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300 | 1,274 | ..
Learning Exact Patterns of Quasi-synchronization
among Spiking Neurons
from Data on Multi-unit Recordings
Laura Martignon
Max Planck Institute
for Psychological Research
Adaptive Behavior and Cognition
80802 Munich, Germany
laura@mpipf-muenchen.mpg.de
Kathryn Laskey
Dept. of Systems Engineering
and the Krasnow In... | 1274 |@word neurophysiology:1 trial:3 seek:1 rhesus:2 covariance:1 carry:1 moment:1 configuration:7 selecting:1 horvitz:1 ka:2 activation:3 artijiciallntelligence:1 follower:1 must:1 written:1 drop:1 update:1 stationary:3 cue:2 selected:1 beginning:1 detecting:2 node:2 simpler:1 correlograms:1 ik:2 persistent:1 pairwis... |
301 | 1,275 | Representation and Induction of Finite
State Machines using Time-Delay Neural
Networks
Daniel S. Clouse
Computer Science & Engineering Dept.
University of California, San Diego
La Jolla, CA 92093-0114
dclouse@ucsd .edu
Bill G. Horne
NEC Research Institute
4 Independence Way
Princeton, NJ 08540
horne@research.nj.nec.co... | 1275 |@word trial:5 stronger:2 simulation:12 pick:1 recursively:1 series:3 contains:3 daniel:1 cleared:1 past:1 current:5 com:2 lang:4 activation:2 ij1:1 must:2 cottrell:5 subsequent:1 confirming:1 offunctions:1 remove:1 plot:2 update:1 fewer:2 short:3 accepting:2 provides:1 completeness:1 node:14 five:2 mathematical:1... |
302 | 1,276 | Neural Network Modeling of Speech and Music
Signals
Axel Robel
Technical University Berlin, Einsteinufer 17, Sekr. EN-8, 10587 Berlin, Germany
Tel: +49-30-31425699, FAX: +49-30-31421143, email: roebel@kgw.tu-berlin.de
Abstract
Time series prediction is one of the major applications of neural networks. After a short i... | 1276 |@word version:1 casdagli:1 tried:1 systeme:1 thereby:1 solid:1 series:25 pub:1 tuned:1 past:1 activation:2 synthesizer:1 universality:1 enables:1 stationary:1 selected:1 tone:7 short:2 detecting:1 become:1 symposium:1 consists:2 expected:1 behavior:1 actual:3 increasing:2 becomes:1 estimating:1 underlying:2 moreo... |
303 | 1,277 | An Hierarchical Model of Visual Rivalry
Peter Dayan
Department of Brain and Cognitive Sciences
E25-21O Massachusetts Institute of Technology
Cambridge, MA 02139
dayan@psyche.mit.edu 1
Abstract
Binocular rivalry is the alternating percept that can result when
the two eyes see different scenes. Recent psychophysical evi... | 1277 |@word version:2 inversion:1 stronger:1 seems:1 simulation:1 r:1 accounting:1 brightness:1 rightmost:1 subjective:1 current:1 activation:3 wx:1 bart:1 generative:8 half:4 fewer:1 reciprocal:1 provides:1 draft:1 wxy:1 successive:1 constructed:1 direct:4 become:1 wale:1 fitting:1 pathway:1 manner:1 expected:2 indeed... |
304 | 1,278 | Reinforcement Learning for Mixed
Open-loop and Closed-loop Control
Eric A. Hansen, Andrew G. Barto, and Shlorno Zilberstein
Department of Computer Science
University of Massachusetts
Amherst, MA 01003
{hansen.barto.shlomo }<Dcs.umass .edu
Abstract
Closed-loop control relies on sensory feedback that is usually assumed... | 1278 |@word interleave:1 open:22 tried:1 incurs:3 thereby:1 minus:1 recursively:1 uma:1 current:6 yet:1 must:11 subsequent:2 shlomo:1 stationary:1 intelligence:3 mccallum:3 short:1 core:3 utile:1 provides:7 node:2 location:2 five:2 along:2 constructed:1 direct:1 prove:3 consists:4 combine:2 manner:1 introduce:1 expecte... |
305 | 1,279 | Adaptively Growing Hierarchical
Mixtures of Experts
Jiirgen Fritsch, Michael Finke, Alex Waibel
{fritsch+,finkem, waibel }@cs.cmu.edu
Interactive Systems Laboratories
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
We propose a novel approach to automatically growing and pruning
Hierarchical Mixtures of Expe... | 1279 |@word version:2 advantageous:1 jacob:4 barney:2 initial:1 contains:4 score:1 exclusively:1 initialisation:1 outperforms:1 existing:1 current:5 nowlan:1 activation:9 assigning:1 visible:1 partition:3 enables:1 hypothesize:1 plot:3 update:2 generative:2 leaf:2 half:1 plane:2 colored:1 node:16 contribute:1 lx:3 heig... |
306 | 128 | 785
ELECTRONIC
RECEPTORS
FOR TACTILE/HAPTIC?
SENSING
Andreas G. Andreou
Electrical and Computer Engineering
The Johns Hopkins University
Baltimore, MD 21218
ABSTRACT
We discuss synthetic receptors for haptic sensing. These are based on
magnetic field sensors (Hall effect structures) fabricated using standard
CMOS ... | 128 |@word cylindrical:1 advantageous:1 proportionality:2 seitz:1 sensed:1 pressure:2 solid:1 electronics:2 configuration:1 amp:1 current:23 lorentz:4 readily:1 john:3 shape:3 designed:1 discrimination:1 device:34 plane:1 short:1 dissertation:1 direct:4 transducer:9 pathway:1 acquired:1 f11:1 terminal:4 provided:2 unde... |
307 | 1,280 | Ensemble Methods for Phoneme
Classification
Steve Waterhouse
Gary Cook
Cambridge University Engineering Department
Cambridge CB2 IPZ, England, Tel: [+44] 1223 332754
Email: srwl00l@eng.cam .ac .uk.gdc@eng .cam .ac.uk
Abstract
This paper investigates a number of ensemble methods for improving the performance of phoneme... | 1280 |@word merrill:1 bigram:3 retraining:1 eng:2 jacob:3 pick:1 reduction:1 initial:2 series:1 united:1 selecting:1 current:2 nowlan:2 additive:1 partition:1 designed:1 intelligence:1 selected:3 cook:7 short:1 record:1 filtered:2 provides:2 boosting:22 postal:1 lexicon:1 firstly:1 along:1 consists:5 combine:5 dan:1 ex... |
308 | 1,281 | Dynamically Adaptable CMOS
Winner-Take-AII Neural Network
Kunihiko Iizuka, Masayuki Miyamoto and Hirofumi Matsui
Information Technology Research Laboratories
Sharp
Tenri, Nara, lAP AN
Abstract
The major problem that has prevented practical application of analog
neuro-LSIs has been poor accuracy due to fluctuating ana... | 1281 |@word nd:1 pulse:9 out1:1 solid:1 current:2 follower:1 realize:1 v:1 device:10 shut:1 node:16 rc:2 become:1 supply:1 compose:1 absorbs:1 deteriorate:1 expected:1 behavior:2 inspired:1 automatically:1 equipped:1 moreover:1 circuit:14 lowest:2 vref:4 cm:2 fabricated:7 guarantee:2 every:1 charge:1 gm1:1 control:5 be... |
309 | 1,282 | Unification of Information Maximization
and Minimization
Ryotaro Kamimura
Information Science Laboratory
Tokai University
1117 Kitakaname Hiratsuka Kanagawa 259-12, Japan
E-mail: ryo@cc.u-tokaLac.jp
Abstract
In the present paper, we propose a method to unify information
maximization and minimization in hidden units. T... | 1282 |@word especially:2 uj:3 concept:5 y2:2 verify:1 normalized:1 majority:1 closely:1 laboratory:1 realized:1 elimination:4 kth:7 pjk:7 smolen:1 initial:6 criterion:1 generalization:14 exclusively:1 formedness:1 asme:1 proposition:1 probable:3 theoretic:1 summation:1 vo:1 mail:1 seven:1 toward:1 assuming:2 e:1 subcom... |
310 | 1,283 | A variational principle for
model-based morphing
Lawrence K. Saul'" and Michael I. Jordan
Center for Biological and Computational Learning
Massachusetts Institute of Technology
79 Amherst Street, EI0-034D
Cambridge, MA 02139
Abstract
Given a multidimensional data set and a model of its density,
we consider how to def... | 1283 |@word calculus:1 seek:1 covariance:4 tr:1 moment:1 initial:1 current:1 assigning:1 must:3 girosi:1 enables:1 cheap:1 parameterization:3 plane:5 short:1 provides:2 location:2 traverse:1 along:4 differential:2 become:1 qualitative:1 combine:1 inside:2 roughly:1 frequently:1 examine:1 mechanic:1 audiovisual:1 little... |
311 | 1,284 | Analytical Mean Squared Error Curves
in Temporal Difference Learning
Satinder Singh
Department of Computer Science
University of Colorado
Boulder, CO 80309-0430
baveja@cs.colorado.edu
Peter Dayan
Brain and Cognitive Sciences
E25-210, MIT
Cambridge, MA 02139
bertsekas@lids.mit.edu
Abstract
We have calculated analytic... | 1284 |@word trial:26 determinant:1 version:1 eliminating:1 inversion:1 stronger:1 confirms:2 simulation:5 covariance:7 harder:1 reduction:3 initial:8 cyclic:2 exclusively:1 omniscient:1 comparing:1 surprising:1 analysed:4 subsequent:1 realistic:1 asymptote:1 plot:2 update:12 alone:1 greedy:14 accordingly:1 dover:1 cave... |
312 | 1,285 | Spectroscopic Detection of Cervical
Pre-Cancer through Radial Basis
Function Networks
Kagan Tumer
kagan@pine.ece.utexas.edu
Dept. of Electrical and Computer Engr.
