Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
0 | 1 | 767
SELF-ORGANIZATION OF ASSOCIATIVE DATABASE
AND ITS APPLICATIONS
Hisashi Suzuki and Suguru Arimoto
Osaka University, Toyonaka, Osaka 560, Japan
ABSTRACT
An efficient method of self-organizing associative databases is proposed together with
applications to robot eyesight systems. The proposed databases can associate ... | 1 |@word trial:3 version:1 compression:3 instruction:1 km:1 delicately:1 recursively:1 initial:3 configuration:1 denoting:1 document:3 past:3 current:1 si:8 universality:1 written:3 must:2 realize:1 subsequent:1 periodically:1 distant:1 succeeding:1 sampl:1 stationary:1 half:2 selected:1 leaf:1 accordingly:6 xk:1 recor... |
1 | 10 | 683
A MEAN FIELD THEORY OF LAYER IV OF VISUAL CORTEX
AND ITS APPLICATION TO ARTIFICIAL NEURAL NETWORKS*
Christopher L. Scofield
Center for Neural Science and Physics Department
Brown University
Providence, Rhode Island 02912
and
Nestor, Inc., 1 Richmond Square, Providence, Rhode Island,
02906.
ABSTRACT
A single cell t... | 10 |@word proportion:1 open:2 independant:1 dramatic:1 reduction:1 initial:1 contains:1 exclusively:1 tuned:1 rearing:1 current:2 cad:1 ixil:1 plasticity:3 nervous:1 postnatal:1 dembo:1 ith:1 dissertation:1 location:2 preference:3 lessening:1 qualitative:1 consists:1 pathway:1 introduce:1 manner:2 proliferation:1 embod... |
2 | 100 | 394
STORING COVARIANCE BY THE ASSOCIATIVE
LONG?TERM POTENTIATION AND DEPRESSION
OF SYNAPTIC STRENGTHS IN THE HIPPOCAMPUS
Patric K. Stanton? and Terrence J. Sejnowski t
Department of Biophysics
Johns Hopkins University
Baltimore, MD 21218
ABSTRACT
In modeling studies or memory based on neural networks, both the select... | 100 |@word determinant:1 longterm:1 unaltered:1 middle:2 hippocampus:22 seems:1 hyperpolarized:2 open:1 pulse:2 covariance:12 lowfrequency:2 reduction:4 series:1 past:1 coactive:2 current:9 activation:5 yet:1 john:1 physiol:2 subsequent:2 hyperpolarizing:2 plasticity:8 aps:2 alone:6 patric:1 math:1 tpresent:1 burst:12 ... |
3 | 1,000 | Bayesian Query Construction for Neural
Network Models
Gerhard Paass
Jorg Kindermann
German National Research Center for Computer Science (GMD)
D-53757 Sankt Augustin, Germany
paass@gmd.de
kindermann@gmd.de
Abstract
If data collection is costly, there is much to be gained by actively selecting particularly informative ... | 1000 |@word trial:5 wcb:3 version:1 simulation:1 concise:1 tr:2 reduction:1 selecting:2 current:16 ixj:1 must:1 numerical:2 informative:1 dydx:1 analytic:1 drop:1 intelligence:1 selected:5 yr:3 beginning:1 toronto:1 wir:2 five:1 prove:1 expected:4 considering:1 project:1 sankt:1 titterington:1 control:1 unit:5 grant:1 ... |
4 | 1,001 | Neural Network Ensembles, Cross
Validation, and Active Learning
Anders Krogh"
Nordita
Blegdamsvej 17
2100 Copenhagen, Denmark
Jesper Vedelsby
Electronics Institute, Building 349
Technical University of Denmark
2800 Lyngby, Denmark
Abstract
Learning of continuous valued functions using neural network ensembles (commi... | 1001 |@word seems:1 thereby:1 solid:6 electronics:1 initial:1 scatter:1 enables:1 drop:1 plot:3 half:1 selected:1 intelligence:1 af3:4 five:3 consists:1 little:1 increasing:2 becomes:1 provided:1 lowest:2 israel:1 kind:2 developed:2 finding:1 ti:1 exactly:3 unit:1 positive:1 fluctuation:1 might:1 chose:1 mateo:2 sugges... |
5 | 1,002 | U sing a neural net to instantiate a
deformable model
Christopher K. I. Williams; Michael D. Revowand Geoffrey E. Hinton
Department of Computer Science, University of Toronto
Toronto, Ontario, Canada M5S lA4
Abstract
Deformable models are an attractive approach to recognizing nonrigid objects which have considerable w... | 1002 |@word deformed:2 trial:1 determinant:1 covariance:1 jacob:2 carry:1 initial:2 current:1 nowlan:1 must:1 readily:1 tot:2 predetermined:1 shape:7 hypothesize:1 designed:2 instantiate:4 guess:2 discovering:1 short:1 postal:1 toronto:4 location:7 sigmoidal:1 five:1 zii:3 along:3 constructed:1 consists:1 fitting:6 all... |
6 | 1,003 | Plasticity-Mediated Competitive Learning
Terrence J. Sejnowski
terry@salk.edu
Nicol N. Schraudolph
nici@salk.edu
Computational Neurobiology Laboratory
The Salk Institute for Biological Studies
San Diego, CA 92186-5800
and
Computer Science & Engineering Department
University of California, San Diego
La Jolla, CA 920... | 1003 |@word version:1 seems:1 seek:2 covariance:2 decorrelate:1 initial:1 past:1 comparing:1 activation:4 scatter:1 must:1 written:1 numerical:1 informative:1 plasticity:27 plot:2 update:1 discrimination:1 provides:3 node:22 preference:1 mathematical:1 prove:1 autocorrelation:1 frequently:1 inappropriate:1 begin:1 medi... |
7 | 1,004 | ICEG Morphology Classification using an
Analogue VLSI Neural Network
Richard Coggins, Marwan Jabri, Barry Flower and Stephen Pickard
Systems Engineering and Design Automation Laboratory
Department of Electrical Engineering J03,
University of Sydney, 2006, Australia.
Email: richardc@sedal.su.oz.au
Abstract
An analogue... | 1004 |@word briefly:1 simulation:2 tried:1 accommodate:1 initial:1 born:1 amp:1 current:11 yet:1 must:2 icds:2 designed:1 update:1 alone:2 implying:1 prohibitive:1 device:6 selected:1 inspection:1 provides:2 node:1 ron:1 firstly:1 tinker:4 five:3 differential:3 m7:2 supply:1 consists:1 resistive:2 isscc:1 introduce:1 a... |
8 | 1,005 | Real-Time Control of a Tokamak Plasma
Using Neural Networks
Chris M Bishop
Neural Computing Research Group
Department of Computer Science
Aston University
Birmingham, B4 7ET, U.K.
c.m .bishop@aston .ac .uk
Paul S Haynes, Mike E U Smith, Tom N Todd,
David L Trotman and Colin G Windsor
AEA Technology, Culham Laboratory... | 1005 |@word cox:2 loading:1 pulse:2 simulation:2 attainable:2 pressure:3 pick:2 thereby:1 solid:2 shot:6 reduction:1 initial:3 configuration:4 series:2 pbx:1 current:7 activation:1 must:4 numerical:3 shape:16 analytic:1 designed:2 plot:5 mounting:1 device:2 parameterization:1 plane:1 smith:7 provides:3 location:1 sigmo... |
9 | 1,006 | Real-Time Control of a Tokamak Plasma
Using Neural Networks
Chris M Bishop
Neural Computing Research Group
Department of Computer Science
Aston University
Birmingham, B4 7ET, U.K.
c.m .bishop@aston .ac .uk
Paul S Haynes, Mike E U Smith, Tom N Todd,
David L Trotman and Colin G Windsor
AEA Technology, Culham Laboratory... | 1006 |@word pulsestream:4 cox:2 chromium:2 loading:1 simulation:2 pulse:9 attainable:2 pressure:3 pick:2 thereby:1 solid:3 shot:6 reduction:1 electronics:2 configuration:4 series:4 contains:2 initial:3 pbx:1 current:15 activation:3 si:26 yet:1 must:7 refresh:1 numerical:3 shape:16 analytic:1 designed:3 plot:5 drop:1 mo... |
10 | 1,007 | Learning To Play the Game of Chess
Sebastian Thrun
University of Bonn
Department of Computer Science III
Romerstr. 164, 0-53117 Bonn, Germany
E-mail: thrun@carbon.informatik.uni-bonn.de
Abstract
This paper presents NeuroChess, a program which learns to play chess from the final
outcome of games. NeuroChess learns che... | 1007 |@word briefly:1 version:6 middle:1 tadepalli:2 open:2 simulation:1 prasad:1 harder:1 recursively:2 initial:1 deepens:1 seriously:1 rightmost:1 current:3 si:2 john:1 shape:1 designed:2 update:1 v:1 alone:1 intelligence:2 half:2 selected:1 item:1 beginning:1 short:3 ebnn:9 contribute:1 attack:3 firstly:1 constructe... |
11 | 1,008 | Multidimensional Scaling and Data Clustering
Thomas Hofmann & Joachim Buhmann
Rheinische Friedrich-Wilhelms-U niversitat
Institut fur Informatik ill, Romerstra6e 164
D-53117 Bonn, Germany
email:{th.jb}@cs.uni-bonn.de
Abstract
Visualizing and structuring pairwise dissimilarity data are difficult combinatorial optimiza... | 1008 |@word covariance:1 euclidian:8 reduction:3 configuration:1 fragment:1 selecting:1 score:1 denoting:1 dx:1 transcendental:1 additive:1 partition:1 hofmann:10 civ:1 stationary:1 half:2 selected:3 accordingly:1 inspection:1 xk:4 provides:1 location:2 afo:2 along:1 constructed:1 ik:2 eiw:1 inside:1 adij:2 introduce:1... |
12 | 1,009 | An experimental comparison
of recurrent neural networks
Bill G. Horne and C. Lee Giles?
