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3,500 | 417 | A Short-Term Memory Architecture for the
Learning of Morphophonemic Rules
Michael Gasser and Chan-Do Lee
Computer Science Department
Indiana University
Bloomington, IN 47405
Abstract
Despite its successes, Rumelhart and McClelland's (1986) well-known approach to the learning of morphophonemic rules suffers from two d... | 417 |@word version:3 simulation:5 accommodate:1 initial:3 hereafter:1 mastery:1 prefix:7 past:14 current:4 surprising:1 activation:1 yet:1 must:1 parsing:1 realistic:1 shape:1 medial:1 pylyshyn:2 fewer:1 item:2 tone:2 beginning:1 short:7 accepting:1 affix:1 combine:1 expected:4 behavior:4 elman:3 window:1 stm:3 provide... |
3,501 | 4,170 | MAP estimation in Binary MRFs via Bipartite
Multi-cuts
Sashank J. Reddi?
IIT Bombay
sashank@cse.iitb.ac.in
Sunita Sarawagi
IIT Bombay
sunita@cse.iitb.ac.in
Sundar Vishwanathan
IIT Bombay
sundar@cse.iitb.ac.in
Abstract
We propose a new LP relaxation for obtaining the MAP assignment of a binary
MRF with pairwise poten... | 4170 |@word kohli:1 faculty:1 version:1 polynomial:3 suitably:1 termination:2 barahona:1 decomposition:2 p0:2 pick:2 multicommodity:2 reduction:1 initial:1 contains:2 score:13 interestingly:1 existing:1 current:2 comparing:1 si:4 j1:9 enables:2 remove:1 progressively:1 update:4 v:1 greedy:1 leaf:1 selected:2 complement... |
3,502 | 4,171 | Bayesian Action-Graph Games
Albert Xin Jiang
Department of Computer Science
University of British Columbia
jiang@cs.ubc.ca
Kevin Leyton-Brown
Department of Computer Science
University of British Columbia
kevinlb@cs.ubc.ca
Abstract
Games of incomplete information, or Bayesian games, are an important gametheoretic mod... | 4171 |@word private:2 version:1 polynomial:12 stronger:1 nd:1 open:2 simulation:1 bn:8 versatile:1 carry:1 initial:1 configuration:12 contains:1 daniel:1 outperforms:1 existing:5 current:1 si:2 yet:1 john:1 cpds:4 implying:1 provides:1 node:57 location:13 firstly:1 vorobeychik:1 mathematical:1 dn:3 become:1 symposium:4... |
3,503 | 4,172 | 000
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Switching state space model for simultaneously
estimating state transitions and nonstationary firing
rates
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Anonymous Author(s)
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email
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We propose an algorithm for simultaneously estimating state transit... | 4172 |@word trial:13 worsens:1 stronger:1 advantageous:1 logit:2 smirnov:2 nd:6 heuristically:2 rhesus:1 tr:1 solid:1 initial:3 series:6 precluding:1 z2:1 informative:1 enables:6 motor:2 plot:11 medial:2 update:1 stationary:2 intelligence:1 selected:4 xk:3 coarse:5 org:1 burst:2 c2:1 xnm:16 persistent:1 consists:4 sust... |
3,504 | 4,173 | Probabilistic latent variable models for distinguishing
between cause and effect
Oliver Stegle
MPI for Biological Cybernetics
T?ubingen, Germany
oliver.stegle@tuebingen.mpg.de
Joris M. Mooij
MPI for Biological Cybernetics
T?ubingen, Germany
joris.mooij@tuebingen.mpg.de
Dominik Janzing
MPI for Biological Cybernetics
T... | 4173 |@word determinant:1 version:1 norm:1 seems:2 nd:1 calculus:1 hyv:3 simulation:1 accounting:1 covariance:3 volkswagen:1 moment:1 initial:1 contains:1 tuned:1 interestingly:1 existing:1 surprising:1 si:1 attracted:1 written:1 additive:26 realistic:1 happen:1 numerical:2 drop:1 designed:1 generative:2 intelligence:4... |
3,505 | 4,174 | Learning sparse dynamic linear systems using
stable spline kernels and exponential hyperpriors
Alessandro Chiuso
Department of Management and Engineering
University of Padova
Vicenza, Italy
alessandro.chiuso@unipd.it
Gianluigi Pillonetto?
Department of Information Engineering
University of Padova
Padova, Italy
giapi@... | 4174 |@word middle:2 version:4 briefly:1 polynomial:1 norm:7 achievable:1 k2hk:1 open:1 carry:1 series:3 hereafter:1 rkhs:2 favouring:1 past:2 ka:2 z2:1 additive:1 distant:1 visible:1 informative:2 numerical:5 stationary:3 selected:1 plane:1 dinuzzo:2 filtered:1 provides:2 pillonetto:3 mathematical:2 chiuso:3 yuan:1 co... |
3,506 | 4,175 | Efficient Relational Learning with
Hidden Variable Detection
Ni Lao, Jun Zhu, Liu Liu, Yandong Liu, William W. Cohen
Carnegie Mellon University
5000 Forbes Avenue, Pittsburgh, PA 15213
{nlao,junzhu,liuliu,yandongl,wcohen}@cs.cmu.edu
Abstract
Markov networks (MNs) can incorporate arbitrarily complex features in modelin... | 4175 |@word nificantly:1 briefly:2 norm:6 seal:1 c0:2 pieter:1 contrastive:11 q1:4 mammal:2 thereby:1 initial:2 liu:3 series:2 exclusively:1 contains:2 paw:2 kurt:1 existing:8 current:2 yet:4 attracted:1 kdd:1 flipper:1 designed:1 update:1 hvs:7 generative:2 greedy:1 discovering:1 mln:1 marine:1 yamada:1 provides:1 par... |
3,507 | 4,176 | Active Learning by Querying
Informative and Representative Examples
Sheng-Jun Huang1
Rong Jin2
Zhi-Hua Zhou1
1
National Key Laboratory for Novel Software Technology,
Nanjing University, Nanjing 210093, China
2
Department of Computer Science and Engineering,
Michigan State University, East Lansing, MI 48824
{huangsj, z... | 4176 |@word repository:1 tried:2 series:1 selecting:1 document:7 outperforms:1 existing:1 ka:2 current:1 partition:1 informative:20 designed:3 v:5 selected:8 beginning:4 short:1 provides:3 cse:1 zhang:1 five:2 incorrect:1 consists:2 combine:4 lansing:1 expected:1 behavior:1 examine:2 multi:2 zhouzh:1 zhi:1 provided:1 f... |
3,508 | 4,177 | Multi-label Multiple Kernel Learning by Stochastic
Approximation: Application to Visual Object Recognition
Serhat S. Bucak?
bucakser@cse.msu.edu
Rong Jin?
rongjin@cse.msu.edu
Dept. of Comp. Sci. & Eng.?
Michigan State University
East Lansing, MI 48824,U.S.A.
Anil K. Jain??
jain@cse.msu.edu
Dept. of Brain & Cogniti... | 4177 |@word norm:6 everingham:3 open:1 confirms:1 r:1 eng:2 p0:1 q1:4 shechtman:1 configuration:1 contains:1 document:2 bhattacharyya:1 ka:5 comparing:1 surprising:1 bie:1 attracted:1 partition:1 blur:1 shape:1 voc2006:4 treating:1 designed:1 update:1 intelligence:3 beginning:1 short:2 core:1 quantized:1 cse:4 org:4 al... |
3,509 | 4,178 | Multivariate Dyadic Regression Trees for Sparse
Learning Problems
Han Liu and Xi Chen
School of Computer Science, Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
We propose a new nonparametric learning method based on multivariate dyadic
regression trees (MDRTs). Unlike traditional dyadic decision trees (DDTs... | 4178 |@word mild:1 trial:3 repository:1 version:2 polynomial:11 norm:1 turlach:1 simulation:1 covariance:1 pick:4 tr:1 recursively:1 liu:4 selecting:1 fbj:1 prefix:2 existing:2 wd:1 nt:6 written:2 must:2 john:2 additive:9 partition:2 realistic:1 numerical:3 remove:1 drop:3 n0:3 bart:2 greedy:7 prohibitive:1 selected:1 ... |
3,510 | 4,179 | Implicitly Constrained Gaussian Process Regression
for Monocular Non-Rigid Pose Estimation
Raquel Urtasun
TTI Chicago
rurtasun@ttic.edu
Mathieu Salzmann
ICSI & EECS, UC Berkeley
TTI Chicago
salzmann@ttic.edu
Abstract
Estimating 3D pose from monocular images is a highly ambiguous problem. Physical constraints can be ... | 4179 |@word deformed:2 middle:3 achievable:1 norm:8 seems:1 triggs:2 seek:1 covariance:3 lepetit:1 configuration:2 liu:1 salzmann:4 outperforms:9 recovered:6 assigning:1 written:1 must:2 mesh:21 chicago:2 partition:2 shape:12 enables:1 moreno:1 plot:2 generative:2 intelligence:1 parameterization:5 phog:7 plane:3 parame... |
3,511 | 418 | A Method for the Efficient Design
of Boltzmann Machines for Classification
Problems
Ajay Gupta and Wolfgang Maass?
Department of Mathematics, Statistics, and Computer Science
University of Illinois at Chicago
Chicago IL, 60680
Abstract
We introduce a method for the efficient design of a Boltzmann machine (or
a Hopfie... | 418 |@word h:3 polynomial:9 nd:1 open:1 simulation:2 configuration:8 cyclic:1 series:1 ka:1 current:3 si:8 schnitger:2 written:1 john:2 synchronicity:1 chicago:3 hajnal:1 analytic:1 update:1 leaf:1 beginning:2 ith:1 compo:1 provides:1 node:22 constructed:3 predecessor:3 consists:2 prove:2 manner:1 introduce:2 rding:1 p... |
3,512 | 4,180 | A Reduction from Apprenticeship Learning to
Classification
Umar Syed?
