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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800 | 1,732 | Optimal sizes of dendritic and axonal arbors
Dmitri B. Chklovskii
Sloan Center for Theoretical Neurobiology
The Salk Institute, La Jolla, CA 92037
mitya@salk.edu
Abstract
I consider a topographic projection between two neuronal layers with different densities of neurons. Given the number of output neurons connected t... | 1732 |@word version:1 termination:1 pulse:2 carry:2 inefficiency:1 existing:2 must:3 readily:1 mesh:3 interrupted:1 numerical:1 shape:3 designed:1 plot:2 overriding:1 v:1 nervous:1 lr:1 location:2 along:1 boycott:1 inter:4 indeed:1 frequently:1 morphology:3 brain:5 uz:1 provided:1 minimizes:4 monkey:4 nj:1 quantitative... |
801 | 1,733 | Maximum entropy discrimination
Tommi Jaakkola
MIT AI Lab
545 Technology Sq.
Cambridge, MA 02139
Marina Meila
MIT AI Lab
545 Technology Sq.
Cambridge, MA 02139
Tony Jebara
MIT Media Lab
20 Ames St.
Cambridge, MA 02139
tommi@ai.mit.edu
mmp@ai. mit. edu
jebara@media. mit. edu
Abstract
We present a general framework... | 1733 |@word determinant:1 achievable:1 polynomial:1 hu:1 covariance:5 reap:1 solid:4 carry:1 must:1 john:2 additive:1 partition:3 informative:2 enables:2 designed:1 discrimination:7 generative:3 fewer:1 warmuth:1 indicative:1 provides:3 boosting:1 node:1 ames:1 preference:2 direct:1 become:1 pairwise:2 multi:1 ol:1 beg... |
802 | 1,734 | Neural System Model of Human Sound
Localization
Craig T. Jin
Department of Physiology and
Department of Electrical Engineering,
Univ. of Sydney, NSW 2006, Australia
Simon Carlile
Department of Physiology
and Institute of Biomedical Research,
Univ. of Sydney, NSW 2006, Australia
Abstract
This paper examines the role ... | 1734 |@word trial:3 version:1 nsw:2 fonn:1 carry:1 existing:1 imaginary:1 current:2 must:1 pertinent:1 plot:1 progressively:2 cue:9 nervous:2 realism:2 short:1 schaik:1 filtered:3 provides:1 location:21 five:2 along:1 qualitative:1 interaural:1 manner:2 indeed:1 nor:1 spherical:2 actual:1 window:1 provided:2 underlying... |
803 | 1,735 | Uniqueness of the SVM Solution
Christopher J .C. Burges
Advanced Technologies,
Bell Laboratories,
Lucent Technologies
Holmdel, New Jersey
burges@iucent.com
David J. Crisp
Centre for Sensor Signal and
Information Processing,
Deptartment of Electrical Engineering,
University of Adelaide, South Australia
dcrisp@eleceng.... | 1735 |@word inversion:1 nd:1 tr:2 necessity:1 contains:4 com:1 z2:3 si:3 attracted:1 must:7 john:2 v_:1 lr:1 hyperplanes:2 mathematical:1 along:1 become:1 scholkopf:4 consists:1 fitting:1 inside:1 themselves:1 nonseparable:1 xz:2 considering:1 becomes:2 notation:2 what:1 minimizes:2 finding:3 classifier:1 zl:2 appear:1... |
804 | 1,736 | Nonlinear Discriminant Analysis using
Kernel Functions
Volker Roth & Volker Steinhage
University of Bonn, Institut of Computer Science III
Romerstrasse 164, D-53117 Bonn, Germany
{roth, steinhag}@cs.uni-bonn.de
Abstract
Fishers linear discriminant analysis (LDA) is a classical multivariate technique both for dimension... | 1736 |@word version:4 polynomial:1 duda:1 replicate:1 hu:1 simulation:2 decomposition:2 covariance:4 tr:1 reduction:2 initial:1 exclusively:2 score:6 pub:1 outperforms:1 current:1 scatter:3 assigning:1 written:1 john:1 oldenbourg:1 numerical:3 partition:1 plot:2 update:1 zik:1 stationary:1 provides:1 direct:1 become:1 ... |
805 | 1,737 | Potential Boosters ?
Nigel Duffy
Department of Computer Science
University of California
Santa Cruz, CA 95064
David Helmbold
Department of Computer Science
University of California
Santa Cruz, CA 95064
nigedufJ@cse. ucsc. edu
dph@~se . ucsc. edu
Abstract
Recent interpretations of the Adaboost algorithm view it as p... | 1737 |@word version:1 minus:1 selecting:1 current:2 yet:1 must:2 written:1 malized:1 john:1 cruz:2 additive:2 warmuth:2 steepest:1 manfred:2 lr:1 boosting:43 cse:1 ron:1 successive:1 sigmoidal:4 ucsc:2 direct:2 prove:2 notably:1 behavior:1 roughly:1 nor:2 examine:2 eurocolt:1 decreasing:6 actual:1 cardinality:1 increas... |
806 | 1,738 | Constrained Hidden Markov Models
Sam Roweis
roweis@gatsby.ucl.ac.uk
Gatsby Unit, University College London
Abstract
By thinking of each state in a hidden Markov model as corresponding to some
spatial region of a fictitious topology space it is possible to naturally define neighbouring states as those which are connec... | 1738 |@word mild:2 private:2 version:3 manageable:1 inversion:3 norm:2 humidity:1 simulation:1 covariance:3 pressure:1 configuration:6 series:5 contains:1 selecting:1 current:2 recovered:2 discretization:1 yet:1 must:2 informative:1 shape:7 plot:2 update:1 generative:2 plane:1 short:3 record:1 location:6 direct:1 persi... |
807 | 1,739 | Algebraic Analysis for Non-Regular
Learning Machines
Sumio Watanabe
Precision and Intelligence Laboratory
Tokyo Institute of Technology
4259 Nagatsuta, Midori-ku, Yokohama 223 Japan
swatanab@pi. titech. ac.jp
Abstract
Hierarchical learning machines are non-regular and non-identifiable
statistical models, whose true p... | 1739 |@word version:1 polynomial:5 open:3 minus:1 xlw:3 recursively:1 contains:1 wd:3 nt:1 z2:1 dx:5 analytic:10 midori:1 intelligence:1 half:2 plane:2 math:5 mathematical:4 c2:1 constructed:1 differential:1 prove:5 consists:1 multi:2 automatically:1 moreover:2 bounded:1 maxo:1 k2:1 zl:1 uo:5 grant:1 yn:1 appear:1 posi... |
808 | 174 | 796
SPEECH RECOGNITION: STATISTICAL AND
NEURAL INFORMATION PROCESSING
APPROACHES
John S. Bridle
Speech Research Unit and
National Electronics Research Initiative in Pattern Recognition
Royal Signals and Radar Establishment
Malvern UK
Automatic Speech Recognition (ASR) is an artificial perception problem: the input
is... | 174 |@word cox:1 mention:1 electronics:1 initial:2 series:1 score:2 contains:1 renewed:1 past:1 current:8 adj:1 yet:1 must:3 john:1 realistic:1 shape:1 offunctions:1 designed:1 update:1 discrimination:3 v:1 generative:1 simpler:1 mathematical:2 constructed:1 symposium:1 initiative:1 replication:1 combine:1 behavior:1 m... |
809 | 1,740 | Low Power Wireless Communication via
Reinforcement Learning
Timothy X Brown
Electrical and Computer Engineering
University of Colorado
Boulder, CO 80309-0530
tirnxb@colorado.edu
Abstract
This paper examines the application of reinforcement learning to a wireless communication problem. The problem requires that channe... | 1740 |@word disk:2 termination:1 simulation:3 seek:1 accounting:1 simplifying:1 pg:4 carry:4 reduction:2 initial:1 configuration:1 contains:1 pub:1 current:3 comparing:1 must:4 readily:2 realistic:1 remove:1 update:2 stationary:1 device:1 short:1 location:1 ron:1 five:2 admission:3 supply:1 introduce:1 thy:1 expected:1... |
810 | 1,741 | Robust Full Bayesian Methods for Neural
Networks
Christophe Andrieu*
Cambridge University
Engineering Department
Cambridge CB2 1PZ
England
ca226@eng.cam.ac.uk
J oao FG de Freitas
UC Berkeley
Computer Science
387 Soda Hall, Berkeley
CA 94720-1776 USA
jfgf@cs.berkeley.edu
Arnaud Doucet
Cambridge University
Engineering ... | 1741 |@word middle:1 version:2 stronger:1 norm:1 simulation:4 eng:3 mention:1 tr:1 mlk:1 phy:1 initial:2 series:2 selecting:1 freitas:6 si:2 yet:1 written:1 enables:1 plot:5 stationary:1 devising:1 smith:1 short:1 sigmoidal:1 firstly:1 c2:4 klx:1 ik:1 prove:1 theoretically:1 expected:2 ra:1 indeed:1 growing:1 ol:6 auto... |
811 | 1,742 | Managing Uncertainty in Cue Combination
Zhiyong Yang
Deparbnent of Neurobiology, Box 3209
Duke University Medical Center
Durham, NC 27710
zhyyang@duke.edu
Richard S. Zemel
Deparbnent of Psychology
University of Arizona
Tucson, AZ 85721
zemel@u.arizona.edu
Abstract
We develop a hierarchical generative model to study c... | 1742 |@word trial:4 middle:1 version:1 judgement:1 polynomial:1 accounting:1 jacob:1 fonn:2 shading:31 contains:3 disparity:1 selecting:1 current:1 si:5 yet:1 must:1 mesh:3 realistic:2 subsequent:1 shape:27 alone:6 cue:70 generative:9 plane:2 provides:3 height:1 along:1 direct:1 vi3:1 combine:1 introduce:1 manner:1 beh... |
812 | 1,743 | The Nonnegative Boltzmann Machine
Oliver B. Downs
Hopfield Group
Schultz Building
Princeton University
Princeton, NJ 08544
obdowns@princeton.edu
David J.e. MacKay
Cavendish Laboratory
Madingley Road
Cambridge, CB3 OHE
United Kingdom
mackay@mrao.cam.ac.uk
Daniel D. Lee
Bell Laboratories
Lucent Technologies
700 Mounta... | 1743 |@word h:2 briefly:1 inversion:1 seems:1 nd:1 open:1 covariance:3 q1:1 kappen:1 initial:1 contains:1 series:1 united:1 daniel:1 suppressing:1 current:2 com:1 si:1 activation:1 dx:3 john:1 numerical:2 plot:1 update:3 progressively:1 generative:5 toronto:2 billiard:1 successive:1 height:1 along:1 direct:1 examine:1 ... |
813 | 1,744 | Kirchoff Law Markov Fields for Analog
Circuit Design
Richard M. Golden *
RMG Consulting Inc.
