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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2,400 | 3,178 | The Epoch-Greedy Algorithm for Contextual
Multi-armed Bandits
Tong Zhang
Department of Statistics
Rutgers University
tongz@rci.rutgers.edu
John Langford
Yahoo! Research
jl@yahoo-inc.com
Abstract
We present Epoch-Greedy, an algorithm for contextual multi-armed bandits (also
known as bandits with side information). Epo... | 3178 |@word exploitation:26 advantageous:1 c0:9 rigged:1 open:1 pick:2 bc:3 current:1 contextual:15 com:1 comparing:1 pothesis:1 john:1 treating:1 designed:1 greedy:35 leaf:1 beginning:2 mannor:1 readability:1 zhang:1 along:1 chakrabarti:1 focs:1 consists:1 combine:1 introduce:1 notably:1 ra:24 expected:21 examine:1 pl... |
2,401 | 3,179 | Stable Dual Dynamic Programming
Tao Wang? Daniel Lizotte Michael Bowling Dale Schuurmans
Department of Computing Science
University of Alberta
{trysi,dlizotte,bowling,dale}@cs.ualberta.ca
Abstract
Recently, we have introduced a novel approach to dynamic programming and reinforcement learning that is based on maintain... | 3179 |@word norm:20 open:1 crucially:1 contraction:12 automat:1 boundedness:1 initial:3 daniel:1 interestingly:2 current:4 yet:2 must:6 drop:1 update:54 stationary:6 greedy:2 alone:1 selected:1 coarse:1 unbounded:2 constructed:1 direct:1 symposium:1 khk:1 prove:2 introduce:1 theoretically:1 expected:1 behavior:2 p1:4 n... |
2,402 | 318 | Evaluation of Adaptive Mixtures
of Competing Experts
Steven J. Nowlan and Geoffrey E. Hinton
Computer Science Dept.
University of Toronto
Toronto, ONT M5S 1A4
Abstract
We compare the performance of the modular architecture, composed of
competing expert networks, suggested by Jacobs, Jordan, Nowlan and
Hinton (1991) t... | 318 |@word middle:1 proportion:6 simulation:12 jacob:8 decomposition:4 tr:2 barney:2 initial:1 selecting:1 o2:1 existing:1 current:2 contextual:1 nowlan:13 assigning:1 alone:1 spec:1 selected:4 cue:4 half:1 discovering:1 toronto:4 five:2 fitting:2 combine:1 ray:1 manner:1 rapid:1 roughly:1 formants:5 ont:1 what:1 watro... |
2,403 | 3,180 | How SVMs can estimate quantiles and the median
Ingo Steinwart
Information Sciences Group CCS-3
Los Alamos National Laboratory
Los Alamos, NM 87545, USA
ingo@lanl.gov
Andreas Christmann
Department of Mathematics
Vrije Universiteit Brussel
B-1050 Brussels, Belgium
andreas.christmann@vub.ac.be
Abstract
We investigate q... | 3180 |@word mention:1 contains:2 selecting:1 rkhs:3 scovel:2 dx:4 realistic:1 device:1 location:1 c2:4 direct:1 beta:1 differential:1 consists:2 prove:1 manner:1 huber:1 decreasing:1 gov:1 little:1 equipped:1 considering:2 increasing:2 estimating:1 moreover:15 bounded:2 klq:4 mass:1 qmin:6 minimizes:1 finding:1 every:1... |
2,404 | 3,181 | Convex Clustering with Exemplar-Based Models
Danial Lashkari
Polina Golland
Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139
{danial, polina}@csail.mit.edu
Abstract
Clustering is often formulated as the maximum likelihood estimation of a mixture
model... | 3181 |@word illustrating:1 compression:2 seek:2 pick:1 solid:1 harder:1 carry:1 initial:2 bibliographic:1 tuned:1 comparing:2 yet:2 assigning:1 shape:3 hofmann:1 remove:1 drop:1 update:2 intelligence:1 guess:1 parametrization:1 parameterizations:1 bijection:1 preference:1 five:2 dn:1 beta:1 fitting:3 introduce:2 pairwi... |
2,405 | 3,182 | Random Features for Large-Scale Kernel Machines
Benjamin Recht
Caltech IST
Pasadena, CA 91125
brecht@ist.caltech.edu
Ali Rahimi
Intel Research Seattle
Seattle, WA 98105
ali.rahimi@intel.com
Abstract
To accelerate the training of kernel machines, we propose to map the input data
to a randomized low-dimensional featur... | 3182 |@word version:1 middle:2 stronger:2 replicate:1 norm:2 retraining:1 d2:2 km:5 seek:1 decomposition:1 pick:1 dramatic:1 versatile:1 moment:1 existing:1 recovered:1 com:1 z2:2 reminiscent:1 written:2 partition:9 kdd:1 analytic:1 designed:1 update:1 hash:2 half:1 rudin:1 isotropic:1 ith:1 core:4 provides:2 completen... |
2,406 | 3,183 | Efficient Inference for Distributions on Permutations
Jonathan Huang
Carnegie Mellon University
jch1@cs.cmu.edu
Carlos Guestrin
Carnegie Mellon University
guestrin@cs.cmu.edu
Leonidas Guibas
Stanford University
guibas@cs.stanford.edu
Abstract
Permutations are ubiquitous in many real world problems, such as voting,
r... | 3183 |@word briefly:1 version:2 kondor:5 nd:1 propagate:1 decomposition:4 tr:14 recursively:1 series:6 exclusively:1 omniscient:3 rightmost:1 past:1 comparing:1 written:2 pertinent:1 update:6 v:1 alone:1 leaf:1 ith:1 infrastructure:1 provides:1 contribute:1 simpler:1 projec:1 mathematical:1 direct:6 prove:1 doubly:3 ad... |
2,407 | 3,184 | Invariant Common Spatial Patterns: Alleviating
Nonstationarities in Brain-Computer Interfacing
Benjamin Blankertz1,2
Motoaki Kawanabe2
Friederike U. Hohlefeld4
Ryota Tomioka3
Vadim Nikulin5
Klaus-Robert M?ller1,2
1
TU Berlin, Dept. of Computer Science, Machine Learning Laboratory, Berlin, Germany
2 Fraunhofer FIR... | 3184 |@word blankertz1:1 neurophysiology:1 trial:8 middle:1 pw:2 stronger:1 norm:2 nd:1 open:2 covariance:10 eng:8 ronchetti:1 moment:2 contains:3 franklin:1 current:1 ida:1 dx:2 oldenbourg:1 chicago:1 visible:2 shape:2 motor:6 plot:7 designed:1 update:1 discrimination:3 v:4 cue:1 device:1 accordingly:1 inspection:2 be... |
2,408 | 3,185 | Learning with Tree-Averaged Densities and
Distributions
Sergey Kirshner
AICML and Dept of Computing Science
University of Alberta
Edmonton, Alberta, Canada T6G 2E8
sergey@cs.ualberta.ca
Abstract
We utilize the ensemble of trees framework, a tractable mixture over superexponential number of tree-structured distribution... | 3185 |@word determinant:1 version:1 inversion:1 repository:2 nd:1 closure:1 cml:1 tr:1 solid:4 wrapper:1 liu:2 series:3 denoting:1 current:1 assigning:1 scatter:1 written:1 treating:1 plot:2 update:3 fund:1 v:1 generative:1 selected:3 underestimating:1 location:1 constructed:2 direct:1 consists:2 fitting:1 introduce:1 ... |
2,409 | 3,186 | Local Algorithms for Approximate Inference in
Minor-Excluded Graphs
Kyomin Jung
Dept. of Mathematics, MIT
kmjung@mit.edu
Devavrat Shah
Dept. of EECS, MIT
devavrat@mit.edu
Abstract
We present a new local approximation algorithm for computing MAP and logpartition function for arbitrary exponential family distribution ... | 3186 |@word trial:1 nd:2 scg:1 mitsubishi:1 decomposition:27 pick:1 euclidian:1 multicommodity:2 recursively:1 outperforms:2 assigning:1 partition:29 remove:3 designed:2 plot:5 update:1 intelligence:3 provides:7 characterization:1 node:10 simpler:1 along:1 become:1 prove:3 specialize:4 combine:1 theoretically:5 x0:4 ex... |
2,410 | 3,187 | A Spectral Regularization Framework for
Multi-Task Structure Learning
Andreas Argyriou
Department of Computer Science
University College London
Gower Street, London WC1E 6BT, UK
a.argyriou@cs.ucl.ac.uk
Charles A. Micchelli
Department of Mathematics and Statistics
SUNY Albany
1400 Washington Avenue
Albany, NY, 12222, ... | 3187 |@word multitask:1 inversion:1 norm:8 lenk:1 d2:8 integrative:1 covariance:4 decomposition:4 accounting:1 mention:1 tr:15 minus:1 initial:2 score:1 tuned:2 renewed:1 denoting:1 ecole:1 err:4 od:3 olkin:1 informative:1 weyl:1 analytic:1 plot:2 intelligence:1 selected:1 authority:1 boosting:1 simpler:2 zhang:2 along... |
2,411 | 3,188 | Catching Change-points with Lasso
Zaid Harchaoui, C?eline L?evy-Leduc
LTCI, TELECOM ParisTech and CNRS
37/39 Rue Dareau, 75014 Paris, France
{zharchao,levyledu}@enst.fr
Abstract
We propose a new approach for dealing with the estimation of the location of
change-points in one-dimensional piecewise constant signals obs... | 3188 |@word ruanaidh:1 version:2 covariance:2 mention:2 carry:1 reduction:1 configuration:4 contains:1 series:5 selecting:3 hereafter:2 yet:3 boysen:1 numerical:1 distant:1 partition:1 zaid:1 remove:1 implying:1 selected:3 beginning:1 provides:4 math:1 detecting:1 evy:1 location:13 mathematical:1 along:1 retrieving:1 q... |
2,412 | 3,189 | Multi-task Gaussian Process Prediction
Edwin V. Bonilla, Kian Ming A. Chai, Christopher K. I. Williams
School of Informatics, University of Edinburgh, 5 Forrest Hill, Edinburgh EH1 2QL, UK
edwin.bonilla@ed.ac.uk, K.M.A.Chai@sms.ed.ac.uk, c.k.i.williams@ed.ac.uk
Abstract
In this paper we investigate multi-task learning... | 3189 |@word multitask:2 determinant:2 version:2 inversion:1 nd:2 covariance:31 decomposition:3 tr:2 nystr:1 klk:3 reduction:1 series:1 score:5 tuned:1 ours:1 past:1 outperforms:3 chu:1 readily:1 john:1 visible:1 numerical:1 informative:2 update:2 bart:1 stationary:1 selected:2 parameterization:1 parametrization:3 ith:1... |
2,413 | 319 | Dynamics of Learning in Recurrent
Feature-Discovery Networks
Todd K. Leen
Department of Computer Science and Engineering
Oregon Graduate Institute of Science & Technology
Beaverton, OR 97006-1999
Abstract
The self-organization of recurrent feature-discovery networks is studied
from the perspective of dynamical system... | 319 |@word inversion:1 simulation:3 reduction:1 configuration:1 activation:1 numerical:1 plasticity:1 stationary:1 liapunov:1 inspection:1 plane:2 ith:7 short:1 lr:1 provides:2 math:2 node:35 location:2 five:1 along:1 constructed:2 become:2 hopf:4 qualitative:1 introduce:1 behavior:2 themselves:1 examine:1 decreasing:1... |
2,414 | 3,190 | Evaluating Search Engines by Modeling the
Relationship Between Relevance and Clicks
Ben Carterette?