Department of Computer and Information Science
University of Pennsylvania
Philadelphia, PA 19104
usyed@cis.upenn.edu
Robert E. Schapire
Department of Computer Science
Princeton University
Princeton, NJ 08540
schapire@cs.princeton.edu
Abstract
We pr... | 4180 |@word exploitation:1 polynomial:1 nd:2 pieter:2 harder:2 moment:1 reduction:6 initial:2 contains:1 exclusively:1 chervonenkis:1 ours:1 interestingly:1 existing:1 must:2 john:3 confirming:1 designed:1 v:1 stationary:5 aside:1 fewer:3 imitate:7 ith:1 simpler:1 si1:1 along:1 doron:1 prove:9 consists:1 combine:2 mann... |
3,513 | 4,181 | Error Propagation for Approximate Policy and
Value Iteration
R?emi Munos
Sequel Project, INRIA Lille
Lille, France
remi.munos@inria.fr
Amir massoud Farahmand
Department of Computing Science
University of Alberta
Edmonton, Canada, T6G 2E8
amirf@ualberta.ca
Csaba Szepesv?ari ?
Department of Computing Science
University... | 4181 |@word norm:21 hu:1 propagate:2 q1:3 initial:2 series:1 daniel:1 tuned:1 past:2 comparing:1 dx:2 john:1 ronald:2 plot:1 stationary:4 greedy:7 selected:2 intelligence:1 amir:3 beginning:1 short:1 erator:1 provides:1 mannor:2 contribute:2 c2:1 direct:1 farahmand:5 qualitative:1 dewen:1 combine:1 inside:1 manner:1 x0... |
3,514 | 4,182 | A Non-Parametric Approach to
Dynamic Programming
Oliver B. Kroemer1,2
1
Jan Peters1,2
Intelligent Autonomous Systems, Technische Universit?t Darmstadt
Robot Learning Lab, Max Planck Institute for Intelligent Systems
{kroemer,peters}@ias.tu-darmstadt.de
2
Abstract
In this paper, we consider the problem of policy eva... | 4182 |@word trial:2 inversion:3 open:1 hu:1 reduction:1 initial:1 series:3 united:1 tuned:2 existing:1 current:2 discretization:1 si:23 must:3 written:1 john:1 ronald:1 numerical:4 plot:1 drop:1 update:1 selected:2 ith:1 provides:1 mannor:1 ron:1 mathematical:3 direct:4 become:2 dewen:1 interscience:1 manner:3 acquired... |
3,515 | 4,183 | Heavy-tailed Distances for
Gradient Based Image Descriptors
Yangqing Jia and Trevor Darrell
UC Berkeley EECS and ICSI
{jiayq,trevor}@eecs.berkeley.edu
Abstract
Many applications in computer vision measure the similarity between images or
image patches based on some statistics such as oriented gradients. These are oft... | 4183 |@word kulis:1 dalal:1 compression:1 stronger:1 d2:3 thres:12 paid:1 solid:1 shot:2 shading:2 carry:1 shechtman:1 series:1 contains:3 score:5 salzmann:1 tuned:1 ours:1 suppressing:1 outperforms:1 existing:4 comparing:3 written:2 numerical:1 informative:1 shape:4 enables:1 designed:2 gist:1 update:1 hash:1 implying... |
3,516 | 4,184 | Maximum Margin Multi-Label Structured Prediction
Christoph H. Lampert
IST Austria (Institute of Science and Technology Austria)
Am Campus 1, 3400 Klosterneuburg, Austria
http://www.ist.ac.at/?chl
chl@ist.ac.at
Abstract
We study multi-label prediction for structured output sets, a problem that occurs,
for example, in ... | 4184 |@word version:1 compression:1 polynomial:2 advantageous:1 zelnik:1 decomposition:1 thereby:2 mcauley:1 initial:2 configuration:2 contains:1 score:9 document:1 existing:3 comparing:1 surprising:1 luo:1 parsing:1 visible:1 hofmann:4 voc2006:1 designed:3 gist:1 greedy:2 plane:2 mccallum:1 core:1 provides:1 boosting:... |
3,517 | 4,185 | Phase transition in the family of p-resistances
Morteza Alamgir
Max Planck Institute for Intelligent Systems
T?ubingen, Germany
morteza@tuebingen.mpg.de
Ulrike von Luxburg
Max Planck Institute for Intelligent Systems
T?ubingen, Germany
ulrike.luxburg@tuebingen.mpg.de
Abstract
We study the family of p-resistances on g... | 4185 |@word version:2 polynomial:1 norm:5 simulation:1 decomposition:1 p0:4 commute:2 reduction:1 contains:4 series:1 sherali:1 current:2 comparing:1 yet:1 informative:2 plot:1 fewer:1 leaf:1 short:1 math:1 node:2 org:2 along:3 c2:3 symposium:1 prove:5 shorthand:1 consists:1 interscience:1 inside:1 introduce:2 indeed:1... |
3,518 | 4,186 | Maximum Covariance Unfolding:
Manifold Learning for Bimodal Data
Vijay Mahadevan
Department of ECE
University of California, San Diego
La Jolla, CA 92093
vmahadev@ucsd.edu
Chi Wah Wong
Department of Radiology
University of California, San Diego
La Jolla, CA 92093
cwwong@ieee.org
Jose Costa Pereira
Department of ECE
... | 4186 |@word trial:1 version:2 cingulate:1 pcc:3 mri:1 d2:3 covariance:8 decomposition:2 u11:1 q1:4 mention:1 tr:5 reduction:9 liu:2 series:3 score:2 document:2 outperforms:1 existing:1 subjective:1 recovered:1 sosa:1 activation:2 written:4 realize:1 concatenate:2 oxygenation:1 plot:3 stationary:1 selected:1 metabolism:... |
3,519 | 4,187 | Crowdclustering
Ryan Gomes?
Caltech
Peter Welinder
Caltech
Andreas Krause
ETH Zurich & Caltech
Pietro Perona
Caltech
Abstract
Is it possible to crowdsource categorization? Amongst the challenges: (a) each
worker has only a partial view of the data, (b) different workers may have different clustering criteria and m... | 4187 |@word open:5 instruction:1 d2:9 tamayo:1 seek:1 covariance:2 tr:1 initial:1 series:1 tuned:1 undiscovered:1 outperforms:3 existing:5 recovered:1 contextual:2 comparing:1 yet:1 must:4 realistic:1 partition:4 informative:1 remove:2 treating:1 update:5 resampling:1 alone:1 intelligence:1 cue:1 generative:1 item:38 s... |
3,520 | 4,188 | Solving Decision Problems with Limited Information
Cassio P. de Campos
IDSIA
Manno, CH 6928
cassio@idsia.ch
Denis D. Mau?a
IDSIA
Manno, CH 6928
denis@idsia.ch
Abstract
We present a new algorithm for exactly solving decision-making problems represented as an influence diagram. We do not require the usual assumptions ... | 4188 |@word cylindrical:1 eliminating:1 polynomial:2 termination:2 d2:1 concise:1 incurs:1 minus:2 recursively:1 initial:1 configuration:1 contains:5 exclusively:1 o2:1 current:1 com:1 surprising:1 must:1 additive:2 remove:1 plot:1 greedy:1 selected:1 device:1 intelligence:3 node:19 denis:2 preference:1 zhang:1 c2:1 di... |
3,521 | 4,189 | Joint 3D Estimation of Objects and Scene Layout
Andreas Geiger
Karlsruhe Institute of Technology
Christian Wojek
MPI Saarbr?ucken
Raquel Urtasun
TTI Chicago
geiger@kit.edu
cwojek@mpi-inf.mpg.de
rurtasun@ttic.edu
Abstract
We propose a novel generative model that is able to reason jointly about the 3D
scene layout... | 4189 |@word mild:1 kohli:1 version:2 middle:1 achievable:1 grey:1 tried:1 covariance:3 accounting:1 pick:1 textonboost:1 harder:1 initial:1 configuration:1 contains:2 score:3 hoiem:3 denoting:1 ours:4 interestingly:1 outperforms:3 existing:3 past:1 si:2 reminiscent:1 parsing:1 chicago:1 concatenate:1 informative:1 shap... |
3,522 | 419 | Transforming Neural-Net Output Levels
to Probability Distributions
John S. Denker and Yann leCun
AT&T Bell Laboratories
Holmdel, NJ 07733
Abstract
(1) The outputs of a typical multi-output classification network do not
satisfy the axioms of probability; probabilities should be positive and sum
to one. This problem ca... | 419 |@word advantageous:1 duda:3 tried:1 harder:1 moment:4 configuration:1 contains:2 ala:1 surprising:1 activation:2 scatter:5 must:1 john:2 additive:1 shape:3 treating:1 plot:5 guess:2 item:1 plane:2 vanishing:1 provides:1 location:1 successive:1 mathematical:1 surprised:1 combine:1 nor:1 multi:2 brain:1 actual:2 cur... |
3,523 | 4,190 | Active Learning with a Drifting Distribution
Liu Yang
Machine Learning Department
Carnegie Mellon University
liuy@cs.cmu.edu
Abstract
We study the problem of active learning in a stream-based setting, allowing the
distribution of the examples to change over time. We prove upper bounds on
the number of prediction mist... | 4190 |@word version:3 achievable:2 stronger:1 seems:1 dekel:1 open:2 d2:1 boundedness:2 initial:1 liu:1 contains:1 series:1 existing:1 must:2 subsequent:1 realistic:1 benign:2 atlas:1 discrimination:1 fewer:1 warmuth:1 core:1 num:1 completeness:1 coarse:1 along:1 persistent:1 prove:5 expected:22 behavior:2 p1:2 examine... |
3,524 | 4,191 | Adaptive Hedge
?
Peter Grunwald
Tim van Erven
Department of Mathematics
VU University
De Boelelaan 1081a
1081 HV Amsterdam, the Netherlands
tim@timvanerven.nl
Centrum Wiskunde & Informatica (CWI)
Science Park 123, P.O. Box 94079
1090 GB Amsterdam, the Netherlands
pdg@cwi.nl
Wouter M. Koolen
CWI and Department of Co... | 4191 |@word version:3 seems:1 open:1 simulation:7 crucially:1 incurs:2 united:1 tuned:3 erven:1 existing:3 current:1 comparing:1 surprising:2 assigning:1 must:1 visible:1 plot:1 designed:1 fewer:1 warmuth:3 provides:2 boosting:1 org:1 become:1 prove:1 introduce:2 excellence:1 ra:1 indeed:1 expected:2 behavior:1 owski:1... |
3,525 | 4,192 | A Denoising View of Matrix Completion
? Carreira-Perpin?
? an
Weiran Wang
Miguel A.
