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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2,500 | 3,268 | Scene Segmentation with Conditional Random Fields
Learned from Partially Labeled Images
Jakob Verbeek and Bill Triggs
INRIA and Laboratoire Jean Kuntzmann, 655 avenue de l?Europe, 38330 Montbonnot, France
Abstract
Conditional Random Fields (CRFs) are an effective tool for a variety of different
data segmentation and ... | 3268 |@word norm:1 triggs:2 plsa:1 disk:2 seek:1 mitsubishi:1 rgb:2 textonboost:3 tr:1 solid:2 reduction:1 initial:1 generatively:1 contains:2 loc:28 series:1 denoting:1 outperforms:2 existing:1 freitas:1 com:1 contextual:4 realistic:1 partition:8 informative:2 subsequent:1 shape:1 designed:1 drop:2 grass:2 alone:1 gen... |
2,501 | 3,269 | DIFFRAC : a discriminative and flexible
framework for clustering
Francis R. Bach
INRIA - Willow Project
?
Ecole
Normale Sup?erieure
45, rue d?Ulm, 75230 Paris, France
francis.bach@mines.org
Za??d Harchaoui
LTCI, TELECOM ParisTech and CNRS
46, rue Barrault
75634 Paris cedex 13, France
zaid.harchaoui@enst.fr
Abstract
... | 3269 |@word repository:1 version:2 inversion:1 polynomial:3 norm:2 advantageous:1 stronger:1 d2:1 closure:2 simulation:5 decomposition:7 tr:5 configuration:1 ecole:1 denoting:1 ours:1 outperforms:1 existing:1 comparing:2 bie:1 must:5 readily:2 numerical:2 partition:11 enables:1 zaid:1 v:1 fewer:1 selected:1 isotropic:1... |
2,502 | 327 | Neural Network Implementation of Admission Control
Rodolfo A. Milito, Isabelle Guyon, and Sara A. SoDa
AT&T Bell Laboratories, Crawfords Corner Rd., Holmdel, NJ 07733
Abstract
A feedforward layered network implements a mapping required to control an
unknown stochastic nonlinear dynamical system. Training is based on ... | 327 |@word open:1 simulation:1 denying:1 contains:1 selecting:1 past:2 outperforms:1 current:2 must:2 john:1 numerical:1 subsequent:1 update:4 stationary:3 congestion:1 idling:1 provides:2 node:1 admission:16 direct:1 supply:1 combine:1 indeed:1 expected:2 rapid:1 behavior:1 nor:2 decreasing:1 increasing:2 becomes:3 pr... |
2,503 | 3,270 | McRank: Learning to Rank Using Multiple
Classification and Gradient Boosting
Ping Li ?
Dept. of Statistical Science
Cornell University
pingli@cornell.edu
Christopher J.C. Burges
Microsoft Research
Microsoft Corporation
cburges@microsoft.com
Qiang Wu
Microsoft Research
Microsoft Corporation
qiangwu@microsoft.com
Abst... | 3270 |@word polynomial:1 mcrank:9 seek:1 tried:1 mention:1 recursively:1 initial:1 liu:1 contains:3 score:32 document:1 outperforms:1 current:1 com:2 si:14 hoboken:1 john:1 additive:1 partition:2 kdd:1 remove:1 plot:3 ainen:1 greedy:1 nq:4 ith:4 renshaw:1 boosting:26 authority:1 node:6 preference:3 simpler:1 zhang:1 fi... |
2,504 | 3,271 | Combined discriminative and generative articulated
pose and non-rigid shape estimation
Leonid Sigal
Alexandru Balan
Michael J. Black
Department of Computer Science
Brown University
Providence, RI 02912
{ls, alb, black}@cs.brown.edu
Abstract
Estimation of three-dimensional articulated human pose and motion from image... | 3271 |@word repository:1 version:1 briefly:1 manageable:1 middle:1 triggs:3 pg:4 shading:1 recursively:1 initial:6 cyclic:1 configuration:1 suppressing:1 current:2 recovered:2 dx:4 must:1 written:2 refines:1 visible:1 mesh:12 shape:94 visibility:1 designed:1 generative:25 intelligence:1 isard:1 parameterization:2 plane... |
2,505 | 3,272 | Discriminative Keyword Selection Using Support
Vector Machines
W. M. Campbell, F. S. Richardson
MIT Lincoln Laboratory
Lexington, MA 02420
wcampbell,frichard@ll.mit.edu
Abstract
Many tasks in speech processing involve classification of long term characteristics
of a speech segment such as language, speaker, dialect, o... | 3272 |@word middle:1 bigram:2 retraining:1 open:1 instruction:1 gish:1 pavel:2 initial:2 wrapper:7 united:1 document:2 reynolds:3 contextual:1 ronan:1 happen:1 shape:1 sponsored:1 cue:1 prohibitive:1 selected:1 item:1 intelligence:1 indicative:1 beginning:2 ith:1 short:2 provides:3 node:6 attack:1 along:3 constructed:2... |
2,506 | 3,273 | An in-silico Neural Model of Dynamic Routing
through Neuronal Coherence
Devarajan Sridharan?? , Brian Percival?? , John Arthur\ and Kwabena Boahen\
?
Program in Neurosciences,
?
Department of Electrical Engineering
and \ Department of Bioengineering
Stanford University
?
These authors contributed equally
{dsridhar, bp... | 3273 |@word trial:1 blindness:1 middle:2 open:1 grey:5 simulation:1 propagate:2 paulsen:1 thereby:5 solid:2 accommodate:1 series:1 tuned:1 current:4 com:1 surprising:1 activation:1 must:3 john:1 periodically:1 motor:3 tone:3 iso:2 mental:1 location:1 constructed:2 become:3 supply:1 fitting:1 combine:1 manner:1 indeed:1... |
2,507 | 3,274 | New Outer Bounds on the Marginal Polytope
David Sontag Tommi Jaakkola
Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139
dsontag,tommi@csail.mit.edu
Abstract
We give a new class of outer bounds on the marginal polytope, and propose a
cutting-plane algorit... | 3274 |@word trial:5 determinant:6 middle:1 seems:1 nd:1 c0:2 open:1 barahona:6 seek:1 tried:1 mitsubishi:1 carry:1 moment:1 initial:1 configuration:1 series:2 contains:1 karger:1 interestingly:1 current:1 chazelle:1 surprising:1 si:14 written:2 must:3 partition:19 j1:3 pseudomarginals:9 intelligence:1 fewer:1 amir:1 ac... |
2,508 | 3,275 | Adaptive Embedded Subgraph Algorithms using
Walk-Sum Analysis
Venkat Chandrasekaran, Jason K. Johnson, and Alan S. Willsky
Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
venkatc@mit.edu, jasonj@mit.edu, willsky@mit.edu
Abstract
We consider the estimation problem in Gau... | 3275 |@word h:2 briefly:1 middle:1 seems:1 nd:1 simulation:2 r:2 propagate:1 covariance:2 thereby:1 solid:1 recursively:1 reduction:6 initial:3 cyclic:3 series:1 loeliger:1 comparing:1 written:1 must:1 additive:1 partition:1 plot:2 update:2 stationary:23 greedy:4 guess:3 beginning:1 walksummable:1 pointer:1 provides:4 ... |
2,509 | 3,276 | Hidden Common Cause Relations in
Relational Learning
Ricardo Silva?
Gatsby Computational Neuroscience Unit
UCL, London, UK WC1N 3AR
rbas@gatsby.ucl.ac.uk
Wei Chu
Center for Computational Learning Systems
Columbia University, New York, NY 10115
chuwei@cs.columbia.edu
Zoubin Ghahramani
Department of Engineering
Univer... | 3276 |@word trial:1 polynomial:1 stronger:2 proportion:1 eng:1 covariance:20 profit:12 tr:1 cyclic:1 contains:1 score:1 bibliographic:1 document:1 past:1 existing:1 com:1 assigning:1 chu:3 written:1 fn:1 happen:1 informative:2 partition:2 shape:1 cheap:1 v:1 half:1 selected:1 parameterization:3 accordingly:1 mccallum:3... |
2,510 | 3,277 | Catching Up Faster in Bayesian
Model Selection and Model Averaging
?
Tim van Erven
Peter Grunwald
Steven de Rooij
Centrum voor Wiskunde en Informatica (CWI)
Kruislaan 413, P.O. Box 94079
1090 GB Amsterdam, The Netherlands
{Tim.van.Erven,Peter.Grunwald,Steven.de.Rooij}@cwi.nl
Abstract
Bayesian model averaging, model s... | 3277 |@word middle:1 achievable:1 compression:1 polynomial:2 stronger:1 km:6 closure:2 automat:1 thereby:2 initial:1 contains:2 erven:2 past:4 current:1 ka:1 od:4 dx:1 must:3 realistic:1 happen:2 drop:1 update:2 v:1 stationary:1 selected:1 guess:1 warmuth:1 xk:3 ith:1 provides:1 characterization:1 math:1 mathematical:1... |
2,511 | 3,278 | Spatial Latent Dirichlet Allocation
Xiaogang Wang and Eric Grimson
Computer Science and Artificial Intelligence Lab
Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
xgwang@csail.mit.edu, welg@csail.mit.edu
Abstract
In recent years, the language model Latent Dirichlet Allocation (LDA), which
clusters c... | 3278 |@word replicate:1 triggs:1 yjd:3 contains:2 document:76 assigning:1 partition:2 shape:1 plot:1 grass:4 intelligence:2 generative:6 selected:1 website:1 discovering:2 blei:3 quantized:2 codebook:5 location:4 welg:1 ik:2 inside:3 roughly:1 freeman:3 window:2 becomes:2 discover:5 finding:1 nj:1 temporal:8 sky:4 part... |
2,512 | 3,279 | Modeling image patches with a directed hierarchy of
Markov random fields
Simon Osindero and Geoffrey Hinton
Department of Computer Science, University of Toronto
6, King?s College Road, M5S 3G4, Canada
osindero,hinton@cs.toronto.edu
Abstract
We describe an efficient learning procedure for multilayer generative models ... | 3279 |@word unaltered:1 version:1 compression:1 seems:1 decomposition:1 covariance:1 contrastive:4 initial:1 configuration:4 tuned:1 document:1 subjective:1 activation:1 must:1 realistic:1 visible:24 partition:1 update:13 generative:9 leaf:1 selected:1 half:1 greedy:1 contribute:2 toronto:3 location:6 five:1 descendant... |
2,513 | 328 | RecNorm: Simultaneous Normalisation and
Classification applied to Speech Recognition
John S. Bridle
Royal Signals and Radar Est.
