Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
1,000 | 1,914 | A tighter bound for graphical models
M.A.R. Leisink* and H.J. Kappe nt
Department of Biophysics
University of Nijmegen, Geert Grooteplein 21
NL 6525 EZ Nijmegen, The Netherlands
{martijn,bert}Cmbfys.kun.nl
Abstract
We present a method to bound the partition function of a Boltzmann machine neural network with any odd ... | 1914 |@word eex:1 middle:2 polynomial:5 grooteplein:1 ld:2 kappen:2 moment:3 reduction:2 contains:1 existing:1 nt:1 si:9 dx:2 must:1 visible:1 numerical:1 j1:7 partition:16 plot:1 intelligence:1 affair:1 node:2 successive:1 sigmoidal:1 direct:2 prove:1 shorthand:1 indeed:1 roughly:1 mbfys:2 mechanic:1 actual:1 notation... |
1,001 | 1,915 | Decomposition of Reinforcement Learning
for Admission Control of Self-Similar
Call Arrival Processes
Jakob Carlstrom
Department of Electrical Engineering, Technion, Haifa 32000, Israel
jakob@ee . technion . ac . il
Abstract
This paper presents predictive gain scheduling, a technique for simplifying reinforcement lear... | 1915 |@word version:2 seems:1 nd:2 simulation:3 decomposition:3 simplifying:1 initial:2 series:3 contains:1 selecting:1 tuned:2 surprising:1 activation:4 must:1 willinger:2 belmont:1 subsequent:1 numerical:2 enables:1 stationary:3 selected:3 inspection:1 xk:7 parametrization:1 haykin:1 accepting:1 regressive:1 location... |
1,002 | 1,916 | Modelling spatial recall, mental imagery and
neglect
Suzanna Becker
Department of Psychology
McMaster University
1280 Main Street West
Hamilton,Ont. Canada L8S 4Kl
becker@mcmaster.ca
Neil Burgess
Department of Anatomy and
Institute of Cognitive Neuroscience, UCL
17 Queen Square
London, UK WCIN 3AR
n.burgess@ucl.ac.uk... | 1916 |@word middle:1 cingulate:1 hippocampus:11 open:1 simulation:9 lobe:1 pressure:1 thereby:2 initial:2 configuration:1 series:1 tuned:6 batista:1 ranck:1 current:1 activation:16 must:1 john:1 subsequent:1 visible:7 plasticity:1 shape:2 motor:1 hypothesize:2 medial:9 cue:4 generative:1 item:1 sys:1 reciprocal:1 ith:1... |
1,003 | 1,917 | New Approaches Towards Robust and
Adaptive Speech Recognition
Herve Bourlard, Samy Bengio and Katrin Weber
IDIAP
P.O. Box 592, rue du Simplon 4
1920 Martigny, Switzerland
{ bourlard, bengio, weber} @idiap. ch
Abstract
In this paper, we discuss some new research directions in automatic
speech recognition (ASR), and w... | 1917 |@word briefly:3 seems:1 nd:1 confirms:1 decomposition:1 pavel:1 q1:1 accommodate:1 initial:2 contains:1 interestingly:1 current:2 dupont:1 stationary:3 xk:1 parametrization:1 short:1 along:1 supply:1 consists:1 inside:1 introduce:1 indeed:1 bocchieri:1 nor:1 multi:15 glotin:1 automatically:2 window:1 provided:1 e... |
1,004 | 1,918 | A variational mean-field theory for
sigmoidal belief networks
c. Bhattacharyya
Computer Science and Automation
Indian Institute of Science
Bangalore, India, 560012
cbchiru@csa.iisc.ernet.in
S. Sathiya Keerthi
Mechanical and Production Engineering
National University of Singapore
mpessk@guppy.mpe.nus.edu.sg
Abstract
A... | 1918 |@word build:1 implemented:1 ye:1 middle:1 inversion:1 ranged:1 uj:1 equality:1 hence:1 approximating:1 objective:2 already:1 g22:6 nonzero:1 stochastic:1 bn:5 attractive:1 kappen:1 leftmost:1 series:5 bhattacharyya:3 pl:1 correction:2 hold:1 temperature:2 around:2 considered:1 variational:11 si:5 activation:4 law... |
1,005 | 1,919 | Direct Classification with Indirect Data
Timothy X Brown
Interdisciplinary Telecommunications Program
Dept. of Electrical and Computer Engineering
University of Colorado, Boulder, 80309-0530
timxb~colorado.edu
Abstract
We classify an input space according to the outputs of a real-valued
function. The function is not g... | 1919 |@word trial:3 norm:3 seek:1 minus:1 mag:1 od:1 si:1 realistic:1 hypothesize:1 drop:1 congestion:1 fewer:2 provides:2 contribute:1 simpler:1 admission:2 direct:1 incorrect:1 prove:1 combine:1 introduce:1 inter:1 expected:3 automatically:1 actual:1 estimating:5 underlying:7 bounded:2 kind:1 fal:1 minimizes:3 nj:4 g... |
1,006 | 192 | 194
Huang and Lippmann
HMM Speech Recognition
with Neural Net Discrimination*
William Y. Huang and Richard P. Lippmann
Lincoln Laboratory, MIT
Room B-349
Lexington, MA 02173-9108
ABSTRACT
Two approaches were explored which integrate neural net classifiers
with Hidden Markov Model (HMM) speech recognizers. Both atte... | 192 |@word advantageous:1 dekker:1 hu:1 covariance:4 decomposition:1 fonn:1 tr:1 contains:1 score:20 selecting:1 current:5 z2:2 skipping:1 speakerindependent:1 shape:1 sponsored:1 discrimination:12 fewer:1 selected:1 ith:1 provides:2 node:13 location:1 nodal:1 along:1 combine:1 expected:1 behavior:1 examine:1 multi:3 l... |
1,007 | 1,920 | Incorporating Second-Order Functional
Knowledge for Better Option Pricing
Charles Dugas, Yoshua Bengio, Fran~ois Belisle, Claude Nadeau:Rene Garcia
CIRANO, Montreal, Qc, Canada H3A 2A5
{du gas ,beng i o y,beli s lf r ,na de a u c }@i ro .umo nt r e a l. ca
garc i ar@c i ran o .qc . ca
Abstract
Incorporating prior kno... | 1920 |@word mild:1 tried:1 solid:1 recursively:1 initial:2 series:1 nt:1 activation:1 universality:1 must:1 readily:1 drop:1 stationary:1 selected:1 xk:2 farther:1 recherche:1 sigmoidal:1 mathematical:1 along:2 maturity:4 prove:2 introduce:1 operationnelle:1 market:2 behavior:1 multi:4 decreasing:1 actual:1 jm:1 pf:1 i... |
1,008 | 1,921 | FaceSync: A linear operator for measuring
synchronization of video facial images and
audio tracks
Malcolm Slaney!
Interval Research
malcolm@ieee.org
Michele Covell2
Interval Research
covell@ieee.org
Abstract
FaceSync is an optimal linear algorithm that finds the degree of synchronization between the audio and image ... | 1921 |@word version:1 norm:1 covariance:3 decomposition:1 brightness:10 asks:1 dramatic:1 fonn:1 shot:1 sychronization:2 recursively:1 series:1 interestingly:1 past:3 current:3 comparing:1 com:1 synthesizer:1 must:1 john:1 grain:1 numerical:1 remove:1 intelligence:1 short:1 location:3 org:2 along:1 direct:2 combine:5 s... |
1,009 | 1,922 | Large Scale Bayes Point Machines
Ralf Herbrich
Statistics Research Group
Computer Science Department
Technical University of Berlin
ralfh@cs.tu-berlin.de
Thore Graepel
Statistics Research Group
Computer Science Department
Technical University of Berlin
guru@cs.tu-berlin.de
Abstract
The concept of averaging over clas... | 1922 |@word trial:1 version:11 pw:4 polynomial:1 advantageous:2 grey:2 covariance:1 tr:1 reduction:1 att:1 tuned:1 interestingly:1 current:2 clash:1 must:1 plot:5 update:3 maximised:1 beginning:1 qjk:1 provides:1 draft:1 toronto:1 herbrich:4 billiard:5 mathematical:1 symposium:1 combine:1 eleventh:1 inside:1 theoretica... |
1,010 | 1,923 | Homeostasis in a Silicon Integrate and Fire
Neuron
Shih-Chii LiD
Institute for Neuroinformatics, ETHIVNIZ
Winterthurstrasse 190, CH-8057 Zurich
Switzerland
shih@ini.phys.ethz.ch
Bradley A. Minch
School of Electrical and Computer Engineering
Cornell University
Ithaca, NY 14853-5401, U.S.A.
minch@ee.comell.edu
Abstract... | 1923 |@word pulse:2 it1:1 electronics:1 liu:2 series:1 efficacy:5 initial:5 document:1 bradley:1 current:17 comell:1 si:2 plasticity:1 remove:2 device:2 floatinggate:1 short:1 fabricating:1 provides:1 intellectual:1 node:1 along:1 c2:1 symposium:1 integrator:1 freeman:1 decreasing:1 prolonged:1 vfg:1 increasing:1 circu... |
1,011 | 1,924 | Active Support Vector Machine
Classification
o. L. Mangasarian
Computer Sciences Dept.
