Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
200 | 1,182 | Balancing between bagging and bumping
Tom Heskes
RWCP Novel Functions SNN Laboratory; University of Nijmegen
Geert Grooteplein 21 , 6525 EZ Nijmegen, The Netherlands
tom@mbfys.kun.nl
Abstract
We compare different methods to combine predictions from neural networks trained on different bootstrap samples of a regressio... | 1182 |@word version:1 seems:1 grooteplein:1 simulation:2 jacob:1 solid:2 initial:3 configuration:2 bootstrapped:2 outperforms:1 yet:1 written:1 dashdot:1 interpretable:1 resampling:3 intelligence:1 leaf:1 toronto:1 consists:1 combine:4 fitting:1 introduce:1 indeed:2 expected:3 mbfys:1 p1:1 themselves:1 snn:2 increasing... |
201 | 1,183 | Adaptive On-line Learning in Changing
Environments
Noboru Murata, Klaus-Robert Miiller, Andreas Ziehe
GMD-First, Rudower Chaussee 5, 12489 Berlin, Germany
{mura.klaus.ziehe}~first.gmd.de
Shun-ichi Amari
Laboratory for Information Representation, RIKEN
Hirosawa 2-1, Wako-shi, Saitama 351-01, Japan
amari~zoo.riken.go.jp... | 1183 |@word version:1 norm:1 seems:1 confirms:1 simulation:3 moment:1 kappen:2 initial:2 wako:1 past:1 existing:1 current:1 z2:1 comparing:1 numerical:1 stationary:2 short:2 compo:1 leakiness:1 sudden:1 provides:1 unmixed:3 math:1 accessed:1 along:2 prove:1 introduce:1 theoretically:2 expected:4 behavior:2 mechanic:2 o... |
202 | 1,184 | Basis Function Networks and Complexity
--_.IIiIIIIIIIIlo. ..............
in Function Learning
10. .......,. ...?. . . . .
JIIIL ....'IIIoo4II? .,JIIIL'IIU"JIIILJIIIL
Adam Krzyzak
Department of Computer Science
Concordia University
Montreal, Canada
krzyzak@cs.concordia.ca
Tamas Linder
Dept. of Math. & Comp. Sci.
Tec... | 1184 |@word version:1 sharpens:1 norm:2 seems:1 hu:1 closure:3 d2:1 pick:1 fonn:2 contains:1 series:1 ala:1 discretization:1 wd:1 activation:5 jkl:1 fn:7 zeger:2 girosi:9 enables:1 offunctions:2 lr:6 characterization:1 math:1 node:3 sigmoidal:2 c2:2 ik:3 consists:1 prove:2 expected:4 decreasing:1 automatically:1 window... |
203 | 1,185 | Learning Decision Theoretic Utilities Through
Reinforcement Learning
Magnus Stensmo
Terrence J. Sejnowski
Computer Science Division
University of California
Berkeley, CA 94720, U.S.A.
magnus@cs.berkeley.edu
Howard Hughes Medical Institute
The Salk Institute
10010 North Torrey Pines Road
La Jolla, CA 92037, U.S.A.
t... | 1185 |@word repository:1 version:1 sex:2 dekker:1 tried:1 paid:2 pressure:1 minus:1 initial:2 series:1 prescriptive:1 subjective:2 past:1 horvitz:1 current:2 yet:1 must:1 benign:1 update:3 alone:1 half:2 selected:1 xk:3 colored:1 num:1 provides:1 preference:7 five:1 supply:1 consists:2 expected:12 behavior:1 company:1 ... |
204 | 1,186 | Text-Based Information Retrieval Using
Exponentiated Gradient Descent
Ron Papka, James P. Callan, and Andrew G. Barto *
Department of Computer Science
University of Massachusetts
Amherst, MA 01003
papka@cs.umass.edu, callan@cs.umass.edu, barto@cs.umass.edu
Abstract
The following investigates the use of single-neuron l... | 1186 |@word attainable:2 profit:1 initial:1 contains:3 uma:3 selecting:1 document:65 current:1 comparing:1 parsing:2 stemming:4 additive:1 update:6 implying:1 warmuth:4 ith:1 record:1 manfred:1 lr:1 institution:1 ire:1 idi:1 ron:1 contribute:1 stopwords:1 ucsc:2 combine:1 allan:1 actual:2 considering:1 notation:1 circu... |
205 | 1,187 | Support Vector Method for Function
Approximation, Regression Estimation,
and Signal Processing?
Vladimir Vapnik
AT&T Research
101 Crawfords Corner
Holmdel, NJ 07733
vlad@research.att .com
Steven E. Golowich
Bell Laboratories
700 Mountain Ave.
Murray Hill, NJ 07974
golowich@bell-Iabs.com
Alex Smola?
GMD First
Rudower... | 1187 |@word murray:1 especially:2 approximating:2 y2:1 indicate:2 polynomial:2 compression:2 differ:1 regularization:1 classical:1 objective:1 open:1 symmetric:1 laboratory:1 bn:3 sin:2 solid:1 require:3 thank:1 mapped:1 berlin:1 phy:1 generalization:1 att:2 chris:1 hill:1 evaluate:1 tt:6 demonstrate:3 tn:4 code:1 com:... |
206 | 1,188 | Spatiotemporal Coupling and Scaling of
Natural Images and Human Visual
Sensitivities
Dawei W. Dong
California Institute of Technology
Mail Code 139-74
Pasadena, CA 91125
dawei@hope.caltech.edu
Abstract
We study the spatiotemporal correlation in natural time-varying
images and explore the hypothesis that the visual sys... | 1188 |@word seems:1 r:3 rgb:1 covariance:1 decorrelate:1 solid:6 shot:1 phy:1 interestingly:1 intriguing:1 physiol:1 shape:2 asymptote:1 plot:2 designed:1 treating:1 raider:1 fitting:1 expected:1 wl1:2 actual:1 increasing:1 underlying:2 what:1 kind:2 suppresses:1 temporal:19 quantitative:1 every:1 nf:1 scaled:10 modula... |
207 | 1,189 | Second-order Learning Algorithm with
Squared Penalty Term
Kazumi Saito
Ryohei Nakano
NTT Communication Science Laboratories
2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-02 Japan
{saito,nakano }@cslab.kecl.ntt.jp
Abstract
This paper compares three penalty terms with respect to the efficiency of supervised learning, b... | 1189 |@word trial:3 scg:2 tr:1 initial:3 hereafter:2 o2:1 wd:1 comparing:1 activation:1 must:1 shape:1 girosi:2 designed:1 update:3 steepest:1 lr:1 successive:1 sigmoidal:1 ryohei:1 consists:1 forgetting:1 os:1 seika:1 frequently:1 examine:1 kazumi:1 decreasing:1 cpu:6 increasing:1 bakker:1 exactly:1 scaled:1 ser:1 uni... |
208 | 119 | 107
SKELETONIZATION:
A TECHNIQUE FOR TRIMMING THE FAT
FROM A NETWORK VIA RELEVANCE ASSESSMENT
Michael C. Mozer
Paul Smolensky
Department of Computer Science &
Institute of Cognitive Science
University of Colorado
Boulder, CO 80309-0430
ABSTRACT
This paper proposes a means of using the knowledge in a network to
determ... | 119 |@word cu:1 version:2 eliminating:3 open:3 simulation:8 concise:1 fifteen:2 thereby:2 mention:1 initial:3 seriously:1 ida:1 surprising:1 yet:1 must:2 subsequent:1 happen:1 informative:1 remove:1 drop:1 update:1 discrimination:2 cue:2 half:1 fewer:1 provides:1 characterization:1 preference:1 simpler:1 lor:1 replicat... |
209 | 1,190 | Source Separation and Density
Estimation by Faithful Equivariant SOM
Juan K. Lin
Department of Physics
University of Chicago
Chicago, IL 60637
jk-lin@uchicago.edu
David G. Grier
Department of Physics
University of Chicago
Chicago, IL 60637
d-grier@uchicago.edu
Jack D. Cowan
Department of Math
University of Chicago
C... | 1190 |@word compression:1 polynomial:1 norm:1 grier:5 simulation:2 seek:1 decorrelate:1 reduction:1 configuration:4 current:1 si:2 must:1 periodically:1 chicago:6 partition:15 update:2 stationary:1 pursued:1 fewer:1 inspection:1 compo:3 provides:3 math:1 coarse:2 hyperplanes:2 along:7 constructed:1 direct:2 introduce:1... |
210 | 1,191 | Regression with Input-Dependent Noise:
A Bayesian Treatment
Christopher M. Bishop
C.M.BishopGaston.ac.uk
Cazhaow S. Qazaz
qazazcsGaston.ac.uk
Neural Computing Research Group
Aston University, Birmingham, B4 7ET, U.K.
http://www.ncrg.aston.ac.uk/
Abstract
In most treatments of the regression problem it is assumed tha... | 1191 |@word determinant:1 covariance:1 jacob:2 tr:1 solid:1 nowlan:1 must:2 additive:1 subsequent:1 realistic:2 plot:4 isotropic:2 parametrization:1 footing:1 toronto:1 simpler:1 consists:1 fitting:1 introduce:1 begin:1 maximizes:2 substantially:1 finding:3 exactly:1 scaled:2 uk:4 gallinari:1 unit:2 uo:1 grant:1 local:... |
211 | 1,192 | Bangs. Clicks, Snaps, Thuds and Whacks:
an Architecture for Acoustic Transient
Processing
Fernando J. Pineda(l)
fernando. pineda@jhuapl.edu
Gert Cauwenberghs(2)
gert@jhunix.hcf.jhu.edu
(iThe Applied Physics Laboratory
The Johns Hopkins University
Laurel, Maryland 20723-6099
R. Timothy Edwards(2)
tim@bach.ece.jhu.edu... | 1192 |@word timefrequency:2 compression:1 achievable:1 norm:2 disk:1 simulation:3 seek:1 excited:1 mammal:2 initial:2 nonexistent:1 tuned:2 interestingly:1 current:4 yet:2 must:3 john:2 remove:1 drop:2 half:1 device:6 accordingly:3 marine:1 lx:1 burst:2 symposium:1 consists:2 expected:1 nor:1 decreasing:1 automatically... |
212 | 1,193 | A New Approach to Hybrid HMMJANN Speech
Recognition Using Mutual Information Neural
Networks
G. Rigoll,
c.