2000 Fresno Road, Plano, Texas 75074
RMGCONSULT@AOL.COM,
www.neural-network.com
Abstract
Three contributions to developing an algorithm for assisting engineers in designing analog circuits are provided in this paper. First,
a... | 1744 |@word illustrating:2 briefly:4 simulation:1 pg:4 initial:1 configuration:2 selecting:1 subjective:2 imaginary:1 current:34 com:2 must:3 designed:1 selected:1 device:1 guess:1 ith:2 consulting:1 node:24 location:1 preference:4 five:1 mathematical:2 constructed:1 direct:2 supply:1 ik:3 consists:1 fitting:1 expected... |
814 | 1,745 | The Infinite Gaussian Mixture Model
Carl Edward Rasmussen
Department of Mathematical Modelling
Technical University of Denmark
Building 321, DK-2800 Kongens Lyngby, Denmark
carl@imm.dtu.dk http://bayes.imm.dtu.dk
Abstract
In a Bayesian mixture model it is not necessary a priori to limit the number of components to be... | 1745 |@word version:1 proportion:6 covariance:4 initial:2 current:2 written:1 must:2 john:1 shape:5 plot:2 update:1 isotropic:1 smith:1 toronto:2 simpler:1 five:1 mathematical:2 beta:1 become:4 ik:1 wild:2 combine:1 introduce:1 roughly:2 themselves:1 growing:1 freeman:1 automatically:1 cpu:1 becomes:2 mass:5 hitherto:1... |
815 | 1,746 | Can VI mechanisms account for
figure-ground and medial axis effects?
Zhaoping Li
Gatsby Computational Neuroscience Unit
University College London
zhaoping~gatsby.ucl.ac.uk
Abstract
When a visual image consists of a figure against a background, V1
cells are physiologically observed to give higher responses to image
re... | 1746 |@word middle:1 stronger:6 simulation:1 solid:4 initial:2 tuned:1 existing:1 contextual:9 kowler:1 visible:4 romero:2 shape:4 enables:1 medial:16 fewer:1 iso:4 compo:1 filtered:2 contribute:1 location:6 preference:1 constructed:1 become:2 consists:3 inside:1 indeed:2 roughly:1 behavior:1 nor:1 brain:1 becomes:1 pr... |
816 | 1,747 | A SNoW-Based Face Detector
Ming-Hsuan Yang
Dan Roth
Narendra Ahuja
Department of Computer Science and the Beckman Institute
University of Illinois at Urbana-Champaign
Urbana, IL 61801
mhyang~vision.ai.uiuc.edu
danr~cs.uiuc.edu
ahuja~vision.ai.uiuc.edu
Abstract
A novel learning approach for human face detection using... | 1747 |@word briefly:1 seems:1 decomposition:1 series:2 outperforms:4 current:7 discretization:1 comparing:1 surprising:1 conjunctive:1 parsing:1 girosi:1 shape:1 update:13 discrimination:2 v:1 intelligence:5 warmuth:2 ith:1 detecting:1 node:11 contribute:1 demoted:1 height:1 along:2 symposium:1 consists:3 dan:1 combine... |
817 | 1,748 | Policy Search via Density Estimation
AndrewY. Ng
Computer Science Division
u.c. Berkeley
Berkeley, CA 94720
ang@cs.berkeley.edu
Ronald Parr
Computer Science Dept.
Stanford University
Stanford, CA 94305
parr@cs.stanjord.edu
Daphne Koller
Computer Science Dept.
Stanford University
Stanford, CA 94305
kolle r@cs.stanjor... | 1748 |@word trial:3 version:1 km:3 covariance:3 contraction:1 thereby:1 initial:2 fragment:1 tuned:1 existing:1 current:2 si:9 artijiciallntelligence:1 must:1 ronald:1 distant:1 enables:1 stationary:1 generative:4 selected:3 accordingly:1 meuleau:1 batmobile:1 node:4 sigmoidal:1 daphne:1 direct:4 become:1 driver:1 cons... |
818 | 1,749 | Churn Reduction in the Wireless Industry
Michael C. Mozer*+, Richard Wolniewicz*, David B. Grimes*+,
Eric Johnson *, Howard Kaushansky*
* Athene Software
+ Department of Computer Science
2060 Broadway, Suite 300
University of Colorado
Boulder, CO 80309-0430
Boulder, CO 80302
Abstract
Competition in the wireless teleco... | 1749 |@word rising:1 judgement:2 proportion:2 logit:4 termination:2 willing:1 grey:1 subscriber:85 profit:1 shading:2 reduction:5 initial:2 series:1 united:2 selecting:1 offering:5 tuned:1 outperforms:1 existing:1 current:1 must:4 realize:1 numerical:1 hoping:1 designed:1 plot:9 intelligence:1 selected:2 beginning:1 sh... |
819 | 175 | 29
"FAST LEARNING IN
MULTI-RESOLUTION HIERARCHIES"
John Moody
Yale Computer Science, P.O. Box 2158, New Haven, CT 06520
Abstract
A class of fast, supervised learning algorithms is presented. They use local representations, hashing, atld multiple scales of resolution to approximate
functions which are piece-wise contin... | 175 |@word polynomial:1 casdagli:2 bn:1 dramatic:1 solid:2 versatile:1 reduction:1 moment:1 cyclic:2 series:4 configuration:1 initial:1 tuned:3 lapedes:5 past:3 z2:1 com:2 yet:2 dx:1 must:1 finest:1 john:2 subsequent:1 designed:1 drop:2 hash:10 mackey:5 v:1 isotropic:1 ith:1 num:1 coarse:1 node:5 successive:1 sigmoidal... |
820 | 1,750 | Scale Mixtures of Gaussians and the
Statistics of Natural Images
Martin J. Wainwright
Stochastic Systems Group
Electrical Engineering & CS
MIT, Building 35-425
Cambridge, MA 02139
mjwain@mit.edu
Eero P. Simoncelli
Ctr. for Neural Science, and
Courant Inst. of Mathematical Sciences
New York University
New York, NY 100... | 1750 |@word compression:2 stronger:1 covariance:2 decomposition:1 q1:1 solid:2 reduction:1 liu:1 z2:1 dx:1 written:1 distant:2 shape:4 plot:5 drop:1 characterization:2 node:5 mathematical:1 along:3 fitting:2 symp:1 behavior:7 themselves:1 examine:2 surge:1 multi:2 freeman:2 quad:1 begin:1 matched:2 underlying:2 moreove... |
821 | 1,751 | A Neurodynamical Approach to Visual Attention
JosefZihl
Gustavo Deco
Institute of Psychology
Siemens AG
Corporate Technology
Neuropsychology
Neural Computation, ZT IK 4
Ludwig-Maximilians-University Munich
Otto-Hahn-Ring 6
Leopoldstr. 13
80802 Munich, Germany
81739 Munich, Germany
Gustavo.Deco@mchp.siemens.de
Abstrac... | 1751 |@word seems:1 bf:1 simulation:2 seek:1 solid:1 necessity:1 disparity:1 suppressing:1 reaction:5 existing:1 current:6 activation:1 yet:1 additive:1 j1:1 aoo:1 hypothesize:1 designed:2 plot:2 v:1 implying:1 item:15 short:1 accepting:1 location:15 clarified:1 registering:1 along:1 differential:5 ik:1 consists:1 ra:1... |
822 | 1,752 | Population Decoding Based on
an Unfaithful Model
s. Wu, H. Nakahara, N. Murata and S. Amari
RIKEN Brain Science Institute
Hirosawa 2-1, Wako-shi, Saitama, Japan
{phwusi, hiro, mura, amari}@brain.riken.go.jp
Abstract
We study a population decoding paradigm in which the maximum likelihood inference is based on an unfait... | 1752 |@word trial:1 simulation:6 covariance:2 aijl:1 initial:1 hereafter:1 denoting:1 tuned:1 interestingly:2 wako:1 com:16 comparing:3 dx:1 motor:1 discrimination:2 guess:1 contribute:1 lx:1 zhang:1 ik:1 prove:1 baldi:2 ra:1 brain:5 decreasing:2 cpu:1 becomes:1 notation:1 maximizes:2 mass:3 exactly:2 normally:1 unit:1... |
823 | 1,753 | An Analysis of Turbo Decoding
with Gaussian Densities
Paat Rusmevichientong and Benjamin Van Roy
Stanford University
Stanford, CA 94305
{paatrus, bvr} @stanford.edu
Abstract
We provide an analysis of the turbo decoding algorithm (TDA)
in a setting involving Gaussian densities. In this context, we are
able to show that... | 1753 |@word nd:1 tedious:1 open:1 covariance:13 q1:1 lq2:2 initial:1 assigning:1 dx:3 must:4 intriguing:1 enables:1 pertinent:1 designed:1 plot:4 selected:1 iterates:3 provides:3 initiative:1 prove:1 f3v:10 behavior:2 shokrollahi:1 freeman:2 revision:1 begin:1 underlying:1 notation:2 bounded:1 maximizes:1 q2:16 develop... |
824 | 1,754 | Learning Factored Representations for Partially
Observable Markov Decision Processes
Brian Sallans
Department of Computer Science
University of Toronto
Toronto M5S 2Z9 Canada
Gatsby Computational Neuroscience Unit*
University College London
London WCIN 3AR U.K.