University of Wisconsin
1210 West Dayton Street
Madison, WI 53706
David R. Musicant
Dept. of Mathematics and Computer Science
Carleton College
One North College Street
Northfield, MN 55057
olvi@cs.wisc.edu
dmusican@carleton.edu
A... | 1924 |@word repository:2 version:4 norm:5 disk:2 open:1 termination:4 eng:1 invoking:1 solid:1 bai:1 necessity:1 contains:1 recovered:1 nt:2 must:1 john:2 belmont:1 numerical:4 partition:1 midway:1 enables:1 designed:1 newest:1 plane:15 reciprocal:1 mulier:1 location:3 successive:2 iset:1 org:1 simpler:4 mathematical:1... |
1,012 | 1,925 | Occam?s Razor
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
Zoubin Ghahramani
Gatsby Computational Neuroscience Unit
University College London
17 Queen Square, London WCIN 3A... | 1925 |@word middle:1 polynomial:1 seems:1 covariance:1 seriously:1 dx:2 must:2 bd:2 realistic:1 additive:1 offunctions:1 generative:1 selected:1 yr:1 sys:1 smith:2 simpler:1 mathematical:1 limd:1 overcomplex:3 become:1 fitting:2 indeed:1 expected:1 themselves:1 coa:1 increasing:1 moreover:1 panel:4 mass:2 kind:2 interp... |
1,013 | 1,926 | Higher-order Statistical Properties
Arising from the Non-stationarity of
Natural Signals
Lucas Parra, Clay Spence
Adaptive Signal and Image Processing, Sarnoff Corporation
{lparra, cspence} @sarnofJ. com
Paul Sajda
Department of Biomedical Engineering, Columbia University
ps629@columbia. edu
Abstract
We present evide... | 1926 |@word version:1 nd:1 covariance:7 paid:1 moment:1 series:3 com:1 comparing:1 surprising:1 fn:2 shape:2 enables:1 motor:1 remove:1 plot:2 stationary:19 half:1 dover:1 short:2 lx:1 mathematical:1 constructed:1 dan:1 ica:2 market:7 behavior:2 p1:2 multi:1 spherical:2 goldman:1 company:1 window:3 dxf:1 provided:1 lin... |
1,014 | 1,927 | Analysis of Bit Error Probability of
Direct-Sequence CDMA Multiuser
Demodulators
Toshiyuki Tanaka
Department of Electronics and Information Engineering
Tokyo Metropolitan University
Hachioji, Tokyo 192-0397, Japan
tanaka@eeLmetro-u.ac.jp
Abstract
We analyze the bit error probability of multiuser demodulators for dire... | 1927 |@word multipoint:2 heuristically:1 r:6 simplifying:1 covariance:1 solid:1 electronics:1 mag:1 multiuser:9 z2:1 si:1 numerical:1 additive:1 mpm:16 realizing:1 provides:1 math:2 af3:1 along:1 direct:3 become:1 indeed:2 expected:1 frequently:1 fez:2 actual:1 increasing:1 estimating:1 moreover:1 what:2 kind:1 suppres... |
1,015 | 1,928 | Kernel expansions with unlabeled examples
Martin Szummer
MIT AI Lab & CBCL
Cambridge, MA
szummer@ai.mit.edu
Tommi Jaakkola
MIT AI Lab
Cambridge, MA
tommi @ai.mit.edu
Abstract
Modern classification applications necessitate supplementing the few
available labeled examples with unlabeled examples to improve classificat... | 1928 |@word version:3 achievable:1 norm:5 covariance:1 tr:1 score:1 current:1 ilxl:6 scatter:2 written:1 readily:4 john:1 reminiscent:1 must:2 partition:2 hofmann:1 enables:1 plot:1 update:1 discrimination:4 implying:2 generative:1 selected:1 mccallum:1 ith:1 provides:1 iterates:1 contribute:1 along:1 become:1 consists... |
1,016 | 1,929 | High-temperature expansions for learning
models of nonnegative data
Oliver B. Downs
Dept. of Mathematics
Princeton University
Princeton, NJ 08544
ob do wn s@ p r in c et o n.edu
Abstract
Recent work has exploited boundedness of data in the unsupervised
learning of new types of generative model. For nonnegative data i... | 1929 |@word h:2 version:1 nd:6 mitsubishi:1 covariance:1 decomposition:1 tr:1 boundedness:1 kappen:3 moment:2 contains:2 efficacy:1 initialisation:2 daniel:1 current:1 activation:2 dx:1 john:1 partition:1 analytic:2 plot:1 generative:9 pursued:1 xk:2 toronto:1 direct:1 reversion:1 calculable:1 indeed:1 embody:2 inspire... |
1,017 | 193 | 694
MacKay and Miller
Analysis of Linsker's Simulations
of Hebbian rules
David J. C. MacKay
Computation and Neural Systems
Caltech 164-30 CNS
Pasadena, CA 91125
mackayOaurel.cns.caltech.edu
Kenneth D. Miller
Department of Physiology
University of California
San Francisco, CA 94143 - 0444
kenOphyb.ucsf.edu
ABSTRACT... | 193 |@word simulation:9 covariance:16 commute:1 initial:1 configuration:2 series:1 existing:1 analysed:2 written:2 numerical:1 subsequent:2 analytic:2 asymptote:1 remove:1 node:8 location:4 along:1 lk2:1 differential:1 become:1 qij:2 examine:1 growing:1 mechanic:1 resolve:1 little:1 increasing:2 becomes:3 project:1 not... |
1,018 | 1,930 | A Mathematical Programming Approach to the
Kernel Fisher Algorithm
Sebastian Mika*, Gunnar Ratsch*, and Klaus-Robert Miiller*+
*GMD FIRST.lDA, KekulestraBe 7, 12489 Berlin, Germany
+University of Potsdam, Am Neuen Palais 10, 14469 Potsdam
{mika, raetsch, klaus}@jirst.gmd.de
Abstract
We investigate a new kernel-based ... | 1930 |@word seems:2 norm:2 open:1 hu:1 simulation:1 crucially:1 r:1 covariance:1 thereby:1 tr:1 outlook:1 solid:1 initial:1 eigensolvers:1 selecting:2 interestingly:1 comparing:2 surprising:1 yet:2 written:2 partition:1 interpretable:1 v:1 alone:1 greedy:1 five:2 mathematical:5 along:2 become:1 scholkopf:1 prove:1 insi... |
1,019 | 1,931 | Active inference in concept learning
Jonathan D. Nelson
Javier R. Movellan
Department of Cogniti ve Science
University of California, San Diego
La Jolla, CA 92093-0515
jnelson@cogsci.ucsd.edu
Department of Cognitive Science
University of California, San Diego
La Jolla, CA 92093-0515
movellan@inc.ucsd.edu
Abstract
... | 1931 |@word trial:32 version:2 instruction:1 simulation:1 seek:1 kent:1 pick:1 solid:2 interestingly:1 subjective:6 current:6 surprising:2 yet:2 informative:4 hypothesize:1 designed:2 guess:1 provides:1 mathematical:2 replication:1 qualitative:1 shorthand:1 consists:2 hci:1 dan:1 expected:9 behavior:7 elman:1 cheetah:1... |
1,020 | 1,932 | Probabilistic Semantic Video Indexing
Milind R. Naphade, Igor Kozintsev and Thomas Huang
Department of Electrical and Computer Engineering
University of Illinois at Urbana-Champaign
{milind, igor,huang}@ifp.uiuc.edu
Abstract
We propose a novel probabilistic framework for semantic video indexing. We define probabilist... | 1932 |@word aircraft:1 kristjansson:1 accounting:6 shot:11 moment:2 configuration:1 series:1 loeliger:1 interestingly:1 qbe:2 ixj:1 fn:1 chicago:3 shape:2 plot:1 sundaram:1 cue:1 discovering:1 fewer:1 ith:1 provides:1 detecting:4 node:25 five:6 direct:1 combine:1 inter:1 uiuc:2 multi:1 audiovisual:1 kozintsev:1 pf:2 be... |
1,021 | 1,933 | Sex with Support Vector Machines
Baback Moghaddam
Mitsubishi Electric Research Laboratory
Cambridge MA 02139, USA
baback<amerl.com
Ming-Hsuan Yang
University of Illinois at Urbana-Champaign
Urbana, IL 61801 USA
mhyang<avision.ai.uiuc.edu
Abstract
Nonlinear Support Vector Machines (SVMs) are investigated for
visual s... | 1933 |@word trial:1 briefly:1 polynomial:3 sex:19 open:1 coombes:1 heuristically:1 mitsubishi:1 harder:1 shot:1 empath:1 ours:1 com:1 attracted:1 must:1 cottrell:2 subsequent:1 partition:1 predetermined:1 girosi:2 shape:1 discrimination:3 v:1 intelligence:2 node:3 location:1 theodoros:1 simpler:1 five:1 mathematical:1 ... |
1,022 | 1,934 | Computing with Finite and Infinite Networks
Ole Winther*
Theoretical Physics, Lund University
SOlvegatan 14 A, S-223 62 Lund, Sweden
wint h e r@ nimis.thep.lu. s e
Abstract
Using statistical mechanics results, I calculate learning curves (average
generalization error) for Gaussian processes (GPs) and Bayesian neural
... | 1934 |@word briefly:1 simulation:9 covariance:7 minus:1 carry:1 moment:1 surprising:1 dx:2 written:4 must:1 partition:1 implying:1 ith:1 vanishing:1 manfred:1 ipi:1 dn:2 direct:1 profound:1 specialize:1 introduce:1 overline:1 expected:3 behavior:1 mechanic:7 uz:2 spherical:1 actual:1 little:1 cardinality:1 becomes:3 li... |
1,023 | 1,935 | Constrained Independent Component
Analysis
Wei Lu and Jagath C. Rajapakse
School of Computer Engineering
Nanyang Technological University, Singapore 639798
email: asjagath@ntu.edu.sg
Abstract
The paper presents a novel technique of constrained independent
component analysis (CICA) to introduce constraints into the cl... | 1935 |@word version:1 briefly:1 eliminating:1 norm:7 simulation:2 configuration:1 contains:2 recovered:4 activation:1 wx:1 stationary:2 urp:1 ith:1 provides:1 ipi:2 c2:1 become:1 symposium:1 manner:6 introduce:2 ica:24 p1:1 growing:1 cct:1 decreasing:1 resolve:1 becomes:1 cm:1 kind:1 unified:1 finding:1 transformation:... |
1,024 | 1,936 | Programmable Reinforcement Learning Agents
David Andre and Stuart J. Russell
Computer Science Division, UC Berkeley, CA 94702
{dandre,russell}@cs.berkeley.edu
Abstract
We present an expressive agent design language for reinforcement learning that allows the user to constrain the policies considered by the learning pr... | 1936 |@word trial:1 reused:1 decomposition:2 rol:1 concise:3 thereby:1 tr:3 reduction:1 initial:2 configuration:1 fragment:1 existing:2 current:2 transferability:1 z2:3 surprising:1 written:4 must:7 tot:1 update:2 smdp:15 half:2 leaf:1 intelligence:2 parameterization:1 accordingly:1 meuleau:1 num:1 provides:3 location:... |
1,025 | 1,937 | Emergence of movement sensitive
neurons' properties by learning a sparse
code for natural moving images
Rafal Bogacz
Dept. of Computer Science
University of Bristol
Bristol BS8 lUB, U.K.