Great Malvern
UK WR143PS
Stephen J. Cox
British Telecom Research Labs.
Ipswich
UK IP57RE
Abstract
A particular form of neural network is described, which has terminals
for acoustic pattern... | 328 |@word cox:5 version:1 inversion:1 seems:1 sex:1 d2:1 propagate:3 tried:1 covariance:2 pick:1 minus:1 reduction:2 initial:1 series:1 current:1 gqj:1 john:1 interpretable:1 short:3 simpler:1 become:1 supply:1 differential:1 li3:1 indeed:1 themselves:1 nor:2 terminal:3 little:1 project:1 estimating:1 linearity:1 spok... |
2,514 | 3,280 | Compressed Regression
Shuheng Zhou? John Lafferty?? Larry Wasserman??
? Computer
Science Department
of Statistics
? Machine Learning Department
? Department
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
Recent research has studied the role of sparsity in high dimensional regression and
signal reconstructi... | 3280 |@word trial:4 private:1 version:2 briefly:2 compression:15 stronger:1 norm:2 turlach:1 heuristically:1 simulation:6 seek:1 bn:35 covariance:3 mention:1 pressed:1 carry:2 celebrated:1 liu:1 selecting:1 recovered:1 current:1 si:1 john:1 additive:2 designed:1 plot:5 fewer:1 parametrization:1 record:2 persistency:2 p... |
2,515 | 3,281 | The Infinite Markov Model
Daichi Mochihashi ?
NTT Communication Science Laboratories
Hikaridai 2-4, Keihanna Science City
Kyoto, Japan 619-0237
daichi@cslab.kecl.ntt.co.jp
Eiichiro Sumita
ATR / NICT
Hikaridai 2-2, Keihanna Science City
Kyoto, Japan 619-0288
eiichiro.sumita@atr.jp
Abstract
We present a nonparametric B... | 3281 |@word msr:1 version:1 bigram:3 compression:3 proportion:2 tr:1 accommodate:1 recursively:4 configuration:1 contains:1 fragment:1 united:3 series:1 daniel:2 document:7 interestingly:1 bejerano:1 current:1 z2:1 nt:13 com:1 must:3 stemming:1 partition:1 remove:2 generative:5 prohibitive:1 leaf:2 item:1 fewer:2 accor... |
2,516 | 3,282 | Variational Inference for Diffusion Processes
Manfred Opper
Technical University Berlin
opperm@cs.tu-berlin.de
C?edric Archambeau
University College London
c.archambeau@cs.ucl.ac.uk
Yuan Shen
Aston University
y.shen2@aston.ac.uk
Dan Cornford
Aston University
d.cornford@aston.ac.uk
John Shawe-Taylor
University Colleg... | 3282 |@word closure:1 simulation:2 covariance:16 tr:4 solid:1 edric:1 moment:1 initial:6 contains:1 series:1 interestingly:1 existing:1 must:1 john:1 numerical:2 additive:2 informative:1 shape:3 cheap:1 sdes:1 update:1 stationary:2 isotropic:1 xk:23 manfred:1 provides:1 simpler:1 mathematical:1 along:2 constructed:1 di... |
2,517 | 3,283 | Ensemble Clustering using Semidefinite
Programming
Vikas Singh
Biostatistics and Medical Informatics
University of Wisconsin ? Madison
Lopamudra Mukherjee
Computer Science and Engineering
State University of New York at Buffalo
vsingh @ biostat.wisc.edu
lm37 @ cse.buffalo.edu
Jiming Peng
Industrial and Enterprise ... | 3283 |@word repository:1 polynomial:3 seems:4 norm:2 yi0:3 surfboard:1 d2:4 tamayo:1 simulation:2 attended:1 pick:1 mention:1 tr:12 initial:1 wedding:1 contains:1 outperforms:1 existing:3 assigning:2 attracted:1 must:5 written:1 subsequent:2 partition:5 shakespeare:1 mislabelled:4 fund:1 resampling:1 intelligence:1 sel... |
2,518 | 3,284 | Gaussian Process Models for
Link Analysis and Transfer Learning
Kai Yu
NEC Laboratories America
Cupertino, CA 95014
Wei Chu
Columbia University, CCLS
New York, NY 10115
Abstract
This paper aims to model relational data on edges of networks. We describe appropriate Gaussian Processes (GPs) for directed, undirected, a... | 3284 |@word trial:3 determinant:3 briefly:2 seems:1 norm:1 c0:1 tried:1 covariance:23 decomposition:1 tr:5 series:1 contains:1 score:3 document:1 interestingly:2 blank:1 recovered:1 chu:3 written:1 numerical:1 informative:1 predetermined:1 update:1 intelligence:3 selected:2 item:3 accordingly:1 earson:2 yamada:1 blei:1... |
2,519 | 3,285 | Linear Programming Analysis of Loopy Belief
Propagation for Weighted Matching
Sujay Sanghavi, Dmitry M. Malioutov and Alan S. Willsky
Laboratory for Information and Decision Systems
Massachusetts Institute of Technology
Cambridge, MA 02139
{sanghavi,dmm,willsky}@mit.edu
Abstract
Loopy belief propagation has been empl... | 3285 |@word trial:5 middle:1 version:2 simulation:2 contains:4 loeliger:1 ours:1 comparing:1 dumbbell:5 remove:1 designed:1 plot:6 update:3 depict:2 drop:1 intelligence:2 leaf:7 fewer:1 short:1 characterization:3 provides:2 node:29 multihop:1 mtj:1 along:1 c2:4 incorrect:2 prove:1 ghi:2 themselves:1 inspired:1 globally... |
2,520 | 3,286 | Efficient multiple hyperparameter
learning for log-linear models
Chuong B. Do
Chuan-Sheng Foo
Andrew Y. Ng
Computer Science Department
Stanford University
Stanford, CA 94305
{chuongdo,csfoo,ang}@cs.stanford.edu
Abstract
In problems where input features have varying amounts of noise, using distinct
regularization hype... | 3286 |@word version:1 inversion:3 bigram:2 proportion:4 simulation:6 covariance:1 recursively:1 reduction:2 initial:2 selecting:1 tuned:2 imaginary:1 existing:2 current:2 yet:3 must:3 parsing:3 written:1 numerical:4 treating:1 v:2 intelligence:1 prohibitive:1 parameterization:2 isotropic:1 scaffold:1 nnsp:3 ith:1 mccal... |
2,521 | 3,287 | A Probabilistic Model for Generating
Realistic Lip Movements from Speech
Gwenn Englebienne
School of Computer Science
University of Manchester
ge@cs.man.ac.uk
Tim F. Cootes
Imaging Science and Biomedical Engineering
University of Manchester
Tim.Cootes@manchester.ac.uk
Magnus Rattray
School of Computer Science
Univer... | 3287 |@word version:1 judgement:1 polynomial:1 seems:1 underst:1 d2:1 bn:7 simplifying:1 covariance:5 weekday:1 reduction:1 series:1 animated:3 outperforms:1 existing:2 current:2 comparing:2 must:2 visible:1 realistic:9 informative:1 shape:8 plot:3 interpretable:1 poritz:1 generative:6 intelligence:1 selected:1 maximis... |
2,522 | 3,288 | Density Estimation under Independent Similarly
Distributed Sampling Assumptions
Tony Jebara, Yingbo Song and Kapil Thadani
Department of Computer Science
Columbia University
New York, NY 10027
{ jebara,yingbo,kapil }@cs.columbia.edu
Abstract
A method is proposed for semiparametric estimation where parametric and nonp... | 3288 |@word kondor:1 version:1 kapil:2 covariance:4 score:7 bhattacharyya:21 recovered:1 comparing:1 nt:3 current:3 yet:1 dx:6 must:1 written:1 john:1 subsequent:1 partition:1 shape:1 analytic:2 designed:1 update:13 discrimination:1 greedy:2 blei:1 provides:2 math:2 simpler:1 direct:1 become:1 above1:1 incorrect:1 spec... |
2,523 | 3,289 | GRIFT: A graphical model for inferring visual
classification features from human data
Michael G. Ross
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
mgross@mit.edu
Andrew L. Cohen
Psychology Department
University of Massachusetts Amherst
Amherst, MA 01003
acohen@ps... | 3289 |@word trial:9 version:1 seems:1 r:3 simulation:4 brightness:7 paid:1 harder:1 initial:4 generatively:1 inefficiency:1 uma:1 efficacy:1 disparity:1 score:1 bc:1 recovered:6 current:2 activation:1 fn:1 additive:4 numerical:1 informative:3 enables:2 remove:1 interpretable:1 discrimination:1 half:1 pursued:1 indicati... |
2,524 | 329 | Discovering Viewpoint-Invariant Relationships
That Characterize Objects
Richard S. Zemel and Geoffrey E. Hinton
Department of Computer Science
University of Toronto
Toronto, ONT M5S lA4
Abstract
Using an unsupervised learning procedure, a network is trained on an ensemble of images of the same two-dimensional object ... | 329 |@word determinant:2 version:3 proportion:1 simulation:1 covariance:2 tr:1 solid:1 contains:3 fragment:2 score:6 bc:1 current:1 comparing:2 nowlan:1 yet:1 must:5 additive:1 realistic:1 shape:18 remove:1 plot:1 update:2 half:26 discovering:4 intelligence:1 toronto:3 location:1 simpler:1 become:1 specialize:2 expecte... |
2,525 | 3,290 | Temporal Difference Updating
without a Learning Rate
Marcus Hutter
RSISE@ANU and SML@NICTA
Canberra, ACT, 0200, Australia
marcus@hutter1.net www.hutter1.net
Shane Legg
IDSIA, Galleria 2, Manno-Lugano CH-6928, Switzerland
shane@vetta.org www.vetta.org/shane
Abstract
We derive an equation for temporal difference learni... | 3290 |@word briefly:1 version:3 middle:1 seems:1 simulation:2 tried:1 tr:1 carry:1 initial:2 configuration:1 tuned:2 bootstrapped:1 past:1 existing:1 current:5 yet:1 must:2 update:12 v:5 stationary:12 half:1 greedy:1 fewer:1 beginning:1 ith:1 normalising:1 provides:1 org:2 prove:1 combine:1 theoretically:1 peng:2 expec... |
2,526 | 3,291 | What Makes Some POMDP Problems Easy to Approximate?
