Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
2,600 | 3,358 | A Constraint Generation Approach to
Learning Stable Linear Dynamical Systems
Sajid M. Siddiqi
Robotics Institute
Carnegie-Mellon University
Pittsburgh, PA 15213
siddiqi@cs.cmu.edu
Byron Boots
Computer Science Department
Carnegie-Mellon University
Pittsburgh, PA 15213
beb@cs.cmu.edu
Geoffrey J. Gordon
Machine Learnin... | 3358 |@word version:1 rising:1 norm:1 nd:1 simulation:1 covariance:2 decomposition:2 asks:1 dramatic:1 concise:1 tr:5 initial:1 series:2 contains:1 elliptical:1 comparing:1 must:1 written:1 realistic:1 treating:1 biosurveillance:3 plot:2 stationary:1 detecting:1 toronto:1 differential:1 jbe:1 qualitative:4 x0:5 ldss:4 ... |
2,601 | 3,359 | C O F I R ANK
Maximum Margin Matrix Factorization for
Collaborative Ranking
Markus Weimer?
Alexandros Karatzoglou?
Quoc Viet Le?
Alex Smola?
Abstract
In this paper, we consider collaborative filtering as a ranking problem. We present
a method which uses Maximum Margin Matrix Factorization and optimizes ranking ins... | 3359 |@word version:1 norm:3 open:1 willing:1 invoking:1 pick:1 tr:15 harder:1 initial:1 score:16 hardy:2 tuned:1 document:1 outperforms:2 existing:1 current:3 com:1 yet:2 chu:1 written:2 realistic:1 hofmann:2 update:1 v:1 intelligence:1 selected:2 item:25 prize:1 core:1 alexandros:1 provides:2 location:2 preference:3 ... |
2,602 | 336 | CAM Storage of Analog Patterns and
Continuous Sequences with 3N 2 Weights
Bill Baird
Dept Mathematics and
Dept Molecular and Cell Biology,
129 LSA, U .C.Berkeley,
Berkeley, Ca. 94720
Frank Eeckman
Lawrence Livermore
National Laboratory,
P.O. Box 808 (L-426),
Livermore, Ca. 94550
Abstract
A simple architecture and al... | 336 |@word middle:2 version:6 inversion:1 simulation:2 pulse:1 heteroassociative:1 tr:1 initial:4 contains:1 diagonalized:1 analysed:1 activation:1 lorentz:1 realize:1 motor:2 designed:1 selected:1 liapunov:2 node:12 mathematical:1 along:2 constructed:4 differential:1 hopf:2 supply:1 become:1 prove:1 combine:2 recogniz... |
2,603 | 3,360 | Congruence between model and human attention
reveals unique signatures of critical visual events
Robert J. Peters?
Department of Computer Science
University of Southern California
Los Angeles, CA 90089
rjpeters@usc.edu
Laurent Itti
Departments of Neuroscience and Computer Science
University of Southern California
Los... | 3360 |@word stronger:1 approved:1 disk:1 propagate:1 attended:2 carry:2 score:17 practiced:1 existing:1 current:10 comparing:1 contextual:1 surprising:2 yet:1 must:2 refresh:1 subsequent:2 thrust:1 iscan:1 gist:1 drop:4 alone:5 intelligence:1 selected:2 item:1 plane:4 beginning:1 provides:2 coarse:1 detecting:2 locatio... |
2,604 | 3,361 | CPR for CSPs: A Probabilistic Relaxation of
Constraint Propagation
Luis E. Ortiz
ECE Dept, Univ. of Puerto Rico, Mayag?uez, PR 00681-9042
leortiz@ece.uprm.edu
Abstract
This paper proposes constraint propagation relaxation (CPR), a probabilistic approach to classical constraint propagation that provides another view o... | 3361 |@word version:2 seems:1 stronger:1 contains:5 pub:3 document:1 interestingly:1 psj:2 current:1 si:3 yet:2 assigning:3 must:2 luis:1 dechter:4 analytic:1 update:1 v:1 intelligence:1 leaf:1 nent:1 short:1 provides:4 completeness:1 node:3 guard:1 become:1 paragraph:1 inside:2 introduce:6 behavior:3 inspired:1 global... |
2,605 | 3,362 | Regulator Discovery from Gene Expression Time
Series of Malaria Parasites: a Hierarchical Approach
Jos?e Miguel Hern?andez-Lobato
Escuela Polit?ecnica Superior
Universidad Aut?onoma de Madrid, Madrid, Spain
Josemiguel.hernandez@uam.es
Tjeerd Dijkstra
Leiden Malaria Research Group
LUMC, Leiden, The Netherlands