Malcolm W. Brown Christophe Giraud-Carrier
Dept. of Anatomy
Dept. of Computer Science
University of Bristol
University of Bristol
B... | 1937 |@word implemented:3 brown:2 middle:1 barlow:1 contain:1 consisted:1 hence:3 direction:7 determining:1 anatomy:1 thick:1 spike:4 grey:1 filter:1 simulation:2 crucially:2 illustrated:1 covariance:1 white:1 x5:1 ll:1 brightness:1 receptive:8 implementing:2 minus:1 cgc:1 gradient:3 outer:1 complete:2 secondly:1 summa... |
1,026 | 1,938 | Learning and Tracking Cyclic Human
Motion
D.Ormoneit
Dept. of Computer Science
Stanford University
Stanford, CA 94305
ormoneitOcs.stanford.edu
H. Sidenbladh
Royal Institute of Technology (KTH),
CVAP/NADA,
S-100 44 Stockholm, Sweden
hedvigOnada.kth.se
M. J. Black
Dept. of Computer Science
Brown University, Box 1910
P... | 1938 |@word briefly:1 seitz:1 mitsubishi:1 decomposition:2 tr:1 recursively:1 carry:1 necessity:1 cyclic:2 series:8 configuration:3 denoting:1 current:1 recovered:1 yet:1 must:2 written:1 numerical:1 periodically:1 j1:1 designed:1 update:1 resampling:1 generative:3 isard:1 plane:1 beginning:1 ith:1 provides:2 coarse:1 ... |
1,027 | 1,939 | Periodic Component Analysis:
An Eigenvalue Method for Representing
Periodic Structure in Speech
Lawrence K. Saul and Jont B. Allen
{lsaul,jba}@research.att.com
AT&T Labs, 180 Park Ave, Florham Park, NJ 07932
Abstract
An eigenvalue method is developed for analyzing periodic structure in
speech. Signals are analyzed by... | 1939 |@word version:1 rising:1 compression:3 seems:1 solid:1 harder:3 carry:1 initial:1 inefficiency:2 series:1 att:1 contains:1 ours:1 rapt:1 imaginary:1 com:1 reminiscent:2 must:1 realistic:1 enables:1 analytic:6 remove:1 designed:2 plot:3 stationary:1 cue:4 half:2 fewer:1 device:1 ith:1 filtered:1 provides:3 detecti... |
1,028 | 194 | 686
Barto, Sutton and Watkins
Sequential Decision Problems
and Neural Networks
A. G. Barto
Dept. of Computer and
Information Science
Univ. of Massachusetts
Amherst, MA 01003
R. S. Sutton
GTE Laboratories Inc.
Waltham, MA 02254
c.
J. C. H. Watkins
25B Framfield
Highbury, London
N51UU
ABSTRACT
Decision making task... | 194 |@word briefly:1 version:1 instruction:1 moment:2 initial:2 series:1 selecting:2 past:1 current:7 yet:1 must:2 numerical:1 update:2 intelligence:2 short:2 provides:3 successive:2 supply:1 symposium:1 consists:1 eleventh:1 manner:2 expected:14 planning:2 brain:1 bellman:3 discounted:3 td:26 actual:3 curse:1 unpredic... |
1,029 | 1,940 | Discovering Hidden Variables:
A Structure-Based Approach
Gal Elidan
Noam Lotner
Nir Friedman
Daphne Koller
Hebrew University
Stanford University
{galel,noaml,nir}@cs.huji.ac.il
koller@cs.stanford.edu
Abstract
A serious problem in learning probabilistic models is the presence of hidden variables. These variable... | 1940 |@word briefly:1 nd:1 bn:2 thereby:1 harder:1 initial:2 born:2 contains:10 score:15 fragment:1 configuration:1 selecting:1 ours:1 outperforms:3 existing:1 current:3 comparing:1 si:2 yet:2 must:1 suermondt:1 subsequent:1 realistic:1 predetermined:1 v:1 stationary:2 pursued:1 discovering:3 greedy:4 half:1 record:1 d... |
1,030 | 1,941 | Multiagent Planning with Factored MDPs
Carlos Guestrin
Computer Science Dept
Stanford University
guestrin@cs.stanford.edu
Daphne Koller
Computer Science Dept
Stanford University
koller@cs.stanford.edu
Ronald Parr
Computer Science Dept
Duke University
parr@cs.duke.edu
Abstract
We present a principled and efficient p... | 1941 |@word version:1 longterm:1 eliminating:1 achievable:2 norm:1 polynomial:1 simplifying:1 q1:1 thereby:2 initial:1 contains:6 siebel:1 selecting:2 recovered:1 current:1 comparing:1 must:7 written:1 dechter:1 ronald:1 stationary:1 greedy:4 fewer:2 instantiate:1 intelligence:5 meuleau:1 tumer:1 provides:3 node:6 simp... |
1,031 | 1,942 | Generalizable Relational Binding from
Coarse-coded Distributed Representations
Randall C. O?Reilly
Department of Psychology
University of Colorado Boulder
345 UCB
Boulder, CO 80309
Richard S. Busby
Department of Psychology
University of Colorado Boulder
345 UCB
Boulder, CO 80309
oreilly@psych.colorado.edu
Richard.B... | 1942 |@word trial:1 proportion:1 holyoak:5 gradual:1 r:4 contrastive:1 thereby:1 initial:1 configuration:1 series:1 loc:6 bc:4 existing:4 comparing:1 contextual:1 activation:7 conjunctive:14 must:6 john:3 realistic:1 shape:5 intelligence:1 fewer:2 selected:1 item:3 short:1 coarse:7 provides:1 location:42 rc:6 replicati... |
1,032 | 1,943 | A Quantitative Model of Counterfactual
Reasoning
Michael Ramscar
Division of Informatics
University of Edinburgh
Edinburgh, Scotland
michael@dai.ed.ac.uk
Daniel Yarlett
Division of Informatics
University of Edinburgh
Edinburgh, Scotland
dany@cogsci.ed.ac.uk
Abstract
In this paper we explore two quantitative approach... | 1943 |@word determinant:1 judgement:7 norm:1 seems:2 proportion:1 calculus:1 simulation:2 crucially:2 propagate:1 simplifying:1 pick:1 accommodate:1 gloss:1 initial:5 daniel:1 current:2 comparing:1 activation:9 yet:1 update:1 aside:1 alone:1 selected:6 accordingly:1 scotland:2 affair:1 dawes:2 provides:1 node:13 contri... |
1,033 | 1,944 | Convergence of Optimistic and
Incremental Q- Learning
Eyal Even-Dar*
Yishay Mansour t
Abstract
Vie sho,v the convergence of tV/O deterministic variants of Qlearning. The first is the widely used optimistic Q-learning, which
initializes the Q-values to large initial values and then follows a
greedy policy with respec... | 1944 |@word mild:1 exploitation:2 polynomial:2 initial:6 tnot:2 current:1 comparing:1 si:12 john:1 belmont:1 update:3 v:1 greedy:18 ith:1 direct:2 prove:1 combine:1 inter:1 behavior:1 derandomization:3 discounted:4 actual:1 bounded:3 maximizes:1 israel:3 maxa:1 developed:1 guarantee:4 quantitative:1 every:11 ti:4 iearn... |
1,034 | 1,945 | Natural Language Grammar Induction using a
Constituent-Context Model
Dan Klein and Christopher D. Manning
Computer Science Department
Stanford University
Stanford, CA 94305-9040
{klein, manning}@cs.stanford.edu
Abstract
This paper presents a novel approach to the unsupervised learning of syntactic analyses of natural ... | 1945 |@word version:3 achievable:1 advantageous:1 open:1 heuristically:1 propagate:1 pressure:3 harder:1 moment:1 initial:1 series:1 score:4 charniak:4 united:1 interestingly:1 contextual:1 comparing:3 surprising:1 must:2 parsing:11 john:2 subsequent:1 chicago:2 wanted:1 seeding:1 plot:1 interpretable:1 v:1 generative:... |
1,035 | 1,946 | On Kernel-Target Alignment
N ello Cristianini
BIOwulf Technologies
nello@support-vector. net
Andre Elisseeff
BIOwulf Technologies
andre@barnhilltechnologies.com
John Shawe-Taylor
Royal Holloway, University of London
john@cs.rhul.ac.uk
Jaz Kandola
Royal Holloway, University of London
jaz@cs.rhul.ac.uk
Abstract
We int... | 1946 |@word h:1 norm:1 lodhi:1 decomposition:1 elisseeff:1 tr:1 reduction:1 selecting:3 err:2 com:1 jaz:2 must:1 written:3 john:3 designed:1 short:1 provides:3 mcdiarmid:5 simpler:2 org:1 prove:2 introduce:2 indeed:3 expected:10 window:9 increasing:1 becomes:1 estimating:2 bounded:5 moreover:1 what:2 pursue:1 eigenvect... |
1,036 | 1,947 | An Efficient Clustering Algorithm Using
Stochastic Association Model and Its
Implementation Using Nanostructures
Takashi Morie, Tomohiro Matsuura, Makoto Nagata, and Atsushi Iwata
Graduate School of Advanced Sciences of Matter, Hiroshima University
Higashi-hiroshima, 739-8526 Japan.