NEe Research Institute
4 Independence Way
Princeton, NJ 08540
{horne.giles}~research.nj.nec.com
Abstract
Many different discrete-time recurrent neural network architectures have been proposed. However, there has been virtually no
e... | 1009 |@word trial:1 seems:2 bptt:2 simulation:2 tried:1 initial:2 series:1 past:3 current:1 com:1 z2:4 surprising:1 lang:1 yet:1 designed:2 aside:1 alone:3 beginning:1 ith:1 node:25 sigmoidal:1 along:1 tdl:3 roughly:1 elman:9 fmm:4 frequently:1 little:1 window:3 provided:1 horne:5 matched:1 watrous:1 string:5 finding:1... |
13 | 101 | 133
TRAINING MULTILAYER PERCEPTRONS WITH THE
EXTENDED KALMAN ALGORITHM
Sharad Singhal and Lance Wu
Bell Communications Research, Inc.
Morristown, NJ 07960
ABSTRACT
A large fraction of recent work in artificial neural nets uses
multilayer perceptrons trained with the back-propagation
algorithm described by Rumelhart e... | 101 |@word briefly:1 version:2 inversion:1 llsed:1 termination:1 simulation:1 covariance:3 eng:1 tr:3 reduction:2 initial:7 configuration:1 series:2 denoting:1 current:1 xand:1 lang:1 update:3 fewer:3 node:17 mathematical:1 constructed:2 become:1 consists:1 examine:3 ol:2 becomes:1 nj:1 morristown:1 zl:5 unit:1 appear:... |
14 | 1,010 | Interference in Learning Internal
Models of Inverse Dynamics in Humans
Reza Shadmehr; Tom Brashers-Krug, and Ferdinando Mussa-lvaldi t
Dept. of Brain and Cognitive Sciences
M. I. T., Cambridge, MA 02139
reza@bme.jhu.edu, tbk@ai.mit.edu, sandro@parker.physio.nwu.edu
Abstract
Experiments were performed to reveal some o... | 1010 |@word mri:1 longterm:2 proportion:1 nd:1 r:1 eng:1 reduction:1 ivaldi:8 initial:10 series:3 interestingly:1 activation:1 john:1 familiarized:1 subsequent:1 chicago:1 plasticity:1 motor:31 designed:2 half:2 manipulandum:1 nervous:2 proficient:1 sys:1 steepest:1 short:3 dissertation:1 lr:1 mathematical:1 along:3 di... |
15 | 1,011 | Active Learning with Statistical Models
David A. Cohn, Zoubin Ghahramani, and Michael I. Jordan
cohnQpsyche.mit.edu. zoubinQpsyche.mit.edu. jordan~syche.mit.edu
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
Abstract
For many types of learners one can compute the ... | 1011 |@word inversion:1 retraining:1 nd:1 simulation:1 covariance:2 concise:1 solid:1 initial:1 series:1 selecting:4 outperforms:1 existing:1 current:1 nowlan:2 dx:1 written:2 shape:1 drop:1 plot:2 atlas:1 v:1 selected:3 provides:1 lx:7 along:1 combine:1 fitting:2 nondeterministic:1 manner:1 jly:1 expected:6 behavior:1... |
16 | 1,012 | A Rapid Graph-based Method for
Arbitrary Transformation-Invariant
Pattern Classification
Alessandro Sperduti
Dipartimento di Informatica
Universita di Pisa
Corso Italia 40
56125 Pisa, ITALY
David G. Stork
Machine Learning and Perception Group
Ricoh California Research Center
2882 Sand Hill Road # 115
Menlo Park, CA US... | 1012 |@word version:2 middle:1 seems:2 simulation:1 seek:1 prasad:1 pick:2 parenthetically:1 solid:1 initial:1 contains:3 tuned:1 ours:3 current:5 com:1 comparing:1 yet:1 must:4 alone:1 half:2 fewer:1 selected:1 pointer:1 node:3 simpler:1 along:1 constructed:1 lowresolution:1 theoretically:1 inter:1 indeed:1 rapid:4 al... |
17 | 1,013 | Ocular Dominance and Patterned Lateral
Connections in a Self-Organizing Model of the
Primary Visual Cortex
Joseph Sirosh and Risto Miikkulainen
Department of Computer Sciences
University of Texas at Austin, Austin, 'IX 78712
email:
sirosh.risto~cs.utexas.edu
Abstract
A neural network model for the self-organization ... | 1013 |@word wiesel:3 stronger:1 replicate:1 risto:6 grey:1 simulation:6 thereby:1 initial:2 tuned:2 biolog:1 activation:7 realistic:1 plasticity:3 eab:1 amir:1 reciprocal:1 short:3 farther:1 location:1 preference:12 mathematical:2 become:7 symposium:1 consists:1 combine:1 becomes:3 begin:1 provided:1 underlying:1 circu... |
18 | 1,014 | Associative Decorrelation Dynamics:
A Theory of Self-Organization and
Optimization in Feedback Networks
Dawei W. Dong*
Lawrence Berkeley Laboratory
University of California
Berkeley, CA 94720
Abstract
This paper outlines a dynamic theory of development and adaptation in neural networks with feedback connections. Give... | 1014 |@word wiesel:3 pg:1 decorrelate:1 tr:1 solid:2 tuned:5 contextual:1 yet:2 tilted:4 physiol:2 shape:1 isotropic:1 short:1 detecting:1 tolhurst:1 preference:1 lor:1 director:1 consists:1 pathway:1 dtij:1 indeed:1 roughly:1 behavior:1 examine:2 ol:1 brain:2 actual:1 bounded:1 what:1 kind:2 kaufman:1 q2:2 developed:5... |
19 | 1,015 | A Connectionist Technique for Accelerated
Textual Input: Letting a Network Do the Typing
Dean A. Pomerleau
pomerlea@cs.cmu.edu
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
Each year people spend a huge amount of time typing. The text people type
typically contains a tremendous a... | 1015 |@word trial:5 middle:1 willing:1 dramatic:1 solid:1 initial:1 configuration:1 contains:3 score:1 prefix:7 past:1 current:4 activation:9 lang:1 must:3 parsing:1 subsequent:3 informative:1 designed:1 v:3 half:3 selected:1 device:1 fewer:3 proficient:1 core:1 location:1 parsec:1 skilled:2 become:2 incorrect:2 manner... |
20 | 1,016 | Connectionist Speaker Normalization
with Generalized
Resource Allocating Networks
Cesare Furlanello
Istituto per La Ricerca
Scientifica e Tecnologica
Povo (Trento), Italy
furlan?lirst. it
Diego Giuliani
Istituto per La Ricerca
Scientifica e Tecnologica
Povo (Trento), Italy
giuliani?lirst.it
Edmondo Trentin
Istituto ... | 1016 |@word version:1 norm:2 dekker:1 d2:2 decomposition:2 covariance:2 decorrelate:1 recursively:1 substitution:1 hardy:2 bootstrapped:1 troller:1 elliptical:3 realize:1 numerical:1 girosi:3 designed:2 v:2 parameterization:1 short:2 provides:1 location:2 simpler:1 unacceptable:1 consists:3 fitting:1 symp:1 sakoe:2 acq... |
21 | 1,017 | A Critical Comparison of Models for
Orientation and Ocular Dominance
Columns in the Striate Cortex
E. Erwin
Beckman Institute
University of Illinois
Urbana, IL 61801, USA
K. Obermayer
Technische Fakultat
U niversitat Bielefeld
33615 Bielefeld, FRG
K. Schulten
Beckman Institute
University of Illinois
Urbana, IL 61801,... | 1017 |@word version:1 briefly:1 wiesel:3 stronger:1 concise:1 tr:1 contains:1 optically:1 interestingly:2 cort:1 elliptical:1 current:2 od:9 must:1 readily:1 v:2 alone:1 spec:2 iso:2 realism:1 filtered:1 location:1 preference:13 conse:1 along:1 autocorrelation:2 paragraph:1 ra:1 indeed:2 rapid:1 roughly:1 anisotropy:4 ... |
22 | 1,018 | Generalization in Reinforcement Learning:
Safely Approximating the Value Function
Justin A. Boyan and Andrew W. Moore
Computer Science Department
Carnegie Mellon University
Pittsburgh, PA 15213
jab@cs.cmu.edu, awm@cs.cmu .edu
Abstract
A straightforward approach to the curse of dimensionality in reinforcement learning... | 1018 |@word exploitation:1 middle:1 polynomial:5 simulation:3 reap:1 harder:1 reduction:2 substitution:1 current:1 surprising:1 yet:1 must:2 benign:2 enables:1 designed:2 greedy:7 short:1 farther:1 underestimating:1 contribute:1 five:1 rollout:3 become:2 prove:1 fitting:2 overhead:1 nondeterministic:1 introduce:1 theor... |
23 | 1,019 | A Mixture Model System for Medical and
Machine Diagnosis
Terrence J. Sejnowski
Magnus Stensmo
Computational Neurobiology Laboratory
The Salk Institute for Biological Studies
10010 North Torrey Pines Road
La Jolla, CA 92037, U.S.A.
{magnus,terry}~salk.edu
Abstract
Diagnosis of human disease or machine fault is a mis... | 1019 |@word repository:1 version:1 reused:1 sex:2 dekker:1 tried:1 covariance:2 pressure:1 initial:1 contains:1 series:1 zij:2 horvitz:1 existing:1 current:2 nowlan:3 written:1 benign:1 shape:1 analytic:1 update:2 intelligence:1 fewer:1 selected:1 record:1 num:1 node:1 complication:1 five:2 specialize:1 fitting:2 intro... |
24 | 102 | 186
AN APPLICATION OF THE PRINCIPLE OF
MAXIMUM INFORMATION PRESERVATION
TO LINEAR SYSTEMS
Ralph Linsker
IBM T. J. Watson Research Center, Yorktown Heights, NY 10598
ABSTRACT
This paper addresses the problem of determining the weights for a
set of linear filters (model "cells") so as to maximize the
ensemble-averaged ... | 102 |@word seek:1 covariance:4 simplifying:1 solid:4 ld:1 initial:1 series:1 past:2 must:2 john:1 numerical:1 realistic:3 additive:2 remove:1 plot:1 selected:2 short:2 provides:1 simpler:1 height:1 mathematical:2 along:1 become:3 manner:1 behavior:2 chap:1 resolve:1 actual:1 considering:1 increasing:1 provided:2 notati... |
25 | 1,020 | A Computational Model of Prefrontal
Cortex Function
Todd S. Braver
Dept. of Psychology
Carnegie Mellon Univ.
Pittsburgh, PA 15213
Jonathan D. Cohen
Dept. of Psychology
Carnegie Mellon Univ .