Center for Intelligent Information Retrieval
University of Massachusetts Amherst
Amherst, MA 01003
carteret@cs.umass.edu
Rosie Jones
Yahoo! Research
3333 Empire Ave
Burbank, CA 91504
jonesr@yahoo-inc.com
Abstract
We p... | 3190 |@word trial:1 judgement:2 stronger:1 logit:1 nd:1 simulation:4 covariance:1 initial:3 series:2 uma:1 score:6 selecting:1 document:68 outperforms:1 past:1 com:1 must:1 additive:3 informative:1 kdd:1 treating:1 sponsored:3 drop:1 v:3 alone:2 obsolete:1 device:1 item:1 fewer:2 reciprocal:1 record:2 filtered:1 provid... |
2,415 | 3,191 | Random Projections for Manifold Learning
Chinmay Hegde
ECE Department
Rice University
ch3@rice.edu
Michael B. Wakin
EECS Department
University of Michigan
wakin@eecs.umich.edu
Richard G. Baraniuk
ECE Department
Rice University
richb@rice.edu
Abstract
We propose a novel method for linear dimensionality reduction of m... | 3191 |@word version:5 compression:2 norm:1 suitably:1 disk:1 termination:1 d2:1 simulation:1 sensed:1 concise:1 tr:1 solid:1 reduction:7 must:1 grassberger:3 subsequent:2 additive:1 plot:2 v:2 greedy:2 device:4 hypersphere:1 provides:1 node:3 mathematical:1 become:1 prove:2 dimen:1 manner:2 acquired:1 pairwise:5 indeed... |
2,416 | 3,192 | Anytime Induction of Cost-sensitive Trees
Saher Esmeir
Computer Science Department
Technion?Israel Institute of Technology
Haifa 32000, Israel
esaher@cs.technion.ac.il
Shaul Markovitch
Computer Science Department
Technion?Israel Institute of Technology
Haifa 32000, Israel
shaulm@cs.technion.ac.il
Abstract
Machine le... | 3192 |@word repository:2 version:5 willing:1 recursively:1 reduction:3 initial:1 selecting:1 genetic:5 tuned:1 interestingly:1 outperforms:2 existing:2 current:1 comparing:1 assigning:2 yet:1 pioneer:1 partition:2 confirming:1 kdd:2 designed:4 interpretable:1 plot:2 aside:1 v:1 greedy:9 leaf:10 selected:2 fewer:1 intel... |
2,417 | 3,193 | Estimating divergence functionals and the likelihood
ratio by penalized convex risk minimization
XuanLong Nguyen
SAMSI & Duke University
Martin J. Wainwright
UC Berkeley
Michael I. Jordan
UC Berkeley
Abstract
We develop and analyze an algorithm for nonparametric estimation of divergence
functionals and the density ... | 3193 |@word mild:1 norm:1 seems:1 unif:4 d2:4 simulation:6 covariance:1 p0:15 reduction:1 series:1 denoting:1 rkhs:11 existing:1 dpn:7 nt:1 partition:4 plot:3 discrimination:1 accordingly:1 characterization:2 provides:1 math:3 lipchitz:1 direct:1 beta:2 become:1 ik:1 symposium:2 hellinger:4 x0:3 behavior:3 increasing:2... |
2,418 | 3,194 | SpAM: Sparse Additive Models
Pradeep Ravikumar? Han Liu?? John Lafferty?? Larry Wasserman??
? Machine
Learning Department
of Statistics
? Computer Science Department
? Department
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
We present a new class of models for high-dimensional nonparametric regression
an... | 3194 |@word trial:1 version:1 norm:6 proportion:1 simulation:2 linearized:1 bn:3 solid:1 carry:1 liu:1 siebel:1 score:4 series:1 current:1 written:1 john:1 additive:22 confirming:1 interpretable:1 update:2 juditsky:1 stationary:3 selected:2 parametrization:1 persistency:1 provides:1 zhang:4 dn:6 ik:2 persistent:4 yuan:... |
2,419 | 3,195 | Learning the structure of manifolds using random
projections
Yoav Freund ?
UC San Diego
Sanjoy Dasgupta ?
UC San Diego
Mayank Kabra
UC San Diego
Nakul Verma
UC San Diego
Abstract
We present a simple variant of the k-d tree which automatically adapts to intrinsic
low dimensional structure in data.
1
Introduction
... | 3195 |@word version:4 compression:1 stronger:1 d2:1 tried:1 covariance:8 pick:3 reduction:3 liu:1 contains:2 partition:7 update:1 half:1 leaf:3 intelligence:1 plane:2 core:1 provides:1 boosting:1 codebook:1 location:2 node:7 along:7 c2:2 become:1 symposium:1 descendant:2 consists:1 fitting:1 manner:4 theoretically:1 ex... |
2,420 | 3,196 | A Probabilistic Approach to Language Change
Alexandre Bouchard-C?ot?e?
Percy Liang?
Thomas L. Griffiths?
?
?
Computer Science Division
Department of Psychology
University of California at Berkeley
Berkeley, CA 94720
Dan Klein?
Abstract
We present a probabilistic approach to language change in which word forms
are re... | 3196 |@word faculty:1 briefly:1 bigram:1 seems:1 open:1 simplifying:1 thereby:1 substitution:4 contains:1 score:1 ours:1 document:1 existing:1 current:2 comparing:3 recovered:2 must:1 portuguese:6 romance:3 evans:1 happen:1 partition:3 plm:3 drop:1 update:1 v:1 generative:7 half:1 eroded:1 selected:1 leaf:1 intelligenc... |
2,421 | 3,197 | Online Linear Regression and Its Application to
Model-Based Reinforcement Learning
Alexander L. Strehl?
Yahoo! Research
New York, NY
strehl@yahoo-inc.com
Michael L. Littman
Department of Computer Science
Rutgers University
Piscataway, NJ USA
mlittman@cs.rutgers.edu
Abstract
We provide a provably efficient algorithm ... | 3197 |@word h:1 exploitation:1 polynomial:10 norm:14 nd:1 decomposition:1 ours:1 past:2 current:9 com:1 discretization:2 yet:1 must:5 written:1 john:1 update:1 intelligence:2 ith:10 dissertation:1 lr:1 provides:1 unbounded:1 direct:1 incorrect:1 prove:4 consists:2 interscience:1 manner:1 apprenticeship:2 behavior:2 pla... |
2,422 | 3,198 | Semi-Supervised Multitask Learning
Qiuhua Liu, Xuejun Liao, and Lawrence Carin
Department of Electrical and Computer Engineering
Duke University
Durham, NC 27708-0291, USA
Abstract
A semi-supervised multitask learning (MTL) framework is presented, in which
M parameterized semi-supervised classifiers, each associated ... | 3198 |@word multitask:9 trial:8 repository:2 version:1 norm:1 proportion:1 replicate:2 seems:1 seek:1 covariance:1 liu:1 exclusively:1 tuned:1 outperforms:4 existing:4 current:1 nt:8 yet:1 must:3 written:1 john:1 distant:1 designed:1 plot:1 alone:1 half:1 selected:1 intelligence:1 beginning:1 provides:1 location:4 trav... |
2,423 | 3,199 | Scan Strategies for Adaptive Meteorological Radars
Victoria Manfredi, Jim Kurose
Department of Computer Science
University of Massachusetts
Amherst, MA USA
{vmanfred,kurose}@cs.umass.edu
Abstract
We address the problem of adaptive sensor control in dynamic resourceconstrained sensor networks. We focus on a meteorolog... | 3199 |@word cox:1 nd:2 km:28 simulation:2 sensed:1 covariance:5 p0:1 tr:11 initial:2 configuration:15 series:2 uma:1 document:1 past:1 existing:2 outperforms:1 current:7 comparing:1 must:4 hypothesize:2 treating:1 fewer:1 coarse:1 location:8 successive:1 mathematical:2 symposium:1 incorrect:1 grupen:1 combine:1 introdu... |
2,424 | 32 | 824
SYNCHRONIZATION IN NEURAL NETS
Jacques J. Vidal
University of California Los Angeles, Los Angeles, Ca. 90024
John Haggerty?