The University of Texas at Austin,
Rebecca Richards-Kortum
kortum@mail.utexas.edu
Biomedical Engineering Program
The University of Texas at Austin
Nirmala R... | 1285 |@word version:1 loading:1 prominence:1 thereby:1 reduction:2 cytology:1 contains:1 selecting:2 current:5 readily:1 designed:2 drop:1 discrimination:4 v:2 half:2 selected:1 intelligence:1 tumer:3 provides:4 location:3 five:1 mathematical:1 direct:1 qualitative:1 consists:2 epithelium:1 acquired:2 inter:1 frequentl... |
313 | 1,286 | Analog VLSI Circuits for
Attention-Based, Visual Tracking
Timothy K. Horiuchi
Computation and Neural Systems
California Institute of Technology
Pasadena, CA 91125
timmer@klab.caltech.edu
Tonia G. Morris
Electrical and Computer Engineering
Georgia Institute of Technology
Atlanta, GA, 30332-0250
tmorris@eecom.gatech.ed... | 1286 |@word middle:1 itdi:2 simulation:1 attended:1 electronics:1 reaction:1 current:8 kowler:2 percep:1 must:2 physiol:1 motor:2 discrimination:1 stationary:1 v:1 selected:5 leaf:1 plane:1 brennan:1 compo:1 provides:2 node:1 location:12 five:1 along:1 supply:1 fixation:2 combine:1 manner:1 behavior:1 td:4 window:3 beg... |
314 | 1,287 | VLSI Implementation of Cortical Visual Motion
Detection Using an Analog Neural Computer
Ralph Etienne-Cummings
Electrical Engineering,
Southern Illinois University,
Carbondale, IL 62901
Naomi Takahashi
The Moore School,
University of Pennsylvania,
Philadelphia, PA 19104
Jan Van der Spiegel
The Moore School,
University... | 1287 |@word neurophysiology:1 middle:1 wiesel:3 donham:3 lobe:1 thereby:1 tuned:10 must:3 reminiscent:1 realize:3 subsequent:1 j1:1 wx:3 cis:1 plot:2 nervous:1 plane:6 compo:1 detecting:1 provides:1 mathematical:1 constructed:2 direct:1 become:1 consists:1 symp:1 market:1 behavior:1 aliasing:2 brain:1 decomposed:1 mmls... |
315 | 1,288 | Minimizing Statistical Bias with Queries
David A. Cohn
Adaptive Systems Group
Harlequin, Inc.
One Cambridge Center
Cambridge, MA 02142
cOhnCharlequin.com
Abstract
I describe a querying criterion that attempts to minimize the error
of a learner by minimizing its estimated squared bias. I describe
experiments with loca... | 1288 |@word kong:2 covariance:2 concise:1 moment:1 initial:1 series:3 selecting:9 bootstrapped:1 outperforms:3 com:1 comparing:1 dx:1 must:7 shape:1 analytic:1 designed:1 drop:2 aside:1 resampling:1 greedy:1 leaf:1 selected:2 lx:3 fitting:3 inside:1 introduce:1 notably:1 expected:6 alspector:1 frequently:1 globally:1 d... |
316 | 1,289 | The CONDENSATION algorithm conditional density propagation and
applications to visual tracking
A. Blake and M. IsardDepartment of Engineering Science,
University of Oxford,
Oxford OXI 3PJ, UK.
Abstract
The power of sampling methods in Bayesian reconstruction of noisy
signals is well known. The extension of sampling t... | 1289 |@word especially:1 recovering:1 iteratively:1 filter:2 stochastic:1 propagate:1 illustrated:1 deal:1 strategy:1 stringent:1 attainable:1 ll:4 during:1 self:1 rt:2 papoulis:1 moment:1 normalise:1 efficacy:1 outline:1 extension:2 motion:6 current:1 z2:1 tracker:2 sufficiently:1 blake:8 camouflaged:1 yet:1 around:1 ... |
317 | 129 | 160
SCALING AND GENERALIZATION IN
NEURAL NETWORKS: A CASE STUDY
Subutai Ahmad
Center for Complex Systems Research
University of Illinois at Urbana-Champaign
508 S. 6th St., Champaign, IL 61820
Gerald Tesauro
IBM Watson Research Center
PO Box 704
Yorktown Heights, NY 10598
ABSTRACT
The issues of scaling and generaliz... | 129 |@word middle:1 version:1 seems:3 d2:1 simulation:5 thereby:1 phy:1 initial:1 selecting:3 current:2 comparing:1 yet:1 must:1 numerical:2 happen:1 analytic:1 plot:3 v:2 implying:1 half:3 nervous:1 contribute:1 height:1 advocate:1 theoretically:1 expected:4 ra:1 behavior:1 examine:3 multi:1 simulator:1 encouraging:1 ... |
318 | 1,290 | Separating Style and Content
Joshua B. Tenenbaum
Dept. of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
jbtGpsyche.mit.edu
William T. Freeman
MERL, Mitsubishi Electric Res. Lab.
201 Broadway
Cambridge, MA 02139
freemanOmerl.com
Abstract
We seek to analyze and manipulate two f... | 1290 |@word calculus:1 seek:2 mitsubishi:1 tried:1 decomposition:1 tr:1 initial:1 bc:8 subjective:1 current:2 com:1 yet:1 must:2 shape:5 extrapolating:1 update:2 sys:1 compo:2 pointer:1 five:1 differential:1 prove:1 consists:1 fitting:5 combine:1 roughly:1 themselves:1 frequently:1 usvt:1 brain:1 multi:1 freeman:5 sphe... |
319 | 1,291 | Contour Organisation with the EM
Algorithm
J. A. F. Leite and E. R. Hancock
Department of Computer Science
University of York, York, Y01 5DD, UK.
Abstract
This paper describes how the early visual process of contour organisation can be realised using the EM algorithm. The underlying
computational representation is ba... | 1291 |@word proportion:4 covariance:1 jacob:1 moment:2 initial:6 substitution:1 series:1 initialisation:1 current:1 si:7 reminiscent:1 readily:1 subsequent:1 intelligence:1 accordingly:2 ith:1 provides:1 coarse:1 iterates:2 location:1 iverson:1 become:1 differential:1 shorthand:1 fitting:8 ra:1 expected:1 themselves:1 ... |
320 | 1,292 | Why did TD-Gammon Work?
Jordan B. Pollack & Alan D. Blair
Computer Science Department
Brandeis University
Waltham, MA 02254
{pollack,blair} @cs.brandeis.edu
Abstract
Although TD-Gammon is one of the major successes in machine learning, it has not led to similar impressive breakthroughs in temporal difference learning ... | 1292 |@word trial:1 middle:1 laurence:1 replicate:1 simulation:1 tried:1 pick:1 maes:2 harder:4 initial:7 angeline:7 genetic:4 reynolds:2 current:3 comparing:1 surprising:1 collude:1 yet:2 must:2 subsequent:2 remove:1 plot:1 mandell:1 alone:1 half:1 intellectual:1 preference:2 successive:1 firstly:1 simpler:3 evaluator... |
321 | 1,293 | Bayesian Unsupervised Learning of
Higher Order Structure
Michael S. Lewicki
Terrence J. Sejnowski
levicki~salk.edu
terry~salk.edu
The Salk Institute
Howard Hughes Medical Institute
Computational Neurobiology Lab
10010 N. Torrey Pines Rd.
La Jolla, CA 92037
Abstract
Multilayer architectures such as those used in Ba... | 1293 |@word seems:1 initial:2 series:1 selecting:1 contextual:1 si:23 must:2 informative:1 remove:1 discrimination:1 alone:1 intelligence:2 discovering:2 selected:2 become:1 nlog2:1 themselves:1 little:2 becomes:3 discover:2 underlying:3 lowest:2 what:1 interpreted:4 finding:2 transformation:1 exactly:1 ro:1 unit:26 me... |
322 | 1,294 | Dynamic features for visual speechreading: A systematic comparison
Michael S. Grayl,a, Javier R. Movellan l , Terrence J. Sejnowski2 ,3
Departments of Cognitive Science l and Biology2
University of California, San Diego
La Jolla, CA 92093
and
Howard Hughes Medical Institute3
Computational Neurobiology Lab
The Salk Ins... | 1294 |@word consisted:1 middle:1 normalized:1 compression:1 psychophysical:1 symmetric:1 pea:1 prasad:1 speechreading:3 centered:1 human:2 tsejnowski:1 speaker:1 separate:1 carry:1 reduction:1 macdonald:1 hmm:1 generalization:1 selecting:1 tt:1 bregler:3 motion:2 current:1 around:1 considered:1 image:14 wise:1 downsamp... |
323 | 1,295 | Limitations of self-organizing maps for
vector quantization and multidimensional
scaling
Arthur Flexer
The Austrian Research Institute for Artificial Intelligence
Schottengasse 3, A-lOlO Vienna, Austria
and
Department of Psychology, University of Vienna
Liebiggasse 5, A-lOlO Vienna, Austria
arthur~ai.univie.ac.at
Ab... | 1295 |@word version:1 compression:1 seems:1 sammon:12 heuristically:1 jacob:1 thereby:1 initial:1 series:2 contains:1 outperforms:1 existing:1 comparing:3 discretization:1 com:1 si:8 partition:5 shape:2 designed:2 sponsored:1 update:4 intelligence:2 xk:1 ith:1 steepest:1 pointer:1 quantizer:4 math:1 codebook:1 direct:1... |
324 | 1,296 | 3D Object Recognition:
A Model of View-Tuned Neurons
Emanuela Bricolo
Tomaso Poggio
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
{emanuela,tp}Gai.mit.edu
Nikos Logothetis
Baylor College of Medicine
Houston, TX 77030
nikosGbcmvision.bcm.tmc.edu
Abstract
In 1990 Po... | 1296 |@word version:2 open:1 grey:1 simulation:5 configuration:3 selecting:1 tuned:19 interestingly:1 current:2 shape:2 designed:1 alone:1 intelligence:2 selected:1 plane:2 farther:1 filtered:4 location:20 successive:1 sigmoidal:2 simpler:1 alert:1 constructed:1 become:1 edelman:11 consists:1 qualitative:1 acquired:1 b... |
325 | 1,297 | Approximate Solutions to
Optimal Stopping Problems
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 propose and analyze an algorithm that approximates solutions
to the problem ... | 1297 |@word norm:2 simulation:2 contraction:6 concise:1 united:1 current:2 written:1 john:1 belmont:1 predetermined:1 shape:1 stationary:1 ith:1 dissertation:1 lr:3 ire:1 provides:2 unbounded:2 introduce:1 expected:3 terminal:1 bellman:1 discounted:3 curse:2 lib:2 becomes:2 begin:1 underlying:3 notation:2 temporal:3 ie... |
326 | 1,298 | 488 Solutions to the XOR Problem
Frans M. Coetzee *
eoetzee@eee.emu.edu
Department of Electrical Engineering
Carnegie Mellon University
Pittsburgh, PA 15213
Virginia L. Stonick
ginny@eee.emu.edu
Department of Electrical Engineering
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