sallans@cs.toronto.edu
Abstract
The problem of reinfo... | 1754 |@word trial:3 version:1 simplifying:1 tr:1 ld:1 contains:1 series:1 tuned:1 past:1 current:2 si:1 tackling:1 artijiciallntelligence:2 must:2 additive:1 update:2 half:2 fewer:3 mccallum:1 ith:2 draft:1 node:1 toronto:4 simpler:1 driver:3 combine:1 expected:3 aliasing:2 multi:1 bellman:1 discounted:2 decomposed:1 d... |
825 | 1,755 | Optimal Kernel Shapes for Local Linear
Regression
Dirk Ormoneit
Trevor Hastie
Department of Statistics
Stanford University
Stanford, CA 94305-4065
ormoneit@stat.stanjord.edu
Abstract
Local linear regression performs very well in many low-dimensional
forecasting problems. In high-dimensional spaces, its performance
ty... | 1755 |@word repository:3 middle:1 briefly:1 polynomial:1 proportion:1 sex:1 simulation:2 covariance:3 rightmost:1 outperforms:1 recovered:1 attracted:1 must:2 written:1 numerical:1 distant:1 shape:21 designed:1 plot:1 intelligence:1 record:1 draft:1 contribute:1 lx:1 sigmoidal:1 five:5 along:2 shorthand:1 consists:1 in... |
826 | 1,757 | Manifold Stochastic Dynamics
for Bayesian Learning
Mark Zlochin
Department of Computer Science
Technion - Israel Institute of Technology
Technion City, Haifa 32000, Israel
zmark@cs.technion.ac.il
YoramBaram
Department of Computer Science
Technion - Israel Institute of Technology
Technion City, Haifa 32000, Israel
bar... | 1757 |@word determinant:1 version:1 norm:1 d2:1 simulation:1 covariance:1 pg:1 pressure:1 initial:1 inefficiency:1 existing:1 discretization:6 written:1 numerical:2 informative:1 implying:1 gear:3 hamiltonian:8 successive:1 lor:1 differential:3 autocorrelation:2 rapid:2 behavior:1 multi:2 ol:1 little:1 actual:2 equippe... |
827 | 1,758 | Channel Noise in Excitable Neuronal
Membranes
Amit Manwani; Peter N. Steinmetz and Christof Koch
Computation and Neural Systems Program, M-S 139-74
California Institute of Technology Pasadena, CA 91125
{quixote,peter,koch } @klab.caltech.edu
Abstract
Stochastic fluctuations of voltage-gated ion channels generate curr... | 1758 |@word neurophysiology:1 determinant:1 version:3 nd:1 open:5 squid:1 simulation:15 linearized:8 covariance:1 solid:2 carry:1 series:1 efficacy:1 mainen:6 current:19 activation:6 written:1 moo:3 physiol:3 numerical:1 realistic:1 nervous:1 iso:2 record:1 filtered:1 hodgkinhuxley:1 detecting:2 contribute:2 putatively... |
828 | 1,759 | Effects of Spatial and Temporal Contiguity on
the Acquisition of Spatial Information
Thea B. Ghiselli-Crippa and Paul W. Munro
Department of Information Science and Telecommunications
University of Pittsburgh
Pittsburgh, PA 15260
tbgst@sis.pitt.edu, munro@sis.pitt.edu
Abstract
Spatial information comes in two forms: ... | 1759 |@word version:2 stronger:1 seems:1 hu:1 simulation:5 initial:1 series:1 hardy:1 reaction:1 si:2 distant:3 additive:1 plot:4 v:9 alone:3 plane:3 short:1 wth:1 provides:1 mental:1 node:19 location:3 contribute:1 direct:4 consists:1 acquired:1 alspector:1 behavior:3 actual:2 project:1 provided:1 moreover:1 panel:14 ... |
829 | 1,760 | Effects of Spatial and Temporal Contiguity on
the Acquisition of Spatial Information
Thea B. Ghiselli-Crippa and Paul W. Munro
Department of Information Science and Telecommunications
University of Pittsburgh
Pittsburgh, PA 15260
tbgst@sis.pitt.edu, munro@sis.pitt.edu
Abstract
Spatial information comes in two forms: ... | 1760 |@word version:2 advantageous:5 stronger:1 seems:1 physik:2 hu:1 simulation:5 eng:1 thereby:1 outlook:1 solid:1 carry:2 initial:1 series:1 contains:1 hardy:1 tuned:5 interestingly:1 reaction:1 recovered:1 si:2 written:3 bd:1 realize:1 physiol:1 additive:1 distant:3 visible:1 shape:4 christian:1 motor:1 plot:4 v:9 ... |
830 | 1,761 | Learning from user feedback in image retrieval
systems
Nuno Vasconcelos
Andrew Lippman
MIT Media Laboratory, 20 Ames St, E15-354, Cambridge, MA 02139,
{nuno,lip} @media.mit.edu,
http://www.media.mit.edwnuno
Abstract
We formulate the problem of retrieving images from visual databases
as a problem of Bayesian inference... | 1761 |@word cox:1 version:1 achievable:1 yisi:2 tedious:1 willing:1 confirms:2 accounting:1 decomposition:1 dramatic:1 harder:1 initial:1 contains:5 score:2 selecting:5 denoting:1 current:1 si:25 attracted:1 written:2 fonnulated:1 exposing:1 must:1 visible:1 happen:1 dct:1 wanted:1 plot:2 update:1 grass:1 selected:3 it... |
831 | 1,762 | Building Predictive Models from Fractal
Representations of Symbolic Sequences
Peter Tioo Georg Dorffner
Austrian Research Institute for Artificial Intelligence
Schottengasse 3, A-101O Vienna, Austria
{petert,georg}@ai.univie.ac.at
Abstract
We propose a novel approach for building finite memory predictive models simil... | 1762 |@word briefly:1 bn:1 contraction:1 homomorphism:1 mention:1 series:11 selecting:1 past:1 o2:1 outperforms:1 blank:1 yet:2 partition:1 fund:1 stationary:2 intelligence:2 selected:1 guess:2 short:1 quantized:1 codebook:6 ron:5 successive:1 constructed:5 become:1 amnesia:2 consists:2 fitting:1 theoretically:1 forget... |
832 | 1,763 | Coastal Navigation with Mobile Robots
Nicholas Roy and Sebastian Thrun
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
{nicholas. roy Isebastian. thrun } @cs.cmu.edu
Abstract
The problem that we address in this paper is how a mobile robot can plan in order
to arrive at its goal with minimum... | 1763 |@word version:1 middle:2 instrumental:1 open:1 grey:5 simplifying:4 ala:1 current:2 si:4 yet:1 dx:2 must:2 additive:1 confirming:1 update:3 intelligence:1 fewer:2 wolfram:1 recherche:1 provides:1 smithsonian:2 location:1 lx:1 along:1 expected:4 pour:1 planning:13 multi:2 bellman:2 inspired:1 actual:1 becomes:3 pr... |
833 | 1,764 | Noisy Neural Networks and
Generalizations
Hava T. Siegelmann
Industrial Eng. and Management, Mathematics
Technion - lIT
Haifa 32000, Israel
iehava@ie.technion.ac.il
Alexander Roitershtein
Mathematics
Technion - lIT
Haifa 32000, Israel
roiterst@math.technion.ac.il
Asa Ben-Hur
Industrial Eng. and Management
Technion - ... | 1764 |@word version:3 pw:12 norm:5 calculus:1 eng:2 doeblin:7 initial:4 liu:1 orponen:2 interestingly:1 current:2 si:1 reminiscent:1 must:1 john:1 update:3 short:1 accepting:3 characterization:1 provides:1 math:2 obser:1 unbounded:1 mathematical:1 c2:3 differential:2 prove:2 combine:1 behavior:1 dist:2 actual:1 lib:1 b... |
834 | 1,765 | Learning Informative Statistics: A
Nonparametric Approach
John W. Fisher III, Alexander T. IhIer, and Paul A. Viola
Massachusetts Institute of Technology
77 Massachusetts Ave., 35-421
Cambridge, MA 02139
{jisher,ihler,viola}@ai.mit.edu
Abstract
We discuss an information theoretic approach for categorizing and modelin... | 1765 |@word mild:1 trial:2 simplifying:1 thereby:1 solid:4 series:1 mmse:3 past:18 current:3 surprising:1 must:1 john:2 fn:3 informative:13 plot:3 discrimination:1 stationary:6 parameterization:1 xk:26 regressive:1 characterization:1 provides:2 math:1 nonpararnetric:2 differential:1 expected:1 market:1 rapid:1 behavior... |
835 | 1,766 | Boosting Algorithms as Gradient Descent
Llew Mason
Research School of Information
Sciences and Engineering
Australian National University
Canberra, ACT, 0200, Australia
lmason@syseng.anu.edu.au
Jonathan Baxter
Research School of Information
Sciences and Engineering
Australian National University
Canberra, ACT, 0200, ... | 1766 |@word repository:3 version:3 proportion:1 twelfth:1 willing:1 queensland:1 tr:1 reduction:1 outperforms:3 existing:2 comparing:1 must:1 additive:1 subsequent:1 greedy:1 intelligence:2 provides:1 characterization:1 boosting:15 hyperplanes:1 lor:1 become:1 supply:1 anyboost:17 prove:2 eleventh:1 theoretically:2 sac... |
836 | 1,767 | Predictive Approaches For Choosing
Hyperparameters in Gaussian Processes
S. Sundararajan
Computer Science and Automation
Indian Institute of Science
Bangalore 560 012, India
sundar@csa.iisc. ernet.in
S. Sathiya Keerthi
Mechanical and Production Engg.
