Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
2,300 | 3,088 | Large Margin Component Analysis
Lorenzo Torresani
Riya, Inc.
lorenzo@riya.com
Kuang-chih Lee
Riya, Inc.
kclee@riya.com
Abstract
Metric learning has been shown to significantly improve the accuracy of k-nearest
neighbor (kNN) classification. In problems involving thousands of features, distance learning algorithms ca... | 3088 |@word trial:2 repository:2 briefly:1 version:7 norm:1 seems:1 nd:1 d2:1 seek:3 covariance:1 decomposition:2 contrastive:1 dramatic:1 carry:1 reduction:17 initial:1 contains:4 tuned:1 com:2 goldberger:2 written:1 must:2 informative:1 update:5 intelligence:1 selected:1 guess:1 steepest:1 dover:1 provides:1 paramete... |
2,301 | 3,089 | Analysis of Empirical Bayesian Methods for
Neuroelectromagnetic Source Localization
David Wipf1 , Rey Ram??rez2 , Jason Palmer1,2 , Scott Makeig2 , & Bhaskar Rao1 ?
1
Signal Processing and Intelligent Systems Lab
2
Swartz Center for Computational Neuroscience
University of California, San Diego 92093
{dwipf,japalmer,b... | 3089 |@word mild:1 neurophysiology:1 version:1 norm:5 willing:1 covariance:20 decomposition:3 simplifying:2 thereby:1 delgado:1 moment:1 initial:1 configuration:4 efficacy:1 interestingly:1 envision:1 ati:1 existing:1 current:13 import:1 readily:1 kiebel:1 realistic:2 subsequent:1 noninformative:1 remove:1 treating:1 a... |
2,302 | 309 | A Recurrent Neural Network Model of
Velocity Storage in the Vestibulo-Ocular Reflex
Thomas J. Anastasio
Department of Otolaryngology
University of Southern California
School of Medicine
Los Angeles, CA 90033
Abstract
A three-layered neural network model was used to explore the organization of
the vestibulo-ocular ref... | 309 |@word neurophysiology:2 unaltered:1 version:1 faculty:1 integrative:1 contraction:1 mammal:2 solid:15 configuration:1 vor:28 subsequent:1 motor:1 plot:1 medial:2 fund:1 alone:1 plane:1 reciprocal:2 short:1 lr:6 filtered:1 sigmoidal:1 alert:2 direct:2 incorrect:1 pathway:1 combine:1 expected:2 behavior:1 themselves... |
2,303 | 3,090 | Inducing Metric Violations in Human Similarity
Judgements
2
Julian Laub1 , Jakob Macke2 , Klaus-Robert M?ller1,3 and Felix A. Wichmann2
1
Fraunhofer FIRST.IDA, Kekulestr. 7, 12489 Berlin, Germany
Max Planck Institut for Biological Cybernetics, Spemannstr. 38, 72076 T?bingen, Germany
3
University of Potsdam, Departmen... | 3090 |@word trial:3 middle:2 judgement:8 norm:2 stronger:1 sex:2 d2:4 decomposition:1 paid:1 series:1 score:1 existing:1 ida:1 yet:1 must:1 written:1 john:1 refresh:1 fn:1 subsequent:3 kyb:1 wanted:1 designed:1 v:2 half:2 selected:4 xk:3 mental:3 constructed:1 laub:1 introduce:5 pairwise:8 indeed:1 roughly:1 mpg:2 freq... |
2,304 | 3,091 | Game theoretic algorithms for Protein-DNA binding
Luis E. Ortiz
CSAIL - MIT
leortiz@csail.mit.edu
Luis P?erez-Breva
CSAIL-MIT
lpbreva@csail.mit.edu
Tommi Jaakkola
CSAIL - MIT
tommi@csail.mit.edu
Chen-Hsiang Yeang
UCSC
chyeang@soe.ucsc.edu
Abstract
We develop and analyze game-theoretic algorithms for predicting coo... | 3091 |@word version:1 briefly:1 simulation:6 ci2:21 franois:1 tr:1 contains:2 genetic:2 rightmost:1 reaction:2 current:3 com:1 si:3 must:1 readily:1 john:3 luis:5 written:1 subsequent:1 numerical:2 succeeding:1 update:2 v:2 intelligence:1 accordingly:1 beginning:1 ith:1 provides:2 location:1 simpler:1 mathematical:2 al... |
2,305 | 3,092 | iLSTD: Eligibility Traces and Convergence Analysis
Alborz Geramifard
Michael Bowling
Martin Zinkevich
Richard S. Sutton
Department of Computing Science
University of Alberta
Edmonton, Alberta
{alborz,bowling,maz,sutton}@cs.ualberta.ca
Abstract
We present new theoretical and empirical results with the iLSTD algorithm... | 3092 |@word trial:3 version:2 maz:1 open:2 dramatic:1 initial:1 inefficiency:1 exclusively:1 selecting:2 recovered:1 current:1 si:2 john:2 update:18 n0:4 aside:1 greedy:26 selected:4 intelligence:1 accordingly:1 ith:1 coarse:1 mathematical:1 prove:3 inside:2 theoretically:1 expected:3 behavior:1 examine:3 discounted:2 ... |
2,306 | 3,093 | Real-time adaptive information-theoretic
optimization of neurophysiology experiments?
Jeremy Lewi?
School of Bioengineering
Georgia Institute of Technology
jlewi@gatech.edu
Robert Butera
School of Electrical and Computer Engineering
Georgia Institute of Technology
rbutera@ece.gatech.edu
Liam Paninski ?
Department of... | 3093 |@word neurophysiology:5 trial:23 briefly:1 polynomial:2 c0:3 d2:7 simulation:5 covariance:6 solid:1 moment:1 reduction:1 past:3 current:1 must:5 realize:1 numerical:1 informative:3 plot:6 update:12 stationary:1 selected:1 desktop:1 ith:3 core:1 provides:2 putatively:1 zhang:1 along:1 above1:1 introduce:1 manner:1... |
2,307 | 3,094 | A Scalable Machine Learning Approach to Go
Lin Wu and Pierre Baldi
School of Information and Computer Sciences
University of California, Irvine
Irvine, CA 92697-3435
lwu,pfbaldi@ics.uci.edu
Abstract
Go is an ancient board game that poses unique opportunities and challenges for AI
and machine learning. Here we develop... | 3094 |@word illustrating:1 version:1 faculty:1 briefly:2 stronger:1 nd:2 reused:1 aske:1 simulation:5 tried:1 reduction:1 configuration:1 contains:3 minmax:1 cobb:1 score:1 icga:1 existing:1 current:2 liva:1 designed:1 joy:1 v:3 intelligence:6 selected:6 plane:21 isotropic:1 desktop:1 beginning:1 short:1 record:3 provi... |
2,308 | 3,095 | Graph-Based Visual Saliency
Jonathan Harel, Christof Koch , Pietro Perona
California Institute of Technology
Pasadena, CA 91125
{harel,koch}@klab.caltech.edu, perona@vision.caltech.edu
Abstract
A new bottom-up visual saliency model, Graph-Based Visual Saliency (GBVS), is
proposed. It consists of two steps: rst formin... | 3095 |@word version:1 compression:1 seems:1 nd:5 termination:3 seek:1 eld:2 mention:1 hereafter:1 offering:1 ours:1 existing:3 nally:2 activation:41 must:2 subsequent:1 distant:1 additive:2 partition:1 informative:1 cant:1 hypothesize:1 treating:1 discrimination:2 selected:1 instantiate:1 plane:2 caveat:1 node:26 locat... |
2,309 | 3,096 | Temporal dynamics of information content carried by
neurons in the primary visual cortex
Danko NikoliC*
Department of Neurophysiology
Max-Planck-Institute for Brain Research,
Frankfurt (Main), Germany
danko@mpih -frankfurt.mpg.de
Stefan Haeusler*
Institute for Theoretical Computer Science
Graz University of Technolog... | 3096 |@word neurophysiology:2 trial:3 nd:3 rint:3 versatile:1 solid:2 carry:3 moment:1 contains:1 series:1 interestingly:1 rightmost:1 past:4 current:2 com:1 blank:1 nt:1 comparing:1 activation:1 refresh:1 synchronicity:1 visible:1 subsequent:3 plot:1 drop:5 v:2 iso:1 short:1 record:1 filtered:1 anesthesia:1 along:1 ma... |
2,310 | 3,097 | Learning Nonparametric Models for
Probabilistic Imitation
David B. Grimes
Daniel R. Rashid
Rajesh P.N. Rao
Department of Computer Science
University of Washington
Seattle, WA 98195
grimes,rashid8,rao@cs.washington.edu
Abstract
Learning by imitation represents an important mechanism for rapid acquisition of
new behavi... | 3097 |@word trial:9 briefly:2 r:1 covariance:1 pressure:3 thereby:1 versatile:1 moment:1 initial:7 configuration:2 series:1 selecting:3 daniel:1 loeliger:1 o2:1 current:2 surprising:1 dx:1 must:1 biomechanical:5 partition:1 motor:3 plot:2 update:1 v:1 infant:1 selected:1 imitate:2 isotropic:1 xk:1 parametrization:1 col... |
2,311 | 3,098 | Multi-Robot Negotiation: Approximating the
Set of Subgame Perfect Equilibria in
General-Sum Stochastic Games
Geo?rey J. Gordon
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213
Chris Murray
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213
Abstract
In real-world planning problems, we... | 3098 |@word version:3 polynomial:1 advantageous:1 nd:12 pick:3 solid:1 carry:1 contains:1 ours:1 poser:1 current:5 must:8 realistic:2 happen:1 cheap:2 remove:1 maxv:1 stationary:5 intelligence:2 item:1 desktop:1 incredible:1 gure:2 hypersphere:1 provides:1 consulting:1 ron:1 preference:1 location:4 pun:2 along:4 direct... |
2,312 | 3,099 | Gaussian and Wishart Hyperkernels
Risi Kondor, Tony Jebara
Computer Science Department, Columbia University
1214 Amsterdam Avenue, New York, NY 10027, U.S.A.
{risi,jebara}@cs.columbia.edu
Abstract
We propose a new method for constructing hyperkenels and define two
promising special cases that can be computed in close... | 3099 |@word kondor:3 dz1:2 covariance:1 elisseeff:1 dramatic:1 tr:3 harder:2 reduction:2 denoting:1 rkhs:5 bhattacharyya:1 kx0:1 current:1 z2:4 com:1 si:1 yet:1 dx:1 reminiscent:1 must:1 written:1 forbidding:1 plot:3 interpretable:1 alone:1 half:1 isotropic:2 core:1 math:1 kvk2:1 become:1 ik:1 qualitative:1 shorthand:1... |
2,313 | 31 | 192
PHASE TRANSITIONS IN NEURAL NETWORKS
Joshua Chover
University of Wisconsin, Madison, WI
53706
ABSTRACT
Various simulat.ions of cort.ical subnetworks have evidenced
something like phase transitions with respect to key parameters.
