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7,100 | 869 | Two Iterative Algorithms for Computing
the Singular Value Decomposition from
Input / Output Samples
Terence D. Sanger
Jet Propulsion Laboratory
MS 303-310
4800 Oak Grove Drive
Pasadena, CA 91109
Abstract
The Singular Value Decomposition (SVD) is an important tool for
linear algebra and can be used to invert or approx... | 869 |@word effect:1 approximating:4 true:1 indicate:2 oae:5 uu:1 zyt:1 arrangement:1 symmetric:3 laboratory:2 memoryless:1 nonzero:1 added:1 rt:5 usual:1 decomposition:14 diagonal:8 during:2 gradient:1 reduction:1 m:2 generalized:8 propulsion:1 score:1 uncontrollable:2 decompose:1 nt:6 relationship:1 trapezoid:1 si:1 m... |
7,101 | 87 | 233
HIGH ORDER NEURAL NETWORKS FOR EFFICIENT
ASSOCIATIVE MEMORY DESIGN
I. GUYON?, L. PERSONNAZ?, J. P. NADAL?? and G. DREYFUS?
? Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris
Laboratoire d'Electronique
10, rue Vauquelin
75005 Paris (France)
?? Ecole Normale Superieure
Groupe de Physique... | 87 |@word briefly:1 lett:6 simulation:4 dramatic:1 initial:3 contains:1 ecole:2 must:2 distant:1 subsequent:1 numerical:1 designed:1 item:1 along:1 retrieving:3 consists:3 paragraph:1 baldi:1 introduce:2 expected:1 multi:2 considering:1 increasing:1 becomes:1 provided:2 discover:1 linearity:1 nadal:3 contiguity:1 tempo... |
7,102 | 870 | Robot Learning: Exploration and
Continuous Domains
David A. Cohn
MIT Dept. of Brain and Cognitive Sciences
Cambridge, MA 02139
The goal of this workshop was to discuss two major issues: efficient exploration of
a learner's state space, and learning in continuous domains. The common themes
that emerged in presentations... | 870 |@word especially:1 validity:1 trial:2 exploitation:1 come:2 concept:1 seems:1 safe:1 moore:2 strategy:6 exploration:6 centered:1 during:2 ensuing:1 landmark:3 initial:1 wall:1 consensus:2 extension:1 useless:1 considered:1 common:1 echoed:1 major:1 ji:2 commonality:1 csm:1 designed:2 discussed:4 he:2 estimation:1 ... |
7,103 | 871 | Comparisoll Training for a Resclleduling
Problem ill Neural Networks
Didier Keymeulen
Artificial Intelligence Laboratory
Vrije Universiteit Brussel
Pleinlaan 2, 1050 Brussels
Belgium
Martine de Gerlache
Prog Laboratory
Vrije Universiteit Brussel
Pleinlaan 2, 1050 Brussels
Belgium
Abstract
Airline companies usually sc... | 871 |@word llsed:1 cha:1 decomposition:1 ld:1 reduction:1 initial:1 substitution:1 score:5 past:1 comparing:12 od:1 yet:1 must:1 heir:2 update:2 intelligence:2 short:1 didier:1 node:5 preference:1 belt:1 mathematical:5 along:1 direct:1 retrieving:1 consists:4 introduce:2 notably:1 indeed:2 alspector:1 p1:2 dist:1 plann... |
7,104 | 872 | Exploiting Chaos to Control the Future
Gary W. Flake*
Guo-Zhen Sun t
Yee-Chun Lee t
Hsing-Hen Chen t
Institute for Advance Computer Studies
University of Maryland
College Park, MD 20742
Abstract
Recently, Ott, Grebogi and Yorke (OGY) [6] found an effective
method to control chaotic systems to unstable fixed point... | 872 |@word version:1 cnls:9 bptt:1 simulation:4 pick:2 initial:1 contains:1 series:1 tuned:1 current:5 yet:1 must:2 realistic:1 visible:1 numerical:4 happen:1 designed:1 update:3 fewer:1 beginning:1 contribute:1 location:2 ipi:1 ditto:1 mathematical:1 along:1 constructed:1 consists:2 manner:1 introduce:1 automatically:... |
7,105 | 873 | x
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oPppPqsr tu OMvPw NE N L \yx Yi K N O lZz w N r NPE1N|{N8}~N?|?... | 873 |@word h:1 cu:1 maz:1 mhn:1 c0:1 sex:1 km:4 d2:1 r:3 t_:1 dba:1 q1:2 tr:1 ld:3 n8:1 xiy:1 bc:1 od:1 si:1 dde:1 fn:1 j1:1 gv:1 yr:4 nq:1 rts:3 xk:1 dn:1 ra:1 ry:2 td:1 jm:1 pf:2 kg:1 sut:1 adc:1 elm:1 w8:6 qm:1 dfu:1 yn:3 ak:1 id:3 ap:1 kml:2 au:1 r4:1 co:1 fot:1 vu:1 lf:1 sxt:1 sq:1 w4:1 oqp:2 got:1 tsr:1 ga:1 py:1... |
7,106 | 874 | How to Choose an Activation Function
H. N. Mhaskar
Department of Mathematics
California State University
Los Angeles, CA 90032
hmhaska@calstatela.edu
c. A. Micchelli
IBM Watson Research Center
P. O. Box 218
Yorktown Heights, NY 10598
cam@watson.ibm.com
Abstract
We study the complexity problem in artificial feedforwa... | 874 |@word version:1 briefly:2 polynomial:4 norm:7 nd:1 km:2 contains:2 com:1 activation:29 must:1 girosi:2 enables:1 designed:1 xk:3 iso:1 provides:2 math:1 sigmoidal:17 height:1 constructed:3 manner:1 theoretically:1 multi:1 kamm:1 estimating:1 bounded:6 notation:2 moreover:2 what:1 z:1 every:1 subclass:1 growth:1 co... |
7,107 | 875 | Efficient Computation of Complex
Distance Metrics Using Hierarchical
Filtering
Patrice Y. Simard
AT&T Bell Laboratories
Holmdel, NJ 07733
Abstract
By their very nature, memory based algorithms such as KNN or
Parzen windows require a computationally expensive search of a
large database of prototypes. In this paper we ... | 875 |@word norm:1 stronger:1 proportion:1 reused:1 decomposition:3 recursively:3 initial:1 complexit:1 score:8 selecting:2 current:1 yet:1 must:7 subsequent:2 remove:1 half:6 selected:4 leaf:1 intelligence:1 ith:1 hyperplanes:2 expected:1 indeed:2 dist:7 window:2 increasing:7 becomes:2 provided:1 developed:1 finding:2 ... |
7,108 | 876 | Illumination-Invariant Face Recognition with a
Contrast Sensitive Silicon Retina
Joachim M. Buhmann
Rheinische Friedrich-Wilhelms-U niversitiit
Institut fUr Informatik II, RomerstraBe 164
0-53117 Bonn, Germany
Martin Lades
Ruhr-Universitiit Bochum
Institut fiir Neuroinformatik
0-44780 Bochum, Germany
Frank Eeckman
La... | 876 |@word neurophysiology:1 deformed:1 version:3 compression:1 seems:1 cco:2 ruhr:1 llo:1 eng:1 thereby:2 tr:1 document:1 ka:1 recovered:1 visible:1 shape:1 enables:1 remove:1 designed:4 discrimination:1 pursued:1 selected:1 device:3 intelligence:1 lamp:2 reciprocal:2 steepest:1 filtered:5 five:1 burr:2 expected:6 beh... |
7,109 | 877 | The "Softmax" Nonlinearity:
Derivation Using Statistical Mechanics
and Useful Properties
as a Multiterminal Analog Circuit
Element
I. M. Elfadel
Research Laboratory of Electronics
Massachusetts Institute of Technology
Cambridge, MA 02139
J. L. Wyatt, Jr.
Research Laboratory of Electronics
Massachusetts Institute of Te... | 877 |@word graded:1 diode:1 concept:1 indicate:1 implies:1 lyapunov:1 added:1 open:1 discontinuous:1 symmetric:1 laboratory:3 stochastic:1 wiring:1 enable:1 gradient:3 implementing:1 tr:1 noted:1 simic:3 thank:1 sci:1 assign:2 electronics:2 generalized:2 theoretic:2 vo:3 im:1 invisible:1 enforcing:1 temperature:1 passi... |
7,110 | 878 | Learning Stochastic Perceptrons Under
k-Blocking Distributions
Mario Marchand
Ottawa-Carleton Institute for Physics
University of Ottawa
Ottawa, Ont., Canada KIN 6N5
mario@physics.uottawa.ca
Saeed Hadjifaradji
Ottawa-Carleton Institute for Physics
University of Ottawa
Ottawa, Ont., Canada KIN 6N5
saeed@physics.uottaw... | 878 |@word trial:1 polynomial:3 harder:1 carry:1 contains:2 reaction:1 err:12 z2:1 ixj:2 activation:10 written:1 must:1 enables:1 remove:1 succeeding:1 discovering:1 device:1 xk:4 provides:1 ron:1 consists:1 prove:2 vitter:2 introduce:1 hardness:1 indeed:1 behavior:2 eurocolt:1 ont:2 provided:1 discover:4 estimating:2 ... |
7,111 | 879 | Nonlinear Image Interpolation using
Manifold Learning
Christoph Bregler
Computer Science Division
University of California
Berkeley, CA 94720
bregler@cs.berkeley.edu
Stephen M. Omohundro'"
Int . Computer Science Institute
1947 Center Street Suite 600
Berkeley, CA 94704
om@research.nj .nec.com
Abstract
The problem of... | 879 |@word middle:1 open:1 sensed:1 jacob:2 brightness:1 reduction:1 initial:2 configuration:3 current:1 com:1 comparing:1 nowlan:1 must:1 realistic:1 distant:1 partition:2 v:2 half:2 leaf:1 short:1 nearness:1 location:2 along:7 constructed:1 consists:3 combine:1 themselves:1 footage:1 globally:1 relying:1 automaticall... |
7,112 | 88 | 144
SPEECH RECOGNITION EXPERIMENTS
WITH PERCEPTRONS
D. J. Burr
Bell Communications Research
Morristown, NJ 07960
ABSTRACT
Artificial neural networks (ANNs) are capable of accurate recognition of
simple speech vocabularies such as isolated digits [1]. This paper looks at two
more difficult vocabularies, the alphabeti... | 88 |@word trial:2 cu:1 illustrating:1 leighton:1 contains:2 score:1 activation:1 must:2 john:1 plot:8 discrimination:1 half:2 beginning:3 short:1 detecting:1 node:2 ron:1 successive:1 five:4 windowed:1 along:2 consists:3 burr:4 autocorrelation:1 sakoe:1 examine:1 multi:2 formants:1 little:2 window:3 sting:1 mass:1 what... |
7,113 | 880 | Using a Saliency Map for Active Spatial Selective
Attention: Implementation & Initial Results
Shumeet Baluja
baluja@cs.cmu.edu
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Dean A. Pomerleau
pomerleau@cs.cmu.edu
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Ab... | 880 |@word proceeded:1 version:1 propagate:1 jacob:2 eng:1 dramatic:1 thereby:1 tr:1 harder:2 initial:1 contains:4 document:1 suppressing:1 current:2 comparing:1 nowlan:1 activation:16 must:1 tilted:1 subsequent:3 additive:3 cottrell:1 designed:3 treating:1 discrimination:1 provides:2 contribute:1 location:5 toronto:1 ... |
7,114 | 881 | Analysis of Unstandardized Contributions
in Cross Connected Networks
Thomas R. Shultz
Yuriko Oshima-Takane
Yoshio Takane
shultz@psych.mcgill.ca
yuriko@psych.mcgill.ca
takane@psych.mcgill.ca
Department of Psychology
McGill University
Montreal, Quebec, Canada H3A IBI
Abstract
Understanding knowledge representation... | 881 |@word middle:1 version:1 loading:5 simulation:1 covariance:13 accounting:1 decomposition:1 contains:1 score:15 existing:1 current:2 activation:10 lang:2 buckingham:2 assigning:1 realistic:1 additive:1 informative:1 shape:12 plot:4 designed:1 v:1 alone:4 generative:2 selected:3 discrimination:1 indicative:1 leamed:... |
7,115 | 882 | Effects of Noise on Convergence and
Generalization in Recurrent Networks
Kam Jim
Bill G. Horne
c.