David Hsu?
Wee Sun Lee?
?
Nan Rong?
?
Department of Computer Science
National University of Singapore
Singapore, 117590, Singapore
Department of Computer Science
Cornell University
Ithaca, NY 14853, USA
Abstract
Point-based algorithms have been surprisingly su... | 3291 |@word polynomial:10 seems:1 suitably:1 open:3 seek:1 simulation:4 condon:1 tr:9 recursively:2 reduction:3 initial:10 contains:4 interestingly:2 o2:1 existing:1 current:4 surprising:1 must:2 mundhenk:1 informative:3 intelligence:7 leaf:2 hamiltonian:3 smith:1 provides:1 node:12 successive:1 zhang:2 height:2 mathem... |
2,527 | 3,292 | Fast Variational Inference
for Large-scale Internet Diagnosis
John C. Platt
Emre K?c?man
Microsoft Research
1 Microsoft Way
Redmond, WA 98052
{jplatt,emrek,dmaltz}@microsoft.com
David A. Maltz
Abstract
Web servers on the Internet need to maintain high reliability, but the cause
of intermittent failures of web trans... | 3292 |@word nd:1 simulation:1 simplifying:1 sgd:3 initial:2 series:2 horvitz:1 rish:1 com:1 router:3 must:3 john:1 numerical:1 analytic:1 enables:1 visibility:1 update:2 v:1 generative:1 fewer:1 selected:1 intelligence:1 short:1 infrastructure:2 coarse:1 attack:3 diagnosing:1 direct:1 beta:10 symposium:1 consists:1 com... |
2,528 | 3,293 | A Game-Theoretic Approach to Apprenticeship
Learning
Umar Syed
Computer Science Department
Princeton University
35 Olden St
Princeton, NJ 08540-5233
usyed@cs.princeton.edu
Robert E. Schapire
Computer Science Department
Princeton University
35 Olden St
Princeton, NJ 08540-5233
schapire@cs.princeton.edu
Abstract
We st... | 3293 |@word version:3 pw:1 polynomial:1 pieter:1 simulation:1 seek:1 invoking:1 reduction:1 initial:4 minmax:1 exclusively:1 selecting:2 tuned:1 ours:1 rightmost:1 yet:1 must:3 remove:2 grass:1 stationary:7 selected:1 fewer:2 ith:2 provides:3 simpler:2 si1:1 direct:1 consists:2 prove:1 apprenticeship:12 notably:1 indee... |
2,529 | 3,294 | Modeling homophily and stochastic equivalence in
symmetric relational data
Peter D. Hoff
Departments of Statistics and Biostatistics
University of Washington
Seattle, WA 98195-4322.
hoff@stat.washington.edu
Abstract
This article discusses a latent variable model for inference and prediction of symmetric relational da... | 3294 |@word version:7 stronger:1 open:1 adrian:1 d2:1 decomposition:5 concise:1 accommodate:1 contains:1 ecole:1 longitudinal:1 current:2 abundantly:1 surprising:1 si:3 written:2 numerical:1 remove:1 update:1 grass:1 half:1 discovering:1 website:1 item:1 beginning:1 core:1 record:1 blei:1 provides:2 math:1 node:33 cont... |
2,530 | 3,295 | Discriminative Batch Mode Active Learning
Yuhong Guo and Dale Schuurmans
Department of Computing Science
University of Alberta
{yuhong, dale}@cs.ualberta.ca
Abstract
Active learning sequentially selects unlabeled instances to label with the goal of
reducing the effort needed to learn a good classifier. Most previous s... | 3295 |@word version:1 retraining:4 tedious:1 corral:3 tried:1 reduction:2 initial:1 configuration:2 score:11 selecting:5 crx:4 outperforms:1 existing:1 current:6 comparing:4 com:1 written:1 import:1 numerical:2 partition:3 informative:7 update:5 greedy:2 selected:12 guess:2 intelligence:2 flare:4 mccallum:2 provides:2 ... |
2,531 | 3,296 | Variational inference for Markov jump processes
Guido Sanguinetti
Department of Computer Science
University of Sheffield, U.K.
guido@dcs.shef.ac.uk
Manfred Opper
Department of Computer Science
Technische Universit?at Berlin
D-10587 Berlin, Germany
opperm@cs.tu-berlin.de
Abstract
Markov jump processes play an importa... | 3296 |@word middle:1 nd:1 mjp:7 open:1 simulation:3 seek:1 git:13 simplifying:1 fifteen:2 minus:1 solid:3 initial:3 selecting:1 pub:1 daniel:1 ours:1 interestingly:1 past:1 existing:1 reaction:3 current:1 analysed:1 must:2 john:1 realistic:1 subsequent:1 happen:1 drop:1 plot:1 update:2 stationary:1 intelligence:1 guess... |
2,532 | 3,297 | Receding Horizon
Differential Dynamic Programming
Yuval Tassa ?
Tom Erez & Bill Smart ?
Abstract
The control of high-dimensional, continuous, non-linear dynamical systems is a
key problem in reinforcement learning and control. Local, trajectory-based methods, using techniques such as Differential Dynamic Programming ... | 3297 |@word cu:2 briefly:1 eliminating:1 polynomial:1 inversion:1 open:4 simulation:2 propagate:1 covariance:1 locomotive:1 euclidian:1 solid:1 reduction:4 moment:1 configuration:3 series:1 synergistically:1 selecting:1 initial:1 reaction:1 current:3 discretization:1 surprising:1 yet:1 reminiscent:2 must:6 numerical:2 ... |
2,533 | 3,298 | Simulated Annealing: Rigorous finite-time guarantees
for optimization on continuous domains
Andrea Lecchini-Visintini
Department of Engineering
University of Leicester, UK
alv1@leicester.ac.uk
John Lygeros
Automatic Control Laboratory
ETH Zurich, Switzerland.
lygeros@control.ee.ethz.ch
Jan Maciejowski
Department of ... | 3298 |@word aircraft:1 trial:2 version:1 polynomial:2 norm:6 simulation:4 eng:1 carry:1 reduction:1 myles:1 configuration:2 contains:1 initial:1 selecting:1 ktv:5 existing:3 yet:1 dx:1 must:2 john:2 enables:1 analytic:1 haario:1 smith:1 provides:1 math:3 location:1 minorization:1 glover:1 constructed:1 become:1 prove:1... |
2,534 | 3,299 | A neural network implementing optimal state
estimation based on dynamic spike train decoding
Omer Bobrowski1 , Ron Meir1 , Shy Shoham2 and Yonina C. Eldar1
Department of Electrical Engineering1 and Biomedical Engineering2
Technion, Haifa 32000, Israel
{bober@tx},{rmeir@ee},{sshoham@bm},{yonina@ee}.technion.ac.il
Abst... | 3299 |@word open:1 d2:1 sensed:1 eng:1 p0:12 thereby:4 mention:1 initial:2 selecting:1 interestingly:1 current:3 nt:10 si:22 yet:1 written:1 realistic:1 tailoring:1 shape:2 analytic:1 motor:1 update:1 implying:2 pursued:1 xk:1 provides:2 characterization:4 statedependent:1 ron:1 location:3 mathematical:5 differential:1... |
2,535 | 33 | 642
LEARNING BY STATE RECURRENCE DETECfION
Bruce E. Rosen, James M. Goodwint, and Jacques J. Vidal
University of California, Los Angeles, Ca. 90024
ABSTRACT
This research investigates a new technique for unsupervised learning of nonlinear
control problems. The approach is applied both to Michie and Chambers BOXES
alg... | 33 |@word trial:18 simulation:8 harder:1 initial:1 configuration:1 genetic:1 ours:1 past:1 current:6 si:3 activation:1 must:3 numerical:1 designed:3 stationary:1 alone:1 intelligence:1 imitated:1 smith:3 hinged:1 short:9 provides:3 ire:1 revisited:1 traverse:3 lor:1 mathematical:1 along:1 differential:1 become:1 consis... |
2,536 | 330 | The Recurrent Cascade-Correlation Architecture
Scott E. Fahlman
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
Recurrent Cascade-Correlation CRCC) is a recurrent version of the CascadeCorrelation learning architecture of Fah Iman and Lebiere [Fahlman, 1990]. RCC
can learn from exa... | 330 |@word trial:9 version:4 glue:1 quickprop:2 propagate:1 dramatic:1 tr:1 harder:1 initial:2 contains:1 score:3 series:3 tram:1 rightmost:2 past:3 existing:2 current:4 yet:1 must:6 visible:1 sponsored:1 aside:2 alone:1 greedy:1 half:2 guess:1 fewer:1 signalling:1 smith:5 short:2 provides:1 node:1 toronto:1 along:2 pr... |
2,537 | 3,300 | Bayesian Inference for Spiking Neuron Models
with a Sparsity Prior
Sebastian Gerwinn