We demonstrate that. such transi t.ions must. indeed exist. in analogous
infinite ... | 31 |@word cox:1 briefly:1 hippocampus:1 extinction:3 simulation:1 tat:1 thereby:1 solid:1 initial:3 configuration:2 efficacy:4 cort:2 intriguing:1 must:4 realistic:1 subsequent:1 plasticity:1 selected:1 patterning:2 ial:2 short:1 record:1 successive:1 simpler:1 become:1 wild:1 manner:1 intricate:1 expected:3 indeed:2 b... |
2,314 | 310 | Closed-Form Inversion of Backpropagation
Networks: Theory and Optimization Issues
Michael L. Rossen
HNC, Inc.
5.501 Oberlin Drive
San Diego, CA 92121
rossen@amos.ucsd.edu
Abstract
We describe a closed-form technique for mapping the output of a trained
backpropagation network int.o input activity space. The mapping is... | 310 |@word briefly:1 inversion:6 true:1 met:1 swing:1 added:2 sllch:1 moore:1 norma:1 vhen:1 question:2 dependence:1 propagat:1 diagonal:1 ll:4 ivation:1 during:1 subspace:1 reversed:1 orlando:1 generalized:4 generalization:1 participate:1 opt:1 outline:1 iple:1 reason:1 extension:1 erms:1 hall:1 image:23 activation:7 ... |
2,315 | 3,100 | Prediction on a Graph with a Perceptron
Mark Herbster, Massimiliano Pontil
Department of Computer Science
University College London
Gower Street, London WC1E 6BT, England, UK
{m.herbster, m.pontil}@cs.ucl.ac.uk
Abstract
We study the problem of online prediction of a noisy labeling of a graph with
the perceptron. We a... | 3100 |@word trial:5 kgk:4 kondor:2 norm:11 vi1:1 hu:3 km:1 crucially:1 incurs:1 contains:1 karger:1 kx0:4 current:1 comparing:1 z2:1 must:1 partition:1 frievald:1 enables:1 kv1:6 update:1 transposition:1 provides:1 math:2 banff:1 simpler:1 zhang:1 five:1 mathematical:1 dn:1 along:1 direct:1 c2:1 prove:6 consists:1 intr... |
2,316 | 3,101 | Adaptor Grammars: A Framework for Specifying
Compositional Nonparametric Bayesian Models
Mark Johnson
Microsoft Research / Brown University
Mark Johnson@Brown.edu
Thomas L. Griffiths
University of California, Berkeley
Tom Griffiths@Berkeley.edu
Sharon Goldwater
Stanford University
sgwater@gmail.com
Abstract
This pa... | 3101 |@word closure:1 bn:5 pick:1 recursively:2 initial:1 contains:2 score:1 selecting:1 prefix:2 past:1 existing:5 current:1 com:2 contextual:1 skipping:2 analysed:3 si:13 gmail:1 written:1 must:2 parsing:1 partition:2 enables:1 generative:2 selected:2 device:1 instantiate:1 ith:2 blei:1 node:8 successive:1 sits:1 con... |
2,317 | 3,102 | Uncertainty, phase and oscillatory hippocampal recall
M?at?e Lengyel and Peter Dayan
Gatsby Computational Neuroscience Unit
University College London
17 Queen Square, London WC1N 3AR, United Kingdom
{lmate,dayan}@gatsby.ucl.ac.uk
Abstract
Many neural areas, notably, the hippocampus, show structured, dynamical, popula... | 3102 |@word trial:2 middle:3 compression:1 hippocampus:7 proportion:1 seems:1 additively:1 simulation:5 paulsen:1 solid:2 initial:1 united:1 ording:1 existing:1 activation:1 must:1 additive:1 numerical:1 shape:1 enables:1 treating:1 drop:1 update:1 plot:1 designed:1 alone:1 cue:10 selected:1 device:2 ith:1 short:1 loca... |
2,318 | 3,103 | A Kernel Subspace Method by Stochastic Realization
for Learning Nonlinear Dynamical Systems
Yoshinobu Kawahara?
Dept. of Aeronautics & Astronautics
The University of Tokyo
Takehisa Yairi Kazuo Machida
Research Center for Advanced Science and Technology
The University of Tokyo
Komaba 4-6-1, Meguro-ku, Tokyo, 153-8904... | 3103 |@word norm:1 simulation:6 decomposition:3 covariance:12 solid:1 reduction:1 initial:2 tuned:1 past:4 yairi:2 si:1 written:1 readily:1 must:2 realistic:1 numerical:2 enables:2 stationary:4 intelligence:1 parameterization:1 pelckmans:1 ith:1 oblique:5 core:1 characterization:1 parameterizations:1 gx:2 mathematical:... |
2,319 | 3,104 | Nonnegative Sparse PCA
Ron Zass
and Amnon Shashua ?
Abstract
We describe a nonnegative variant of the ?Sparse PCA? problem. The goal is to
create a low dimensional representation from a collection of points which on the
one hand maximizes the variance of the projected points and on the other uses
only parts of the o... | 3104 |@word version:2 polynomial:1 norm:5 d2:1 seek:1 decomposition:7 covariance:3 pg:1 thereby:2 reduction:3 initial:1 mudassir:1 contains:1 dspca:3 past:1 yet:1 must:1 hou:1 subsequent:1 informative:3 drop:2 update:3 alone:1 greedy:2 guess:1 provides:1 ron:1 zhang:1 c2:3 direct:2 become:3 symposium:1 introduce:2 cons... |
2,320 | 3,105 | Temporal and Cross-Subject Probabilistic Models
for fMRI Prediction Tasks
Alexis Battle
Gal Chechik
Daphne Koller
Department of Computer Science
Stanford University
Stanford, CA 94305-9010
{ajbattle,gal,koller}@cs.stanford.edu
Abstract
We present a probabilistic model applied to the fMRI video rating prediction task
o... | 3105 |@word trial:1 cox:1 version:4 mri:2 r:24 covariance:1 pbaic:10 reduction:1 series:2 contains:2 selecting:2 score:1 tuned:1 interestingly:1 subjective:7 current:2 comparing:1 nt:1 activation:5 lang:1 yet:1 written:1 oxygenation:1 remove:1 update:2 v:11 stationary:1 alone:2 selected:11 mccallum:1 beginning:1 short:... |
2,321 | 3,106 | Attentional Processing on a Spike-Based VLSI Neural
Network
Yingxue Wang, Rodney Douglas, and Shih-Chii Liu
Institute of Neuroinformatics
University of Zurich and ETH Zurich
Winterthurerstrasse 190
CH-8057 Zurich, Switzerland
yingxue,rjd,shih@ini.phys.ethz.ch
Abstract
The neurons of the neocortex communicate by asynch... | 3106 |@word version:1 stronger:2 pulse:1 simulation:2 attended:5 initial:1 liu:4 substitution:1 efficacy:3 current:1 activation:2 plot:2 intelligence:1 selected:2 reciprocal:1 pointer:56 infrastructure:2 characterization:1 provides:1 location:6 firstly:2 five:1 mathematical:1 along:2 m7:2 symposium:1 transceiver:1 qual... |
2,322 | 3,107 | Convex Repeated Games and Fenchel Duality
1
Shai Shalev-Shwartz1 and Yoram Singer1,2
School of Computer Sci. & Eng., The Hebrew University, Jerusalem 91904, Israel
2
Google Inc. 1600 Amphitheater Parkway, Mountain View, CA 94043, USA
Abstract
We describe an algorithmic framework for an abstract game which we term a ... | 3107 |@word trial:6 briefly:1 norm:13 dekel:1 open:2 eng:1 initial:1 denoting:1 existing:1 yet:1 additive:1 benign:1 enables:4 update:9 greedy:1 warmuth:1 beginning:1 ith:1 core:1 provides:1 boosting:27 simpler:1 mathematical:2 along:1 constructed:1 direct:1 differential:3 consists:1 combine:2 redefine:1 p1:5 multi:1 e... |
2,323 | 3,108 | Relational Learning with Gaussian Processes
Wei Chu
CCLS
Columbia Univ.
New York, NY 10115
Vikas Sindhwani
Dept. of Comp. Sci.
Univ. of Chicago
Chicago, IL 60637
Zoubin Ghahramani
Dept. of Engineering
Univ. of Cambridge
Cambridge, UK
S. Sathiya Keerthi
Yahoo! Research
Media Studios North
Burbank, CA 91504
Abstract... | 3108 |@word trial:3 faculty:1 advantageous:1 heuristically:1 r:8 tried:1 covariance:18 carry:2 moment:2 contains:2 score:1 hereafter:1 tuned:2 document:11 outperforms:1 comparing:2 chu:4 written:3 numerical:1 chicago:2 informative:1 partition:2 treating:1 update:2 v:3 alone:1 intelligence:2 fx1:2 selected:2 fewer:1 acc... |
2,324 | 3,109 | Learning Motion Style Synthesis
from Perceptual Observations
Lorenzo Torresani
Riya, Inc.
lorenzo@riya.com
Peggy Hackney
Integrated Movement Studies
pjhackney@aol.com
Christoph Bregler
New York University
chris.bregler@nyu.edu
Abstract
This paper presents an algorithm for synthesis of human motion in specified styl... | 3109 |@word version:3 briefly:1 achievable:1 seek:1 initial:2 configuration:3 series:2 fragment:16 shum:1 tuned:1 animated:2 existing:2 recovered:1 com:2 yet:1 numerical:1 realistic:1 shape:2 enables:1 designed:1 resampling:1 selected:1 parameterization:1 destined:1 beginning:1 realism:2 sudden:1 coarse:1 provides:1 pa... |
2,325 | 311 | A Multiscale Adaptive Network Model of
Motion Computation in Primates
H. Taichi Wang
Dimal Mathur
Science Center, A18
Rockwell International
1049 Camino Dos Rios
Thousand Oaks, CA 91360
Science Center, A7A
Computation & Neural Systems
Caltech,216-76
Rockwell International
Pasadena, CA 91125
1049 Camino Dos Rios
Tho... | 311 |@word middle:2 briefly:1 open:1 simulation:3 series:1 discretization:5 si:1 tenned:1 written:2 finest:1 must:1 plot:1 mounting:1 alone:1 stationary:1 lrc:1 coarse:5 provides:2 brandt:2 oak:2 become:2 profound:1 incorrect:2 consists:1 pathway:1 nor:2 multi:5 totally:1 becomes:1 estimating:1 directionselective:1 wha... |
2,326 | 3,110 | A Kernel Method for the Two-Sample-Problem
Arthur Gretton
MPI for Biological Cybernetics
T?ubingen, Germany
arthur@tuebingen.mpg.de
Karsten M. Borgwardt
Ludwig-Maximilians-Univ.
