Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
1,600 | 2,454 | Probability Estimates for Multi-class
Classification by Pairwise Coupling
Ting-Fan Wu
Chih-Jen Lin
Department of Computer Science
National Taiwan University
Taipei 106, Taiwan
Ruby C. Weng
Department of Statistics
National Chenechi University
Taipei 116, Taiwan
Abstract
Pairwise coupling is a popular multi-class cla... | 2454 |@word version:3 seems:1 solid:1 initial:2 selecting:2 zij:2 seriously:1 document:1 interestingly:1 outperforms:2 existing:2 bradley:1 com:1 assigning:1 written:1 remove:1 stationary:2 intelligence:1 selected:1 ith:1 revisited:1 five:7 direct:1 qij:5 consists:1 combine:2 pairwise:12 expected:1 p1:7 examine:1 multi... |
1,601 | 2,455 | Autonomous helicopter flight
via Reinforcement Learning
Andrew Y. Ng
Stanford University
Stanford, CA 94305
H. Jin Kim, Michael I. Jordan, and Shankar Sastry
University of California
Berkeley, CA 94720
Abstract
Autonomous helicopter flight represents a challenging control problem,
with complex, noisy, dynamics. In th... | 2455 |@word aircraft:1 briefly:1 middle:1 polynomial:1 retraining:1 simulation:4 tried:1 pick:1 mention:1 solid:5 blade:4 carry:3 reduction:1 moment:1 cyclic:4 series:1 initial:2 selecting:1 longitudinal:1 franklin:1 err:1 current:2 comparing:1 kmk:3 must:1 tilted:2 numerical:2 thrust:7 wanted:1 plot:3 v:1 intelligence... |
1,602 | 2,456 | Approximate Policy Iteration
with a Policy Language Bias
Alan Fern and SungWook Yoon and Robert Givan
Electrical and Computer Engineering, Purdue University, W. Lafayette, IN 47907
Abstract
We explore approximate policy iteration, replacing the usual costfunction learning step with a learning step in policy space. We... | 2456 |@word trial:3 exploitation:1 version:1 briefly:1 polynomial:1 eliminating:1 norm:1 c0:2 hector:1 heuristically:3 simulation:4 seek:1 uncovers:3 dramatic:1 initial:22 configuration:2 selecting:2 daniel:1 genetic:1 existing:1 current:6 comparing:1 yet:3 must:3 written:1 ronald:1 enables:1 designed:1 v:1 alone:1 pur... |
1,603 | 2,457 | Information Bottleneck for
Gaussian Variables
Gal Chechik?
Amir Globerson?
Naftali Tishby
Yair Weiss
{ggal,gamir,tishby,yweiss}@cs.huji.ac.il
School of Computer Science and Engineering and
The Interdisciplinary Center for Neural Computation
The Hebrew University of Jerusalem, 91904, Israel
?
Both authors contributed e... | 2457 |@word determinant:1 version:2 achievable:1 compression:15 norm:4 covariance:8 carry:1 moment:1 reduction:2 contains:3 series:2 nii:1 document:1 interestingly:1 current:2 intriguing:2 written:1 must:1 analytic:7 plot:1 interpretable:1 amir:1 ith:1 provides:3 matrix1:1 characterization:3 allerton:1 scholkopf:1 cons... |
1,604 | 2,458 | An Autonomous Robotic System
For Mapping Abandoned Mines
D. Ferguson1 , A. Morris1 , D. H?ahnel2 , C. Baker1 , Z. Omohundro1 , C. Reverte1
S. Thayer1 , C. Whittaker1 , W. Whittaker1 , W. Burgard2 , S. Thrun3
1
The Robotics Institute
Carnegie Mellon University
Pittsburgh, PA
2
Computer Science Department
University of... | 2458 |@word inversion:4 nd:2 open:1 closure:6 linearized:1 covariance:1 pick:1 eld:6 thereby:1 recursively:3 cyclic:2 contains:1 past:3 existing:1 recovered:2 current:1 yet:1 numerical:1 entrance:1 predetermined:1 shape:2 enables:1 remove:1 atlas:1 accordingly:1 xk:7 hallway:4 fastslam:2 short:2 provides:2 node:2 locat... |
1,605 | 2,459 | Ambiguous model learning made unambiguous
with 1/f priors
G. S. Atwal
Department of Physics
Princeton University
Princeton, NJ 08544
gatwal@princeton.edu
William Bialek
Department of Physics
Princeton University
Princeton, NJ 08544
wbialek@princeton.edu
Abstract
What happens to the optimal interpretation of noisy da... | 2459 |@word exploitation:1 seems:1 proportionality:1 crucially:1 pick:1 thereby:1 carry:1 initial:1 series:1 envision:1 imaginary:1 surprising:1 dx:1 must:3 perturbative:2 physiol:1 numerical:2 stationary:2 indefinitely:1 provides:1 height:5 differential:1 viable:1 qualitative:1 introduce:2 expected:2 rapid:1 behavior:... |
1,606 | 246 | 84
Wilson and Bower
Computer Simulation of Oscillatory Behavior
in Cerebral Cortical Networks
Matthew A. Wilson and James M. Bower!
Computation and Neural Systems Program
Division of Biology, 216-76
California Institute of Technology
Pasadena, CA 9 1125
ABSTRACT
It has been known for many years that specific region... | 246 |@word trial:7 middle:1 replicate:2 adrian:2 termination:1 simulation:11 initial:1 reaction:1 current:2 activation:1 intriguing:1 physiol:1 distant:2 realistic:1 alone:1 nervous:1 provides:1 location:4 successive:1 preference:1 five:1 direct:1 sustained:1 fitting:1 olfactory:9 inter:1 behavior:17 mnc:1 examine:1 br... |
1,607 | 2,460 | Reconstructing MEG Sources
with Unknown Correlations
Maneesh Sahani
W. M. Keck Foundation Center
for Integrative Neuroscience,
UC, San Francisco, CA 94143-0732
maneesh@phy.ucsf.edu
Srikantan S. Nagarajan
Biomagnetic Imaging Laboratory,
Department of Radiology,
UC, San Francisco, CA 94143-0628
sri@radiology.ucsf.edu
A... | 2460 |@word trial:2 sri:1 inversion:2 seems:1 norm:4 squid:1 integrative:1 simulation:8 seek:1 covariance:1 simplifying:1 eng:4 tr:4 moment:2 electronics:1 configuration:1 series:2 contains:4 phy:1 mosher:1 elaborating:1 past:1 existing:4 current:4 recovered:1 activation:7 must:1 realistic:1 visible:1 benign:1 drop:1 a... |
1,608 | 2,461 | Out-of-Sample Extensions for LLE, Isomap,
MDS, Eigenmaps, and Spectral Clustering
Yoshua Bengio, Jean-Franc?ois Paiement, Pascal Vincent
Olivier Delalleau, Nicolas Le Roux and Marie Ouimet
D?epartement d?Informatique et Recherche Op?erationnelle
Universit?e de Montr?eal
Montr?eal, Qu?ebec, Canada, H3C 3J7
{bengioy,vin... | 2461 |@word cox:4 version:2 proportion:1 norm:1 seems:1 d2:4 seek:1 minus:1 reduction:6 epartement:2 initial:1 comparing:1 si:9 dx:1 numerical:2 short:1 recherche:2 recompute:2 provides:1 toronto:1 five:2 mathematical:1 direct:1 shorthand:1 x0:8 pairwise:1 expected:1 indeed:1 p1:1 nor:1 multi:3 globally:1 considering:3... |
1,609 | 2,462 | Self-calibrating Probability Forecasting
Vladimir Vovk
Computer Learning Research Centre
Department of Computer Science
Royal Holloway, University of London
Egham, Surrey TW20 0EX, UK
vovk@cs.rhul.ac.uk
Glenn Shafer
Rutgers School of Business
Newark and New Brunswick
180 University Avenue
Newark, NJ 07102, USA
gshafe... | 2462 |@word briefly:1 version:6 compression:1 achievable:1 confirms:1 forecaster:6 simplifying:1 solid:1 carry:1 contains:1 prefix:1 mishra:1 assigning:1 dx:1 written:2 john:3 cruz:1 visible:1 partition:2 happen:2 plot:1 n0:6 intelligence:2 warmuth:2 reappears:1 manfred:2 multiset:1 constructed:1 direct:1 become:1 symp... |
1,610 | 2,463 | When Does Non-Negative Matrix Factorization
Give a Correct Decomposition into Parts?