Pittsburgh , PA 15213
David Servan-Schreiber
Dept. of Psychiatry
Univ . of Pittsburgh
Pittsburgh , PA 15232
Abstract
Accumulat... | 1020 |@word neurophysiology:1 trial:29 version:4 middle:1 instrumental:1 seems:1 nd:1 simulation:11 lobe:2 reduction:1 responsivity:1 reaction:6 current:2 contextual:1 comparing:2 activation:2 must:1 blur:1 motor:1 stroop:1 cue:16 nervous:2 tone:2 short:15 provides:6 simpler:2 incorrect:4 qualitative:1 sustained:3 fitt... |
26 | 1,021 | The Gamma MLP for Speech Phoneme
Recognition
Steve
Lawrence~
Ah Chung Tsoi, Andrew D. Back
{lawrence,act,back}Oelec.uq.edu.au
Department of Electrical and Computer Engineering
University of Queensland
St. Lucia Qld 4072 Australia
Abstract
We define a Gamma multi-layer perceptron (MLP) as an MLP
with the usual syna... | 1021 |@word casdagli:5 simulation:3 queensland:1 minus:1 reduction:1 electronics:1 initial:3 series:2 contains:2 pub:1 past:1 current:3 com:1 comparing:1 lang:3 activation:1 designed:1 succeeding:1 update:7 yi1:1 short:1 node:1 c2:3 direct:1 become:1 consists:1 inside:1 manner:1 uncoupling:1 roughly:1 themselves:2 mult... |
27 | 1,022 | A Multiscale Attentional Framework for
Relaxation Neural Networks
Dimitris I. Tsioutsias
Dept. of Electrical Engineering
Yale University
New Haven, CT 06520-8285
Eric Mjolsness
Dept. of Computer Science & Engineering
University of California, San Diego
La Jolla, CA 92093-0114
tsioutsias~cs.yale.edu
emj~cs.ucsd.edu
... | 1022 |@word version:1 seems:2 norm:4 nd:1 termination:1 seek:1 pick:1 tr:3 yaleu:1 reduction:4 configuration:1 series:1 pub:1 interestingly:1 past:1 current:2 elliptical:1 activation:1 reminiscent:1 readily:1 periodically:1 partition:4 localise:1 update:2 greedy:1 selected:1 steepest:2 coarse:3 cse:1 x128:1 along:1 int... |
28 | 1,023 | Correlated Neuronal Response:
Time Scales and Mechanisms
Wyeth Bair
Howard Hughes Medical Inst.
NYU Center for Neural Science
4 Washington PI., Room 809
New York, NY 10003
Ehud Zohary
Dept. of Neurobiology
Institute of Life Sciences
The Hebrew University, Givat Ram
Jerusalem, 91904 ISRAEL
Christof Koch
Computation an... | 1023 |@word trial:24 seems:1 r:1 minus:1 responsivity:3 score:1 current:2 yet:1 interrupted:2 remove:1 plot:1 half:2 indicative:1 short:10 provides:1 contribute:1 height:2 correlograms:3 burst:4 fixation:1 pathway:1 autocorrelation:1 inter:2 expected:2 roughly:1 isi:4 little:1 window:3 zohary:9 panel:2 barrel:1 null:3 ... |
29 | 1,024 | Onset-based Sound Segmentation
Leslie S. Smith
CCCN jDepartment of Computer Science
University of Stirling
Stirling FK9 4LA
Scotland
Abstract
A technique for segmenting sounds using processing based on mammalian early auditory processing is presented. The technique is
based on features in sound which neuron spike rec... | 1024 |@word neurophysiology:1 version:1 middle:1 stronger:1 nd:1 pulse:4 tried:1 gradual:1 simulation:1 pressure:1 tr:1 carry:1 initial:1 liu:2 neurophys:1 activation:2 yet:1 dx:1 olive:1 visible:1 motor:1 medial:1 alone:2 tone:2 scotland:1 smith:12 short:2 schaik:2 filtered:1 leakiness:1 sudden:1 provides:1 math:1 bur... |
30 | 1,025 | A model of transparent motion and
non-transparent motion aftereffects
Alexander Grunewald*
Max-Planck Institut fur biologische Kybernetik
Spemannstrafie 38
D-72076 Tubingen, Germany
Abstract
A model of human motion perception is presented. The model
contains two stages of direction selective units. The first stage co... | 1025 |@word sharpens:1 stronger:1 grey:2 simulation:9 contains:4 tuned:8 activation:5 subsequent:1 enables:1 short:1 contribute:1 location:3 become:1 differential:1 consists:1 grunewald:4 behavior:1 prolonged:3 becomes:1 monkey:1 act:4 ensured:1 unit:61 rungekutta:1 grant:1 appear:1 planck:1 before:1 sutherland:2 local... |
31 | 1,026 | A MODEL OF AUDITORY STREAMING
Susan L. McCabe & Michael J. Denham
Neurodynamics Research Group
School of Computing
University of Plymouth
Plymouth PL4 8AA, u.K.
ABSTRACT
An essential feature of intelligent sensory processing is the ability to
focus on the part of the signal of interest against a background of
distract... | 1026 |@word judgement:6 seems:2 replicate:2 simulation:1 attended:1 thereby:1 moment:1 initial:2 series:1 tuned:1 current:5 activation:2 interrupted:2 subsequent:2 partition:1 tone:29 dissertation:1 complication:1 successive:1 along:1 c2:2 direct:1 consists:1 expected:1 rapid:1 frequently:1 distractor:4 integrator:1 ol... |
32 | 1,027 | REMAP: Recursive Estimation and
Maximization of A Posteriori
Probabilities - Application to
Transition-Based Connectionist Speech
Recognition
Yochai Konig, Herve Bourlard~ and Nelson Morgan
{konig, bourlard,morgan }@icsi.berkeley.edu
International Computer Science Institute
1947 Center Street Berkeley, CA 94704, USA.
... | 1027 |@word tr:1 reduction:1 initial:4 series:1 current:1 ixj:2 reminiscent:2 must:1 realistic:1 alone:1 dhmm:2 beginning:1 dissertation:1 provides:1 ron:1 prove:2 consists:2 introduce:2 indeed:1 themselves:2 multi:1 kamm:1 actual:1 csl:1 window:3 provided:1 estimating:1 project:1 q2:8 developed:2 spoken:5 finding:1 gu... |
33 | 1,028 | Exponentially many local minima for single
neurons
Peter Auer
Manfred K. Warmuth
Mark Herbster
Department of Computer Science
Santa Cruz, California
{pauer,mark,manfred} @cs.ucsc.edu
Abstract
We show that for a single neuron with the logistic function as the transfer
function the number of local minima of the erro... | 1028 |@word open:2 ithere:1 contains:2 cruz:2 additive:1 visible:1 update:2 intelligence:1 warmuth:6 haykin:1 manfred:3 sigmoidal:1 unbounded:2 along:1 ucsc:2 pairing:1 consists:1 prove:2 nonrealizable:1 introduce:1 tesi:1 p1:1 curse:1 increasing:6 becomes:3 notation:2 bounded:11 circuit:1 watrous:1 extremum:1 brady:1 ... |
34 | 1,029 | A Practical Monte Carlo Implementation
of Bayesian Learning
Carl Edward Rasmussen
Department of Computer Science
University of Toronto
Toronto, Ontario, M5S 1A4, Canada
carl@cs.toronto.edu
Abstract
A practical method for Bayesian training of feed-forward neural
networks using sophisticated Monte Carlo methods is pres... | 1029 |@word exploitation:1 version:4 eliminating:1 tedious:1 simulation:3 tried:1 thereby:1 initial:2 series:1 pub:1 initialisation:2 outperforms:1 comparing:1 written:1 additive:1 partition:3 shape:3 update:6 half:1 selected:3 cursory:1 hamiltonian:2 normalising:1 toronto:5 five:2 direct:4 differential:1 consists:2 co... |
35 | 103 | 653
AN ADAPTIVE NETWORK THAT LEARNS
SEQUENCES OF TRANSITIONS
C. L. Winter
Science Applications International Corporation
5151 East Broadway, Suite 900
Tucson, Auizona 85711
ABSTRACT
We describe an adaptive network, TIN2, that learns the transition
function of a sequential system from observations of its behavior. It
... | 103 |@word version:2 simulation:5 tried:1 initial:1 contains:2 current:6 blank:1 si:2 must:7 partition:5 motor:1 remove:1 leaf:2 ith:2 characterization:7 provides:2 node:43 math:1 contribute:1 lor:1 constructed:1 skilled:1 become:1 ik:1 prove:1 inside:1 behavior:9 frequently:1 actual:1 unpredictable:1 provided:1 circui... |
36 | 1,030 | Neuron-MOS Temporal Winner Search
Hardware for Fully-Parallel Data
Processing
Tadashi SHIBATA, Tsutomu NAKAI, Tatsuo MORIMOTO
Ryu KAIHARA, Takeo YAMASHITA, and Tadahiro OHMI
Department of Electronic Engineering
Tohoku University
Aza-Aoba, Aramaki, Aobaku, Sendai 980-77 JAPAN
Abstract
A unique architecture of winner s... | 1030 |@word version:1 inversion:2 loading:1 nd:1 simulation:11 simplifying:1 solid:1 carry:1 moment:1 configuration:2 selecting:1 current:8 must:1 takeo:1 civ:1 designed:5 v:3 discrimination:1 device:4 short:1 core:1 quantizer:1 node:4 location:1 rc:1 direct:2 become:2 supply:1 kotani:2 isscc:3 sendai:1 manner:2 rampin... |
37 | 1,031 | Dynamics of Attention as Near
Saddle-Node Bifurcation Behavior
Hiroyuki Nakahara"
Kenji Doya
General Systems Studies
U ni versi ty of Tokyo
3-8-1 Komaba, Meguro
Tokyo 153, Japan
nakahara@vermeer.c.u-tokyo.ac.jp
ATR Human Information Processing
Research Laboratories
2-2 Hikaridai, Seika, Soraku
Kyoto 619-02, Japan
d... | 1031 |@word rising:1 advantageous:1 pulse:1 simulation:6 initial:1 past:3 repelling:1 activation:2 yet:2 john:1 visible:16 enables:1 motor:7 hypothesize:1 plot:1 stationary:4 cue:2 item:6 xk:1 node:18 sigmoidal:3 five:2 qualitative:3 inside:4 theoretically:1 expected:1 indeed:1 behavior:21 seika:1 food:26 underlying:1 ... |
38 | 1,032 | VLSI Model of Primate Visual Smooth Pursuit
Ralph Etienne-Cummings
Jan Van der Spiegel
Department of Electrical Engineering,
Southern Illinois University, Carbondale,
IL 62901
Moore School of Electrical Engineering,
University of Pennsylvania, Philadelphia,
PA 19104
Paul Mueller
Corticon, Incorporated,
3624 Market... | 1032 |@word version:1 inversion:1 compression:1 cm2:1 pulse:9 t_:1 foveal:1 tuned:1 current:7 must:2 realize:1 additive:1 motor:4 plot:2 update:4 v:3 nervous:1 plane:5 reciprocal:3 provides:2 location:1 mathematical:1 c2:1 direct:1 profound:1 driver:3 prove:1 resistive:1 combine:1 autocorrelation:1 market:1 multi:1 int... |
39 | 1,033 | Gradient and Hamiltonian Dynamics
Applied to Learning in Neural Networks
James W. Howse
Chaouki T. Abdallah
Gregory L. Heileman
Department of Electrical and Computer Engineering
University of New Mexico
Albuquerque, NM 87131
Abstract
The process of machine learning can be considered in two stages: model
selection a... | 1033 |@word polynomial:4 simulation:1 decomposition:2 excited:1 thereby:1 solid:7 irnxn:1 initial:3 contains:2 selecting:1 z2:6 comparing:1 must:3 zll:2 partition:2 shape:1 designed:5 plot:3 half:1 p7:3 plane:2 steepest:2 hamiltonian:10 ith:1 provides:1 location:3 height:4 mathematical:2 along:8 constructed:3 different... |
40 | 1,034 | Is Learning The n-th Thing Any Easier Than
Learning The First?