ABSTRACT
The paper presents an artificial neural network concept (the
Synchronizable Oscillator Networks) where the instants of individual
firings in the form of point processes constitute ... | 32 |@word neurophysiology:1 version:1 pulse:2 propagate:1 simulation:1 accounting:1 dramatic:1 initial:2 freitas:1 activation:4 must:3 john:2 shape:1 pursued:1 short:1 implemen:1 quantized:1 node:8 contribute:1 simpler:1 burst:1 along:1 become:2 differential:2 sustained:1 inter:1 indeed:2 rapid:1 behavior:4 multi:1 bra... |
2,425 | 320 | Exploratory Feature Extraction in Speech Signals
Nathan Intrator
Center for Neural Science
Brown U ni versity
Providence, RI 02912
Abstract
A novel unsupervised neural network for dimensionality reduction which
seeks directions emphasizing multimodality is presented, and its connection to exploratory projection pursu... | 320 |@word version:1 polynomial:4 duda:2 simulation:2 seek:4 moment:2 reduction:6 nowlan:2 written:1 must:1 john:1 happen:1 j1:1 plasticity:2 remove:1 fewer:1 beginning:3 dissertation:1 node:1 location:1 sigmoidal:1 mathematical:1 burst:5 c2:1 constructed:1 differential:1 direct:1 consists:1 multimodality:4 huber:5 exp... |
2,426 | 3,200 | Fixing Max-Product: Convergent Message Passing
Algorithms for MAP LP-Relaxations
Amir Globerson Tommi Jaakkola
Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139
gamir,tommi@csail.mit.edu
Abstract
We present a novel message passing algorithm for approxima... | 3200 |@word eliminating:1 advantageous:1 offering:1 ours:1 rightmost:1 surprising:1 must:1 belmont:1 partition:1 koetter:2 plot:2 update:17 intelligence:3 amir:2 parameterization:1 xk:8 provides:1 parameterizations:1 node:11 allerton:1 constructed:1 direct:1 symposium:1 replication:1 prove:1 shorthand:1 introduce:2 pai... |
2,427 | 3,201 | A Kernel Statistical Test of Independence
Arthur Gretton
MPI for Biological Cybernetics
T?ubingen, Germany
arthur@tuebingen.mpg.de
Le Song
NICTA, ANU
and University of Sydney
lesong@it.usyd.edu.au
Kenji Fukumizu
Inst. of Statistical Mathematics
Tokyo Japan
fukumizu@ism.ac.jp
Bernhard Sch?olkopf
MPI for Biological Cyb... | 3201 |@word norm:9 tried:1 covariance:9 moment:1 reduction:1 series:1 lqr:1 ours:1 rkhs:3 denoting:1 jyv:1 outperforms:1 com:2 comparing:2 exy:3 gmail:2 yet:1 must:1 written:1 dx:1 john:1 partition:2 kyb:1 drop:1 plot:5 designed:1 resampling:2 stationary:2 mvar:1 v:1 spec:3 short:2 record:1 accepting:2 eskin:1 math:1 h... |
2,428 | 3,202 | PSVM: Parallelizing Support Vector Machines
on Distributed Computers
Edward Y. Chang?, Kaihua Zhu, Hao Wang, Hongjie Bai,
Jian Li, Zhihuan Qiu, & Hang Cui
Google Research, Beijing, China
Abstract
Support Vector Machines (SVMs) suffer from a widely recognized scalability
problem in both memory use and computational tim... | 3202 |@word msr:1 loading:3 replicate:1 nd:2 open:3 termination:1 decomposition:2 tr:2 ipm:17 reduction:1 initial:2 bai:1 rkhs:2 outperforms:1 com:2 nt:1 si:2 mushroom:1 chu:3 must:4 john:1 chicago:1 kdd:1 remove:1 plot:1 update:2 n0:5 ith:1 svmguide1:1 core:2 record:2 infrastructure:2 provides:1 iterates:1 node:1 simp... |
2,429 | 3,203 | Predictive Matrix-Variate t Models
Shenghuo Zhu
Kai Yu
Yihong Gong
NEC Labs America, Inc.
10080 N. Wolfe Rd. SW3-350
Cupertino, CA 95014
{zsh,kyu,ygong}@sv.nec-labs.com
Abstract
It is becoming increasingly important to learn from a partially-observed random
matrix and predict its missing elements. We assume that the e... | 3203 |@word determinant:12 loading:1 norm:1 nd:1 calculus:2 confirms:1 gradual:1 decomposition:1 covariance:22 tr:4 contains:2 interestingly:1 outperforms:2 existing:1 recovered:1 com:1 comparing:2 chu:1 written:3 j1:1 treating:1 depict:1 generative:1 beginning:1 provides:2 node:3 simpler:1 five:1 direct:1 ik:2 fitting... |
2,430 | 3,204 | Modelling motion primitives and their timing
in biologically executed movements
Ben H Williams
School of Informatics
University of Edinburgh
5 Forrest Hill, EH1 2QL, UK
ben.williams@ed.ac.uk
Marc Toussaint
TU Berlin
Franklinstr. 28/29, FR 6-9
10587 Berlin, Germany
mtoussai@cs.tu-berlin.de
Amos J Storkey
School of In... | 3204 |@word version:1 briefly:1 km:8 covariance:2 pressure:3 thereby:1 kappen:1 ivaldi:1 initial:1 score:2 current:2 com:1 activation:6 scatter:3 written:5 shape:1 motor:13 plot:4 stationary:3 generative:19 intelligence:1 provides:3 contribute:1 differential:1 become:1 consists:1 introduce:2 indeed:1 rapid:1 behavior:3... |
2,431 | 3,205 | The pigeon as particle filter
Nathaniel D. Daw
Center for Neural Science
and Department of Psychology
New York University
daw@cns.nyu.edu
Aaron C. Courville
D?partement d?Informatique
et de recherche op?rationnelle
Universit? de Montr?al
aaron.courville@gmail.com
Abstract
Although theorists have interpreted classica... | 3205 |@word trial:23 middle:2 judgement:1 seems:3 proportion:1 nd:4 extinction:1 d2:4 gradual:2 simulation:7 crucially:1 accounting:1 covariance:5 delicately:1 thereby:2 recursively:1 carry:2 initial:2 exclusively:1 suppressing:1 o2:2 current:1 com:1 comparing:3 gmail:1 must:2 tenet:1 subsequent:5 periodically:1 realis... |
2,432 | 3,206 | Learning the 2-D Topology of Images
Yoshua Bengio
University of Montreal
yoshua.bengio@umontreal.ca
Nicolas Le Roux
University of Montreal
nicolas.le.roux@umontreal.ca
Marc Joliveau
?
Ecole
Centrale Paris
marc.joliveau@ecp.fr
Pascal Lamblin
University of Montreal
lamblinp@umontreal.ca
Bal?azs K?egl
LAL/LRI, Univer... | 3206 |@word advantageous:1 hyv:1 grey:1 reduction:4 score:4 ecole:1 document:1 subjective:1 recovered:3 com:1 surprising:3 lang:1 must:1 informative:1 remove:3 v:2 intelligence:1 fewer:1 cook:1 accordingly:1 xk:2 farther:1 boosting:1 location:6 preference:1 plumbley:1 along:1 incorrect:1 combine:1 inside:1 indeed:1 ica... |
2,433 | 3,207 | Comparing Bayesian models for multisensory cue
combination without mandatory integration
Konrad P. K?ording
Rehabilitation Institute of Chicago
Northwestern University, Dept. PM&R
Chicago, IL 60611
konrad@koerding.com
Ulrik R. Beierholm
Computation and Neural Systems
California Institute of Technology
Pasadena, CA 91... | 3207 |@word beep:3 determinant:1 trial:8 judgement:1 open:1 simulation:1 jacob:1 excited:1 pick:1 thereby:1 solid:2 contains:1 disparity:12 ording:1 imaginary:1 comparing:1 com:2 si:13 gmail:1 must:1 realize:1 chicago:2 analytic:1 remove:1 designed:1 cue:31 generative:5 selected:1 nervous:2 filtered:1 location:7 five:1... |
2,434 | 3,208 | Probabilistic Matrix Factorization
Ruslan Salakhutdinov and Andriy Mnih
Department of Computer Science, University of Toronto
6 King?s College Rd, M5S 3G4, Canada
{rsalakhu,amnih}@cs.toronto.edu
Abstract
Many existing approaches to collaborative filtering can neither handle very large
datasets nor easily deal with use... | 3208 |@word middle:1 version:3 norm:4 tried:1 decomposition:1 covariance:11 simplifying:1 tr:2 contains:2 score:5 selecting:1 outperforms:2 existing:2 mishra:1 comparing:1 michal:1 nowlan:1 realistic:1 hofmann:1 remove:1 update:2 v:1 half:1 fewer:3 selected:1 item:1 greedy:1 steepest:2 prize:1 ith:1 provides:3 toronto:... |
2,435 | 3,209 | On Higher-Order Perceptron Algorithms ?
Cristian Brotto
DICOM, Universit`a dell?Insubria
Claudio Gentile
DICOM, Universit`a dell?Insubria
cristian.brotto@gmail.com
claudio.gentile@uninsubria.it
Fabio Vitale
DICOM, Universit`a dell?Insubria
fabiovdk@yahoo.com
Abstract
A new algorithm for on-line learning linear-th... | 3209 |@word trial:15 version:9 polynomial:4 norm:22 seems:2 justice:1 advantageous:1 flexiblity:1 nd:1 dekel:1 additively:1 tried:3 pick:2 thereby:2 minus:1 initial:2 contains:2 past:4 existing:1 outperforms:2 current:2 com:3 comparing:1 gmail:1 readily:1 additive:1 plot:4 update:20 v:9 discrimination:1 half:1 selected... |
2,436 | 321 | Adaptive Range Coding
Bruce E. Rosen, James M. Goodwin, and Jacques J. Vidal
Distributed Machine Intelligence Laboratory
Computer Science Department
University of California, Los Angeles
Los Angeles, CA 90024
Abstract
This paper examines a class of neuron based
learning systems for dynamic control that rely on
adaptiv... | 321 |@word effect:1 trial:11 consisted:1 true:2 comparatively:1 differ:2 move:4 rei:1 believe:1 laboratory:1 receptive:1 simulation:1 subsequently:1 alp:1 during:2 self:3 require:1 simulated:1 initial:4 evenly:1 disparity:1 preliminary:1 hill:1 polytope:2 tuned:2 toward:1 adjusted:1 current:6 considered:1 activation:2 ... |
2,437 | 3,210 | Configuration Estimates Improve Pedestrian Finding
Duan Tran?