http://www.dsl.hiroshima-u.ac.jp
mo... | 1947 |@word version:2 compression:1 seems:1 pulse:3 simulation:13 solid:3 initial:2 series:1 comparing:2 si:2 scatter:1 yet:1 ikeda:1 shape:1 update:2 selected:1 device:7 core:1 height:2 dn:2 c2:3 along:1 constructed:2 consists:2 sendai:3 fabricate:1 introduce:1 nor:3 mechanic:1 multi:1 lena:1 inspired:1 detects:1 beco... |
1,037 | 1,948 | Risk Sensitive Particle Filters
Sebastian Thrun, John Langford, Vandi Verma
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
thrun,jcl,vandi @cs.cmu.edu
Abstract
We propose a new particle filter that incorporates a model of costs when
generating particles. The approach is motivated by the o... | 1948 |@word mild:1 proportion:1 simulation:1 pick:1 incurs:1 recursively:2 initial:7 liu:1 tuned:1 freitas:1 must:1 john:1 numerical:1 resampling:1 generative:1 selected:1 fewer:1 isard:1 intelligence:1 gear:1 wolfram:1 location:7 height:1 differential:1 consists:1 combine:1 elderly:2 indeed:1 expected:2 planning:1 ry:... |
1,038 | 1,949 | A Parallel Mixture of SVMs for Very Large Scale
Problems
Ronan Collobert*
Universite de Montreal, DIRG
CP 6128, Succ. Centre-Ville
Montreal, Quebec, Canada
collober?iro.umontreal.ca
Samy Bengio
IDIAP
CP 592, rue du Simp Ion 4
1920 Martigny, Switzerland
bengio?idiap.ch
Yoshua Bengio
Universite de Montreal, DIRG
CP 612... | 1949 |@word briefly:1 seems:3 termination:1 queensland:1 covariance:1 jacob:1 decomposition:1 series:5 pub:1 current:1 comparing:1 nowlan:1 assigning:1 realistic:2 ronan:2 partition:1 girosi:1 lue:6 selected:5 website:1 ith:1 short:1 rc:1 consists:1 combine:2 introduce:2 ra:1 indeed:2 rapid:1 multi:1 actual:1 cpu:5 win... |
1,039 | 195 | Training Stochastic Model Recognition Algorithms
Training Stochastic Model Recognition
Algorithms as Networks can lead to Maximum
Mutual Information Estimation of Parameters
John s. Bridle
Royal Signals and Radar Establishment
Great Malvern
Worcs.
UK
WR143PS
ABSTRACT
One of the attractions of neural network approache... | 195 |@word seems:1 seek:1 covariance:2 specialises:1 pick:1 minus:1 barney:1 score:5 written:1 must:3 john:1 enables:1 drop:1 interpretable:1 discrimination:9 poritz:1 patterning:1 isotropic:1 lr:1 provides:1 firstly:1 mathematical:1 constructed:2 incorrect:1 introduce:1 expected:3 p1:1 multi:3 ol:1 insist:1 spherical:... |
1,040 | 1,950 | A hierarchical model of complex cells in
visual cortex for the binocular perception
of motion-in-depth
Silvio P. Sabatini, Fabio Solari, Giulia Andreani,
Chiara Bartolozzi, and Giacomo M. Bisio
Department of Biophysical and Electronic Engineering
University of Genoa, 1-16145 Genova, ITALY
silvio@dibe.unige.it
Abstrac... | 1950 |@word middle:1 sabatini:1 d2:3 maes:1 carry:2 disparity:34 tuned:4 erms:1 si:1 written:1 tilted:2 realistic:1 shape:1 plot:2 discrimination:1 cue:2 selected:1 accordingly:1 beginning:1 location:2 preference:1 arctan:1 c22:1 along:3 c2:1 direct:1 differential:1 acquired:1 indeed:1 freeman:1 considering:3 project:3... |
1,041 | 1,951 | A Sequence Kernel and its Application to
Speaker Recognition
William M. Campbell
Motorola Human Interface Lab
7700 S. River Parkway
Tempe, AZ 85284
Bill.Campbell@motorola.com
Abstract
A novel approach for comparing sequences of observations using an
explicit-expansion kernel is demonstrated. The kernel is derived usi... | 1951 |@word polynomial:15 decomposition:1 covariance:3 dramatic:1 tr:1 reduction:5 series:1 score:5 reynolds:1 com:1 comparing:3 activation:1 must:2 written:1 john:3 reminiscent:1 ronan:1 designed:1 generative:1 smith:1 shorthand:1 combine:2 manner:1 motorola:2 window:1 considering:1 becomes:3 notation:1 maximizes:1 in... |
1,042 | 1,952 | Agglomerative Multivariate Information
Bottleneck
Noam Sionim Nir Friedman Naftali Tishby
School of Computer Science & Engineering, Hebrew University, Jerusalem 91904, Israel
{noamm, nir, tishby } @cs.huji.ac.il
Abstract
The information bottleneck method is an unsupervised model independent data
organization techniqu... | 1952 |@word briefly:1 version:1 compression:6 tamayo:1 tr:1 carry:1 reduction:1 electronics:2 contains:4 document:12 current:1 lang:1 must:2 partition:10 informative:11 enables:1 v:2 stationary:4 greedy:4 leaf:1 half:1 eshkol:1 noamm:1 merger:22 mccallum:1 ecir:1 characterization:1 provides:4 ames:1 traverse:1 allerton... |
1,043 | 1,953 | Reinforcement Learning
Memory
Bram Bakker
Dept. of Psychology, Leiden University / IDSIA
P.O. Box 9555; 2300 RB, Leiden; The Netherlands
bbakker@fsw.leidenuniv. nl
Abstract
This paper presents reinforcement learning with a Long ShortTerm Memory recurrent neural network: RL-LSTM. Model-free
RL-LSTM using Advantage(,x)... | 1953 |@word longterm:1 version:3 seems:1 nd:1 bptt:8 open:1 simulation:3 moment:2 contains:1 past:2 outperforms:1 current:7 surprising:1 activation:12 must:9 yep:1 realistic:1 designed:4 update:3 greedy:1 discovering:2 mccallum:2 beginning:2 short:5 hinged:1 meuleau:1 indefinitely:3 location:1 along:1 corridor:13 consi... |
1,044 | 1,954 | Active Information Retrieval
Tommi Jaakkola
MIT AI Lab
Cambridge, MA
tommi@ai.mit.edu
Hava Siegelmann
MIT LIDS
Cambridge, MA
hava@mit.edu
Abstract
In classical large information retrieval systems, the system responds
to a user initiated query with a list of results ranked by relevance.
The users may further refine th... | 1954 |@word stronger:1 tedious:1 km:1 additively:2 contrastive:3 moment:1 reduction:1 initial:1 contains:1 karger:1 document:23 past:1 current:1 si:2 scatter:4 written:1 readily:1 subsequent:2 informative:1 update:4 xex:4 greedy:1 selected:2 half:1 item:1 short:2 el1:1 provides:2 successive:1 along:1 consists:1 combine... |
1,045 | 1,955 | Switch Packet Arbitration via Queue-Learning
Timothy X Brown
Electrical and Computer Engineering
Interdisciplinary Telecommunications
University of Colorado
Boulder, CO 80309-0530
timxb@colorado.edu
Abstract
In packet switches, packets queue at switch inputs and contend for outputs. The contention arbitration policy ... | 1955 |@word polynomial:6 loading:1 simulation:3 simplifying:1 decomposition:4 gabow:1 reduction:3 initial:2 existing:1 current:8 must:1 john:1 realistic:1 enables:1 update:1 selected:3 destined:9 beginning:1 accepting:1 provides:1 uncoordinated:1 five:2 admission:3 consists:1 headed:1 expected:6 multi:1 bellman:1 disco... |
1,046 | 1,956 | The Infinite Hidden Markov Model
Matthew J. Beal
Zoubin Ghahramani
Carl Edward Rasmussen
Gatsby Computational Neuroscience Unit
University College London
17 Queen Square, London WC1N 3AR, England
http://www.gatsby.ucl.ac.uk
m.beal,zoubin,edward @gatsby.ucl.ac.uk
Abstract
We show that it is possible to extend hid... | 1956 |@word middle:2 proportion:7 neigbours:1 invoking:1 initial:1 selecting:1 nii:1 denoting:1 initialisation:1 existing:6 yet:1 subsequent:1 shape:1 treating:1 update:5 generative:3 instantiate:1 discovering:1 item:1 consulting:1 toronto:1 successive:2 five:1 along:1 become:1 underfitting:1 introduce:2 expected:5 exa... |
1,047 | 1,957 | Why neuronal dynamics should control
synaptic learning rules
Jesper Tegner
Stockholm Bioinformatics Center
Dept. of Numerical Analysis
& Computing Science
Royal Institute for Technology
S-10044 Stockholm, Sweden
jespert@nada.kth.se
Adam Kepecs
Volen Center for Complex Systems
Brandeis University
Waltham, MA 02454
kep... | 1957 |@word version:3 simulation:5 versatile:1 initial:1 interestingly:1 wd:1 yet:1 written:1 tot:3 additive:13 realistic:1 numerical:4 plasticity:9 analytic:1 opin:1 update:2 ith:1 short:1 provides:4 parameterizations:1 ouput:1 pairing:1 combine:1 dan:1 introduce:1 manner:1 expected:2 rapid:3 nor:1 examine:4 little:1 ... |
1,048 | 1,958 | Kernel Machines and Boolean Functions
Adam Kowalczyk
Telstra Research Laboratories
Telstra, Clayton, VIC 3168
a.kowalczyk@trl.oz.au
Alex J. Smola, Robert C. Williamson
RSISE, MLG and TelEng
ANU, Canberra, ACT, 0200
Alex.Smola, Bob.Williamson @anu.edu.au
Abstract
We give results about the learnability and required c... | 1958 |@word version:4 achievable:1 polynomial:18 norm:2 simplifying:1 contains:1 score:2 rkhs:3 percep:2 current:1 od:1 must:1 belmont:2 partition:1 wx:1 girosi:1 cwd:1 update:5 half:1 leaf:9 selected:2 nq:2 rts:2 device:1 greedy:3 realizing:1 vanishing:1 institution:1 provides:1 boosting:2 cse:1 org:1 mathematical:2 d... |
1,049 | 1,959 | EM-DD: An Improved Multiple-Instance
Learning Technique
Qi Zhang
Department of Computer Science
Washington University
St. Louis, MO 63130-4899
Sally A. Goldman
Department of Computer Science
Washington University
St. Louis, MO 63130-4899
qz@cs. wustl. edu
sg@cs. wustl. edu
Abstract
We present a new multiple-inst a... | 1959 |@word version:2 briefly:1 termination:1 cml:1 ratan:1 pick:2 tr:1 initial:2 contains:1 series:2 tuned:3 outperforms:2 past:1 current:2 must:1 numerical:1 partition:1 shape:3 drop:1 generative:3 selected:7 guess:1 half:1 fewer:2 intelligence:1 dissertation:2 provides:1 zhang:2 along:1 consists:2 combine:3 dan:1 ra... |
1,050 | 196 | A Computer Modeling Approach to Understanding
A computer modeling approach to understanding the
inferior olive and its relationship to the cerebellar
cortex in rats
Maurice Lee and James M. Bower
Computation and Neural Systems Program
California Institute of Technology
Pasadena, CA 91125
ABSTRACT
This paper presents... | 196 |@word proceeded:1 briefly:1 replicate:1 open:1 simulation:6 nicholson:1 lobe:4 systeme:1 tr:1 shading:3 cytology:1 series:1 current:3 anterior:4 si:1 yet:1 olive:21 physiol:2 distant:1 medial:1 nervous:2 plane:2 perioral:2 paulin:1 record:1 supplying:1 compo:4 provides:1 contribute:2 accessed:1 correlograms:1 cons... |
1,051 | 1,960 | Speech Recognition with Missing Data using
Recurrent Neural Nets
S. Parveen
Speech and Hearing Research Group
Department of Computer Science
University of Sheffield
Sheffield S14DP, UK
s.parveen@dcs.shef.ac.uk
P.D. Green
Speech and Hearing Research Group
Department of Computer Science
University of Sheffield
Sheffield... | 1960 |@word version:1 seek:1 covariance:1 tr:1 solid:3 initial:3 series:1 past:1 existing:1 contextual:2 activation:2 realistic:1 additive:2 eleven:1 generative:1 cue:1 intelligence:2 node:1 windowed:1 direct:3 consists:1 introduce:1 mask:5 alspector:1 elman:4 encouraging:1 jm:2 window:1 actual:1 matched:1 bounded:3 de... |
1,052 | 1,961 | Laplacian Eigenmaps and Spectral
Techniques for Embedding and Clustering
Mikhail Belkin and Partha Niyogi
Depts. of Mathematics and Computer Science
The University of Chicago
Hyde Park , Chicago, IL 60637.