Jakob H Macke
Matthias Seeger
Matthias Bethge
Max Planck Institute for Biological Cybernetics
Spemannstrasse 41
72076 Tuebingen, Germany
{firstname.surname}@tuebingen.mpg.de
Abstract
Generalized linear models are the most commonly... | 3300 |@word seems:2 hippocampus:1 seek:1 covariance:9 exitatory:1 moment:1 contains:2 score:1 selecting:1 series:2 readily:1 refresh:1 tilted:1 numerical:1 informative:2 enables:1 kyb:1 drop:1 plot:4 update:2 intelligence:1 leaf:1 short:2 provides:1 characterization:1 burst:1 consists:3 sustained:1 combine:2 fitting:1 ... |
2,538 | 3,301 | Experience-Guided Search:
A Theory of Attentional Control
Michael C. Mozer
Department of Computer Science and
Institute of Cognitive Science
University of Colorado
mozer@colorado.edu
David Baldwin
Department of Computer Science
Indiana University
Bloomington, IN 47405
baldwind@indiana.edu
Abstract
People perform a r... | 3301 |@word trial:18 briefly:1 proportion:2 replicate:2 nd:1 d2:2 simulation:9 mention:1 thereby:1 accommodate:1 contains:2 series:2 tuned:5 ours:1 suppressing:1 past:2 imaginary:1 reaction:1 current:6 contextual:1 percep:1 activation:14 intriguing:1 must:3 written:1 cottrell:2 distant:2 tilted:4 j1:1 shape:1 wanted:2 ... |
2,539 | 3,302 | Privacy-Preserving Belief Propagation and Sampling
Michael Kearns, Jinsong Tan, and Jennifer Wortman
Department of Computer and Information Science
University of Pennsylvania, Philadelphia, PA 19104
Abstract
We provide provably privacy-preserving versions of belief propagation, Gibbs
sampling, and other local algorit... | 3302 |@word private:16 briefly:2 version:10 polynomial:11 stronger:2 invoking:1 initial:3 cyclic:1 contains:1 selecting:1 ours:1 current:10 collude:1 yet:1 assigning:1 must:4 numerical:3 partition:1 unmask:1 resampling:2 alone:4 stationary:1 leaf:7 intelligence:4 decrypted:1 xk:16 beginning:1 node:15 along:1 direct:2 b... |
2,540 | 3,303 | Progressive mixture rules are deviation suboptimal
Jean-Yves Audibert
Willow Project - Certis Lab
ParisTech, Ecole des Ponts
77455 Marne-la-Vall?ee, France
audibert@certis.enpc.fr
Abstract
We consider the learning task consisting in predicting as well as the best function
in a finite reference set G up to the smalles... | 3303 |@word logit:3 c0:2 open:1 ecole:1 tuned:1 existing:1 enpc:1 surprising:1 si:6 additive:2 juditsky:1 warmuth:2 zhang:1 c2:2 prove:5 inside:1 introduce:2 expected:4 indeed:2 behavior:1 inspired:1 ming:3 project:1 bounded:4 what:1 concave:2 finance:1 universit:1 wrong:2 control:2 converse:1 yn:2 producing:4 positive... |
2,541 | 3,304 | Kernels on Attributed Pointsets with Applications
Mehul Parsana1
mehul.parsana@gmail.com
Sourangshu Bhattacharya1
sourangshu@gmail.com
Chiranjib Bhattacharyya1
chiru@csa.iisc.ernet.in
K. R. Ramakrishnan2
krr@ee.iisc.ernet.in
Abstract
This paper introduces kernels on attributed pointsets, which are sets of vectors ... | 3304 |@word kondor:1 norm:1 km:1 gradual:1 seek:1 decomposition:2 eng:1 shot:8 bai:1 score:2 existing:6 ka:2 com:4 comparing:3 current:1 gmail:2 cruz:1 shape:6 hypothesize:1 intelligence:1 fewer:1 prohibitive:1 selected:1 affair:1 ith:2 kyoung:1 detecting:2 firstly:1 org:1 zhang:2 five:2 along:3 become:1 tagging:10 kar... |
2,542 | 3,305 | A General Boosting Method and its Application to
Learning Ranking Functions for Web Search
Zhaohui Zheng? Hongyuan Zha? Tong Zhang? Olivier Chapelle? Keke Chen? Gordon Sun?
?
Yahoo! Inc.
701 First Avene
Sunnyvale, CA 94089
{zhaohui,tzhang,chap,kchen,gzsun}@yahoo-inc.com
?
College of Computing
Georgia Institute of Tec... | 3305 |@word kgk:3 illustrating:1 norm:1 relevancy:1 d2:3 seek:1 pick:2 eld:1 arti:1 reduction:1 initial:1 contains:1 tuned:1 document:37 outperforms:2 existing:3 com:1 si:1 dx:4 readily:1 numerical:1 ranka:1 wx:1 cant:4 enables:1 update:1 greedy:2 leaf:2 guess:1 item:2 intelligence:1 xk:1 boosting:18 node:2 preference:... |
2,543 | 3,306 | Regret Minimization in Games with Incomplete
Information
Martin Zinkevich
maz@cs.ualberta.ca
Michael Johanson
johanson@cs.ualberta.ca
Carmelo Piccione
Computing Science Department
University of Alberta
Edmonton, AB Canada T6G2E8
carm@cs.ualberta.ca
Michael Bowling
Computing Science Department
University of Alberta
Ed... | 3306 |@word private:1 version:5 maz:1 achievable:1 proportion:1 coarseness:1 approachability:1 stronger:5 szafron:1 tried:1 selecting:1 prefix:3 past:2 outperforms:1 current:1 yet:2 must:1 additive:1 partition:8 enables:1 update:1 half:2 selected:2 fewer:2 intelligence:3 item:1 core:3 node:1 five:4 along:1 consists:2 i... |
2,544 | 3,307 | Colored Maximum Variance Unfolding
Le Song? , Alex Smola? , Karsten Borgwardt? and Arthur Gretton?
?
National ICT Australia, Canberra, Australia
?
University of Cambridge, Cambridge, United Kingdom
?
MPI for Biological Cybernetics, T?ubingen, Germany
{le.song,alex.smola}@nicta.com.au
kmb51@eng.cam.ac.uk,arthur.gretton... | 3307 |@word version:1 norm:4 confirms:1 covariance:4 eng:1 invoking:1 thereby:1 tr:25 fortuitous:1 reduction:4 initial:1 united:1 document:10 rkhs:1 interestingly:1 existing:1 ka:1 com:1 current:1 exy:2 stemmed:1 intriguing:1 written:2 goldberger:1 subsequent:1 happen:1 shape:1 christian:1 remove:1 drop:1 sys:2 vanishi... |
2,545 | 3,308 | Cooled and Relaxed Survey Propagation for MRFs
Hai Leong Chieu1,2 , Wee Sun Lee2
1
Singapore MIT Alliance
2
Department of Computer Science
National University of Singapore
Yee-Whye Teh
Gatsby Computational Neuroscience Unit
University College London
ywteh@gatsby.ucl.ac.uk
haileong@nus.edu.sg,leews@comp.nus.edu.sg
A... | 3308 |@word trial:1 version:1 pcc:2 seems:1 tried:2 recursively:1 kappen:2 initial:1 configuration:49 contains:1 loeliger:1 document:5 outperforms:5 current:1 comparing:2 ocurring:1 si:3 yet:1 conjunctive:1 written:1 must:1 partition:3 enables:1 remove:1 plot:2 update:5 n0:5 v:5 greedy:5 intelligence:2 item:3 node:4 or... |
2,546 | 3,309 | The Infinite Gamma-Poisson Feature Model
Michalis K. Titsias
School of Computer Science,
University of Manchester, UK
mtitsias@cs.man.ac.uk
Abstract
We present a probability distribution over non-negative integer valued matrices
with possibly an infinite number of columns. We also derive a stochastic process
that repr... | 3309 |@word middle:2 briefly:1 proportion:1 covariance:2 series:1 yni:9 must:1 partition:13 shape:4 plot:3 update:4 zik:1 occlude:1 generative:1 blei:1 location:14 firstly:2 five:3 unbounded:1 relabelling:1 dn:4 ewens:8 constructed:1 consists:1 combine:2 inside:1 introduce:1 frequently:1 uiuc:1 freeman:1 decreasing:1 d... |
2,547 | 331 | Natural Dolphin Echo Recog~ition Using an Integrator
Gateway Network
Herbert L. Roitblat
Department of Psychology, University
of Hawaii, Honolulu, HI 96822
Patrick W. B Moore, Paul E.
Nachtigall, & Ralph H. Penner
Naval Ocean Systems Center, Hawaii
Laboratory, Kailua, Hawaii, 96734
Abstract
We have been studying the... | 331 |@word trial:2 manageable:2 open:1 seek:1 simulation:2 pulse:1 accounting:1 mammal:1 solid:1 ne1work:2 initial:2 series:2 atlantic:3 current:1 activation:3 assigning:3 shape:1 designed:1 update:1 half:1 selected:1 plane:1 marine:1 short:1 provides:1 location:1 successive:10 along:1 terrace:1 combine:2 behavioral:1 ... |
2,548 | 3,310 | Infinite State Bayesian Networks
Max Welling?, Ian Porteous, Evgeniy Bart?