U.Illinois at Urbana-Champaign
Urbana, IL 61801 USA
ddtran2@uiuc.edu
D.A. Forsyth
U.Illinois at Urbana-Champaign
Urbana, IL 61801 USA
daf@uiuc.edu
Abstract
Fair discriminative pedestrian finders are now available. In fact, these pedestrian
finders make mo... | 3210 |@word hierachy:2 version:2 dalal:14 replicate:1 triggs:15 decomposition:1 lepetit:1 initial:1 configuration:56 series:1 score:8 contains:1 selecting:1 daniel:1 bootstrapped:1 outperforms:2 brien:1 current:4 comparing:1 protection:1 must:5 concatenate:2 happen:1 shape:5 plot:2 update:1 mounting:1 alone:1 half:1 cu... |
2,438 | 3,211 | Using Deep Belief Nets to Learn Covariance Kernels
for Gaussian Processes
Ruslan Salakhutdinov and Geoffrey Hinton
Department of Computer Science, University of Toronto
6 King?s College Rd, M5S 3G4, Canada
rsalakhu,hinton@cs.toronto.edu
Abstract
We show how to use unlabeled data and a deep belief net (DBN) to learn a ... | 3211 |@word version:2 middle:1 tried:2 covariance:19 decomposition:1 contrastive:2 carry:1 initial:1 contains:4 series:1 tuned:3 document:8 outperforms:1 existing:2 comparing:1 jaz:1 activation:2 scatter:2 stemmed:1 must:1 readily:2 john:1 visible:11 subsequent:1 plot:4 update:1 v:2 discrimination:2 generative:4 greedy... |
2,439 | 3,212 | Learning Bounds for Domain Adaptation
John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman
Department of Computer and Information Science
University of Pennsylvania, Philadelphia, PA 19146
{blitzer,crammer,kulesza,pereira,wortmanj}@cis.upenn.edu
Abstract
Empirical risk minimization offers ... | 3212 |@word illustrating:1 version:1 stronger:1 vldb:1 blender:1 electronics:2 contains:2 series:1 document:2 comparing:3 com:1 ida:1 assigning:1 john:2 distant:2 numerical:1 happen:1 shape:3 plot:6 depict:2 alone:1 website:1 record:1 detecting:1 boosting:2 appliance:1 tagger:1 height:1 shorthand:1 prove:1 consists:3 m... |
2,440 | 3,213 | Unconstrained Online Handwriting Recognition with
Recurrent Neural Networks
Alex Graves
TUM, Germany
alex@idsia.ch
Santiago Fern?andez
IDSIA, Switzerland
santiago@idsia.ch
Horst Bunke
University of Bern, Switzerland
bunke@iam.unibe.ch
Marcus Liwicki
University of Bern, Switzerland
liwicki@iam.unibe.ch
?
Jurgen
Schm... | 3213 |@word arabic:1 briefly:1 bigram:5 seems:1 johansson:1 termination:1 hu:1 eng:1 thereby:1 pressed:1 recursively:1 reduction:2 substitution:1 contains:4 score:13 initialisation:1 document:6 prefix:2 past:1 blank:11 current:1 activation:5 assigning:1 written:2 shape:1 remove:1 designed:6 drop:1 progressively:1 devic... |
2,441 | 3,214 | Markov Chain Monte Carlo with People
Adam N. Sanborn
Psychological and Brain Sciences
Indiana University
Bloomington, IN 47045
asanborn@indiana.edu
Thomas L. Griffiths
Department of Psychology
University of California
Berkeley, CA 94720
tom griffiths@berkeley.edu
Abstract
Many formal models of cognition implicitly u... | 3214 |@word trial:19 version:2 seems:1 instruction:1 uncovers:1 paid:1 solid:1 subjective:13 bradley:1 current:9 recovered:1 john:1 shape:3 plot:1 stationary:8 generative:1 selected:2 plane:1 beginning:1 accepting:1 mental:4 provides:2 contribute:1 height:4 mathematical:4 along:1 constructed:2 theoretically:1 acquired:... |
2,442 | 3,215 | Learning with Transformation Invariant Kernels
Christian Walder
Max Planck Institute for Biological Cybernetics
72076 T?ubingen, Germany
christian.walder@tuebingen.mpg.de
Olivier Chapelle
Yahoo! Research
Santa Clara, CA
chap@yahoo-inc.com
Abstract
This paper considers kernels invariant to translation, rotation and d... | 3215 |@word repository:1 version:1 polynomial:4 norm:5 seems:3 nd:1 r:1 mention:1 configuration:1 series:1 existing:1 current:1 com:1 define1:1 surprising:1 analysed:1 clara:1 written:1 john:1 numerical:3 christian:2 update:1 v:1 implying:1 alone:1 flare:1 accordingly:2 xk:1 provides:1 math:1 bijection:4 hyperplanes:1 ... |
2,443 | 3,216 | Bayesian binning beats approximate alternatives:
estimating peristimulus time histograms
Dominik Endres, Mike Oram, Johannes Schindelin and Peter F?oldi?ak
School of Psychology
University of St. Andrews
KY16 9JP, UK
{dme2,mwo,js108,pf2}@st-andrews.ac.uk
Abstract
The peristimulus time histogram (PSTH) and its more con... | 3216 |@word neurophysiology:3 briefly:1 reused:1 proportionality:1 termination:1 km:47 overwritten:1 lobe:1 stsa:4 thereby:1 solid:1 carry:1 initial:1 configuration:2 contains:1 series:1 selecting:1 outperforms:1 discretization:1 anterior:5 yet:1 must:1 subsequent:1 informative:1 shape:1 analytic:1 wanted:1 treating:1 ... |
2,444 | 3,217 | Learning Visual Attributes
Vittorio Ferrari ?
University of Oxford (UK)
Andrew Zisserman
University of Oxford (UK)
Abstract
We present a probabilistic generative model of visual attributes, together with an efficient
learning algorithm. Attributes are visual qualities of objects, such as ?red?, ?striped?, or
?spotte... | 3217 |@word deformed:1 briefly:1 dalal:1 middle:1 proportion:2 triggs:1 open:1 pick:3 moment:1 initial:3 liu:1 contains:10 ours:1 rightmost:1 current:5 blank:1 si:3 yet:1 activation:4 must:3 grain:1 refines:1 subsequent:2 j1:7 confirming:2 shape:13 enables:2 cheap:1 plot:2 update:4 alone:1 generative:6 leaf:1 selected:... |
2,445 | 3,218 | Convex Learning with Invariances
Choon Hui Teo
Australian National University
choonhui.teo@anu.edu.au
Amir Globerson
CSAIL, MIT
gamir@csail.mit.edu
Sam Roweis
Department of Computer Science
University of Toronto
roweis@cs.toronto.edu
Alexander J. Smola
NICTA
Canberra, Australia
alex.smola@gmail.com
Abstract
Incorp... | 3218 |@word version:1 polynomial:2 norm:1 gradual:1 pick:1 solid:1 substitution:2 document:2 bhattacharyya:1 existing:4 current:2 com:1 comparing:1 surprising:1 si:6 gmail:1 yet:1 kft:2 numerical:1 kdd:3 shape:1 analytic:1 hofmann:1 drop:1 update:3 generative:1 fewer:1 half:1 selected:1 amir:1 footing:1 infrastructure:... |
2,446 | 3,219 | Active Preference Learning with Discrete Choice Data
Eric Brochu, Nando de Freitas and Abhijeet Ghosh
Department of Computer Science
University of British Columbia
Vancouver, BC, Canada
{ebrochu, nando, ghosh}@cs.ubc.ca
Abstract
We propose an active learning algorithm that learns a continuous valuation model
from dis... | 3219 |@word trial:7 exploitation:2 judgement:3 nd:1 tedious:2 simulation:4 tried:1 seek:1 covariance:1 solid:1 offload:1 series:1 selecting:2 daniel:1 tuned:1 bc:1 ours:2 interestingly:1 animated:1 subjective:1 freitas:1 bradley:1 current:1 comparing:1 past:1 si:1 chu:9 must:2 numerical:1 realistic:2 predetermined:1 en... |
2,447 | 322 | INTERACTION AMONG OCULARITY,
RETINOTOPY AND ON-CENTER/OFFCENTER PATHWAYS DURING
DEVELOPMENT
Shigeru Tanaka
Fundamental Research Laboratories, NEC Corporation,
34 Miyukigaoka, Tsukuba, Ibaraki 305, Japan
ABSTRACT
The development of projections from the retinas to the cortex is
mathematically analyzed according to the p... | 322 |@word middle:2 wiesel:7 seems:3 oncenter:1 simplecell:1 simulation:12 thereby:1 harder:1 initial:2 must:1 physiol:1 plasticity:1 nq:1 mastronarde:2 hamiltonian:2 compo:1 mathematical:2 become:1 pathway:18 roughly:1 behavior:3 terminal:15 decreasing:1 considering:2 innervation:1 project:1 retinotopic:6 panel:3 monk... |
2,448 | 3,220 | Receptive Fields without Spike-Triggering
Jakob H Macke
j a k o b@ t u e bi n g e n . mpg . de
Max Planck Institute for Biological Cybernetics
S pemannstrasse 41
72076 T u? bingen, Germany
?
G unther
Zeck
z e c k @ n e u r o . mpg . de
Max Planck Institute of Neurobiology
Am Klopferspitze 1 8
8 21 52 Martinsried, Germ... | 3220 |@word trial:1 nd:6 simulation:1 seek:2 tried:1 covariance:8 decomposition:2 arti:1 concise:1 eld:40 carry:1 reduction:4 contains:2 score:1 xand:1 recovered:2 negentropy:1 readily:2 numerical:1 informative:5 wx:1 shape:1 interspike:1 eichhorn:2 hofmann:1 plot:2 designed:1 opin:1 aside:1 cult:2 short:2 colored:1 pr... |
2,449 | 3,221 | Extending position/phase-shift tuning to motion
energy neurons improves velocity discrimination
Stanley Yiu Man Lam and Bertram E. Shi
Department of Electronic and Computer Engineering
Hong Kong Univeristy of Science and Technology
Clear Water Bay, Kowloon, Hong Kong
{eelym,eebert}@ee.ust.hk
Abstract
We extend positio... | 3221 |@word neurophysiology:1 kong:3 seems:1 grey:1 mammal:1 solid:2 disparity:27 tuned:44 imaginary:3 current:1 comparing:3 recovered:1 ust:1 reminiscent:2 enables:1 plot:2 discrimination:7 v:1 cue:1 half:2 nervous:1 plane:1 location:3 along:3 constructed:4 become:1 combine:2 autocorrelation:1 expected:1 behavior:1 in... |
2,450 | 3,222 | Heterogeneous Component Analysis
3,2
?