A globally convergent homotop... | 1298 |@word deformed:1 trial:6 illustrating:1 briefly:1 polynomial:1 norm:1 seems:1 open:1 simulation:1 thereby:1 initial:10 contains:1 selecting:1 ferrier:1 comparing:1 attracted:1 numerical:3 plot:1 stationary:10 characterization:1 node:6 five:3 mathematical:2 dn:3 constructed:1 become:1 frans:1 multi:2 globally:2 td... |
327 | 1,299 | The Neurothermostat:
Predictive Optimal Control of
Residential Heating Systems
Michael C. Mozer t , Lucky Vidmart , Robert H. Dodiert
tDepartment of Computer Science
tDepartment of Civil, Environmental, and Architectural Engineering
University of Colorado, Boulder, CO 80309-0430
Abstract
The Neurothermostat is an ada... | 1299 |@word proportion:2 humidity:1 hu:10 simulation:5 initial:1 contains:1 past:2 outperforms:2 reaction:1 current:6 yet:1 must:6 readily:1 drop:1 depict:1 update:1 v:2 half:2 device:1 shut:1 beginning:1 short:1 detecting:1 quantized:1 appliance:1 provides:4 preference:3 nishi:2 five:1 rc:4 replication:2 combine:1 non... |
328 | 13 | 297
TEMPORAL PATTERNS OF ACTIVITY IN
NEURAL NETWORKS
Paolo Gaudiano
Dept. of Aerospace Engineering Sciences,
University of Colorado, Boulder CO 80309, USA
January 5, 1988
Abstract
Patterns of activity over real neural structures are known to exhibit timedependent behavior. It would seem that the brain may be capable ... | 13 |@word seems:1 simulation:1 cyclic:5 series:1 selecting:1 past:1 existing:1 current:1 must:3 subsequent:1 numerical:1 realistic:1 atlas:2 update:1 pacemaker:1 short:1 indefinitely:1 location:1 sustained:2 olfactory:2 behavior:9 brain:4 inspired:2 decreasing:1 lowest:1 what:1 string:3 fuzzy:1 developed:2 finding:1 te... |
329 | 130 | 586
STATISTICAL PREDICTION WITH KANERVA'S
SPARSE DISTRmUTED MEMORY
David Rogers
Research Institute for Advanced Computer Science
MS 230-5, NASA Ames Research Center
Moffett Field, CA 94035
ABSTRACT
A new viewpoint of the processing performed by Kanerva's sparse
distributed memory (SDM) is presented. In conditions of ... | 130 |@word open:1 contains:3 genetic:2 bitwise:1 current:1 activation:23 tenned:1 must:2 written:2 riacs:1 partition:2 informative:7 noninformative:2 v:1 intelligence:1 selected:13 guess:1 nervous:1 math:1 location:27 ames:3 lor:1 mathematical:3 become:1 combine:2 tagging:1 behavior:5 dist:1 nor:1 love:1 brain:1 food:3... |
330 | 1,301 | Reconstructing Stimulus Velocity from
Neuronal Responses in Area MT
Wyeth Bair, James R. Cavanaugh, J. Anthony Movshon
Howard Hughes Medical Institute, and
Center for Neural Science
New York University
4 Washington Place, Room 809
New York, NY 10003
wyeth@cns.nyu.edu, jamesc@cns.nyu.edu, tony@cns.nyu.edu
Abstract
We ... | 1301 |@word trial:7 r:2 lobe:4 minus:1 extrastriate:1 document:1 neurophys:1 nowlan:2 visible:1 plot:2 alone:1 half:8 cavanaugh:3 beginning:1 filtered:1 preference:1 height:2 rc:1 along:1 burst:2 anesthesia:1 manner:1 ra:1 expected:2 rapid:1 roughly:1 examine:1 nor:2 aliasing:1 borst:2 little:2 window:4 matched:1 panel... |
331 | 1,302 | On-line Policy Improvement using
Monte-Carlo Search
Gerald Tesauro
IBM T. J. Watson Research Center
P. O. Box 704
Yorktown Heights, NY 10598
Gregory R. Galperin
MIT AI Lab
545 Technology Square
Cambridge, MA 02139
Abstract
We present a Monte-Carlo simulation algorithm for real-time policy
improvement of an adaptive c... | 1302 |@word trial:19 middle:1 longterm:1 stronger:3 seems:1 simulation:9 dramatic:1 reduction:8 initial:11 configuration:1 score:3 current:1 surprising:1 yet:1 belmont:1 v:1 greedy:1 selected:1 fewer:1 provides:2 node:6 location:1 preference:1 zhang:3 evaluator:6 height:1 rollout:14 suspicious:1 consists:1 prove:1 comb... |
332 | 1,303 | Size of multilayer networks for exact
learning: analytic approach
Andre Elisseefl'
Mathematiques et Informatique
Ecole Normale Superieure de Lyon
46 allee d'Italie
F69364 Lyon cedex 07, FRANCE
D~pt
Helene Paugam-Moisy
LIP, URA 1398 CNRS
Ecole Normale Superieure de Lyon
46 allee d'Italie
F69364 Lyon cedex 07, FRANCE
... | 1303 |@word inversion:1 polynomial:1 compression:2 seems:1 open:3 elisseeff:4 recursively:1 ecole:2 activation:1 written:1 must:3 realize:1 j1:1 analytic:6 funahashi:1 recherche:1 compo:1 math:1 sigmoidal:2 direct:1 symposium:2 prove:4 consists:1 themselves:1 decomposed:1 lyon:4 little:1 considering:2 project:1 notatio... |
333 | 1,304 | Spatial Decorrelation in Orientation
Tuned Cortical Cells
Alexander Dimitrov
Department of Mathematics
University of Chicago
Chicago, IL 60637
a-dimitrov@uchicago.edu
Jack D. Cowan
Department of Mathematics
University of Chicago
Chicago, IL 60637
cowan@math.uchicago.edu
Abstract
In this paper we propose a model for ... | 1304 |@word selforganization:1 inversion:1 compression:2 trotter:2 tried:1 decorrelate:2 pick:1 thereby:1 solid:1 reduction:3 necessity:1 series:1 selecting:1 tuned:5 denoting:1 si:1 intriguing:1 readily:1 realistic:2 chicago:4 pertinent:1 plot:1 v:1 isotropic:1 math:1 mathematical:1 along:2 constructed:1 pathway:6 aut... |
334 | 1,305 | Rapid Visual Processing using Spike Asynchrony
Simon J. Thorpe & Jacques Gautrais
Centre de Recherche Cerveau & Cognition
F-31062 Toulouse
France
email thorpe@cerco.ups-tlseJr
Abstract
We have investigated the possibility that rapid processing in the visual
system could be achieved by using the order of firing in dif... | 1305 |@word neurophysiology:2 version:2 middle:1 proportion:2 oncenter:1 trotter:2 simulation:8 lobe:2 excited:1 extrastriate:1 initial:5 contains:1 efficacy:1 mainen:2 seriously:1 tuned:2 interestingly:1 surprising:1 activation:8 realistic:1 informative:1 plot:1 progressively:3 short:3 recherche:1 filtered:1 location:... |
335 | 1,306 | Practical confidence and prediction
intervals
Tom Heskes
RWCP Novel Functions SNN Laboratory; University of Nijmegen
Geert Grooteplein 21, 6525 EZ Nijmegen, The Netherlands
tom@mbfys.kun.nl
Abstract
We propose a new method to compute prediction intervals. Especially for small data sets the width of a prediction inter... | 1306 |@word middle:1 grooteplein:1 minus:2 solid:8 ours:1 existing:2 yet:2 written:1 resampling:2 aside:1 liberal:2 simpler:6 lor:1 mathematical:2 direct:1 become:1 qualitative:1 mbfys:1 themselves:1 nor:2 snn:2 actual:2 considering:2 becomes:1 estimating:2 underlying:1 what:1 interpreted:1 minimizes:1 bootstrapping:6 ... |
336 | 1,307 | Noisy Spiking Neurons with Temporal
Coding have more Computational Power
than Sigmoidal Neurons
Wolfgang Maass
Institute for Theoretical Computer Science
Technische Universitaet Graz, Klosterwiesgasse 32/2
A-80lO Graz, Austria, e-mail: maass@igLtu-graz.ac.at
Abstract
We exhibit a novel way of simulating sigmoidal neu... | 1307 |@word cu:8 rising:1 norm:1 nd:1 simulation:4 lobe:1 thereby:1 carry:1 initial:2 pub:2 current:2 activation:7 si:3 additive:1 realistic:1 subsequent:1 interspike:2 shape:2 ctu:2 inspection:1 provides:3 math:1 node:1 sigmoidal:29 consists:1 prove:1 compose:2 cta:2 manner:1 pairwise:3 nor:1 torque:1 automatically:1 ... |
337 | 1,308 | Softening Discrete Relaxation
Andrew M. Finch, Richard C. Wilson and Edwin R. Hancock
Department of Computer Science,
University of York, York, Y01 5DD, UK
Abstract
This paper describes a new framework for relational graph matching. The starting point is a recently reported Bayesian consistency
measure which gauges s... | 1308 |@word version:1 briefly:1 solid:1 initial:2 configuration:3 series:3 genetic:2 outperforms:2 current:2 contextual:2 ka:1 must:2 evans:1 partition:1 plot:1 update:16 leaf:1 ith:1 provides:2 node:13 location:2 contribute:1 firstly:1 mathematical:1 rc:3 constructed:1 symposium:1 consists:1 doubly:1 manner:2 ra:4 jm:... |
338 | 1,309 | Interpreting images by propagating
Bayesian beliefs
Yair Weiss
Dept. of Brain and Cognitive Sciences
Massachusetts Institute of Technology
E10-120, Cambridge, MA 02139, USA
yweiss<opsyche.mit.edu
Abstract
A central theme of computational vision research has been the realization that reliable estimation of local scene... | 1309 |@word version:1 calculus:4 propagate:2 tr:2 denoting:1 ours:1 existing:1 current:1 yet:1 dx:2 written:2 subsequent:1 enables:1 plot:2 update:24 generative:3 cue:3 inspection:1 provides:1 location:6 successive:1 mathematical:1 along:8 become:1 qualitative:1 combine:2 allan:1 multi:2 brain:1 freeman:1 globally:1 td... |
339 | 131 | 384
MODELING SMALL OSCILLATING
BIOLOGICAL NETWORKS IN ANALOG VLSI
Sylvie Ryckebusch, James M. Bower, and Carver Mead
California Instit ute of Technology
Pasadena, CA 91125
ABSTRACT
We have used analog VLSI technology to model a class of small oscillating biological neural circuits known as central pattern generators ... | 131 |@word eliminating:1 pulse:6 simulation:4 initial:1 contains:2 reaction:1 current:5 yet:1 follower:3 must:2 john:1 physiol:2 enables:1 motor:5 designed:1 sponsored:1 pacemaker:1 accordingly:1 reciprocal:4 marine:1 judith:1 cpg:28 rc:1 burst:5 constructed:1 c2:9 consists:1 introduce:1 behavior:2 themselves:1 nor:1 i... |
340 | 1,310 | Salient Contour Extraction by Temporal Binding
in a Cortically-Based Network
Shih.Cheng Yen and Leif H. Finkel
Department of Bioengineering and
Institute of Neurological Sciences
University of Pennsylvania
Philadelphia, PA 19104, U. S. A.