t.dijks... | 3362 |@word version:1 open:2 grey:2 propagate:1 p0:2 pick:2 series:3 contains:3 denoting:1 interestingly:1 reaction:1 si:3 yet:1 additive:1 realistic:2 remove:1 plot:1 update:6 fewer:1 histone:1 selected:1 provides:1 simpler:1 five:2 along:2 direct:2 inside:1 introduce:3 roughly:1 themselves:1 relying:1 becomes:1 spain... |
2,606 | 3,363 | Sparse Feature Learning for Deep Belief Networks
Marc?Aurelio Ranzato1
Y-Lan Boureau2,1
Yann LeCun1
1
Courant Institute of Mathematical Sciences, New York University
2
INRIA Rocquencourt
{ranzato,ylan,yann@courant.nyu.edu}
Abstract
Unsupervised learning algorithms aim to discover the structure hidden in the data,
and... | 3363 |@word mild:1 norm:1 advantageous:1 seems:2 indiscriminate:1 contrastive:5 thereby:3 delgado:1 configuration:1 interestingly:2 recovered:1 current:1 com:1 activation:1 rocquencourt:1 must:1 partition:11 shape:2 update:2 greedy:1 discovering:1 short:1 provides:1 quantized:3 coarse:1 location:1 gx:1 simpler:1 mathem... |
2,607 | 3,364 | Contraction Properties of VLSI Cooperative
Competitive Neural Networks of Spiking Neurons
Emre Neftci1 , Elisabetta Chicca1 , Giacomo Indiveri1 , Jean-Jacques Slotine2 , Rodney Douglas1
1 Institute of Neuroinformatics, UNI|ETH, Zurich
2 Nonlinear Systems Laboratory, MIT, Cambridge, Massachusetts, 02139
emre@ini.phys.e... | 3364 |@word trial:15 stronger:1 nd:2 open:1 contraction:45 somplinsky:1 carry:2 reduction:1 initial:17 configuration:7 contains:1 selecting:1 suppressing:1 current:2 written:3 wx:2 plasticity:2 shape:1 hofmann:1 plot:2 half:2 selected:1 device:2 intelligence:1 infrastructure:1 sigmoidal:1 mathematical:1 along:2 differe... |
2,608 | 3,365 | HM-BiTAM: Bilingual Topic Exploration, Word
Alignment, and Translation
Bing Zhao
IBM T. J. Watson Research
zhaob@us.ibm.com
Eric P. Xing
Carnegie Mellon University
epxing@cs.cmu.edu
Abstract
We present a novel paradigm for statistical machine translation (SMT), based on
a joint modeling of word alignment and the top... | 3365 |@word version:1 bf:3 gloss:1 contains:1 score:5 hereafter:1 united:1 document:45 outperforms:3 existing:1 current:3 com:1 contextual:2 must:1 john:1 fn:6 informative:1 confirming:1 enables:1 update:1 v:1 generative:2 selected:1 intelligence:1 beginning:1 sys:2 chiang:1 blei:1 provides:1 iterates:1 contribute:1 le... |
2,609 | 3,366 | Modeling Natural Sounds
with Modulation Cascade Processes
Richard E. Turner and Maneesh Sahani
Gatsby Computational Neuroscience Unit
17 Alexandra House, Queen Square, London, WC1N 3AR, London
Abstract
Natural sounds are structured on many time-scales. A typical segment of speech,
for example, contains features that s... | 3366 |@word middle:6 timefrequency:2 norm:8 grey:1 d2:4 pulse:1 km:3 decomposition:3 simplifying:1 tr:1 carry:1 contains:4 initialisation:4 past:1 current:1 z2:1 surprising:1 analysed:1 yet:1 dx:1 must:4 subsequent:1 opin:1 progressively:2 update:1 stationary:2 generative:23 fewer:3 prohibitive:2 tone:1 plane:1 sys:1 s... |
2,610 | 3,367 | The Distribution Family of Similarity Distances
Gertjan J. Burghouts?
Arnold W. M. Smeulders
Intelligent Systems Lab Amsterdam
Informatics Institute
University of Amsterdam
Jan-Mark Geusebroek ?
Abstract
Assessing similarity between features is a key step in object recognition and scene
categorization tasks. We arg... | 3367 |@word illustrating:1 version:2 norm:6 triggs:1 open:2 d2:1 covariance:1 papoulis:1 substitution:1 contains:2 current:1 comparing:2 com:1 si:7 assigning:2 conjunctive:1 realistic:6 shape:7 enables:1 sponsored:1 intelligence:3 selected:2 generative:1 provides:1 traverse:1 mathematical:1 along:2 prove:1 fitting:1 mu... |
2,611 | 3,368 | Multi-Task Learning via Conic Programming
Tsuyoshi Kato,?, Hisashi Kashima? , Masashi Sugiyama? , Kiyoshi Asai,
Graduate School of Frontier Sciences, The University of Tokyo,
?
Institute for Bioinformatics Research and Development (BIRD),
Japan Science and Technology Agency (JST)
?