Shigeyuki Oba1 , Motoaki Kawanabe2 , Klaus Robert Muller
, and Shin Ishii4,1
1. Graduate School of Information Science, Nara Institute of Science and Technology, Japan
2. Fraunhofer FIRST.IDA, Germany
3. Department of Computer Science, Technical University Berlin, Germany
4. Grad... | 3222 |@word loading:32 norm:1 underline:1 simulation:1 covariance:1 decomposition:1 initial:3 contains:1 selecting:1 interestingly:1 existing:5 current:1 ida:1 trustworthy:1 interpretable:1 v:1 stationary:2 greedy:18 generative:1 device:5 selected:5 implying:1 accordingly:1 yamada:1 colored:2 num:1 contribute:1 five:1 ... |
2,451 | 3,223 | Discovering Weakly-Interacting Factors in a Complex
Stochastic Process
Charlie Frogner
School of Engineering and Applied Sciences
Harvard University
Cambridge, MA 02138
frogner@seas.harvard.edu
Avi Pfeffer
School of Engineering and Applied Sciences
Harvard University
Cambridge, MA 02138
avi@eecs.harvard.edu
Abstract
... | 3223 |@word advantageous:2 seek:1 propagate:3 tried:1 decomposition:1 minus:1 tr:1 carry:1 initial:2 contains:2 score:38 series:1 interestingly:1 outperforms:1 recovered:3 surprising:1 must:1 partition:4 enables:2 treating:1 designed:1 half:1 discovering:1 fewer:2 intelligence:7 indicative:1 merger:2 batmobile:1 node:8... |
2,452 | 3,224 | Inferring Elapsed Time
from Stochastic Neural Processes
Misha B. Ahrens and Maneesh Sahani
Gatsby Computational Neuroscience Unit, UCL
Alexandra House, 17 Queen Square, London, WC1N 3AR
{ahrens, maneesh}@gatsby.ucl.ac.uk
Abstract
Many perceptual processes and neural computations, such as speech recognition,
motor cont... | 3224 |@word trial:1 exploitation:1 judgement:3 replicate:1 gradual:1 teich:1 covariance:9 thereby:1 tr:2 lq2:1 initial:1 necessity:1 tuned:1 subjective:1 existing:1 timer:5 attracted:1 must:2 physiol:1 realistic:1 motor:1 plot:1 alone:1 stationary:2 pacemaker:1 short:1 footing:1 farther:1 contribute:1 obser:1 simpler:5... |
2,453 | 3,225 | A Unified Near-Optimal Estimator For Dimension Reduction in l?
(0 < ? ? 2) Using Stable Random Projections
Ping Li
Department of Statistical Science
Faculty of Computing and Information Science
Cornell University
pingli@cornell.edu
Trevor J. Hastie
Department of Statistics
Department of Health, Research and Policy
Sta... | 3225 |@word illustrating:2 briefly:1 faculty:1 norm:24 disk:2 widom:1 d2:5 vldb:1 seek:2 simulation:5 mention:1 moment:2 reduction:12 celebrated:1 contains:1 series:1 karger:1 outperforms:1 comparing:2 z2:1 tackling:1 must:1 subsequent:1 numerical:1 fama:1 plot:6 update:1 device:1 cormode:1 mathematical:2 direct:1 beco... |
2,454 | 3,226 | People Tracking with the Laplacian Eigenmaps
Latent Variable Model
Zhengdong Lu
CSEE, OGI, OHSU
? Carreira-Perpin?
? an
Miguel A.
EECS, UC Merced
Cristian Sminchisescu
University of Bonn
zhengdon@csee.ogi.edu
http://eecs.ucmerced.edu
sminchisescu.ins.uni-bonn.de
Abstract
Reliably recovering 3D human pose from mon... | 3226 |@word middle:1 briefly:1 proportion:7 perpin:1 decomposition:1 covariance:4 tr:2 reduction:11 initial:1 configuration:1 contains:1 fragment:2 score:1 initialisation:4 tuned:1 ours:1 existing:3 recovered:1 wd:1 yet:3 must:1 realistic:1 remove:1 drop:1 plot:5 update:1 resampling:1 xdx:1 generative:7 parameterizatio... |
2,455 | 3,227 | Cluster Stability for Finite Samples
Ohad Shamir? and Naftali Tishby??
? School of Computer Science and Engineering
? Interdisciplinary Center for Neural Computation
The Hebrew University
Jerusalem 91904, Israel
{ohadsh,tishby}@cs.huji.ac.il
Abstract
Over the past few years, the notion of stability in data clustering ... | 3227 |@word mild:2 trial:6 middle:2 open:2 invoking:1 elisseeff:1 series:1 denoting:1 past:2 assigning:1 realistic:1 happen:1 plot:2 intelligence:2 selected:1 complementing:1 detecting:1 mcdiarmid:2 mathematical:1 direct:1 become:4 prove:4 consists:1 wassily:1 excellence:1 theoretically:1 periodograms:1 indeed:1 roughl... |
2,456 | 3,228 | Transfer Learning using Kolmogorov Complexity:
Basic Theory and Empirical Evaluations
M. M. Hassan Mahmud
Department of Computer Science
University of Illinois at Urbana-Champaign
mmmahmud@uiuc.edu
Sylvian R. Ray
Department of Computer Science
University of Illinois at Urbana-Champaign
ray@cs.uiuc.edu
Abstract
In tr... | 3228 |@word h:1 multitask:1 repository:5 version:1 briefly:1 compression:3 seems:1 nd:2 c0:4 simulation:1 p0:2 recursively:1 contains:7 denoting:1 bc:4 interestingly:1 prefix:1 outperforms:1 existing:2 freitas:1 current:4 comparing:1 yet:1 universality:1 mushroom:3 drop:1 intelligence:3 fewer:2 leaf:1 xk:2 ith:1 pointe... |
2,457 | 3,229 | Inferring Neural Firing Rates from Spike Trains
Using Gaussian Processes
John P. Cunningham1 , Byron M. Yu1,2,3 , Krishna V. Shenoy1,2
1
Department of Electrical Engineering,
2
Neurosciences Program, Stanford University, Stanford, CA 94305
{jcunnin,byronyu,shenoy}@stanford.edu
Maneesh Sahani3
Gatsby Computational Neuro... | 3229 |@word trial:23 cox:1 middle:1 determinant:2 inversion:5 sgf:1 simulation:1 covariance:4 p0:4 thereby:1 carry:1 reduction:1 series:2 hereafter:1 unintended:1 batista:1 optican:1 outperforms:1 current:1 ka:4 neurophys:1 dx:2 written:3 must:2 john:1 vere:1 numerical:1 shape:1 plot:1 v:5 alone:1 generative:1 selected... |
2,458 | 323 | Generalization by Weight-Elimination
with Application to Forecasting
Andreas S. Weigend
Physics Department
Stanford University
Stanford, CA 94305
David E. Rumelhart
Psychology Department
Stanford University
Stanford, CA 94305
Bernardo A. Huberman
Dynamics of Computation
XeroxPARC
Palo Alto, CA 94304
Abstract
Inspir... | 323 |@word briefly:1 eliminating:1 polynomial:1 justice:1 casdagli:1 grey:1 pressure:1 pick:2 thereby:1 solid:2 series:12 interestingly:1 past:2 activation:2 yet:1 must:1 happen:1 remove:1 precaution:1 half:1 fewer:1 device:3 preference:1 monday:4 sigmoidal:2 five:1 become:1 fitting:2 deteriorate:1 expected:3 indeed:1 ... |
2,459 | 3,230 | Bundle Methods for Machine Learning
Alexander J. Smola, S.V. N. Vishwanathan, Quoc V. Le
NICTA and Australian National University, Canberra, Australia
Alex.Smola@gmail.com, {SVN.Vishwanathan, Quoc.Le}@nicta.com.au
Abstract
We present a globally convergent method for regularized risk minimization problems. Our method ... | 3230 |@word mild:1 polynomial:1 norm:2 initial:1 score:3 past:2 existing:2 current:2 com:2 comparing:1 gmail:1 yet:1 written:4 subsequent:1 additive:1 partition:1 kdd:2 hofmann:2 cheap:2 analytic:1 numerical:1 plot:3 designed:1 update:6 greedy:1 parameterization:1 provides:1 successive:1 firstly:1 zhang:1 along:1 direc... |
2,460 | 3,231 | An Analysis of Inference with the Universum
Fabian H. Sinz
Max Planck Institute for biological Cybernetics
Spemannstrasse 41, 72076, T?ubingen, Germany
fabee@tuebingen.mpg.de
Alekh Agarwal
University of California Berkeley
387 Soda Hall Berkeley, CA 94720-1776
alekh@eecs.berkeley.edu
Olivier Chapelle
Yahoo! Research
... | 3231 |@word cu:18 briefly:2 version:3 inversion:1 seems:1 covariance:14 thereby:1 mention:1 carry:1 series:2 score:4 contains:2 exclusively:1 rkhs:1 suppressing:1 com:1 analysed:1 clara:1 must:2 written:1 bd:1 additive:1 motor:2 v:1 device:1 isotropic:1 filtered:1 contribute:1 readability:1 fabee:1 mathematical:1 along... |
2,461 | 3,232 | Retrieved context and the discovery of semantic
structure
Vinayak A. Rao, Marc W. Howard?
Syracuse University
Department of Psychology
430 Huntington Hall
Syracuse, NY 13244
vrao@gatsby.ucl.ac.uk, marc@memory.syr.edu
Abstract
Semantic memory refers to our knowledge of facts and relationships between concepts. A succes... | 3232 |@word middle:1 version:4 proportion:2 hippocampus:4 proportionality:1 instruction:1 open:1 grey:1 simulation:10 lobe:3 moment:2 initial:1 series:2 contains:1 denoting:1 past:1 existing:1 current:3 contextual:33 comparing:1 yet:1 must:1 realistic:4 plasticity:1 unchanging:1 enables:8 update:1 medial:4 cue:27 selec... |
2,462 | 3,233 | Fitted Q-iteration in continuous action-space MDPs
Andr?as Antos
Computer and Automation Research Inst.
of the Hungarian Academy of Sciences
Kende u. 13-17, Budapest 1111, Hungary
antos@sztaki.hu
R?emi Munos
SequeL project-team, INRIA Lille
59650 Villeneuve d?Ascq, France
remi.munos@inria.fr
Csaba Szepesv?ari?