Munich, Germany
kb@dbs.ifi.lmu.de
Bernhard Sch?olkopf
MPI for Biological Cybernetics
T?ubingen, Germany
bs@tuebingen.mpg.de
Malte Rasch
Gr... | 3110 |@word repository:1 version:1 briefly:1 norm:3 smirnov:4 arcones:1 nd:1 d2:1 bn:1 moment:3 series:1 ecole:1 rkhs:8 kurt:1 outperforms:1 current:1 comparing:5 analysed:1 yet:3 assigning:1 must:3 written:2 john:1 subsequent:1 kdd:3 cheap:1 kyb:1 designed:1 half:1 accepting:1 provides:3 detecting:1 mathematical:2 sym... |
2,327 | 3,111 | A Humanlike Predictor of Facial
Attractiveness
Amit Kagian*1, Gideon Dror?2, Tommer Leyvand *3, Daniel Cohen-Or *4, Eytan Ruppin*5
*
School of Computer Sciences, Tel-Aviv University, Tel-Aviv, 69978, Israel.
?
The Academic College of Tel-Aviv-Yaffo, Tel-Aviv, 64044, Israel.
Email: {1 kagianam, 3 tommer, 4dcor, 5rupp... | 3111 |@word cu:1 proportion:1 open:1 tried:2 rgb:1 photographer:1 euclidian:1 carry:1 anthropological:1 cyclic:1 series:1 score:29 selecting:2 contains:1 daniel:1 genetic:1 ours:1 interestingly:1 franklin:1 subjective:1 existing:1 current:1 surprising:1 intriguing:1 wherefore:1 shape:2 designed:1 infant:2 half:2 fewer:... |
2,328 | 3,112 | Efficient Learning of Sparse Representations
with an Energy-Based Model
Marc?Aurelio Ranzato Christopher Poultney Sumit Chopra Yann LeCun
Courant Institute of Mathematical Sciences
New York University, New York, NY 10003
{ranzato,crispy,sumit,yann}@cs.nyu.edu
Abstract
We describe a novel unsupervised method for learn... | 3112 |@word briefly:1 version:2 compression:2 seems:2 norm:2 nd:1 propagate:1 decomposition:1 contrastive:1 sparsifies:1 inpainting:2 initial:2 inefficiency:1 contains:2 document:1 current:3 wd:17 com:1 assigning:1 must:6 reminiscent:3 additive:2 numerical:1 update:1 progressively:1 generative:1 half:1 selected:1 recor... |
2,329 | 3,113 | A Collapsed Variational Bayesian Inference
Algorithm for Latent Dirichlet Allocation
Yee Whye Teh
David Newman and Max Welling
Gatsby Computational Neuroscience Unit Bren School of Information and Computer Science
University College London
University of California, Irvine
17 Queen Square, London WC1N 3AR, UK
CA 92697-... | 3113 |@word version:2 seems:1 proportion:2 confirms:1 crucially:1 accounting:1 xtest:2 series:1 zij:21 njk:9 document:17 current:4 com:1 assigning:1 reminiscent:1 must:1 update:4 fund:1 generative:1 mccallum:1 ith:1 blei:2 completeness:1 provides:1 vjk:5 combine:1 overhead:1 indeed:1 expected:3 rapid:1 growing:1 become... |
2,330 | 3,114 | A Theory of Retinal Population Coding
Eizaburo Doi
Center for the Neural Basis of Cognition
Carnegie Mellon University
Pittsburgh, PA 15213
edoi@cnbc.cmu.edu
Michael S. Lewicki
Center for the Neural Basis of Cognition
Carnegie Mellon University
Pittsburgh, PA 15213
lewicki@cnbc.cmu.edu
Abstract
Efficient coding mode... | 3114 |@word h:3 neurophysiology:1 version:1 proportion:1 nd:1 simulation:2 covariance:2 tr:7 reduction:1 initial:1 valois:1 current:2 elliptical:1 recovered:1 comparing:1 must:2 physiol:1 numerical:1 additive:5 blur:16 shape:1 analytic:1 remove:1 update:1 v:1 implying:1 half:1 fewer:1 accordingly:3 es:1 provides:3 char... |
2,331 | 3,115 | A Local Learning Approach for Clustering
Mingrui Wu, Bernhard Sch?olkopf
Max Planck Institute for Biological Cybernetics
72076 T?ubingen, Germany
{mingrui.wu, bernhard.schoelkopf}@tuebingen.mpg.de
Abstract
We present a local learning approach for clustering. The basic idea is that a good
clustering result should have... | 3115 |@word middle:1 briefly:2 norm:1 km:7 seek:1 tried:2 decomposition:2 electronics:1 contains:3 document:5 err:1 current:2 discretization:3 attracted:1 written:2 numerical:2 partition:7 analytic:1 spec:5 fewer:2 guess:1 selected:2 dover:1 node:2 rc:3 constructed:2 consists:2 combine:2 upenn:1 mpg:1 multi:1 ol:3 enco... |
2,332 | 3,116 | Stability of K-Means Clustering
Alexander Rakhlin
Department of Computer Science
UC Berkeley
Berkeley, CA 94720
rakhlin@cs.berkeley.edu
Andrea Caponnetto
Department of Computer Science
University of Chicago
Chicago, IL 60637
and
D.I.S.I., Universit`a di Genova, Italy
caponnet@uchicago.edu
Abstract
We phrase K-means c... | 3116 |@word version:1 polynomial:1 norm:2 nd:1 d2:1 elisseeff:1 tr:1 boundedness:1 selecting:2 comparing:1 scatter:6 written:1 chicago:2 partition:3 plot:1 alone:1 discovering:1 characterization:2 c2:1 become:2 symposium:1 prove:5 theoretically:3 expected:3 indeed:2 andrea:1 actual:1 little:1 considering:1 increasing:2... |
2,333 | 3,117 | Using Combinatorial Optimization
within Max-Product Belief Propagation
John Duchi
Daniel Tarlow
Gal Elidan
Daphne Koller
Department of Computer Science
Stanford University
Stanford, CA 94305-9010
{jduchi,dtarlow,galel,koller}@cs.stanford.edu
Abstract
In general, the problem of computing a maximum a posteriori (MAP) a... | 3117 |@word kohli:2 mri:1 complying:2 propagate:1 covariance:1 thereby:1 carry:2 initial:1 contains:5 score:32 series:2 karger:1 daniel:1 interestingly:1 amp:5 outperforms:3 existing:1 current:5 comparing:1 must:3 john:1 distant:1 partition:5 leaf:1 plane:1 beginning:1 tarlow:1 provides:1 node:18 location:4 preference:... |
2,334 | 3,118 | Conditional mean field
Nando de Freitas
Department of Computer Science
University of British Columbia
Vancouver, BC, Canada V6T 1Z4
nando@cs.ubc.ca
Peter Carbonetto
Department of Computer Science
University of British Columbia
Vancouver, BC, Canada V6T 1Z4
pcarbo@cs.ubc.ca
Abstract
Despite all the attention paid to v... | 3118 |@word nd:1 simulation:5 paid:1 solid:1 kappen:1 moment:1 configuration:5 initial:1 pub:1 tuned:1 bc:2 denoting:2 offering:1 freitas:4 recovered:1 current:1 surprising:1 yet:1 dx:8 must:2 subsequent:1 partition:21 analytic:1 remove:1 designed:2 plot:3 update:3 progressively:2 resampling:2 stationary:1 greedy:2 dep... |
2,335 | 3,119 | Modelling transcriptional regulation using Gaussian
processes
Neil D. Lawrence
School of Computer Science
University of Manchester, U.K.
neill@cs.man.ac.uk
Guido Sanguinetti
Department of Computer Science
University of Sheffield, U.K.