David Donoho
Department of Statistics
Stanford University
Stanford, CA 94305
donoho@stat.stanford.edu
Victoria Stodden
Department of Statistics
Stanford University
Stanford, CA 94305
vcs@stat.stanford.edu
Abstract
We interpret non-n... | 2463 |@word hyv:1 seek:1 sensed:1 decomposition:2 contains:14 series:1 recovered:1 surprising:1 written:1 must:3 realistic:1 happen:1 alone:1 generative:9 inspection:1 short:1 core:1 characterization:1 hyperplanes:1 plumbley:2 along:1 welldefined:1 consists:1 prove:1 ray:21 inside:5 introduce:2 x0:2 ica:1 indeed:5 them... |
1,611 | 2,464 | Semi-Supervised Learning with Trees
Charles Kemp, Thomas L. Griffiths, Sean Stromsten & Joshua B. Tenenbaum
Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139
{ckemp,gruffydd,sean s,jbt}@mit.edu
Abstract
We describe a nonparametric Bayesian approach to generalizing from
few labeled examples, guided ... | 2464 |@word trial:1 repository:2 version:1 proportion:2 simplifying:1 dramatic:1 shading:1 substitution:1 contains:1 karger:1 genetic:1 tuned:1 outperforms:1 existing:1 current:1 enables:1 motor:1 treating:1 plot:1 stationary:1 greedy:2 leaf:7 instantiate:1 agglom:3 provides:3 node:8 toronto:1 simpler:3 five:1 phylogen... |
1,612 | 2,465 | Link Prediction in Relational Data
Ben Taskar Ming-Fai Wong Pieter Abbeel Daphne Koller
{btaskar, mingfai.wong, abbeel, koller}@cs.stanford.edu
Stanford University
Abstract
Many real-world domains are relational in nature, consisting of a set of objects
related to each other in complex ways. This paper focuses on pred... | 2465 |@word trial:1 faculty:6 pw:1 eliminating:1 proportion:7 seems:1 logit:1 pieter:1 tried:5 thereby:1 mention:2 harder:1 reduction:1 generatively:1 contains:1 siebel:1 denoting:2 document:2 bc:1 outperforms:2 current:1 com:1 must:1 realistic:1 partition:1 informative:1 alone:1 intelligence:1 selected:4 website:1 mcc... |
1,613 | 2,466 | Hierarchical Topic Models and
the Nested Chinese Restaurant Process
David M. Blei
blei@cs.berkeley.edu
Thomas L. Griffiths
gruffydd@mit.edu
Michael I. Jordan
jordan@cs.berkeley.edu
Joshua B. Tenenbaum
jbt@mit.edu
University of California, Berkeley
Berkeley, CA 94720
Massachusetts Institute of Technology
Cambridge,... | 2466 |@word version:2 proportion:8 open:1 solid:1 moment:1 contains:4 ecole:1 document:46 rightmost:2 existing:1 current:5 recovered:1 ka:1 must:3 readily:1 john:1 subsequent:2 partition:8 hofmann:1 generative:4 leaf:8 selected:2 item:2 blei:3 provides:3 node:7 toronto:1 sits:2 simpler:1 five:1 unbounded:1 along:5 c2:1... |
1,614 | 2,467 | Unsupervised context sensitive language
acquisition from a large corpus
Zach Solan, David Horn, Eytan Ruppin
Sackler Faculty of Exact Sciences
Tel Aviv University
Tel Aviv, Israel 69978
{rsolan,horn,ruppin}@post.tau.ac.il
Shimon Edelman
Department of Psychology
Cornell University
Ithaca, NY 14853, USA
se37@cornell.ed... | 2467 |@word illustrating:1 version:1 faculty:1 briefly:1 proportion:2 solan:2 crucially:2 concise:1 solid:3 recursively:1 initial:1 configuration:2 score:10 wanna:1 prefix:1 existing:3 current:1 contextual:1 comparing:1 activation:3 must:1 written:1 tenet:1 refines:1 subsequent:1 chicago:2 enables:2 praeger:1 designed:... |
1,615 | 2,468 | No Unbiased Estimator of the Variance of
K-Fold Cross-Validation
Yoshua Bengio and Yves Grandvalet
Dept. IRO, Universit?e de Montr?eal
C.P. 6128, Montreal, Qc, H3C 3J7, Canada
{bengioy,grandvay}@iro.umontreal.ca
Abstract
Most machine learning researchers perform quantitative experiments to
estimate generalization erro... | 2468 |@word version:1 covariance:13 decomposition:2 arti:1 moment:3 score:1 comparing:3 must:1 bs2:1 stemming:1 realistic:1 numerical:1 analytic:1 remove:1 resampling:1 v:3 intelligence:1 cult:2 provides:3 ron:1 unbiasedly:2 consists:2 introduce:1 indeed:1 expected:9 behavior:1 themselves:1 decomposed:1 inappropriate:1... |
1,616 | 2,469 | Efficient Exact k-NN and Nonparametric
Classification in High Dimensions
Ting Liu
Computer Science Dept.
Carnegie Mellon University
Pittsburgh, PA 15213
tingliu@cs.cmu.edu
Andrew W. Moore
Computer Science Dept.
Carnegie Mellon University
Pittsburgh, PA 15213
awm@cs.cmu.edu
Alexander Gray
Computer Science Dept.
Carneg... | 2469 |@word repository:1 version:2 nd:1 c0:2 twelfth:2 open:3 vldb:2 accounting:1 citeseer:3 q1:1 dramatic:2 shot:2 liu:1 contains:1 tuned:1 must:2 kdd:4 remove:2 designed:1 update:1 v:1 intelligence:2 leaf:10 short:1 record:7 num:9 hypersphere:1 node:77 attack:4 org:1 mathematical:2 constructed:1 become:2 supply:1 sym... |
1,617 | 2,470 | Sparse Greedy Minimax Probability Machine
Classification
Thomas R. Strohmann
Department of Computer Science
University of Colorado, Boulder
strohman@cs.colorado.edu
Andrei Belitski
Department of Computer Science
University of Colorado, Boulder
Andrei.Belitski@colorado.edu
Gregory Z. Grudic
Department of Computer Scie... | 2470 |@word briefly:1 version:1 polynomial:3 c0:6 open:1 bn:1 covariance:6 reduction:1 contains:1 selecting:1 tuned:1 strohman:2 bhattacharyya:2 current:1 z2:1 olkin:3 assigning:1 must:1 numerical:1 subsequent:2 plot:2 tsa:8 greedy:6 selected:1 mpm:3 accordingly:1 provides:1 boosting:2 math:1 five:2 mathematical:1 dire... |
1,618 | 2,471 | Minimising Contrastive Divergence in Noisy,
Mixed-mode VLSI Neurons
Hsin Chen, Patrice Fleury and Alan F. Murray
School of Engineering and Electronics
Edinburgh University
Mayfield Rd., Edinburgh
EH9 3JL, UK
{hc, pcdf, afm}@ee.ed.ac.uk
Abstract
This paper presents VLSI circuits with continuous-valued probabilistic be... | 2471 |@word h:3 rising:1 open:1 simulation:1 pulse:17 contrastive:7 q1:2 solid:1 o2i:2 initial:2 electronics:3 contains:1 amp:2 past:1 current:15 si:15 chu:1 refresh:2 periodically:1 subsequent:1 visible:4 asymptote:1 designed:2 update:2 vmin:5 device:1 ckq:2 sigmoidal:1 five:1 along:1 become:1 supply:1 differential:5 ... |
1,619 | 2,472 | A Recurrent Model of Orientation Maps
with Simple and Complex Cells
Paul Merolla and Kwabena Boahen
Department of Bioengineering
University of Pennsylvania
Philadelphia, PA 19104
{pmerolla,boahen} @seas.upenn.edu
Abstract
We describe a neuromorphic chip that utilizes transistor
heterogeneity, introduced by the fabric... | 2472 |@word briefly:1 wiesel:1 open:1 pulse:2 excited:1 solid:1 initial:2 configuration:2 tuned:5 current:22 must:2 physiol:1 realistic:1 periodically:1 shape:1 designed:2 plot:2 isotropic:1 reciprocal:2 short:2 core:1 node:1 location:3 preference:3 mathematical:1 rc:1 incorrect:1 consists:4 resistive:1 qualitative:1 a... |
1,620 | 2,473 | Design of experiments via information theory ?
Liam Paninski
Center for Neural Science
New York University
New York, NY 10003
liam@cns.nyu.edu
Abstract
We discuss an idea for collecting data in a relatively efficient manner. Our
point of view is Bayesian and information-theoretic: on any given trial,
we want to adapt... | 2473 |@word neurophysiology:1 trial:4 version:2 briefly:1 seems:1 open:2 closure:1 calculus:1 simulation:1 p0:4 citeseer:1 initial:1 necessity:1 subjective:1 bradley:1 current:2 comparing:2 com:2 surprising:2 must:2 designed:1 implying:1 guess:1 draft:1 math:1 location:1 sigmoidal:1 differential:2 become:1 calculable:1... |
1,621 | 2,474 | A Model for Learning the Semantics of Pictures
V. Lavrenko, R. Manmatha, J. Jeon
Center for Intelligent Information Retrieval
Computer Science Department,
University of Massachusetts Amherst
{lavrenko,manmatha,jeon}@cs.umass.edu
Abstract
We propose an approach to learning the semantics of images which allows us to au... | 2474 |@word aircraft:1 private:1 version:1 proportion:1 c0:1 covariance:1 pg:8 pick:3 manmatha:3 contains:4 uma:1 selecting:1 document:1 outperforms:6 freitas:2 current:2 comparing:2 nt:2 partition:1 shape:3 hypothesize:1 grass:4 generative:7 half:1 guess:1 selected:2 item:3 intelligence:1 blei:6 provides:3 lexicon:1 l... |
1,622 | 2,475 | A Functional Architecture for Motion
Pattern Processing in MSTd
Scott A. Beardsley
Dept. of Biomedical Engineering
Boston University
Boston, MA 02215
sbeardsl@bu.edu
Lucia M. Vaina
Dept. of Biomedical Engineering
Boston University
Boston, MA 02215
vaina@bu.edu
Abstract
Psychophysical studies suggest the existence of... | 2475 |@word neurophysiology:5 middle:1 seems:1 integrative:1 simulation:13 contraction:4 extrastriate:1 cyclic:5 series:4 com:22 comparing:1 reminiscent:1 distant:1 visible:1 hypothesize:1 designed:1 medial:2 discrimination:13 v:2 cue:1 ith:3 contribute:1 location:5 preference:2 five:1 constructed:1 direct:1 fixation:2... |
1,623 | 2,476 | All learning is local:
Multi-agent learning in global reward games
Yu-Han Chang
MIT CSAIL
Cambridge, MA 02139
ychang@csail.mit.edu
Tracey Ho
LIDS, MIT
Cambridge, MA 02139
trace@mit.edu
Leslie Pack Kaelbling
MIT CSAIL
Cambridge, MA 02139
lpk@csail.mit.edu
Abstract
In large multiagent games, partial observability, co... | 2476 |@word trial:1 exploitation:1 version:1 middle:1 inversion:1 seems:1 rigged:1 simplifying:1 covariance:4 initial:3 efficacy:1 pt0:3 past:2 current:8 realistic:2 additive:1 shape:1 wanted:1 drop:1 plot:1 update:8 stationary:5 intelligence:2 half:2 guess:2 ith:1 tumer:3 provides:5 contribute:1 location:5 node:12 zha... |
1,624 | 2,477 | Sparseness of Support Vector Machines?Some
Asymptotically Sharp Bounds
Ingo Steinwart
Modeling, Algorithms, and Informatics Group, CCS-3, Mail Stop B256
Los Alamos National Laboratory
Los Alamos, NM 87545, USA
ingo@lanl.gov
Abstract
The decision functions constructed by support vector machines (SVM?s)
usually depend ... | 2477 |@word polynomial:2 stronger:1 c0:4 open:1 bn:1 series:1 rkhs:11 scovel:1 kft:5 girosi:1 analytic:7 intelligence:1 fewer:1 vanishing:2 math:1 herbrich:1 dn:4 constructed:1 become:1 consists:1 prove:3 introduce:1 x0:6 nor:1 gov:1 considering:1 increasing:1 becomes:1 begin:1 moreover:4 notation:1 bounded:1 kind:1 ne... |
1,625 | 2,478 | Multiple Instance Learning via
Disjunctive Programming Boosting
Stuart Andrews
Department of Computer Science
Brown University, Providence, RI, 02912
stu@cs.brown.edu
Thomas Hofmann
Department of Computer Science
Brown University, Providence, RI, 02912
th@cs.brown.edu
Abstract
Learning from ambiguous training data is... | 2478 |@word briefly:1 version:2 stronger:2 norm:1 flach:1 ambig:1 closure:1 ratan:1 recapitulate:1 pick:1 tr:1 solid:1 shading:1 reduction:9 contains:1 score:5 selecting:2 document:2 current:3 written:2 john:1 dashdot:1 hofmann:2 plot:2 sponsored:1 intelligence:3 selected:1 prohibitive:1 vanishing:1 provides:1 boosting... |
1,626 | 2,479 | Human and Ideal Observers for Detecting Image
Curves
Alan Yuille
Department of Statistics & Psychology
University of California Los Angeles
Los Angeles CA
yuille@stat.ucla.edu
Fang Fang
Psychology, University of Minnesota
Minneapolis MN 55455
fang0057@tc.umn.edu
Paul Schrater
Psychology, University of Minnesota
Minne... | 2479 |@word briefly:1 seems:1 stronger:1 closure:2 simulation:2 pg:14 harder:1 phy:1 initial:1 series:1 fragment:4 daniel:1 practiced:1 comparing:1 forbidding:1 must:2 realistic:3 shape:17 enables:2 clumping:17 generative:3 cue:6 ith:1 coughlan:2 detecting:9 preference:1 five:2 consists:2 ijcv:1 theoretically:1 expecte... |
