Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
700 | 1,640 | Approximate inference algorithms for two-layer
Bayesian networks
AndrewY. Ng
Computer Science Division
UC Berkeley
Berkeley, CA 94720
ang@cs.berkeley.edu
Michael I. Jordan
Computer Science Division and
Department of Statistics
UC Berkeley
Berkeley, CA 94720
jordan@cs.berkeley.edu
Abstract
We present a class of appro... | 1640 |@word version:5 briefly:1 suitably:1 simulation:2 thereby:1 solid:2 moment:1 configuration:1 contains:1 horvitz:1 current:3 written:1 numerical:1 j1:3 plot:1 intelligence:5 fewer:1 short:1 provides:1 math:1 node:19 lor:1 five:1 diagnosing:1 dn:1 viable:1 inside:1 expected:1 behavior:1 roughly:2 inspired:1 provide... |
701 | 1,641 | Correctness of belief propagation in Gaussian
graphical models of arbitrary topology
Yair Weiss
Computer Science Division
UC Berkeley, 485 Soda Hall
Berkeley, CA 94720-1776
Phone: 510-642-5029
William T. Freeman
Mitsubishi Electric Research Lab
201 Broadway
Cambridge, MA 02139
Phone: 617-621-7527
yweiss@cs.berkeley.... | 1641 |@word version:1 inversion:1 compression:1 stronger:1 simulation:2 mitsubishi:1 covariance:8 fonn:1 dramatic:2 unwrappings:2 com:1 visible:1 happen:1 tenn:2 leaf:10 selected:3 czt:1 ith:1 node:50 successive:2 allerton:1 five:1 constructed:1 incorrect:5 pairwise:3 indeed:1 hardness:1 expected:1 growing:1 freeman:8 ... |
702 | 1,642 | Robust Learning of Chaotic Attractors
Rembrandt Bakker*
Chemical Reactor Engineering
Delft Univ. of Technology
r.bakker@stm.tudelft?nl
Jaap C. Schouten
Marc-Olivier Coppens
Chemical Reactor Engineering Chemical Reactor Engineering
Eindhoven Univ. of Technology
Delft Univ. of Technology
J.C.Schouten@tue.nl
coppen... | 1642 |@word deformed:1 trial:1 simulation:2 tried:1 t_:1 jacob:4 decomposition:4 euclidian:1 recursively:1 reduction:2 initial:1 series:14 contains:2 selecting:1 lapedes:2 existing:1 current:2 com:1 surprising:1 written:1 must:1 partition:1 shape:3 enables:1 compution:1 extrapolating:1 designed:2 plot:1 tenn:2 selected... |
703 | 1,643 | Correctness of belief propagation in Gaussian
graphical models of arbitrary topology
Yair Weiss
Computer Science Division
UC Berkeley, 485 Soda Hall
Berkeley, CA 94720-1776
Phone: 510-642-5029
William T. Freeman
Mitsubishi Electric Research Lab
201 Broadway
Cambridge, MA 02139
Phone: 617-621-7527
yweiss@cs.berkeley.... | 1643 |@word trial:1 version:1 inversion:1 compression:1 stronger:1 simulation:4 mitsubishi:1 covariance:13 fonn:1 dramatic:2 catastrophically:1 kappen:1 unwrappings:2 score:1 contains:3 skd:1 err:1 com:1 si:13 visible:2 happen:1 alone:1 tenn:2 leaf:10 selected:3 czt:1 half:1 intelligence:2 ith:1 provides:2 node:101 suc... |
704 | 1,644 | Generalized Model Selection For Unsupervised
Learning In High Dimensions
Shivakumar Vaithyanathan
IBM Almaden Research Center
650 Harry Road
San Jose, CA 95136
Shiv@almaden.ibm.com
Byron Dom
IBM Almaden Research Center
650 Harry Road
San Jose, CA 95136
dom@almaden.ibm.com
Abstract
We describe a Bayesian approach to m... | 1644 |@word uev:1 msr:1 pw:2 seems:1 simplifying:2 tr:1 ld:1 reduction:1 initial:1 selecting:1 document:27 existing:1 com:2 comparing:2 written:1 partition:3 kdd:1 hypothesize:1 plot:1 selected:1 indicative:1 smith:1 provides:2 dn:1 beta:4 ood:1 consists:4 interscience:1 isi:1 encouraging:1 begin:2 estimating:1 underly... |
705 | 1,645 | On input selection with reversible jump
Markov chain Monte Carlo sampling
Peter Sykacek
Austrian Research Institute for Artificial Intelligence (OFAI)
Schottengasse 3, A-10lO Vienna, Austria
peter@ai. univie. ac. at
Abstract
In this paper we will treat input selection for a radial basis function
(RBF) like classifier... | 1645 |@word repository:1 nd:3 bn:1 covariance:4 mlk:2 ld:2 carry:1 contains:1 denoting:3 freitas:1 current:1 informative:1 remove:2 update:10 intelligence:1 smith:1 core:1 provides:3 contribute:1 wkd:1 dn:1 beta:1 consists:1 introduce:2 roughly:1 provided:1 project:1 alto:2 null:1 cm:3 proposing:3 unobserved:1 nj:1 qua... |
706 | 1,646 | Memory Capacity of Linear vs. Nonlinear
Models of Dendritic Integration
Panayiota Poirazi*
Biomedical Engineering Department
University of Southern California
Los Angeles, CA 90089
Bartlett W. Mel*
Biomedical Engineering Department
University of Southern California
Los Angeles, CA 90089
poirazi@sc/. usc. edu
mel@ln... | 1646 |@word repository:1 hippocampus:1 simulation:3 thereby:1 solid:2 configuration:1 contains:2 efficacy:2 terion:1 interestingly:1 comparing:1 activation:2 written:1 readily:1 must:1 numerical:3 realistic:1 plot:2 v:17 discrimination:1 fewer:1 short:1 contribute:2 sigmoidal:2 misinterpreted:1 along:2 differential:1 l... |
707 | 1,647 | An Information-Theoretic Framework for
Understanding Saccadic Eye Movements
Tai Sing Lee *
Department of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Stella X. Yu
Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213
tai@es.emu.edu
stella@enbe.emu.edu
Abstract
In this paper, we pro... | 1647 |@word mcconkie:3 neurophysiology:1 seems:1 stronger:1 integrative:1 rayner:3 shot:1 carry:1 reduction:2 moment:3 initial:1 foveal:1 contains:2 disparity:1 tuned:2 subjective:2 current:1 contextual:3 intake:1 yet:1 must:1 takeo:1 mesh:1 periodically:1 romero:1 motor:4 remove:1 half:1 cue:1 greedy:1 intelligence:1 ... |
708 | 1,648 | Online Independent Component Analysis
With Local Learning Rate Adaptation
Nicol N. Schraudolph
Xavier Giannakopoulos
nic<Didsia.ch
xavier<Didsia.ch
IDSIA, Corso Elvezia 36
6900 Lugano, Switzerland
http://www.idsia.ch/
Abstract
Stochastic meta-descent (SMD) is a new technique for online adaptation of local learnin... | 1648 |@word version:2 t_:1 jacob:1 tr:1 npt:3 uma:2 pub:6 past:3 current:3 com:1 must:3 cruz:1 additive:1 vietri:2 update:9 intelligence:1 warmuth:2 scotland:1 provides:1 along:1 ucsc:1 direct:1 symposium:1 notably:1 expected:1 indeed:1 forgetting:1 ica:6 multi:2 globally:1 pitfall:1 actual:1 etl:1 differentiation:1 ev... |
709 | 1,649 | Local probability propagation for factor
analysis
Brendan J. Frey
Computer Science, University of Waterloo, Waterloo, Ontario, Canada
Abstract
Ever since Pearl's probability propagation algorithm in graphs with
cycles was shown to produce excellent results for error-correcting
decoding a few years ago, we have been cu... | 1649 |@word determinant:3 achievable:6 loading:1 reused:1 iki:1 tried:1 covariance:1 minus:1 solid:1 wellapproximated:1 reduction:1 phy:1 contains:2 current:4 comparing:2 must:1 numerical:1 drop:1 plot:1 update:9 aside:1 generative:2 selected:1 lx:1 successive:1 along:2 become:1 ik:1 consists:3 reinterpreting:1 ra:1 gl... |
710 | 165 | 537
A MASSIVELY PARALLEL SELF-TUNING
CONTEXT-FREE PARSER!
Eugene Santos Jr.
Department of Computer Science
Brown University
Box 1910, Providence, RI 02912
eSj@cs.brown.edu
ABSTRACT
The Parsing and Learning System(PALS) is a massively
parallel self-tuning context-free parser. It is capable of
parsing sentences of unbo... | 165 |@word manageable:1 tried:1 tr:1 charniak:2 esj:1 rightmost:1 blank:2 current:4 adj:2 si:1 parsing:13 drop:1 bart:1 leaf:3 accordingly:1 node:12 toronto:1 unbounded:2 height:1 constructed:1 incorrect:4 consists:1 combine:1 introduce:1 frequently:2 terminal:1 totally:2 project:1 easiest:1 santos:8 what:1 differing:1... |
711 | 1,650 | A MCMC approach to Hierarchical Mixture
Modelling
Christopher K. I. Williams
Institute for Adaptive and Neural Computation
Division of Informatics, University of Edinburgh
5 Forrest Hill, Edinburgh EHI 2QL, Scotland, UK
ckiw@dai.ed.ac.uk
http://anc.ed.ac.uk
Abstract
There are many hierarchical clustering algorithms a... | 1650 |@word middle:1 version:3 proportion:1 twelfth:1 simulation:1 crucially:1 covariance:9 recursively:1 initial:3 configuration:15 denoting:1 rightmost:1 past:1 current:2 comparing:1 hpp:7 must:1 visible:2 hofmann:1 remove:1 update:1 discrimination:1 generative:11 leaf:4 intelligence:2 discovering:1 ccj:1 xk:3 scotla... |
712 | 1,651 | Agglomerative Information Bottleneck
Noam Slonim
Naftali Tishby*
Institute of Computer Science and
Center for Neural Computation
The Hebrew University
Jerusalem, 91904 Israel
email: {noamm.tishby}(Qcs.huji.ac.il
Abstract
We introduce a novel distributional clustering algorithm that maximizes the mutual information pe... | 1651 |@word version:3 compression:4 seek:1 tr:1 reduction:5 contains:2 current:4 z2:4 recovered:1 xiyi:1 lang:1 yet:1 must:1 john:1 alphanumeric:1 partition:29 hofmann:1 shape:1 plot:1 xex:2 update:2 v:6 greedy:5 noamm:1 plane:10 mccallum:1 pointer:1 provides:1 lexicon:1 lx:1 direct:2 ik:1 introduce:1 expected:1 indeed... |
713 | 1,652 | Support Vector Method for Multivariate
Density Estimation
Vladimir N. Vapnik
Royal Halloway College and
AT &T Labs, 100 Schultz Dr.