EECS, University of California, Merced
Zhengdong Lu
Microsoft Research Asia, Beijing
http://eecs.ucmerced.edu
zhengdol@microsoft.com
Abstract
In matrix completion, we are given a matrix where the values of only some of the
entries a... | 4192 |@word version:2 proportion:5 perpin:1 covariance:2 ality:1 decomposition:1 pick:1 harder:1 carry:1 initial:4 celebrated:1 contains:2 seriously:1 ours:1 existing:1 com:1 wd:2 reminiscent:2 must:1 john:3 mesh:1 subsequent:1 numerical:1 remove:2 plot:2 update:5 stationary:3 leaf:1 selected:3 item:1 epanechnikov:1 lo... |
3,526 | 4,193 | Multi-View Learning of Word Embeddings via CCA
Paramveer S. Dhillon
Dean Foster
Lyle Ungar
Computer & Information Science
Statistics
Computer & Information Science
University of Pennsylvania, Philadelphia, PA, U.S.A
{dhillon|ungar}@cis.upenn.edu, foster@wharton.upenn.edu
Abstract
Recently, there has been substantial ... | 4193 |@word multitask:1 bigram:1 norm:1 decomposition:2 covariance:3 xtest:1 tr:1 reduction:6 contains:1 score:5 tuned:2 document:1 prefix:2 past:4 existing:1 current:9 com:1 chicago:1 informative:1 remove:1 plot:1 generative:1 greedy:1 ihr:1 data2:1 eigenfeatures:1 core:4 short:3 lr:48 institution:1 provides:2 lexicon... |
3,527 | 4,194 | Hierarchical Multitask Structured Output Learning
for Large-Scale Sequence Segmentation
Nico G?ornitz1
Technical University Berlin,
Franklinstr. 28/29, 10587 Berlin, Germany
Nico.Goernitz@tu-berlin.de
Christian Widmer1
FML of the Max Planck Society
Spemannstr. 39, 72070 T?ubingen, Germany
Christian.Widmer@tue.mpg.de
... | 4194 |@word multitask:17 nd:1 suitably:1 mers:2 jacob:2 contains:4 fragment:1 score:5 series:1 kahles:2 genetic:1 outperforms:1 existing:1 current:3 com:2 nt:1 gmail:1 interrupted:1 numerical:1 distant:1 confirming:1 hofmann:2 christian:2 remove:1 update:1 v:1 intelligence:1 leaf:3 selected:2 accordingly:1 plane:11 mcc... |
3,528 | 4,195 | A Two-Stage Weighting Framework for Multi-Source
Domain Adaptation
Qian Sun? , Rita Chattopadhyay?, Sethuraman Panchanathan, Jieping Ye
Computer Science and Engineering, Arizona State University, AZ 85287
{Qian Sun, rchattop, panch, Jieping.Ye}@asu.edu
Abstract
Discriminative learning when training and test data belo... | 4195 |@word h:3 repository:1 d2:6 blender:1 reduction:1 electronics:3 contains:1 document:4 past:1 existing:7 outperforms:2 nt:4 surprising:1 si:2 readily:1 chicago:1 kdd:3 enables:1 motor:1 drop:1 sponsored:1 n0:1 intelligence:2 asu:1 device:1 selected:1 weighing:1 sys:2 chua:1 mental:1 boosting:1 location:1 mcdiarmid... |
3,529 | 4,196 | Sparse Features for PCA-Like Linear Regression
Petros Drineas
Computer Science Department
Rensselaer Polytechnic Institute
Troy, NY 12180
drinep@cs.rpi.edu
Christos Boutsidis
Mathematical Sciences Department
IBM T. J. Watson Research Center
Yorktown Heights, New York
cboutsi@us.ibm.com
Malik Magdon-Ismail
Computer S... | 4196 |@word trial:2 cu:2 repository:1 madelon:1 polynomial:2 norm:10 loading:1 nd:1 open:1 seek:4 decomposition:4 elisseeff:1 pick:1 thereby:1 contains:5 selecting:1 ours:3 spambase:1 existing:1 ka:1 com:1 rpi:3 must:1 additive:1 numerical:2 benign:1 update:1 greedy:5 selected:2 kyk:1 xk:15 propack:4 eigenfeatures:17 i... |
3,530 | 4,197 | Inverting Grice?s Maxims to Learn Rules from
Natural Language Extractions
Mohammad Shahed Sorower, Thomas G. Dietterich, Janardhan Rao Doppa
Walker Orr, Prasad Tadepalli, and Xiaoli Fern
School of Electrical Engineering and Computer Science
Oregon State University
Corvallis, OR 97331
{sorower,tgd,doppa,orr,tadepall,xf... | 4197 |@word kong:1 exploitation:1 version:1 proportion:2 tadepalli:2 twelfth:1 open:1 seek:2 prasad:1 concise:3 mention:66 initial:1 born:3 contains:1 score:3 configuration:2 seriously:2 document:9 fa8750:1 current:1 com:1 nell:1 must:2 olive:1 john:1 subsequent:1 chicago:1 remove:1 treating:1 drop:1 generative:2 selec... |
3,531 | 4,198 | Approximating Semidefinite Programs in Sublinear
Time
Elad Hazan
Technion - Israel Institute of Technology
Haifa 32000 Israel
ehazan@ie.technion.ac.il
Dan Garber
Technion - Israel Institute of Technology
Haifa 32000 Israel
dangar@cs.technion.ac.il
Abstract
In recent years semidefinite optimization has become a tool o... | 4198 |@word polynomial:1 norm:6 q1:1 mention:1 tr:1 reduction:1 woodruff:2 denoting:2 hardy:1 past:1 current:1 attracted:1 john:1 additive:4 garud:2 remove:1 update:3 prohibitive:1 xk:4 ith:1 simpler:1 unbounded:1 direct:1 become:2 symposium:7 focs:2 prove:1 naor:1 dan:1 assaf:1 inside:1 excellence:1 x0:8 indeed:2 sdp:... |
3,532 | 4,199 | Advice Refinement in Knowledge-Based SVMs
Gautam Kunapuli
Univ. of Wisconsin-Madison
1300 University Avenue
Madison, WI 53705
kunapuli@wisc.edu
Richard Maclin
Univ. of Minnesota, Duluth
1114 Kirby Drive
Duluth, MN 55812
rmaclin@d.umn.edu
Jude W. Shavlik
Univ. of Wisconsin-Madison
1300 University Avenue
Madison, WI 5... | 4199 |@word repository:1 briefly:1 version:1 norm:2 termination:2 additively:1 pressure:2 contains:2 pub:2 genetic:2 document:1 current:4 comparing:1 incidence:2 toh:1 dx:3 must:1 written:1 refines:1 interpretable:4 farkas:1 alone:2 intelligence:3 fewer:1 selected:1 leaf:1 smith:2 provides:1 iterates:1 contribute:2 gau... |
3,533 | 42 | 632
STATIC AND DYNAMIC ERROR PROPAGATION
NETWORKS WITH APPLICATION TO SPEECH
CODING
A J Robinson, F Fallside
Cambridge University Engineering Department
Trumpington Street, Cambridge, England
Abstract
Error propagation nets have been shown to be able to learn a variety of tasks in
which a static input pattern is mappe... | 42 |@word version:3 compression:4 proportionality:1 pulse:1 propagate:1 jacob:1 fifteen:2 tr:2 series:1 itp:1 denoting:1 past:9 usillg:1 current:4 comparing:1 od:1 nt:1 activation:8 must:5 yep:1 written:1 cottrell:1 enables:1 remove:1 update:1 discrimination:1 half:2 ith:1 steepest:1 bup:1 node:1 firstly:1 five:1 diffe... |
3,534 | 420 | EVOLUTION AND LEARNING IN
NEURAL NETWORKS: THE NUMBER
AND DISTRIBUTION OF LEARNING
TRIALS AFFECT THE RATE OF
EVOLUTION
Ron Keesing and David G. Stork*
Ricoh California Research Center
2882 Sand Hill Road Suite 115
Menlo Park, CA 94025
stork@crc.ricoh.com
and
*Dept. of Electrical Engineering
Stanford University
Stanfo... | 420 |@word trial:3 simulation:4 pressure:3 initial:5 score:3 genetic:12 com:1 nowlan:2 alone:4 selected:3 iso:4 coarse:1 ron:1 five:6 along:1 replication:1 consists:1 hague:1 little:3 precursor:1 moreover:1 evolved:1 psych:1 suite:1 every:4 unit:8 appear:1 before:1 engineering:1 local:3 offspring:1 despite:1 black:1 mi... |
3,535 | 4,200 | Unifying Framework for Fast Learning Rate of
Non-Sparse Multiple Kernel Learning
Taiji Suzuki
Department of Mathematical Informatics
The University of Tokyo
Tokyo 113-8656, Japan
s-taiji@stat.t.u-tokyo.ac.jp
Abstract
In this paper, we give a new generalization error bound of Multiple Kernel Learning (MKL) for a gener... | 4200 |@word version:1 polynomial:1 seems:1 norm:46 advantageous:1 c0:2 km:5 decomposition:1 q1:1 boundedness:1 series:1 rkhs:6 interestingly:1 bhattacharyya:1 outperforms:2 existing:7 sharpley:1 recovered:1 comparing:1 scovel:1 numerical:3 additive:2 device:1 rp1:2 isotropic:6 characterization:1 simpler:1 unbounded:2 m... |
3,536 | 4,201 | A Pylon Model for Semantic Segmentation
Victor Lempitsky
Andrea Vedaldi
Andrew Zisserman
Visual Geometry Group, University of Oxford?
{vilem,vedaldi,az}@robots.ox.ac.uk
Abstract
Graph cut optimization is one of the standard workhorses of image segmentation since for
binary random field representations of the image, i... | 4201 |@word kohli:2 briefly:1 middle:1 polynomial:2 interleave:1 achievable:1 open:1 grey:1 tried:1 rgb:1 accounting:1 textonboost:1 shading:1 substitution:2 contains:3 series:1 selecting:2 hoiem:1 reynolds:2 existing:2 current:3 comparing:1 si:14 assigning:1 olive:1 additive:2 partition:1 shape:4 hofmann:1 grass:2 alo... |
3,537 | 4,202 | On U -processes and clustering performance
St?ephan Cl?emenc?on?