Donald Bren School of Information and Computer Sciences
University of California Irvine
Irvine, CA 92697-3425 USA
{welling,iporteou}@ics.uci.edu, bart@caltech.edu
Abstract
A general modeling framework is proposed that unifies nonparametric-Baye... | 3310 |@word middle:1 version:4 plsa:1 d2:3 confirms:1 seek:1 propagate:1 bn:3 solid:1 harder:1 carry:3 contains:1 document:13 interestingly:1 existing:3 z2:6 skipping:1 assigning:1 dechter:1 academia:1 j1:4 remove:3 bart:2 intelligence:1 leaf:2 item:23 mccallum:3 ji2:2 urp:1 blei:5 node:10 direct:1 become:2 consists:3 ... |
2,549 | 3,311 | Hippocampal Contributions to Control:
The Third Way
M?at?e Lengyel
Collegium Budapest Institute for Advanced Study
2 Szenth?aroms?ag u, Budapest, H-1014, Hungary
and
Computational & Biological Learning Lab
Cambridge University Engineering Department
Trumpington Street, Cambridge CB2 1PZ, UK
lmate@gatsby.ucl.ac.uk
Pete... | 3311 |@word trial:2 exploitation:2 version:2 middle:1 instrumental:1 hippocampus:6 seems:1 steck:1 simulation:8 seek:1 covariance:1 solid:2 moment:1 initial:1 past:1 outperforms:1 comparing:1 crippled:1 must:1 readily:1 realistic:1 happen:1 numerical:3 plot:1 medial:3 v:1 stationary:2 cue:1 alone:1 caveat:1 provides:4 ... |
2,550 | 3,312 | On Sparsity and Overcompleteness in Image Models
Pietro Berkes, Richard Turner, and Maneesh Sahani
Gatsby Computational Neuroscience Unit, UCL
Alexandra House, 17 Queen Square, London WC1N 3AR
Abstract
Computational models of visual cortex, and in particular those based on sparse
coding, have enjoyed much recent atte... | 3312 |@word deformed:1 version:2 briefly:1 proportion:1 open:2 grey:1 simulation:10 attainable:1 solid:1 initial:1 configuration:1 series:1 initialisation:1 tuned:1 current:1 comparing:2 recovered:1 activation:1 tackling:1 must:3 additive:1 visible:1 shape:1 pertinent:2 designed:1 update:2 generative:7 leaf:1 discoveri... |
2,551 | 3,313 | Sparse deep belief net model for visual area V2
Honglak Lee
Chaitanya Ekanadham
Andrew Y. Ng
Computer Science Department
Stanford University
Stanford, CA 94305
{hllee,chaitu,ang}@cs.stanford.edu
Abstract
Motivated in part by the hierarchical organization of the cortex, a number of algorithms have recently been propo... | 3313 |@word neurophysiology:1 trial:1 version:1 middle:1 wiesel:1 seems:1 replicate:1 hyv:1 decomposition:1 contrastive:4 pick:1 interestingly:2 current:2 com:1 activation:6 visible:8 subsequent:1 additive:1 shape:2 treating:1 plot:1 update:4 stationary:1 greedy:4 characterization:1 node:1 simpler:1 five:2 along:7 path... |
2,552 | 3,314 | Classification via Minimum Incremental Coding
Length (MICL)
John Wright?, Yi Ma
Coordinated Science Laboratory
University of Illinois at Urbana-Champaign
{jnwright,yima}@uiuc.edu
Yangyu Tao, Zhouchen Lin, Heung-Yeung Shum
Visual Computing Group
Microsoft Research Asia
{v-yatao,zhoulin,hshum}@microsoft.com
Abstract
We... | 3314 |@word trial:1 version:8 polynomial:3 compression:5 seek:1 simulation:2 covariance:6 jacob:1 eng:1 tr:1 carry:1 reduction:2 contains:1 series:1 njk:5 shum:2 document:2 interestingly:1 outperforms:7 existing:2 recovered:1 com:1 readily:1 john:1 shape:1 v:3 plane:1 xk:1 isotropic:2 ith:1 provides:5 detecting:1 codeb... |
2,553 | 3,315 | Collective Inference on Markov Models
for Modeling Bird Migration
Daniel Sheldon
M. A. Saleh Elmohamed
Dexter Kozen
Cornell University
Ithaca, NY 14853
{dsheldon,kozen}@cs.cornell.edu
saleh@cam.cornell.edu
Abstract
We investigate a family of inference problems on Markov models, where many
sample paths are drawn from... | 3315 |@word koopmans:1 briefly:1 polynomial:3 km:3 seek:3 decomposition:2 mention:1 yih:2 reduction:3 contains:1 united:1 charniak:1 daniel:2 current:1 nt:15 assigning:1 import:1 parsing:1 must:3 written:1 partition:5 generative:2 operationally:1 leaf:1 website:1 selected:1 half:1 mccallum:1 short:1 record:1 colored:1 ... |
2,554 | 3,316 | A configurable analog VLSI neural network with
spiking neurons and self-regulating plastic synapses
which classifies overlapping patterns
M. Giulioni?
Italian National Inst. of Health, Rome, Italy
INFN-RM2, Rome, Italy
giulioni@roma2.infn.it
D. Badoni
INFN-RM2, Rome, Italy
M. Pannunzi
Italian National Inst. of Health... | 3316 |@word trial:17 briefly:2 version:1 nd:1 simulation:6 solid:2 initial:1 efficacy:8 tuned:2 past:1 current:2 plasticity:2 v:1 implying:1 half:3 discrimination:2 device:5 beginning:1 short:3 characterization:1 provides:2 along:2 dn:2 profound:1 symposium:2 ouput:1 qualitative:1 vpre:2 paragraph:2 theoretically:2 beh... |
2,555 | 3,317 | A Bayesian LDA-based model for semi-supervised
part-of-speech tagging
Kristina Toutanova
Microsoft Research
Redmond, WA
kristout@microsoft.com
Mark Johnson
Brown University
Providence, RI
Mark Johnson@brown.edu
Abstract
We present a novel Bayesian model for semi-supervised part-of-speech tagging.
Our model extends t... | 3317 |@word version:2 plsa:13 contrastive:2 reduction:2 cyclic:1 contains:5 document:4 outperforms:6 com:1 comparing:1 si:48 parsing:1 john:1 hofmann:1 remove:2 reproducible:1 kristina:2 generative:1 selected:1 smith:2 blei:1 coarse:1 completeness:1 provides:1 contribute:1 tagger:2 along:1 constructed:2 c2:1 become:2 c... |
2,556 | 3,318 | Reinforcement Learning in Continuous Action Spaces
through Sequential Monte Carlo Methods
Alessandro Lazaric Marcello Restelli Andrea Bonarini
Department of Electronics and Information
Politecnico di Milano
piazza Leonardo da Vinci 32, I-20133 Milan, Italy
{bonarini,lazaric,restelli}@elet.polimi.it
Abstract
Learning ... | 3318 |@word h:1 trial:4 briefly:1 tr:1 initial:2 liu:1 contains:2 electronics:1 selecting:2 outperforms:2 hasselt:1 current:10 discretization:3 must:1 belmont:1 shape:4 remove:1 progressively:2 update:15 resampling:16 v:1 greedy:2 selected:2 fewer:1 intelligence:1 beginning:2 realizing:1 core:1 epanechnikov:1 provides:... |
2,557 | 3,319 | Adaptive Online Gradient Descent
Elad Hazan
IBM Almaden Research Center
650 Harry Road
San Jose, CA 95120
hazan@us.ibm.com
Peter L. Bartlett
Division of Computer Science
Department of Statistics
UC Berkeley
Berkeley, CA 94709
bartlett@cs.berkeley.edu
Alexander Rakhlin ?
Division of Computer Science
UC Berkeley
Berke... | 3319 |@word h:5 version:3 achievable:1 norm:22 d2:14 carry:1 current:1 com:1 yet:1 dx:1 must:1 remove:1 update:3 implying:1 warmuth:2 kyk:3 provides:2 shorthand:1 consists:1 prove:1 introduce:1 x0:2 indeed:2 considering:1 increasing:1 becomes:1 provided:3 bounded:3 linearity:1 moreover:1 notation:1 kind:2 minimizes:1 g... |
2,558 | 332 | Asymptotic slowing down of the
nearest- neighbor classifier
Robert R. Snapp
CS lEE Department
University of Vermont
Burlington, VT 05405
Demetri Psaltis
Electrical Engineering
Caltech 116-81
Pasadena, CA 91125
Santosh S. Venkatesh
Electrical Engineering
University of Pennsylvania
Philadelphia, PA 19104
Abstract
If ... | 332 |@word trial:2 achievable:1 duda:2 annoying:1 concise:1 reduction:1 contains:1 selecting:1 denoting:1 wd:1 surprising:1 assigning:1 dx:1 must:1 readily:1 john:1 numerical:2 happen:1 j1:2 benign:1 analytic:3 depict:1 cue:1 selected:5 guess:1 fewer:1 slowing:4 plane:1 reciprocal:1 caveat:1 provides:1 lx:1 c2:1 qualit... |
2,559 | 3,320 | Locality and low-dimensions in the prediction of
natural experience from fMRI
Franc?ois G. Meyer
Center for the Study of Brain, Mind and Behavior,
Program in Applied and Computational Mathematics
Princeton University
fmeyer@colorado.edu
Greg J. Stephens
Center for the Study of Brain, Mind and Behavior,
Department of P... | 3320 |@word h:1 middle:1 open:1 instruction:13 confirms:1 seek:2 covariance:2 commute:2 reduction:5 configuration:2 series:9 offering:1 interestingly:1 subjective:1 activation:3 eleven:1 motor:1 designed:1 drop:1 selected:1 parametrization:7 short:1 provides:7 contribute:1 location:3 node:1 along:1 constructed:1 direct... |
2,560 | 3,321 | FilterBoost: Regression and Classification on Large
Datasets
Robert E. Schapire
Department of Computer Science
Princeton University
Princeton, NJ 08540
schapire@cs.princeton.edu
Joseph K. Bradley
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA 15213
jkbradle@cs.cmu.edu
Abstract
We study boosting... | 3321 |@word repository:2 briefly:1 version:1 polynomial:1 seems:2 q1:1 forestry:1 interestingly:1 outperforms:3 existing:3 bradley:1 current:4 yet:3 must:4 realistic:1 additive:4 designed:4 plot:1 interpretable:1 resampling:8 v:4 fewer:3 accepting:1 filtered:1 provides:4 boosting:33 iterates:1 complication:1 ron:1 simp... |
2,561 | 3,322 | Measuring Neural Synchrony by Message Passing
Justin Dauwels
Amari Research Unit
RIKEN Brain Science Institute
Wako-shi, Saitama, Japan
justin@dauwels.com
Franc?ois Vialatte, Tomasz Rutkowski, and Andrzej Cichocki
Advanced Brain Signal Processing Laboratory
RIKEN Brain Science Institute
Wako-shi, Saitama, Japan
{fvial... | 3322 |@word mild:2 neurophysiology:2 seems:1 propagate:2 bn:4 minus:1 initial:1 cyclic:8 series:2 loeliger:1 interestingly:1 wako:2 existing:1 com:1 attracted:1 readily:1 tot:1 grassberger:2 fn:2 remove:1 update:9 n0:16 v:2 half:1 leaf:1 greedy:1 plane:2 xk:13 filtered:1 mental:2 detecting:2 node:6 c22:1 five:3 height:... |