syen @jupiter.seas.upenn.edu
leif@jupiter.seas.upenn.edu
Abstract
It has been su... | 1310 |@word neurophysiology:1 trial:2 seems:1 stronger:1 open:15 closure:4 simulation:7 pick:1 initial:1 contains:2 series:1 interestingly:1 shape:2 plot:1 discrimination:1 intelligence:2 plane:1 reciprocal:1 short:1 detecting:1 mathematical:1 along:2 alert:1 qualitative:2 pathway:1 baldi:2 inter:2 upenn:2 ra:1 rapid:1... |
341 | 1,311 | Consistent Classification, Firm and Soft
Yoram Baram*
Department of Computer Science
Technion, Israel Institute of Technology
Haifa 32000, Israel
baram@cs.technion.ac.il
Abstract
A classifier is called consistent with respect to a given set of classlabeled points if it correctly classifies the set. We consider classi... | 1311 |@word economically:1 version:1 cnn:6 open:2 profit:5 reduction:6 contains:2 uncovered:1 denoting:1 assigning:1 yet:2 written:2 intelligence:1 amir:1 record:1 ames:1 simpler:1 along:3 become:2 incorrect:3 consists:1 expected:15 behavior:2 brain:1 spherical:4 classifies:3 israel:4 kind:4 finding:1 every:2 classifie... |
342 | 1,312 | Statistically Efficient Estimation Using
Cortical Lateral Connections
Alexandre Pouget
alex@salk.edu
Kechen Zhang
zhang@salk.edu
Abstract
Coarse codes are widely used throughout the brain to encode sensory and motor variables. Methods designed to interpret these
codes, such as population vector analysis, are either i... | 1312 |@word trial:3 briefly:1 seems:1 simulation:4 euclidian:1 solid:2 shot:1 initial:4 disparity:2 tuned:5 suppressing:1 current:1 com:8 od:1 activation:4 yet:1 must:1 readily:1 attracted:1 oml:1 shape:4 motor:3 designed:1 discrimination:2 guess:1 liapunov:1 coarse:11 provides:2 contribute:3 location:1 zhang:6 five:2 ... |
343 | 1,313 | Interpolating Earth-science Data using RBF
Networks and Mixtures of Experts
E.VVan
D.Bone
Division of Infonnation Technology
Canberra Laboratory, CSIRO
GPO Box 664, Canberra, ACT, 2601, Australia
{ernest, don} @cbr.dit.csiro.au
Abstract
We present a mixture of experts (ME) approach to interpolate sparse,
spatially c... | 1313 |@word norm:4 seems:2 simulation:2 jacob:6 decomposition:1 covariance:1 solid:2 initial:1 selecting:1 ala:2 current:1 nowlan:1 written:1 partition:7 girosi:2 stationary:4 accordingly:1 isotropic:7 ji2:1 provides:1 location:4 lx:1 along:3 viable:1 consists:1 combine:1 fitting:2 indeed:1 expected:1 globally:2 spheri... |
344 | 1,314 | Smoothing Regularizers for
Projective Basis Function Networks
John E. Moody and Thorsteinn S. Rognvaldsson *
Department of Computer Science, Oregon Graduate Institute
PO Box 91000, Portland, OR 97291
moody@cse.ogi.edu denni@cca.hh.se
Abstract
Smoothing regularizers for radial basis functions have been studied extensi... | 1314 |@word cox:1 polynomial:1 norm:2 dekker:1 heretofore:1 simulation:2 series:1 efficacy:1 comparing:1 nt:1 bie:1 john:2 additive:1 girosi:4 drop:1 plot:1 sponsored:1 v:1 parametrization:1 parameterizations:1 cse:1 sigmoidal:2 simpler:1 along:1 sii:1 direct:2 differential:1 absorbs:1 expected:1 ra:1 becomes:3 deutsch... |
345 | 1,315 | One-unit Learning Rules for
Independent Component Analysis
Aapo Hyvarinen and Erkki Oja
Helsinki University of Technology
Laboratory of Computer and Information Science
Rakentajanaukio 2 C, FIN-02150 Espoo, Finland
email: {Aapo.Hyvarinen.Erkki.Oja}(Qhut.fi
Abstract
Neural one-unit learning rules for the problem of In... | 1315 |@word version:2 polynomial:3 norm:3 seems:1 hyv:12 covariance:1 moment:1 initial:1 contains:1 series:1 kurt:12 current:1 recovered:1 yet:1 must:6 wll:1 enables:1 stationary:1 simpler:1 mathematical:3 combine:1 inside:1 introduce:4 ica:13 equivariant:1 roughly:1 nor:1 multi:1 becomes:1 begin:1 estimating:2 moreove... |
346 | 1,316 | Recursive algorithms for approximating
probabilities in graphical models
Tommi S. Jaakkola and Michael I. Jordan
{tommi,jordan}Opsyche.mit.edu
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
Abstract
We develop a recursive node-elimination formalism for efficiently... | 1316 |@word eliminating:2 twelfth:1 bn:2 reap:1 solid:2 initial:3 denoting:1 existing:1 ida:1 si:5 yet:1 written:1 must:1 subsequent:1 partition:15 plot:1 intelligence:2 hja:1 slh:5 provides:2 node:5 constructed:1 direct:1 become:2 ik:6 qualitative:1 consists:1 introduce:1 manner:1 indeed:1 brain:1 relying:1 little:1 c... |
347 | 1,317 | Exploiting Model Uncertainty Estimates
for Safe Dynamic Control Learning
Jeff G. Schneider
The Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213
schneide@cs.cmu.edu
Abstract
Model learning combined with dynamic programming has been shown to
be effective for learning control of continuous state dynami... | 1317 |@word trial:10 exploitation:1 nd:1 simulation:1 covariance:1 incurs:1 harder:1 initial:4 lqr:10 existing:1 yet:1 must:5 designed:1 drop:1 update:9 plot:1 v:2 xk:3 record:2 simpler:1 mathematical:1 along:1 constructed:1 symposium:2 combine:1 fitting:1 expected:2 planning:2 discretized:3 globally:1 td:1 little:1 ca... |
348 | 1,318 | Triangulation by Continuous Embedding
Marina MeiHl and Michael I. Jordan
{mmp, jordan }@ai.mit.edu
Center for Biological & Computational Learning
Massachusetts Institute of Technology
45 Carleton St. E25-201
Cambridge, MA 02142
Abstract
When triangulating a belief network we aim to obtain a junction
tree of minimum s... | 1318 |@word uev:3 trial:2 cu:1 version:1 suitably:2 open:1 simulation:1 decomposition:2 solid:1 recursively:1 initial:1 ours:1 outperforms:2 current:1 surprising:1 additive:1 j1:2 meilii:3 plot:1 progressively:1 implying:1 half:1 provides:1 node:7 contribute:1 readability:1 gio:2 mathematical:1 constructed:1 qualitativ... |
349 | 1,319 | Cholinergic Modulation Preserves Spike
Timing Under Physiologically Realistic
Fluctuating Input
Akaysha C. Tang
The Salk Institute
Howard Hughes Medical Institute
Computational Neurobiology Laboratory
La Jolla, CA 92037
Andreas M. Bartels
Zoological Institute
University of Zurich
Ziirich
Switzerland
Terrence J. Sejn... | 1319 |@word trial:6 cu:1 hippocampus:1 grey:1 km:1 pulse:10 simulation:4 extrastriate:1 reduction:6 series:1 mainen:5 existing:1 current:8 must:2 evans:1 realistic:3 plot:1 alone:1 half:1 nervous:1 zoological:1 five:1 constructed:2 direct:1 c2:1 profound:1 differential:1 fitting:1 behavioral:1 inter:3 expected:1 behavi... |
350 | 132 | 451
THEORY OF SELF-ORGANIZATION OF
CORTICAL MAPS
Shigeru Tanaka
Fundamental Research Laboratorys, NEC Corporation
1-1 Miyazaki 4-Chome, Miyamae-ku, Kawasaki, Kanagawa 213, Japan
ABSTRACT
We have mathematically shown that cortical maps in the
primary sensory cortices can be reproduced by using three
hypotheses which h... | 132 |@word neurophysiology:1 wiesel:6 open:1 simulation:7 dx:2 bd:3 john:1 subsequent:1 half:1 plane:1 destined:1 hamiltonian:2 short:2 mathematical:1 rc:4 differential:1 become:1 qualitative:2 pathway:2 paragraph:1 behavior:2 themselves:1 terminal:3 considering:2 project:1 begin:1 retinotopic:1 miyazaki:1 monkey:3 dep... |
351 | 1,320 | An Orientation Selective Neural Network
for Pattern Identification in Particle
Detectors
Halina Abramowicz, David Horn, Ury Naftaly, Carmit Sahar- Pikielny
School of Physics and Astronomy, Tel Aviv University
Tel Aviv 69978, Israel
halinaOpost.tau.ac.il, horn~neuron.tau.ac.il
ury~ost.tau.ac.il, carmitOpost.tau.ac.il
A... | 1320 |@word middle:1 manageable:1 wiesel:1 proportion:1 duda:1 open:2 physik:1 grey:1 carry:1 inefficiency:2 contains:1 denby:1 selecting:1 tuned:1 existing:1 incidence:1 activation:3 yet:2 si:1 physiol:1 predetermined:1 selected:1 device:1 isotropic:1 detecting:1 contribute:1 location:7 node:1 five:3 along:2 construct... |
352 | 1,321 | Edges are the 'Independent Components' of
Natural Scenes.
