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100 | 1,089 | Beating a Defender in Robotic Soccer:
Memory-Based Learning of a Continuous
FUnction
Peter Stone
Department of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Manuela Veloso
Department of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
Learning how to adjust to an opponent's... | 1089 |@word trial:10 eliminating:1 proportion:1 twelfth:1 solid:1 shot:3 initial:10 score:3 practiced:1 document:1 interestingly:1 past:1 existing:1 current:2 yet:1 must:2 partition:1 enables:1 designed:3 sponsored:1 v:3 stationary:2 ficial:1 selected:1 intelligence:2 short:2 location:6 simpler:1 along:1 symposium:1 re... |
101 | 109 | 206
APPLICATIONS OF
~RROR BACK-PROPAGATION
TO PHONETIC CLASSIFICATION
Hong C. Leung & Victor W. Zue
Spoken Language Systems Group
Laboratory for Computer Science
Massachusetts Institute of Technology
Cambridge, MA 02139
ABSTRACT
This paper is concerced with the use of error back-propagation
in phonetic classification... | 109 |@word covariance:4 past:1 comparing:1 contextual:3 subsequent:1 numerical:2 drop:1 designed:1 lr:1 location:1 along:1 become:1 consists:2 inter:1 expected:1 examine:1 multi:2 formants:1 little:1 encouraging:1 becomes:1 provided:1 underlying:1 what:1 substantially:1 spoken:11 temporal:1 multidimensional:1 classifie... |
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103 | 1,091 | Independent Component Analysis
of Electroencephalographic Data
Scott Makeig
Naval Health Research Center
P.O. Box 85122
San Diego CA 92186-5122
Anthony J. Bell
Computational Neurobiology Lab
The Salk Institute, P.O. Box 85800
San Diego, CA 92186-5800
scott~cplJmmag.nhrc.navy.mil
tony~salk.edu
Tzyy-Ping Jung
Naval ... | 1091 |@word stronger:1 nd:1 decomposition:2 pick:1 initial:3 configuration:1 series:2 uncovered:1 contains:3 current:1 subcomponents:3 j1:1 wx:1 remove:1 half:2 selected:1 tone:1 short:1 record:1 filtered:3 location:1 burst:4 alert:4 sustained:2 behavioral:2 symp:1 expected:1 ica:24 coa:1 brain:13 window:2 estimating:1... |
104 | 1,092 | Clustering data through an analogy to
the Potts model
Marcelo Blatt, Shai Wiseman and Eytan Domany
Department of Physics of Complex Systems,
The Weizmann Institute of Science, Rehovot 76100, Israel
Abstract
A new approach for clustering is proposed. This method is based
on an analogy to a physical model; the ferromagn... | 1092 |@word advantageous:1 d2:1 simulation:1 emperature:1 dramatic:1 solid:2 initial:3 configuration:3 paramagnetic:18 si:8 assigning:1 partition:2 enables:1 plane:1 ith:2 vanishing:4 short:2 hamiltonian:3 sudden:1 mathematical:1 along:1 become:2 consists:1 autocorrelation:1 introduce:1 inter:2 indeed:1 roughly:1 behav... |
105 | 1,094 | Plasticity of Center-Surround Opponent
Receptive Fields in Real and Artificial
Neural Systems of Vision
S. Yasui
Kyushu Institute of Technology
lizuka 820, Japan
T. Furukawa
Kyushu Institute of Technology
lizuka 820, Japan
M. Yamada
Electrotechnical Laboratory
Tsukuba 305, Japan
T. Saito
Tsukuba University
Tsukuba ... | 1094 |@word pw:1 seems:1 open:1 cm2:1 brightness:2 mention:1 shading:1 initial:2 series:4 interestingly:1 rightmost:1 past:1 activation:1 physiol:1 plasticity:10 shape:4 remove:1 update:1 metabolism:1 nervous:1 accordingly:3 short:1 yamada:4 record:3 compo:1 location:1 height:1 constructed:1 consists:2 pathway:1 inside... |
106 | 1,095 | Worst-case Loss Bounds
for Single Neurons
David P. Helmbold
Department of Computer Science
University of California, Santa Cruz
Santa Cruz, CA 95064
USA
Jyrki Kivinen
Department of Computer Science
P.O. Box 26 (Teollisuuskatu 23)
FIN-00014 University of Helsinki
Finland
Manfred K. Warmuth
Department of Computer Scie... | 1095 |@word trial:5 version:1 achievable:2 norm:12 seems:2 nd:1 suitably:1 open:1 tried:1 initial:2 contains:1 interestingly:2 past:1 current:1 comparing:1 ilxl:1 activation:1 must:4 written:1 cruz:6 realize:1 additive:1 treating:1 update:6 warmuth:14 plane:1 manfred:1 unbounded:1 ucsc:2 direct:1 become:2 prove:4 combi... |
107 | 1,096 | Predictive Q-Routing: A Memory-based
Reinforcement Learning Approach to
Adaptive Traffic Control
Samuel P.M. Choi, Dit-Yan Yeung
Department of Computer Science
Hong Kong University of Science and Technology
Clear Water Bay, Kowloon, Hong Kong
{pmchoi,dyyeung}~cs.ust.hk
Abstract
In this paper, we propose a memory-base... | 1096 |@word kong:2 exploitation:5 middle:3 version:1 manageable:1 seems:1 simulation:3 initial:7 selecting:1 past:2 current:7 router:1 ust:1 must:3 realistic:1 subsequent:2 update:3 depict:1 stationary:2 congestion:9 selected:2 slowing:1 short:1 prespecified:1 node:53 along:16 become:4 overhead:1 manner:1 peng:2 expect... |
108 | 1,097 | Using Unlabeled Data for Supervised
Learning
Geoffrey Towell
Siemens Corporate Research
755 College Road East
Princeton, NJ 08540
Abstract
Many classification problems have the property that the only costly
part of obtaining examples is the class label. This paper suggests
a simple method for using distribution infor... | 1097 |@word middle:1 version:1 briefly:1 achievable:1 contains:2 efficacy:1 genetic:1 interestingly:1 current:2 contextual:1 si:1 reminiscent:2 written:1 eleven:1 aside:1 intelligence:1 guess:1 ith:1 record:1 supplying:1 provides:1 five:1 incorrect:2 combine:1 eleventh:2 expected:2 roughly:2 behavior:2 nor:1 gener:1 li... |
109 | 1,098 | Discovering Structure in Continuous
Variables Using Bayesian Networks
Reimar Hofmann and Volker Tresp*
Siemens AG, Central Research
Otto-Hahn-Ring 6
81730 Munchen, Germany
Abstract
We study Bayesian networks for continuous variables using nonlinear conditional density estimators. We demonstrate that useful structures... | 1098 |@word msr:1 open:1 covariance:1 pick:1 tr:2 initial:4 contains:2 score:12 recovered:1 comparing:1 partition:1 hofmann:5 remove:1 update:8 depict:1 greedy:1 discovering:4 intelligence:1 xk:6 provides:1 node:2 location:2 ipi:4 consists:2 introduce:2 expected:2 ra:1 roughly:1 examine:1 decomposed:1 window:3 consider... |
110 | 1,099 | Adaptive Mixture of Probabilistic Transducers
Yoram Singer
AT&T Bell Laboratories
singer@research.att.com
Abstract
We introduce and analyze a mixture model for supervised learning of
probabilistic transducers. We devise an online learning algorithm that
efficiently infers the structure and estimates the parameters of ... | 1099 |@word briefly:2 middle:1 compression:1 trofimov:2 simulation:2 jacob:1 recursively:2 cru:1 att:1 exclusively:1 past:1 current:3 com:1 nowlan:1 si:1 yet:1 update:6 half:1 leaf:7 warmuth:2 p7:1 beginning:1 smith:1 short:1 manfred:1 node:18 ron:2 traverse:1 desantis:1 five:1 unbounded:1 tagger:1 shtarkov:1 direct:2 ... |
111 | 11 | 515
MICROELECTRONIC IMPLEMENTATIONS OF CONNECTIONIST
NEURAL NETWORKS
Stuart Mackie, Hans P. Graf, Daniel B. Schwartz, and John S. Denker
AT&T Bell Labs, Holmdel, NJ 07733
Abstract
In this paper we discuss why special purpose chips are needed for useful
implementations of connectionist neural networks in such applicat... | 11 |@word instruction:5 r:1 solid:1 accommodate:1 reduction:1 series:2 contains:6 exclusively:1 daniel:1 current:12 must:2 reminiscent:1 john:1 enables:3 designed:7 update:1 device:2 short:1 node:5 five:6 along:2 fabricate:1 expected:3 brain:3 cpu:2 pf:1 circuit:10 maximizes:1 lowest:1 what:2 kind:1 string:3 fabricated... |
112 | 110 | 761
Adaptive Neural Networks Using MOS Charge Storage
D. B. Schwartz 1, R. E. Howard and W. E. Hubbard
AT&T Bell Laboratories
Crawfords Corner Rd.
