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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1,400 | 2,274 | A Formulation for Minimax Probability
Machine Regression
Thomas Strohmann
Department of Computer Science
University of Colorado, Boulder
strohman@cs.colorado.edu
Gregory Z. Grudic
Department of Computer Science
University of Colorado, Boulder
grudic@cs.colorado.edu
Abstract
We formulate the regression problem as one... | 2274 |@word trial:3 repository:1 version:1 yi0:3 open:1 covariance:9 pick:1 initial:1 contains:3 efficacy:3 bc:8 strohman:1 bhattacharyya:1 existing:1 ka:1 olkin:1 must:2 numerical:1 prohibitive:1 mpm:4 math:1 mathematical:1 along:2 direct:3 incorrect:1 consists:1 indeed:1 actual:1 becomes:1 estimating:2 underlying:2 b... |
1,401 | 2,275 | Self Supervised Boosting
Max Welling, Richard S. Zemel, and Geoffrey E. Hinton
Department of Computer Science
University of Toronto
10 King?s College Road
Toronto, M5S 3G5 Canada
Abstract
Boosting algorithms and successful applications thereof abound for classification and regression learning problems, but not for un... | 2275 |@word version:1 seems:1 proportion:1 norm:3 stronger:1 contrastive:3 pick:1 solid:1 initial:1 existing:1 current:10 yet:1 assigning:1 must:4 realize:2 visible:4 additive:7 j1:1 informative:1 shape:1 remove:1 drop:1 plot:6 update:6 stationary:2 greedy:1 intelligence:1 isotropic:2 iso:1 provides:2 boosting:29 toron... |
1,402 | 2,276 | Stochastic Neighbor Embedding
Geoffrey Hinton and Sam Roweis
Department of Computer Science, University of Toronto
10 King?s College Road, Toronto, M5S 3G5 Canada
hinton,roweis @cs.toronto.edu
Abstract
We describe a probabilistic approach to the task of placing objects, described by high-dimensional vectors or by p... | 2276 |@word cox:2 version:17 compression:1 norm:1 seems:2 proportion:8 tedious:1 cleanly:4 simulation:1 pick:3 reduction:5 fragment:1 selecting:2 document:8 bitmap:2 neuneier:1 current:1 nowlan:1 yet:1 must:1 cottrell:2 distant:1 enables:1 update:2 generative:2 fewer:1 intelligence:1 item:1 warmuth:2 mccallum:2 steepes... |
1,403 | 2,277 | A Prototype for Automatic Recognition of
Spontaneous Facial Actions
M.S. Bartlett, G. Littlewort, B. Braathen, T.J. Sejnowski , and J.R. Movellan
Institute for Neural Computation and Department of Biology
University of California, San Diego
and Howard Hughes Medical Institute at the Salk Institute
Email: marni, gwen, ... | 2277 |@word cingulate:1 closure:2 contraction:2 brightness:1 tr:2 series:1 current:3 comparing:1 activation:1 yet:1 takeo:1 mesh:1 realistic:3 partition:1 shape:1 enables:1 motor:4 designed:2 discrimination:2 alone:1 v:6 selected:4 intelligence:2 caucasian:1 plane:10 inspection:1 beginning:1 smith:1 detecting:1 consult... |
1,404 | 2,278 | A Maximum Entropy Approach To
Collaborative Filtering in Dynamic, Sparse,
High-Dimensional Domains
David M. Pennock
Overture Services, Inc.
74 N. Pasadena Ave., 3rd floor
Pasadena, CA 91103,
david.pennock@overture.com
Dmitry Y. Pavlov
NEC Laboratories America
4 Independence Way
Princeton, NJ 08540,
dpavlov@nec-labs.c... | 2278 |@word repository:1 bigram:6 nd:1 open:2 vldb:1 carolina:1 decomposition:1 citeseer:1 maes:1 tr:3 reduction:2 series:3 contains:1 score:1 tuned:1 document:47 outperforms:1 past:3 current:5 com:3 written:1 bd:3 must:1 hofmann:1 interpretable:1 greedy:2 fewer:1 selected:2 item:6 intelligence:4 accordingly:1 indicati... |
1,405 | 2,279 | Multiclass Learning by Probabilistic Embeddings
Ofer Dekel and Yoram Singer
School of Computer Science & Engineering
The Hebrew University, Jerusalem 91904, Israel
{oferd,singer}@cs.huji.ac.il
Abstract
We describe a new algorithmic framework for learning multiclass categorization problems. In this framework a multicl... | 2279 |@word repository:1 version:1 dekel:1 twelfth:1 c0:17 additively:1 initial:1 contains:1 selecting:1 denoting:1 document:1 prefix:1 outperforms:1 current:3 comparing:1 z2:2 com:1 john:1 additive:2 partition:2 girosi:1 update:3 v:3 stationary:1 intelligence:1 selected:1 warmuth:2 complementing:1 manfred:1 boosting:3... |
1,406 | 228 | 650
Lincoln and Skrzypek
Synergy Of Clustering Multiple Back Propagation Networks
William P. Lincoln* and Josef Skrzypekt
UCLA Machine Perception Laboratory
Computer Science Department
Los Angeles, CA 90024
ABSTRACT
The properties of a cluster of multiple back-propagation (BP) networks
are examined and compared to ... | 228 |@word aircraft:1 selforganization:1 advantageous:1 retraining:4 open:1 simulation:2 solid:1 initial:5 configuration:1 past:2 current:1 selected:1 node:6 five:4 direct:1 incorrect:1 consists:1 expected:2 behavior:2 decreasing:2 company:1 actual:1 increasing:4 becomes:1 begin:1 underlying:1 funtion:1 interpreted:1 s... |
1,407 | 2,280 | Multiplicative Updates for Nonnegative Quadratic
Programming in Support Vector Machines
Fei Sha1 , Lawrence K. Saul1 , and Daniel D. Lee2
1
Department of Computer and Information Science
2
Department of Electrical and System Engineering
University of Pennsylvania
200 South 33rd Street, Philadelphia, PA 19104
{feisha,l... | 2280 |@word repository:2 version:1 polynomial:3 seems:1 solid:1 reduction:1 series:1 daniel:1 comparing:1 intriguing:1 must:3 distant:1 numerical:1 additive:1 plot:1 update:54 warmuth:1 xk:7 ith:3 provides:2 clarified:1 location:2 hyperplanes:1 mathematical:1 ik:1 prove:3 shorthand:1 underfitting:1 upenn:2 expected:1 r... |
1,408 | 2,281 | Using Tarjan?s Red Rule for Fast Dependency
Tree Construction
Dan Pelleg and Andrew Moore
School of Computer Science
Carnegie-Mellon University
Pittsburgh, PA 15213 USA
dpelleg@cs.cmu.edu, awm@cs.cmu.edu
Abstract
We focus on the problem of efficient learning of dependency trees. It
is well-known that given the pairwis... | 2281 |@word repository:1 version:1 polynomial:1 disk:1 pick:1 moment:1 initial:1 liu:3 contains:4 interestingly:1 outperforms:2 current:1 discretization:1 yet:1 danny:1 must:1 mst:8 numerical:1 happen:1 kdd:3 v:2 greedy:1 intelligence:1 indicative:1 inspection:1 dover:1 record:20 draft:1 iterates:1 node:5 math:1 simple... |
1,409 | 2,282 | A Hierarchical Bayesian Markovian Model for
Motifs in Biopolymer Sequences
Eric P. Xing, Michael I. Jordan, Richard M. Karp and Stuart Russell
Computer Science Division
University of California, Berkeley
Berkeley, CA 94720
epxing,jordan,karp,russell @cs.berkeley.edu
Abstract
We propose a dynamic Bayesian model for ... | 2282 |@word proportion:1 cml:1 seek:1 simplifying:1 dramatic:1 solid:1 initial:1 liu:4 contains:1 score:1 genetic:1 interestingly:1 outperforms:1 current:1 nt:10 readily:1 realistic:1 concatenate:1 informative:3 pqd:1 shape:4 confirming:1 update:2 generative:3 discovering:2 guess:1 parameterization:1 short:2 detecting:... |
1,410 | 2,283 | Modeling Midazolam' s Effect on the
__
H_il!Jlocampus and Recognition Memor!
Kenneth J'" .I\'lalJrnbeJ~2
Departn1ent of Psychology
Indiana V'uiversity
Bloomington, IN' 47405
Rene
Le!ele:nD~er2
Department of rS'/cnOlCHIV
Indiana University
Bloomington, IN 47405
rzeelenb(~~indiana.edu
Richard 1\'1.. Sbiffrin
Departm... | 2283 |@word hippocampus:3 replicate:1 nd:5 anterograde:2 ences:1 simulation:1 r:3 crite:1 ld:1 tuned:1 interestingly:1 subjective:1 emory:6 contextual:1 com:1 yet:1 vere:3 v:1 cue:8 leaf:1 tenn:1 item:9 es:1 ith:2 short:1 contribute:4 tvl:2 oak:1 gillund:1 rc:1 along:1 ect:1 viable:1 amnesia:3 re2:1 consists:1 frequen:... |
1,411 | 2,284 | Bayesian Models of Inductive Generalization
Neville E. Sanjana & Joshua B. Tenenbaum
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
nsanjana, jbt @mit.edu
Abstract
We argue that human inductive generalization is best explained in a
Bayesian framework, rather tha... | 2284 |@word trial:3 version:1 seems:4 duda:1 seal:1 nd:1 hairiness:1 open:1 seek:1 pick:1 mammal:19 contains:3 score:3 tuned:2 outperforms:1 subjective:2 blank:2 afflict:2 yet:3 assigning:2 must:2 invitation:1 shape:1 alone:1 generative:2 fewer:1 item:1 smith:1 provides:1 node:1 preference:2 simpler:1 five:4 along:1 co... |
1,412 | 2,285 | A Probabilistic Model for Learning
Concatenative Morphology
Matthew G. Snover
Department of Computer Science
Washington University
St Louis, MO, USA, 63130-4809
ms9@cs.wustl.edu
Michael R. Brent
Department of Computer Science
Washington University
St Louis, MO, USA, 63130-4809
brent@cs.wustl.edu
Abstract
This paper ... | 2285 |@word faculty:1 version:1 middle:1 seems:1 stronger:1 pick:1 initial:3 contains:1 score:4 comparing:1 written:2 john:1 informative:1 remove:4 designed:2 generative:5 fewer:1 discovering:2 intelligence:1 beginning:1 rescoring:1 node:14 lexicon:19 location:1 mathematical:1 along:2 pairing:1 consists:1 manner:2 nor:... |
1,413 | 2,286 | Recovering Intrinsic Images from a Single Image
Marshall F Tappen
William T Freeman
Edward H Adelson
MIT Artificial Intelligence Laboratory
Cambridge, MA 02139
mtappen@ai.mit.edu, wtf@ai.mit.edu, adelson@ai.mit.edu
Abstract
We present an algorithm that uses multiple cues to recover shading and
reflectance intrinsic i... | 2286 |@word version:1 cleanly:1 propagate:5 rgb:2 decomposition:1 simplifying:1 mitsubishi:1 brightness:1 shading:50 contains:1 recovered:2 must:3 additive:1 shape:2 intelligence:1 cue:4 generative:3 filtered:1 boosting:3 node:10 simpler:1 along:8 c2:8 consists:1 combine:1 manner:1 mccann:1 expected:1 freeman:5 little:... |
1,414 | 2,287 | Derivative observations in Gaussian Process
Models of Dynamic Systems
E. Solak
Dept. Elec. & Electr. Eng.,
Strathclyde University,
Glasgow G1 1QE,
Scotland, UK.