Anthony J. Bell and Terrence J. Sejnowski
Computational Neurobiology Laboratory
The Salk Institute
10010 N. Torrey Pines Road
La Jolla, California 92037
tony@salk.edu, terry@salk.edu
Abstract
Field (1994) has suggested that neurons with line and edge selectivit... | 1321 |@word determinant:1 version:3 wiesel:2 open:1 proportionality:1 simulation:1 covariance:2 pick:1 reduction:2 series:1 contains:1 activation:1 yet:1 must:2 physiol:1 subsequent:1 wx:2 remove:1 selected:1 leaf:1 preference:1 sigmoidal:1 direct:1 suspicious:1 theoretically:1 expected:1 ica:32 equivariant:1 roughly:2... |
353 | 1,322 | A spike based learning neuron in analog
VLSI
Philipp Hiifliger
Institute of Neuroinformatics
ETHZjUNIZ
Gloriastrasse 32
CH-8006 Zurich
Switzerland
e-mail: haftiger@neuroinf.ethz.ch
tel: ++41 1 257 26 84
Misha Mahowald
Institute of Neuroinformatics
ETHZjUNIZ
Gloriastrasse 32
CH-8006 Zurich
Switzerland
e-mail: misha@neu... | 1322 |@word middle:2 stronger:3 open:1 simulation:2 propagate:1 simplifying:2 ttn:2 contains:1 efficacy:1 current:5 com:1 timer:1 clara:1 must:1 written:1 designed:1 update:2 fewer:1 nervous:1 core:1 record:1 philipp:1 become:1 differential:3 combine:1 inter:1 spine:1 behavior:2 brain:1 increasing:1 revision:1 circuit:... |
354 | 1,323 | NeuroScale: Novel Topographic Feature
Extraction using RBF Networks
David Lowe
D.LoweOaston.ac.uk
Michael E. Tipping
H.E.TippingOaston.ac.uk
Neural Computing Research Group
Aston University, Aston Triangle, Birmingam B4 7ET1 UK
http://www.ncrg.aston.ac.uk/
.
Abstract
Dimension-reducing feature extraction neural net... | 1323 |@word inversion:1 norm:1 sammon:6 seek:1 reduction:2 configuration:2 subjective:1 must:2 informative:1 predetermined:1 analytic:1 provides:1 location:1 organising:1 firstly:1 overcomplex:1 inter:3 themselves:1 spherical:1 automatically:1 considering:1 psychometrika:1 provided:2 linearity:1 transformation:18 quant... |
355 | 1,324 | Probabilistic Interpretation of Population
Codes
Richard S. Zemel
zemeleu.arizona.edu
Peter Dayan
dayaneai.mit.edu
Alexandre Pouget
alexesalk.edu
Abstract
We present a theoretical framework for population codes which
generalizes naturally to the important case where the population
provides information about a whole ... | 1324 |@word h:1 version:4 middle:1 hippocampus:1 proportion:1 seems:1 proportionality:1 r:1 aijl:1 xlw:16 shot:1 carry:1 contains:1 tuned:1 existing:3 current:1 comparing:1 xlr:6 dx:9 yet:1 must:1 subsequent:1 treating:2 fund:1 discrimination:1 implying:1 intelligence:1 lr:1 institution:1 characterization:2 provides:2 ... |
356 | 1,325 | Combined Weak Classifiers
Chuanyi Ji and Sheng Ma
Department of Electrical, Computer and System Engineering
Rensselaer Polytechnic Institute, Troy, NY 12180
chuanyi@ecse.rpi.edu, shengm@ecse.rpi.edu
Abstract
To obtain classification systems with both good generalization performance and efficiency in space and time, we... | 1325 |@word cnn:1 version:1 polynomial:2 approved:1 simulation:2 paid:1 contains:1 att:1 selecting:1 pub:1 existing:4 com:1 rpi:2 written:4 realize:1 partition:2 discrimination:6 v_:1 intelligence:1 selected:3 guess:1 record:1 boosting:2 hyperplanes:1 sigmoidal:1 symposium:1 consists:2 multi:2 little:2 cpu:2 curse:1 pr... |
357 | 1,326 | Estimating Equivalent Kernels For Neural
Networks: A Data Perturbation Approach
A. Neil Burgess
Department of Decision Science
London Business School
London, NW1 4SA, UK
(N.Burgess@lbs.lon.ac.uk)
ABSTRACT
We describe the notion of "equivalent kernels" and suggest that this
provides a framework for comparing different ... | 1326 |@word retraining:1 open:1 simulation:2 fonn:3 minus:1 initial:1 contains:2 existing:2 err:1 current:1 comparing:1 dx:1 fonnulated:1 additive:3 shape:3 analytic:1 treating:1 drop:1 selected:1 accordingly:1 xk:3 num:1 provides:2 sigmoidal:1 fitting:2 combine:2 absorbs:2 manner:1 expected:2 themselves:2 multi:1 auto... |
358 | 1,327 | Compositionality, MDL Priors, and
Object Recognition
Elie Bienenstock (elie@dam.brown.edu)
Stuart Geman (geman@dam.brown.edu)
Daniel Potter (dfp@dam.brown.edu)
Division of Applied Mathematics,
Brown University, Providence, RI 02912 USA
Abstract
Images are ambiguous at each of many levels of a contextual hierarchy. Ne... | 1327 |@word advantageous:2 open:1 essay:1 seek:1 amply:1 idl:1 recursively:2 carry:3 configuration:1 contains:1 daniel:1 denoting:1 interestingly:1 rightmost:1 blank:1 contextual:1 emory:1 must:2 john:1 realize:1 happen:1 plasticity:2 pylyshyn:2 half:1 leaf:3 greedy:1 nervous:1 cursory:1 short:1 mental:1 location:1 mat... |
359 | 1,328 | MIMIC: Finding Optima by Estimating
Probability Densities
Jeremy S. De Bonet, Charles L. Isbell, Jr., Paul Viola
Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139
Abstract
In many optimization problems, the structure of solutions reflects
complex relationships between the di... | 1328 |@word trial:1 middle:2 twelfth:1 pbil:9 propagate:1 pick:1 fortuitous:1 initial:2 genetic:8 interestingly:1 existing:1 current:1 ixj:1 lang:2 yet:1 dx:1 must:5 subsequent:1 happen:1 update:1 intelligence:1 half:2 greedy:1 colored:1 node:2 successive:2 lor:1 constructed:2 become:1 viable:1 ik:1 initiative:1 prove:... |
360 | 1,329 | ARTEX: A Self-Organizing Architecture
for Classifying Image Regions
Stephen Grossberg and James R. Williamson
{steve, jrw}@cns.bu.edu
Center for Adaptive Systems and
Department of Cognitive and Neural Systems
Boston University
677 Beacon Street,
Boston, MA 02215
Abstract
A self-organizing architecture is developed fo... | 1329 |@word version:1 middle:7 simulation:1 brightness:14 thereby:1 tr:1 carry:2 outperforms:2 current:1 activation:5 informative:1 grass:2 discrimination:1 half:1 fewer:1 detecting:1 node:3 location:1 five:4 height:1 consists:1 notably:1 compensating:1 grj:1 automatically:1 actual:1 becomes:1 begin:1 project:1 consoli... |
361 | 133 | 519
A BACK-PROPAGATION ALGORITHM
WITH OPTIMAL USE OF HIDDEN UNITS
Yves Chauvin
Thomson-CSF, Inc
(and Psychology Department, Stanford University)
630, Hansen Way (Suite 250)
Palo Alto, CA 94306
ABSTRACT
This paper presents a variation of the back-propagation algorithm that makes optimal use of a network hidden units b... | 133 |@word middle:1 pw:1 proportion:1 simulation:2 propagate:1 initial:1 interestingly:1 current:1 activation:24 yet:1 written:2 shape:1 asymptote:1 become:1 fitting:1 expected:1 behavior:3 elman:3 growing:1 simulator:1 decreasing:1 automatically:2 actual:3 little:1 becomes:1 notation:1 moreover:1 alto:1 null:1 what:1 ... |
362 | 1,330 | Orientation contrast sensitivity from
long-range interactions in visual cortex
Klaus R. Pawelzik, Udo Ernst, Fred Wolf, Theo Geisel
Institut fur Theoretische Physik and SFB 185 Nichtlineare Dynamik,
Universitat Frankfurt, D-60054 Frankfurt/M., and
MPI fur Stromungsforschung, D-37018 Gottingen, Germany
email: {klaus.ud... | 1330 |@word wiesel:2 norm:1 physik:1 grey:3 simulation:5 exitatory:1 tuned:3 interestingly:1 recovered:1 current:1 neurophys:1 surprising:2 activation:10 distant:3 shape:1 nichtlineare:1 stationary:1 alone:1 pursued:1 amir:1 isotropic:1 iso:2 provides:1 location:1 preference:10 rc:2 differential:3 become:1 consists:1 i... |
363 | 1,331 | Recovering Perspective Pose with a Dual
Step EM Algorithm
Andrew D.J. Cross and Edwin R. Hancock,
Department of Computer Science,
University of York,
York, YOl 5DD, UK.