Holmdel, N.J. 07733
Abstract
MOS charge storage has been demonstrated as an effective method to store
the weights in VLSI implementations of neural network models by severa... | 110 |@word cox:2 middle:1 version:1 inversion:1 eliminating:1 humidity:1 cha:1 linearized:1 decomposition:1 mention:1 minus:2 solid:5 initial:1 configuration:1 contains:1 current:3 com:1 ida:1 follower:1 must:5 written:3 refresh:1 treating:1 designed:1 update:1 v:1 v_:1 device:7 steepest:2 tcp:3 coarse:1 node:9 sigmoid... |
113 | 1,100 | Tempering Backpropagation Networks:
Not All Weights are Created Equal
Nicol N. Schraudolph
EVOTEC BioSystems GmbH
Grandweg 64
22529 Hamburg, Germany
nici@evotec.de
Terrence J. Sejnowski
Computational Neurobiology Lab
The Salk Institute for BioI. Studies
San Diego, CA 92186-5800, USA
terry@salk.edu
Abstract
Backprop... | 1100 |@word proportion:2 instrumental:1 confirms:1 jacob:2 reap:1 tr:1 initial:2 pub:1 existing:1 anterior:1 nt:1 nowlan:1 activation:7 yet:1 must:3 subsequent:1 shape:1 cheap:1 update:10 v:1 guess:1 accordingly:2 directory:1 isotropic:3 steepest:1 haykin:2 plaut:3 node:15 toronto:1 become:1 consists:1 acti:3 introduce... |
114 | 1,101 | Finite State Automata that Recurrent
Cascade-Correlation Cannot Represent
Stefan C. Kremer
Department of Computing Science
University of Alberta
Edmonton, Alberta, CANADA T6H 5B5
Abstract
This paper relates the computational power of Fahlman' s Recurrent
Cascade Correlation (RCC) architecture to that of fInite state ... | 1101 |@word version:1 simulation:1 fmite:3 fif:1 ld:1 initial:3 substitution:1 contains:3 exclusively:1 current:3 comparing:1 activation:16 must:11 subsequent:1 happen:1 treating:1 implying:1 selected:1 device:1 accepting:2 provides:1 node:12 simpler:1 five:1 constructed:1 become:1 consists:1 prove:5 expected:1 elman:1... |
115 | 1,102 | Hierarchical Recurrent Neural Networks for
Long-Term Dependencies
Yoshua Bengio?
Dept. Informatique et
Recherche Operationnelle
Universite de Montreal
Montreal, Qc H3C-3J7
bengioyGiro.umontreal.ca
Salah El Hihi
Dept. Informatique et
Recherche Operationnelle
Universite de Montreal
Montreal, Qc H3C-3J7
elhihiGiro.umont... | 1102 |@word trial:3 longterm:1 compression:1 polynomial:1 coarseness:1 propagate:1 decomposition:1 systeme:1 harder:1 carry:1 initial:3 configuration:1 series:2 past:2 current:1 lang:3 must:1 intelligence:4 beginning:1 short:3 farther:1 recherche:2 coarse:2 simpler:1 five:1 introduce:3 operationnelle:2 theoretically:1 ... |
116 | 1,103 | Optimizing Cortical Mappings
Geoffrey J. Goodhill
The Salk Institute
10010 North Torrey Pines Road
La Jolla, CA 92037, USA
Steven Finch
Human Communication Research Centre
University of Edinburgh, 2 Buccleuch Place
Edinburgh EH8 9LW, GREAT BRITAIN
Terrence J. Sejnowski
The Howard Hughes Medical Institute
The Salk Ins... | 1103 |@word seems:1 simulation:1 solid:2 reduction:1 contains:1 series:1 pub:1 current:1 written:1 distant:3 half:16 pursued:1 bijection:1 unbounded:1 mathematical:1 c2:5 direct:1 become:3 qualitative:2 consists:1 edelman:1 inter:2 behavior:2 frequently:1 examine:1 brain:3 globally:3 decreasing:2 increasing:3 becomes:3... |
117 | 1,104 | KODAK lMAGELINK? OCR
Alphanumeric Handprint Module
Alexander Shustorovich and Christopher W. Thrasher
Business Imaging Systems, Eastman Kodak Company, Rochester, NY 14653-5424
ABSTRACT
This paper describes the Kodak Imageliok TM OCR alphanumeric
handprint module. There are two neural network algorithms at its
cme: the... | 1104 |@word middle:3 version:1 stronger:1 minus:1 applicatioo:2 moment:1 contains:1 united:2 document:1 blank:2 current:1 activation:7 alphanumeric:8 remove:1 designed:2 intelligence:1 guess:2 node:10 successive:1 five:4 height:3 along:3 m7:1 driver:2 replication:1 inside:1 introduce:1 alspector:1 globally:1 company:1 ... |
118 | 1,105 | Cholinergic suppression of transmission may
allow combined associative memory function and
self-organization in the neocortex.
Michael E. Hasselmo and Milos Cekic
Department of Psychology and Program in Neurosciences,
Harvard University, 33 Kirkland St., Cambridge, MA 02138
hasselmo@katIa.harvard.edu
Abstract
Selectiv... | 1105 |@word selforganization:1 hippocampus:2 stronger:2 termination:1 simulation:4 r:1 fonn:2 initial:1 contains:2 reynolds:1 comparing:1 activation:4 si:1 must:2 subsequent:1 tenn:1 detecting:1 location:1 wir:1 height:1 along:5 become:1 gustafsson:2 incorrect:1 combine:1 olfactory:2 brain:5 little:2 becomes:2 project:... |
119 | 1,106 | Handwritten Word Recognition using Contextual
Hybrid Radial Basis Function NetworklHidden
Markov Models
Bernard Lemarie
La Poste/SRTP
10, Rue de l'lle-Mabon
F-44063 Nantes Cedex France
lemarie@srtp.srt-poste.fr
Michel Gilloux
La Poste/SRTP
10, Rue de l'1le-Mabon
F-44063 Nantes Cede x France
gilloux@srtp.srt-poste.fr
... | 1106 |@word proportion:1 nd:2 cha:1 simplifying:2 decomposition:3 covariance:2 initial:1 score:3 itp:3 document:3 interestingly:1 outperforms:1 bitmap:9 current:4 contextual:15 manuel:1 yet:4 written:1 numerical:1 girosi:4 designed:3 discrimination:2 half:5 intelligence:2 lr:1 postal:2 contribute:1 predecessor:1 combin... |
120 | 1,107 | Human Reading and the Curse of
Dimensionality
Gale L. Martin
MCC Austin, TX 78613 galem@mcc.com
Abstract
Whereas optical character recognition (OCR) systems learn to classify single characters; people learn to classify long character strings
in parallel, within a single fixation . This difference is surprising
becaus... | 1107 |@word mcconkie:2 determinant:1 version:1 middle:1 wiesel:2 simulation:3 jacob:2 rayner:9 paid:1 thereby:2 carry:1 reduction:1 com:1 surprising:1 must:3 shape:3 asymptote:3 designed:1 ccj:2 plane:2 beginning:2 sys:1 provides:1 node:8 location:3 five:1 height:1 skilled:1 replication:2 ooj:4 fixation:12 roughly:1 fr... |
121 | 1,108 | Sample Complexity for Learning
Recurrent Percept ron Mappings
Bhaskar Dasgupta
Department of Computer Science
University of Waterloo
Waterloo, Ontario N2L 3G 1
CANADA
Eduardo D. Sontag
Department of Mathematics
Rutgers University
New Brunswick, NJ 08903
USA
bdasgupt~daisy.uwaterloo.ca
sontag~control.rutgers.edu
Ab... | 1108 |@word briefly:2 jlf:1 polynomial:11 duda:2 open:1 essay:1 decomposition:1 pick:3 contains:2 series:1 chervonenkis:4 current:1 written:1 must:2 fn:15 offunctions:1 alone:1 selected:1 warmuth:1 record:1 lr:7 cjx:1 filtered:1 provides:2 quantized:3 ron:1 toronto:1 prove:4 fitting:1 introduce:2 roughly:2 lll:2 cardin... |
122 | 1,109 | Generalization in Reinforcement
Learning: Successful Examples Using
Sparse Coarse Coding
Richard S. Sutton
University of Massachusetts
Amherst, MA 01003 USA
richOcs.umass.edu
Abstract
On large problems, reinforcement learning systems must use parameterized function approximators such as neural networks in order to ge... | 1109 |@word trial:9 seems:1 simulation:1 tr:1 moment:1 initial:1 series:2 uma:1 selecting:1 o2:1 current:1 must:3 plot:2 update:1 greedy:4 selected:5 intelligence:1 ria:1 coarse:3 zhang:2 rollout:1 along:1 become:1 rife:1 theoretically:1 expected:1 indeed:1 behavior:1 roughly:1 planning:3 brain:1 terminal:3 torque:4 ol... |
123 | 111 | 568
DYNAMICS OF ANALOG NEURAL
NETWORKS WITH TIME DELAY
C.M. Marcus and RM. Westervelt
Division of Applied Sciences and Department of Physics
Harvard University, Cambridge Massachusetts 02138
ABSTRACT
A time delay in the response of the neurons in a network can
induce sustained oscillation and chaos. We present a stab... | 111 |@word version:1 open:1 simulation:1 linearized:2 ci2:1 biomathematics:1 initial:5 configuration:4 contains:1 imaginary:3 surprising:1 si:4 written:1 must:1 transcendental:1 realize:1 numerical:3 update:2 mackey:3 half:5 device:1 plane:7 coleman:3 short:1 math:1 sigmoidal:3 mathematical:1 along:6 constructed:1 diff... |
124 | 1,111 | Fast Learning by Bounding Likelihoods
in Sigmoid Type Belief Networks
Tommi Jaakkola
tommi@psyche.mit.edu