1,627 | 248 | 226
Mann
The Effects of Circuit Integration on a Feature
Map Vector Quantizer
Jim lVIann
MIT Lincoln Laboratory
244 Wood St.
Lexington, ~IA 02173
email: mann@vlsi.ll.mit.edu
ABSTRACT
The effects of parameter modifications imposed by hardware constraints on a self-organizing feature map algorithm were examined.
Perf... | 248 |@word effect:9 implemented:1 requiring:1 version:2 establish:1 read:1 volt:1 laboratory:1 added:1 quantity:1 gradual:1 simulation:6 subsequently:1 illustrated:2 occurs:1 ll:1 latch:1 self:4 virtual:1 mann:6 lends:1 speaker:3 noted:1 distance:4 berlin:1 gracefully:1 degrade:1 performs:2 current:1 ground:1 activatio... |
1,628 | 2,480 | From Algorithmic to Subjective Randomness
Thomas L. Griffiths & Joshua B. Tenenbaum
{gruffydd,jbt}@mit.edu
Massachusetts Institute of Technology
Cambridge, MA 02139
Abstract
We explore the phenomena of subjective randomness as a case study in
understanding how people discover structure embedded in noise. We
present a... | 2480 |@word illustrating:1 version:1 stronger:1 seems:2 proportion:2 solid:1 contains:1 score:4 prefix:1 subjective:18 comparing:2 com:1 assigning:2 universality:1 partition:2 informative:1 designed:1 v:1 alone:1 half:3 discovering:1 item:3 short:3 characterization:2 provides:6 ire:1 preference:1 firstly:1 simpler:1 ac... |
1,629 | 2,481 | Warped Gaussian Processes
Edward Snelson?
Carl Edward Rasmussen?
Zoubin Ghahramani?
?
Gatsby Computational Neuroscience Unit
University College London
17 Queen Square, London WC1N 3AR, UK
{snelson,zoubin}@gatsby.ucl.ac.uk
?
Max Planck Institute for Biological Cybernetics
Spemann Stra?e 38, 72076 T?ubingen, German... | 2481 |@word aircraft:1 repository:1 inversion:2 seems:1 grey:1 covariance:18 accounting:1 incurs:1 concise:1 solid:2 series:3 initialisation:1 current:1 comparing:1 surprising:1 yet:1 must:2 written:1 shape:4 enables:1 cheap:1 camacho:1 plot:2 stationary:2 yr:2 ntrain:2 isotropic:1 toronto:2 hermite:1 fitting:1 manner:... |
1,630 | 2,482 | Salient Boundary Detection using Ratio Contour
Song Wang, Toshiro Kubota
Dept. Computer Science & Engineering
University of South Carolina
Columbia, SC 29208
{songwang|kubota}@cse.sc.edu
Jeffrey Mark Siskind
School Electrical & Comput. Engr.
Purdue University
West Lafayette, IN 47906
qobi@purdue.edu
Abstract
This pa... | 2482 |@word cox:1 middle:1 polynomial:7 open:4 closure:8 confirms:1 seek:1 carolina:1 jacob:3 solid:38 reduction:4 initial:2 contains:2 fragment:62 interestingly:1 contextual:1 must:2 shape:1 designed:1 fund:1 intelligence:8 selected:1 cook:1 amir:2 ith:1 short:1 detecting:5 cse:1 traverse:2 along:3 constructed:7 consi... |
1,631 | 2,483 | Approximate Analytical Bootstrap Averages for
Support Vector Classifiers
D?orthe Malzahn1,2
Manfred Opper3
Informatics and Mathematical Modelling, Technical University of Denmark,
R.-Petersens-Plads, Building 321, Lyngby DK-2800, Denmark
2
Institute of Mathematical Stochastics, University of Karlsruhe,
Englerstr. 2, K... | 2483 |@word polynomial:1 retraining:2 suitably:1 simulation:5 pg:3 outlook:1 moment:3 series:3 united:1 pub:1 bootstrapped:10 existing:1 z2:1 surprising:1 activation:1 si:13 must:2 written:1 fn:1 numerical:1 partition:4 analytic:1 update:1 resampling:1 xk:1 manfred:1 ttrain:3 contribute:1 simpler:2 mathematical:2 const... |
1,632 | 2,484 | Insights from Machine Learning Applied to
Human Visual Classification
Arnulf B. A. Graf and Felix A. Wichmann
Max Planck Institute for Biological Cybernetics
Spemannstra?e 38
72076 T?ubingen, Germany
{arnulf.graf, felix.wichmann}@tuebingen.mpg.de
Abstract
We attempt to understand visual classification in humans using ... | 2484 |@word illustrating:1 judgement:1 seems:5 duda:1 grey:1 paid:1 contains:1 reaction:3 comparing:1 scatter:3 intriguing:1 written:1 john:1 numerical:1 shape:9 bmcv:1 plot:5 update:1 cue:1 selected:1 caucasian:1 characterization:1 revisited:1 postal:1 hyperplanes:7 enterprise:1 constructed:1 incorrect:1 manner:3 mpg:... |
1,633 | 2,485 | Approximate Planning in POMDPs with
Macro-Actions
Georgios Theocharous
MIT AI Lab
200 Technology Square
Cambridge, MA 02139
theochar@ai.mit.edu
Leslie Pack Kaelbling
MIT AI Lab
200 Technology Square
Cambridge, MA 02139
lpk@ai.mit.edu
Abstract
Recent research has demonstrated that useful POMDP solutions do not
require... | 2485 |@word compression:1 proportion:1 seems:2 nd:1 termination:1 propagate:1 initial:6 contains:2 current:6 discretization:4 john:1 j1:4 qmdp:7 wanted:1 designed:2 update:7 smdp:3 hash:1 intelligence:7 fewer:2 greedy:1 node:1 location:1 along:1 constructed:1 become:1 corridor:8 expected:1 behavior:1 planning:6 simulat... |
1,634 | 2,486 | Feature Selection in Clustering Problems
Volker Roth and Tilman Lange
ETH Zurich, Institut f. Computational Science
Hirschengraben 84, CH-8092 Zurich
Tel: +41 1 6323179
{vroth, tilman.lange}@inf.ethz.ch
Abstract
A novel approach to combining clustering and feature selection is presented. It implements a wrapper strat... | 2486 |@word middle:3 version:1 proportion:1 advantageous:1 seems:2 norm:1 replicate:1 suitably:1 turlach:1 grey:6 covariance:2 simplifying:1 solid:1 harder:1 initial:1 wrapper:6 contains:3 score:4 selecting:8 rightmost:2 current:1 si:2 must:2 partition:47 hofmann:1 interpretable:1 resampling:2 v:1 selected:23 problemsp... |
1,635 | 2,487 | Application of SVMs for Colour Classification
and Collision Detection with AIBO Robots
Michael J. Quinlan, Stephan K. Chalup and Richard H. Middleton?
School of Electrical Engineering & Computer Science
The University of Newcastle, Callaghan 2308, Australia
{mquinlan,chalup,rick}@eecs.newcastle.edu.au
Abstract
This ar... | 2487 |@word version:1 open:2 seek:1 simulation:2 tr:2 harder:1 reduction:1 initial:8 contains:2 series:1 past:1 existing:1 bitmap:1 com:1 yet:1 must:2 shape:7 designed:1 half:1 selected:1 device:1 provides:1 detecting:1 location:1 firstly:1 org:1 five:2 height:1 along:1 constructed:3 become:1 symposium:1 consists:2 fit... |
1,636 | 2,488 | Sample Propagation
Mark A. Paskin
Computer Science Division
University of California, Berkeley
Berkeley, CA 94720
mark@paskin.org
Abstract
Rao?Blackwellization is an approximation technique for probabilistic inference that flexibly combines exact inference with sampling. It is useful
in models where conditioning on s... | 2488 |@word trial:1 kong:1 version:1 willing:1 covariance:1 tr:1 harder:1 initial:2 contains:1 wcn:1 bc:8 interestingly:1 freitas:1 current:5 wd:1 z2:3 comparing:1 must:10 dechter:3 distant:1 update:5 resampling:2 stationary:1 half:1 leaf:3 instantiate:4 fewer:1 intelligence:2 recompute:2 draft:1 node:1 toronto:1 succe... |
1,637 | 2,489 | How to Combine Expert (or Novice) Advice
when Actions Impact the Environment
Daniela Pucci de Farias?