Tokyo Research Laboratory, IBM R... | 3368 |@word multitask:2 version:4 advantageous:1 norm:1 mers:1 heuristically:1 simulation:4 p0:1 pick:1 contains:2 score:2 existing:6 cruz:1 shape:1 enables:1 v:7 kint:3 implying:1 node:3 location:1 preference:1 five:3 constructed:2 ucsc:1 ik:5 consists:1 fitting:2 manner:1 introduce:4 theoretically:1 expected:4 indeed... |
2,612 | 3,369 | A Bayesian Model of Conditioned Perception
Alan A. Stocker? and Eero P. Simoncelli
Howard Hughes Medical Institute,
Center for Neural Science,
and Courant Institute of Mathematical Sciences
New York University
New York, NY-10003, U.S.A.
We argue that in many circumstances, human observers evaluate sensory evidence
sim... | 3369 |@word trial:16 version:2 eliminating:2 manageable:1 norm:1 stronger:1 instruction:1 decomposition:1 brightness:2 liquid:2 ording:1 subjective:1 past:1 contextual:7 yet:1 additive:1 subsequent:14 plot:1 v:2 alone:2 half:7 cue:7 generative:1 selected:6 sudden:2 provides:1 appliance:4 mathematical:1 incorrect:1 fixa... |
2,613 | 337 | A Reinforcement Learning Variant for Control
Scheduling
Aloke Guha
Honeywell Sensor and System Development Center
3660 Technology Drive
Minneapolis, MN 55417
Abstract
We present an algorithm based on reinforcement and state recurrence
learning techniques to solve control scheduling problems. In particular, we
have de... | 337 |@word trial:11 version:1 emperature:1 invoking:1 initial:3 contains:1 current:6 si:1 must:7 realize:1 plot:3 update:1 discrimination:1 intelligence:1 beginning:1 ith:2 short:2 dissertation:1 provides:1 successive:1 constructed:1 prove:2 examine:1 curse:1 window:8 considering:1 provided:3 developed:1 ahc:5 every:1 ... |
2,614 | 3,370 | Automatic Generation of Social Tags for Music
Recommendation
Douglas Eck?
Sun Labs, Sun Microsystems
Burlington, Mass, USA
douglas.eck@umontreal.ca
Paul Lamere
Sun Labs, Sun Microsystems
Burlington, Mass, USA
paul.lamere@sun.com
Thierry Bertin-Mahieux
Sun Labs, Sun Microsystems
Burlington, Mass, USA
bertinmt@iro.umon... | 3370 |@word middle:2 proportion:2 seems:3 nd:1 seek:1 tried:2 document:11 subjective:1 current:4 com:3 comparing:1 si:1 must:1 john:2 audioscrobbler:4 realistic:1 wanted:1 remove:1 plot:1 aside:1 alone:1 selected:5 website:1 item:1 fewer:1 short:3 boosting:2 simpler:1 tagger:1 mahieux:2 constructed:1 become:2 autocorre... |
2,615 | 3,371 | The Price of Bandit Information
for Online Optimization
Thomas P. Hayes
Toyota Technological Institute
Chicago, IL 60637
hayest@tti-c.org
Varsha Dani
Department of Computer Science
University of Chicago
Chicago, IL 60637
varsha@cs.uchicago.edu
Sham M. Kakade
Toyota Technological Institute
Chicago, IL 60637
sham@tti-... | 3371 |@word version:1 achievable:4 norm:1 suitably:1 rigged:1 seek:1 decomposition:1 incurs:2 boundedness:1 celebrated:1 contains:1 past:2 current:1 nt:19 dx:1 must:3 chicago:4 additive:3 designed:1 update:1 v:1 website:1 warmuth:2 manfred:1 provides:4 boosting:1 org:3 mathematical:1 along:4 symposium:3 prove:3 expecte... |
2,616 | 3,372 | A New View of Automatic Relevance Determination
David Wipf and Srikantan Nagarajan, ?
Biomagnetic Imaging Lab, UC San Francisco
{david.wipf, sri}@mrsc.ucsf.edu
Abstract
Automatic relevance determination (ARD) and the closely-related sparse
Bayesian learning (SBL) framework are effective tools for pruning large numbers... | 3372 |@word determinant:1 version:3 sri:1 achievable:1 briefly:1 seems:1 norm:16 nd:1 simulation:1 covariance:5 simplifying:1 delgado:1 series:2 selecting:5 kx0:1 existing:1 current:3 surprising:1 yet:1 dx:1 must:3 readily:3 refines:1 subsequent:2 shape:1 mrsc:1 treating:1 designed:1 update:17 plot:4 stationary:2 gener... |
2,617 | 3,373 | An Analysis of Convex Relaxations for MAP Estimation
M. Pawan Kumar
Dept. of Computing
Oxford Brookes University
V. Kolmogorov
Computer Science
University College London
P.H.S. Torr
Dept. of Computing
Oxford Brookes University
pkmudigonda@brookes.ac.uk
vnk@adastral.ucl.ac.uk
philiptorr@brookes.ac.uk
Abstract
The... | 3373 |@word polynomial:2 stronger:1 barahona:1 moment:2 inefficiency:1 contains:3 sherali:1 kahl:1 comparing:1 cad:1 surprising:1 john:1 additive:2 subsequent:1 partition:1 j1:2 uak:1 enables:1 v:3 persistency:1 provides:4 node:3 mathematical:2 ik:12 prove:6 naor:1 introduce:1 pairwise:11 sdp:3 multi:1 begin:1 xx:5 not... |
2,618 | 3,374 | Boosting Algorithms for Maximizing the Soft Margin
Manfred K. Warmuth?