ercan.solak@strath.ac.uk
D. J. Leith
Hamilton Institute,
National Univ. of
Ireland, Maynooth,
Co. Kildare, Ireland
doug.leith@may.ie
R. Murray-Smith
De... | 2287 |@word middle:1 norm:1 simulation:5 covariance:21 eng:1 reduction:1 pub:1 visible:1 numerical:3 cheap:1 analytic:1 plot:2 alone:1 electr:1 scotland:2 smith:6 draft:1 location:2 along:3 become:1 combine:3 fitting:1 dimen:1 manner:3 frequently:1 multi:1 underlying:1 cm:2 fuzzy:2 differentiation:1 every:1 bernardo:1 ... |
1,415 | 2,288 | Learning about Multiple Objects in Images:
Factorial Learning without Factorial Search
Christopher K. I. Williams and Michalis K. Titsias
School of Informatics, University of Edinburgh, Edinburgh EH1 2QL, UK
c.k.i.williams@ed.ac.uk
M.Titsias@sms.ed.ac.uk
Abstract
We consider data which are images containing views of ... | 2288 |@word version:1 eliminating:1 decomposition:1 carry:1 initial:1 configuration:2 undiscovered:1 current:1 cad:1 yet:2 must:8 written:1 realistic:1 eigentracking:1 shape:2 remove:4 designed:1 update:3 occlude:1 stationary:13 greedy:12 discovering:1 detecting:1 location:1 toronto:1 five:4 combine:1 fitting:1 introdu... |
1,416 | 2,289 | Real Time Voice Processing with Audiovisual
Feedback: Toward Autonomous Agents
with Perfect Pitch
Lawrence K. Saul1 , Daniel D. Lee2 , Charles L. Isbell3 , and Yann LeCun4
1
Department of Computer and Information Science
2
Department of Electrical and System Engineering
University of Pennsylvania, 200 South 33rd St, P... | 2289 |@word proportionality:1 cos2:1 shot:1 noll:2 initial:1 contains:2 liquid:1 daniel:1 tuned:1 interestingly:1 rightmost:1 atlantic:1 current:2 com:1 comparing:2 recovered:1 embarrassment:1 remove:2 designed:2 update:4 infant:1 half:3 intelligence:1 cue:3 device:1 filtered:2 detecting:8 provides:1 triumph:1 simpler:... |
1,417 | 229 | A Cost Function for Internal Representations
A Cost Function for Internal Representations
Anders Krogh
The Niels Bohr Institute
Blegdamsvej 17
2100 Copenhagen
Denmark
G. I. Thorbergsson
Nordita
Blegdamsvej 17
2100 Copenhagen
Denmark
John A. Hertz
Nordita
Blegdamsvej 17
2100 Copenhagen
Denmark
ABSTRACT
We introduce... | 229 |@word trial:1 bf:1 simulation:4 tried:1 tuned:1 ours:2 recovered:2 activation:4 john:1 j1:3 plot:3 stationary:1 sits:1 along:1 introduce:1 themselves:1 actual:1 cpu:1 considering:1 increasing:1 becomes:2 lowest:1 modeles:1 kind:1 finding:1 every:4 ro:1 unit:22 local:1 limit:13 encoding:6 might:1 studied:1 relaxing... |
1,418 | 2,290 | Intrinsic Dimension Estimation Using Packing
Numbers
Bal?azs K?egl
Department of Computer Science and Operations Research
University of Montreal
CP 6128 succ. Centre-Ville, Montr?eal, Canada H3C 3J7
kegl@iro.umontreal.ca
Abstract
We propose a new algorithm to estimate the intrinsic dimension of data
sets. The method i... | 2290 |@word cox:2 polynomial:2 seems:6 nd:2 underline:1 open:4 covariance:2 profit:1 carry:1 reduction:4 contains:3 existing:2 must:2 grassberger:1 designed:2 generative:3 greedy:2 intelligence:2 short:1 iterates:1 node:2 constructed:2 become:4 symposium:2 psfrag:17 focs:1 consists:1 redefine:2 manner:1 introduce:3 x0:... |
1,419 | 2,291 | Improving a Page Classifier with Anchor
Extraction and Link Analysis
William W. Cohen
Center for Automated Learning and Discovery,
Carnegie-Mellon University
5000 Forbes Ave, Pittsburgh, PA 15213
william@wcohen.com
Abstract
Most text categorization systems use simple models of documents and
document collections. In t... | 2291 |@word version:2 seems:3 d2:1 wrapper:22 exclusively:1 bibtex:1 document:15 existing:2 current:1 com:1 written:2 informative:1 hofmann:1 designed:1 half:2 selected:1 generative:1 item:1 intelligence:1 discovering:1 mccallum:1 blei:6 detecting:1 node:1 toronto:1 constructed:1 combine:1 eleventh:1 introduce:1 x0:5 t... |
1,420 | 2,292 | Clustering with the Fisher Score
Koji Tsuda, Motoaki Kawanabe and Klaus-Robert Muller
?
AIST
CBRC, 2-41-6, Aomi, Koto-ku, Tokyo, 135-0064, Japan
Fraunhofer FIRST, Kekul?estr. 7, 12489 Berlin, Germany
Dept. of CS, University of Potsdam, A.-Bebel-Str. 89, 14482 Potsdam, Germany
koji.tsuda@aist.go.jp,
nabe,k... | 2292 |@word tried:1 covariance:3 ld:1 reduction:1 initial:6 contains:3 score:38 tuned:1 document:2 comparing:1 partition:6 shape:1 designed:1 discrimination:1 generative:1 denison:1 smith:1 contribute:2 mathematical:2 constructed:2 become:1 replication:1 laub:1 consists:2 prove:2 pairwise:1 huber:1 expected:1 terminal:... |
1,421 | 2,293 | Learning Graphical Models
with Mercer Kernels
Francis R. Bach
Division of Computer Science
University of California
Berkeley, CA 94720
fbach@cs.berkeley.edu
Michael I. Jordan
Computer Science and Statistics
University of California
Berkeley, CA 94720
jordan@cs.berkeley.edu
Abstract
We present a class of algorithms fo... | 2293 |@word determinant:2 briefly:1 repository:2 polynomial:1 simulation:2 covariance:17 decomposition:4 invoking:1 score:2 current:2 discretization:7 must:1 partition:1 enables:1 treating:1 v:2 greedy:1 generative:2 discovering:1 assurance:1 intelligence:2 accordingly:1 isotropic:1 footing:2 provides:2 characterizatio... |
1,422 | 2,294 | Evidence Optimization Techniques
for Estimating Stimulus-Response Functions
Maneesh Sahani
Gatsby Unit, UCL
17 Queen Sq., London, WC1N 3AR, UK.
maneesh@gatsby.ucl.ac.uk
Jennifer F. Linden
Keck Center, UCSF
San Francisco, CA 94143?0732, USA.
