 pulse:15 simulation:9 initial:3 cyclic:1 ecole:1 document:1 reynolds:1 current:1 activation:1 must:2 designed:1 plot:1 discrimination:28 v:2 half:2 selected:4 guess:1 ith:1 record:1 node:15 incorrect:3 inside:1 ra:1 proliferation:2 examine:1 actual:4 becom... |
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