Lee Giles*
NEC Research Institute, Inc., 4 Independence Way, Princeton, NJ 08540
{kamjim,horne,giles}~research.nj.nec.com
*Also with
UMIACS, University of Maryland, College Park, MD 20742
Abstract
We introduce and st... | 882 |@word version:2 rising:1 nd:4 bptt:1 simulation:5 simplifying:1 aijl:2 tr:2 initial:1 att:1 current:1 com:1 john:1 additive:21 hypothesize:1 plot:1 update:2 v:9 beginning:2 coarse:1 node:8 become:1 consists:3 introduce:2 expected:1 multi:1 td:1 encouraging:1 unrolling:1 increasing:2 lib:1 horne:6 string:20 develop... |
7,116 | 883 | Pairwise Neural Network Classifiers with
Probabilistic Outputs
David Price
A2iA and ESPCI
3 Rue de l'Arrivee, BP 59
75749 Paris Cedex 15, France
a2ia@dialup.francenet.fr
Stefan Knerr
ESPCI and CNRS (UPR AOOO5)
10, Rue Vauquelin, 75005 Paris, France
knerr@neurones.espci.fr
Leon Personnaz, Gerard Dreyfus
ESPeI, Labora... | 883 |@word duda:2 retraining:1 covariance:1 tr:1 serie:1 reduction:1 series:2 score:1 existing:1 partition:1 informative:2 interpretable:1 intelligence:1 postal:5 idi:3 revisited:1 sigmoidal:2 along:1 viable:1 ik:1 consists:1 combine:1 upr:2 pairwise:17 multi:2 little:1 becomes:1 kaufman:1 string:2 finding:1 transforma... |
7,117 | 884 | Spatial Representations in the Parietal
Cortex May Use Basis Functions
Alexandre Pouget
alex@salk.edu
Terrence J. Sejnowski
terry@salk.edu
Howard Hughes Medical Institute
The Salk Institute
La Jolla, CA 92037
and
Department of Biology
University of California, San Diego
Abstract
The parietal cortex is thought to re... | 884 |@word hippocampus:1 simulation:1 decomposition:1 minus:1 contains:1 extrapersonal:1 recovered:1 current:1 activation:1 must:1 readily:1 subsequent:1 motor:5 plot:1 plane:1 location:13 sigmoidal:2 simpler:1 along:2 asanuma:1 direct:1 symposium:1 presumed:1 behavior:1 themselves:1 nor:1 brain:2 actual:1 retinotopic:... |
7,118 | 885 | A Comparison of Discrete-Time Operator Models
for Nonlinear System Identification
Andrew D. Back, Ah Chung Tsoi
Department of Electrical and Computer Engineering,
University of Queensland
St. Lucia, Qld 4072. Australia.
e-mail: {back.act}@elec.uq.oz.au
Abstract
We present a unifying view of discrete-time operator mod... | 885 |@word trial:1 inversion:2 simulation:3 queensland:1 decomposition:1 thereby:1 initial:1 series:1 interestingly:1 current:1 yet:2 readily:1 realize:1 j1:4 update:4 selected:1 fewer:1 plane:1 ith:2 feedfoward:1 node:2 successive:1 simpler:1 constructed:1 direct:3 become:1 c2:11 consists:1 introduce:1 manner:2 themse... |
7,119 | 886 | Implementation of Neural Hardware with
the Neural VLSI of URAN in Applications
with Reduced Representations
ll-Song Han
Korea Telecom Research Laboratories
17, Woomyun-dong, Suhcho-ku
Seoul 137-140, KOREA
Ki-Chul Kim
Dept. of Info and Comm
KAIST
Seoul, 130-012, Korea
Hwang-Soo Lee
Dept. of Info and Comm
KAIST
Seoul... | 886 |@word reconstructible:2 implemented:1 effect:1 murray:1 loading:1 added:1 emulation:1 laboratory:1 realized:1 filter:1 pulse:2 simulation:4 ll:2 during:2 implementing:1 speaker:2 die:1 capacity:1 reduction:1 m:1 configuration:1 contains:1 decoder:1 selecting:1 biological:1 indispensible:1 past:1 interface:2 curren... |
7,120 | 887 | Finding Structure in Reinforcement Learning
Sebastian Thrun
University of Bonn
Department of Computer Science nr
R6merstr. 164, D-53117 Bonn, Germany
E-mail: thrun@carbon.informatik.uni-bonn.de
Anton Schwartz
Dept. of Computer Science
Stanford University
Stanford, CA 94305
Email: schwartz@cs.stanford.edu
Abstract
Re... | 887 |@word middle:1 open:2 seek:2 simulation:1 r:3 decomposition:1 pick:2 harder:1 configuration:1 contains:1 exclusively:4 franklin:1 current:2 comparing:1 artijiciallntelligence:2 yet:1 must:1 grain:1 partition:1 happen:1 designed:1 update:3 greedy:1 selected:2 discovering:1 accordingly:1 firstly:1 simpler:1 along:1 ... |
7,121 | 888 | A Novel Reinforcement Model of
Birdsong Vocalization Learning
Kenji Doya
ATR Human Infonnation Processing
Research Laboratories
2-2 Hikaridai, Seika, Kyoto 619-02, Japan
Terrence J. Sejnowski
Howard Hughes Medical Institute
UCSD and Salk Institute,
San Diego, CA 92186-5800, USA
Abstract
Songbirds learn to imitate a ... | 888 |@word trial:5 replicate:1 simulation:1 simplifying:1 solid:2 initial:1 contains:1 practiced:1 tuned:1 interestingly:1 current:3 anterior:5 activation:3 synthesizer:1 explorative:1 plasticity:1 motor:25 medial:2 implying:1 nervous:1 imitate:1 accordingly:1 short:1 provides:4 simpler:1 five:3 along:2 constructed:3 d... |
7,122 | 889 | Reinforcement Learning Methods for
Continuous-Time Markov Decision
Problems
Steven J. Bradtke
Computer Science Department
University of Massachusetts
Amherst, MA 01003
bradtkeGcs.umass.edu
Michael O. Duff
Computer Science Department
University of Massachusetts
Amherst, MA 01003
duffGcs.umass.edu
Abstract
Semi-Markov... | 889 |@word version:4 contraction:1 uma:2 selecting:1 must:3 moo:1 realize:1 update:8 smdp:16 greedy:2 selected:2 intelligence:1 along:3 c2:3 direct:1 consists:1 expected:6 bellman:4 discounted:2 td:12 underlying:1 panel:3 unspecified:1 minimizes:2 rtdp:17 developed:3 finding:1 nj:1 temporal:2 every:1 act:2 usefully:2 i... |
7,123 | 89 | 127
Neural Network Implementation Approaches
for the
Connection Machine
Nathan H. Brown, Jr.