LTCI UMR Telecom ParisTech/CNRS No. 5141
Institut Telecom, Paris, 75634 Cedex 13, France
stephan.clemencon@telecom-paristech.fr
Abstract
Many clustering techniques aim at optimizing empirical criteria that are of the
form of a U -statistic of degree two... | 4202 |@word mild:1 briefly:1 norm:4 underline:1 nd:1 d2:1 seek:1 crucially:1 bn:16 decomposition:2 euclidian:2 carry:1 moment:4 celebrated:2 selecting:1 denoting:2 scatter:3 dx:7 subsequent:3 partition:25 resampling:1 intelligence:1 selected:1 ck2:4 provides:1 math:1 location:1 zhang:1 mathematical:2 walther:1 introduc... |
3,538 | 4,203 | Greedy Model Averaging
Dong Dai
Department of Statistics Rutgers University, New Jersey, 08816
dongdai916@gmail.com
Tong Zhang
Department of Statistics, Rutgers University, New Jersey, 08816
tzhang@stat.rutgers.edu
Abstract
This paper considers the problem of combining multiple models to achieve a
prediction accuracy... | 4203 |@word version:3 bsm:3 reduction:2 tuned:3 existing:1 current:2 com:1 ka:1 surprising:1 si:1 gmail:1 informative:1 juditsky:1 greedy:14 simpler:1 zhang:1 five:2 replication:2 prove:2 combine:2 theoretically:2 indeed:1 decreasing:2 becomes:2 provided:1 notation:1 moreover:7 competes:2 argmin:3 supplemental:5 unobse... |
3,539 | 4,204 | Dynamic Pooling and Unfolding Recursive
Autoencoders for Paraphrase Detection
Richard Socher, Eric H. Huang, Jeffrey Pennington? , Andrew Y. Ng, Christopher D. Manning
Computer Science Department, Stanford University, Stanford, CA 94305, USA
?
SLAC National Accelerator Laboratory, Stanford University, Stanford, CA 943... | 4204 |@word multitask:1 trial:2 merrill:2 briefly:1 norm:2 open:1 tried:1 recursively:6 initial:4 contains:1 ours:1 outperforms:1 counterterrorism:1 current:1 wd:7 recovered:1 comparing:2 activation:2 bd:3 parsing:5 subsequent:2 partition:2 interannotator:1 malaysia:2 designed:1 intelligence:2 leaf:7 selected:1 plane:1... |
3,540 | 4,205 | Emergence of Multiplication in a Biophysical Model
of a Wide-Field Visual Neuron for Computing Object
Approaches: Dynamics, Peaks, & Fits
Matthias S. Keil?
Department of Basic Psychology
University of Barcelona
E-08035 Barcelona, Spain
matskeil@ub.edu
Abstract
Many species show avoidance reactions in response to loomi... | 4205 |@word neurophysiology:11 trial:2 version:4 seems:1 norm:6 simulation:3 tr:1 carry:1 moment:2 rind:7 configuration:1 series:1 exclusively:1 disparity:2 initial:2 reaction:1 current:1 comparing:1 discretization:4 adj:2 activation:1 guez:3 rizzolatti:1 numerical:2 shape:3 motor:1 plot:1 drop:1 implying:1 half:4 cue:... |
3,541 | 4,206 | History distribution matching method for predicting
effectiveness of HIV combination therapies
Jasmina Bogojeska
Max-Planck Institute for Computer Science
Campus E1 4
66123 Saarbr?ucken, Germany
jasmina@mpi-inf.mpg.de
Abstract
This paper presents an approach that predicts the effectiveness of HIV combination therapie... | 4206 |@word version:4 covariance:1 thereby:1 carry:2 liu:1 contains:3 score:5 selecting:4 genetic:1 suppressing:1 past:4 outperforms:2 current:6 comparing:2 virus:5 montaner:2 tackling:1 yet:1 john:1 stemming:2 distant:2 partition:1 realistic:1 enables:1 treating:3 update:1 fewer:1 schapiro:1 short:1 contribute:1 simpl... |
3,542 | 4,207 | Variance Penalizing AdaBoost
Tony Jebara
Department of Compter Science
Columbia University, New York NY
jebara@cs.columbia.edu
Pannagadatta K. Shivaswamy
Department of Computer Science
Cornell University, Ithaca NY
pannaga@cs.cornell.edu
Abstract
This paper proposes a novel boosting algorithm called VadaBoost which ... | 4207 |@word briefly:1 seems:1 open:1 termination:2 incurs:1 versatile:1 didate:1 inefficiency:1 selecting:1 wj2:5 denoting:1 spambase:2 outperforms:1 current:2 yet:1 mushroom:2 written:1 reminiscent:1 plot:3 drop:1 update:2 v:1 ith:1 boosting:12 unbounded:1 roughly:1 behavior:1 multi:1 actual:4 enumeration:5 considerin... |
3,543 | 4,208 | Spectral Methods for
Learning Multivariate Latent Tree Structure
Animashree Anandkumar
UC Irvine
Kamalika Chaudhuri
UC San Diego
Daniel Hsu
Microsoft Research
a.anandkumar@uci.edu
kamalika@cs.ucsd.edu
dahsu@microsoft.com
Sham M. Kakade
Microsoft Research &
University of Pennsylvania
Le Song
Carnegie Mellon Unive... | 4208 |@word polynomial:2 norm:3 tarsus:1 thereby:1 tr:2 moment:9 initial:1 configuration:2 liu:1 exclusively:1 daniel:1 genetic:1 tuned:1 current:3 com:2 z2:32 comparing:1 tackling:1 john:1 subsequent:1 additive:1 partition:1 remove:2 fund:1 implying:1 greedy:1 leaf:12 intelligence:2 isotropic:1 core:2 short:1 fa9550:1... |
3,544 | 4,209 | Learning Higher-Order Graph Structure with
Features by Structure Penalty
Shilin Ding1?, Grace Wahba1,2,3? , and Xiaojin Zhu2?
Department of { Statistics, 2 Computer Sciences, 3 Biostatistics and Medical Informatics}
University of Wisconsin-Madison, WI 53705
{sding, wahba}@stat.wisc.edu, jerryzhu@cs.wisc.edu
1
Abstrac... | 4209 |@word norm:5 c0:1 simulation:2 covariance:2 jacob:3 attainable:1 liu:3 born:1 score:1 murder:1 series:1 rkhs:3 outperforms:1 bradley:2 elliptical:1 recovered:4 com:1 john:1 partition:1 remove:2 designed:3 ugms:6 greedy:9 selected:2 intelligence:1 cook:1 parameterization:3 fpr:3 ith:1 node:22 firstly:1 constructed... |
3,545 | 421 | Analog Computation at a Critical Point: A Novel
Function for Neuronal Oscillations?
Leonid Kruglyak and Willianl Bialek
Depart.ment of Physics
University of California at Berkeley
Berkeley, California 94720
and NEC Research Institute?
4 Independence vVay
Princeton, New Jersey 08540
Abstract
\Ve show that a simple spi... | 421 |@word simulation:6 excited:2 emperature:1 thereby:1 shot:1 tice:1 current:6 erms:1 must:1 john:1 fn:2 realistic:3 numerical:2 i1l:1 interspike:1 shape:2 drop:1 fund:1 v:2 hamiltonian:5 short:2 filtered:3 correlat:4 mathematical:1 direct:1 profound:1 cray:1 poised:1 indeed:2 expected:2 behavior:1 mechanic:2 td:1 ac... |
3,546 | 4,210 | Learning Patient-Specific Cancer Survival
Distributions as a Sequence of Dependent Regressors
Chun-Nam Yu, Russell Greiner, Hsiu-Chin Lin
Department of Computing Science
University of Alberta
Edmonton, AB T6G 2E8
Vickie Baracos
Department of Oncology
University of Alberta
Edmonton, AB T6G 1Z2
{chunnam,rgreiner,hsiuc... | 4210 |@word multitask:1 cox:36 middle:3 innovates:1 proportion:3 prognostic:5 norm:1 sex:1 hu:1 steck:1 tried:1 forecaster:1 creatinine:1 pick:1 gamerman:1 initial:1 series:5 score:3 contains:2 current:1 z2:1 si:2 yet:1 chu:1 additive:2 subsequent:2 kdd:1 shape:2 designed:1 drop:1 plot:3 update:2 selected:1 device:1 mc... |
3,547 | 4,211 | Modelling Genetic Variations with
Fragmentation-Coagulation Processes
Yee Whye Teh, Charles Blundell and Lloyd T. Elliott
Gatsby Computational Neuroscience Unit, UCL
17 Queen Square, London WC1N 3AR, United Kingdom
{ywteh,c.blundell,elliott}@gatsby.ucl.ac.uk
Abstract
We propose a novel class of Bayesian nonparametric... | 4211 |@word version:1 middle:2 proportion:4 mjp:7 open:1 multipoint:1 essay:1 simulation:1 simplifying:1 incurs:1 accommodate:1 initial:4 series:5 fragment:1 united:1 zij:2 initialisation:1 ecole:1 genetic:13 document:1 outperforms:1 existing:8 current:3 subsequent:9 partition:28 treating:1 update:1 v:1 stationary:4 ge... |
3,548 | 4,212 | Fast and Balanced: Efficient Label Tree Learning for
Large Scale Object Recognition
Jia Deng1,2 , Sanjeev Satheesh1 , Alexander C. Berg3 , Li Fei-Fei1
Computer Science Department, Stanford University1
Computer Science Department, Princeton University2
Computer Science Department, Stony Brook University3
Abstract
We p... | 4212 |@word repository:1 briefly:2 eliminating:1 polynomial:2 willing:1 seek:2 pick:3 sgd:6 recursively:3 score:1 ours:4 outperforms:1 current:3 beygelzimer:2 stony:1 must:1 john:1 visible:1 partition:16 enables:1 update:3 v:7 intelligence:1 prohibitive:1 leaf:7 fewer:1 filtered:1 node:34 codebook:1 org:1 simpler:1 zha... |
3,549 | 4,213 | ShareBoost: Efficient Multiclass Learning with
Feature Sharing
Shai Shalev-Shwartz?
Yonatan Wexler?
Amnon Shashua?
Abstract
Multiclass prediction is the problem of classifying an object into a relevant target
class. We consider the problem of learning a multiclass predictor that uses only
few features, and in partic... | 4213 |@word mild:4 deformed:2 version:5 polynomial:1 norm:28 advantageous:2 stronger:2 seems:1 duda:1 turlach:1 wexler:1 mention:1 solid:1 reduction:1 initial:1 series:1 score:2 minw2rk:1 contains:1 selecting:1 document:5 outperforms:1 current:2 comparing:2 com:1 tackling:2 must:1 written:1 john:1 additive:2 enables:1 ... |
3,550 | 4,214 | Estimating time-varying input signals and ion
channel states from a single voltage trace of a neuron
Ryota Kobayashi?