Lawrence K. Saul
lksaul@psyche.mit.edu
Michael I. Jordan
jordan@psyche.mit.edu
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
Abstract
Sigmoid type belief ... | 1111 |@word autoassociator:1 inversion:1 manageable:1 thereby:1 tr:1 solid:1 harder:2 configuration:3 tuned:1 recovered:1 activation:5 si:8 written:1 realistic:1 visible:2 numerical:1 informative:1 motor:7 alone:1 generative:1 intelligence:1 detecting:1 sigmoidal:1 wijsj:1 become:2 reinterpreting:1 introduce:2 expected... |
125 | 1,112 | Learning Model Bias
Jonathan Baxter
Department of Computer Science
Royal Holloway College, University of London
jon~dcs.rhbnc.ac.uk
Abstract
In this paper the problem of learning appropriate domain-specific
bias is addressed. It is shown that this can be achieved by learning
many related tasks from the same domain, a... | 1112 |@word mild:1 advantageous:1 simulation:4 reduction:1 pub:1 must:3 cruz:1 remove:1 plot:1 update:1 ihr:3 lr:3 draft:1 node:8 dn:10 constructed:1 ouput:1 qualitative:1 expected:1 automatically:2 actual:1 little:1 increasing:1 notation:1 bounded:3 didn:1 agnostic:1 kind:2 differing:1 pseudo:1 every:1 xd:1 exactly:1 ... |
126 | 1,113 | Generalized Learning Vector
Quantization
Atsushi Sato & Keiji Yamada
Information Technology Research Laboratories,
NEC Corporation
1-1, Miyazaki 4-chome, Miyamae-ku,
Kawasaki, Kanagawa 216, Japan
E-mail: {asato.yamada}@pat.cl.nec.co.jp
Abstract
We propose a new learning method, "Generalized Learning Vector Quantizati... | 1113 |@word version:3 d2:22 heretofore:1 initial:5 document:3 wd:3 must:4 aft:11 written:2 happen:1 wll:2 update:1 half:2 accordingly:1 beginning:1 steepest:5 yamada:7 location:1 prove:2 examine:1 multi:2 ol:1 window:4 increasing:2 becomes:1 linearity:3 miyazaki:1 what:1 kind:2 minimizes:2 corporation:1 act:2 positive:... |
127 | 1,114 | Stock Selection via Nonlinear
Multi-Factor Models
Asriel U. Levin
BZW Barclays Global Investors
Advanced Strategies and Research Group
45 Fremont Street
San Francisco CA 94105
email: asriel.levin@bglobal.com
Abstract
This paper discusses the use of multilayer feed forward neural networks for predicting a stock's exces... | 1114 |@word eliminating:1 loading:1 replicate:1 t_:1 jacob:3 profit:1 reduction:1 series:1 pub:1 past:1 com:1 must:1 john:1 fama:2 v:1 selected:2 yr:4 beginning:3 short:12 record:2 sigmoidal:1 constructed:3 direct:1 persistent:1 consists:3 manner:2 market:14 expected:8 alspector:1 multi:4 company:1 window:1 increasing:... |
128 | 1,115 | A New Learning Algorithm for Blind
Signal Separation
s. Amari*
University of Tokyo
Bunkyo-ku, Tokyo 113, JAPAN
amari@sat.t. u-tokyo.ac.jp
A. Cichocki
Lab. for Artificial Brain Systems
FRP, RIKEN
Wako-Shi, Saitama, 351-01, JAPAN
cia@kamo.riken.go.jp
H. H. Yang
Lab. for Information Representation
FRP, RIKEN
Wako-Shi, S... | 1115 |@word polynomial:2 simulation:5 recursively:2 moment:4 wako:3 ka:2 z2:1 od:1 activation:16 si:4 yet:2 negentropy:2 written:1 wx:2 update:3 stationary:1 selected:1 xk:6 coarse:1 provides:1 hermite:2 mathematical:1 h4:1 direct:1 differential:2 symposium:1 ica:6 equivariant:6 brain:1 decreasing:2 becomes:1 estimatin... |
129 | 1,116 | Classifying Facial Action
Marian Stewart Bartlett, Paul A. Viola,
Terrence J. Sejnowski, Beatrice A. Golomb
Howard Hughes Medical Institute
The Salk Institute, La Jolla, CA 92037
marni, viola, terry, beatrice @salk.edu
Jan Larsen
The Niels Bohr Institute
2100 Copenhagen
Denmark
jlarsen@fys.ku.dk
Joseph C. Hager
Pau... | 1116 |@word sex:1 closure:1 contraction:4 brightness:1 hager:9 initial:2 contains:1 score:1 cottrell:5 visible:1 subsequent:1 shape:1 alone:2 cue:3 beginning:1 detecting:2 consulting:1 provides:4 location:4 wrinkling:2 five:1 along:4 symposium:1 surprised:2 behavioral:3 au1:1 themselves:1 frequently:1 examine:1 plannin... |
130 | 1,117 | Parallel Optimization of Motion
Controllers via Policy Iteration
J. A. Coelho Jr., R. Sitaraman, and R. A. Grupen
Department of Computer Science
University of Massachusetts, Amherst, 01003
Abstract
This paper describes a policy iteration algorithm for optimizing the
performance of a harmonic function-based controller... | 1117 |@word version:2 nd:3 open:1 simplifying:1 automat:2 initial:8 configuration:19 series:1 existing:1 current:2 discretization:1 numerical:1 plot:2 update:1 chua:4 characterization:1 completeness:1 node:12 simpler:1 direct:1 grupen:10 consists:2 resistive:14 behavior:1 p1:2 planning:2 discretized:1 globally:1 td:1 c... |
131 | 1,118 | A Framework for Non-rigid Matching
and Correspondence
Suguna Pappu, Steven Gold, and Anand Rangarajan 1
Departments of Diagnostic Radiology and Computer Science
and the Yale Neuroengineering and Neuroscience Center
Yale University New Haven, CT 06520-8285
Abstract
Matching feature point sets lies at the core of many ... | 1118 |@word decomposition:1 tr:1 accommodate:1 initial:1 shape:1 mislabelled:1 concert:1 update:8 alone:3 rnxn:1 intelligence:2 plane:1 nonspatial:1 core:1 provides:1 location:3 p8:11 expected:1 alspector:1 nor:1 discretized:1 decomposed:1 becomes:1 begin:3 provided:1 null:1 minimizes:1 developed:3 finding:1 transforma... |
132 | 1,119 | A Smoothing Regularizer for Recurrent
Neural Networks
Lizhong Wu and John Moody
Oregon Graduate Institute, Computer Science Dept., Portland, OR 97291-1000
Abstract
We derive a smoothing regularizer for recurrent network models by
requiring robustness in prediction performance to perturbations of
the training data. The... | 1119 |@word version:1 norm:1 dekker:1 covariance:2 p0:1 simplifying:1 fonn:1 initial:1 series:7 selecting:2 pub:1 ours:1 past:1 current:1 comparing:1 nowlan:4 activation:2 yet:2 written:1 john:2 additive:2 partition:2 girosi:2 plaut:3 sigmoidal:1 constructed:1 become:1 supply:1 consists:2 fitting:1 expected:4 behavior:... |
133 | 112 | 802
CRICKET WIND DETECTION
John P. Miller
Neurobiology Group, University of California,
Berkeley, California 94720, U.S.A.
A great deal of interest has recently been focused on theories concerning
parallel distributed processing in central nervous systems. In particular,
many researchers have become very interested... | 112 |@word implemented:1 cereal:4 direction:12 question:1 occurs:1 spike:4 filter:2 primary:2 receptive:2 deal:1 jacob:4 during:1 self:1 uniquely:1 cricket:9 mapped:2 carry:1 contains:1 explanation:1 presenting:1 dendritic:4 cellular:1 current:3 relationship:1 ground:1 ratio:1 great:1 recently:1 john:2 physiol:3 robert... |
134 | 1,120 | Optimization Principles for the Neural
Code
Michael DeWeese
Sloan Center, Salk Institute
La Jolla, CA 92037
deweese@salk.edu
Abstract
Recent experiments show that the neural codes at work in a wide
range of creatures share some common features. At first sight, these
observations seem unrelated. However, we show that t... | 1120 |@word trial:2 illustrating:2 briefly:1 adrian:3 pulse:3 minus:1 carry:1 substitution:1 current:2 comparing:1 must:4 shape:1 offunctions:1 remove:1 asymptote:1 aside:1 stationary:3 half:1 nervous:2 iso:1 vanishing:1 dissertation:1 record:1 filtered:8 height:1 unbounded:1 become:2 shorthand:1 inside:2 inter:1 indee... |
135 | 1,121 | Optimal Asset Allocation
?
uSIng
Adaptive Dynamic Programming
Ralph Neuneier*
Siemens AG, Corporate Research and Development
Otto-Hahn-Ring 6, D-81730 Munchen, Germany
Abstract
In recent years, the interest of investors has shifted to computerized asset allocation (portfolio management) to exploit the growing
dynamic... | 1121 |@word trial:2 exploitation:2 middle:1 seems:1 willing:1 p0:1 tr:2 initial:1 series:3 liquid:2 past:1 neuneier:6 current:3 od:1 must:1 realistic:1 drop:2 plot:1 update:3 stationary:1 imitate:1 beginning:2 short:1 institution:1 successive:1 constructed:2 consists:3 prove:1 compose:1 expected:7 market:10 behavior:2 ... |
136 | 1,122 | Recursive Estimation of Dynamic
Modular RBF Networks
Visakan Kadirkamanathan
Automatic Control & Systems Eng. Dept.