Dept. of Engineering
University of California
Santa Cruz, CA, U.S.A.
Karen Glocer
Dept. of Engineering
University of California
Santa Cruz, CA, U.S.A.
Gunnar R?atsch
Friedrich Miescher Laboratory
Max Planck Society
T?ubingen, Germany
Abstract
We... | 3374 |@word repository:1 version:3 c0:1 termination:1 d2:1 simulation:1 minus:1 solid:1 initial:2 cyclic:1 series:1 contains:3 denoting:1 current:4 surprising:1 analysed:1 must:1 cruz:3 remove:1 designed:1 plot:1 update:9 discrimination:1 v:1 half:1 fewer:1 intelligence:1 rudin:1 warmuth:5 record:1 manfred:2 boosting:2... |
2,619 | 3,375 | On Ranking in Survival Analysis: Bounds on the
Concordance Index
Vikas C. Raykar, Harald Steck, Balaji Krishnapuram
CAD and Knowledge Solutions (IKM CKS), Siemens Medical Solutions Inc., Malvern, USA
{vikas.raykar,harald.steck,balaji.krishnapuram}@siemens.com
Cary Dehing-Oberije, Philippe Lambin
Maastro Clinic, Univers... | 3375 |@word trial:2 cox:30 version:4 norm:2 prognostic:1 suitably:1 steck:2 series:1 score:2 seriously:1 rkhs:1 interestingly:1 com:1 cad:1 written:3 numerical:2 designed:1 intelligence:1 ith:2 steepest:1 renshaw:1 provides:2 boosting:1 preference:4 herbrich:1 hospitalized:1 five:5 mathematical:1 become:1 kalbfleisch:1... |
2,620 | 3,376 | Statistical Analysis of Semi-Supervised Regression
John Lafferty
Computer Science Department
Carnegie Mellon University
Pittsburgh, PA 15213
lafferty@cs.cmu.edu
Larry Wasserman
Department of Statistics
Carnegie Mellon University
Pittsburgh, PA 15213
larry@stat.cmu.edu
Abstract
Semi-supervised methods use unlabeled d... | 3376 |@word kondor:2 version:3 polynomial:2 nd:1 suitably:1 heuristically:2 hu:2 bn:6 tr:7 reduction:2 moment:1 contains:1 score:1 existing:1 current:1 dx:1 written:1 john:1 hou:1 chicago:1 realistic:1 j1:2 informative:1 shape:5 intelligence:1 xk:1 math:1 org:1 positing:1 constructed:1 c2:4 differential:1 incorrect:1 x... |
2,621 | 3,377 | EEG-Based Brain-Computer Interaction: Improved
Accuracy by Automatic Single-Trial Error Detection
Pierre W. Ferrez
IDIAP Research Institute
Centre du Parc
Av. des Pr?es-Beudin 20
1920 Martigny, Switzerland
pierre.ferrez@idiap.ch
Jos?e del R. Mill?an
IDIAP Research Institute
Centre du Parc
Av. des Pr?es-Beudin 20
1920... | 3377 |@word neurophysiology:3 trial:32 biosemi:1 briefly:1 cingulate:8 exploitation:1 seems:2 cincotti:1 confirms:2 minus:1 cp2:1 exclusively:1 past:1 reaction:2 current:3 anterior:7 activation:2 realistic:1 motor:18 intelligence:1 selected:6 device:4 cp3:1 short:3 idling:2 filtered:1 mental:17 provides:2 detecting:1 b... |
2,622 | 3,378 | Learning Transformational
Invariants from Natural Movies
Charles F. Cadieu & Bruno A. Olshausen
Helen Wills Neuroscience Institute
University of California, Berkeley
Berkeley, CA 94720
{cadieu, baolshausen}@berkeley.edu
Abstract
We describe a hierarchical, probabilistic model that learns to extract complex motion from... | 3378 |@word repository:1 hyv:1 seek:1 linearized:1 simulation:1 decomposition:3 contraction:1 initial:2 tuned:1 rightmost:1 imaginary:4 current:1 nowlan:1 yet:1 must:1 written:1 additive:1 shape:1 enables:1 designed:1 update:2 generative:7 cue:1 half:2 pursued:1 intelligence:1 plane:2 short:1 filtered:1 provides:4 loca... |
2,623 | 3,379 | Gates
Tom Minka
Microsoft Research Ltd.
Cambridge, UK
John Winn
Microsoft Research Ltd.