MRJlPerkin Elmer, 10467 White Granite Dr. (Suite 304), Oakton, Va. 22124
ABSlRACf
The SIMD parallelism of the Connection Machine (eM) allows the construction of
neural network simulations by the use of simple data and control... | 89 |@word instruction:3 cm2:2 simulation:10 pset:2 reduction:3 configuration:1 inefficiency:1 efficacy:1 selecting:1 current:8 activation:48 must:1 ust:1 grain:1 numerical:1 plot:2 update:30 v:2 tenn:1 selected:6 pointer:4 provides:1 location:3 five:2 constructed:1 direct:1 overhead:3 roughly:2 becomes:1 provided:3 xx:... |
7,124 | 890 | A Model of the Neural Basis of the Rat's
Sense of Direction
William E. Skaggs
James J. Knierim
Hemant S. Kudrimoti
bill@nsma.arizona. edu
jim@nsma.arizona. edu
hemant@nsma. arizona. edu
Bruce L. McNaughton
bruce@nsma. arizona. edu
ARL Division of Neural Systems, Memory, And Aging
344 Life Sciences North, Univers... | 890 |@word cingulate:1 hippocampus:1 stronger:3 mammal:1 thereby:1 moment:1 necessity:1 initial:1 contains:3 series:1 ranck:4 interestingly:1 current:1 com:1 anterior:4 activation:1 yet:1 physiol:1 distant:1 plasticity:1 shape:2 motor:1 plot:1 stationary:1 cue:17 half:2 vanishing:1 cognit:1 detecting:3 optokinetic:1 lo... |
7,125 | 891 | Morphogenesis of the Lateral Geniculate
Nucleus: How Singularities Affect Global
Structure
Svilen Tzonev
Beckman Institute
University of Illinois
Urbana, IL 61801
svilen@ks.uiuc.edu
Klaus Schulten
Beckman Institute
University of Illinois
Urbana, IL 61801
kschulte@ks.uiuc.edu
Joseph G. Malpeli
Psychology Department
Un... | 891 |@word trial:2 maz:1 proportion:3 rhesus:1 propagate:2 gradual:1 initial:2 configuration:1 contains:1 foveal:12 past:1 must:3 visible:1 realistic:3 midway:1 shape:3 reappeared:2 accordingly:1 plane:3 lr:1 location:5 ipi:2 along:5 become:1 maturity:1 consists:1 intricate:1 behavior:2 roughly:2 examine:1 uiuc:3 morph... |
7,126 | 892 | Glove-TalkII: Mapping Hand Gestures to
Speech Using Neural Networks
S. Sidney Fels
Department of Computer Science
University of Toronto
Toronto, ON, M5S lA4
ssfels@ai.toronto.edu
Geoffrey Hinton
Department of Computer Science
University of Toronto
Toronto, ON, M5S lA4
hinton@ai.toronto.edu
Abstract
Glove-TaikII is a... | 892 |@word middle:4 version:1 open:2 tr:1 versatile:1 reduction:1 initial:5 configuration:14 franklin:1 current:2 analysed:1 activation:1 synthesizer:15 must:1 additive:3 shape:2 device:3 plane:2 beginning:1 flexing:1 provides:2 quantized:1 toronto:7 height:3 along:1 direct:1 qualitative:2 consists:2 sidney:5 wild:1 ma... |
7,127 | 893 | A Growing Neural Gas Network Learns
Topologies
Bernd Fritzke
Institut fur Neuroinformatik
Ruhr-Universitat Bochum
D-44 780 Bochum
Germany
Abstract
An incremental network model is introduced which is able to learn
the important topological relations in a given set of input vectors by
means of a simple Hebb-like learni... | 893 |@word maz:2 briefly:1 ruhr:1 simulation:4 reduction:1 initial:4 neighbors1:1 existing:1 current:1 yet:1 must:1 realize:1 remove:3 obsolete:1 indefinitely:1 node:1 mathematical:1 direct:3 introduce:1 alspector:1 behavior:1 growing:19 decreasing:1 discover:1 moreover:1 what:1 kind:2 finding:1 growth:2 unit:32 local:... |
7,128 | 894 | JPMAX: Learning to Recognize Moving
Objects as a Model-fitting Problem
Suzanna Becker
Department of Psychology, McMaster University
Hamilton, Onto L8S 4K1
Abstract
Unsupervised learning procedures have been successful at low-level
feature extraction and preprocessing of raw sensor data. So far,
however, they have had... | 894 |@word version:2 simulation:1 covariance:3 fifteen:1 moment:2 initial:1 configuration:3 interestingly:1 must:1 subsequent:1 shape:7 update:1 ilii:1 discrimination:1 selected:2 fewer:1 item:1 steepest:1 location:5 successive:3 preference:1 simpler:1 five:8 along:2 edelman:2 qij:8 fitting:2 expected:1 roughly:1 multi... |
7,129 | 895 | Factorial Learning and the EM Algorithm
Zoubin Ghahramani
zoubin@psyche.mit.edu
Department of Brain & Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
Abstract
Many real world learning problems are best characterized by an
interaction of multiple independent causes or factors. Discovering ... | 895 |@word seems:2 simulation:1 jacob:2 thereby:2 tr:1 solid:1 configuration:3 series:2 current:2 soules:1 si:15 partition:5 shape:2 asymptote:1 generative:1 discovering:2 intelligence:2 slh:1 quantizer:4 idi:1 location:2 toronto:2 si1:1 mathematical:1 become:1 consists:4 inside:1 pairwise:1 expected:3 alspector:1 absc... |
7,130 | 896 | Learning Local Error Bars
for Nonlinear Regression
David A.Nix
Department of Computer Science
and Institute of Cognitive Science
University of Colorado
Boulder, CO 80309-0430
dnix@cs.colorado.edu
Andreas S. Weigend
Department of Computer Science
and Institute of Cognitive Science
University of Colorado
Boulder, CO 80... | 896 |@word grey:2 covariance:1 solid:2 moment:1 initial:6 series:11 pub:2 seriously:1 past:4 existing:1 current:1 comparing:1 nowlan:1 activation:3 yet:1 must:5 additive:1 subsequent:1 shape:2 drop:1 designed:1 update:6 v:1 half:2 ria:1 underestimating:1 filtered:1 sudden:1 provides:2 characterization:1 location:2 dire... |
7,131 | 897 | PREDICTIVE CODING WITH
NEURAL NETS: APPLICATION TO
TEXT COMPRESSION
J iirgen Schmidhuber
Stefan Heil
Fakultat fiir Informatik
Technische Universitat Miinchen
80290 Miinchen, Germany
Abstract
To compress text files, a neural predictor network P is used to approximate the conditional probability distribution of possib... | 897 |@word version:1 compression:27 proportion:3 retraining:1 simulation:2 pick:1 tr:1 outlook:1 cleary:1 harder:1 initial:1 contains:1 prefix:1 outperforms:1 current:3 z2:1 com:1 activation:1 written:1 update:1 une:1 beginning:2 short:5 compo:1 node:9 miinchen:2 become:1 behavioral:1 paragraph:1 expected:3 pour:1 nor:... |
7,132 | 898 | Direction Selectivity In Primary Visual
Cortex Using Massive Intracortical
Connections
Christof Koch
CNS Program 216-76
Caltech
Pasadena, CA 91125
Humbert Suarez
CNS Program 216-76
Caltech
Pasadena, CA 91125
Rodney Douglas
MRC Anatomical Neuropharmacology Unit
University of Oxford
Oxford
UK
Abstract
Almost all mode... | 898 |@word wiesel:2 open:1 simulation:2 initial:2 exclusively:1 cort:1 current:6 must:1 physiol:3 realistic:1 shape:1 plot:2 n0:1 v_:1 short:2 provides:3 ohl:1 mathematical:1 schweitzer:1 consists:1 indeed:2 morphology:1 little:3 totally:1 provided:1 linearity:3 underlying:1 circuit:4 null:14 compressive:1 transformati... |
7,133 | 899 | SIMPLIFYING NEURAL NETS BY
DISCOVERING FLAT MINIMA
Sepp Hochreiter"
Jiirgen Schmidhuber t
Fakultat fiir Informatik, H2
Technische Universitat Miinchen
80290 Miinchen, Germany
Abstract
We present a new algorithm for finding low complexity networks
with high generalization capability. The algorithm searches for
large co... | 899 |@word norm:1 stronger:1 simplifying:5 pick:2 euclidian:1 analoguous:1 reduction:2 initial:1 outperforms:3 wd:1 nowlan:2 activation:7 john:1 hochreit:1 designed:1 half:1 discovering:6 fewer:1 provides:1 miinchen:2 lor:1 supply:1 underfitting:3 introduce:1 huber:1 expected:6 indeed:2 alspector:2 market:4 embody:1 br... |
7,134 | 9 | 22
LEARNING ON A GENERAL NETWORK
Amir F. Atiya
Department of Electrical Engineering
California Institute of Technology
Ca 91125
Abstract
This paper generalizes the backpropagation method to a general network containing feedback t;onnections. The network model considered consists of interconnected groups of neurons,
... | 9 |@word norm:1 tr:1 initial:4 configuration:1 nt:3 si:2 attracted:1 update:3 amir:1 kyk:1 beginning:2 ith:5 short:1 dissertation:1 iterates:1 differential:1 become:2 consists:2 prove:1 behavioral:1 manner:2 inter:1 expected:1 behavior:2 uz:2 becomes:1 begin:1 project:1 bounded:1 insure:1 what:1 developed:1 every:2 y3:... |