Neukirchen
Gerhard-Mercator-University Duisburg
Faculty of Electrical Engineering
Department of Computer Science
Bismarckstr. 90, Duisburg, Germany
ABSTRACT
This paper presents a new approach to speech recogni... | 1193 |@word exploitation:1 briefly:2 faculty:2 pw:1 bigram:1 qly:1 initial:2 existing:1 activation:1 yet:2 si:1 written:3 realize:2 cook:1 characterization:1 quantizer:9 contribute:1 codebook:1 lx:1 mathematical:1 profound:1 introduce:1 manner:2 crossword:3 expected:1 indeed:1 behavior:5 considering:2 becomes:2 provide... |
213 | 1,194 | An Apobayesian Relative of Winnow
Nick Littlestone
NEC Research Institute
4 Independence Way
Princeton, NJ 08540
Chris Mesterharm
NEC Research Institute
4 Independence Way
Princeton, NJ 08540
Abstract
We study a mistake-driven variant of an on-line Bayesian learning algorithm (similar to one studied by Cesa-Bianchi,... | 1194 |@word trial:21 version:2 stronger:1 simulation:9 mention:1 initial:1 past:1 existing:1 current:5 comparing:3 si:7 must:2 cruz:1 additive:1 analytic:1 plot:4 update:6 leaf:1 warmuth:2 beginning:1 ith:3 record:2 provides:1 ucsc:1 symp:1 inside:1 manner:1 theoretically:1 forgetting:1 expected:3 behavior:2 themselves... |
214 | 1,195 | Hebb Learning of Features
based on their Information Content
Ferdinand Peper
Hideki Noda
Communications Research Laboratory
588-2, Iwaoka, Iwaoka-cho
Nishi-ku, Kobe 651-24
Japan
peper@crl.go.jp
Kyushu Institute of Technology
Dept. Electr., Electro., and Compo Eng.
1-1 Sensui-cho, Tobata-ku
Kita-Kyushu 804, Japan
nod... | 1195 |@word auu:1 cos2:1 simulation:4 eng:1 covariance:4 tr:2 boundedness:2 carry:1 initial:1 suppressing:3 anne:1 si:1 written:1 periodically:1 shape:2 stationary:18 implying:2 electr:1 compo:1 detecting:1 math:1 nishi:1 traverse:1 sigmoidal:3 unbounded:4 mathematical:2 along:5 c2:1 differential:2 prove:1 combine:1 sy... |
215 | 1,196 | Competition Among Networks
Improves Committee Performance
Paul W. Munro
Department of Infonnation Science
and Telecommunications
University of Pittsburgh
Pittsburgh PA 15260
Bambang Parman to
Department of Health Infonnation
Management
University of Pittsburgh
Pittsburgh PA 15260
munro@sis.pitt.edu
parmanto+@pitt.e... | 1196 |@word middle:1 simulation:2 jacob:2 fonn:1 thereby:1 tr:1 reduction:3 initial:1 interestingly:1 err:1 comparing:1 nowlan:1 si:3 yet:2 reminiscent:1 partition:1 plot:2 resampling:1 stationary:1 item:2 direct:1 become:1 acheived:1 manner:1 introduce:1 pairwise:1 expected:1 brain:2 ol:1 panel:1 finding:1 guarantee:1... |
216 | 1,197 | Computing with infinite networks
Christopher K. I. Williams
Neural Computing Research Group
Department of Computer Science and Applied Mathematics
Aston University, Birmingham B4 7ET, UK
c.k.i.williamsGaston.ac.nk
Abstract
For neural networks with a wide class of weight-priors, it can be
shown that in the limit of an... | 1197 |@word version:1 inversion:1 polynomial:1 advantageous:1 diametrically:1 simulation:1 covariance:29 shot:1 moment:1 series:1 selecting:1 denoting:1 comparing:2 written:3 must:1 girosi:3 analytic:2 stationary:11 isotropic:2 draft:1 provides:1 sigmoidal:9 height:1 become:1 ik:2 combine:1 introduce:1 manner:2 expecte... |
217 | 1,198 | ARC-LH: A New Adaptive Resampling
Algorithm for Improving ANN Classifiers
Friedrich Leisch
Friedrich.Leisch@ci.tuwien.ac.at
Kurt Hornik
Kurt.Hornik@ci.tuwien.ac.at
Institut fiir Statistik und Wahrscheinlichkeitstheorie
Technische UniversWit Wien
A-I040 Wien, Austria
Abstract
We introduce arc-Ih, a new algorithm for... | 1198 |@word kong:3 version:1 grey:1 covariance:1 decomposition:5 pick:1 harder:1 initial:1 kurt:2 current:2 assigning:2 update:1 aps:3 resampling:13 boosting:4 node:5 toronto:1 gx:1 simpler:1 dn:3 constructed:3 replication:3 combine:9 introduce:3 ra:1 behavior:1 roughly:1 decomposed:1 tuwien:2 actual:1 mountain:1 nj:1 ... |
218 | 1,199 | ?
Neural network models of chemotaxis In
the nematode Caenorhabditis elegans
Thomas C. Ferree, Ben A. Marcotte, Shawn R. Lockery
Institute of Neuroscience, University of Oregon, Eugene, Oregon 97403
Abstract
We train recurrent networks to control chemotaxis in a computer
model of the nematode C. elegans. The model pr... | 1199 |@word neurophysiology:2 cylindrical:1 contraction:3 pressure:1 thereby:1 searle:1 initial:6 score:2 current:1 com:1 anterior:2 yet:1 must:1 physiol:1 realistic:3 biomechanical:2 underly:1 predetermined:1 motor:10 nervous:8 beginning:1 ith:1 short:1 provides:1 math:1 location:1 successive:1 sigmoidal:2 cpg:3 along... |
219 | 12 | 783
USING NEURAL NETWORKS TO IMPROVE
COCHLEAR IMPLANT SPEECH PERCEPTION
Manoel F. Tenorio
School of Electrical Engineering
Purdue University
West Lafayette, IN 47907
ABSTRACT
-
An increasing number of profoundly deaf patients suffering from sensorineural deafness are using cochlear implants as prostheses. Mter the
... | 12 |@word trial:2 kong:2 compression:4 replicate:1 instruction:1 laryngology:4 seek:1 wexler:1 mention:1 carry:2 initial:2 series:1 selecting:1 subjective:1 existing:1 yet:2 must:1 evans:1 disables:1 wanted:1 discrimination:3 device:8 nervous:3 provides:1 parkin:1 symposium:1 maturity:1 recognizable:1 inside:1 introduc... |
220 | 120 | 560
A MODEL OF NEURAL OSCILLATOR FOR A UNIFIED SUEt10DULE
A.B.Kirillov, G.N.Borisyuk, R.M.Borisyuk,
Ye.I.Kovalenko, V.I.Makarenko,V.A.Chulaevsky,
V.I.Kryukov
Research Computer Center
USSR Academy of Sciences
Pushchino, Moscow Region
142292 USSR
AmTRACT
A new model of a controlled neuron oscillatJOr,
proposed earlier ... | 120 |@word ye:2 lation:3 middle:1 come:1 counterpart:1 hence:1 added:1 already:1 discontinuous:1 owing:1 spike:3 tat:8 tried:2 simulation:2 consecutively:2 stochastic:1 sin:1 exitatory:1 dependence:2 dramatic:1 usual:1 exhibit:4 regulated:3 excitation:3 sci:1 behaviour:1 reduction:1 initial:2 m:1 me:1 biological:1 unst... |
221 | 1,200 | Visual Cortex Circuitry and Orientation
Tuning
Trevor M undel
Department of Neurology
University of Chicago
Chicago, IL 60637
mundel@math.uchicago.edu
Alexander Dimitrov
Department of Mathematics
University of Chicago
Chicago, IL 60637
a-dimitrov@ucllicago.edu
Jack D. Cowan
Departments of Mathematics and Neurology
U... | 1200 |@word wiesel:1 wenderoth:5 trigonometry:1 open:1 simulation:4 solid:1 reduction:2 tuned:3 current:3 attracted:1 distant:1 chicago:6 plot:1 alone:1 iso:4 detecting:1 provides:1 math:2 preference:9 dell:2 mathematical:1 along:3 direct:7 differential:4 manner:3 grieve:1 rapid:1 brain:3 freeman:1 increasing:2 circuit... |
222 | 1,201 | Combining Neural Network Regression
Estimate1s with Regularized Linear
Weights
Christopher J. Merz and Michael J. Pazzani
Dept. of Information and Computer Science
University of California, Irvine, CA 92717-3425 U.S.A.
{cmerz,pazzani }@ics.uci.edu
Category: Algorithms and Architectures.
Abstract
When combining a set... | 1201 |@word trial:3 repository:1 eliminating:1 covariance:4 simplifying:1 tr:3 initial:1 existing:2 must:1 written:4 john:1 cruz:1 belmont:1 alone:1 intelligence:2 selected:1 warmuth:3 smith:4 lr:9 lrc:3 provides:4 toronto:1 simpler:1 along:1 ucsc:1 fitting:1 underfitting:1 stolfo:3 nor:1 automatically:1 cpu:4 increasi... |
223 | 1,202 | Dual Kalman Filtering Methods for
Nonlinear Prediction, Smoothing, and
Estimation
Eric A. Wan
ericwan@ee.ogi.edu
Alex T. Nelson
atnelson@ee.ogi.edu
Department of Electrical Engineering
Oregon Graduate Institute
P.O. Box 91000 Portland, OR 97291
Abstract
Prediction, estimation, and smoothing are fundamental to signal... | 1202 |@word cu:1 retraining:1 simulation:1 covariance:2 tr:2 accommodate:1 reduction:1 initial:1 series:18 contains:1 tram:5 past:4 existing:1 current:2 written:2 readily:2 must:1 john:2 additive:2 concatenate:1 atlas:1 sponsored:1 update:1 alone:1 short:1 colored:2 provides:1 severa:1 lx:5 successive:1 acheived:1 wild... |
224 | 1,203 | Time Series Prediction Using Mixtures of
Experts
Assaf J. Zeevi
Information Systems Lab
Department of Electrical Engineering
Stanford University
Stanford, CA. 94305
Ron Meir
Department of Electrical Engineering
Technion
Haifa 32000, Israel
rmeir~ee.technion.ac.il
azeevi~isl.stanford.edu
Robert J. Adler
Department o... | 1203 |@word version:3 polynomial:2 seems:1 seek:1 carolina:1 jacob:2 decomposition:1 tr:1 series:15 pub:2 past:2 existing:1 comparing:1 surprising:1 yet:3 must:1 fn:7 numerical:2 additive:1 partition:1 enables:1 analytic:1 stationary:5 pursued:2 credence:1 accordingly:1 inspection:1 completeness:1 ron:1 sigmoidal:2 dn:... |
225 | 1,204 | For valid generalization, the size of the
weights is more important than the size
of the network
Peter L. Bartlett
Department of Systems Engineering
Research School of Information Sciences and Engineering
Australian National University
Canberra, 0200 Australia
Peter .BartlettClanu .edu.au
Abstract
This paper shows tha... | 1204 |@word version:5 norm:2 proportion:3 seems:1 cm2:1 closure:1 pub:1 chervonenkis:3 comparing:1 nt:3 john:1 informative:1 warmuth:1 lr:4 quantized:1 successive:1 lipchitz:1 h4:1 symposium:1 incorrect:1 prove:1 compose:1 emma:1 roughly:1 multi:2 brain:1 eurocolt:1 encouraging:1 jm:1 considering:2 provided:2 classifie... |
226 | 1,205 | An Adaptive WTA using Floating Gate
Technology
w.