National University of Singapore
10 Kentridge Crescent, Singapore 1... | 1767 |@word version:2 termination:1 simulation:5 bn:2 covariance:8 phy:1 initial:3 existing:2 comparing:1 si:1 dx:1 written:1 readily:1 additive:1 partition:4 engg:1 alone:1 ith:14 toronto:2 lx:9 become:1 introduce:1 ra:1 behavior:1 iisc:1 estimating:1 circuit:1 minimizes:1 developed:1 scaled:2 demonstrates:1 uk:1 ser:... |
837 | 1,768 | Robust Neural Network Regression for Offline
and Online Learning
Thomas Briegel*
Siemens AG, Corporate Technology
D-81730 Munich, Germany
thomas.briegel@mchp.siemens.de
Volker Tresp
Siemens AG, Corporate Technology
D-81730 Munich, Germany
volker.tresp@mchp.siemens.de
Abstract
We replace the commonly used Gaussian no... | 1768 |@word middle:2 seems:2 covariance:2 decomposition:1 contains:2 score:2 series:1 outperforms:1 freitas:1 current:1 z2:1 john:1 stemming:1 visible:1 additive:5 wanted:1 plot:4 update:4 stationary:3 selected:1 metrika:2 argm:2 provides:1 location:1 mathematical:1 supply:1 tlog:1 huber:2 expected:5 behavior:1 multi:2... |
838 | 1,769 | Understanding stepwise generalization of
Support Vector Machines: a toy model
Sebastian Risau-Gusman and Mirta B. Gordon
DRFMCjSPSMS CEA Grenoble, 17 avo des Martyrs
38054 Grenoble cedex 09, France
Abstract
In this article we study the effects of introducing structure in the
input distribution of the data to be learn... | 1769 |@word version:2 compression:1 polynomial:1 seems:2 norm:2 simulation:1 contraction:1 independant:1 dramatic:1 contains:1 hereafter:1 partition:1 pertinent:2 drop:1 selected:1 isotropic:4 vanishing:3 short:1 compo:1 hypersphere:2 math:1 five:1 rc:10 along:2 become:1 symposium:1 buhot:2 overline:1 expected:1 ra:4 r... |
839 | 177 | 314
NEURAL NETWORK STAR PATTERN
RECOGNITION FOR SPACECRAFT ATTITUDE
DETERMINATION AND CONTROL
Phillip Alvelda, A. Miguel San Martin
The Jet Propulsion Laboratory,
California Institute of Technology,
Pasadena, Ca. 91109
ABSTRACT
Currently, the most complex spacecraft attitude determination
and control tasks are ultimat... | 177 |@word achievable:1 simulation:4 brightness:1 thereby:1 electronics:1 initial:1 contains:1 document:1 outperforms:1 current:3 od:1 must:4 realistic:1 designed:1 drop:1 half:1 intelligence:1 device:2 obsolete:1 kbytes:2 core:1 smithsonian:1 become:1 incorrect:1 prove:2 consists:1 inside:1 acquired:1 interplanetary:3... |
840 | 1,770 | State Abstraction in MAXQ Hierarchical
Reinforcement Learning
Thomas G. Dietterich
Department of Computer Science
Oregon State University
Corvallis, Oregon 97331-3202
tgd@cs.orst.edu
Abstract
Many researchers have explored methods for hierarchical reinforcement learning (RL) with temporal abstractions, in which abstr... | 1770 |@word version:1 briefly:1 eliminating:1 norm:2 termination:7 decomposition:3 contraction:2 pick:1 recursively:7 initial:1 selecting:1 tuned:1 current:4 si:1 must:4 deposited:1 belmont:1 subsequent:1 partition:1 lue:1 plot:1 update:1 stationary:2 intelligence:1 greedy:1 yr:1 leaf:2 beginning:1 meuleau:1 compo:2 pr... |
841 | 1,771 | Leveraged Vector Machines
Yoram Singer
Hebrew University
singer@cs.huji.ac.il
Abstract
We describe an iterative algorithm for building vector machines used in
classification tasks. The algorithm builds on ideas from support vector
machines, boosting, and generalized additive models. The algorithm can
be used with vari... | 1771 |@word mild:1 repository:2 version:5 middle:3 polynomial:2 norm:8 seems:1 twelfth:1 eng:1 parenthetically:1 reduction:1 att:5 denoting:1 current:2 comparing:1 john:1 numerical:4 additive:5 partition:1 plot:4 half:1 sys:1 dover:1 ith:2 provides:1 boosting:13 differential:1 incorrect:1 combine:1 inside:1 indeed:1 be... |
842 | 1,772 | Monte Carlo POMDPs
Sebastian Thrun
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
We present a Monte Carlo algorithm for learning to act in partially observable
Markov decision processes (POMDPs) with real-valued state and action spaces.
Our approach uses importance sampling for r... | 1772 |@word mild:1 version:3 open:2 grey:1 simulation:4 tr:1 solid:1 accommodate:1 recursively:1 moment:1 initial:7 configuration:1 contains:1 denoting:1 past:4 existing:1 current:2 bd:1 must:3 numerical:2 subsequent:1 plot:1 stationary:2 greedy:1 location:7 unbounded:1 mathematical:1 height:5 along:1 symposium:1 expec... |
843 | 1,773 | Large Margin DAGs for
Multiclass Classification
John C. Platt
Microsoft Research
1 Microsoft Way
Redmond, WA 98052
jpiatt@microsojt.com
Nello Cristianini
Dept. of Engineering Mathematics
University of Bristol
Bristol, BS8 1TR - UK
nello.cristianini@bristol.ac.uk
John Shawe-Taylor
Department of Computer Science
Royal... | 1773 |@word repository:1 polynomial:1 proportionality:1 decomposition:1 tr:1 contains:2 current:1 com:1 comparing:1 must:3 john:2 numerical:1 partition:1 xex:1 half:2 leaf:6 selected:1 ith:2 short:2 revisited:1 node:57 traverse:1 hyperplanes:4 simpler:1 scholkopf:2 consists:3 combine:3 introduce:3 pairwise:1 themselves... |
844 | 1,774 | Effective Learning Requires Neuronal
Remodeling of Hebbian Synapses
Gal Chechik Isaac Meilijson Eytan Ruppin
School of Mathematical Sciences
Tel-Aviv University Tel Aviv, Israel
ggal@math.tau.ac.il isaco@math.tau.ac.il ruppin@math.tau.ac.il
Abstract
This paper revisits the classical neuroscience paradigm of Hebbian
l... | 1774 |@word classical:1 already:1 self:2 capacity:2 potentiation:1 efficacy:5 ruppin:2 yet:1 recently:1 must:2 difficult:1 enables:1 alone:2 neuron:2 homeostasis:1 successfully:1 math:3 mathematical:1 synap:1 driven:3 scenario:1 manner:2 dependent:1 brain:2 pattern:1 paradigm:3 tau:3 memory:2 bounded:1 israel:1 hebbian... |
845 | 1,775 | Reinforcement Learning for
Spoken Dialogue Systems
Satinder Singh
Michael Keams
Diane Litman
Marilyn Walker
AT&T Labs
AT&T Labs
AT&T Labs
AT&T Labs
{baveja,mkeams,diane,walker} @research.att.com
Abstract
Recently, a number of authors have proposed treating dialogue systems as Markov
decision processes (MDPs). ... | 1775 |@word seems:2 open:1 instruction:1 confirms:1 tat:1 asks:2 mention:1 holy:1 initial:2 series:2 att:1 score:4 interestingly:1 prefix:1 existing:1 current:1 com:1 synthesizer:1 yet:1 written:1 must:1 confirming:3 treating:2 plot:3 v:1 beginning:1 ith:2 short:1 caveat:1 contribute:1 five:1 dn:1 symposium:2 initiativ... |
846 | 1,776 | LTD Facilitates Learning In a Noisy
Environment
Paul Munro
School of Information Sciences
University of Pittsburgh
Pittsburgh PA 15260
pwm+@pitt.edu
Gerardina Hernandez
Intelligent Systems Program
University of Pittsburgh
Pittsburgh PA 15260
gehst5+@pitt.edu
Abstract
Long-term potentiation (LTP) has long been held ... | 1776 |@word trial:1 hippocampus:3 nd:4 r:1 simulation:6 covariance:3 fonn:1 solid:2 initial:1 series:4 efficacy:3 suppressing:1 activation:1 must:1 physiol:1 plasticity:2 designed:1 aps:1 alone:6 tenn:2 half:1 provides:2 math:1 contribute:1 zhang:2 mathematical:1 rc:1 direct:2 fth:1 pathway:1 expected:1 behavior:3 exam... |
847 | 1,777 | Acquisition in Autoshaping
Sham Kakade
Peter Dayan
Gatsby Computational Neuroscience Unit
17 Queen Square, London, England, WC1N 3AR.
sharn@gatsby.ucl.ac.uk
dayan@gatsby.ucl.ac.uk
Abstract
Quantitative data on the speed with which animals acquire behavioral responses during classical conditioning experiments should
p... | 1777 |@word h:3 trial:18 version:2 extinction:3 proportionality:4 jacob:2 paid:1 current:1 nt:1 yet:1 must:2 john:1 additive:2 subsequent:3 asymptote:1 designed:1 drop:2 half:1 leaf:1 underestimating:1 lr:2 successive:1 five:1 rc:3 c2:1 direct:1 differential:1 become:1 gallistel:6 terrace:3 behavioral:5 inter:1 ra:2 ex... |
848 | 1,778 | Better Generative Models for Sequential
Data Problems: Bidirectional Recurrent
Mixture Density Networks
Mike Schuster
ATR Interpreting Telecommunications Research Laboratories
2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-02, JAPAN
gustl@itl.atr.co.jp
Abstract
This paper describes bidirectional recurrent mixture de... | 1778 |@word version:1 grey:2 covariance:6 decomposition:1 simplifying:1 contains:1 xiy:5 past:2 comparing:1 contextual:1 yet:1 must:1 remove:1 treating:1 stationary:1 generative:8 fewer:1 simpler:1 become:1 consists:1 theoretically:2 expected:1 subdividing:1 seika:1 frequently:1 multi:7 automatically:1 unfolded:1 xti:2... |
849 | 1,779 | Data Visualization and Feature Selection:
New Algorithms for Nongaussian Data
Howard Hua Yang and John Moody
Oregon Graduate Institute of Science and Technology
20000 NW, Walker Rd., Beaverton, OR97006, USA
hyang@ece.ogi.edu, moody@cse.ogi.edu, FAX:503 7481406
Abstract
Data visualization and feature selection methods... | 1779 |@word timefrequency:1 version:1 eliminating:2 d2:1 pulse:13 reduction:1 series:2 efficacy:1 selecting:2 existing:5 yet:1 john:1 informative:1 v:1 selected:8 fewer:3 xk:11 provides:1 cse:1 lx:1 symposium:1 consists:1 ica:11 curse:1 project:1 underlying:1 minimizes:1 finding:1 every:2 classifier:5 wrong:1 unit:1 gr... |
850 | 178 | 444
A MODEL FOR RESOLUTION ENHANCEMENT
(HYPERACUITY) IN SENSORY REPRESENTATION
Jun Zhang and John P. Miller
Neurobiology Group, University of California,
Berkeley, California 94720, U.S.A.
