Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
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7,000 | 777 | Bayesian Modeling and Classification of
Neural Signals
Michael S. Lewicki
Computation and Neural Systems Program
California Institute of Technology 216-76
Pasadena, CA 91125
lewickiOcns.caltech.edu
Abstract
Signal processing and classification algorithms often have limited
applicability resulting from an inaccurate... | 777 |@word decomposition:6 solid:1 wellapproximated:1 moment:1 contains:1 si:1 must:1 shape:22 aps:9 selected:1 rts:1 record:1 math:1 five:1 dn:1 along:1 ipx:1 fitting:4 pairwise:1 expected:1 frequently:1 decomposed:1 cpu:1 window:1 becomes:1 underlying:3 moreover:1 circuit:1 what:3 finding:2 differentiation:1 certaint... |
7,001 | 778 | A Learning Analog Neural Network Chip
with Continuous-Time Recurrent
Dynamics
Gert Cauwenberghs*
California Institute of Technology
Department of Electrical Engineering
128-95 Caltech, Pasadena, CA 91125
E-mail: gertalcco. cal tech. edu
Abstract
We present experimental results on supervised learning of dynamical feat... | 778 |@word illustrating:1 norm:2 pulse:2 accommodate:4 tapering:1 configuration:1 contains:1 initial:6 tuned:1 current:13 activation:4 perturbative:2 chu:1 john:1 refresh:5 periodically:1 tailoring:1 j1:1 shape:2 remove:1 update:8 xk:1 provides:3 location:1 sigmoidal:1 five:3 rc:1 along:2 constructed:1 differential:4 s... |
7,002 | 779 | Address Block Location with a Neural Net System
Eric Cosatto
Hans Peter Graf
AT&T Bell Laboratories
Crawfords Corner Road
Holmdel, NJ 07733, USA
Abstract
We developed a system for finding address blocks on mail pieces that can
process four images per second. Besides locating the address block, our
system also determ... | 779 |@word tried:1 t1r:1 solid:2 moment:1 contains:2 united:3 tuned:3 existing:1 written:2 subsequent:1 designed:1 discrimination:1 alone:1 half:1 selected:1 sram:2 provides:1 coarse:1 postal:4 location:17 height:4 along:2 consists:1 combine:1 inside:2 deteriorate:1 decreasing:1 company:1 actual:1 increasing:1 totally:... |
7,003 | 78 | 358
LEARNING REPRESENTATIONS BY RECIRCULATION
Geoffrey E. Hinton
Computer Science and Psychology Departments, University of Toronto,
Toronto M5S lA4, Canada
James L. McClelland
Psychology and Computer Science Departments, Carnegie-Mellon University,
Pittsburgh, PA 15213
ABSTRACT
We describe a new learning procedure fo... | 78 |@word cu:1 compression:1 simulation:10 harder:2 initial:2 contains:2 series:2 current:1 yet:2 must:1 cottrell:1 visible:76 happen:1 designed:1 exploded:2 update:2 tenn:6 intelligence:1 steepest:9 ith:1 toronto:2 simpler:1 mathematical:2 direct:1 become:1 consists:1 manner:1 introduce:3 expected:1 elman:1 considerin... |
7,004 | 780 | Dynamic Modulation of Neurons and Networks
Eve Marder
Center for Complex Systems
Brandeis University
Waltham, MA 02254 USA
Abstract
Biological neurons have a variety of intrinsic properties because of the
large number of voltage dependent currents that control their activity.
Neuromodulatory substances modify both th... | 780 |@word hyperpolarized:1 open:2 pulse:4 lowfrequency:1 dramatic:1 electronics:1 efficacy:2 current:13 si:1 must:2 evans:1 hyperpolarizing:3 physiol:3 underly:1 motor:7 tenn:1 nervous:7 signalling:2 coleman:2 short:5 kepler:2 simpler:1 burst:5 sustained:4 behavioral:2 theoretically:1 rapid:5 behavior:3 terminal:2 jm:... |
7,005 | 781 | A Comparison of Dynamic Reposing and
Tangent Distance for Drug Activity
Prediction
Thomas G. Dietterich
Arris Pharmaceutical Corporation and Oregon State University
Corvallis, OR 97331-3202
Ajay N. Jain
Arris Pharmaceutical Corporation
385 Oyster Point Blvd., Suite 3
South San Francisco, CA 94080
Richard H. Lathrop an... | 781 |@word deformed:1 briefly:1 norm:1 initial:2 cyclic:1 contains:1 series:1 selecting:1 perret:1 outperforms:1 existing:3 current:1 must:4 written:1 john:2 subsequent:1 shape:4 webster:2 drop:1 discrimination:2 intelligence:1 selected:3 guess:1 plane:3 provides:1 iterates:1 complication:1 location:3 intramolecular:1 ... |
7,006 | 782 | Optimal Unsupervised Motor Learning
Predicts the Internal Representation of
Barn Owl Head Movements
Terence D. Sanger
Jet Propulsion Laboratory
MS 303-310
4800 Oak Grove Drive
Pasadena, CA 91109
Abstract
(Masino and Knudsen 1990) showed some remarkable results which
suggest that head motion in the barn owl is control... | 782 |@word oae:2 polynomial:6 simulation:9 moment:1 initial:1 tuned:3 nt:3 yet:1 motor:36 hypothesize:1 half:4 intelligence:1 plane:1 short:7 provides:4 toronto:1 oak:1 height:2 become:1 incorrect:1 qualitative:1 autocorrelation:3 manner:1 behavior:1 brain:1 automatically:1 becomes:1 project:1 unrelated:1 circuit:2 max... |
7,007 | 783 | A Hodgkin-Huxley Type Neuron Model
That Learns Slow Non-Spike Oscillation
Kenji Doya*
Allen I. Selverston
Department of Biology
University of California, San Diego
La Jolla, CA 92093-0357, USA
Peter F. Rowat
Abstract
A gradient descent algorithm for parameter estimation which is
similar to those used for continuous... | 783 |@word sba:1 trial:1 neurophysiology:4 version:1 replicate:1 calculus:2 simulation:2 covariance:3 solid:1 carry:1 initial:2 sah:1 tuned:2 pna:1 current:37 ka:1 activation:7 yet:1 obi:5 must:1 update:2 cleland:1 pacemaker:1 accordingly:1 five:1 mathematical:1 differential:1 symposium:1 olfactory:1 manner:1 behavior:... |
7,008 | 784 | Learning Mackey-Glass from 25
examples, Plus or Minus 2
Mark Plutowski? Garrison Cottrell? Halbert White??
Institute for Neural Computation
*Department of Computer Science and Engineering
**Department of Economics
University of California, San Diego
La J oHa, CA 92093
Abstract
We apply active exemplar selection (Plut... | 784 |@word compression:1 r:1 pick:1 concise:4 minus:5 solid:1 initial:2 series:7 selecting:7 denoting:1 lapedes:3 current:6 comparing:3 cottrell:5 fn:20 subsequent:1 informative:2 mackey:9 greedy:1 fewer:2 selected:13 half:1 beginning:2 cse:1 simpler:1 five:2 along:1 fitting:3 overhead:3 manner:1 introduce:1 rapid:1 gr... |
7,009 | 786 | Solvable Models of Artificial Neural
Networks
Sumio Watanabe
Information and Communication R&D Center
Ricoh Co., Ltd.
3-2-3, Shin-Yokohama, Kohoku-ku, Yokohama, 222 Japan
sumio@ipe.rdc.ricoh.co.jp
Abstract
Solvable models of nonlinear learning machines are proposed, and
learning in artificial neural networks is studie... | 786 |@word toda:1 true:5 pw:5 pii:1 seems:1 qd:4 met:1 lvh:1 parametric:1 eg:3 decomposition:3 white:1 enable:1 ll:1 uniquely:2 generalization:3 equiv:1 reason:3 paramet:1 strictly:1 z2:2 wd:8 sufficiently:1 condit:1 normal:1 yet:1 dx:3 bd:2 zll:1 aft:1 exp:9 mapping:1 bj:2 sigmoid:1 minimizing:4 ricoh:2 difficult:2 ad... |
7,010 | 787 | Computational Elements of the Adaptive
Controller of the Human Arm
Reza Shadmehr and Ferdinando A. Mussa-Ivaldi
Dept . of Brain and Cognitive Sciences
M. I. T ., Cambridge , MA 02139
Email : reza@ai.mit.edu , sandro@ai .mit.edu
Abstract
We consider the problem of how the CNS learns to control dynamics of a mechanical... | 787 |@word trial:1 seems:1 gradual:1 linearized:1 pick:1 solid:2 ivaldi:12 configuration:1 practiced:2 com:1 activation:2 yet:1 written:2 motor:12 designed:1 wimberly:2 manipulandum:1 nervous:1 accepting:1 simpler:1 along:2 direct:1 become:1 edelman:1 raibert:2 combine:1 symp:2 indeed:2 expected:4 market:1 rapid:1 beha... |
7,011 | 788 | U sing Local Trajectory Optimizers To
Speed Up Global Optimization In
Dynamic Programming
Christopher G. Atkeson
Department of Brain and Cognitive Sciences and
the Artificial Intelligence Laboratory
Massachusetts Institute of Technology, NE43-771
545 Technology Square, Cambridge, MA 02139
617-253-0788, cga@ai.mit.edu
... | 788 |@word version:2 seems:1 d2:1 initial:7 contains:1 zuk:1 current:3 optim:1 motor:1 intelligence:1 xk:4 provides:4 node:1 location:1 along:6 become:1 differential:2 inside:1 planning:2 growing:2 brain:1 torque:1 bellman:1 globally:9 curse:3 increasing:2 becomes:1 provided:2 underlying:1 lowest:1 what:1 minimizes:1 d... |
7,012 | 789 | Assessing the Quality of Learned Local Models
Stefan Schaal
Christopher G. Atkeson
Department of Brain and Cognitive Sciences & The Artifical Intelligence Laboratory
Massachusetts Institute of Technology
545 Technology Square, Cambridge, MA 02139
email: sschaal@ai.mit.edu, cga@ai.mit.edu
Abstract
An approach is pre... | 789 |@word trial:4 version:5 briefly:1 casdagli:1 simulation:2 tried:2 kent:1 ld:1 reduction:1 moment:2 initial:3 series:1 interestingly:1 troller:1 longitudinal:1 err:1 current:6 trustworthy:1 additive:1 wx:1 thrust:1 motor:1 plot:2 mandell:1 update:1 intelligence:1 plane:3 sudden:1 provides:1 contribute:2 along:2 con... |
7,013 | 79 | 840
LEARNING IN NETWORKS OF
NONDETERMINISTIC ADAPTIVE LOGIC ELEMENTS
Richard C. Windecker*
AT&T Bell Laboratories, Middletown, NJ 07748
ABSTRACT
This paper presents a model of nondeterministic adaptive automata that are
constructed from simpler nondeterministic adaptive information processing
elements. The first half ... | 79 |@word trial:10 seems:1 proportion:1 simulation:6 uncovers:1 thereby:1 moment:1 configuration:2 series:1 contains:1 selecting:4 past:5 existing:1 current:3 z2:1 dx:1 must:4 readily:2 realize:3 subsequent:1 motor:1 designed:1 alone:1 half:6 intelligence:1 selected:4 nervous:4 complementing:1 accordingly:1 fewer:1 ith... |
7,014 | 790 | A Connectionist Model of the Owl's
Sound Localization System
D alliel J. Rosen?