Red Bank, NJ 07701
vlad@research.att.com
Sayan Mukherjee
CBCL, MIT E25-201
Cambridge, MA 02142
sayan@ai.mit.edu
Abstract
A new method for multivariate density estimation is developed
ba... | 1652 |@word trial:6 involves:1 indicate:1 true:2 norm:3 proportion:1 smirnov:3 hence:1 regularization:6 closely:1 analytically:1 nonzero:1 micchelli:1 simulation:1 stochastic:2 avtomatika:1 covariance:2 accounting:1 ll:3 parametric:4 width:2 diagonal:2 speaker:2 berlin:1 g0:1 att:1 rkhs:5 reynolds:1 performs:1 l1:3 fj:... |
714 | 1,653 | Semiparametric Approach to Multichannel
Blind Deconvolution
of Nonminimum Phase Systems
L.-Q. Zhang, S. Amari and A. Cichocki
Brain-style Information Systems Research Group, BSI
The Institute of Physical and Chemical Research
Wako shi, Saitama 351-0198, JAPAN
zha@open.brain.riken.go.jp
{amari,cia }@brain.riken.go.jp
... | 1653 |@word version:1 polynomial:1 open:1 decomposition:4 carry:1 liu:1 series:1 score:21 rpz:1 wako:1 recovered:1 si:12 dx:4 john:1 numerical:1 remove:1 parameterization:1 xk:2 nnsp:1 zhang:6 dn:1 c2:1 differential:2 ik:1 introduce:5 inter:1 ica:1 equivariant:2 growing:1 brain:3 automatically:1 pf:1 increasing:1 provi... |
715 | 1,654 | Learning the Similarity of Documents:
An Information-Geometric Approach to
Document Retrieval and Categorization
Thomas Hofmann
Department of Computer Science
Brown University, Providence, RI
hofmann@cs.brown.edu, www.cs.brown.edu/people/th
Abstract
The project pursued in this paper is to develop from first
informati... | 1654 |@word aircraft:1 msr:1 repository:2 version:1 proportion:1 coarseness:1 seems:1 nd:1 additively:1 simulation:1 tried:1 dealer:1 decomposition:11 simplifying:1 thereby:2 profit:1 tr:2 reduction:2 series:1 exclusively:1 united:1 score:2 document:37 current:1 ka:1 yet:2 import:1 written:1 grain:2 evans:1 additive:1 ... |
716 | 1,655 | Search for Information Bearing
Components in Speech
Howard Hua Yang and Hynek Hermansky
Department of Electrical and Computer Engineering
Oregon Graduate Institute of Science and Technology
20000 NW, Walker Rd., Beaverton, OR97006, USA
{hyang,hynek}@ece.ogi.edu, FAX:503 7481406
Abstract
In this paper, we use mutual i... | 1655 |@word timefrequency:2 relevancy:6 carry:3 reduction:6 contains:1 xiy:2 interestingly:1 current:7 written:1 realistic:1 plot:1 vuuren:2 plane:2 short:1 quantized:1 manner:1 theoretically:1 nor:1 multi:1 shirt:1 globally:1 little:1 curse:1 window:2 increasing:1 underlying:1 bounded:1 temporal:5 schwartz:1 overestim... |
717 | 1,656 | Resonance in a Stochastic Neuron Model
with Delayed Interaction
Toru Ohira*
Sony Computer Science Laboratory
3-14-13 Higashi-gotanda
Shinagawa, Tokyo 141, Japan
ohira@csl.sony.co.jp
Yuzuru Sato
Institute of Physics,
Graduate School of Arts and Science, University of Tokyo
3-8-1 Komaba, Meguro, Tokyo 153 Japan
ysato@sa... | 1656 |@word version:1 suitably:1 simulation:5 q1:1 solid:3 initial:1 series:1 tuned:3 wako:1 past:4 discretization:1 yet:2 realistic:1 numerical:1 chicago:2 interspike:3 shape:3 plot:3 stationary:2 math:1 firstly:1 sigmoidal:1 lor:1 height:5 consists:1 sustained:1 theoretically:1 intricate:2 behavior:4 p1:3 frequently:... |
718 | 1,657 | Information Capacity and Robustness of
Stochastic Neuron Models
Elad Schneidman Idan Segev
N aftali Tishby
Institute of Computer Science,
Department of Neurobiology and
Center for Neural Computation,
Hebrew University
Jerusalem 91904, Israel
{ elads, tishby} @cs.huji.ac.il, idan@lobster.ls.huji.ac.il
Abstract
The reli... | 1657 |@word seems:1 nd:1 open:4 simulation:1 carry:1 extrastriate:1 liu:1 mainen:2 interestingly:1 current:23 i3n:1 activation:1 yet:2 physiol:3 opin:1 plot:1 alone:1 half:4 imitate:1 five:2 along:1 direct:1 ozaki:1 elads:1 inter:2 expected:2 behavior:5 examine:1 brain:1 discretized:2 decreasing:1 window:1 increasing:2... |
719 | 1,658 | Spike-based learning rules and stabilization of
persistent neural activity
Xiaohui Xie and H. Sebastian Seung
Dept. of Brain & Cog. Sci., MIT, Cambridge, MA 02139
{xhxie, seung}@mit.edu
Abstract
We analyze the conditions under which synaptic learning rules based
on action potential timing can be approximated by learn... | 1658 |@word middle:1 seems:1 open:1 simulation:2 pulse:1 lobe:4 electrosensory:1 t_:1 thres:1 tr:2 series:1 tuned:7 current:4 cad:1 si:3 yet:1 activation:2 must:3 realistic:1 interspike:1 plasticity:10 shape:1 motor:1 succeeding:1 update:3 accordingly:1 ith:2 reciprocal:1 short:4 core:1 zhang:1 burst:11 along:1 differe... |
720 | 1,659 | Boosting with Multi-Way Branching in
Decision Trees
Yishay Mansour
David McAllester
AT&T Labs-Research
180 Park Ave
Florham Park NJ 07932
{mansour, dmac }@research.att.com
Abstract
It is known that decision tree learning can be viewed as a form
of boosting. However, existing boosting theorems for decision tree
learn... | 1659 |@word seems:2 reduction:2 initial:2 att:1 selecting:4 existing:1 current:1 com:1 assigning:2 must:5 written:1 designed:1 greedy:1 leaf:33 selected:8 ith:1 argm:1 short:1 provides:1 boosting:24 node:27 ih1:1 constructed:1 symposium:1 prove:6 fitting:2 nondeterministic:1 roughly:2 isi:1 growing:1 multi:15 globally:... |
721 | 166 | 712
A PROGRAMMABLE ANALOG NEURAL COMPUTER
AND SIMULATOR
Paul Mueller*, Jan Vander Spiegel, David Blackman*, Timothy Chiu, Thomas Clare,
Joseph Dao, Christopher Donham, Tzu-pu Hsieh, Marc Loinaz
*Dept.of Biochem. Biophys., Dept. of Electrical Engineering.
University of Pennsylvania, Philadelphia Pa.