2,562 | 3,323 | The Tradeoffs of Large Scale Learning
L?eon Bottou
NEC laboratories of America
Princeton, NJ 08540, USA
leon@bottou.org
Olivier Bousquet
Google Z?urich
8002 Zurich, Switzerland
olivier.bousquet@m4x.org
Abstract
This contribution develops a theoretical framework that takes into account the
effect of approximate optim... | 3323 |@word polynomial:3 loading:1 duda:1 nd:8 cleanly:1 d2:3 decomposition:4 covariance:1 pick:1 sgd:10 tr:4 carry:1 initial:2 series:2 chervonenkis:2 ecole:1 current:1 scovel:2 surprising:1 john:1 fn:21 realistic:1 numerical:1 kdd:1 update:2 intelligence:1 leaf:1 short:1 provides:4 iterates:2 complication:1 ron:1 org... |
2,563 | 3,324 | Augmented Functional Time Series Representation
and Forecasting with Gaussian Processes
Nicolas Chapados and Yoshua Bengio
Department of Computer Science and Operations Research
University of Montr?eal
Montr?eal, Qu?ebec, Canada H3C 3J7
{chapados,bengioy}@iro.umontreal.ca
Abstract
We introduce a functional representa... | 3324 |@word open:3 willing:1 simulation:1 covariance:19 profit:3 solid:1 ytn:1 series:44 tuned:1 past:1 existing:1 current:2 comparing:2 yet:1 must:4 readily:2 grain:2 periodically:1 realistic:1 chicago:1 predetermined:1 remove:1 plot:3 succeeding:1 progressively:2 half:1 selected:3 smith:1 short:11 farther:1 seasonali... |
2,564 | 3,325 | A general agnostic active learning algorithm
Sanjoy Dasgupta
UC San Diego
dasgupta@cs.ucsd.edu
Daniel Hsu
UC San Diego
djhsu@cs.ucsd.edu
Claire Monteleoni
UC San Diego
cmontel@cs.ucsd.edu
Abstract
We present an agnostic active learning algorithm for any hypothesis class
of bounded VC dimension under arbitrary data ... | 3325 |@word trial:1 polynomial:2 nd:1 open:1 solid:2 reduction:10 initial:1 substitution:1 contains:5 chervonenkis:2 daniel:1 err:19 comparing:2 beygelzimer:2 dx:13 must:2 additive:1 benign:1 confirming:1 atlas:5 ainen:2 plot:3 v:1 intelligence:2 prohibitive:2 fewer:2 plane:1 core:1 prespecified:1 coarse:1 complication... |
2,565 | 3,326 | Predicting Brain States from fMRI Data:
Incremental Functional Principal Component
Regression
S. Ghebreab
ISLA/HCS lab, Informatics Institute
University of Amsterdam, The Netherlands
ghebreab@science.uva.nl
A.W.M. Smeulders
ISLA lab, Informatics Institute
University of Amsterdam, The Netherlands
smeulders@science.uva... | 3326 |@word collinearity:1 proportion:1 open:1 pbil:4 r:2 uncovers:1 covariance:1 pbaic:3 solid:1 carry:1 moment:1 reduction:5 score:5 denoting:1 genetic:2 subjective:1 existing:1 activation:1 exposing:1 evans:1 enables:1 haxby:1 designed:1 atlas:2 update:1 alone:1 selected:3 item:1 short:1 core:4 mental:1 provides:3 b... |
2,566 | 3,327 | Rapid Inference on a Novel AND/OR graph for
Object Detection, Segmentation and Parsing
Yuanhao Chen
Department of Automation
University of Science and Technology of China
yhchen4@ustc.edu.cn
Chenxi Lin
Microsoft Research Asia
chenxil@microsoft.com
Long (Leo) Zhu
Department of Statistics
University of California, Los ... | 3327 |@word polynomial:4 grey:1 harder:2 recursively:3 configuration:34 contains:4 score:6 liu:1 outperforms:1 com:2 must:4 parsing:15 dechter:1 visible:1 partition:1 refines:3 shape:8 enables:3 designed:1 intelligence:2 fewer:1 leaf:18 half:2 cue:2 coughlan:2 colored:1 detecting:4 provides:1 node:116 location:2 succes... |
2,567 | 3,328 | Supervised topic models
Jon D. McAuliffe
Department of Statistics
University of Pennsylvania,
Wharton School
Philadelphia, PA
mcjon@wharton.upenn.edu
David M. Blei
Department of Computer Science
Princeton University
Princeton, NJ
blei@cs.princeton.edu
Abstract
We introduce supervised latent Dirichlet allocation (sLD... | 3328 |@word version:5 proportion:4 seems:1 suitably:1 proportionality:2 essay:1 r:2 harder:1 carry:1 moment:2 reduction:3 contains:4 document:47 rightmost:1 existing:1 recovered:1 com:2 wd:2 written:1 numerical:3 update:10 generative:4 mccallum:2 short:1 supplying:1 blei:7 provides:4 nonexchangeable:1 mathematical:1 di... |
2,568 | 3,329 | Optimistic Linear Programming gives Logarithmic
Regret for Irreducible MDPs
Ambuj Tewari
Computer Science Division
Univeristy of California, Berkeley
Berkeley, CA 94720, USA
ambuj@cs.berkeley.edu
Peter L. Bartlett
Computer Science Division and Department of Statistics
University of California, Berkeley
Berkeley, CA 9... | 3329 |@word exploitation:3 polynomial:1 seems:1 norm:3 simulation:1 contains:1 omniscient:1 current:5 nt:33 must:1 nt1:4 john:1 numerical:1 unichain:2 v:1 intelligence:1 accordingly:1 simpler:6 become:2 katehakis:5 prove:2 shorthand:1 inside:1 expected:5 roughly:1 themselves:1 behavior:1 inspired:2 provided:1 bounded:1... |
2,569 | 333 | Neural Dynamics of
Motion Segmentation and Grouping
Ennio Mingolla
Center for Adaptive Systems, and
Cognitive and Neural Systems Program
Boston University
111 Cummington Street
Boston, MA 02215
Abstract
A neural network model of motion segmentation by visual cortex is described. The model clarifies how preprocessing ... | 333 |@word middle:1 version:1 stronger:1 horizonta:1 seek:1 contains:2 tuned:4 activation:1 must:3 arrayed:1 shape:1 accordingly:1 short:2 contribute:1 location:1 successive:1 preference:5 node:1 along:5 become:2 consists:2 sustained:12 combine:3 introduce:1 notably:2 presumed:1 rapid:1 roughly:2 decreasing:2 consideri... |
2,570 | 3,330 | Distributed Inference for Latent Dirichlet Allocation
David Newman, Arthur Asuncion, Padhraic Smyth, Max Welling
Department of Computer Science
University of California, Irvine
newman,asuncion,smyth,welling @ics.uci.edu
Abstract
We investigate the problem of learning a widely-used latent-variable model ? the
Latent ... | 3330 |@word version:3 briefly:1 proportion:2 norm:1 plsa:1 simulation:2 initial:3 configuration:1 score:1 zij:1 exclusively:1 document:19 o2:1 current:3 z2:1 com:2 scatter:2 assigning:1 chu:1 partition:1 asymptote:1 plot:1 designed:1 update:8 generative:3 half:2 pursued:1 item:1 mccallum:3 blei:2 infrastructure:1 five:... |
2,571 | 3,331 | TrueSkill Through Time:
Revisiting the History of Chess
Pierre Dangauthier
INRIA Rhone Alpes
Grenoble, France
pierre.dangauthier@imag.fr
Ralf Herbrich
Microsoft Research Ltd.
Cambridge, UK
rherb@microsoft.com
Tom Minka
Microsoft Research Ltd.
Cambridge, UK
minka@microsoft.com
Thore Graepel
Microsoft Research Ltd.
C... | 3331 |@word pw:2 stronger:2 proportion:1 seems:1 crucially:1 propagate:2 thoreg:1 solid:2 carry:1 initial:1 necessity:1 series:5 score:1 denoting:1 interestingly:1 past:11 trueskill:27 current:3 com:5 bradley:2 comparing:1 si:3 pioneer:1 subsequent:1 plot:2 designed:1 update:9 initialises:1 selected:1 beginning:1 provi... |
2,572 | 3,332 | Learning and using relational theories
Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum
Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139
{ckemp,ndg,jbt}@mit.edu
Abstract
Much of human knowledge is organized into sophisticated systems that are often
called intuitive theories. We propose that intu... | 3332 |@word seems:2 nd:1 eld:2 minus:1 harder:1 score:2 subjective:13 outperforms:1 current:2 com:1 yet:1 conjunctive:2 must:2 realize:1 chicago:2 designed:1 plot:3 alone:1 infant:5 selected:1 item:2 short:1 gure:1 mental:2 provides:4 contribute:1 readability:1 simpler:1 along:1 initiative:1 expected:2 brain:1 inspired... |
2,573 | 3,333 | Bayes-Adaptive POMDPs
St?ephane Ross
McGill University
Montr?eal, Qc, Canada
sross12@cs.mcgill.ca
Brahim Chaib-draa
Laval University
Qu?ebec, Qc, Canada
chaib@ift.ulaval.ca
Joelle Pineau
McGill University
Montr?eal, Qc, Canada
jpineau@cs.mcgill.ca
Abstract
Bayesian Reinforcement Learning has generated substantial i... | 3333 |@word trial:2 exploitation:4 polynomial:1 open:2 seek:1 simulation:5 initial:4 freitas:1 current:4 wd:2 yet:1 must:4 analytic:1 remove:1 update:6 stationary:1 greedy:1 fewer:2 selected:1 intelligence:2 smith:1 recherche:1 lr:1 provides:2 banff:1 five:1 unbounded:1 mathematical:1 along:1 constructed:2 beta:1 becom... |
2,574 | 3,334 | Estimating disparity with confidence from energy
neurons
Eric K. C. Tsang
Dept. of Electronic and Computer Engr.
Hong Kong Univ. of Sci. and Tech.
Kowloon, HONG KONG SAR
eeeric@ee.ust.hk
Bertram E. Shi
Dept. of Electronic and Computer Engr.
Hong Kong Univ. of Sci. and Tech.
Kowloon, HONG KONG SAR
eebert@ee.ust.hk
Abs... | 3334 |@word neurophysiology:2 kong:5 trotter:1 gradual:1 tr:3 initial:2 disparity:113 efficacy:2 tuned:33 outperforms:1 imaginary:2 comparing:1 activation:2 dx:2 ust:2 finest:2 refines:1 discrimination:2 cue:1 half:2 stereoacuity:1 coarse:12 provides:1 location:18 successive:1 five:1 alert:1 constructed:1 incorrect:3 c... |
2,575 | 3,335 | Testing for Homogeneity
with Kernel Fisher Discriminant Analysis
Za??d Harchaoui
LTCI, TELECOM ParisTech and CNRS
46, rue Barrault, 75634 Paris cedex 13, France
zaid.harchaoui@enst.fr
Francis Bach
Willow Project, INRIA-ENS
45, rue d?Ulm, 75230 Paris, France
francis.bach@mines.org
?