Fritz Kruger, Paul Hasler, Bradley A. Minch, and Christ of Koch
California Institute of Technology
Pasadena, CA 91125
(818) 395 - 2812
stretch@klab.caltech.edu
Abstract
We have designed, fabricated, and tested an adaptive WinnerTake-All (WTA) circuit based upon the cl... | 1205 |@word middle:1 propagate:1 thereby:1 initial:5 substitution:1 bradley:1 current:64 must:1 designed:1 drop:2 kv1:1 half:2 device:1 iso:2 supplying:1 node:5 location:1 c2:2 differential:6 introduce:1 roughly:3 behavior:3 terminal:1 decreasing:2 considering:2 increasing:3 becomes:1 begin:2 circuit:18 null:1 lowest:1... |
227 | 1,206 | A Micropower Analog VLSI
HMM State Decoder for Wordspotting
John Lazzaro and John Wawrzynek
CS Division, UC Berkeley
Berkeley, CA 94720-1776
lazzaroGcs.berkeley.edu. johnwGcs.berkeley.edu
Richard Lippmann
MIT Lincoln Laboratory
Room S4-121, 244 Wood Street
Lexington, MA 02173-0073
rplGsst.ll.mit.edu
Abstract
We descr... | 1206 |@word version:3 inversion:3 simulation:1 excited:2 configuration:1 contains:1 series:1 united:1 current:15 follower:2 must:2 john:2 enables:1 plot:4 sponsored:1 update:2 device:2 p7:6 beginning:1 short:3 detecting:1 kingsbury:1 dn:1 differential:1 qualitative:1 consists:2 dan:1 p8:1 expected:1 behavior:7 integrat... |
228 | 1,207 | GTM: A Principled Alternative
to the Self-Organizing Map
Christopher M. Bishop
Markus Svensen
Christopher K. I. Williams
C.M .Bishop@aston.ac.uk
svensjfm@aston.ac.uk
C.K.r. Williams@aston.ac.uk
Neural Computing Research Group
Aston University, Birmingham, B4 7ET, UK
http://www.ncrg.aston.ac.uk/
Abstract
The Self... | 1207 |@word version:4 inversion:1 seek:2 tiw:2 fifteen:1 configuration:2 dx:1 must:1 plot:3 generative:5 assurance:1 provides:1 codebook:2 revisited:1 node:3 location:1 along:1 become:1 consists:2 introduce:1 indeed:1 multi:3 spherical:1 automatically:2 cpu:1 metaphor:1 considering:2 becomes:1 provided:5 project:2 what... |
229 | 1,208 | A Mixture of Experts Classifier with
Learning Based on Both Labelled and
Unlabelled Data
David J. Miller and Hasan S. Uyar
Department of Electrical Engineering
The Pennsylvania State University
University Park, Pa. 16802
miller@perseus.ee.psu.edu
Abstract
We address statistical classifier design given a mixed training... | 1208 |@word trial:1 briefly:2 covariance:1 jacob:1 cla:1 plentiful:2 selecting:1 tuned:1 interestingly:1 envision:1 existing:1 neuneier:1 must:2 written:2 concert:1 update:3 selected:1 item:1 sys:1 realizing:1 clarified:1 c2:1 qualitative:1 consists:5 ascend:2 roughly:1 ol:1 ote:1 little:1 estimating:1 moreover:1 what:... |
230 | 1,209 | Complex-Cell Responses Derived from
Center-Surround Inputs: The Surprising
Power of Intradendritic Computation
Bartlett W. Mel and Daniel L. Ruderman
Department of Biomedical Engineering
University of Southern California
Los Angeles, CA 90089
Kevin A. Archie
Neuroscience Program
University of Southern California
Los A... | 1209 |@word jlf:1 polynomial:1 wiesel:3 oncenter:1 simulation:5 initial:1 series:1 exclusively:1 hereafter:1 mainen:1 daniel:1 disparity:1 coactive:1 current:1 surprising:1 must:1 john:1 physiol:2 plasticity:1 discrimination:2 selected:1 beginning:1 postnatal:1 sys:1 filtered:2 provides:1 location:2 preference:1 simple... |
231 | 121 | 468
LEARNING THE SOLUTION TO THE
APERTURE PROBLEM FOR PATTERN
MOTION WITH A HEBB RULE
Martin I. Sereno
Cognitive Science C-015
University of California, San Diego
La Jolla, CA 92093-0115
ABSTRACT
The primate visual system learns to recognize the true direction of
pattern motion using local detectors only capable of d... | 121 |@word trial:1 f32:1 proportion:3 open:2 tr:1 shading:1 contains:2 series:1 tuned:9 activation:1 yet:1 mst:1 realistic:4 subsequent:1 visible:1 shape:1 seelen:1 update:1 v:1 infant:2 cue:2 half:1 tertiary:1 detecting:3 location:7 unbiological:1 simpler:1 height:1 constructed:3 direct:1 consists:1 pathway:1 behavior... |
232 | 1,210 | Self-Organizing and Adaptive Algorithms for
Generalized Eigen-Decomposition
Chanchal Chatterjee
Vwani P. Roychowdhury
Newport Corporation
1791 Deere Avenue, Irvine, CA 92606
Electrical Engineering Department
UCLA, Los Angeles, CA 90095
ABSTRACT
The paper is developed in two parts where we discuss a new approach
to ... | 1210 |@word nd:1 wtm:1 decomposition:16 heteroassociative:1 covariance:1 twolayer:1 thereby:1 tr:2 ld:2 denoting:1 current:2 od:1 scatter:4 written:1 numerical:2 l7i:2 update:2 stationary:3 liapunov:1 accordingly:1 xk:6 ith:3 provides:1 equi:1 firstly:1 constructed:1 differential:4 consists:1 prove:4 rapid:1 themselves... |
233 | 1,211 | Learning Bayesian belief networks with
neural network estimators
Stefano Monti*
Gregory F. Cooper*''''
*Intelligent Systems Program
University of Pittsburgh
901M CL, Pittsburgh, PA - 15260
"Center for Biomedical Informatics
University of Pittsburgh
8084 Forbes Tower, Pittsburgh, PA - 15261
smonti~isp.pitt.edu
gfc... | 1211 |@word briefly:1 seems:1 nd:1 gfc:1 simulation:8 simplifying:1 solid:1 reduction:1 contains:1 score:2 selecting:1 recovered:2 comparing:3 current:1 written:1 suermondt:1 subsequent:1 informative:1 hofmann:1 greedy:2 fewer:2 discovering:1 parametrization:2 provides:1 node:7 become:1 descendant:1 introduce:1 plannin... |
234 | 1,212 | Selective Integration: A Model for
Disparity Estimation
Michael S. Gray, Alexandre Pouget, Richard S. Zemel,
Steven J. Nowlan, Terrence J. Sejnowski
Departments of Biology and Cognitive Science
University of California, San Diego
La Jolla, CA 92093
and
Howard Hughes Medical Institute
Computational Neurobiology Lab
The... | 1212 |@word middle:2 nd:2 open:1 jacob:2 configuration:1 disparity:98 tuned:3 nowlan:13 activation:7 must:2 readily:1 john:1 realistic:2 discrimination:1 cue:4 intelligence:1 filtered:1 location:17 qualitative:1 pathway:12 fitting:1 introduce:1 swets:2 multi:2 freeman:2 actual:2 estimating:2 medium:1 kind:1 substantial... |
235 | 1,213 | On the Effect of Analog Noise in
Discrete-Time Analog Computations
Pekka Orponen
Department of Mathematics
University of Jyvaskylat
Wolfgang Maass
Institute for Theoretical Computer Science
Technische Universitat Graz*
Abstract
We introduce a model for noise-robust analog computations with
discrete time that is flex... | 1213 |@word mild:2 version:2 briefly:1 norm:1 ithere:1 recursively:1 initial:2 contains:1 pub:1 orponen:13 current:2 surprising:1 activation:1 yet:1 happen:1 partition:3 drop:2 chile:1 beginning:1 accepting:1 provides:3 math:4 completeness:1 sigmoidal:9 mathematical:5 replication:1 prove:3 manner:2 introduce:3 ra:2 exp... |
236 | 1,214 | A Convergence Proof for the Softassign
Quadratic Assignment Algorithm
Anand Rangarajan
Department of Diagnostic Radiology
Yale University School of Medicine
New Haven, CT 06520-8042
e-mail: anand<Onoodle. med. yale. edu
Steven Gold
CuraGen Corporation
322 East Main Street
Branford, CT 06405
e-mail: gold-steven<ocs. ya... | 1214 |@word manageable:1 r:1 simulation:2 thereby:1 initial:1 reran:1 written:3 readily:1 engg:1 webster:1 designed:1 plot:3 update:2 intelligence:1 smith:1 compo:1 node:1 org:1 rc:6 along:1 kettlewell:1 doubly:11 manner:1 inter:1 roughly:1 decreasing:1 increasing:1 begin:4 ocs:2 provided:1 bounded:1 argmin:1 cm:2 deve... |
237 | 1,215 | LSTM CAN SOLVE HARD
LO G TIME LAG PROBLEMS
Sepp Hochreiter
Fakultat fur Informatik
Technische Universitat Munchen
80290 Miinchen, Germany
Jiirgen Schmidhuber
IDSIA
Corso Elvezia 36
6900 Lugano, Switzerland
Abstract
Standard recurrent nets cannot deal with long minimal time lags
between relevant signals. Several recen... | 1215 |@word trial:12 briefly:1 version:1 compression:1 nd:1 bptt:1 open:2 r:19 simulation:1 tried:1 mention:1 tr:1 initial:4 contains:1 outperforms:2 current:1 activation:9 must:1 hochreit:2 update:2 intelligence:1 fewer:1 beginning:1 vanishing:2 short:5 smith:1 miinchen:1 consists:1 paragraph:1 peng:1 alspector:1 elma... |
238 | 1,216 | Reinforcement Learning for Dynamic
C?h annel Allocation in Cellular Telephone
Systems
Satinder Singh
Department of Computer Science
University of Colorado
Boulder, CO 80309-0430
bavej a@cs.colorado.edu
Dimitri Bertsekas
Lab. for Info. and Decision Sciences
MIT
Cambridge, MA 02139
bertsekas@lids.mit.edu
Abstract