ABSTRACT
Heiligenberg (1987) recently proposed a model to explain how sensory maps could enhance resolution through orderly arr... | 178 |@word effect:3 coverage:1 concept:1 eliminating:1 polynomial:17 implies:1 arrangement:2 question:1 deal:1 decomposition:1 jacob:4 ll:1 width:3 uniquely:2 cricket:1 dp:1 wrap:1 excitation:2 configuration:1 generalized:2 degrade:1 argue:1 extent:2 tuned:3 cellular:1 extension:2 experientia:1 code:1 heiligenberg:11 a... |
851 | 1,780 | Learning Statistically Neutral Tasks
without Expert Guidance
Ton Weijters
Information Technology,
Eindhoven University,
The Netherlands
Antal van den Bosch
ILK,
Tilburg University,
The Netherlands
Eric Postma
Computer Science,
Universiteit Maastricht,
The Netherlands
Abstract
In this paper, we question the necessit... | 1780 |@word middle:2 decomposition:4 necessity:4 contains:1 exclusively:1 activation:7 must:2 numerical:1 interpretable:1 selected:3 mental:1 characterization:1 toronto:1 simpler:2 five:1 become:4 consists:2 indeed:1 hardness:1 behavior:1 multi:2 ol:1 decomposed:3 automatically:1 provided:1 moreover:1 what:1 interprete... |
852 | 1,781 | An Analog VLSI Model of
Periodicity Extraction
Andre van Schaik
Computer Engineering Laboratory
J03, University of Sydney, NSW 2006
Sydney, Australia
andre@ee.usyd.edu.au
Abstract
This paper presents an electronic system that extracts the
periodicity of a sound. It uses three analogue VLSI building
blocks: a silicon ... | 1781 |@word version:2 simulation:1 nsw:1 solid:2 contains:2 series:1 current:4 john:1 evans:1 realistic:1 synchronicity:8 entrance:1 shape:2 drop:2 plot:1 half:10 cue:1 tone:9 schaik:5 filtered:1 along:8 direct:1 become:1 consists:1 manner:1 ra:1 indeed:1 expected:1 oscilloscope:1 nor:1 roughly:2 brain:2 ol:1 detects:1... |
853 | 1,782 | Evolv . . . . .
JIiIIIIIIo.
Bradley Tookes
Dept of Comp. Sci. and Elec. Engineering
University of Queensland
Queensland, 4072
Australia
btonkes@csee.uq. edu. au
Alan Blair
Department of Computer Science
University of Melbourne
Parkville, Victoria, 3052
Australia
blair@cs. mu. oz. au
Janet Wiles
Dept of Comp. Sci. a... | 1782 |@word seems:4 simulation:19 queensland:4 decomposition:1 simplifying:1 paid:1 maes:1 fortuitous:1 recursively:1 initial:2 series:10 selecting:1 mag:1 tuned:1 bc:1 past:1 bradley:2 current:2 surprising:1 activation:1 must:5 written:1 subsequent:2 eleven:1 infant:2 intelligence:1 leaf:1 device:2 selected:1 guess:1 ... |
854 | 1,783 | Predictive Sequence Learning in Recurrent
Neocortical Circuits*
R.P.N.Rao
T. J. Sejnowski
Computational Neurobiology Lab and
Sloan Center for Theoretical Neurobiology
The Salk Institute, La Jolla, CA 92037
rao@salk.edu
Computational Neurobiology Lab and
Howard Hughes Medical Institute
The Salk Institute, La Jolla, ... | 1783 |@word trial:10 cu:1 unaltered:1 hippocampus:1 mehta:1 simulation:3 pulse:4 fonn:1 thereby:1 tr:1 solid:2 initial:1 efficacy:2 mainen:1 l__:1 past:1 current:4 nt:3 activation:2 si:4 universality:1 subsequent:1 plasticity:13 shape:1 motor:1 plot:6 succeeding:1 progressively:1 location:1 preference:1 zhang:1 alert:3... |
855 | 1,784 | Hierarchical Image Probability (HIP) Models
Clay D. Spence and Lucas Parra
Sarnoff Corporation
CN5300
Princeton, NJ 08543-5300
{cspence, lparra} @samoff.com
Abstract
We formulate a model for probability distributions on image spaces. We
show that any distribution of images can be factored exactly into conditional dis... | 1784 |@word mild:1 aircraft:4 compression:1 polynomial:1 seems:1 proportionality:1 tried:2 covariance:1 series:1 united:1 denoting:1 current:1 com:1 written:1 john:1 visible:1 blur:1 nian:1 shape:1 discrimination:4 v:1 stationary:1 leaf:2 plane:2 ial:1 short:1 detecting:2 coarse:4 characterization:1 location:2 lx:1 alo... |
856 | 1,785 | Image representations for facial expression
coding
Marian Stewart Bartlett*
V.C. San Diego
marni<Osalk.edu
Gianluca Donato
Persona, Redwood City, CA
glanlucad<Odigitalpersona.com
Di~ital
Javier R. Movellan
V.C. San Diego
movellan<ocogsci.ucsd.edu
Joseph C. Hager
Network Information Res., SLC, Utah
jchager<Oibm.com... | 1785 |@word compression:1 advantageous:1 speechreading:1 contraction:1 covariance:2 decomposition:3 brightness:1 ld:2 hager:5 reduction:3 recovered:1 com:3 cottrell:2 subsequent:2 drop:1 discrimination:1 alone:1 v:1 fewer:2 selected:2 intelligence:3 consulting:1 location:4 symposium:2 surprised:1 behavioral:3 affective... |
857 | 1,786 | Actor-Critic Algorithms
Vijay R. Konda
John N. Tsitsiklis
Laboratory for Information and Decision Systems ,
Massachusetts Institute of Technology,
Cambridge, MA, 02139.
konda@mit.edu, jnt@mit.edu
Abstract
We propose and analyze a class of actor-critic algorithms for
simulation-based optimization of a Markov decision ... | 1786 |@word version:1 stronger:1 norm:1 advantageous:1 open:1 termination:2 simulation:8 q1:3 carry:2 reduction:2 initial:1 contains:3 exclusively:1 ours:1 past:2 existing:1 current:4 od:1 yet:1 readily:1 john:1 belmont:1 update:12 stationary:5 parameterization:5 xk:13 along:1 differential:1 become:1 prove:1 introduce:... |
858 | 1,787 | Speech Modelling Using Subspace and EM
Techniques
Gavin Smith
Cambridge University
Engineering Department
Cambridge CB2 1PZ
England
gas1 oo3@eng.cam.ac.uk
Joao FG de Freitas
Computer Science Division
487 Soda Hall
UC Berkeley
CA 94720-1776, USA.
jfgf@cs.berkeley.edu 1
Tony Robinson
Cambridge University
Engineering D... | 1787 |@word version:1 polynomial:1 instrumental:3 norm:1 eng:2 decomposition:1 covariance:3 tr:1 initial:3 initialisation:12 past:3 freitas:5 africa:1 current:1 analysed:1 john:1 numerical:3 plot:3 initialises:1 stationary:4 device:1 smith:7 toronto:1 firstly:4 simpler:1 consists:1 fitting:1 manner:1 expected:1 multi:2... |
859 | 1,788 | Neural Network Based Model Predictive
Control
Stephen Piche
Pavilion Technologies
Austin, TX 78758
spiche@pav.com
Jim Keeler
Pavilion Technologies
Austin, TX 78758
jkeeler@pav.com
Greg Martin
Pavilion Technologies
Austin, TX 78758
gmartin@pav.com
Gene Boe
Pavilion Technologies
Austin, TX 78758
gboe@pav.com
Doug Joh... | 1788 |@word open:4 seborg:2 simulation:1 linearized:1 tried:1 dramatic:1 initial:3 contains:2 series:1 selecting:1 past:3 current:1 com:6 must:3 readily:1 cracking:1 update:1 selected:1 postprocess:1 provides:1 revisited:1 constructed:1 become:2 symposium:1 combine:1 proliferation:1 grade:3 rawlings:1 company:2 food:4 ... |
860 | 1,789 | Improved Output Coding for Classification
Using Continuous Relaxation
Koby Crammer and Yoram Singer
School of Computer Science & Engineering
The Hebrew University, Jerusalem 91904, Israel
{kob i cs ,sing e r }@ c s.huji.a c .il
Abstract
Output coding is a general method for solving multiclass problems by
reducing them... | 1789 |@word kong:1 repository:1 polynomial:3 norm:2 seems:1 twelfth:1 fonn:2 tr:1 reduction:1 contains:2 att:1 comparing:2 assigning:2 reminiscent:1 partition:2 enables:2 plot:6 intelligence:2 selected:1 shut:1 short:3 argm:2 boosting:1 height:1 constructed:2 viable:2 consists:1 combine:1 pairwise:1 indeed:1 behavior:1... |
861 | 179 | 777
TRAINING A
LIMITED-INTERCONNECT,
SYNTHETIC NEURAL IC
M.R. Walker. S. Haghighi. A. Afghan. and L.A. Akers
Center for Solid State Electronics Research
Arizona State University
Tempe. AZ 85287-6206
mwalker@enuxha.eas.asu.edu
ABSTRACT
Hardware implementation of neuromorphic algorithms is hampered by
high degrees of c... | 179 |@word duda:2 seek:1 fonn:1 euclidian:1 solid:1 electronics:1 series:2 current:2 comparing:2 nowlan:1 activation:1 dx:3 must:4 tenn:2 asu:1 realizing:1 plaut:2 node:9 hyperplanes:1 c2:1 direct:5 consists:1 behavior:1 actual:1 valve:1 increasing:1 becomes:1 transformation:2 y3:4 act:1 xd:1 unit:7 positive:1 encoding... |
862 | 1,790 | Regularization with Dot-Product Kernels
Alex J. SIDola, Zoltan L. Ovari, and Robert C. WilliaIDson
Department of Engineering
Australian National University
Canberra, ACT, 0200
Abstract
In this paper we give necessary and sufficient conditions under
which kernels of dot product type k(x, y) = k(x . y) satisfy Mercer's... | 1790 |@word rreg:1 briefly:1 polynomial:21 norm:1 open:1 bn:2 commute:1 series:13 contains:1 yet:1 dx:2 written:3 numerical:1 benign:1 analytic:4 generative:2 leaf:2 rp1:1 lr:2 provides:1 math:1 simpler:1 mathematical:1 scholkopf:4 prove:3 intricate:1 bnp:2 ry:2 spherical:8 td:2 pf:2 moreover:5 finding:1 act:1 nutshell... |
863 | 1,791 | Sparse Kernel
Principal Component Analysis
Michael E. Tipping
Microsoft Research
St George House, 1 Guildhall St
Cambridge CB2 3NH, U.K.