(misha@math.uchicago.edu,niyogi@cs.uchicago.edu)
Abstract
Drawing on the correspondence between the graph Laplac... | 1961 |@word middle:1 bigram:1 closure:2 tr:2 reduction:5 initial:1 necessity:1 etric:2 fragment:1 contains:2 series:1 existing:1 yet:1 must:1 written:2 chicago:2 enables:1 remove:1 intelligence:1 selected:1 xk:2 ith:2 core:1 short:1 erator:3 lr:5 provides:3 math:1 node:5 simpler:1 constructed:1 become:1 differential:1 ... |
1,053 | 1,962 | Fast, large-scale transformation-invariant
clustering
Brendan J. Frey
Machine Learning Group
University of Toronto
www.psi.toronto.edu/?frey
Nebojsa Jojic
Vision Technology Group
Microsoft Research
www.ifp.uiuc.edu/?jojic
Abstract
In previous work on ?transformed mixtures of Gaussians? and
?transformed hidden Markov... | 1962 |@word version:1 norm:1 open:1 covariance:3 tr:2 reduction:1 initial:1 document:1 culprit:1 must:1 readily:1 written:1 realistic:1 x240:4 update:5 nebojsa:1 generative:6 greedy:1 fewer:1 intelligence:2 provides:1 toronto:2 along:1 direct:2 acheived:1 consists:1 xz:1 uiuc:1 multi:1 considering:1 becomes:2 confused:... |
1,054 | 1,963 | On the Convergence of Leveraging
Gunnar R?atsch, Sebastian Mika and Manfred K. Warmuth
RSISE, Australian National University, Canberra, ACT 0200 Australia
Fraunhofer FIRST, Kekul?estr. 7, 12489 Berlin, Germany
University of California at Santa Cruz, CA 95060, USA
raetsch@csl.anu.edu.au, mika@first.fhg.de, manfred@cse.... | 1963 |@word mild:2 version:2 briefly:2 norm:7 open:1 tr:2 contains:1 selecting:3 pub:1 existing:1 com:1 luo:2 yet:1 cruz:1 additive:2 numerical:4 analytic:1 designed:1 update:2 greedy:2 selected:2 warmuth:5 beginning:1 steepest:1 manfred:2 lr:3 boosting:8 cse:1 revisited:1 complication:1 toronto:2 org:1 simpler:1 zhang... |
1,055 | 1,964 | A kernel method for multi-labelled classification
Andr?e Elisseeff and Jason Weston
BIOwulf Technologies, 305 Broadway, New York, NY 10007
andre,jason @barhilltechnologies.com
Abstract
This article presents a Support Vector Machine (SVM) like learning system to handle multi-label problems. Such problems are usually... | 1964 |@word middle:1 polynomial:4 grey:1 elisseeff:2 configuration:1 contains:1 document:2 err:1 current:1 com:2 comparing:2 leaf:2 metabolism:1 mccallum:1 boosting:1 hyperplanes:1 simpler:2 phylogenetic:2 direct:3 expected:1 indeed:3 multi:24 decomposed:1 food:2 considering:3 becomes:2 argmin:1 interpreted:2 minimizes... |
1,056 | 1,965 | Entropy and Inference, Revisited
Ilya Nemenman,1,2 Fariel Shafee,3 and William Bialek1,3
NEC Research Institute, 4 Independence Way, Princeton, New Jersey 08540
2
Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106
3
Department of Physics, Princeton University, Princeton, New Jersey 08... | 1965 |@word schurmann:3 middle:1 briefly:1 seems:2 mers:1 trofimov:2 open:1 essay:1 simulation:2 p0:2 minus:1 holy:1 moment:5 itp:1 hardy:2 emory:1 surprising:1 yet:2 dx:1 must:2 grassberger:3 happen:1 shape:1 drop:1 plot:2 v:1 alone:2 half:1 dover:1 record:1 parameterizations:1 revisited:1 org:1 simpler:1 shtarkov:1 c... |
1,057 | 1,966 | Linking motor learning to function
approximation: Learning in an
unlearnable force field
Opher Donchin and Reza Shadmehr
Dept. of Biomedical Engineering
Johns Hopkins University, Baltimore, MD 21205
Email: opher@bme.jhu.edu, reza@bme.jhu.edu
Abstract
Reaching movements require the brain to generate motor commands that... | 1966 |@word neurophysiology:1 trial:8 seems:1 simulation:8 accounting:1 solid:2 ivaldi:2 substitution:1 series:1 initial:1 current:2 comparing:1 surprising:1 activation:1 dx:1 must:1 john:1 subsequent:3 shape:6 motor:10 plot:2 drop:1 stationary:1 num:4 successive:1 simpler:1 mathematical:1 along:1 become:2 fitting:5 in... |
1,058 | 1,967 | Partially labeled classification with Markov
random walks
Tommi Jaakkola
MIT AI Lab
Cambridge, MA 02139
tommi@ai.mit.edu
Martin Szummer
MIT AI Lab & CBCL
Cambridge, MA 02139
szummer@ai.mit.edu
Abstract
To classify a large number of unlabeled examples we combine a limited number of labeled examples with a Markov rando... | 1967 |@word kondor:1 version:1 pw:1 crucially:1 pick:2 thereby:1 reduction:1 document:5 current:1 surprising:1 written:1 must:3 distant:1 happen:1 plot:1 treating:1 update:1 discrimination:2 implying:1 v:1 stationary:1 iterates:1 provides:1 node:7 location:1 c6:6 along:2 constructed:1 become:2 combine:2 manner:1 expect... |
1,059 | 1,968 | (Not) Bounding the True Error
John Langford
Department of Computer Science
Carnegie-Mellon University
Pittsburgh, PA 15213
jcl+@cs.cmu.edu
Rich Caruana
Department of Computer Science
Cornell University
Ithaca, NY 14853
caruana@cs.cornell.edu
Abstract
We present a new approach to bounding the true error rate of a con... | 1968 |@word repository:1 citeseer:1 harder:1 reduction:8 initial:1 mag:1 err:2 current:2 com:1 yet:1 john:2 visible:2 realistic:1 wx:1 shape:4 analytic:1 plot:5 v:2 implying:1 greedy:1 half:1 guess:1 fewer:1 theoretician:2 isotropic:1 contribute:1 node:2 direct:1 specialize:4 introduce:1 manner:3 expected:8 roughly:2 s... |
1,060 | 1,969 | Keywords: portfolio management, financial forecasting, recurrent neural networks.
Active Portfolio-Management
based on Error Correction Neural Networks
Hans Georg Zimmermann, Ralph Neuneier and Ralph Grothmann
Siemens AG
Corporate Technology
D-81730 M?unchen, Germany
Abstract
This paper deals with a neural network ar... | 1969 |@word complying:3 proportion:7 grey:1 covariance:4 series:1 neuneier:3 com:1 recovered:1 activation:1 mulated:1 plot:1 designed:1 fund:4 overshooting:4 reciprocal:1 short:5 haykin:1 provides:1 constructed:1 consists:2 inter:1 market:15 expected:5 behavior:2 manager:1 actual:1 increasing:1 spain:1 underlying:5 mor... |
1,061 | 197 | 642
Chauvin
Dynamic Behavior of Constrained
Back-Propagation Networks
Yves Chauvin!
Thomson-CSF, Inc.