linden@phy.ucsf.edu
Abstract
An essential step in understanding the function... | 2294 |@word trial:4 version:1 polynomial:1 seek:1 pulse:8 gfih:1 covariance:10 accounting:1 decomposition:1 dramatic:1 minus:1 tr:4 moment:1 reduction:2 substitution:3 series:1 contains:1 phy:1 rightmost:1 current:1 discretization:2 yet:1 evans:1 realistic:1 partition:1 designed:1 alone:2 nervous:1 tone:4 along:4 becom... |
1,423 | 2,295 | Constraint Classification for Multiclass
Classification and Ranking
Sariel Har-Peled
Dan Roth
Dav Zimak
Department of Computer Science
University of Illinois
Urbana, IL 61801
sariel,danr,davzimak @uiuc.edu
Abstract
The constraint classification framework captures many flavors of multiclass classification including ... | 2295 |@word repository:3 duda:1 advantageous:1 seems:1 closure:1 seek:1 solid:2 outperforms:2 existing:1 current:1 written:1 realize:1 multioutput:1 j1:1 enables:1 update:3 discrimination:1 intelligence:1 sys:1 characterization:1 provides:8 hyperplanes:1 firstly:1 demoted:1 along:1 direct:1 symposium:1 learing:1 incorr... |
1,424 | 2,296 | Adaptive Caching by Refetching
Robert B. Gramacy , Manfred K. Warmuth, Scott A. Brandt, Ismail Ari
Department of Computer Science, UCSC
Santa Cruz, CA 95064
rbgramacy, manfred, scott, ari @cs.ucsc.edu
Abstract
We are constructing caching policies that have 13-20% lower miss rates
than the best of twelve baseli... | 2296 |@word trial:2 middle:1 achievable:1 advantageous:1 disk:3 bf:3 seek:1 pick:1 moment:1 initial:1 selecting:1 past:8 current:7 must:3 john:1 cruz:1 additive:1 realistic:1 partition:2 wanted:1 update:15 v:1 selected:1 fewer:3 device:1 warmuth:10 record:2 manfred:2 filtered:1 provides:1 brandt:2 accessed:1 five:1 alo... |
1,425 | 2,297 | Kernel Dependency Estimation
Jason Weston, Olivier Chapelle, Andre Elisseeff,
Bernhard Scholkopf and Vladimir Vapnik*
Max Planck Institute for Biological Cybernetics, 72076 Tubingen, Germany
*NEC Research Institute, Princeton, NJ 08540 USA
Abstract
We consider the learning problem of finding a dependency between
a ge... | 2297 |@word trial:1 middle:1 inversion:1 polynomial:1 lodhi:1 open:1 hu:1 tried:1 decomposition:2 elisseeff:2 euclidian:1 carry:1 score:1 subjective:1 outperforms:1 comparing:1 surprising:1 must:2 parsing:2 john:1 cruz:1 fn:2 offunctions:1 enables:1 kyb:1 v:5 half:8 selected:1 provides:1 postal:1 herbrich:1 five:2 cons... |
1,426 | 2,298 | Boosting Density Estimation
Saharon Rosset
Department of Statistics
Stanford University
Stanford, CA, 94305
saharon@stat.stanford.edu
Eran Segal
Computer Science Department
Stanford University
Stanford, CA, 94305
eran@cs.stanford.edu
Abstract
Several authors have suggested viewing boosting as a gradient descent sear... | 2298 |@word mild:2 illustrating:1 version:5 sgf:1 repository:1 norm:3 stronger:1 seems:1 duda:1 nd:1 msr:1 tr:2 series:1 contains:1 current:12 assigning:1 written:1 john:1 additive:1 happen:2 partition:2 kdd:1 christian:1 asymptote:1 plot:2 interpretable:1 implying:1 greedy:2 selected:6 steepest:1 vanishing:1 boosting:... |
1,427 | 2,299 | Bias-Optimal Incremental Problem Solving
Jurgen
?
Schmidhuber
IDSIA, Galleria 2, 6928 Manno-Lugano, Switzerland
juergen@idsia.ch
Abstract
Given is a problem sequence and a probability distribution (the bias) on
programs computing solution candidates. We present an optimally fast
way of incrementally solving each task... | 2299 |@word version:1 nd:3 disk:11 c0:2 instruction:51 unbeatable:1 invoking:1 profit:1 harder:2 nonexistent:2 cyclic:1 initial:12 genetic:2 ours:1 prefix:23 existing:1 current:18 com:1 yet:3 assigning:1 interrupted:3 additive:1 subsequent:2 remove:1 hoping:1 half:1 selected:1 discovering:4 intelligence:2 short:3 point... |
1,428 | 23 | 715
A COMPUTER SIMULATION OF CEREBRAL NEOCORTEX:
COMPUTATIONAL CAPABILITIES OF NONLINEAR NEURAL NETWORKS
Alexander Singer* and John P. Donoghue**
*Department of Biophysics, Johns Hopkins University,
Baltimore, MD 21218 (to whom all correspondence should
be addressed)
**Center for Neural Science, Brown University,
Pr... | 23 |@word deformed:1 trial:2 neurophysiology:1 middle:1 stronger:1 replicate:1 hyperpolarized:1 simulation:32 initial:1 uncovered:1 suppressing:1 existing:1 current:4 si:2 must:2 john:2 grain:14 realistic:1 predetermined:1 motor:1 plot:6 designed:1 fewer:1 selected:1 record:1 detecting:1 provides:1 contribute:2 locatio... |
1,429 | 230 | 258
Seibert and Waxman
Learning Aspect Graph Representations
from View Sequences
Michael Seibert and Allen M. Waxnlan
Lincoln Laborat.ory, l\IIassachusetts Institute of Technology
Lexington, MA 02173-9108
ABSTRACT
In our effort to develop a modular neural system for invariant learning and recognition of 3D objects,... | 230 |@word nd:1 simulation:1 postsynaptically:1 rol:1 tr:1 solid:1 initial:3 att:1 selecting:1 existing:2 current:1 erms:1 cad:1 activation:1 assigning:1 must:3 periodically:2 happen:1 partition:1 shape:2 designed:1 sponsored:1 v:1 cue:1 leaf:1 short:3 node:40 contribute:1 constructed:1 differential:3 redirected:1 cons... |
1,430 | 2,300 | On the Complexity of Learning
the Kernel Matrix
Olivier Bousquet, Daniel J. L. Herrmann
MPI for Biological Cybernetics
Spemannstr. 38, 72076 T?ubingen
Germany
olivier.bousquet, daniel.herrmann @tuebingen.mpg.de
Abstract
We investigate data based procedures for selecting the kernel when learning with Support Vector M... | 2300 |@word repository:2 middle:1 polynomial:6 norm:8 seems:2 termination:1 closure:2 decomposition:1 elisseeff:1 pick:2 commute:1 versatile:1 n8:1 contains:1 selecting:1 daniel:2 tuned:1 rkhs:3 current:1 written:1 parameterization:3 hyperplanes:2 simpler:2 direct:1 combine:1 introduce:1 sublinearly:1 indeed:6 behavior... |
1,431 | 2,301 | An Asynchronous Hidden Markov Model
for Audio-Visual Speech Recognition
Samy Bengio
Dalle Molle Institute for Perceptual Artificial Intelligence (IDIAP)
CP 592, rue du Simplon 4,
1920 Martigny, Switzerland
bengio@idiap.ch.http://www.idiap.ch/-bengio
Abstract
This paper presents a novel Hidden Markov Model architectur... | 2301 |@word kong:1 version:1 laurence:1 contains:1 series:2 tuned:1 yet:1 additive:3 informative:1 shape:1 enables:1 dupont:2 designed:2 steeneken:1 intelligence:1 selected:1 yr:3 farther:1 provides:1 along:2 become:1 introduce:1 speaks:1 expected:1 indeed:2 nor:1 inspired:1 considering:1 becomes:1 begin:1 project:3 mo... |
1,432 | 2,302 | Prediction of Protein Topologies Using
Generalized IOHMMs and RNNs
Gianluca Pollastri and Pierre Baldi
Department of Information and Computer Science
University of California, Irvine
Irvine, CA 92697-3425
gpollast,pfbaldi@ics.uci.edu
Alessandro Vullo and Paolo Frasconi
Dipartimento di Sistemi e Informatica
Universit`
a... | 2302 |@word private:1 faculty:1 version:2 exploitation:9 open:1 simulation:6 nsw:1 recursively:1 configuration:1 contains:1 score:10 pub:1 terminus:1 past:1 current:2 contextual:2 yet:1 must:1 realize:1 numerical:1 distant:5 remove:1 half:1 plane:14 coarse:8 node:6 location:1 casp:1 constructed:1 direct:4 beta:1 combin... |
1,433 | 2,303 | Speeding up the Parti-Game Algorithm
Maxim Likhachev
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
maxim+@cs.cmu.edu
Sven Koenig
College of Computing
Georgia Institute of Technology
Atlanta, GA 30312-0280
skoenig@cc.gatech.edu
Abstract
In this paper, we introduce an efficient replanning ... | 2303 |@word version:1 d2:7 git:1 incurs:1 initial:1 series:1 contains:4 bc:5 ours:2 o2:1 imaginary:5 current:10 discretization:13 yet:1 refines:3 shape:1 remove:5 update:6 half:1 leaf:1 record:1 coarse:3 constructed:1 predecessor:4 become:2 prove:3 combine:1 nondeterministic:4 introduce:1 indeed:2 behavior:2 planning:1... |
1,434 | 2,304 | Minimax Differential Dynamic Programming:
An Application to Robust Biped Walking
Jun Morimoto
Human Information Science Labs,
Department 3, ATR International
Keihanna Science City,
Kyoto, JAPAN, 619-0288
xmorimo@atr.co.jp
Christopher G. Atkeson ?
The Robotics Institute and HCII,
Carnegie Mellon University
5000 Forbes... | 2304 |@word trial:1 ankle:8 simulation:2 p0:2 initial:6 minmax:1 tuned:10 current:1 neuneier:1 must:1 designed:3 half:1 provides:5 five:4 height:2 glover:1 along:2 differential:9 ik:3 x0:18 acquired:1 terminal:4 torque:9 automatically:1 actual:2 mass:4 qw:6 kg:3 viscous:2 developed:2 finding:1 control:32 before:3 local... |
1,435 | 2,305 | Real-time Particle Filters
Cody Kwok
Dieter Fox
Marina Meil?a
Dept. of Computer Science & Engineering, Dept. of Statistics
University of Washington
Seattle, WA 98195
ctkwok,fox @cs.washington.edu, mmp@stat.washington.edu
Abstract
Particle filters estimate the state of dynamical systems from sensor informa... | 2305 |@word thereby:4 recursively:1 reduction:3 contains:1 series:1 outperforms:1 freitas:1 skipping:3 interrupted:1 numerical:1 informative:2 plot:1 sponsored:1 update:20 resampling:2 intelligence:3 device:1 accordingly:1 hallway:2 beginning:1 short:1 along:2 corridor:1 overhead:2 introduce:3 indeed:1 detects:3 decrea... |
1,436 | 2,306 | Using Manifold Structure for Partially
Labelled Classification
Mikhail B e lkin
University of Chicago
Department of Mathematics
misha@math .uchicago .edu
Partha Niyogi
University of Chicago
Depts of Computer Science and Statistics
niyogi@cs.uchicago .edu
Abstract
We consider the general problem of utilizing both lab... | 2306 |@word trial:1 version:1 middle:1 seems:5 norm:2 bn:1 tr:1 reduction:2 series:2 etric:1 contains:2 document:2 err:1 comparing:2 adj:1 surprising:1 written:2 readily:1 john:1 chicago:3 plot:1 alone:2 plane:3 mccallum:2 lr:1 erator:1 provides:2 math:1 node:1 location:1 traverse:1 five:1 along:1 constructed:2 discret... |
1,437 | 2,307 | A Model for Real-Time Computation in Generic
Neural Microcircuits
Wolfgang Maass , Thomas Natschl?ager
Institute for Theoretical Computer Science
Technische Universitaet Graz, Austria
maass, tnatschl @igi.tu-graz.ac.at
Henry Markram
Brain Mind Institute
EPFL, Lausanne, Switzerland
henry.markram@epfl.ch
Abstract
... | 2307 |@word version:2 norm:2 nd:1 simulation:1 thereby:2 solid:1 carry:2 moment:4 initial:1 contains:1 score:4 liquid:13 current:7 surprising:1 universality:1 written:1 realistic:5 designed:2 short:2 filtered:1 provides:2 lsm:6 sigmoidal:1 five:1 provisional:1 mathematical:2 constructed:9 differential:1 psfrag:7 qualit... |
1,438 | 2,308 | Feature Selection in Mixture-Based Clustering
Martin H. Law, Anil K. Jain
Dept. of Computer Science and Eng.