In c... | 1216 |@word termination:2 simulation:3 tried:1 accounting:1 profit:2 thereby:1 configuration:14 existing:2 current:3 must:2 belmont:2 partition:2 happen:1 plot:2 update:2 intelligence:1 short:1 accepting:2 zhang:6 admission:2 become:2 qualitative:1 consists:1 combine:1 introduce:1 market:2 expected:1 rapid:2 themselves... |
239 | 1,217 | Clustering Sequences with Hidden
Markov Models
Padhraic Smyth
Information and Computer Science
University of California, Irvine
CA 92697-3425
smyth~ics.uci.edu
Abstract
This paper discusses a probabilistic model-based approach to clustering sequences, using hidden Markov models (HMMs) . The problem can be framed as a ... | 1217 |@word grey:1 seek:1 covariance:2 initial:6 contains:1 series:2 selecting:1 comparing:1 surprising:1 si:4 must:2 subsequent:1 partition:2 analytic:1 generative:1 discovering:1 pursued:1 accordingly:2 detecting:1 simpler:1 lor:1 qualitative:1 consists:2 fitting:3 baldi:1 manner:2 pairwise:1 behavior:1 automatically... |
240 | 1,218 | On a Modification to the Mean Field EM
Algorithm in Factorial Learning
A. P. Dunmur
D. M. Titterington
Department of Statistics
Maths Building
University of Glasgow
Glasgow G12 8QQ, UK
alan~stats.gla.ac.uk
mike~stats.gla.ac.uk
Abstract
A modification is described to the use of mean field approximations in the E step... | 1218 |@word version:3 seems:1 simulation:6 covariance:2 dramatic:1 mention:1 tr:1 harder:1 reduction:1 initial:1 contains:2 series:2 current:1 wd:2 must:1 numerical:1 realistic:2 hofmann:2 intelligence:1 fewer:1 provides:1 math:1 zhang:3 along:1 become:5 ik:1 qualitative:1 consists:1 introduce:1 pairwise:1 indeed:1 fre... |
241 | 1,219 | MLP can provably generalise much better
than VC-bounds indicate.
A. Kowalczyk and H. Ferra
Telstra Research Laboratories
770 Blackburn Road, Clayton, Vic. 3168, Australia
({ a.kowalczyk, h.ferra}@trl.oz.au)
Abstract
Results of a study of the worst case learning curves for a particular class of probability distribution... | 1219 |@word determinant:1 version:2 briefly:1 cnn:1 polynomial:1 closure:1 chervonenkis:1 tco:2 wd:1 activation:1 fn:1 csc:1 partition:2 aoo:1 analytic:1 plot:3 selected:1 warmuth:1 iog2:1 provides:1 characterization:1 sigmoidal:3 mathematical:1 shatter:3 dn:9 director:1 introduce:2 behavior:1 telstra:2 mechanic:1 mult... |
242 | 122 | 141
GEMINI: GRADIENT ESTIMATION
THROUGH MATRIX INVERSION
AFTER NOISE INJECTION
Yann Le Cun 1 Conrad C. Galland and Geoffrey E. Hinton
Department of Computer Science
University of Toronto
10 King's College Rd
Toronto, Ontario M5S 1A4
Canada
ABSTRACT
Learning procedures that measure how random perturbations of unit act... | 122 |@word inversion:4 simulation:6 propagate:1 thereby:2 initial:1 inefficiency:1 contains:2 must:4 update:3 fewer:3 accordingly:1 node:1 toronto:2 successive:1 expected:1 actual:4 little:1 estimating:1 linearity:3 alto:1 modeles:1 kind:1 nj:1 pseudo:1 fellow:1 control:4 unit:50 grant:1 positive:1 engineering:1 local:... |
243 | 1,220 | The Generalisation Cost of RAMnets
Richard Rohwer and Michal Morciniec
rohwerrj~cs.aston.ac.uk
morcinim~cs.aston.ac.uk
Neural Computing Research Group
Aston University
Aston Triangle, Birmingham B4 7ET, UK.
Abstract
Given unlimited computational resources, it is best to use a criterion of minimal expected generalisa... | 1220 |@word nd:1 ljo:1 covariance:7 tr:1 solid:3 carry:1 denoting:1 current:2 michal:1 analysed:1 readily:2 numerical:3 shape:1 selected:1 toronto:1 gx:1 thermometer:2 mathematical:1 constructed:1 differential:1 supply:2 qij:1 consists:1 introductory:1 sacrifice:1 indeed:1 expected:7 multi:1 txp:3 actual:1 becomes:1 pr... |
244 | 1,221 | Multi-Task Learning for Stock Selection
Joumana Ghosn
Dept. Informatique et
Recherche Operationnelle
Universite de Montreal
Montreal, Qc H3C-3J7
Yoshua Bengio *
Dept. Informatique et
Recherche Operationnelle
Universite de Montreal
Montreal, Qc H3C-3J7
ghosn~iro.umontreal.ca
bengioy~iro.umontreal . ca
Abstract
Arti... | 1221 |@word multitask:2 worsens:1 covariance:1 profit:2 initial:4 series:6 past:1 current:4 must:1 cruz:1 additive:1 shape:1 fund:1 selected:1 yr:1 ith:1 short:1 recherche:3 toronto:2 firstly:1 beta:7 rnl:1 incorrect:1 edelman:2 fitting:1 operationnelle:3 theoretically:1 acquired:2 market:8 expected:3 multi:12 company:... |
245 | 1,222 | Extraction of temporal features in the
electrosensory system of weakly electric
fish
Fabrizio GabbianiDivision of Biology
139-74 Caltech
Pasadena, CA 91125
Walter Metzner
Department of Biology
Univ. of Cal. Riverside
Riverside, CA 92521-0427
RalfWessel
Department of Biology
Univ. of Cal. San Diego
La J oBa, CA 92093... | 1222 |@word bf:1 open:1 electrosensory:9 lobe:3 electroreceptors:4 covariance:2 accounting:1 thereby:1 carry:2 cytology:1 blank:1 comparing:1 anterior:1 exposing:1 physiol:2 subsequent:3 motor:1 plot:1 discrimination:3 v:1 half:1 nervous:1 short:3 compo:4 location:1 burst:19 pathway:2 upstroke:1 autocorrelation:1 manne... |
246 | 1,223 | Learning Appearance Based Models:
Mixtures of Second Moment Experts
Christoph 8regler and Jitendra Malik
Computer Science Division
University of California at Berkeley
Berkeley, CA 94720
email: bregler@cs.berkeley.edu, malik@cs.berkeley.edu
Abstract
This paper describes a new technique for object recognition based o... | 1223 |@word version:2 advantageous:1 seems:1 jacob:3 decomposition:6 covariance:5 pick:1 brightness:1 volkswagen:2 shot:1 moment:22 contains:1 interestingly:1 current:1 shape:4 enables:1 wanted:1 motor:1 discrimination:2 aside:1 cue:2 selected:2 fewer:1 compo:1 coarse:2 location:1 five:2 height:1 windowed:2 along:1 dir... |
247 | 1,224 | Learning From Demonstration
Stefan Schaal
sschaal @cc .gatech.edu; http://www.cc.gatech.edulfac/Stefan.Schaal
College of Computing, Georgia Tech, 801 Atlantic Drive, Atlanta, GA 30332-0280
ATR Human Information Processing, 2-2 Hikaridai, Seiko-cho, Soraku-gun, 619-02 Kyoto
Abstract
By now it is widely accepted that le... | 1224 |@word trial:30 version:2 achievable:1 seems:2 open:1 instruction:1 simulation:6 valuefunction:1 delicately:1 profit:6 tr:3 shot:3 initial:12 series:2 lqr:9 interestingly:1 past:2 atlantic:1 nally:1 current:4 comparing:1 trustworthy:1 surprising:2 yet:2 must:1 oml:1 subsequent:1 realistic:1 concert:1 update:1 v:2 ... |
248 | 1,225 | Dynamics of Training
Siegfried Bos*
Lab for Information Representation
RIKEN, Hirosawa 2-1, Wako-shi
Saitama 351-01, Japan
Manfred Opper
Theoretical Physics III
University of Wiirzburg
97074 Wiirzburg, Germany
Abstract
A new method to calculate the full training process of a neural network is introduced. No sophisti... | 1225 |@word version:1 briefly:1 loading:1 simulation:3 solid:2 moment:1 initial:1 series:1 wako:1 realistic:1 numerical:1 guess:1 manfred:1 ik:1 introduce:2 expected:3 behavior:8 examine:1 mechanic:3 ol:1 spherical:1 actual:4 becomes:1 provided:1 what:1 cm:1 developed:1 pseudo:1 nf:1 exactly:1 demonstrates:1 unit:1 loc... |
249 | 1,226 | Viewpoint invariant face recognition using
independent component analysis and
attractor networks
Marian Stewart Bartlett
University of California San Diego
The Salk Institute
La Jolla, CA 92037
marni@salk.edu
Terrence J. Sejnowski
University of California San Diego
Howard Hughes Medical Institute
The Salk Institute, L... | 1226 |@word open:3 simulation:4 lobe:1 moment:2 selecting:1 recovered:1 current:1 neurobio:1 activation:3 assigning:1 cottrell:2 update:3 v:1 compo:3 filtered:1 provides:1 location:1 five:2 sustained:1 acquired:2 ica:9 brain:1 automatically:1 provided:1 begin:2 project:1 maximizes:1 sloping:1 kaufman:1 monkey:1 develop... |
250 | 1,227 | U sing Curvature Information for
Fast Stochastic Search
Genevieve B. Orr
Dept of Computer Science
Willamette University
900 State Street
Salem, OR 97301
gorr@willamette.edu
Todd K. Leen
Dept of Computer Science and Engineering
Oregon Graduate Institute of
Science and Technology
P.O.Box 91000, Portland, Oregon 97291-10... | 1227 |@word version:1 inversion:3 simulation:1 linearized:1 minus:1 efficacy:1 current:1 must:3 readily:1 written:2 john:2 subsequent:1 j1:8 christian:2 plot:1 update:5 credence:1 leaf:1 prohibitive:1 detecting:1 provides:1 cse:1 node:7 lending:1 mathematical:1 become:2 consists:2 paragraph:1 expected:2 alspector:1 beh... |
251 | 1,228 | Efficient Nonlinear Control with
Actor-Tutor Architecture
Kenji Doya*
A.TR Human Information Processing Research Laboratories
2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-02, Japan.