mtipping~microsoft.com
Abstract
'Kernel' principal component analysis (PCA) is an elegant nonlinear generalisation of the popular linear data analysis method,
where a kernel functio... | 1791 |@word inversion:1 compression:1 covariance:12 pick:1 reduction:2 series:2 efficacy:1 offering:1 psarrou:1 current:1 com:1 subsequent:1 kleen:1 informative:1 shape:1 analytic:1 plot:3 update:3 rpn:1 inspection:1 isotropic:2 maximised:1 rc:1 overhead:1 expected:1 themselves:1 multi:1 psychometrika:1 xx:1 notation:1... |
864 | 1,792 | Redundancy and Dimensionality Reduction in
Sparse-Distributed Representations of Natural
Objects in Terms of Their Local Features
Penio S. Penev*
Laboratory of Computational Neuroscience
The Rockefeller University
1230 York Avenue, New York, NY 10021
penev@rockefeller.edu http://venezia.rockefeller.edu/
Abstract
Low-... | 1792 |@word middle:1 briefly:1 compression:1 accounting:1 solid:2 reduction:11 initial:2 contains:1 pub:2 tuned:1 recovered:1 current:1 jaynes:2 activation:2 must:1 numerical:1 girosi:2 greedy:4 intelligence:1 shut:1 beginning:1 filtered:1 provides:2 characterization:1 contribute:1 location:2 successive:2 five:1 mathem... |
865 | 1,793 | Permitted and Forbidden Sets in
Symmetric Threshold-Linear Networks
Richard H.R. Hahnloser and H. Sebastian Seung
Dept. of Brain & Cog. Sci., MIT
Cambridge, MA 02139 USA
rh~ai.mit.edu,
seung~mit.edu
Abstract
Ascribing computational principles to neural feedback circuits is an
important problem in theoretical neurosc... | 1793 |@word longterm:1 excited:1 initial:7 tuned:1 past:2 coactive:3 comparing:1 surprising:1 activation:3 dx:1 must:7 written:1 realize:1 provides:1 characterization:2 completeness:1 unbounded:2 along:1 constructed:2 differential:1 become:1 qualitative:1 prove:2 introduce:1 indeed:1 behavior:1 brain:1 inspired:2 globa... |
866 | 1,794 | Minimum Bayes Error Feature Selection for
Continuous Speech Recognition
George Saon and Mukund Padmanabhan
IBM T. 1. Watson Research Center, Yorktown Heights, NY, 10598
E-mail: {saon.mukund}@watson.ibm.com. Phone: (914)-945-2985
Abstract
We consider the problem of designing a linear transformation () E lRPx n,
of ran... | 1794 |@word manageable:1 duda:1 sensed:1 covariance:5 simplifying:1 searle:1 reduction:1 series:1 selecting:1 bhattacharyya:11 com:1 si:2 dx:2 numerical:1 j1:1 analytic:1 update:1 discrimination:3 stationary:1 short:1 provides:3 argmax1:1 height:1 mathematical:1 rnl:1 prove:1 consists:1 umbach:1 introduce:1 pairwise:3 ... |
867 | 1,795 | Active Learning for Parameter Estimation
in Bayesian Networks
Simon Tong
Computer Science Department
Stanford University
simon. tong@cs.stanford.edu
Daphne Koller
Computer Science Department
Stanford University
koller@cs.stanford.edu
Abstract
Bayesian networks are graphical representations of probability distributio... | 1795 |@word sri:1 bn:9 decomposition:1 simplifying:1 pick:1 dramatic:1 thereby:2 reduction:2 initial:1 contains:2 selecting:3 zij:1 imaginary:1 existing:2 current:5 yet:1 must:1 cpds:2 designed:1 update:9 greedy:2 fewer:1 assurance:1 intelligence:1 short:1 provides:2 node:28 simpler:2 daphne:1 five:1 incorrect:2 consis... |
868 | 1,796 | The Early Word Catches the Weights
Mark A. Smith
Garrison W. Cottrell
Karen L. Anderson
Department of Computer Science
University of California at San Diego
La Jolla, CA 92093
{masmith,gary,kanders}@cs.ucsd.edu
Abstract
The strong correlation between the frequency of words and their naming
latency has been well do... | 1796 |@word determinant:1 version:1 stronger:1 nd:1 simulation:1 paid:1 interestingly:1 reaction:2 com:1 surprising:1 yet:2 written:1 moo:1 must:1 cottrell:1 realistic:1 plasticity:2 eleven:1 wanted:1 plot:2 reproducible:1 hvs:1 v:8 fewer:1 item:1 beginning:1 smith:1 provides:2 contribute:2 five:1 become:3 surprised:1 ... |
869 | 1,797 | A Gradient-Based Boosting Algorithm for
Regression Problems
Richard S. Zemel
Toniann Pitassi
Department of Computer Science
University of Toronto
Abstract
In adaptive boosting, several weak learners trained sequentially
are combined to boost the overall algorithm performance. Recently adaptive boosting methods for cl... | 1797 |@word version:1 polynomial:1 seems:1 decomposition:2 jacob:1 contrastive:1 tr:2 harder:1 initial:1 series:1 tuned:1 ours:1 existing:1 riitsch:1 current:1 comparing:2 nowlan:1 yet:1 must:1 readily:1 additive:3 j1:1 update:3 greedy:2 steepest:1 reciprocal:1 provides:1 boosting:38 node:2 toronto:1 lx:8 five:1 constr... |
870 | 1,798 | Bayes Networks on Ice:
Robotic Search for Antarctic Meteorites
Liam Pedersen-, Dimi Apostolopoulos, Red Whittaker
Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213
{pedersen+, dalv, red}@ri.cmu.edu
Abstract
A Bayes network based classifier for distinguishing terrestrial
rocks from meteorites is impl... | 1798 |@word trial:1 retraining:2 suitably:1 tr:1 harder:1 carry:1 initial:2 hunting:1 contains:1 series:1 xiy:1 current:1 recovered:1 surprising:1 yet:1 must:5 deposited:1 subsequent:1 visible:2 realistic:1 shape:1 cheap:1 designed:1 update:1 chile:1 caveat:1 quantized:4 complication:2 location:2 node:6 along:1 ect:1 v... |
871 | 1,799 | Who Does What? A Novel Algorithm to
Determine Function Localization
Ranit Aharonov-Barki
Interdisciplinary Center for Neural Computation
The Hebrew University, Jerusalem 91904, Israel
ranit@alice.nc.huji.ac.il
Isaac Meilijson and Eytan Ruppin
School of Mathematical Sciences
Tel-Aviv University, Tel-Aviv, Israel
isaco... | 1799 |@word seems:1 simulation:1 pressure:1 solid:1 initial:2 configuration:10 existing:1 current:3 incidence:1 activation:3 si:1 yet:2 attracted:1 john:1 chicago:2 shape:3 motor:6 designed:1 alone:2 intelligence:1 nervous:5 math:2 contribute:1 mathematical:1 qualitative:6 consists:1 prove:1 behavioral:3 manner:1 intro... |
872 | 18 | 564
PROGRAMMABLE SYNAPTIC CHIP FOR
ELECTRONIC NEURAL NETWORKS
A. Moopenn, H. Langenbacher, A.P. Thakoor, and S.K. Khanna
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, CA 91009
ABSTRACT
A binary synaptic matrix chip has been developed for electronic
neural networks. The matrix chip contains a p... | 18 |@word cu:4 open:1 proportionality:1 grey:4 simulation:1 reduction:1 electronics:4 configuration:5 contains:3 series:4 past:1 duong:1 current:14 must:3 readily:1 deposited:8 john:1 shape:1 designed:3 sponsored:1 cue:1 selected:1 patterning:1 device:3 plane:1 provides:1 quantized:1 lor:1 rc:2 become:1 consists:3 resi... |
873 | 180 | 116
THE BOLTZMANN PERCEPTRON NETWORK:
A MULTI-LAYERED FEED-FORWARD NETWORK
EQUIVALENT TO THE BOLTZMANN MACHINE
Eyal Yair
and
Allen Gersho
Center for Infonnation Processing Research
Department of Electrical & Computer Engineering
University of California, Santa Barbara, CA 93106
ABSTRACT
The concept of the stochast... | 180 |@word tedious:1 d2:2 simulation:2 propagate:1 thereby:2 solid:1 harder:1 score:6 selecting:1 activation:2 written:3 partition:6 update:2 tenn:3 steepest:1 equi:1 yeb:2 lx:7 unacceptable:1 become:2 comb:1 manner:1 pairwise:1 behavior:2 examine:1 multi:1 ol:1 decomposed:2 actual:3 jm:1 increasing:1 becomes:7 provide... |
874 | 1,800 | Learning continuous distributions:
Simulations with field theoretic priors
lIya Nemenman1 ,2 and William Bialek2
of Physics, Princeton University, Princeton, New Jersey 08544
2NEC Research Institute, 4 Independence Way, Princeton, New Jersey 08540
nemenman@research.nj.nec.com, bialek@research.nj.nec.com
1 Department
... | 1800 |@word determinant:4 version:2 achievable:1 advantageous:1 seems:1 open:1 simulation:4 holy:1 phy:1 celebrated:1 series:1 efficacy:1 selecting:1 pub:2 current:1 com:2 discretization:1 yet:1 dx:1 must:3 john:1 numerical:3 subsequent:1 analytic:1 remove:1 asymptote:1 plot:1 selected:1 parameterization:4 short:1 comp... |
875 | 1,801 | Place Cells and Spatial Navigation based on
2d Visual Feature Extraction, Path Integration,
and Reinforcement Learning
A. Arleo*
F. Smeraldi
S. Hug
W. Gerstner
Centre for Neuro-Mimetic Systems, MANTRA
Swiss Federal Institute of Technology Lausanne,
CH-1015 Lausanne EPFL, Switzerland
Abstract
We model hippocampal plac... | 1801 |@word trial:4 exploitation:2 determinant:1 hippocampus:5 open:1 grey:3 confirms:1 azimuthal:1 decomposition:2 contains:1 efficacy:1 selecting:1 tuned:2 ranck:1 current:4 si:2 activation:1 must:1 wx:2 enables:1 motor:1 remove:1 medial:1 update:2 discrimination:1 cue:7 greedy:2 plane:3 mccallum:1 eminent:1 argm:1 d... |
876 | 1,802 | The Kernel Gibbs Sampler
Thore Graepel
Statistics Research Group
Computer Science Department
Technical University of Berlin
Berlin, Germany
guru@cs.tu-berlin.de
Ralf Herbrich
Statistics Research Group
Computer Science Department
Technical University of Berlin
Berlin, Germany
ralfh@cs.tu-berlin.de
Abstract
We present... | 1802 |@word pw:3 norm:1 covariance:1 tr:3 shading:1 reduction:1 denoting:1 interestingly:1 outperforms:1 current:2 gv:1 designed:1 plot:1 depict:1 v:1 plane:1 maximised:1 provides:3 toronto:1 herbrich:3 lx:2 bixi:1 along:1 beta:2 shorthand:1 eleventh:1 introduce:1 abscissa:1 multi:2 decomposed:1 equipped:1 increasing:1... |
877 | 1,803 | Error-correcting Codes on a Bethe-like Lattice
Renato Vicente
David Saad
The Neural Computing Research Group
Aston University, Birmingham, B4 7ET, United Kingdom
{vicenter,saadd}@aston.ac.uk
Yoshiyuki Kabashima
Department of Computational Intelligence and Systems Science
Tokyo Institute of Technology, Yokohama 2268502... | 1803 |@word open:1 attainable:1 tr:1 recursively:1 initial:4 united:1 tuned:1 interestingly:1 recovered:1 assigning:1 perturbative:1 written:2 numerical:2 v:1 implying:1 intelligence:1 selected:1 mpm:2 vanishing:2 hamiltonian:4 provides:1 node:6 inside:1 expected:1 mechanic:1 multi:2 rem:1 actual:2 increasing:2 becomes... |
878 | 1,804 | The Manhattan World Assumption:
Regularities in scene statistics which
enable Bayesian inference
James M. Coughlan
Smith-Kettlewell Eye Research Inst.