Department of Mechanical Engineering
Massachusetts Institute of Technology
Cambridge, MA 02139
pucci@mit.edu
Nimrod Megiddo
IBM Almaden Research Center
650 Harry Road, K53-B2
San Jose, CA 95120
megiddo@almaden.ibm.co... | 2489 |@word h:4 exploitation:1 middle:1 seems:2 rigged:1 instruction:1 willing:1 tat:5 pick:1 exclusively:2 denoting:1 past:6 current:3 com:1 yet:1 must:4 stationary:1 selected:8 guess:1 warmuth:1 revisited:1 become:1 ik:12 consists:1 prove:1 combine:3 symp:1 introduce:2 expected:3 indeed:1 behavior:6 themselves:1 nor:... |
1,638 | 249 | 28
Lockery t Fang and Sejnowski
Neu.?al Network Analysis of
Distributed Representations of Dynamical
Sensory-Motor rrransformations in the Leech
Shawn R. LockerYt Van Fang t and Terrence J. Sejnowski
Computational Neurobiology Laboratory
Salk Institute for Biological Studies
Box 85800, San Diego, CA 92138
ABSTRACT
... | 249 |@word middle:1 open:1 pulse:4 simulation:1 contraction:1 excited:2 fonn:2 pressure:1 shading:1 initial:1 longitudinal:3 current:8 blank:1 surprising:1 activation:5 yet:1 must:1 physiol:8 realistic:1 shape:1 motor:41 alone:1 nervous:2 compo:5 contribute:1 sigmoidal:1 qualitative:1 behavioral:2 introduce:1 pairwise:... |
1,639 | 2,490 | .
Reasoning about Time and Knowledge In
Neural-Symbolic Learning Systems
Artur S. d' Avila Garcez" and Luis C. Lamb A
"Dept. of Computing, City University London
London, EC1V OHB, UK (aag@soi.city.ac.uk)
ADept. of Computing Theory, PPGC-II-UFRGS
Porto Alegre, RS 91501-970, Brazil (lamb@inf.ufrgs.br)
Abstract
We show... | 2490 |@word complying:1 open:1 grey:1 r:1 paid:1 asks:1 epistemic:1 initial:2 contains:1 fragment:1 zurada:2 current:1 activation:11 yet:1 must:11 luis:2 intelligence:3 provides:3 along:1 tomorrow:2 prove:1 shorthand:2 introduce:1 acquired:2 themselves:1 nor:1 multi:3 little:1 becomes:3 nuffield:1 what:3 kind:1 interpr... |
1,640 | 2,491 | Decoding V1 Neuronal Activity using Particle
Filtering with Volterra Kernels
Ryan Kelly
Center for the Neural Basis of Cognition
Carnegie-Mellon University
Pittsburgh, PA 15213
rkelly@cs.cmu.edu
Tai Sing Lee
Center for the Neural Basis of Cognition
Carnegie-Mellon University
Pittsburgh, PA 15213
tai@cnbc.cmu.edu
Abs... | 2491 |@word neurophysiology:2 trial:11 version:1 hippocampus:2 covariance:1 decomposition:1 moment:1 initial:1 series:2 contains:1 interestingly:1 past:1 existing:2 recovered:1 current:1 comparing:1 ka:1 scatter:2 romero:2 plasticity:1 motor:3 plot:2 update:1 resampling:7 fewer:1 core:1 filtered:1 provides:1 location:1... |
1,641 | 2,492 | A Low-Power Analog VLSI Visual
Collision Detector
Reid R. Harrison
Department of Electrical and Computer Engineering
University of Utah
Salt Lake City, UT 84112
harrison@ece.utah.edu
Abstract
We have designed and tested a single-chip analog VLSI sensor that
detects imminent collisions by measuring radially expansive ... | 2492 |@word version:2 inversion:1 advantageous:1 simulation:2 series:2 suppressing:1 current:10 nt:1 follower:1 must:2 physiol:2 ota:7 designed:1 mounting:1 provides:1 detecting:1 five:1 positing:1 rc:1 become:1 supply:1 differential:7 consists:1 pathway:1 pnp:1 introduce:1 behavior:2 monopolar:3 integrator:4 otas:3 de... |
1,642 | 2,493 | Factorization with uncertainty and
missing data: exploiting temporal
coherence
Amit Gruber and Yair Weiss
School of Computer Science and Engineering
The Hebrew University of Jerusalem
91904 Jerusalem, Israel
{amitg,yweiss}@cs.huji.ac.il
Abstract
The problem of ?Structure From Motion? is a central problem in
vision: gi... | 2493 |@word mild:1 middle:1 inversion:1 km:1 seitz:1 seek:2 covariance:2 jacob:9 decomposition:1 contains:1 shum:3 existing:6 bradley:1 yet:1 realistic:3 shape:1 update:2 depict:1 half:2 intelligence:1 rts:1 plane:2 vtp:2 short:2 location:13 along:1 fitting:1 polyhedral:1 cp0:2 subvectors:1 becomes:1 israel:1 minimizes... |
1,643 | 2,494 | Subject-Independent Magnetoencephalographic
Source Localization by a Multilayer Perceptron
Sung C. Jun
Biological and Quantum Physics Group
MS-D454, Los Alamos National Laboratory
Los Alamos, NM 87545, USA
jschan@lanl.gov
Barak A. Pearlmutter
Hamilton Institute
NUI Maynooth
Maynooth, Co. Kildare, Ireland
barak@cs.may... | 2494 |@word neurophysiology:1 trial:4 retraining:1 proportionality:1 squid:2 additively:2 moment:4 initial:3 contains:1 series:2 mosher:1 tuned:1 interestingly:1 subjective:1 reaction:1 current:2 recovered:1 marquardt:3 activation:8 yet:2 dx:2 realistic:3 numerical:1 analytic:2 motor:1 ainen:6 v:1 discrimination:1 gues... |
1,644 | 2,495 | Algorithms for Interdependent Security Games
Michael Kearns
Luis E. Ortiz
Department of Computer and Information Science
University of Pennsylvania
1 Introduction
Inspired by events ranging from 9/11 to the collapse of the accounting firm Arthur Andersen, economists Kunreuther and Heal [5] recently introduced an inte... | 2495 |@word polynomial:2 willing:1 simulation:9 accounting:1 minus:1 reduction:1 moment:1 initial:4 contains:1 current:1 must:2 luis:1 partition:3 j1:2 enables:1 plot:4 intelligence:2 fewer:1 plane:1 xk:1 short:1 record:2 provides:1 completeness:1 constructed:1 install:1 direct:9 ik:2 suspicious:1 qualitative:1 owner:3... |
1,645 | 2,496 | Semi-supervised protein classification using
cluster kernels
Jason Weston?
Max Planck Institute for Biological Cybernetics,
72076 T?ubingen, Germany
weston@tuebingen.mpg.de
Christina Leslie
Department of Computer Science,
Columbia University
cleslie@cs.columbia.edu
Dengyong Zhou, Andre Elisseeff
Max Planck Institute ... | 2496 |@word version:3 additively:1 tried:1 gish:1 elisseeff:1 plentiful:1 score:19 outperforms:1 current:1 com:1 must:3 kyb:3 enables:1 plot:2 generative:4 smith:4 eskin:2 detecting:2 zhang:2 become:1 combine:1 inside:2 blast:30 x0:4 pairwise:6 sublinearly:1 roughly:1 mpg:5 nor:1 multi:1 little:1 window:1 moreover:2 st... |
1,646 | 2,497 | Unsupervised Color Decomposition of
Histologically Stained Tissue Samples
A. Rabinovich
Department of Computer Science
University of California, San Diego
amrabino@ucsd.edu
C. A. Laris
Q3DM, Inc.
claris@q3dm.com
S. Agarwal
Department of Computer Science
University of California, San Diego
sagarwal@cs.ucsd.edu
J.H. P... | 2497 |@word version:1 norm:3 proportion:1 nd:4 hyv:1 rgb:1 decomposition:13 brightness:2 shading:1 series:1 suppressing:2 envision:1 current:2 com:1 comparing:2 must:1 john:1 subsequent:1 additive:1 visible:1 designed:1 half:1 generative:1 intelligence:1 plane:1 ith:2 core:1 filtered:1 provides:1 differential:2 doubly:... |
1,647 | 2,498 | Approximability of Probability Distributions
Alina Beygelzimer?
IBM T. J. Watson Research Center
Hawthorne, NY 10532
beygel@cs.rochester.edu
Irina Rish
IBM T. J. Watson Research Center
Hawthorne, NY 10532
rish@us.ibm.com
Abstract
We consider the question of how well a given distribution can be approximated with prob... | 2498 |@word illustrating:1 eliminating:2 polynomial:2 achievable:11 nd:1 willing:1 decomposition:2 minus:2 moment:2 reduction:1 liu:2 contains:2 rish:3 com:1 current:1 beygelzimer:2 yet:1 must:3 readily:2 written:1 john:1 dechter:1 additive:4 plot:1 v:2 half:1 vanishing:1 realizing:1 node:15 contribute:1 mathematical:1... |
1,648 | 2,499 | Attractive People: Assembling Loose-Limbed
Models using Non-parametric Belief Propagation
Leonid Sigal
Department of Computer Science
Brown University
Providence, RI 02912
ls@cs.brown.edu
Michael Isard
Microsoft Research Silicon Valley
Mountain View, CA 94043
misard@microsoft.com
Benjamin H. Sigelman
Department of C... | 2499 |@word version:1 triggs:1 simulation:1 covariance:5 tr:1 configuration:8 neighbors1:1 freitas:1 com:1 discretization:2 must:2 attracted:1 written:1 realistic:2 shape:1 enables:1 plot:1 treating:2 update:1 n0:5 isard:6 cue:2 parameterization:1 plane:3 calf:2 sys:2 coughlan:2 davison:1 provides:1 node:12 location:5 ... |
1,649 | 25 | 422
COMPUTING MOTION USING RESISTIVE NETWORKS
Christof Koch, Jin Luo, Carver Mead
California Institute of Technology, 216-76, Pasadena, Ca. 91125
James Hutchinson
Jet Propulsion Laboratory, California Institute of Technology
Pasadena, Ca. 91125
INTRODUCTION
To us, and to other biological organisms, vision seems effort... | 25 |@word version:1 seems:2 open:1 calculus:1 brightness:6 solid:1 initial:4 configuration:3 contains:2 current:6 luo:1 yet:1 must:1 visibility:1 sponsored:1 update:2 ilii:1 stationary:3 node:11 location:8 llii:2 along:2 constructed:1 resistive:15 combine:1 inside:2 undifferentiated:1 behavior:1 themselves:1 encouragin... |
1,650 | 250 | 598
Le Cun, Denker and Solla
Optimal Brain Damage
Yann Le Cun, John S. Denker and Sara A. Sol1a
AT&T Bell Laboratories, Holmdel, N. J. 07733
ABSTRACT
We have used information-theoretic ideas to derive a class of practical and nearly optimal schemes for adapting the size of a neural
network. By removing unimportant ... | 250 |@word polynomial:1 retraining:4 hu:2 simulation:1 simplifying:1 thereby:1 outlook:1 initial:2 npt:1 series:2 chervonenkis:3 ours:1 comparing:2 marquardt:1 must:4 john:1 additive:1 half:1 fewer:1 tems:1 postal:1 become:4 consists:1 excise:1 introduce:2 theoretically:1 expected:1 rapid:1 brain:9 decreasing:1 automat... |
1,651 | 2,500 | An iterative improvement procedure for
hierarchical clustering
David Kauchak
Department of Computer Science
University of California, San Diego
dkauchak@cs.ucsd.edu
Sanjoy Dasgupta
Department of Computer Science
University of California, San Diego
dasgupta@cs.ucsd.edu
Abstract
We describe a procedure which finds a h... | 2500 |@word briefly:1 seems:2 seek:2 tried:3 decomposition:1 pick:3 mention:1 recursively:1 reduction:1 initial:2 contains:2 series:1 ours:1 existing:1 yet:1 must:3 realize:1 partition:1 enables:1 wanted:1 depict:2 update:2 greedy:3 leaf:13 plane:1 merger:1 short:1 node:29 contribute:1 successive:1 location:3 traverse:... |
1,652 | 2,501 | Limiting form of the sample covariance
eigenspectrum in PCA and kernel PCA
David C. Hoyle & Magnus Rattray
Department of Computer Science,
University of Manchester,
Manchester M13 9PL, UK.