guido@dcs.shef.ac.uk
Magnus Rattray
School of Computer Science
University of Manch... | 3119 |@word grey:2 crucially:1 accounting:2 covariance:18 tr:1 solid:2 carry:2 initial:3 liu:1 efficacy:1 ours:1 affymetrix:3 mishra:1 current:1 discretization:1 nt:1 analysed:1 must:1 realistic:3 plot:1 treating:1 intelligence:1 monk:1 xk:2 ith:1 smith:1 provides:3 firstly:1 sigmoidal:1 five:2 differential:2 become:1 ... |
2,336 | 312 | Bumptrees for Efficient Function, Constraint, and
Classification Learning
Stephen M. Omohundro
International Computer Science Institute
1947 Center Street. Suite 600
Berkeley. California 94704
Abstract
A new class of data structures called "bumptrees" is described. These
structures are useful for efficiently implement... | 312 |@word version:1 open:1 simulation:1 decomposition:1 fonn:1 dramatic:1 asks:1 tr:1 recursively:1 current:1 comparing:1 yet:1 must:2 partition:3 update:1 leaf:8 fewer:2 discovering:1 math:1 node:8 location:2 contribute:1 five:1 along:2 compose:1 inside:1 expected:2 behavior:1 themselves:2 planning:1 multi:2 inspired... |
2,337 | 3,120 | A PAC-Bayes Risk Bound for General Loss Functions
Pascal Germain
D?epartement IFT-GLO
Universit?e Laval
Qu?ebec, Canada
Pascal.Germain.1@ulaval.ca
Alexandre Lacasse
D?epartement IFT-GLO
Universit?e Laval
Qu?ebec, Canada
Alexandre.Lacasse@ift.ulaval.ca
Franc?ois Laviolette
D?epartement IFT-GLO
Universit?e Laval
Qu?eb... | 3120 |@word repository:1 version:1 r:10 minus:1 moment:2 epartement:4 series:2 interestingly:1 err:2 mushroom:5 written:1 john:3 numerical:4 half:3 provides:3 boosting:14 contribute:1 consists:1 indeed:5 expected:10 behavior:6 examine:1 discretized:1 decreasing:2 automatically:1 encouraging:1 increasing:1 becomes:1 pro... |
2,338 | 3,121 | Kernel Maximum Entropy Data Transformation
and an Enhanced Spectral Clustering Algorithm
Robert Jenssen1?, Torbj?rn Eltoft1 , Mark Girolami2 and Deniz Erdogmus3
Department of Physics and Technology, University of Troms?, Norway
Department of Computing Science, University of Glasgow, Scotland
3
Department of Computer S... | 3121 |@word repository:1 middle:1 briefly:1 norm:1 seek:1 covariance:1 innermost:1 pick:2 carry:3 reduction:5 series:1 dx:6 must:3 deniz:1 realize:1 update:1 selected:1 scotland:1 epanechnikov:1 five:2 mathematical:1 along:2 consists:3 troms:1 introduce:1 torbj:1 expected:1 nor:1 decomposed:1 decreasing:1 actual:1 wind... |
2,339 | 3,122 | A Nonparametric Approach to
Bottom-Up Visual Saliency
Wolf Kienzle, Felix A. Wichmann, Bernhard Sch?olkopf, and Matthias O. Franz
Max Planck Institute for Biological Cybernetics,
Spemannstr. 38, 72076 T?ubingen, Germany
{kienzle,felix,bs,mof}@tuebingen.mpg.de
Abstract
This paper addresses the bottom-up influence of l... | 3122 |@word neurophysiology:1 version:1 briefly:1 tried:1 photographer:1 mention:1 carry:1 initial:5 score:4 interestingly:1 past:1 existing:9 surprising:1 must:1 visible:1 shape:1 plot:1 designed:1 v:1 alone:1 intelligence:2 selected:2 accordingly:1 coughlan:1 location:20 preference:2 height:1 along:1 constructed:1 qu... |
2,340 | 3,123 | Temporal Coding using the Response Properties
of Spiking Neurons
Thomas Voegtlin
INRIA - Campus Scientifique, B.P. 239
F-54506 Vandoeuvre-Les-Nancy Cedex, FRANCE
voegtlin@loria.fr
Abstract
In biological neurons, the timing of a spike depends on the timing of synaptic
currents, in a way that is classically described b... | 3123 |@word trial:12 version:3 rising:2 grey:5 simulation:2 dominique:1 paulsen:1 moment:1 initial:2 current:21 comparing:1 must:2 realistic:1 visible:3 plasticity:2 shape:5 remove:1 update:2 half:1 beginning:2 short:1 coarse:1 burst:4 differential:1 become:1 introduce:1 inter:2 isi:6 xz:1 multi:1 inspired:1 little:1 a... |
2,341 | 3,124 | Multiple Instance Learning for Computer Aided
Diagnosis
Glenn Fung, Murat Dundar, Balaji Krishnapuram, R. Bharat Rao
CAD & Knowledge Solutions, Siemens Medical Solutions USA, Malvern, PA 19355
{glenn.fung, murat.dundar, balaji.krishnapuram, bharat.rao}@siemens.com
Abstract
Many computer aided diagnosis (CAD) problems... | 3124 |@word illustrating:1 version:10 mri:1 eliminating:1 covariance:2 thereby:1 initial:1 substitution:1 series:1 contains:3 pub:1 tuned:2 outperforms:1 existing:3 current:2 com:1 cad:20 scatter:1 distant:1 happen:1 chicago:1 shape:1 hofmann:1 designed:1 plot:1 treating:1 half:1 fewer:1 leaf:1 guess:1 selected:1 intel... |
2,342 | 3,125 | Ordinal Regression by Extended Binary Classification
Hsuan-Tien Lin
Learning Systems Group
California Institute of Technology
htlin@caltech.edu
Ling Li
Learning Systems Group
California Institute of Technology
ling@caltech.edu
Abstract
We present a reduction framework from ordinal regression to binary classification... | 3125 |@word mild:1 trial:1 briefly:2 stronger:1 replicate:1 nd:2 suitably:1 flach:1 hu:4 bn:4 paid:1 reduction:21 contains:2 tuned:2 existing:6 current:1 comparing:1 chu:9 must:1 readily:1 john:1 happen:1 designed:1 intelligence:3 accordingly:1 farther:1 balc:1 provides:1 preference:2 herbrich:2 simpler:1 constructed:4... |
2,343 | 3,126 | Image Retrieval and Classification
Using Local Distance Functions
Andrea Frome
Department of Computer Science
UC Berkeley
Berkeley, CA 94720
andrea.frome@gmail.com
Yoram Singer
Google, Inc.
Mountain View, CA 94043
singer@google.com
Jitendra Malik
Department of Computer Science
UC Berkeley
malik@cs.berkeley.edu
Abst... | 3126 |@word version:1 norm:2 fifteen:1 tr:1 wjf:1 configuration:1 contains:3 series:1 score:1 denoting:1 elliptical:1 com:2 gmail:1 yet:2 must:2 visible:1 blur:15 shape:16 designed:1 alone:2 generative:4 fewer:2 beaver:1 ith:1 location:4 hsv:1 attack:1 zhang:6 five:1 direct:1 become:1 combine:2 fitting:1 manner:1 intro... |
2,344 | 3,127 | Similarity by Composition
Oren Boiman
Michal Irani
Dept. of Computer Science and Applied Math
The Weizmann Institute of Science
76100 Rehovot, Israel
Abstract
We propose a new approach for measuring similarity between two signals, which
is applicable to many machine learning tasks, and to many signal types. We say
th... | 3127 |@word briefly:1 seek:1 tried:1 gish:1 decomposition:3 accommodate:1 harder:1 shechtman:1 initial:1 configuration:5 contains:1 score:36 fragment:2 current:2 comparing:1 michal:1 di2:1 blank:1 informative:1 shape:2 motor:1 fund:1 bart:1 stationary:1 generative:1 discovering:1 accordingly:1 rav:1 short:3 lr:3 provid... |
2,345 | 3,128 | Learning with Hypergraphs: Clustering,
Classification, and Embedding
Dengyong Zhou? , Jiayuan Huang? , and Bernhard Sch?
olkopf?
?
NEC Laboratories America, Inc.
4 Independence Way, Suite 200, Princeton, NJ 08540, USA
?
School of Computer Science, University of Waterloo
Waterloo ON, N2L3G1, Canada
?
Max Planck Institut... | 3128 |@word trial:1 illustrating:1 version:1 middle:1 norm:1 seal:3 nd:1 vldb:1 zelnik:1 euclidian:1 mention:1 tr:2 moment:1 initial:2 contains:3 series:1 egfr:1 imaginary:1 current:1 incidence:1 si:2 assigning:1 mushroom:2 written:1 parsing:3 numerical:2 partition:10 shape:1 remove:1 designed:1 v:1 stationary:2 intell... |
2,346 | 3,129 | Speakers optimize information density through
syntactic reduction
Roger Levy
Department of Linguistics
UC San Diego
9500 Gilman Drive
La Jolla, CA 92093-0108, USA
rlevy@ling.ucsd.edu
T. Florian Jaeger
Department of Linguistics & Department of Psychology
Stanford University & UC San Diego
9500 Gilman Drive
La Jolla, C... | 3129 |@word faculty:1 version:1 bigram:1 addressee:2 open:1 seek:1 decomposition:1 prominence:1 pressure:1 recursively:1 reduction:41 loc:2 contains:1 charniak:7 bootstrapped:2 yet:2 written:2 parsing:4 subsequent:1 sponsored:1 n0:1 alone:2 cue:11 generative:1 cook:1 intelligence:1 beginning:3 probablity:1 provides:4 n... |
2,347 | 313 | Applications of Neural Networks in
Video Signal Processing
John C. Pearson, Clay D. Spence and Ronald Sverdlove
David Sarnoff Research Center
CN5300
Princeton, NJ 08543-5300
Abstract
Although color TV is an established technology, there are a number of
longstanding problems for which neural networks may be suited. Im... | 313 |@word version:1 instruction:1 simulation:6 tried:1 rgb:1 hsieh:1 reduction:2 electronics:5 configuration:1 series:1 contains:1 subjective:1 outperforms:1 current:7 com:1 john:4 ronald:1 numerical:1 visible:1 motor:1 remove:3 plot:1 designed:1 short:1 colored:2 filtered:1 node:7 successive:1 five:1 interprocessor:1... |
2,348 | 3,130 | Sparse Representation for Signal Classification
Ke Huang and Selin Aviyente
Department of Electrical and Computer Engineering
Michigan State University, East Lansing, MI 48824
{kehuang, aviyente}@egr.msu.edu
Abstract
In this paper, application of sparse representation (factorization) of signals over
an overcomplete b... | 3130 |@word version:1 norm:5 duda:1 nd:1 simulation:1 decomposition:5 inpainting:4 contains:4 outperforms:1 recovered:2 current:1 scatter:1 written:2 j1:12 enables:2 discrimination:31 v:1 intelligence:3 selected:4 greedy:1 xk:1 huo:1 detecting:1 location:1 toronto:1 zhang:1 constructed:2 direct:3 combine:3 combinationa... |
2,349 | 3,131 | Subordinate class recognition using relational object
models
Aharon Bar Hillel
Department of Computer Science
The Hebrew university of Jerusalem
aharonbh@cs.huji.ac.il
Daphna Weinshall
Department of Computer Science
The Hebrew university of Jerusalem
daphna@cs.huji.ac.il
Abstract
We address the problem of sub-ordinat... | 3131 |@word briefly:2 middle:1 seems:3 covariance:2 simplifying:1 anthropological:1 contains:2 score:2 denoting:2 existing:1 current:3 comparing:1 dct:2 realistic:1 informative:2 shape:1 motor:2 plot:1 drop:1 v:2 discrimination:5 generative:14 leaf:1 prohibitive:1 half:2 accordingly:1 sys:1 filtered:1 characterization:... |
2,350 | 3,132 | MLLE: Modified Locally Linear Embedding Using
Multiple Weights
Zhenyue Zhang
Department of Mathematics
Zhejiang University, Yuquan Campus,
Hangzhou, 310027, P. R. China
zyzhang@zju.edu.cn
Jing Wang
College of Information Science and Engineering
Huaqiao University
Quanzhou, 362021, P. R. China
Dep. of Mathematics, Zhe... | 3132 |@word middle:4 nd:3 suitably:1 open:1 d2:1 r:1 decomposition:1 pick:1 tr:2 solid:2 reduction:8 daniel:1 denoting:1 com:1 si:34 written:1 john:1 numerical:3 wanted:1 plot:5 selected:6 toronto:1 zhang:4 dn:1 constructed:1 along:2 scholkopf:1 prove:1 consists:1 theoretically:1 indeed:2 roughly:1 behavior:1 dist:2 gl... |
2,351 | 3,133 | Adaptive Spatial Filters with predefined Region of
Interest for EEG based Brain-Computer-Interfaces
Moritz Grosse-Wentrup
Institute of Automatic Control Engineering
Technische Universit?at M?unchen
80333 M?unchen, Germany
moritz@tum.de
Klaus Gramann
Department Psychology
Ludwig-Maximilians-Universit?at M?unchen
80802... | 3133 |@word neurophysiology:5 trial:16 version:1 briefly:3 middle:1 stronger:1 duda:1 nd:2 open:1 instruction:2 heuristically:2 covariance:15 harder:1 moment:7 mosher:1 suppressing:1 imaginary:11 current:2 written:1 realistic:1 numerical:1 enables:2 motor:48 designed:2 plot:3 v:1 implying:1 discrimination:1 nervous:1 p... |
2,352 | 3,134 | Logistic Regression for Single Trial EEG
Classification
Ryota Tomioka?