University of Sheffield, Sheffield Sl 4DU, UK
visakan@acse.sheffield.ac.uk
Maha Kadirkamanathan
Dragon Systems UK
Cheltenham GL52 4RW, UK
maha@dragon.co.uk
Abstract
In this paper, recursive estimation a... | 1122 |@word briefly:1 covariance:1 eng:1 excited:1 jacob:2 recursively:3 initial:1 score:1 past:1 o2:4 current:2 nowlan:2 must:2 xk:1 lr:4 normalising:1 detecting:1 consists:1 combine:1 expected:2 alspector:1 multi:2 ol:4 increasing:1 phj:2 underlying:11 developed:9 differing:1 ti:1 growth:1 ro:6 demonstrates:1 uk:6 co... |
137 | 1,123 | Silicon Models
for
A uditory Scene Analysis
John Lazzaro and John Wawrzynek
CS Division
UC Berkeley
Berkeley, CA 94720-1776
lazzaroOcs.berkeley.edu. johnvOcs.berkeley.edu
Abstract
We are developing special-purpose, low-power analog-to-digital
converters for speech and music applications, that feature analog
circuit m... | 1123 |@word middle:2 compression:1 d2:2 km:1 pulse:7 sensed:1 carry:2 initial:1 disparity:1 tuned:1 current:1 john:2 timestamps:1 shape:4 designed:1 uditory:1 half:1 device:1 tone:4 short:1 filtered:1 five:2 height:1 burst:1 supply:1 viable:1 qualitative:1 autocorrelation:6 behavior:1 multi:11 lyon:4 circuit:8 sparcsta... |
138 | 1,124 | Memory-based Stochastic Optimization
Andrew W. Moore and Jeff Schneider
School of Computer Science
Carnegie-Mellon University
Pittsburgh, PA 15213
Abstract
In this paper we introduce new algorithms for optimizing noisy
plants in which each experiment is very expensive. The algorithms
build a global non-linear model o... | 1124 |@word trial:1 briefly:2 version:6 polynomial:7 simulation:2 tried:1 sensed:1 covariance:2 pick:1 tr:2 initial:2 inefficiency:1 tuned:2 reaction:2 current:5 comparing:1 must:1 numerical:1 motor:1 greedy:1 fewer:2 intelligence:1 accordingly:1 beginning:1 steepest:4 ith:2 detecting:2 location:4 simpler:1 five:1 math... |
139 | 1,125 | Parallel analog VLSI architectures for
computation of heading direction and
time-to-contact
Giacomo Indiveri
giacomo@klab.caltech .edu
Jorg Kramer
kramer@klab .caltech.edu
Christof Koch
koch@klab.caltech.edu
Division of Biology
California Institute of Technology
Pasadena, CA 91125
Abstract
We describe two parallel... | 1125 |@word disk:1 integrative:1 confirms:2 pulse:3 simulation:6 carolina:1 contraction:2 brightness:1 current:23 comparing:2 yet:1 tilted:1 designed:4 update:2 half:1 selected:4 device:3 plane:3 inspection:1 reciprocal:1 short:1 detecting:1 location:7 successive:1 along:4 qualitative:8 symp:1 diffuser:2 expected:1 rap... |
140 | 1,126 | Recurrent Neural Networks for Missing or
Asynchronous Data
Yoshua Bengio Dept. Informatique et
Recherche Operationnelle
Universite de Montreal
Montreal, Qc H3C-3J7
Francois Gingras
Dept. Informatique et
Recherche Operationnelle
Universite de Montreal
Montreal, Qc H3C-3J7
bengioy~iro.umontreal.ca
gingra8~iro.umontre... | 1126 |@word trial:3 repository:1 middle:1 covariance:2 initial:2 pub:1 neuneier:2 comparing:1 si:1 lang:2 periodically:1 half:1 discovering:1 weighing:1 recherche:2 firstly:1 simpler:1 consists:2 inside:1 introduce:1 operationnelle:2 indeed:1 expected:3 alspector:2 behavior:1 multi:1 company:1 estimating:5 linearity:1 ... |
141 | 1,127 | A Realizable Learning Task which
Exhibits Overfitting
Siegfried Bos
Laboratory for Information Representation, RIKEN,
Hirosawa 2-1, Wako-shi, Saitama, 351-01, Japan
email: boes@zoo.riken.go.jp
Abstract
In this paper we examine a perceptron learning task. The task is
realizable since it is provided by another perceptr... | 1127 |@word effect:1 trial:1 concept:1 normalized:1 implies:2 indicate:3 norm:3 evolution:2 concentrate:2 guided:2 hypercube:1 already:6 correct:3 laboratory:1 quantity:1 simulation:8 realized:1 eg:18 occurs:1 parametric:6 strategy:4 during:5 dependence:1 exhibit:5 gradient:1 outlook:1 solid:8 thank:1 capacity:1 transp... |
142 | 1,128 | Modeling Saccadic Targeting in Visual Search
Gregory J. Zelinsky
Center for Visual Science
University of Rochester
Rochester, NY 14627
greg@cvs.rochester.edu
Rajesh P. N. Rao
Computer Science Department
University of Rochester
Rochester, NY 14627
rao@cs.rochester.edu
Mary M. Hayhoe
Center for Visual Science
Universit... | 1128 |@word trial:3 middle:1 sri:1 instruction:1 r:1 crucially:1 dramatic:1 thereby:1 initial:1 foveal:1 exclusively:1 current:8 comparing:1 kowler:1 skipping:1 nowlan:1 activation:1 yet:1 parsing:1 cottrell:1 distant:1 subsequent:3 chicago:1 motor:2 intelligence:2 selected:1 plane:2 smith:1 prespecified:1 yamada:1 coa... |
143 | 1,129 | Simulation of a Thalamocortical Circuit for
Computing Directional Heading in the Rat
Hugh T. Blair*
Department of Psychology
Yale University
New Haven, CT 06520-8205
tadb@minerva.cis.yale.edu
Abstract
Several regions of the rat brain contain neurons known as head-direction celis, which encode the animal's directional... | 1129 |@word version:1 proportion:2 hippocampus:1 anterograde:1 simulation:16 r:3 propagate:1 thereby:1 initial:3 tuned:1 ranck:6 current:5 anterior:14 activation:1 must:3 celis:2 motor:1 plot:1 stationary:1 shut:1 slowing:1 plane:1 provides:1 characterization:1 location:1 preference:4 zhang:3 five:1 rc:1 become:1 incor... |
144 | 113 | 224
USE OF MULTI-LAYERED NETWORKS FOR
CODING SPEECH WITH PHONETIC FEATURES
Piero Cosi
Centro di Studio per Ie
Ricerche di Fonetica, C.N.R.,
Via Oberdan,10,
35122 Padova, Italy
Yoshua Bengio, Regis Cardin
and Renato De Mori
Computer Science Dept.
McGill University
Montreal, Canada H3A2A7
ABSTRACT
Preliminary results ... | 113 |@word beep:1 middle:1 seems:1 glue:1 gradual:1 independant:3 contains:1 tuned:1 current:1 delgutte:5 designed:3 intelligence:4 selected:1 mln:3 short:1 node:2 simpler:2 become:1 combine:1 pathway:1 rapid:1 behavior:1 pr1:1 multi:9 formants:3 morphology:1 automatically:2 window:2 kiang:3 synchro:1 transformation:2 ... |
145 | 1,130 | Some results on convergent unlearning
algorithm
Serguei A. Semenov &: Irina B. Shuvalova
Institute of Physics and Technology
Prechistenka St. 13/7
Moscow 119034, Russia
Abstract
In this paper we consider probabilities of different asymptotics of
convergent unlearning algorithm for the Hopfield-type neural network (Pl... | 1130 |@word private:1 simulation:4 llo:2 contraction:1 pick:1 reduction:1 initial:3 elaborating:1 past:1 ts2:3 current:1 nt:1 si:2 universality:1 written:1 realize:2 subsequent:1 numerical:2 treating:1 update:1 vanishing:2 realizing:1 along:2 c2:1 supply:1 retrieving:1 prove:3 unlearning:22 expected:1 isi:4 examine:1 r... |
146 | 1,131 | Discriminant Adaptive Nearest Neighbor
Classification and Regression
Trevor Hastie
Department of Statistics
Sequoia Hall
Stanford University
California 94305
trevor@playfair .stanford.edu
Robert Tibshirani
Department of Statistics
University of Toronto
tibs@utstat .toronto.edu
Abstract
Nearest neighbor classificatio... | 1131 |@word deformed:2 version:3 middle:3 briefly:1 proportion:1 polynomial:1 km:4 grey:3 simulation:1 crucially:1 covariance:6 decomposition:2 solid:2 reduction:4 myles:2 series:1 xiy:1 ours:3 outperforms:1 visible:1 shape:1 designed:1 discrimination:2 intelligence:1 short:5 toronto:2 combine:1 inside:1 frequently:1 m... |
147 | 1,132 | EM Optimization of Latent-Variable
Density Models
Christopher M Bishop, Markus Svensen and Christopher K I Williams
Neural Computing Research Group
Aston University, Birmingham, B4 7ET, UK
c.m.bishop~aston.ac.uk svensjfm~aston.ac.uk c.k.i.williams~aston.ac.uk
Abstract
There is currently considerable interest in devel... | 1132 |@word version:1 inversion:1 seek:1 covariance:1 decomposition:1 tiw:1 paid:1 ld:1 initial:1 configuration:6 current:2 dx:2 must:4 readily:1 john:1 realistic:1 j1:1 drop:1 plot:3 update:1 provides:1 revisited:1 along:1 become:1 consists:2 introduce:4 multi:5 spherical:1 td:1 little:2 project:3 discover:2 underlyin... |
148 | 1,133 | Stable Fitted Reinforcement Learning
Geoffrey J. Gordon
Computer Science Department
Carnegie Mellon University
Pittsburgh PA 15213
ggordon@cs.cmu.edu
Abstract
We describe the reinforcement learning problem, motivate algorithms which seek an approximation to the Q function, and present
new convergence results for two ... | 1133 |@word trial:2 exploitation:1 version:4 norm:12 twelfth:2 seek:1 contraction:12 initial:3 contains:1 series:1 united:1 must:3 numerical:1 subsequent:2 benign:1 predetermined:1 update:9 fewer:1 mln:1 direct:3 prove:1 paragraph:1 introduce:1 expected:8 behavior:1 frequently:2 terminal:2 bellman:2 discounted:5 td:5 a... |
149 | 1,134 | Competence Acquisition in an
Autonomous Mobile Robot using
Hardware Neural Techniques.
Geoff Jackson and Alan F. Murray
Department of Electrical Engineering
Edinburgh University
Edinburgh, ER9 3JL
Scotland, UK
gbj@ee.ed.ac.uk,afm@ee.ed.ac.uk
Abstract
In this paper we examine the practical use of hardware neural
netwo... | 1134 |@word pw:3 pulse:22 tried:1 versatile:1 carry:2 configuration:1 tuned:1 surprising:1 must:5 readily:1 refresh:3 motor:7 designed:4 drop:1 progressively:2 intelligence:1 device:5 scotland:1 indefinitely:1 provides:1 along:2 constructed:1 direct:7 become:1 profound:1 predecessor:1 overhead:3 inter:1 rapid:1 roughly... |
150 | 1,135 | Information through a Spiking Neuron
Charles F. Stevens and Anthony Zador
Salk Institute MNL/S
La J olIa, CA 92037
zador@salk.edu
Abstract
While it is generally agreed that neurons transmit information
about their synaptic inputs through spike trains, the code by which
this information is transmitted is not well und... | 1135 |@word trial:2 nd:1 thereby:1 minus:1 solid:4 papoulis:2 initial:2 series:2 current:2 si:2 yet:6 must:3 interspike:2 alone:1 filtered:1 colored:1 provides:3 height:1 mathematical:1 theoretically:1 isi:21 themselves:1 perkel:1 ol:1 decreasing:1 actual:1 considering:3 increasing:1 becomes:1 provided:3 estimating:2 b... |
151 | 1,136 | The Capacity of a Bump
Gary William Flake?