ABSTRACT
This repo... | 166 |@word version:2 seems:1 donham:1 bining:1 hsieh:1 attainable:1 solid:1 contains:4 envision:1 current:11 com:1 activation:1 must:2 numerical:1 v:1 selected:3 beginning:1 provides:1 direct:2 differential:1 transducer:1 expected:1 alspector:2 simulator:7 quad:1 conv:1 provided:1 linearity:1 circuit:7 pennits:1 fabric... |
722 | 1,660 | Reconstruction of Sequential Data with
Probabilistic Models and Continuity Constraints
Miguel A. Carreira-Perpifian
Dept. of Computer Science, University of Sheffield, UK
miguel@dcs.shefac.uk
Abstract
We consider the problem of reconstructing a temporal discrete sequence
of multidimensional real vectors when part of ... | 1660 |@word version:2 briefly:2 confirms:1 tried:1 covariance:1 configuration:1 outperforms:1 current:1 comparing:1 deteriorating:1 neuneier:1 analysed:1 must:1 john:1 shape:2 generative:3 selected:2 greedy:1 isotropic:4 codebook:1 location:1 constructed:1 become:1 ray:1 introduce:1 scatterometer:1 multi:2 torque:1 aud... |
723 | 1,661 | Inference for the Generalization Error
Claude Nadeau
CIRANO
2020, University,
Montreal, Qc, Canada, H3A 2A5
jcnadeau@altavista.net
Yoshua Bengio
CIRANO and Dept. IRO
Universite de Montreal
Montreal, Qc, Canada, H3C 3J7
bengioy@iro.umontreal.ca
Abstract
In order to to compare learning algorithms, experimental results ... | 1661 |@word version:3 norm:1 proportion:1 underline:1 nd:1 simulation:7 covariance:1 solid:2 pub:1 comparing:3 si:1 ij1:2 j1:14 aoo:8 enables:1 device:1 toronto:1 liberal:4 pun:1 unbiasedly:2 five:3 mathematical:1 constructed:1 advocate:1 theoretically:1 li3:6 expected:2 indeed:2 roughly:1 behavior:1 decreasing:1 actua... |
724 | 1,662 | Application of Blind Separation of Sources to
Optical Recording of Brain Activity
Holger Schoner, Martin Stetter, Ingo Schie61
Department of Computer Science
Technical University of Berlin Germany
{hjsch,moatl,ingos}@cs.tu-berlin.de
John E. W. Mayhew
University of Sheffield, UK
j. e.mayhew@sheffield.ac.uk
Jennifer S... | 1662 |@word trial:4 middle:5 version:2 norm:3 seems:1 simulation:1 mammal:1 shot:1 initial:1 schoner:1 series:2 contains:1 selecting:2 blank:1 od:3 activation:1 si:2 must:3 john:1 plot:6 selected:2 record:1 provides:1 preference:7 differential:1 incorrect:1 consists:1 introduce:2 multi:5 brain:7 underlying:2 medium:1 m... |
725 | 1,663 | Model Selection for Support Vector Machines
Olivier Chapelle*,t, Vladimir Vapnik*
* AT&T Research Labs, Red Bank, NJ
t LIP6, Paris, France
{ chapelle, vlad} @research.au.com
Abstract
New functionals for parameter (model) selection of Support Vector Machines are introduced based on the concepts of the span of support ... | 1663 |@word trial:1 version:1 proportion:1 norm:3 covariance:2 yih:1 initial:1 series:1 com:1 surprising:1 dx:1 shape:2 enables:2 treating:1 plot:1 prohibitive:1 xk:1 provides:3 postal:5 hyperplanes:2 constructed:1 ik:1 scholkopf:2 consists:4 introduce:3 indeed:1 nonseparable:1 actual:3 becomes:2 notation:1 bounded:1 m... |
726 | 1,664 | Approximate Planning in Large POMDPs
via Reusable Trajectories
Michael Kearns
AT&T Labs
mkearns@research.att.com
Yishay Mansour
Tel Aviv University
mansour@math.tau.ac.il
AndrewY. Ng
UC Berkeley
ang@cs.berkeley.edu
Abstract
We consider the problem of reliably choosing a near-best strategy from
a restricted class of... | 1664 |@word trial:1 version:4 briefly:1 achievable:1 stronger:1 polynomial:1 seems:1 simulation:2 seek:1 tr:22 harder:4 recursively:1 mkearns:1 att:1 err:1 current:6 com:1 yet:1 must:6 readily:1 reminiscent:1 treating:1 generative:21 leaf:1 greedy:1 intelligence:3 provides:3 math:1 contribute:1 node:12 along:2 prove:2 ... |
727 | 1,665 | An Environment Model for N onstationary
Reinforcement Learning
Samuel P. M. Choi
pmchoi~cs.ust.hk
Dit-Yan Yeung
Nevin L. Zhang
dyyeung~cs.ust.hk
lzhang~cs.ust.hk
Department of Computer Science, Hong Kong University of Science and Technology
Clear Water Bay, Kowloon, Hong Kong
Abstract
Reinforcement learning in n... | 1665 |@word kong:2 exploitation:1 briefly:1 seems:1 tried:1 pick:2 initial:2 past:1 outperforms:1 current:4 comparing:1 si:1 yet:1 ust:3 must:3 realistic:1 remove:1 drop:3 stationary:3 fewer:2 item:1 boosting:1 zhang:5 consists:1 expected:2 aliasing:1 inspired:1 cpu:1 little:1 equipped:1 increasing:1 moreover:1 formali... |
728 | 1,666 | Rules and Similarity in Concept Learning
Joshua B. Tenenbaum
Department of Psychology
Stanford University, Stanford, CA 94305
jbt@psych.stanford.edu
Abstract
This paper argues that two apparently distinct modes of generalizing concepts - abstracting rules and computing similarity to exemplars - should
both be seen as... | 1666 |@word trial:16 briefly:1 sharpens:1 proportion:1 norm:2 instruction:1 pick:2 mention:1 selecting:1 glh:2 yet:1 assigning:1 written:1 must:1 numerical:1 additive:2 shape:2 blickets:1 designed:1 v:3 alone:1 oldest:1 smith:2 short:2 num:1 provides:1 contribute:1 preference:1 five:1 height:1 mathematical:8 along:1 co... |
729 | 1,667 | Reinforcement Learning Using Approximate
Belief States
Andres Rodriguez *
Artificial Intelligence Center
SRI International
333 Ravenswood Avenue, Menlo Park, CA 94025
rodriguez@ai.sri.com
Ronald Parr, Daphne Koller
Computer Science Department
Stanford University
Stanford, CA 94305
{parr,koller}@cs.stanford.edu
Abstr... | 1667 |@word aircraft:15 trial:1 version:1 sri:2 manageable:1 simulation:2 decomposition:4 reduction:1 contains:1 current:1 com:1 comparing:1 si:5 artijiciallntelligence:1 must:2 ronald:2 visible:2 additive:1 visibility:1 designed:3 update:1 intelligence:1 fewer:1 discovering:1 hallway:1 mccallum:1 utile:1 provides:1 ma... |
730 | 1,668 | Learning sparse codes with a
mixture-of-Gaussians prior
Bruno A. Olshausen
Department of Psychology and
Center for Neuroscience, UC Davis
1544 Newton Ct.
Davis, CA 95616
baolshausen@ucdavis.edu
K. Jarrod Millman
Center for Neuroscience, UC Davis
1544 Newton Ct.
Davis, CA 95616
kjmillman@ucdavis. edu
Abstract
We desc... | 1668 |@word isil:1 seek:1 covariance:1 current:1 comparing:1 si:22 assigning:1 must:5 update:1 along:1 sii:9 become:2 manner:1 roughly:1 freeman:1 considering:1 notation:2 lowest:1 what:1 ti:3 scaled:1 grant:1 appear:2 dropped:1 local:1 sd:1 despite:1 plus:1 initialization:1 suggests:1 collect:1 challenging:1 bi:1 aver... |
731 | 1,669 | An Oscillatory Correlation Framework for
Computational Auditory Scene Analysis
GuyJ.Brown
Department of Computer Science
University of Sheffield
Regent Court, 211 Portobello Street,
Sheffield S 1 4DP, UK
Email: g.brown@dcs.shefac.uk
DeLiang L. Wang
Department of Computer and Information
Science and Centre for Cognitiv... | 1669 |@word middle:1 grey:1 simulation:1 excited:1 n8:3 tlo:1 fragment:1 existing:1 recovered:1 si:1 must:3 subsequent:1 remove:1 drop:1 implying:1 half:1 tone:1 accordingly:2 xk:1 core:1 short:1 contribute:1 windowed:1 along:2 burst:1 consists:2 regent:1 autocorrelation:5 olfactory:1 introduce:1 discontiguous:1 spine:... |
732 | 167 | 794
NEURAL ARCHITECTURE
Valentino Braitenberg
Max Planck Institute
Federal Republic of Germany
While we are waiting for the ultimate biophysics of cell membranes and synapses
to be completed, we may speculate on the shapes of neurons and on the patterns of
their connections. Much of this will be significant whatever ... | 167 |@word read:1 excitation:2 dendritic:2 reason:1 besides:1 geometrical:1 length:1 must:1 statement:1 shape:2 defeat:1 cerebral:1 perform:1 device:1 significant:1 refer:2 neuron:5 isotropic:2 plane:1 measurement:1 reciprocal:1 federal:1 situation:1 cortex:3 inhibition:2 differential:1 connection:1 brain:2 able:1 patt... |
733 | 1,670 | Emergence of Topography and Complex
Cell Properties from Natural Images
using Extensions of ICA
Aapo Hyviirinen and Patrik Hoyer
Neural Networks Research Center
Helsinki University of Technology
P.O. Box 5400, FIN-02015 HUT, Finland
aapo.hyvarinen~hut.fi, patrik.hoyer~hut.fi
http://www.cis.hut.fi/projects/ica/
Abstr... | 1670 |@word version:1 norm:11 seems:1 decomposition:2 ours:1 interestingly:1 existing:1 si:21 generative:4 selected:1 location:7 ik:1 consists:3 wild:1 combine:1 inside:5 manner:1 ica:12 expected:1 multi:1 window:1 considering:1 project:1 provided:1 moreover:2 kind:1 interpreted:1 finding:1 transformation:1 temporal:1 ... |
734 | 1,671 | Training Data Selection
for Optimal Generalization
in Trigonometric Polynomial Networks
Masashi Sugiyama*and Hidemitsu Ogawa
Department of Computer Science, Tokyo Institute of Technology,
2-12-1, O-okayama, Meguro-ku, Tokyo, 152-8552, Japan.
sugi@cs. titeck. ac.jp
Abstract
In this paper, we consider the problem of ac... | 1671 |@word especially:1 trial:1 c:4 version:2 implies:3 polynomial:10 society:1 hence:1 regularization:1 lyp:2 objective:1 tokyo:2 moore:4 filter:1 simulation:4 fa:2 exploration:1 covariance:3 uniquely:1 tr:2 solid:1 ja:1 schatten:2 initial:1 ao:1 generalization:22 criterion:9 pub:2 decompose:1 proposition:2 generaliz... |
735 | 1,672 | Variational Inference for Bayesian
Mixtures of Factor Analysers
Zoubin Ghahramani and Matthew J. Beal
Gatsby Computational Neuroscience Unit
University College London
17 Queen Square, London WC1N 3AR, England
{zoubin,m.beal}Ggatsby.ucl.ac.uk
Abstract
We present an algorithm that infers the model structure of a mixtur... | 1672 |@word determinant:1 middle:2 loading:5 proportion:2 seek:1 yja:1 covariance:9 pick:2 tr:3 solid:1 reduction:2 initial:1 dx:2 happen:1 treating:1 drop:1 plot:1 v:1 generative:1 discovering:2 fewer:1 intelligence:1 ria:1 sys:3 compo:1 toronto:1 penalises:1 along:1 fitting:1 manner:1 introduce:1 indeed:1 expected:2 ... |
736 | 1,673 | Mixture Density Estimation
Jonathan Q. Li
Department of Statistics
Yale University
P.O. Box 208290
New Haven, CT 06520
Andrew R. Barron
Department of Statistics
Yale University
P.O. Box 208290
New Haven, CT 06520
Qiang.Li@aya.yale. edu
Andrew. Barron@yale. edu
Abstract
Gaussian mixtures (or so-called radial basis ... | 1673 |@word manageable:1 achievable:1 norm:2 seek:1 covariance:1 pick:1 tr:2 initial:1 surprising:1 yet:1 dx:7 fn:2 analytic:1 xex:1 greedy:7 reciprocal:1 dissertation:1 provides:2 location:3 successive:1 sigmoidal:3 c2:1 ik:8 prove:2 fitting:2 assaf:1 inside:1 manner:2 introduce:2 indeed:3 roughly:1 behavior:1 decreas... |
737 | 1,674 | Model selection in clustering by uniform
convergence bounds*
Joachim M. Buhmann and Marcus Held
Institut flir Informatik III,
RomerstraBe 164, D-53117 Bonn, Germany
{jb,held}@cs.uni-bonn.de
Abstract
Unsupervised learning algorithms are designed to extract structure from data samples. Reliable and robust inference req... | 1674 |@word version:3 achievable:1 open:1 simulation:1 tr:1 solid:1 reduction:1 moment:1 series:1 denoting:1 document:1 protection:1 v21:1 finest:1 explorative:1 hofmann:1 enables:1 designed:2 plot:1 v:1 generative:6 dover:1 five:2 c2:1 interscience:1 introduce:1 theoretically:1 expected:14 mechanic:3 growing:2 cardina... |
738 | 1,675 | Image Recognition in Context: Application to
Microscopic Urinalysis
XuboSong*
Department of Electrical and Computer Engineering
Oregon Graduate Institute of Science and Technology
Beaverton, OR 97006
xubosong@ece.ogi.edu
Joseph Sill
Department of Computation and Neural Systems
California Institute of Technology
Pasade... | 1675 |@word grey:1 nicholson:1 reduction:2 moment:1 contains:1 efficacy:1 current:1 contextual:8 readily:1 amir:1 detecting:1 recompute:1 provides:1 dn:2 c2:3 consists:2 ray:1 manner:3 expected:1 roughly:1 detects:1 increasing:1 becomes:1 null:1 what:1 developed:1 finding:3 corporation:2 ti:3 ofa:1 rm:1 unit:2 appear:1... |
739 | 1,676 | Recurrent cortical competition: Strengthen or
weaken?