Michigan State University,
East Lansing, MI 48824
U.S.A.
M?ario A. T. Figueiredo
Instituto de Telecomunicac?o? es,
Instituto Superior T?ecnico
1049-001 Lisboa
Portugal
Abstract
There exist many approaches to ... | 2308 |@word repository:2 version:1 sri:1 dekker:1 eng:2 covariance:5 mention:1 solid:1 initial:3 liu:2 wrapper:5 score:1 selecting:4 genetic:1 freitas:1 bradley:1 portuguese:1 john:2 subsequent:1 partition:1 treating:2 update:2 discrimination:1 generative:1 selected:3 intelligence:2 denison:1 contribute:1 cse:3 lx:1 be... |
1,439 | 2,309 | The Stability of Kernel Principal
Components Analysis and its Relation to
the Process Eigenspectrum
John Shawe-Taylor
Royal Holloway
University of London
john?cs.rhul.ac.uk
Christopher K. I. Williams
School of Informatics
University of Edinburgh
c.k.i.williams?ed.ac.uk
Abstract
In this paper we analyze the relations... | 2309 |@word cpe:2 version:1 polynomial:1 compression:1 norm:13 covariance:2 decomposition:3 tr:1 dzp:2 comparing:2 com:1 dx:10 written:1 john:2 numerical:2 analytic:1 enables:1 plot:1 ith:1 short:1 provides:1 mathematical:1 prove:1 introduce:1 expected:6 frequently:1 lll:1 provided:1 project:2 underlying:1 baker:2 nota... |
1,440 | 231 | 388
Smith and Miller
Bayesian Inference of Regular Grammar
and Markov Source Models
Kurt R. Smith and Michael I. Miller
Biomedical Computer Laboratory
and
Electronic Signals and Systems Research Laboratory
Washington University, SL Louis. MO 63130
ABSTRACT
In this paper we develop a Bayes criterion which includes t... | 231 |@word fmite:1 fonn:1 disallows:1 kurt:1 current:1 incidence:6 si:1 must:4 written:1 enables:1 treating:2 plot:2 v:1 implying:1 alone:1 exl:2 ji2:1 smith:8 provides:1 five:3 mathematical:1 incorrect:1 introduce:1 terminal:4 decreasing:1 considering:2 becomes:2 begin:2 notation:1 maximizes:2 pel:1 kind:1 string:8 de... |
1,441 | 2,310 | Adaptation and Unsupervised Learning
Peter Dayan Maneesh Sahani Gr?egoire Deback
Gatsby Computational Neuroscience Unit
17 Queen Square, London, England, WC1N 3AR.
dayan, maneesh @gatsby.ucl.ac.uk, gdeback@ens-lyon.fr
Abstract
Adaptation is a ubiquitous neural and psychological phenomenon, with
a wealth of instantia... | 2310 |@word neurophysiology:1 determinant:1 version:5 proportion:2 wenderoth:1 bf:1 d2:2 seek:1 covariance:11 thereby:1 tr:1 solid:9 reduction:10 series:2 contains:1 current:2 activation:1 attracted:1 john:2 tilted:1 visible:3 plasticity:6 shape:1 treating:1 progressively:2 rpn:1 discrimination:10 generative:9 half:1 s... |
1,442 | 2,311 | Classifying Patterns of Visual Motion a Neuromorphic Approach
Jakob Heinzle and Alan Stocker
Institute of Neuroinformatics
University and ETH Z?urich
Winterthurerstr. 190, 8057 Z?urich, Switzerland
jakob,alan @ini.phys.ethz.ch
Abstract
We report a system that classifies and can learn to classify patterns of
visu... | 2311 |@word seems:1 simulation:2 outlook:1 solid:3 moment:1 reduction:1 initial:1 contains:1 existing:1 discretization:1 activation:4 must:1 subsequent:1 realistic:1 plot:3 v:2 device:3 short:2 firstly:2 sigmoidal:2 five:2 become:1 differential:1 dsn:11 qualitative:1 consists:3 dayhoff:1 behavior:2 nor:1 inspired:1 act... |
1,443 | 2,312 | Maximally Informative Dimensions: Analyzing
Neural Responses to Natural Signals
Tatyana Sharpee , Nicole C. Rust , and William Bialek
Sloan?Swartz Center for Theoretical Neurobiology, Department of Physiology
University
of California at San Francisco, San Francisco, California 94143?0444
Center for Neural Sc... | 2312 |@word trial:3 r:18 covariance:9 eng:1 solid:3 moment:2 necessity:1 reduction:2 phy:1 odour:1 current:1 comparing:1 recovered:1 yet:1 written:1 additive:1 informative:2 remove:1 extrapolating:1 plot:2 plane:1 marine:1 short:1 provides:2 successive:1 allerton:1 along:8 become:1 shapley:1 dimen:1 olfactory:1 manner:... |
1,444 | 2,313 | Gaussian Process Priors With Uncertain Inputs
Application to Multiple-Step Ahead Time Series
Forecasting
Agathe Girard
Department of Computing Science
University of Glasgow
Glasgow, G12 8QQ
agathe@dcs.gla.ac.uk
Carl Edward Rasmussen
Gatsby Unit
University College London
London, WC1N 3AR
edward@gatsby.ucl.ac.uk
?
Joa... | 2313 |@word briefly:1 seborg:1 simulation:4 covariance:11 minus:1 tr:1 moment:1 initial:1 series:13 past:1 current:3 comparing:2 numerical:7 realistic:2 additive:1 plot:1 mackey:3 stationary:1 smith:3 provides:2 toronto:2 successive:1 firstly:1 mathematical:1 direct:3 consists:1 fitting:1 manner:1 multi:3 brain:1 inspi... |
1,445 | 2,314 | Monaural Speech Separation
Guoning Hu
Biophysics Program
The Ohio State University
Columbus, OH 43210
hu.117@osu.edu
DeLiang Wang
Department of Computer and Information
Science & Center of Cognitive Science
The Ohio State University, Columbus, OH 43210
dwang@cis.ohio-state.edu
Abstract
Monaural speech separation has... | 2314 |@word version:1 timefrequency:1 stronger:5 nd:1 hu:4 hyv:1 simulation:1 decomposition:2 tr:1 solid:1 n8:3 initial:8 contains:2 current:1 comparing:4 od:1 must:1 subsequent:1 remove:3 e22:1 n0:3 half:3 selected:2 cue:1 tone:2 accordingly:1 dover:1 smith:1 dissertation:2 filtered:2 provides:1 passbands:1 burst:1 co... |
1,446 | 2,315 | Bayesian Image Super-Resolution
Michael E. Tipping and Christopher M. Bishop
Microsoft Research
Cambridge, CB3 OFB, U.K.
{ mtipping, cmbishop} @microsoft.com
http://research.microsoft.com/ "-'{ mtipping,cmbishop}
Abstract
The extraction of a single high-quality image from a set of lowresolution images is an important... | 2315 |@word inversion:2 consequential:1 tried:1 covariance:1 thereby:1 shot:1 initial:1 tuned:1 com:2 must:2 readily:2 blur:1 plot:2 generative:3 intelligence:1 parameterization:1 successive:2 registering:1 direct:1 lowresolution:1 combine:1 fitting:2 psf:12 multi:1 ol:1 resolve:1 little:1 inappropriate:1 becomes:1 pro... |
1,447 | 2,316 | Learning Semantic Similarity
Jaz Kandola
John Shawe-Taylor
Royal Holloway, University of London
{jaz, john}@cs.rhul.ac.uk
N ella Cristianini
University of California, Berkeley
nello@support-vector.net
Abstract
The standard representation of text documents as bags of words
suffers from well known limitations, mostly ... | 2316 |@word kondor:1 proportion:3 lodhi:1 open:1 seek:1 decomposition:2 thereby:1 recursively:1 reduction:1 contains:1 series:1 document:34 existing:1 comparing:1 jaz:3 must:1 john:4 partition:1 hofmann:1 enables:1 selected:3 prohibitive:1 item:3 parameterization:1 beginning:1 provides:1 node:9 lexicon:3 gx:1 construct... |
1,448 | 2,317 | shorter argument and much tighter than previous margin bounds.
There are two mathematical flavors of margin bound dependent upon the weights
Wi of the vote and the features Xi that the vote is taken over.
1. Those ([12], [1]) with a bound on Li w~ and Li x~
("bib" bounds).