7,135 | 90 | 584
PHASOR NEURAL NETVORKS
Andr~
J. Noest, N.I.B.R., NL-ll0S AZ Amsterdam, The Netherlands.
ABSTRACT
A novel network type is introduced which uses unit-length 2-vectors
for local variables. As an example of its applications, associative
memory nets are defined and their performance analyzed. Real systems
correspondi... | 90 |@word version:1 loading:4 c0:1 tedious:1 concise:1 ld:1 initial:2 si:1 scatter:1 readily:1 realistic:1 selected:1 device:1 accordingly:1 hamiltonian:1 sigmoidal:1 become:1 consists:2 hermitian:6 olfactory:1 ra:1 roughly:1 isi:2 behavior:1 multi:1 brain:1 increasing:1 becomes:3 notation:1 suffice:1 circuit:1 develop... |
7,136 | 900 | Active Learning for Function
Approximation
Kah Kay Sung
(sung@ai.mit.edu)
Massachusetts Institute of Technology
Artificial Intelligence Laboratory
545 Technology Square
Cambridge, MA 02139
Partha Niyogi
(pn@ai.mit.edu)
Massachusetts Institute of Technology
Artificial Intelligence Laboratory
545 Technology Square
Camb... | 900 |@word polynomial:14 seems:2 nd:1 seek:1 tried:1 simulation:5 pressure:1 solid:2 carry:1 selecting:3 ours:1 outperforms:2 recovered:1 comparing:1 si:2 dx:1 numerical:1 j1:2 girosi:2 offunctions:1 treating:1 plot:1 intelligence:2 fewer:4 accepting:1 cjx:1 location:15 mathematical:1 dn:12 incorrect:1 consists:1 prove... |
7,137 | 901 | Higher Order Statistical Decorrelation without
Information Loss
Gustavo Deco
SiemensAG
Central Research
Otto-Hahn-Ring 6
81739 Munich
GeIIDany
Wilfried Brauer
Technische UniversiUit MUnchen
Institut fur InfoIIDatik
Arcisstr. 21
80290 Munich
GeIIDany
Abstract
A neural network learning paradigm based on information the... | 901 |@word determinant:3 version:1 compression:1 polynomial:8 simulation:1 covariance:2 decorrelate:3 fonn:1 twolayer:1 papoulis:3 moment:1 reduction:4 dx:1 must:1 written:1 remove:1 plot:1 update:1 nervous:1 beginning:1 successive:1 direct:1 consists:1 nor:1 brain:1 factorized:2 kind:1 interpreted:1 developed:2 transf... |
7,138 | 902 | A Neural Model of Delusions and
Hallucinations in Schizophrenia
Eytan Ruppin and James A. Reggia
Department of Computer Science
University of Maryland, College Park, MD 20742
ruppin@cs.umd .edu reggia@cs.umd.edu
David Horn
School of Physics and Astronomy,
Tel Aviv University, Tel Aviv 69978, Israel
horn@vm.tau.ac.il
... | 902 |@word trial:3 hippocampus:2 rhesus:1 simulation:2 lobe:4 initial:3 denoting:1 tuned:1 current:2 comparing:1 si:2 activation:2 yet:1 aft:4 numerical:1 plasticity:2 analytic:3 motor:1 cue:4 item:1 wijsj:1 berndt:1 persistent:2 behavioral:2 affective:2 buchanan:1 manner:4 acquired:1 indeed:1 behavior:5 frequently:2 g... |
7,139 | 903 | A Silicon Axon
Bradley A. Minch, Paul Hasler, Chris Diorio, Carver Mead
Physics of Computation Laboratory
California Institute of Technology
Pasadena, CA 91125
bminch, paul, chris, carver@pcmp.caltech.edu
Abstract
We present a silicon model of an axon which shows promise as a
building block for pulse-based neural comp... | 903 |@word briefly:1 open:2 pulse:55 propagate:4 simplifying:1 initial:7 necessity:1 series:1 bradley:5 current:9 activation:1 additonally:1 must:3 happen:1 shape:2 plot:1 depict:1 nervous:1 reciprocal:1 short:1 provides:1 node:9 neuromimes:1 successive:2 five:3 along:7 consists:2 ramped:1 resistive:1 overhead:1 rapid:... |
7,140 | 904 | A solvable connectionist model of
immediate recall of ordered lists
Neil Burgess
Department of Anatomy, University College London
London WC1E 6BT, England
(e-mail: n.burgessOucl.ac.uk)
Abstract
A model of short-term memory for serially ordered lists of verbal
stimuli is proposed as an implementation of the 'articulat... | 904 |@word trial:4 simulation:3 selecting:2 existing:1 current:2 activation:16 yet:1 plasticity:6 shape:1 motor:1 selected:7 item:106 positron:1 short:15 normalising:1 transposition:1 provides:2 node:22 bowed:1 successive:1 sigmoidal:1 ik:2 consists:1 indeed:1 planning:1 nor:1 brain:1 totally:1 stm:2 begin:1 wfj:5 temp... |
7,141 | 905 | oqp
r
s
t
u
! "# $ "%
% & ' )(+*, # -
QSRUTWVXZYJa1[]\\Z.+[^_`V1R/1RbuW02VaU/4Xmc1vx36dewy\S5 fgR1Yb/8^z|{k79dA7;fPhi:XZ< 0dZ[]^}|:<>j\~=c V XZ[k\Zl]XSc QmRT V XZY aO[k?AZ\Z[^@CnoVRBERpT DFV aXScv/Gd 7Iw?\S... | 905 |@word d2:1 cml:1 r:3 pg:1 tr:3 ytn:1 mxt:1 n8:1 xb0:1 dzp:1 amp:2 xz0:2 ka:9 od:8 bd:2 tot:2 wx:3 gv:2 yiz:1 v:1 yr:2 v1r:2 nq:2 rts:9 fpr:1 tvl:3 tvo:1 c6:1 dn:2 vxw:1 ik:2 nrtp:2 tuy:1 x0:2 xz:7 rvu:1 uz:1 ol:1 trg:2 pf:1 mek:1 kg:2 sut:1 tvg:1 ehk:2 acbed:1 otp:2 xd:2 k2:2 zl:5 dfu:1 uo:1 pfe:1 yn:3 sd:1 xv:1 e... |
7,142 | 906 | An Alternative Model for Mixtures of
Experts
Lei Xu
Dept. of Computer Science, The Chinese University of Hong Kong
Shatin, Hong Kong, Emaillxu@cs.cuhk.hk
Michael I. Jordan
Dept. of Brain and Cognitive Sciences
MIT
Cambridge, MA 02139
Geoffrey E. Hinton
Dept. of Computer Science
University of Toronto
Toronto, M5S lA4,... | 906 |@word kong:2 f32:1 polynomial:5 simulation:4 jacob:9 solid:1 current:1 comparing:1 wd:1 nowlan:2 com:6 neuneier:1 must:1 written:1 partition:1 update:1 maxv:2 accordingly:1 prespecified:2 provides:2 parameterizations:1 toronto:2 lx:11 direct:1 become:1 consists:3 combine:2 manner:1 alspector:2 brain:1 becomes:4 mo... |
7,143 | 907 | Classifying with Gaussian Mixtures and
Clusters
Nanda Kambhatla and Todd K. Leen
Department of Computer Science and Engineering
Oregon Graduate Institute of Science & Technology
P.O. Box 91000 Portland, OR 97291-1000
nanda@cse.ogi.edu, tleen@cse.ogi.edu
Abstract
In this paper, we derive classifiers which are winner-ta... | 907 |@word determinant:1 version:1 pw:1 duda:2 proportion:2 covariance:13 thereby:1 series:1 nanda:6 com:1 nowlan:6 surprising:1 assigning:1 written:1 john:1 partition:2 selected:2 quantizer:5 node:3 cse:2 consists:2 fitting:2 multi:1 becomes:1 underlying:1 alto:1 lowest:1 minimizes:1 spoken:1 acoust:1 ajn:1 exactly:1 ... |
7,144 | 908 | The Use of Dynamic Writing Information
in a Connectionist On-Line Cursive
Handwriting Recognition System
Stefan Manke
Michael Finke
University of Karlsruhe
Computer Science Department
D-76128 Karlsruhe, Germany
mankeCO)ira. uka.de, finkem@ira.uka.de
Alex Waibel
Carnegie Mellon University
School of Computer Science... | 908 |@word middle:2 instruction:1 score:1 past:1 bitmap:10 current:1 lang:1 written:3 fn:1 visible:1 remove:2 designed:1 npen:6 qij:3 consists:3 combine:7 multi:7 terminal:1 automatically:1 window:3 provided:4 maximizes:2 cm:1 kind:2 interpreted:1 kaufman:1 developed:1 finding:1 temporal:10 unit:7 normally:1 digitizer:... |
7,145 | 909 | Forward dynamic models in human
motor control: Psychophysical evidence
Daniel M. Wolpert
wolpert@psyche .mit .edu
Zouhin Ghahramani
zoubin@psyche.mit.edu
Michael I. Jordan
jordan@psyche.mit.edu
Department of Brain & Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
Abstract
Based on comp... | 909 |@word trial:2 integrative:1 gradual:1 sensed:1 simulation:7 covariance:3 thereby:2 ttn:1 solid:1 accommodate:1 recursively:1 initial:5 configuration:2 contains:1 daniel:6 current:3 yet:1 written:1 must:1 additive:3 midway:1 motor:29 asymptote:1 update:1 stationary:1 selected:1 manipulandum:6 nervous:1 tone:2 plane... |
7,146 | 91 | 652
Scaling Properties of Coarse-Coded Symbol Memories
Ronald Rosenfeld
David S. Touretzky
Computer Science Department
Carnegie Mellon University
Pittsburgh, Pennsylvania 15213
Abstract: Coarse-coded symbol memories have appeared in several neural network
symbol processing models. In order to determine how these mode... | 91 |@word trial:1 version:1 attainable:2 thereby:1 tr:1 accommodate:1 recursively:1 initial:1 born:1 exclusively:1 tuned:2 surprising:2 yet:2 must:5 john:2 ronald:1 numerical:4 happen:1 visibility:2 half:2 selected:1 short:2 coarse:14 mathematical:5 along:1 constructed:3 become:2 qualitative:1 consists:1 behavioral:1 p... |
7,147 | 910 | Unsupervised Classification of 3D Objects
from 2D Views
Satoshi Suzuki Hiroshi Ando
ATR Human Information Processing Research Laboratories
2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-02, Japan
satoshi@hip.atr.co.jp, ando@hip.atr.co.jp
Abstract
This paper presents an unsupervised learning scheme for categorizing
3... | 910 |@word version:1 eliminating:1 compression:1 duda:2 simulation:19 jacob:5 reduction:2 contains:1 selecting:2 existing:1 recovered:9 nowlan:1 si:1 john:1 cottrell:2 selected:5 plane:1 steepest:1 five:4 direct:1 become:1 symposium:1 edelman:5 consists:3 combine:1 tommy:1 acquired:1 expected:1 seika:1 examine:5 multi:... |
7,148 | 911 | A Study of Parallel Perturbative
Gradient Descent
D. Lippe? J. Alspector
Bellcore
Morristown, NJ 07960
Abstract
We have continued our study of a parallel perturbative learning
method [Alspector et al., 1993] and implications for its implementation in analog VLSI. Our new results indicate that, in most cases,
a single ... | 911 |@word briefly:1 simulation:6 initial:5 contains:1 duong:1 surprising:1 perturbative:9 must:1 chu:1 realistic:1 j1:5 update:5 stationary:3 math:1 five:2 along:1 become:1 yuhas:1 theoretically:2 expected:1 alspector:13 frequently:1 little:2 becomes:1 provided:1 begin:1 circuit:1 developed:1 proposing:1 nj:1 impracti... |
7,149 | 912 | Advantage Updating Applied to
a Differential Game
Mance E. Harmon
Wright Laboratory
WL/AAAT Bldg. 635 2185 Avionics Circle
Wright-Patterson Air Force Base, OH 45433-7301
harmonme@aa.wpafb.mil
Leemon C. Baird III?