Peter Adorjan*, Lars Schwabe,
Christian Piepenbrock* , and Klaus Obennayer
Dept. of Compo Sci., FR2-I, Technical University Berlin
Franklinstrasse 28/29 10587 Berlin, Germany
adorjan@epigenomics.com, {schwabe, oby} @cs.tu-berlin.de,
piepenbrock@epigenomics.com
http:... | 1676 |@word middle:1 wiesel:1 sharpens:2 stronger:4 advantageous:1 simulation:3 simplifying:1 fonn:1 tr:1 solid:2 carry:1 initial:2 series:1 efficacy:3 pub:1 tuned:16 denoting:1 current:2 com:2 comparing:1 physiol:1 additive:5 realistic:1 plasticity:3 christian:1 piepenbrock:6 stationary:1 half:1 beginning:2 short:5 co... |
740 | 1,677 | The Entropy Regularization
Information Criterion
Alex J. Smola
Dept. of Engineering and RSISE
Australian National University
Canberra ACT 0200, Australia
Alex.Smola@anu.edu.au
John Shawe-Taylor
Royal Holloway College
University of London
Egham, Surrey 1W20 OEX, UK
john@dcs.rhbnc.ac.uk
Bernhard Scholkopf
Microsoft Re... | 1677 |@word rreg:1 norm:3 tedious:1 r:3 decomposition:3 tr:2 carry:1 initial:2 contains:1 chervonenkis:1 current:2 com:1 yet:1 written:2 readily:1 john:2 additive:1 girosi:1 offunctions:2 characterization:1 boosting:1 herbrich:1 become:1 scholkopf:4 qij:2 inside:1 expected:3 globally:1 decreasing:1 eurocolt:1 automatic... |
741 | 1,678 | Information Factorization in
Connectionist Models of Perception
Javier R. Movellan
Department of Cognitive Science
Institute for Neural Computation
University of California San Diego
James L. McClelland
Center for the Neural Bases of Cognition
Department of Psychology
Carnegie Mellon University
Abstract
We examine a... | 1678 |@word version:3 pick:1 hereafter:1 rpz:2 kcr:1 ka:1 activation:12 dx:1 additive:1 partition:1 v:1 ith:1 short:2 lx:4 mathematical:1 direct:3 differential:2 consists:1 cnbc:1 behavior:2 examine:4 brain:1 szs:1 little:2 becomes:1 notation:1 moreover:1 factorized:2 mass:1 tic:1 z:15 offour:1 fuzzy:1 every:1 sai:1 ti... |
742 | 1,679 | Modeling High-Dimensional Discrete Data with
Multi-Layer Neural Networks
Samy Bengio *
IDIAP
CP 592, rue du Simplon 4,
1920 Martigny, Switzerland
bengio@idiap.ch
Yoshua Bengio
Dept.IRO
Universite de Montreal
Montreal, Qc, Canada, H3C 317
bengioy@iro.umontreal.ca
Abstract
The curse of dimensionality is severe when mod... | 1679 |@word polynomial:8 duda:1 smirnov:1 heuristically:1 tried:2 selecting:1 tuned:1 past:1 z2:2 assigning:1 mushroom:3 must:1 partition:1 selected:1 item:2 regressive:1 ire:1 node:2 simpler:1 direct:1 combine:1 pairwise:3 multi:11 steffen:1 inspired:1 actual:1 curse:3 encouraging:1 considering:1 null:1 what:1 interpr... |
743 | 168 | 410
NEURAL CONTROL OF SENSORY ACQUISITION:
THE VESTIBULO-OCULAR REFLEX.
Michael G. Paulin, Mark E. Nelson and James M. Bower
Division of Biology
California Institute of Technology
Pasadena, CA 91125
ABSTRACT
We present a new hypothesis that the cerebellum plays a key role in actively controlling the acquisition of se... | 168 |@word neurophysiology:1 seems:1 simulation:1 rhesus:2 contraction:1 eng:2 series:1 tuned:1 optican:2 past:1 existing:1 current:2 comparing:1 activation:1 must:2 vor:49 distant:1 thrust:1 motor:7 hypothesize:1 plot:7 treating:1 nemal:2 cue:2 device:1 nervous:4 accordingly:4 plane:1 paulin:7 short:1 characterization... |
744 | 1,680 | Broadband Direction-Of-Arrival Estimation
Based On Second Order Statistics
Justinian Rosca
Joseph 6 Ruanaidh
Alexander Jourjine
Scott Rickard
{rosca,oruanaidh,jourjine,rickard}@scr.siemens.com
Siemens Corporate Research, Inc.
755 College Rd E
Princeton, NJ 08540
Abstract
N wideband sources recorded using N closely spa... | 1680 |@word ruanaidh:5 determinant:5 version:2 proportion:1 d2:6 r:1 covariance:1 dramatic:1 tr:1 versatile:1 substitution:1 document:1 com:1 z2:5 yet:1 written:1 must:1 distant:2 designed:1 drop:1 plot:1 v:2 cue:2 pursued:1 device:1 core:1 filtered:2 along:2 c2:7 direct:6 weinstein:1 frans:1 redefine:1 theoretically:1... |
745 | 1,681 | Dual Estimation and the Unscented
Transformation
EricA. Wan
ericwan@ece.ogi.edu
Rudolph van der Merwe
rudmerwe@ece.ogi.edu
Alex T. Nelson
atneison@ece.ogi.edu
Oregon Graduate Institute of Science & Technology
Department of Electrical and Computer Engineering
20000 N.W. Walker Rd., Beaverton, Oregon 97006
Abstract
... | 1681 |@word version:1 norm:2 simulation:1 propagate:1 linearized:2 covariance:11 wgn:2 minus:1 tr:1 klk:12 recursively:2 ld:1 initial:1 series:8 mmse:1 past:2 freitas:1 current:7 activation:1 additive:3 enables:1 plot:3 sponsored:1 update:2 mackey:4 stationary:1 kyk:1 xk:20 provides:2 direct:1 symposium:1 consists:1 co... |
746 | 1,682 | Independent Factor Analysis with
Temporally Structured Sources
Hagai Attias
hagai@gatsby.ucl.ac.uk
Gatsby Unit, University College London
17 Queen Square
London WCIN 3AR, U.K.
Abstract
We present a new technique for time series analysis based on dynamic probabilistic networks. In this approach, the observed data
are ... | 1682 |@word h:1 version:1 middle:1 stronger:1 advantageous:1 covariance:7 solid:2 recursively:1 reduction:5 initial:1 configuration:2 series:4 si:1 j1:1 mstep:1 update:3 v:2 isotropic:9 parametrization:1 ith:1 sys:3 provides:2 contribute:1 become:1 combine:1 fitting:2 manner:2 indeed:1 expected:2 ica:7 themselves:1 act... |
747 | 1,683 | Recognizing Evoked Potentials in a Virtual
Environment *
Jessica D. Bayliss and Dana H. Ballard
Department of Computer Science
University of Rochester
Rochester, NY 14627
{bayliss,dana}@cs.rochester.edu
Abstract
Virtual reality (VR) provides immersive and controllable experimental environments. It expands the bounds ... | 1683 |@word neurophysiology:1 trial:11 pulse:1 tried:1 covariance:2 pick:1 tr:1 ld:1 reduction:2 substitution:1 series:1 score:2 existing:1 must:1 john:1 numerical:2 visible:1 enables:1 motor:1 remove:1 iscan:1 grass:1 generative:1 mental:1 provides:1 detecting:1 five:1 become:1 inside:1 manner:5 ica:7 expected:3 behav... |
748 | 1,685 | Bayesian Network Induction via Local
Neighborhoods
Dimitris Margaritis
Department of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
D.Margaritis@cs.cmu.edu
Sebastian Thrun
Department of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
S. Thrun@cs.cmu.edu
Abstract
In recent years, Ba... | 1685 |@word mild:2 h:2 version:13 polynomial:2 advantageous:1 heuristically:1 simulation:1 propagate:1 bn:1 pressure:1 dramatic:1 mention:1 tr:1 plentiful:1 contains:1 score:3 selecting:1 series:1 liu:2 denoting:1 current:1 comparing:1 surprising:1 attracted:1 nb2:1 happen:1 partition:2 remove:5 greedy:1 beginning:1 pr... |
749 | 1,686 | Audio-Vision:
Using Audio-Visual Synchrony to Locate
Sounds
John Hershey ..