2318 Fillmore St.
San Francisco, CA 94115
A.L. Yuille
Smith-Kettlewell Eye Research Inst.
2318 Fillmore St.
San Francisco, CA 94115
coughlan@ski.org
yuille@ski.org
... | 1804 |@word briefly:1 thereby:1 contains:1 current:1 comparing:1 enables:2 cue:2 intelligence:1 plane:3 coughlan:5 smith:3 vanishing:4 core:1 detecting:3 coarse:1 quantized:2 location:3 ames:5 org:2 five:2 constructed:1 kettlewell:3 lopez:1 combine:2 indeed:1 growing:1 eil:5 little:1 provided:1 pof:6 panel:4 maximizes:... |
879 | 1,805 | On iterative Krylov-dogleg trust-region
steps for solving neural networks
nonlinear least squares problems
Eiji Mizutani
Department of Computer Science
National Tsing Hua University
Hsinchu, 30043 TAIWAN R.O.C.
eiji@wayne.cs.nthu.edu.tw
James w. Demmel
Mathematics and Computer Science
University of California at Berk... | 1805 |@word tsing:1 version:2 instrumental:1 adrian:1 linearized:1 decomposition:3 solid:1 reduction:2 initial:1 katoh:1 current:1 marquardt:8 yet:1 must:1 john:1 numerical:2 alone:1 prohibitive:1 fewer:1 device:1 dembo:2 steepest:3 ith:2 short:1 iterates:1 math:1 node:4 characterization:1 sigmoidal:1 constructed:1 dir... |
880 | 1,806 | Ensemble Learning and Linear Response Theory
for leA
Pedro A.d.F.R. Hfljen-Sflrensen l , Ole Winther2 , Lars Kai Hansen l
of Mathematical Modelling, Technical University of Denmark B321
DK-2800 Lyngby, Denmark, ph s , l k h a n s en @imrn. d tu. dk
2Theoretical Physics, Lund University, SOlvegatan 14 A
S-223 62 Lund, ... | 1806 |@word trial:1 simulation:1 covariance:4 tr:5 solid:1 moment:2 kappen:2 initial:1 ts2:1 si:2 additive:1 plot:3 hts:1 intelligence:1 lr:15 draft:1 mathematical:1 direct:2 ica:2 estimating:1 notation:1 factorized:3 sisi:1 temporal:8 act:2 overestimate:1 positive:1 treat:2 consequence:1 modulation:1 ap:1 might:1 stud... |
881 | 1,807 | Recognizing Hand-written Digits Using
Hierarchical Products of Experts
Guy Mayraz & Geoffrey E. Hinton
Gatsby Computational Neuroscience Unit
University College London
17 Queen Square, London WCIN 3AR, u.K.
Abstract
The product of experts learning procedure [1] can discover a set of
stochastic binary features that co... | 1807 |@word version:2 polynomial:2 replicate:1 logit:1 open:1 simulation:1 contrastive:3 q1:2 pick:1 tr:1 initial:2 contains:3 score:20 tuned:1 current:3 comparing:1 surprising:1 mayraz:1 si:3 written:1 must:1 john:2 visible:12 additive:1 treating:1 update:2 discrimination:2 generative:8 five:1 scholkopf:1 incorrect:1 ... |
882 | 1,808 | Efficient Learning of Linear Perceptrons
Shai Ben-David
Department of Computer Science
Technion
Haifa 32000, Israel
Hans Ulrich Simon
Fakultat fur Mathematik
Ruhr Universitat Bochum
D-44780 Bochum, Germany
shai~cs.technion.ac.il
simon~lmi.ruhr-uni-bochum.de
Abstract
We consider the existence of efficient algorithm... | 1808 |@word version:1 achievable:1 polynomial:24 norm:1 open:6 ruhr:2 accounting:2 profit:16 tr:1 reduction:11 contains:2 yet:1 must:3 readily:1 written:2 j1:1 wx:2 half:12 plane:23 lr:2 provides:1 become:1 symposium:1 prove:4 consists:2 inside:1 introduce:1 hardness:5 bsh:7 considering:1 classifies:6 notation:1 maximi... |
883 | 1,809 | Multiple times cales of adaptation in a neural
code
Adrienne L. Fairhall, Geoffrey D. Lewen, William Bialek,
and Robert R. de Ruyter van Steveninck
NEe Research Institute
4 Independence Way
Princeton, New Jersey 08540
adrienne!geofflbialeklruyter@ research. nj. nec. com
Abstract
Many neural systems extend their dynam... | 1809 |@word trial:3 version:1 seems:1 adrian:2 gradual:1 solid:1 moment:2 series:1 com:1 si:5 must:2 happen:2 shape:3 designed:1 half:2 nervous:1 short:1 record:1 compo:1 filtered:1 sudden:2 provides:1 along:1 constructed:2 undetectable:1 consists:1 indeed:2 rapid:1 oscilloscope:1 examine:2 discretized:1 insist:1 windo... |
884 | 181 | 149
FIXED POINT ANALYSIS FOR RECURRENT
NETWORKS
Mary B. Ottaway
Patrice Y. Simard
Dept. of Computer Science
University of Rochester
Rochester NY 14627
Dana H. Ballard
ABSTRACT
This paper provides a systematic analysis of the recurrent backpropagation (RBP) algorithm, introducing a number of new results. The main
li... | 181 |@word trial:1 simulation:5 cla:1 perfo:1 dramatic:1 mention:1 solid:2 recursively:4 initial:4 lapedes:3 current:1 surprising:1 activation:9 must:1 luis:1 visible:3 enables:1 update:2 accordingly:1 ith:1 indefinitely:1 provides:2 ron:1 successive:3 along:1 become:3 incorrect:6 introduce:1 indeed:1 behavior:3 examin... |
885 | 1,810 | Beyond maximum likelihood and density
estimation: A sample-based criterion for
unsupervised learning of complex models
Sepp Hochreiter and Michael C. Mozer
Department of Computer Science
University of Colorado
Boulder, CO 80309- 0430
{hochreit,mozer}~cs.colorado.edu
Abstract
The goal of many unsupervised learning pro... | 1810 |@word duda:1 simulation:3 tried:2 covariance:1 minus:1 tr:1 recovered:4 repelling:1 surprising:1 yet:1 dx:1 must:2 readily:1 distant:1 analytic:2 hochreit:1 designed:1 update:3 generative:12 plane:1 xk:3 transposition:1 characterization:2 location:6 toronto:1 obser:1 sigmoidal:1 unbounded:1 prove:3 recognizable:1... |
886 | 1,811 | Exact Solutions to Time-Dependent MDPs
Justin A. Boyan?