Abstract
This paper describes a new approach to extracting 3D perspective
structure from 2D point-sets. The novel feature is to unify the
tasks of es... | 1331 |@word kong:1 disk:1 covariance:2 jacob:2 invoking:1 initial:3 configuration:9 esj:1 outperforms:1 current:3 si:6 yet:1 reminiscent:1 must:1 readily:2 seeding:1 update:2 intelligence:1 selected:1 item:1 accordingly:2 desktop:1 steepest:1 provides:3 node:8 idi:1 simpler:1 registering:1 expected:5 boissonnat:1 estim... |
364 | 1,332 | Mapping a manifold of perceptual observations
Joshua B. Tenenbaum
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology, Cambridge, MA 02139
jbt@psyche.mit.edu
Abstract
Nonlinear dimensionality reduction is formulated here as the problem of trying to
find a Euclidean feature-space embedding... | 1332 |@word cox:4 version:1 polynomial:1 grey:1 seek:3 tried:3 cos2:2 gradual:1 reduction:4 configuration:1 selecting:1 recovered:2 z2:3 discretization:1 must:4 cottrell:2 subsequent:1 realistic:1 informative:1 gci:2 girosi:3 plot:2 interpretable:1 v:1 alone:3 generative:2 discovering:1 greedy:2 plane:1 ith:1 haykin:1 ... |
365 | 1,333 | The Efficiency and The Robustness of
Natural Gradient Descent Learning Rule
Howard Hua Yang
Department of Computer Science
Oregon Graduate Institute
PO Box 91000, Portland, OR 97291, USA
hyang@cse.ogi.edu
Shun-ichi Amari
Lab. for Information Synthesis
RlKEN Brain Science Institute
Wako-shi, Saitama 351-01, JAPAN
amar... | 1333 |@word trial:1 briefly:1 unbiased:2 true:4 tensor:2 direction:2 leibler:1 d2:1 fa:1 simulation:6 stochastic:6 vc:1 i2:1 ogi:1 sin:2 gradient:30 implementing:2 tr:3 shun:1 distance:1 initial:2 pdf:1 demonstrate:1 tjt:1 wako:1 ati:1 o2:3 exploring:1 performs:1 l1:2 length:1 cramer:4 iiw:1 additive:1 partition:2 phys... |
366 | 1,334 | Features as Sufficient Statistics
D. Geiger ?
Department of Computer Science
Courant Institute
and Center for Neural Science
New York University
A. Rudra t
Department of Computer Science
Courant Institute
New York University
archi~cs.nyu.edu
geiger~cs.nyu.edu
L. Maloney t
Departments of Psychology and Neural Science... | 1334 |@word h:3 version:1 compression:1 seek:2 gainesville:1 recursively:1 contains:2 si:21 perturbative:3 remove:2 generative:2 selected:1 discovering:1 accordingly:1 contribute:1 along:3 direct:6 prove:1 fitting:1 interscience:1 introduce:1 manner:1 expected:1 themselves:1 xhk:1 little:1 pf:1 increasing:1 estimating:... |
367 | 1,335 | Training Methods for Adaptive Boosting
of Neural Networks
Holger Schwenk
Dept.IRO
Universite de Montreal
2920 Chemin de la Tour,
Montreal, Qc, Canada, H3C 317
schwenk@iro.umontreal.ca
Yoshua Bengio
Dept.IRO
Universite de Montreal
and AT&T Laboratories, NJ
bengioy@iro.umontreal.ca
Abstract
"Boosting" is a general meth... | 1335 |@word effect:1 especially:1 normalized:1 version:10 true:1 classical:1 direction:1 assigned:1 objective:1 correct:3 laboratory:1 satisfactory:1 vc:1 centered:1 deal:1 round:1 guessing:1 distance:8 explains:1 capacity:1 reduction:2 c1ass:1 ofneural:1 contains:1 score:6 generalization:7 collected:1 diabolo:12 iro:4... |
368 | 1,336 | Minimax and Hamiltonian Dynamics of
Excitatory-Inhibitory Networks
H. S. Seung, T. J. Richardson
Bell Labs, Lucent Technologies
Murray Hill, NJ 07974
{seungltjr}~bell-labs.com
J. C. Lagarias
AT&T Labs-Research
180 Park Ave. D-130
Florham Park, NJ 07932
J. J. Hopfield
Dept. of Molecular Biology
Princeton University
P... | 1336 |@word version:2 tedious:1 open:1 closure:1 simulation:1 r:3 att:1 itp:1 existing:1 com:2 comparing:1 must:1 written:2 numerical:1 happen:2 half:1 nervous:2 hamiltonian:13 lr:2 mathematical:3 constructed:3 become:1 supply:1 differential:1 consists:1 sustained:2 prove:1 olfactory:1 introduce:1 roughly:1 behavior:11... |
369 | 1,337 | Analog VLSI Model of Intersegmental
Coordination With Nearest-Neighbor Coupling
Girish N. Patel
girish@ece.gatech.edu
Jeremy H. Holleman
jeremy@ece.gatech.edu
Stephen P. DeWeerth
steved@ece.gatech.edu
School of Electrical and Computer Engineering
Georgia Institute of Technology
Atlanta, Ga. 30332-0250
Abstract
We ... | 1337 |@word version:1 inversion:2 stronger:4 dekker:2 simulation:1 configuration:1 lowermost:1 current:12 must:2 ota:1 eleven:2 motor:10 civ:1 designed:2 fund:1 device:2 nervous:1 isotropic:1 ith:1 short:2 compo:1 mental:1 provides:1 math:2 five:1 mathematical:3 along:5 burst:1 become:1 viable:1 qualitative:1 consists:... |
370 | 1,338 | Dynamic Stochastic Synapses as
Computational Units
Wolfgang Maass
Institute for Theoretical Computer Science
Technische Universitat Graz,
A-B01O Graz, Austria.
email: maass@igi.tu-graz.ac.at
Anthony M. Zador
The Salk Institute
La Jolla, CA 92037, USA
email: zador@salk.edu
Abstract
In most neural network models, syna... | 1338 |@word version:1 hippocampus:1 simulation:1 attainable:1 carry:1 efficacy:3 current:2 activation:1 readily:3 visible:1 interspike:15 plasticity:6 motor:1 ith:1 short:5 detecting:1 provides:1 five:1 rc:3 burst:4 become:1 edelman:1 consists:1 indeed:1 expected:1 examine:1 terminal:3 td:2 window:2 increasing:1 become... |
371 | 1,339 | Modeling acoustic correlations by
factor analysis
Lawrence Saul and Mazin Rahim
{lsaul.mazin}~research.att.com
AT&T Labs - Research
180 Park Ave, D-130
Florham Park, NJ 07932
Abstract
Hidden Markov models (HMMs) for automatic speech recognition
rely on high dimensional feature vectors to summarize the shorttime prop... | 1339 |@word version:1 briefly:1 inversion:1 loading:2 d2:1 covariance:20 decomposition:1 tr:1 reduction:7 initial:2 att:1 score:5 com:1 must:1 numerical:1 alphanumeric:2 informative:1 speakerindependent:1 enables:2 plot:5 update:4 stationary:1 parameterization:1 short:2 toronto:2 lx:1 along:1 xtl:1 consists:1 overhead:... |
372 | 134 | 40
EFFICIENT PARALLEL LEARNING
ALGORITHMS FOR NEURAL NETWORKS
Alan H. Kramer and A. Sangiovanni-Vincentelli
Department of EECS
U .C. Berkeley
Berkeley, CA 94720
ABSTRACT
Parallelizable optimization techniques are applied to the problem of
learning in feedforward neural networks. In addition to having superior converg... | 134 |@word trial:9 exploitation:1 uee:2 norm:1 termination:1 d2:1 tried:2 recursively:1 electronics:1 initial:1 contains:3 series:1 current:1 written:1 must:2 numerical:2 alone:1 pursued:1 fewer:1 half:1 ith:1 steepest:12 num:1 node:5 successive:1 lor:1 mathematical:2 fitting:1 manner:1 expected:1 weightspace:3 roughly... |
373 | 1,340 | Generalization in decision trees and DNF:
Does size matter?
Mostefa Golea\ Peter L. Bartlett h , Wee Sun Lee2 and Llew Mason 1
1 Department of Systems Engineering
Research School of Information
Sciences and Engineering
Australian National University
Canberra, ACT, 0200, Australia
2 School of Electrical Engineering
Uni... | 1340 |@word proportion:2 seems:2 ld:1 uncovered:2 written:3 pcp:1 belmont:1 fn:1 numerical:2 greedy:1 leaf:32 intelligence:2 ith:1 boosting:3 node:2 simpler:1 prove:1 inside:1 multi:1 m8:2 actual:1 considering:1 increasing:1 becomes:1 begin:1 bounded:1 what:1 mostefa:1 every:8 act:2 ti:19 growth:2 nation:1 voting:1 cla... |
374 | 1,341 | Nonlinear Markov Networks for Continuous
Variables
Reimar Hofmann and Volker Tresp*
Siemens AG, Corporate Technology
Information and Communications
81730 Munchen, Germany
Abstract
We address the problem oflearning structure in nonlinear Markov networks
with continuous variables. This can be viewed as non-Gaussian mul... | 1341 |@word middle:1 briefly:1 seems:1 stronger:1 grey:1 decomposition:1 covariance:2 contains:5 score:13 current:2 scatter:1 dx:2 must:2 bd:1 john:1 realize:1 hofmann:5 remove:3 plot:1 update:1 alone:1 pursued:1 tenn:2 intelligence:1 node:4 location:2 mathematical:1 direct:9 consists:2 introduce:1 pairwise:2 expected:... |
375 | 1,342 | Multiplicative Updating Rule
for Blind Separation Derived
from the Method of Scoring
Howard Hua Yang
Department of Computer Science
Oregon Graduate Institute
PO Box 91000, Portland, OR 97291, USA
hyang@cse.ogi.edu
Abstract
For blind source separation, when the Fisher information matrix is
used as the Riemannian metr... | 1342 |@word verify:1 true:2 former:1 equality:1 direction:2 bl:2 fij:2 tensor:6 bn:1 diagonal:4 ogi:1 enable:1 gradient:34 ow:1 tr:4 ptr:1 initial:2 nx:1 gg:1 pdf:1 avec:5 tt:1 reason:2 l1:1 pham:3 hold:1 around:2 hall:1 geometrical:1 si:5 exp:1 meaning:1 written:1 equilibrium:4 mapping:1 instantaneous:1 fi:1 minimizin... |
376 | 1,343 | Extended leA Removes Artifacts from
Electroencephalographic Recordings
Tzyy-Ping JungI, Colin Humphries!, Te-Won Lee!, Scott Makeig 2 ,3,
Martin J. McKeown!, Vicente IraguP, Terrence J. SejnowskF
1 Howard
Hughes Medical Institute and Computational Neurobiology Lab
The Salk Institute, P.O . Box 85800 , San Diego, CA 9... | 1343 |@word middle:7 version:2 eliminating:5 inversion:1 confirms:1 simulation:1 decomposition:2 accounting:1 initial:1 series:5 contains:1 molenaar:1 current:1 comparing:1 activation:6 lang:1 numerical:1 wx:1 remove:8 lue:1 selected:5 inspection:1 record:10 filtered:1 provides:1 location:1 five:3 unacceptable:1 along:... |
377 | 1,344 | Analytical study of the interplay between
architecture and predictability
Avner Priel, Ido Kanter, David A. Kessler
Minerva Center and Department of Physics, Bar Ilan University,
Ramat-Gan 52900, Israel.