david.c.hoyle@man.ac.uk magnus@cs.man.ac.uk
Abstract
We derive the limiting form of the eigenvalue spectrum for sample covariance... | 2501 |@word determinant:1 polynomial:2 proportion:1 simulation:2 covariance:26 decomposition:1 solid:2 carry:1 moment:2 bai:2 contains:5 ours:1 current:1 perturbative:2 must:2 plot:2 stationary:3 isotropic:14 ith:1 vanishing:1 provides:3 math:2 along:1 become:1 qualitative:1 edelman:1 indeed:1 expected:1 mechanic:5 glo... |
1,653 | 2,502 | Nonlinear Filtering of Electron
Micrographs by Means of Support Vector
Regression
R. Vollgraf1 , M. Scholz1 , I. A. Meinertzhagen2 , K. Obermayer1
1
Department of Electrical Engineering and Computer Science
Berlin University of Technology, Germany
{vro,idefix,oby}@cs.tu-berlin.de
2
Dalhousie University, Halifax, Canad... | 2502 |@word achievable:1 nd:1 tedious:2 decomposition:2 dramatic:1 cyclic:1 uncovered:1 contains:5 exclusively:1 genetic:3 current:1 written:1 must:2 numerical:2 shape:1 plot:2 interpretable:1 aside:1 alone:1 greedy:2 parameterization:1 haykin:1 provides:2 detecting:1 location:11 constructed:1 direct:1 incorrect:1 wild... |
1,654 | 2,503 | Extending Q-Learning to General Adaptive
Multi-Agent Systems
Gerald Tesauro
IBM Thomas J. Watson Research Center
19 Skyline Drive, Hawthorne, NY 10532 USA
tesauro@watson.ibm.com
Abstract
Recent multi-agent extensions of Q-Learning require knowledge of other
agents? payoffs and Q-functions, and assume game-theoretic p... | 2503 |@word trial:1 version:2 rising:1 achievable:2 seems:1 advantageous:1 hu:2 simulation:2 tried:1 profit:1 thereby:1 versatile:1 reduction:4 initial:2 cyclic:1 series:1 selecting:1 hereafter:1 omniscient:11 outperforms:3 current:6 com:1 discretization:7 rish:1 yet:1 reminiscent:1 realistic:2 informative:3 plot:10 up... |
1,655 | 2,504 | Measure Based Regularization
Olivier Bousquet, Olivier Chapelle, Matthias Hein
Max Planck Institute for Biological Cybernetics, 72076 T?
ubingen, Germany
{first.last}@tuebingen.mpg.de
Abstract
We address in this paper the question of how the knowledge of
the marginal distribution P (x) can be incorporated in a learni... | 2504 |@word determinant:1 version:1 inversion:1 norm:10 tried:2 reduction:3 rkhs:3 existing:3 dx:7 must:1 additive:1 girosi:1 intelligence:1 isotropic:1 xk:4 preference:1 along:1 c2:2 differential:1 ik:1 scholkopf:1 khk:8 manner:1 indeed:2 behavior:3 mpg:1 automatically:1 td:3 pf:1 window:1 increasing:2 becomes:1 provi... |
1,656 | 2,505 | Generalised Propagation for Fast Fourier
Transforms with Partial or Missing Data
Amos J Storkey
School of Informatics, University of Edinburgh
5 Forrest Hill, Edinburgh UK
a.storkey@ed.ac.uk
Abstract
Discrete Fourier transforms and other related Fourier methods have
been practically implementable due to the fast Four... | 2505 |@word proportion:1 disk:1 propagate:1 bn:5 covariance:2 tr:2 recursively:1 initialisation:1 loeliger:1 outperforms:1 must:1 numerical:2 partition:1 predetermined:1 enables:1 update:1 discrimination:1 stationary:1 generative:1 leaf:3 selected:1 plane:1 short:1 provides:5 node:34 simpler:1 c2:4 consists:2 prove:1 c... |
1,657 | 2,506 | Learning with Local and Global Consistency
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal,
Jason Weston, and Bernhard Sch?olkopf
Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany
{firstname.secondname}@tuebingen.mpg.de
Abstract
We consider the general problem of learning from labeled and unl... | 2506 |@word trial:2 kondor:2 version:1 proportion:3 elisseeff:1 initial:7 contains:2 ours:1 document:5 skipping:1 activation:3 attracted:1 john:1 happen:1 enables:1 v:1 fewer:1 accordingly:1 realizing:1 simpler:1 rc:1 along:2 constructed:2 fitting:2 introduce:1 pairwise:3 mpg:1 inspired:1 encouraging:1 decoste:1 increa... |
1,658 | 2,507 | Convex Methods for Transduction
Tijl De Bie
ESAT-SCD/SISTA, K.U.Leuven
Kasteelpark Arenberg 10
3001 Leuven, Belgium
tijl.debie@esat.kuleuven.ac.be
Nello Cristianini
Department of Statistics, U.C.Davis
360 Kerr Hall One Shields Ave.
Davis, CA-95616
nello@support-vector.net
Abstract
The 2-class transduction problem, as... | 2507 |@word middle:1 version:1 polynomial:3 norm:1 elisseeff:1 pick:1 minus:1 reduction:1 contains:2 score:2 current:1 nt:29 bie:2 written:3 distant:2 fund:1 intelligence:2 guess:2 parameterization:4 ith:3 provides:1 become:1 consists:1 indeed:4 sdp:17 encouraging:1 becomes:2 provided:1 notation:2 null:3 what:1 unrelax... |
1,659 | 2,508 | Parameterized Novelty Detection for
Environmental Sensor Monitoring
Cynthia Archer, Todd K. Leen, Antonio Baptista
OGI School of Science & Engineering
Oregon Health & Science University
20000 N. W. Walker Road
Beaverton, OR 97006
archer@cse.ogi.edu, tleen@cse.ogi.edu, baptista@ccalmr.ogi.edu
Abstract
As part of an en... | 2508 |@word version:1 covariance:1 tr:4 reduction:3 initial:2 contains:4 series:6 efficacy:1 past:1 current:3 yet:1 must:1 numerical:1 plot:2 drop:1 alone:1 half:2 fewer:1 xk:3 beginning:3 farther:1 infrastructure:1 detecting:2 provides:1 cse:2 location:1 district:1 five:2 constructed:1 fitting:1 expected:1 roughly:2 b... |
1,660 | 2,509 | Learning Non-Rigid 3D Shape from 2D Motion
Lorenzo Torresani
Stanford University
ltorresa@cs.stanford.edu
Aaron Hertzmann
University of Toronto
hertzman@dgp.toronto.edu
Christoph Bregler
New York University
chris.bregler@nyu.edu
Abstract
This paper presents an algorithm for learning the time-varying shape of a
non-... | 2509 |@word version:1 inversion:1 covariance:2 jacob:1 dramatic:1 tr:2 initial:2 contains:2 series:2 selecting:1 shum:1 current:1 nt:3 must:1 written:1 visible:1 shape:76 larization:1 update:6 fund:1 isard:1 toronto:3 simpler:1 become:1 consists:2 combine:2 fitting:3 polyhedral:1 expected:1 behavior:1 p1:1 spherical:2 ... |
1,661 | 251 | 332
Hormel
A Sell-organizing Associative
Memory System lor Control
Applications
Michael Bormel
Department of Control Theory and Robotics
Technical University of Darmstadt
Schlossgraben 1
6100 Darmstadt/W.-Ger.any
ABSTRACT
The CHAC storage scheme has been used as a basis
for a software implementation of an associati... | 251 |@word effect:3 concept:4 hypercube:1 ization:1 advantageous:1 nd:1 direction:1 question:1 merged:1 strategy:1 simulation:2 receptive:6 deal:1 during:8 self:15 virtual:2 distance:2 frg:1 mapped:1 lateral:2 behaviour:1 darmstadt:3 multivariable:1 maryland:1 generalization:12 leinhos:1 preliminary:1 presenting:1 stra... |
1,662 | 2,510 | Fast Embedding of
Sparse Music Similarity Graphs
John C. Platt
Microsoft Research
1 Microsoft Way
Redmond, WA 98052 USA
jplatt@microsoft.com
Abstract
This paper applies fast sparse multidimensional scaling (MDS) to a large
graph of music similarity, with 267K vertices that represent artists, albums, and tracks; and 3.... | 2510 |@word cox:2 compression:1 nd:4 sammon:1 d2:1 heuristically:1 r:6 nystr:6 reduction:1 contains:1 score:1 leeuw:1 subjective:2 existing:2 outperforms:1 current:2 com:1 bradley:1 must:3 john:1 subsequent:2 numerical:2 limp:1 enables:2 treating:1 designed:1 prohibitive:1 ith:2 farther:1 iterates:1 provides:1 location... |
1,663 | 2,511 | Learning curves for stochastic gradient descent
in linear feedforward networks
Justin Werfel
Dept. of EECS
MIT
Cambridge, MA 02139
jkwerfel@mit.edu
Xiaohui Xie
Dept. of Molecular Biology
Princeton University
Princeton, NJ 08544
xhx@princeton.edu
H. Sebastian Seung
HHMI
Dept. of Brain & Cog. Sci.