ITA Software
Building 400
One Kendall Square
Cambridge, MA 02139
jab@itasoftware.com
Michael L. Littman
AT&T Labs-Research
and Duke University
180 Park Ave. Room A275
Florham Park, NJ 07932-0971 USA
mlittman@research.att. com
Abstract
We describe an extension o... | 1811 |@word seems:1 nd:1 heuristically:1 closure:2 r:2 commute:2 carry:1 initial:2 att:1 tram:1 rightmost:1 com:2 discretization:1 must:3 john:2 ronald:1 realistic:1 numerical:1 visible:1 seeding:1 visibility:2 intelligence:2 item:1 lr:2 ames:7 location:2 five:1 symposium:1 consists:1 introduce:1 inter:1 ra:2 expected:... |
887 | 1,812 | Bayesian video shot segmentation
Nuno Vasconcelos
Andrew Lippman
MIT Media Laboratory, 20 Ames St, E15-354, Cambridge, MA 02139,
{nuno,lip}@media.mit.edu,
http://www.media.mit.edurnuno
Abstract
Prior knowledge about video structure can be used both as a means to
improve the peiformance of content analysis and to ex... | 1812 |@word achievable:1 norm:1 coarseness:1 seems:1 decomposition:1 covariance:1 shot:53 initial:1 selecting:1 current:3 comparing:1 od:1 nt:1 parsing:1 visible:1 shape:1 plot:1 v:2 stationary:1 inspection:1 characterization:6 provides:1 ames:1 successive:3 retrieving:1 prove:1 consists:1 wild:1 introduce:2 inter:3 ex... |
888 | 1,813 | Finding the Key to a Synapse
Thomas Natschlager & Wolfgang Maass
Institute for Theoretical Computer Science
Technische Universitat Graz, Austria
{tnatschl, maass}@igi.tu-graz.ac.at
Abstract
Experimental data have shown that synapses are heterogeneous: different
synapses respond with different sequences of amplitudes ... | 1813 |@word neurophysiology:1 stronger:1 seems:1 heuristically:1 seek:1 carry:1 initial:3 series:1 current:3 discretization:2 com:1 belmont:1 numerical:1 interspike:2 designed:1 xk:15 provides:2 height:1 mathematical:3 burst:3 differential:1 behavior:2 nor:1 ming:1 grotschel:1 underlying:1 circuit:4 maximizes:2 panel:2... |
889 | 1,814 | Incremental and Decremental Support Vector
Machine Learning
Gert Cauwenberghs*
CLSP, ECE Dept.
Johns Hopkins University
Baltimore, MD 21218
gert@jhu.edu
Tomaso Poggio
CBCL, BCS Dept.
Massachusetts Institute of Technology
Cambridge, MA 02142
tp@ai.mit.edu
Abstract
An on-line recursive algorithm for training support v... | 1814 |@word manageable:1 t_:1 covariance:1 recursively:1 initial:1 liu:1 bc:4 si:1 must:1 john:1 partition:1 girosi:1 remove:1 extrapolating:1 update:7 v:1 intelligence:1 accordingly:4 contribute:1 along:2 become:2 differential:1 scholkopf:5 qij:4 incorrect:2 overhead:1 unlearning:10 pairwise:1 tomaso:1 adiabatically:1... |
890 | 1,815 | Sequentially fitting "inclusive" trees for
inference in noisy-OR networks
Brendan J. Frey!, Relu Patrascu l
1
Intelligent Algorithms Lab
University of Toronto
www.cs.toronto.edu/~frey
,
Tommi S. Jaakkola\ Jodi Moranl
Computer Science and
Electrical Engineering
Massachusetts Institute of Technology
2
Abstract
An ... | 1815 |@word version:2 tried:1 tr:1 configuration:6 horvitz:1 current:3 written:1 must:3 plot:2 intelligence:4 vanishing:2 core:1 node:1 toronto:3 allerton:1 simpler:1 diagnosing:1 dn:14 fitting:3 absorbs:1 introduce:1 roughly:1 freeman:2 considering:1 increasing:1 factorized:1 minimizes:1 finding:7 ti:1 oscillates:1 de... |
891 | 1,816 | Divisive and Subtractive Mask Effects:
Linking Psychophysics and Biophysics
Barbara Zenger
Division of Biology
Caltech 139-74
Pasadena, CA 91125
Christof Koch
Computation and Neural Systems
Caltech 139-74
Pasadena, CA 91125
barbara@klab.caltech. edu
koch@klab.caltech.edu
Abstract
We describe an analogy between psy... | 1816 |@word trial:2 stronger:1 open:1 grey:1 simulation:3 solid:3 configuration:1 rightmost:1 blank:1 current:2 contextual:1 protection:1 written:1 john:1 realistic:1 numerical:1 visible:1 happen:1 remove:1 discrimination:12 cue:1 half:1 shut:1 contribute:1 location:1 sigmoidal:1 become:1 fixation:1 sustained:1 fitting... |
892 | 1,817 | NIPS '00
The Use of Classifiers in Sequential Inference
Vasin Punyakanok
Dan Roth
Department of Computer Science
University of Illinois at Urbana-Champaign
Urbana, IL 61801
punyakan@cs.uiuc.edu
danr@cs.uiuc. edu
Abstract
We study the problem of combining the outcomes of several different
classifiers in a way that pr... | 1817 |@word f32:2 polynomial:1 seems:1 open:5 confirms:1 tried:2 harder:3 initial:2 score:1 selecting:3 existing:1 recovered:2 current:3 od:1 comparing:2 nt:1 si:4 activation:3 conjunctive:1 parsing:9 stationary:2 intelligence:3 selected:1 item:1 indicative:1 mccallum:2 beginning:1 provides:3 location:2 simpler:1 along... |
893 | 1,818 | The Unscented Particle Filter
Rudolph van der Merwe
Oregon Graduate Institute
Electrical and Computer Engineering
P.O. Box 91000,Portland,OR 97006, USA
rvdmerwe@ece.ogi.edu
N ando de Freitas
UC Berkeley, Computer Science
387 Soda Hall, Berkeley
CA 94720-1776 USA
jfgf@cs.berkeley.edu
Arnaud Doucet
Cambridge University... | 1818 |@word seems:1 simulation:2 propagate:1 eng:1 covariance:5 tr:3 recursively:1 series:4 precluding:1 outperforms:1 freitas:11 nt:3 surprising:1 must:1 plot:1 update:1 hts:1 resampling:3 stationary:1 alone:1 smith:2 short:1 sudden:1 provides:2 firstly:1 welg:1 symposium:1 xtl:1 indeed:1 multi:1 pf:3 considering:1 be... |
894 | 1,819 | The Interplay of Symbolic and Subsymbolic
Processes
in Anagram Problem Solving
David B. Grimes and Michael C. Mozer
Department of Computer Science and Institute of Cognitive Science
University of Colorado, Boulder, CO 80309-0430 USA
{gr imes ,mo z er}@c s .co l ora d o .edu
Abstract
Although connectionist models have ... | 1819 |@word bigram:33 seems:1 grey:1 simulation:3 tr:1 harder:1 initial:2 contains:4 score:1 hereafter:1 outperforms:1 surprising:2 assigning:1 must:5 readily:2 grain:1 numerical:1 subsequent:1 motor:1 update:5 item:1 beginning:3 short:2 core:1 mental:1 consulting:1 lexicon:9 toronto:1 five:3 mathematical:1 along:1 bec... |
895 | 182 | 485
GENESIS: A SYSTEM FOR SIMULATING NEURAL
NETWOfl.KS
Matthew A. Wilson, Upinder S. Bhalla, John D. Uhley, James M. Bower.
Division of Biology
California Institute of Technology
Pasadena, CA 91125
ABSTRACT
We have developed a graphically oriented, general purpose
simulation system to facilitate the modeling of neura... | 182 |@word briefly:1 version:1 simulation:45 dramatic:1 xform:1 contains:2 selecting:1 current:4 written:1 olive:1 john:1 physiol:1 numerical:2 realistic:1 motor:1 designed:2 fund:2 device:1 nervous:1 directory:1 kbytes:1 provides:3 five:1 constructed:1 director:1 consists:5 olfactory:6 behavior:1 growing:1 simulator:1... |
896 | 1,820 | Combining ICA and top-down attention
for robust speech recognition
Un-Min Bae and Soo-Young Lee
Department of Electrical Engineering and Computer Science
and Brain Science Research Center
Korea Advanced Institute of Science and Technology
373-1 Kusong-dong, Yusong-gu, Taejon, 305-701, Korea
bum@neuron.kaist.ac.kr, sy... | 1820 |@word retraining:1 papoulis:1 electronics:1 score:1 recovered:4 additive:1 aoo:1 sponsored:1 update:1 stationary:3 alone:1 intelligence:1 record:1 provides:1 unmixed:3 contribute:3 supply:1 consists:1 introduce:1 ica:38 expected:3 equivariant:1 roughly:1 multi:2 brain:2 inspired:1 window:1 considering:4 becomes:1... |
897 | 1,821 | Automated State Abstraction for Options using
the U-Tree Algorithm
Anders Jonsson, Andrew G. Barto
Department of Computer Science
University of Massachusetts
Amherst, MA 01003
{ajonsson,barto}@cs.umass.edu
Abstract
Learning a complex task can be significantly facilitated by defining a
hierarchy of subtasks. An agent ... | 1821 |@word exploitation:1 version:4 smirnov:1 termination:1 decomposition:1 pick:6 solid:1 uma:1 tuned:1 current:2 must:1 realistic:1 periodically:1 enables:1 drop:5 designed:1 update:3 alone:1 intelligence:3 leaf:22 selected:4 mccallum:4 oldest:1 beginning:1 provides:1 node:17 location:3 five:1 constructed:1 symposiu... |
898 | 1,822 | A Linear Programming Approach to
Novelty Detection
Colin Campbell
Dept. of Engineering Mathematics,
Bristol University, Bristol
Bristol, BS8 1TR,
United Kingdon
C. Campbell@bris.ac.uk
Kristin P. Bennett
Dept. of Mathematical Sciences
Rensselaer Polytechnic Institute
Troy, New York 12180-3590
United States
bennek@rpi.... | 1822 |@word aircraft:1 cox:1 msr:2 proportion:1 tr:3 solid:2 substitution:1 contains:1 united:2 genetic:1 interestingly:1 od:1 repelling:1 rpi:1 must:1 distant:1 drop:1 plot:2 fewer:1 hypersphere:5 boosting:1 hyperplanes:1 org:1 simpler:2 mathematical:1 constructed:1 scholkopf:4 ypma:1 introduce:1 xji:1 considering:1 t... |
899 | 1,823 | A Support Vector Method for Clustering
AsaBen-Hur
Faculty of IE and Management
Technion, Haifa 32000, Israel
Hava T. Siegelmann
Lab for Inf. & Decision Systems
MIT Cambridge, MA 02139, USA
David Horn
School of Physics and Astronomy
Tel Aviv University, Tel Aviv 69978, Israel
Vladimir Vapnik
AT&T Labs Research
100 S... | 1823 |@word repository:2 faculty:1 polynomial:1 norm:1 seems:1 contains:2 written:1 numerical:1 partition:2 shape:6 core:2 provides:1 c2:1 become:1 scholkopf:1 qualitative:1 consists:1 interscience:1 inside:1 introduce:1 pairwise:1 behavior:1 globally:1 decreasing:2 window:3 increasing:4 begin:1 underlying:3 bounded:16... |
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