Depar... | 3233 |@word mild:3 version:1 norm:1 open:1 hu:1 r:1 kalyanakrishnan:1 initial:2 selecting:1 past:2 ka:6 assigning:1 dx:3 yet:1 written:1 must:1 update:1 fund:1 stationary:6 greedy:10 selected:3 intelligence:1 lr:3 iterates:2 complication:1 mathematical:2 become:2 shorthand:1 prove:1 introduce:1 manner:1 x0:2 ra:1 indee... |
2,463 | 3,234 | Topmoumoute online natural gradient algorithm
Pierre-Antoine Manzagol
University of Montreal
manzagop@iro.umontreal.ca
Nicolas Le Roux
University of Montreal
nicolas.le.roux@umontreal.ca
Yoshua Bengio
University of Montreal
yoshua.bengio@umontreal.ca
Abstract
Guided by the goal of obtaining an optimization algorithm... | 3234 |@word kgk:1 version:1 inversion:1 norm:6 c0:1 grey:1 tried:1 covariance:30 thereby:2 profit:1 tr:1 moment:1 contains:1 selecting:2 denoting:1 existing:2 surprising:1 yet:2 dx:1 must:5 readily:1 numerical:1 shape:1 cheap:1 designed:2 drop:2 progressively:2 update:2 aside:1 intelligence:1 prohibitive:1 steepest:1 r... |
2,464 | 3,235 | Sparse Overcomplete Latent Variable Decomposition
of Counts Data
Madhusudana Shashanka
Mars, Incorporated
Hackettstown, NJ
shashanka@cns.bu.edu
Bhiksha Raj
Mitsubishi Electric Research Labs
Cambridge, MA
bhiksha@merl.com
Paris Smaragdis
Adobe Systems
Newton, MA
paris@adobe.com
Abstract
An important problem in many f... | 3235 |@word h:1 version:1 proportion:3 norm:1 plsa:8 simulation:1 mitsubishi:1 decomposition:15 wgn:1 thereby:2 initial:1 configuration:2 document:7 com:2 must:7 transcendental:1 informative:1 hofmann:1 hypothesize:1 update:5 fewer:1 blei:2 characterization:2 contribute:1 firstly:1 become:3 compose:7 combine:2 manner:1... |
2,465 | 3,236 | Second Order Bilinear Discriminant Analysis for
single-trial EEG analysis
Christoforos Christoforou
Department of Computer Science
The Graduate Center of the City University of New York
365 Fifth Avenue
New York, NY 10016-4309
cchristoforou@gc.cuny.edu
Paul Sajda
Department of Biomedical Engineering
Columbia Universit... | 3236 |@word neurophysiology:2 trial:23 seems:1 norm:2 decomposition:1 eng:4 thereby:1 existing:1 imaginary:1 ida:1 luo:1 scatter:1 written:1 readily:1 realistic:1 analytic:1 motor:2 reproducible:1 plot:1 discrimination:2 v:1 half:1 selected:1 device:1 sys:1 ith:1 lr:2 filtered:3 provides:1 boosting:1 contribute:1 five:... |
2,466 | 3,237 | Learning Horizontal Connections in a Sparse Coding
Model of Natural Images
Pierre J. Garrigues
Department of EECS
Redwood Center for Theoretical Neuroscience
Univ. of California, Berkeley
Berkeley, CA 94720
garrigue@eecs.berkeley.edu
Bruno A. Olshausen
Helen Wills Neuroscience Inst.
School of Optometry
Redwood Center... | 3237 |@word determinant:1 compression:1 norm:3 nd:1 hyv:2 covariance:1 decomposition:1 garrigues:1 contains:1 selecting:1 current:1 recovered:2 activation:2 si:19 written:2 optometry:1 visible:1 informative:1 update:2 stationary:1 generative:4 intelligence:1 amir:1 colored:1 node:2 zhang:1 mathematical:1 symposium:1 in... |
2,467 | 3,238 | One-Pass Boosting
Zafer Barutcuoglu
zbarutcu@cs.princeton.edu
Philip M. Long
plong@google.com
Rocco A. Servedio
rocco@cs.columbia.edu
Abstract
This paper studies boosting algorithms that make a single pass over a set of base
classifiers.
We first analyze a one-pass algorithm in the setting of boosting with diverse ... | 3238 |@word briefly:2 version:3 stronger:1 seems:1 advantageous:1 duda:1 d2:1 tried:1 bn:30 reap:1 pick:1 reduction:1 initial:6 contains:2 selecting:1 current:3 com:2 yet:2 must:3 grain:1 additive:1 designed:1 update:1 half:1 item:1 xk:1 mccallum:1 filtered:2 boosting:36 complication:1 dn:2 constructed:2 incorrect:1 pr... |
2,468 | 3,239 | Stability Bounds for Non-i.i.d. Processes
Mehryar Mohri
Courant Institute of Mathematical Sciences
and Google Research
251 Mercer Street
New York, NY 10012
Afshin Rostamizadeh
Department of Computer Science
Courant Institute of Mathematical Sciences
251 Mercer Street
New York, NY 10012
mohri@cims.nyu.edu
rostami@cs... | 3239 |@word h:42 eor:1 version:4 middle:1 briefly:1 seems:2 stronger:1 norm:1 elisseeff:2 thereby:1 series:8 denoting:1 past:3 existing:3 z2:2 si:15 must:4 realistic:3 designed:1 discrimination:1 stationary:22 haykin:1 math:1 boosting:1 ron:1 mcdiarmid:5 quantit:1 mathematical:2 learing:1 prove:4 shorthand:1 interscien... |
2,469 | 324 | Discrete Affine Wavelet Transforms For Analysis
And Synthesis Of Feedforward Neural Networks
Y. c. Pati and P. S. Krishnaprasad
Systems Research Center and Department of Electrical Engineering
University of Maryland, College Park, MD 20742
Abstract
In this paper we show that discrete affine wavelet transforms can pro... | 324 |@word briefly:1 simulation:3 bn:1 decomposition:1 thereby:1 tr:1 solid:2 series:4 lapedes:1 emn:1 z2:1 activation:2 john:1 fn:5 designed:2 plane:1 isotropic:2 lr:2 provides:1 contribute:1 node:7 sigmoidal:6 mathematical:1 constructed:7 manner:2 globally:1 pitfall:1 provided:2 bounded:1 what:1 developed:1 guarantee... |
2,470 | 3,240 | Message Passing for Max-weight Independent Set
Sujay Sanghavi
LIDS, MIT
sanghavi@mit.edu
Devavrat Shah
Dept. of EECS, MIT
devavrat@mit.edu
Alan Willsky
Dept. of EECS, MIT
willsky@mit.edu
Abstract
We investigate the use of message-passing algorithms for the problem of finding
the max-weight independent set (MWIS) in... | 3240 |@word version:1 briefly:2 polynomial:1 suitably:1 open:1 mitsubishi:1 pick:1 reduction:4 lightweight:2 denoting:1 existing:1 current:1 yet:1 must:1 happen:1 j1:7 update:9 v:1 infrastructure:2 provides:4 certificate:3 node:45 math:1 along:1 constructed:1 direct:1 ik:1 incorrect:2 prove:1 manner:1 nor:1 freeman:1 r... |
2,471 | 3,241 | Iterative Non-linear Dimensionality Reduction by
Manifold Sculpting
Mike Gashler, Dan Ventura, and Tony Martinez ?
Brigham Young University
Provo, UT 84604
Abstract
Many algorithms have been recently developed for reducing dimensionality by
projecting data onto an intrinsic non-linear manifold. Unfortunately, existin... | 3241 |@word cos2:1 seek:4 decomposition:1 reduction:10 contains:1 outperforms:1 existing:3 current:8 com:1 gmail:1 yet:1 must:1 john:1 mesh:1 visible:1 distant:1 visibility:1 designed:1 fewer:3 selected:3 hallway:1 provides:1 zhang:1 qualitative:1 dan:1 manner:3 expected:3 frequently:1 informational:2 globally:6 little... |
2,472 | 3,242 | Comparison of objective functions for estimating
linear-nonlinear models
Tatyana O. Sharpee
Computational Neurobiology Laboratory,
the Salk Institute for Biological Studies, La Jolla, CA 92037
sharpee@salk.edu
Abstract
This paper compares a family of methods for characterizing neural feature selectivity with natural s... | 3242 |@word h:10 trial:2 version:1 compression:3 proportion:1 polynomial:1 open:2 bining:1 simulation:6 covariance:6 eng:2 tr:2 solid:3 reduction:1 exclusively:1 selecting:1 current:1 dx:4 subsequent:1 additive:1 numerical:3 informative:2 remove:1 aside:2 rebrik:1 short:1 filtered:2 provides:2 math:1 revisited:1 tolhur... |
2,473 | 3,243 | A Risk Minimization Principle
for a Class of Parzen Estimators
Kristiaan Pelckmans, Johan A.K. Suykens, Bart De Moor
Department of Electrical Engineering (ESAT) - SCD/SISTA
K.U.Leuven University
Kasteelpark Arenberg 10, Leuven, Belgium
Kristiaan.Pelckmans@esat.kuleuven.be
Abstract
This paper1 explores the use of a Max... | 3243 |@word faculty:1 polynomial:1 twelfth:1 tr:5 moment:1 score:5 denoting:1 chu:2 must:1 written:1 numerical:2 additive:1 cheap:3 enables:1 plot:1 n0:1 bart:1 intelligence:1 pelckmans:5 plane:1 provides:1 boosting:1 herbrich:1 mcdiarmid:1 along:3 dn:3 direct:2 become:1 constructed:2 prev:1 eleventh:1 expected:5 ry:5 ... |
2,474 | 3,244 | Optimal models of sound localization by barn owls
Brian J. Fischer
Division of Biology
California Institute of Technology
Pasadena, CA
fischerb@caltech.edu
Abstract
Sound localization by barn owls is commonly modeled as a matching procedure
where localization cues derived from auditory inputs are compared to stored t... | 3244 |@word duda:1 azimuthal:1 simulation:4 solid:1 contains:1 colburn:1 subjective:1 comparing:1 must:4 luis:1 written:1 physiol:2 evans:1 distant:2 additive:1 shape:1 motor:1 plot:1 cue:40 tone:5 plane:15 provides:3 location:3 along:2 direct:1 interaural:14 behavioral:8 expected:7 behavior:24 examine:2 resolve:5 litt... |
2,475 | 3,245 | Learning Monotonic Transformations for
Classification
Andrew G. Howard
Department of Computer Science
Columbia University
New York, NY 10027
ahoward@cs.columbia.edu
Tony Jebara
Department of Computer Science
Columbia University
New York, NY 10027
jebara@cs.columbia.edu
Abstract
A discriminative method is proposed for... | 3245 |@word trial:1 version:2 faculty:1 polynomial:2 seems:1 middle:1 kondor:1 sex:1 rgb:1 solid:2 moment:3 substitution:1 series:1 uncovered:1 tuned:1 document:4 outperforms:1 current:1 wd:1 z2:2 yet:2 written:1 subsequent:3 plot:1 v:12 greedy:4 leaf:1 intelligence:1 ith:1 location:1 simpler:1 five:1 along:1 construct... |
2,476 | 3,246 | Agreement-Based Learning
Percy Liang
Computer Science Division
University of California
Berkeley, CA 94720
Dan Klein
Computer Science Division
University of California
Berkeley, CA 94720
Michael I. Jordan
Computer Science Division
University of California
Berkeley, CA 94720
pliang@cs.berkeley.edu
klein@cs.berkeley... | 3246 |@word version:1 seems:1 advantageous:1 contains:1 past:1 existing:1 current:1 z2:4 yet:1 must:2 written:1 partition:3 siepel:3 alone:1 intelligence:1 leaf:1 de1:2 mccallum:2 provides:3 node:2 complication:2 phylogenetic:8 along:1 direct:1 become:1 consists:3 dan:1 expected:4 p1:14 decomposed:1 encouraging:1 actua... |
2,477 | 3,247 | Boosting the Area Under the ROC Curve
Philip M. Long
plong@google.com
Rocco A. Servedio
rocco@cs.columbia.edu
Abstract
We show that any weak ranker that can achieve an area under the ROC curve
slightly better than 1/2 (which can be achieved by random guessing) can be efficiently boosted to achieve an area under the ... | 3247 |@word briefly:1 version:2 middle:1 d2:1 solid:1 gloss:1 contains:1 series:2 past:2 bradley:1 com:1 comparing:1 assigning:1 must:9 additive:4 designed:1 v:1 rudin:2 guess:2 item:4 beginning:1 provides:1 boosting:25 node:33 ron:1 preference:2 along:1 constructed:3 prove:3 consists:1 paragraph:1 theoretically:1 swet... |
2,478 | 3,248 | Direct Importance Estimation with Model Selection
and Its Application to Covariate Shift Adaptation
Masashi Sugiyama
Tokyo Institute of Technology
sugi@cs.titech.ac.jp
Hisashi Kashima
IBM Research
hkashima@jp.ibm.com
Shinichi Nakajima
Nikon Corporation
nakajima.s@nikon.co.jp
?