2. Those ([11], [6]) with a bound on Li Wi a... | 2317 |@word open:1 fortuitous:1 tr:1 z2:1 dx:2 must:4 john:3 wx:1 joy:1 implying:1 isotropic:2 boosting:2 herbrich:2 simpler:1 mathematical:1 along:1 prove:1 behavior:1 ol:1 eurocolt:1 classifies:1 notation:2 bounded:2 developed:1 every:6 voting:2 megiddo:1 classifier:35 demonstrates:1 tricky:1 uk:1 unit:2 appear:1 pos... |
1,449 | 2,318 | Adaptive Nonlinear System Identification
with Echo State Networks
Herbert Jaeger
International University Bremen
D-28759 Bremen, Germany
h.jaeger@iu-bremen. de
Abstract
Echo state networks (ESN) are a novel approach to recurrent neural network training. An ESN consists of a large, fixed, recurrent
"reservoir" network,... | 2318 |@word private:1 exploitation:1 version:2 achievable:1 suitably:1 open:1 simulation:1 excited:1 prokhorov:4 incurs:1 solid:3 reduction:1 initial:4 liquid:1 bppt:2 existing:1 current:2 activation:3 subsequent:1 numerical:3 wx:1 plot:3 update:5 v:4 stationary:3 short:2 ire:1 lsm:1 become:1 yuan:1 consists:1 recogniz... |
1,450 | 2,319 | Exponential Family PCA for Belief Compression
in POMDPs
Nicholas Roy
Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213
nickr@ri.cmu.edu
Geoffrey Gordon
Department of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
ggordon@cs.cmu.edu
Abstract
Standard value function approaches to find... | 2319 |@word trial:3 compression:1 tried:2 decomposition:3 ality:1 pick:2 reduction:13 initial:1 daniel:1 past:2 existing:2 discretization:1 must:3 localise:1 v:1 half:3 fewer:2 leaf:1 guess:1 intelligence:3 hallway:1 ron:1 daphne:1 along:2 direct:1 become:1 corridor:8 fitting:1 inside:1 introduce:1 acquired:1 expected:... |
1,451 | 232 | 702
Obradovic and Pclrberry
Analog Neural Networks of Limited Precision I:
Computing with Multilinear Threshold Functions
(Preliminary Version)
Zoran Obradovic and Ian Parberry
Department of Computer Science.
Penn State University.
University Park. Pa. 16802.
ABSTRACT
Experimental evidence has shown analog neural n... | 232 |@word determinant:1 version:5 polynomial:12 stronger:1 minus:1 carry:2 configuration:1 franklin:1 nonmonotone:5 si:1 must:1 wll:1 intelligence:2 device:1 quantized:3 hyperplanes:1 simpler:1 constructed:4 prove:1 expected:1 wier:2 multi:1 increasing:7 provided:2 bounded:6 notation:3 circuit:11 what:1 every:10 ser:1... |
1,452 | 2,320 | Combining Features for BCI
Guido Dornhege1?, Benjamin Blankertz1 , Gabriel Curio2 , Klaus-Robert M?ller1,3
1 Fraunhofer FIRST.IDA, Kekul?str. 7, 12489 Berlin, Germany
2 Neurophysics Group, Dept. of Neurology, Klinikum Benjamin Franklin,
Freie Universit?t Berlin, Hindenburgdamm 30, 12203 Berlin, Germany
3 University of... | 2320 |@word blankertz1:1 trial:27 proceeded:1 version:1 open:2 cincotti:1 tried:1 accounting:1 covariance:5 eng:5 thereby:2 harder:1 reduction:1 series:1 denoting:1 franklin:1 ida:1 comparing:1 activation:2 pothesis:1 must:1 visible:1 hofmann:1 enables:1 motor:10 toro:1 v:2 discrimination:1 selected:5 device:3 nervous:... |
1,453 | 2,321 | Feature Selection and Classification on
Matrix Data: From Large Margins To
Small Covering Numbers
Sepp Hochreiter and Klaus Obermayer
Department of Electrical Engineering and Computer Science
Technische Universit?at Berlin
10587 Berlin, Germany
{hochreit,oby}@cs.tu-berlin.de
Abstract
We investigate the problem of lea... | 2321 |@word mild:1 norm:1 tamayo:2 decomposition:3 tr:1 contains:2 outperforms:1 dx:1 must:4 additive:1 hofmann:1 hochreit:1 selected:7 nervous:1 provides:1 ih1:5 herbrich:2 downing:1 along:1 laub:1 introduce:1 pairwise:11 expected:1 indeed:2 brain:1 becomes:1 provided:2 bounded:4 sdorra:1 lowest:1 what:2 kind:2 interp... |
1,454 | 2,322 | Optimality of Reinforcement Learning
Algorithms with Linear Function
Approximation
Ralf Schoknecht
ILKD
University of Karlsruhe , Germany
ralf.schoknecht@ilkd.uni-karlsruhe.de
Abstract
There are several reinforcement learning algorithms that yield approximate solutions for the problem of policy evaluation when the
va... | 2322 |@word version:2 inversion:1 norm:14 twelfth:1 initial:2 selecting:2 comparing:1 analysed:1 si:7 written:1 must:3 aft:1 belmont:1 update:2 intelligence:1 tdp:2 lr:1 along:1 direct:2 inside:1 dpr:2 theoretically:1 expected:2 frequently:1 bellman:9 discounted:1 decomposed:1 td:39 becomes:2 moreover:6 notation:1 inte... |
1,455 | 2,323 | Unsupervised Color Constancy
Kinh Tieu
Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139
tieu@ai.mit.edu
Erik G. Miller
Computer Science Division
UC Berkeley
Berkeley, CA 94720
egmil@cs.berkeley.edu
Abstract
In [1] we introduced a linear statistical model of joint color cha... | 2323 |@word middle:2 eliminating:1 achievable:1 polynomial:2 seems:1 version:2 c0:2 nd:1 rgb:7 covariance:1 brightness:5 dramatic:1 accommodate:1 contains:1 score:1 selecting:1 franklin:1 rightmost:1 outperforms:1 recovered:1 assigning:1 must:1 numerical:1 wanted:2 alone:1 intelligence:1 selected:1 short:1 colored:1 hs... |
1,456 | 2,324 | Inferring a Semantic Representation of Text
via Cross-Language Correlation Analysis
Alexei Vinokourov
John Shawe-Taylor
Dept. Computer Science
Royal Holloway, University of London
Egham, Surrey, UK, TW20 0EX
alexei@cs.rhul.ac.uk
john@cs.rhul.ac.uk
Nello Cristianini
Dept. Statistics
UC Davis, Berkeley, US
nello@suppor... | 2324 |@word briefly:1 version:4 middle:2 norm:1 seems:1 decomposition:5 carry:1 initial:1 series:1 fragment:1 selecting:1 document:54 rkhs:1 outperforms:1 atlantic:2 existing:5 comparing:2 analysed:2 written:1 readily:1 john:3 grain:4 realistic:1 subsequent:1 fund:1 generative:2 marine:1 short:2 provides:3 scientifique... |
1,457 | 2,325 | Fast Exact Inference with a Factored Model for
Natural Language Parsing
Dan Klein
Department of Computer Science
Stanford University
Stanford, CA 94305-9040
Christopher D. Manning
Department of Computer Science
Stanford University
Stanford, CA 94305-9040
klein@cs.stanford.edu
manning@cs.stanford.edu
Abstract
We pr... | 2325 |@word faculty:1 version:3 bigram:1 polynomial:1 seems:1 propagate:1 contrastive:2 mention:1 tr:1 carry:1 initial:2 configuration:2 score:32 charniak:4 fragment:1 tuned:1 past:1 contextual:1 wd:7 assigning:1 yet:1 must:3 parsing:37 enables:1 remove:2 alone:5 generative:4 selected:1 intelligence:2 item:5 hwd:1 rera... |
1,458 | 2,326 | Developing Topography and Ocular Dominance
Using two aVLSI Vision Sensors and a
Neurotrophic Model of Plasticity
Terry Elliott
Dept. Electronics & Computer Science
University of Southampton
Highfield
Southampton, SO17 1BJ
United Kingdom
te@ecs.soton.ac.uk
J?org Kramer
Institute of Neuroinformatics
University of Z?uri... | 2326 |@word neurophysiology:1 wiesel:2 simulation:2 pulse:1 brightness:1 thereby:1 solid:1 shading:1 initial:1 electronics:1 disparity:35 united:1 od:1 realistic:1 interspike:1 plasticity:9 half:2 selected:2 plane:1 short:1 coarse:1 location:1 successive:1 org:2 simpler:1 height:1 mathematical:2 burst:1 along:2 become:... |
1,459 | 2,327 | String Kernels, Fisher Kernels and Finite
State Automata
John Shawe-Taylor
Alexei Vinokourov
Department of Computer Science
Royal Holloway, University of London
Email: { craig, j st, alexei }?lcs. rhul. ac. uk
Craig Saunders
Abstract
In this paper we show how the generation of documents can be
thought of as a k-stag... | 2327 |@word briefly:1 lodhi:2 idl:5 tr:1 carry:1 score:4 selecting:1 document:29 outperforms:1 recovered:1 comparing:2 di2:1 surprising:1 com:1 yet:1 john:1 cruz:1 grain:1 visible:1 informative:4 enables:1 beginning:2 sys:1 normalising:1 contribute:1 herbrich:1 constructed:2 direct:1 become:2 ucsc:1 ik:1 introduce:1 in... |
1,460 | 2,328 | A Note on the Representational Incompatibility
of Function Approximation and Factored
Dynamics
Eric Allender
Computer Science Department
Rutgers University
allender@cs.rutgers.edu
Sanjeev Arora
Computer Science Department
Princeton University
arora@cs.princeton.edu
Michael Kearns
Department of Computer and Informati... | 2328 |@word private:1 polynomial:14 seems:2 stronger:3 open:2 willing:1 seek:1 simulation:2 asks:1 reduction:1 moment:2 configuration:11 mkearns:1 ours:1 current:1 mundhenk:1 parameterization:1 record:1 accepting:4 characterization:1 cse:1 node:4 traverse:1 unbounded:2 along:1 prove:4 manner:1 upenn:1 expected:6 indeed... |
1,461 | 2,329 | A Neural Edge-Detection Model for
Enhanced Auditory Sensitivity in
Modulated Noise
Alon Fishbach and Bradford J. May
Department of Biomedical Engineering and Otolaryngology-HNS
Johns Hopkins University
Baltimore, MD 21205
fishbach@northwestern.edu
Abstract
Psychophysical data suggest that temporal modulations of stim... | 2329 |@word rising:3 compression:2 pulse:1 simulation:2 p0:6 pressure:3 solid:1 carry:1 reduction:3 series:1 efficacy:2 interestingly:1 suppressing:1 od:1 intriguing:1 john:1 shape:3 hypothesize:1 plot:1 progressively:1 cue:2 tone:35 beginning:2 short:1 sudden:1 provides:1 contribute:2 revisited:1 zhang:1 along:2 const... |
1,462 | 233 | 558
Rohwer
The 'Moving Targets' Training Algorithm
Richard Rohwer
Centre for Speech Technology Research
Edinburgh University
80, South Bridge
Edinburgh EH1 1HN SCOTLAND
ABSTRACT
A simple method for training the dynamical behavior of a neural network is derived. It is applicable to any training problem
in discrete-ti... | 233 |@word version:4 inversion:2 pulse:3 simulation:3 decomposition:1 thereby:1 tr:1 initial:1 contains:1 past:1 attainability:1 activation:12 dx:1 must:1 distant:3 numerical:3 plot:1 update:1 alone:1 scotland:1 provides:1 node:26 become:1 qualitative:2 manner:1 behavior:3 frequently:1 cpu:2 becomes:1 estimating:1 nota... |
