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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2,200 | 2,998 | Stochastic Relational Models
for Discriminative Link Prediction
Wei Chu
CCLS, Columbia University
New York, NY 10115
Kai Yu
NEC Laboratories America
Cupertino, CA 95014
Shipeng Yu, Volker Tresp, Zhao Xu
Siemens AG, Corporate Research & Technology,
81739 Munich, Germany
Abstract
We introduce a Gaussian process (GP) f... | 2998 |@word deformed:1 version:1 briefly:1 nd:3 open:1 covariance:9 contains:1 hereafter:1 interestingly:2 current:2 si:2 chu:2 fn:1 informative:9 hofmann:1 enables:1 noninformative:4 update:2 generative:3 prohibitive:2 cue:2 item:3 parameterization:1 selected:1 intelligence:5 yamada:1 provides:1 authority:2 iterates:1... |
2,201 | 2,999 | Computation of Similarity Measures for
Sequential Data using Generalized Suffix Trees
Konrad Rieck
Fraunhofer FIRST.IDA
Kekul?estr. 7
12489 Berlin, Germany
rieck@first.fhg.de
Pavel Laskov
Fraunhofer FIRST.IDA
Kekul?estr. 7
12489 Berlin, Germany
laskov@first.fhg.de
S?oren Sonnenburg
Fraunhofer FIRST.IDA
Kekul?estr. 7... | 2999 |@word version:1 polynomial:2 nd:1 lodhi:1 mers:1 pavel:1 reduction:1 contains:3 document:1 rkhs:1 prefix:3 ida:3 must:4 cruz:1 numerical:2 enables:1 hash:1 leaf:10 selected:1 beginning:2 fried:1 tcp:2 eskin:3 provides:1 detecting:2 node:13 contribute:1 traverse:1 attack:3 herbrich:1 unbounded:1 along:1 constructe... |
2,202 | 3 | 52
Supervised Learning of Probability Distributions
by Neural Networks
Eric B. Baum
Jet Propulsion Laboratory, Pasadena CA 91109
Frank Wilczek t
Department of Physics,Harvard University,Cambridge MA 02138
Abstract:
We propose that the back propagation algorithm for supervised learning can be generalized, put on a sat... | 3 |@word duda:1 advantageous:1 seems:1 covariance:1 tr:1 moment:1 past:1 activation:2 must:2 john:1 subsequent:3 device:1 footing:2 dissertation:1 record:1 ire:1 node:18 constructed:1 fitting:2 behavioral:1 fabricate:1 manner:1 expected:1 indeed:5 themselves:1 frequently:1 brain:1 little:2 considering:1 bounded:2 what:... |
2,203 | 30 | 794
A 'Neural' Network that Learns to Play Backgammon
G. Tesauro
Center for Complex Systems Research, University of Illinois
at Urbana-Champaign, 508 S. Sixth St., Champaign, IL 61820
T. J. Sejnowski
Biophysics Dept., Johns Hopkins University, Baltimore, MD 21218
ABSTRACT
We describe a class of connectionist networ... | 30 |@word trial:1 illustrating:1 judgement:2 seems:3 instruction:1 tried:1 fonn:1 pick:1 tr:1 necessity:1 configuration:1 contains:2 score:10 initial:6 mastery:1 legality:1 envision:1 existing:1 current:5 surprising:1 must:4 readily:1 john:1 numerical:1 designed:2 half:2 selected:1 leaf:2 intelligence:1 beginning:1 pai... |
2,204 | 300 | Multi-Layer Perceptrons
with B-SpIine Receptive Field Functions
Stephen H. Lane, Marshall G. Flax, David A. Handelman and JackJ. Gelfand
Human Information Processing Group
Department of Psychology
Princeton University
Princeton, New Jersey 08544
ABSTRACT
Multi-layer perceptrons are often slow to learn nonlinear funct... | 300 |@word trial:1 version:1 wiesel:2 polynomial:4 simulation:4 initial:2 tuned:1 current:1 lang:4 activation:2 reminiscent:1 partition:11 blur:1 shape:4 enables:1 extrapolating:1 cfo:1 plot:1 v:4 half:1 sys:1 steepest:1 ith:1 coarse:1 node:43 sigmoidal:1 constructed:1 combine:3 fitting:2 symp:1 manner:1 theoretically:... |
2,205 | 3,000 | On the Relation Between Low Density Separation, Spectral
Clustering and Graph Cuts
Hariharan Narayanan
Department of Computer Science
University of Chicago
Chicago IL 60637
hari@cs.uchicago.edu
Mikhail Belkin
Department of Computer Science and Engineering
The Ohio State University
Columbus, OH 43210
mbelkin@cse.ohio-... | 3000 |@word version:2 middle:1 seems:1 tr:3 existing:1 b1c:2 dx:8 fn:1 chicago:5 partition:9 n0:5 short:2 node:1 contribute:1 cse:1 mcdiarmid:4 along:2 c2:4 direct:1 scholkopf:1 prove:4 introduce:1 coifman:1 roughly:1 considering:1 underlying:4 moreover:1 panel:9 notation:1 mass:1 linearity:1 what:1 bounded:1 kevrekidi... |
2,206 | 3,001 | Learning to be Bayesian without Supervision
Martin Raphan
Courant Inst. of Mathematical Sciences
New York University
raphan@cims.nyu.edu
Eero P. Simoncelli
Center for Neural Science, and
Courant Inst. of Mathematical Sciences
New York University
eero.simoncelli@nyu.edu
Bayesian estimators are defined in terms of the... | 3001 |@word illustrating:1 version:1 inversion:1 polynomial:2 pw:10 blu:1 nd:1 simulation:11 pick:1 fifteen:2 score:1 selecting:1 denoting:1 dx:5 written:9 must:5 john:1 additive:11 partition:1 asymptote:1 update:1 alone:1 selected:1 parameterization:2 short:1 provides:3 parameterizations:2 mathematical:2 along:1 const... |
2,207 | 3,002 | Linearly-solvable Markov decision problems
Emanuel Todorov
Department of Cognitive Science
University of California San Diego
todorov@cogsci.ucsd.edu
Abstract
We introduce a class of MPDs which greatly simplify Reinforcement Learning.
They have discrete state spaces and continuous control spaces. The controls have
th... | 3002 |@word mild:1 version:1 seems:1 nd:3 willing:1 simulation:1 simplifying:2 q1:4 paid:1 initial:1 series:1 suppressing:1 outperforms:1 clari:1 current:6 surprising:1 yet:1 reminiscent:2 must:5 written:1 numerical:2 cant:1 enables:1 remove:1 update:1 greedy:1 offpolicy:1 along:2 constructed:2 differential:1 become:1 ... |
2,208 | 3,003 | Efficient Structure Learning of Markov Networks
using L1-Regularization
Su-In Lee Varun Ganapathi Daphne Koller
Department of Computer Science
Stanford University
Stanford, CA 94305-9010
{silee,varung,koller}@cs.stanford.edu
Abstract
Markov networks are commonly used in a wide variety of applications, ranging
from com... | 3003 |@word eliminating:1 polynomial:1 norm:3 nd:3 termination:3 decomposition:1 pick:1 thereby:4 harder:1 kappen:1 contains:2 score:4 selecting:3 genetic:6 document:1 interestingly:1 outperforms:3 current:6 must:5 written:1 john:1 numerical:2 partition:2 shape:1 analytic:1 remove:1 designed:2 treating:1 plot:1 aside:1... |
2,209 | 3,004 | Towards a general independent subspace analysis
Fabian J. Theis
Max Planck Institute for Dynamics and Self-Organisation &
Bernstein Center for Computational Neuroscience
Bunsenstr. 10, 37073 G?ottingen, Germany
fabian@theis.name
Abstract
The increasingly popular independent component analysis (ICA) may only be applie... | 3004 |@word norm:2 seems:1 hyv:3 confirms:2 simulation:2 decomposition:14 covariance:3 tr:2 reduction:1 contains:1 interestingly:2 existing:3 diagonalized:1 recovered:5 comparing:1 si:12 scatter:1 must:2 readily:1 partition:6 wx:1 shape:3 enables:1 plot:1 update:1 generative:1 short:1 misinterpreted:1 along:2 construct... |
2,210 | 3,005 | Large-Scale Sparsified Manifold Regularization
Ivor W. Tsang
James T. Kwok
Department of Computer Science and Engineering
The Hong Kong University of Science and Technology
Clear Water Bay, Kowloon, Hong Kong
{ivor,jamesk}@cse.ust.hk
Abstract
Semi-supervised learning is more powerful than supervised learning by using ... | 3005 |@word kong:2 inversion:3 norm:10 decomposition:1 nystr:1 series:1 tuned:1 rkhs:6 existing:3 recovered:2 discretization:1 ust:2 readily:1 written:1 griebel:2 girosi:3 designed:1 treating:1 intelligence:1 core:8 cse:1 simpler:1 five:1 along:1 constructed:1 khk:4 artner:2 inside:2 introduce:2 sublinearly:1 indeed:1 ... |
2,211 | 3,006 | Optimal Change-Detection and Spiking Neurons
Angela J. Yu
CSBMB, Princeton University
Princeton, NJ 08540
ajyu@princeton.edu
Abstract
Survival in a non-stationary, potentially adversarial environment requires animals
to detect sensory changes rapidly yet accurately, two oft competing desiderata.