Department of Psychology
Stanford University
Stanford, CA 94305
David E. Rumelhart
Department of Psychology
Stanford University
Stanford, CA 94305
Eric. I. Knudsen
Department of Neurobiology
Stanford University
Stanford, CA 94305
Abstra... | 790 |@word neurophysiology:2 trial:7 blindness:1 middle:1 seems:1 integrative:1 simulation:2 accounting:1 veigend:1 exclusively:1 tuned:2 current:1 activation:5 yet:1 must:3 plasticity:3 midway:1 shape:1 motor:1 designed:3 progressively:1 cue:5 nervous:1 core:1 dissertation:1 provides:2 location:8 sigmoidal:1 mathemati... |
7,015 | 791 | Supervised Learning with Growing Cell
Structures
Bernd Fritzke
Institut fiir Neuroinformatik
Ruhr-U niversitat Bochum
Germany
Abstract
We present a new incremental radial basis function network suitable for classification and regression problems. Center positions
are continuously updated through soft competitive lear... | 791 |@word version:2 seems:2 ruhr:1 simulation:3 thereby:1 tr:1 harder:1 initial:3 existing:3 current:4 activation:6 lang:6 readily:1 distant:1 girosi:2 item:4 plane:1 core:1 coarse:1 sigmoidal:2 direct:7 consists:2 expected:1 behavior:1 frequently:1 growing:8 multi:1 little:1 considering:1 classifies:1 moreover:2 what... |
7,016 | 792 | Figure of Merit Training for Detection and
Spotting
Eric I. Chang and Richard P. Lippmann
MIT Lincoln Laboratory
Lexington, MA 02173-0073, USA
Abstract
Spotting tasks require detection of target patterns from a background of
richly varied non-target inputs. The performance measure of interest for
these tasks, called t... | 792 |@word version:1 proportion:2 covariance:1 dramatic:1 initial:1 score:25 fragment:1 past:1 current:1 comparing:1 must:2 shape:2 plot:1 sponsored:1 update:1 v:2 discrimination:1 beginning:1 short:2 detecting:2 quantized:1 node:17 location:2 sigmoidal:1 sii:1 roughly:3 arrhythmia:1 frequently:2 automatically:1 little... |
7,017 | 793 | Directional Hearing by the Mauthner
System
.Audrey L. Gusik
Department of Psychology
University of Colorado
Boulder, Co. 80309
Robert c. Eaton
E. P. O. Biology
University of Colorado
Boulder, Co. 80309
Abstract
We provide a computational description of the function of the Mauthner system. This is the brainstem circu... | 793 |@word version:2 compression:1 simulation:1 sensed:1 saccular:2 pressure:34 offending:1 initial:3 contains:1 exclusively:1 tuned:1 activation:3 yet:1 must:3 herring:1 physiol:1 realistic:2 asymptote:1 treating:1 interpretable:2 discrimination:3 plane:1 compo:1 provides:1 contribute:2 sigmoidal:1 simpler:1 five:1 al... |
7,018 | 794 | Recognition-based Segmentation of
On-line Cursive Handwriting
Nicholas S. Flann
Department of Computer Science
Utah State University
Logan, UT 84322-4205
flannGnick.cs.usu.edu
Abstract
This paper introduces a new recognition-based segmentation approach to recognizing on-line cursive handwriting from a database
of 10,... | 794 |@word faculty:1 schomaker:3 orf:1 initial:1 fragment:21 score:4 prefix:4 jyv:1 current:1 lang:3 written:2 must:2 j1:1 enables:1 remove:1 designed:2 v:1 intelligence:2 device:1 beginning:1 lexicon:9 five:4 differential:1 edelman:3 consists:6 expected:1 ry:1 multi:1 terminal:2 automatically:1 little:1 actual:1 windo... |
7,019 | 795 | Analysis of Short Term Memories for Neural
Networks
Jose C. Principe, Hui-H. Hsu and Jyh-Ming Kuo
Computational NeuroEngineering Laboratory
Department of Electrical Engineering
University of Florida, CSE 447
Gainesville, FL 32611
principe@synapse.ee.ufi.edu
Abstract
Short term memory is indispensable for the processin... | 795 |@word version:1 bptt:1 open:1 gainesville:1 carry:1 moment:1 contains:1 series:1 selecting:1 past:1 yet:2 must:3 realize:1 additive:1 devising:1 xk:2 short:7 cse:1 location:1 along:2 constructed:1 burst:1 become:3 combine:1 growing:2 ming:1 window:4 equipped:1 increasing:2 becomes:7 project:1 provided:3 bounded:1 ... |
7,020 | 796 | Robust Parameter Estimation And
Model Selection For Neural Network
Regression
Yong Liu
Department of Physics
Institute for Brain and Neural Systems
Box 1843, Brown University
Providence, RI 02912
yong~cns.brown.edu
Abstract
In this paper, it is shown that the conventional back-propagation
(BPP) algorithm for neural n... | 796 |@word nd:1 simulation:3 covariance:2 qly:2 tr:1 accommodate:2 ld:1 liu:9 contains:2 surprising:1 must:1 wx:2 cook:1 ith:1 stahel:1 characterization:1 detecting:1 math:1 provides:1 sigmoidal:3 unbounded:1 symposium:1 prove:1 huber:1 expected:2 weisberg:1 brain:1 chi:1 td:3 becomes:2 provided:1 estimating:3 underlyi... |
7,021 | 797 | Learning in Computer Vision and Image
Understanding
Hayit Greenspan
Department of Electrical Engineering
California Institute of Technology, 116-81
Pasadena, CA 91125
There is an increasing interest in the area of Learning in Computer Vision and
Image Understanding, both from researchers in the learning community and... | 797 |@word concept:1 contain:1 classical:2 forum:1 question:1 open:1 imaged:1 carolina:1 deal:1 human:3 dependence:1 viewing:1 during:1 distance:1 argued:1 mel:1 contains:1 chris:1 discriminant:1 motion:1 image:7 ground:1 yet:1 common:2 rotation:1 difficult:1 major:1 shape:1 girosi:1 synthesizing:1 recognizer:1 discuss... |
7,022 | 798 | Autoencoders, Minimum Description Length
and Helmholtz Free Energy
Geoffrey E. Hinton
Department of Computer Science
University of Toronto
6 King's College Road
Toronto M5S lA4, Canada
Richard S. Zemel
Computational Neuroscience Laboratory
The Salk Institute
10010 North Torrey Pines Road
La Jolla, CA 92037
Abstract
... | 798 |@word briefly:1 version:2 d2:1 simulation:1 pick:6 configuration:2 recovered:1 current:1 activation:1 must:4 predetermined:1 generative:12 selected:1 intelligence:1 quantizer:1 quantized:2 toronto:3 height:1 become:1 consists:1 combine:1 fitting:3 baldi:2 expected:10 alspector:1 considering:1 provided:1 discover:1... |
7,023 | 799 | Catastrophic interference in
connectionist networks: Can it be
predicted, can it be prevented?