1,463 | 2,330 | Learning in Spiking Neural Assemblies
David Barber
Institute for Adaptive and Neural Computation
Edinburgh University
5 Forrest Hill, Edinburgh, EH1 2QL, U.K.
dbarber@anc.ed.ac.uk
Abstract
We consider a statistical framework for learning in a class of networks of spiking neurons. Our aim is to show how optimal local
... | 2330 |@word achievable:1 seems:1 simulation:1 simplifying:1 minus:1 recursively:1 carry:1 kappen:1 necessity:1 contains:1 efficacy:3 past:1 readily:6 visible:1 subcomponent:1 realistic:3 plasticity:2 shape:1 enables:1 plot:1 drop:1 update:2 aps:1 aside:1 inconvenience:1 realism:1 core:1 direct:1 become:1 qualitative:1 ... |
1,464 | 2,331 | Neuromorphic Bistable VLSI Synapses with
Spike-Timing-Dependent Plasticity
Giacomo Indiveri
Institute of Neuroinformatics
University/ETH Zurich
CH-8057 Zurich, Switzerland
giacomo@ini.phys.ethz.ch
Abstract
We present analog neuromorphic circuits for implementing bistable synapses with spike-timing-dependent plasticit... | 2331 |@word middle:2 version:1 hippocampus:1 nd:1 pulse:11 simulation:1 liu:4 series:3 efficacy:29 contains:2 o2:1 current:7 discretization:2 comparing:1 refresh:2 periodically:2 subsequent:1 additive:1 realistic:3 plasticity:6 asymptote:9 plot:3 succeeding:1 designed:1 aps:1 device:5 vtp:3 short:9 node:2 successive:1 ... |
1,465 | 2,332 | Theory-Based Causal Inference
Joshua B. Tenenbaum & Thomas L. Griffiths
Department of Brain and Cognitive Sciences
MIT, Cambridge, MA 02139
jbt, gruffydd @mit.edu
Abstract
People routinely make sophisticated causal inferences unconsciously, effortlessly, and from very little data ? often from just one or a few obse... | 2332 |@word trial:42 version:3 nd:1 open:9 simulation:3 shot:2 necessity:1 series:1 score:1 interestingly:1 subjective:1 comparing:1 surprising:1 activation:8 yet:1 must:2 written:2 subsequent:1 additive:2 realistic:1 blickets:11 plot:1 alone:15 stationary:1 instantiate:1 weighing:1 leaf:1 cue:2 intelligence:2 beginnin... |
1,466 | 2,333 | Discriminative Learning for Label
Sequences via Boosting
Yasemin Altun, Thomas Hofmann and Mark Johnson*
Department of Computer Science
*Department of Cognitive and Linguistics Sciences
Brown University, Providence, RI 02912
{altun,th}@cs.brown.edu, Mark_Johnson@brown.edu
Abstract
This paper investigates a boosting a... | 2333 |@word seems:1 willing:1 pick:1 tr:1 ld:2 interestingly:1 past:2 current:4 comparing:1 yet:1 written:2 readily:1 parsing:1 additive:4 partition:1 hofmann:1 designed:1 sponsored:1 update:2 stationary:1 generative:3 selected:2 reranking:1 item:1 xk:1 beginning:1 mccallum:1 lr:1 boosting:26 location:3 bixi:3 along:1 ... |
1,467 | 2,334 | Spectro-Temporal Receptive Fields of
Subthreshold Responses in Auditory Cortex
Christian K. Machens, Michael Wehr, Anthony M. Zador
Cold Spring Harbor Laboratory
One Bungtown Rd
Cold Spring Harbor, NY 11724
machens, wehr, zador @cshl.edu
Abstract
How do cortical neurons represent the acoustic environment? This ques... | 2334 |@word trial:7 timefrequency:2 seems:1 proportion:1 seal:1 termination:1 liu:1 series:1 surprising:1 yet:1 slb:1 shape:1 christian:1 plot:1 tone:7 short:1 record:2 characterization:2 provides:3 revisited:1 complication:1 five:1 burst:1 constructed:1 direct:1 eleventh:1 pharmacologically:1 rapid:1 roughly:1 multi:1... |
1,468 | 2,335 | How Linear are Auditory Cortical Responses?
Maneesh Sahani
Gatsby Unit, UCL
17 Queen Sq., London, WC1N 3AR, UK.
maneesh@gatsby.ucl.ac.uk
Jennifer F. Linden
Keck Center, UCSF
San Francisco, CA 94143?0732.
linden@phy.ucsf.edu
Abstract
By comparison to some other sensory cortices, the functional properties of cells in ... | 2335 |@word neurophysiology:1 trial:6 achievable:1 proportion:1 approved:1 polynomial:2 smirnov:1 pulse:8 simulation:3 covariance:1 thereby:1 minus:1 tr:1 moment:4 reduction:3 substitution:1 series:2 fragment:1 hereafter:1 selecting:1 phy:1 denoting:1 tuned:1 current:1 discretization:3 yet:1 scatter:7 must:7 evans:1 ad... |
1,469 | 2,336 | Discriminative Densities from Maximum
Contrast Estimation
Peter Meinicke
Neuroinformatics Group
University of Bielefeld
Bielefeld, Germany
pmeinick@techfak.uni-bielefeld.de
Thorsten Twellmann
Neuroinformatics Group
University of Bielefeld
Bielefeld, Germany
ttwellma@techfak.uni-bielefeld.de
Helge Ritter
Neuroinformat... | 2336 |@word mild:1 repository:2 norm:9 duda:1 meinicke:2 suitably:1 denoting:1 bhattacharyya:1 ida:1 assigning:1 written:1 realize:3 partition:3 larization:1 depict:1 generative:1 selected:4 isotropic:1 parametrization:7 vanishing:1 colored:2 coarse:2 provides:1 herbrich:1 five:1 unbounded:2 direct:1 scholkopf:1 consis... |
1,470 | 2,337 | Dynamical Constraints on Computing
with Spike Timing in the Cortex
Arunava Banerjee and Alexandre Pouget
Department of Brain and Cognitive Sciences
University of Rochester, Rochester, New York 14627
{arunavab, alex} @bcs.rochester.edu
Abstract
If the cortex uses spike timing to compute, the timing of the spikes
must ... | 2337 |@word neurophysiology:1 briefly:1 rising:2 norm:3 proportionality:1 simulation:12 propagate:1 solid:3 initial:13 configuration:1 contains:1 interestingly:1 amp:1 past:2 current:1 must:4 subsequent:1 hyperpolarizing:1 numerical:1 enables:1 stationary:3 half:4 short:3 colored:1 provides:1 contribute:1 successive:5 ... |
1,471 | 2,338 | Concurrent Object Recognition and
Segmentation by Graph Partitioning
Stella
x. YuH, Ralph Gross t and Jianbo Shit
Robotics Institute t
Carnegie Mellon University
Center for the Neural Basis of Cognition +
5000 Forbes Ave, Pittsburgh, PA 15213-3890
{stella.yu, rgross, jshi}@cs.cmu.edu
Abstract
Segmentation and recog... | 2338 |@word middle:5 seek:1 brightness:1 carry:1 configuration:9 contains:1 current:1 recovered:1 si:1 dx:1 must:1 readily:1 subsequent:1 partition:2 discrimination:2 alone:6 cue:11 detecting:1 provides:1 node:17 location:2 five:3 along:2 constructed:2 supply:1 consists:1 combine:1 introduce:1 manner:1 falsely:2 pairwi... |
1,472 | 2,339 | Fractional Belief Propagation
Wim Wiegerinck and Tom Heskes
SNN, University of Nijmegen
Geert Grooteplein 21, 6525 EZ, Nijmegen, the Netherlands
wimw,tom @snn.kun.nl
Abstract
We consider loopy belief propagation for approximate inference in probabilistic graphical models. A limitation of the standard algorithm is t... | 2339 |@word grooteplein:1 simulation:2 mention:1 kappen:2 substitution:2 contains:2 loeliger:1 tuned:2 recovered:2 scatter:1 guez:1 written:1 ikeda:1 numerical:1 partition:3 plot:3 update:7 stationary:1 implying:1 greedy:1 provides:1 node:13 contribute:1 successive:1 c6:1 constructed:1 direct:1 introduce:2 pairwise:1 i... |
1,473 | 234 | 178
Lang and Hinton
Dimensionality Reduction and Prior Knowledge in
E-set Recognition
Geoffrey E. Hinton
Computer Science Dept.
University of Toronto
Toronto, Ontario M5S lA4
Canada
Kevin J. Lang1
Computer Science Dept.
