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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600 | 1,549 | Bayesian peA
Christopher M. Bishop
Microsoft Research
St. George House, 1 Guildhall Street
Cambridge CB2 3NH, u.K.
cmbishop@microsoft.com
Abstract
The technique of principal component analysis (PCA) has recently been
expressed as the maximum likelihood solution for a generative latent
variable model. In this paper we... | 1549 |@word compression:1 advantageous:1 wla:1 seek:1 covariance:6 tr:2 solid:1 plentiful:1 suppressing:2 recovered:1 com:1 dx:1 must:2 written:1 visible:1 additive:1 wx:2 plot:5 stationary:1 generative:2 provides:1 location:1 become:2 fitting:1 introduce:2 expected:1 wml:1 automatically:5 armed:1 considering:1 becomes... |
601 | 155 | 739
AN ANALOG SELF-ORGANIZING
NEURAL NElWORK CHIP
James R. Mann
MIT Lincoln Laboratory
244 Wood Street
Lexington, MA 02173"()()73
Sheldon Gilbert
4421 West Estes
Lincolnwood, IL 60646
ABSTRACT
A design for a fully analog version of a self-organizing feature map neural
network has been completed. Several parts of thi... | 155 |@word version:11 instrumental:1 d2:1 simulation:3 descnbed:2 thereby:1 tr:1 minus:1 accommodate:2 selecting:1 t7:1 l__:1 past:1 existing:1 current:28 activation:4 yet:1 must:2 refresh:9 realize:1 periodically:1 drop:1 plot:5 update:1 sponsored:1 v:2 selected:4 device:4 mln:1 sram:1 short:1 quantizer:2 quantized:1 ... |
602 | 1,550 | Robust. Efficient, Globally-Optimized
Reinforcement Learning with the
Parti-Game Algorithm
Mohammad A. AI-Ansari and Ronald J. Williams
College of Computer Science, 161 CN
Northeastern University
Boston, MA 02115
alansar@ccs.neu.edu, rjw@ccs.neu.edu
Abstract
Parti-game (Moore 1994a; Moore 1994b; Moore and Atkeson 199... | 1550 |@word deformed:1 trial:8 proceeded:1 version:5 termination:1 pick:1 thereby:1 nonexistent:1 configuration:1 disparity:1 past:1 outperforms:3 current:2 comparing:1 ronald:1 partition:35 thrust:2 shape:1 designed:1 fewer:4 plane:1 indefinitely:1 coarse:2 provides:1 location:1 successive:1 five:1 along:14 corridor:1... |
603 | 1,551 | Reinforcement Learning for Trading
John Moody and Matthew Saffell*
Oregon Graduate Institute , CSE Dept.
P.O . Box 91000 , Portland, OR 97291-1000
{moody, saffell }@cse.ogi.edu
Abstract
We propose to train trading systems by optimizing financial objective functions via reinforcement learning. The performance function... | 1551 |@word trial:4 version:2 simulation:4 dramatic:1 twolayer:1 profit:13 solid:1 moment:1 initial:2 series:6 denoting:1 past:1 outperforms:1 current:1 neuneier:2 si:1 must:2 john:1 numerical:1 subsequent:2 additive:2 enables:1 designed:1 treating:1 fund:2 update:1 plot:2 short:6 record:1 provides:3 cse:2 zhang:2 alon... |
604 | 1,552 | Visualizing Group Structure*
Marcus Held, Jan Puzicha, and Joachim M. Buhmann
Institut fur Informatik III,
RomerstraBe 164, D-53117 Bonn, Germany
email: {heldjanjb}.cs.uni-bonn.de.
VVVVVV: http://vvv-dbv.cs.uni-bonn.de
Abstract
Cluster analysis is a fundamental principle in exploratory data
analysis, providing the us... | 1552 |@word cox:2 sammon:2 seek:1 inefficiency:1 denoting:1 document:1 recovered:2 assigning:1 visible:1 partition:3 hofmann:3 plot:3 generative:3 intelligence:1 inspection:5 iso:1 provides:7 detecting:1 vistex:4 consists:2 fitting:1 introduce:1 pairwise:1 inter:4 discretized:1 becomes:1 underlying:2 moreover:1 israel:... |
605 | 1,553 | Direct Optimization of Margins Improves
Generalization in Combined Classifiers
Llew Mason,Peter Bartlett, Jonathan Baxter
Department of Systems Engineering
Australian National University, Canberra, ACT 0200, Australia
{lmason, bartlett, jon }@syseng.anu.edu.au
Abstract
Cumulative training margin distributions for Ada... | 1553 |@word repository:1 version:2 proportion:1 suitably:1 willing:1 thereby:1 reduction:2 initial:1 subsequent:1 plot:5 update:1 v:1 intelligence:1 selected:2 plane:1 short:1 provides:1 boosting:5 node:1 complication:1 constructed:1 direct:6 sacrifice:2 examine:1 multi:2 becomes:1 provided:4 cleveland:1 what:2 tic:1 p... |
606 | 1,554 | Neuronal Regulation Implements
Efficient Synaptic Pruning
Gal Chechik and Isaac Meilijson
School of Mathematical Sciences
Tel Aviv University, Tel Aviv 69978, Israel
ggal@math.tau.ac.il isaco@math.tau.ac.il
Eytan Ruppin
Schools of Medicine and Mathematical Sciences
Tel Aviv University, Tel Aviv 69978, Israel
ruppin@ma... | 1554 |@word determinant:1 simulation:4 mammal:1 initial:2 efficacy:12 z2:1 activation:1 additive:1 numerical:1 analytic:1 remove:3 plot:3 half:2 nervous:2 ith:2 math:3 sigmoidal:2 unbounded:1 mathematical:2 along:2 behavioral:1 manner:6 theoretically:2 indeed:1 brain:12 terminal:1 underlying:1 feigel:1 israel:2 what:1 ... |
607 | 1,555 | Computation of Smooth Optical Flow in a
Feedback Connected Analog Network
Alan Stocker *
Institute of Neuroinforrnatics
University and ETH Zi.irich
Winterthurerstrasse 190
8057 Zi.irich, Switzerland
Rodney Douglas
Institute of Neuroinforrnatics
University and ETH Zi.irich
Winterthurerstrasse 190
8057 Zi.irich, Switze... | 1555 |@word norm:1 calculus:1 brightness:5 liu:1 current:7 luo:2 dx:2 must:1 follower:1 numerical:1 exl:1 half:1 intelligence:1 device:1 core:2 lr:1 detecting:1 provides:2 node:6 location:2 five:1 qualitative:1 consists:1 resistive:10 manner:1 actual:2 little:1 becomes:1 perceives:1 circuit:3 differentiation:1 fabricat... |
608 | 1,556 | Distributional Population Codes and
Multiple Motion Models
Richard S. Zemel
University of Arizona
Peter Dayan
Gatsby Computational Neuroscience Unit
zemel@u.arizona.edu
dayan@gatsby.ucl.ac.uk
Abstract
Most theoretical and empirical studies of population codes make
the assumption that underlying neuronal activities ... | 1556 |@word neurophysiology:1 trial:3 version:1 proportion:3 simulation:1 llo:4 inefficiency:1 schoner:1 o2:1 existing:1 current:2 recovered:1 comparing:1 must:2 mst:1 additive:2 informative:1 motor:2 treating:1 plot:5 pursued:1 metamerization:1 lr:1 provides:3 putatively:1 five:1 consists:1 fitting:1 behavioral:4 grun... |
609 | 1,557 | Computation of Smooth Optical Flow in a
Feedback Connected Analog Network
Alan Stocker *
Institute of Neuroinforrnatics
University and ETH Zi.irich
Winterthurerstrasse 190
8057 Zi.irich, Switzerland
Rodney Douglas
Institute of Neuroinforrnatics
University and ETH Zi.irich
Winterthurerstrasse 190
8057 Zi.irich, Switze... | 1557 |@word version:2 norm:1 instruction:73 calculus:1 brightness:5 pick:1 harder:2 liu:1 series:1 uma:1 selecting:1 punishes:1 bc:1 eustace:2 current:10 luo:2 lang:1 dx:2 must:2 follower:1 written:3 john:1 numerical:1 remove:1 hypothesize:1 exl:1 half:1 intelligence:1 device:1 greedy:5 discovering:1 beginning:2 core:2... |
610 | 1,558 | A Micropower CMOS Adaptive Amplitude and
Shift Invariant Vector Quantiser
Richard J. Coggins, Raymond J.W. Wang and Marwan A. Jabri
Computer Engineering Laboratory
School of Electrical and Infonnation Engineering, J03
University of Sydney, 2006, Australia.
{richardc, jwwang, marwan} @seda1.usyd.edu.au
Abstract
In thi... | 1558 |@word trial:1 middle:1 inversion:3 achievable:1 compression:17 loading:2 version:1 simulation:5 eng:1 thereby:1 solid:3 reduction:1 electronics:1 contains:1 exclusively:1 series:1 transfonn:3 denoting:1 existing:2 err:1 current:12 must:2 numerical:3 subsequent:1 icds:3 enables:2 remove:2 designed:1 stationary:1 t... |
611 | 1,559 | Computational Differences between
Asymmetrical and Symmetrical Networks
Zhaoping Li
Peter Dayan
Gatsby Computational Neuroscience Unit
17 Queen Square, London, England, WCIN 3AR.
zhaoping@gatsby.ucl.ac.uk
dayan@gatsby.ucl.ac.uk
Abstract
Symmetrically connected recurrent networks have recently been
used as models of a ... | 1559 |@word neurophysiology:1 kong:1 h:2 rising:1 hippocampus:1 stronger:1 simulation:1 simplifying:1 commute:1 solid:3 tuned:12 wd:1 activation:2 yet:3 dx:1 plasticity:1 pertinent:1 motor:1 designed:2 rinzel:1 v:4 selected:2 hallucinate:3 short:2 node:1 location:3 zhang:2 mathematical:1 along:1 pairing:1 qualitative:1... |
612 | 156 | 177
COMPARING BIASES FOR MINIMAL NETWORK
CONSTRUCTION WITH BACK-PROPAGATION
Stephen Jo~ Hansont
Bell Communications Research
Morristown. New Jersey 07960
Lorien Y. Pratt
Rutgers University
New Brunswick. New Jersey 08903
ABSTRACT
Rumelhart (1987). has proposed a method for choosing minimal or
"simple" representatio... | 156 |@word collinearity:1 gradual:1 fonn:1 t7:1 past:2 comparing:6 must:3 john:1 mesh:4 update:1 tenn:5 plane:1 ith:2 along:1 differential:2 replication:5 consists:1 combine:1 introduce:2 theoretically:2 expected:3 behavior:3 examine:1 rawlings:2 decreasing:1 automatically:1 becomes:3 provided:1 begin:1 underlying:1 ma... |
613 | 1,560 | Graphical Models for Recognizing
Human Interactions
Nuria M. Oliver, Barbara Rosario and Alex Pentland
20 Ames Street, E15-384C,
Media Arts and Sciences Laboratory, MIT
Cambridge, MA 02139
{nuria, rosario, sandy}@media.mit.edu
Abstract
We describe a real-time computer vision and machine learning system for modeling a... | 1560 |@word version:1 open:2 simulation:1 rgb:1 covariance:2 recursively:1 initial:2 configuration:1 contains:1 series:2 hereafter:2 practiced:1 current:3 od:1 must:1 shape:2 enables:1 generative:2 selected:2 cue:1 une:1 detecting:1 coarse:1 ames:1 location:1 simpler:1 mathematical:2 alert:1 along:1 kov:1 combine:2 com... |
614 | 1,561 | Probabilistic Visualisation of
High-dimensional Binary Data
Michael E. Tipping
Microsoft Research,
St George House, 1 Guildhall Street,
Cambridge CB2 3NH, U.K.