Eric
Moulines
LTCI, TELECOM ParisT... | 3335 |@word version:1 polynomial:1 norm:5 smirnov:1 bf:1 c0:6 d2:7 bn:7 covariance:18 pg:1 moment:3 series:1 rkhs:6 reynolds:1 diagonalized:1 scovel:1 comparing:1 dx:2 readily:1 fn:3 zaid:1 accepting:1 barrault:2 org:1 mathematical:1 dn:6 consists:3 prove:5 introduce:1 theoretically:1 indeed:2 behavior:1 p1:23 nor:1 pl... |
2,576 | 3,336 | Near-Maximum Entropy Models for Binary
Neural Representations of Natural Images
Matthias Bethge and Philipp Berens
Max Planck Institute for Biological Cybernetics
Spemannstrasse 41, 72076, T?ubingen, Germany
mbethge,berens@tuebingen.mpg.de
Abstract
Maximum entropy analysis of binary variables provides an elegant way ... | 3336 |@word trial:1 cox:1 middle:2 seems:5 grey:1 km:2 seek:1 covariance:12 pg:1 tkacik:1 outlook:1 solid:3 garrigues:1 reduction:2 contains:1 seriously:1 current:2 jaynes:1 surprising:1 activation:3 yet:1 scatter:2 si:6 intriguing:1 numerical:5 partition:1 happen:1 kyb:1 remove:1 plot:3 drop:1 update:1 extrapolating:1... |
2,577 | 3,337 | Discriminative Log-Linear Grammars
with Latent Variables
Slav Petrov and Dan Klein
Computer Science Department, EECS Division
University of California at Berkeley, Berkeley, CA, 94720
{petrov, klein}@cs.berkeley.edu
Abstract
We demonstrate that log-linear grammars with latent variables can be practically
trained using... | 3337 |@word norm:1 open:1 contrastive:2 thereby:1 reduction:1 subordinating:1 generatively:2 score:18 charniak:2 tuned:1 bc:7 skipping:1 written:1 parsing:31 reminiscent:1 subsequent:1 numerical:2 partition:2 hoping:1 interpretable:2 update:2 v:4 generative:36 fewer:4 prohibitive:1 item:3 selected:1 reranking:4 mccallu... |
2,578 | 3,338 | Hierarchical Penalization
Marie Szafranski 1 , Yves Grandvalet 1, 2 and Pierre Morizet-Mahoudeaux 1
Heudiasyc 1 , UMR CNRS 6599
Universit?e de Technologie de Compi`egne
BP 20529, 60205 Compi`egne Cedex, France
IDIAP Research Institute 2
Av. des Pr?es-Beudin 20
P.O. Box 592, 1920 Martigny, Switzerland
marie.szafranski@h... | 3338 |@word repository:1 norm:4 turlach:1 tedious:1 sex:5 solid:2 initial:1 series:2 current:2 z2:1 mahoudeaux:1 bie:1 stemming:1 j1:1 shape:1 enables:2 remove:1 update:2 infant:2 half:1 leaf:2 selected:5 plane:2 egne:2 iterates:1 node:3 toronto:1 height:6 h4:1 yuan:1 consists:5 prove:1 fitting:3 combine:1 inside:1 int... |
2,579 | 3,339 | Support Vector Machine Classification
with Indefinite Kernels
Ronny Luss
ORFE, Princeton University
Princeton, NJ 08544
rluss@princeton.edu
Alexandre d?Aspremont
ORFE, Princeton University
Princeton, NJ 08544
aspremon@princeton.edu
Abstract
In this paper, we propose a method for support vector machine classification ... | 3339 |@word repository:2 version:1 eliminating:1 nd:2 decomposition:5 citeseer:1 tr:8 minus:1 initial:2 contains:2 score:2 ati:2 current:4 comparing:1 yet:2 written:1 must:1 numerical:4 analytic:11 cheap:2 plot:1 update:6 stationary:1 intelligence:3 plane:9 beginning:1 ith:6 steepest:1 epanechnikov:1 provides:2 zhang:1... |
2,580 | 334 | VLSI Implementations of Learning
and Memory Systems: A Review
Mark A. Holler
Intel Corporation
2250 Mission College Blvd.
Santa Clara, Ca. 95052-8125
ABSTRACT
A large number of VLSI implementations of neural network models
have been reported. The diversity of these implementations is
noteworthy. This paper attempts ... | 334 |@word proportion:1 replicate:1 open:1 instruction:1 pulse:4 mitsubishi:2 seek:1 etann:1 solid:3 minus:1 carry:1 disparity:1 current:1 comparing:2 clara:2 yet:1 activation:1 must:4 crawling:1 refresh:2 john:1 numerical:1 motor:1 designed:3 update:1 v:2 leaf:1 device:15 fewer:1 sram:1 steepest:1 provides:2 precison:... |
2,581 | 3,340 | Kernel Measures of Conditional Dependence
Kenji Fukumizu
Institute of Statistical Mathematics
4-6-7 Minami-Azabu, Minato-ku
Tokyo 106-8569 Japan
fukumizu@ism.ac.jp
Arthur Gretton
Max-Planck Institute for Biological Cybernetics
Spemannstra?e 38, 72076 T?ubingen, Germany
arthur.gretton@tuebingen.mpg.de
Xiaohai Sun
Max... | 3340 |@word h:9 briefly:1 middle:1 norm:10 seems:1 covariance:16 decomposition:1 creatinine:1 tr:2 reduction:2 moment:3 series:7 rkhs:8 bootstrapped:1 outperforms:1 yet:1 written:1 grassberger:1 plot:1 n0:1 colored:1 provides:1 math:1 gx:4 herbrich:1 direct:2 prove:2 consists:1 pairwise:1 theoretically:1 behavior:1 mpg... |
2,582 | 3,341 | Selecting Observations against Adversarial Objectives
Andreas Krause
SCS, CMU
H. Brendan McMahan
Google, Inc.
Carlos Guestrin
SCS, CMU
Anupam Gupta
SCS, CMU
Abstract
In many applications, one has to actively select among a set of expensive observations before making an informed decision. Often, we want to select o... | 3341 |@word trial:2 faculty:1 version:2 polynomial:4 norm:1 achievable:2 km:1 simulation:2 linearized:1 covariance:10 thereby:1 tr:3 reduction:14 initial:18 contains:1 score:9 selecting:4 united:1 tuned:3 interestingly:1 outperforms:6 discretization:1 z2:9 si:1 must:3 additive:1 realistic:2 informative:4 kdd:1 v:1 gree... |
2,583 | 3,342 | Collapsed Variational Inference for HDP
Yee Whye Teh
Gatsby Unit
University College London
Kenichi Kurihara
Dept. of Computer Science
Tokyo Institute of Technology
Max Welling
ICS
UC Irvine
ywteh@gatsby.ucl.ac.uk
kurihara@mi.cs.titech.ac.jp
welling@ics.uci.edu
Abstract
A wide variety of Dirichlet-multinomial ?to... | 3342 |@word middle:2 seems:1 proportion:1 nd:8 simulation:1 xtest:4 harder:1 initial:2 zij:1 denoting:2 ours:1 document:11 comparing:1 yet:1 readily:1 subsequent:1 plot:1 update:8 generative:1 intelligence:2 xk:2 ith:1 indefinitely:1 blei:3 caveat:2 completeness:1 firstly:2 simpler:1 five:1 along:1 direct:1 beta:4 prov... |
2,584 | 3,343 | Trans-dimensional MCMC for Bayesian Policy
Learning
Matt Hoffman
Dept. of Computer Science
University of British Columbia
hoffmanm@cs.ubc.ca
Arnaud Doucet
Depts. of Statistics and Computer Science
University of British Columbia
arnaud@cs.ubc.ca
Nando de Freitas
Dept. of Computer Science
University of British Columbi... | 3343 |@word version:1 simulation:7 tried:1 carry:2 initial:4 denoting:1 freitas:3 existing:1 current:4 written:1 must:1 porta:1 informative:1 klaas:1 enables:2 wanted:2 analytic:1 plot:8 motor:1 update:11 aside:1 intelligence:4 xk:19 parametrization:1 location:1 simpler:2 five:1 become:2 symposium:1 consists:2 reinterp... |
2,585 | 3,344 | Nearest-Neighbor-Based Active Learning for Rare
Category Detection
Jingrui He
School of Computer Science
Carnegie Mellon University
jingruih@cs.cmu.edu
Jaime Carbonell
School of Computer Science
Carnegie Mellon University
jgc@cs.cmu.edu
Abstract
Rare category detection is an open challenge for active learning, espec... | 3344 |@word repository:2 interleave:14 proportion:8 open:1 r:10 accounting:1 pick:2 asks:1 tr:3 score:7 selecting:2 undiscovered:1 existing:5 current:2 beygelzimer:1 si:9 yet:2 must:1 shape:1 update:1 generative:1 selected:12 xk:1 record:2 detecting:3 coarse:1 contribute:1 location:1 firstly:1 c2:3 differential:1 nnk:2... |
2,586 | 3,345 | The Value of Labeled and Unlabeled Examples when
the Model is Imperfect
Mikahil Belkin
Dept. of Computer Science and Engineering
Ohio State University
Columbus, OH 43210
mbelkin@cse.ohio-state.edu
Kaushik Sinha
Dept. of Computer Science and Engineering
Ohio State University
Columbus, OH 43210
sinhak@cse.ohio-state.edu... | 3345 |@word version:4 polynomial:1 seems:2 norm:8 d2:1 covariance:6 reduction:2 series:1 yet:1 intriguing:1 perror:40 dx:1 must:2 realistic:1 shape:1 alone:3 intelligence:1 provides:1 cse:2 along:1 scholkopf:1 incorrect:1 consists:1 fitting:24 expected:1 behavior:8 p1:15 frequently:1 roughly:3 spherical:6 actual:2 beco... |
2,587 | 3,346 | Robust Regression with Twinned Gaussian Processes
Andrew Naish-Guzman & Sean Holden
Computer Laboratory
University of Cambridge
Cambridge, CB3 0FD. United Kingdom
{agpn2,sbh11}@cl.cam.ac.uk
Abstract
We propose a Gaussian process (GP) framework for robust inference in which a
GP prior on the mixing weights of a two-co... | 3346 |@word version:1 inversion:1 proportion:1 grey:1 seek:2 crucially:1 covariance:5 accommodate:1 moment:10 series:2 united:1 mseeger:1 o2:1 current:1 recovered:2 must:3 refresh:2 tilted:3 distant:1 fn:23 remove:1 update:6 generative:1 fewer:1 parameterization:1 isotropic:1 provides:3 firstly:1 simpler:1 five:4 burst... |