Paul von Bunau
Technical University Ber... | 3248 |@word trial:6 faculty:1 seems:1 advantageous:1 open:1 simulation:2 covariance:2 tr:23 carry:1 liblinear:1 score:4 existing:2 abundantly:1 com:1 current:1 ida:1 yet:1 dx:6 subsequent:2 remove:1 implying:1 half:2 xk:9 direct:2 consists:1 baldi:1 manner:1 x0:3 planning:1 brain:1 equipped:3 project:1 estimating:8 not... |
2,479 | 3,249 | Computational Equivalence of Fixed Points and No
Regret Algorithms, and Convergence to Equilibria
Satyen Kale
Computer Science Department,
Princeton University
35 Olden St.
Princeton, NJ 08540
satyen@cs.princeton.edu
Elad Hazan
IBM Almaden Research Center
650 Harry Road
San Jose, CA 95120
hazan@us.ibm.com
Abstract
W... | 3249 |@word version:3 achievable:1 stronger:3 norm:3 approachability:1 polynomial:1 crucially:1 mention:1 reduction:1 contains:1 past:1 com:1 must:1 additive:1 update:1 stationary:1 warmuth:1 xk:1 ith:1 math:1 become:1 supply:1 clairvoyant:1 prove:2 focs:2 manner:1 x0:6 indeed:2 market:6 expected:3 behavior:3 multi:1 n... |
2,480 | 325 | Learning by Combining Memorization
and Gradient Descent
John C. Platt
Synaptics, Inc.
2860 Zanker Road, Suite 206
San Jose, CA 95134
ABSTRACT
We have created a radial basis function network that allocates a
new computational unit whenever an unusual pattern is presented
to the network. The network learns by allocatin... | 325 |@word cox:1 longterm:1 polynomial:6 series:5 tuned:2 lapedes:2 existing:2 blank:1 current:1 yet:1 john:2 refines:1 girosi:3 mackey:7 hash:4 fewer:2 short:1 record:1 ire:2 coarse:2 become:1 differential:1 consists:2 cray:1 fitting:1 ahij:1 sublinearly:1 roughly:1 multi:1 automatically:1 cpu:1 window:1 provided:2 ci... |
2,481 | 3,250 | Theoretical Analysis of Heuristic Search Methods for
Online POMDPs
St?ephane Ross
McGill University
Montr?eal, Qc, Canada
sross12@cs.mcgill.ca
Joelle Pineau
McGill University
Montr?eal, Qc, Canada
jpineau@cs.mcgill.ca
Brahim Chaib-draa
Laval University
Qu?ebec, Qc, Canada
chaib@ift.ulaval.ca
Abstract
Planning in par... | 3250 |@word compression:3 seems:1 reused:2 reduction:4 initial:2 atb:2 contains:2 selecting:1 past:1 lave:4 current:11 yet:2 dx:2 must:2 bd:4 john:1 subsequent:1 update:1 intelligence:2 selected:1 leaf:2 ith:1 smith:1 recherche:1 provides:2 completeness:1 node:51 location:1 toronto:1 mathematical:1 descendant:1 consist... |
2,482 | 3,251 | Managing Power Consumption and Performance of
Computing Systems Using Reinforcement Learning
Gerald Tesauro, Rajarshi Das, Hoi Chan, Jeffrey O. Kephart,
Charles Lefurgy? , David W. Levine and Freeman Rawson?
IBM Watson and Austin? Research Laboratories
{gtesauro,rajarshi,hychan,kephart,lefurgy,dwl,frawson}@us.ibm.com
... | 3251 |@word middle:1 stronger:1 advantageous:1 seems:1 decomposition:1 thereby:1 blade:19 reduction:1 initial:8 series:1 selecting:1 united:1 existing:2 current:7 com:2 comparing:1 protection:1 tackling:1 dx:1 must:1 realistic:2 concatenate:1 shape:1 enables:1 designed:2 drop:1 update:1 plot:2 icac:4 devising:1 short:1... |
2,483 | 3,252 | Multiple-Instance Active Learning
Burr Settles Mark Craven
University of Wisconsin
Madison, WI 5713 USA
{bsettles@cs,craven@biostat}.wisc.edu
Soumya Ray
Oregon State University
Corvallis, OR 97331 USA
sray@eecs.oregonstate.edu
Abstract
We present a framework for active learning in the multiple-instance (MI) setting.... | 3252 |@word version:1 eliminating:1 seek:1 reduction:1 electronics:1 initial:9 contains:3 selecting:1 document:6 existing:1 current:2 must:1 readily:1 stemming:1 numerical:1 hofmann:1 christian:1 plot:1 atlas:1 update:1 alone:1 half:1 selected:4 fewer:1 intelligence:1 sys:2 ith:1 short:4 coarse:3 location:3 org:1 along... |
2,484 | 3,253 | Hierarchical Apprenticeship Learning, with
Application to Quadruped Locomotion
J. Zico Kolter, Pieter Abbeel, Andrew Y. Ng
Department of Computer Science
Stanford University
Stanford, CA 94305
{kolter, pabbeel, ang}@cs.stanford.edu
Abstract
We consider apprenticeship learning?learning from expert demonstrations?in
th... | 3253 |@word briefly:1 eliminating:1 polynomial:1 seems:2 advantageous:1 pieter:2 r:1 seek:1 decomposition:10 minus:1 initial:1 loc:1 hereafter:1 past:6 outperforms:3 bradley:1 current:9 must:2 ronald:1 subsequent:2 hofmann:1 littledog:3 motor:1 designed:2 stationary:1 greedy:3 intelligence:4 desktop:1 institution:2 pro... |
2,485 | 3,254 | Object Recognition by Scene Alignment
Bryan C. Russell Antonio Torralba Ce Liu Rob Fergus William T. Freeman
Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambrige, MA 02139 USA
{brussell,torralba,celiu,fergus,billf}@csail.mit.edu
Abstract
Current object recognition sys... | 3254 |@word norm:1 everingham:1 r:1 seek:1 covariance:2 liu:1 configuration:8 score:2 hoiem:1 tuned:2 outperforms:4 current:1 contextual:1 si:5 scatter:2 assigning:1 dde:1 wiewiora:1 noninformative:1 shape:3 voc2006:1 plot:3 gist:9 depict:1 alone:2 intelligence:2 cue:1 generative:1 instantiate:1 core:1 geospatial:1 ble... |
2,486 | 3,255 | Adaptive Bayesian Inference
Umut A. Acar?
Toyota Tech. Inst.
Chicago, IL
umut@tti-c.org
Alexander T. Ihler
U.C. Irvine
Irvine, CA
ihler@ics.uci.edu
Ramgopal R. Mettu?
Univ. of Massachusetts
Amherst, MA
mettu@ecs.umass.edu
? ur
? Sumer
?