Javier Movellan
jhershey~cogsci.ucsd.edu
movellan~cogsci.ucsd.edu
Department of Cognitive Science
University of California, San Diego
La Jolla, CA 92093-0515
Department of Cognitive Science
University of California, San Diego
La Jolla, CA ... | 1686 |@word tried:2 rgb:1 covariance:5 decomposition:1 recursively:1 carry:1 series:2 past:2 current:1 nt:1 must:2 john:1 realistic:1 subsequent:1 plasticity:1 designed:1 stationary:1 cue:1 device:1 accordingly:1 ith:1 provides:1 contribute:2 location:5 lx:2 mathematical:1 direct:1 driver:2 persistent:1 tagging:1 brain... |
750 | 1,687 | A Geometric Interpretation of v-SVM
Classifiers
David J. Crisp
Centre for Sensor Signal and
Information Processing,
Deptartment of Electrical Engineering,
University of Adelaide, South Australia
Christopher J.C. Burges
Advanced Technologies,
Bell Laboratories,
Lucent Technologies
Holmdel, New Jersey
dcrisp@eleceng.ad... | 1687 |@word version:2 tr:1 substitution:1 comparing:1 com:1 rpi:1 xiyi:1 must:1 written:2 shape:1 remove:1 half:1 sits:1 hyperplanes:1 along:2 scholkopf:4 prove:2 introduce:1 indeed:1 nonseparable:1 becomes:1 moreover:2 notation:1 lowest:1 interpreted:1 finding:4 every:3 exactly:2 classifier:5 scaled:1 appear:3 positiv... |
751 | 1,688 | Graded grammaticality in Prediction
Fractal Machines
Shan Parfitt, Peter Tiilo and Georg Dorffner
Austrian Research Institute for Artificial Intelligence,
Schottengasse 3, A-IOIO Vienna, Austria.
{ shan,petert,georg} @ai. univie. ac. at
Abstract
We introduce a novel method of constructing language models,
which avoids... | 1688 |@word version:7 middle:1 briefly:1 interleave:1 tr:1 harder:1 omidvar:1 initial:1 score:1 document:2 ours:1 interestingly:1 past:1 current:1 comparing:1 activation:2 must:1 happen:1 pertinent:1 remove:1 designed:1 fund:1 discrimination:1 infant:1 intelligence:3 cue:1 device:2 accordingly:1 leamed:2 codebook:10 no... |
752 | 1,689 | Some Theoretical Results Concerning the
Convergence of Compositions of Regularized
Linear Functions
Tong Zhang
Mathematical Sciences Department
IBM T.1. Watson Research Center
Yorktown Heights, NY 10598
tzhang@watson.ibm.com
Abstract
Recently, sample complexity bounds have been derived for problems involving linear f... | 1689 |@word version:2 briefly:1 polynomial:1 norm:5 bn:1 decomposition:1 fonn:1 tr:4 contains:1 series:1 chervonenkis:1 com:1 unction:2 afl:1 john:1 ixil:2 numerical:1 update:1 exl:4 greedy:1 wth:1 provides:1 boosting:5 math:1 lx:1 sigmoidal:1 zhang:5 height:1 mathematical:1 rc:1 differential:1 symposium:1 introduce:1 ... |
753 | 169 | 687
AN ANALOG VLSI CHIP FOR
THIN-PLATE SURFACE INTERPOLATION
John G. Harris
California Institute of Technology
Computation and Neural Systeins Option, 216-76
Pasadena, CA 91125
ABSTRACT
Reconstructing a surface from sparse sensory data is a well-known
problem iIi computer vision. This paper describes an experimental
... | 169 |@word aircraft:1 version:2 open:1 calculus:1 arti:1 solid:5 interestingly:2 current:6 luo:2 follower:3 must:2 written:1 john:1 mesh:1 analytic:3 designed:3 device:7 supplying:1 compo:1 sudden:1 provides:2 node:10 location:2 supply:1 resistive:3 combine:1 expected:1 terminal:8 becomes:1 provided:2 circuit:9 vref:2 ... |
754 | 1,690 | Bifurcation Analysis of a Silicon Neuron
Girish N. Patel] , Gennady s. Cymbalyuk2,3,
Ronald L. Calabrese2 , and Stephen P. DeWeerth 1
lSchool of Electrical and Computer Engineering
Georgia Institute of Technology
Atlanta, Ga. 30332-0250
{girish.patel, steve.deweerth} @ece.gatech.edu
2Department of Biology
Emory Univer... | 1690 |@word simulation:1 gennady:1 solid:2 configuration:1 pub:1 current:28 emory:2 activation:4 follower:1 ronald:1 ota:5 motor:2 rinzel:1 alone:1 half:2 selected:1 dissertation:1 provides:3 node:2 location:1 mathematical:17 burst:1 c2:3 differential:3 m7:1 supply:2 hopf:2 prove:1 inside:1 theoretically:1 ra:1 behavio... |
755 | 1,691 | Greedy importance sampling
Dale Schuurmans
Department of Computer Science
University of Waterloo
dale@cs.uwaterloo.ca
Abstract
I present a simple variation of importance sampling that explicitly searches for important regions in the target distribution. I prove that the technique yields unbiased estimates, and show e... | 1691 |@word mild:1 version:1 middle:1 heuristically:1 simulation:2 crucially:1 reduction:2 initial:2 series:1 elliptical:1 z2:1 current:1 must:3 evans:1 partition:6 drop:1 xex:2 depict:2 greedy:27 selected:1 fewer:1 isard:1 ebf:1 intelligence:2 xk:3 math:1 simpler:2 unbounded:1 constructed:1 direct:3 predecessor:4 prov... |
756 | 1,692 | Lower Bounds on the Complexity of
Approximating Continuous Functions by
Sigmoidal Neural Networks
Michael Schmitt
Lehrstuhl Mathematik und Informatik
FakuWit ftir Mathematik
Ruhr-Universitat Bochum
D-44780 Bochum, Germany
mschmitt@lmi.ruhr-uni-bochum.de
Abstract
We calculate lower bounds on the size of sigmoidal neur... | 1692 |@word briefly:1 polynomial:28 norm:4 seems:2 open:2 ruhr:2 harder:1 contains:1 chervonenkis:6 existing:1 comparing:1 activation:6 schnitger:3 si:1 must:3 assigning:2 dx:1 readily:2 partition:1 offunctions:1 analytic:1 node:39 sigmoidal:33 mathematical:3 shatter:4 along:1 constructed:1 predecessor:1 become:1 consi... |
757 | 1,693 | Dynamics of Supervised Learning with
Restricted Training Sets and Noisy Teachers
A.C.C. Coolen
Dept of Mathematics
King's College London
The Strand, London WC2R 2LS, UK
tcoolen@mth.kc1.ac.uk
C.W.H.Mace
Dept of Mathematics
King's College London
The Strand, London WC2R 2LS, UK
cmace@mth.kc1.ac.uk
Abstract
We generaliz... | 1693 |@word private:1 closure:2 simulation:7 fonn:2 solid:1 moment:1 xiy:3 recovered:1 yet:6 dx:8 written:1 numerical:6 dydx:4 shape:1 designed:1 update:1 accordingly:1 short:2 iterates:1 complication:1 simpler:2 mathematical:1 along:1 introduce:1 indeed:3 xz:3 mechanic:1 ry:5 multi:1 cpu:1 increasing:1 underlying:2 qw... |
758 | 1,694 | Efficient Approaches to Gaussian Process
Classification
Lehel Csato, Ernest Fokoue, Manfred Opper, Bernhard Schottky
Neural Computing Research Group
School of Engineering and Applied Sciences
Aston University Birmingham B4 7ET, UK.
{opperm,csatol}~aston.ac.uk
Ole Winther
Theoretical Physics II, Lund University, Solveg... | 1694 |@word determinant:1 inversion:2 polynomial:1 seems:2 simulation:4 covariance:11 thereby:1 moment:2 selecting:1 imaginary:1 z2:1 wd:1 com:1 written:3 must:2 numerical:1 subsequent:1 partition:3 j1:1 treating:1 plot:1 ti7:2 update:6 stationary:1 intelligence:1 plane:1 steepest:1 manfred:1 toronto:1 simpler:4 specia... |
759 | 1,695 | A generative model for attractor dynamics
Richard S. Zemel
Department of Psychology
University of Arizona
Tucson, AZ 85721
Michael C. Mozer
Department of Computer Science
University of Colorado
Boulder, CO 80309-0430
zemel@u.arizona.edu
mozer@colorado.edu
Abstract
Attractor networks, which map an input space to a d... | 1695 |@word eliminating:1 proportion:2 hippocampus:1 simulation:7 covariance:1 solid:1 accommodate:1 initial:3 selecting:2 rightmost:1 existing:1 current:1 wd:1 activation:2 yet:1 must:1 cottrell:1 visible:1 partition:2 midway:1 shape:2 update:8 generative:15 selected:1 item:2 beginning:1 dissertation:1 provides:2 char... |
760 | 1,696 | Robust Recognition of Noisy and Superimposed
Patterns via Selective Attention
Soo-Young Lee
Brain Science Research Center
Korea Advanced Institute of Science & Technology
Yusong-gu, Taejon 305-701 Korea
Michael C. Mozer
Department of Computer Science
University of Colorado at Boulder
Boulder, CO 80309 USA
sylee@ee.ka... | 1696 |@word briefly:1 inversion:1 seems:1 heuristically:1 simulation:2 attended:5 solid:1 contextual:1 activation:1 must:1 visible:1 treating:1 update:1 cue:1 selected:3 xk:3 location:2 along:2 consists:1 expected:1 roughly:1 nor:1 brain:3 ol:4 considering:2 increasing:1 panel:5 what:2 quantitative:1 y3:1 act:1 bipolar... |
761 | 1,697 | Statistical Dynamics of Batch Learning
s. Li and K. Y. Michael Wong
Department of Physics, Hong Kong University of Science and Technology
Clear Water Bay, Kowloon, Hong Kong
{phlisong, phkywong}@ust.hk
Abstract
An important issue in neural computing concerns the description of
learning dynamics with macroscopic dynamic... | 1697 |@word kong:3 version:3 briefly:1 open:1 mee:1 simulation:2 covariance:1 thereby:1 versatile:1 solid:1 series:1 denoting:1 reaction:2 comparing:1 activation:19 perturbative:1 ust:1 additive:2 realistic:1 subsequent:2 enables:2 beginning:1 coarse:1 node:3 ron:5 along:1 direct:1 introduce:4 theoretically:1 sacrifice... |
762 | 1,698 | Bayesian Reconstruction of 3D Human Motion
from Single-Camera Video
Nicholas R. Howe
Department of Computer Science
Cornell University
Ithaca, NY 14850
nihowe@cs.comell.edu
Michael E. Leventon
Artificial Intelligence Lab
Massachusetts Institute of Technology
Cambridge, MA 02139
leventon@ai.mit.edu
William T. Freeman... | 1698 |@word version:1 proportion:1 nd:1 open:1 seek:1 mitsubishi:2 decomposition:1 covariance:1 shot:1 contains:4 com:1 comell:1 yet:2 must:2 realistic:1 informative:1 eleven:1 treating:1 stationary:1 intelligence:1 isard:1 plane:4 short:6 provides:2 toronto:1 successive:2 along:2 consists:1 combine:1 fitting:1 manner:... |
763 | 1,699 | Transductive Inference for Estimating
Values of Functions
Olivier Chapelle*, Vladimir Vapnik*,t, Jason Westontt.t,*
* AT&T Research Laboratories, Red Bank, USA.
t Royal Holloway, University of London, Egham, Surrey, UK.
tt Barnhill BioInformatics.com, Savannah, Georgia, USA.