Cambridge, UK
Abstract
Gates are a new notation for representing mixture models and context-sensitive
independence in factor graphs. Factor graphs provide a natural representation for
message-passing algorithms, such as expectatio... | 3379 |@word pick:7 tr:1 moment:1 initial:1 loeliger:1 genetic:5 existing:2 si:7 must:1 readily:1 john:1 blur:3 update:3 implying:1 intelligence:4 leaf:1 xk:1 node:5 contribute:1 allerton:1 height:1 constructed:1 incorrect:2 inside:21 introduce:1 behavior:1 p1:3 nor:1 considering:1 increasing:1 becomes:2 notation:16 und... |
2,624 | 338 | Discovering Discrete Distributed Representations
with Iterative Competitive Learning
Michael C. Mozer
Department of Computer Science
and Institute of Cognitive Science
University of Colorado
Boulder, CO 80309-0430
Abstract
Competitive learning is an unsupervised algorithm that classifies input patterns into mutually ... | 338 |@word trial:3 version:1 compression:13 norm:1 retraining:1 simulation:2 mention:1 shading:1 initial:2 contains:1 selecting:1 outperforms:1 surprising:1 activation:2 yet:2 lang:1 must:4 readily:2 cottrell:7 subsequent:1 partition:1 j1:1 extensional:1 update:2 intelligence:1 discovering:4 selected:1 fewer:1 assuranc... |
2,625 | 3,380 | Sparse probabilistic projections
C?edric Archambeau
Department of Computer Science
University College London, United Kingdom
c.archambeau@cs.ucl.ac.uk
Francis R. Bach
INRIA - Willow Project
Ecole Normale Sup?erieure, Paris, France
francis.bach@mines.org
Abstract
We present a generative model for performing sparse pr... | 3380 |@word middle:1 compression:1 norm:1 hu:1 seek:2 covariance:2 tr:3 edric:1 moment:1 reduction:2 series:1 dspca:4 united:2 initialisation:1 ecole:1 interestingly:1 existing:1 com:1 yet:1 shape:2 designed:1 update:6 generative:5 device:3 isotropic:2 maximised:1 wolfram:1 provides:1 node:1 evy:1 toronto:1 attack:3 or... |
2,626 | 3,381 | Look Ma, No Hands: Analyzing the Monotonic
Feature Abstraction for Text Classification
Doug Downey
Electrical Engineering and Computer Science Department
Northwestern University
Evanston, IL 60208
ddowney@eecs.northwestern.edu
Oren Etzioni
Turing Center, Department of Computer Science and Engineering
University of Wash... | 3381 |@word version:1 hyponym:1 reduction:3 configuration:1 exclusively:2 daniel:1 document:13 bootstrapped:2 outperforms:1 existing:2 must:1 partition:2 informative:2 enables:1 sponsored:1 alone:5 selected:2 mccallum:2 provides:2 detecting:1 direct:1 become:3 prove:2 dan:1 x0:2 theoretically:1 expected:2 nor:2 multi:1... |
2,627 | 3,382 | An ideal observer model of infant object perception
Charles Kemp
Department of Psychology
Carnegie Mellon University
ckemp@cmu.edu
Fei Xu
Department of Psychology
University of British Columbia
fei@psych.ubc.ca
Abstract
Before the age of 4 months, infants make inductive inferences about the motions
of physical object... | 3382 |@word mild:1 version:7 seems:3 stronger:1 nd:1 eld:53 initial:3 contains:1 tuned:1 ours:1 past:1 existing:3 current:2 recovered:1 surprising:3 yet:2 must:7 interrupted:1 subsequent:3 shape:7 infant:46 generative:2 stationary:6 alone:1 cult:1 core:2 short:1 provides:1 characterization:2 contribute:1 location:6 pre... |
2,628 | 3,383 | Spectral Hashing
3
Yair Weiss1,3
School of Computer Science,
Hebrew University,
91904, Jerusalem, Israel
Antonio Torralba1
1
CSAIL, MIT,
32 Vassar St.,
Cambridge, MA 02139
yweiss@cs.huji.ac.il
torralba@csail.mit.edu
2
Rob Fergus2
Courant Institute, NYU,
715 Broadway,
New York, NY 10003
fergus@cs.nyu.edu
Abstrac... | 3383 |@word version:1 middle:1 polynomial:1 proportion:4 seek:4 tr:1 nystr:1 document:1 outperforms:1 reaction:1 comparing:1 yet:1 dx:2 written:1 ronald:1 partition:10 analytic:2 enables:1 plot:3 gist:5 hash:4 pursued:2 intelligence:1 item:15 inspection:1 short:2 shortlist:2 boosting:23 location:1 org:1 simpler:1 outer... |
2,629 | 3,384 | A mixture model for the evolution of gene expression