mtipping@microsoit.com
Abstract
We present a probabilistic latent-variable framework for data visualisation, a key feature of which is its applicability to bi... | 1561 |@word briefly:1 inversion:1 covariance:1 ld:1 reduction:2 series:1 initialisation:1 current:1 com:1 dx:1 must:1 readily:1 numerical:1 plot:3 update:4 v:1 implying:1 generative:3 alone:1 intelligence:2 indicative:1 maximised:1 location:4 firstly:1 hermite:1 mathematical:1 fitting:1 manner:1 notably:2 indeed:1 expe... |
615 | 1,562 | Fast Neural Network Emulation of Dynamical
Systems for Computer Animation
Radek Grzeszczuk 1
Demetri Terzopoulos
1 Intel Corporation
Microcomputer Research Lab
2200 Mission College Blvd.
Santa Clara, CA 95052, USA
2
Geoffrey Hinton
2
2 University of Toronto
Department of Computer Science
10 King's College Road
T... | 1562 |@word reused:1 r:2 tried:1 simulation:14 contraction:1 dramatic:2 incurs:1 thereby:1 initial:3 r5t:2 brien:1 clara:1 yet:1 must:2 john:1 realistic:6 biomechanical:1 numerical:13 enables:2 motor:1 update:1 cook:1 beginning:1 realism:3 compo:1 math:1 contribute:1 toronto:5 sigmoidal:3 simpler:1 constructed:1 direct... |
616 | 1,563 | Shrinking the Thbe:
A New Support Vector Regression Algorithm
Bernhard SchOikopr?,*, Peter Bartlett*, Alex Smola?,r, Robert Williamson*
? GMD FIRST, Rudower Chaussee 5, 12489 Berlin, Germany
* FEITIRSISE, Australian National University, Canberra 0200, Australia
bs, smola@first.gmd.de, Peter.Bartlett, Bob.Williamson@an... | 1563 |@word trial:1 determinant:1 version:1 briefly:1 middle:1 seems:2 compression:1 open:1 seek:1 decomposition:1 tr:1 harder:1 stitson:3 substitution:1 selecting:1 interestingly:1 recovered:1 com:1 comparing:1 analysed:1 must:2 additive:1 happen:1 shape:3 girosi:3 drop:1 aside:1 implying:1 v:1 provides:1 contribute:2... |
617 | 1,564 | ?
Inference in Multilayer Networks VIa
Large Deviation Bounds
Michael Kearns and Lawrence Saul
AT&T Labs - Research
Shannon Laboratory
180 Park A venue A-235
Florham Park, NJ 07932
{mkearns ,lsaul}Oresearch.att. com
Abstract
We study probabilistic inference in large, layered Bayesian networks represented as directed ... | 1564 |@word briefly:1 n8:1 mkearns:1 att:1 contains:1 denoting:1 rightmost:1 com:1 reminiscent:1 must:1 fn:1 numerical:1 additive:1 treating:1 n0:1 aside:1 generative:1 intelligence:3 xk:6 ith:1 lr:1 provides:1 node:38 become:4 prove:2 fth:4 inside:1 manner:1 expected:1 indeed:2 roughly:1 behavior:2 frequently:1 mechan... |
618 | 1,565 | Barycentric Interpolators for Continuous
Space & Time Reinforcement Learning
Remi Munos & Andrew Moore
Robotics Institute, Carnegie Mellon University
Pittsburgh, PA 15213, USA.
E-mail: {munos, awm }@cs.cmu.edu
Abstract
In order to find the optimal control of continuous state-space and
time reinforcement learning (RL)... | 1565 |@word open:1 grey:3 contraction:7 ytn:1 initial:3 current:2 discretization:1 nt:5 dx:1 must:1 numerical:1 j1:1 sponsored:1 update:1 intelligence:1 ivo:3 provides:2 c2:1 differential:2 prove:4 hjb:5 inside:6 introduce:1 indeed:1 multi:5 terminal:1 bellman:1 discretized:1 discounted:4 increasing:1 moreover:4 bounde... |
619 | 1,567 | Probabilistic Modeling for Face Orientation
Discrimination:
Learning from Labeled and Unlabeled Data
Shumeet Baluja
baluja@cs.cmu.edu
Justsystem Pittsburgh Research Center &
School of Computer Science, Carnegie Mellon University
Abstract
This paper presents probabilistic modeling methods to solve the problem of discr... | 1567 |@word version:1 briefly:1 middle:1 termination:2 fonn:1 thereby:1 tr:1 liu:4 contains:1 exclusively:1 series:1 document:3 mages:1 must:2 readily:2 cottrell:2 discrimination:11 alone:1 cue:1 selected:1 half:2 fewer:1 generative:2 intelligence:2 mccallum:4 provides:2 draft:1 node:1 location:2 ofo:1 five:8 driver:1 ... |
620 | 1,568 | Blind Separation of Filtered Sources
Using State-Space Approach
Liqing Zhang? and Andrzej Cichocki t
Laboratory for Open Information Systems,
Brain Science Institute, RIKEN
Saitama 351-0198, Wako shi, JAPAN
Email: {zha.cia}@open.brain.riken.go.jp
Abstract
In this paper we present a novel approach to multichannel blin... | 1568 |@word kong:1 determinant:1 suitably:1 open:2 simulation:4 propagate:1 covariance:4 jacob:1 tr:3 moment:1 initial:3 wako:1 current:1 si:1 activation:3 dx:2 numerical:2 enables:1 designed:2 plot:1 update:7 stationary:1 xk:1 nnsp:2 filtered:3 zhang:5 differential:3 symposium:1 introduce:3 expected:2 equivariant:2 gr... |
621 | 1,569 | Discontinuous Recall Transitions Induced By
Competition Between Short- and Long-Range
Interactions in Recurrent Networks
N.S. Skantzos, C.F. Beckmann and A.C.C. Coolen
Dept of Mathematics, King's College London, Strand, London WC2R 2LS, UK
E-mail: skantzos@mth.kcl.ac.uktcoolen@mth.kcl.ac.uk
Abstract
We present exact ... | 1569 |@word advantageous:1 physik:1 bn:1 covariance:2 tr:2 solid:1 initial:1 imaginary:1 written:1 must:2 realistic:1 enables:1 cue:1 plane:5 inspection:1 short:14 brandt:1 five:1 mathematical:1 along:4 inter:1 indeed:1 mechanic:2 eil:1 increasing:1 becomes:1 provided:1 lowest:1 what:1 textbook:1 gutfreund:2 lone:1 tra... |
622 | 157 | 634
ON THE K-WINNERS-TAKE-ALL NETWORK
E. Majani
Jet Propulsion Laboratory
California Institute of Technology
R. Erlanson, Y. Abu-Mostafa
Department of Electrical Engineering
California Institute of Technology
ABSTRACT
We present and rigorously analyze a generalization of the WinnerTake-All Network: the K-Winners-Take... | 157 |@word uj:24 graded:1 implies:1 evolution:1 already:1 symmetric:1 laboratory:3 ryckebusch:1 linearized:1 eg:1 september:1 self:3 material:1 tr:1 thank:1 ja:2 sci:1 initial:6 propulsion:3 generalization:2 assuming:2 fj:1 majani:5 around:1 si:1 must:3 equilibrium:32 john:1 sigmoid:5 additive:1 mostafa:5 a2:1 winner:1... |
623 | 1,570 | The Belief in TAP
Yoshiyuki Kabashima
Dept. of Compt. IntI. & Syst. Sci.