Robert M. French
Computer Science Department
Willamette University
Salem, Oregon 97301
french@willamette.edu
1
OVERVIEW
Catastrophic forgetting occurs when connectionist networks learn new information,
and by so doing, fo... | 799 |@word effect:1 predicted:1 resemble:1 eliminating:2 consisted:1 true:1 symmetric:1 occurs:1 receptive:1 simulation:1 elimination:1 virtual:1 separate:1 speaker:1 generalization:5 relationship:2 activation:4 recently:1 robert:2 overview:1 ecn:1 he:3 item:8 significant:2 node:4 had:1 het:1 lor:1 windowed:1 surface:2... |
7,024 | 8 | 242
THE SIGMOID NONLINEARITY IN PREPYRIFORM CORTEX
Frank H. Eeckman
University of California, Berkeley, CA 94720
ABSlRACT
We report a study ?on the relationship between EEG amplitude values and unit
spike output in the prepyriform cortex of awake and motivated rats. This relationship
takes the form of a sigmoid curve,... | 8 |@word disk:1 pulse:17 systeme:1 mammal:1 mohm:2 initial:1 series:3 score:2 existing:1 current:1 neurophys:1 written:1 physiol:1 shape:1 selected:1 nervous:2 beginning:1 record:1 compo:1 filtered:2 location:1 sigmoidal:2 burst:6 direct:1 become:1 vertebres:1 olfactory:22 manner:1 multi:2 brain:2 freeman:12 rall:1 win... |
7,025 | 80 | 9
Stochastic Learning Networks and their Electronic Implementation
Joshua Alspector*. Robert B. Allen. Victor Hut. and Srinagesh Satyanarayanat
Bell Communications Research. Morristown. NJ 01960
We describe a family of learning algorithms that operate on a recurrent, symmetrically
connected. neuromorphic network that.... | 80 |@word proceeded:1 trial:3 version:1 proportion:2 seems:2 stronger:1 replicate:1 simulation:2 propagate:1 tried:2 r:1 thereby:2 minus:4 solid:1 noll:1 electronics:1 tuned:1 envision:1 current:2 com:1 activation:13 si:1 must:3 plasticity:1 designed:1 update:2 v:1 aside:1 half:1 selected:1 tenn:1 device:5 liapunov:1 d... |
7,026 | 800 | Foraging in an Uncertain Environment Using
Predictive Hebbian Learning
P. Read Montague: Peter Dayan, and Terrence J. Sejnowski
Computational Neurobiology Lab, The Salk Institute,
100 ION. Torrey Pines Rd,
La Jolla, CA, 92037, USA
read~bohr.bcm.tmc.edu
Abstract
Survival is enhanced by an ability to predict the availa... | 800 |@word trial:8 selforganization:1 version:3 instrumental:3 proportion:3 simulation:2 sensed:1 r:5 solid:1 harder:3 ld:2 series:1 selecting:1 past:1 current:2 intake:1 nowlan:1 yet:1 distant:1 biomechanical:1 subsequent:1 plasticity:3 shape:1 motor:1 drop:1 plot:1 hts:1 beginning:1 short:1 mental:1 provides:1 prefer... |
7,027 | 801 | Use of Bad Training Data For Better
Predictions
Tal Grossman
Complex Systems Group (T13) and CNLS
LANL, MS B213 Los Alamos N.M. 87545
Alan Lapedes
Complex Systems Group (T13)
LANL, MS B213 Los Alamos N.M. 87545
and The Santa Fe Institute, Santa Fe, New Mexico
Abstract
We show how randomly scrambling the output classe... | 801 |@word polynomial:1 cnls:1 crite:1 initial:4 score:3 l__:2 tuned:1 lapedes:5 subjective:1 numerical:1 cheap:1 plot:1 update:1 v:2 parameterization:1 contribute:1 ron:1 prediciton:1 fitting:1 wild:1 expected:2 behavior:3 elman:1 ol:2 relying:1 actual:1 increasing:1 becomes:1 circuit:1 nj:1 safely:1 preferable:1 proh... |
7,028 | 802 | Constructive Learning Using Internal
Representation Conflicts
Laurens R. Leerink and Marwan A. J abri
Systems Engineering & Design Automation Laboratory
Department of Electrical Engineering
The University of Sydney
Sydney, NSW 2006, Australia
Abstract
We present an algorithm for the training of feedforward and recurr... | 802 |@word simulation:5 nsw:1 moment:1 reduction:1 initial:2 selecting:1 past:2 current:2 comparing:1 stemmed:1 subsequent:1 predetermined:1 wynne:4 update:13 fewer:1 short:2 provides:1 detecting:1 node:9 constructed:1 fitting:1 manner:2 acquired:1 notably:1 behavior:1 elman:5 frequently:1 growing:2 multi:6 brain:1 det... |
7,029 | 803 | Learning Curves: Asymptotic Values and
Rate of Convergence
Corinna Cortes, L. D. Jackel, Sara A. Solla, Vladimir Vapnik,
and John S. Denker
AT&T Bell Laboratories
Holmdel, NJ 07733
Abstract
Training classifiers on large databases is computationally demanding. It is desirable to develop efficient procedures for a reli... | 803 |@word effect:1 validity:4 predicted:5 normalized:2 especially:1 unchanged:1 norm:1 quantify:1 lenet:3 assigned:1 already:2 symmetric:1 laboratory:1 strategy:2 costly:1 illustrated:4 implementing:3 inferior:2 contains:1 generalization:1 suitability:3 unrealizable:3 exploring:1 existing:1 etest:4 hold:2 considered:2... |
7,030 | 804 | Asynchronous Dynamics of Continuous
Time Neural Networks
Xin Wang
Computer Science Department
University of California at Los Angeles
Los Angeles, CA 90024
Qingnan Li
Department of Mathematics
University of Southern California
Los Angeles, CA 90089-1113
Edward K. Blum
Department of Mathematics
University of Southern... | 804 |@word version:3 norm:2 closure:1 simulation:3 contraction:1 eng:1 celebrated:1 reine:1 bc:1 existing:2 discretization:1 activation:4 yet:2 must:1 ixil:1 fn:2 numerical:2 additive:6 update:2 math:1 complication:1 sigmoidal:2 mathematical:5 differential:6 symposium:1 consists:2 sustained:1 discretized:2 globally:4 p... |
7,031 | 805 | ?
Probabilistic Anomaly Detection In
Dynamic Systems
Padhraic Smyth
Jet Propulsion Laboratory 238-420
California Institute of Technology
4800 Oak Grove Drive
Pasadena, CA 91109
Abstract
This paper describes probabilistic methods for novelty detection
when using pattern recognition methods for fault monitoring of
dyna... | 805 |@word stronger:1 dubuisson:2 datagenerating:1 necessity:1 configuration:1 tachometer:3 past:1 current:2 wd:3 activation:2 yet:1 must:4 informative:1 confirming:1 motor:1 designed:3 drop:1 discrimination:2 alone:1 generative:13 inspection:1 short:1 record:1 accepting:1 provides:1 detecting:1 sigmoidal:1 oak:1 const... |
7,032 | 806 | Observability of Neural Network
Behavior
Max Garzon
Fernanda Botelho
sarzonmOherme ?. msci.mem.t.edu
botelhoflherme ?. msci.mem.t.edu
Institute for Intelligent Systems
Department of Mathematical Sciences
Memphis State University
Memphis, TN 38152 U.S.A.
Abstract
We prove that except possibly for small exceptional sets... | 806 |@word determinant:2 open:2 simulation:5 propagate:1 contraction:1 decomposition:1 initial:1 configuration:6 contains:3 electronics:1 franklin:2 surprising:1 activation:27 numerical:1 leaf:3 device:1 xk:12 ith:1 feedfoward:1 characterization:3 iterates:1 math:2 lx:1 sigmoidal:6 arctan:2 mathematical:1 along:2 prove... |
7,033 | 807 | Memory-Based Methods for Regression
and Classification
Thomas G. Dietterich and Dietrich Wettschereck
Department of Computer Science
Oregon State University
Corvallis, OR 97331-3202
Chris G. Atkeson
MIT AI Lab
545 Technology Square
Cambridge, MA 02139
Andrew W. Moore
School of Computer Science
Carnegie Mellon Universi... | 807 |@word dietterich:2 hence:1 lenet:3 question:1 open:1 moore:4 seek:1 nettalk:1 width:3 kth:1 distance:12 chris:2 manifold:2 omohundro:1 performs:1 code:1 relationship:1 activation:1 must:2 john:1 common:1 distant:1 informative:1 shape:3 girosi:2 purpose:1 recognizer:1 he:1 mellon:1 corvallis:1 cambridge:1 ai:1 benc... |
7,034 | 808 | Training Neural Networks with
Deficient Data
Volker Tresp
Siemens AG
Central Research
81730 Munchen
Germany
tresp@zfe.siemens.de
Subutai Ahmad
Interval Research Corporation
1801-C Page Mill Rd.
Palo Alto, CA 94304
ahmad@interval.com
Ralph N euneier
Siemens AG
Central Research
81730 Munchen
Germany
ralph@zfe.siemens.d... | 808 |@word briefly:1 duda:2 tr:1 klk:1 series:1 t7:1 tuned:1 xnj:1 neuneier:4 com:1 current:1 surprising:1 si:4 dx:5 john:1 realize:1 additive:1 blur:1 dydx:1 cheap:1 plot:1 update:2 stationary:1 half:2 selected:1 xk:2 ith:2 location:1 ik:1 consists:1 oflocally:1 expected:1 fez:2 pitfall:1 becomes:4 underlying:3 notati... |
7,035 | 809 | Bayesian Self-Organization
Alan L. Yuille
Division of Applied Sciences
Harvard University
Cambridge, MA 02138
Stelios M. Smirnakis
Lyman Laboratory of Physics
Harvard University
Cambridge, MA 02138
Lei Xu *
Dept. of Computer Science
HSH ENG BLDG, Room 1006
The Chinese University of Hong Kong
Shatin, NT
Hong Kong
Ab... | 809 |@word kong:2 version:1 seems:3 eng:1 pick:3 thereby:3 moment:1 initial:1 disparity:5 nt:1 od:2 si:8 must:3 readily:1 written:1 realize:1 reminiscent:1 additive:4 analytic:2 update:1 infant:1 steepest:1 node:1 suspicious:1 dan:1 introduce:2 bility:1 distractor:2 multi:1 brain:1 freeman:1 automatically:1 company:1 a... |
7,036 | 81 | 830
Invariant Object Recognition Using a Distributed Associative Memory
Harry Wechsler and George Lee Zimmerman
Department or Electrical Engineering
University or Minnesota
Minneapolis, MN 55455
Abstract
This paper describes an approach to 2-dimensional object recognition. Complex-log conformal mapping is combined wi... | 81 |@word version:3 compression:1 sharpens:1 nd:1 grey:1 simulation:1 lobe:1 heteroassociative:1 paid:1 dramatic:1 tr:1 solid:1 carry:5 contains:1 selecting:1 contextual:1 assigning:1 dx:1 must:2 written:1 tilted:1 visible:1 shape:2 leaf:1 plane:4 isotropic:1 filtered:2 characterization:2 quantized:1 lor:1 mathematical... |
7,037 | 810 | A Hybrid Radial Basis Function Neurocomputer
and Its Applications
Steven S. Watkins
ECE Department
Paul M. Chau
ECE Department
UCSD
La Jolla. CA. 92093
UCSD
La Jolla, CA. 92093
Raoul Tawel
JPL
Caltech
Pasadena. CA. 91109
Bjorn Lambrigtsen
JPL
Caltech
Pasadena. CA. 91109
Mark Plutowski
CSE Department
UCSD
La Jol... | 810 |@word eliminating:1 advantageous:1 simulation:2 pressure:3 solid:2 applicatioo:1 reduction:1 series:7 tuned:1 current:4 comparing:2 lang:1 john:3 remove:1 plot:1 designed:1 sponsored:1 mackey:9 v:3 half:1 intelligence:1 provides:1 detecting:1 cse:1 sigmoidal:11 five:1 constructed:1 symposium:1 consists:1 combine:1... |
7,038 | 811 | Generalization Error and The Expected
Network Complexity
Chuanyi Ji
Dept. of Elec., Compt. and Syst Engl' .