Wright Laboratory
baird@cs.usafa.af.mil
A. Harry Klopr
Wright Laboratory
klopfah@aa.wpafb.mil
Category: ... | 912 |@word aircraft:1 seek:2 prasad:2 simulation:12 tr:2 solid:1 initial:3 united:1 current:1 comparing:2 yet:1 must:3 john:1 update:3 v:2 alone:1 plane:16 short:1 differential:11 consists:1 combine:1 fitting:1 expected:2 behavior:2 planning:2 bellman:8 discounted:3 notation:1 maximizes:1 maxa:3 developed:1 nj:1 suite:... |
7,150 | 913 | Generalisation in Feedforward Networks
Adam Kowalczyk and Herman Ferra
Telecom Australia, Research Laboratories
770 Blackburn Road, Clayton, Vic. 3168, Australia
(a.kowalczyk@trl.oz.au, h.ferra@trl.oz.au)
Abstract
We discuss a model of consistent learning with an additional restriction on the probability distribution... | 913 |@word eex:1 version:1 maz:2 polynomial:1 achievable:1 substitution:2 series:1 chervonenkis:2 current:2 si:1 dx:1 realistic:1 analytic:5 plot:1 drop:1 warmuth:1 affair:1 short:1 bup:7 provides:2 ron:3 attack:1 sigmoidal:1 along:1 director:1 symp:1 manner:1 introduce:3 indeed:1 themselves:1 abscissa:1 nor:1 mechanic... |
7,151 | 914 | A Rigorous Analysis Of
Linsker-type Hebbian Learning
J. Feng
Mathematical Department
University of Rome "La Sapienza?
P. Ie A. Moro, 00185 Rome, Italy
H. Pan
V. P. Roychowdhury
School of Electrical Engineering
Purdue University
West Lafayette, IN 47907
feng~at.uniroma1.it
hpan~ecn.purdue.edu
vwani~drum.ecn.purdue.... | 914 |@word faculty:1 stronger:2 oncenter:1 nd:1 grey:1 d2:6 simulation:5 r:3 eng:2 covariance:5 mention:1 initial:3 series:1 bc:2 must:1 numerical:2 analytic:1 motor:1 progressively:1 item:2 plane:2 short:1 core:2 provides:1 mathematical:1 rc:6 qualitative:2 qij:2 prove:1 uniroma1:1 kdk2:1 introduce:1 ra:1 expected:1 b... |
7,152 | 915 | Template-Based Algorithms for
Connectionist Rule Extraction
Jay A. Alexander and Michael C. Mozer
Department of Computer Science and
Institute for Cognitive Science
University of Colorado
Boulder, CO 80309--0430
Abstract
Casting neural network weights in symbolic terms is crucial for
interpreting and explaining the be... | 915 |@word repository:5 briefly:2 version:3 manageable:1 polynomial:2 simulation:7 fonn:1 concise:1 accommodate:1 initial:2 offering:1 bc:1 ours:1 nt:8 activation:6 yet:1 must:1 numerical:1 subsequent:1 benign:1 shape:1 instantiate:1 selected:1 monk:10 inputj:1 sigmoidal:7 simpler:1 thermometer:1 mathematical:1 direct:... |
7,153 | 916 | An Actor/Critic Algorithm that
Equivalent to Q-Learning
?
IS
Robert H. Crites
Computer Science Department
University of Massachusetts
Amherst, MA 01003
Andrew G. Barto
Computer Science Department
University of Massachusetts
Amherst, MA 01003
crites~cs.umass.edu
barto~cs.umass.edu
Abstract
We prove the convergence... | 916 |@word illustrating:1 version:1 open:1 tried:1 initial:1 uma:2 selecting:1 current:6 z2:2 must:4 readily:1 numerical:1 update:7 intelligence:1 location:3 supply:1 prove:3 indeed:1 expected:3 planning:1 discounted:2 decreasing:2 td:2 increasing:4 provided:1 estimating:2 underlying:1 maximizes:2 what:1 kind:1 interpr... |
7,154 | 917 | Temporal Dynamics of Generalization
Neural Networks
Changfeng Wang
Department of Systems Engineering
University Of Pennsylvania
Philadelphia, PA 19104
fwang~ender.ee.upenn.edu
?
In
Santosh S. Venkatesh
Department of Electrical Engineering
University Of Pennsylvania
Philadelphia, PA 19104
venkateshGee.upenn.edu
Abst... | 917 |@word achievable:1 open:2 gradual:1 bn:1 covariance:1 decomposition:1 ld:3 reduction:6 initial:7 substitution:1 contains:1 demarcated:2 activation:1 dx:3 written:1 readily:1 fn:1 additive:3 numerical:1 ith:2 characterization:4 math:1 dn:2 direct:1 differential:1 fitting:2 upenn:2 indeed:1 expected:1 behavior:1 inc... |
7,155 | 918 | Limits on Learning Machine Accuracy
Imposed by Data Quality
Corinna Cortes, L. D. Jackel, and Wan-Ping Chiang
AT&T Bell Laboratories
Holmdel, NJ 07733
Abstract
Random errors and insufficiencies in databases limit the performance of any classifier trained from and applied to the database.
In this paper we propose a met... | 918 |@word seems:1 termination:1 carry:2 series:2 john:1 ronald:1 designed:2 plot:2 fewer:1 device:1 short:2 record:1 chiang:5 codebook:6 along:1 constructed:1 install:1 become:2 underfitting:1 behavior:3 mechanic:1 automatically:1 window:2 increasing:11 becomes:1 provided:1 estimating:3 begin:1 underlying:1 domestic:1... |
7,156 | 919 | Diffusion of Credit in Markovian Models
Yoshua Bengio?
Dept. I.R.O., Universite de Montreal,
Montreal, Qc, Canada H3C-3J7
bengioyCIRO.UMontreal.CA
Paolo Frasconi
Dipartimento di Sistemi e Informatica
Universita di Firenze, Italy
paoloCmcculloch.ing.unifi.it
Abstract
This paper studies the problem of diffusion in Mark... | 919 |@word trial:6 version:1 longterm:1 norm:3 stronger:1 aia2:1 proportionality:2 simulation:2 propagate:1 contraction:2 decomposition:4 initial:8 cyclic:2 series:2 incidence:5 written:1 numerical:1 wanted:1 drop:1 update:1 stationary:1 half:1 ith:1 short:1 lr:1 firstly:1 mathematical:2 become:2 consists:1 introduce:2... |
7,157 | 92 | 544
MURPHY: A Robot that Learns by Doing
Bartlett W. Mel
Center for Complex Systems Research
University of Illinois
508 South Sixth Street
Champaign, IL 61820
January 2, 1988
Abstract
MURPHY consists of a camera looking at a robot arm, with a connectionist network
architecture situated in between. By moving its arm t... | 92 |@word eor:1 hampson:3 nd:1 decomposition:1 tr:1 colby:1 moment:2 initial:1 configuration:21 series:2 efficacy:1 contains:1 tuned:4 envision:3 current:6 activation:5 conjunct:5 conjunctive:4 must:5 reminiscent:1 yet:2 realize:1 readily:1 happen:2 blur:2 shape:1 motor:9 designed:2 progressively:2 discrimination:1 inf... |
7,158 | 921 | The NilOOO: High Speed Parallel VLSI
for Implementing Multilayer Perceptrons
Michael P. Perrone
Thomas J. Watson Research Center
P.O. Box 704
Yorktown Heights, NY 10598
mppGwatson.ibm.com
Leon N Cooper
Institute for Brain and Neural Systems
Brown University
Providence, Ri 02912
IncGcns.brown.edu
Abstract
In this pap... | 921 |@word coprocessor:1 version:2 norm:9 willing:1 simulation:4 thereby:1 minus:2 reduction:1 initial:1 configuration:1 contains:1 current:1 com:1 must:2 j1:1 enables:1 designed:1 plot:1 drop:2 v:1 ith:3 hypersphere:2 math:1 height:1 along:2 beta:1 incorrect:1 overhead:1 behavioral:1 behavior:1 examine:1 brain:2 eil:1... |
7,159 | 922 | PCA-Pyramids for Image Compression*
Horst Bischof
Department for Pattern Recognition
and Image Processing
Technical University Vienna
Treitlstraf3e 3/1832
A-1040 Vienna, Austria
bis@prip.tuwien.ac.at
Kurt Hornik
Institut fur Statistik und
Wahrscheinlichkeitstheorie
Technische UniversiUit Wien
Wiedner Hauptstraf3e 8-1... | 922 |@word compression:30 retraining:1 grey:2 decomposition:2 awij:1 reduction:7 kurt:6 past:1 recovered:2 com:1 must:1 cottrell:6 extensional:1 half:2 fewer:1 intelligence:1 quantizer:3 quantized:2 successive:1 simpler:1 five:1 constructed:1 symposium:1 consists:1 combine:3 baldi:5 introduce:2 tuwien:2 automatically:1... |
7,160 | 923 | Learning Saccadic Eye Movements
Using Multiscale Spatial Filters
Rajesh P.N. Rao and Dana H. Ballard
Department of Computer Science
University of Rochester
Rochester, NY 14627
{rao)dana}~cs.rochester.edu
Abstract
We describe a framework for learning saccadic eye movements using a
photometric representation of target ... | 923 |@word version:1 suitably:1 simulation:1 gradual:3 reduction:1 initial:1 foveal:6 contains:2 current:6 comparing:1 activation:2 assigning:2 dx:1 si:2 realize:1 subsequent:1 analytic:1 motor:30 update:2 depict:1 discrimination:1 infant:6 intelligence:1 fewer:1 plane:1 scienc:1 beginning:1 smith:1 location:17 success... |
7,161 | 924 | Extracting Rules from Artificial Neural Networks
with Distributed Representations
Sebastian Thrun
University of Bonn
Department of Computer Science III
Romerstr. 164, D-53117 Bonn, Germany
E-mail: thrun@carbon.informatik.uni-bonn.de
Abstract
Although artificial neural networks have been applied in a variety of real-w... | 924 |@word version:1 grey:3 confirms:1 mitsubishi:1 xout:4 tr:1 c1ass:2 configuration:10 initial:5 existing:1 current:1 activation:21 intriguing:1 must:2 written:1 john:1 refines:1 numerical:1 succeeding:1 update:1 half:1 monk:3 xk:4 short:3 detecting:2 node:2 zhang:1 five:2 along:1 constructed:2 tomorrow:1 incorrect:1... |
7,162 | 925 | From Data Distributions to
Regularization in Invariant Learning
Todd K. Leen
Department of Computer Science and Engineering
Oregon Graduate Institute of Science and Technology
20000 N.W. Walker Rd
Beaverton, Oregon 97006
tieen@cse.ogi.edu
Abstract
Ideally pattern recognition machines provide constant output when