e-mail: priel@mail.cc.biu.ac.il
(web-page: http://faculty.biu.ac.il/ ""'priel)
Abstract
We study model feed forwa... | 1344 |@word faculty:1 casdagli:2 open:1 km:1 simulation:3 linearized:2 bn:1 decomposition:1 initial:9 series:11 current:1 analysed:1 activation:1 yet:1 si:3 written:2 attracted:1 lang:1 numerical:2 stationary:9 mln:3 inspection:1 short:4 characterization:2 detecting:1 ron:1 priel:6 c2:6 hopf:1 qualitative:1 consists:1 ... |
378 | 1,345 | Multi-modular Associative Memory
Nir Levy
David Horn
School of Physics and Astronomy
Tel-Aviv University Tel Aviv 69978, Israel
Eytan Ruppin
Departments of Computer Science & Physiology
Tel-Aviv University Tel Aviv 69978, Israel
Abstract
Motivated by the findings of modular structure in the association
cortex, we stu... | 1345 |@word trial:3 version:1 seems:1 simulation:4 solid:2 shading:2 accommodate:2 necessity:1 initial:1 contains:1 efficacy:2 z2:1 od:2 plot:1 treating:1 v:5 cue:4 ith:4 sigmoidal:3 five:1 constructed:1 become:1 symposium:1 ik:1 consists:1 inside:1 manner:2 acquired:1 inter:9 embody:1 behavior:1 multi:11 brain:1 israe... |
379 | 1,346 | A Framework for Multiple-Instance Learning
Oded Maron
Tomas Lozano-Perez
NE43-836a
AI Lab, M .I.T.
Cambridge, MA 02139
tlp@ai.mit.edu
NE43-755
AI Lab, M.I. T.
Cambridge, MA 02139
oded@ai.mit.edu
Abstract
Multiple-instance learning is a variation on supervised learning, where the
task is to learn a concept given pos... | 1346 |@word middle:4 polynomial:1 bn:1 pick:1 minus:1 tr:1 harder:1 series:4 contains:4 yet:2 arest:1 shape:14 hoping:1 tlp:1 plot:1 half:2 instantiate:1 fewer:2 selected:1 intelligence:1 xk:1 argm:1 provides:1 contribute:1 location:8 five:1 along:3 inter:1 roughly:2 multi:1 becomes:1 provided:1 xx:1 underlying:1 what:... |
380 | 1,347 | Incorporating Test Inputs into Learning
Zebra Cataltepe
Learning Systems Group
Department of Computer Science
California Institute of Technology
Pasadena, CA 91125
zehra@cs.caltech.edu
Malik Magdon-Ismail
Learning Systems Group
Department of Electrical Engineering
California Institute of Technology
Pasadena, CA 91125... | 1347 |@word effect:1 especially:3 c:1 version:1 contain:1 unbiased:1 prof:1 hence:2 concentrate:1 malik:1 cco:1 radius:1 nonzero:1 shahshahani:2 occurs:2 nicholson:1 rt:1 covariance:2 vx:2 md:1 wol:1 tr:9 thank:2 die:1 necessarily:3 criterion:1 ei1:2 proposition:3 tt:1 extension:1 l1:1 nt:1 credit:2 ic:1 hall:1 image:2... |
381 | 1,348 | A Solution for Missing Data in Recurrent Neural
Networks With an Application to Blood Glucose
Prediction
Volker Tresp and Thomas Briegel *
Siemens AG
Corporate Technology
Otto-Hahn-Ring 6
81730 Miinchen, Germany
Abstract
We consider neural network models for stochastic nonlinear dynamical
systems where measurements o... | 1348 |@word linearized:1 t_:1 covariance:4 tr:4 initial:1 series:7 tuned:1 interestingly:1 past:1 o2:1 current:1 intake:2 john:1 realize:1 additive:2 confirming:1 treating:2 update:1 alone:1 leaf:1 metabolism:2 yr:2 sys:1 provides:1 miinchen:1 digestive:1 lbo:1 five:1 beta:1 consists:1 dpr:1 introduce:1 acquired:1 inde... |
382 | 1,349 | Learning to Schedule Straight-Line Code
Eliot Moss, Paul Utgoff, John Cavazos
Doina Precup, Darko Stefanovic .
Dept. of Compo Sci., Univ. of Mass.
Amherst, MA 01003
Carla Brodley, David Scheeff
Sch. of Elec. and Compo Eng.
Purdue University
W. Lafayette, IN 47907
Abstract
Program execution speed on modem computers i... | 1349 |@word seems:1 suitably:1 instruction:79 simulation:1 eng:1 attainable:1 minus:1 outlook:2 harder:1 configuration:1 selecting:2 genetic:2 interestingly:1 eustace:2 existing:2 bradley:1 current:5 comparing:1 lang:1 must:4 written:3 john:1 realize:1 update:1 aside:1 v:1 greedy:3 selected:3 fewer:1 item:1 une:1 begin... |
383 | 135 | 248
A CONNECTIONIST EXPERT SYSTEM
THAT ACTUALLY WORKS
Gary Bradshaw
Psychology
Richard Fozzard
Computer Science
University of Colorado
Boulder, CO 80302
fozzard@boulder.colorado.edu
LouisCeci
Computer Science
ABSTRACf
The Space Environment Laboratory in Boulder has collaborated
with the University of Colorado to co... | 135 |@word cu:1 middle:1 simulation:3 forecaster:4 c1ass:1 past:1 existing:1 current:2 activation:10 yet:3 intriguing:1 john:1 drop:1 update:1 intelligence:2 discovering:1 guess:1 flare:18 desktop:1 ith:2 bowed:1 five:1 skilled:2 constructed:4 sidney:1 behavior:1 elman:2 examine:1 multi:1 brain:2 simulator:3 freeman:1 ... |
384 | 1,350 | Analysis of Drifting Dynamics with
Neural Network Hidden Markov Models
J. Kohlmorgen
GMD FIRST
Rudower Chaussee 5
12489 Berlin, Germany
K.-R. Miiller
GMD FIRST
Rudower Chaussee 5
12489 Berlin, Germany
K. Pawelzik
MPI f. Stromungsforschung
Bunsenstr. 10
37073 Gottingen, Germany
Abstract
We present a method for the an... | 1350 |@word middle:1 underline:1 open:1 jacob:1 pressure:1 electronics:1 initial:2 series:23 selecting:1 tuned:1 interestingly:1 past:1 nowlan:1 dx:1 distant:1 shape:1 net1:1 plot:1 mackey:4 stationary:4 prohibitive:1 selected:1 short:1 provides:1 detecting:1 differential:1 symposium:1 ik:1 consists:1 manner:1 multi:1 ... |
385 | 1,351 | 2D Observers for Human 3D Object Recognition?