Kazuyuki Aihara?
Dept. of Mathematical Informatics,
IST, The University of Tokyo,
113-8656 Tokyo, Japan.
ryotat@first.fhg.de
aihara@sat.t.u-tokyo.ac.jp
Klaus-Robert M?
uller?
Dept. of Computer Science,
Technical University of Berlin,
Franklinstr. 2... | 3134 |@word trial:14 eliminating:1 norm:2 proportion:1 logit:3 open:1 simulation:1 decomposition:4 covariance:11 eng:4 tr:5 recursively:1 contains:1 imaginary:5 err:1 current:1 ida:1 written:2 motor:9 plot:4 reproducible:1 generative:2 half:2 cue:2 device:1 parameterization:3 sys:2 short:1 lr:9 filtered:2 provides:1 pa... |
2,353 | 3,135 | Branch and Bound for
Semi-Supervised Support Vector Machines
Olivier Chapelle1
Max Planck Institute
T?
ubingen, Germany
chapelle@tuebingen.mpg.de
Vikas Sindhwani
University of Chicago
Chicago, USA
vikass@cs.uchicago.edu
S. Sathiya Keerthi
Yahoo! Research
Santa Clara, USA
selvarak@yahoo-inc.com
Abstract
Semi-supervi... | 3135 |@word polynomial:1 seems:1 retraining:1 open:2 termination:1 recursively:3 selecting:2 current:4 com:2 clara:1 yet:2 assigning:1 john:1 chicago:2 kyb:1 treating:1 update:1 intelligence:1 leaf:6 selected:1 beginning:1 node:14 traverse:1 constructed:1 direct:1 consists:2 paragraph:1 expected:1 indeed:2 mpg:2 examin... |
2,354 | 3,136 | A Nonparametric Bayesian Method for Inferring
Features From Similarity Judgments
Daniel J. Navarro
School of Psychology
University of Adelaide
Adelaide, SA 5005, Australia
daniel.navarro@adelaide.edu.au
Thomas L. Griffiths
Department of Psychology
UC Berkeley
Berkeley, CA 94720, USA
tom griffiths@berkeley.edu
Abstrac... | 3136 |@word seems:1 proportion:1 open:1 seek:2 tried:1 simulation:4 heiser:1 pick:2 harder:1 accommodate:1 series:3 contains:1 daniel:2 existing:3 current:2 recovered:1 elliptical:1 surprising:1 written:2 indonesia:2 fn:2 additive:22 numerical:1 chicago:2 analytic:1 plot:2 interpretable:1 update:1 resampling:2 intellig... |
2,355 | 3,137 | Differential Entropic Clustering of Multivariate
Gaussians
Jason V. Davis
Inderjit Dhillon
Dept. of Computer Science
University of Texas at Austin
Austin, TX 78712
{jdavis,inderjit}@cs.utexas.edu
Abstract
Gaussian data is pervasive and many learning algorithms (e.g., k-means) model
their inputs as a single sample draw... | 3137 |@word determinant:1 version:1 norm:1 duda:1 humidity:4 nd:1 open:1 d2:2 seek:1 covariance:32 tr:12 solid:1 initial:1 series:3 score:1 selecting:1 contains:1 document:3 interestingly:3 ours:1 existing:1 current:1 wd:1 surprising:1 si:9 assigning:1 dx:2 written:1 must:1 john:1 plot:2 update:4 discovering:2 website:... |
2,356 | 3,138 | High-Dimensional Graphical Model Selection
Using `1-Regularized Logistic Regression
Martin J. Wainwright
Department of Statistics
Department of EECS
Univ. of California, Berkeley
Berkeley, CA 94720
Pradeep Ravikumar
Machine Learning Dept.
Carnegie Mellon Univ.
Pittsburgh, PA 15213
John D. Lafferty
Computer Science De... | 3138 |@word briefly:1 version:10 norm:1 r:4 bn:6 covariance:1 thereby:1 minus:2 liu:1 series:1 score:1 denoting:1 document:1 recovered:1 current:1 attracted:1 must:2 john:1 written:1 partition:1 plot:1 v:1 intelligence:2 provides:2 node:20 allerton:1 constructed:2 become:1 prove:1 shorthand:1 introduce:1 falsely:2 pair... |
2,357 | 3,139 | Scalable Discriminative Learning
for Natural Language Parsing and Translation
Joseph Turian, Benjamin Wellington, and I. Dan Melamed
{lastname}@cs.nyu.edu
Computer Science Department
New York University
New York, New York 10003
Abstract
Parsing and translating natural languages can be viewed as problems of predicting... | 3139 |@word illustrating:1 norm:1 heuristically:1 simplifying:1 recursively:2 initial:2 configuration:1 contains:1 score:2 charniak:2 pub:1 current:2 comparing:1 must:3 parsing:26 john:1 treating:1 sponsored:1 update:4 headword:1 v:1 generative:19 leaf:26 half:2 item:24 rudin:1 accordingly:1 graehl:2 reranking:1 beginn... |
2,358 | 314 | Second Order Properties of Error Surfaces :
Learning Time and Generalization
Yann Le Cun
Ido Kanter
AT &T Bell Laboratories Department of Physics
Bar Ilan University
Crawfords Corner Rd.
Holmdel, NJ 07733, USA Ramat Gan, 52100 Israel
Sara A. Sona
AT&T Bell Laboratories
Crawfords Corner Rd.
Holmdel, NJ 07733, USA
Abs... | 314 |@word trial:2 open:1 simulation:1 propagate:1 covariance:6 reduction:1 initial:1 configuration:1 elliptical:1 surprising:2 si:4 activation:5 yet:1 must:1 numerical:2 analytic:2 update:3 characterization:1 provides:7 clarified:1 along:3 constructed:1 behavior:4 multi:6 actual:1 considering:1 becomes:1 provided:1 es... |
2,359 | 3,140 | A recipe for optimizing a time-histogram
Hideaki Shimazaki
Department of Physics, Graduate School of Science
Kyoto University
Kyoto 606-8502, Japan
shimazaki@ton.scphys.kyoto-u.ac.jp
Shigeru Shinomoto
Department of Physics, Graduate School of Science
Kyoto University
Kyoto 606-8502, Japan
shinomoto@scphys.kyoto-u.ac.j... | 3140 |@word h:1 trial:6 neurophysiology:1 middle:1 adrian:1 decomposition:3 solid:1 selecting:3 hereafter:1 universality:1 must:1 vere:1 john:2 shape:1 extrapolating:2 plot:4 v:1 implying:1 selected:2 ith:3 vanishing:2 provides:1 psth:10 height:3 mathematical:1 constructed:4 direct:1 autocorrelation:1 manner:1 theoreti... |
2,360 | 3,141 | Causal inference in sensorimotor integration
Konrad P. Ko? rding
Department of Physiology and PM&R
Northwestern University
Chicago, IL 60611
konrad@koerding.com
Joshua B. Tenenbaum
Massachusetts Institute of Technology
Cambridge, MA 02139
jbt@mit.edu
Abstract
Many recent studies analyze how data from different modal... | 3141 |@word trial:9 version:2 seems:2 unif:3 sensed:1 tried:1 jacob:1 solid:1 ivaldi:1 configuration:4 exclusively:2 disparity:2 daniel:2 com:1 must:2 readily:2 stemming:1 subsequent:1 chicago:1 motor:13 plot:2 infant:2 cue:39 generative:1 weighing:3 nervous:6 tone:5 short:1 cognit:1 provides:1 along:2 fixation:2 combi... |
2,361 | 3,142 | An Approach to Bounded Rationality
Eli Ben-Sasson
Department of Computer Science
Technion ? Israel Institute
of Technology
Adam Tauman Kalai
Department of Computer Science
College of Computing
Georgia Tech
Ehud Kalai
MEDS Department
Kellogg Graduate School of Management
Northwestern University
Abstract
A central qu... | 3142 |@word exploitation:1 briefly:1 polynomial:3 achievable:3 seems:1 willing:2 noregret:1 q1:3 paid:1 minus:1 accommodate:1 shot:1 reduction:1 contains:2 selecting:2 existing:2 surprising:1 si:15 yet:2 must:4 realistic:1 chicago:1 enables:1 congestion:5 intelligence:1 selected:1 ith:1 prize:1 short:2 math:1 node:2 ea... |
2,362 | 3,143 | Multi-Task Feature Learning
Andreas Argyriou
Department of Computer Science
University College London
Gower Street, London WC1E 6BT, UK
a.argyriou@cs.ucl.ac.uk
Theodoros Evgeniou
Technology Management and Decision Sciences,
INSEAD,
Bd de Constance, Fontainebleau 77300, France
theodoros.evgeniou@insead.edu
Massimilia... | 3143 |@word middle:2 briefly:2 norm:21 lenk:1 seems:1 disk:1 covariance:1 accounting:1 serie:1 series:1 tuned:1 ecole:1 past:1 ka:2 current:1 od:4 bd:2 plot:3 depict:1 update:1 v:2 intelligence:1 selected:2 provides:2 boosting:1 preference:2 theodoros:2 simpler:1 zhang:2 along:2 c2:1 direct:1 kak22:2 consists:3 combine... |
2,363 | 3,144 | Randomized PCA Algorithms with Regret Bounds
that are Logarithmic in the Dimension
Manfred K. Warmuth
Computer Science Department
University of California - Santa Cruz
manfred@cse.ucsc.edu
Dima Kuzmin
Computer Science Department
University of California - Santa Cruz
dima@cse.ucsc.edu
Abstract