{ chapelle, vlad, weston} @research.att.com
... | 1699 |@word cu:1 repository:1 simulation:2 tr:1 ld:3 series:1 att:1 denoting:1 outperforms:2 com:2 realistic:1 partition:1 plot:2 discrimination:1 leaf:1 provides:1 postal:1 five:2 direct:2 consists:1 introduce:1 expected:2 considering:1 increasing:1 estimating:14 minimizes:4 finding:1 xd:1 classifier:1 uk:1 control:1 ... |
764 | 17 | 622
LEARNING A COLOR ALGORITHM FROM EXAMPLES
Anya C. Hurlbert and Tomaso A. Poggio
Artificial Intelligence Laboratory and Department of Brain and Cognitive Sciences,
Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
ABSTRACT
A lightness algorithm that separates surface reflectance from illumin... | 17 |@word middle:1 seems:2 norm:1 grey:1 simulation:1 llo:1 brightness:4 thereby:1 shading:1 configuration:1 disparity:1 correspondin:1 interestingly:2 existing:1 err:1 recovered:1 surprising:1 assigning:1 yet:2 must:3 numerical:1 shape:3 alone:2 intelligence:2 half:4 inspection:1 iso:3 draft:1 location:2 simpler:1 con... |
765 | 170 | 795
SONG LEARNING IN BIRDS
M. Konishi
Division of Biology
California Institute of Technology
Birds sing to communicate. Male birds use song to advertise their territories and
attract females. Each bird species has a unique song or set of songs. Song conveys
both species and individual identity. In most species, young... | 170 |@word tutor:2 white:3 during:1 memorized:3 link:2 series:1 contains:2 liquid:1 crystal:1 patterning:1 neuron:6 sensitive:2 sing:1 compo:1 stokes:1 crowned:3 own:2 female:1 recognizable:1 selectivity:1 acoustic:1 advertise:1 california:1 learned:1 behavior:1 adult:1 brain:1 attract:1 proceeds:1 pattern:2 reproduce:... |
766 | 1,700 | Regular and Irregular Gallager-type
Error-Correcting Codes
Y. Kabashirna and T. Murayarna
Dept. of Compt. IntI. & Syst. Sci.
Tokyo Institute of Technology
Yokohama 2268502, Japan
D. Saad and R. Vicente
Neural Computing Research Group
Aston University
Birmingham B4 7ET, UK
Abstract
The performance of regular and irre... | 1700 |@word version:2 achievable:1 seems:1 simulation:5 tr:1 solid:1 initial:9 mag:1 longitudinal:1 current:3 comparing:1 paramagnetic:13 si:5 assigning:1 dx:2 attracted:1 must:1 yet:1 additive:1 numerical:14 partition:1 enables:1 analytic:1 treating:1 selected:3 devising:1 parameterization:1 complementing:1 sys:1 hami... |
767 | 1,702 | Differentiating Functions of the Jacobian
with Respect to the Weights
Gary William Flake
NEC Research Institute
4 Independence Way
Princeton, NJ 08540
jiake@research.nj.nec.com
Barak A. Pearlmutter
Dept of Computer Science, FEC 313
University of New Mexico
Albuquerque, NM 87131
bap@cs.unm.edu
Abstract
For many probl... | 1702 |@word ruanaidh:1 determinant:1 seems:1 series:2 contains:3 prescriptive:1 existing:1 com:1 dx:1 must:1 john:1 numerical:1 analytic:1 alone:1 selected:1 node:4 sigmoidal:1 five:1 mathematical:2 differential:7 fitting:1 frans:1 introduce:2 behavior:1 mechanic:1 notation:4 moreover:1 kevrekidis:1 mass:3 transformati... |
768 | 1,703 | Distributed Synchrony of Spiking Neurons
in a Hebbian Cell Assembly
David Horn Nir Levy
School of Physics and Astronomy,
Raymond and Beverly Sackler Faculty of Exact Sciences,
Tel Aviv University, Tel Aviv 69978, Israel
horn~neuron.tau.ac.il
nirlevy~post.tau.ac.il
Isaac Meilijson Eytan Ruppin
School of Mathematical ... | 1703 |@word trial:1 longterm:1 faculty:2 seems:2 stronger:1 open:1 grey:1 simulation:8 excited:2 initial:1 cyclic:3 efficacy:8 current:3 activation:1 yet:2 analytic:5 enables:1 aps:1 v:1 stationary:3 tone:1 short:1 compo:1 math:2 zhang:5 mathematical:1 differential:1 prove:1 sustained:3 manner:3 theoretically:1 inter:1... |
769 | 1,704 | Constructing Heterogeneous Committees
Using Input Feature Grouping:
Application to Economic Forecasting
Yuansong Liao and John Moody
Department of Computer Science, Oregon Graduate Institute,
P.O.Box 91000, Portland, OR 97291-1000
Abstract
The committee approach has been proposed for reducing model
uncertainty and im... | 1704 |@word h:2 achievable:1 tr:2 reduction:1 initial:1 contains:1 series:11 selecting:6 genetic:1 outperforms:1 existing:1 john:1 designed:1 plot:1 resampling:1 ith:2 tumer:1 successive:2 constructed:2 supply:1 market:1 themselves:1 increasing:1 totally:1 becomes:1 underlying:1 bounded:1 notation:1 ghosh:1 bootstrappi... |
770 | 1,705 | A recurrent model of the interaction between
Prefrontal and Inferotemporal cortex in delay
tasks
ALFONSO RENART, NESTOR PARGA
Departamento de F{sica Te6rica
Universidad Aut6noma de Madrid
Canto Blanco, 28049 Madrid, Spain
http://www.ft.uam.es/neurociencialGRUPO/grup0.1!nglish.html
and
EDMUND T. ROLLS
Oxford Universit... | 1705 |@word trial:12 proceeded:2 solid:3 initial:2 configuration:1 series:1 efficacy:2 contains:1 past:1 current:14 nt:3 partition:1 v:1 stationary:1 cue:9 alone:7 realism:1 short:2 provides:4 characterization:1 troll:1 differential:4 persistent:1 consists:1 sustained:3 inside:1 inter:3 indeed:1 behavior:3 themselves:1... |
771 | 1,706 | From Coexpression to Coregulation: An
Approach to Inferring Transcriptional
Regulation among Gene Classes from
Large-Scale Expression Data
Eric Mjolsness
Jet Propulsion Laboratory
California Institute of Technology
Pasadena CA 91109-8099
mjolsness@jpl.nasa.gov
Tobias Mann
Jet Propulsion Laboratory
California Institut... | 1706 |@word nd:1 proportionality:1 simulation:1 covariance:1 excited:2 pick:2 thereby:1 minus:1 solid:1 reduction:2 initial:1 series:2 score:10 genetic:1 existing:3 current:2 od:1 activation:1 must:1 plot:3 drop:1 guess:1 nervous:1 beginning:1 smith:1 provides:1 sigmoidal:1 zhang:1 five:1 provisional:1 scie:1 direct:1 ... |
772 | 1,707 | An MEG Study of Response Latency and
Variability in the Human Visual System
During a Visual-Motor Integration Task
Akaysha C. Tang
Dept. of Psychology
University of New Mexico
Albuquerque, NM 87131
akaysha@unm.edu
Barak A. Pearlmutter
Dept. of Computer Science
University of New Mexico
Albuquerque, NM 87131
bap@cs. un... | 1707 |@word neurophysiology:1 trial:14 middle:1 cyprus:1 simulation:1 lobe:3 brightness:1 pressed:1 solid:1 extrastriate:1 contains:1 mainen:1 reaction:3 bitmap:1 current:1 neurophys:2 surprising:1 cad:1 activation:1 scatter:1 bd:1 motor:5 remove:1 plot:1 tone:1 rts:2 beginning:1 short:1 detecting:1 location:1 firstly:... |
773 | 1,708 | The Parallel Problems Server: an Interactive Tool
for Large Scale Machine Learning
Charles Lee Isbell, Jr.