Zili Liu
NEC Research Institute
Daniel Kersten
University of Minnesota
. Abstract
Converging evidence has shown that human object recognition
depends on familiarity with the images of an object. Further,
the greater the similarity between objects, the stronger is the
dep... | 1351 |@word trial:2 stronger:1 d2:2 decomposition:1 tr:5 shading:2 liu:6 daniel:1 hpp:2 dx:1 must:3 v:1 discrimination:1 selected:2 yr:2 accordingly:2 plane:2 provides:3 si1:1 sii:1 edelman:2 advocate:1 pairwise:1 indeed:1 nor:1 distractor:1 td:3 increasing:4 becomes:1 provided:3 lowest:1 what:3 weinshall:1 cm:1 minimi... |
386 | 1,352 | A Simple and Fast Neural Network
Approach to Stereovision
Rolf D. Henkel
Institute of Theoretical Physics
University of Bremen
P.O. Box 330 440, D-28334 Bremen
http://axon.physik.uni-bremen.de/-rdh
Abstract
A neural network approach to stereovision is presented based on
aliasing effects of simple disparity estimators... | 1352 |@word sri:1 middle:4 seems:1 physik:2 dedi:1 configuration:1 disparity:62 tuned:1 zerocrossings:1 denoting:1 imaginary:1 current:1 readily:1 realize:1 christian:1 leipzig:1 half:3 inspection:1 coarse:4 kiel:3 albrechts:1 along:3 direct:2 diplopia:3 overhead:1 combine:1 inter:1 notably:1 aliasing:6 multi:1 freeman... |
387 | 1,353 | Stacked Density Estimation
Padhraic Smyth *
Information and Computer Science
University of California, Irvine
CA 92697-3425
smythCics.uci.edu
David Wolpert
NASA Ames Research Center
Caelum Research
MS 269-2, Mountain View, CA 94035
dhwCptolemy.arc.nasa.gov
Abstract
In this paper, the technique of stacking, previousl... | 1353 |@word covariance:1 jacob:2 accounting:1 recounted:1 barney:1 contains:3 score:2 selecting:2 outperforms:2 john:1 partition:9 shape:6 plot:1 selected:2 inspection:1 ith:1 smith:2 te3t:1 contribute:1 location:1 ames:1 simpler:1 incorrect:1 shorthand:1 consists:1 combine:2 multimodality:1 manner:2 roughly:2 behavior... |
388 | 1,354 | Phase transitions and the perceptual
organization of video sequences
Yair Weiss
Dept. of Brain and Cognitive Sciences
Massachusetts Institute of Technology
ElO-120, Cambridge, MA 02139
http://www-bcs.mit.edu;-yweiss
Abstract
Estimating motion in scenes containing multiple moving objects
remains a difficult problem in... | 1354 |@word version:1 compression:1 seems:1 simulation:1 harder:1 initial:2 contains:1 existing:1 current:1 comparing:1 subsequent:2 additive:1 kdd:1 occludes:2 enables:1 plot:1 plane:1 location:1 direct:1 fitting:1 parallax:1 introduce:1 expected:1 indeed:1 mechanic:1 brain:2 automatically:1 researched:1 little:1 esti... |
389 | 1,355 | A Neural Network Model of Naive Preference
and Filial Imprinting in the Domestic Chick
Lucy E. Hadden
Department of Cognitive Science
University of California, San Diego
La Jolla, CA 92093
hadden@cogsci.ucsd.edu
Abstract
Filial imprinting in domestic chicks is of interest in psychology, biology,
and computational mod... | 1355 |@word trial:1 replicate:1 open:1 simulation:15 accounting:1 moment:1 initial:4 score:13 genetic:1 interestingly:1 reaction:1 current:2 comparing:1 activation:6 yet:3 exposing:1 remove:1 medial:1 v:2 infant:2 leaf:1 beginning:2 short:1 provides:1 detecting:1 node:1 location:1 preference:42 five:2 qualitative:2 con... |
390 | 1,356 | On Parallel Versus Serial Processing:
A Computational Study of Visual Search
Eyal Cohen
Department of Psychology
Tel-Aviv University Tel Aviv 69978, Israel
eyalc@devil. tau .ac .il
Eytan Ruppin
Departments of Computer Science & Physiology
Tel-Aviv University Tel Aviv 69978, Israel
ruppin@math.tau .ac.il
Abstract
A no... | 1356 |@word compression:5 underline:1 glue:1 simulation:2 reduction:1 contains:3 interestingly:2 reaction:2 assigning:1 must:1 cottrell:2 subsequent:1 tilted:2 numerical:2 shape:2 discrimination:2 item:12 short:1 detecting:2 math:1 location:2 five:2 mathematical:1 along:3 constructed:1 supply:1 viable:1 qualitative:1 p... |
391 | 1,357 | The Canonical Distortion Measure in Feature
Space and I-NN Classification
Jonathan Baxter*and Peter Bartlett
Department of Systems Engineering
Australian National University
Canberra 0200, Australia
{jon,bartlett}@syseng.anu.edu.au
Abstract
We prove that the Canonical Distortion Measure (CDM) [2, 3] is the
optimal di... | 1357 |@word version:1 norm:1 document:1 subjective:1 com:9 analysed:1 si:1 must:3 benign:1 selected:1 provides:1 constructed:1 symposium:1 qualitative:1 prove:1 consists:1 expected:2 considering:1 becomes:2 provided:2 exotic:1 notation:1 bounded:1 agnostic:1 argmin:1 transformation:1 ife:1 every:2 classifier:13 grant:1... |
392 | 1,358 | Monotonic Networks
Joseph Sill
Computation and Neural Systems program
California Institute of Technology
MC 136-93, Pasadena, CA 91125
email: joe@cs.caltech.edu
Abstract
Monotonicity is a constraint which arises in many application domains. We present a machine learning model, the monotonic network, for which monoton... | 1358 |@word seems:1 stronger:1 nicholson:1 si:1 issuing:1 must:1 chu:1 john:1 enables:2 update:1 amir:1 plane:7 supplying:1 hyperplanes:7 sigmoidal:1 along:2 direct:3 consists:2 theoretically:1 indeed:1 market:2 roughly:1 themselves:1 decreasing:3 company:1 increasing:4 bounded:7 lowest:1 developed:1 transformation:1 c... |
393 | 1,359 | On Parallel Versus Serial Processing:
A Computational Study of Visual Search
Eyal Cohen
Department of Psychology
Tel-Aviv University Tel Aviv 69978, Israel
eyalc@devil. tau .ac .il
Eytan Ruppin
Departments of Computer Science & Physiology
Tel-Aviv University Tel Aviv 69978, Israel
ruppin@math.tau .ac.il
Abstract
A no... | 1359 |@word cu:2 repository:2 compression:5 underline:1 glue:1 nd:1 simulation:2 cla:5 tr:1 reduction:1 contains:3 pub:1 chervonenkis:1 relabelled:1 interestingly:2 reaction:2 thre:1 analysed:1 assigning:1 must:4 john:2 tilted:2 numerical:2 subsequent:2 cottrell:2 partition:1 shape:2 offunctions:1 benign:1 belmont:1 di... |
394 | 136 | 602
AUTOMATIC LOCAL ANNEALING
Jared Leinbach
Deparunent of Psychology
Carnegie-Mellon University
Pittsburgh, PA 15213
ABSTRACT
This research involves a method for finding global maxima
in constraint satisfaction networks. It is an annealing
process butt unlike most others t requires no annealing
schedule. Temperatur... | 136 |@word trial:2 simulation:4 pick:1 initial:1 born:1 ala:12 current:2 activation:24 yet:1 must:9 distant:1 predetermined:1 shape:2 update:12 beginning:1 short:1 provides:1 mathematical:1 become:2 mechanic:1 ry:1 globally:1 decreasing:1 little:3 increasing:1 becomes:1 begin:2 what:2 substantially:1 spends:1 developed... |
395 | 1,360 | Silicon Retina with Adaptive Filtering
Properties
Shih-Chii Liu
Computation and Neural Systems
136-93 California Institute of Technology
Pasadena, CA 91125
shih@pcmp.caltech.edu
Abstract
This paper describes a small, compact circuit that captures the
temporal and adaptation properties both of the photoreceptor and
of... | 1360 |@word middle:1 q1:4 tr:1 electronics:1 liu:4 document:1 bradley:1 current:7 written:1 predetermined:1 plot:3 drop:1 stationary:1 yr:1 fabricating:1 node:3 five:5 supply:1 consists:2 behavior:3 monopolar:1 vertebrate:1 circuit:67 lowest:1 q2:1 fabricated:3 temporal:11 act:3 ti:3 bipolar:1 control:2 imager:1 discha... |
396 | 1,361 | Recurrent Neural Networks Can Learn to
Implement Symbol-Sensitive Counting
Paul Rodriguez
Janet Wiles
Department of Cognitive Science
University of California, San Diego
La Jolla, CA. 92093
prodrigu@cogsci.ucsd.edu
School of Information Technology and
Department of Psychology
University of Queensland
Brisbane, Quee... | 1361 |@word version:3 fjij:6 hu:4 simulation:2 queensland:2 contraction:4 fmite:1 solid:2 harder:4 initial:2 orponen:2 activation:3 must:2 unchanging:1 enables:1 plot:3 treating:1 update:1 half:11 plane:2 inspection:3 short:1 dissertation:1 accepting:2 node:7 location:1 along:3 differential:1 ouput:1 transducer:2 consi... |
397 | 1,362 | Multi-time Models for Temporally Abstract
Planning
Doina Precup, Richard S. Sutton
University of Massachusetts
Amherst, MA 01003
{dprecuplrich}@cs.umass.edu
Abstract
Planning and learning at multiple levels of temporal abstraction is a key
problem for artificial intelligence. In this paper we summarize an approach to... | 1362 |@word achievable:1 seems:1 twelfth:1 closure:1 korf:2 fonn:1 initial:1 contains:1 uma:1 current:2 numerical:1 happen:1 enables:2 update:1 maxv:1 v:3 intelligence:1 half:1 greedy:1 hallway:9 iso:2 location:1 mathematical:2 along:6 direct:1 inside:1 introduce:2 peng:2 expected:2 behavior:3 planning:26 multi:17 bell... |
398 | 1,363 | Active Data Clustering
Thomas Hofmann
Center for Biological and Computational Learning, MIT
Cambridge, MA 02139, USA, hofmann@ai.mit.edu
Joachim M. Buhmann
Institut fur Informatik III, Universitat Bonn
RomerstraBe 164, D-53117 Bonn, Germany, jb@cs.uni-bonn.de
Abstract
Active data clustering is a novel technique for c... | 1363 |@word faculty:1 interleave:1 advantageous:1 seems:1 additively:1 pick:1 profit:1 moment:1 initial:1 configuration:1 series:1 selecting:2 document:6 comparing:2 optim:1 com:1 assigning:1 yet:1 realistic:1 hofmann:13 enables:1 update:2 maxv:1 fund:1 greedy:1 prohibitive:1 selected:1 intelligence:2 ria:1 core:1 shor... |
399 | 1,364 | An Analog VLSI Neural Network for Phasebased Machine Vision
Bertram E. Shi
Department of Electrical and Electronic
Engineering
Hong Kong University of Science and
Technology
Clear Water Bay, Kowloon, Hong Kong
KwokFaiHui
Fujitsu Microelectronics Pacific Asia Ltd.
Suite 1015-20, Tower 1
Grand Century Place
193 Prince ... | 1364 |@word kong:4 cnn:5 cox:1 eliminating:1 achievable:2 middle:2 propagate:1 fonn:1 solid:2 disparity:3 amp:1 imaginary:5 vg2:1 current:12 must:4 ota:1 enables:1 designed:3 v:3 selected:1 farther:1 fabricating:1 node:3 location:1 nodal:1 dn:1 differential:1 supply:1 symposium:1 consists:1 otas:1 globally:1 chap:1 jsi... |
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