Tokyo Institute of Technology
Yokohama 226, Japan
David Saad
Neural Computing Research Group
Aston University
Birmingham B4 7ET, UK
Abstract
We show the similarity between belief propagation and TAP, for
decoding corrupted messages encoded by So... | 1570 |@word open:1 tr:2 solid:1 initial:4 efficacy:4 mag:1 reaction:1 current:2 si:8 assigning:1 additive:1 numerical:4 enables:1 selected:1 indicative:1 hamiltonian:3 vanishing:1 ire:1 provides:3 firstly:1 mathematical:1 direct:1 ik:7 retrieving:1 introduce:1 manner:1 expected:2 behavior:1 isi:3 examine:3 mechanic:6 e... |
624 | 1,571 | Classification on Pairwise Proximity Data
Thore Graepel t , Ralf Herbrich i ,
Peter Bollmann-Sdorra t , Klaus Obermayert
Technical University of Berlin,
t Statistics Research Group, Sekr. FR 6-9,
t
Neural Information Processing Group, Sekr . FR 2-1 ,
Franklinstr. 28/29, 10587 Berlin, Germany
Abstract
We investigate ... | 1571 |@word determinant:1 inversion:1 norm:1 covariance:1 decomposition:1 jacob:1 contains:1 series:1 comparing:1 numerical:1 hofmann:2 plot:2 intelligence:1 item:19 plane:2 vanishing:1 provides:1 characterization:1 herbrich:3 become:1 borg:1 consists:2 introduce:1 pairwise:16 behavior:3 themselves:1 examine:2 frequent... |
625 | 1,572 | Information Maximization in Single Neurons
Martin Stemmler and Christof Koch
Computation and Neural Systems Program
Caltech 139-74
Pasadena, CA 91 125
Email: stemmler@klab.caltech.edu.koch@klab.caltech.edu
Abstract
Information from the senses must be compressed into the limited range
of firing rates generated by spik... | 1572 |@word compression:1 open:2 seek:1 thereby:1 carry:1 phosphorylation:1 initial:1 contains:3 imoto:1 current:6 activation:4 yet:1 dx:1 must:5 moo:2 periodically:2 additive:1 hofmann:1 implying:1 fewer:1 selected:1 nervous:1 compo:2 burst:1 constructed:1 consists:1 sustained:1 avery:1 behavior:3 monopolar:1 brain:1 ... |
626 | 1,573 | Learning Instance-Independent Value Functions
to Enhance Local Search
Robert Moll Andrew G. Barto Theodore J. Perkins
Department of Computer Science
University of Massachusetts, Amherst, MA 01003
Richard S. Sutton
AT&T Shannon Laboratory, 180 Park Avenue, Florham Park, NJ 07932
Abstract
Reinforcement learning methods... | 1573 |@word version:1 termination:1 bn:3 pick:5 thereby:1 necessity:1 initial:2 current:5 yet:1 must:2 belmont:1 subsequent:1 predetermined:1 midway:1 drop:5 intelligence:1 selected:3 plane:1 math:1 location:2 zhang:6 five:3 constructed:1 direct:1 driver:1 consists:1 combine:3 inside:1 sacrifice:1 indeed:1 expected:2 m... |
627 | 1,574 | Analyzing and Visualizing Single-Trial
Event-Related Potentials
Tzyy-Ping Jung 1 ,2, Scott Makeig 2,3, Marissa Westerfield 2
Jeanne Townsend 2, Eric Courchesne 2, Terrence J. Sejnowskp,2
1 Howard
Hughes Medical Institute and Computational Neurobiology Laboratory
The Salk Institute, P.O. Box 85800, San Diego, CA 92186-... | 1574 |@word trial:50 middle:7 version:1 decomposition:1 accounting:5 attended:6 solid:2 rightmost:1 reaction:11 activation:7 visible:1 wx:2 arrayed:1 analytic:1 motor:1 remove:2 plot:5 selected:2 nent:2 record:20 detecting:1 location:12 successive:1 five:7 along:1 fixation:4 westerfield:2 introduce:1 ica:19 behavior:1 ... |
628 | 1,575 | Semiparametric Support Vector and
Linear Programming Machines
Alex J. Smola, Thilo T. Frie6, and Bernhard Scholkopf
GMD FIRST, Rudower Chaussee 5, 12489 Berlin
{smola, friess, bs }@first.gmd.de
Abstract
Semiparametric models are useful tools in the case where domain
knowledge exists about the function to be estimated... | 1575 |@word rreg:1 trial:1 inversion:1 polynomial:3 advantageous:1 decomposition:1 pick:1 tr:2 outlook:1 solid:3 carry:1 rkhs:2 outperforms:2 existing:1 current:1 yet:1 written:1 realistic:1 additive:2 wx:1 happen:1 visible:1 treating:1 rpn:1 v:5 accordingly:1 math:1 preference:2 direct:1 scholkopf:5 backfitting:4 expe... |
629 | 1,576 | Gradient Descent for General
Reinforcement Learning
Leemon Baird
leemon@cs.cmu.edu
www.cs.cmu.edu/- Ieemon
Computer Science Department
5000 Forbes Avenue
Carnegie Mellon University
Pittsburgh, PA 15213-3891
Andrew Moore
awm@cs.cmu .edu
www.cs.cmu.edu/-awm
Computer Science Department
5000 Forbes Avenue
Carnegie Mellon... | 1576 |@word trial:8 armand:1 seems:1 norm:1 twelfth:1 open:1 simulation:5 initial:2 series:1 genetic:1 rightmost:1 past:2 existing:1 unprimed:3 current:7 com:1 outperforms:1 must:5 happen:1 girosi:1 plot:1 sponsored:1 aps:7 update:4 v:1 alone:3 greedy:14 intelligence:1 mccallum:1 short:1 dissertation:2 lx:1 vaps:5 alon... |
630 | 1,577 | Using Analytic QP and Sparseness to Speed
Training of Support Vector Machines
John C. Platt
Microsoft Research
1 Microsoft Way
Redmond, WA 98052
jplatt@microsoft.com
Abstract
Training a Support Vector Machine (SVM) requires the solution of a very
large quadratic programming (QP) problem. This paper proposes an algori... | 1577 |@word msr:1 repository:1 version:1 polynomial:1 decomposition:7 invoking:1 tr:1 reduction:1 series:3 att:1 com:1 nt:1 must:3 written:1 john:1 numerical:9 girosi:1 analytic:5 update:2 ith:2 short:1 provides:1 along:3 scholkopf:3 consists:2 overhead:3 rapid:1 surge:1 discretized:1 cpu:3 window:1 solver:1 cache:6 ka... |
631 | 1,578 | Dynamics of Supervised Learning with
Restricted Training Sets
A.C.C. Coolen
Dept of Mathematics
King's College London
Strand, London WC2R 2LS, UK
tcoolen @mth.kcl.ac.uk
D. Saad
Neural Computing Research Group
Aston University
Birmingham B4 7ET, UK
saadd@aston.ac.uk
Abstract
We study the dynamics of supervised learni... | 1578 |@word middle:1 suitably:1 closure:3 simulation:11 x2p:1 thereby:1 solid:1 moment:1 xiy:2 ours:1 activation:1 yet:1 dx:7 must:1 laii:1 numerical:5 dydx:1 enables:1 update:2 tjw:1 short:1 iterates:1 provides:1 math:2 five:2 along:1 lyir:2 indeed:3 mechanic:1 ry:8 ol:1 underlying:1 what:1 q2:2 developed:1 finding:1 ... |
632 | 1,579 | Dynamics of Supervised Learning with
Restricted Training Sets
A.C.C. Coolen
Dept of Mathematics
King's College London
Strand, London WC2R 2LS, UK
tcoolen @mth.kcl.ac.uk
D. Saad
Neural Computing Research Group
Aston University
Birmingham B4 7ET, UK
saadd@aston.ac.uk
Abstract
We study the dynamics of supervised learni... | 1579 |@word middle:1 suitably:1 closure:3 simulation:11 x2p:1 thereby:1 solid:1 moment:1 xiy:2 ours:1 activation:1 yet:1 dx:7 must:1 laii:1 numerical:5 dydx:1 enables:1 update:2 tjw:1 short:1 iterates:1 provides:1 math:2 five:2 along:1 lyir:2 indeed:3 mechanic:1 ry:8 ol:1 underlying:1 what:1 q2:2 developed:1 finding:1 ... |
633 | 158 | 332
NEURAL NETWORKS THAT LEARN TO
DISCRIMINATE SIMILAR KANJI CHARACTERS
Yoshihiro Morl
Kazuhiko Yokosawa
ATR Auditory and Visual Perception Research Laboratories
2-1-61 Shiromi Higashiku Osaka 540 Japan
ABSTRACT
A neural network is applied to the problem of
recognizing Kanji characters. Using a b a c k
propagation ne... | 158 |@word chinese:3 concept:1 hence:1 direction:2 correct:1 laboratory:1 strategy:3 simulation:1 covariance:2 human:1 ll:2 diagonal:1 fonn:1 atr:1 current:1 length:1 rudimentary:1 normal:1 yet:1 visually:1 written:3 handprinted:2 common:1 difficult:4 mesh:3 wx:1 ji:1 teach:1 early:2 designed:1 recognizer:1 implementat... |
634 | 1,580 | Learning a Continuous Hidden Variable
Model for Binary Data
Daniel D. Lee
Bell Laboratories
Lucent Technologies
Murray Hill, NJ 07974
ddlee~bell-labs.com
Haim Sompolinsky
Racah Institute of Physics and
Center for Neural Computation
Hebrew University
Jerusalem, 91904, Israel
haim~fiz.huji . ac.il
Abstract
A directed ... | 1580 |@word compression:1 seems:1 reduction:2 configuration:7 daniel:1 diagonalized:3 com:1 si:11 yet:1 cottrell:1 visible:3 partition:1 drop:1 generative:18 half:2 xk:1 location:1 hyperplanes:3 sigmoidal:1 constructed:2 acti:1 multi:1 psychometrika:1 xx:3 underlying:4 bounded:1 israel:2 what:1 transformation:4 nj:1 im... |
635 | 1,581 | Classification in Non-Metric Spaces
Daphna Weinshall l ,2 David W. Jacobs l Yoram Gdalyahu 2
1 NEC Research Institute , 4 Independence Way, Princeton, NJ 08540, USA
2Inst. of Computer Science, Hebrew University of Jerusalem, Jerusalem 91904 , Israel
Abstract
A key question in vision is how to represent our knowledge ... | 1581 |@word weins:1 cox:1 cnn:1 c0:1 open:1 d2:1 simulation:4 jacob:5 pick:1 contains:1 score:2 selecting:1 rightmost:1 past:1 existing:4 current:3 elliptical:1 yet:1 must:5 applicant:1 john:1 treating:1 plot:2 designed:1 half:1 item:1 plane:2 compo:1 provides:1 math:1 five:1 mathematical:1 dn:2 consists:2 redefine:1 p... |
636 | 1,582 | Semi-Supervised Support Vector
Machines
Kristin P. Bennett
Department of Mathematical Sciences
Rensselaer Polytechnic Institute
Troy, NY 12180 bennek@rpi.edu
Ayhan Demiriz
Department of Decision Sciences and Engineering Systems
Rensselaer Polytechnic Institute
Troy, NY 12180 demira@rpi.edu
Abstract
We introduce a se... | 1582 |@word trial:1 repository:1 mri:1 briefly:1 version:1 norm:12 prognostic:1 open:1 reduction:2 chervonenkis:1 past:1 bradley:2 rpi:2 girosi:1 discrimination:4 alone:2 plane:5 provides:1 math:1 mathematical:7 constructed:4 introduce:1 g4:1 theoretically:1 indeed:1 globally:1 little:1 solver:1 becomes:3 provided:2 es... |
637 | 1,583 | Learning a Continuous Hidden Variable
Model for Binary Data
Daniel D. Lee
Bell Laboratories
Lucent Technologies
Murray Hill, NJ 07974
ddlee~bell-labs.com
Haim Sompolinsky
Racah Institute of Physics and
Center for Neural Computation
Hebrew University
Jerusalem, 91904, Israel
haim~fiz.huji . ac.il
Abstract
A directed ... | 1583 |@word trial:2 compression:1 seems:1 stronger:3 willing:1 simulation:1 contraction:2 cla:1 concise:1 profit:1 minus:1 reduction:2 configuration:7 series:1 exclusively:1 daniel:1 tuned:1 past:1 diagonalized:3 existing:1 neuneier:10 com:1 current:3 si:11 yet:1 import:1 john:1 cottrell:1 visible:3 partition:1 happen:... |
638 | 1,584 | Learning Lie Groups for Invariant Visual Perception*
Rajesb P. N. Rao and Daniel L. Ruderman
Sloan Center for Theoretical Neurobiology
The Salk Institute
La Jolla, CA 92037
{rao,ruderrnan}@salk.edu
Abstract
One of the most important problems in visual perception is that of visual invariance: how are objects perceived ... | 1584 |@word open:1 covariance:1 fonn:2 thereby:1 tr:1 initial:4 series:5 efficacy:1 transfonn:1 daniel:1 imaginary:2 current:1 comparing:1 written:3 reminiscent:1 realistic:1 eigentracking:1 plot:2 generative:9 tenn:1 plane:1 ith:1 dissertation:1 coarse:2 location:2 lx:1 mathematical:1 become:1 supply:1 differential:1 ... |
639 | 1,585 | Multiple Paired Forward-Inverse Models
for Human Motor Learning and Control
Masahiko Haruno*
mharuno@hip .atr.co.jp
Daniel M. Wolpert t
wolpert@hera.ucl.ac.uk
Mitsuo Kawato* o
kawato(Q) hip.atr.co.jp
* ATR Human Information Processing Research Laboratories
2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-02, Japan.