Parry Husbands
isbell @research.att.com
AT&T Labs
180 Park Avenue Room A255
Florham Park, NJ 07932-0971
PIRHusbands@lbl.gov
Lawrence Berkeley National LaboratorylNERSC
1 Cyclotron Road, MS 50F
Berkeley, CA 947... | 1708 |@word luk:1 briefly:1 version:3 judgement:2 loading:2 disk:1 decomposition:2 cleary:1 wrapper:1 contains:2 att:1 efficacy:1 score:2 document:26 africa:5 brien:1 current:1 com:1 si:1 issuing:1 written:1 visible:1 academia:1 shape:1 enables:2 remove:2 plot:1 half:4 guess:1 provides:2 noisereduction:1 along:2 direct... |
774 | 1,709 | Bayesian averaging is well-temperated
Lars Kai Hansen
Department of Mathematical Modelling
Technical University of Denmark B321
DK-2800 Lyngby, Denmark
lkhansen@imm .dtu.dk
Abstract
Bayesian predictions are stochastic just like predictions of any other
inference scheme that generalize from a finite sample. While a si... | 1709 |@word stronger:1 decomposition:1 series:1 denoting:2 recovered:1 dx:5 must:1 oml:6 analytic:1 resampling:1 intelligence:1 cult:1 manfred:1 boosting:2 location:1 mathematical:1 become:1 indeed:2 expected:2 kamm:1 becomes:2 kind:1 quantitative:1 berkeley:1 voting:1 whatever:1 control:1 unit:6 planck:1 positive:2 li... |
775 | 171 | 366
NEURONAL MAPS FOR SENSORY-MOTOR
CONTROL IN THE BARN OWL
C.D. Spence, J.C. Pearson, JJ. Gelfand, and R.M. Peterson
David Sarnoff Research Center
Subsidiary of SRI International
CN5300
Princeton, New Jersey 08543-5300
W.E. Sullivan
Department of Biology
Princeton University
Princeton, New Jersey 08544
ABSTRACT
The ... | 171 |@word private:2 version:2 sri:1 seems:1 simulation:4 tried:2 pick:3 mention:1 minus:1 shading:1 contains:1 tuned:1 current:1 neurophys:1 activation:2 yet:3 must:1 realistic:2 motor:15 fund:1 v:9 alone:1 cue:4 fewer:1 nervous:3 imitate:1 tone:2 core:2 compo:2 provides:1 location:4 sigmoidal:1 along:1 constructed:1 ... |
776 | 1,710 | Learning to Parse Images
Geoffrey E. Hinton and Zoubin Ghahramani
Gatsby Computational Neuroscience Unit
University College London
London, United Kingdom WC1N 3AR
{hinton,zoubin}@gatsby.ucl.ac.uk
Vee Whye Tah
Department of Computer Science
University of Toronto
Toronto, Ontario, Canada M5S 3G4
ywteh@cs.utoronto.ca
Ab... | 1710 |@word middle:1 version:2 simulation:1 harder:1 contains:1 united:1 past:1 current:3 si:5 activation:3 partition:1 shape:2 remove:1 discrimination:1 generative:2 leaf:2 intelligence:2 node:6 toronto:2 location:1 become:1 combine:1 manner:1 g4:1 terminal:1 inspired:1 freeman:1 company:1 actual:3 inappropriate:1 mor... |
777 | 1,711 | Probabilistic methods for Support Vector
Machines
Peter Sollich
Department of Mathematics, King's College London
Strand, London WC2R 2LS, U.K. Email: peter.sollich@kcl.ac.uk
Abstract
I describe a framework for interpreting Support Vector Machines
(SVMs) as maximum a posteriori (MAP) solutions to inference
problems wit... | 1711 |@word private:1 determinant:1 polynomial:1 open:1 grey:1 km:1 covariance:7 tr:1 solid:2 contains:1 recovered:1 comparing:1 analysed:1 dx:3 numerical:1 additive:1 shape:1 plot:2 alone:2 intelligence:1 manfred:1 contribute:1 hyperplanes:1 sigmoidal:2 scholkopf:4 inside:1 roughly:1 actual:2 lib:1 becomes:2 underlyin... |
778 | 1,712 | Bayesian Transduction
Thore Graepel, Ralf Herbrich and Klaus Obermayer
Department of Computer Science
Technical University of Berlin
Franklinstr. 28/29, 10587 Berlin, Germany
{graepeI2, raith, oby} @cs.tu-berlin.de
Abstract
Transduction is an inference principle that takes a training sample and aims at estimating the... | 1712 |@word repository:1 version:12 nd:1 tr:2 carry:1 reduction:1 itp:1 current:2 bd:3 offunctions:1 treating:1 plot:1 update:1 v:1 alone:1 accordingly:1 jwi:1 record:1 hypersphere:2 coarse:1 provides:2 postal:1 herbrich:5 billiard:10 hyperplanes:2 mathematical:1 direct:1 indeed:1 considering:1 project:1 estimating:3 b... |
779 | 1,713 | Policy Gradient Methods for
Reinforcement Learning with Function
Approximation
Richard S. Sutton, David McAllester, Satinder Singh, Yishay Mansour
AT&T Labs - Research, 180 Park Avenue, Florham Park, NJ 07932
Abstract
Function approximation is essential to reinforcement learning, but
the standard approach of approxim... | 1713 |@word q7f:9 version:2 twelfth:2 simulation:1 r:1 valuefunction:1 tr:1 selecting:1 ours:2 si:1 written:1 must:2 update:1 v:3 stationary:3 greedy:1 selected:1 assurance:1 implying:1 parameterization:5 es:1 parameterizations:1 vaps:5 simpler:1 direct:2 prove:5 theoretically:1 expected:6 rapid:1 roughly:1 elman:1 nor... |
780 | 1,714 | U nmixing Hyperspectral Data
Lucas Parra, Clay Spence, Paul Sajda
Sarnoff Corporation, CN-5300, Princeton, NJ 08543, USA
{lparra, cspence,psajda} @sarnoff.com
Andreas Ziehe, Klaus-Robert Miiller
GMD FIRST.lDA, Kekulestr. 7, 12489 Berlin, Germany
{ziehe,klaus}@first.gmd.de
Abstract
In hyperspectral imagery one pixel ... | 1714 |@word determinant:2 inversion:1 proportion:1 underline:1 tedious:1 open:3 simulation:2 sensed:1 decomposition:2 brightness:1 solid:2 moment:1 substitution:1 series:1 contains:2 united:1 pub:1 interestingly:1 current:1 com:1 contextual:1 recovered:2 scatter:3 yet:1 additive:1 distant:1 remove:1 plot:3 v:3 selected... |
781 | 1,715 | U nmixing Hyperspectral Data
Lucas Parra, Clay Spence, Paul Sajda
Sarnoff Corporation, CN-5300, Princeton, NJ 08543, USA
{lparra, cspence,psajda} @sarnoff.com
Andreas Ziehe, Klaus-Robert Miiller
GMD FIRST.lDA, Kekulestr. 7, 12489 Berlin, Germany
{ziehe,klaus}@first.gmd.de
Abstract
In hyperspectral imagery one pixel ... | 1715 |@word determinant:2 version:1 inversion:1 polynomial:2 proportion:1 underline:1 nd:1 tedious:1 open:3 hu:1 simulation:3 seek:2 sensed:1 covariance:14 crucially:1 decomposition:2 brightness:1 pick:1 tr:1 solid:2 moment:1 substitution:1 series:1 contains:2 united:1 selecting:1 pub:1 tuned:1 interestingly:3 ati:1 ri... |
782 | 1,716 | Bayesian Map Learning in Dynamic
Environments
Kevin P. Murphy
Computer Science Division
University of California
Berkeley, CA 94720-1776
murphyk@cs.berkeley.edu
Abstract
We consider the problem of learning a grid-based map using a robot
with noisy sensors and actuators. We compare two approaches:
online EM, where the ... | 1716 |@word briefly:1 version:1 bf:1 open:2 tried:1 initial:2 liu:3 contains:1 selecting:1 ours:1 rightmost:1 past:1 freitas:1 current:1 must:4 motor:3 update:5 resampling:1 alone:1 greedy:1 intelligence:1 es:2 lr:1 location:12 simpler:1 become:3 corridor:2 introduce:2 pairwise:1 forgetting:1 expected:2 indeed:1 nor:1 ... |
783 | 1,717 | An Improved Decomposition Algorithm
for Regression Support Vector Machines
Pavel Laskov
Department of Computer and Information Sciences
University of Delaware
Newark, DE 19718
laskov@asel. udel. edu
Abstract
A new decomposition algorithm for training regression Support
Vector Machines (SVM) is presented. The algorith... | 1717 |@word termination:4 decomposition:21 pavel:1 mention:1 minus:1 tr:1 initial:2 selecting:2 outperforms:1 current:2 incidence:1 rpi:17 si:1 must:3 numerical:1 kdd:4 girosi:1 plot:1 selected:3 nnsp:1 dissertation:1 completeness:1 provides:1 iterates:1 unbounded:1 become:1 fitting:1 behavior:1 td:1 company:1 cache:1 ... |
784 | 1,718 | A Multi-class Linear Learning Algorithm
Related to Winnow
Chris Mesterhann*
Rutgers Computer Science Department
110 Frelinghuysen Road
Piscataway, NJ 08854
mesterha@paul.rutgers.edu
Abstract
In this paper, we present Committee, a new multi-class learning algorithm related to the Winnow family of algorithms. Committee... | 1718 |@word trial:8 version:1 stronger:1 duda:1 open:1 simulation:1 tried:1 pick:1 solid:1 series:1 contains:1 document:1 current:2 comparing:2 z2:4 must:1 cruz:1 additive:1 remove:4 update:7 selected:1 warmuth:2 smith:1 manfred:1 provides:1 ucsc:1 incorrect:1 freitag:1 combine:2 introduce:1 behavior:2 multi:23 actual:... |
785 | 1,719 | The Relevance Vector Machine
Michael E. Tipping
Microsoft Research
St George House, 1 Guildhall Street
Cambridge CB2 3NH, U.K.
mtipping~microsoft.com
Abstract
The support vector machine (SVM) is a state-of-the-art technique
for regression and classification, combining excellent generalisation
properties with a sparse ... | 1719 |@word determinant:1 wla:3 heuristically:1 covariance:1 paid:2 dramatic:1 solid:1 reduction:1 current:1 com:1 must:2 written:1 john:1 update:2 implying:2 alone:1 fewer:4 selected:1 intelligence:1 location:1 preference:1 liberal:1 along:3 direct:1 qualitative:1 fitting:2 combine:1 manner:1 introduce:4 notably:1 ra:... |
786 | 172 | 678
ALOW-POWER CMOS CIRCUIT WHICH EMULATES
TEMWORALELECTIDCALPROPERTIES OF NEURONS
Jack L. Meador and Clint S. Cole
Electrical and Computer Engineering Dept.