Sebastian Thrun I
Computer Science Department
Carnegie Mellon University
Pittsburgh, PA 15213-3891
World Wide Web: http://www.cs.cmu.edul'''thrun
Abstract
This paper investigates learning in a lifelong context. Lifelong learning
addresses situations in wh... | 1034 |@word briefly:1 version:1 seems:1 open:1 brightness:1 euclidian:1 tr:2 carry:1 initial:1 contains:4 exclusively:2 series:1 past:1 current:1 comparing:1 coke:2 dx:1 intriguing:1 stemming:1 fn:1 shape:1 motor:1 v:1 selected:1 xk:5 ebnn:12 provides:1 location:1 preference:1 five:3 become:1 ik:3 edelman:2 repro:2 inc... |
41 | 1,035 | A Dynamical Model of Context Dependencies for the
Vestibulo-Ocular Reflex
Terrence J. Sejnowskit
Olivier J.M.D. Coenen*
Computational Neurobiology Laboratory
Howard Hughes Medical Institute
The Salk Institute for Biological Studies
10010 North Torrey Pines Road
La Jolla, CA 92037, U.S.A.
Departments oftBiology and *t... | 1035 |@word neurophysiology:10 open:1 rhesus:1 sensed:4 solid:1 contains:1 anterior:1 activation:1 must:3 written:3 vor:18 distant:1 motor:1 v:1 stationary:1 nervous:1 plane:4 reciprocal:1 filtered:1 sudden:1 provides:1 location:11 along:3 constructed:1 direct:1 ooj:2 fixation:1 combine:2 pathway:3 fitting:1 inside:1 m... |
42 | 1,036 | Improved Gaussian Mixture Density
Estimates Using Bayesian Penalty Terms
and Network Averaging
Dirk Ormoneit
Institut fur Informatik (H2)
Technische Universitat Munchen
80290 Munchen, Germany
ormoneit@inJormatik.tu-muenchen.de
Volker Tresp
Siemens AG
Central Research
81730 Munchen, Germany
Volker. Tresp@zJe.siemens.d... | 1036 |@word version:2 seems:1 advantageous:1 contraction:1 dramatic:1 tr:1 ld:1 selecting:1 interestingly:1 current:1 nowlan:1 attracted:1 written:1 numerical:1 partition:1 j1:6 analytic:1 update:3 resampling:5 intelligence:1 xk:4 beginning:1 lr:2 provides:1 toronto:1 five:2 viable:1 consists:5 fitting:1 expected:1 rou... |
43 | 1,037 | Quadratic-Type Lyapunov Functions for
Competitive Neural Networks with
Different Time-Scales
Anke Meyer-Base
Institute of Technical Informatics
Technical University of Darmstadt
Darmstadt, Germany 64283
Abstract
The dynamics of complex neural networks modelling the selforganization process in cortical maps must includ... | 1037 |@word mild:1 establish:1 concept:2 selforganization:1 normalized:1 evolution:2 lyapunov:28 exhibiting:1 symmetric:1 modifying:1 simulation:1 illustrated:1 centered:1 yty:1 traditional:1 ll:1 during:1 hassan:1 dii:1 self:3 exhibit:1 excitation:2 lateral:4 behaviour:1 darmstadt:2 criterion:1 efficacy:1 l__:2 biolog... |
44 | 1,038 | Stable Linear Approximations to
Dynamic Programming for Stochastic
Control Problems with Local Transitions
Benjamin Van Roy and John N. Tsitsiklis
Laboratory for Information and Decision Systems
Massachusetts Institute of Technology
Cambridge, MA 02139
e-mail: bvr@mit.edu, jnt@mit.edu
Abstract
We consider the solutio... | 1038 |@word version:3 f32:1 norm:7 seems:1 contraction:7 initial:1 celebrated:1 configuration:2 exclusively:1 nii:1 current:2 must:1 john:1 belmont:1 happen:1 update:1 rrt:1 fewer:1 guess:1 ith:2 steepest:1 record:1 lr:2 provides:1 parameterizations:1 become:2 prove:4 notably:1 expected:1 behavior:1 bellman:1 discounte... |
45 | 1,039 | Context-Dependent Classes in a Hybrid
Recurrent Network-HMM Speech
Recognition System
Dan Kershaw
Tony Robinson
Mike Hochberg ?
Cambridge University Engineering Department,
Trumpington Street, Cambridge CB2 1PZ, England.
Tel: [+44]1223332800, Fax: [+44]1223332662.
Email: djk.ajr@eng.cam.ac.uk
Abstract
A method for in... | 1039 |@word version:1 bigram:1 seems:1 eng:1 jacob:3 reduction:1 past:1 existing:1 current:2 contextual:1 must:2 drop:2 discrimination:1 fewer:1 cook:4 short:1 lexicon:1 lx:1 along:1 become:1 dan:1 manner:1 roughly:1 multi:1 becomes:1 lowest:1 tying:1 whilst:2 differing:1 finding:1 spoken:2 guarantee:1 every:1 scaled:1... |
46 | 104 | 728
DIGITAL REALISATION OF SELF-ORGANISING MAPS
Nigel M. Allinson
M~rtin J. Johnson
Department of Electronics
University of York
York
Y015DD
England
Kevin J. Moon
ABSTRACT
A digital realisation of two-dimensional self-organising feature
maps is presented.
The method is based on subspace
Weight vector
classification... | 104 |@word version:1 proportion:1 termination:1 simulation:2 reduction:1 electronics:1 swansea:1 current:1 must:2 readily:1 realize:1 subsequent:1 shape:1 fund:1 half:1 inspection:1 desktop:1 ith:1 compo:1 filtered:1 quantized:1 location:1 organising:8 hyperplanes:1 unbounded:1 c2:2 become:1 combine:1 mask:1 multi:1 br... |
47 | 1,040 | Empirical Entropy Manipulation for
Real-World Problems
Paul Viola: Nicol N. Schraudolph, Terrence J. Sejnowski
Computational Neurobiology Laboratory
The Salk Institute for Biological Studies
10010 North Torrey Pines Road
La Jolla, CA 92037-1099
viola@salk.edu
Abstract
No finite sample is sufficient to determine the de... | 1040 |@word trial:1 mri:12 polynomial:2 duda:2 grey:2 simulation:1 covariance:4 tr:1 reduction:1 contains:1 interestingly:1 existing:1 current:1 attracted:1 informative:1 plasticity:1 shape:1 wll:2 update:1 discrimination:1 half:1 selected:2 positron:1 oblique:1 record:1 mathematical:1 along:3 constructed:2 differentia... |
48 | 1,041 | The Geometry of Eye Rotations
and Listing's Law
Amir A. Handzel*
Tamar Flash t
Department of Applied Mathematics and Computer Science
Weizmann Institute of Science
Rehovot, 76100 Israel
Abstract
We analyse the geometry of eye rotations, and in particular
saccades, using basic Lie group theory and differential geometr... | 1041 |@word neurophysiology:1 briefly:2 eliminating:1 advantageous:2 bf:1 calculus:1 decomposition:2 mention:1 minus:1 contains:1 series:3 denoting:1 current:1 comparing:1 analysed:1 written:4 enables:2 leaf:1 amir:1 parameterization:2 plane:18 short:1 lr:1 provides:1 parameterizations:3 math:1 lx:4 cosets:1 mathematic... |
49 | 1,042 | Reinforcement Learning by Probability
Matching
Philip N. Sabes
Michael I. Jordan
sabes~psyche.mit.edu
jordan~psyche.mit.edu
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
Abstract
We present a new algorithm for associative reinforcement learning. The algorithm... | 1042 |@word trial:6 exploitation:2 version:1 advantageous:1 simulation:4 jacob:4 covariance:2 tr:2 solid:1 reduction:1 series:1 past:1 existing:3 nowlan:1 yet:1 dx:1 must:4 zll:1 dydx:5 designed:1 plot:1 update:11 v:1 half:1 selected:1 preference:1 height:1 along:1 qualitative:2 manner:1 peng:2 p8:8 expected:7 roughly:... |
50 | 1,043 | Neural Control for Nonlinear Dynamic Systems
Ssu-Hsin Yu
Department of Mechanical Engineering
Massachusetts Institute of Technology
Cambridge, MA 02139
Email: hsin@mit.edu
Anuradha M. Annaswamy
Department of Mechanical Engineering
Massachusetts Institute of Technology
Cambridge, MA 02139
Email: aanna@mit.edu
Abstrac... | 1043 |@word mjp:1 open:4 simulation:5 linearized:1 recursively:1 ld:1 initial:5 past:1 surprising:1 written:2 must:1 bd:1 belmont:1 pertinent:2 along:1 constructed:1 direct:1 differential:1 compose:1 introduce:1 actual:1 pf:1 becomes:1 bounded:3 ttl:1 minimizes:1 developed:1 guarantee:1 every:6 y3:2 expands:1 control:2... |
51 | 1,044 | Learning with ensembles: How
over-fitting can be useful
Peter Sollich
Department of Physics
University of Edinburgh, U.K.