t... | 1585 |@word neurophysiology:1 johansson:1 bf:2 open:1 simulation:4 jacob:1 thereby:1 initial:1 contains:2 series:1 united:1 selecting:1 daniel:1 iple:1 interestingly:1 past:1 existing:1 current:5 contextual:8 nowlan:1 activation:1 must:3 periodically:1 visible:1 shape:4 motor:28 plot:1 update:1 fund:1 cue:3 selected:4 ... |
640 | 1,586 | Learning Macro-Actions in Reinforcement
Learning
Jette Randlttv
Niels Bohr Inst., Blegdamsvej 17,
University of Copenhagen,
DK-21 00 Copenhagen 0, Denmark
randlov@nbi.dk
Abstract
We present a method for automatically constructing macro-actions from
scratch from primitive actions during the reinforcement learning proc... | 1586 |@word trial:10 stronger:2 seems:2 open:2 km:1 simulation:1 korf:2 tried:4 pick:3 thereby:1 tr:1 carry:1 reduction:1 initial:1 uma:1 andreae:2 steiner:1 yet:1 must:4 thrust:3 update:1 half:1 greedy:1 selected:2 intelligence:5 mccallum:2 ith:2 meuleau:1 draft:1 coarse:1 five:1 consists:3 combine:1 ravindran:3 indee... |
641 | 1,587 | Phase Diagram and Storage Capacity of
Sequence Storing Neural Networks
A. During
Dept. of Physics
Oxford University
Oxford OX 1 3NP
United Kingdom
a.duringl @physics.oxford.ac .uk
A. C. C. Coolen
Dept. of Mathematics
King 's College
London WC2R 2LS
United Kingdom
tcoolen @mth.kc1.ac.uk
D. Sherrington
Dept. of Physic... | 1587 |@word version:1 simulation:8 solid:1 initial:2 bai:1 united:3 past:1 current:1 paramagnetic:2 osh:1 activation:4 dx:1 numerical:4 analytic:1 remove:1 designed:1 update:2 stationary:3 accordingly:1 plane:1 short:1 slh:1 provides:1 math:2 persistent:2 introduce:1 theoretically:1 overline:1 roughly:1 frequently:1 pr... |
642 | 1,588 | Approximate Learning of Dynamic Models
Xavier Boyen
Computer Science Dept. 1A
Stanford, CA 94305-9010
xb@cs.stanford.edu
Daphne Koller
Computer Science Dept. 1A
Stanford, CA 94305-9010
koller@cs.stanford.edu
Abstract
Inference is a key component in learning probabilistic models from partially observable data. When l... | 1588 |@word illustrating:1 seems:1 suitably:1 additively:1 propagate:3 tried:1 contraction:7 b39:1 thereby:2 ld:1 reduction:1 initial:1 fragment:2 ours:1 interestingly:1 past:1 current:4 si:2 realistic:1 update:8 v:1 prohibitive:1 parameterization:1 xk:3 es:6 short:3 indefinitely:1 batmobile:1 iterates:1 recompute:1 re... |
643 | 1,589 | Perceiving without Learning: from Spirals to
Inside/Outside Relations
Ke Chen" and DeLiang L. Wang
Department of Computer and Information Science and Center for Cognitive Science
The Ohio State University, Columbus, OH 43210-1277, USA
{kchen,dwang}@cis.ohio-state.edu
Abstract
As a benchmark task, the spiral problem i... | 1589 |@word version:1 open:1 simulation:13 propagate:2 mention:1 initial:3 contains:1 lang:2 yet:1 activation:5 si:4 readily:2 realize:1 shape:5 plane:2 xk:1 detecting:1 provides:3 along:2 become:1 qualitative:1 inside:18 introduce:1 theoretically:1 rapid:1 behavior:7 examine:1 brain:2 globally:1 considering:1 becomes:... |
644 | 159 | 748
Performance of a Stochastic Learning Microchip
Joshua Alspector, Bhusan Gupta, ? and Robert B. Allen
Bellcore, Morristown, NJ 07960
We have fabricated a test chip in 2 micron CMOS that can perform supervised
learning in a manner similar to the Boltzmann machine. Patterns can be
presented to it at 100,000 per seco... | 159 |@word trial:2 version:1 compression:1 proportion:1 loading:1 hu:1 simulation:12 pulse:1 contains:1 ours:1 existing:1 blank:1 activation:1 synthesizer:1 yet:2 written:1 v:1 intelligence:2 selected:1 leaf:2 device:1 record:1 ire:1 codebook:1 attack:1 oak:2 simpler:1 five:5 direct:1 differential:3 replication:1 micro... |
645 | 1,590 | Bayesian Modeling of Facial Similarity
Baback Moghaddam
Mitsubishi Electric Research Laboratory
201 Broadway
Cambridge , MA 02139 , USA
babackCOmerl.com
Tony Jebara and Alex Pentland
Massachusettes Institute of Technology
20 Ames St .
Cambridge, MA 02139 , USA
{jebara,sandy}COmedia.mit.edu
Abstract
In previous work [... | 1590 |@word norm:4 advantageous:1 mitsubishi:1 decomposition:1 extrapersonal:9 score:4 existing:1 current:1 com:2 surprising:1 yet:1 v:3 alone:1 intelligence:5 discovering:1 plane:2 eigenfeatures:1 completeness:1 location:2 ames:1 simpler:2 direct:1 ik:2 consists:1 manner:1 swets:1 roughly:1 ara:1 moreover:1 matched:1 ... |
646 | 1,591 | USING COLLECTIVE INTELLIGENCE
TO ROUTE INTERNET TRAFFIC
David H. Wolpert
NASA Ames Research Center
Moffett Field, CA 94035
dhw@ptolemy.arc.nasa.gov
Kagan Turner
NASA Ames Research Center
Moffett Field, CA 94035
kagan@ptolemy.arc.nasa.gov
Jeremy Frank
NASA Ames Research Center
Moffett Field, CA 94035
frank@ptolemy.arc... | 1591 |@word cu:1 simulation:1 paid:1 thereby:1 yet:1 router:31 must:2 visible:1 designed:1 plot:1 update:3 v:1 intelligence:12 selected:1 assurance:1 accordingly:3 iso:1 cursory:1 tumer:4 provides:2 node:3 ames:3 traverse:1 mathematical:3 along:3 admission:1 pairing:1 indeed:2 market:1 behavior:7 nor:1 planning:1 multi... |
647 | 1,592 | Unsupervised Classification with
Non-Gaussian Mixture Models using ICA
Te-Won Lee, Michael S. Lewicki and Terrence Sejnowski
Howard Hughes Medical Institute
Computational Neurobiology Laboratory
The Salk Institute
10010 N. Torrey Pines Road
La Jolla, California 92037, USA
{tewon,lewicki,terry}Osalk.edu
Abstract
We p... | 1592 |@word compression:2 duda:3 comparing:2 si:2 additive:2 wx:1 update:1 selected:1 xk:1 awex:1 provides:1 unmixed:1 lx:1 five:3 mathematical:1 prove:1 ica:39 equivariant:1 automatically:2 underlying:1 kg:1 finding:2 medical:2 sd:1 switching:1 encoding:8 ak:5 au:1 relaxing:2 bi:1 statistically:1 hughes:1 laheld:3 bel... |
648 | 1,593 | A Randomized Algorithm for Pairwise Clustering
Yoram Gdalyahu, Daphna Weinshall, Michael Werman
Institute of Computer Science, The Hebrew University, 91904 Jerusalem, Israel
{yoram,daphna,werman}@cs.huji.ac.il
Abstract
We present a stochastic clustering algorithm based on pairwise similarity of datapoints. Our method... | 1593 |@word weins:2 version:2 eliminating:1 polynomial:2 duda:1 contraction:12 tr:1 configuration:1 selecting:1 karger:1 existing:2 current:1 must:3 subsequent:8 partition:25 hofmann:1 shape:1 remove:2 greedy:1 selected:2 half:1 item:6 plane:1 smith:1 provides:2 parameterizations:1 node:7 constructed:2 direct:3 c2:1 pa... |
649 | 1,594 | Learning Nonlinear Dynamical Systems
using an EM Algorithm
Zoubin Ghahramani and Sam T. Roweis
Gatsby Computational Neuroscience Unit
University College London
London WC1N 3AR, U.K.
http://www.gatsby.ucl.ac.uk/
Abstract
The Expectation-Maximization (EM) algorithm is an iterative procedure for maximum likelihood param... | 1594 |@word middle:2 clts:1 linearized:4 covariance:13 tr:1 solid:3 initial:1 substitution:1 series:4 tuned:1 current:1 si:4 written:1 readily:1 additive:1 analytic:2 plot:1 fund:1 v:1 stationary:2 half:1 provides:2 complication:4 along:1 become:2 pairing:1 prove:1 consists:1 fitting:3 notably:1 examine:2 xz:1 automati... |
650 | 1,596 | Learning to Find Pictures of People
Sergey Ioffe
Computer Science Division
U.C. Berkeley
Berkeley CA 94720
iojJe (Cj)cs. be1?keley. edu
David Forsyth
Computer Sciencp Division
U.C. Berkeley
Berkeley CA 94720
daf@cs.beTkeley. edv
Abstract
Finding articulated objects, like people, in pictures present.s a particularly ... | 1596 |@word version:2 nd:1 open:1 shading:2 ld:1 configuration:19 selecting:2 tuned:1 document:1 current:1 nt:1 si:10 must:3 readily:1 realistic:1 visible:1 wx:2 shape:1 greedy:2 half:4 selected:1 plane:3 rch:1 lua:1 boosting:2 node:13 preference:1 five:1 along:2 constructed:2 direct:1 ik:1 j3j:1 consists:1 inside:1 au... |
651 | 1,597 | Restructuring Sparse High Dimensional Data for
Effective Retrieval
Charles Lee Isbell, Jr.