Washington State University
Pullman WA. 99164-2752
ABSTRACf
This paper describes a CMOS artificial neuron. The circuit is
directly derived from the voltage-gat... | 172 |@word pulsestream:1 seems:3 open:1 pulse:1 gradual:2 simulation:3 thereby:1 moment:1 initial:1 configuration:2 amp:2 current:15 activation:26 yet:1 must:1 physiol:1 numerical:2 analytic:1 asymptote:1 designed:1 v:2 device:1 nervous:1 vtp:1 beginning:1 smith:1 node:1 neuromimes:3 become:1 differential:1 viable:1 ma... |
787 | 1,720 | A Neuromorphic VLSI System for Modeling
the Neural Control of Axial Locomotion
Girish N. Patel
girish@ece.gatech.edu
Edgar A. Brown
ebrown@ece.gatech.edu
Stephen P. DeWeerth
steved@ece.gatech.edu
School of Electrical and Computer Engineering
Georgia Institute of Technology
Atlanta, Ga. 30332-0250
Abstract
We have ... | 1720 |@word illustrating:1 rising:2 stronger:4 replicate:2 propagate:1 disparity:1 current:5 timer:1 comparing:1 motor:7 plot:1 designed:1 progressively:1 half:2 device:2 beginning:1 short:1 detecting:2 provides:2 node:4 cpg:11 mathematical:2 along:3 burst:1 consists:3 rostral:1 rapid:2 behavior:13 brain:1 inspired:1 i... |
788 | 1,721 | v-Arc: Ensemble Learning
in the Presence of Outliers
t
G. Ratsch t , B. Scholkopf1, A. Smola",
K.-R. Miillert, T. Onodatt , and S. Mikat
t GMD FIRST, Rudower Chaussee 5,12489 Berlin, Germany
Microsoft Research, 1 Guildhall Street, Cambridge CB2 3NH, UK
* Dep. of Engineering, ANU, Canberra ACT 0200, Australia
tt CRIEP... | 1721 |@word repository:2 version:1 briefly:1 seems:1 queensland:1 eng:1 tr:1 substitution:1 riitsch:3 com:1 ida:1 nt:1 written:1 additive:1 shape:1 gv:13 interpretable:1 prohibitive:1 compo:1 provides:1 boosting:15 become:2 scholkopf:4 combine:1 inside:1 introduce:1 expected:2 indeed:1 behavior:1 roughly:1 eurocolt:1 i... |
789 | 1,722 | Bayesian model selection for Support
Vector machines, Gaussian processes and
other kernel classifiers
Matthias Seeger
Institute for Adaptive and Neural Computation
University of Edinburgh
5 Forrest Hill, Edinburgh EHI 2QL
seeger@dai.ed.ac.uk
Abstract
We present a variational Bayesian method for model selection over
f... | 1722 |@word repository:1 briefly:1 polynomial:2 norm:1 seems:1 logit:1 open:2 covariance:7 series:1 score:1 rkhs:3 ours:1 existing:1 ka:1 comparing:1 written:2 john:1 aside:1 discrimination:1 generative:4 lr:2 manfred:1 transposition:1 coarse:1 location:1 lx:6 attack:2 toronto:1 introduce:1 sacrifice:1 expected:1 indee... |
790 | 1,723 | Support Vector Method for Novelty Detection
Bernhard Scholkopf*, Robert Williamson?,
Alex Smola?, John Shawe-Taylor t , John Platt*
?
* Microsoft Research Ltd., 1 Guildhall Street, Cambridge, UK
Department of Engineering, Australian National University, Canberra 0200
t Royal Holloway, University of London, Egham, UK
... | 1723 |@word msr:1 briefly:1 version:3 norm:5 stronger:1 nd:1 thereby:1 tr:1 contains:2 recovered:1 com:1 current:1 yet:1 dx:3 must:1 written:1 john:3 mesh:1 analytic:1 mislabelled:2 xex:1 alone:1 implying:1 intelligence:1 accordingly:1 lr:3 detecting:1 hyperplanes:1 firstly:2 direct:1 become:1 scholkopf:8 inside:1 intr... |
791 | 1,724 | Spiking Boltzmann Machines
Geoffrey E. Hinton
Gatsby Computational Neuroscience Unit
University College London
London WCIN 3AR, UK
hinton@gatsby. ucl. ac. uk
Andrew D. Brown
Department of Computer Science
University of Toronto
Toronto, Canada
andy@cs.utoronto.ca
Abstract
We first show how to represent sharp posterio... | 1724 |@word proportion:1 seems:1 simulation:7 covariance:5 solid:1 awij:1 initial:1 series:2 tuned:1 activation:2 si:3 conjunctive:1 must:3 distant:1 additive:1 visible:20 shape:1 treating:2 hourglass:3 extrapolating:1 stationary:1 generative:3 intelligence:1 caveat:1 coarse:2 toronto:2 location:2 consists:1 fitting:1 ... |
792 | 1,725 | A Winner-Take-All Circuit with
Controllable Soft Max Property
Shih-Chii Lin
Institute for Neuroinformatics, ETHjUNIZ
Winterthurstrasse 190, CH-8057 Zurich
Switzerland
shih@ini.phys.ethz.ch
Abstract
I describe a silicon network consisting of a group of excitatory neurons and a global inhibitory neuron. The output of t... | 1725 |@word simulation:1 liu:3 document:1 current:41 discrimination:1 ial:1 fabricating:1 node:11 differential:3 consists:4 expected:1 behavior:10 increasing:1 provided:1 circuit:14 directionselective:2 kaufman:1 fabricated:4 temporal:1 act:2 unit:1 grant:1 positive:1 engineering:1 mead:1 mateo:1 range:2 grossberg:3 ac... |
793 | 1,726 | A Variational Bayesian Framework for
Graphical Models
Hagai Attias
hagai@gatsby.ucl.ac.uk
Gatsby Unit, University College London
17 Queen Square
London WC1N 3AR, U.K.
Abstract
This paper presents a novel practical framework for Bayesian model
averaging and model selection in probabilistic graphical models.
Our approa... | 1726 |@word repository:1 proportion:5 open:1 heretofore:1 r:5 covariance:6 tr:2 solid:2 contains:1 denoting:2 ours:1 existing:1 current:1 dx:1 visible:2 informative:1 update:1 v:1 intelligence:2 provides:2 node:16 mathematical:1 become:3 overhead:2 manner:1 themselves:1 automatically:1 becomes:4 provided:1 bounded:1 fa... |
794 | 1,727 | The Relaxed Online
Maximum Margin Algorithm
Yi Li and Philip M. Long
Department of Computer Science
National University of Singapore
Singapore 119260, Republic of Singapore
{liyi,p/ong}@comp.nus.edu.sg
Abstract
We describe a new incremental algorithm for training linear threshold functions: the Relaxed Online Maximum ... | 1727 |@word trial:25 polynomial:2 d2:2 t_:1 tr:1 born:1 contains:2 att:1 ours:1 bhattacharyya:1 err:4 percep:4 com:1 must:2 john:1 cruz:1 enables:1 update:5 plane:1 xk:2 simpler:1 scholkopf:2 prove:6 consists:1 manner:2 expected:1 brain:2 little:1 abound:1 classifies:2 moreover:1 maximizes:2 what:1 kaufman:1 guarantee:... |
795 | 1,728 | Wiring optimization in the brain
Dmitri B. Chklovskii
Sloan Center for
Theoretical Neurobiology
The Salk Institute
La Jolla, CA 92037
mitya@salk.edu
Charles F. Stevens
Howard Hughes Medical Institute
and Molecular Neurobiology Lab
The Salk Institute
La Jolla, CA 92037
stevens@salk.edu
Abstract
The complexity of cort... | 1728 |@word eliminating:1 proportion:1 km:1 propagate:1 reduction:1 contains:2 series:1 existing:3 must:4 written:1 physiol:2 plot:1 half:2 nervous:2 become:1 sacrifice:1 brain:13 decreasing:2 rall:1 actual:8 increasing:1 becomes:3 retinotopic:1 cherniak:3 circuit:6 formidable:1 what:6 monkey:1 transformation:1 attenua... |
796 | 1,729 | Topographic Transformation as a
Discrete Latent Variable
Nebojsa Jojic
Beckman Institute
University of Illinois at Urbana
www.ifp.uiuc.edu/",jojic
Brendan J. Frey
Computer Science
University of Waterloo
www.cs.uwaterloo.ca/ ... frey
Abstract
Invariance to topographic transformations such as translation and
shearing i... | 1729 |@word deformed:2 version:1 proportion:2 loading:1 tried:1 covariance:2 tr:2 accommodate:1 tmg:20 golem:2 contains:1 series:1 denoting:1 document:1 written:1 must:1 gurion:1 shape:1 update:1 nebojsa:1 generative:10 selected:3 fewer:1 intelligence:1 ire:1 toronto:1 rc:1 along:1 jac:1 shearing:11 behavior:1 uiuc:1 o... |
797 | 173 | 356
USING BACKPROPAGATION
WITH TEMPORAL WINDOWS
TO LEARN THE DYNAMICS
OF THE CMU DIRECT-DRIVE ARM II
K. Y. Goldberg and B. A. Pearlmutter
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
ABSTRACT
Computing the inverse dynamics of a robot ann is an active area of research
in the control liter... | 173 |@word effect:2 especially:1 move:1 correct:1 filter:1 highfrequency:1 pick:1 link:1 generalization:1 pearlmutter:1 predict:1 sigmoid:1 difficult:1 physical:3 unknown:1 mellon:1 hope:1 sensor:1 robot:4 direct:2 tenns:1 torque:5 window:2 saturation:1 ii:2 relates:1 linearity:1 difficulty:1 force:1 interpreted:1 dist... |
798 | 1,730 | Perceptual Organization Based on
Temporal Dynamics
Xiuwen Liu and DeLiang L. Wang
Department of Computer and Information Science
Center for Cognitive Science
The Ohio State University, Columbus, OR 43210-1277
Email: {liux, dwang}@cis.ohio-state.edu
Abstract
A figure-ground segregation network is proposed based on a n... | 1730 |@word version:1 decomposition:3 solid:1 shiota:1 liu:4 configuration:2 fragment:1 initial:1 existing:2 current:1 tilted:1 occludes:1 shape:6 praeger:1 plot:1 update:1 occlude:1 cue:2 selected:1 detecting:1 provides:1 node:37 preference:1 mathematical:1 differential:1 become:1 excitatorily:1 shapley:1 behavior:4 a... |
799 | 1,731 | Spectral Cues in Human Sound Localization
Craig T. Jin
Department of Physiology and
Department of Electrical Engineering,
Univ. of Sydney, NSW 2006, Australia
Anna Corderoy
Department of Physiology
Univ. of Sydney, NSW 2006, Australia
Simon Carlile
Department of Physiology
and Institute of Biomedical Research
Univ. ... | 1731 |@word trial:4 version:1 briefly:1 judgement:1 duda:1 tried:1 nsw:4 score:18 surprising:1 shape:4 plot:4 alone:1 cue:115 iso:10 short:1 schaik:4 filtered:5 provides:1 location:45 five:5 height:1 mathematical:2 along:9 differential:1 interaural:38 manner:4 themselves:1 ol:6 resolve:1 inappropriate:1 provided:2 line... |
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