630 Hansen Way, Suite 250
Palo Alto, CA. 94304
ABSTRACT
The learning dynamics of the back-propagation algorithm are investigated when complexity constraints are added to the standard
Least Mean Square (LMS) cost fun... | 197 |@word polynomial:3 seems:1 simulation:3 reduction:2 initial:1 series:1 interestingly:1 franklin:1 comparing:1 activation:5 reminiscent:1 designed:1 contribute:1 cbp:12 successive:1 differential:1 fitting:4 baldi:1 acquired:1 forgetting:1 behavior:5 multi:1 decreasing:1 prolonged:1 increasing:2 provided:1 linearity... |
1,062 | 1,970 | Means. Correlations and Bounds
M.A.R. Leisink and H.J. Kappen
Department of Biophysics
University of Nijmegen , Geert Grooteplein 21
NL 6525 EZ Nijmegen, The Netherlands
{martijn,bert}@mbfys.kun.nl
Abstract
The partition function for a Boltzmann machine can be bounded
from above and below. We can use this to bound th... | 1970 |@word briefly:3 polynomial:1 grooteplein:1 eld:1 solid:1 kappen:2 existing:1 si:4 written:2 partition:14 plot:1 intelligence:2 beginning:1 affair:1 ial:1 math:1 along:2 become:1 prove:1 shorthand:1 roughly:2 mbfys:1 ijw:1 increasing:1 bounded:6 notation:1 moreover:2 panel:4 unwanted:2 exactly:1 zl:5 appear:1 befo... |
1,063 | 1,971 | Grammar Transfer in a Second Order
Recurrent Neural Network
Michiro N egishi
Department of Psychology
Rutgers University
101 Warren St. Smith Hall #301
Newark, NJ 07102
negishi@psychology.rutgers.edu
Stephen Jose Hanson
Psychology Department
Rutgers University
101 Warren St. Smith Hall #301
Newark, NJ 07102
jose @ps... | 1971 |@word termination:1 simulation:6 minus:2 reduction:5 initial:6 correspondin:1 past:1 current:5 shape:1 hypothesize:2 plot:1 update:1 discrimination:7 cue:1 selected:1 smith:2 short:2 accepting:4 node:8 along:1 become:1 consists:2 acquired:4 forgetting:1 elman:1 examine:1 nor:1 little:2 l20:1 what:2 string:2 devel... |
1,064 | 1,972 | Self-regulation Mechanism of Temporally
Asymmetric Hebbian Plasticity
Narihisa Matsumoto
Graduate School of Science and Engineering
Saitama University:
RIKEN Brain Science Institute
Saitama 351-0198, Japan
xmatumo@brain.riken.go.jp
Masato Okada
RIKEN Brain Science Institute
Saitama 351-0198, Japan
okada@brain.riken.g... | 1972 |@word trial:2 version:1 loading:10 simulation:4 covariance:15 solid:2 initial:4 efficacy:1 hereafter:1 kitano:2 periodically:1 plasticity:11 enables:2 designed:1 aps:1 stationary:2 half:1 zhang:1 mathematical:4 become:1 theoretically:1 rapid:1 examine:4 brain:5 window:1 becomes:3 circuit:1 didn:1 finding:5 tempor... |
1,065 | 1,973 | Prod uct Analysis:
Learning to model observations as
products of hidden variables
Brendan J. Freyl, Anitha Kannan l , Nebojsa Jojic 2
1
Machine Learning Group, University of Toronto, www.psi.toronto.edu
2 Vision Technology Group, Microsoft Research
Abstract
Factor analysis and principal components analysis can be us... | 1973 |@word polynomial:1 loading:1 covariance:3 tr:4 reduction:1 moment:3 cytology:1 pub:1 denoting:1 outperforms:1 assigning:1 written:1 benign:4 shape:1 remove:2 xlclass:1 nebojsa:1 generative:4 intelligence:1 parameterization:1 plane:1 ith:1 dissertation:1 provides:1 toronto:3 rc:1 ik:1 scholkopf:1 baldi:2 introduce... |
1,066 | 1,974 | Modularity in the motor system: decomposition
of muscle patterns as combinations of
time-varying synergies
Andrea d?Avella and Matthew C. Tresch
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology, E25-526
Cambridge, MA 02139
davel, mtresch @ai.mit.edu
Abstract
The question of whether t... | 1974 |@word neurophysiology:1 duda:1 ankle:1 decomposition:3 thereby:1 ivaldi:1 existing:1 anterior:2 activation:10 scatter:2 realistic:1 motor:11 remove:1 plot:2 opin:1 update:2 v:5 selected:2 nervous:3 location:1 five:1 height:1 mathematical:1 burst:1 c2:2 tresch:4 consists:2 ra:7 rapid:1 andrea:1 behavior:10 examine... |
1,067 | 1,975 | Characterizing neural gain control using
spike-triggered covariance
Odelia Schwartz
Center for Neural Science
New York University
odelia@cns.nyu.edu
E. J. Chichilnisky
Systems Neurobiology
The Salk Institute
ej@salk.edu
Eero P. Simoncelli
Howard Hughes Medical Inst.
Center for Neural Science
New York University
eero... | 1975 |@word neurophysiology:1 middle:1 stronger:1 simulation:7 covariance:11 decomposition:1 eng:1 reduction:2 initial:1 contains:1 selecting:1 recovered:12 current:1 elliptical:1 scatter:5 must:1 physiol:2 additive:1 realistic:1 subsequent:1 shape:3 drop:1 plot:5 v:4 selected:2 nervous:1 plane:5 short:2 characterizati... |
1,068 | 1,976 | Adaptive Sparseness Using Jeffreys Prior
M?ario A. T. Figueiredo
Institute of Telecommunications,
and Department of Electrical and Computer Engineering.
Instituto Superior T?ecnico
1049-001 Lisboa, Portugal
mtf @lx.it.pt
Abstract
In this paper we introduce a new sparseness inducing prior which does not involve any (h... | 1976 |@word sri:1 inversion:1 norm:2 turlach:1 open:1 covariance:2 decomposition:3 solid:1 pub:2 bibliographic:1 interestingly:1 outperforms:3 current:2 yet:1 portuguese:1 john:1 numerical:1 partition:2 informative:3 noninformative:1 girosi:2 remove:1 treating:1 update:1 generative:1 intelligence:2 accordingly:2 mulier... |
1,069 | 1,977 | Learning spike-based correlations and
conditional probabilities in silicon
Aaron P. Shon
David Hsu
Chris Diorio
Department of Computer Science and Engineering
University of Washington
Seattle, WA 98195-2350 USA
{aaron, hsud, diorio}@cs.washington.edu
Abstract
We have designed and fabricated a VLSI synapse that can le... | 1977 |@word mild:1 briefly:1 weq:1 pulse:5 thereby:1 solid:1 efficacy:1 past:1 current:20 ihei:2 activation:1 plasticity:1 enables:2 remove:1 designed:2 plot:2 update:10 drop:2 v:5 aps:1 device:7 floatinggate:1 provides:3 along:1 m7:3 qualitative:1 expected:2 behavior:1 themselves:1 nor:1 compensating:1 inspired:2 m8:3... |
1,070 | 1,978 | Incremental Learning and Selective
Sampling via Parametric Optimization
Framework for SVM
Shai Fine
IBM T. J. Watson Research Center
fshai@us.ibm.com
Katya Scheinberg
IBM T. J. Watson Research Center
katyas@us.ibm.com
Abstract
We propose a framework based on a parametric quadratic programming (QP) technique to solve... | 1978 |@word repository:2 version:3 polynomial:1 norm:8 pick:3 initial:3 pub:1 bhattacharyya:1 existing:2 current:3 com:2 si:2 must:1 partition:12 hoping:1 drop:1 update:3 v:2 infant:1 greedy:1 selected:1 guess:1 accordingly:1 iso:1 iterates:1 provides:1 qij:1 overhead:1 introduce:2 expected:5 rapid:1 behavior:2 examine... |
1,071 | 1,979 | Iterative Double Clustering for
Unsupervised and Semi-Supervised
Learning
Ran El-Yaniv
Oren Souroujon
Computer Science Department
Technion - Israel Institute of Technology
(rani,orenso)@cs.technion.ac.il
Abstract
We present a powerful meta-clustering technique called Iterative Double Clustering (IDC). The IDC method ... | 1979 |@word trial:4 version:3 rani:1 compression:1 advantageous:2 norm:2 open:1 simulation:1 reduction:1 contains:2 denoting:2 document:20 outperforms:3 si:2 john:1 subsequent:1 numerical:1 partition:4 predetermined:1 plot:1 progressively:1 v:3 half:2 greedy:2 selected:1 ecir:1 ng4:3 ith:1 agglom:1 filtered:2 coarse:1 ... |
1,072 | 198 | 828
Cowan
Neural networks: the early days
J.D. Cowan
Department of Mathematics, Committee on
Neurobiology, and Brain Research Institute,
The University of Chicago, 5734 S. Univ. Ave.,
Chicago, Illinois 60637
ABSTRACT
A short account is given of various investigations of neural network
properties, beginning with the... | 198 |@word neurophysiology:1 validity:1 concept:1 hence:1 already:1 imaginable:1 calculus:1 essay:1 said:1 exhibit:1 material:2 prodigy:1 noted:2 mapped:1 me:2 investigation:2 proposition:2 subjective:1 written:1 common:1 pitt:12 subsequent:2 chicago:6 happen:2 sought:1 early:5 conditioning:1 designed:1 purpose:1 somet... |
1,073 | 1,980 | BLIND SOURCE SEPARATION VIA
MULTINODE SPARSE REPRESENTATION
Michael Zibulevsky
Department of Electrical Engineering
Technion, Haifa 32000, Israel
mzib@ee.technion.ac. if
Pavel Kisilev
Department of Electrical Engineering
Technion, Haifa 32000, Israel
paufk@tx.technion.ac. if
Yehoshua Y. Zeevi
Department of Electric... | 1980 |@word middle:3 stronger:1 norm:2 simulation:2 decomposition:11 pavel:1 attainable:1 selecting:1 rightmost:1 outperforms:1 recovered:1 si:1 scatter:13 finest:2 dct:1 visible:1 additive:1 offunctions:1 remove:1 plot:14 stationary:1 half:1 selected:2 short:1 provides:1 node:16 lx:1 c22:1 along:8 constructed:1 consis... |
1,074 | 1,981 | Direct value-approxiIllation for factored MDPs
Dale Schuurmans and ReIn Patrascll
Department of Computer Science
University of Waterloo
{dale, rpatrasc} @cs.'Uwaterloo.ca
Abstract
We present a simple approach for computing reasonable policies
for factored Markov decision processes (MDPs), when the optimal value funct... | 1981 |@word version:1 seems:2 norm:1 simulation:1 tried:1 decomposition:2 incurs:1 concise:3 reduction:1 contains:1 current:3 yet:1 must:1 tot:1 tenet:1 realize:1 additive:1 alone:1 greedy:6 intelligence:1 indicative:1 provides:3 lx:7 simpler:2 direct:7 predecessor:1 introduce:1 manner:1 ra:5 indeed:2 expected:5 nor:2 ... |