Department of Human and Computer Intelligence, Ritsumeikan University
Siga 525-8577, Japan
kobayashi@cns.ci.ritsumei.ac.jp
Yasuhiro Tsubo
Laboratory for Neural Circuit Theory, Brain Science Institute, R... | 4214 |@word nd:1 open:4 simplifying:1 eld:1 series:1 denoting:1 wako:1 current:11 wd:1 ka:1 si:2 bd:1 written:1 additive:1 realistic:2 numerical:2 s21:1 motor:1 alone:2 intelligence:1 selected:1 advancement:1 accordingly:1 ith:4 smith:1 funahashi:1 provides:2 math:1 mathematical:2 along:1 constructed:1 consists:1 manne... |
3,551 | 4,215 | Demixed Principal Component Analysis
Ranulfo Romo
Instituto de Fisiolog?a Celular
Universidad Nacional Aut?noma de M?xico
Mexico City, Mexico
Wieland Brendel
Ecole Normale Sup?rieure, Paris, France
Champalimaud Neuroscience Programme
Lisbon, Portugal
Christian K. Machens
Ecole Normale Sup?rieure, Paris, France
Champ... | 4215 |@word trial:1 middle:2 norm:2 seek:2 covariance:13 decomposition:2 thereby:3 tr:5 carry:1 reduction:5 ndez:1 series:1 ecole:2 z2:1 noma:1 yet:2 written:2 informative:1 kdd:1 christian:1 drop:1 plot:3 update:1 discrimination:1 leaf:1 complementing:1 accordingly:1 isotropic:1 provides:1 dn:1 constructed:2 along:6 p... |
3,552 | 4,216 | Optimal learning rates for least squares SVMs using
Gaussian kernels
M. Eberts, I. Steinwart
Institute for Stochastics and Applications
University of Stuttgart
D-70569 Stuttgart
{eberts,ingo.steinwart}@mathematik.uni-stuttgart.de
Abstract
We prove a new oracle inequality for support vector machines with Gaussian RBF
k... | 4216 |@word version:3 polynomial:1 norm:3 seems:2 nd:3 open:1 d2:21 q1:1 minmax:4 neeman:1 rkhs:7 ours:1 scovel:2 lorentz:1 analytic:2 n0:1 beginning:1 math:6 mathematical:1 c2:6 differential:1 prove:3 consists:1 introduce:1 x0:2 indeed:1 expected:1 behavior:1 multi:2 cardinality:1 begin:1 estimating:1 bounded:9 moreov... |
3,553 | 4,217 | Reinforcement Learning using Kernel-Based
Stochastic Factorization
Andr?e M. S. Barreto
School of Computer Science
McGill University
Montreal, Canada
amsb@cs.mcgill.ca
Doina Precup
School of Computer Science
McGill University
Montreal, Canada
dprecup@cs.mcgill.ca
Joelle Pineau
School of Computer Science
McGill Univer... | 4217 |@word neurophysiology:1 version:6 briefly:1 polynomial:1 seems:1 norm:1 compression:1 hippocampus:1 pulse:3 decomposition:3 homomorphism:4 pick:1 harder:2 reduction:3 contains:1 series:1 outperforms:1 ka:9 current:1 surprising:2 si:11 must:3 john:2 numerical:1 sorg:3 designed:1 plot:1 update:3 stationary:1 greedy... |
3,554 | 4,218 | Minimax Localization of Structural Information in
Large Noisy Matrices
Mladen Kolar??
mladenk@cs.cmu.edu
Sivaraman Balakrishnan??
sbalakri@cs.cmu.edu
Alessandro Rinaldo??
arinaldo@stat.cmu.edu
Aarti Singh?
aarti@cs.cmu.edu
?
School of Computer Science and ?? Department of Statistics, Carnegie Mellon University
Abs... | 4218 |@word stronger:1 seems:2 nd:1 open:2 hu:1 simulation:3 decomposition:8 covariance:5 tr:5 harder:1 bai:1 liu:2 contains:1 dspca:1 score:4 series:1 outperforms:1 existing:2 comparing:3 surprising:2 activation:2 attracted:1 john:1 additive:1 numerical:1 plot:1 discovering:1 huo:1 fa9550:1 characterization:2 detectin... |
3,555 | 4,219 | Automated Refinement of Bayes Networks?
Parameters based on Test Ordering Constraints
Omar Zia Khan & Pascal Poupart
David R. Cheriton School of Computer Science
University of Waterloo
Waterloo, ON Canada
{ozkhan,ppoupart}@cs.uwaterloo.ca
John Mark Agosta?
Intel Labs
Santa Clara, CA, USA
johnmark.agosta@gmail.com
Ab... | 4219 |@word trial:1 version:1 polynomial:3 confirms:1 carry:1 selecting:1 ours:1 bc:2 existing:1 current:3 com:1 discretization:1 comparing:1 surprising:1 clara:1 gmail:1 must:1 john:4 ronald:1 subsequent:1 informative:2 remove:2 implying:1 greedy:11 leaf:1 intelligence:8 scotland:1 accepting:1 provides:3 parameterizat... |
3,556 | 422 | A Model of Distributed Sensorimotor Control in
the Cockroach Escape Turn
R.D. Beer 1 ,2, G.J. Kacmarcik 1 , R.E. Ritzmann 2 and H.J. Chie1 2
Departments of lComputer Engineering and Science, and 2Biology
Case Western Reserve University
Cleveland, OR 44106
Abstract
In response to a puff of wind, the American cockroach... | 422 |@word neurophysiology:1 middle:2 proportion:2 retraining:1 initial:7 tuned:3 existing:1 current:1 yet:1 must:6 cottrell:1 thrust:1 plasticity:1 motor:6 intelligence:1 nervous:1 accordingly:1 compo:5 proprioceptor:2 sudden:1 caveat:1 attack:1 constructed:1 direct:1 rohrer:1 incorrect:2 consists:1 pathway:1 behavior... |
3,557 | 4,220 | Collective Graphical Models
Thomas G. Dietterich
Oregon State University
tgd@eecs.oregonstate.edu
Daniel Sheldon
Oregon State University
sheldon@eecs.oregonstate.edu
Abstract
There are many settings in which we wish to fit a model of the behavior of individuals but where our data consist only of aggregate informatio... | 4220 |@word trial:1 replicate:1 nd:21 d2:1 seek:1 crucially:1 decomposition:1 initial:1 configuration:13 series:1 selecting:1 daniel:2 ours:1 existing:1 qbe:1 current:1 nt:10 yet:3 must:11 readily:1 fn:7 realistic:1 partition:4 happen:1 plot:2 concert:1 update:2 n0:2 v:5 stationary:1 braz:1 instantiate:2 fewer:1 yr:9 a... |
3,558 | 4,221 | Additive Gaussian Processes
David Duvenaud
Department of Engineering
Cambridge University
dkd23@cam.ac.uk
Hannes Nickisch
MPI for Intelligent Systems
T?ubingen, Germany
hn@tue.mpg.de
Carl Edward Rasmussen
Department of Engineering
Cambridge University
cer54@cam.ac.uk
Abstract
We introduce a Gaussian process model of... | 4221 |@word polynomial:6 nd:4 suitably:1 d2:2 covariance:4 eng:1 decomposition:3 stitson:2 contains:1 efficacy:3 selecting:1 series:1 ka:1 z2:13 recovered:1 must:2 john:1 distant:2 additive:72 remove:1 plot:2 zik:1 intelligence:1 website:1 parameterization:3 provides:1 node:1 contribute:1 location:2 org:1 five:2 along:... |
3,559 | 4,222 | Universal low-rank matrix recovery
from Pauli measurements
Yi-Kai Liu
Applied and Computational Mathematics Division
National Institute of Standards and Technology
Gaithersburg, MD, USA
yi-kai.liu@nist.gov
Abstract
We study the problem of reconstructing an unknown matrix M of rank r and dimension d using O(rd poly log... | 4222 |@word seems:1 norm:50 nd:1 c0:6 stronger:4 open:1 km:13 d2:3 decomposition:1 commute:2 tr:7 outlook:1 carry:1 liu:5 kmk:1 recovered:1 ka:5 si:3 must:3 reminiscent:1 data2:1 short:1 certificate:2 math:5 contribute:1 simpler:1 c2:6 ik:1 consists:1 prove:6 combine:1 kraus:1 introduce:1 expected:2 roughly:1 p1:3 cand... |
3,560 | 4,223 | Committing Bandits
Loc Bui?
MS&E Department
Stanford University
Ramesh Johari?
MS&E Department
Stanford University
Shie Mannor?
EE Department
Technion
Abstract
We consider a multi-armed bandit problem where there are two phases. The first
phase is an experimentation phase where the decision maker is free to explore
... | 4223 |@word exploitation:3 briefly:1 version:3 achievable:1 unif:11 simulation:6 mention:1 initial:1 celebrated:1 loc:1 configuration:1 past:3 contextual:3 surprising:1 yet:1 chu:1 must:8 numerical:2 realistic:1 subsequent:1 predetermined:1 n0:2 greedy:1 eba:11 provides:1 mannor:4 revisited:1 zhang:1 c2:4 consists:1 in... |
3,561 | 4,224 | Learning person-object interactions for
action recognition in still images
Vincent Delaitre?
?
Ecole
Normale Sup?erieure
Josef Sivic*
INRIA Paris - Rocquencourt
Ivan Laptev*
INRIA Paris - Rocquencourt
Abstract
We investigate a discriminatively trained model of person-object interactions for
recognizing common human... | 4224 |@word cu:1 version:1 msr:1 dalal:1 stronger:1 seems:1 everingham:2 triggs:1 seek:1 covariance:2 q1:2 initial:1 configuration:13 contains:4 score:6 selecting:1 hoiem:2 jimenez:1 ecole:2 interestingly:1 outperforms:3 contextual:2 activation:2 rocquencourt:2 si:1 attracted:1 must:1 realistic:3 additive:1 informative... |
3,562 | 4,225 | Finite-Time Analysis of Strati?ed Sampling
for Monte Carlo
R?
emi Munos
INRIA Lille - Nord Europe
remi.munos@inria.fr
Alexandra Carpentier
INRIA Lille - Nord Europe
alexandra.carpentier@inria.fr
Abstract
We consider the problem of strati?ed sampling for Monte-Carlo integration.