We design an on-line al... | 3144 |@word trial:13 version:4 norm:2 seems:1 bn:12 covariance:5 decomposition:1 pick:1 incurs:4 tr:20 accommodate:1 recursively:1 reduction:1 moment:1 initial:2 tuned:1 past:2 current:4 olkin:1 written:1 cruz:2 benign:1 remove:1 drop:1 plot:4 update:11 greedy:1 warmuth:9 beginning:1 ith:1 manfred:7 boosting:1 cse:2 al... |
2,364 | 3,145 | Geometric entropy minimization (GEM) for anomaly
detection and localization
Alfred O Hero, III
University of Michigan
Ann Arbor, MI 48109-2122
hero@umich.edu
Abstract
We introduce a novel adaptive non-parametric anomaly detection approach, called
GEM, that is based on the minimal covering properties of K-point entropi... | 3145 |@word version:1 proportion:2 disk:1 simulation:2 decomposition:1 p0:2 minus:1 versatile:2 contains:1 score:2 comparing:1 dx:4 must:1 mst:23 additive:1 partition:1 plot:3 v:2 greedy:6 selected:1 mpm:2 short:1 detecting:4 dell:1 height:1 mathematical:1 along:1 constructed:2 direct:1 symposium:1 scholkopf:2 clairvoy... |
2,365 | 3,146 | Part-based Probabilistic Point Matching using
Equivalence Constraints
Graham McNeill, Sethu Vijayakumar
Institute of Perception, Action and Behavior
School of Informatics, University of Edinburgh, Edinburgh, UK. EH9 3JZ
[graham.mcneill, sethu.vijayakumar]@ed.ac.uk
Abstract
Correspondence algorithms typically struggle... | 3146 |@word covariance:1 decomposition:8 jacob:2 initial:18 contains:1 selecting:1 current:1 luo:1 tackling:1 yet:1 must:1 written:1 partition:1 informative:1 shape:46 designed:1 update:4 maxv:1 generative:2 fewer:2 firstly:2 constructed:1 combine:1 fitting:2 introduce:1 expected:1 behavior:1 themselves:2 frequently:1 ... |
2,366 | 3,147 | Particle Filtering for Nonparametric
Bayesian Matrix Factorization
Frank Wood
Department of Computer Science
Brown University
Providence, RI 02912
fwood@cs.brown.edu
Thomas L. Griffiths
Department of Psychology
University of California, Berkeley
Berkeley, CA 94720
tom griffiths@berkeley.edu
Abstract
Many unsupervise... | 3147 |@word trial:3 middle:2 inversion:1 norm:1 nd:1 simulation:1 covariance:2 accounting:1 tr:3 recursively:3 initial:1 liu:1 contains:1 freitas:1 comparing:1 must:1 partition:1 analytic:2 plot:1 resampling:2 v:3 generative:2 intelligence:1 accordingly:1 ith:4 record:2 location:1 toronto:1 simpler:1 unbounded:4 ik:2 p... |
2,367 | 3,148 | Learning on Graph with Laplacian Regularization
Rie Kubota Ando
IBM T.J. Watson Research Center
Hawthorne, NY 10532, U.S.A.
rie1@us.ibm.com
Tong Zhang
Yahoo! Inc.
New York City, NY 10011, U.S.A.
tzhang@yahoo-inc.com
Abstract
We consider a general form of transductive learning on graphs with Laplacian
regularization,... | 3148 |@word version:1 norm:3 tr:1 harder:1 reduction:27 configuration:2 contains:1 series:1 practiced:3 tuned:1 outperforms:2 err:14 com:3 wd:1 comparing:1 written:1 realistic:1 remove:1 implying:1 core:9 provides:1 node:29 zhang:2 five:2 mathematical:1 become:1 prove:3 consists:3 manner:1 introduce:2 indeed:1 expected... |
2,368 | 3,149 | Bayesian Model Scoring in Markov Random Fields
Sridevi Parise
Bren School of Information and Computer Science
UC Irvine
Irvine, CA 92697-3425
sparise@ics.uci.edu
Max Welling
Bren School of Information and Computer Science
UC Irvine
Irvine, CA 92697-3425
welling@ics.uci.edu
Abstract
Scoring structures of undirected gr... | 3149 |@word seems:1 covariance:8 contrastive:3 tr:1 carry:1 moment:2 contains:3 score:28 uma:1 document:1 cxn:1 comparing:1 happen:3 partition:6 plot:1 alone:2 intelligence:4 mccallum:2 lr:24 node:10 firstly:1 direct:1 become:2 ik:6 freitag:1 doubly:1 introduce:1 deteriorate:1 pairwise:2 expected:2 multi:1 freeman:1 en... |
2,369 | 315 | A B-P ANN Commodity Trader
Joseph E. Collard
Martingale Research Corporation
100 Allentown Pkwy., Suite 211
Allen, Texas 75002
Abstract
An Artificial Neural Network (ANN) is trained to
recognize a buy/sell (long/short) pattern for a
particular commodity future contract.
The BackPropagation of errors algorithm was use... | 315 |@word implemented:1 consisted:5 trading:16 open:2 ti8:1 exclusive:1 round:1 pkwy:1 profit:15 pao:1 noted:1 bc:1 allen:1 past:1 percent:2 relationship:2 meaning:1 activation:1 yet:1 si:1 predict:1 john:1 numerical:1 negative:1 plot:3 volume:1 twenty:1 rts:4 agrees:1 eighteen:3 short:10 optional:1 mit:1 always:1 had... |
2,370 | 3,150 | Map-Reduce for Machine Learning on Multicore
Cheng-Tao Chu ?
chengtao@stanford.edu
Sang Kyun Kim ?
skkim38@stanford.edu
YuanYuan Yu ?
yuanyuan@stanford.edu
Gary Bradski ??
garybradski@gmail
Yi-An Lin ?
ianl@stanford.edu
Andrew Y. Ng ?
ang@cs.stanford.edu
Kunle Olukotun ?
kunle@cs.stanford.edu
?
. CS. Department, ... | 3150 |@word repository:1 version:1 briefly:1 inversion:3 covariance:3 decomposition:2 incurs:1 reaping:1 reduction:2 electronics:1 liu:1 contains:1 series:2 undiscovered:1 silvescu:1 com:1 gmail:1 chu:1 written:3 yet:2 assigning:1 must:1 john:1 subsequent:1 numerical:2 kdd:2 confirming:1 sponsored:1 update:6 device:1 x... |
2,371 | 3,151 | An Application of Reinforcement Learning to
Aerobatic Helicopter Flight
Pieter Abbeel, Adam Coates, Morgan Quigley, Andrew Y. Ng
Computer Science Dept.
Stanford University
Stanford, CA 94305
Abstract
Autonomous helicopter flight is widely regarded to be a highly challenging control
problem. This paper presents the fi... | 3151 |@word aircraft:1 polynomial:3 open:2 pieter:1 d2:1 simulation:4 linearized:3 harder:1 blade:8 moment:2 electronics:1 cyclic:3 contains:1 series:5 initial:5 lqr:7 longitudinal:2 current:7 must:2 wx:2 thrust:10 remove:1 designed:3 plot:1 stationary:1 half:2 indefinitely:2 provides:5 attack:2 airflow:1 rc:2 along:2 ... |
2,372 | 3,152 | Isotonic Conditional Random Fields
and Local Sentiment Flow
Yi Mao
School of Elec. and Computer Engineering
Purdue University - West Lafayette, IN
ymao@ecn.purdue.edu
Guy Lebanon
Department of Statistics, and
School of Elec. and Computer Engineering
Purdue University - West Lafayette, IN
lebanon@stat.purdue.edu
Abstr... | 3152 |@word version:4 inversion:2 yi0:2 accounting:1 photographer:1 solid:1 harder:1 series:1 denoting:1 document:20 interestingly:1 existing:1 current:1 activation:1 written:5 numerical:1 plot:1 selected:1 parameterization:3 mccallum:1 iso:1 characterization:1 node:1 simpler:1 incorrect:1 combine:1 introduce:2 manner:... |
2,373 | 3,153 | The Neurodynamics of Belief Propagation on Binary
Markov Random Fields
Ruedi Stoop
Institute of Neuroinformatics
ETH/UNIZH Zurich
Switzerland
ruedi@ini.phys.ethz.ch
Thomas Ott
Institute of Neuroinformatics
ETH/UNIZH Zurich
Switzerland
tott@ini.phys.ethz.ch
Abstract
We rigorously establish a close relationship between... | 3153 |@word seems:2 open:1 grey:3 outlook:1 kappen:1 reduction:2 configuration:1 initial:1 initialisation:5 activation:1 written:1 must:3 john:1 additive:1 realistic:2 update:4 intelligence:2 trapping:1 gtg:1 haykin:1 provides:2 node:6 along:1 constructed:1 direct:1 profound:1 pairwise:3 mechanic:1 brain:3 inspired:2 g... |
2,374 | 3,154 | Boosting Structured Prediction
for Imitation Learning
Nathan Ratliff, David Bradley, J. Andrew Bagnell, Joel Chestnutt
Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213
{ndr, dbradley, dbagnell, joel.chestnutt}@ri.cmu.edu
Abstract
The Maximum Margin Planning (MMP) (Ratliff et al., 2006) algorithm sol... | 3154 |@word briefly:1 version:7 simulation:1 solid:2 reduction:3 configuration:2 series:1 denoting:1 tuned:1 interestingly:1 outperforms:1 bradley:1 current:10 comparing:1 beygelzimer:2 si:1 yet:1 must:1 john:1 visible:2 hofmann:1 hypothesize:1 plot:2 designed:1 update:1 v:4 greedy:1 website:2 parameterization:1 imitat... |
2,375 | 3,155 | Sparse Multinomial Logistic Regression via Bayesian
L1 Regularisation
Gavin C. Cawley
School of Computing Sciences
University of East Anglia
Norwich, Norfolk, NR4 7TJ, U.K.