AT&T Labs
180 Park Avenue Room A255
Florham Park, NJ 07932-0971
Paul Viola
Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139
Abstract
The task in text retrieval is to find the subset ... | 1597 |@word version:1 briefly:1 norm:1 seems:1 decomposition:2 tr:1 reduction:4 contains:3 document:83 africa:6 brien:1 assigning:1 must:2 written:1 john:1 subsequent:1 remove:2 designed:1 aside:1 intelligence:1 discovering:1 leaf:1 short:1 characterization:1 mathematical:1 along:4 constructed:1 fitting:1 ica:8 ol:1 co... |
652 | 1,598 | Global Optimisation of Neural Network
Models Via Sequential Sampling
J oao FG de Freitas
Cambridge University
Engineering Department
Cambridge CB2 1PZ England
jfgf@eng.cam.ac.uk
[Corresponding author]
Arnaud Doucet
Cambridge University
Engineering Department
Cambridge CB2 1PZ England
ad2@eng.cam.ac.uk
Mahesan Niranj... | 1598 |@word simulation:2 eng:7 covariance:4 tr:3 recursively:1 initial:3 past:2 freitas:12 africa:1 nell:1 yet:2 distant:1 plot:1 update:1 resampling:4 stationary:2 credence:1 xk:3 smith:2 consists:1 vwv:1 multimodality:1 multi:3 wki:1 joao:1 interpreted:1 pseudo:1 uk:8 engineering:4 local:1 oxford:1 lrm:1 might:1 chos... |
653 | 1,599 | Optimizing admission control while ensuring
quality of service in multimedia networks via
reinforcement learning*
Timothy X Brown t , Hui Tong t , Satinder Singh+
t Electrical and Computer Engineering
+Computer Science
University of Colorado
Boulder, CO 80309-0425
{timxb, tongh, baveja}@colorado.edu
Abstract
This pap... | 1599 |@word termination:2 simulation:2 tried:1 profit:2 initial:1 configuration:5 existing:3 current:2 nt:1 must:1 readily:1 enables:1 drop:1 update:3 smdp:3 v:4 greedy:12 leamed:2 haykin:1 accepting:2 admission:14 combine:2 paragraph:1 expected:1 rapid:1 examine:1 multi:4 bellman:2 discounted:2 becomes:1 underlying:1 ... |
654 | 16 | 219
Network Generality, Training Required,
and PrecisIon Required
John S. Denker and Ben S. Wittner
AT&T Bell Laboratories
Holmdel, New Jersey 07733
1
Keep your hand on your wallet.
- Leon Cooper, 1987
Abstract
We show how to estimate (1) the number of functions that can be implemented by a
particular network archi... | 16 |@word implemented:3 private:1 version:2 implies:1 memorize:2 concentrate:1 objective:1 question:1 realized:1 laboratory:1 nonzero:1 occurs:1 white:1 usual:1 exhibit:1 bin:10 versatile:1 require:1 initial:2 configuration:1 generalization:4 score:2 daniel:1 complete:3 secondly:1 designate:1 water:1 assuming:1 code:1 ... |
655 | 160 | 626
ANALYZING THE ENERGY LANDSCAPES
OF DISTRIBUTED
WINNER-TAKE-ALL NETWORKS
David S. Touretzky
School of Computer Science
Carnegie Mellon University
Pittsburgh, P A 15213
ABSTRACT
DCPS (the Distributed Connectionist Production System) is a neural
network with complex dynamical properties. Visualizing the energy
lands... | 160 |@word version:3 reduction:1 activation:1 must:2 visible:2 shape:6 drop:1 update:1 v:1 half:3 fewer:2 location:1 five:1 along:3 become:1 ik:1 uphill:2 behavior:2 examine:1 growing:1 little:2 actual:1 null:2 ttl:1 spends:1 substantially:1 safely:1 unusually:1 unwanted:2 exactly:1 wrong:1 unit:47 grant:1 appear:1 bef... |
656 | 1,600 | Non-linear PI Control Inspired by
Biological Control Systems
Lyndon J. Brown
Gregory E. Gonye
James S. Schwaber *
Experimental Station, E.!. DuPont deNemours & Co. Wilmington, DE 19880
Abstract
A non-linear modification to PI control is motivated by a model
of a signal transduction pathway active in mammalian blood p... | 1600 |@word trial:1 achievable:2 open:2 iki:2 pulse:1 sensed:1 simulation:1 invoking:1 pressure:8 mammal:1 ld:1 reduction:1 series:1 ala:1 ati:2 reaction:5 com:3 nt:6 comparing:1 activation:7 must:1 numerical:1 plasticity:1 dupont:4 remove:1 designed:3 device:1 nervous:3 tone:2 sudden:1 provides:1 kiel:1 pathway:13 ray... |
657 | 1,601 | The Role of Lateral Cortical Competition
in Ocular Dominance Development
Christian Piepenbrock and Klaus Obermayer
Dept. of Computer Science, Technical University of Berlin
FR 2-1; Franklinstr. 28-29; 10587 Berlin, Germany'
{piep,oby}@cs.tu-berlin.de; http://www.ni.cs.tu-berlin.de
Abstract
Lateral competition within ... | 1601 |@word sharpens:1 stronger:4 simulation:11 covariance:1 reduction:1 moment:3 series:2 loc:1 contains:2 past:1 diagonalized:1 od:6 activation:1 yet:1 realistic:2 additive:2 shape:5 christian:1 piepenbrock:5 analytic:4 plot:2 half:1 lr:1 ron:1 location:4 lx:1 become:3 beta:1 qualitative:1 paragraph:1 inside:1 introd... |
658 | 1,602 | Boxlets: a Fast Convolution Algorithm for
Signal Processing and Neural Networks
Patrice Y. Simard?, Leon Botton, Patrick Haffner and Yann LeCnn
AT&T Labs-Research
100 Schultz Drive, Red Bank, NJ 07701-7033
patrice@microsoft.com
{leon b ,haffner ,yann }@research.att.com
Abstract
Signal processing and pattern recogniti... | 1602 |@word polynomial:29 advantageous:1 recursively:2 ld:1 uncovered:1 att:1 exclusively:1 series:1 recovered:1 com:2 comparing:1 yet:1 written:4 must:4 botton:1 partition:4 shape:1 greedy:7 leaf:1 beginning:1 quantized:3 location:2 simpler:1 along:1 dn:2 become:2 consists:2 combine:1 inside:1 introduce:1 indeed:2 bra... |
659 | 1,603 | The effect of eligibility traces on finding optimal memoryless
policies in partially observable Markov decision processes
John Loch
Department of Computer Science
University of Colorado
Boulder, CO 80309-0430
loch@cs.colorado.edu
Abstract
Agents acting in the real world are confronted with the problem of
making good d... | 1603 |@word eor:1 trial:3 twelfth:1 simulation:1 initial:1 selecting:1 past:1 current:4 surprising:1 must:1 john:1 update:2 intelligence:2 greedy:2 selected:2 short:2 provides:1 lor:1 loll:2 manner:1 expected:2 behavior:1 planning:1 discounted:1 decreasing:1 provided:1 kaufman:1 developed:1 finding:7 every:1 control:1 ... |
660 | 1,604 | A Theory of Mean Field Approximation
T.Tanaka
Department of Electronics and Information Engineering
Tokyo Metropolitan University
I-I, Minami-Osawa, Hachioji , Tokyo 192-0397 Japan
Abstract
I present a theory of mean field approximation based on information geometry. This theory includes in a consistent way the naive... | 1604 |@word fjij:3 nd:1 simulation:1 pick:1 kappen:3 reduction:1 electronics:1 mag:1 reaction:1 si:4 happen:1 hofmann:1 aside:1 stationary:4 leaf:17 advancement:1 accordingly:1 provides:2 math:1 differential:1 ik:1 shorthand:1 introduce:2 pairwise:1 expected:2 behavior:1 frequently:1 ol:1 considering:1 sager:1 becomes:... |
661 | 1,605 | Tight Bounds for the VC-Dimension of
Piecewise Polynomial Networks
Akito Sakurai
School of Knowledge Science
Japan Advanced Institute of Science and Technology
Nomi-gun, Ishikawa 923-1211, Japan.
CREST, Japan Science and Technology Corporation.
ASakurai@jaist.ac.jp
Abstract
O(ws(s log d+log(dqh/ s))) and O(ws((h/ s) ... | 1605 |@word version:1 polynomial:32 open:1 tr:1 series:1 chervonenkis:1 ours:1 activation:14 realize:1 compo:1 draft:1 clarified:1 sigmoidal:3 unbounded:2 shatter:2 c2:2 symposium:2 consists:2 uphill:4 roughly:2 ol:4 ifm:1 bounded:4 notation:1 underlying:1 moreover:1 circuit:1 cm:2 emerging:1 developed:1 corporation:1 ... |
662 | 1,606 | Tight Bounds for the VC-Dimension of
Piecewise Polynomial Networks
Akito Sakurai
School of Knowledge Science
Japan Advanced Institute of Science and Technology
Nomi-gun, Ishikawa 923-1211, Japan.
CREST, Japan Science and Technology Corporation.