Neurons subserving su... | 3006 |@word trial:6 exploitation:1 briefly:1 noradrenergic:1 stronger:2 termination:3 propagate:1 p0:3 incurs:2 vigorously:1 initial:1 configuration:1 ati:1 current:5 comparing:1 activation:1 yet:1 import:3 reminiscent:1 must:1 plot:2 update:4 discrimination:2 stationary:4 generative:7 implying:1 cue:2 nervous:2 inspec... |
2,212 | 3,007 | Attribute-efficient learning
of decision lists and linear threshold functions
under unconcentrated distributions
Philip M. Long
Google
Mountain View, CA
plong@google.com
Rocco A. Servedio
Department of Computer Science
Columbia University
New York, NY
rocco@cs.columbia.edu
Abstract
We consider the well-studied proble... | 3007 |@word hampson:1 version:4 briefly:1 polynomial:1 norm:10 twelfth:1 d2:9 tried:1 initial:4 pub:1 wj2:2 ours:1 franklin:1 current:1 com:1 must:2 reminiscent:1 numerical:1 hajnal:1 update:1 intelligence:2 selected:1 xk:12 core:2 boosting:27 complication:1 along:2 c2:1 direct:1 symposium:2 focs:1 prove:3 kdk2:5 manne... |
2,213 | 3,008 | Clustering Under Prior Knowledge with Application
to Image Segmentation
M?ario A. T. Figueiredo
Instituto de Telecomunicac?o? es
Instituto Superior T?ecnico
Technical University of Lisbon
Portugal
Dong Seon Cheng, Vittorio Murino
Vision, Image Processing, and Sound Laboratory
Dipartimento di Informatica
University of... | 3008 |@word mild:1 briefly:1 inversion:1 compression:1 sri:1 verona:1 open:1 rgb:2 covariance:1 tr:1 accommodate:1 configuration:1 liu:1 pub:1 denoting:2 current:2 portuguese:1 additive:1 kdd:1 update:5 v:1 stationary:1 generative:4 intelligence:1 lr:2 provides:1 math:1 location:2 lx:1 preference:3 direct:2 ik:1 consis... |
2,214 | 3,009 | Emergence of conjunctive visual features by
quadratic independent component analysis
J.T. Lindgren
Department of Computer Science
University of Helsinki
Finland
jtlindgr@cs.helsinki.fi
Aapo Hyv?arinen
HIIT Basic Research Unit
University of Helsinki
Finland
aapo.hyvarinen@cs.helsinki.fi
Abstract
In previous studies, ... | 3009 |@word collinearity:2 briefly:1 polynomial:6 norm:2 replicate:1 simplecell:1 open:1 hyv:4 decomposition:6 covariance:1 mention:1 reduction:2 configuration:1 contains:4 selecting:1 ours:1 reaction:1 err:1 current:3 comparing:1 si:3 conjunct:2 conjunctive:9 must:1 yet:1 shape:3 drop:1 plot:1 alone:2 selected:1 chara... |
2,215 | 301 | Constructing Hidden Units
using Examples and Queries
Eric B. Baum
Kevin J. Lang
NEC Research Institute
4 Independence Way
Princeton, NJ 08540
ABSTRACT
While the network loading problem for 2-layer threshold nets is
NP-hard when learning from examples alone (as with backpropagation), (Baum, 91) has now proved that a le... | 301 |@word trial:4 middle:1 polynomial:3 loading:1 seems:2 grey:1 heuristically:1 tried:1 invoking:1 shading:2 initial:4 configuration:2 contains:1 existing:4 surprising:1 nowlan:1 lang:5 yet:1 must:3 plot:1 alone:3 fewer:1 selected:1 plane:13 node:1 location:2 ron:1 five:1 constructed:1 direct:1 viable:1 behavior:1 mu... |
2,216 | 3,010 | Robotic Grasping of Novel Objects
Ashutosh Saxena, Justin Driemeyer, Justin Kearns, Andrew Y. Ng
Computer Science Department
Stanford University, Stanford, CA 94305
{asaxena,jdriemeyer,jkearns,ang}@cs.stanford.edu
Abstract
We consider the problem of grasping novel objects, specifically ones that are being seen for the... | 3010 |@word trial:1 illustrating:1 version:1 seems:1 open:1 proportionality:1 closure:1 pick:6 carry:1 initial:1 cellphone:5 contains:1 fa8750:1 past:1 must:1 mesh:1 realistic:1 additive:1 visible:2 shape:4 plot:1 ashutosh:1 cue:2 intelligence:1 item:2 plane:5 realism:2 colored:1 ire:1 location:8 simpler:2 zhang:1 alon... |
2,217 | 3,011 | Neurophysiological Evidence of Cooperative
Mechanisms for Stereo Computation
Jason M. Samonds
Brian R. Potetz
Tai Sing Lee
Center for the Neural Basis
CNBC and Computer
CNBC and Computer
of Cognition (CNBC)
Science Department
Science Department
Carnegie Mellon University Carnegie Mellon University Carnegie Mellon Univ... | 3011 |@word neurophysiology:2 trial:7 wiesel:1 stronger:1 covariance:5 solid:2 accommodate:1 moment:1 initial:2 disparity:84 liquid:1 tuned:8 bootstrapped:1 anterior:1 si:5 slanted:1 refines:1 chicago:2 eleven:1 remove:1 medial:1 discrimination:1 half:3 cue:1 indicative:1 plane:4 smith:1 provides:1 location:6 preferenc... |
2,218 | 3,012 | Handling Advertisements of Unknown Quality
in Search Advertising
Sandeep Pandey
Carnegie Mellon University
spandey@cs.cmu.edu
Christopher Olston
Yahoo! Research
olston@yahoo-inc.com
Abstract
We consider how a search engine should select advertisements to display
with search results, in order to maximize its revenue. ... | 3012 |@word trial:1 exploitation:16 version:3 mehta:1 willing:1 d2:1 simulation:3 incurs:1 carry:2 exclusively:2 selecting:2 renewed:1 past:1 existing:1 current:4 com:1 contextual:2 comparing:1 yet:2 must:3 realistic:1 happen:1 drop:1 plot:2 update:1 greedy:21 selected:2 accordingly:1 short:4 along:1 become:2 supply:2 ... |
2,219 | 3,013 | Near-Uniform Sampling of Combinatorial Spaces
Using XOR Constraints
Carla P. Gomes
Ashish Sabharwal
Bart Selman
Department of Computer Science
Cornell University, Ithaca NY 14853-7501, USA
{gomes,sabhar,selman}@cs.cornell.edu ?
Abstract
We propose a new technique for sampling the solutions of combinatorial problems in... | 3013 |@word version:4 polynomial:1 stronger:1 nd:1 c0:5 open:1 simplifying:1 dramatic:2 thereby:1 solid:1 phy:1 series:1 selecting:1 fa8750:1 current:5 conjunctive:1 must:1 happen:2 remove:2 designed:1 plot:1 progressively:1 bart:1 stationary:5 half:1 selected:1 v:1 nq:1 ith:2 core:1 provides:1 iterates:1 math:1 readab... |
2,220 | 3,014 | Generalized Regularized Least-Squares Learning
with Predefined Features in a Hilbert Space
Wenye Li, Kin-Hong Lee, Kwong-Sak Leung
Department of Computer Science and Engineering
The Chinese University of Hong Kong
Shatin, Hong Kong, China
{wyli, khlee, ksleung}@cse.cuhk.edu.hk
Abstract
Kernel-based regularized learnin... | 3014 |@word kong:4 trial:1 polynomial:4 norm:1 plsa:4 seek:1 decomposition:1 contains:3 rkhs:6 document:2 existing:3 comparing:1 deteriorating:1 com:1 written:1 john:1 kdd:1 hofmann:1 girosi:2 leaf:2 selected:2 mccallum:2 provides:1 math:4 cse:1 herbrich:1 along:3 constructed:3 direct:2 ik:1 consists:3 combine:1 fittin... |
2,221 | 3,015 | Stratification Learning: Detecting Mixed Density and
Dimensionality in High Dimensional Point Clouds
Gloria Haro, Gregory Randall, and Guillermo Sapiro
IMA and Electrical and Computer Engineering
University of Minnesota, Minneapolis, MN 55455
haro@ima.umn.edu,randall@fing.edu.uy,guille@umn.edu
Abstract
The study of p... | 3015 |@word version:1 briefly:1 polynomial:1 proportion:2 thereby:1 ld:8 reduction:6 necessity:1 initial:1 contains:1 document:1 pless:2 current:2 com:1 si:2 written:2 realize:1 numerical:1 mstep:1 alone:2 pursued:1 discovering:2 guess:1 core:2 colored:2 detecting:1 quantized:1 gpca:1 allerton:1 dn:3 c2:1 consists:3 co... |
2,222 | 3,016 | Dynamic Foreground/Background Extraction from
Images and Videos using Random Patches
Le Lu?
Integrated Data Systems Department
Siemens Corporate Research
Princeton, NJ 08540
le-lu@siemens.com
Gregory Hager
Department of Computer Science
Johns Hopkins University
Baltimore, MD 21218
hager@cs.jhu.edu
Abstract
In this p... | 3016 |@word briefly:1 manageable:1 triggs:1 cha:4 grey:1 rgb:3 covariance:1 brightness:2 pick:1 hager:3 moment:1 reduction:4 configuration:1 score:3 hoiem:1 shum:2 tuned:1 colburn:1 current:3 com:1 si:9 assigning:1 must:1 john:2 partition:10 shape:1 enables:1 remove:1 resampling:11 greedy:1 selected:1 fewer:1 unaccepta... |
2,223 | 3,017 | Bayesian Detection of Infrequent Differences in
Sets of Time Series with Shared Structure
Jennifer Listgarten? , Radford M. Neal? , Sam T. Roweis? Rachel Puckrin? and Sean Cutler?
?