in non-homogeneous datasets
Gerald Quon1 , Yee Whye Teh2 , Esther Chan3 , Timothy Hughes3 , Michael Brudno1,3 ,
Quaid Morris3
1
Department of Computer Science, and 3 Banting and Best Department of Medical Research,
University of Toronto, Canada,
2
Gatsby Computation... | 3384 |@word pcc:11 stronger:1 smirnov:1 replicate:1 covariance:1 mammal:2 tr:3 configuration:1 contains:1 efficacy:1 united:1 denoting:1 indispensible:1 genetic:2 existing:2 current:1 comparing:1 assigning:1 remove:3 designed:1 plot:1 depict:1 half:2 leaf:1 selected:1 nervous:1 xk:1 short:4 characterization:1 detecting... |
2,630 | 3,385 | Multi-task Gaussian Process Learning of Robot
Inverse Dynamics
Kian Ming A. Chai
Christopher K. I. Williams
Stefan Klanke
Sethu Vijayakumar
School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, UK
{k.m.a.chai, c.k.i.williams, s.klanke, sethu.vijayakumar}@ed.ac.uk
Abstract
The inverse ... | 3385 |@word multitask:1 inversion:1 loading:1 advantageous:3 simulation:1 propagate:1 decomposition:5 covariance:11 tr:5 harder:1 igp:9 carry:1 initial:2 configuration:1 selecting:1 denoting:1 outperforms:1 current:1 comparing:3 com:1 surprising:1 written:1 realistic:1 happen:1 motor:1 drop:1 interpretable:1 update:1 p... |
2,631 | 3,386 | Optimization on a Budget: A Reinforcement
Learning Approach
Ian Fasel
Department of Computer Sciences
University of Texas at Austin
ianfasel@cs.utexas.edu
Paul Ruvolo
Department of Computer Science
University of California San Diego
La Jolla, CA 92093
pruvolo@cs.ucsd.edu
Javier Movellan
Machine Perception Laboratory... | 3386 |@word trial:1 version:1 middle:1 brightness:1 dramatic:1 reduction:12 initial:2 series:1 selecting:1 document:4 past:1 current:14 marquardt:11 update:2 greedy:1 selected:3 ruvolo:1 xk:19 steepest:1 detecting:1 boosting:3 math:1 location:2 zhang:1 height:2 along:5 become:4 consists:2 ijcv:1 combine:3 fitting:1 x0:... |
2,632 | 3,387 | Efficient Direct Density Ratio Estimation for
Non-stationarity Adaptation and Outlier Detection
Takafumi Kanamori
Nagoya University
Nagoya, Japan
kanamori@is.nagoya-u.ac.jp
Shohei Hido
IBM Research
Kanagawa, Japan
hido@jp.ibm.com
Masashi Sugiyama
Tokyo Institute of Technology
Tokyo, Japan
sugi@cs.titech.ac.jp
Abstr... | 3387 |@word trial:10 inversion:1 norm:1 seems:3 advantageous:1 covariance:2 tr:27 versatile:1 contains:2 score:7 existing:6 current:1 com:1 ida:3 dx:6 numerical:3 visibility:1 succeeding:2 n0:2 half:2 forb:1 direct:4 fitting:2 introduce:1 manner:2 theoretically:3 planning:1 brain:1 pitfall:1 cpu:1 curse:2 equipped:2 so... |
2,633 | 3,388 | Risk Bounds for Randomized Sample Compressed
Classifiers
Mohak Shah
Centre for Intelligent Machines
McGill University
Montreal, QC, Canada, H3A 2A7
mohak@cim.mcgill.ca
Abstract
We derive risk bounds for the randomized classifiers in Sample Compression setting where the classifier-specification utilizes two sources of ... | 3388 |@word h:2 version:3 inversion:4 compression:43 briefly:1 seek:1 r:12 chervonenkis:1 existing:2 recovered:1 john:3 cruz:2 intelligence:1 selected:1 fewer:1 warmuth:4 ith:1 manfred:1 provides:1 preference:1 along:1 direct:1 prove:1 consists:1 inside:1 manner:3 indeed:1 expected:2 themselves:1 decreasing:1 actual:1 ... |
2,634 | 3,389 | Unsupervised Learning of Visual Sense Models for
Polysemous Words
Kate Saenko
MIT CSAIL
Cambridge, MA
saenko@csail.mit.edu
Trevor Darrell
UC Berkeley EECS / ICSI
Berkeley, CA
trevor@eecs.berkeley.edu
Abstract
Polysemy is a problem for methods that exploit image search engines to build object category models. Existin... | 3389 |@word trial:1 nd:2 hyponym:1 downloading:1 initial:3 contains:1 fragment:1 document:8 outperforms:1 existing:4 wd:1 assigning:1 issuing:1 must:1 plot:1 wnd:1 v:2 generative:1 discovering:1 device:3 website:1 selected:2 mccallum:1 short:1 harvesting:1 blei:3 detecting:1 provides:1 codebook:2 location:1 zhang:1 fiv... |
2,635 | 339 | Oscillation Onset
?