We model this problem in a multi-armed ... | 4225 |@word trial:2 exploitation:4 proportion:2 seems:1 nd:2 open:1 simulation:3 crucially:1 simplifying:1 mention:1 reduction:2 disparity:7 ours:1 past:2 outperforms:1 current:2 com:1 wd:12 yet:1 numerical:4 informative:1 shape:2 enables:2 etor:11 plot:7 stationary:4 selected:1 cult:1 xk:4 beginning:1 characterization... |
3,563 | 4,226 | Semantic Labeling of 3D Point Clouds for
Indoor Scenes
Hema Swetha Koppula? , Abhishek Anand? , Thorsten Joachims, and Ashutosh Saxena
Department of Computer Science, Cornell University.
{hema,aa755,tj,asaxena}@cs.cornell.edu
Abstract
Inexpensive RGB-D cameras that give an RGB image together with depth data
have becom... | 4226 |@word illustrating:1 middle:2 version:1 printer:1 stronger:1 dalal:1 triggs:1 rgb:19 n8:4 electronics:1 configuration:7 contains:3 zij:24 hoiem:2 ours:1 past:1 contextual:8 si:4 scatter:3 written:1 partition:1 shape:37 enables:1 hofmann:1 designed:2 gist:1 ashutosh:1 drop:3 v:1 plot:1 cue:2 half:7 selected:1 plan... |
3,564 | 4,227 | PAC-Bayesian Analysis of Contextual Bandits
Yevgeny Seldin1,4 Peter Auer2 Franc?ois Laviolette3 John Shawe-Taylor4 Ronald Ortner2
1
Max Planck Institute for Intelligent Systems, T?ubingen, Germany
2
Chair for Information Technology, Montanuniversit?at Leoben, Austria
3
D?epartement d?informatique, Universit?e Laval, Qu... | 4227 |@word version:2 seems:1 decomposition:3 pick:2 moment:1 epartement:1 substitution:1 daniel:2 existing:4 current:1 contextual:6 nt:4 beygelzimer:5 yet:1 john:10 ronald:2 subsequent:1 shawetaylor:1 dive:1 enables:1 remove:1 update:2 joy:1 bart:1 intelligence:1 selected:1 greedy:1 amir:1 provides:7 allerton:1 org:2 ... |
3,565 | 4,228 | Probabilistic Joint Image Segmentation and Labeling?
Adrian Ion1,2 , Joao Carreira1, Cristian Sminchisescu1
Faculty of Mathematics and Natural Sciences, University of Bonn
PRIP, Vienna University of Technology & Institute of Science and Technology, Austria
1
2
{ion,carreira,cristian.sminchisescu}@ins.uni-bonn.de
Ab... | 4228 |@word kohli:2 version:1 faculty:1 achievable:1 everingham:1 adrian:1 decomposition:2 textonboost:1 offending:2 harder:1 configuration:16 cyclic:1 score:20 selecting:3 contains:3 hoiem:2 trainval:2 ours:1 existing:1 freitas:1 current:3 si:22 yet:4 assigning:1 parsing:1 additive:1 partition:21 visible:1 shape:1 ana... |
3,566 | 4,229 | Manifold Pr?ecis: An Annealing Technique for Diverse
Sampling of Manifolds
Nitesh Shroff ?, Pavan Turaga ?, Rama Chellappa ?
?Department of Electrical and Computer Engineering, University of Maryland, College Park
?School of Arts, Media, Engineering and ECEE, Arizona State University
{nshroff,rama}@umiacs.umd.edu, ptu... | 4229 |@word determinant:1 briefly:1 seems:1 norm:2 vldb:1 seitz:1 seek:3 covariance:1 decomposition:4 pick:23 thereby:1 accommodate:1 shot:1 hager:1 recursively:1 carry:1 liu:4 series:2 reduction:2 selecting:5 initial:1 tuned:1 document:6 current:1 yet:1 written:1 ecis:3 numerical:4 shape:30 analytic:3 plot:1 update:6 ... |
3,567 | 423 | The Tempo 2 Algorithm: Adjusting Time-Delays By
Supervised Learning
Ulrich Bodenhausen and Alex Waibel
School of Computer Science
Carnegie Mellon University
Pittsbwgh, PA 15213
Abstract
In this work we describe a new method that adjusts time-delays and the widths of
time-windows in artificial neural networks automati... | 423 |@word neurophysiology:1 simulation:3 solid:1 existing:4 activation:4 lang:2 predetermined:1 shape:1 enables:2 designed:1 cue:1 short:1 provides:2 along:1 behavior:1 brain:2 automatically:5 kamm:1 encouraging:1 window:41 kind:4 interpreted:1 temporal:10 fat:1 unit:26 positive:1 engineering:1 local:2 id:3 might:1 su... |
3,568 | 4,230 | Confidence Sets for Network Structure
Patrick Wolfe
School of Engineering and Applied Sciences
Harvard University
Cambridge, MA 02138
patrick@seas.harvard.edu
David S. Choi
School of Engineering and Applied Sciences
Harvard University
Cambridge, MA 02138
dchoi@seas.harvard.edu
Edoardo M. Airoldi
Department of Statist... | 4230 |@word trial:2 version:3 proportion:3 stronger:1 open:2 seek:1 simulation:4 p0:4 fifteen:1 solid:1 score:1 united:1 pub:1 denoting:1 longitudinal:3 recovered:1 comparing:2 current:1 yet:1 additive:1 partition:16 visible:1 enables:2 parameterization:2 inspection:1 undertook:1 blei:1 provides:2 math:2 node:10 prefer... |
3,569 | 4,231 | Sparse recovery by thresholded
non-negative least squares
Martin Slawski and Matthias Hein
Department of Computer Science
Saarland University
Campus E 1.1, Saarbr?ucken, Germany
{ms,hein}@cs.uni-saarland.de
Abstract
Non-negative data are commonly encountered in numerous fields, making nonnegative least squares regres... | 4231 |@word norm:4 seems:1 bf:3 r:6 covariance:2 pick:1 solid:1 substitution:1 series:4 contains:1 selecting:1 configuration:2 denoting:2 outperforms:1 recovered:1 comparing:1 yet:1 intriguing:1 subsequent:1 numerical:2 realistic:1 plot:1 maxv:1 v:1 alone:1 greedy:1 xk:1 equi:2 readability:1 allerton:1 org:1 zhang:2 sa... |
3,570 | 4,232 | Complexity of Inference in Latent Dirichlet
Allocation
David Sontag
New York University?
Daniel M. Roy
University of Cambridge
Abstract
We consider the computational complexity of probabilistic inference in Latent Dirichlet Allocation (LDA). First, we study the problem of finding
the maximum a posteriori (MAP) assign... | 4232 |@word cu:6 briefly:1 polynomial:8 open:2 eng:1 reduction:8 configuration:1 contains:1 daniel:1 document:32 existing:1 comparing:1 nt:15 si:1 dx:1 must:2 additive:1 realistic:1 partition:4 plot:1 ainen:1 update:1 greedy:3 intelligence:1 warmuth:2 mccallum:2 beginning:1 ith:1 blei:7 characterization:1 provides:2 pa... |
3,571 | 4,233 | 1
INTRODUCTION
1
Video Annotation and Tracking with Active Learning
Carl Vondrick
UC Irvine
Deva Ramanan
UC Irvine
vondrick@mit.edu
dramanan@ics.uci.edu
Abstract
We introduce a novel active learning framework for video annotation. By judiciously choosing which frames a user should annotate, we can obtain highly... | 4233 |@word dalal:1 polynomial:1 c0:1 triggs:1 seitz:1 bn:3 simplifying:2 rgb:4 covariance:1 citeseer:1 pick:2 kristjansson:1 hsieh:1 minus:1 liblinear:2 initial:8 liu:2 score:4 exclusively:1 selecting:1 existing:1 err:9 current:3 beygelzimer:1 must:2 readily:1 visible:1 blur:1 shape:1 v:1 stationary:5 intelligence:2 r... |
3,572 | 4,234 | The Impact of Unlabeled Patterns in Rademacher
Complexity Theory for Kernel Classifiers
Davide Anguita, Alessandro Ghio, Luca Oneto, Sandro Ridella
Department of Biophysical and Electronic Engineering
University of Genova
Via Opera Pia 11A, I-16145 Genova, Italy
{Davide.Anguita,Alessandro.Ghio} @unige.it
{Luca.Oneto,S... | 4234 |@word trial:1 version:4 open:1 elisseeff:1 reduction:1 selecting:9 chervonenkis:1 recovered:1 comparing:1 assigning:2 must:2 v:2 selected:4 haykin:1 oneto:4 mcdiarmid:3 five:1 unbounded:1 x1l:1 c2:2 rnl:3 scholkopf:1 consists:3 expected:6 behavior:1 inspired:2 actual:1 ivanov:2 cardinality:4 increasing:1 solver:1... |
3,573 | 4,235 | Penalty Decomposition Methods for Rank
Minimization ?
Zhaosong Lu ?
Yong Zhang ?
Abstract
In this paper we consider general rank minimization problems with rank appearing in either objective function or constraint. We first show that a class of matrix
optimization problems can be solved as lower dimensional vector op... | 4235 |@word milenkovic:1 version:7 norm:18 suitably:3 termination:2 km:1 decomposition:23 eld:1 tr:2 inpainting:4 reduction:4 liu:2 series:1 bc:2 outperforms:2 existing:4 err:1 current:3 recovered:5 optim:1 si:2 toh:2 written:1 numerical:6 shape:2 stationary:4 half:1 ith:2 math:4 location:1 successive:1 zhang:6 enterpr... |
3,574 | 4,236 | Image Parsing via Stochastic Scene Grammar
Yibiao Zhao?