1,075 | 1,982 | A Maximum-Likelihood Approach to
Modeling Multisensory Enhancement
Hans Colonius*
Institut fUr Kognitionsforschung
Carl von Ossietzky Universitat
Oldenburg, D-26111
hans. colonius@uni-oldenburg.de
Adele Diederich
School of Social Sciences
International University Bremen
Bremen, D-28725
a. diederich @iu-bremen.de
Abs... | 1982 |@word neurophysiology:1 achievable:1 open:1 dramatic:1 thereby:1 ld:3 series:1 oldenburg:3 pub:1 reaction:2 current:1 surprising:2 must:6 visibility:1 discrimination:2 alone:1 cue:3 v:1 short:2 detecting:1 behavioral:7 swets:1 behavior:2 planning:1 wallace:3 distractor:1 brain:3 decreasing:1 moreover:2 deutsche:1... |
1,076 | 1,983 | Predictive Representations of State
Michael L. Littman
Richard S. Sutton
AT&T Labs-Research, Florham Park, New Jersey
{mlittman,sutton}~research.att.com
Satinder Singh
Syntek Capital, New York, New York
baveja~cs.colorado.edu
Abstract
We show that states of a dynamical system can be usefully represented by multi-ste... | 1983 |@word polynomial:3 seems:1 open:1 decomposition:1 ithere:1 accommodate:1 recursively:4 initial:2 series:1 att:1 ours:1 rightmost:2 past:3 ati:1 current:1 com:1 soules:1 subsequent:1 happen:2 enables:1 treating:1 update:2 implying:1 generative:5 selected:2 discovering:1 fewer:1 intelligence:5 mccallum:2 ith:2 reci... |
1,077 | 1,984 | Playing is believing:
The role of beliefs in multi-agent learning
Yu-Han Chang
Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, Massachusetts 02139
ychang@ai.mit.edu
Leslie Pack Kaelbling
Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, Massachuset... | 1984 |@word h:1 trial:1 version:1 eliminating:1 seems:3 open:1 hu:3 q1:2 thereby:1 solid:1 shot:6 yasuo:1 cyclic:3 score:1 past:2 existing:8 current:5 must:2 dilemna:3 designed:1 plot:1 update:8 stationary:15 intelligence:5 half:1 unbounded:3 along:1 prove:1 redefine:1 inter:1 indeed:2 expected:2 behavior:3 roughly:2 e... |
1,078 | 1,985 | Discriminative Direction for Kernel Classifiers
Polina Golland
Artificial Intelligence Lab
Massachusetts Institute of Technology
Cambridge, MA 02139
polina@ai.mit.edu
Abstract
In many scientific and engineering applications, detecting and understanding differences between two groups of examples can be reduced
to a cla... | 1985 |@word mri:1 briefly:1 polynomial:2 hippocampus:3 hu:1 simplifying:1 covariance:1 functions2:1 solid:2 selecting:1 comparing:1 surprising:1 assigning:1 dx:18 must:1 written:1 john:1 visible:1 shape:14 medial:1 v:2 generative:3 intelligence:1 xk:9 short:1 colored:1 detecting:1 provides:1 complication:1 location:2 n... |
1,079 | 1,986 | The Steering Approach for Multi-Criteria
Reinforcement Learning
Shie Mannor and Nahum Shimkin
Department of Electrical Engineering
Technion, Haifa 32000, Israel
{shie,shimkin}@{tx,ee}.technion.ac.il
Abstract
We consider the problem of learning to attain multiple goals in a dynamic environment, which is initially unkn... | 1986 |@word exploitation:1 version:4 briefly:3 polynomial:2 norm:3 faculty:1 approachability:9 humidity:11 closure:1 bn:4 attainable:1 mention:1 thereby:1 reduction:1 moment:2 initial:5 current:4 happen:1 fund:1 stationary:12 intelligence:2 selected:1 accordingly:1 vrieze:1 provides:2 mannor:2 math:1 location:1 success... |
1,080 | 1,987 | Gaussian Process Regression with
Mismatched Models
Peter Sollich
Department of Mathematics, King's College London
Strand, London WC2R 2LS, U.K. Email peter.sollich@kcl.ac . uk
Abstract
Learning curves for Gaussian process regression are well understood
when the 'student' model happens to match the 'teacher' (true dat... | 1987 |@word achievable:1 confirms:1 simulation:7 covariance:22 tr:16 solid:2 initial:1 yet:3 dx:1 must:1 numerical:2 shape:1 asymptote:1 stationary:1 alone:1 provides:2 contribute:1 ron:1 successive:1 simpler:1 along:1 c2:1 become:7 differential:1 persistent:1 doubly:1 fitting:8 expected:1 indeed:1 abbreviating:1 trg:1... |
1,081 | 1,988 | Neural Implementation of Bayesian
Inference in Population Codes
Si Wu
Computer Science Department
Sheffield University, UK
Shun-ichi Amari
Lab. for Mathematic Neuroscience,
RIKEN Brain Science Institute, JAPAN
Abstract
This study investigates a population decoding paradigm, in which
the estimation of stimulus in the... | 1988 |@word trial:1 nd:1 simulation:1 initial:1 inefficiency:1 tuned:1 outperforms:1 current:1 z2:4 comparing:1 si:1 xlr:2 realize:1 girosi:1 enables:1 shape:2 nervous:1 nq:1 accordingly:1 ith:1 provides:1 clarified:1 successive:1 zhang:4 rc:2 constructed:1 consists:1 biologic:1 indeed:1 behavior:1 brain:8 ol:1 inspire... |
1,082 | 1,989 | A Rational Analysis of Cognitive Control
in a Speeded Discrimination Task
Michael C. Mozer
, Michael D. Colagrosso , David E. Huber
Department
of Computer Science
Department
of Psychology
Institute of Cognitive Science
University of Colorado
Boulder, CO 80309
mozer,colagrom,dhuber @colorado.edu
... | 1989 |@word trial:23 version:1 cingulate:3 briefly:1 proportion:1 instruction:1 simulation:14 thereby:1 solid:2 score:1 reaction:33 existing:1 current:7 anterior:3 activation:1 must:4 hypothesize:1 plot:2 remove:1 stroop:1 discrimination:9 implying:1 slowing:1 accordingly:1 inspection:1 dissertation:1 provides:2 coarse... |
1,083 | 199 | 324
Jordan and Jacobs
Learning to Control an Unstable System with
Forward Modeling
Michael I. Jordan
Brain and Cognitive Sciences
MIT
Cambridge, MA 02139
Robert A. Jacobs
Computer and Information Sciences
University of Massachusetts
Amherst, MA 01003
ABSTRACT
The forward modeling approach is a methodology for lear... | 199 |@word briefly:1 simulation:3 jacob:5 thereby:2 minus:1 initial:1 configuration:4 current:4 activation:1 yet:1 must:4 numerical:1 motor:1 half:1 smith:2 provides:4 welldefined:1 indeed:1 degress:1 behavior:1 brain:2 torque:1 jm:1 considering:1 provided:3 mass:2 minimizes:1 transformation:3 corporation:1 nj:2 tempor... |
1,084 | 1,990 | Information-Geometrical Significance of
Sparsity in Gallager Codes
Toshiyuki Tanaka
Department of Electronics and Information Engineering
Tokyo Metropolitan University
Tokyo 192-0397, Japan
tanaka@eei.metro-u.ac.jp
Shiro Ikeda
Kyushu Institute of Technology & JST
Fukuoka 808-0196, Japan
shiro@brain.kyutech.ac.jp
Shun... | 1990 |@word c0:2 p0:9 kappen:3 electronics:1 equimarginal:2 mag:1 existing:1 current:1 attracted:1 ikeda:4 v:1 guess:1 mpm:2 short:2 characterization:1 provides:1 contribute:1 math:1 mathematical:1 c2:4 transl:1 compose:1 manner:1 introduce:1 expected:2 behavior:1 p1:1 brain:3 underlying:3 notation:1 mass:1 what:2 ag:1... |
1,085 | 1,991 | A Rotation and Translation Invariant Discrete
Saliency Network
Lance R. Williams
Dept. of Computer Science
Univ. of New Mexico
Albuquerque, NM 87131
John W. Zweck
Dept. of CS and EE
Univ. of Maryland Baltimore County
Baltimore, MD 21250
Abstract
We describe a neural network which enhances and completes salient
close... | 1991 |@word nd:1 initial:2 series:2 ka:1 must:3 john:2 shape:3 remove:1 update:1 half:3 intelligence:2 plane:1 isotropic:2 short:1 dissertation:1 constructed:1 iverson:1 differential:1 ik:1 consists:1 combine:1 brain:1 inspired:1 freeman:2 globally:1 little:1 provided:2 mass:1 sharpening:1 transformation:7 temporal:1 d... |
1,086 | 1,992 | Spectral Relaxation for K-means
Clustering
Hongyuan Zha & Xiaofeng He
Dept. of Compo Sci. & Eng.
The Pennsylvania State University
University Park, PA 16802
{zha,xhe}@cse.psu.edu
Chris Ding & Horst Simon
NERSC Division
Lawrence Berkeley National Lab.
UC Berkeley, Berkeley, CA 94720
{chqding,hdsimon}@lbl.gov
Ming Gu
... | 1992 |@word msr:1 version:1 seems:1 norm:7 advantageous:1 eng:1 decomposition:6 pick:2 tr:1 electronics:1 initial:2 contains:2 document:8 interestingly:1 bradley:2 si:10 assigning:1 written:3 john:1 stemming:1 partition:5 christian:1 plot:1 fund:1 v:2 greedy:1 selected:1 xk:1 mccallum:2 ng4:3 ith:1 sys:2 compo:1 math:1... |
1,087 | 1,993 | Causal Categorization with Bayes Nets
Bob Rehder
Department of Psychology
New York University
New York, NY 10012
bob .rehder@nyu.edu
Abstract
A theory of categorization is presented in which knowledge of
causal relationships between category features is represented as a
Bayesian network. Referred to as causal-model t... | 1993 |@word open:1 holyoak:1 accounting:1 rol:1 shrimp:3 current:3 yet:1 assigning:1 subsequent:1 enables:2 fund:1 v:3 cue:1 fewer:1 rehder:4 mental:2 node:2 contribute:1 fitting:2 pairwise:2 indeed:1 expected:2 nor:1 examine:1 chi:1 little:1 provided:1 moreover:1 kind:1 kaufman:1 developed:1 finding:1 nj:1 configural:... |
1,088 | 1,994 | Eye movements and the maturation of cortical
orientation selectivity
Michele Rucci and Antonino Casile
Department of Cognitive and Neural Systems, Boston University, Boston, MA 02215.