2,588 | 3,347 | Computing Robust Counter-Strategies
Michael Johanson
johanson@cs.ualberta.ca
Martin Zinkevich
maz@cs.ualberta.ca
Michael Bowling
Computing Science Department
University of Alberta
Edmonton, AB Canada T6G2E8
bowling@cs.ualberta.ca
Abstract
Adaptation to other initially unknown agents often requires computing an effe... | 3347 |@word private:3 version:4 maz:1 exploitation:8 stronger:1 szafron:1 dramatic:2 reduction:1 selecting:1 past:1 current:1 yet:2 must:1 realistic:1 subcomponent:1 treating:1 designed:3 plot:1 drop:1 intelligence:4 selected:2 item:1 iterates:1 provides:2 node:1 five:3 become:1 symposium:1 consists:3 compose:1 introdu... |
2,589 | 3,348 | Fast and Scalable Training of Semi-Supervised CRFs
with Application to Activity Recognition
Maryam Mahdaviani
Computer Science Department
University of British Columbia
Vancouver, BC, Canada
Tanzeem Choudhury
Intel Research
1100 NE 45th Street
Seattle, WA 98105,USA
Abstract
We present a new and efficient semi-superv... | 3348 |@word cu:4 briefly:1 version:2 advantageous:1 norm:1 yi0:29 humidity:1 tedious:1 pressure:1 reduction:2 initial:1 contains:3 selecting:1 bc:1 document:1 outperforms:5 existing:2 freitas:1 current:1 contextual:1 comparing:1 additive:1 informative:1 cheap:1 update:2 generative:1 selected:2 intelligence:3 mccallum:4... |
2,590 | 3,349 | Theoretical Analysis of Learning with
Reward-Modulated Spike-Timing-Dependent
Plasticity
Robert Legenstein, Dejan Pecevski, Wolfgang Maass
Institute for Theoretical Computer Science
Graz University of Technology
A-8010 Graz, Austria
{legi,dejan,maass}@igi.tugraz.at
Abstract
Reward-modulated spike-timing-dependent pla... | 3349 |@word h:1 trial:2 pulse:2 pipa:1 simulation:25 solid:3 shading:1 initial:2 current:3 si:5 written:1 visible:1 realistic:2 plasticity:7 shape:5 analytic:1 drop:3 update:1 fund:1 stationary:2 half:1 beginning:1 short:2 provides:2 direct:1 differential:2 pairing:2 consists:1 behavioral:1 introduce:1 theoretically:1 ... |
2,591 | 335 | Stereopsis by a Neural Network
Which Learns the Constraints
Alireza Khotanzad and Ying-Wung Lee
Image Processing and Analysis Laboratory
Electrical Engineering Department
Southern Methodist University
Dallas, Texas 75275
Abstract
This paper presents a neural network (NN) approach to the problem of
stereopsis. The corr... | 335 |@word trial:2 tried:1 shot:1 initial:23 contains:1 disparity:11 ours:1 past:1 existing:1 si:2 happen:1 shape:3 progressively:1 half:2 selected:3 device:1 plane:4 record:1 node:20 along:6 constructed:1 consists:4 manner:1 inter:1 multi:1 automatically:2 actual:2 moreover:1 underlying:1 linearity:1 evolved:1 kind:1 ... |
2,592 | 3,350 | Random Sampling of States in Dynamic
Programming
Christopher G. Atkeson and Benjamin Stephens
Robotics Institute, Carnegie Mellon University
cga@cmu.edu, bstephens@cmu.edu
www.cs.cmu.edu/?cga, www.cs.cmu.edu/?bstephe1
Abstract
We combine three threads of research on approximate dynamic programming:
sparse random samp... | 3350 |@word middle:1 version:1 seems:1 nd:1 open:1 simulation:2 simplifying:1 initial:1 configuration:3 series:4 lqr:12 existing:5 current:11 discretization:1 yet:1 must:1 periodically:2 numerical:1 motor:1 plot:4 update:4 rrt:1 half:1 selected:4 greedy:1 intelligence:3 xk:4 smith:1 accepting:1 provides:5 recompute:1 l... |
2,593 | 3,351 | The Generalized FITC Approximation
Andrew Naish-Guzman & Sean Holden
Computer Laboratory
University of Cambridge
Cambridge, CB3 0FD. United Kingdom
{agpn2,sbh11}@cl.cam.ac.uk
Abstract
We present an efficient generalization of the sparse pseudo-input Gaussian process (SPGP) model developed by Snelson and Ghahramani [1... | 3351 |@word version:1 inversion:2 advantageous:1 nd:2 nonsensical:1 bn:5 covariance:13 p0:8 decomposition:1 reduction:2 moment:7 initial:3 series:1 united:1 pub:1 pt0:4 mseeger:1 err:4 current:1 ida:1 comparing:1 must:5 readily:1 refresh:3 fn:12 tilted:2 numerical:1 informative:5 partition:2 shape:1 distant:1 hypothesi... |
2,594 | 3,352 | A Randomized Algorithm for Large Scale Support
Vector Learning
Krishnan S.
Department of Computer Science and Automation, Indian Institute of Science, Bangalore-12
krishi@csa.iisc.ernet.in
Chiranjib Bhattacharyya
Department of Computer Science and Automation, Indian Institute of Science, Bangalore-12
chiru@csa.iisc.er... | 3352 |@word norm:3 termination:1 d2:1 covariance:2 pick:2 reduction:1 wrapper:1 contains:1 selecting:1 document:4 bhattacharyya:1 existing:1 current:1 com:1 written:1 kdd:1 plane:1 iterates:1 hyperplanes:2 unbounded:1 become:1 symposium:2 prove:2 consists:3 considering:2 solver:12 becomes:4 iisc:2 cardinality:1 bounded... |
2,595 | 3,353 | Consistent Minimization of Clustering Objective
Functions
Ulrike von Luxburg
Max Planck Institute for Biological Cybernetics
S?ebastien Bubeck
INRIA Futurs Lille, France
ulrike.luxburg@tuebingen.mpg.de
sebastien.bubeck@inria.fr
Stefanie Jegelka
Max Planck Institute for Biological Cybernetics
Michael Kaufmann
Unive... | 3353 |@word repository:7 polynomial:6 stronger:1 tried:1 simplifying:1 commute:3 pick:1 recursively:1 contains:3 selecting:1 denoting:2 current:2 ida:1 assigning:1 fn:59 partition:28 happen:1 wanted:1 designed:1 greedy:1 discovering:1 fx1:2 math:1 mcdiarmid:2 zhang:1 constructed:2 become:1 prove:3 consists:1 introduce:... |
2,596 | 3,354 | Loop Series and Bethe Variational Bounds
in Attractive Graphical Models
Erik B. Sudderth and Martin J. Wainwright
Electrical Engineering & Computer Science, University of California, Berkeley
sudderth@eecs.berkeley.edu, wainwrig@eecs.berkeley.edu
Alan S. Willsky
Electrical Engineering & Computer Science, Massachusetts... | 3354 |@word polynomial:5 calculus:1 accounting:1 kappen:2 moment:6 configuration:1 series:23 contains:1 loeliger:1 wainwrig:1 existing:1 recovered:1 comparing:1 must:2 partition:34 analytic:1 hypothesize:1 plot:1 update:2 stationary:1 leaf:1 parameterization:1 xk:2 short:1 core:8 provides:3 characterization:4 node:38 i... |
2,597 | 3,355 | Sequential Hypothesis Testing under Stochastic
Deadlines
Peter I. Frazier
ORFE
Princeton University
Princeton, NJ 08544
pfrazier@princeton.edu
Angela J. Yu
CSBMB
Princeton University
Princeton, NJ 08544
ajyu@princeton.edu
Abstract
Most models of decision-making in neuroscience assume an infinite horizon,
which yields... | 3355 |@word trial:4 version:2 seems:1 open:2 simulation:10 p0:2 q1:10 pressure:3 minus:1 solid:5 recursively:1 contains:3 series:1 past:1 timer:5 current:2 yet:1 must:5 written:2 numerical:4 happen:1 shape:2 plot:3 v:2 implying:1 xk:1 fa9550:1 mental:1 math:1 org:1 constructed:1 incorrect:1 behavioral:2 inside:1 introd... |
2,598 | 3,356 | Efficient Convex Relaxation for
Transductive Support Vector Machine
Zenglin Xu
Dept. of Computer Science & Engineering
The Chinese University of Hong Kong
Shatin, N.T., Hong Kong
zlxu@cse.cuhk.edu.hk
Rong Jin
Dept. of Computer Science & Engineering
Michigan State University
East Lansing, MI, 48824
rongjin@cse.msu.edu... | 3356 |@word kong:5 cu:4 version:1 pw:2 trial:2 advantageous:1 polynomial:1 retraining:1 nd:1 propagate:1 nemirovsky:1 contains:2 tuned:1 current:2 comparing:2 bie:1 attracted:1 written:1 import:1 drop:1 designed:1 intelligence:1 fewer:1 provides:4 cse:3 consists:1 introduce:3 lansing:1 valizadegan:1 zlxu:1 sdp:11 multi... |
2,599 | 3,357 | A Learning Framework for Nearest Neighbor Search
Sanjoy Dasgupta
Department of Computer Science
University of California, San Diego
dasgupta@cs.ucsd.edu
Lawrence Cayton
Department of Computer Science
University of California, San Diego
lcayton@cs.ucsd.edu
Abstract
Can we leverage learning techniques to build a fast ... | 3357 |@word repository:1 version:3 seems:1 stronger:2 norm:1 scg:1 p0:2 q1:5 pick:3 liu:1 series:1 tuned:3 ours:1 outperforms:1 z2:1 comparing:1 beygelzimer:1 si:3 must:3 subsequent:1 partition:6 kdd:3 moreno:1 designed:1 greedy:2 half:1 leaf:2 fewer:1 selected:1 ith:1 core:1 provides:1 node:1 location:5 traverse:1 sim... |
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