Department of Computer Science, ? Department of Botany,
University of Toronto, Toronto, Ontario, M5S 3G4
{jenn,radford,roweis}@cs.toronto.... | 3017 |@word version:3 middle:5 inversion:1 proportion:2 replicate:1 seek:1 crucially:2 decomposition:1 covariance:1 thereby:3 absorbance:1 xkn:1 initial:2 cyclic:2 series:58 contains:1 score:1 liquid:5 existing:1 current:5 com:1 z2:2 comparing:2 tackling:1 yet:2 r1c:1 visible:1 informative:1 shape:2 remove:1 aside:1 ge... |
2,224 | 3,018 | Recursive ICA
Honghao Shan, Lingyun Zhang, Garrison W. Cottrell
Department of Computer Science and Engineering
University of California, San Diego
La Jolla, CA 92093-0404
{hshan,lingyun,gary}@cs.ucsd.edu
Abstract
Independent Component Analysis (ICA) is a popular method for extracting independent features from visual d... | 3018 |@word version:1 seems:3 grey:1 simulation:1 tried:1 carry:1 reduction:5 initial:1 contains:2 tuned:1 rightmost:1 blank:1 surprising:1 activation:19 si:7 must:4 exposing:1 cottrell:1 additive:1 wiewiora:1 shape:3 enables:1 hypothesize:1 remove:1 plot:2 progressively:2 stationary:1 generative:2 leaf:1 metabolism:2 ... |
2,225 | 3,019 | Mixture Regression for Covariate Shift
Amos J Storkey
Institute of Adaptive and Neural Computation
School of Informatics, University of Edinburgh
a.storkey@ed.ac.uk
Masashi Sugiyama
Department of Computer Science
Tokyo Institute of Technology
sugi@cs.titech.ac.jp
Abstract
In supervised learning there is a typical pr... | 3019 |@word trial:1 middle:1 proportion:11 duda:1 covariance:1 jacob:2 shot:1 contains:1 selecting:2 denoting:1 interestingly:1 existing:1 current:4 comparing:1 nowlan:1 yet:1 must:1 mst:4 happen:1 plot:1 update:4 alone:3 generative:5 provides:2 location:1 herbrich:1 prediciton:1 become:1 consists:4 interscience:1 bald... |
2,226 | 302 | Phase-coupling in Two-Dimensional
Networks of Interacting Oscillators
Ernst Niebur, Daniel M. Kammen, Christof Koch,
Daniel Ruderman! & Heinz G. Schuster2
Computation and Neural Systems
Caltech 216-76
Pasadena, CA 91125
ABSTRACT
Coherent oscillatory activity in large networks of biological or artificial neural units m... | 302 |@word physik:1 dramatic:1 volkswagen:1 solid:1 initial:6 configuration:1 series:1 daniel:2 reaction:1 neurophys:1 parsing:1 numerical:1 j1:1 seeding:1 plot:2 progressively:1 alone:1 selected:1 nervous:1 short:1 location:2 kiel:1 constructed:1 olfactory:2 inter:1 rapid:2 behavior:4 multi:1 brain:1 heinz:1 freeman:2... |
2,227 | 3,020 | Multi-dynamic Bayesian Networks
Karim Filali and Jeff A. Bilmes
Departments of Computer Science & Engineering and Electrical Engineering
University of Washington
Seattle, WA 98195
{karim@cs,bilmes@ee}.washington.edu
Abstract
We present a generalization of dynamic Bayesian networks to concisely describe
complex probab... | 3020 |@word kong:1 briefly:1 version:1 polynomial:1 nd:1 cloned:3 recursively:4 configuration:6 interestingly:1 existing:1 current:4 activation:1 yet:2 readily:1 happen:1 generative:2 intelligence:1 weighing:1 indefinitely:1 pointer:1 node:23 successive:1 simpler:1 unbounded:1 along:7 become:3 consists:4 combine:2 intr... |
2,228 | 3,021 | Unsupervised Learning of a Probabilistic Grammar
for Object Detection and Parsing
Long (Leo) Zhu
Department of Statistics
University of California at Los Angeles
Los Angeles, CA 90095
lzhu@stat.ucla.edu
Yuanhao Chen
Department of Automation
University of Science and Technology of China
Hefei, Anhui 230026 P.R.China
y... | 3021 |@word calculus:1 seek:1 covariance:2 configuration:1 contains:1 score:17 liu:1 existing:2 recovered:1 current:3 must:1 parsing:6 hoboken:1 dechter:1 enables:1 update:2 generative:2 leaf:1 selected:1 intelligence:5 plane:1 mccallum:2 short:1 detecting:1 node:15 location:2 firstly:1 simpler:1 height:2 constructed:1... |
2,229 | 3,022 | Learning Structural Equation Models for fMRI
Amos J. Storkey
School of Informatics
University of Edinburgh
Stephen Lawrie
Division of Psychiatry
University of Edinburgh
Enrico Simonotto
Division of Psychiatry
University of Edinburgh
Lawrence Murray
School of Informatics
University of Edinburgh
Heather Whalley
Divisi... | 3022 |@word briefly:1 version:2 cingulate:1 mri:1 seems:2 middle:2 stronger:1 d2:3 covariance:17 contraction:5 asks:1 tr:1 cyclic:4 generatively:1 series:2 genetic:2 current:1 comparing:2 sosa:1 analysed:1 activation:2 john:1 visible:2 motor:2 medial:1 alone:2 generative:6 selected:2 guess:1 half:1 tone:2 intelligence:... |
2,230 | 3,023 | Unified Inference for Variational Bayesian Linear
Gaussian State-Space Models
David Barber
IDIAP Research Institute
rue du Simplon 4, Martigny, Switzerland
david.barber@idiap.ch
Silvia Chiappa
IDIAP Research Institute
rue du Simplon 4, Martigny, Switzerland
silvia.chiappa@idiap.ch
Abstract
Linear Gaussian State-Spac... | 3023 |@word neurophysiology:1 advantageous:1 suitably:1 covariance:10 decomposition:7 carry:1 series:3 contains:1 genetic:1 expositional:1 recovered:2 si:1 written:3 readily:1 john:1 visible:4 numerical:4 enables:1 designed:1 plot:1 update:12 smith:1 filtered:1 mental:1 completeness:1 org:1 simpler:1 si1:2 become:1 sup... |
2,231 | 3,024 | Max-margin classification of incomplete data
2
Gal Chechik1 , Geremy Heitz2 ,
Gal Elidan1 , Pieter Abbeel 1 , Daphne Koller 1
1
Department of Computer Science, Stanford University, Stanford CA, 94305
Department of Electrical Engineering, Stanford University, Stanford CA, 94305
Email for correspondence: gal@ai.stanfor... | 3024 |@word version:1 polynomial:2 norm:3 pieter:1 covariance:1 thereby:1 harder:1 mcar:1 series:2 contains:1 fragment:1 interestingly:1 outperforms:2 reaction:14 current:4 si:13 assigning:1 written:1 must:2 concatenate:1 wx:1 shape:1 generative:2 guess:1 xk:1 ith:3 iterates:1 math:1 daphne:1 five:3 along:1 constructed... |
2,232 | 3,025 | Accelerated Variational Dirichlet Process Mixtures
Kenichi Kurihara
Dept. of Computer Science
Tokyo Institute of Technology
Tokyo, Japan
kurihara@mi.cs.titech.ac.jp
Max Welling
Bren School of Information and Computer Science
UC Irvine
Irvine, CA 92697-3425
welling@ics.uci.edu
Nikos Vlassis
Informatics Institute
Unive... | 3025 |@word trial:1 hu:1 covariance:1 citeseer:2 recursively:1 moment:1 reduction:1 initial:1 contains:3 tuned:1 document:5 offering:1 ecole:1 com:1 must:1 parsing:1 partition:2 entertaining:1 plot:2 update:10 greedy:1 generative:1 tcp:1 blei:7 provides:1 cse:1 toronto:1 node:17 five:2 unbounded:2 direct:2 beta:5 consi... |
2,233 | 3,026 | Blind source separation for over-determined delayed
mixtures
Lars Omlor, Martin Giese?
Laboratory for Action Representation and Learning
Department of Cognitive Neurology,
Hertie Institute for Clinical Brain Research
University of T?ubingen, Germany
Abstract
Blind source separation, i.e. the extraction of unknown sour... | 3026 |@word illustrating:1 timefrequency:2 seems:2 norm:1 nd:1 hyv:1 pulse:1 simulation:1 covariance:2 lacquaniti:1 minus:2 moment:2 reduction:4 outperforms:4 existing:2 recovered:1 comparing:3 si:2 activation:1 written:1 john:1 physiol:1 wx:6 motor:1 plot:3 interpretable:5 update:2 ainen:1 generative:2 fewer:1 prohibi... |
2,234 | 3,027 | A Novel Gaussian Sum Smoother for Approximate
Inference in Switching Linear Dynamical Systems
David Barber and Bertrand Mesot
IDIAP Research Institute
Martigny 1920, Switzerland
david.barber/bertrand.mesot@idiap.ch
Abstract
We introduce a method for approximate smoothed inference in a class of switching
linear dynami... | 3027 |@word version:1 briefly:1 heuristically:1 covariance:10 decomposition:1 recursively:1 carry:1 kappen:1 moment:5 series:5 contains:2 freitas:1 reminiscent:1 readily:1 additive:1 numerical:8 visible:4 eleven:3 treating:2 designed:1 update:1 resampling:2 alone:1 generative:1 intelligence:1 rts:1 filtered:11 provides... |
2,235 | 3,028 | A selective attention multi?chip system with dynamic
synapses and spiking neurons
Chiara Bartolozzi
Institute of neuroinformatics
UNI-ETH Zurich
Wintherthurerstr. 190, 8057, Switzerland
chiara@ini.phys.ethz.ch
Giacomo Indiveri
Institute of neuroinformatics
UNI-ETH Zurich
Wintherthurerstr. 190, 8057, Switzerland
giaco... | 3028 |@word middle:3 pulse:3 solid:1 initial:1 liu:1 foveal:1 efficacy:1 current:24 motor:1 designed:2 plot:9 intelligence:1 selected:3 device:4 desktop:2 core:1 short:4 infrastructure:2 node:7 symposium:1 transceiver:1 isscc:1 combine:1 rapid:1 behavior:2 multi:10 integrator:1 brain:2 inspired:3 decreasing:1 begin:1 p... |
2,236 | 3,029 | Learning to Model Spatial Dependency:
Semi-Supervised Discriminative Random Fields
Chi-Hoon Lee
Department of Computing Science
University of Alberta
chihoon@cs.ualberta.ca
Feng Jiao
Department of Computing Science
University of Waterloo
fjiao@cs.uwaterloo.ca
Shaojun Wang ?