the ... | 925 |@word covariance:1 pavel:1 tr:1 carry:1 series:1 dx:4 must:2 john:2 analytic:3 leaf:2 selected:1 plane:1 ith:1 provides:2 node:2 cse:1 lx:1 along:1 become:1 fitting:2 inside:1 introduce:2 shearing:3 indeed:1 expected:1 mock:1 mechanic:1 becomes:1 notation:1 insure:1 bounded:1 lowest:1 what:1 transformation:23 ough... |
7,163 | 926 | On the Computational Complexity of Networks of
Spiking Neurons
(Extended Abstract)
Wolfgang Maass
Institute for Theoretical Computer Science
Technische Universitaet Graz
A-80lO Graz, Austria
e-mail: maass@igi.tu-graz.ac.at
Abstract
We investigate the computational power of a formal model for networks of spiking neuro... | 926 |@word version:3 polynomial:3 norm:1 open:1 simulation:7 pulse:1 tr:1 recursively:1 carry:1 initial:3 chervonenkis:1 current:5 comparing:1 activation:6 numerical:1 realistic:5 shape:3 drop:1 short:2 provides:1 location:1 mathematical:5 along:1 symposium:1 prove:4 consists:3 combine:1 introduce:1 sacrifice:1 intrica... |
7,164 | 927 | Optimal Movement Primitives
Terence D. Sanger
Jet Propulsion Laboratory
MS 303-310
4800 Oak Grove Drive
Pasadena, CA 91109
(818) 354-9127 tds@ai.mit .edu
Abstract
The theory of Optimal Unsupervised Motor Learning shows how
a network can discover a reduced-order controller for an unknown
nonlinear system by representi... | 927 |@word trial:1 oae:3 compression:3 simulation:2 linearized:1 decomposition:5 reduction:2 ivaldi:1 configuration:1 current:1 nt:1 written:2 shape:2 motor:27 designed:2 stationary:1 provides:1 successive:1 oak:1 direct:1 prove:1 consists:2 autocorrelation:2 behavior:2 examine:1 cpu:1 window:2 increasing:2 becomes:2 d... |
7,165 | 928 | Reinforcement Learning Predicts the Site
of Plasticity for Auditory Remapping in
the Barn Owl
Alexandre Pougett
Cedric Deffayett
Terrence J. Sejnowskit
cedric@salk.edu
terry@salk.edu
alex@salk .edu
tHoward Hughes Medical Institute
The Salk Institute
La Jolla, CA 92037
Department of Biology
University of California, San... | 928 |@word trial:2 unaltered:1 simulation:4 thereby:1 ulus:1 initial:2 configuration:1 contains:4 series:1 foveal:1 tuned:1 existing:1 comparing:1 nowlan:1 activation:1 readily:1 subsequent:1 plasticity:17 update:1 cue:1 half:1 accordingly:1 core:1 location:8 instructs:1 prove:1 pathway:6 interaural:2 introduce:1 expec... |
7,166 | 929 | Hierarchical Mixtures of Experts Methodology Applied to
Continuous Speech Recognition
Ying Zhao, Richard Schwartz, Jason Sroka*: John Makhoul
BBN System and Technologies
70 Fawcett Street
Cambridge MA 02138
Abstract
In this paper, we incorporate the Hierarchical Mixtures of Experts (HME)
method of probability estimat... | 929 |@word inversion:1 reduction:2 c1ass:1 initial:2 current:1 od:1 reminiscent:1 john:4 distant:1 alone:3 rescoring:1 parameterizations:1 sigmoidal:9 simpler:1 direct:1 consists:1 fitting:5 combine:1 manner:1 multi:3 automatically:3 actual:1 snn:7 increasing:1 project:1 kaufman:1 interpreted:1 developed:3 spoken:1 nj:... |
7,167 | 93 | 477
A COMPUTATIONA.LLY ROBUST
ANATOlVIICAL MODEL FOR RETIN.AL
DIRECTIONAL SELECTI\l ITY
Norberto M. Grzywacz
Center BioI. Inf. Processing
MIT, E25-201
Cambridge, MA 02139
Franklin R. Amthor
Dept. Psychol.
Univ. Alabama Birmingham
Birmingham, AL 35294
ABSTRACT
We analyze a mathematical model for retinal directionally... | 93 |@word version:2 seems:2 d2:1 simulation:1 dramatic:1 reduction:1 series:1 tuned:1 franklin:1 current:1 activation:1 must:1 physiol:1 additive:1 hyperpolarizing:1 plot:3 progressively:3 discrimination:1 farther:1 compo:1 mathematical:1 along:3 borg:1 sustained:3 ra:3 tomaso:2 brain:1 increasing:1 provided:1 null:10 ... |
7,168 | 930 | FINANCIAL APPLICATIONS OF
LEARNING FROM HINTS
Yaser s. Abu-Mostafa
California Institute of Technology
and
NeuroDollars, Inc.
e-mail: yaser@caltech.edu
Abstract
The basic paradigm for learning in neural networks is 'learning from
examples' where a training set of input-output examples is used to
teach the network the t... | 930 |@word version:1 inversion:1 seems:1 approved:1 dramatic:1 past:1 informative:2 plot:3 alone:1 half:2 amir:1 characterization:1 math:1 provides:3 along:2 differential:4 ouput:1 fitting:1 paragraph:1 indeed:1 market:28 roughly:1 behavior:1 nor:1 decreasing:1 window:1 what:5 inputting:1 hit:2 whatever:1 negligible:1 ... |
7,169 | 931 | Asymptotics of Gradient-based
Neural Network 'fraining Algorithms
Sayandev Mukherjee
Terrence L. Fine
saymukh~ee.comell.edu
tlfine~ee.comell.edu
School of Electrical Engineering
Cornell University
Ithaca, NY 14853
School of Electrical Engineering
Cornell University
Ithaca, NY 14853
Abstract
We study the asymptot... | 931 |@word trial:2 version:2 norm:7 smirnov:1 suitably:1 seek:1 linearized:2 bn:3 simulation:2 thereby:2 n8:2 moment:7 initial:2 series:1 amp:1 cleared:1 comell:2 activation:4 yet:1 laii:1 remove:2 update:1 stationary:2 implying:1 selected:1 vanishing:2 lr:1 iterates:9 node:2 successive:1 sigmoidal:1 mathematical:1 dir... |
7,170 | 932 | Instance-Based State Identification for
Reinforcement Learning
R. Andrew McCallum
Department of Computer Science
University of Rochester
Rochester, NY 14627-0226
mccallumCcs.rochester.edu
Abstract
This paper presents instance-based state identification, an approach
to reinforcement learning and hidden state that buil... | 932 |@word trial:4 version:1 proportion:1 advantageous:1 casdagli:1 reused:1 open:1 instruction:1 simulation:1 dramatic:1 tr:2 ld:1 initial:1 inefficiency:1 contains:1 genetic:2 past:1 current:7 comparing:1 surprising:1 must:3 wanted:1 update:4 maxv:1 fewer:4 mccallum:16 oldest:1 short:5 record:6 utile:7 mental:1 provi... |
7,171 | 933 | Learning with Preknowledge:
Clustering with Point and Graph
Matching Distance Measures
Steven Gold!, Anand Rangarajan 1 and Eric Mjolsness 2
Department of Computer Science
Yale University
New Haven, CT 06520-8285
Abstract
Prior constraints are imposed upon a learning problem in the form
of distance measures. Prototyp... | 933 |@word decomposition:1 jacob:2 tr:2 yaleu:2 initial:3 series:4 selecting:1 recovered:1 comparing:1 analytic:1 designed:1 intelligence:1 selected:2 oblique:1 compo:1 mental:1 node:3 toronto:1 constructed:1 consists:1 manner:2 introduce:1 expected:1 multi:1 brain:1 ctan:1 decomposed:2 cpu:1 begin:2 bounded:1 null:1 f... |
7,172 | 934 | Reinforcement Learning Predicts the Site
of Plasticity for Auditory Remapping in
the Barn Owl
Alexandre Pougett
Cedric Deffayett
Terrence J. Sejnowskit
cedric@salk.edu
terry@salk.edu
alex@salk .edu
tHoward Hughes Medical Institute
The Salk Institute
La Jolla, CA 92037
Department of Biology
University of California, San... | 934 |@word trial:3 unaltered:1 seems:3 norm:1 casdagli:2 bptt:2 simulation:4 seek:1 gainesville:1 thereby:1 ulus:1 necessity:1 configuration:1 contains:6 series:41 foveal:1 exclusively:1 initial:5 tuned:1 lapedes:2 past:2 existing:1 current:1 comparing:1 nowlan:1 activation:1 si:1 must:5 readily:1 realize:1 subsequent:... |
7,173 | 935 | A Model for Chemosensory Reception
Rainer Malaka J Thomas Ragg
Institut fUr Logik, Komplexitat und Oeduktionssysteme
Universitat Karlsruhe, PO Box
0-76128 Karlsruhe, Germany
e-mail: malaka@ira.uka.de.ragg@ira.uka.de
Martin Hammer
Institut fur Neurobiologie
Freie Universitat Berlin
0-14195 Berlin, Germany
e-mail: mham... | 935 |@word version:1 hyperpolarized:1 vogt:1 open:1 simulation:12 minus:1 substitution:3 series:1 reaction:13 blank:1 current:1 activation:3 must:1 physiol:1 additive:1 recept:1 alone:1 signalling:1 marine:1 compo:1 provides:1 simpler:1 mathematical:1 constructed:1 direct:1 transducer:31 consists:1 fitting:3 olfactory:... |
7,174 | 936 | SARDNET: A Self-Organizing Feature
Map for Sequences
Daniel L. James and Risto Miikkulainen
Department of Computer Sciences
The University of Texas at Austin
Austin, TX 78712
dljames,risto~cs.utexas.edu
Abstract
A self-organizing neural network for sequence classification called
SARDNET is described and analyzed expe... | 936 |@word briefly:1 reused:1 risto:6 simulation:1 initial:1 series:2 daniel:5 past:2 current:2 contextual:1 activation:13 yet:1 must:1 subsequent:1 designed:1 selected:2 short:1 core:1 node:39 successive:1 five:2 scholtes:2 become:2 consists:3 manner:1 indeed:1 behavior:1 themselves:1 examine:1 multi:1 integrator:1 in... |
7,175 | 938 | Estimating Conditional Probability
Densities for Periodic Variables
Chris M Bishop and Claire Legleye
Neural Computing Research Group
Department of Computer Science and Applied Mathematics
Aston University
Birmingham, B4 7ET, U.K.
c.m.bishop@aston.ac.uk
Abstract
Most of the common techniques for estimating conditional... | 938 |@word km:1 calculus:1 seek:1 jacob:2 thereby:1 solid:1 existing:1 legleye:3 incidence:1 nowlan:1 activation:2 tackling:1 written:1 must:1 aft:1 numerical:1 plot:3 plane:1 provides:1 location:2 along:2 inside:1 introduce:3 themselves:1 scatterometer:3 frequently:1 multi:2 spherical:1 inappropriate:1 considering:1 e... |
7,176 | 939 | Learning Prototype Models for Tangent
Distance
Trevor Hastie?