MIT
Cambridge, MA 021... | 2511 |@word trial:1 private:1 fiete:2 simulation:1 thereby:1 solid:2 must:1 written:1 numerical:1 additive:3 wx:1 shape:1 remove:1 update:21 v:1 fewer:1 xk:1 isotropic:1 ith:1 node:14 successive:2 mathematical:1 direct:13 become:2 baldi:1 introduce:1 expected:1 behavior:3 frequently:2 nor:1 examine:1 brain:1 decreasing... |
1,664 | 2,512 | Computing Gaussian Mixture Models with EM
using Equivalence Constraints
Noam Shental
Computer Science & Eng.
Center for Neural Computation
Hebrew University of Jerusalem
Jerusalem, Israel 91904
fenoam@cs.huji.ac.il
Aharon Bar-Hillel
Computer Science & Eng.
Center for Neural Computation
Hebrew University of Jerusalem
... | 2512 |@word repository:3 version:3 closure:2 simulation:1 eng:4 covariance:2 pavel:1 initial:2 score:2 document:1 subjective:1 must:2 readily:2 happen:1 partition:2 plot:1 update:6 v:1 generative:4 selected:1 mccallum:1 node:3 location:1 successive:1 uncoordinated:2 consists:2 fitting:1 manner:2 pairwise:1 acquired:1 i... |
1,665 | 2,513 | Kernel Dimensionality Reduction for Supervised
Learning
Kenji Fukumizu
Institute of Statistical
Mathematics
Tokyo 106-8569 Japan
fukumizu@ism.ac.jp
Francis R. Bach
CS Division
University of California
Berkeley, CA 94720, USA
fbach@cs.berkeley.edu
Michael I. Jordan
CS Division and Statistics
University of California
... | 2513 |@word determinant:2 repository:2 covariance:16 decomposition:2 thereby:1 reduction:27 hereafter:1 genetic:1 rkhs:6 suppressing:2 bc:1 detc:1 outperforms:2 existing:1 recovered:1 current:1 comparing:1 exy:1 must:1 import:1 additive:1 numerical:1 informative:1 greedy:2 fewer:1 selected:7 half:1 characterization:4 p... |
1,666 | 2,514 | Eye micro-movements improve stimulus
detection beyond the Nyquist limit in the
peripheral retina
Matthias H. Hennig and Florentin W?org?otter
Computational Neuroscience
Psychology
University of Stirling
FK9 4LR Stirling, UK
{hennig,worgott}@cn.stir.ac.uk
Abstract
Even under perfect fixation the human eye is under ste... | 2514 |@word version:1 briefly:1 wiesel:1 stronger:1 grey:2 simulation:5 excited:1 mention:1 solid:1 initial:1 foveal:2 bradley:1 comparing:2 surprising:1 activation:1 must:1 john:1 physiol:2 visible:2 realistic:4 remove:1 designed:1 implying:1 half:7 short:1 record:1 lr:1 characterization:1 contribute:1 location:8 chen... |
1,667 | 2,515 | A Computational Geometric Approach to Shape
Analysis in Images
Washington Mio
Department of Mathematics
Florida State University
Tallahassee, FL 32306
mio@math.fsu.edu
Anuj Srivastava
Department of Statistics
Florida State University
Tallahassee, FL 32306
anuj@stat.fsu.edu
Xiuwen Liu
Department of Computer Science
Fl... | 2515 |@word exploitation:1 middle:4 closure:2 simulation:1 covariance:11 decomposition:1 initial:4 liu:1 contains:1 series:1 selecting:1 disallows:1 rightmost:1 past:6 existing:1 must:2 john:1 numerical:3 shape:100 enables:1 analytic:1 remove:1 intelligence:3 parametrization:3 realizing:1 short:1 provides:3 math:2 equi... |
1,668 | 2,516 | Dynamical Modeling with Kernels for Nonlinear
Time Series Prediction
Liva Ralaivola
Laboratoire d?Informatique de Paris 6
Universit?e Pierre et Marie Curie
8, rue du capitaine Scott
F-75015 Paris, FRANCE
liva.ralaivola@lip6.fr
Florence d?Alch?e?Buc
Laboratoire d?Informatique de Paris 6
Universit?e Pierre et Marie Curi... | 2516 |@word version:1 inversion:1 polynomial:9 open:1 km:2 paid:1 tr:1 series:34 denoting:1 yet:1 liva:2 written:1 dx:1 john:1 numerical:1 girosi:1 aside:1 mackey:3 isotropic:1 nnsp:1 ith:1 core:1 provides:1 symposium:1 consists:1 symp:1 introduce:4 indeed:1 market:1 behavior:2 multi:8 actual:1 kohlmorgen:1 considering... |
1,669 | 2,517 | Extreme Components Analysis
Max Welling
Department of Computer Science
University of Toronto
10 King?s College Road
Toronto, M5S 3G5 Canada
welling@cs.toronto.edu
Felix Agakov, Christopher K. I. Williams
Institute for Adaptive and Neural Computation
School of Informatics
University of Edinburgh
5 Forrest Hill, Edinbu... | 2517 |@word version:3 middle:1 pw:5 norm:2 stronger:1 nd:2 open:1 covariance:16 decomposition:1 pg:4 tr:6 solid:2 reduction:1 configuration:1 contains:3 series:1 z2:4 must:3 dashdot:1 shape:2 remove:1 drop:1 plot:1 depict:1 v:2 implying:3 generative:1 leaf:1 stationary:2 intelligence:1 plane:5 isotropic:2 provides:1 ch... |
1,670 | 2,518 | AUC Optimization vs. Error Rate Minimization
Corinna Cortes? and Mehryar Mohri
AT&T Labs ? Research
180 Park Avenue, Florham Park, NJ 07932, USA
{corinna, mohri}@research.att.com
Abstract
The area under an ROC curve (AUC) is a criterion used in many applications to measure the quality of a classification algorithm. H... | 2518 |@word repository:2 seems:1 flach:1 a02:2 d2:1 salcedo:1 minus:2 configuration:2 att:1 score:2 selecting:4 document:4 outperforms:1 existing:2 com:2 assigning:1 belmont:1 kdd:2 pertinent:1 designed:7 plot:2 update:1 n0:7 v:2 selected:1 ith:2 boosting:4 preference:1 prove:1 x0:13 pairwise:1 swets:1 indeed:2 expecte... |
1,671 | 2,519 | Simplicial Mixtures of Markov Chains:
Distributed Modelling of Dynamic User Profiles
Mark Girolami
Department of Computing Science
University of Glasgow
Glasgow, UK
girolami@dcs.gla.ac.uk
Ata Kab?an
School of Computer Science
University of Birmingham
Birmingham, UK
a.kaban@cs.bham.ac.uk
Abstract
To provide a compact... | 2519 |@word briefly:1 bigram:1 interleave:2 proportion:2 plsa:2 simulation:1 decomposition:1 weekday:1 fifteen:1 minus:1 solid:3 reduction:1 initial:2 necessity:1 series:1 occupational:1 score:1 cadez:1 document:1 prefix:1 past:1 existing:2 com:1 surprising:1 realistic:1 hofmann:1 plot:2 interpretable:3 update:3 statio... |
1,672 | 252 | Can Simple Cells Learn Curves? A Hebbian Model in a Structured Environment
Can Simple Cells Learn Curves? A
Hebbian Model in a Structured
Environment
William R. Softky
Divisions of Biology and Physics
103-33 Caltech
Pasadena, CA 91125
bill@aurel.caltech.edu
Daniel M. Kammen
Divisions of Biology and Engineering
216-7... | 252 |@word version:2 grey:1 confirms:1 heretofore:1 tried:1 simulation:2 lobe:2 configuration:1 contains:2 efficacy:2 daniel:1 tuned:4 rearing:4 current:2 neurophys:2 must:2 exposing:1 physiol:1 plasticity:9 shape:2 remove:1 half:1 filtered:8 location:1 mathematical:3 direct:1 pathway:1 indeed:1 behavior:1 roughly:1 br... |
1,673 | 2,520 | Mutual Boosting for
Contextual Inference
Michael Fink
Center for Neural Computation
Hebrew University of Jerusalem
Jerusalem, Israel 91904
fink@huji.ac.il
Pietro Perona
Electrical Engineering Department
California Institute of Technology
Pasadena, CA 91125
perona@vision.caltech.edu
Abstract
Mutual Boosting is a meth... | 2520 |@word covariance:4 initial:1 configuration:7 selecting:1 outperforms:1 existing:7 contextual:30 comparing:1 neurophys:1 yet:1 tackling:1 informative:1 gist:2 update:1 selected:1 fewer:1 agglomerating:1 supplying:1 detecting:2 boosting:53 provides:1 five:1 height:1 direct:1 pairing:1 combine:2 indeed:1 rapid:1 mul... |
1,674 | 2,521 | Necessary Intransitive Likelihood-Ratio
Classifiers
Gang Ji and Jeff Bilmes
SSLI-Lab, Department of Electrical Engineering
University of Washington
Seattle, WA 98195-2500
{gang,bilmes}@ee.washington.edu
Abstract
In pattern classification tasks, errors are introduced because of differences between the true model and th... | 2521 |@word repository:2 duda:1 corral:1 tried:1 ci2:1 covariance:2 attainable:1 thereby:2 initial:1 contains:1 score:1 crx:1 past:1 o2:1 current:1 comparing:1 marquardt:1 dx:5 must:1 john:3 additive:1 numerical:1 interpretable:1 nynex:1 generative:2 selected:1 flare:1 dover:1 provides:2 detecting:1 preference:2 simple... |
1,675 | 2,522 | The Diffusion Mediated Biochemical Signal
Relay Channel
Peter J. Thomas?, Donald J. Spencer?