Scuola Superiore S. Anna, Pisa, Italy
Abstract
Neural activity appears to be a crucial component for shaping the receptive fields... | 1994 |@word replicate:3 simulation:2 lobe:3 covariance:6 mammal:1 initial:1 series:1 efficacy:1 coactive:1 activation:2 yet:1 must:1 physiol:1 additive:1 plasticity:10 motor:2 selected:2 mastronarde:1 core:1 filtered:1 provides:1 contribute:2 preference:1 mathematical:1 along:1 direct:1 become:1 fixation:11 pathway:1 e... |
1,089 | 1,995 | Generating velocity tuning by asymmetric
recurrent connections
Xiaohui Xie and Martin A. Giese
Dept. of Brain and Cognitive Sciences and CBCL
Massachusetts Institute of Technology
Cambridge, MA 02139
Dept. for Cognitive Neurology,
University Clinic T?ubingen
Max-Planck-Institute for Biological Cybernetics
72076... | 1995 |@word sabatini:1 simulation:8 linearized:3 pulse:23 excited:4 solid:2 series:1 contains:1 denoting:1 activation:12 written:3 must:1 john:1 numerical:2 realistic:1 shape:1 analytic:1 treating:1 plot:4 stationary:11 nervous:2 accordingly:1 feedfoward:1 provides:1 contribute:2 five:1 mathematical:8 differential:3 be... |
1,090 | 1,996 | Learning a Gaussian Process Prior
for Automatically Generating Music Playlists
John C. Platt
Christopher J. C. Burges
Steven Swenson
Christopher Weare
Alice Zheng
Microsoft Corporation
1 Microsoft Way
Redmond, WA 98052
jplatt,cburges,sswenson,chriswea @microsoft.com, alicez@cs.berkeley.edu
Abstract
This paper pr... | 1996 |@word trial:5 version:1 instrumental:1 norm:5 tedious:1 covariance:11 decomposition:1 elisseeff:1 tr:1 harder:1 contains:2 score:6 selecting:1 tuned:1 existing:1 current:3 com:1 must:3 john:1 chicago:1 enables:1 remove:3 designed:5 half:1 selected:5 fewer:1 intelligence:1 preference:23 simpler:1 qualitative:1 con... |
1,091 | 1,997 | Probabilistic Inference of Hand Motion from Neural
Activity in Motor Cortex
Y. Gao
M. J. Black
E. Bienenstock
S. Shoham
J. P. Donoghue
Division of Applied Mathematics, Brown University, Providence, RI 02912
Dept.
of Computer Science, Brown University, Box 1910, Providence, RI 02912
Princeton Univ... | 1997 |@word neurophysiology:2 trial:4 kolaczyk:1 seek:2 propagate:2 initial:1 tuned:2 current:2 com:1 comparing:1 written:2 must:1 john:1 numerical:1 motor:12 plot:2 update:2 generative:2 isard:1 manipulandum:3 wessberg:1 short:2 record:1 provides:7 location:1 along:1 ijcv:2 combine:1 behavioral:1 introduce:1 expected:... |
1,092 | 1,998 | KLD-Sampling: Adaptive Particle Filters
Dieter Fox
Department of Computer Science & Engineering
University of Washington
Seattle, WA 98195
Email: fox@cs.washington.edu
Abstract
Over the last years, particle filters have been applied with great success to
a variety of state estimation problems. We present a statistica... | 1998 |@word cox:1 version:2 polynomial:1 proportion:1 open:1 seek:1 simulation:1 thereby:1 recursively:1 carry:1 initial:2 series:1 genetic:2 rightmost:1 existing:2 freitas:1 current:1 discretization:2 john:1 pioneer:1 numerical:1 timestamps:2 realistic:1 cant:1 shape:2 plot:2 update:9 intelligence:1 hallway:1 prespeci... |
1,093 | 1,999 | Tempo Tracking
Rhythm
by Sequential Monte
Ali Taylan Ce:mgil and Bert Kappen
SNN, University of Nijmegen
NL 6525 EZ Nijmegen
The Netherlands
{cemgil,bert}@mbfys.kun.nl
Abstract
We present a probabilistic generative model for timing deviations
in expressive music. performance. The structure of the proposed
model is eq... | 1999 |@word termination:1 dz1:2 simulation:1 covariance:1 pressed:1 kappen:3 liu:1 contains:1 score:10 selecting:1 accompaniment:4 past:1 existing:1 freitas:2 parsing:1 realistic:2 timestamps:1 designed:1 update:1 polyphonic:2 resampling:1 generative:2 intelligence:1 selected:1 parameterization:2 slowing:1 accordingly:... |
1,094 | 2 | 184
THE CAPACITY OF THE KANERVA ASSOCIATIVE MEMORY IS EXPONENTIAL
P. A. Choul
Stanford University. Stanford. CA 94305
ABSTRACT
The capacity of an associative memory is defined as the maximum
number of vords that can be stored and retrieved reliably by an address
vithin a given sphere of attraction. It is shown by sphe... | 2 |@word version:1 polynomial:3 propagate:2 tr:1 moment:1 surprising:1 must:2 riacs:1 afn:4 fewer:1 selected:1 ith:9 provides:1 node:1 location:13 tvo:2 fitting:1 behavior:2 increasing:1 provided:3 begin:1 bounded:1 mountain:1 unified:1 nj:1 vhich:3 every:4 ti:1 growth:6 exactly:1 grant:1 yn:1 io:1 meet:1 doctoral:1 co... |
1,095 | 20 | 31
AN ARTIFICIAL NEURAL NETWORK FOR SPATIOTEMPORAL BIPOLAR PATTERNS: APPLICATION TO
PHONEME CLASSIFICATION
Toshiteru Homma
Les E. Atlas
Robert J. Marks II
Interactive Systems Design Laboratory
Department of Electrical Engineering, Ff-l0
University of Washington
Seattle, Washington 98195
ABSTRACT
An artificial neural... | 20 |@word norm:1 duda:1 calculus:1 simulation:2 thereby:2 electronics:1 interestingly:1 past:2 existing:3 activation:4 yet:1 synthesizer:1 realize:1 numerical:1 additive:1 wx:1 atlas:3 lky:1 precaution:1 pursued:1 nervous:1 accordingly:2 record:1 node:4 lx:1 sigmoidal:2 five:1 zii:1 mathematical:1 along:2 constructed:2... |
1,096 | 200 | 68
Baird
Associative Memory in a Simple Model of
Oscillating Cortex
Bill Baird
Dept Molecular and Cell Biology,
U .C.Berkeley, Berkeley, Ca. 94720
ABSTRACT
A generic model of oscillating cortex, which assumes "minimal"
coupling justified by known anatomy, is shown to function as an associative memory, using previous... | 200 |@word seems:1 grey:1 hu:1 simulation:2 pulse:2 series:1 contains:1 hereafter:1 seriously:1 imaginary:1 must:2 jkl:1 stemming:1 realize:1 additive:1 analytic:2 motor:2 designed:1 half:1 fewer:1 nervous:1 liapunov:1 node:1 location:1 contribute:1 sigmoidal:4 arctan:4 phylogenetic:1 mathematical:3 along:1 direct:2 ol... |
1,097 | 2,000 | Reinforcement Learning and Time
Perception - a Model of Animal
Experiments
J. L. Shapiro
Department of Computer Science
University of Manchester
Manchester, M13 9PL U.K.
jls@cs.man.ac.uk
John Wearden
Department of Psychology
University of Manchester
Manchester, M13 9PL U.K.
Abstract
Animal data on delayed-reward con... | 2000 |@word trial:32 cu:1 middle:2 merrill:2 pulse:1 simulation:8 covariance:7 excited:1 thereby:1 tr:2 solid:1 moment:1 contains:1 tuned:1 subjective:2 current:1 si:5 written:1 must:2 john:7 berthier:2 shape:1 stationary:3 ith:4 dover:1 short:2 coarse:1 node:19 contribute:1 liberal:1 mathematical:1 consists:2 expected... |
1,098 | 2,001 | The Unified Propagation and Scaling Algorithm
Max Welling
Gatsby Computational Neuroscience Unit
University College London
17 Queen Square
London WC1N 3AR U.K.
welling@gatsby.ucl.ac.uk
Yee Whye Teh
Department of Computer Science
University of Toronto
10 King?s College Road
Toronto M5S 3G4 Canada
ywteh@cs.toronto.edu
... | 2001 |@word bounced:1 seems:1 replicate:1 confirms:1 simulation:1 contains:1 past:1 current:3 yet:1 must:1 plot:3 update:23 stationary:4 intelligence:2 leaf:3 node:39 toronto:3 ditto:1 firstly:1 mathematical:2 become:1 viable:2 deming:1 introduce:2 g4:1 pairwise:1 expected:1 nor:1 frequently:2 inspired:1 freeman:1 incr... |
1,099 | 2,002 | Spectral Kernel Methods for Clustering
N ello Cristianini
BIOwulf Technologies
nello@support-vector.net
John Shawe-Taylor
Jaz Kandola
Royal Holloway, University of London
{john, jaz} @cs.rhul.ac.uk
Abstract
In this paper we introduce new algorithms for unsupervised learning based on the use of a kernel matrix. All t... | 2002 |@word repository:1 version:1 middle:3 norm:2 proportion:2 lodhi:1 grey:1 decomposition:2 elisseeff:1 tr:1 solid:1 comparing:1 jaz:3 assigning:1 john:4 distant:1 partition:2 enables:1 remove:1 lue:1 plot:6 selected:1 normalising:1 num:1 provides:4 characterization:1 node:1 contribute:1 org:1 constructed:1 introduc... |
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