Carnegie Mellon University
Pittsburgh, PA 15213
USA
ABSTRACT
It is well known that when an auto... | 234 |@word middle:1 version:4 compression:1 fonn:1 reduction:7 contains:3 disparity:1 score:1 current:1 lang:8 activation:5 attracted:1 informative:1 designed:1 plot:2 discrimination:4 tenn:1 selected:2 fewer:1 cue:5 short:1 dissertation:1 detecting:1 provides:1 toronto:2 along:1 burst:2 roughly:1 multi:1 decreasing:1 ... |
1,474 | 2,340 | An Impossibility Theorem for Clustering
Jon Kleinberg
Department of Computer Science
Cornell University
Ithaca NY 14853
Abstract
Although the study of clustering is centered around an intuitively
compelling goal, it has been very difficult to develop a unified
framework for reasoning about it at a technical level, an... | 2340 |@word briefly:1 faculty:1 version:3 achievable:1 duda:1 nd:1 d2:1 seek:4 methodologically:1 initial:1 celebrated:1 ours:1 horvitz:1 assigning:2 must:5 written:1 john:1 additive:1 partition:44 hofmann:2 generative:3 node:1 location:5 preference:1 unbounded:1 mathematical:2 prove:5 consists:2 inside:1 introduce:1 x... |
1,475 | 2,341 | Location Estimation with a Differential Update
Network
Ali Rahimi and Trevor Darrell
Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139
{ali,trevor}@mit.edu
Abstract
Given a set of hidden variables with an a-priori Markov structure, we
derive an online algorithm which approxim... | 2341 |@word middle:2 version:1 open:1 covariance:8 simplifying:2 automat:1 brightness:1 accommodate:1 cyclic:1 liu:1 shum:1 denoting:1 ours:1 rightmost:1 past:2 existing:1 recovered:4 current:2 dx:2 takeo:1 subsequent:2 happen:1 shape:3 wanted:1 plot:1 update:24 intelligence:5 parametrization:1 smith:1 node:5 location:... |
1,476 | 2,342 | Binary Coding in Auditory Cortex
Michael R. DeWeese and Anthony M. Zador
Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724
deweese@cshl.edu, zador@cshl.edu
Abstract
Cortical neurons have been reported to use both rate and temporal
codes. Here we describe a novel mode in which each neuron
generates exactly 0... | 2342 |@word trial:28 norm:1 seems:1 seal:1 open:3 simulation:1 propagate:2 pulse:1 dramatic:2 carry:1 born:1 tuned:1 interestingly:1 surprising:1 scatter:1 subsequent:1 interspike:1 plot:1 progressively:2 hash:1 v:2 half:2 selected:1 fewer:1 discrimination:1 tone:40 patterning:1 short:2 record:1 filtered:1 provides:1 t... |
1,477 | 2,343 | Dynamic Bayesian Networks with
Deterministic Latent Tables
David Barber
Institute for Adaptive and Neural Computation
Edinburgh University
5 Forrest Hill, Edinburgh, EH1 2QL, U.K.
dbarber@anc.ed.ac.uk
Abstract
The application of latent/hidden variable Dynamic Bayesian Networks is constrained by the complexity of margi... | 2343 |@word version:1 heteroassociative:1 recursively:2 carry:1 initial:3 series:11 contains:5 denoting:1 past:3 freitas:1 yet:1 concatenate:1 visible:24 enables:1 alone:1 generative:1 short:1 node:2 five:2 along:3 consists:3 fitting:1 admirably:1 indeed:1 roughly:1 multi:1 underlying:2 what:1 whilst:6 temporal:4 toyam... |
1,478 | 2,344 | Adaptive Quantization and Density
Estimation in Silicon
David Hsu
Seth Bridges
Miguel Figueroa
Chris Diorio
Department of Computer Science and Engineering
University of Washington
114 Sieg Hall, Box 352350
Seattle, WA 98195-2350 USA
{hsud, seth, miguel, diorio}@cs.washington.edu
Abstract
We present the bump mixture m... | 2344 |@word version:2 compression:2 covariance:1 solid:1 score:1 selecting:1 loeliger:1 denoting:1 current:13 comparing:1 com:1 assigning:1 refresh:1 additive:1 shape:1 plot:2 update:6 v:2 selected:1 device:8 floatinggate:1 ith:9 reciprocal:1 record:1 quantizer:8 provides:1 codebook:1 location:1 sieg:1 along:1 c2:9 con... |
1,479 | 2,345 | Error Bounds for Transductive Learning via
Compression and Clustering
Philip Derbeko
Ran El-Yaniv
Ron Meir
Technion - Israel Institute of Technology
{philip,rani}@cs.technion.ac.il rmeir@ee.technion.ac.il
Abstract
This paper is concerned with transductive learning. Although transduction appears to be an easier task t... | 2345 |@word h:5 rani:1 briefly:1 compression:23 advantageous:1 stronger:1 contains:1 current:1 realistic:1 partition:4 sponsored:1 fund:1 statis:1 implying:1 selected:4 devising:1 guess:3 dembo:1 affair:1 provides:1 node:2 ron:1 herbrich:1 constructed:3 c2:1 combine:1 interscience:1 manner:2 indeed:1 expected:1 p1:4 li... |
1,480 | 2,346 | Predicting Speech Intelligibility from a
Population of Neurons
Jeff Bondy
Dept. of Electrical Engineering
McMaster University
Hamilton, ON
jeff@soma.crl.mcmaster.ca
Ian C. Bruce
Dept. of Electrical Engineering
McMaster University
Hamilton, ON
ibruce@ieee.org
Suzanna Becker
Dept. of Psychology
McMaster University
bec... | 2346 |@word middle:2 polynomial:5 nd:5 open:1 simulation:2 eng:1 initial:1 score:7 hereafter:1 current:1 remove:1 designed:2 discrimination:1 steeneken:8 tone:2 dissertation:1 haykin:2 provides:2 org:1 combine:1 fitting:3 introduce:1 behavior:2 mechanic:1 encouraging:1 becomes:1 project:1 underlying:3 linearity:1 lowes... |
1,481 | 2,347 | Markov Models for Automated ECG Interval
Analysis
Nicholas P. Hughes, Lionel Tarassenko and Stephen J. Roberts
Department of Engineering Science
University of Oxford
Oxford, 0X1 3PJ, UK
{nph,lionel,sjrob}@robots.ox.ac.uk
Abstract
We examine the use of hidden Markov and hidden semi-Markov models for automatically segm... | 2347 |@word trial:1 timefrequency:1 compression:1 nd:1 uon:1 q1:3 moment:1 initial:1 series:2 score:1 tuned:3 subjective:1 o2:2 outperforms:1 current:2 must:1 subsequent:1 partition:1 informative:3 predetermined:1 shape:1 numerical:1 designed:1 alone:1 generative:1 stationary:2 intelligence:2 selected:1 vanishing:1 cor... |
1,482 | 2,348 | Perception of the structure of the physical world
using unknown multimodal sensors and effectors
D. Philipona
Sony CSL, 6 rue Amyot
75005 Paris, France
david.philipona@m4x.org
J.K. O?Regan
Laboratoire de Psychologie Exp?erimentale, CNRS
Universit?e Ren?e Descartes, 71, avenue Edouard Vaillant
92774 Boulogne-Billancour... | 2348 |@word briefly:2 r:1 simulation:12 decomposition:3 euclidian:2 thereby:1 moment:1 configuration:10 reaction:1 issuing:1 written:2 realize:1 informative:1 motor:22 alone:2 device:10 provides:1 successive:3 org:1 uncoordinated:1 parametrizable:1 mathematical:5 along:1 constructed:1 direct:1 differential:2 combine:1 ... |
1,483 | 2,349 | Finding the M Most Probable
Configurations Using Loopy Belief
Propagation
Chen Yanover and Yair Weiss
School of Computer Science and Engineering
The Hebrew University of Jerusalem
91904 Jerusalem, Israel
{cheny,yweiss}@cs.huji.ac.il
Abstract
Loopy belief propagation (BP) has been successfully used in a number of diff... | 2349 |@word heuristically:1 seek:2 simulation:4 initial:1 configuration:31 score:3 interestingly:1 existing:2 freitas:1 rish:1 must:4 dechter:1 numerical:2 partition:7 koetter:1 half:1 greedy:8 intelligence:2 selected:1 xk:2 pointer:1 node:7 location:5 ik:9 inside:1 introduce:1 pairwise:3 indeed:1 frequently:1 freeman:... |
1,484 | 235 | 60
Nelson and Bower
Computational Efficiency:
A Common Organizing Principle for
Parallel Computer Maps and Brain Maps?
Mark E. Nelson James M. Bower
Computation and Neural Systems Program
Division of Biology, 216-76
California Institute of Technology
Pasadena, CA 91125
ABSTRACT
It is well-known that neural response... | 235 |@word version:1 maz:2 seems:3 simulation:3 carry:1 inefficiency:1 interestingly:1 must:1 john:1 grain:1 discernible:1 update:1 metabolism:1 nervous:7 provides:1 location:1 constructed:1 interprocessor:2 differential:1 supply:1 assayed:1 overhead:22 behavioral:1 olfactory:6 brain:33 prolonged:1 actual:1 little:1 de... |
1,485 | 2,350 | Nonstationary Covariance Functions for
Gaussian Process Regression
Christopher J. Paciorek and Mark J. Schervish
Department of Statistics
Carnegie Mellon University
Pittsburgh, PA 15213
paciorek@alumni.cmu.edu,mark@stat.cmu.edu
Abstract
We introduce a class of nonstationary covariance functions for Gaussian
process (... | 2350 |@word trial:2 version:4 manageable:1 seems:1 logit:2 r:1 covariance:43 decomposition:3 thereby:2 contains:1 series:1 bc:1 outperforms:3 ka:1 yet:1 intriguing:1 readily:1 drop:1 stationary:24 greedy:1 discovering:1 denison:1 parameterization:1 indicative:1 isotropic:1 ith:1 smith:2 short:1 provides:1 parameterizat... |
1,486 | 2,351 | A Kullback-Leibler Divergence Based Kernel for
SVM Classification in Multimedia Applications
Pedro J. Moreno Purdy P. Ho
Hewlett-Packard
Cambridge Research Laboratory
Cambridge, MA 02142, USA
{pedro.moreno,purdy.ho}@hp.com
Nuno Vasconcelos
UCSD ECE Department
9500 Gilman Drive, MC 0407
La Jolla, CA 92093-0407
nuno@ec... | 2351 |@word version:2 polynomial:4 rgb:1 covariance:20 pick:1 tr:4 contains:5 score:12 interestingly:2 outperforms:3 current:1 com:1 comparing:1 dx:2 john:1 dct:1 numerical:2 moreno:3 remove:1 plot:1 v:3 generative:19 indicative:1 smith:1 five:1 direct:3 become:1 combine:6 introduce:1 upenn:1 encouraging:1 window:2 und... |
1,487 | 2,352 | Discriminative Fields for Modeling Spatial
Dependencies in Natural Images
Sanjiv Kumar and Martial Hebert
The Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213
{skumar,hebert}@ri.cmu.edu
Abstract
In this paper we present Discriminative Random Fields (DRF), a discriminative framework for the classific... | 2352 |@word version:2 stronger:1 contrastive:3 accommodate:3 harder:1 configuration:1 contains:1 denoting:1 outperforms:1 past:1 recovered:1 contextual:3 current:1 si:10 written:1 john:1 sanjiv:1 partition:2 informative:2 statis:1 v:1 generative:7 isotropic:3 mccallum:1 ith:4 detecting:1 node:4 location:1 simpler:1 fiv... |
1,488 | 2,353 | Learning a Rare Event Detection Cascade by
Direct Feature Selection
Jianxin Wu James M. Rehg Matthew D. Mullin
College of Computing and GVU Center, Georgia Institute of Technology
{wujx, rehg, mdmullin}@cc.gatech.edu
Abstract
Face detection is a canonical example of a rare event detection problem, in which target patt... | 2353 |@word advantageous:1 seems:1 d2:1 mitsubishi:1 dramatic:1 reduction:1 initial:1 series:1 shum:1 bootstrapped:2 current:2 must:6 takeo:1 shape:2 remove:1 designed:2 update:1 greedy:1 selected:1 intelligence:6 item:1 core:1 pisarevsky:1 detecting:3 boosting:8 node:33 attack:1 simpler:1 zhang:2 dn:1 constructed:5 di... |
1,489 | 2,354 | Denoising and untangling graphs using
degree priors
Quaid D Morris, Brendan J Frey, and Christopher J Paige
University of Toronto
Electrical and Computer Engineering
10 King?s College Road, Toronto, Ontario, M5S 3G4
Canada
{quaid, frey}@psi.utoronto.ca, paige@uhnres.utoronto.ca
Abstract
This paper addresses the probl... | 2354 |@word version:1 nd:2 d2:5 decomposition:1 electronics:2 configuration:3 contains:5 score:2 loeliger:1 current:1 comparing:3 ij1:3 must:2 koetter:1 enables:1 analytic:1 e22:1 designed:1 update:4 generative:3 selected:1 half:2 intelligence:1 record:1 detecting:1 node:2 toronto:3 allerton:2 si1:2 along:1 enterprise:... |