Rensselaer Polytechnic Inst.itu( e
Troy, NY 12180-3590
chuanyi@ecse.rpi.edu
Abstract
For two layer networks with n sigmoidal hidden units, the generalization error is
shown to be bounded by
O(E~)
l N)
K + O( ... | 811 |@word version:1 briefly:1 norm:1 rno:1 nd:6 open:3 gaussion:2 simplifying:1 complexit:1 series:1 existing:1 erms:1 nt:1 nowlan:1 rpi:1 john:1 tot:1 fn:9 happen:1 applica:3 plot:1 alone:1 fewer:1 nq:3 lr:1 provides:2 sigmoidal:3 direct:1 consists:1 veight:1 ra:1 expected:16 dist:2 bility:1 actual:2 little:1 bounded... |
7,039 | 812 | Analyzing Cross Connected Networks
Thomas R. Shultz
Department of Psychology &
McGill Cognitive Science Centre
McGill University
Montreal, Quebec, Canada H3A IB 1
shultz@psych.mcgill.ca
and
Jeffrey L. Elman
Center for Research on Language
Department of Cognitive Science
University of California at San Diego
LaJolla, ... | 812 |@word middle:2 version:3 proportion:1 loading:4 seems:1 simulation:4 accounting:2 carry:1 score:12 current:1 activation:21 lang:2 buckingham:2 written:1 yet:2 assigning:1 tilted:1 additive:1 realistic:1 informative:1 distant:1 shape:2 plot:7 designed:2 hourglass:2 discrimination:1 alone:3 generative:4 selected:5 h... |
7,040 | 813 | Functional Models of Selective Attention
and Context Dependency
Thomas H. Hildebrandt
Department of Electrical Engineering and Computer Science
Room 304 Packard Laboratory
19 Memorial Drive West
Lehigh University
Bethlehem PA 18015-3084
thildebr@aragorn.eecs.lehigh.edu
Scope
This workshop reviewed and classified the ... | 813 |@word conquer:1 concept:3 briefly:1 inductive:1 drawback:1 laboratory:1 goldfarb:2 parametric:1 carolina:1 public:1 distance:2 link:1 daniel:1 biological:1 adjusted:1 reaction:1 initio:1 contextual:1 hold:1 coke:1 activation:2 ratio:2 scope:1 superior:1 functional:5 sought:1 commonality:1 negative:1 omitted:1 rise... |
7,041 | 814 | Surface Learning with Applications to
Lipreading
Christoph Bregler *.**
*Computer Science Division
University of California
Berkeley, CA 94720
Stephen M. Omohundro **
**Int. Computer Science Institute
1947 Center Street Suite 600
Berkeley, CA 94704
Abstract
Most connectionist research has focused on learning mapping... | 814 |@word polynomial:1 glue:1 thereby:1 initial:4 configuration:2 selecting:1 ka:2 current:1 must:6 grain:1 partition:2 shape:3 drop:1 v:1 location:1 successive:1 lbo:1 along:7 ood:1 consists:1 combine:1 globally:1 reprojecting:1 project:1 discover:2 kind:2 finding:3 suite:1 berkeley:2 quantitative:1 act:1 growth:1 ex... |
7,042 | 815 | Hoo Optimality Criteria for LMS and
Backpropagation
Babak Hassibi
Information Systems Laboratory
Stanford University
Stanford, CA 94305
Ali H. Sayed
Dept. of Elec. and Compo Engr.
University of California Santa Barbara
Santa Barbara, CA 93106
Thomas Kailath
Information Systems Laboratory
Stanford University
Stanford... | 815 |@word version:1 norm:14 seems:1 linearized:2 mention:2 recursively:1 initial:3 celebrated:1 surprising:1 must:1 readily:1 john:1 j1:3 update:2 guess:3 ji2:1 ith:2 haykin:2 compo:1 filtered:1 provides:1 math:1 idi:1 ire:1 preference:1 glover:2 along:2 become:1 ik:1 introduce:1 sayed:8 expected:2 conv:1 begin:1 prov... |
7,043 | 816 | Optimal Stopping and Effective Machine
Complexity in Learning
Changfeng Wang
Department of SystE'IIlS Sci. (Iud Ell/!,.
UJliversity of PPIIIlsylv1I.Ili(l
Philadelphin, PA, U.S.A. I!HlJ4
Salltosh S. Venkatesh
Dp?artn}(,llt (If Elf'drical EugiJlPprinJ!,
UIIi v('rsi ty (If Ppnllsyl va nia
Philadelphia, PA, U.S.A. 19104
... | 816 |@word llsed:1 polynomial:1 seems:1 bf:1 open:2 seek:1 moment:1 kappen:1 initial:5 series:1 ording:1 current:1 wd:1 nt:1 ka:1 must:2 written:1 john:1 numerical:1 treating:1 accordingly:1 ial:1 dear:2 provides:1 ron:1 lx:1 sigmoidal:1 symposium:1 prove:1 fitting:11 baldi:1 expected:1 behavior:1 p1:1 frequently:1 nor... |
7,044 | 817 | Grammatical Inference by
Attentional Control of Synchronization
in an Oscillating Elman Network
Bill Baird
Dept Mathematics,
U.C.Berkeley,
Berkeley, Ca. 94720,
baird@math.berkeley.edu
Todd Troyer
Dept of Phys.,
U.C.San Francisco,
513 Parnassus Ave.
San Francisco, Ca. 94143,
todd@phy.ucsf.edu
Frank Eeckman
Lawrence L... | 817 |@word briefly:1 simulation:1 attended:2 moment:1 initial:1 cordinates:1 phy:1 must:2 tilted:1 motor:4 designed:3 discrimination:1 selected:2 leaf:1 plane:1 beginning:1 quantized:2 node:2 math:1 mathematical:1 along:1 constructed:6 direct:3 differential:2 hopf:1 consists:3 behavioral:2 olfactory:3 introduce:1 manne... |
7,045 | 818 | Grammatical Inference by
Attentional Control of Synchronization
in an Oscillating Elman Network
Bill Baird
Dept Mathematics,
U.C.Berkeley,
Berkeley, Ca. 94720,
baird@math.berkeley.edu
Todd Troyer
Dept of Phys.,
U.C.San Francisco,
513 Parnassus Ave.
San Francisco, Ca. 94143,
todd@phy.ucsf.edu
Frank Eeckman
Lawrence L... | 818 |@word briefly:1 polynomial:1 seek:2 simulation:1 attended:2 concise:1 moment:1 initial:3 cordinates:1 phy:1 tuned:2 outperforms:1 existing:1 current:1 yet:1 must:2 mesh:1 belmont:1 tilted:1 additive:1 girosi:1 motor:4 designed:3 discrimination:1 selected:2 leaf:1 plane:1 beginning:1 provides:1 math:1 node:2 quanti... |
7,046 | 819 | Globally Trained Handwritten Word
Recognizer using Spatial Representation,
Convolutional Neural Networks and
Hidden Markov Models
Yoshua Bengio . .
Dept. Informatique et Recherche Operationnelle
Universite de Montreal
Montreal, Qc H3C-3J7
Yann Le Cun
AT&T Bell Labs
Holmdel NJ 07733
Donnie Henderson
AT&T Bell Labs
Ho... | 819 |@word economically:1 version:2 advantageous:1 seems:1 nd:1 heuristically:2 simplifying:1 amaps:2 thereby:1 reduction:1 configuration:1 contains:2 score:5 series:2 substitution:1 past:1 atlantic:1 bitmap:1 written:1 readily:1 must:5 j1:2 predetermined:1 designed:1 drop:4 device:2 core:3 recherche:1 node:1 location:... |
7,047 | 82 | 82
SIMULATIONS SUGGEST
INFORMATION PROCESSING ROLES
FOR THE DIVERSE CURRENTS IN
HIPPOCAMPAL NEURONS
Lyle J. Borg-Graham
Harvard-MIT Division of Health Sciences and Technology and
Center for Biological Information Processing,
Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
ABSTRACT
A computer mode... | 82 |@word hippocampus:2 integrative:1 simulation:13 contraction:2 solid:1 biomathematics:1 reduction:1 initial:2 series:1 undiscovered:1 tine:5 past:1 current:65 comparing:1 nt:1 activation:6 yet:1 must:2 shape:18 eleven:2 motor:1 designed:1 progressively:1 v:1 nervous:1 slowing:1 short:4 contribute:1 burst:4 along:1 d... |
7,048 | 820 | Temporal Difference Learning of
Position Evaluation in the Game of Go
Nicol N. Schraudolph
schraudo~salk.edu
Peter Dayan
dayan~salk.edu
Terrence J. Sejnowski
terry~salk.edu
Computational Neurobiology Laboratory
The Salk Institute for Biological Studies
San Diego, CA 92186-5800
Abstract
The game of Go has a high bra... | 820 |@word worsens:1 eliminating:1 stronger:1 proportion:1 disk:2 q1:1 pick:4 golem:2 initial:1 score:1 selecting:1 tuned:1 past:2 existing:1 com:2 yet:2 must:3 embarrassment:1 destiny:1 happen:1 informative:1 enables:2 alone:3 intelligence:2 accordingly:1 trapping:1 oldest:1 beginning:1 record:1 lr:1 commensurately:1 ... |
7,049 | 821 | Backpropagation Convergence Via
Deterministic Nonmonotone Perturbed
Minimization
o.