P.SollichGed.ac.uk
Anders Krogh'"
NORDITA, Blegdamsvej 17
2100 Copenhagen, Denmark
kroghGsanger.ac.uk
Abstract
We study the characteristics of learning with ensembles. Solving
exactly the simple ... | 1044 |@word briefly:1 achievable:1 seems:1 advantageous:1 stronger:1 confirms:1 solid:2 contains:1 denoting:1 written:1 realistic:4 additive:2 remove:1 plot:4 update:1 alone:1 implying:1 t2j:1 become:2 fitting:3 indeed:1 globally:4 decreasing:1 considering:1 increasing:1 becomes:1 provided:1 confused:1 kind:1 akl:3 sub... |
52 | 1,045 | SEEMORE: A View-Based Approach to
3-D Object Recognition Using Multiple
Visual Cues
Bartlett W. Mel
Department of Biomedical Engineering
University of Southern California
Los Angeles, CA 90089
mel@quake.usc.edu
Abstract
A neurally-inspired visual object recognition system is described
called SEEMORE, whose goal is to ... | 1045 |@word trial:3 proportion:1 open:2 cos2:1 simplifying:1 configuration:1 contains:1 series:5 surprising:1 must:2 subsequent:1 numerical:1 informative:1 shape:33 v:1 cue:3 half:1 leaf:1 plane:5 sys:1 ith:1 oblique:1 record:1 mental:1 coarse:1 lx:1 belt:1 five:1 edelman:3 consists:2 qualitative:1 dan:1 verbalize:1 re... |
53 | 1,046 | Analog VLSI Processor Implementing the
Continuous Wavelet Transform
R. Timothy Edwards and Gert Cauwenberghs
Department of Electrical and Computer Engineering
Johns Hopkins University
3400 North Charles Street
Baltimore, MD 21218-2686
{tim,gert}@bach.ece.jhu.edu
Abstract
We present an integrated analog processor for ... | 1046 |@word illustrating:1 middle:2 inversion:1 simulation:2 decomposition:12 hannonic:1 fonn:1 solid:2 accommodate:1 electronics:1 liu:1 imaginary:1 yet:2 john:1 subsequent:1 shape:1 pertinent:1 remove:2 designed:1 v:1 filtered:7 successive:1 mathematical:1 consists:1 overhead:1 fitting:1 expected:2 actual:1 lyon:2 pr... |
54 | 1,047 | Selective Attention for Handwritten
Digit Recognition
Ethem Alpaydm
Department of Computer Engineering
Bogazi<1i U ni versi ty
Istanbul, TR-SOS15 Turkey
alpaydin@boun.edu.tr
Abstract
Completely parallel object recognition is NP-complete. Achieving
a recognizer with feasible complexity requires a compromise between pa... | 1047 |@word seems:1 simulation:1 propagate:1 covariance:1 attended:1 brightness:1 tr:3 carry:1 reduction:1 initial:1 series:1 bitmap:1 current:3 activation:1 conjunctive:1 blur:3 motor:2 designed:1 update:2 fund:1 implying:1 half:1 fewer:1 cue:1 filtered:1 coarse:1 location:4 successive:1 ron:1 simpler:1 rc:1 skilled:1... |
55 | 1,048 | Gaussian Processes for Regression
Christopher K. I. Williams
Neural Computing Research Group
Aston University
Birmingham B4 7ET, UK
Carl Edward Rasmussen
Department of Computer ,Science
University of Toronto
Toronto , ONT, M5S lA4, Canada
c.k.i.williams~aston.ac.uk
carl~cs.toronto.edu
Abstract
The Bayesian analysi... | 1048 |@word version:3 inversion:2 seems:1 simulation:1 covariance:19 thereby:1 initial:1 series:2 wd:1 discretization:1 must:1 additive:1 girosi:4 plot:4 implying:1 stationary:1 hamiltonian:2 draft:1 provides:1 toronto:4 lx:2 five:3 constructed:1 become:1 differential:1 consists:1 manner:1 ont:1 encouraging:1 little:1 ... |
56 | 1,049 | Modern Analytic Techniques to Solve the
Dynamics of Recurrent Neural Networks
A.C.C. Coolen
Dept. of Mathematics
King's College London
Strand, London WC2R 2LS, U.K.
S.N. Laughton
Dept. of Physics - Theoretical Physics
University of Oxford
1 Keble Road, Oxford OX1 3NP, U.K.
D. Sherrington ..
Center for Non-linear Stu... | 1049 |@word version:6 closure:2 r:9 simulation:10 paid:1 thereby:1 solid:6 initial:1 current:1 comparing:1 nt:1 yet:1 ddc:1 written:1 numerical:1 subsequent:1 analytic:4 drop:1 slowing:2 plane:3 complication:2 location:2 simpler:1 mathematical:1 along:1 become:1 qualitative:1 consists:2 indeed:1 xji:1 themselves:1 mech... |
57 | 105 | 340
BACKPROPAGATION AND ITS
APPLICATION TO HANDWRITTEN
SIGNATURE VERIFICATION
Dorothy A. Mighell
Electrical Eng. Dept.
Info. Systems Lab
Stanford University
Stanford, CA 94305
Timothy S. Wilkinson
Electrical Eng. Dept.
Info. Systems Lab
Stanford University
Stanford, CA 94305
Joseph W. Goodman
Electrical Eng. Dept.
I... | 105 |@word eliminating:1 nd:1 confirms:1 simulation:2 eng:3 pressure:2 initial:4 score:1 selecting:1 offering:1 document:2 protection:1 yet:1 written:5 must:1 john:1 shape:1 plot:6 fund:1 update:1 v:1 alone:1 implying:1 discrimination:1 device:2 short:1 record:1 accepting:1 lr:1 detecting:1 math:1 ire:1 five:2 consists... |
58 | 1,050 | Family Discovery
Stephen M. Omohundro
NEC Research Institute
4 Independence Way, Princeton, NJ 08540
om@research.nj.nec.com
Abstract
"Family discovery" is the task of learning the dimension and structure of a parameterized family of stochastic models. It is especially appropriate when the training examples are partit... | 1050 |@word covariance:1 jacob:4 selecting:2 document:1 com:1 additive:1 partition:1 speakerindependent:1 discrimination:1 generative:1 selected:1 parameterization:3 plane:1 xk:1 provides:1 toronto:1 successive:2 simpler:1 five:3 consists:1 pmap:1 fitting:1 manner:2 alspector:1 formants:1 spherical:1 p9:2 becomes:1 pro... |
59 | 1,051 | Neural Networks with Quadratic VC
Dimension
Pascal Koiran*
Lab. de l'Informatique du Paraltelisme
Ecole Normale Superieure de Lyon - CNRS
69364 Lyon Cedex 07, France
Eduardo D. Sontag t
Department of Mathematics
Rutgers University
New Brunswick, NJ 08903, USA
Abstract
This paper shows that neural networks which use c... | 1051 |@word version:2 polynomial:3 open:4 simulation:1 tr:10 recursively:1 exclusively:1 chervonenkis:4 ecole:1 current:2 surprising:1 activation:15 assigning:1 must:2 partition:1 predetermined:1 shape:1 rote:1 guess:1 warmuth:1 completeness:1 node:18 sigmoidal:6 shatter:6 constructed:1 c2:2 predecessor:1 symposium:1 c... |
60 | 1,052 | Learning the structure of similarity
Joshua B. Tenenbaum
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
jbt~psyche.mit.edu
Abstract
The additive clustering (ADCL US) model (Shepard & Arabie, 1979)
treats the similarity of two stimuli as a weighted additive measure... | 1052 |@word trial:2 middle:1 proportion:1 seek:1 accounting:3 b39:1 arti:1 concise:1 tr:1 configuration:14 subjective:2 current:4 wd:1 recovered:9 assigning:1 written:2 realistic:2 numerical:3 additive:15 treating:2 generative:5 discovering:2 fewer:1 leaf:1 complementing:1 supplying:1 provides:1 contribute:1 toronto:1 ... |
61 | 1,053 | Reorganisation of Somatosensory Cortex after
Tactile Training
Rasmus S. Petersen
John G. Taylor
Centre for Neural Networks, King's College London
Strand, London WC2R 2LS, UK
Abstract
Topographic maps in primary areas of mammalian cerebral cortex reorganise as a result of behavioural training. The nature of this reorg... | 1053 |@word seems:3 disk:2 simulation:3 attended:1 mammal:1 minus:1 series:1 bc:1 past:1 analysed:1 dx:2 must:1 john:1 beginning:1 short:1 compo:1 math:2 location:2 sigmoidal:1 edelman:1 consists:1 manner:2 roughly:1 brain:2 decreasing:1 becomes:1 discover:1 underlying:2 monkey:3 ought:1 laterally:1 willshaw:3 ro:8 uk:... |
62 | 1,054 | Implementation Issues in the Fourier
Transform Algorithm
Yishay Mansour" Sigal Sahar t
Computer Science Dept.
Tel-Aviv University
Tel-Aviv, ISRAEL
Abstract
The Fourier transform of boolean functions has come to play an
important role in proving many important learnability results. We
aim to demonstrate that the Fouri... | 1054 |@word trial:2 briefly:3 polynomial:4 instrumental:1 norm:1 thereby:2 recursively:1 series:1 pub:2 prefix:4 merrick:2 shape:1 enables:1 plot:1 implying:1 selected:1 guess:1 ith:1 smith:1 provides:3 math:1 node:1 contribute:2 lx:1 accessed:1 supply:2 consists:1 introduce:2 deteriorate:1 indeed:1 expected:5 xz:1 dec... |
63 | 1,055 | Adaptive Retina with Center-Surround
Receptive Field
Shih-Chii Lin and Kwabena Boahen
Computation and Neural Systems
139-74 California Institute of Technology
Pasadena, CA 91125
shih@pcmp.caltech.edu, buster@pcmp.caltech.edu
Abstract
Both vertebrate and invertebrate retinas are highly efficient in extracting contrast... | 1055 |@word middle:1 version:2 open:1 grey:1 sensed:2 excited:4 tr:1 initial:3 liu:3 past:6 bradley:1 current:15 analysed:1 subsequent:1 remove:1 discrimination:1 reciprocal:1 node:2 five:3 c2:1 direct:2 supply:1 symposium:1 consists:5 behavior:3 p1:1 monopolar:2 vertebrate:12 becomes:1 project:1 increasing:2 provided:... |
64 | 1,056 | Forward-backward retraining of recurrent
neural networks
Andrew Senior ?