Institute for Advance Computer Studies
University of Maryland
College Park, MD 20742
Abstract
Recently, several researchers have reported encouraging experimental results when using Gaussian or bump-like activation functions in multilayer
perceptrons. Networks of this type u... | 1136 |@word version:1 pick:1 chervonenkis:1 current:1 com:1 comparing:2 activation:6 yet:1 additive:1 girosi:1 plot:3 tenn:1 fewer:1 short:4 recompute:1 node:1 hyperplanes:2 sigmoidal:2 become:2 prove:1 comb:1 encouraging:2 becomes:1 moreover:3 interpreted:1 nj:1 growth:1 unit:6 positive:1 local:1 limit:8 consequence:1... |
152 | 1,138 | A Dynamical Systems Approach for a Learnable Autonomous Robot
J un Tani and N aohiro Fukumura
Sony Computer Science Laboratory Inc.
Takanawa Muse Building, 3-14-13 Higashi-gotanda, Shinagawa-ku,Tokyo, 141 JAPAN
Abstract
This paper discusses how a robot can learn goal-directed navigation tasks using local sensory inpu... | 1138 |@word bptt:1 open:1 simulation:1 moment:2 initial:4 cyclic:4 past:2 current:2 recovered:1 activation:2 chicago:1 j1:2 pertinent:1 motor:7 intelligence:1 selected:2 node:1 location:3 firstly:1 along:1 interprocessor:1 qualitative:1 consists:1 behavior:3 elman:3 frequently:1 examine:1 multi:1 kuiper:3 actual:1 equi... |
153 | 1,139 | Rapid Quality Estimation of Neural
Network Input Representations
Kevin J. Cherkauer
Jude W. Shav lik
Computer Sciences Department, University of Wisconsin-Madison
1210 W. Dayton St., Madison, WI 53706
{cherkauer,shavlik}@cs.wisc.edu
Abstract
The choice of an input representation for a neural network can have
a profou... | 1139 |@word d2:1 heuristically:1 r:14 tried:1 minus:1 reduction:1 initial:2 wrapper:1 score:2 selecting:3 pfleger:1 kitano:1 comparing:1 john:3 ecis:1 planet:1 partition:1 cheap:1 v:4 implying:1 intelligence:2 leaf:30 selected:1 greedy:1 sys:1 detecting:1 simpler:1 constructed:2 profound:1 incorrect:1 freitag:2 introdu... |
154 | 114 | 527
IMPLICATIONS OF
RECURSIVE DISTRIBUTED REPRESENTATIONS
Jordan B. Pollack
Laboratory for AI Research
Ohio State University
Columbus, OH -'3210
ABSTRACT
I will describe my recent results on the automatic development of fixedwidth recursive distributed representations of variable-sized hierarchal data
structures. One ... | 114 |@word middle:1 version:1 faculty:1 compression:6 seems:2 retraining:1 inversion:1 seek:1 fonn:2 incurs:1 harder:1 recursively:2 reduction:2 electronics:2 initial:2 seriously:1 lapedes:4 current:1 comparing:2 od:2 surprising:1 activation:4 yet:3 intriguing:1 must:7 universality:2 john:3 distant:1 shape:1 plot:1 pyl... |
155 | 1,140 | Active Learning in Multilayer
Perceptrons
Kenji Fukumizu
Information and Communication R&D Center, Ricoh Co., Ltd.
3-2-3, Shin-yokohama, Yokohama, 222 Japan
E-mail: fuku@ic.rdc.ricoh.co.jp
Abstract
We propose an active learning method with hidden-unit reduction.
which is devised specially for multilayer perceptrons (... | 1140 |@word briefly:1 eliminating:1 simulation:1 tr:8 reduction:12 necessity:1 initial:2 hereafter:1 existing:2 john:1 ouly:1 ith:2 steepest:1 ron:1 sigmoidal:2 pairing:1 introduce:1 expected:1 actual:4 minimizes:1 unit:29 appear:2 positive:4 local:1 sd:4 ure:1 resembles:1 mateo:1 suggests:1 co:2 statistically:2 averag... |
156 | 1,141 | Investment Learning
with Hierarchical PSOMs
Jorg Walter and Helge Ritter
Department of Information Science
University of Bielefeld, D-33615 Bielefeld, Germany
Email: {walter.helge}@techfak.uni-bielefeld.de
Abstract
We propose a hierarchical scheme for rapid learning of context dependent
"skills" that is based on the ... | 1141 |@word manageable:2 polynomial:1 seems:1 gradual:1 decomposition:1 kappen:1 initial:1 contains:1 fragment:1 disparity:1 offering:1 tuned:2 current:2 marquardt:1 yet:2 must:2 attracted:2 subsequent:1 visible:1 partition:2 girosi:1 enables:1 motor:4 drop:1 intelligence:1 leaf:1 short:1 provides:2 location:5 height:1... |
157 | 1,142 | Explorations with the Dynamic Wave
Model
Thomas P. Rebotier
Jeffrey L. Elman
Department of Cognitive Science
UCSD, 9500 Gilman Dr
LA JOLLA CA 92093-0515
rebotier@cogsci.ucsd .edu
Department of Cognitive Science
UCSD, 9500 Gilman Dr
LA JOLLA CA 92093-0515
elman@cogsci.ucsd.edu
Abstract
Following Shrager and Johnson ... | 1142 |@word effect:2 consisted:1 true:1 indicate:1 come:1 avb:2 normalized:1 move:1 death:1 simulation:1 gradual:1 exploration:2 human:1 deal:1 primary:1 gradient:1 implementing:1 wrap:1 distance:3 excitation:1 iller:1 initial:4 series:1 ini:6 hereafter:1 investigation:1 pdf:1 genetic:2 rightmost:1 around:1 novel:1 gre... |
158 | 1,143 | Improving Policies without Measuring
Merits
Peter Dayan!
CBCL
E25-201, MIT
Cambridge, MA 02139
Satinder P Singh
Harlequin, Inc
1 Cambridge Center
Cambridge, MA 02142
dayan~ai.mit.edu
singh~harlequin.com
Abstract
Performing policy iteration in dynamic programming should only
require knowledge of relative rather than... | 1143 |@word version:2 suitably:1 calculus:1 simulation:1 tried:1 r:3 incurs:1 initial:1 ivaldi:2 lqr:7 existing:2 current:2 com:1 yet:1 written:2 must:3 distant:1 wx:1 girosi:2 update:1 alone:1 globalized:1 leaf:1 selected:1 intelligence:1 xk:5 argm:1 provides:2 location:1 lx:2 along:3 differential:7 prove:1 hjb:1 nota... |
159 | 1,144 | Factorial Hidden Markov Models
Zoubin Ghahramani
zoubin@psyche.mit.edu
Department of Computer Science
University of Toronto
Toronto, ON M5S 1A4
Canada
Michael I. Jordan
jordan@psyche.mit.edu
Department of Brain & Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
USA
Abstract
We present a fr... | 1144 |@word covariance:2 initial:1 configuration:1 series:6 current:2 si:2 must:2 partition:3 mstep:1 drop:1 update:1 intelligence:1 quantizer:3 provides:1 iterates:1 toronto:2 successive:1 simpler:1 multinomially:1 viable:1 combine:1 baldi:2 introduce:1 expected:3 rapid:1 alspector:1 elman:1 roughly:1 brain:1 decompos... |
160 | 1,145 | A Predictive Switching Model of
Cerebellar Movement Control
Andrew G. Barto
.J ay T. Buckingham
Department of Computer Science
University of Massachusetts
Amherst, MA 01003-4610
barto@cs.umass.edu
.J ames C. Houk
Department of Physiology
Northwestern University Medical School
303 East Chicago Ave
Chicago, Illinois 606... | 1145 |@word trial:2 version:4 simulation:3 pulse:13 t_:1 moment:4 initial:7 series:1 uma:1 past:1 current:2 activation:2 buckingham:7 must:1 olive:1 physiol:1 realistic:3 chicago:2 berthier:3 motor:20 plot:4 intelligence:1 selected:1 plane:3 beginning:1 smith:1 short:1 compo:1 ames:1 simpler:1 height:2 mathematical:1 a... |
161 | 1,146 | The Role of Activity in Synaptic
Competition at the Neuromuscular
Junction
Samuel R. H. Joseph
Centre for Cognitive Science
Edinburgh University
Edinburgh, U.K.
email: sam@cns.ed.ac.uk
David J. Willshaw
Centre for Cognitive Science
Edinburgh University
Edinburgh, U.K.
email: david@cns.ed.ac.uk
Abstract
An extended v... | 1146 |@word version:1 hippocampus:1 seems:3 anterograde:1 proportion:2 simulation:5 crucially:1 soleus:2 initial:2 uncovered:1 efficacy:2 reaction:10 activation:2 regenerating:1 physiol:1 subsequent:2 plasticity:1 wll:1 motor:18 displace:1 selected:1 nervous:3 postnatal:1 smith:1 organising:1 mathematical:1 direct:2 co... |
162 | 1,147 | Geometry of Early Stopping in Linear
Networks
Robert Dodier *
Dept. of Computer Science
University of Colorado
Boulder, CO 80309
Abstract
A theory of early stopping as applied to linear models is presented.