Department of Statistics
University of California, Los Angeles
Los Angeles, CA 90095
ybzhao@ucla.edu
Song-Chun Zhu
Department of Statistics and Computer Science
University of California, Los Angeles
Los Angeles, CA 90095
sczhu@stat.ucla.edu
Abstract
This paper ... | 4236 |@word middle:1 decomposition:2 textonboost:1 bai:1 configuration:10 contains:2 liu:1 initial:1 hoiem:6 outperforms:1 existing:2 current:3 contextual:20 parsing:11 partition:1 hofmann:1 v:1 generative:5 greedy:1 mccallum:1 hinged:7 filtered:1 detecting:3 node:19 revisited:1 successive:1 preference:1 become:2 corri... |
3,575 | 4,237 | Query-Aware MCMC
Andrew McCallum
Department of Computer Science
University of Massachusetts
Amherst, MA
mccallum@cs.umass.edu
Michael Wick
Department of Computer Science
University of Massachusetts
Amherst, MA
mwick@cs.umass.edu
Abstract
Traditional approaches to probabilistic inference such as loopy belief propagat... | 4237 |@word repository:1 kmh:2 pw:3 polynomial:2 proportion:1 norm:3 adnan:1 adrian:1 km:1 vldb:3 simulation:1 pick:1 dramatic:2 initial:4 configuration:1 uma:2 score:7 ktv:8 exclusively:2 selecting:1 document:1 franklin:1 miklau:2 outperforms:1 existing:2 current:3 si:1 yet:1 danny:1 must:1 conjunctive:1 partition:1 e... |
3,576 | 4,238 | Clustering via Dirichlet Process Mixture Models for
Portable Skill Discovery
Scott Niekum
Andrew G. Barto
Department of Computer Science
University of Massachusetts Amherst
Amherst, MA 01003
{sniekum,barto}@cs.umass.edu
Abstract
Skill discovery algorithms in reinforcement learning typically identify single
states or ... | 4238 |@word h:1 trial:1 version:1 seems:1 advantageous:1 tadepalli:1 termination:38 confirms:1 simulation:1 prasad:1 rgb:1 covariance:1 decomposition:1 initial:1 configuration:2 series:2 uma:1 inefficiency:1 contains:1 denoting:1 dpmms:1 o2:1 current:2 pickett:1 activation:1 must:6 written:2 shape:1 enables:1 designed:... |
3,577 | 4,239 | Submodular Multi-Label Learning
James Petterson
NICTA/ANU
Canberra, Australia
Tiberio Caetano
NICTA/ANU
Sydney/Canberra, Australia
Abstract
In this paper we present an algorithm to learn a multi-label classifier which
attempts at directly optimising the F -score. The key novelty of our formulation is that we explici... | 4239 |@word polynomial:2 norm:1 proportion:1 seek:1 initial:1 score:17 document:1 ours:1 current:1 yet:1 written:3 partition:1 informative:3 hofmann:1 enables:5 plot:4 alone:1 selected:2 mccallum:1 certificate:2 herbrich:1 zhang:2 direct:1 incorrect:2 consists:4 prove:1 ectively:3 fitting:1 focs:1 theoretically:1 excel... |
3,578 | 424 | Stochastic Neurodynamics
J.D. Cowan
Department of Mathematics, Committee on
Neurobiology, and Brain Research Institute,
The University of Chicago, 5734 S. Univ. Ave.,
Chicago, Illinois 60637
Abstract
The main point of this paper is that stochastic neural networks have a
mathematical structure that corresponds quite c... | 424 |@word effect:1 facility:1 physik:1 closely:2 symmetric:2 laboratory:1 pulse:1 stochastic:8 matsubara:1 during:1 d2a:1 eqns:1 mann:1 thank:1 moment:5 initial:5 configuration:2 contains:2 efficacy:2 generalization:1 liquid:1 lagrangians:4 investigation:1 probable:1 generalized:1 lapedes:1 im:1 schrodinger:1 complete... |
3,579 | 4,240 | Blending Autonomous Exploration and
Apprenticeship Learning
Thomas J. Walsh
Center for Educational
Testing and Evaluation
University of Kansas
Lawrence, KS 66045
twalsh@ku.edu
Daniel Hewlett
Clayton T. Morrison
School of Information:
Science, Technology and Arts
University of Arizona
Tucson, AZ 85721
{dhewlett@cs,cla... | 4240 |@word h:2 trial:7 exploitation:1 version:5 judgement:1 polynomial:13 seems:2 stronger:1 proportion:3 open:1 pieter:2 seek:1 simulation:3 diuk:1 pick:6 hunting:1 daniel:1 outperforms:1 existing:2 bradley:1 current:2 com:1 yet:1 conjunctive:2 must:8 subsequent:2 informative:1 designed:1 plot:5 update:2 drop:1 alone... |
3,580 | 4,241 | Robust Multi-Class Gaussian Process Classification
Daniel Hern?andez-Lobato
ICTEAM - Machine Learning Group
Universit?e catholique de Louvain
Place Sainte Barbe, 2
Louvain-La-Neuve, 1348, Belgium
danielhernandezlobato@gmail.com
Jos?e Miguel Hern?andez-Lobato
Department of Engineering
University of Cambridge
Trumpingto... | 4241 |@word oostenveld:1 repository:3 version:1 seems:1 adnan:1 confirms:1 eng:1 covariance:13 carry:2 contains:1 united:1 daniel:2 outperforms:2 existing:1 recovered:1 com:1 comparing:1 gmail:1 written:1 readily:1 must:1 additive:1 dupont:2 update:12 stationary:1 intelligence:3 selected:4 generative:1 data2:1 provides... |
3,581 | 4,242 | Sparse Bayesian Multi-Task Learning
C?edric Archambeau, Shengbo Guo, Onno Zoeter
Xerox Research Centre Europe
{Cedric.Archambeau, Shengbo.Guo, Onno.Zoeter}@xrce.xerox.com
Abstract
We propose a new sparse Bayesian model for multi-task regression and classification. The model is able to capture correlations between tas... | 4242 |@word multitask:4 middle:2 inversion:2 seems:1 open:1 integrative:1 seek:1 covariance:17 jacob:1 elisseeff:1 tr:4 edric:1 liu:1 contains:2 score:1 current:1 com:1 nt:1 luo:1 chu:1 attracted:1 numerical:2 xrce:1 enables:2 dupont:1 update:9 discrimination:1 selected:1 fewer:1 lr:2 boosting:2 idi:1 zhang:3 five:2 co... |
3,582 | 4,243 | Environmental statistics and the trade-off between
model-based and TD learning in humans
Dylan A. Simon
Department of Psychology
New York University
New York, NY 10003
dylex@nyu.edu
Nathaniel D. Daw
Center for Neural Science and Department of Psychology
New York University
New York, NY 10003
nathaniel.daw@nyu.edu
Abs... | 4243 |@word trial:15 determinant:1 version:1 achievable:1 seems:2 approved:1 instrumental:2 willing:1 d2:1 simulation:3 propagate:1 confirms:1 covariance:1 linearized:1 eng:1 rhesus:1 pressed:1 harder:2 shot:2 selecting:1 past:1 current:2 yet:1 dx:1 must:7 john:3 realistic:1 visible:2 subsequent:2 treating:1 designed:1... |
3,583 | 4,244 | The Fixed Points of Off-Policy TD
J. Zico Kolter
Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139
kolter@csail.mit.edu
Abstract
Off-policy learning, the ability for an agent to learn about a policy other than the
one it is following, is a key element of... | 4244 |@word illustrating:2 version:1 briefly:3 norm:4 open:3 simulation:2 contraction:4 covariance:1 pg:2 commute:1 mention:1 tr:1 sepulchre:1 carry:1 outperforms:1 existing:1 wd:20 subsequent:1 numerical:2 wiewiora:1 plot:1 treating:1 update:1 stationary:19 intelligence:2 rrt:1 offpolicy:1 provides:1 characterization:... |
3,584 | 4,245 | N EWTRON: an Efficient Bandit algorithm for Online
Multiclass Prediction
Elad Hazan
Department of Industrial Engineering
Technion - Israel Institute of Technology
Haifa 32000 Israel
ehazan@ie.technion.ac.il
Satyen Kale
Yahoo! Research
4301 Great America Parkway
Santa Clara, CA 95054
skale@yahoo-inc.com
Abstract
We pr... | 4245 |@word middle:1 version:7 seems:2 stronger:2 norm:6 open:6 d2:4 jacob:2 p0:1 initial:1 contains:1 tuned:1 document:1 current:3 com:1 contextual:1 clara:1 yet:1 john:1 additive:2 plot:2 v:2 greedy:1 item:1 ith:1 short:1 zhang:1 incorrect:3 prove:2 newtron:1 expected:3 behavior:1 problems1:1 roughly:2 multi:2 euters... |
3,585 | 4,246 | Sparse Manifold Clustering and Embedding
Ren?e Vidal
Center for Imaging Science
Johns Hopkins University
rvidal@cis.jhu.edu
Ehsan Elhamifar
Center for Imaging Science
Johns Hopkins University
ehsan@cis.jhu.edu
Abstract
We propose an algorithm called Sparse Manifold Clustering and Embedding
(SMCE) for simultaneous cl... | 4246 |@word middle:3 norm:1 d2:1 decomposition:1 pick:1 reduction:24 contains:5 selecting:5 document:1 existing:2 current:1 john:2 informative:1 kdd:2 plot:3 intelligence:3 selected:1 huo:1 farther:2 provides:2 node:9 location:1 zhang:1 five:3 along:3 consists:1 eleventh:1 manner:1 expected:1 themselves:1 examine:1 aut... |
3,586 | 4,247 | Distributed Delayed Stochastic Optimization
Alekh Agarwal
John C. Duchi
Department of Electrical Engineering and Computer Sciences
University of California, Berkeley
Berkeley, CA 94720
{alekh,jduchi}@eecs.berkeley.edu
Abstract
We analyze the convergence of gradient-based optimization algorithms
whose updates depend o... | 4247 |@word version:5 norm:4 nd:1 dekel:6 cleanly:1 moment:2 reduction:1 cyclic:15 series:2 current:6 comparing:1 nt:3 john:1 numerical:2 remove:3 plot:4 update:25 juditsky:2 leaf:3 idling:1 draft:1 node:31 org:4 height:1 mathematical:4 become:1 prove:1 consists:1 combine:1 expected:2 multi:1 becomes:1 notation:1 under... |
3,587 | 4,248 | Composite Multiclass Losses
Elodie Vernet
ENS Cachan