gcc@cmp.uea.ac.uk
Nicola L. C. Talbot
School of Computing Sciences
University of East Anglia
Norwich, Norfolk, NR4 7TJ, U.K.
nlct@cmp.uea.ac.uk
... | 3155 |@word version:1 eliminating:2 proportion:1 seems:1 seek:1 accommodate:1 cyclic:1 series:3 score:2 xnj:6 existing:3 virus:1 marquardt:1 yet:1 dx:1 written:3 john:1 additive:1 informative:1 remove:1 update:2 intelligence:3 selected:2 plane:1 beginning:1 scotland:1 ith:1 steepest:1 normalising:2 provides:3 contribut... |
2,376 | 3,156 | In-Network PCA and Anomaly Detection
Ling Huang
University of California
Berkeley, CA 94720
hling@cs.berkeley.edu
Michael I. Jordan
University of California
Berkeley, CA 94720
jordan@cs.berkeley.edu
XuanLong Nguyen
University of California
Berkeley, CA 94720
xuanlong@cs.berkeley.edu
Anthony Joseph
University of Cal... | 3156 |@word kolaczyk:1 version:5 norm:6 proportion:1 nd:1 scalably:1 covariance:5 decomposition:1 curtail:2 moment:1 bai:1 reduction:4 series:10 contains:1 initial:1 ours:1 current:1 com:2 adj:1 od:2 yet:1 must:3 periodically:1 subsequent:2 weyl:1 plot:7 update:17 discovering:1 device:1 selected:1 indicative:1 yno:4 xk... |
2,377 | 3,157 | Aggregating Classification Accuracy across
Time: Application to Single Trial EEG
Steven Lemm ?
Intelligent Data Analysis Group,
Fraunhofer Institute FIRST,
Kekulestr. 7
12489 Berlin,
Germany
Christin Sch?
afer
Intelligent Data Analysis Group,
Fraunhofer Institute FIRST,
Kekulestr. 7
12489 Berlin,
Germany
Gabriel Cur... | 3157 |@word neurophysiology:1 trial:18 briefly:1 duda:1 nd:1 cincotti:1 covariance:5 eng:5 solid:2 series:1 contains:1 exclusively:1 franklin:1 outperforms:1 imaginary:4 ida:1 activation:1 dx:1 must:1 john:1 subsequent:1 motor:11 discrimination:4 resampling:1 pursued:1 cue:2 device:1 v:2 sys:2 compo:1 detecting:1 provi... |
2,378 | 3,158 | Dirichlet-Enhanced Spam Filtering
based on Biased Samples
Steffen Bickel and Tobias Scheffer
Max-Planck-Institut f?ur Informatik, Saarbr?ucken, Germany
{bickel, scheffer}@mpi-inf.mpg.de
Abstract
We study a setting that is motivated by the problem of filtering spam messages
for many users. Each user receives messages ... | 3158 |@word trial:1 proportion:3 reused:1 incurs:1 reduction:8 contains:3 outperforms:5 existing:3 com:2 wouters:1 yet:1 fn:1 succeeding:1 update:3 v:2 stationary:1 alone:1 selected:2 prohibitive:1 xk:4 steal:1 prize:1 dissertation:1 blei:1 org:4 relayed:1 zhang:1 privacy:1 expected:7 mpg:1 nor:2 multi:1 steffen:1 reso... |
2,379 | 3,159 | An EM Algorithm for Localizing Multiple Sound
Sources in Reverberant Environments
Michael I. Mandel, Daniel P. W. Ellis
LabROSA, Dept. of Electrical Engineering
Columbia University
New York, NY
{mim,dpwe}@ee.columbia.edu
Tony Jebara
Dept. of Computer Science
Columbia University
New York, NY
jebara@cs.columbia.edu
Abs... | 3159 |@word mild:1 middle:1 eliminating:1 achievable:1 timefrequency:1 duda:2 brandstein:1 tedious:1 simulation:4 paid:1 minus:1 tr:1 accommodate:1 carry:1 contains:3 zij:11 daniel:4 outperforms:2 current:3 discretization:1 comparing:1 assigning:1 must:1 realistic:2 additive:1 numerical:1 informative:1 designed:3 plot:... |
2,380 | 316 | Learning Trajectory and Force Control
of an Artificial Muscle Arm
by Parallel-hierarchical Neural Network Model
Masazumi Katayama
Mitsuo Kawato
Cognitive Processes Department
ATR Auditory and Visual Perception Research Laboratories
Seika-cho. Soraku-gun. Kyoto 619-02. JAPAN
Abstract
We propose a new parallel-hierarch... | 316 |@word norm:2 km:1 rhesus:1 simulation:3 pressure:1 arti:1 moment:2 electronics:1 od:1 must:2 realize:1 motor:26 centrifugal:1 ficial:1 selected:1 nervous:2 firstly:1 mathematical:2 symposium:1 consists:3 behavioral:3 acquired:4 rapid:4 behavior:1 seika:1 multi:2 torque:10 resolve:4 moreover:3 musculo:4 monkey:1 ps... |
2,381 | 3,160 | Context Effects in Category Learning:
An Investigation of Four Probabilistic Models
+
Michael C. Mozer+ , Michael Jones? , Michael Shettel+
Dept. of Computer Science, ? Dept. of Psychology, and Institute of Cognitive Science
University of Colorado, Boulder, CO 80309-0430
{mozer,mike.jones,shettel}@colorado.edu
A... | 3160 |@word trial:77 version:1 seems:4 proportion:2 open:2 d2:3 simulation:15 covariance:2 solid:4 reduction:1 initial:4 interestingly:1 reaction:1 current:4 must:2 readily:1 numerical:1 distant:1 subsequent:2 pertinent:1 motor:1 plot:1 update:4 generative:2 fewer:1 half:3 item:6 realizing:1 mental:1 provides:3 cse:1 l... |
2,382 | 3,161 | Learning to classify complex patterns using a VLSI
network of spiking neurons
Srinjoy Mitra? , Giacomo Indiveri? and Stefano Fusi ??
? Institute of Neuroinformatics, UZH|ETH, Zurich
? Center for Theoretical Neuroscience, Columbia University, New York
srinjoy|giacomo|fusi@ini.phys.ethz.ch
Abstract
We propose a compact... | 3161 |@word trial:3 middle:1 version:1 pulse:2 overwritten:1 solid:1 outlook:1 carry:1 initial:1 efficacy:2 current:20 yet:1 refresh:2 realistic:1 happen:1 plasticity:14 shape:1 enables:1 motor:1 plot:3 update:10 device:7 accordingly:1 indefinitely:1 infrastructure:1 provides:1 node:3 traverse:1 along:2 dn:1 become:1 s... |
2,383 | 3,162 | Structured Learning with Approximate Inference
Alex Kulesza and Fernando Pereira?
Department of Computer and Information Science
University of Pennsylvania
{kulesza, pereira}@cis.upenn.edu
Abstract
In many structured prediction problems, the highest-scoring labeling is hard to
compute exactly, leading to the use of a... | 3162 |@word version:1 middle:1 norm:3 seems:1 proportion:1 suitably:3 seek:2 yih:2 tr:1 shading:1 configuration:9 cyclic:1 score:5 daniel:1 yet:1 assigning:1 must:5 john:1 distant:1 remove:1 drop:1 update:8 alone:2 intelligence:3 fewer:1 xk:8 mccallum:3 colored:1 characterization:1 provides:1 node:27 complication:1 pre... |
2,384 | 3,163 | Competition adds complexity
Judy Goldsmith
Department of Computer Science
University of Kentucky
Lexington, KY
goldsmit@cs.uky.edu
Martin Mundhenk
Friedrich-Schiller-Universit?at Jena
Jena, Germany
mundhenk@cs.uni-jena.de
Abstract
It is known that determinining whether a DEC-POMDP, namely, a cooperative
partially ob... | 3163 |@word polynomial:3 nd:1 open:1 asks:1 tr:4 harder:2 reduction:2 initial:19 daniel:1 bitwise:1 yet:1 written:1 subsequent:1 mundhenk:3 shlomo:1 stationary:14 intelligence:1 leaf:1 guess:3 accordingly:1 short:5 characterization:1 completeness:2 math:1 constructed:3 consists:3 prove:1 fitting:1 nondeterministic:3 ha... |
2,385 | 3,164 | Efficient Principled Learning of Thin Junction Trees
Anton Chechetka Carlos Guestrin
Carnegie Mellon University
Abstract
We present the first truly polynomial algorithm for PAC-learning the structure of
bounded-treewidth junction trees ? an attractive subclass of probabilistic graphical
models that permits both the co... | 3164 |@word version:1 briefly:2 polynomial:16 vldb:1 seek:1 decomposition:8 q1:3 minus:1 liu:9 inefficiency:1 contains:1 njk:1 karger:6 score:7 ours:1 bc:1 outperforms:1 si:5 suermondt:1 subcomponent:1 partition:7 shape:1 remove:1 greedy:2 leaf:1 selected:1 nq:2 record:2 provides:1 math:1 node:2 location:3 chechetka:1 ... |
2,386 | 3,165 | A Bayesian Framework for Cross-Situational
Word-Learning
Michael C. Frank, Noah D. Goodman, and Joshua B. Tenenbaum
Department of Brain and Cognitive Science
Massachusetts Institute of Technology
{mcfrank, ndg, jbt}@mit.edu
Abstract
For infants, early word learning is a chicken-and-egg problem. One way to learn
a wor... | 3165 |@word seems:1 simulation:1 attended:1 pick:1 contains:1 score:11 exclusively:1 preverbal:1 outperforms:1 existing:1 current:1 contextual:1 surprising:4 si:2 must:6 visible:2 informative:1 plot:2 childes:2 v:2 infant:18 cue:45 generative:2 guess:2 half:1 pasek:2 smith:1 contribute:2 lexicon:47 location:1 positing:... |
2,387 | 3,166 | Ultrafast Monte Carlo for Kernel Estimators
and Generalized Statistical Summations
Michael P. Holmes, Alexander G. Gray, and Charles Lee Isbell, Jr.