Department of Computer Science and Engineeri... | 3029 |@word version:1 tedious:1 covariance:2 thereby:1 harder:1 configuration:1 series:2 score:6 selecting:1 tuned:1 document:3 yni:10 current:2 contextual:2 assigning:1 yet:1 written:1 john:1 tenet:1 fn:1 partition:2 shape:1 moreno:1 update:1 generative:3 p7:2 mccallum:2 lr:6 provides:1 node:10 zhang:1 along:1 drfs:19... |
2,237 | 303 | ART2/BP architecture for adaptive estimation of
dynamic processes
Einar S~rheim *
Department of Computer Science
UNIK, Kjeller
University of Oslo
N-2007 Norway
Abstract
The goal has been to construct a supervised artificial neural network that
learns incrementally an unknown mapping. As a result a network consisting... | 303 |@word version:3 coarseness:1 norm:2 grey:1 simulation:6 tried:1 quickprop:1 ljo:1 reduction:1 cyclic:1 liquid:8 outperforms:2 existing:1 current:2 comparing:1 yet:8 plasticity:1 girosi:1 v:1 selected:1 short:1 provides:1 node:18 location:2 consists:2 expected:1 roughly:2 simulator:1 becomes:1 kind:1 developed:1 ev... |
2,238 | 3,030 | Approximate Correspondences in High Dimensions
Kristen Grauman
Department of Computer Sciences
University of Texas at Austin
grauman@cs.utexas.edu
Trevor Darrell
CS and AI Laboratory
Massachusetts Institute of Technology
trevor@csail.mit.edu
Abstract
Pyramid intersection is an efficient method for computing an appro... | 3030 |@word version:1 compression:1 stronger:1 d2:1 confirms:1 decomposition:6 innermost:1 thereby:1 minus:1 solid:1 recursively:2 initial:3 contains:3 score:14 document:2 rightmost:1 existing:1 current:3 comparing:1 must:6 finest:1 indistinguishably:1 realistic:1 partition:10 subsequent:1 distant:1 shape:4 plot:10 dep... |
2,239 | 3,031 | Predicting spike times from subthreshold dynamics of
a neuron
Ryota Kobayashi
Department of Physics
Kyoto University
Kyoto 606-8502, Japan
kobayashi@ton.scphys.kyoto-u.ac.jp
Shigeru Shinomoto
Department of Physics
Kyoto University
Kyoto 606-8502, Japan
shinomoto@scphys.kyoto-u.ac.jp
Abstract
It has been established th... | 3031 |@word middle:2 cm2:2 simulation:3 simplifying:1 series:1 mainen:1 past:2 current:22 universality:1 dx:1 realize:1 physiol:2 hyperpolarizing:1 realistic:2 plasticity:1 shape:3 drop:1 cue:2 half:1 cult:2 realism:1 gure:1 ire:1 quantized:1 gx:1 along:2 direct:1 differential:3 qualitative:1 consists:1 n22:1 expected:... |
2,240 | 3,032 | Learning Time-Intensity Profiles of Human Activity
using Non-Parametric Bayesian Models
Alexander T. Ihler
Padhraic Smyth
Donald Bren School of Information and Computer Science
U.C. Irvine
ihler@ics.uci.edu
smyth@ics.uci.edu
Abstract
Data sets that characterize human activity over time through collections of timestam... | 3032 |@word cox:1 version:2 nd:2 c0:2 closure:1 carolina:1 weekday:17 commute:2 solid:2 accommodate:1 cyclic:1 series:2 contains:2 selecting:1 denoting:1 rightmost:1 nally:1 discretization:1 yet:1 dx:1 must:1 readily:1 stemming:1 cruz:4 visible:1 additive:7 happen:1 subsequent:1 shape:6 entrance:1 treating:1 interpreta... |
2,241 | 3,033 | Modeling Dyadic Data with Binary Latent Factors
Edward Meeds
Department of Computer Science
University of Toronto
ewm@cs.toronto.edu
Zoubin Ghahramani
Department of Engineering
Cambridge University
zoubin@eng.cam.ac.uk
Radford Neal
Department of Computer Science
University of Toronto
radford@cs.toronto.edu
Sam Rowe... | 3033 |@word briefly:1 version:3 grey:1 tamayo:1 eng:1 decomposition:3 covariance:2 accommodate:1 initial:2 configuration:4 document:1 current:5 activation:1 written:1 additive:1 shape:1 update:7 generative:1 selected:1 half:12 item:5 intelligence:1 yamada:1 toronto:6 location:1 five:1 downing:1 along:2 constructed:1 di... |
2,242 | 3,034 | Implicit Surfaces with Globally Regularised and
Compactly Supported Basis Functions
?
Christian Walder?? , Bernhard Sch?olkopf? & Olivier Chapelle?
Max Planck Institute for Biological Cybernetics, 72076 T?ubingen, Germany
?
The University of Queensland, Brisbane, Queensland 4072, Australia
first.last@tuebingen.mpg.de... | 3034 |@word briefly:1 inversion:1 compression:1 norm:6 nd:1 km:1 seek:1 simulation:1 queensland:2 covariance:5 thereby:2 celebrated:1 series:3 contains:1 denoting:1 rkhs:3 brien:2 comparing:1 si:3 yet:1 written:1 evans:1 numerical:2 partition:1 benign:1 shape:6 christian:2 aside:1 mccallum:1 herbrich:1 simpler:2 five:2... |
2,243 | 3,035 | Learning to Traverse Image Manifolds
Piotr Doll?ar, Vincent Rabaud and Serge Belongie
University of California, San Diego
{pdollar,vrabaud,sjb}@cs.ucsd.edu
Abstract
We present a new algorithm, Locally Smooth Manifold Learning (LSML), that
learns a warping function from a point on an manifold to its neighbors. Importa... | 3035 |@word middle:2 version:2 compression:5 seems:1 open:3 seek:2 r:1 decomposition:2 accommodate:1 shading:2 reduction:3 contains:4 series:2 quadrilateral:1 bitmap:1 recovered:7 comparing:2 must:3 written:1 shape:1 plot:1 v:1 generative:2 fewer:1 plane:8 cse:1 location:1 traverse:4 successive:2 zhang:1 five:1 height:... |
2,244 | 3,036 | A Bayesian Approach to Diffusion Models of
Decision-Making and Response Time
Michael D. Lee?
Department of Cognitive Sciences
University of California, Irvine
Irvine, CA, 92697-5100.
mdlee@uci.edu
Ian G. Fuss
Defence Science and Technology Organisation
PO Box 1500, Edinburgh, SA 5111, Australia
ian.fuss@dsto.defence.... | 3036 |@word trial:4 proportion:8 replicate:1 instruction:20 accounting:1 solid:2 harder:1 series:1 daniel:2 past:1 reaction:3 current:3 dx:1 intriguing:1 realistic:1 visible:1 informative:2 shape:1 fuss:2 analytic:1 motor:1 designed:1 stationary:1 generative:1 accordingly:2 beginning:1 smith:2 core:1 short:1 supplying:... |
2,245 | 3,037 | Bayesian Image Super-resolution, Continued
Lyndsey C. Pickup, David P. Capel? , Stephen J. Roberts Andrew Zisserman
Information Engineering Building, Dept. of Eng. Science, Parks Road, Oxford, OX1 3PJ, UK
{elle,sjrob,az}@robots.ox.ac.uk
?
2D3, d.capel@2d3.com
Abstract
This paper develops a multi-frame image super-res... | 3037 |@word middle:1 open:1 km:1 scg:2 eng:1 covariance:4 minus:1 configuration:1 series:1 efficacy:1 score:1 daniel:1 err:2 recovered:1 com:2 current:2 dx:11 must:1 visible:2 realistic:2 blur:1 partition:1 analytic:1 plot:1 generative:3 half:1 device:1 intelligence:1 plane:2 isotropic:1 dissertation:1 registering:1 co... |
2,246 | 3,038 | Implicit Online Learning with Kernels
Li Cheng
S.V. N. Vishwanathan
National ICT Australia
li.cheng@nicta.com.au
SVN.Vishwanathan@nicta.com.au
Shaojun Wang
Department of Computer Science and Engineering
Wright State University
shaojun.wang@wright.edu
Dale Schuurmans
Department of Computing Science
University of Alber... | 3038 |@word mild:1 trial:4 version:6 middle:1 briefly:1 norm:1 stronger:1 polynomial:1 seems:1 nd:1 dekel:1 d2:10 outlook:1 initial:2 series:1 tuned:1 rkhs:8 past:5 outperforms:3 existing:1 current:5 com:3 comparing:1 must:2 written:4 reminiscent:1 john:1 numerical:2 plot:3 update:27 v:4 stationary:6 alone:1 fewer:1 wa... |
2,247 | 3,039 | Unsupervised Regression with Applications to
Nonlinear System Identification
Ali Rahimi
Intel Research Seattle
Seattle, WA 98105
ali.rahimi@intel.com
Ben Recht
California Institute of Technology
Pasadena, CA 91125
brecht@ist.caltech.edu
Abstract
We derive a cost functional for estimating the relationship between hig... | 3039 |@word trial:1 determinant:2 version:2 eliminating:1 norm:2 c0:3 open:2 calculus:1 seek:1 accounting:1 covariance:8 tr:5 reduction:2 moment:1 series:7 tuned:1 rkhs:1 existing:1 err:1 recovered:26 com:1 surprising:1 must:2 jkl:3 john:1 informative:1 confirming:1 shape:1 plot:1 juditsky:1 stationary:1 generative:1 a... |
2,248 | 304 | Reconfigurable Neural Net Chip with 32K
Connections
H.P. Graf, R. Janow, D. Henderson, and R. Lee
AT&T Bell Laboratories, Room 4G320, Holmdel, NJ 07733
Abstract
We describe a CMOS neural net chip with a reconfigurable network architecture. It contains 32,768 binary, programmable connections arranged in
256 'building ... | 304 |@word coprocessor:1 loading:3 instruction:1 donham:1 hsieh:1 solid:1 electronics:1 configuration:2 contains:5 selecting:1 tuned:1 current:4 janow:4 designed:2 alone:1 half:3 selected:1 ajd:1 device:1 sram:1 coarse:1 consists:4 indeed:1 multi:5 simulator:1 provided:1 circuit:9 developed:1 fabricated:1 nj:1 every:1 ... |
2,249 | 3,040 | Analysis of Contour Motions
Ce Liu William T. Freeman Edward H. Adelson
Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139, USA
{celiu,billf,adelson}@csail.mit.edu
Abstract
A reliable motion estimation algorithm must function under a wide range of conditi... | 3040 |@word illustrating:1 briefly:1 nd:1 open:3 d2:3 seek:1 covariance:2 decomposition:1 brightness:2 carry:1 liu:1 series:1 fragment:69 selecting:1 existing:1 current:1 comparing:1 nowlan:1 must:1 visible:1 happen:1 eleven:1 remove:1 designed:1 intelligence:3 cue:6 selected:1 postprocess:1 short:1 feris:1 detecting:1... |
2,250 | 3,041 | Parameter Expanded Variational Bayesian Methods
Tommi S. Jaakkola
MIT CSAIL
32 Vassar street
Cambridge, MA 02139
tommi@csail.mit.edu
Yuan (Alan) Qi
MIT CSAIL
32 Vassar street
Cambridge, MA 02139
alanqi@csail.mit.edu
Abstract
Bayesian inference has become increasingly important in statistical machine
learning. Exact ... | 3041 |@word polynomial:1 norm:2 c0:3 d2:9 thereby:1 moment:1 reduction:9 liu:8 recovered:1 current:1 luo:2 dx:2 alanqi:1 numerical:1 remove:2 update:31 hwit:4 stationary:2 leaf:1 fewer:1 parameterization:1 accordingly:3 dissertation:1 pc0:1 probablity:1 c2:4 constructed:1 become:2 yuan:1 consists:1 combine:1 introduce:... |
2,251 | 3,042 | Statistical Modeling of Images with
Fields of Gaussian Scale Mixtures
Siwei Lyu
Eero. P. Simoncelli
Howard Hughes Medical Institute
Center for Neural Science, and
Courant Institute of Mathematical Sciences
New York University, New York, NY 10003
Abstract
The local statistical properties of photographic images, when re... | 3042 |@word version:1 middle:2 compression:1 nd:1 hyv:1 simplifying:1 decomposition:5 covariance:5 accounting:1 solid:2 initial:1 substitution:1 series:1 envision:1 past:1 diagonalized:1 current:5 recovered:1 comparing:1 elliptical:1 si:3 distant:1 additive:1 numerical:2 treating:1 stationary:1 indicative:1 cult:1 sys:... |
2,252 | 3,043 | Hyperparameter Learning for Graph Based
Semi-supervised Learning Algorithms
Xinhua Zhang?