In
Neural Delayed Feedback
Andre Longtin
Complex Systems Group and Center for Nonlinear Studies
Theoretical Division B213, Los Alamos National Laboratory
Los Alamos, NM 87545
Abstract
This paper studies dynamical aspects of neural systems with delayed negative feedback modelled by nonlinear delay-... | 339 |@word neurophysiology:1 stronger:1 simulation:3 serie:1 biomathematics:1 initial:1 series:2 past:2 dx:1 dde:9 realistic:1 numerical:3 additive:2 christian:1 mackey:11 pacemaker:3 nervous:1 short:1 colored:1 provides:1 math:5 differential:8 hopf:18 become:1 qualitative:5 pathway:2 behavior:6 frequently:1 nor:1 decr... |
2,636 | 3,390 | Efficient Exact Inference in Planar Ising Models
Nicol N. Schraudolph
Dmitry Kamenetsky
nips@schraudolph.org
dkamen@cecs.anu.edu.au
National ICT Australia, Locked Bag 8001, Canberra ACT 2601, Australia
& RSISE, Australian National University, Canberra ACT 0200, Australia
Abstract
We give polynomial-time algorithm... | 3390 |@word determinant:1 version:1 polynomial:5 disk:1 open:1 seek:1 decomposition:2 tr:1 solid:2 offending:1 configuration:2 cyclic:2 contains:2 elaborating:1 current:1 com:2 yet:2 must:2 readily:1 subsequent:2 partition:10 numerical:1 hofmann:2 designed:1 depict:1 aps:1 v:4 half:3 leaf:2 selected:2 item:1 intelligen... |
2,637 | 3,391 | Hebbian Learning of Bayes Optimal Decisions
Bernhard Nessler?, Michael Pfeiffer?, and Wolfgang Maass
Institute for Theoretical Computer Science
Graz University of Technology
A-8010 Graz, Austria
{nessler,pfeiffer,maass}@igi.tugraz.at
Abstract
Uncertainty is omnipresent when we perceive or interact with our environmen... | 3391 |@word trial:6 version:5 bn:6 initial:2 current:5 written:3 plasticity:5 shape:1 enables:2 update:11 fund:1 v:1 stationary:4 generative:2 intelligence:1 accordingly:1 xk:24 beginning:1 vanishing:1 provides:3 node:3 mathematical:2 direct:1 beta:4 symposium:1 prove:1 combine:1 dan:1 manner:1 x0:42 theoretically:1 ex... |
2,638 | 3,392 | Joint support recovery under high-dimensional scaling:
Benefits and perils of `1,?-regularization
Sahand Negahban
Department of Electrical Engineering and Computer Sciences
University of California, Berkeley
Berkeley, CA 94720-1770
sahand n@eecs.berkeley.edu
Martin J. Wainwright
Department of Statistics, and Departmen... | 3392 |@word polynomial:1 turlach:1 norm:14 stronger:1 simulation:6 seek:1 r:3 decomposition:1 thereby:3 reduction:1 contains:2 series:1 genetic:1 wainwrig:1 surprising:1 numerical:1 plot:2 larization:1 fewer:1 accordingly:1 ith:1 provides:3 characterization:1 buldygin:1 simpler:1 mathematical:1 c2:6 incorrect:1 prove:3... |
2,639 | 3,393 | Bayesian Kernel Shaping for Learning Control
Jo-Anne Ting1 , Mrinal Kalakrishnan1 , Sethu Vijayakumar2 and Stefan Schaal1,3
1
Computer Science, U. of Southern California, Los Angeles, CA 90089, USA
2
School of Informatics, University of Edinburgh, Edinburgh, EH9 3JZ, UK
3
ATR Computational Neuroscience Labs, Kyoto 619... | 3393 |@word inversion:1 polynomial:6 open:2 calculus:1 covariance:15 accommodate:1 initial:4 configuration:1 series:3 offering:1 envision:1 existing:1 current:1 z2:1 comparing:1 anne:1 si:4 yet:1 activation:1 bd:1 must:1 realize:1 additive:1 numerical:1 shape:2 girosi:1 update:8 hwit:2 stationary:17 aside:1 guess:1 pla... |
2,640 | 3,394 | Self-organization using synaptic plasticity
Vicenc? G?omez1
vgomez@iua.upf.edu
Hilbert J Kappen1
b.kappen@science.ru.nl
Andreas Kaltenbrunner2
andreas.kaltenbrunner@upf.edu
Vicente L?opez2
vicente.lopez@barcelonamedia.org
1
Department of Biophysics
Radboud University Nijmegen
6525 EZ Nijmegen, The Netherlands
2
Ba... | 3394 |@word trial:1 briefly:1 pulse:1 propagate:1 simulation:7 minus:1 kappen:1 initial:8 configuration:3 efficacy:4 bc:1 activation:1 guez:1 must:2 plasticity:15 shape:1 analytic:2 plot:5 mandell:1 progressively:1 update:13 selected:1 nervous:1 provides:1 math:1 org:1 mandelbrot:1 ik:16 lopez:1 sustained:4 introduce:1... |