Abstract
A new reinforcement learning architecture for nonlinear control is
proposed. A direct feedback controller, or the actor, is t... | 1228 |@word trial:5 mri:1 version:2 nd:1 confirms:1 simulation:4 tr:1 current:3 activation:1 enables:2 motor:6 plot:1 medial:1 selected:1 plane:1 short:1 provides:2 cambrigde:1 height:1 constructed:1 direct:8 differential:1 hjb:1 expected:2 behavior:1 seika:2 multi:1 brain:4 bellman:1 torque:4 td:11 curse:1 project:1 p... |
252 | 1,229 | Multidimensional Triangulation and
Interpolation for Reinforcement Learning
Scott Davies
scottd@cs.cmu.edu
Department of Computer Science, Carnegie Mellon University
5000 Forbes Ave, Pittsburgh, PA 15213
Abstract
Dynamic Programming, Q-Iearning and other discrete Markov Decision
Process solvers can be -applied to cont... | 1229 |@word trial:20 seems:1 amply:1 pick:1 dramatic:1 harder:1 initial:1 current:1 discretization:2 must:1 reminiscent:1 mesh:2 thrust:2 cheap:1 update:3 stationary:1 half:1 plane:1 iso:1 coarse:5 location:2 height:1 rc:1 along:3 driver:1 inside:1 manner:2 expected:1 roughly:1 behavior:1 frequently:1 aborted:1 fared:4... |
253 | 123 | 65
LINEAR LEARNING: LANDSCAPES AND ALGORITHMS
Pierre Baldi
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, CA 91109
What follows extends some of our results of [1] on learning from examples in layered feed-forward networks of linear units. In particular we examine what happens when the ntunber... | 123 |@word compression:2 aia2:1 heuristically:1 simulation:2 propagate:1 covariance:1 thereby:1 tr:1 reduction:1 must:1 readily:1 cottrell:2 extensional:1 j1:7 transposition:1 detecting:1 iterates:1 along:1 baldi:6 behavior:2 examine:3 uz:1 decomposed:1 xx:7 notation:1 what:5 ail:1 eigenvector:3 nj:1 assert:1 every:1 x... |
254 | 1,230 | Local Bandit Approximation
for Optimal Learning Problems
Michael o. Duff
Andrew G. Barto
Department of Computer Science
University of Massachusetts
Amherst, MA 01003
{duff.barto}Ccs.umass.edu
Abstract
In general, procedures for determining Bayes-optimal adaptive
controls for Markov decision processes (MDP's) require ... | 1230 |@word exploitation:2 version:2 manageable:1 hyperstates:5 twelfth:1 willing:2 seek:1 simulation:1 bn:1 decomposition:1 moment:1 initial:1 uma:1 past:1 current:2 must:4 bd:1 written:1 happen:2 reappeared:1 update:3 maxv:1 greedy:1 prohibitive:1 accordingly:1 compelled:1 short:1 ijb:1 provides:2 node:2 ire:1 math:2... |
255 | 1,231 | Promoting Poor Features to Supervisors:
Some Inputs Work Better as Outputs
Rich Caruana
JPRC and
Carnegie Mellon University
Pittsburgh, PA 15213
caruana@cs.cmu.edu
Virginia R. de Sa
Sloan Center for Theoretical Neurobiology and
W . M. Keck Center for Integrative Neuroscience
University of California, San Francisco CA... | 1231 |@word multitask:3 trial:6 version:2 nd:1 integrative:1 reap:1 tr:1 harder:1 phy:1 surprising:2 must:1 asymptote:1 plot:1 v:1 alone:1 intelligence:1 selected:1 steepest:1 ebnn:1 toronto:2 along:1 constructed:1 direct:1 become:2 combine:1 expected:1 examine:1 simulator:4 discretized:1 provided:2 what:3 finding:1 ex... |
256 | 1,232 | Gaussian Processes for Bayesian
Classification via Hybrid Monte Carlo
David Barber and Christopher K. I. Williams
Neural Computing Research Group
Department of Computer Science and Applied Mathematics
Aston University, Birmingham B4 7ET, UK
d.barber~aston.ac.uk
c.k.i.williams~aston.ac.uk
Abstract
The full Bayesian m... | 1232 |@word briefly:1 inversion:1 sex:3 hu:1 simulation:3 tried:2 covariance:11 initial:3 contains:1 pub:1 wd:1 discretization:1 nt:1 activation:5 must:1 readily:2 analytic:1 guess:1 xk:1 parametrization:1 ith:1 hamiltonian:1 record:1 iterates:1 successive:1 five:2 direct:1 become:2 differential:1 combine:1 grj:1 actua... |
257 | 1,233 | Fast Network Pruning and Feature
Extraction Using the Unit-OBS Algorithm
Achim Stahlberger and Martin Riedmiller
Institut fur Logik , Komplexitiit und Deduktionssysteme
Universitiit Karlsruhe, 76128 Karlsruhe, Germany
email: stahlb@ira.uka.de . riedml@ira.uka.de
Abstract
The algorithm described in this article is bas... | 1233 |@word especially:1 eliminating:1 already:1 leeds:1 attribute:11 simulation:2 neue:1 q1:1 initial:2 criterion:1 generalized:6 really:1 selecting:1 evident:2 complete:1 considers:2 comparing:1 index:2 ql:2 mt:9 major:1 smallest:1 remove:15 stork:7 volume:1 update:1 unknown:2 refer:1 monk:10 neuron:3 benchmark:3 loo... |
258 | 1,234 | A Constructive Learning Algorithm for
Discriminant Tangent Models
Diego Sona
Alessandro Sperduti
Antonina Starita
Dipartimento di Informatica, Universita di Pisa
Corso Italia, 40, 56125 Pisa, Italy
email: {sona.perso.starita}di.unipi.it
Abstract
To reduce the computational complexity of classification systems
using t... | 1234 |@word trial:1 version:3 seems:2 norm:2 heuristically:1 grey:1 decomposition:1 ours:1 document:1 err:3 comparing:1 must:4 interpretable:1 fewer:2 become:1 ik:1 consists:1 combine:2 introduce:1 behavior:1 automatically:4 td:7 increasing:2 becomes:1 underlying:1 moreover:3 notation:1 minimizes:1 developed:6 transfor... |
259 | 1,235 | Blind separation of delayed and convolved
sources.
Te-Won Lee
Max-Planck-Society, GERMANY,
AND Interactive Systems Group
Carnegie Mellon University
Pittsburgh, PA 15213, USA
tewonOes. emu. edu
Anthony J. Bell
Computational Neurobiology,
The Salk Institute
10010 N. Torrey Pines Road
La Jolla, California 92037, USA
tony... | 1235 |@word polynomial:1 simulation:2 pick:1 metre:1 si:2 tailoring:1 wll:1 remove:2 half:1 ith:1 filtered:2 firstly:1 become:1 combine:1 symp:1 inside:3 hermitian:1 roughly:1 equivariant:1 multi:1 bounded:1 linearity:3 notation:1 medium:1 cm:2 spoken:1 differentiation:1 temporal:1 interactive:1 platt:2 unit:4 grant:1 ... |
260 | 1,236 | Neural Models for Part-Whole Hierarchies
Maximilian Riesenhuber
Peter Dayan
Department of Brain & Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
{max,dayan}~ai.mit.edu
Abstract
We present a connectionist method for representing images that explicitly addresses their hierarchical nature. I... | 1236 |@word neurophysiology:1 illustrating:1 version:4 inversion:1 middle:4 seems:3 compression:2 trotter:1 attended:1 dramatic:1 cobb:1 contains:1 current:2 anterior:3 activation:8 must:1 shape:2 motor:1 depict:1 v:1 generative:20 selected:2 intelligence:1 short:1 provides:2 location:3 traverse:1 provisional:1 direct:... |
261 | 1,238 | Support Vector Regression Machines
Harris Drucker? Chris J.C. Burges" Linda Kaufman"
Alex Smola?? Vladimir Vapoik +
*Bell Labs and Monmouth University
Department of Electronic Engineering
West Long Branch. NJ 07764
**BellLabs +AT&T Labs
Abstract
A new regression technique based on Vapnik's concept of support
vectors ... | 1238 |@word trial:4 version:1 polynomial:6 norm:1 seems:2 tried:6 t_:1 fonn:1 necessity:1 comparing:2 surprising:1 must:3 subsequent:1 yr:2 directory:1 five:1 prove:1 combine:1 inside:1 expected:1 fared:1 project:1 linda:2 kaufman:6 minimizes:2 developed:1 nj:1 berkeley:2 ti:1 xd:2 ull:1 yn:2 positive:1 engineering:1 l... |
262 | 1,239 | Multilayer neural networks:
one or two hidden layers?
G. Brightwell
Dept of Mathematics
LSE, Houghton Street
London WC2A 2AE, U.K.
c.