The backpropagation learning algorithm is modeled as gradient
descent in continuous time. Given a training set a... | 1147 |@word simulation:2 covariance:3 pick:2 reduction:2 initial:6 nt:3 must:3 additive:2 j1:1 shape:1 half:1 plane:14 xk:1 beginning:2 indefinitely:2 lx:1 sigmoidal:2 unbounded:1 mathematical:2 along:3 constructed:1 direct:1 baldi:2 indeed:1 expected:2 alspector:1 considering:2 becomes:1 suffice:1 mass:4 what:4 q2:2 t... |
163 | 1,148 | -
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... | 1148 |@word cu:1 cah:1 nd:1 bf:1 ckd:1 bn:2 t_:2 k7:1 n8:3 ghj:1 si:1 dx:2 fn:1 wx:1 cwd:3 v:1 dcfe:1 nq:2 rts:2 lr:1 c6:1 c2:2 vxw:1 ik:1 tuy:1 g4:4 isi:1 p1:1 xz:1 uz:1 ry:1 gou:1 jm:2 pof:1 sut:1 x5p:2 ag:1 ahc:2 ro:1 qm:2 k2:1 uo:1 t1:3 xv:3 sd:1 io:1 yd:15 kml:3 dut:1 co:1 bi:1 qkf:4 uy:1 ond:1 lf:1 xr:1 ga:2 bh:5... |
164 | 1,149 | Laterally Interconnected Self-Organizing
Maps in Hand-Written Digit Recognition
Yoonsuck Choe, Joseph Sirosh, and Risto Miikkulainen
Department of Computer Sciences
The University of Texas at Austin
Austin, TX 78712
yschoe,sirosh,risto@cs. u texas .ed u
Abstract
An application of laterally interconnected self-organiz... | 1149 |@word version:1 risto:2 simulation:2 crucially:1 decorrelate:4 thereby:1 shading:1 initial:5 bitmap:8 activation:11 ij1:2 written:4 must:1 alphanumeric:1 eab:1 alone:1 beginning:1 short:1 mnf:1 coarse:1 ron:5 direct:1 become:4 consists:2 indeed:1 roughly:1 nor:1 touchstone:1 decreasing:1 gov:1 actual:1 becomes:1 ... |
165 | 115 | 577
HETEROGENEOUS NEURAL NETWORKS FOR
ADAPTIVE BEHAVIOR IN DYNAMIC ENVIRONMENTS
Leon S. Sterling
Hillel J. Chiel
Randall D. Beer
CS Dept.
Dept. of Computer Engineering and Science and
Biology Dept.
& CAISR
Center for Automation and Intelligent Systems Research
& CAISR
CWRU
CWRU
Case Western Reserve University
Clevelan... | 115 |@word beep:1 middle:3 open:1 simulation:1 excited:1 solid:1 initial:1 necessity:1 contains:1 series:2 existing:1 donner:3 current:9 nt:1 yet:1 must:4 interrupted:1 periodically:1 hyperpolarizing:1 plasticity:1 motor:7 pacemaker:7 selected:3 nervous:16 shut:1 ji2:1 simpler:2 uncoordinated:1 rc:2 burst:8 surprised:1... |
166 | 1,150 | Primitive Manipulation Learning with
Connectionism
Yoky Matsuoka
The Artificial Intelligence Laboratory
NE43-819
Massachusetts Institute of Techonology
Cambridge, MA 02139
Abstract
Infants' manipulative exploratory behavior within the environment
is a vehicle of cognitive stimulation[McCall 1974]. During this time,
in... | 1150 |@word laboratory:2 strategy:1 imbedded:1 human:1 during:2 implementing:1 initial:1 contains:1 connectionism:2 shear:1 stimulation:1 physical:1 infant:2 intelligence:2 unknown:1 significant:1 cambridge:1 situation:1 mit:1 become:1 behavioral:1 manipulation:3 learned:1 hardness:1 detect:1 behavior:1 brook:1 muscle:... |
167 | 1,151 | Learning long-term dependencies
is not as difficult with NARX networks
Tsungnan Lin*
Department of Electrical Engineering
Princeton University
Princeton, NJ 08540
Peter Tiiio
Dept. of Computer Science and Engineering
Slovak Technical University
Ilkovicova 3, 812 19 Bratislava, Slovakia
Bill G. Horne
NEC Research Insti... | 1151 |@word trial:1 compression:1 leighton:1 bptt:3 simulation:7 eng:2 tr:3 initial:2 series:1 past:4 must:3 readily:1 written:1 wx:1 hypothesize:1 plot:10 designed:1 update:1 drop:1 tenn:2 discovering:1 weighing:1 vanishing:3 short:4 farther:1 node:2 become:1 pathway:2 inside:1 comb:1 indeed:1 roughly:1 behavior:2 fel... |
168 | 1,152 | Extracting Thee-Structured
Representations of Thained Networks
Mark W. Craven and Jude W. Shavlik
Computer Sciences Department
University of Wisconsin-Madison
1210 West Dayton St.
Madison, WI 53706
craven@cs.wisc.edu, shavlik@cs.wisc.edu
Abstract
A significant limitation of neural networks is that the representations... | 1152 |@word proportion:2 concise:1 liu:2 selecting:4 existing:1 current:1 yet:1 conjunctive:1 must:2 partition:2 remove:2 greedy:1 leaf:5 selected:2 fewer:1 intelligence:2 node:26 along:1 symposium:1 manner:2 growing:1 considering:1 becomes:1 begin:1 classifies:1 discover:1 cleveland:1 substantially:1 fuzzy:1 developed... |
169 | 1,153 | Does the Wake-sleep Algorithm
Produce Good Density Estimators?
Brendan J. Frey, Geoffrey E. Hinton
Department of Computer Science
University of Toronto
Toronto, ON M5S 1A4, Canada
{frey, hinton} @cs.toronto.edu
Peter Dayan
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA ... | 1153 |@word version:4 compression:5 simulation:2 tried:1 bn:7 initial:1 configuration:2 series:1 past:1 must:1 realistic:1 visible:20 hoping:1 aside:1 generative:19 selected:2 device:1 intelligence:2 pointer:1 quantized:1 postal:1 toronto:4 successive:1 sigmoidal:1 simpler:1 five:3 constructed:1 beta:2 consists:2 fitti... |
170 | 1,154 | Control of Selective Visual Attention:
Modeling the "Where" Pathway
Ernst Niebur?
Computation and Neural Systems 139-74
California Institute of Technology
Christof Koch
Computation and Neural Systems 139-74
California Institute of Technology
Abstract
Intermediate and higher vision processes require selection of a sub... | 1154 |@word neurophysiology:1 version:1 instruction:1 rhesus:1 rgb:1 lobe:1 attended:5 thereby:1 outlook:1 initial:1 current:1 john:1 cottrell:2 subsequent:1 distant:1 synchronicity:1 plasticity:1 update:1 selected:3 inspection:2 short:2 yamada:2 provides:2 location:10 five:1 along:1 constructed:1 consists:1 sustained:... |
171 | 1,155 | Exploiting Tractable Substructures
in Intractable Networks
Lawrence K. Saul and Michael I. Jordan
{lksaul.jordan}~psyche.mit.edu
Center for Biological and Computational Learning
Massachusetts Institute of Technology
79 Amherst Street, ElO-243
Cambridge, MA 02139
Abstract
We develop a refined mean field approximation ... | 1155 |@word briefly:1 simulation:1 b39:1 ithere:1 reduction:1 configuration:1 contains:1 denoting:1 si:8 numerical:1 partition:4 treating:1 update:1 v_:1 core:2 compo:1 provides:2 simpler:1 five:1 become:1 consists:1 manner:1 inter:4 themselves:1 decomposed:1 unfolded:1 correlator:1 factorized:4 backbone:1 string:1 dev... |
172 | 1,156 | Adaptive Back-Propagation in On-Line
Learning of Multilayer Networks
Ansgar H. L. West 1 ,2 and David Saad 2
1 Department of Physics , University of Edinburgh
Edinburgh EH9 3JZ, U.K.
2Neural Computing Research Group, University of Aston
Birmingham B4 7ET, U.K.
Abstract
An adaptive back-propagation algorithm is studie... | 1156 |@word achievable:1 norm:5 seems:2 open:1 linearized:1 bn:3 harder:1 ld:2 reduction:1 initial:2 favouring:1 surprising:1 analysed:1 activation:8 readily:1 numerical:4 plot:1 designed:1 update:4 trapping:3 isotropic:6 provides:1 math:1 node:22 contribute:1 sigmoidal:2 unbounded:1 become:2 differential:3 qij:10 spec... |
173 | 1,157 | Temporal coding
in the sub-millisecond range:
Model of barn owl auditory pathway
Richard Kempter*
Institut fur Theoretische Physik
Physik-Department der TU Munchen
D-85748 Garching bei Munchen
Germany
J. Leo van Hemmen
Institut fur Theoretische Physik
Physik-Department der TU Munchen
0-85748 Garching bei Munchen
Germ... | 1157 |@word rising:1 seems:2 physik:7 pulse:3 azimuthal:2 simulation:1 tr:3 solid:3 reduction:1 initial:1 current:3 nt:1 must:4 physiol:1 realistic:1 plot:2 half:1 beginning:1 compo:2 along:3 become:2 consists:3 pathway:9 interaural:2 brain:2 window:5 considering:2 project:1 deutsche:1 developed:1 finding:1 temporal:28... |
174 | 1,158 | On the Computational Power of Noisy
Spiking Neurons
Wolfgang Maass
Institute for Theoretical Computer Science, Technische Universitaet Graz
Klosterwiesgasse 32/2, A-8010 Graz, Austria, e-mail: maass@igi.tu-graz.ac.at
Abstract
It has remained unknown whether one can in principle carry out
reliable digital computations... | 1158 |@word cu:3 version:1 rising:1 polynomial:2 simulation:3 bn:9 carry:5 unction:1 activation:1 si:2 realize:1 additive:1 subsequent:1 realistic:6 shape:3 pacemaker:1 inspection:1 realism:1 short:3 record:1 provides:5 location:1 sigmoidal:1 unbounded:1 constructed:2 persistent:1 prove:1 introduce:1 manner:3 frequentl... |
175 | 1,159 | A Novel Channel Selection System in
Cochlear Implants Using Artificial Neural
Network
Marwan A. Jabri &
Raymond J. Wang
Systems Engineering and Design Automation Laboratory
Department of Electrical Engineering
The University of Sydney
NSW 2006, Australia
{marwan,jwwang}Osedal.usyd.edu.au
Abstract
State-of-the-art spee... | 1159 |@word simulation:3 pulse:1 kent:2 nsw:1 contains:2 score:3 selecting:1 existing:1 synthesizer:1 shape:1 selected:8 device:1 short:2 successive:1 tinker:1 airflow:2 constructed:2 direct:1 symposium:1 consists:3 multi:7 brain:2 morphology:7 window:4 considering:1 confused:1 provided:1 moreover:1 matched:11 churchil... |
176 | 116 | 511
CONVERGENCE AND PATTERN STABILIZATION
IN THE BOLTZMANN MACHINE
MosheKam
Dept. of Electrical and Computer Eng.