Tony Robinson
Cambridge University Engineering Department
Trumpington Street, Cambridge, England
Abstract
This paper describes the training of a recurrent neural network
as the letter posterior probability estimator for a hidden Markov
model, off... | 1056 |@word middle:1 retraining:8 eng:1 recursively:1 necessity:1 initial:4 series:4 pub:2 document:3 comparing:1 activation:4 must:6 written:2 subsequent:1 shape:2 remove:1 half:2 postal:1 lexicon:1 successive:1 five:1 height:2 constructed:1 become:1 incorrect:1 consists:1 manner:1 indeed:1 multi:2 automatically:1 act... |
65 | 1,057 | When is an Integrate-and-fire Neuron
like a Poisson Neuron?
Charles F. Stevens
Salk Institute MNL/S
La Jolla, CA 92037
cfs@salk.edu
Anthony Zador
Salk Institute MNL/S
La Jolla, CA 92037
zador@salk.edu
Abstract
In the Poisson neuron model, the output is a rate-modulated Poisson process (Snyder and Miller, 1991); the... | 1057 |@word trial:1 nd:1 minus:1 solid:1 initial:5 current:3 yet:6 must:3 interspike:1 shape:2 asymptote:1 alone:1 short:5 farther:1 filtered:2 provides:1 sigmoidal:1 ouput:2 qualitative:2 retrieving:1 behavior:3 themselves:1 little:1 increasing:1 begin:1 agnostic:1 what:3 ret:6 temporal:1 quantitative:1 exactly:2 k2:2... |
66 | 1,058 | From Isolation to Cooperation:
An Alternative View of a System of Experts
Stefan Schaal:!:*
sschaal@cc.gatech.edu
http://www.cc.gatech.eduifac/Stefan.Schaal
Christopher C. Atkeson:!:
cga@cc.gatech.edu
http://www.cc.gatech.eduifac/Chris.Atkeson
+College of Computing, Georgia Tech, 801 Atlantic Drive, Atlanta, GA 30332... | 1058 |@word eliminating:1 casdagli:1 dekker:1 simulation:3 jacob:4 solid:1 reduction:2 initial:1 configuration:1 series:2 atlantic:1 err:3 current:2 diagonalized:1 nowlan:2 activation:4 written:1 must:1 eduifac:2 subsequent:1 happen:1 additive:1 shape:5 enables:1 motor:1 update:9 pursued:1 greedy:1 intelligence:1 iso:2... |
67 | 1,059 | From Isolation to Cooperation:
An Alternative View of a System of Experts
Stefan Schaal:!:*
sschaal@cc.gatech.edu
http://www.cc.gatech.eduifac/Stefan.Schaal
Christopher C. Atkeson:!:
cga@cc.gatech.edu
http://www.cc.gatech.eduifac/Chris.Atkeson
+College of Computing, Georgia Tech, 801 Atlantic Drive, Atlanta, GA 30332... | 1059 |@word version:1 eliminating:1 seems:1 casdagli:1 dekker:1 simulation:3 tried:1 jacob:4 pick:4 solid:1 recursively:1 ld:1 reduction:2 initial:3 configuration:2 series:7 atlantic:1 err:3 current:3 diagonalized:1 nowlan:2 activation:4 yet:1 written:2 must:2 eduifac:2 subsequent:1 happen:1 additive:1 shape:6 enables:... |
68 | 106 | 2
CONSTRAINTS ON ADAPTIVE NETWORKS
FOR MODELING HUMAN GENERALIZATION
M. Pavel
Mark A. Gluck
Van Henkle
Departm?1Il of Psychology
Stanford University
Stanford. CA 94305
ABSTRACT
The potential of adaptive networks to learn categorization rules and to
model human performance is studied by comparing how natural and
a... | 106 |@word trial:2 hampson:2 briefly:1 version:1 proportion:5 simulation:1 pavel:14 solid:1 initial:17 configuration:2 comparing:1 activation:3 assigning:1 must:1 half:2 selected:2 indicative:1 rehder:1 provides:1 characterization:1 node:3 ames:1 preference:1 draft:1 lor:2 mathematical:1 loll:2 incorrect:4 consists:1 e... |
69 | 1,060 | Statistical Theory of Overtraining - Is
Cross-Validation Asymptotically
Effective?
s. Amari, N. Murata, K.-R. Miiller*
Dept. of Math. Engineering and Inf. Physics, University of Tokyo
Hongo 7-3-1, Bunkyo-ku, Tokyo 113, Japan
M. Finke
Inst. f. Logik , University of Karlsruhe
76128 Karlsruhe, Germany
H. Yang
Lab . f. In... | 1060 |@word trial:1 version:1 advantageous:1 d2:1 simulation:8 covariance:1 tr:2 kappen:2 initial:3 rapt:1 comparing:1 yet:1 numerical:1 partition:1 girosi:2 analytic:1 xk:1 short:1 math:1 firstly:1 mathematical:1 constructed:1 direct:1 consists:1 compose:2 ray:10 theoretically:1 ofwo:1 behavior:3 elman:1 nor:1 brain:1... |
70 | 1,061 | Stable Dynamic Parameter Adaptation
Stefan M. Riiger
Fachbereich Informatik, Technische Universitat Berlin
Sekr. FR 5-9, Franklinstr. 28/29
10587 Berlin, Germany
async~cs. tu-berlin.de
Abstract
A stability criterion for dynamic parameter adaptation is given. In
the case of the learning rate of backpropagation, a clas... | 1061 |@word trial:1 version:1 norm:1 uncovers:1 jacob:1 initial:3 series:1 genetic:1 outperforms:1 urgently:1 dx:1 must:2 numerical:1 motor:1 update:1 greedy:1 fewer:1 leaf:1 vanishing:1 provides:1 node:1 location:1 along:2 c2:1 differential:4 symposium:1 prove:1 overhead:2 expected:4 indeed:2 behavior:2 relying:1 glob... |
71 | 1,062 | Universal Approximation and Learning
of Trajectories Using Oscillators
Pierre Baldi*
Division of Biology
California Institute of Technology
Pasadena, CA 91125
pfbaldi@juliet.caltech.edu
Kurt Hornik
Technische Universitat Wien
Wiedner Hauptstra8e 8-10/1071
A-1040 Wien, Austria
Kurt.Hornik@tuwien.ac.at
Abstract
Natura... | 1062 |@word trial:1 briefly:3 polynomial:3 norm:1 nd:1 open:3 willing:1 meansquare:1 decomposition:2 pick:1 juliet:1 initial:3 configuration:1 series:2 kurt:3 comparing:1 yet:1 must:4 readily:1 written:1 fn:1 visible:3 subsequent:1 analytic:3 motor:1 leaf:1 amir:1 plane:2 short:2 iterates:1 sigmoidal:1 along:2 construc... |
72 | 1,063 | Learning Fine Motion by Markov
Mixtures of Experts
Marina Meilii
Dept. of Elec. Eng . and Computer Sci.
Massachussetts Inst . of Technology
Cambridge, MA 02139
mmp@ai .mit.edu
Michael I. J Ol'dan
Dept.of Brain and Cognitive Sciences
Massachussetts Inst. of Technology
Cambridge, MA 02139
jordan@psyche.mit .edu
Abstra... | 1063 |@word version:1 simulation:5 eng:1 jacob:2 moment:1 initial:4 configuration:5 contains:1 outperforms:1 current:3 discretization:1 nowlan:1 yet:1 must:1 additive:1 happen:1 partition:2 shape:2 meilii:1 update:1 selected:1 vmin:1 sys:1 node:1 tems:1 direct:1 dan:1 expected:1 planning:1 ol:1 brain:1 little:1 actual:... |
73 | 1,064 | Estimating the Bayes Risk from Sample Data
Robert R. Snapp? and Tong Xu
Computer Science and Electrical Engineering Department
University of Vermont
Burlington, VT 05405
Abstract
A new nearest-neighbor method is described for estimating the Bayes risk
of a multiclass pattern claSSification problem from sample data (... | 1064 |@word trial:2 briefly:1 duda:3 series:1 contains:2 selecting:1 dx:1 john:1 predetermined:1 analytic:1 enables:1 discrimination:2 stationary:1 intelligence:2 provides:1 math:1 consulting:1 five:1 c2:2 constructed:1 incorrect:1 manner:1 expected:2 behavior:2 nor:1 examine:1 ol:1 resolve:1 increasing:1 begin:1 estim... |
74 | 1,065 | A Unified Learning Scheme:
Bayesian-Kullback Ying-Yang Machine
Lei Xu
1. Computer Science Dept., The Chinese University of HK, Hong Kong
2. National Machine Perception Lab, Peking University, Beijing
Abstract
A Bayesian-Kullback learning scheme, called Ying-Yang Machine,
is proposed based on the two complement but eq... | 1065 |@word kong:1 nd:1 rint:1 open:2 heuristically:1 initial:1 xiy:2 selecting:2 interestingly:1 existing:9 si:7 fertilization:1 update:2 item:1 provides:1 clarified:1 symposium:1 consists:2 fitting:5 indeed:1 multi:1 window:2 munder:1 becomes:1 estimating:1 moreover:3 p02:1 minimizes:1 developed:2 unified:13 temporal... |
75 | 1,066 | On Neural Networks with Minimal
Weights
J ehoshua Bruck
Vasken Bohossian
California Institute of Technology
Mail Code 136-93
Pasadena, CA 91125
E-mail: {vincent, bruck }?Iparadise. cal tech. edu
Abstract
Linear threshold elements are the basic building blocks of artificial
neural networks. A linear threshold elemen... | 1066 |@word polynomial:5 ecole:1 comparing:1 written:4 v:1 implying:1 inspection:1 smith:3 ire:1 math:2 mathematical:2 rc:1 prove:4 consists:1 interscience:1 indeed:2 behavior:1 growing:3 brain:1 inspired:1 goldman:2 circuit:8 developed:2 finding:2 subclass:1 growth:2 tie:1 unit:1 grant:2 engineering:2 limit:2 switchin... |
76 | 1,067 | SPERT-II: A Vector Microprocessor
System and its Application to Large
Problems in Backpropagation Training
John Wawrzynek, Krste Asanovic, & Brian Kingsbury
University of California at Berkeley
Department of Electrical Engineering and Computer Sciences
Berkeley, CA 94720-1776
{johnw ,krste,bedk }@cs.berkeley.edu
James... | 1067 |@word coprocessor:4 version:2 nd:1 instruction:15 r:5 invoking:1 thereby:1 contains:5 existing:2 current:3 timer:1 activation:6 issuing:1 must:3 written:3 john:3 readily:1 datapath:2 cant:1 enables:1 designed:1 update:1 alone:1 selected:1 device:3 sram:2 core:2 provides:3 math:1 ron:1 kingsbury:4 constructed:1 ov... |
77 | 1,068 | A Neural Network Model of 3-D
Lightness Perception
Luiz Pessoa
Federal Univ. of Rio de Janeiro
Rio de Janeiro, RJ, Brazil
pessoa@cos.ufrj.br
William D. Ross
Boston University
Boston, MA 02215
bill@cns.bu.edu
Abstract
A neural network model of 3-D lightness perception is presented
which builds upon the FACADE Theory ... | 1068 |@word neurophysiology:1 middle:3 stronger:1 disk:2 simulation:8 excited:1 brightness:8 minus:2 reduction:1 initial:3 configuration:4 disparity:2 zij:1 tuned:1 rightmost:2 current:3 comparing:1 activation:4 aside:1 cue:2 provides:2 location:1 direct:1 shapley:2 pathway:1 mccann:2 brain:1 detects:1 retinotopic:1 ma... |
78 | 1,069 | How Perception Guides Production
Birdsong Learning
?