Kenyon, H. Paugam-Moisy
LIP, URA 1398 CNRS
ENS Lyon, 46 alIee d'Italie
F69364 Lyon cedex, FRANCE
Abstract
We study the number of hidden layers required by a multilayer neural network... | 1239 |@word version:1 seems:2 nd:1 open:3 closure:4 grey:1 configuration:8 contains:2 existing:1 comparing:1 skipping:1 written:1 realize:2 distant:1 subsequent:1 partition:1 remove:1 drop:1 farkas:4 implying:1 fewer:1 plane:2 realizing:5 math:1 node:1 successive:1 hyperplanes:22 sigmoidal:1 unbounded:4 along:2 become:... |
263 | 124 | 65
LINEAR LEARNING: LANDSCAPES AND ALGORITHMS
Pierre Baldi
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, CA 91109
What follows extends some of our results of [1] on learning from examples in layered feed-forward networks of linear units. In particular we examine what happens when the ntunber... | 124 |@word middle:1 briefly:1 wiesel:5 compression:2 aia2:1 cortez:1 nd:1 open:1 heuristically:1 grey:2 simulation:12 propagate:1 closure:1 covariance:1 decomposition:1 innervating:2 thereby:1 tr:1 reduction:1 initial:2 series:1 exclusively:5 selecting:1 err:1 activation:2 must:1 readily:1 written:1 cottrell:2 extensio... |
264 | 1,240 | Predicting Lifetimes in Dynamically
Allocated Memory
David A. Cohn
Adaptive Systems Group
Harlequin, Inc.
Menlo Park, CA 94025
Satinder Singh
Department of Computer Science
University of Colorado
Boulder, CO 80309
cohn~harlequin.com
baveja~cs.colorado.edu
Abstract
Predictions oflifetimes of dynamically allocated o... | 1240 |@word cu:1 instruction:1 tried:1 exclusively:1 pub:1 tuned:2 seriously:1 existing:2 com:1 adj:3 assigning:1 must:1 belmont:1 subsequent:1 predetermined:1 cheap:1 designed:1 overriding:1 intelligence:1 leaf:2 scotland:1 short:21 oblique:1 pointer:1 complication:1 cse:2 simpler:1 along:1 become:1 incorrect:1 wild:1... |
265 | 1,241 | Ordered Classes and Incomplete Examples
in Classification
Mark Mathieson
Department of Statistics, University of Oxford
1 South Parks Road, Oxford OXI 3TG, UK
E-mail: mathies@stats.ox.ac.uk
Abstract
The classes in classification tasks often have a natural ordering, and the
training and testing examples are often inco... | 1241 |@word proportion:1 seems:1 simulation:3 covariance:1 incurs:3 solid:1 shading:1 contains:1 score:1 series:1 itp:1 past:2 reaction:1 existing:1 si:1 assigning:1 must:2 motor:4 plot:1 interpretable:1 fewer:1 selected:2 parameterization:1 ith:1 wth:1 node:6 hyperplanes:1 simpler:1 incorrect:1 fitting:2 manager:1 lit... |
266 | 1,242 | Unsupervised Learning by
Convex and Conic Coding
D. D. Lee and H. S. Seung
Bell Laboratories, Lucent Technologies
Murray Hill, NJ 07974
{ddlee I seung}Obell-labs. com
Abstract
Unsupervised learning algorithms based on convex and conic encoders are proposed. The encoders find the closest convex or conic
combination of... | 1242 |@word polynomial:1 r:2 simulation:1 covariance:1 initial:1 contains:3 com:1 yet:1 must:1 visible:1 kdd:1 interpretable:2 update:2 ajd:1 characterization:1 math:1 become:1 scholkopf:1 consists:1 actual:1 little:1 becomes:1 project:1 circuit:2 kaufman:1 finding:2 transformation:4 nj:1 scaled:1 understood:1 local:3 ... |
267 | 1,243 | Learning with Noise and Regularizers
Multilayer Neural Networks
David Saad
Dept. of Compo Sci. & App. Math.
Aston University
Birmingham B4 7ET, UK
D .Saad@aston.ac.uk
?
In
Sara A. Solla
AT &T Research Labs
Holmdel, NJ 07733, USA
solla@research .at t .com
Abstract
We study the effect of noise and regularization in ... | 1243 |@word polynomial:1 norm:5 bn:7 covariance:1 initial:3 recovered:1 com:1 activation:5 written:1 additive:5 numerical:5 realistic:1 analytic:1 update:2 stationary:1 imitate:1 trapping:1 xk:1 isotropic:4 short:1 compo:1 math:2 node:2 differential:1 become:1 qualitative:3 introduce:1 behavior:2 examine:2 frequently:2... |
268 | 1,244 | A neural model of visual contour
integration
Zhaoping Li
Computer Science, Hong Kong University of Science and Technology
Clear Water Bay, Hong Kong
zhaoping~uxmail.ust.hkl
Abstract
We introduce a neurobiologically plausible model of contour integration from visual inputs of individual oriented edges. The model
is co... | 1244 |@word kong:3 middle:1 stronger:3 open:4 closure:1 solid:1 reduction:1 contains:1 tuned:2 existing:3 universality:1 yet:1 ust:1 must:1 j1:4 plot:3 v:1 fewer:1 hallucinate:1 draft:1 contribute:1 location:3 gx:9 preference:1 lor:1 along:2 iverson:1 become:1 compose:1 olfactory:2 introduce:3 expected:1 indeed:3 rough... |
269 | 1,245 | Sequential Tracking in Pricing Financial
Options using Model Based and Neural
Network Approaches
Mahesan Niranjan
Cambridge University Engineering Department
Cambridge CB2 IPZ, England
niranjan@eng.cam.ac.uk
Abstract
This paper shows how the prices of option contracts traded in financial markets can be tracked sequen... | 1245 |@word trial:2 cox:1 d2:7 simulation:3 eng:1 covariance:3 tr:2 recursively:1 n8:1 initial:1 series:2 past:1 candy:2 stationary:1 simpler:1 five:1 become:1 maturity:8 consists:2 manner:2 market:6 formants:1 feldkamp:2 window:3 considering:1 becomes:1 underlying:7 what:1 nj:1 esti:1 pseudo:1 finance:3 uk:1 unit:1 po... |
270 | 1,247 | Genetic Algorithms and Explicit Search Statistics
Shumeet 8a1uja
baluja@cs.cmu.edu
Justsystem Pittsburgh Research Center &
School of Computer Science, Carnegie Mellon University
Abstract
The genetic algorithm (GA) is a heuristic search procedure based on mechanisms
abstracted from population genetics. In a previous pa... | 1247 |@word trial:1 version:3 unaltered:1 middle:1 open:1 grey:1 pbil:36 tried:4 covariance:1 pressure:1 tr:1 initial:2 selecting:2 genetic:27 tuned:1 optim:4 tenned:1 numerical:2 subsequent:2 designed:2 reproducible:1 update:5 v:1 selected:2 leaf:1 lr:4 provides:2 node:2 location:1 successive:1 hyperplanes:1 simpler:3... |
271 | 1,248 | Neural Learning in Structured
Parameter Spaces
Natural Riemannian Gradient
Shun-ichi Amari
RIKEN Frontier Research Program, RIKEN,
Hirosawa 2-1, Wako-shi 351-01, Japan
amari@zoo.riken.go.jp
Abstract
The parameter space of neural networks has a Riemannian metric structure. The natural Riemannian gradient should be use... | 1248 |@word version:3 norm:2 reused:1 kappen:2 initial:1 series:1 t7:2 wako:1 current:5 z2:1 wd:3 written:2 implying:1 steepest:5 simpler:1 c2:6 become:1 differential:1 prove:4 ewe:1 introduce:1 manner:1 expected:2 equivariant:1 behavior:10 nor:1 mechanic:1 ol:2 becomes:2 begin:1 notation:1 moreover:1 mass:1 what:1 min... |
272 | 1,249 | An Analog Implementation of the
Constant Statistics Constraint
For Sensor Calibration
John G. Harris and Yu-Ming Chiang
Computational Neuro-Engineering Laboratory
Department of Computer and Electrical Engineering
University of Florida
Gainesville, FL 32611
Abstract
We use the constant statistics constraint to calibrat... | 1249 |@word middle:2 version:2 seems:1 norm:1 gainesville:1 brightness:1 thereby:1 initial:1 contains:1 past:1 current:5 yet:1 written:1 must:3 john:1 periodically:2 additive:4 designed:2 v:1 stationary:1 plane:3 short:1 chiang:4 filtered:1 provides:2 height:1 rc:2 c2:2 symposium:1 expected:1 nor:1 ming:1 window:1 incr... |
273 | 125 | 494
TRAINING A 3-NODE NEURAL NETWORK
IS NP-COMPLETE
Avrim Blum'"
MIT Lab. for Computer Science
Cambridge, Mass. 02139 USA
Ronald L. Rivest t
MIT Lab. for Computer Science
Cambridge, Mass. 02139 USA
ABSTRACT
We consider a 2-layer, 3-node, n-input neural network whose nodes
compute linear threshold functions of their ... | 125 |@word polynomial:2 open:3 reduction:3 contains:3 nt:2 si:6 must:2 zll:1 ronald:1 partition:2 j1:1 plane:15 ith:1 provides:1 completeness:7 jkj:1 node:41 location:3 hyperplanes:2 c2:2 constructed:1 symposium:1 consists:1 polyhedral:1 p1:3 surge:1 multi:1 inspired:2 freeman:1 ming:1 considering:1 totally:1 rivest:4 ... |