Computational Neurobiology Laboratory
(Terrence J. Sejnowski, Director)
Salk Institute for Biological Studies
La Jolla, CA 92037
Sierra K. Hampton, Peter Park, Joseph P. Zurkus
Department of Electrical and Computer Engineering... | 2522 |@word version:2 seems:1 simulation:5 carry:1 moment:1 cyclic:2 contains:1 selecting:1 amp:2 reaction:1 comparing:1 activation:2 must:1 underly:1 numerical:3 additive:5 remove:1 half:1 nervous:1 plane:1 filtered:1 detecting:1 contribute:1 location:2 sigmoidal:1 burst:1 become:1 director:2 introduce:1 x0:2 expected... |
1,676 | 2,523 | Phonetic Speaker Recognition with Support
Vector Machines
W. M. Campbell, J. P. Campbell, D. A. Reynolds, D. A. Jones, and T. R. Leek
MIT Lincoln Laboratory
Lexington, MA 02420
wcampbell,jpc,dar,daj,tleek@ll.mit.edu
Abstract
A recent area of significant progress in speaker recognition is the use
of high level features... | 2523 |@word trial:2 bigram:12 tried:1 dramatic:2 solid:1 reduction:2 contains:1 score:12 united:2 document:4 reynolds:5 current:2 comparing:2 john:2 ronan:1 confirming:1 designed:1 plot:6 sponsored:1 half:1 cue:2 selected:1 item:1 spk1:6 beginning:1 short:2 characterization:3 provides:1 five:1 become:2 incorrect:1 intr... |
1,677 | 2,524 | Sensory Modality Segregation
Virginia R. de Sa
Department of Cognitive Science
University of California, San Diego
La Jolla, CA 92093-0515
desa@ucsd.edu
Abstract
Why are sensory modalities segregated the way they are? In this paper
we show that sensory modalities are well designed for self-supervised
cross-modal lear... | 2524 |@word version:2 crucially:1 imaginary:1 current:1 comparing:1 written:1 partition:4 informative:1 designed:3 selfsupervised:1 update:3 selected:1 short:1 coarse:1 successive:1 simpler:1 differential:1 become:1 combine:1 eleventh:1 multimodality:1 indeed:1 alspector:1 multi:2 brain:1 muslea:1 window:1 humphrey:1 p... |
1,678 | 2,525 | Fast Algorithms for Large-State-Space HMMs with
Applications to Web Usage Analysis
Pedro F. Felzenszwalb1 , Daniel P. Huttenlocher2 , Jon M. Kleinberg2
2
1
AI Lab, MIT, Cambridge MA 02139
Computer Science Dept., Cornell University, Ithaca NY 14853
Abstract
In applying Hidden Markov Models to the analysis of massive ... | 2525 |@word briefly:1 rising:1 norm:1 gradual:1 accounting:1 downloading:1 q1:2 citeseer:1 pick:1 recursively:1 initial:1 series:2 contains:1 daniel:1 offering:1 rightmost:1 o2:1 current:2 si:7 readily:1 subsequent:2 happen:1 blur:1 visible:1 shape:1 enables:1 drop:1 plot:3 update:1 prohibitive:1 fewer:1 item:18 select... |
1,679 | 2,526 | Learning the k in k-means
Greg Hamerly, Charles Elkan
{ghamerly,elkan}@cs.ucsd.edu
Department of Computer Science and Engineering
University of California, San Diego
La Jolla, California 92093-0114
Abstract
When clustering a dataset, the right number k of clusters to use is often
not obvious, and choosing k automatic... | 2526 |@word repository:1 middle:2 compression:1 nd:1 open:1 d2:1 covariance:5 simplifying:1 reduction:4 initial:1 wrapper:3 score:6 document:1 current:1 elliptical:1 comparing:3 ka:1 yet:1 must:2 written:2 partition:4 remove:1 plot:10 aside:1 intelligence:2 discovering:1 item:1 problemspecific:1 ith:1 revisited:1 locat... |
1,680 | 2,527 | Fast Feature Selection from Microarray
Expression Data via Multiplicative
Large Margin Algorithms
Claudio Gentile
DICOM, Universit`a dell?Insubria
Via Mazzini, 5, 21100 Varese, Italy
gentile@dsi.unimi.it
Abstract
New feature selection algorithms for linear threshold functions are described which combine backward elim... | 2527 |@word trial:5 briefly:1 version:4 eliminating:2 norm:31 seems:2 bf:1 tried:1 elisseeff:1 thereby:2 mention:1 carry:1 reduction:1 wrapper:3 contains:5 seriously:1 outperforms:1 bradley:1 current:7 assigning:1 readily:1 john:1 additive:3 numerical:1 limp:1 remove:1 drop:1 update:11 progressively:2 discrimination:2 ... |
1,681 | 2,528 | Online Learning via Global Feedback
for Phrase Recognition
Xavier Carreras
Llu??s M`arquez
TALP Research Center, LSI Department
Technical University of Catalonia (UPC)
Campus Nord UPC, E?08034 Barcelona
{carreras,lluism}@lsi.upc.es
Abstract
This work presents an architecture based on perceptrons to recognize
phrase s... | 2528 |@word achievable:1 polynomial:2 nd:1 open:1 additively:1 recursively:2 initial:1 score:25 fragment:2 past:1 current:1 comparing:1 assigning:1 parsing:5 realistic:1 additive:2 plot:5 update:7 progressively:1 selected:1 website:1 provides:1 org:1 simpler:1 tagger:1 incorrect:3 consists:8 inside:2 tagging:3 indeed:1... |
1,682 | 2,529 | A Fast Multi-Resolution Method for Detection of
Significant Spatial Disease Clusters
Daniel B. Neill
Department of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
neill@cs.cmu.edu
Andrew W. Moore
Department of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
awm@cs.cmu.edu
Abstract
G... | 2529 |@word version:3 eliminating:1 c0:2 simulation:1 anthrax:1 q1:5 tr:1 ptot:11 recursively:3 score:4 daniel:1 si:4 must:7 tot:5 partition:1 half:2 selected:1 mdr:14 intelligence:1 beginning:1 record:2 coarse:1 node:1 location:1 replication:5 prove:1 inside:4 manner:1 expected:4 themselves:1 examine:2 multi:3 relying... |
1,683 | 253 | 638
Zipser
Subgrouping Reduces Complexity and Speeds Up
Learning in Recurrent Networks
David Zipser
Department of Cognitive Science
University of California, San Diego
La Jolla, CA 92093
1 INTRODUCTION
Recurrent nets are more powerful than feedforward nets because they allow simulation of
dynamical systems. Everyth... | 253 |@word effect:1 come:1 proportion:1 bptt:8 added:1 simulation:1 illustrated:1 exploration:1 viewing:1 everything:1 backpropagating:1 subnet:1 microstructure:1 past:3 assuming:1 balance:1 cognition:1 nw:1 twist:1 smallest:1 update:5 cambridge:1 beginning:1 record:1 t:1 truncated:1 keu:1 hinton:3 ever:1 mit:1 pdp:1 s... |
1,684 | 2,530 | Model Uncertainty in Classical Conditioning
A. C. Courville*1,3 , N. D. Daw2,3 , G. J. Gordon4 , and D. S. Touretzky2,3
1
Robotics Institute, 2 Computer Science Department,
3
Center for the Neural Basis of Cognition,
4
Center for Automated Learning and Discovery
Carnegie Mellon University, Pittsburgh, PA 15213
{aaronc... | 2530 |@word trial:41 advantageous:1 extinction:3 additively:1 simulation:2 simplifying:1 dramatic:1 carry:1 reduction:1 configuration:1 contains:1 current:1 yet:2 subsequent:1 realistic:1 analytic:1 enables:1 plot:1 update:1 discrimination:1 stationary:1 generative:5 alone:2 patterning:2 tone:2 indicative:1 provides:1 ... |
1,685 | 2,531 | Log-Linear Models for Label Ranking
Ofer Dekel
Computer Science & Eng.
Hebrew University
Christopher D. Manning
Computer Science Dept.
Stanford University
Yoram Singer
Computer Science & Eng.
Hebrew University
oferd@cs.huji.ac.il manning@cs.stanford.edu singer@cs.huji.ac.il
Abstract
Label ranking is the task of in... | 2531 |@word middle:1 proportion:1 stronger:1 dekel:2 eng:2 decomposition:18 elisseeff:1 accommodate:1 reduction:2 initial:1 cyclic:1 contains:5 denoting:1 com:1 si:11 must:1 parsing:2 informative:1 enables:2 update:1 aside:1 stationary:2 leaf:6 core:1 boosting:15 preference:33 incorrect:1 prove:1 tagging:1 upenn:1 nota... |
1,686 | 2,532 | Boosting versus Covering
Kohei Hatano?
Tokyo Institute of Technology
hatano@is.titech.ac.jp
Manfred K. Warmuth
UC Santa Cruz
manfred@cse.ucsc.edu
Abstract
We investigate improvements of AdaBoost that can exploit the fact
that the weak hypotheses are one-sided, i.e. either all its positive
(or negative) predictions ar... | 2532 |@word version:2 norm:1 nd:3 open:1 arti:2 moment:1 reduction:1 initial:4 uncovered:1 past:2 current:3 surprising:1 pothesis:1 must:1 readily:1 cruz:2 additive:1 happen:1 update:9 v:1 greedy:14 half:7 fewer:1 warmuth:3 manfred:3 num:1 boosting:17 cse:1 ucsc:1 become:1 consists:1 indeed:1 eap:1 totally:1 becomes:1 ... |
1,687 | 2,533 | Entrainment of Silicon Central Pattern Generators
for Legged Locomotory Control
Francesco Tenore1, Ralph Etienne-Cummings1,2, M. Anthony Lewis3
Dept. of Electrical & Computer Eng., Johns Hopkins University, Baltimore, MD 21218
2
Institute of Systems Research, University of Maryland, College Park, MD 20742
3
Iguana Robo... | 2533 |@word illustrating:1 version:2 pw:4 achievable:1 stronger:1 nd:1 pulse:9 eng:1 thereby:2 versatile:1 configuration:1 contains:2 current:14 com:1 si:1 yet:1 exposing:1 john:1 happen:1 motor:6 remove:1 designed:1 drop:1 alone:1 pacemaker:6 device:2 sram:1 smith:1 characterization:1 digestive:1 cpg:9 constructed:2 d... |
1,688 | 2,534 | Different Cortico-Basal Ganglia Loops
Specialize in Reward Prediction on
Different Time Scales
Saori Tanaka
Kenji Doya
Nara Institute of Science and Technology
ATR Computational Neuroscience Laboratories
CREST, Japan Science and Technology Corporation
Kyoto, Japan
xsaori@atr.co.jp
doya@atr.co.jp
Go Okada
Kazutaka Ued... | 2534 |@word trial:12 cingulate:1 mri:2 hippocampus:2 specialises:1 tr:2 solid:2 necessity:1 selecting:1 current:1 anterior:2 activation:4 subsequent:1 motor:2 opin:1 medial:5 v:5 half:1 selected:1 short:2 characterization:1 five:2 differential:1 specialize:1 fixation:1 pathway:1 behavioral:1 rostral:1 orbital:2 acquire... |
1,689 | 2,535 | On the Dynamics of Boosting?