In
Christopher L. Fry
cfry@cogsci.ucsd.edu
Department of Cognitive Science
University of California at San Diego
La Jolla, CA 92093-0515
Abstract
A c.:omputational model of song learning in the song sparrow
(M elospiza melodia) learns to categorize the different ... | 1069 |@word trial:1 replicate:1 reused:1 essay:1 gradual:1 pick:1 solid:1 n8:3 initial:2 current:1 anterior:2 activation:2 refines:1 underly:1 motor:7 plot:1 medial:1 half:1 selected:1 short:2 institution:1 provides:3 smithsonian:1 preference:3 five:1 sustained:1 wild:1 pathway:8 ra:5 behavior:2 elman:1 frequently:1 br... |
79 | 107 | 323
NEURAL NETWORK RECOGNIZER FOR
HAND-WRITTEN ZIP CODE DIGITS
J. S. Denker, W. R. Gardner, H. P. Graf, D. Henderson, R. E. Howard,
W. Hubbard, L. D. Jackel, H. S. Baird, and I. Guyon
AT &T Bell Laboratories
Holmdel, New Jersey 07733
ABSTRACT
This paper describes the construction of a system that recognizes hand-prin... | 107 |@word version:1 pw:5 wiesel:2 duda:2 grey:1 tried:1 solid:1 moment:3 configuration:1 contains:1 substitution:1 comparing:1 activation:1 yet:2 written:9 must:6 john:2 realistic:1 happen:1 predetermined:1 shape:1 remove:5 designed:3 reproducible:1 intelligence:1 coarse:2 provides:1 contribute:2 location:5 postal:2 a... |
80 | 1,070 | A Bound on the Error of Cross Validation Using
the Approximation and Estimation Rates, with
Consequences for the Training-Test Split
Michael Kearns
AT&T Research
1 INTRODUCTION
We analyze the performance of cross validation 1 in the context of model selection and
complexity regularization. We work in a setting in whic... | 1070 |@word mild:1 trial:2 version:2 polynomial:2 seems:2 contains:1 score:1 chervonenkis:2 pprox:1 mari:1 unction:1 must:2 realistic:1 partition:1 refuted:1 remove:1 plot:24 progressively:1 v:1 tenn:3 selected:2 dun:1 coarse:1 ron:1 sigmoidal:1 become:1 qualitative:6 shorthand:1 consists:2 combine:1 fitting:6 introduc... |
81 | 1,071 | A Model of Spatial Representations in
Parietal Cortex Explains Hemineglect
Alexandre Pouget
Dept of Neurobiology
UCLA
Los Angeles, CA 90095-1763
alex@salk.edu
Terrence J. Sejnowski
Howard Hughes Medical Institute
The Salk Institute
La Jolla, CA 92037
terry@salk.edu
Abstract
We have recently developed a theory of spa... | 1071 |@word replicate:1 bf:6 thereby:2 solid:2 contains:2 selecting:2 tuned:2 interestingly:1 rightmost:1 reaction:8 current:1 must:1 motor:1 designed:1 alone:1 half:1 selected:6 item:5 short:1 location:8 c2:9 asanuma:1 fixation:1 expected:2 behavior:7 nor:1 brain:3 considering:1 project:2 retinotopic:19 moreover:1 wha... |
82 | 1,072 | Dynamics of On-Line Gradient Descent
Learning for Multilayer Neural Networks
David Saad"
Dept. of Comp o Sci. & App. Math.
Aston University
Birmingham B4 7ET, UK
Sara A. Solla t
CONNECT, The Niels Bohr Institute
Blegdamsdvej 17
Copenhagen 2100, Denmark
Abstract
We consider the problem of on-line gradient descent lea... | 1072 |@word norm:7 bf:1 proportionality:1 r:1 linearized:1 bn:8 initial:3 o2:1 current:1 activation:6 written:2 numerical:1 subsequent:1 analytic:2 update:1 leaf:1 imitate:3 imitated:2 trapping:3 xk:2 isotropic:2 math:2 node:22 successive:1 height:1 differential:1 become:1 behavior:3 themselves:1 examine:1 decreasing:1... |
83 | 1,073 | Improving Elevator Performance Using
Reinforcement Learning
Robert H. Crites
Computer Science Department
University of Massachusetts
Amherst, MA 01003-4610
critesGcs.umass.edu
Andrew G. Barto
Computer Science Department
University of Massachusetts
Amherst, MA 01003-4610
bartoGcs.umass.edu
Abstract
This paper describ... | 1073 |@word version:1 seems:1 open:1 hu:1 simulation:2 pick:1 pressed:1 configuration:1 contains:1 uma:2 omniscient:1 past:1 current:2 activation:3 yet:1 attracted:1 must:2 john:1 subsequent:3 nq:2 reciprocal:1 complication:2 location:1 grupen:1 specialize:1 headed:1 inter:2 notably:1 expected:3 elman:1 examine:1 simul... |
84 | 1,074 | Visual gesture-based robot guidance
with a modular neural system
E. Littmann,
A. Drees, and H. Ritter
Abt. Neuroinformatik, Fak. f. Informatik
Universitat Ulm, D-89069 Ulm, FRG
enno@neuro.informatik.uni-ulm.de
AG Neuroinformatik, Techn. Fakultat
Univ. Bielefeld, D-33615 Bielefeld, FRG
andrea,helge@techfak.uni-biele... | 1074 |@word exploitation:1 mee:4 simulation:1 crucially:1 jacob:2 pick:4 euclidian:1 configuration:1 loc:1 meyering:2 current:5 comparing:1 nowlan:2 yet:1 tackling:1 must:2 john:1 visible:1 subsequent:2 realistic:2 shape:1 designed:2 discrimination:1 device:5 filtered:1 provides:2 node:5 location:18 along:2 consists:3 ... |
85 | 1,075 | Improving Committee Diagnosis with
Resampling Techniques
Bambang Parmanto
Department of Information Science
University of Pittsburgh
Pittsburgh, PA 15260
parmanto@li6.pitt. edu
Paul W. Munro
Department of Information Science
University of Pittsburgh
Pittsburgh, PA 15260
munro@li6.pitt. edu
Howard R. Doyle
Pittsburgh... | 1075 |@word repository:2 middle:2 replicate:5 confirms:1 simulation:1 tr:2 outlook:1 reduction:4 initial:1 cytology:1 contains:2 pub:1 existing:1 current:1 partition:1 benign:2 shape:1 resampling:6 v:1 half:2 item:16 along:1 constructed:2 consists:2 combine:1 expected:3 roughly:1 decreasing:1 project:1 estimating:1 fin... |
86 | 1,076 | Learning Sparse Perceptrons
Jeffrey C. Jackson
Mathematics & Computer Science Dept.
Duquesne University
600 Forbes Ave
Pittsburgh, PA 15282
jackson@mathcs.duq.edu
Mark W. Craven
Computer Sciences Dept.
University of Wisconsin-Madison
1210 West Dayton St.
Madison, WI 53706
craven@cs.wisc.edu
Abstract
We introduce a n... | 1076 |@word version:2 polynomial:5 seems:1 nd:3 open:1 termination:1 selecting:1 interestingly:1 current:1 yet:2 conjunctive:1 must:3 readily:1 cruz:1 remove:1 designed:1 v:1 intelligence:2 fewer:1 boosting:16 lor:1 symposium:1 prove:1 specialize:1 consists:1 introduce:1 pairwise:1 nor:1 frequently:2 multi:5 brain:2 co... |
87 | 1,077 | A Neural Network Autoassociator for
Induction Motor Failure Prediction
Thomas Petsche, Angelo Marcantonio, Christian Darken,
Stephen J. Hanson, Gary M. Kuhn and Iwan Santoso
[PETSCHE, ANGELO, DARKEN, JOSE, GMK, NIS]@SCR.SIEMENS.COM
Siemens Corporate Research, Inc.