274 | 1,250 | Effective Training of a Neural Network
Character Classifier for Word Recognition
Larry Yaeger
Apple Computer
5540 Bittersweet Rd.
Morgantown, IN 46160
larryy@apple.com
Richard Lyon
Apple Computer
1 Infinite Loop, MS301-3M
Cupertino, CA 95014
lyon@apple.com
Brandyn Webb
The Future
4578 Fieldgate Rd.
Oceanside, CA 9205... | 1250 |@word version:1 eliminating:1 seems:1 ount:1 gish:2 cla:9 pressure:5 minus:1 tr:2 ld:1 reduction:3 initial:1 mag:1 mmse:2 current:1 com:3 skipping:4 nowlan:1 activation:2 yet:1 must:2 readily:1 written:1 predetermined:1 disables:1 drop:1 v:2 alone:1 half:1 selected:2 fewer:1 provides:1 replication:1 consists:2 in... |
275 | 1,251 | Multi-effect Decompositions
for Financial Data Modeling
Lizhong Wu & John Moody
Oregon Graduate Institute, Computer Science Dept.,
PO Box 91000, Portland, OR 97291
also at:
Nonlinear Prediction Systems,
PO Box 681, University Station, Portland, OR 97207
Abstract
High frequency foreign exchange data can be decomposed ... | 1251 |@word kong:1 briefly:1 r13:4 confirms:1 decomposition:21 jacob:2 pick:1 solid:1 reduction:1 moment:2 liu:2 series:7 contains:2 seriously:1 hearn:1 current:1 recovered:1 si:1 must:2 john:1 additive:1 analytic:1 tenn:8 short:6 successive:1 glover:2 c2:1 mandelbrot:4 become:2 m22:1 autocorrelation:7 ica:9 expected:1... |
276 | 1,252 | A comparison between neural networks
and other statistical techniques for
modeling the relationship between
tobacco and alcohol and cancer
Tony Plate
BC Cancer Agency
601 West 10th Ave, Epidemiology
Vancouver BC Canada V5Z 1L3
tap@comp.vuw.ac.nz
Pierre Band
BC Cancer Agency
601 West 10th Ave, Epidemiology
Vancouver B... | 1252 |@word version:2 seems:1 reduction:3 series:1 selecting:1 bc:10 amp:1 comparing:1 yet:1 must:2 john:1 belmont:1 additive:4 partition:1 designed:1 aside:1 half:1 selected:5 nervous:1 record:4 provides:1 detecting:1 contribute:1 complication:1 five:1 mathematical:1 replication:7 prove:1 fitting:2 pairwise:1 inter:4 ... |
277 | 1,253 | Improving the Accuracy and Speed of
Support Vector Machines
Chris J.C. Burges
Bell Laboratories
Lucent Technologies , Room 3G429
101 Crawford 's Corner Road
Holmdel , NJ 07733-3030
burges@bell-Iabs.com
Bernhard Scholkopf"
Max-Planck-Institut fur
biologische Kybernetik ,
Spemannstr. 38
72076 Tubingen , Germany
bs@mpik... | 1253 |@word inversion:1 polynomial:1 r:9 solid:1 contains:1 interestingly:1 err:3 current:1 com:1 si:1 yet:1 must:2 readily:1 drop:1 fewer:2 selected:1 plane:1 record:1 provides:1 along:1 become:2 scholkopf:7 combine:2 expected:1 roughly:1 mpg:1 torque:1 decreasing:1 increasing:2 becomes:1 estimating:1 bonus:1 substant... |
278 | 1,254 | The effect of correlated input data on the
dynamics of learning
S~ren
Halkjrer and Ole Winther
CONNECT, The Niels Bohr Institute
Blegdamsvej 17
2100 Copenhagen, Denmark
halkjaer>winther~connect.nbi.dk
Abstract
The convergence properties of the gradient descent algorithm in the
case of the linear perceptron may be o... | 1254 |@word inversion:1 simulation:1 covariance:3 recursively:1 initial:1 series:1 zij:2 imaginary:3 written:2 readily:1 must:3 john:1 numerical:4 remove:4 implying:1 metabolism:1 contribute:1 lx:1 sigmoidal:1 along:1 become:1 pairing:1 consists:1 increasing:1 becomes:2 qtw:1 finding:4 transformation:18 collecting:1 co... |
279 | 1,255 | A Model of Recurrent Interactions in
Primary Visual Cortex
ElDanuel Todorov, Athanassios Siapas and David SOlDers
Dept. of Brain and Cognitive Sciences
E25-526, MIT, Cambridge, MA 02139
Email: {emo, thanos,somers }@ai.mit.edu
Abstract
A general feature of the cerebral cortex is its massive interconnectivity - it has ... | 1255 |@word eex:1 version:1 wiesel:1 simulation:2 solid:7 hunting:1 initial:1 contains:1 interestingly:1 current:4 yet:1 extraclassical:2 realistic:2 shape:1 alone:2 half:2 guess:1 iso:7 provides:1 contribute:1 location:2 gx:1 five:1 c2:1 direct:2 consists:1 grieve:1 ra:1 behavior:2 roughly:1 brain:3 freeman:1 increasi... |
280 | 1,256 | The Learning Dynamics of
a Universal Approximator
Ansgar H. L. West 1 ,2
David Saad 1
Ian T. N abneyl
A.H.L.West~aston.ac.uk
D.Saad~aston.ac.uk
I.T.Nabney~aston.ac.uk
1 Neural
Computing Research Group, University of Aston
Birmingham B4 7ET, U.K.
http://www.ncrg.aston.ac.uk/
2Department of Physics, University of ... | 1256 |@word polynomial:1 norm:5 stronger:1 simulation:3 bn:2 initial:16 configuration:2 o2:5 nt:1 surprising:1 activation:1 attracted:2 fn:1 realistic:1 numerical:5 update:5 imitate:1 theoretician:1 isotropic:6 tjw:3 lr:3 provides:1 node:7 sigmoidal:2 along:3 become:3 differential:2 qij:9 consists:2 notably:1 ra:2 mech... |
281 | 1,257 | Removing Noise in On-Line Search using
Adaptive Batch Sizes
Genevieve B. Orr
Department of Computer Science
Willamette University
900 State Street
Salem, Oregon 97301
gorr@willamette.ed-u
Abstract
Stochastic (on-line) learning can be faster than batch learning.
However, at late times, the learning rate must be anneal... | 1257 |@word norm:1 simulation:15 scg:1 bn:3 concise:2 tr:2 solid:2 moment:1 initial:1 selecting:1 current:3 nt:8 yet:1 must:2 written:1 remove:2 plot:1 update:7 v:2 greedy:1 ith:2 haykin:1 simpler:1 along:2 zkj:1 theoretically:1 expected:3 roughly:1 behavior:4 examine:2 increasing:3 maximizes:1 what:1 proposing:1 impra... |
282 | 1,258 | A Constructive RBF Network
for Writer Adaptation
John C. Platt and Nada P. Matic
Synaptics, Inc.
2698 Orchard Parkway
San Jose, CA 95134
platt@synaptics.com, nada@synaptics.com
Abstract
This paper discusses a fairly general adaptation algorithm which
augments a standard neural network to increase its recognition accu... | 1258 |@word qthat:1 version:1 cox:1 polynomial:1 retraining:1 tuned:1 com:2 contextual:2 od:1 nowlan:1 must:1 written:2 john:1 partition:2 shape:1 treating:1 designed:1 update:1 lor:1 five:2 incorrect:6 sacrifice:1 inspired:1 decreasing:2 actual:2 little:1 cpu:1 substantially:2 pseudo:1 quantitative:2 every:3 ti:1 clas... |
283 | 1,259 | Are Hopfield Networks Faster Than
Conventional Computers?
Ian Parberry* and Hung-Li Tsengt
Department of Computer Sciences
University of North Texas
P.O. Box 13886
Denton, TX 76203-6886
Abstract
It is shown that conventional computers can be exponentiallx faster
than planar Hopfield networks: although there are plana... | 1259 |@word polynomial:16 leighton:3 seems:1 versatile:1 shading:1 configuration:2 contains:3 yet:1 must:3 written:1 mesh:8 device:2 imitate:1 plane:1 steepest:1 completeness:13 node:8 mehlhorn:2 symposium:2 prove:4 consists:1 insist:1 becomes:1 discover:1 bounded:1 circuit:1 developed:2 finding:8 every:5 exactly:2 dem... |
284 | 126 | 419
COMPUTER MODELING OF ASSOCIATIVE LEARNING
DANIEL L. ALKON'
FRANCIS QUEK 2a
THOMAS P. VOGL 2b
1. Laboratory for Cellular and Molecular
NeurobiologYt NINCDS t NIH t Bethesda t MD 20892
2. Environmental Research Institute of Michigan
a) P.O. Box 8G18 t Ann Arbor t MI 48107
b) 1501 Wilson Blvd. t Suite 1105 t Arlin... | 126 |@word neurophysiology:1 stronger:2 replicate:1 hyperpolarized:2 extinction:1 open:4 simulation:3 pulse:23 r:1 eng:1 thereby:1 reduction:1 initial:2 necessity:1 efficacy:1 daniel:1 past:1 medi:1 current:5 must:5 subsequent:2 happen:1 predetermined:1 alone:3 nervous:1 indicative:1 rsk:2 beginning:1 marine:3 prespeci... |
285 | 1,260 | Clustering via Concave Minimization
P. S. Bradley and O. L. Mangasarian
Computer Sciences Department
University of Wisconsin
1210 West Dayton Street
Madison, WI 53706
email: paulb@es.wise.edu, olvi@es.wise.edu
w. N. Street
Computer Science Department
Oklahoma State University
205 Mathematical Sciences
Stillwater, OK 7... | 1260 |@word repository:1 f32:1 proportion:1 norm:14 prognostic:4 harder:1 carry:1 initial:1 bradley:4 comparing:1 assigning:2 scatter:1 moo:1 numerical:1 kdd:2 update:1 discrimination:2 stationary:4 intelligence:1 characterization:2 completeness:1 node:1 successive:1 five:2 mathematical:6 along:1 constructed:1 polyhedr... |
286 | 1,261 | Continuous sigmoidal belief networks
trained using slice sampling
Brendan J. Frey
Department of Computer Science, University of Toronto
6 King's College Road, Toronto, Canada M5S 1A4
Abstract
Real-valued random hidden variables can be useful for modelling
latent structure that explains correlations among observed var... | 1261 |@word version:1 simulation:2 pick:2 versatile:1 selecting:1 activation:1 visible:3 j1:2 hofmann:2 shape:1 designed:1 intelligence:2 selected:1 prohibitive:1 discovering:1 xk:1 short:1 prespecified:1 revisited:1 toronto:2 successive:1 sigmoidal:14 five:1 along:1 become:1 consists:2 eleventh:1 roughly:1 behavior:6 ... |