Statistical Machine Learning Program
National ICT Australia, Canberra, Australia
and CSL, RSISE, ANU, Canberra, Australia
xinhua.zhang@nicta.com.au
Wee Sun Lee
Department of Computer Science
National University of Singapore
3 Sc... | 3043 |@word mild:1 repository:1 version:10 inversion:4 kondor:1 advantageous:1 d2:1 gradual:1 pick:1 carry:1 reduction:1 initial:2 score:2 selecting:1 efficacy:2 exclusively:1 denoting:2 rightmost:1 outperforms:1 existing:1 current:1 com:1 comparing:1 discretization:1 jaz:1 dx:2 written:3 must:1 john:3 numerical:1 info... |
2,253 | 3,044 | Cross-Validation Optimization for Large Scale
Hierarchical Classification Kernel Methods
Matthias W. Seeger
Max Planck Institute for Biological Cybernetics
P.O. Box 2169, 72012 T?ubingen, Germany
seeger@tuebingen.mpg.de
Abstract
We propose a highly efficient framework for kernel multi-class models with a
large and str... | 3044 |@word repository:1 norm:2 seems:1 c0:9 covariance:2 p0:3 innermost:1 pick:1 mention:1 tr:2 profit:1 tice:1 carry:1 initial:2 score:4 document:2 bc:3 rkhs:2 ours:3 outperforms:1 existing:1 must:2 stemming:1 numerical:5 partition:5 hofmann:3 kyb:2 cheap:2 update:1 v:1 stationary:1 half:1 leaf:7 fewer:1 greedy:1 acc... |
2,254 | 3,045 | Multiple timescales and uncertainty in motor
adaptation
Konrad P. Ko? rding
Rehabilitation Institute of Chicago
Northwestern University, Dept. PM&R
Chicago, IL 60611
konrad@koerding.com
Joshua B. Tenenbaum
Massachusetts Institute of Technology
Cambridge, MA 02139
jbt@mit.edu
Reza Shadmehr
Johns Hopkins University
Ba... | 3045 |@word trial:4 version:1 stronger:1 seems:4 extinction:2 initial:3 interestingly:1 current:4 com:1 kowler:1 surprising:1 must:1 written:1 john:2 physiol:1 visible:1 subsequent:2 chicago:2 happen:2 plasticity:2 motor:29 hypothesize:1 plot:2 progressively:6 generative:2 cue:1 nervous:8 smith:3 short:5 characterizati... |
2,255 | 3,046 | Approximate inference using planar graph
decomposition
Amir Globerson Tommi Jaakkola
Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139
gamir,tommi@csail.mit.edu
Abstract
A number of exact and approximate methods are available for inference calculations i... | 3046 |@word trial:2 determinant:1 middle:1 briefly:1 polynomial:10 barahona:1 grey:2 decomposition:15 innermost:1 solid:1 contains:2 outperforms:1 current:1 surprising:1 assigning:1 written:1 belmont:1 numerical:1 partition:38 update:1 stationary:1 intelligence:1 alone:1 amir:2 plane:8 provides:1 characterization:2 nod... |
2,256 | 3,047 | Multi-Instance Multi-Label Learning with
Application to Scene Classification
Zhi-Hua Zhou
Min-Ling Zhang
National Laboratory for Novel Software Technology
Nanjing University, Nanjing 210093, China
{zhouzh,zhangml}@lamda.nju.edu.cn
Abstract
In this paper, we formalize multi-instance multi-label learning, where each tr... | 3047 |@word version:4 open:1 ratan:1 bn:2 contains:4 zuk:1 score:3 document:3 africa:2 existing:1 ka:2 comparing:1 luo:1 assigning:1 numerical:1 additive:1 partition:2 intelligence:2 item:1 ith:4 core:1 boosting:12 contribute:2 zhang:4 five:1 along:2 consists:1 expected:1 multi:78 zhouzh:1 goldman:1 zhi:1 tenfold:2 dis... |
2,257 | 3,048 | Greedy Layer-Wise Training of Deep Networks
Yoshua Bengio, Pascal Lamblin, Dan Popovici, Hugo Larochelle
Universit?e de Montr?eal
Montr?eal, Qu?ebec
{bengioy,lamblinp,popovicd,larocheh}@iro.umontreal.ca
Abstract
Complexity theory of circuits strongly suggests that deep architectures can be much
more efficient (someti... | 3048 |@word repository:1 c0:1 d2:3 stracuzzi:2 propagate:1 covariance:1 contrastive:8 pick:1 minus:1 initial:3 contains:2 series:1 tuned:1 past:2 activation:2 yet:1 visible:3 uncooperative:2 shape:2 hypothesize:1 update:7 v:1 greedy:33 generative:4 intelligence:1 selected:3 pb1:1 complementing:1 provides:1 sigmoidal:3 ... |
2,258 | 3,049 | Doubly Stochastic Normalization for Spectral
Clustering
Ron Zass
and Amnon Shashua ?
Abstract
In this paper we focus on the issue of normalization of the affinity matrix in spectral clustering. We show that the difference between N-cuts and Ratio-cuts is
in the error measure being used (relative-entropy versus L1 no... | 3049 |@word kulis:1 version:2 briefly:1 polynomial:2 norm:23 seems:1 open:1 d2:2 decomposition:2 thereby:1 tr:2 reduction:1 contains:1 past:1 existing:1 spambase:3 outperforms:2 discretization:1 must:3 readily:1 kdd:1 plot:1 v:2 intelligence:1 mpm:1 provides:2 math:1 ron:1 successive:4 five:1 above1:1 consists:2 doubly... |
2,259 | 3,050 | Nonlinear physically-based models for decoding
motor-cortical population activity
Gregory Shakhnarovich Sung-Phil Kim Michael J. Black
Department of Computer Science
Brown University
Providence, RI 02912
{gregory,spkim,black}@cs.brown.edu
Abstract
Neural motor prostheses (NMPs) require the accurate decoding of motor ... | 3050 |@word neurophysiology:4 trial:3 norm:4 open:1 eng:1 solid:1 contains:1 ours:1 nordhausen:1 bc:1 imaginary:1 recovered:1 ka:9 current:2 activation:2 readily:1 john:1 biomechanical:1 subsequent:1 realistic:3 motor:31 hypothesize:2 generative:2 selected:1 device:6 manipulandum:4 wessberg:1 plane:2 parametrization:1 ... |
2,260 | 3,051 | Large Margin Hidden Markov Models for
Automatic Speech Recognition
Fei Sha
Computer Science Division
University of California
Berkeley, CA 94720-1776
feisha@cs.berkeley.edu
Lawrence K. Saul
Department of Computer Science and Engineering
University of California (San Diego)
La Jolla, CA 92093-0404
saul@cs.ucsd.edu
Ab... | 3051 |@word mild:1 briefly:1 manageable:1 proportion:3 nd:1 open:1 termination:1 covariance:2 simplifying:1 substitution:1 liu:1 score:6 denoting:1 mishra:1 current:1 recovered:1 must:1 numerical:1 hofmann:1 designed:1 update:2 half:1 parameterization:1 mccallum:1 short:1 core:1 infrastructure:1 provides:1 c6:1 simpler... |
2,261 | 3,052 | Logarithmic Online Regret Bounds for Undiscounted
Reinforcement Learning
Peter Auer
Ronald Ortner
University of Leoben, Franz-Josef-Strasse 18,
8700 Leoben, Austria
{auer,rortner}@unileoben.ac.at
Abstract
We present a learning algorithm for undiscounted reinforcement learning. Our
interest lies in bounds for the algo... | 3052 |@word trial:2 exploitation:5 version:1 polynomial:5 seems:3 nd:2 c0:1 open:2 p0:4 outlook:1 initial:2 ala:1 outperforms:1 nt:20 si:3 john:1 ronald:2 subsequent:2 wiewiora:1 happen:1 unichain:8 update:1 n0:1 v:1 stationary:3 greedy:1 fund:1 accordingly:1 provides:1 mannor:1 math:1 simpler:2 katehakis:2 apostolos:1... |
2,262 | 3,053 | The Robustness-Performance Tradeoff in Markov
Decision Processes
Huan Xu, Shie Mannor
Department of Electrical and Computer Engineering
McGill University
Montreal, Quebec, Canada, H3A2A7
xuhuan@cim.mcgill.ca
shie@ece.mcgill.ca
Abstract
Computation of a satisfactory control policy for a Markov decision process when
the... | 3053 |@word trial:1 version:1 briefly:1 simulation:4 r:9 attainable:1 tr:1 initial:1 series:1 selecting:1 denoting:1 subjective:1 past:1 current:1 neuneier:1 si:8 john:2 happen:1 unichain:4 treating:1 stationary:6 guess:2 plane:1 ith:6 record:1 provides:1 mannor:1 preference:1 prove:2 consists:1 polyhedral:1 expected:5... |
2,263 | 3,054 | Clustering appearance and shape by learning jigsaws
Anitha Kannan, John Winn, Carsten Rother
Microsoft Research Cambridge
[ankannan, jwinn, carrot]@microsoft.com
Abstract
Patch-based appearance models are used in a wide range of computer vision applications. To learn such models it has previously been necessary to sp... | 3054 |@word middle:1 rgb:2 contains:2 selecting:1 initialisation:1 ours:1 existing:4 com:1 comparing:1 surprising:1 readily:1 john:1 visible:1 shape:37 enables:1 hoping:1 treating:1 plot:1 depict:1 alone:2 cue:1 generative:7 selected:2 fewer:1 nebojsa:1 colored:2 provides:1 location:1 lx:1 five:1 height:1 along:1 const... |
2,264 | 3,055 | Theory and Dynamics of Perceptual Bistability
Paul R. Schrater?