Robert E. Schapire
Cynthia Rudin
Ingrid Daubechies
Princeton University
Princeton University
Department of Computer Science
Progr. Appl. & Comp. Math.
35 Olden St.
Fine Hall
Princeton, NJ 08544
Washington Road
schapire@cs.princeton.edu
Princeton, NJ 08544-1000
{crudin,ingrid}@math.princeto... | 2535 |@word achievable:1 stronger:1 seems:1 open:1 d2:1 tried:1 contraction:3 concise:1 mention:1 harder:1 reduction:2 initial:1 cyclic:2 current:1 yet:2 must:2 subsequent:1 happen:1 j1:1 gv:15 plot:1 designed:1 update:14 v:1 implying:1 credence:1 rudin:1 selected:2 intelligence:1 warmuth:4 manfred:2 provides:2 math:2 ... |
1,690 | 2,536 | Kernels for Structured Natural Language Data
Jun Suzuki, Yutaka Sasaki, and Eisaku Maeda
NTT Communication Science Laboratories, NTT Corp.
2-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0237 Japan
{jun, sasaki, maeda}@cslab.kecl.ntt.co.jp
Abstract
This paper devises a novel kernel function for structured natural la... | 2536 |@word polynomial:1 norm:1 lodhi:1 hu:1 q1:2 tr:7 cyclic:1 contains:1 score:1 document:1 existing:1 comparing:1 skipping:1 tackling:1 written:5 parsing:2 cruz:2 numerical:2 remove:1 designed:1 yokoo:1 accordingly:1 node:41 location:2 gx:2 lexicon:1 tagger:2 constructed:3 direct:2 consists:1 hci:11 combine:1 introd... |
1,691 | 2,537 | Auction Mechanism Design for Multi-Robot
Coordination
Curt Bererton, Geoff Gordon, Sebastian Thrun, Pradeep Khosla
{curt,ggordon,thrun,pkk}@cs.cmu.edu
Carnegie Mellon University
5000 Forbes Ave
Pittsburgh, PA 15217
Abstract
The design of cooperative multi-robot systems is a highly active research
area in robotics. Two... | 2537 |@word willing:1 seek:1 simulation:3 decomposition:10 asks:1 incurs:1 thereby:1 harder:1 carry:1 initial:2 contains:1 kitano:1 existing:2 current:1 yet:1 must:9 written:1 ronald:1 realistic:1 cheap:1 remove:1 designed:1 intelligence:2 guess:1 beginning:1 ith:1 short:1 node:2 location:4 attack:4 uncoordinated:1 rol... |
1,692 | 2,538 | Pairwise Clustering and Graphical Models
Noam Shental
Computer Science & Eng.
Center for Neural Computation
Hebrew University of Jerusalem
Jerusalem, Israel 91904
fenoam@cs.huji.ac.il
Assaf Zomet
Computer Science & Eng.
Hebrew University of Jerusalem
Jerusalem, Israel 91904
zomet@cs.huji.ac.il
Tomer Hertz
Computer Sc... | 2538 |@word repository:1 middle:1 polynomial:1 seems:1 advantageous:1 propagate:1 eng:4 initial:2 selecting:1 scatter:2 must:2 subsequent:1 partition:11 hofmann:1 shape:1 plot:2 intelligence:3 xk:2 provides:1 node:9 direct:1 consists:3 combine:1 assaf:1 pairwise:25 indeed:1 expected:1 nor:1 freeman:1 automatically:2 li... |
1,693 | 2,539 | ?
Nonlinear processing in LGN neurons
Vincent Bonin* , Valerio Mante and Matteo Carandini
Smith-Kettlewell Eye Research Institute
2318 Fillmore Street
San Francisco, CA 94115, USA
Institute of Neuroinformatics
University of Zurich and ETH Zurich
Winterthurerstrasse 190
CH-8046 Zurich, Switzerland
{vincent,valerio,ma... | 2539 |@word version:2 wiesel:2 stronger:1 integrative:1 meansquare:1 solid:4 moment:1 tuned:1 surprising:1 si:1 dx:1 extraclassical:1 physiol:4 shape:1 progressively:1 cleland:3 alone:2 cavanaugh:2 smith:1 filtered:2 provides:3 org:1 along:1 kettlewell:1 qualitative:1 dan:3 shapley:3 fitting:2 pathway:1 manner:1 mask:1... |
1,694 | 254 | 44
Beer and Chiel
Neural Implementation of Motivated Behavior:
Feeding in an Artificial Insect
Randall D. Beerl,2 and Hillel J. Chiel 2
Departments of 1 Computer Engineering and Science, and 2Biology
and the Center for Automation and Intelligent Systems Research
Case Western Reserve University
Cleveland, OH 44106
AB... | 254 |@word stronger:1 open:3 sensed:1 tr:1 initial:4 contains:2 si:1 yet:1 must:4 attracted:1 physiol:1 shape:1 motor:4 hypothesize:1 designed:1 drop:1 overriding:1 progressively:1 plot:2 v:1 intelligence:1 pacemaker:2 patterning:1 nervous:1 beginning:1 marine:2 dissertation:1 compo:1 successive:2 simpler:2 burst:1 alo... |
1,695 | 2,540 | Gaussian Process Latent Variable Models for
Visualisation of High Dimensional Data
Neil D. Lawrence
Department of Computer Science,
University of Sheffield,
Regent Court, 211 Portobello Street,
Sheffield, S1 4DP, U.K.
neil@dcs.shef.ac.uk
Abstract
In this paper we introduce a new underlying probabilistic model for pri... | 2540 |@word version:1 open:1 scg:3 covariance:7 decomposition:1 tr:2 shot:4 reduction:1 selecting:1 denoting:1 existing:1 recovered:1 must:1 written:1 informative:2 remove:1 plot:2 generative:5 provides:1 node:1 location:2 herbrich:1 along:3 vxw:1 regent:1 manner:2 introduce:1 indeed:1 ica:1 multi:1 little:1 considerin... |
1,696 | 2,541 | GPPS: A Gaussian Process Positioning System
for Cellular Networks
Anton Schwaighofer?, Marian Grigoras?, Volker Tresp, Clemens Hoffmann
Siemens Corporate Technology, Information and Communications
81730 Munich, Germany
http://www.igi.tugraz.at/aschwaig
Abstract
In this article, we present a novel approach to solving t... | 2541 |@word msr:1 version:2 achievable:1 open:1 crucially:1 covariance:3 tr:1 shot:1 initial:1 configuration:1 series:1 selecting:2 existing:1 current:1 discretization:1 z2:1 yet:6 must:3 john:1 numerical:1 subsequent:1 informative:1 plot:3 selected:1 parameterization:1 isotropic:1 infrastructure:2 coarse:1 provides:1 ... |
1,697 | 2,542 | Image Reconstruction by Linear Programming
Koji Tsuda?? and Gunnar R?atsch??
Max Planck Institute for Biological Cybernetics
Spemannstr. 38, 72076 T?ubingen, Germany
?
AIST CBRC, 2-43 Aomi, Koto-ku, Tokyo, 135-0064, Japan
?
Fraunhofer FIRST, Kekul?estr. 7, 12489 Berlin, Germany
?
{koji.tsuda,gunnar.raetsch}@tuebingen.... | 2542 |@word inversion:4 norm:8 d2:4 confirms:1 minus:1 solid:2 tr:1 score:9 interestingly:1 outperforms:1 existing:3 recovered:1 shape:3 enables:1 kyb:1 plot:2 update:1 boosting:1 along:1 become:1 inside:1 polyhedral:1 manner:1 introduce:3 notably:1 huber:6 mpg:2 considering:1 increasing:1 becomes:1 project:2 moreover:... |
1,698 | 2,543 | Machine Learning Applied to Perception:
Decision-Images for Gender Classification
Felix A. Wichmann and Arnulf B. A. Graf
Max Planck Institute for Biological Cybernetics
T?ubingen, Germany
felix.wichmann@tuebingen.mpg.de
Eero P. Simoncelli
Howard Hughes Medical Institute
Center for Neural Science
New York University,... | 2543 |@word trial:7 proportion:5 sex:1 grey:1 confirms:1 covariance:1 paid:1 thereby:1 reduction:1 past:1 reaction:2 current:2 comparing:1 attracted:1 written:3 must:1 distant:1 kyb:1 christian:1 discrimination:8 cue:1 intelligence:1 rts:1 inspection:1 supplying:1 location:1 hyperplanes:1 five:2 along:9 become:1 pairin... |
1,699 | 2,544 | A Large Deviation Bound
for the Area Under the ROC Curve
Shivani Agarwal? , Thore Graepel? , Ralf Herbrich? and Dan Roth?
?
?
Dept. of Computer Science
University of Illinois
Urbana, IL 61801, USA
Microsoft Research
7 JJ Thomson Avenue
Cambridge CB3 0FB, UK
{sagarwal,danr}@cs.uiuc.edu
{thoreg,rherb}@microsoft.com
... | 2544 |@word seek:2 rayner:1 thoreg:1 contains:2 document:8 interestingly:1 com:1 comparing:1 exy:1 dx:2 remove:1 v:1 intelligence:2 xk:12 boosting:1 herbrich:3 preference:1 mcdiarmid:8 simpler:1 mathematical:1 dn:2 retrieving:1 dan:1 manner:1 expected:21 uiuc:1 begin:1 notation:2 underlying:1 linearity:1 bounded:2 fact... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.