College Of Computing
Georgia Institute of Technology
Atlanta, GA 30327
{mph, agray, isbell}@cc.gatech.edu
Abstract
Machine learning contains many computational bottleneck... | 3166 |@word h:2 termination:1 simulation:2 covariance:7 innermost:1 thereby:1 solid:2 accommodate:1 recursively:6 reduction:3 moment:1 contains:1 score:3 series:3 past:1 freitas:1 yet:1 intriguing:1 dx:1 written:1 must:1 john:1 klaas:1 enables:1 v:2 xxz:1 intelligence:3 leaf:1 assurance:1 fewer:1 xk:2 provides:1 charac... |
2,388 | 3,167 | Regularized Boost for Semi-Supervised Learning
Ke Chen and Shihai Wang
School of Computer Science
The University of Manchester
Manchester M13 9PL, United Kingdom
{chen,swang}@cs.manchester.ac.uk
Abstract
Semi-supervised inductive learning concerns how to learn a decision rule from a
data set containing both labeled a... | 3167 |@word trial:1 repository:2 version:7 briefly:2 termination:3 tr:1 carry:1 reduction:1 series:1 uncovered:1 united:1 document:1 outperforms:2 existing:8 yet:1 must:1 j1:2 informative:1 treating:1 joy:1 v:6 generative:1 accordingly:2 xk:8 mccallum:1 record:1 boosting:57 node:1 org:1 simpler:1 five:6 mathematical:1 ... |
2,389 | 3,168 | Simplified Rules and Theoretical Analysis for
Information Bottleneck Optimization and PCA with
Spiking Neurons
Lars Buesing, Wolfgang Maass
Institute for Theoretical Computer Science
Graz University of Technology
A-8010 Graz, Austria
{lars,maass}@igi.tu-graz.at
Abstract
We show that under suitable assumptions (primar... | 3168 |@word illustrating:1 version:2 briefly:1 advantageous:1 open:1 hu:3 simulation:9 simplifying:1 covariance:7 tif:2 solid:2 series:1 wj2:4 written:1 numerical:5 realistic:2 plasticity:7 shape:1 enables:2 fund:1 stationary:5 xk:1 filtered:2 provides:1 allerton:1 simpler:4 differential:3 consists:1 specialize:3 manne... |
2,390 | 3,169 | Predicting human gaze using low-level saliency
combined with face detection
Jonathan Harel
Electrical Engineering
California Institute of Technology
Pasadena, CA 91125
harel@klab.caltech.edu
Moran Cerf
Computation and Neural Systems
California Institute of Technology
Pasadena, CA 91125
moran@klab.caltech.edu
Wolfgang ... | 3169 |@word trial:12 version:1 nd:1 open:2 tried:1 accounting:1 photographer:1 solid:1 cleary:1 shot:1 score:1 outperforms:1 current:1 contextual:2 rizzolatti:1 subsequent:1 chicago:1 treating:1 depict:3 mounting:1 v:1 infant:1 half:2 alone:4 device:1 intelligence:4 plane:1 short:1 pisarevsky:1 provides:1 contribute:1 ... |
2,391 | 317 | Qualitative structure from motion
Daphna Weinshall
Center for Biological Information Processing
MIT, E25-201, Cambridge MA 02139
Abstract
Exact structure from motion is an ill-posed computation and therefore
very sensitive to noise. In this work I describe how a qualitative shape
representation, based on the sign of ... | 317 |@word cylindrical:1 trial:3 middle:1 judgement:1 nd:1 plication:1 pick:1 necessity:1 configuration:4 disparity:5 tuned:1 recovered:2 od:1 clara:1 must:1 subsequent:1 girosi:2 shape:7 designed:2 v:1 cue:2 intelligence:2 plane:1 location:6 successive:1 along:1 direct:1 ect:1 surprised:1 qualitative:13 incorrect:2 ed... |
2,392 | 3,170 | Expectation Maximization and Posterior Constraints
Jo?ao V. Grac?a
L2 F INESC-ID
INESC-ID
Lisboa, Portugal
Kuzman Ganchev
Computer & Information Science
University of Pennsylvania
Philadelphia, PA
Ben Taskar
Computer & Information Science
University of Pennsylvania
Philadelphia, PA
Abstract
The expectation maximiza... | 3170 |@word mild:1 middle:1 heuristically:1 shading:1 initial:1 configuration:1 series:1 zij:9 daniel:1 animated:1 current:2 mari:1 bd:1 romance:1 belmont:1 v:1 generative:2 half:1 leaf:2 mccallum:1 smith:1 provides:1 node:1 philipp:1 simpler:1 become:1 consists:1 dan:1 introduce:5 manner:1 notably:1 ra:2 expected:5 be... |
2,393 | 3,171 | Mining Internet-Scale Software Repositories
Erik Linstead, Paul Rigor, Sushil Bajracharya, Cristina Lopes and Pierre Baldi
Donald Bren School of Information and Computer Science
University of California, Irvine
Irvine, CA 92697-3435
{elinstea,prigor,sbajrach,lopes,pfbaldi}@ics.uci.edu
Abstract
Large repositories of s... | 3171 |@word repository:19 version:1 faculty:1 private:3 mri:1 nd:2 open:5 decomposition:1 kent:1 downloading:1 cristina:1 contains:2 lightweight:1 score:1 practiced:1 tuned:1 document:25 united:1 pub:1 current:2 comparing:1 michal:1 manuel:1 assigning:1 crawling:3 written:1 parsing:6 import:1 john:2 kdd:2 pertinent:1 r... |
2,394 | 3,172 | Continuous Time Particle Filtering for fMRI
Lawrence Murray
School of Informatics
University of Edinburgh
lawrence.murray@ed.ac.uk
Amos Storkey
School of Informatics
University of Edinburgh
a.storkey@ed.ac.uk
Abstract
We construct a biologically motivated stochastic differential model of the neural and hemodynamic a... | 3172 |@word cu:1 seems:1 seek:1 propagate:1 simulation:1 covariance:1 ttn:1 tr:4 solid:4 accommodate:1 moment:1 series:1 efficacy:3 duong:1 current:1 si:7 activation:1 numerical:1 informative:1 oxygenation:1 resampling:4 generative:1 prohibitive:1 cue:1 selected:2 isard:2 provides:2 node:2 organising:1 org:1 simpler:1 ... |
2,395 | 3,173 | Feature Selection Methods for Improving Protein
Structure Prediction with Rosetta
Ben Blum, Michael I. Jordan
Department of Electrical Engineering and Computer Science
University of California at Berkeley
Berkeley, CA 94305
{bblum,jordan}@cs.berkeley.edu
David E. Kim, Rhiju Das, Philip Bradley, David Baker
Department o... | 3173 |@word achievable:2 bf:4 heuristically:1 excited:1 attainable:1 pick:1 fifteen:4 tif:2 initial:4 born:1 series:2 score:1 fragment:4 past:1 bradley:3 di2:2 must:1 visible:2 distant:1 subsequent:1 informative:1 designed:2 plot:3 resampling:25 selected:5 leaf:15 beginning:2 tertiary:1 farther:1 detecting:1 node:2 fiv... |
2,396 | 3,174 | An online Hebbian learning rule that performs
Independent Component Analysis
Claudia Clopath
School of Computer Science and Brain Mind Institute
Ecole polytechnique federale de Lausanne
1015 Lausanne EPFL
claudia.clopath@epfl.ch
Andre Longtin
Center for Neural Dynamics
University of Ottawa
150 Louis Pasteur, Ottawa
a... | 3174 |@word trial:1 inversion:1 norm:1 simulation:2 solid:9 moment:3 initial:1 liu:1 series:1 efficacy:2 ecole:2 tuned:3 recovered:5 ka:1 si:9 negentropy:1 written:3 must:1 plasticity:3 remove:2 update:5 aps:1 colored:1 detecting:1 math:1 revisited:1 location:1 mathematical:1 become:1 ik:1 ouput:1 dan:1 interscience:1 ... |
2,397 | 3,175 | Non-Parametric Modeling of Partially Ranked Data
Guy Lebanon
Department of Statistics, and
School of Elec. and Computer Engineering
Purdue University - West Lafayette, IN
lebanon@stat.purdue.edu
Yi Mao
School of Elec. and Computer Engineering
Purdue University - West Lafayette, IN
ymao@ecn.purdue.edu
Abstract
Statist... | 3175 |@word inversion:16 manageable:1 decomposition:5 necessity:1 contains:9 score:3 denoting:1 existing:1 realize:1 refines:1 partition:1 enables:2 v:1 generative:1 selected:1 item:31 xk:2 transposition:3 bijection:1 location:3 preference:2 node:1 simpler:1 cosets:5 five:1 become:1 psfrag:4 manner:1 surge:1 election:2... |
2,398 | 3,176 | Discriminative K-means for Clustering
Jieping Ye
Arizona State University
Tempe, AZ 85287
jieping.ye@asu.edu
Zheng Zhao
Arizona State University
Tempe, AZ 85287
zhaozheng@asu.edu
Mingrui Wu
MPI for Biological Cybernetics
T?ubingen, Germany
mingrui.wu@tuebingen.mpg.de
Abstract
We present a theoretical study on the d... | 3176 |@word kulis:1 repository:1 nd:1 covariance:2 simplifying:1 decomposition:1 reduction:8 initial:5 liu:1 att:1 pub:1 tuned:1 com:1 scatter:4 john:1 kyb:1 treating:1 sponsored:1 update:2 generative:1 asu:2 provides:3 five:1 leigs:1 direct:1 become:1 consists:1 inter:1 mpg:2 nor:1 sdp:5 examine:1 automatically:1 curs... |
2,399 | 3,177 | Exponential Family
Predictive Representations of State
David Wingate
Computer Science and Engineering
University of Michigan
wingated@umich.edu
Satinder Singh
Computer Science and Engineering
University of Michigan
baveja@umich.edu
Abstract
In order to represent state in controlled, partially observable, stochastic ... | 3177 |@word determinant:3 briefly:2 version:3 middle:1 repository:1 nd:2 mitsubishi:1 covariance:1 contrastive:2 tr:1 initial:1 series:1 selecting:4 o2:2 existing:1 past:1 current:3 jaynes:2 dx:1 must:10 partition:1 designed:1 update:2 implying:1 half:1 fewer:1 prohibitive:1 intelligence:2 mccallum:2 provides:1 node:2 ... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.