Ozg
Uni. of Chicago
Chicago, IL
osumer@cs.uchicago.edu
Abstract
Motivated by s... | 3255 |@word cu:2 open:2 simulation:5 contraction:9 recursively:3 configuration:2 uma:1 selecting:1 loeliger:1 ours:1 interestingly:1 past:1 current:1 delcher:4 must:3 john:1 subsequent:1 chicago:3 opin:1 acar:5 plot:1 update:26 intelligence:1 leaf:9 inspection:1 dunbrack:1 core:1 recompute:2 node:55 org:1 simpler:1 alo... |
2,487 | 3,256 | Neural characterization in partially observed
populations of spiking neurons
Jonathan W. Pillow
Peter Latham
Gatsby Computational Neuroscience Unit, UCL
17 Queen Square, London WC1N 3AR, UK
pillow@gatsby.ucl.ac.uk
pel@gatsby.ucl.ac.uk
Abstract
Point process encoding models provide powerful statistical methods for und... | 3256 |@word achievable:1 stronger:1 hippocampus:1 simulation:3 accounting:1 contains:2 past:4 current:3 z2:2 surprising:1 v:1 provides:2 characterization:2 psth:3 mathematical:3 burst:1 direct:1 goaldirected:1 shorthand:1 pathway:3 fitting:2 introduce:2 pairwise:1 expected:4 examine:3 fared:1 brain:4 multi:3 begin:1 es... |
2,488 | 3,257 | Convex Relaxations of Latent Variable Training
Yuhong Guo and Dale Schuurmans
Department of Computing Science
University of Alberta
{yuhong, dale}@cs.ualberta.ca
Abstract
We investigate a new, convex relaxation of an expectation-maximization (EM)
variant that approximates a standard objective while eliminating local m... | 3257 |@word middle:2 version:1 eliminating:2 seems:1 invoking:2 thereby:2 tr:20 initial:1 configuration:9 series:1 interestingly:1 recovered:4 com:1 yet:1 grapheme:1 must:8 bie:1 numerical:1 j1:1 remove:1 drop:1 update:7 implying:1 alone:1 fewer:2 selected:1 parameterization:1 ith:1 core:2 provides:4 node:5 simpler:1 c... |
2,489 | 3,258 | Incremental Natural Actor-Critic Algorithms
Shalabh Bhatnagar
Department of Computer Science & Automation, Indian Institute of Science, Bangalore, India
Richard S. Sutton, Mohammad Ghavamzadeh, Mark Lee
Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
Abstract
We present four new rein... | 3258 |@word version:4 inversion:1 seems:2 valuefunction:1 recursively:2 reduction:1 initial:1 uma:1 tuned:1 renewed:1 rightmost:1 existing:1 discretization:1 si:1 yet:1 written:4 update:26 stationary:3 intelligence:1 parameterization:1 dissertation:2 iterates:1 parameterizations:1 along:3 differential:6 welldefined:1 p... |
2,490 | 3,259 | The Noisy-Logical Distribution and its Application to
Causal Inference
Alan Yuille
Department of Statistics
University of California at Los Angeles
Los Angeles, CA 90095
yuille@stat.ucla.edu
Hongjing Lu
Department of Psychology
University of California at Los Angeles
Los Angeles, CA 90095
hongjing@ucla.edu
Abstract
W... | 3259 |@word proportion:3 kokkinos:1 c0:1 adnan:1 holyoak:1 d2:4 simulation:3 accounting:1 recursively:1 selecting:1 current:1 comparing:1 conjunctive:2 must:5 intelligence:4 cue:3 generative:1 completeness:2 firstly:1 simpler:2 c2:49 prove:3 combine:1 darwiche:1 introduce:2 p1:2 frequently:1 inspired:1 provided:2 circu... |
2,491 | 326 | Flight Control in the Dragonfly:
A Neurobiological Simulation
William E. Faller and Marvin W. Luttges
Aerospace Engineering Sciences,
University of Colorndo, Boulder, Colorado 80309-0429.
ABSTRACT
Neural network simulations of the dragonfly flight neurocontrol system
have been developed to understand how this insect u... | 326 |@word briefly:1 middle:1 meso:1 simulation:19 innervating:1 carry:1 initial:1 efficacy:1 past:1 activation:3 must:1 interspike:1 motor:6 cue:1 nervous:2 record:2 contribute:1 sigmoidal:1 burst:1 along:1 direct:2 behavioral:1 rostral:3 inter:1 behavior:2 roughly:3 mechanic:1 ol:2 discretized:2 decomposed:1 resolve:... |
2,492 | 3,260 | Bayesian Co-Training
Shipeng Yu, Balaji Krishnapuram, Romer Rosales, Harald Steck, R. Bharat Rao
CAD & Knowledge Solutions, Siemens Medical Solutions USA, Inc.
firstname.lastname@siemens.com
Abstract
We propose a Bayesian undirected graphical model for co-training, or more generally for semi-supervised multi-view lea... | 3260 |@word faculty:3 mri:1 seems:1 retraining:1 steck:1 tried:2 covariance:4 citeseer:3 pick:1 harder:1 accommodate:2 moment:1 contains:2 score:1 hereafter:1 ours:1 document:4 existing:1 abundantly:1 com:1 current:1 cad:1 intriguing:1 john:1 concatenate:1 additive:1 shape:1 cheap:1 v:1 stationary:6 obsolete:1 xk:3 cha... |
2,493 | 3,261 | Subspace-Based Face Recognition in Analog
VLSI
Gonzalo Carvajal, Waldo Valenzuela and Miguel Figueroa
Department of Electrical Engineering, Universidad de Concepci?n
Casilla 160-C, Correo 3, Concepci?n, Chile
{gcarvaja, waldovalenzuela, miguel.figueroa}@udec.cl
Abstract
We describe an analog-VLSI neural network for fa... | 3261 |@word version:2 inversion:1 norm:2 open:1 pulse:4 simulation:2 covariance:2 euclidian:1 reduction:24 configuration:2 current:10 comparing:2 activation:1 assigning:1 scatter:2 written:1 must:1 fn:1 remove:2 designed:2 plot:2 update:8 v:1 half:2 selected:1 device:12 floatinggate:1 intelligence:1 xk:7 chile:1 simple... |
2,494 | 3,262 | Blind channel identification for speech
dereverberation using l1-norm sparse learning
?
Yuanqing Lin? , Jingdong Chen? , Youngmoo Kim? , Daniel D. Lee?
GRASP Laboratory, Department of Electrical and Systems Engineering, University of Pennsylvania
?
Bell Laboratories, Alcatel-Lucent
?
Department of Electrical and Comp... | 3262 |@word norm:22 advantageous:1 open:3 simulation:13 jingdong:1 decomposition:16 excited:1 dramatic:1 solid:1 daniel:1 existing:2 current:1 recovered:1 written:4 john:1 remove:1 plot:1 update:5 stationary:2 ith:1 short:1 provides:1 contribute:1 preference:1 direct:2 become:1 consists:1 combine:1 manner:1 theoretical... |
2,495 | 3,263 | Optimal ROC Curve for a Combination of Classifiers
Marco Barreno
Alvaro A. C?ardenas
J. D. Tygar
Computer Science Division
University of California at Berkeley
Berkeley, California 94720
{barreno,cardenas,tygar}@cs.berkeley.edu
Abstract
We present a new analysis for the combination of binary classifiers. Our analysi... | 3263 |@word repository:2 seems:1 flach:7 initial:1 series:1 score:1 selecting:3 united:1 ours:2 outperforms:1 comparing:1 must:2 fn:4 kdd:1 plot:3 treating:1 resampling:1 greedy:1 half:1 intelligence:1 fa9550:1 lr:13 boosting:6 preference:2 five:2 mathematical:1 prove:6 consists:1 doubly:1 combine:2 introduce:2 rapid:1... |
2,496 | 3,264 | The discriminant center-surround hypothesis for
bottom-up saliency
Dashan Gao
Vijay Mahadevan
Nuno Vasconcelos
Department of Electrical and Computer Engineering
University of California, San Diego
{dgao, vmahadev, nuno}@ucsd.edu
Abstract
The classical hypothesis, that bottom-up saliency is a center-surround process, ... | 3264 |@word neurophysiology:2 wiesel:1 compression:2 replicate:3 decomposition:6 initial:1 series:1 tuned:1 existing:1 baddeley:1 current:1 comparing:1 scatter:2 dx:1 subsequent:1 informative:2 plot:4 designed:1 drop:1 discrimination:1 v:2 generative:1 leaf:1 item:1 dashan:1 fpr:3 cognit:1 provides:1 quantized:1 detect... |
2,497 | 3,265 | Multiple-Instance Pruning For Learning Efficient
Cascade Detectors
Cha Zhang and Paul Viola
Microsoft Research
One Microsoft Way, Redmond, WA 98052
{chazhang,viola}@microsoft.com
Abstract
Cascade detectors have been shown to operate extremely rapidly, with high accuracy, and have important applications such as face de... | 3265 |@word trial:1 version:1 seems:1 reused:2 cha:1 instruction:1 pick:1 reduction:1 score:15 shum:1 ours:1 past:2 existing:4 current:3 com:1 nowlan:2 surprising:1 luo:1 yet:2 must:4 subsequent:1 visible:1 additive:1 shape:1 drop:1 update:4 v:1 greedy:1 selected:1 guess:1 unacceptably:1 destined:2 xk:5 core:1 record:1... |
2,498 | 3,266 | Bayesian Agglomerative Clustering with Coalescents
Yee Whye Teh
Gatsby Unit
University College London
Hal Daum?e III
School of Computing
University of Utah
Daniel Roy
CSAIL
MIT
ywteh@gatsby.ucl.ac.uk
me@hal3.name
droy@mit.edu
Abstract
We introduce a new Bayesian model for hierarchical clustering based on a prior... | 3266 |@word middle:1 version:1 sri:10 norm:1 duda:1 essay:1 tried:1 covariance:2 pick:2 arti:1 recursively:1 reaping:1 initial:1 series:1 efficacy:1 score:3 daniel:1 document:2 ours:1 past:2 spambase:5 outperforms:1 nepali:1 surprising:1 si:1 romance:7 portuguese:1 distant:2 partition:6 discernible:1 atlas:2 update:3 d... |
2,499 | 3,267 | Unsupervised Feature Selection for Accurate
Recommendation of High-Dimensional Image Data
Sabri Boutemedjet
DI, Universite de Sherbrooke
2500 boulevard de l?Universit?e
Sherbrooke, QC J1K 2R1, Canada
sabri.boutemedjet@usherbrooke.ca
Djemel Ziou
DI, Universite de Sherbrooke
2500 boulevard de l?Universit?e
Sherbrooke, ... | 3267 |@word faculty:1 pcc:2 proportion:1 open:1 covariance:1 elisseeff:1 weekday:1 minus:1 initial:1 t7:1 denoting:1 document:4 existing:1 contextual:4 si:1 periodically:1 hofmann:1 shape:3 remove:1 generative:3 half:1 intelligence:4 urp:4 provides:1 characterization:1 location:2 preference:14 five:3 mathematical:1 bet... |
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