1,490 | 2,355 | Discriminating deformable shape classes
S. Ruiz-Correa? , L. G. Shapiro? , M. Meil?a? and G. Berson?
?Department of Electrical Engineering
?Department of Statistics
?
Division of Medical Genetics, School of Medicine
University of Washington, Seattle, WA 98105
Abstract
We present and empirically test a novel approach ... | 2355 |@word version:2 norm:1 jacob:1 accommodate:1 configuration:6 series:2 contains:1 genetic:1 outperforms:1 existing:1 current:1 comparing:1 chazelle:1 yet:1 must:3 mesh:39 realistic:1 shape:62 designed:1 discrimination:7 v:2 intelligence:2 plane:3 beginning:1 smith:1 detecting:1 characterization:1 location:1 five:1... |
1,491 | 2,356 | Dopamine modulation in a basal ganglio-cortical
network implements saliency-based gating of
working memory
Aaron J. Gruber1,2 , Peter Dayan3 , Boris S. Gutkin3 , and Sara A. Solla2,4
Biomedical Engineering1 , Physiology2 , and Physics and Astronomy4 ,
Northwestern University, Chicago, IL, USA.
Gatsby Computational Neu... | 2356 |@word middle:1 open:2 grey:4 crucially:2 thereby:2 solid:5 initial:3 ours:2 suppressing:1 interestingly:1 reynolds:1 existing:1 current:9 contextual:1 neurophys:2 activation:5 perturbative:1 must:2 numerical:1 chicago:1 subsequent:2 plasticity:1 motor:1 opin:1 plot:8 gv:1 cue:1 accordingly:1 funahashi:1 provides:... |
1,492 | 2,357 | Prediction on Spike Data
Using Kernel Algorithms
Jan Eichhorn, Andreas Tolias, Alexander Zien, Malte Kuss,
Carl Edward Rasmussen, Jason Weston, Nikos Logothetis and Bernhard Sch o? lkopf
Max Planck Institute for Biological Cybernetics
72076 T?ubingen, Germany
first.last@tuebingen.mpg.de
Abstract
We report and compare... | 2357 |@word neurophysiology:1 trial:7 norm:1 approved:1 covariance:7 elisseeff:1 thereby:1 carry:1 series:3 score:10 wd:1 comparing:2 analysed:1 si:6 written:1 readily:1 must:1 john:1 concatenate:1 numerical:1 informative:1 shape:1 eichhorn:1 motor:2 designed:2 interpretable:1 n0:1 v:4 stationary:1 device:1 beginning:1... |
1,493 | 2,358 | Probabilistic Inference of Speech Signals from
Phaseless Spectrograms
Kannan Achan, Sam T. Roweis, Brendan J. Frey
Machine Learning Group
University of Toronto
Abstract
Many techniques for complex speech processing such as denoising and
deconvolution, time/frequency warping, multiple speaker separation, and
multiple ... | 2358 |@word middle:1 inversion:3 norm:1 disk:1 open:1 tried:1 simplifying:1 pg:1 reap:1 initial:1 configuration:3 selecting:1 loeliger:1 current:1 comparing:1 recovered:1 ka:1 si:5 assigning:1 must:1 written:2 john:1 numerical:1 lengthen:1 drop:1 plot:1 update:1 generative:3 short:9 node:6 toronto:3 windowed:1 burst:1 ... |
1,494 | 2,359 | Locality Preserving Projections
Xiaofei He
Department of Computer Science
The University of Chicago
Chicago, IL 60637
xiaofei@cs.uchicago.edu
Partha Niyogi
Department of Computer Science
The University of Chicago
Chicago, IL 60637
niyogi@cs.uchicago.edu
Abstract
Many problems in information processing involve some f... | 2359 |@word illustrating:1 middle:1 repository:1 norm:2 open:1 crucially:1 lpp:36 incurs:1 solid:1 klk:1 moment:1 reduction:11 contains:2 exclusively:1 series:1 document:2 rkhs:1 outperforms:1 yet:1 dx:7 written:1 john:1 chicago:4 designed:2 plot:2 v:1 xdx:7 intelligence:1 discovering:2 plane:1 ith:4 short:1 farther:1 ... |
1,495 | 236 | 614
Gish and Blanz
Comparing the Performance of Connectionist
and Statistical Classifiers on an Image
Segmentation Problem
Sheri L. Gish
w. E. Blanz
IBM Almaden Research Center
650 Harry Road
San Jose, CA 95120
ABSTRACT
In the development of an image segmentation system for real time
image processing applications, w... | 236 |@word trial:2 cox:1 polynomial:9 duda:1 nd:3 grey:3 simulation:1 gish:7 cla:1 series:2 ours:1 comparing:7 activation:1 readily:1 ronald:1 designed:2 alone:1 petkovic:2 provides:2 simpler:1 lce:1 constructed:2 viable:1 qualitative:2 consists:1 multi:1 automatically:2 actual:3 nre:3 underlying:1 straub:1 sheri:3 qua... |
1,496 | 2,360 | Online Passive-Aggressive Algorithms
Koby Crammer Ofer Dekel Shai Shalev-Shwartz Yoram Singer
School of Computer Science & Engineering
The Hebrew University, Jerusalem 91904, Israel
{kobics,oferd,shais,singer}@cs.huji.ac.il
Abstract
We present a unified view for online classification, regression, and uniclass problem... | 2360 |@word version:2 briefly:1 norm:6 dekel:1 minus:2 reduction:1 initial:1 current:1 z2:3 comparing:1 written:1 additive:8 enables:1 update:12 half:2 warmuth:7 short:1 provides:3 constructed:1 direct:1 prove:5 manner:1 x0:3 equipped:1 becomes:2 provided:1 bounded:1 moreover:1 israel:1 argmin:1 unified:6 pseudo:1 mult... |
1,497 | 2,361 | Geometric Clustering using the Information
Bottleneck method
Susanne Still
Department of Physics
Princeton Unversity, Princeton, NJ 08544
susanna@princeton.edu
William Bialek
Department of Physics
Princeton Unversity, Princeton, NJ 08544
wbialek@princeton.edu
L?eon Bottou
NEC Laboratories America
4 Independence Way, P... | 2361 |@word version:1 compression:6 norm:1 proportionality:3 p0:2 initial:23 denoting:1 ecole:1 must:1 numerical:1 shape:1 plot:1 update:1 guess:1 advancement:1 merger:1 math:1 location:12 allerton:1 org:7 c6:1 mathematical:1 along:1 become:1 prove:1 symp:1 x0:8 houches:1 p1:1 mechanic:1 globally:3 inappropriate:1 beco... |
1,498 | 2,362 | Sequential Bayesian Kernel Regression
Jaco Vermaak, Simon J. Godsill, Arnaud Doucet
Cambridge University Engineering Department
Cambridge, CB2 1PZ, U.K.
{jv211, sjg, ad2}@eng.cam.ac.uk
Abstract
We propose a method for sequential Bayesian kernel regression. As is
the case for the popular Relevance Vector Machine (RVM)... | 2362 |@word proportion:1 eng:1 vermaak:2 dramatic:1 tr:1 recursively:5 initial:2 series:1 initialisation:1 mmse:3 existing:4 freitas:2 current:2 reminiscent:2 written:2 john:1 subsequent:1 partition:2 additive:1 remove:3 update:7 resampling:8 intelligence:2 leaf:1 fewer:1 xk:1 isotropic:1 smith:1 normalising:3 provides... |
1,499 | 2,363 | Training a Quantum Neural Network
Bob Ricks
Department of Computer Science
Brigham Young University
Provo, UT 84602
cyberbob@cs.byu.edu
Dan Ventura
Department of Computer Science
Brigham Young University
Provo, UT 84602
ventura@cs.byu.edu
Abstract
Most proposals for quantum neural networks have skipped over the prob... | 2363 |@word repository:1 briefly:1 inversion:1 polynomial:2 seems:1 version:1 open:1 steck:2 simulation:1 tried:1 mention:1 initial:2 current:1 yet:1 must:4 written:1 update:3 selected:1 item:1 node:41 mathematical:1 ik:1 consists:1 dan:4 combine:1 lov:2 roughly:1 themselves:1 mechanic:6 decreasing:2 gov:4 little:2 beg... |
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