Departments of Psychology and Computer Sci. & Eng.
University of Minnesota
Minneapolis, MN 55455
schrater@umn.edu
Rashmi Sundareswara
Department of Computer Sci. & Eng.
University of Minnesota
sundares@cs.umn.edu
Abstract
Perceptual Bistability refers t... | 3055 |@word mild:2 trial:1 middle:2 simulation:1 eng:2 pick:1 solid:1 series:2 selecting:1 current:5 surprising:1 si:5 must:3 written:1 shape:4 update:20 clumping:1 stationary:1 cue:1 selected:1 ith:4 core:1 sudden:1 provides:2 math:1 location:1 org:1 height:4 along:1 direct:2 qualitative:3 qij:4 fixation:2 fitting:1 b... |
2,265 | 3,056 | Kernels on Structured Objects Through Nested
Histograms
Marco Cuturi
Institute of Statistical Mathematics
Minami-azabu 4-6-7, Minato ku,
Tokyo, Japan.
Kenji Fukumizu
Institute of Statistical Mathematics
Minami-azabu 4-6-7, Minato ku,
Tokyo, Japan.
Abstract
We propose a family of kernels for structured objects which ... | 3056 |@word kondor:3 polynomial:3 coarseness:1 seems:1 underline:1 reused:1 rgb:1 decomposition:2 p0:7 euclidian:1 minus:1 recursively:1 moment:1 initial:1 substitution:1 series:6 score:2 contains:1 tuned:2 current:1 contextual:2 comparing:1 toh:1 written:2 finest:4 cruz:1 subsequent:1 partition:37 shape:1 plot:4 updat... |
2,266 | 3,057 | Inferring Network Structure from Co-Occurrences
Michael G. Rabbat
Electrical and Computer Eng.
University of Wisconsin
Madison, WI 53706
rabbat@cae.wisc.edu
M?ario A.T. Figueiredo
Instituto de Telecomunicac?o? es
Instituto Superior T?ecnico
Lisboa, Portugal
mtf@lx.it.pt
Robert D. Nowak
Electrical and Computer Eng.
U... | 3057 |@word sri:1 version:2 polynomial:5 stronger:1 sensed:1 eng:2 accounting:1 thereby:1 initial:5 configuration:1 liu:1 disparity:1 score:1 genetic:1 document:4 outperforms:1 current:3 com:1 activation:1 assigning:1 router:1 must:2 readily:2 written:1 portuguese:1 john:1 treating:4 drop:2 update:5 plot:2 intelligence... |
2,267 | 3,058 | Tighter PAC-Bayes Bounds
Amiran Ambroladze
Dep. of Mathematics
Lund University/LTH
Box 118, S-221 00 Lund, SWEDEN
amiran.ambroladze@math.lth.se
Emilio Parrado-Hern?andez
Dep. of Signal Processing and Communications
University Carlos III of Madrid
Legan?es, 28911, SPAIN
emipar@tsc.uc3m.es
John Shawe-Taylor
Dep. of Co... | 3058 |@word repository:1 version:2 briefly:1 seems:2 covariance:7 uphold:1 reduction:1 configuration:2 selecting:1 tuned:1 document:1 comparing:1 dx:1 must:2 john:1 partition:7 j1:1 informative:1 selected:5 plane:1 xk:1 kkwk:1 completeness:1 math:1 provides:2 along:2 constructed:1 learing:1 consists:1 combine:1 excelle... |
2,268 | 3,059 | An Efficient Method for Gradient-Based Adaptation
of Hyperparameters in SVM Models
S. Sathiya Keerthi
Vikas Sindhwani
Olivier Chapelle
Yahoo! Research
3333 Empire Avenue
Burbank, CA 91504
Department of Computer Science
University of Chicago
Chicago, IL 60637
MPI for Biological Cybernetics
Spemannstra?e 38
72076 T?... | 3059 |@word erate:9 version:6 manageable:1 yct:1 seems:1 proportion:1 termination:1 heuristically:1 tried:3 decomposition:2 reusage:1 score:1 tuned:7 interestingly:1 com:1 ida:1 mari:1 written:1 chicago:2 partition:6 cheap:1 plot:2 treating:2 half:2 plane:2 provides:1 iterates:1 sigmoidal:6 five:1 direct:4 become:2 inc... |
2,269 | 306 | Cholinergic Modulation May Enhance Cortical
Associative Memory Function
Michael E. Hasselmo?
Computation and
Neural Systems
Caltech 216-76
Pasadena, CA 91125
Brooke P. Andersont
Computation and
Neural Systems
Caltech 139-74
Pasadena, CA 91125
James M. Bower
Computation and
Neural Systems
Caltech 216-76
Pasadena, CA ... | 306 |@word version:2 duda:2 simulation:1 awij:1 activation:3 intriguing:1 designed:1 update:1 v:1 nervous:1 differential:1 become:2 symposium:1 behavioral:2 paragraph:1 olfactory:8 lehtio:1 brain:3 ol:1 preclude:1 psychopharmacology:1 becomes:1 circuit:1 what:2 psych:1 suppresses:2 developed:2 quantitative:1 every:2 ro... |
2,270 | 3,060 | Graph Laplacian Regularization for Large-Scale
Semidefinite Programming
Fei Sha
Computer Science Division
UC Berkeley, CA 94720
feisha@cs.berkeley.edu
Kilian Q. Weinberger
Dept of Computer and Information Science
U of Pennsylvania, Philadelphia, PA 19104
kilianw@seas.upenn.edu
Qihui Zhu
Dept of Computer and Informatio... | 3060 |@word version:1 seems:2 decomposition:1 tr:6 reduction:7 initial:2 series:1 toh:1 yet:1 must:3 written:2 additive:1 subsequent:1 recasting:1 drop:1 plot:3 intelligence:2 leaf:1 plane:4 desktop:1 ith:1 colored:1 provides:3 node:25 location:21 traverse:2 banff:1 mathematical:2 viable:1 inside:2 introduce:1 manner:1... |
2,271 | 3,061 | A Probabilistic Algorithm Integrating Source
Localization and Noise Suppression of MEG and
EEG Data
Johanna M. Zumer
Biomagnetic Imaging Lab
Department of Radiology
Joint Graduate Group in Bioengineering
University of California, San Francisco
San Francisco, CA 94143-0628
johannaz@mrsc.ucsf.edu
Hagai T. Attias
Golden ... | 3061 |@word trial:5 middle:5 sri:1 inversion:1 mri:1 bun:4 m100:2 simulation:9 r:1 lobe:2 covariance:5 eng:1 solid:2 npost:3 configuration:2 series:2 suppressing:1 past:1 existing:1 current:2 com:1 dx:1 realistic:2 oxygenation:1 shape:1 motor:1 mrsc:2 remove:1 plot:8 update:3 alone:1 generative:3 half:1 device:2 select... |
2,272 | 3,062 | Combining causal and similarity-based reasoning
Charles Kemp, Patrick Shafto, Allison Berke & Joshua B. Tenenbaum
Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139
{ckemp,shafto,berke,jbt}@mit.edu
Abstract
Everyday inductive reasoning draws on many kinds of knowledge, including
knowledge about relat... | 3062 |@word version:1 seems:3 replicate:1 nd:3 proportion:1 seek:2 arti:3 eld:1 mammal:3 carry:3 initial:1 score:4 genetic:1 o2:5 blank:1 comparing:1 nitesimal:1 must:3 distant:1 happen:1 cpds:1 remove:1 designed:3 alone:2 generative:4 leaf:2 intelligence:2 parameterization:1 cult:3 ith:2 rehder:5 smith:1 fa9550:1 prov... |
2,273 | 3,063 | Detecting Humans via Their Pose
Alessandro Bissacco
Computer Science Department
University of California, Los Angeles
Los Angeles, CA 90095
bissacco@cs.ucla.edu
Ming-Hsuan Yang
Honda Research Institute
800 California Street
Mountain View, CA 94041
mhyang@ieee.org
Stefano Soatto
Computer Science Department
University... | 3063 |@word pw:1 dalal:1 proportion:7 instrumental:1 norm:2 triggs:4 additively:1 decomposition:1 brightness:2 harder:1 reduction:1 initial:1 configuration:5 contains:1 score:6 efficacy:1 document:15 outperforms:1 recovered:1 comparing:1 assigning:2 shape:1 plot:1 update:1 v:1 alone:1 generative:11 cue:1 isard:1 discov... |
2,274 | 3,064 | Information Bottleneck for Non Co-Occurrence Data
Yevgeny Seldin?
Noam Slonim?
Naftali Tishby??
?
School of Computer Science and Engineering
Interdisciplinary Center for Neural Computation
The Hebrew University of Jerusalem
?