L. Mangasarian & M. v. Solodov
Computer Sciences Department
University of Wisconsin
Madison, WI 53706
Email: olvi@cs.wisc.edu, solodov@cs.wisc.edu
Abstract
The fundamental backpropagation (BP) algorithm for training artificial neural ... | 821 |@word version:1 advantageous:1 seems:2 stronger:1 paid:1 boundedness:1 series:2 denoting:1 nonmonotone:10 luo:4 attracted:1 fn:1 periodically:1 numerical:2 partition:1 l7i:1 enables:1 burdick:2 stationary:5 intelligence:1 iterates:6 provides:2 mendel:1 zhang:2 mathematical:5 symposium:1 incorrect:1 consists:3 mann... |
7,050 | 822 | Bounds on the complexity of recurrent
neural network implementations of finite
state machines
Bill G. Horne
NEC Research Institute
4 Independence Way
Princeton, NJ 08540
Don R. Hush
EECE Department
University of New Mexico
Albuquerque, NM 87131
Abstract
In this paper the efficiency of recurrent neural network implem... | 822 |@word version:3 polynomial:1 open:3 simulation:1 decomposition:2 recursively:1 initial:1 series:4 must:1 device:2 sys:2 ith:1 compo:1 node:70 lor:1 direct:1 prove:2 terminal:1 decomposed:1 jm:1 totally:1 becomes:1 provided:1 horne:3 bounded:1 circuit:3 flog:8 developed:1 nj:1 control:1 unit:10 appear:1 before:2 iq... |
7,051 | 823 | A Comparative Study Of A Modified
Bumptree Neural Network With Radial Basis
Function Networks and the Standard MultiLayer Perceptron.
Richard T .J. Bostock and Alan J. Harget
Department of Computer Science & Applied Mathematics
Aston University
Binningham
England
Abstract
Bumptrees are geometric data structures introd... | 823 |@word inversion:2 confirms:1 decomposition:1 thereby:1 tr:1 shot:1 initial:7 contains:1 series:1 selecting:1 genetic:2 past:1 current:1 activation:6 assigning:2 partition:3 leaf:1 fewer:1 inspection:1 ith:5 zmax:1 provides:3 location:1 firstly:1 five:1 symposium:1 viable:1 acti:1 manner:2 subdividing:1 multi:12 au... |
7,052 | 824 | COMBINED NEURAL NETWORKS
FOR TIME SERIES ANALYSIS
Iris Ginzburg and David Horn
School of Physics and Astronomy
Raymond and Beverly Sackler Faculty of Exact Science
Tel-Aviv University
Tel-A viv 96678, Israel
Abstract
We propose a method for improving the performance of any network designed to predict the next value of... | 824 |@word faculty:1 advantageous:1 tried:1 simplifying:1 reduction:2 initial:1 series:16 selecting:2 unprimed:2 nowlan:2 must:1 fn:1 numerical:1 remove:1 designed:1 nervous:1 beginning:1 short:4 compo:2 sigmoidal:4 five:5 mathematical:1 constructed:2 direct:4 differential:1 advocate:2 combine:1 manner:1 interdependenc... |
7,053 | 825 | Fast Non-Linear Dimension Reduction
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
Abstract
We present a fast algorithm for non-linear dimension reduction.
The algorithm builds a local linear mode... | 825 |@word middle:2 eliminating:1 compression:2 norm:6 nd:1 simulation:1 tried:1 covariance:6 decomposition:1 sgd:7 solid:2 reduction:26 configuration:1 empath:1 nanda:1 activation:1 written:1 john:1 realize:1 cottrell:9 numerical:1 partition:3 half:1 prohibitive:2 provides:2 quantizer:1 node:7 timeaveraged:1 five:14 r... |
7,054 | 826 | Decoding Cursive Scripts
Yoram Singer
and
Naftali Tishby
Institute of Computer Science and
Center for Neural Computation
Hebrew University, Jerusalem 91904, Israel
Abstract
Online cursive handwriting recognition is currently one of the most
intriguing challenges in pattern recognition. This study presents a
novel app... | 826 |@word briefly:1 grey:1 excited:1 accommodate:1 recursively:1 initial:3 series:1 contains:1 selecting:1 score:5 denoting:1 past:1 existing:1 current:1 si:1 yet:3 intriguing:1 must:1 written:3 wx:2 enables:1 motor:4 remove:1 update:1 selected:1 imitate:1 beginning:1 short:1 quantized:8 location:8 ron:2 simpler:1 bec... |
7,055 | 827 | Connectionist Models for
A uditory Scene Analysis
Richard
o. Duda
Department of Electrical Engineering
San Jose State University
San Jose, CA 95192
Abstract
Although the visual and auditory systems share the same basic
tasks of informing an organism about its environment, most connectionist work on hearing to date ... | 827 |@word neurophysiology:1 briefly:1 duda:10 nd:1 seek:1 simulation:1 azimuthal:1 accounting:1 pressure:2 document:1 colburn:2 existing:1 current:2 surprising:1 yet:1 must:3 olive:3 written:1 john:4 physiol:1 happen:1 shape:2 uditory:2 medial:1 alone:1 cue:5 half:2 stationary:1 tone:1 plane:2 short:2 dissertation:3 c... |
7,056 | 828 | Connectionist Modeling and
Parallel Architectures
Joachim Diederich
Ah Chung Tsoi
Neurocomputing Research Centre
Department of Electrical and
Computer Engineering
School of Computing Science
Queensland University of Technology
University of Queensland
St Lucia, Queensland 4072, Australia
Brisbane Q 400 1 Australia
T... | 828 |@word coprocessor:1 implemented:1 establish:1 functioning:1 chemical:1 arrangement:1 functionality:1 simulation:3 queensland:5 occupies:1 australia:2 traditional:1 programmer:1 die:1 topic:1 dendritic:1 complete:2 trivial:1 interface:1 systolic:2 code:1 considered:1 mimd:1 consideration:1 written:1 common:2 mesh:2... |
7,057 | 829 | Neurobiology, Psychophysics, and
Computational Models of Visual
Attention
Ernst Niebur
Computation and Neural Systems
California Institute of Technology
Pasadena, CA 91125, USA
Bruno A. Olshausen
Department of Anatomy and Neurobiology
Washington University School of Medicine
St. Louis, MO 63110
The purpose of this w... | 829 |@word effect:1 hypothesized:1 predicted:1 classical:1 judge:1 psychophysical:1 anatomy:1 realized:1 spike:5 flashed:1 rhesus:1 covary:1 receptive:12 primary:2 responds:1 during:2 attended:2 width:1 attentional:4 mapped:1 majority:1 originate:1 crf:2 modeled:1 relationship:1 activation:1 preferentially:1 mo:1 poten... |
7,058 | 83 | 72
ANALYSIS AND COMPARISON OF DIFFERENT LEARNING
ALGORITHMS FOR PATTERN ASSOCIATION PROBLEMS
J. Bernasconi
Brown Boveri Research Center
CH-S40S Baden, Switzerland
ABSTRACT
We investigate the behavior of different learning algorithms
for networks of neuron-like units. As test cases we use simple pattern association pr... | 83 |@word version:7 seems:4 suitably:2 open:4 dramatic:1 mention:1 solid:1 carry:1 reduction:1 initial:10 configuration:1 contains:1 past:1 activation:5 net1:1 update:1 beginning:1 direct:1 become:3 qualitative:1 consists:5 expected:1 behavior:10 automatically:1 lll:1 becomes:1 moreover:2 what:1 weisbuch:1 differentiat... |
7,059 | 830 | A Computational Model
for Cursive Handwriting
Based on the Minimization Principle
Yasuhiro Wada
*
Yasuharu Koike
Eric Vatikiotis-Bateson
Mitsuo Kawato
ATR Human Infonnation Processing Research Laboratories
2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-02, Japan
ABSTRACT
We propose a trajectory planning and cont... | 830 |@word version:1 compression:2 seems:1 fonn:1 solid:1 blade:1 ivaldi:3 comparing:1 cooker:2 assigning:1 must:2 j1:1 motor:5 zacks:1 rjo:1 selected:2 nervous:1 accordingly:1 dear:3 completeness:1 location:2 honda:1 mathematical:2 along:2 become:1 edelman:2 qualitative:1 seika:1 planning:2 examine:1 brain:1 torque:10... |
7,060 | 831 | Learning Classification with Unlabeled Data
Virginia R. de Sa
desa@cs.rochester.edu
Department of Computer Science
University of Rochester
Rochester, NY 14627
Abstract
One of the advantages of supervised learning is that the final error metric is available during training. For classifiers, the algorithm can directly
... | 831 |@word trial:1 version:2 proportion:1 sensed:1 barney:2 initial:6 configuration:1 current:1 comparing:1 nowlan:2 activation:1 moo:3 must:1 subsequent:1 update:3 provides:1 codebook:39 pairing:2 consists:1 expected:1 themselves:1 elman:1 multi:3 formants:1 actual:2 increasing:1 distri:1 minimizes:2 finding:1 transfo... |
7,061 | 832 | Classifying Hand Gestures with a View-based
Distributed Representation
Trevor J. Darrell
Perceptual Computing Group
MIT Media Lab
Alex P. Pentland
Perceptual Computing Group
MIT Media Lab
Abstract
We present a method for learning, tracking, and recognizing human hand
gestures recorded by a conventional CCD camera wi... | 832 |@word trial:3 gish:1 initial:1 series:1 score:14 tuned:1 existing:1 current:1 subsequent:1 shape:1 plot:3 cue:1 selected:1 half:2 record:1 coarse:1 loworder:1 location:2 simpler:1 constructed:1 qualitative:2 edelman:1 behavioral:1 parallax:1 sakoe:1 acquired:1 roughly:2 multi:2 bellman:1 automatically:1 actual:3 b... |
7,062 | 833 | Backpropagation without Multiplication
Patrice Y. Simard
AT &T Bell Laboratories
Holmdel, NJ 07733
Hans Peter Graf
AT&T Bell Laboratories
Holmdel, NJ 07733
Abstract
The back propagation algorithm has been modified to work without any multiplications and to tolerate comput.ations with a low
resolution, which makes it... | 833 |@word version:1 middle:1 nd:1 simulation:3 tried:2 reduction:2 discretization:7 unction:1 surprising:1 activation:6 written:1 interrupted:1 j1:2 nemal:1 drop:1 update:8 erat:1 filtered:1 quantized:1 ron:1 five:1 discret:1 veight:1 indeed:1 behavior:1 discretized:2 little:1 increasing:1 becomes:1 provided:1 moreove... |
7,063 | 834 | Event-Driven Simulation of Networks of
Spiking l'Ieurons
Lloyd Watts
Synaptics Inc.