287 | 1,262 | Temporal Low-Order Statistics of Natural
Sounds
H. Attias? and C.E. Schreinert
Sloan Center for Theoretical Neurobiology and
W.M. Keck Foundation Center for Integrative Neuroscience
University of California at San Francisco
San Francisco, CA 94143-0444
Abstract
In order to process incoming sounds efficiently, it is ad... | 1262 |@word advantageous:1 nd:1 integrative:1 phy:2 tuned:1 ka:1 atop:2 must:1 confirming:1 designed:1 discrimination:2 alone:1 implying:1 stationary:2 characterization:2 location:2 successive:1 along:2 sii:2 fitting:1 roughly:2 behavior:2 examine:2 voss:2 ol:1 td:1 provided:1 bounded:1 acoust:1 transformation:1 guaran... |
288 | 1,263 | Training Algorithms for Hidden Markov Models
Using Entropy Based Distance Functions
Yoram Singer
AT&T Laboratories
600 Mountain Avenue
Murray Hill, NJ 07974
singer@research.att.com
Manfred K. Warmuth
Computer Science Department
University of California
Santa Cruz, CA 95064
manfred@cse.ucsc.edu
Abstract
We present new... | 1263 |@word version:5 seems:1 si8:1 seek:1 contraction:1 b39:1 initial:5 contains:1 att:1 past:1 current:7 com:1 comparing:1 dx:1 must:1 readily:1 cruz:1 partition:1 predetermined:1 treating:1 update:52 xex:10 fewer:1 warmuth:7 parameterization:2 manfred:2 filtered:1 lr:1 cse:1 ron:1 along:1 ucsc:1 baldi:1 indeed:2 exp... |
289 | 1,264 | Hidden Markov decision trees
Michael I. Jordan*, Zoubin Ghahramani t , and Lawrence K. Saul*
{jordan.zoubin.lksaul}~psyche.mit.edu
*Center for Biological and Computational Learning
Massachusetts Institute of Technology
Cambridge, MA USA 02139
t Department of Computer Science
University of Toronto
Toronto, ON Canada M... | 1264 |@word repository:1 middle:2 simulation:2 jacob:4 decomposition:3 covariance:1 pick:1 recursively:1 moment:2 initial:1 configuration:2 series:6 current:4 z2:3 reminiscent:1 must:1 remove:1 drop:1 leaf:2 selected:1 parameterization:1 provides:2 coarse:4 node:30 toronto:2 lx:2 five:1 incorrect:1 consists:1 pathway:1... |
290 | 1,265 | A Silicon Model of
Amplitude Modulation Detection
in the Auditory Brainstem
And~
van Schaik, Eric Fragniere, Eric Vittoz
MANIRA Center for Neuromimetic Systems
Swiss Federal Institute of Technology
CH-lOlS Lausanne
email: Andre.van_Schaik@di.epfl.ch
Abstract
Detectim of the periodicity of amplitude modulatim is a ma... | 1265 |@word briefly:1 rising:5 chopping:16 thereby:1 series:1 current:16 must:1 drop:1 half:1 tone:2 short:1 schaik:7 completeness:1 provides:1 detecting:4 node:1 psth:6 burst:1 differential:1 supply:1 ik:3 become:1 resistive:2 pathway:2 expected:2 multi:1 brain:2 decreasing:1 resolve:1 actual:2 little:1 increasing:2 b... |
291 | 1,266 | Learning temporally persistent
hierarchical representations
Suzanna Becker
Department of Psychology
McMaster University
Hamilton, Onto L8S 4K1
becker@mcmaster.ca
Abstract
A biologically motivated model of cortical self-organization is proposed. Context is combined with bottom-up information via a
maximum likelihood c... | 1266 |@word middle:2 version:1 compression:1 proportion:2 hippocampus:1 simulation:6 tried:1 covariance:1 jacob:2 thereby:2 series:1 tuned:1 current:4 contextual:15 comparing:1 nowlan:5 activation:5 must:1 realistic:1 partition:1 eleven:1 motor:1 remove:1 update:3 mounting:1 progressively:1 cue:1 discovering:1 intellig... |
292 | 1,267 | Adaptive Access Control Applied to Ethernet Data
Timothy X Brown
Dept. of Electrical and Computer Engineering
University of Colorado, Boulder, CO 80309-0530
timxb@colorado.edu
Abstract
This paper presents a method that decides which combinations of traffic
can be accepted on a packet data link, so that quality of ser... | 1267 |@word trial:10 loading:1 simulation:2 incurs:1 accommodate:1 carry:2 moment:1 plentiful:1 contains:2 mag:1 interestingly:2 outperforms:1 si:15 router:1 must:4 willinger:1 realistic:3 analytic:5 remove:2 treating:2 plot:1 fewer:1 beginning:1 ith:1 short:2 record:1 accepting:3 detecting:1 five:1 admission:2 direct:... |
293 | 1,268 | A mean field algorithm for Bayes learning
in large feed-forward neural networks
Manfred Opper
Institut fur Theoretische Physik
Julius-Maximilians-Universitat, Am Hubland
D-97074 Wurzburg, Germany
opperOphysik.Uni-Wuerzburg.de
Ole Winther
CONNECT
The Niels Bohr Institute
Blegdamsvej 17
2100 Copenhagen, Denmark
winther... | 1268 |@word seems:1 open:1 physik:1 simulation:3 tr:2 moment:1 mag:1 l__:1 si:4 written:1 realistic:1 numerical:1 partition:2 remove:1 v:1 isotropic:1 compo:1 manfred:1 provides:1 ron:3 become:5 specialize:2 expected:4 mechanic:6 multi:1 considering:1 becomes:1 distri:1 deutsche:1 developed:1 ull:1 exactly:2 control:1 ... |
294 | 1,269 | Analysis of Temporal-Difference Learning
with Function Approximation
John N. Tsitsiklis and Benjamin Van Roy
Laboratory for Information and Decision Systems
Massachusetts Institute of Technology
Cambridge, MA 02139
e-mail: jnt@mit.edu, bvr@mit.edu
Abstract
We present new results about the temporal-difference learning... | 1269 |@word instrumental:1 stronger:2 norm:4 twelfth:1 open:1 contraction:4 concise:1 recursively:1 series:2 past:1 current:4 yet:1 john:1 belmont:2 subsequent:1 predetermined:1 enables:1 update:1 aside:1 ith:1 characterization:1 provides:4 unbounded:1 constructed:1 ik:1 prove:2 manner:1 introduce:2 indeed:1 expected:3... |
295 | 127 | 91
OPTIMIZATION BY MEAN FIELD ANNEALING
Griff Bilbro
ECE Dept.
NCSU
Raleigh, NC 27695
Reinhold Mann
Eng. Physics and Math. Div.
Oak Ridge Natl. Lab.
Oak Ridge, TN 37831
Thomas K. Miller
ECE Dept.
NCSU
Raleigh, NC 27695
Wesley. E. Snyder
ECE Dept.
NCSU
Raleigh, NC 27695
David E. Van den Bout
ECE Dept.
NCSU
Raleigh,... | 127 |@word trial:1 version:1 seems:1 carolina:1 eng:1 initial:4 selecting:1 mag:1 past:1 si:5 assigning:1 attracted:1 must:1 analytic:3 update:2 fewer:2 hamiltonian:5 bipartitions:2 math:1 node:17 sigmoidal:1 oak:2 become:1 differential:1 consists:1 manner:1 uphill:1 behavior:4 decreasing:2 frustrate:1 little:1 becomes... |
296 | 1,270 | Monotonicity Hints
Joseph Sill
Computation and Neural Systems program
California Institute of Technology
email: joe@cs.caltech.edu
Yaser S. Abu-Mostafa
EE and CS Deptartments
California Institute of Technology
email: yaser@cs.caltech.edu
Abstract
A hint is any piece of side information about the target function to
b... | 1270 |@word repository:1 middle:2 tried:1 covariance:1 pick:1 profit:1 contains:1 pub:1 current:1 applicant:5 visible:1 partition:1 v:2 half:1 indicative:1 accordingly:1 ron:1 direct:3 fitting:1 market:3 roughly:1 frequently:1 decreasing:3 increasing:3 lowest:1 cm:1 transformation:1 iearning:1 refenes:1 classifier:2 uk... |
297 | 1,271 | Online learning from finite training sets:
An analytical case study
Peter Sollich*
Department of Physics
University of Edinburgh
Edinburgh EH9 3JZ, U.K.
P.SollichOed.ac.uk
David Barber t
Neural Computing Research Group
Department of Applied Mathematics
Aston University
Birmingham B4 7ET, U.K.
D.BarberOaston.ac.uk
Ab... | 1271 |@word version:1 inversion:1 eliminating:1 advantageous:1 confirms:1 simulation:2 solid:1 kappen:1 seriously:1 interestingly:1 comparing:1 surprising:1 luo:1 numerical:2 realistic:1 additive:1 drop:1 update:19 v:3 alone:1 isotropic:2 steepest:1 hypersphere:1 provides:1 ron:1 lx:1 along:2 differential:1 become:1 de... |
298 | 1,272 | Bayesian Model Comparison
by Monte Carlo Chaining
David Barber
Christopher M. Bishop
D.Barber~aston.ac.uk
C.M.Bishop~aston.ac.uk
Neural Computing Research Group
Aston University, Birmingham, B4 7ET, U.K.
http://www.ncrg.aston.ac.uk/
Abstract
The techniques of Bayesian inference have been applied with great
success... | 1272 |@word pw:1 lnh:1 tr:1 ka:2 current:1 written:1 periodically:2 additive:1 numerical:1 shape:1 plot:4 alone:1 isotropic:1 beginning:1 hamiltonian:1 provides:1 toronto:1 successive:3 location:1 consists:2 fitting:1 indeed:1 expected:1 themselves:2 multi:1 increasing:1 lowest:1 kind:1 developed:1 transformation:1 uk:... |
299 | 1,273 | Multi-Grid Methods for Reinforcement
Learning in Controlled Diffusion Processes
Stephan Pareigis
stp@numerik.uni-kiel.de
Lehrstuhl Praktische Mathematik
Christian-Albrechts-Universi tat Kiel
Kiel, Germany
Abstract
Reinforcement learning methods for discrete and semi-Markov decision problems such as Real-Time Dynamic ... | 1273 |@word nd:1 simulation:2 tat:1 tr:2 ld:1 reduction:1 initial:1 current:1 discretization:7 optim:1 dx:1 finest:5 numerical:5 christian:1 plot:2 coarse:2 provides:2 universi:1 kiel:3 albrechts:1 mathematical:1 differential:2 hjb:3 inside:1 multi:20 discretized:3 bellman:3 discounted:1 decreasing:1 actual:1 bounded:1... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.