Statistics Department
Sequoia Hall
Stanford University
Stanford, CA 94305
email: trevor@playfair .stanford .edu
Patrice Simard
AT&T Bell Laboratories
Crawfords Corner Road
Holmdel, NJ 07733
email: patrice@neural.att.com
Eduard Siickinger
AT &T Bell Laborat... | 939 |@word middle:1 briefly:1 norm:3 seems:2 grey:1 tried:3 decomposition:3 accounting:1 covariance:1 initial:1 att:2 ours:1 current:4 com:2 bd:1 partition:2 pertinent:1 drop:2 stationary:1 guess:1 plane:1 parametrization:1 argm:1 location:3 hyperplanes:1 fitting:2 combine:1 redefine:1 indeed:1 behavior:1 themselves:1 ... |
7,177 | 94 | 195
LEARNING WITH TEMPORAL DERIVATIVES IN
PULSE-CODED NEURONAL SYSTEMS
Mark Gluck
David B. Parker
Eric S. Reifsnider
Department of Psychology
Stanford University
Stanford, CA 94305
Abstract
A number of learning models have recently been proposed which
involve calculations of temporal differences (or derivatives in... | 94 |@word trial:5 version:3 seems:1 nd:1 extinction:4 pulse:57 simulation:3 solid:2 electronics:1 series:5 efficacy:7 unintended:1 groundwork:1 current:4 activation:13 intriguing:1 must:2 fonnulated:2 vor:1 subsequent:1 realistic:2 analytic:1 update:1 bart:1 es:1 dissertation:1 record:1 sudden:1 provides:1 draft:1 node... |
7,178 | 940 | ?
?
!#"%$ "'&)(*,+.-0/
?
?
H IKJ` 13LNR 25^ 46MO25JI57cdPi83QIE9!e R!: T3S.TVPdjUWI5Z ;PX5Y[I IEZWklX]\ Pj
gvh P_wxt IEeyo Z iWzR S ` R{ Rey|~} R
? R c?{ h I ep? `????d? ??? ????
?V?[?V??? ?V??[?3?[?a?3? ?a? ??]? ???
?
?
?... | 940 |@word ixx:1 km:1 r:2 n8:7 ld:1 kuf:1 bc:4 ts2:1 ka:1 ixj:1 dx:1 bd:5 obi:3 wx:3 aps:1 rts:1 tdp:1 eba:1 lr:1 lx:2 h4:1 mnc:1 xz:2 ee6:1 td:5 wbi:1 gqi:1 qw:1 kg:2 sut:1 jik:1 x5p:1 ag:2 j62:1 uk:1 j24:1 uo:1 cdp:1 io:2 api:1 iie:1 dnm:1 ap:1 kml:3 ibi:1 au:1 eb:9 p_:2 bi:2 uy:7 fah:5 yj:1 vu:2 epr:1 xr:2 ofe:1 fbh... |
7,179 | 941 | Deterministic Annealing Variant
of the EM Algorithm
N aonori U eda
Ryohei N alcano
ueda@cslab.kecl.ntt.jp nakano@cslab.kecl.ntt.jp
NTT Communication Science Laboratories
Hikaridai, Seika-cho, Soraku-gun,
Kyoto 619-02 Japan
Abstract
We present a deterministic annealing variant of the EM algorithm
for maximum likelihoo... | 941 |@word version:1 cha:1 jacob:2 simplifying:1 klk:1 initial:5 configuration:1 interestingly:1 current:1 nowlan:3 fn:1 partition:3 plot:2 parameterization:1 successive:1 ryohei:5 become:1 introduce:1 theoretically:1 indeed:2 expected:1 seika:1 mechanic:3 brain:1 ote:1 increasing:3 becomes:3 begin:1 moreover:3 maximiz... |
7,180 | 942 | Patterns of damage in neural networks:
The effects of lesion area, shape and
number
Eytan Ruppin and James A. Reggia ?
Department of Computer Science
A.V. Williams Bldg.
University of Maryland
College Park, MD 20742
ruppin@cs.umd.edu reggia@cs.umd.edu
Abstract
Current understanding of the effects of damage on neural ... | 942 |@word mild:1 trial:2 stronger:1 gradual:1 simulation:9 shading:4 series:1 hereafter:1 denoting:1 past:2 existing:1 current:1 z2:1 surprising:1 si:1 numerical:5 j1:1 shape:9 analytic:4 enables:1 plot:1 v:1 cue:2 selected:1 device:1 short:1 berndt:1 viable:2 qualitative:1 manner:1 theoretically:1 examine:3 growing:1... |
7,181 | 943 | Phase-Space Learning
Fu-Sheng Tsung
Chung Tai Ch'an Temple
56, Yuon-fon Road, Yi-hsin Li, Pu-li
Nan-tou County, Taiwan 545
Republic of China
Garrison W. Cottrell?
Institute for Neural Computation
Computer Science & Engineering
University of California, San Diego
La Jolla, California 92093
Abstract
Existing recurrent... | 943 |@word unaltered:1 briefly:1 bptt:7 simulation:3 crucially:1 solid:2 reduction:2 series:1 lapedes:1 existing:5 current:3 recovered:1 activation:3 dx:2 must:3 cottrell:6 visible:13 plot:2 half:1 selected:1 short:2 provides:2 detecting:1 simpler:1 five:1 become:3 differential:1 incorrect:2 consists:1 inside:4 introdu... |
7,182 | 944 | Synchrony and Desynchrony
in Neural Oscillator Networks
DeLiang Wang
David Terman
Department of Computer and Information Science
and Center for Cognitive Science
The Ohio State University
Columbus, Ohio 43210, USA
dwang@cis.ohio-state.edu
Department of Mathematics
The Ohio State University
Columbus, Ohio 43210, USA
... | 944 |@word middle:1 indiscriminate:1 open:1 simulation:5 reentrant:1 fragment:1 past:1 current:1 cad:1 si:3 activation:1 readily:1 distant:2 selected:1 plane:2 xk:1 beginning:5 short:3 cognit:1 provides:1 math:1 successive:1 along:2 direct:1 differential:1 ik:1 edelman:1 consists:1 inter:1 indeed:1 rapid:2 behavior:2 x... |
7,183 | 945 | The Electrotonic Transformation:
a Tool for Relating Neuronal Form to Function
Nicholas T. Carnevale
Department of Psychology
Yale University
New Haven, CT 06520
Kenneth Y. Tsai
Department of Psychology
Yale University
New Haven, CT 06520
Brenda J. Claiborne
Division of Life Sciences
University of Texas
San Antonio,... | 945 |@word cylindrical:3 middle:2 loading:1 termination:1 cm2:1 simulation:1 pressure:1 fonn:1 thereby:1 initial:1 series:1 mainen:2 current:9 activation:3 yet:1 must:2 readily:1 physiol:1 additive:2 j1:1 plasticity:2 plot:2 designed:1 drop:1 v:2 device:1 nervous:1 postnatal:1 core:1 characterization:1 contribute:1 loc... |
7,184 | 946 | Boosting the Performance
of RBF Networks
with Dynamic Decay Adjustment
Michael R. Berthold
Forschungszentrum Informatik
Gruppe ACID (Prof. D. Schmid)
Haid-und-Neu-Strasse 10-14
76131 Karlsruhe, Germany
eMail: berthold@fzLde
Jay Diamond
Intel Corporation
2200 Mission College Blvd.
Santa Clara, CA, USA
95052 MS:SC9-15
... | 946 |@word bounced:2 cylindrical:1 simulation:1 contains:1 tuned:3 existing:1 com:1 clara:1 activation:9 lang:2 must:4 eleven:1 v:1 fewer:1 liapunov:1 boosting:3 node:4 location:3 direct:1 introduce:2 roughly:1 examine:1 growing:1 multi:2 inspired:1 pf:1 project:2 underlying:1 what:1 substantially:1 developed:1 elbaum:... |