ASakurai@jaist.ac.jp
Abstract
O(ws(s log d+log(dqh/ s))) and O(ws((h/ s) ... | 1606 |@word version:2 polynomial:32 tedious:1 open:1 simulation:12 ttn:1 tr:7 solid:1 zbl:1 initial:3 series:1 chervonenkis:1 ours:1 past:1 current:1 activation:14 dx:1 realize:1 numerical:8 reproducible:1 update:1 inspection:1 reappears:1 short:1 compo:1 draft:1 provides:2 math:1 clarified:1 ron:2 sigmoidal:3 unbounde... |
663 | 1,607 | Improved Switching
among Temporally Abstract Actions
Richard S. Sutton Satinder Singh
AT&T Labs
Florham Park, NJ 07932
{sutton,baveja}@research.att.com
Doina Precup Balaraman Ravindran
University of Massachusetts
Amherst, MA 01003-4610
{dprecup,ravi}@cs.umass.edu
Abstract
In robotics and other control applications it... | 1607 |@word proportion:1 seems:1 nd:1 twelfth:1 termination:8 simulation:1 thereby:1 tr:1 att:1 uma:1 selecting:2 existing:2 reaction:1 current:4 com:1 discretization:1 si:2 must:1 written:1 interrupted:10 numerical:1 update:1 smdp:15 stationary:1 intelligence:1 selected:4 accordingly:1 plane:6 hallway:1 manfred:1 comp... |
664 | 1,608 | Exploring Unknown Environments with
Real-Time Search or Reinforcement Learning
Sven Koenig
College of Computing, Georgia Institute of Technology
skoenig@cc.gatech.edu
Abstract
Learning Real-Time A* (LRTA*) is a popular control method that interleaves planning and plan execution and has been shown to solve search prob... | 1608 |@word version:1 polynomial:1 interleave:3 advantageous:1 smirnov:5 open:1 simulation:1 korf:8 tried:1 solid:1 contains:3 current:11 si:3 interrupted:3 realistic:2 subsequent:1 hofmann:1 update:7 intelligence:8 selected:1 iterates:1 provides:1 location:5 traverse:6 along:1 direct:1 ik:1 chakrabarti:1 prove:1 consi... |
665 | 1,609 | Support Vector Machines Applied to Face
Recognition
P. Jonathon Phillips
National Institute of Standards and Technology
Bldg 225/ Rm A216
Gaithersburg. MD 20899
Tel 301.975.5348; Fax 301.975.5287
jonathon@nist.gov
Abstract
Face recognition is a K class problem. where K is the number of known
individuals; and support... | 1609 |@word harder:1 contains:1 score:5 si:1 must:1 remove:2 extrapolating:1 designed:1 half:2 selected:4 devising:1 inspection:1 eigenfeatures:6 c2:3 reinterpreting:1 introduce:1 gov:1 pf:12 increasing:1 notation:1 minimizes:1 eigenvector:1 developed:2 rm:1 classifier:8 facto:1 unit:1 positive:2 meet:1 probablistic:1 ... |
666 | 161 | 264
NEURAL APPROACH FOR TV IMAGE COMPRESSION
USING A HOPFIELD TYPE NETWORK
Martine NAILLON
Jean-Bernard THEETEN
Laboratoire d'Electronique et de Physique Appliquee *
3 Avenue DESCARTES, BP 15
94451 LIMEIL BREVANNES Cedex FRANCE.
ABSTRACT
A self-organizing Hopfield network has been
developed in the context of Vector ... | 161 |@word compression:7 nd:1 open:1 grey:1 initial:1 configuration:1 ati:1 nt:1 si:1 cottrell:1 mesh:1 visible:1 plot:1 designed:1 selected:2 nq:1 gure:1 provides:1 quantizer:1 math:2 codebook:15 along:1 ra:1 inspired:1 little:2 notation:2 pel:4 what:1 developed:3 gutfreund:2 impl:3 classifier:1 whatever:1 tends:1 lim... |
667 | 1,610 | Linear Hinge Loss and Average Margin
Claudio Gentile
DSI, Universita' di Milano,
Via Comelico 39,
20135 Milano. Italy
gentile@dsi.unimi.it
Manfred K. Warmuth?
Computer Science Department,
University of California,
95064 Santa Cruz, USA
manfred@cse.ucsc.edu
Abstract
We describe a unifying method for proving relative ... | 1610 |@word trial:8 version:11 seems:1 norm:1 current:3 wd:7 activation:5 artijiciallntelligence:1 must:1 cruz:2 fs98:2 subsequent:1 additive:6 update:23 v:2 warmuth:9 beginning:1 short:1 manfred:2 provides:1 cse:1 ucsc:1 prove:3 consists:1 introduce:2 os:1 increasing:2 becomes:4 abound:1 notation:1 what:1 finding:1 ev... |
668 | 1,611 | Synergy and redundancy among brain
cells of behaving monkeys
Itay Gat?
Institute of Computer Science and
Center for Neural Computation
The Hebrew University, Jerusalem 91904, Israel
Naftali Tishby t
NEC Research Institute
4 Independence Way
Princeton NJ 08540
Abstract
Determining the relationship between the activi... | 1611 |@word trial:5 tried:1 dramatic:1 born:1 xiy:1 current:1 written:1 additive:1 plasticity:1 motor:1 drop:1 reproducible:1 v:3 iog2:1 caveat:1 detecting:1 location:2 psth:2 behavioral:16 falsely:2 pairwise:1 inter:1 indeed:1 expected:4 behavior:11 multi:1 brain:9 globally:1 estimating:1 underlying:3 israel:2 what:1 ... |
669 | 1,612 | Sparse Code Shrinkage: Denoising by
Nonlinear Maximum Likelihood Estimation
Aapo Hyvarinen, Patrik Hoyer and Erkki Oja
Helsinki University of Technology
Laboratory of Computer and Information Science
P.O. Box 5400, FIN-02015 HUT, Finland
aapo.hyvarinen@hut.fi,patrik.hoyer@hut.fi,erkki.oja@hut.fi
http://www.cis.hut.fi/p... | 1612 |@word version:2 inversion:1 seems:1 solid:2 moment:1 reduction:2 series:2 denoting:2 imaginary:1 si:11 yet:1 additive:1 wx:3 predetermined:1 shape:1 remove:1 plot:3 alone:1 parameterization:1 parameterizations:3 introduce:1 ica:7 window:3 project:1 begin:1 estimating:6 moreover:1 linearity:1 underlying:1 argmin:1... |
670 | 1,613 | An Integrated Vision Sensor for the
Computation of Optical Flow Singular Points
Charles M. Higgins and Christof Koch
Division of Biology, 139-74
California Institute of Technology
Pasadena, CA 91125
[chuck,koch]@klab.caltech.edu
Abstract
A robust, integrative algorithm is presented for computing the position of
the f... | 1613 |@word briefly:1 integrative:1 contains:1 gexp:2 current:11 dx:2 periodically:1 shape:1 drop:1 intelligence:1 leaf:2 yr:6 provides:1 location:8 along:1 expected:1 sublinearly:1 prolonged:1 increasing:1 estimating:1 bounded:1 deutsche:1 null:1 compressive:1 temporal:2 scaled:1 control:3 normally:1 christof:1 positi... |
671 | 1,614 | Reinforcement Learning based on
On-line EM Algorithm
Masa-aki Sato t
Information Processing Research Laboratories
masaaki@hip.atr.co.jp
Seika, Kyoto 619-0288, Japan
t ATR Human
Shin Ishii +t
tNara Institute of Science and Technology
Ikoma, Nara 630-0101, Japan
ishii@is.aist-nara.ac.jp
Abstract
In this article, we pr... | 1614 |@word trial:2 version:1 covariance:1 tr:3 solid:1 initial:2 cp2:4 series:2 necessity:1 current:9 yet:7 partition:2 selected:4 provides:1 lx:1 height:1 along:1 introduce:1 manner:1 forgetting:1 expected:1 seika:1 multi:1 torque:4 discounted:1 td:1 becomes:1 xx:2 moreover:1 notation:1 maximizes:1 lowest:1 interpret... |
672 | 1,615 | Regularizing AdaBoost
Gunnar Riitsch, Takashi Onoda; Klaus R. M iiller
GMD FIRST, Rudower Chaussee 5, 12489 Berlin, Germany
{raetsch, onoda, klaus }@first.gmd.de
Abstract
Boosting methods maximize a hard classification margin and are
known as powerful techniques that do not exhibit overfitting for low
noise cases. Al... | 1615 |@word repository:1 middle:3 version:3 norm:2 thereby:1 yih:1 solid:1 tr:1 reduction:1 punishes:1 seriously:1 interestingly:1 riitsch:2 written:1 must:1 tot:2 numerical:2 partition:3 analytic:1 update:2 flare:1 coarse:1 boosting:23 five:2 c2:1 direct:2 ect:1 introduce:5 indeed:1 multi:1 increasing:2 becomes:3 proj... |
673 | 1,616 | m
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WYX<Z X\[<]^E P
d0u vxw<y0z a b a z dYzYn<a c f|{^n<}0} ~3cYB?
???.?t?... | 1616 |@word h:2 c0:1 hu:1 km:1 r:1 p0:1 d3d:3 tr:1 bc:2 wd:1 bd:1 pqd:3 gv:3 v_:2 v:1 rts:3 hsv:1 c6:1 vxw:2 c2:2 ipx:1 ik:1 ksvd:1 f3v:1 li3:1 thy:2 ra:1 wxd:1 ol:1 td:1 jm:1 dha:1 sfa:1 acbed:1 otp:1 sox:1 dti:2 y3:1 xd:1 qm:2 uk:1 pfe:1 id:1 yd:2 kml:3 ap:2 plu:1 dtp:4 g7:2 ppq:1 tsu:1 vu:3 w4:2 oqp:1 gkb:1 vr2:3 bh... |
674 | 1,617 | Tractable Variational Structures for
Approximating Graphical Models
David Barber
Wim Wiegerinck
{davidb,wimw}@mbfys,kun,nl
RWCP* Theoretical Foundation SNNt University of Nijmegen
6525 EZ Nijmegen, The Netherlands.
Abstract
Graphical models provide a broad probabilistic framework with applications in speech recogniti... | 1617 |@word briefly:1 unaltered:1 polynomial:1 open:1 simulation:1 q1:6 solid:2 carry:1 reduction:1 expositional:1 current:1 z2:2 si:4 perturbative:1 readily:1 i1l:2 visible:4 partition:6 remove:1 plot:1 intelligence:4 slh:1 node:21 jbe:1 qualitative:1 consists:2 calculable:1 introduce:1 mbfys:1 considering:2 increasin... |
675 | 1,618 | A VI model of pop out and asymmetry
visual search
?