Drexel University, Philadelphia PA 19104
Roger Cheng
Dept. of Electrical Eng.
Princeton University, NJ 08544
ABSTRACT
The Boltzmann Machine has been introduced as a means to perform
global optimization f... | 116 |@word polynomial:4 seek:2 bn:1 eng:2 llo:1 contraction:2 reap:1 initial:3 attracted:1 fn:1 predetermined:4 shape:1 designed:1 interpretable:1 update:1 devising:1 item:1 pwc:3 ith:4 short:1 prespecified:1 chua:2 sigmoidal:1 along:3 supply:1 introduce:1 uphill:1 behavior:1 themselves:1 multi:2 decreasing:1 lib:1 inc... |
177 | 1,160 | Harmony Networks Do Not Work
Rene Gourley
School of Computing Science
Simon Fraser University
Burnaby, B.C., V5A 1S6, Canada
gourley@mprgate.mpr.ca
Abstract
Harmony networks have been proposed as a means by which connectionist models can perform symbolic computation. Indeed, proponents claim that a harmony network ca... | 1160 |@word cu:1 polynomial:1 twelfth:1 seek:2 tr:4 smolen:3 initial:2 surprising:1 activation:17 must:10 happen:1 shape:1 drop:2 alone:1 intelligence:3 plane:4 ck2:1 draft:1 node:4 mathematical:2 constructed:1 indeed:2 multi:1 brain:1 considering:1 becomes:1 awe:1 maximizes:1 interpreted:2 string:2 eigenvector:3 diffe... |
178 | 1,161 | An Information-theoretic Learning
Algorithm for Neural Network
Classification
David J. Miller
Department of Electrical Engineering
The Pennsylvania State University
State College, Pa: 16802
Ajit Rao, Kenneth Rose, and Allen Gersho
Department of Electrical and Computer Engineering
University of California
Santa Barbara,... | 1161 |@word repository:1 simulation:1 seek:2 concise:1 tr:5 series:1 tram:1 tuned:1 outperforms:1 must:3 belmont:1 partition:11 girosi:2 designed:1 recept:1 accordingly:2 ial:1 steepest:3 provides:1 quantizer:1 ional:1 five:1 along:1 direct:2 become:1 ik:1 specialize:1 combine:1 introduce:2 expected:6 nor:1 mechanic:1 ... |
179 | 1,162 | Experiments with Neural Networks for Real
Time Implementation of Control
P. K. Campbell, M. Dale, H. L. Ferra and A. Kowalczyk
Telstra Research Laboratories
770 Blackburn Road Clayton, Vic. 3168, Australia
{p.campbell, m.dale, h.ferra, a.kowalczyk}@trl.oz.au
Abstract
This paper describes a neural network based control... | 1162 |@word achievable:1 replicate:1 gradual:1 simulation:5 thereby:1 initial:2 tuned:1 current:1 yet:1 must:2 designed:1 alone:1 greedy:1 selected:3 quantized:1 node:1 firstly:1 along:1 direct:2 become:3 director:2 manner:1 inter:2 mask:8 ra:1 rapid:1 alspector:1 telstra:5 inspired:1 automatically:1 researched:2 encou... |
180 | 1,163 | Generalisation of A Class of Continuous
Neural Networks
John Shawe-Taylor
Dept of Computer Science,
Royal Holloway, University of London,
Egham, Surrey TW20 OEX, UK
Email: johnCdcs.rhbnc.ac . uk
Jieyu Zhao*
IDSIA, Corso Elvezia 36,
6900-Lugano, Switzerland
Email: jieyuCcarota.idsia.ch
Abstract
We propose a way of us... | 1163 |@word version:3 polynomial:5 suitably:1 simulation:4 tr:1 carry:1 moment:1 series:4 chervonenkis:1 tco:3 comparing:2 surprising:1 activation:2 assigning:2 must:2 john:2 happen:1 treating:1 intelligence:1 monk:14 lr:1 node:12 sigmoidal:4 incorrect:1 consists:1 manner:1 introduce:2 expected:1 indeed:1 multi:1 brain... |
181 | 1,164 | Learning to Predict
Visibility and Invisibility
from Occlusion Events
Jonathan A. Marshall
Richard K. Alley
Robert S. Hubbard
Department of Computer Science, CB 3175, Sitterson Hall
University of North Carolina, Chapel Hill, NC 27599-3175, U.S.A.
marshall@cs.unc.edu, 919-962-1887, fax 919-962-1799
Abstract
Visual occ... | 1164 |@word neurophysiology:1 faculty:2 version:1 stronger:1 simulation:8 carolina:2 propagate:2 reappearance:2 r:1 thereby:1 solid:4 shading:3 carry:3 moment:2 initial:3 tuned:1 subjective:2 contextual:1 activation:19 yet:1 must:4 visible:37 enables:1 visibility:12 reappeared:2 asymptote:2 discrimination:1 alone:5 v:1... |
182 | 1,165 | Constructive Algorithms for Hierarchical
Mixtures of Experts
S.R.Waterhouse
A.J.Robinson
Cambridge University Engineering Department,
Trumpington St., Cambridge, CB2 1PZ, England.
Tel: [+44] 1223 332754, Fax: [+44] 1223 332662,
Email: srw1001.ajr@eng.cam.ac.uk
Abstract
We present two additions to the hierarchical mix... | 1165 |@word version:1 simulation:2 eng:1 jacob:3 didate:1 recursively:1 series:2 selecting:2 zij:1 recovered:1 current:1 activation:4 reminiscent:1 partition:2 shape:1 update:1 v:3 selected:1 node:43 sits:1 firstly:1 along:1 become:1 consists:1 fitting:2 introduce:1 manner:1 themselves:1 growing:14 multi:2 ol:1 termina... |
183 | 1,166 | Model Matching and SFMD
Computation
Steve Rehfuss and Dan Hammerstrom
Department of Computer Science and Engineering
Oregon Graduate Institute of Science and Technology
P.O.Box 91000, Portland, OR 97291-1000 USA
stever@cse.ogi.edu, strom@asi.com
Abstract
In systems that process sensory data there is frequently a mode... | 1166 |@word version:1 nd:1 instruction:20 simulation:3 dramatic:1 initial:2 inefficiency:1 contains:1 existing:5 current:4 com:1 yet:1 assigning:1 written:1 must:7 realistic:3 partition:2 plot:1 v:1 leaf:7 cse:1 location:1 node:7 interprocessor:1 pairing:2 consists:1 dan:1 sync:2 introduce:2 inter:4 expected:4 frequent... |
184 | 1,167 | Bayesian Methods for Mixtures of Experts
Steve Waterhouse
Cambridge University
Engineering Department
Cambridge CB2 1PZ
England
Tel: [+44] 1223 332754
srw1001@eng.cam.ac.uk
David MacKay
Cavendish Laboratory
Madingley Rd.
Cambridge CB3 OHE
England
Tel: [+44] 1223 337238
mackay@mrao .cam.ac.uk
Tony Robinson
Cambridge U... | 1167 |@word advantageous:1 simulation:1 covariance:2 eng:2 jacob:4 moment:1 series:7 selecting:2 pub:1 rightmost:1 must:1 additive:1 wx:1 generative:1 selected:2 record:1 toronto:1 lx:4 constructed:1 become:2 consists:6 fitting:11 overhead:1 introduce:1 manner:1 expected:2 themselves:1 considering:1 estimating:1 underl... |
185 | 1,168 | Human Face Detection in Visual Scenes
Henry A. Rowley
Shumeet Baluja
Takeo Kanade
har@cs.cmu.edu
baluja@cs.cmu.edu
tk@cs.cmu.edu
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Abstract
We present a neural network-based face detection system. A retinally
connected neural network exam... | 1168 |@word version:1 eliminating:1 tried:1 harder:1 initial:2 contains:2 selecting:1 ours:1 document:1 activation:1 lang:1 must:4 takeo:2 occludes:1 plot:1 alone:1 half:1 fewer:1 plane:1 smith:1 draft:1 location:9 simpler:1 along:2 consists:5 combine:2 manner:3 tomaso:2 behavior:1 multi:1 chi:1 detects:4 actual:2 enco... |
186 | 1,169 | Temporal Difference Learning in
Continuous Time and Space
Kenji Doya
doya~hip.atr.co.jp
ATR Human Information Processing Research Laboratories
2-2 Hikaridai, Seika.-cho, Soraku-gun, Kyoto 619-02, Japan
Abstract
A continuous-time, continuous-state version of the temporal difference (TD) algorithm is derived in order ... | 1169 |@word trial:9 version:5 nd:1 simulation:2 lup:1 tr:1 past:2 intelligence:1 mgl:1 cambrigde:1 height:1 rc:1 differential:1 hjb:1 manner:1 expected:1 seika:1 discretized:1 torque:7 bellman:1 discounted:1 td:22 provided:1 bounded:1 maximizes:2 ret:2 differentiation:4 nj:2 temporal:8 act:1 iearning:1 rm:2 control:37 ... |
187 | 1,170 | A Neural Network Classifier for
the 11000 OCR Chip
John C. Platt and Timothy P. Allen
Synaptics, Inc.