755 College Road East
Princeton, NJ 08853
Abstract
We... | 1077 |@word autoassociator:17 schoen:2 hippocampus:2 initial:1 contains:2 series:2 existing:3 current:10 com:1 protection:1 must:1 periodically:1 christian:1 motor:104 designed:7 half:3 selected:4 intelligence:1 core:1 record:2 filtered:2 location:1 five:4 along:1 supply:1 ik:2 behavior:1 frequently:2 nor:1 detects:1 d... |
88 | 1,078 | Modeling Interactions of the Rat's Place and
Head Direction Systems
A. David Redish and David S. Touretzky
Computer Science Department & Center for the Neural Basis of Cognition
Carnegie Mellon University, Pittsburgh PA 15213-3891
Internet: {dredi sh, ds t}@es . emu. edu
Abstract
We have developed a computational the... | 1078 |@word cylindrical:2 trial:4 faculty:1 open:2 simulation:3 smolen:1 initial:6 denoting:1 tuned:4 ranck:1 current:6 anterior:2 must:6 enables:1 alone:2 cue:30 leaf:1 smith:1 record:1 mental:1 location:9 disoriented:3 five:2 along:1 viable:2 pairing:1 behavioral:3 behavior:1 elman:1 integrator:25 brain:1 food:1 pane... |
89 | 1,079 | Active Gesture Recognition using
Learned Visual Attention
Trevor Darrell and Alex Pentland
Perceptual Computing Group
MIT Media Lab
20 Ames Street, Cambridge MA, 02138
trevor,sandy~media.mit.edu
Abstract
We have developed a foveated gesture recognition system that runs
in an unconstrained office environment with an ac... | 1079 |@word trial:2 briefly:1 maes:1 tr:2 configuration:2 score:1 selecting:1 existing:1 current:3 contextual:1 must:1 girosi:1 motor:2 update:2 selected:1 accordingly:1 plane:2 mccallum:2 farther:1 compo:2 provides:2 coarse:1 ames:1 location:6 along:1 direct:1 driver:1 surprised:1 consists:2 combine:2 indeed:1 behavio... |
90 | 108 | 618
NEURAL NETWORKS FOR MODEL
MATCHING AND PERCEPTUAL
ORGANIZATION
Gene Gindi
EE Department
Yale University
New Haven, CT 06520
Eric Mjolsness
CS Department
Yale University
New Haven, CT 06520
P. Anandan
CS Department
Yale University
New Haven, CT 06520
ABSTRACT
We introduce an optimization approach for solving pro... | 108 |@word polynomial:1 simulation:1 solid:1 carry:1 yaleu:1 initial:1 contains:2 must:1 john:1 plasticity:1 shape:1 cheap:1 discrimination:2 intelligence:2 parameterization:1 plane:6 short:1 pointer:5 provides:1 location:2 along:3 m7:1 consists:1 behavioral:1 recognizable:1 manner:1 introduce:1 expected:3 brain:3 prec... |
91 | 1,080 | Symplectic Nonlinear Component
Analysis
Lucas C. Parra
Siemens Corporate Research
755 College Road East, Princeton, NJ 08540
lucas@scr.siemens.com
Abstract
Statistically independent features can be extracted by finding a factorial representation of a signal distribution. Principal Component
Analysis (PCA) accomplishe... | 1080 |@word version:1 polynomial:1 nd:2 additively:1 covariance:1 solid:2 papoulis:2 reduction:2 moment:3 series:2 com:1 activation:1 additive:3 partition:3 analytic:1 update:1 realizing:1 hermite:1 introduce:1 ica:1 abscissa:1 mechanic:1 multi:3 company:2 considering:4 estimating:2 linearity:1 kind:1 substantially:2 f... |
92 | 1,081 | Using the Future to "Sort Out" the
Present: Rankprop and Multitask
Learning for Medical Risk Evaluation
Rich Caruana, Shumeet Baluja, and Tom Mitchell
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213
(caruana, baluja, mitchell)@cs.cmu.edu
Abstract
A patient visits the doctor; the doctor re... | 1081 |@word multitask:11 trial:4 economically:2 achievable:1 prognostic:1 sex:1 pulse:1 pressure:1 asks:1 contains:2 lave:1 current:1 si:1 yet:2 lang:1 must:4 alone:2 short:1 prespecified:1 record:1 detecting:1 location:1 preference:1 simpler:3 hospitalized:6 five:1 admission:1 become:2 acquired:1 market:1 overtrain:1 ... |
93 | 1,082 | Prediction of Beta Sheets in Proteins
Anders Krogh
The Sanger Centre
Hinxton, Carobs CBIO IRQ, UK.
Email: krogh@sanger.ac. uk
S~ren Kamaric Riis
Electronics Institute, Building 349
Technical University of Denmark
2800 Lyngby, Denmark
Email: riis@ei.dtu.dk
Abstract
Most current methods for prediction of protein second... | 1082 |@word version:1 middle:1 seems:1 electronics:1 configuration:2 contains:1 initial:3 current:2 surprising:1 activation:1 si:7 written:1 must:1 hypothesize:1 implying:1 alone:1 leaf:1 selected:2 greedy:1 tertiary:1 compo:1 firstly:1 along:2 beta:4 qij:1 consists:2 combine:2 introduce:1 inspired:1 globally:1 encoura... |
94 | 1,083 | Unsupervised Pixel-prediction
William R. Softky
Math Resp.arch Branch
NIDDK, NIH
9190 Wisconsin Ave #350
Bethesda, MD 20814
bill@homer.niddk.nih.gov
Abstract
When a sensory system constructs a model of the environment
from its input, it might need to verify the model's accuracy. One
method of verification is multivar... | 1083 |@word middle:3 propagate:1 brightness:1 pick:1 thereby:1 configuration:1 series:2 contains:2 tuned:11 existing:1 current:1 comparing:2 contextual:1 must:3 subsequent:1 designed:1 v:1 generative:1 half:1 discovering:1 nervous:1 provides:1 math:1 coarse:2 location:2 five:1 constructed:1 iverson:1 become:3 different... |
95 | 1,084 | High-Speed Airborne Particle Monitoring
Using Artificial Neural Networks
Alistair Ferguson
ERDC, Univ. of Hertfordshire
A.Ferguson@herts.ac.uk
Theo Sabisch
Dept. Electrical and Electronic Eng.
Univ. of Hertfordshire
Paul Kaye
ERDC, Univ. of Hertfordshire
Laurence C. Dixon
NOC, Univ. of Hertfordshire
Hamid Bolouri
... | 1084 |@word laurence:1 grey:5 tried:1 eng:1 bolouri:4 incurs:1 electronics:1 initial:1 offering:1 current:2 noc:1 activation:2 si:1 scatter:1 must:2 j1:1 shape:6 plot:1 selected:2 device:5 steepest:1 short:1 node:10 location:4 traverse:1 sigmoidal:2 constructed:1 consists:1 overhead:2 notably:1 expected:1 spherical:1 l... |
96 | 1,085 | ?
?
(
?!?
)*
+
,
?
)-
.0/
% 21
?
,
/
!
354687
?
#"$&% '
*:9
<;
?
='>@? ACBEDGFIHGJKFLHEMN DGOQPRFQSUTV
WX? YZ>\[[]>@^-? _a` D
bdcfehg-ikjmlXconIj0prqCs0ptl eIuQjvcxwzy\{x| }~c]n{??
? w ?x?@p-n yhj g jvc??5nh| ???@cxw... | 1085 |@word knd:5 ona:1 lup:1 wog:1 t_:2 r:2 gfih:1 pg:3 q1:1 d3d:2 n8:1 ld:1 t7:1 k1d:10 bc:1 ka:2 z2:4 cxn:1 si:5 bd:4 ctn:2 wx:3 j1:2 xb1:1 gv:2 dcfe:1 yr:1 nq:6 rts:1 xk:2 mef:1 ik:1 ghi:1 g4:1 opc:1 chi:2 ijw:1 jm:6 mpj:2 k1q:3 qw:1 xed:1 jmp:2 cm:2 m_:2 q2:4 ag:2 nj:3 ptl:1 ti:7 nf:1 mkm:1 uk:1 csk:5 m3d:5 t1:1 e... |
97 | 1,086 | Examples of learning curves from a modified
VC-formalism.
A. Kowalczyk & J. Szymanski
Telstra Research Laboratories
770 Blackbtun Road,
Clayton, Vic. 3168, Australia
{akowalczyk,j.szymanski }@trl.oz.au)
P.L. Bartlett & R.C. Williamson
Department of Systems Engineering
Australian National University
Canberra, ACT 0200,... | 1086 |@word determinant:1 version:1 polynomial:1 simulation:1 solid:3 ld:3 initial:1 series:1 contains:1 chervonenkis:1 universality:1 numerical:2 partition:3 j1:5 shape:2 drop:1 plot:1 selected:1 warmuth:1 lr:3 compo:1 sudden:1 provides:1 five:1 lor:1 rc:1 director:1 qualitative:1 introduce:2 theoretically:1 sacrifice... |
98 | 1,087 | Using Feedforward Neural Networks to
Monitor Alertness from Changes in EEG
Correlation and Coherence
Scott Makeig
Naval Health Research Center, P.O. Box 85122
San Diego, CA 92186-5122
Tzyy-Ping Jung
Naval Health Research Center and
Computational Neurobiology Lab
The Salk Institute, P.O. Box 85800
San Diego, CA 92186-58... | 1087 |@word luk:1 replicate:1 solid:1 papoulis:1 initial:1 series:7 molenaar:1 yet:1 subsequent:1 implying:1 half:2 selected:3 tone:1 short:1 record:2 postal:1 contribute:1 five:1 burst:1 become:1 sustained:2 behavioral:3 pairwise:1 brain:6 chap:1 actual:6 window:10 increasing:2 estimating:2 panel:2 what:1 electroencep... |
99 | 1,088 | Softassign versus Softmax: Benchmarks
in Combinatorial Optimization
Steven Gold
Department of Computer Science
Yale University
New Haven, CT 06520-8285
Anand Rangarajan
Dept. of Diagnostic Radiology
Yale University
New Haven, CT 06520-8042
Abstract
A new technique, termed soft assign, is applied for the first time
t... | 1088 |@word middle:2 version:8 series:3 current:1 must:8 john:1 update:7 intelligence:1 fewer:2 math:1 node:6 simpler:2 along:1 become:1 doubly:5 introduce:1 expected:1 roughly:1 armed:1 lll:1 moreover:1 notation:1 mitigate:1 partitioning:19 unit:2 positive:3 local:2 subscript:1 enforces:2 pappu:1 implement:2 thought:1... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.