In
Zhaoping Li
University College London, z.li@ucl.ac.uk
Abstract
Visual search is the task of finding a target in an image against a
background of distractors. Unique features of targets enable them
to pop out against the background, while targets defined by lack... | 1618 |@word stronger:2 open:1 closure:2 simulation:1 brightness:1 solid:4 initial:1 disparity:2 score:1 tuned:1 current:2 contextual:5 surprising:1 cad:1 si:1 yet:1 readily:1 tilted:2 visible:3 enables:2 plot:1 v:7 alone:1 short:3 compo:1 filtered:2 location:6 preference:1 gx:2 sits:1 five:1 along:1 become:1 qualitativ... |
676 | 1,619 | Source Separation as a
By-Product of Regularization
J urgen Schmidhuber
Sepp Hochreiter
Fakultat fur lnformatik
Technische Universitat Munchen
80290 Munchen, Germany
IDSIA
Corso Elvezia 36
6900 Lugano, Switzerland
hochreit~informatik.tu-muenchen.de
juergen~idsia.ch
Abstract
This paper reveals a previously ignored... | 1619 |@word autoassociator:2 version:2 compression:1 bf:2 hu:1 simplifying:1 pick:1 tr:1 shot:1 reduction:1 loc:5 punishes:1 tuned:1 outperforms:1 activation:10 si:2 subsequent:2 realistic:1 hochreit:1 wlm:1 generative:1 fewer:1 discovering:3 num:1 contribute:2 miinchen:1 consists:1 indeed:1 ica:14 alspector:1 embody:1... |
677 | 162 | 49
Mapping Classifier Systems
Into Neural Networks
Lawrence Davis
BBN Laboratories
BBN Systems and Technologies Corporation
10 Moulton Street
Cambridge, MA 02238
January 16, 1989
Abstract
Classifier systems are machine learning systems incotporating a genetic algorithm as the learning mechanism. Although they respond ... | 162 |@word proportion:3 open:1 holyoak:1 paid:1 accommodate:1 carry:3 contains:1 series:1 genetic:14 current:2 activation:8 yet:1 import:2 must:6 written:2 john:1 periodically:1 remove:1 half:1 intelligence:1 inspection:1 record:1 pointer:1 provides:1 node:62 ron:1 complication:2 supply:1 prove:1 introduce:1 behavior:7... |
678 | 1,620 | Temporally Asymmetric Hebbian Learning,
Spike Timing and Neuronal Response Variability
L.F. Abbott and Sen Song
Volen Center and Department of Biology
Brandeis University
Waltham MA 02454
Abstract
Recent experimental data indicate that the strengthening or weakening of
synaptic connections between neurons depends on ... | 1620 |@word trial:1 stronger:1 hippocampus:3 hyperpolarized:1 pulse:1 covariance:1 dramatic:1 solid:2 efficacy:2 past:1 existing:1 current:3 must:3 physiol:1 realistic:1 plasticity:3 interspike:1 motor:2 drop:1 plot:1 aps:1 compo:3 provides:1 zhang:4 rc:1 along:1 correlograms:1 direct:1 become:1 pairing:2 gustafsson:2 ... |
679 | 1,621 | Optimizing Correlation Algorithms for
Hardware-based Transient Classification
R. Timothy Edwards l , Gert Cauwenberghsl, and Fernando J. Pineda2
1 Electrical
2
and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218
Applied Physics Laboratory, Johns Hopkins University, Laurel, MD 20723
e-mail: {tim, ge... | 1621 |@word kong:1 achievable:1 norm:1 disk:1 simulation:9 tried:1 accounting:1 minus:1 reduction:1 contains:1 current:2 surprising:1 written:1 must:8 john:2 drop:2 aside:1 half:4 realizing:1 core:1 short:3 provides:1 quantized:1 contribute:1 location:1 simpler:2 direct:1 differential:1 symposium:2 pairwise:5 expected:... |
680 | 1,622 | General Bounds on Bayes Errors for
Regression with Gaussian Processes
Manfred Opper
Neural Computing Research Group
Dept. of Electronic Engineering
and Computer Science,
Aston University,
Birmingham, B4 7ET
United Kingdom
oppermGaston.ac.uk
Francesco Vivarelli
Centro Ricerche Ambientali
Montecatini,
via Ciro Menotti,... | 1622 |@word simulation:4 covariance:7 tr:6 solid:2 phy:1 united:1 chervonenkis:1 pub:1 rightmost:1 dx:2 written:2 numerical:2 kj0:1 manfred:1 completeness:2 toronto:1 simpler:1 dn:1 along:1 direct:2 ra:1 expected:1 mechanic:1 becomes:2 begin:1 pel:1 akl:1 concave:1 xd:2 abk:1 classifier:1 uk:4 normally:1 unit:1 yn:1 en... |
681 | 1,623 | Basis Selection For Wavelet Regression
Kevin R. Wheeler
Caelum Research Corporation
NASA Ames Research Center
Mail Stop 269-1
Moffett Field, CA 94035
kwheeler@mail .arc.nasa.gov
Atam P. Dhawan
College of Engineering
University of Toledo
2801 W. Bancroft Street
Toledo, OH 43606
adhawan@eng.utoledo.edu
Abstract
A wave... | 1623 |@word trial:1 version:2 willing:1 carolina:2 eng:1 decomposition:13 pick:1 moment:3 initial:1 series:3 selecting:1 recovered:7 written:3 finest:1 shape:3 analytic:1 selected:5 vanishing:3 math:1 ames:1 mathematical:1 consists:1 manner:1 expected:1 gov:1 underlying:2 lowest:1 what:1 substantially:1 corporation:1 b... |
682 | 1,624 | Neural Networks for Density Estimation
Amir Atiya
Malik Magdon-Ismail*
magdon~cco.caltech.edu
amir~deep.caltech.edu
Caltech Learning Systems Group
Department of Electrical Engineering
California Institute of Technology
136-93 Pasadena, CA, 91125
Caltech Learning Systems Group
Department of Electrical Engineering
C... | 1624 |@word version:1 norm:1 cco:1 simulation:3 pg:1 pick:1 moment:1 series:1 comparing:1 unction:1 activation:2 dx:1 must:1 john:1 realistic:2 amir:2 ith:4 provides:1 node:1 five:1 mathematical:1 introduce:3 manner:1 theoretically:2 expected:1 behavior:1 themselves:1 decreasing:1 window:4 becomes:1 provided:1 estimati... |
683 | 1,625 | Unsupervised and supervised clustering:
the mutual information between
parameters and observations
Didier Herschkowitz
Jean-Pierre Nadal
Laboratoire de Physique Statistique de l'E.N.S .*
Ecole Normale Superieure
24 , rue Lhomond - 75231 Paris cedex 05, France
herschko@lps.ens.fr
nadal@lps.ens.fr
http://www.lps .ens.... | 1625 |@word determinant:1 implies:1 equality:1 direction:7 arnaud:1 already:1 laboratory:1 calculus:1 quantity:3 bib:1 centered:1 covariance:1 independant:1 vc:1 conditionally:1 self:1 echnique:1 solid:2 dio:5 noted:1 behaviour:13 thank:1 ecole:1 probable:1 toward:1 extension:1 assuming:1 cramer:4 relationship:1 exp:2 ... |
684 | 1,626 | A Phase Space Approach to Minimax
Entropy Learning and the Minutemax
Approximations
A.L.Yuille
James M. Coughlan
Smith-Kettlewell Inst.
San Francisco, CA 94115
Smith-Kettlewell Inst.
San Francisco, CA 94115
Abstract
There has been much recent work on measuring image statistics
and on learning probability distributi... | 1626 |@word private:1 version:1 proportionality:1 covariance:4 moment:1 ours:1 reaction:1 xand:1 discretization:1 stemming:1 additive:2 concatenate:1 partition:1 informative:2 shape:2 analytic:2 enables:1 alone:3 half:1 coughlan:12 smith:3 core:1 coarse:4 quantized:3 along:1 constructed:1 kettlewell:3 prove:2 introduce... |
685 | 1,627 | A Polygonal Line Algorithm for Constructing
Principal Curves
Balazs Kegl, Adam Krzyzak
Dept. of Computer Science
Concordia University
1450 de Maisonneuve Blvd. W.
Montreal, Canada H3G IM8
kegl@cs.concordia.ca
krzyzak@cs.concordia.ca
Tamas Linder
Dept. of Mathematics
and Statistics
Queen's University
Kingston, Ontario... | 1627 |@word h:27 middle:1 isil:2 open:1 simulation:3 ld:1 carry:1 moment:2 nonexistent:1 contains:1 hereafter:2 o2:1 elliptical:1 si:11 yet:1 additive:3 partition:2 subsequent:1 zeger:6 shape:4 leipzig:2 plot:2 half:5 accordingly:1 steepest:2 mulier:2 revisited:1 mathematical:1 direct:1 consists:1 symp:1 manner:1 excel... |
686 | 1,628 | Utilizing Time: Asynchronous Binding
Bradley C. Love
Department of Psychology
Northwestern University
Evanston, IL 60208
Abstract
Historically, connectionist systems have not excelled at representing and manipulating complex structures. How can a system composed of simple neuron-like computing elements encode complex... | 1628 |@word neurophysiology:2 trial:2 seems:1 simulation:5 guarding:1 interestingly:2 reaction:1 bradley:1 activation:2 yet:1 must:6 john:4 periodically:2 blur:2 shape:1 treating:1 intelligence:1 discovering:1 device:1 short:1 mental:3 provides:1 revisited:1 location:2 mathematical:1 become:3 combine:1 behavioral:5 man... |
687 | 1,629 | Learning to estimate scenes from images
William T. Freeman and Egon C. Pasztor
MERL , Mitsubishi Electric Research Laboratory
201 Broadway; Cambridge, MA 02139
freeman@merl.com, pasztor@merl.com
Abstract
We seek the scene interpretation that best explains image data.