7,185 | 947 | A Charge-Based CMOS Parallel Analog
Vector Quantizer
Gert Cauwenberghs
Johns Hopkins University
ECE Department
3400 N. Charles St.
Baltimore, MD 21218-2686
gert@jhunix.hcf.jhu.edu
Volnei Pedroni
California Institute of Technology
EE Department
Mail Code 128-95
Pasadena, CA 91125
pedroni@romeo.caltech.edu
Abstract
We... | 947 |@word inversion:3 compression:4 mb1:1 pulse:1 accommodate:1 contains:2 tuned:1 current:9 ixj:3 follower:5 written:2 readily:1 john:1 refresh:1 subsequent:1 resent:1 plot:3 globalized:3 plane:1 beginning:2 vanishing:1 core:1 compo:1 quantizer:6 location:1 along:4 differential:2 supply:4 consists:1 resistive:1 isscc... |
7,186 | 948 | Computational structure of coordinate
transformations: A generalization study
Zoubin Ghahramani
zoubin@psyche.mit.edu
Daniel M. Wolpert
wolpert@psyche.mit.edu
Michael I. Jordan
jordan@psyche.mit.edu
Department of Brain & Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
Abstract
One of t... | 948 |@word neurophysiology:1 unaltered:1 inversion:1 seems:1 proportion:1 open:3 gradual:2 simulation:2 jacob:2 pick:2 thereby:1 solid:1 shading:1 configuration:2 daniel:6 tuned:2 contextual:5 nowlan:1 yet:1 must:1 subsequent:3 plasticity:2 girosi:2 motor:6 designed:1 plot:2 nervous:2 tone:2 plane:1 record:1 provides:1... |
7,187 | 949 | Inferring Ground Truth from Subjective
Labelling of Venus Images
Padhraic Smyth, Usama Fayyad
Jet Propulsion Laboratory 525-3660,
Caltech, 4800 Oak Grove Drive,
Pasadena, CA 91109
Michael Burl, Pietro Perona
Department of Electrical Engineering
Caltech, MS 116-81,
Pasadena, CA 91125
Pierre Baldi*
Jet Propulsion Labo... | 949 |@word km:5 accounting:3 carry:1 initial:1 contains:1 score:1 subjective:16 current:1 comparing:3 planet:1 realistic:1 visible:5 plot:1 intelligence:1 geologic:1 item:1 smith:1 provides:1 quantized:1 detecting:1 location:1 oak:2 chester:1 consists:1 baldi:5 manner:1 spacecraft:1 expected:1 rapid:1 examine:2 provide... |
7,188 | 95 | 305
ALVINN:
AN AUTONOMOUS LAND VEHICLE IN A
NEURAL NETWORK
Dean A. Pomerleau
Computer Science Department
Carnegie Mellon University
Pittsburgh, PA 15213
ABSTRACT
ALVINN (Autonomous Land Vehicle In a Neural Network) is a 3-layer
back-propagation network designed for the task of road following. Currently ALVINN takes i... | 95 |@word version:1 middle:1 simulation:2 excited:1 brightness:1 dramatic:1 accommodate:1 initial:1 contains:1 current:7 activation:12 lang:1 follower:1 must:3 realistic:2 tailoring:1 designed:2 v:1 half:1 intelligence:1 accordingly:1 provides:2 contribute:1 location:2 successive:1 along:2 become:1 initiative:1 consist... |
7,189 | 950 | Using Voice Transformations to Create
Additional Training Talkers for Word Spotting
Eric I. Chang and Richard P. Lippmann
MIT Lincoln Laboratory
Lexington, MA 02173-0073, USA
eichang@sst.ll.mit.edu and rpl@sst.ll.mit.edu
Abstract
Speech recognizers provide good performance for most users but the
error rate often incr... | 950 |@word nd:1 llo:1 perfo:2 carry:1 series:2 score:4 past:1 existing:2 comparing:1 wakita:3 john:1 partition:1 shape:1 plot:2 sponsored:1 provides:1 boosting:1 unacceptable:1 symposium:1 ra:1 rapid:1 alspector:1 ming:1 increasing:1 provided:2 project:1 developed:1 lexington:1 transformation:21 temporal:1 transfonnati... |
7,190 | 951 | Reinforcement Learning Algorithm for
Partially Observable Markov Decision
Problems
Tommi Jaakkola
tommi@psyche.mit.edu
Satinder P. Singh
singh@psyche.mit.edu
Michael I. Jordan
jordan@psyche.mit.edu
Department of Brain and Cognitive Sciences, BId. E10
Massachusetts Institute of Technology
Cambridge, MA 02139
Abstrac... | 951 |@word illustrating:1 version:4 inversion:1 paid:1 carry:1 contains:1 past:1 current:8 comparing:1 si:1 must:1 readily:1 written:1 enables:2 implying:1 v1r:1 accordingly:1 vanishing:1 provides:1 direct:4 become:1 eleventh:1 alm:15 theoretically:2 indeed:1 expected:5 behavior:1 nor:1 brain:1 bellman:1 discounted:2 t... |
7,191 | 952 | Hyperparameters, Evidence and
Generalisation for an Unrealisable Rule
Glenn Marion and David Saad
glennyGed.ac.uk, D.SaadGed.ac.uk
Department of Physics, University of Edinburgh,
Edinburgh, EH9 3JZ, U.K.
Abstract
Using a statistical mechanical formalism we calculate the evidence,
generalisation error and consistency m... | 952 |@word seek:2 attainable:1 pick:2 reduction:1 contains:1 nt:1 surprising:1 plot:1 plane:1 compo:2 math:3 ron:1 preference:2 firstly:3 fitting:2 advocate:1 introduce:1 indeed:1 examine:4 mechanic:2 trg:1 increasing:1 begin:1 underlying:2 linearity:4 heidbreder:1 what:6 pursue:1 whilst:1 act:2 f3f:1 preferable:1 uk:2... |
7,192 | 953 | A Non-linear Information Maximisation
Algorithm that Performs
Blind Separation.
Anthony J. Bell
tonylOsalk.edu
Terrence J. Sejnowski
terrylOsalk.edu
Computational Neurobiology Laboratory
The Salk Institute
10010 N. Torrey Pines Road
La Jolla, California 92037-1099
and
Department of Biology
University of California a... | 953 |@word determinant:2 polynomial:1 nd:1 simulation:1 papoulis:3 reduction:1 moment:3 contains:1 series:2 recovered:1 yet:1 written:2 must:3 wx:5 enables:1 alone:2 maximised:2 sys:1 steepest:1 ith:1 compo:1 caveat:1 node:4 toronto:1 sigmoidal:5 five:3 become:3 supply:1 olfactory:1 manner:1 expected:1 presumed:1 ica:4... |
7,193 | 954 | Anatomical origin and computational role
of diversity in the response properties of
cortical neurons
Kalanit Grill Spectort
Shimon Edelmant
Rafael Malacht
Depts of tApplied Mathematics and Computer Science and tN eurobiology
The Weizmann Institute of Science
Rehovot 76100, Israel
{kalanit.edelman. malach }~wisdom . we... | 954 |@word neurophysiology:1 wiesel:5 disk:2 confirms:1 simulation:7 accounting:1 tuned:5 denoting:1 scatter:1 must:1 girosi:2 v:3 device:1 amir:1 short:1 num:1 provides:1 location:2 preference:4 successive:1 mathematical:1 edelman:6 inter:2 expected:1 brain:3 freeman:1 versity:1 provided:1 moreover:1 israel:1 monkey:1... |
7,194 | 955 | Factorial Learning by Clustering Features
Joshua B. Tenenbaum and Emanuel V. Todorov
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
{jbt.emo}~psyche . mit.edu
Abstract
We introduce a novel algorithm for factorial learning, motivated
by segmentation problems in com... | 955 |@word cleanly:1 simulation:2 tried:1 simplifying:1 decomposition:1 harder:1 configuration:3 assigning:3 must:3 visible:1 plot:1 prk:1 update:2 generative:8 fewer:3 ith:1 simpler:3 five:1 become:1 compose:1 introduce:1 expected:1 behavior:1 alspector:1 embody:1 mechanic:3 brain:1 inspired:1 decreasing:1 eil:1 actua... |
7,195 | 956 | Predicting the Risk of Complications in Coronary
Artery Bypass Operations using Neural Networks
Richard P. Lippmann, Linda Kukolich
MIT Lincoln Laboratory
244 Wood Street
Lexington, MA 02173-0073
Dr. David Shahian
Lahey Clinic
Burlington, MA 01805
Abstract
Experiments demonstrated that sigmoid multilayer perceptron (... | 956 |@word version:1 middle:1 replicate:2 logit:1 open:2 edema:1 initial:2 united:2 selecting:1 bradley:1 current:1 comparing:1 anne:1 treating:1 drop:1 sponsored:1 resampling:1 congestion:1 half:2 selected:6 record:1 provides:2 complication:27 node:10 sigmoidal:1 simpler:1 mathematical:1 become:1 replication:1 fitting... |
7,196 | 957 | An Analog Neural Network Inspired by
Fractal Block Coding
Fernando J. Pineda
Andreas G. Andreou
The Applied Physics Laboratory
The Johns Hopkins University
Johns Hokins Road
Laurel, MD 20723-6099
Dept. of Electrical & Computer
Engineering
The Johns Hopkins University
34th & Charles St.
Baltimore, MD 21218
Abstract
... | 957 |@word briefly:1 compression:1 simulation:2 ajj:1 recursively:1 electronics:1 configuration:1 initial:1 amp:1 current:10 must:4 john:3 transcendental:1 happen:1 shape:1 half:4 device:1 accordingly:1 realizing:1 dissertation:1 simpler:1 ik:5 qualitative:1 consists:1 prove:1 indeed:1 behavior:1 inspired:4 globally:1 ... |
7,197 | 958 | A Lagrangian Formulation For
Optical Backpropagation Training In
Kerr-Type Optical Networks
James E. Steck
Mechanical Engineering
Wichita State University
Wichita, KS 67260-0035
Steven R. Skinner
Electrical Engineering
Wichita State University
Wichita, KS 67260-0044
Alvaro A. Cruz-Cabrara
Electrical Engineering
Wich... | 958 |@word cm2:1 steck:2 simulation:3 propagate:1 pg:1 fonn:1 thereby:1 optically:6 comparing:1 dx:1 cruz:1 pertinent:1 update:4 discrimination:1 tenn:1 half:2 device:3 plane:5 beginning:1 ith:1 location:3 sigmoidal:1 simpler:1 along:1 constructed:1 prove:1 mask:1 nor:1 integrator:1 discretized:1 increasing:2 becomes:2... |
7,198 | 959 | Learning Many Related Tasks at the
Same Time With Backpropagation
Rich Caruana
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
caruana@cs.cmu.edu
Abstract
Hinton [6] proposed that generalization in artificial neural nets
should improve if nets learn to represent the domain's underlying
regu... | 959 |@word multitask:17 trial:1 version:1 briefly:1 stronger:1 seems:1 nd:1 hu:4 pulse:1 pressure:1 harder:1 initial:1 loc:1 outperforms:1 lang:1 yet:2 synthesizer:2 must:4 john:1 realistic:1 shape:1 cheap:1 concert:1 sponsored:1 v:1 alone:3 half:1 selected:2 fewer:1 intelligence:2 provides:3 location:13 preference:3 c... |
7,199 | 960 | Non-linear Prediction of Acoustic Vectors
Using Hierarchical Mixtures of Experts
S.R.Waterhouse
A.J.Robinson
Cambridge University Engineering Department,
Trumpington St ., Cambridge, CB2 1PZ, England.
Tel: [+44] 223 332800, Fax: [+44] 223 332662,
Email: srwlO01.ajr@eng.cam.ac.uk
URL: http://svr-www.eng.cam.ac.ukr srw1... | 960 |@word cu:1 compression:1 yct:3 nd:1 simulation:1 eng:2 jacob:5 covariance:7 decomposition:1 solid:4 recursively:1 reduction:1 moment:2 initial:3 series:14 outperforms:1 past:1 current:1 com:1 activation:5 assigning:1 yet:2 additive:1 fram:1 plot:3 leaf:1 short:1 record:1 regressive:1 provides:2 quantized:1 node:1 ... |
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