The Lewis-Sigler Institute for Integrative Genomics
Princeton University
{seldin,tishby}@c... | 3064 |@word compression:7 integrative:1 reduction:1 configuration:1 series:2 document:7 existing:1 current:1 z2:8 com:2 hohmann:1 must:1 readily:1 reminiscent:1 john:1 realistic:1 numerical:1 partition:13 informative:2 hofmann:1 enables:1 designed:1 joy:1 generative:1 greedy:1 prohibitive:1 amir:1 core:2 provides:3 nod... |
2,275 | 3,065 | Data Integration for Classification Problems
Employing Gaussian Process Priors
Mark Girolami
Department of Computing Science
University of Glasgow
Scotland, UK
girolami@dcs.gla.ac.uk
Mingjun Zhong
IRISA, Campus de Beaulieu
F-35042 Rennes Cedex
France
zmingjun@irisa.fr
Abstract
By adopting Gaussian process priors a f... | 3065 |@word version:1 logit:1 seek:1 covariance:25 moment:4 series:1 denoting:1 tuned:1 yni:1 bie:1 fn:19 subsequent:1 additive:1 partition:6 informative:2 analytic:4 kyb:1 update:1 discrimination:1 intelligence:1 devising:2 scotland:1 smith:1 short:1 eskin:1 provides:5 location:1 herbrich:2 become:1 overhead:1 manner:... |
2,276 | 3,066 | An Oracle Inequality for Clipped Regularized Risk
Minimizers
Ingo Steinwart, Don Hush, and Clint Scovel
Modelling, Algorithms and Informatics Group, CCS-3
Los Alamos National Laboratory
Los Alamos, NM 87545
{ingo,dhush,jcs}@lanl.gov
Abstract
We establish a general oracle inequality for clipped approximate minimizers o... | 3066 |@word version:3 norm:3 p0:11 rkhs:3 scovel:3 realistic:1 girosi:1 half:1 beginning:1 math:1 prove:1 consists:2 combine:1 multi:1 gov:1 actual:1 equipped:2 cardinality:2 becomes:1 begin:2 estimating:1 moreover:7 substantially:1 minimizes:2 guarantee:1 exactly:1 k2:1 classifier:2 unit:3 yn:2 appear:2 before:2 t1:7 ... |
2,277 | 3,067 | Denoising and Dimension Reduction in Feature Space
Mikio L. Braun
Fraunhofer Institute1
FIRST.IDA
Kekul?estr. 7, 12489 Berlin
mikio@first.fhg.de
Joachim Buhmann
Inst. of Computational Science
ETH Zurich
CH-8092 Z?urich
jbuhmann@inf.ethz.ch
2,1
?
Klaus-Robert Muller
Technical University of Berlin2
Computer Science
Fr... | 3067 |@word norm:4 seems:2 underline:1 nd:1 simulation:1 tried:1 tr:7 reduction:1 contains:2 series:1 denoting:2 rkhs:1 kcr:4 err:1 ida:1 comparing:1 si:2 dx:1 written:2 informative:1 shape:1 enables:2 plot:3 v:1 selected:2 flare:2 ith:1 provides:2 math:1 location:2 zhang:1 along:1 become:1 ik:1 consists:1 fitting:2 ma... |
2,278 | 3,068 | Learnability and the Doubling Dimension
Yi Li
Genome Institute of Singapore
liy3@gis.a-star.edu.sg
Philip M. Long
Google
plong@google.com
Abstract
Given a set F of classifiers and a probability distribution over their domain, one
can define a metric by taking the distance between a pair of classifiers to be the
prob... | 3068 |@word achievable:1 polynomial:1 stronger:1 r:1 wexler:1 series:2 com:1 beygelzimer:1 yet:1 must:2 fn:2 alone:1 half:1 item:2 desh:1 math:1 location:1 mendel:1 simpler:1 zhang:2 mathematical:1 c2:7 incorrect:1 prove:3 consists:4 focs:2 expected:1 behavior:1 frequently:1 inspired:1 decreasing:1 begin:1 classifies:1... |
2,279 | 3,069 | Fundamental Limitations of Spectral Clustering
Boaz Nadler?, Meirav Galun
Department of Applied Mathematics and Computer Science
Weizmann Institute of Science, Rehovot, Israel 76100
boaz.nadler,meirav.galun@weizmann.ac.il
Abstract
Spectral clustering methods are common graph-based approaches to clustering of
data. Sp... | 3069 |@word polynomial:1 norm:1 nd:1 disk:6 zelnik:1 bn:1 covariance:1 decomposition:1 pg:2 commute:1 recursively:1 reduction:1 initial:1 contains:1 series:1 reaction:1 comparing:1 yet:1 numerical:1 partition:11 shape:1 dupont:1 plot:2 fund:2 v:1 half:1 parameterization:1 isotropic:4 coarse:1 node:2 location:3 brandt:3... |
2,280 | 307 | An Attractor Neural Network Model of Recall
and Recognition
Eytan Ruppin
Yechezkel Yeshurun
Department of Computer Science Department of Computer Science
School of Mathematical Sciences School of Mathematical Sciences
Sackler Faculty of Exact Sciences Sackler Faculty of Exact Sciences
Tel Aviv University
Tel Aviv Univ... | 307 |@word mild:1 faculty:2 jijsj:1 simulation:3 accounting:1 pg:4 idl:1 initial:5 fragment:2 denoting:1 timer:1 comparing:1 si:3 yet:1 arest:1 cue:4 selected:1 item:9 beginning:2 reciprocal:1 gillund:1 mathematical:2 consists:1 kej:1 unlearning:1 paragraph:1 jly:1 inter:2 expected:1 behavior:1 mechanic:1 growing:1 ol:... |
2,281 | 3,070 | Information Bottleneck Optimization and
Independent Component Extraction with Spiking
Neurons
Stefan Klampfl, Robert Legenstein, Wolfgang Maass
Institute for Theoretical Computer Science
Graz University of Technology
A-8010 Graz, Austria
{klampfl,legi,maass}@igi.tugraz.at
Abstract
The extraction of statistically indep... | 3070 |@word trial:1 middle:1 open:1 hyv:1 simulation:1 accounting:2 thereby:1 solid:4 initial:2 contains:1 efficacy:6 denoting:2 xnj:4 current:5 attracted:1 written:2 plasticity:3 drop:1 update:5 fund:1 v:1 xk:8 yi1:1 filtered:1 allerton:1 simpler:1 burst:4 direct:1 become:1 consists:2 manner:1 theoretically:1 ica:2 ro... |
2,282 | 3,071 | A Small World Threshold
for Economic Network Formation
Eyal Even-Dar
Computer and Information Science
University of Pennsylvania
Philadelphia, PA 19104
evendar@seas.upenn.edu
Michael Kearns
Computer and Information Science
University of Pennsylvania
Philadelphia, PA 19104
mkearns@cis.upenn.edu
Abstract
We introduce a... | 3071 |@word private:1 briefly:2 version:1 polynomial:5 stronger:1 c0:13 d2:3 simulation:4 wexler:1 recursively:1 carry:1 initial:1 configuration:1 mkearns:1 contains:1 series:1 ours:2 existing:2 current:2 si:12 yet:2 must:2 distant:3 subsequent:1 asymptote:1 v:2 generative:1 greedy:7 selected:2 xk:4 short:6 parkes:1 co... |
2,283 | 3,072 | Generalized Maximum Margin Clustering and
Unsupervised Kernel Learning
Hamed Valizadegan
Computer Science and Engineering
Michigan State University
East Lansing, MI 48824
valizade@msu.edu
Rong Jin
Computer Science and Engineering
Michigan State University
East Lansing, MI 48824
rongjin@cse.msu.edu
Abstract
Maximum m... | 3072 |@word repository:2 briefly:1 seems:2 km:1 zelnik:1 elisseeff:1 interestingly:1 existing:2 written:2 realize:1 remove:2 designed:1 intelligence:2 ith:1 provides:1 math:1 cse:1 consists:1 lansing:2 introduce:3 acquired:1 valizadegan:1 examine:1 sdp:1 multi:1 automatically:3 resolve:2 equipped:1 considering:1 become... |
2,284 | 3,073 | Simplifying Mixture Models
through Function Approximation
Kai Zhang
James T. Kwok
Department of Computer Science and Engineering
The Hong Kong University of Science and Technology
Clear Water Bay, Kowloon, Hong Kong
{twinsen, jamesk}@cse.ust.hk
Abstract
Finite mixture model is a powerful tool in many statistical lear... | 3073 |@word kong:2 version:2 compression:1 norm:6 nd:1 simplifying:6 covariance:5 contraction:1 initial:2 selecting:1 ours:2 interestingly:1 subjective:1 existing:1 si:33 goldberger:1 dx:7 ust:1 written:4 intriguing:1 subsequent:1 partition:6 shape:1 designed:1 plot:1 intelligence:2 selected:1 ith:1 iterates:1 provides... |
2,285 | 3,074 | On Transductive Regression
Corinna Cortes
Google Research
76 Ninth Avenue
New York, NY 10011
corinna@google.com
Mehryar Mohri
Courant Institute of Mathematical Sciences
and Google Research
251 Mercer Street
New York, NY 10012
mohri@cs.nyu.edu
Abstract
In many modern large-scale learning applications, the amount of u... | 3074 |@word version:2 inversion:5 compression:1 norm:1 seems:1 adrian:1 thereby:1 tr:1 carry:1 series:1 score:1 selecting:1 document:1 existing:1 com:1 surprising:1 jaz:1 yet:1 dx:1 must:1 readily:2 john:1 chicago:1 partition:2 alone:1 intelligence:1 prohibitive:2 selected:2 provides:3 node:2 contribute:1 location:2 ro... |
2,286 | 3,075 | Correcting Sample Selection Bias by Unlabeled Data
Jiayuan Huang
School of Computer Science
Univ. of Waterloo, Canada
j9huang@cs.uwaterloo.ca
Alexander J. Smola
NICTA, ANU
Canberra, Australia
Alex.Smola@anu.edu.au
Karsten M. Borgwardt
Ludwig-Maximilians-University
Munich, Germany
kb@dbs.ifi.lmu.de
Arthur Gretton
MP... | 3075 |@word rreg:2 trial:4 briefly:1 middle:1 polynomial:3 proportion:5 nd:1 tamayo:1 gish:1 simplifying:1 q1:1 attended:1 selecting:2 denoting:1 rkhs:4 outperforms:2 existing:1 err:1 surprising:1 si:9 must:1 saal:1 benign:1 hofmann:1 resampling:2 v:1 selected:1 ith:1 renshaw:1 provides:2 successive:1 direct:2 borg:1 i... |
2,287 | 3,076 | A Complexity-Distortion Approach to
Joint Pattern Alignment
Andrea Vedaldi
Stefano Soatto
Department of Computer Science
University of California at Los Angeles
Los Angeles, CA 90035
{vedaldi,soatto}@cs.ucla.edu
Abstract
Image Congealing (IC) is a non-parametric method for the joint alignment of a collection of image... | 3076 |@word deformed:2 illustrating:1 middle:4 compression:1 norm:1 duda:1 d2:2 seek:2 simplifying:1 reduction:1 series:1 must:1 written:1 john:1 wx:4 enables:2 remove:2 plot:1 update:1 v:1 alone:1 fewer:1 plane:1 xk:3 realizing:1 fa9550:1 coarse:1 quantized:1 lx:1 simpler:3 unacceptable:1 along:4 direct:2 differential... |
2,288 | 3,077 | Learning annotated hierarchies from relational data
Daniel M. Roy, Charles Kemp, Vikash K. Mansinghka, and Joshua B. Tenenbaum
CSAIL, Dept. of Brain & Cognitive Sciences, MIT, Cambridge, MA 02139
{droy, ckemp, vkm, jbt}@mit.edu
Abstract
The objects in many real-world domains can be organized into hierarchies, where
e... | 3077 |@word faculty:3 duda:1 seal:1 nd:1 open:1 cleanly:1 pick:2 mammal:2 tr:11 initial:1 contains:1 daniel:1 recovered:3 comparing:1 distant:1 partition:63 informative:1 yf3:4 remove:1 interpretable:3 stationary:1 generative:12 leaf:16 discovering:6 yr:1 item:1 infant:1 oldest:1 yamada:1 provides:2 node:26 five:2 phyl... |
2,289 | 3,078 | Modeling Human Motion
Using Binary Latent Variables
Graham W. Taylor, Geoffrey E. Hinton and Sam Roweis
Dept. of Computer Science
University of Toronto
Toronto, M5S 2Z9 Canada
{gwtaylor,hinton,roweis}@cs.toronto.edu
Abstract
We propose a non-linear generative model for human motion data that uses an
undirected model ... | 3078 |@word version:2 contrastive:5 pick:1 tr:1 solid:1 reduction:2 initial:1 configuration:5 contains:4 series:4 selecting:1 cyclic:2 liu:1 shum:1 document:1 past:6 brien:1 current:7 activation:2 must:1 visible:39 realistic:1 concatenate:1 wanted:1 utml:1 treating:2 plot:1 update:4 designed:1 generative:3 leaf:1 websi... |
2,290 | 3,079 | TrueSkill
TM :
A Bayesian Skill Rating System
Ralf Herbrich
Tom Minka
Microsoft Research Ltd.