2698 Orchard Parkway
San Jose CA 95134
11oydGsynaptics.com
Abstract
A fast event-driven software simulator has been developed for simulating large networks of spiking neurons and synapses. The primitive network elements are designed t... | 834 |@word disk:1 pulse:11 azimuthal:1 simulation:15 simplifying:1 gradual:1 carry:1 series:1 current:22 com:1 nowlan:1 yet:1 written:1 john:4 realistic:5 designed:3 plot:1 tone:1 short:1 dfl:1 successive:1 cpg:4 burst:1 edelman:1 consists:1 presumed:1 behavior:14 simulator:8 chi:1 vertebrate:1 circuit:27 kiang:1 strin... |
7,064 | 835 | Statistics of Natural Images:
Scaling in the Woods
Daniel L. Ruderman* and William Bialek
NEe Research Institute
4 Independence Way
Princeton, N.J. 08540
Abstract
In order to best understand a visual system one should attempt
to characterize the natural images it processes. We gather images
from the woods and find th... | 835 |@word seems:1 solid:2 foveal:1 daniel:1 current:1 atop:1 must:1 john:1 tilted:1 physiol:1 distant:1 additive:1 shape:1 remove:2 plot:11 guess:1 compo:1 characterization:1 location:1 downing:1 along:4 schweitzer:1 consists:1 examine:1 aliasing:1 equipped:1 what:2 ag:1 sky:1 act:1 control:6 unit:2 yn:1 positive:1 lo... |
7,065 | 836 | When Will a Genetic Algorithm
Outperform Hill Climbing?
Melanie Mitchell
Santa Fe Institute
1660 Old Pecos Trail, Suite A
Santa Fe, NM 87501
John H. HoUand
Dept. of Psychology
University of Michigan
Ann Arbor, MI 48109
Stephanie Forrest
Dept. of Computer Science
University of New Mexico
Albuquerque, NM 87131
Abstra... | 836 |@word version:1 proportion:2 multipoint:1 bourgine:1 carry:1 contains:3 genetic:16 comparing:1 si:2 yet:2 must:3 john:1 realistic:1 subsequent:1 designed:4 cue:1 selected:1 steepest:1 short:2 dissertation:1 contribute:2 c6:1 simpler:1 mathematical:2 along:2 c2:1 consists:1 wild:1 combine:2 inside:1 theoretically:1... |
7,066 | 837 | Agnostic PAC-Learning of Functions on
Analog Neural Nets
(Extended Abstract)
Wolfgang Maass
Institute for Theoretical Computer Science
Technische Universitaet Graz
Klosterwiesgasse 32/2
A-BOlO Graz, Austria
e-mail: maass@igi.tu-graz.ac.at
Abstract:
There exist a number of negative results ([J), [BR), [KV]) about
lea... | 837 |@word briefly:1 version:3 polynomial:14 r:1 lpp:1 pick:1 thereby:1 recursively:1 carry:1 reduction:1 chervonenkis:1 com:1 activation:22 written:1 realistic:4 p7:1 xk:1 provides:1 math:1 node:22 successive:1 along:1 predecessor:2 symposium:2 consists:2 expected:1 isi:1 multi:2 touchstone:1 actual:1 provided:1 bound... |
7,067 | 838 | Clustering with a Domain-Specific
Distance Measure
Steven Gold, Eric Mjolsness and Anand Rangarajan
Department of Computer Science
Yale University
New Haven, CT 06520-8285
Abstract
With a point matching distance measure which is invariant under
translation, rotation and permutation, we learn 2-D point-set objects, by... | 838 |@word decomposition:1 jacob:2 recursively:1 initial:3 selecting:1 comparing:1 shape:1 analytic:1 drop:1 succeeding:1 update:1 intelligence:1 selected:2 greedy:1 beginning:1 along:1 constructed:2 consists:1 manner:1 expected:1 begin:3 moreover:1 notation:1 lowest:1 substantially:1 fuzzy:2 finding:1 transformation:6... |
7,068 | 839 | Discontinuous Generalization in Large
Committee Machines
H. Schwarze
Dept. of Theoretical Physics
Lund University
Solvegatan 14A
223 62 Lund
Sweden
J. Hertz
Nordita
Blegdamsvej 17
2100 Copenhagen 0
Denmark
Abstract
The problem of learning from examples in multilayer networks is
studied within the framework of statis... | 839 |@word h:1 version:3 nd:1 open:1 r:4 simulation:5 carry:1 initial:1 numerical:1 partition:1 j1:1 drop:2 location:5 qualitative:2 incorrect:1 introduce:1 behavior:5 mechanic:4 increasing:2 becomes:1 finding:1 quantitative:2 unit:17 grant:1 local:3 limit:4 ak:1 studied:3 mateo:3 suggests:1 palmer:1 averaged:3 impleme... |
7,069 | 84 | 154
PRESYNApnC NEURAL INFORMAnON PROCESSING
L. R. Carley
Department of Electrical and Computer Engineering
Carnegie Mellon University, Pittsburgh PA 15213
ABSTRACT
The potential for presynaptic information processing within the arbor
of a single axon will be discussed in this paper. Current knowledge about
the activi... | 84 |@word gradual:1 simulation:1 pulse:9 contraction:3 innervating:1 minus:1 ulus:6 initial:1 hunting:1 terminus:1 past:7 current:3 com:4 must:1 ulation:1 physiol:6 subsequent:4 designed:4 medial:1 v:4 implying:1 half:1 nervous:1 short:1 num:1 characterization:1 node:10 location:1 along:9 nodal:1 burst:2 differential:2... |
7,070 | 840 | Learning in Compositional Hierarchies:
Inducing the Structure of Objects from Data
Joachim Utans
Oregon Graduate Institute
Department of Computer Science and Engineering
P.O. Box 91000
Portland, OR 97291-1000
utans@cse.ogi.edu
Abstract
I propose a learning algorithm for learning hierarchical models for object recogni... | 840 |@word simulation:2 covariance:1 jacob:2 pick:1 tr:2 recursively:1 yaleu:1 initial:4 contains:1 tuned:1 current:2 z2:1 recovered:1 ixj:1 must:6 shape:5 designed:2 generative:2 leaf:3 intelligence:1 parameterization:1 xk:1 cse:1 node:64 constructed:1 become:5 consists:2 advocate:1 pairwise:1 roughly:1 themselves:1 f... |
7,071 | 841 | Hoeffding Races: Accelerating Model
Selection Search for Classification and
Function Approximation
Oded Maron
Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139
Andrew W. Moore
Robotics Institute
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Abst... | 841 |@word private:1 eliminating:1 open:1 pick:1 thereby:1 tr:1 shot:1 reduction:1 initial:6 configuration:1 selecting:3 united:1 current:3 cheap:2 treating:1 update:1 v:2 intelligence:2 fewer:1 zhang:3 constructed:2 viable:2 incorrect:1 prove:1 wassily:1 fitting:2 combine:1 market:2 indeed:1 examine:1 discretized:1 in... |
7,072 | 842 | A Network Mechanism for the Determination of
Shape-From-Texture
Ko Sakai and Leif H. Finkel
Department of Bioengineering and
Institute of Neurological Sciences
University of Pennsylvania
220 South 33rd Street, Philadelphia, PA 19104-6392
ko@ganymede.seas.upenn.edu, leif@ganymede.seas.upenn.edu
Abstract
We propose a co... | 842 |@word middle:1 compression:14 simulation:3 decomposition:1 tr:1 carry:1 moment:1 tuned:1 dx:1 slanted:2 realize:1 tilted:2 shape:19 discrimination:1 cue:6 half:1 plane:7 steepest:1 characterization:5 simpler:1 along:2 constructed:2 direct:1 qualitative:2 consists:2 mask:1 upenn:2 themselves:1 examine:1 simulator:1... |
7,073 | 843 | Robust Reinforcement Learning
Motion Planning
?
In
Satinder P. Singh'"
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
singh@psyche.mit.edu
Andrew G. Barto, Roderic Grupen, and Christopher Connolly
Department of Computer Science
University of Massachusetts
Amherst... | 843 |@word trial:8 inversion:1 proportion:4 open:1 grey:3 simulation:3 solid:3 initial:1 configuration:5 contains:2 series:1 selecting:1 outperforms:2 current:1 mesh:1 plot:2 update:1 stationary:1 greedy:1 fewer:1 intelligence:1 ith:1 location:10 successive:3 height:1 unacceptable:3 along:1 direct:1 differential:1 corr... |
7,074 | 844 | Complexity Issues in Neural
Computation and Learning
V. P. Roychowdhnry
School of Electrical Engineering
Purdue University
West Lafayette, IN 47907
Email: vwani@ecn.purdue.edu
K.-Y. Sin
Dept.. of Electrical & Compo Engr.