Robert C. Williamson
ANU and NICTA
Mark D. Reid
ANU and NICTA
evernet@ens-cachan.fr
Bob.Williamson@anu.edu.au
Mark.Reid@anu.edu.au
Abstract
We consider loss functions for multiclass prediction problems. We show when
a multiclass loss can be expressed as a ?pro... | 4248 |@word version:1 stronger:1 nd:1 twelfth:1 open:1 adrian:1 decomposition:1 p0:9 carry:1 liu:1 erven:1 existing:3 surprising:1 yet:1 guez:1 written:2 dx:1 fn:6 partition:1 half:1 intelligence:3 plane:1 characterization:3 boosting:1 hyperplanes:1 arctan:1 simpler:1 zhang:2 org:1 mathematical:1 along:1 eleventh:1 pol... |
3,588 | 4,249 | Learning to Agglomerate Superpixel Hierarchies
Viren Jain
Janelia Farm Research Campus
Howard Hughes Medical Institute
Srinivas C. Turaga
Brain & Cognitive Sciences
Massachusetts Institute of Technology
Kevin L. Briggman, Moritz N. Helmstaedter, Winfried Denk
Department of Biomedical Optics
Max Planck Institute for M... | 4249 |@word polynomial:1 advantageous:1 termination:1 tried:1 brightness:1 maes:1 ultrathin:1 briggman:7 moment:2 initial:5 fragment:1 suppressing:1 current:5 attracted:1 gpu:2 must:3 connectomics:1 nanoscale:1 partition:1 shape:4 enables:1 opin:1 remove:1 designed:1 alone:1 greedy:1 leaf:1 selected:1 intelligence:2 cu... |
3,589 | 425 | Adjoint-Functions and Temporal Learning
Algorithms in Neural Networks
N. Toomarian and J. Barhen
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, CA 91109
Abstract
The development of learning algorithms is generally based upon the minimization of an energy function. It is a fundamental requireme... | 425 |@word briefly:1 simulation:1 eng:1 commute:1 dramatic:1 mention:1 thereby:1 moment:1 necessity:1 reduction:2 initial:9 selecting:3 activation:7 must:5 written:2 numerical:5 partition:1 enables:2 update:1 selected:1 sys:2 math:3 sigmoidal:1 mathematical:2 along:2 differential:1 shorthand:1 combine:1 behavioral:1 ma... |
3,590 | 4,250 | Learning a Tree of Metrics
with Disjoint Visual Features
Sung Ju Hwang
University of Texas
Austin, TX 78701
Kristen Grauman
University of Texas
Austin, TX 78701
Fei Sha
University of Southern California
Los Angeles, CA 90089
sjhwang@cs.utexas.edu
grauman@cs.utexas.edu
feisha@usc.edu
Abstract
We introduce an appr... | 4250 |@word aircraft:1 kulis:3 briefly:1 norm:4 nd:1 suitably:1 hu:3 seek:2 r:4 rgb:1 llo:1 mammal:1 locomotive:1 recursively:1 configuration:1 loc:1 score:1 selecting:3 contains:1 salzmann:1 tuned:1 seriously:1 bc:1 ours:1 document:3 outperforms:3 existing:1 od:3 nt:2 babenko:1 si:1 yet:1 must:2 finest:1 numerical:1 i... |
3,591 | 4,251 | Speedy Q-Learning
Mohammad Gheshlaghi Azar
Radboud University Nijmegen
Geert Grooteplein 21N, 6525 EZ
Nijmegen, Netherlands
m.azar@science.ru.nl
Remi Munos
INRIA Lille, SequeL Project
40 avenue Halley
59650 Villeneuve d?Ascq, France
r.munos@inria.fr
Mohammad Ghavamzadeh
INRIA Lille, SequeL Project
40 avenue Halley
5... | 4251 |@word mild:1 kgk:1 version:6 polynomial:3 norm:3 km:1 grooteplein:2 contraction:1 q1:1 kappen:3 initial:3 contains:1 existing:5 hasselt:1 current:3 comparing:1 com:1 belmont:1 remove:1 update:12 v:1 stationary:1 generative:1 intelligence:1 xk:1 provides:1 mannor:1 readability:1 successive:1 along:1 prove:7 kej:3 ... |
3,592 | 4,252 | Prismatic Algorithm for Discrete D.C. Programming Problem
Yoshinobu Kawahara? and Takashi Washio
The Institute of Scientific and Industrial Research (ISIR)
Osaka University
8-1 Mihogaoka, Ibaraki-shi, Osaka 567-0047 JAPAN
{kawahara,washio}@ar.sanken.osaka-u.ac.jp
Abstract
In this paper, we propose the first exact alg... | 4252 |@word kohli:1 repository:2 version:1 norm:9 seems:1 nd:1 tried:1 bn:5 decomposition:3 p0:8 isir:1 tr:3 mcauley:1 initial:4 generatively:2 contains:1 series:1 outperforms:1 existing:2 current:2 comparing:1 si:4 attracted:1 written:1 partition:5 kdd:1 bilp:10 remove:1 update:2 greedy:4 selected:1 generative:4 intel... |
3,593 | 4,253 | Signal Estimation Under Random Time-Warpings
and Nonlinear Signal Alignment
Sebastian Kurtek Anuj Srivastava Wei Wu
Department of Statistics
Florida State University, Tallahassee, FL 32306
skurtek,anuj,wwu@stat.fsu.edu
Abstract
While signal estimation under random amplitudes, phase shifts, and additive noise
is studi... | 4253 |@word trial:1 version:3 seems:2 norm:3 open:1 closure:1 q1:6 attainable:1 sychronization:1 moment:2 series:1 ours:1 past:1 ka:2 current:1 john:2 additive:4 partition:1 motor:1 update:2 selected:1 provides:1 bijection:1 location:2 height:2 mathematical:2 along:2 differential:2 prove:1 fitting:1 introduce:1 pairwis... |
3,594 | 4,254 | Relative Density-Ratio Estimation
for Robust Distribution Comparison
Makoto Yamada
Tokyo Institute of Technology
yamada@sg.cs.titech.ac.jp
Takafumi Kanamori
Nagoya University
kanamori@is.nagoya-u.ac.jp
Taiji Suzuki
The University of Tokyo
s-taiji@stat.t.u-tokyo.ac.jp
Hirotaka Hachiya Masashi Sugiyama
Tokyo Institute ... | 4254 |@word trial:1 illustrating:2 version:1 middle:1 repository:3 norm:2 advantageous:2 twelfth:1 willing:1 p0:18 tr:2 reduction:3 contains:2 score:3 series:1 rkhs:3 existing:3 bradley:1 comparing:2 ida:13 tackling:1 dx:2 written:1 numerical:2 happen:1 n0:11 v:12 implying:1 half:3 intelligence:2 flare:1 yamada:2 accep... |
3,595 | 4,255 | The Kernel Beta Process
Yingjian Wang?
Electrical & Computer Engineering Dept.
Duke University
Durham, NC 27708
yw65@duke.edu
Lu Ren?
Electrical & Computer Engineering Dept.
Duke University
Durham, NC 27708
lr22@duke.edu
David Dunson
Department of Statistical Science
Duke University
Durham, NC 27708
dunson@stat.duke.e... | 4255 |@word loading:3 reused:1 d2:1 calculus:1 bn:4 wgn:3 thereby:1 series:1 kx0:6 recovered:3 current:1 com:1 assigning:1 dx:3 readily:1 cruz:1 analytic:1 remove:1 designed:1 concert:1 update:7 n0:1 implying:1 generative:3 selected:3 alone:1 accordingly:1 ith:1 short:1 evy:16 location:2 successive:1 club:1 five:1 phyl... |
3,596 | 4,256 | Recovering Intrinsic Images with a Global Sparsity
Prior on Reflectance
Peter Vincent Gehler
Max Planck Institut for Informatics
Carsten Rother
Microsoft Research Cambridge
pgehler@mpii.de
carrot@microsoft.com
Martin Kiefel, Lumin Zhang, Bernhard Sch?olkopf
Max Planck Institute for Intelligent Systems
{mkiefel,lumi... | 4256 |@word version:1 briefly:1 seems:2 nd:2 confirms:1 propagate:1 rgb:8 decomposition:5 brightness:1 mention:1 shading:38 reduction:2 necessity:1 configuration:1 contains:2 score:6 liu:1 initial:7 com:1 comparing:2 si:4 yet:1 must:1 stemming:1 visible:1 numerical:1 blur:1 informative:1 shape:1 treating:1 v:2 alone:3 ... |
3,597 | 4,257 | Dynamical segmentation of single trials
from population neural data
Biljana Petreska
Gatsby Computational Neuroscience Unit
University College London
biljana@gatsby.ucl.ac.uk
John P. Cunningham
Dept of Engineering
University of Cambridge
jpc74@cam.ac.uk
Byron M. Yu
ECE and BME
Carnegie Mellon University
byronyu@cmu.e... | 4257 |@word trial:42 briefly:1 rising:1 norm:1 rhesus:1 lobe:1 covariance:8 simplifying:1 decomposition:1 jacob:1 thereby:1 solid:1 shot:1 carry:1 initial:1 schoner:1 score:1 prefix:1 outperforms:1 reaction:7 current:2 surprising:1 analysed:1 john:1 visible:1 partition:1 informative:1 subsequent:1 motor:5 asymptote:1 p... |
3,598 | 4,258 | The Doubly Correlated Nonparametric Topic Model
Dae Il Kim and Erik B. Sudderth
Department of Computer Science
Brown University, Providence, RI 02906
daeil@cs.brown.edu, sudderth@cs.brown.edu
Abstract
Topic models are learned via a statistical model of variation within document collections, but designed to extract me... | 4258 |@word middle:1 version:2 nd:2 bf:5 covariance:8 series:1 score:8 document:37 interestingly:1 recovered:2 activation:1 realistic:1 subsequent:2 designed:2 update:4 resampling:2 leaf:1 assurance:1 mccallum:2 colored:1 blei:6 provides:2 simpler:2 unbounded:8 dn:5 along:1 direct:1 ik:8 doubly:5 behavior:1 themselves:... |
3,599 | 4,259 | The Local Rademacher Complexity of `p-Norm
Multiple Kernel Learning
Marius Kloft?
Machine Learning Laboratory
TU Berlin, Germany
kloft@tu-berlin.de
Gilles Blanchard
Department of Mathematics
University of Potsdam, Germany
gilles.blanchard@math.uni-potsdam.de
Abstract
We derive an upper bound on the local Rademacher ... | 4259 |@word h:3 version:1 norm:29 nd:3 open:1 km:7 d2:4 simulation:1 crucially:2 covariance:3 decomposition:2 tr:10 searle:1 moment:1 series:3 rkhs:2 interestingly:2 past:1 current:1 comparing:1 recovered:1 readily:1 subsequent:2 additive:1 plot:1 spec:2 selected:1 intelligence:3 warmuth:1 vanishing:1 lrc:4 math:1 simp... |
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