2698 Orchard Parkway
San Jose, CA 95134
platt@synaptics.com, tpa@synaptics.com
Abstract
This paper describes a neural network classifier for the 11000 chip, which
optically reads the E13B font characters at the bottom... | 1170 |@word agc:2 thereby:1 contains:2 optically:2 current:1 com:2 lang:1 must:1 bd:1 john:1 designed:1 alone:1 location:1 simpler:2 along:1 constructed:1 become:4 incorrect:2 behavior:3 multi:1 company:1 window:1 considering:1 provided:1 circuit:1 fabricated:1 every:4 collecting:1 ro:1 classifier:34 platt:6 control:1 ... |
188 | 1,171 | Using Pairs of Data-Points to Define
Splits for Decision Trees
Geoffrey E. Hinton
Department of Computer Science
University of Toronto
Toronto, Ontario, M5S lA4, Canada
hinton@cs.toronto.edu
Michael Revow
Department of Computer Science
University of Toronto
Toronto, Ontario, M5S lA4, Canada
revow@cs.toronto.edu
Abst... | 1171 |@word version:1 proportion:1 twelfth:1 willing:1 pick:1 tr:6 selecting:2 bc:5 existing:1 comparing:1 must:1 john:1 belmont:1 midway:1 eleven:1 discrimination:1 selected:4 plane:3 node:15 toronto:6 hyperplanes:15 simpler:1 along:2 pairwise:2 terminal:7 jlt:1 little:1 considering:1 estimating:1 maximizes:1 ag:1 sui... |
189 | 1,172 | Stochastic Hillclimbing as a Baseline
Method for Evaluating Genetic
Algorithms
Ari Juels
Department of Computer Science
University of California at Berkeley?
Martin Wattenberg
Department of Mathematics
University of California at Berkeleyt
Abstract
We investigate the effectiveness of stochastic hillclimbing as a bas... | 1172 |@word trial:1 version:5 open:1 d2:1 seek:1 pick:1 accommodate:1 initial:2 series:1 contains:2 selecting:1 genetic:17 document:1 icga:3 current:6 must:2 john:1 csc:1 remove:1 designed:1 leaf:2 selected:2 cook:1 problemspecific:1 steepest:1 record:1 math:1 node:12 successive:1 simpler:1 height:1 mathematical:1 dire... |
190 | 1,173 | hnproved Silicon Cochlea
?
uSIng
Compatible Lateral Bipolar Transistors
Andre van Schalk, Eric Fragniere, Eric Vittoz
MANTRA Center for Neuromimetic Systems
Swiss Federal Institute of Technology
CH-IOI5 Lausanne
email: vschaik@di.epfl.ch
Abstract
Analog electronic cochlear models need exponentially scaled filters.
CM... | 1173 |@word version:1 briefly:1 inversion:7 simulation:1 solid:2 liu:1 bc:1 current:55 si:1 yet:3 readily:1 cruz:1 tilted:3 visible:1 drop:2 precaution:1 device:8 beginning:4 along:2 resistive:20 inside:1 introduce:1 notably:1 mechanic:1 terminal:1 decreasing:5 lyon:4 actual:3 matched:3 circuit:6 acoust:1 fabricated:1 ... |
191 | 1,174 | d
j
!
" # $ &%('*) + , - ' ) + .
c
*/ 02HJ143I 571E6!DL8:KM29;<9NP3=!O >?M 6 5A@BD 5C5C6 1EDF5G6
6
6
R
Q
M
J
H
M
5
E
1
D
r _`ks7SU?mg TB??ht VR`a?eu4VR??]wWX?Ev SA?E?LgAY[xy{ZA?Uz \^_F}Gdo]<?Gj_a??x|`cn~?7be} de}7fEf4?7g... | 1174 |@word h:1 pw:1 rno:1 nd:1 d2:1 hu:2 r:2 nks:1 pg:1 q1:1 gnm:1 sah:1 dff:1 zij:1 skd:3 ghj:1 q1e:1 od:1 si:2 bd:2 neq:1 fn:1 xyu:1 rts:1 xk:1 d2d:3 ron:1 gx:1 rc:10 ik:5 adk:1 qij:1 ra:5 p1:4 xz:1 uz:12 gjk:1 ry:1 fcz:1 jm:1 kg:1 xkx:1 adc:1 ag:3 acbed:1 qm:2 k2:1 uk:1 t1:1 diu:1 xv:1 sd:1 io:1 xiv:1 kml:1 au:4 eb... |
192 | 1,175 | Generating Accurate and Diverse
Members of a Neural-Network Ensemble
David w. Opitz
Computer Science Department
University of Minnesota
Duluth, MN 55812
opitz@d.umn.edu
Jude W. Shavlik
Computer Sciences Department
University of Wisconsin
Madison, WI 53706
shavlik@cs.wisc.edu
Abstract
Neural-network ensembles have bee... | 1175 |@word seems:2 tried:1 pick:1 yih:1 initial:9 contains:5 score:3 united:1 genetic:12 existing:2 current:8 comparing:2 activation:1 yet:1 refines:1 partition:3 happen:1 nynex:1 intelligence:3 boosting:1 node:2 direct:1 consists:2 regent:10 combine:2 eleventh:2 pairwise:1 expected:1 frequently:1 growing:1 decreasing... |
193 | 1,176 | Statistical Mechanics of the Mixture of
Experts
Kukjin Kang and Jong-Hoon Oh
Department of Physics
Pohang University of Science and Technology
Hyoja San 31, Pohang, Kyongbuk 790-784, Korea
E-mail: kkj.jhohOgalaxy.postech.ac.kr
Abstract
We study generalization capability of the mixture of experts learning from example... | 1176 |@word implemented:1 consisted:2 conquer:4 come:1 functioning:1 hence:1 assigned:1 fij:1 closely:1 correct:1 symmetric:4 already:1 strategy:1 vc:1 postech:2 jacob:2 sgn:2 cusp:4 branching:2 subspace:4 solid:1 thank:1 won:1 assign:1 generalized:1 generalization:17 mail:1 hill:1 probable:1 temperature:2 considered:1... |
194 | 1,177 | Microscopic Equations in Rough Energy
Landscape for Neural Networks
K. Y. Michael Wong
Department of Physics,
The Hong Kong University of Science and Technology,
Clear Water Bay, Kowloon, Hong Kong.
E-mail: phkywong@usthk.ust.hk
Abstract
We consider the microscopic equations for learning problems in
neural networks. ... | 1177 |@word kong:3 version:1 loading:2 simulation:6 solid:1 moment:1 initial:2 configuration:1 reaction:1 comparing:1 hkust:1 perturbative:2 ust:1 subsequent:1 j1:3 provides:4 math:1 node:10 ron:1 mathematical:2 consists:1 introduce:2 mechanic:2 begin:1 underlying:2 what:2 minimizes:1 ag:1 onsager:1 adatron:2 appear:1 ... |
195 | 1,178 | An Architectural Mechanism for
Direction-tuned Cortical Simple Cells:
The Role of Mutual Inhibition
Silvio P. Sabatini
silvio@dibe.unige.it
Fabio Solari
fabio@dibe .unige.it
Giacomo M. Bisio
bisio@dibe.unige.it
Department of Biophysical and Electronic Engineering
PSPC Research Group
Genova, 1-16145, Italy
Abstract
... | 1178 |@word sabatini:5 sex:2 d2:1 simulation:2 initial:2 efficacy:1 tuned:4 k1d:1 dx:1 tilted:1 wx:2 shape:1 analytic:1 seelen:1 plot:4 nervous:1 accordingly:2 plane:1 postnatal:1 location:1 lx:1 mathematical:1 along:2 direct:3 combine:1 pathway:1 shapley:1 inter:1 expected:1 indeed:2 behavior:3 multi:1 brain:1 freeman... |
196 | 1,179 | Maximum Likelihood Blind Source
Separation: A Context-Sensitive
Generalization of ICA
Barak A. Pearlmutter
Computer Science Dept, FEC 313
University of New Mexico
Albuquerque, NM 87131
bap@cs.unm.edu
Lucas C. Parra
Siemens Corporate Research
755 College Road East
Princeton, NJ 08540-6632
lucas@scr.siemens.com
Abstrac... | 1179 |@word kong:1 version:1 inversion:1 seems:2 simulation:1 efficacy:1 pub:1 recovered:7 com:1 contextual:1 si:3 assigning:1 must:3 wx:1 update:1 cue:1 generative:3 fewer:1 intelligence:1 parameterization:2 colored:2 filtered:2 complication:1 sigmoidal:1 five:2 along:1 constructed:1 symposium:1 combine:1 introduce:1 ... |
197 | 118 | 73
LEARNING BY CHOICE
OF INTERNAL REPRESENTATIONS
Tal Grossman, Ronny Meir and Eytan Domany
Department of Electronics, Weizmann Institute of Science
Rehovot 76100 Israel
ABSTRACT
We introduce a learning algorithm for multilayer neural networks composed of binary linear threshold elements. Whereas existing algorithms ... | 118 |@word version:8 briefly:2 simulation:1 pick:1 initial:4 electronics:1 contains:1 existing:2 current:7 nowlan:1 si:3 partition:1 j1:1 enables:1 treating:2 v:3 guess:3 accordingly:1 probi:1 vanishing:1 accepting:1 iterates:1 plaut:2 wijsj:1 consists:1 manner:3 introduce:2 indeed:1 behavior:2 aborted:1 actual:1 becom... |
198 | 1,180 | Representing Face Images for Emotion
Classification
Curtis Padgett
Department of Computer Science
University of California, San Diego
La Jolla, CA 92034
Garrison Cottrell
Department of Computer Science
University of California, San Diego
La Jolla, CA 92034
Abstract
We compare the generalization performance of three ... | 1180 |@word trial:2 sex:1 open:2 seek:2 accommodate:1 shot:1 hager:1 reduction:1 initial:1 score:3 empath:1 comparing:1 activation:1 cottrell:16 pertinent:1 v:1 discrimination:2 intelligence:1 selected:1 eigenfeatures:3 provides:2 consulting:1 node:1 location:1 along:1 constructed:1 consists:1 mask:1 expected:3 examine... |
199 | 1,181 | Early Brain Damage
Volker Tresp, Ralph Neuneier and Hans Georg Zimmermann*
Siemens AG, Corporate Technologies
Otto-Hahn-Ring 6
81730 Miinchen, Germany
Abstract
Optimal Brain Damage (OBD) is a method for reducing the number of weights in a neural network. OBD estimates the increase in
cost function if weights are prun... | 1181 |@word hahn:1 repository:1 requiring:1 true:1 advantageous:1 regularization:2 retraining:2 direction:1 already:2 quantity:3 correct:1 damage:9 simplifying:1 diagonal:7 during:1 gradient:2 dramatic:1 ow:1 razor:1 initial:1 criterion:1 contains:3 generalization:1 pub:1 really:1 ridge:1 demonstrate:2 cellular:1 exten... |
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