U ni versit.y of California at Irvine
Irvine, CA 92717
Email: siu@balboa.eng.uci.edu
The general... | 844 |@word establish:1 c:1 concept:1 boun:1 polynomial:1 classical:1 thwt:1 open:1 maass:1 primary:1 vc:4 rt:1 eng:1 sin:1 during:1 gradient:1 hitachi:1 speaker:1 ld:1 carry:1 capacity:2 generalization:3 topic:3 theoretic:1 complete:1 toward:1 minimizat:1 performs:1 pl:1 bring:1 bost:1 activation:1 ratio:1 fi:1 algorit... |
7,075 | 845 | Developing Population Codes By
Minimizing Description Length
Richard S. Zemel
CNL, The Salk Institute
10010 North Torrey Pines Rd.
La J oUa, CA 92037
Geoffrey E. Hinton
Department of Computer Science
University of Toronto
Toronto M5S 1A4 Canada
Abstract
The Minimum Description Length principle (MDL) can be used to
t... | 845 |@word version:2 pressure:1 solid:1 reduction:1 tuned:2 existing:1 current:1 must:4 realize:1 j1:1 shape:16 cheap:1 enables:2 plot:2 intelligence:1 short:1 toronto:3 location:3 height:1 constructed:1 x0:1 inter:1 expected:1 alspector:1 nor:1 brain:1 little:1 actual:3 considering:1 xx:4 underlying:5 panel:2 what:4 k... |
7,076 | 846 | A Unified Gradient-Descent/Clustering
Architecture for
Finite State Machine Induction
Sreerupa Das and Michael C. Mozer
Department of Computer Science
University of Colorado
Boulder, CO 80309-0430
Abstract
Although recurrent neural nets have been moderately successful
in learning to emulate finite-state machines (FSMs... | 846 |@word version:7 judgement:1 eliminating:1 compression:1 open:1 simulation:3 pressure:1 solid:1 initial:5 current:2 must:1 subsequent:1 underly:1 happen:1 drop:1 depict:1 pursued:1 selected:3 half:2 discovering:1 plane:1 location:1 along:1 constructed:1 c2:1 become:1 replication:3 consists:3 manner:1 roughly:1 elma... |
7,077 | 848 | Transition Point Dynamic Programming
Kenneth M. Buckland'"
Dept. of Electrical Engineering
University of British Columbia
Vancouver, B.C, Canada V6T 1Z4
buckland@pmc-sierra.bc.ca
Peter D. Lawrence
Dept. of Electrical Engineering
University of British Columbia
Vancouver, B.C, Canada V6T 1Z4
peterl@ee.ubc.ca
Abstract
... | 848 |@word trial:4 illustrating:1 version:1 middle:1 cu:1 nd:2 heuristically:1 simplifying:1 thereby:1 reduction:5 initial:4 bc:1 yvt:1 existing:3 must:8 readily:1 john:1 shape:1 remove:1 designed:1 drop:1 update:1 ouly:1 intelligence:1 indefinitely:1 quantized:1 direct:4 consists:1 expected:1 rapid:1 roughly:1 themsel... |
7,078 | 849 | Structured Machine Learning For 'Soft'
Classification with Smoothing Spline
ANOVA and Stacked Tuning, Testing
and Evaluation
Yuedong Wang
Dept of Statistics
University of Wisconsin
Madison, WI 53706
Grace Wahba
Dept of Statistics
University of Wisconsin
Madison, WI 53706
Chong Gu
Dept of Statistics
Purdue University
W... | 849 |@word trial:3 version:2 polynomial:2 logit:2 casdagli:1 decomposition:2 q1:2 tr:4 series:2 score:1 pub:2 att:1 lapedes:1 com:1 incidence:1 yet:1 ronald:1 numerical:1 l2l:1 plot:1 interpretable:1 v:1 half:2 prohibitive:1 selected:1 yr:4 directory:1 ith:2 record:1 math:2 cheney:1 attack:1 along:2 consists:1 fitting:... |
7,079 | 85 | 850
Strategies for Teaching Layered Networks
Classification Tasks
Ben S. Wittner 1 and John S. Denker
AT&T Bell Laboratories
Holmdel, New Jersey 07733
Abstract
There is a widespread misconception that the delta-rule is in some sense guaranteed to
work on networks without hidden units. As previous authors have mention... | 85 |@word h:1 illustrating:1 private:1 duda:2 r:1 simulation:2 solid:1 shading:2 initial:1 john:2 numerical:1 remove:1 nynex:1 leaf:1 guess:1 plane:2 beginning:1 vanishing:1 draft:1 along:2 become:2 prove:1 ray:4 behavior:1 little:1 begin:3 provided:1 bounded:2 what:4 kind:2 finding:1 guarantee:5 ifs:1 wrong:1 unit:14 ... |
7,080 | 850 | How to Describe Neuronal Activity:
Spikes, Rates, or Assemblies?
Wulfram Gerstner and J. Leo van Hemmen
Physik-Department der TU Miinchen
D-85748 Garching bei Miinchen, Germany
Abstract
What is the 'correct' theoretical description of neuronal activity?
The analysis of the dynamics of a globally connected network of
... | 850 |@word wiesel:2 physik:1 simulation:3 pulse:3 tr:15 solid:1 reduction:2 efficacy:3 past:2 reaction:1 si:1 must:1 zll:1 stationary:9 inspection:1 short:2 timeaveraged:1 contribute:1 miinchen:2 lor:1 become:1 consists:1 fth:1 inter:3 behavior:2 nor:1 globally:3 pf:1 window:2 lll:1 becomes:3 deutsche:1 mcculloch:2 wha... |
7,081 | 851 | Learning Temporal Dependencies in
Connectionist Speech Recognition
Steve Renals
Mike Hocbberg
Tony Robinson
Cambridge University Engineering Department
Cambridge CB2 IPZ, UK
{sjr,mmh,ajr}@eng.cam.ac.uk
Abstract
Hybrid connectionistfHMM systems model time both using a Markov
chain and through properties of a connec... | 851 |@word bigram:1 eng:1 tr:1 feb91:2 initial:2 series:1 contains:1 past:3 comparing:1 must:1 j1:1 speakerindependent:1 enables:1 designed:1 fewer:1 xk:3 filtered:3 provides:1 c6:1 c2:2 fullyconnected:1 introduce:1 g4:4 expected:1 rapid:1 multi:4 automatically:1 window:3 increasing:1 becomes:1 project:1 interpreted:1 ... |
7,082 | 852 | Emergence of Global Structure from
Local Associations
Thea B. Ghiselli-Crippa
Paul W. Munro
Department of Infonnation Science
University of Pittsburgh
Pittsburgh PA 15260
Department of Infonnation Science
University of Pittsburgh
Pittsburgh PA 15260
ABSTRACT
A variant of the encoder architecture, where units at th... | 852 |@word middle:3 version:2 seems:1 open:1 fonn:1 pressure:1 tr:1 configuration:2 hardy:1 subjective:1 current:1 comparing:1 readily:1 chicago:1 plot:3 v:1 selected:2 beginning:1 mental:1 coarse:1 provides:2 node:13 location:6 incorrect:1 acquired:2 behavior:2 elman:3 ol:1 globally:2 actual:1 considering:1 increasing... |
7,083 | 853 | Classification of Multi-Spectral Pixels
by the
Binary Diamond Neural Network
Yehuda Salu
Department of Physics and CSTEA, Howard University, Washington, DC 20059
Abstract
A new neural network, the Binary Diamond, is presented and its use
as a classifier is demonstrated and evaluated. The network is of the
feed-forwar... | 853 |@word manageable:1 propagate:3 simplifying:1 shot:2 contains:5 existing:1 current:2 com:1 comparing:1 activation:1 assigning:1 bd:2 visible:1 happen:1 enables:1 treating:1 grass:1 infant:2 selected:2 item:26 beginning:1 core:1 pointer:2 node:13 become:1 consists:2 inside:1 introduce:1 nor:3 growing:2 multi:6 discr... |
7,084 | 854 | Correlation Functions in a Large
Stochastic Neural Network
Iris Ginzburg
School of Physics and Astronomy
Raymond and Beverly Sackler Faculty of Exact Sciences
Tel-Aviv University
Tel-Aviv 69978, Israel
Haim Sompolinsky
Racah Institute of Physics and Center for Neural Computation
Hebrew University
Jerusalem 91904, Isra... | 854 |@word faculty:1 indicate:1 aperiodic:1 symmetric:1 spike:1 stochastic:8 dependence:4 exhibit:2 microscopic:1 quiet:1 sand:2 thank:1 iris:1 linearizing:1 investigation:2 unstable:1 secondly:1 hold:6 ground:2 si:2 exp:5 hebrew:1 written:1 equilibrium:2 cij:4 functional:1 holding:1 a2:5 binational:1 cerebral:2 v:1 st... |
7,085 | 855 | Classification of Electroencephalogram using
Artificial Neural Networks
A C Tsoi*, D S C So*, A Sergejew**
*Department of Electrical Engineering
**Department of Psychiatry
University of Queensland
St Lucia, Queensland 4072
Australia
Abstract
In this paper, we will consider the problem of classifying electroencephalog... | 855 |@word neurophysiology:1 advantageous:1 queensland:2 contraction:1 initial:1 series:4 contains:1 denoting:1 suppressing:1 current:1 surprising:1 activation:3 alone:1 stationary:1 accordingly:1 indicative:1 cursory:1 short:1 filtered:1 direct:2 become:1 beta:1 ocd:14 consists:3 introduce:1 acquired:1 inter:2 multi:4... |
7,086 | 856 | Postal Address Block Location Using A
Convolutional Locator Network
Ralph Wolf and John C. Platt
Synaptics, Inc.
2698 Orchard Parkway
San Jose, CA 95134
Abstract
This paper describes the use of a convolutional neural network
to perform address block location on machine-printed mail pieces.