Thore Graepel
Microsoft Research Ltd.
Cambridge, UK
Microsoft Research Ltd.
Cambridge, UK
rherb@microsoft.com
Cambridge, UK
minka@microsoft.com
thoreg@microsoft.com
Abstract
We present a new Bayesian skill ratin... | 3079 |@word achievable:1 d2:2 bn:2 thoreg:1 tr:6 solid:1 moment:3 score:1 loeliger:1 past:1 trueskill:29 bradley:1 com:5 nt:1 surprising:1 si:10 yet:1 subsequent:1 realistic:1 additive:3 informative:1 plot:2 update:10 v:3 selected:1 cult:1 beginning:1 node:4 herbrich:1 provisional:1 c2:2 skilled:1 beta:3 ect:1 a2j:1 co... |
2,291 | 308 | A Framework for the Cooperation
of Learning Algorithms
Leon Bottou
Patrick Gallinari
Laboratoire de Recherche en Informatique
Universite de Paris XI
91405 Orsay Cedex
France
Abstract
We introduce a framework for training architectures composed of several
modules. This framework, which uses a statistical formulation... | 308 |@word decomposition:7 euclidian:6 series:1 ala:1 dx:1 wx:5 analytic:1 yr:1 une:1 xk:17 recherche:1 provides:4 contribute:1 simpler:1 along:1 constructed:1 ik:1 consists:2 combine:4 introduce:5 expected:6 behavior:2 nor:1 multi:2 globally:1 estimating:1 kaufman:2 unified:1 finding:1 gallinari:8 control:1 unit:1 gra... |
2,292 | 3,080 | PG-means: learning the number of clusters in data
Yu Feng
Greg Hamerly
Computer Science Department
Baylor University
Waco, Texas 76798
{yu feng, greg hamerly}@baylor.edu
Abstract
We present a novel algorithm called PG-means which is able to learn the number
of clusters in a classical Gaussian mixture model. Our metho... | 3080 |@word compression:1 smirnov:6 d2:1 simulation:7 seek:1 covariance:10 pg:62 initial:2 wrapper:4 contains:1 score:6 rightmost:1 existing:2 ka:1 comparing:3 yet:1 must:1 readily:1 additive:1 analytic:1 remove:1 plot:3 n0:3 intelligence:2 fewer:2 provides:3 postal:1 location:3 along:5 ryohei:1 walther:1 consists:1 da... |
2,293 | 3,081 | Efficient Methods for Privacy Preserving Face
Detection
Shai Avidan
Mitsubishi Electric Research Labs
201 Broadway
Cambridge, MA 02139
avidan@merl.com
Moshe Butman
Department of Computer Science
Bar Ilan University
Ramat-Gan, Israel
butmanm@cs.biu.edu
Abstract
Bob offers a face-detection web service where clients can... | 3081 |@word stronger:1 mitsubishi:1 elisseeff:1 pick:1 solid:2 carry:1 contains:1 score:5 current:1 com:1 si:3 john:1 chicago:1 additive:1 informative:1 kdd:1 update:1 greedy:2 selected:8 half:2 plane:1 directory:1 mental:1 completeness:1 boosting:2 allerton:1 symposium:1 ik:1 symp:1 introduce:1 manner:1 privacy:25 rap... |
2,294 | 3,082 | No-regret Algorithms for Online Convex Programs
Geoffrey J. Gordon
Department of Machine Learning
Carnegie Mellon University
Pittsburgh, PA 15213
ggordon@cs.cmu.edu
Abstract
Online convex programming has recently emerged as a powerful primitive for
designing machine learning algorithms. For example, OCP can be used f... | 3082 |@word trial:3 middle:1 version:4 polynomial:1 norm:2 approachability:1 open:1 dealer:14 pick:2 tr:1 solid:1 carry:1 contains:1 ours:1 current:3 si:4 must:3 cruz:1 additive:2 happen:2 shape:1 hofmann:1 designed:2 drop:1 update:3 implying:1 half:1 item:1 kyk:1 warmuth:3 core:1 short:1 manfred:2 provides:1 boosting:... |
2,295 | 3,083 | Fast Discriminative Visual Codebooks
using Randomized Clustering Forests
Frank Moosmann?, Bill Triggs and Frederic Jurie
GRAVIR-CNRS-INRIA, 655 avenue de l?Europe, Montbonnot 38330, France
firstname.lastname@inrialpes.fr
Abstract
Some of the most effective recent methods for content-based image classification work by... | 3083 |@word trial:2 briefly:1 seems:2 everingham:1 triggs:3 tried:1 brightness:1 pick:1 tr:1 recursively:2 lepetit:1 liu:1 contains:2 score:3 selecting:1 fragment:1 document:1 outperforms:2 current:2 comparing:1 assigning:2 yet:1 reminiscent:1 numerical:1 partition:2 visibility:1 discrimination:2 alone:2 generative:1 s... |
2,296 | 3,084 | Bayesian Ensemble Learning
Hugh A. Chipman
Department of Mathematics and Statistics
Acadia University
Wolfville, NS, Canada
Edward I. George
Department of Statistics
The Wharton School
University of Pennsylvania
Philadelphia, PA 19104-6302
Robert E. McCulloch
Graduate School of Business
University of Chicago
Chicago... | 3084 |@word middle:2 version:2 simulation:1 pick:4 tr:1 born:1 tuned:1 qth:1 current:1 dx:4 must:1 additive:8 chicago:4 enables:1 plot:6 interpretable:1 bart:22 half:1 greedy:1 cook:5 inspection:1 iterates:1 boosting:14 node:21 provides:3 successive:1 draft:1 mathematical:1 direct:1 become:1 replication:2 consists:1 ba... |
2,297 | 3,085 | Blind Motion Deblurring Using Image Statistics
Anat Levin?
School of Computer Science and Engineering
The Hebrew University of Jerusalem
Abstract
We address the problem of blind motion deblurring from a single image, caused
by a few moving objects. In such situations only part of the image may be blurred,
and the scen... | 3085 |@word version:1 stronger:4 simplifying:1 decomposition:1 photographer:1 inpainting:1 liu:1 contains:2 score:1 selecting:1 existing:3 current:1 recovered:5 com:1 surprising:1 assigning:1 yet:3 blur:79 hofmann:1 enables:1 shape:2 remove:2 plot:1 stationary:1 cue:3 selected:5 rav:1 provides:1 detecting:2 favaro:1 di... |
2,298 | 3,086 | Support Vector Machines on a Budget
Ofer Dekel and Yoram Singer
School of Computer Science and Engineering
The Hebrew University
Jerusalem 91904, Israel
{oferd,singer}@cs.huji.ac.il
Abstract
The standard Support Vector Machine formulation does not provide its user with
the ability to explicitly control the number of ... | 3086 |@word briefly:1 version:2 norm:86 seems:1 dekel:2 calculus:1 q1:6 dramatic:1 minus:1 contains:1 selecting:1 rkhs:3 bc:5 sharpley:1 existing:1 recovered:1 current:1 yet:1 must:2 written:3 limp:1 girosi:1 analytic:1 plot:1 update:2 v:1 device:1 xk:5 beginning:1 short:1 farther:1 mathematical:2 direct:1 prove:1 comb... |
2,299 | 3,087 | Natural Actor-Critic for Road Traffic Optimisation
Silvia Richter
Albert-Ludwigs-Universit?at
Freiburg, Germany
Douglas Aberdeen
National ICT Australia
Canberra, Australia
Jin Yu
National ICT Australia
Canberra, Australia.
si.richter@web.de
doug.aberdeen@anu.edu.au
jin.yu@anu.edu.au
Abstract
Current road-traffic... | 3087 |@word version:3 inversion:1 advantageous:1 approved:1 unif:1 d2:1 simulation:4 simplifying:1 pg:17 tuned:4 ours:1 existing:4 current:11 surprising:1 analysed:1 si:3 written:1 readily:1 must:3 interrupted:1 realistic:5 cheap:1 update:8 stationary:2 alone:1 fewer:2 half:1 signalling:1 short:1 provides:1 authority:2... |
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