For example, we may want to infer the projected ve... | 1629 |@word compression:3 heuristically:1 seek:1 mitsubishi:1 propagate:4 reduction:1 initial:4 contains:1 series:1 mmse:1 reaction:1 com:2 nowlan:1 yet:1 visible:1 realistic:1 girosi:1 shape:1 occlude:1 cue:1 intelligence:1 isard:1 xk:3 sys:1 ith:1 provides:1 quantizer:1 node:22 quantized:2 successive:1 simpler:1 alon... |
688 | 163 | 502
LINKS BETWEEN MARKOV MODELS AND
MULTILAYER PERCEPTRONS
H. Bourlard t,t & C.J. Wellekens t
(t)Philips Research Laboratory
Brussels, B-1170 Belgium.
mInt. Compo Science Institute
Berkeley, CA 94704 USA.
ABSTRACT
Hidden Markov models are widely used for automatic speech recognition. They inherently incorporate the ... | 163 |@word version:1 norm:1 contains:1 karger:1 denoting:1 current:10 contextual:15 comparing:1 lang:1 must:4 written:3 predetermined:1 discrimination:3 selected:1 short:1 compo:1 provides:3 quantized:3 successive:1 along:2 consists:1 inside:1 indeed:7 elman:2 decreasing:1 window:6 cardinality:1 becomes:1 provided:1 no... |
689 | 1,630 | Mechanisms of generalization
perceptual learning
Zili Lin
Rutgers University, Newark
?
In
Daphna Weinshall
Hebrew University, Israel
Abstract
The learning of many visual perceptual tasks has been shown to be
specific to practiced stimuli, while new stimuli require re-Iearning
from scratch. Here we demonstrate gene... | 1630 |@word trial:13 middle:2 simulation:2 brightness:1 dramatic:1 ld:2 initial:1 liu:5 practiced:1 existing:1 current:2 yet:1 readily:1 informative:13 plasticity:1 discrimination:24 v:3 half:3 beginning:1 sys:1 oblique:1 location:1 direct:4 qualitative:1 edelman:1 shapley:1 acquired:1 inter:1 expected:2 behavior:1 bra... |
690 | 1,631 | Learning a Hierarchical Belief Network of
Independent Factor Analyzers
H. Attias*
hagai@gatsby.ucl.ac.uk
Sloan Center for Theoretical Neurobiology, Box 0444
University of California at San Francisco
San Francisco, CA 94143-0444
Abstract
Many belief networks have been proposed that are composed of
binary units. Howeve... | 1631 |@word proportion:2 r:1 covariance:4 configuration:2 current:1 recovered:1 si:19 yet:1 intriguing:1 must:1 readily:1 drop:1 generative:7 intelligence:1 yr:15 accordingly:1 affair:1 ith:2 sigmoidal:1 simpler:1 along:1 become:1 consists:2 compose:1 introduce:2 manner:1 ica:10 examine:1 brain:1 inappropriate:1 become... |
691 | 1,632 | Convergence of The Wake-Sleep Algorithm
Shiro Ikeda
PRESTO,JST
Wako, Saitama, 351-0198, Japan
shiro@brain.riken.go.jp
Shun-ichi Amari
RIKEN Brain Science Institute
Wako, Saitama, 351-0198,Japan
amari@brain.riken.go.jp
Hiroyuki Nakahara
RIKEN Brain Science Institute
hiro@brain.riken.go.jp
Abstract
The W-S (Wake-Slee... | 1632 |@word version:4 come:1 society:1 seems:1 loading:1 stochastic:1 neal:4 rt:20 covariance:1 crt:2 ll:1 diagonal:1 jst:1 gradient:5 tr:2 shun:3 thank:1 berlin:1 won:1 generalized:1 series:2 manifold:4 tt:5 reason:3 invisible:1 wako:2 crg:1 extension:1 hold:2 geometrical:5 od:1 exp:2 minimizing:3 ikeda:4 john:1 visib... |
692 | 1,633 | Signal Detection in Noisy Weakly-Active
Dendrites
Amit Manwani and Christof Koch
{quixote,koch}@klab.caltech.edu
Computation and Neural Systems Program
California Institute of Technology
Pasadena, CA 91125
Abstract
Here we derive measures quantifying the information loss of a synaptic
signal due to the presence of ne... | 1633 |@word cu:1 seems:1 open:11 propagate:1 invoking:1 fonn:2 papoulis:2 initial:1 series:1 efficacy:2 contains:1 mainen:4 longitudinal:1 current:15 si:1 activation:1 dx:1 moo:5 realistic:1 j1:1 plot:1 drop:1 stationary:1 ith:1 short:1 filtered:1 equi:1 location:6 five:1 along:6 differential:3 ik:2 consists:1 inter:1 ... |
693 | 1,634 | Maximum-Likelihood Continuity Mapping
(MALCOM): An Alternative to HMMs
David A. Nix
dnix@lanl.gov
Computer Research & Applications
CIC-3, MS B265
Los Alamos National Laboratory
Los Alamos, NM 87545
John E. Hogden
hogden@lanl.gov
Computer Research & Applications
CIC-3, MS B265
Los Alamos National Laboratory
Los Alamos... | 1634 |@word middle:1 covariance:4 simplifying:1 tr:1 carry:1 initial:3 configuration:2 series:3 xiy:4 contains:1 liquid:1 current:2 must:5 john:1 numerical:1 realistic:1 partition:4 remove:1 designed:1 v:1 fewer:1 guess:2 dissertation:1 filtered:2 codebook:1 location:1 mathematical:1 along:3 windowed:1 viable:1 consist... |
694 | 1,635 | Spike-Based Compared to Rate-Based
Hebbian Learning
Richard Kempter*
Institut fur Theoretische Physik
Technische Universitat Munchen
D-85747 Garching, Germany
Wulfram Gerstner
Swiss Federal Institute of Technology
Center of Neuromimetic Systems, EPFL-DI
CH-1015 Lausanne, Switzerland
J. Leo van Hemmen
Institut fur Th... | 1635 |@word proportionality:1 physik:3 simulation:1 pulse:1 pick:1 carry:1 substitution:1 series:1 efficacy:7 contains:2 existing:2 nt:2 dx:2 must:4 numerical:1 interspike:2 tone:1 short:1 zhang:3 mathematical:1 differential:2 qij:7 consists:1 inside:2 introduce:1 expected:6 indeed:2 behavior:1 window:11 provided:1 not... |
695 | 1,636 | Neural Computation with Winner-Take-All as
the only Nonlinear Operation
Wolfgang Maass
Institute for Theoretical Computer Science
Technische UniversWit Graz
A-8010 Graz, Austria
email: maass@igi.tu-graz.ac.at
http://www.cis.tu-graz.ac.atiigi/maass
Abstract
Everybody "knows" that neural networks need more than a singl... | 1636 |@word neurophysiology:1 version:1 polynomial:7 pulse:2 bn:3 thereby:1 exclusively:1 current:1 surprising:1 si:1 universality:1 plasticity:2 device:1 plane:1 ith:1 short:1 lr:1 provides:3 hyperplanes:3 sigmoidal:1 height:1 roughly:1 multi:2 brain:1 bounded:3 circuit:26 mcculloch:2 substantially:1 nj:1 temporal:1 d... |
696 | 1,637 | Bayesian modelling of tMRI time series
Pedro A. d. F. R. H~jen-S~rensen, Lars K. Hansen and Carl Edward Rasmussen
Department of Mathematical Modelling, Building 321
Technical University of Denmark
DK-2800 Lyngby, Denmark
phs,lkhansen,carl@imrn.dtu.dk
Abstract
We present a Hidden Markov Model (HMM) for inferring the ... | 1637 |@word h:2 trial:8 mri:1 seems:2 grey:2 simulation:1 covariance:3 minus:1 series:8 hemodynamic:7 current:2 activation:16 si:2 readily:3 explorative:2 zeger:2 noninformative:1 enables:1 analytic:1 plot:2 update:5 stroop:1 stationary:1 generative:2 pursued:1 half:2 alone:1 indicative:1 vanishing:1 short:1 accepting:... |
697 | 1,638 | An Oeulo-Motor System with Multi-Chip
Neuromorphie Analog VLSI Control
Oliver Landolt*
CSEMSA
2007 Neuchatel / Switzerland
E-mail: landolt@caltech.edu
Steve Gyger
CSEMSA
2007 Neuchatel / Switzerland
E-mail: steve.gyger@csem.ch
Abstract
A system emulating the functionality of a moving eye-hence the name
oculo-motor s... | 1638 |@word version:2 disk:2 open:2 pulse:7 brightness:1 attended:1 thereby:10 solid:1 electronics:1 contains:3 current:7 must:1 periodically:1 visible:2 shape:1 pertinent:1 motor:29 enables:1 designed:1 plot:2 update:1 alone:1 cue:1 selected:1 device:9 shut:1 plane:1 provides:1 location:11 preference:1 lowresolution:1... |
698 | 1,639 | Algorithms for Independent Components
Analysis and Higher Order Statistics
Daniel D. Lee
Bell Laboratories
Lucent Technologies
Murray Hill, NJ 07974
Uri Rokni and Haim Sompolinsky
Racah Institute of Physics and
Center for Neural Computation
Hebrew University
Jerusalem, 91904, Israel
Abstract
A latent variable genera... | 1639 |@word version:2 nd:1 crucially:1 decomposition:1 covariance:2 tr:1 reduction:1 daniel:1 kurt:2 perturbative:1 must:1 additive:1 visible:4 update:6 generative:20 discovering:1 isotropic:7 haykin:2 lx:1 wijsj:1 become:1 fitting:1 lj2:1 indeed:2 ica:22 behavior:6 p1:5 globally:1 becomes:3 underlying:1 factorized:3 i... |
699 | 164 | 545
DYNAMIC, NON?LOCAL ROLE BINDINGS AND
INFERENCING IN A LOCALIST NETWORK FOR
NATURAL LANGUAGE UNDERSTANDING?
Trent E. Lange
Michael G. Dyer
Artificial Intelligence Laboratory
Computer Science Department
University of California, Los Angeles
Los Angeles, CA 90024
ABSTRACT
This paper introduces a means to handle the ... | 164 |@word propagate:3 tr:1 solid:2 necessity:1 selecting:1 existing:1 current:5 activation:62 yet:1 si:4 must:2 assigning:1 john:7 parsing:1 cottrell:2 distant:1 overriding:1 v:1 alone:1 intelligence:3 pursued:1 selected:6 pacemaker:5 plane:5 node:45 location:1 along:8 pathway:4 inside:19 roughly:1 brain:1 perkel:1 au... |
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