Locating the address block i... | 856 |@word middle:1 grey:1 versatile:1 past:1 must:3 john:1 shape:7 enables:1 remove:1 designed:1 guess:1 provides:1 postal:5 location:10 five:2 consists:1 combine:1 detects:1 decreasing:1 cpu:1 window:3 becomes:1 confused:1 provided:1 substantially:1 suppresses:1 finding:4 impractical:1 every:1 demonstrates:1 platt:7 ... |
7,087 | 857 | Learning Complex Boolean Functions:
Algorithms and Applications
Arlindo L. Oliveira and Alberto Sangiovanni- Vincentelli
Dept. of EECS
UC Berkeley
Berkeley CA 94720
Abstract
The most commonly used neural network models are not well suited
to direct digital implementations because each node needs to perform a large nu... | 857 |@word pulse:2 solid:1 electronics:1 contains:1 xiy:2 selecting:1 current:1 luo:1 yet:1 written:1 must:1 partition:3 informative:1 designed:1 greedy:3 selected:6 intelligence:1 warmuth:1 smith:3 short:1 prespecified:1 provides:4 completeness:1 node:29 simpler:1 direct:1 prove:1 combine:1 indeed:1 multi:3 terminal:4... |
7,088 | 858 | Lipreading by neural networks:
Visual preprocessing, learning
and sensory integration
Gregory J. Wolff
Ricoh California Research Center
2882 Sand Hill Road Suite 115
Menlo Park, CA 94025-7022
wolff@crc.ricoh.com
David G. Stork
Ricoh California Research Center
2882 Sand Hill Road Suite 115
Menlo Park, CA 94025-7022
sto... | 858 |@word manageable:2 eliminating:1 seems:1 nd:1 disk:1 open:3 closure:1 prasad:6 speechreading:3 efficacy:1 ours:1 interestingly:1 com:3 ida:1 visible:3 shape:2 remove:2 alone:1 fewer:1 contribute:1 successive:1 sigmoidal:1 five:3 height:1 burst:1 along:1 direct:2 consists:2 yuhas:2 combine:1 manner:1 expected:1 rou... |
7,089 | 859 | Connectionism for Music and Audition
Andreas S. Weigend
Department of Computer Science
and Institute of Cognitive Science
University of Colorado
Boulder, CO 80309-0430
Abstract
This workshop explored machine learning approaches to 3 topics:
(1) finding structure in music (analysis, continuation, and completion of an u... | 859 |@word c:1 normalized:1 dat:1 question:3 primary:1 human:4 traditional:1 distance:1 speaker:1 die:1 rhythm:2 mcauley:5 coincides:1 attentional:1 thank:2 series:2 wall:1 anonymous:1 baroque:1 me:1 topic:1 connectionism:1 past:1 motion:1 interface:1 com:1 considered:2 harmonic:1 ohio:1 lawrence:1 cognition:3 devin:2 ... |
7,090 | 86 | 467
SPONTANEOUS AND INFORMATION-TRIGGERED SEGMENTS OF SERIES
OF HUMAN BRAIN ELECTRIC FIELD MAPS
D. lehmann, D. Brandeis*, A. Horst, H. Ozaki* and I. Pal*
Neurol09Y Department, University Hospital, 8091 Zurich, Switzerland
ABSTRACT
The brain works in a state-dependent manner: processin9
strate9ies and access to stored ... | 86 |@word open:1 prominence:1 invoking:1 paid:2 minus:2 ld:1 configuration:12 series:14 offering:1 reaction:6 anterior:1 analysed:1 activation:2 must:1 physiol:1 shape:1 praeger:1 atlas:1 v:9 discrimination:1 grass:1 selected:1 metabolism:1 libet:1 accordingly:1 record:1 psychiat:3 filtered:1 location:10 successive:2 o... |
7,091 | 860 | Encoding Labeled Graphs by Labeling
RAAM
Alessandro Sperduti*
Department of Computer Science
Pisa University
Corso Italia 40, 56125 Pisa, Italy
Abstract
In this paper we propose an extension to the RAAM by Pollack.
This extension, the Labeling RAAM (LRAAM), can encode labeled graphs with cycles by representing pointer... | 860 |@word trial:1 version:2 briefly:1 termination:1 simulation:1 tried:1 covariance:1 decomposition:1 tr:3 harder:1 recursively:1 ld:1 initial:1 configuration:3 cyclic:1 current:1 activation:6 must:4 happen:1 intelligence:4 leaf:2 discovering:1 beginning:1 record:2 pointer:55 node:11 contribute:1 toronto:1 firstly:1 a... |
7,092 | 861 | SPEAKER RECOGNITION USING
NEURAL TREE NETWORKS
Kevin R. Farrell and Richard J. Marnrnone
CAIP Center, Rutgers University
Core Building, Frelinghuysen Road
Piscataway, New Jersey 08855
Abstract
A new classifier is presented for text-independent speaker recognition. The
new classifier is called the modified neural tree... | 861 |@word polynomial:1 norm:4 twelfth:1 simulation:1 simplifying:1 recursively:1 score:14 imposter:4 outperforms:1 si:1 belmont:1 partition:1 predetermined:1 update:1 leaf:11 selected:2 core:1 provides:4 codebook:5 node:11 five:2 become:1 consists:5 combine:1 growing:1 multi:1 ol:1 window:1 becomes:1 minimizes:2 devel... |
7,093 | 862 | Non-linear Statistical Analysis and
Self-Organizing Hebbian Networks
Jonathan L. Shapiro and Adam Priigel-Bennett
Department of Computer Science
The University, Manchester
Manchester, UK
M139PL
Abstract
Neurons learning under an unsupervised Hebbian learning rule can
perform a nonlinear generalization of principal co... | 862 |@word effect:1 multiplier:2 differ:1 direction:3 objective:9 question:1 closely:1 correct:1 pea:8 stochastic:1 usual:1 responds:1 viewing:1 ll:1 self:4 said:1 subspace:1 minus:1 ambiguous:1 argued:1 softky:5 initial:1 generalized:1 contains:1 generalization:10 tuned:3 extension:1 length:1 relationship:6 analysed:1... |
7,094 | 863 | Non-Intrusive Gaze Tracking Using Artificial
Neural Networks
Shumeet Baluja
Dean Pomerleau
baluja@cs.cmu.edu
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
pomerleau @cs.cmu.edu
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
We have developed an artif... | 863 |@word trial:1 willing:1 simulation:2 thereby:1 initial:2 selecting:2 document:1 current:3 comparing:2 activation:1 yet:1 follower:1 must:2 additive:1 discrimination:1 stationary:2 v:1 alone:1 device:3 provides:2 location:6 along:1 hci:2 fitting:1 manner:2 behavior:1 nor:1 chi:1 automatically:1 actual:1 window:5 ma... |
7,095 | 864 | Digital Boltzmann VLSI for
constraint satisfaction and learning
Michael Murray t
Ming-Tak Leung t
Kan Boonyanit t
Kong Kritayakirana t
James B. Burrt*
Gregory J. Wolff+
Takahiro Watanabe+
Edward Schwartz+
David G. Storktt
Allen M. Petersont
t Department of Electrical Engineering
Stanford University
Stanford, ... | 864 |@word kong:1 selforganization:1 version:3 seems:1 instruction:1 simulation:3 solid:1 initial:2 ours:1 omniscient:1 amp:1 current:6 com:1 protection:1 activation:29 written:1 refresh:1 designed:1 update:13 reciprocal:1 steepest:1 yamada:1 node:1 location:1 along:1 driver:2 supply:1 symposium:1 consists:2 sustained:... |
7,096 | 865 | Monte Carlo Matrix Inversion and
Reinforcement Learning
Andrew Barto and Michael Duff
Computer Science Department
University of Massachusetts
Amherst, MA 01003
Abstract
We describe the relationship between certain reinforcement learning (RL) methods based on dynamic programming (DP) and a class
of unorthodox Monte C... | 865 |@word trial:2 version:1 inversion:6 advantageous:1 suitably:2 simulation:1 reduction:4 initial:1 series:4 prefix:1 z2:1 od:2 assigning:1 must:1 written:1 analytic:2 plot:2 stationary:1 yr:1 ith:1 provides:1 math:2 successive:2 symposium:1 ik:1 expected:7 discounted:5 td:12 automatically:1 actual:4 considering:1 be... |
7,097 | 866 | Two-Dimensional Object Localization by
Coarse-to-Fine Correlation Matching
Chien-Ping Lu and Eric Mjolsness
Department of Computer Science
Yale University
New Haven, CT 06520-8285
Abstract
We present a Mean Field Theory method for locating twodimensional objects that have undergone rigid transformations.
The resultin... | 866 |@word private:1 version:1 configuration:1 ida:1 si:3 readily:2 numerical:1 implying:1 greedy:1 discovering:1 fewer:1 trapping:1 ith:1 short:1 provides:1 coarse:5 node:1 location:4 simpler:1 along:1 c2:1 pairing:1 roughly:1 frequently:1 discretized:1 rem:1 actual:1 considering:1 becomes:3 matched:4 notation:1 under... |
7,098 | 867 | The Role of MT Neuron Receptive Field
Surrounds in Computing Object Shape from
Velocity Fields
G.T.Buracas & T.D.Albright
Vision Center Laboratory, The Salk Institute,
P.O.Box 85800, San Diego, California 92138-9216
Abstract
The goal of this work was to investigate the role of primate
MT neurons in solving the struct... | 867 |@word version:1 middle:5 judgement:1 seems:1 norm:2 nd:2 rhesus:1 simplifying:1 solid:1 born:3 series:2 indispensible:1 dx:1 must:1 readily:1 mst:2 additive:1 wx:1 shape:8 plot:1 sponsored:1 v:1 cue:2 half:1 iso:1 filtered:1 zhang:1 along:1 c2:4 differential:6 fixation:2 combine:2 redefine:1 parallax:1 introduce:1... |
7,099 | 868 | Development of Orientation and Ocular
Dominance Columns in Infant Macaques
Klaus Obermayer
Howard Hughes Medical Institute
Salk-Institute
La Jolla, CA 92037
Lynne Kiorpes
Center for Neural Science
New York University
New York, NY 10003
Gary G. Blasdel
Department of Neurobiology
Harvard Medical School
Boston, MA 0211... | 868 |@word wiesel:2 seems:3 brightness:2 mammal:1 reduction:1 liu:1 contains:2 optically:1 seriously:1 interestingly:1 past:1 elliptical:1 od:1 john:1 shape:1 analytic:1 plot:3 infant:9 inspection:1 postnatal:1 iso:7 coleman:1 compo:3 location:1 preference:21 along:1 differential:1 autocorrelation:1 expected:1 nor:1 gr... |
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