2,641 | 3,395 | Variational Mixture of Gaussian Process Experts
Chao Yuan and Claus Neubauer
Siemens Corporate Research
Integrated Data Systems Department
755 College Road East, Princeton, NJ 08540
{chao.yuan,claus.neubauer}@siemens.com
Abstract
Mixture of Gaussian processes models extended a single Gaussian process with
ability of ... | 3395 |@word determinant:1 version:1 middle:1 c0:1 jacob:2 covariance:4 pick:1 solid:1 carry:1 reduction:1 contains:1 score:1 selecting:3 current:2 com:1 recovered:1 nowlan:1 chu:1 enables:1 remove:1 plot:10 update:4 v:3 generative:5 selected:2 prohibitive:1 greedy:2 discovering:1 intelligence:2 beginning:1 location:1 f... |
2,642 | 3,396 | Characteristic Kernels on Groups and Semigroups
Kenji Fukumizu
Institute of Statistical Mathematics
4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569 Japan
fukumizu@ism.ac.jp
Arthur Gretton
MPI for Biological Cybernetics
Spemannstra?e 38, 72076 T?ubingen, Germany
arthur.gretton@tuebingen.mpg.de
Bharath Sriperumbudur
Depa... | 3396 |@word briefly:1 open:6 closure:1 covariance:1 homomorphism:3 tr:12 reduction:2 moment:1 series:3 hereafter:1 rkhs:11 dx:8 must:1 additive:1 weyl:1 enables:1 remove:1 rudin:1 provides:2 arctan:1 direct:1 prove:2 consists:1 fitting:1 interscience:1 ica:1 mpg:2 becomes:2 provided:2 bounded:8 transformation:1 guarant... |
2,643 | 3,397 | Particle Filter-based Policy Gradient in POMDPs
Romain Deguest?
CMAP, Ecole Polytechnique
deguest@cmapx.polytechnique.fr
Pierre-Arnaud Coquelin
CMAP, Ecole Polytechnique
coquelin@cmapx.polytechnique.fr
R?emi Munos
INRIA Lille - Nord Europe, SequeL project,
remi.munos@inria.fr
Abstract
Our setting is a Partially Obs... | 3397 |@word version:1 seems:1 proportion:1 replicate:1 simulation:7 bn:11 covariance:1 reduction:1 initial:2 selecting:1 ecole:2 past:4 freitas:1 current:2 activation:1 dx:1 written:5 must:1 additive:1 numerical:3 informative:1 enables:1 plot:1 update:1 resampling:10 greedy:2 intelligence:1 selected:1 smith:1 math:1 al... |
2,644 | 3,398 | Syntactic Topic Models
David Blei
Department of Computer Science
35 Olden Street
Princeton University
Princeton, NJ 08540
blei@cs.princeton.edu
Jordan Boyd-Graber
Department of Computer Science
35 Olden Street
Princeton University
Princeton, NJ 08540
jbg@cs.princeton.edu
Abstract
We develop the syntactic topic model... | 3398 |@word kintsch:1 version:1 proportion:2 johansson:1 laurence:1 uncovers:2 decomposition:1 brochure:3 contains:1 document:61 subjective:1 blank:1 z2:1 adj:1 protection:1 must:2 parsing:9 visible:1 remove:1 treating:1 plot:2 update:4 fund:1 implying:1 generative:2 selected:1 intelligence:1 scotland:1 emperical:1 ble... |
2,645 | 3,399 | Partially Observed Maximum Entropy
Discrimination Markov Networks
Jun Zhu?
Eric P. Xing?
Bo Zhang?
?
State Key Lab of Intelligent Tech & Sys, Tsinghua National TNList Lab, Dept. Comp Sci & Tech,
Tsinghua University, Beijing China. jun-zhu@mails.thu.edu.cn; dcszb@thu.edu.cn
?
School of Comp. Sci., Carnegie Mellon Un... | 3399 |@word version:2 polynomial:1 triggs:1 open:1 calculus:1 grey:1 covariance:1 p0:35 tnlist:1 reduction:2 initial:1 score:4 document:1 interestingly:1 existing:9 surprising:1 stemmed:1 yet:1 intriguing:1 written:1 fn:1 realistic:2 hofmann:2 designed:2 discrimination:8 intelligence:1 leaf:3 generative:2 item:2 parame... |
2,646 | 34 | 114
A Computer Simulation of Olfactory Cortex With Functional Implications for
Storage and Retrieval of Olfactory Information
Matthew A. Wilson and James M. Bower
Computation and Neural Systems Program
Division of Biology, California Institute of Technology, Pasadena, CA 91125
ABSTRACT
Based on anatomical and physiolo... | 34 |@word trial:15 version:1 middle:2 hippocampus:1 adrian:1 simulation:17 excited:1 fonn:1 initial:1 contains:3 current:10 activation:1 distant:1 hyperpolarizing:1 progressively:1 discrimination:1 alone:6 half:2 selected:1 accordingly:1 reciprocal:1 record:2 compo:4 location:3 mathematical:1 along:2 constructed:1 burs... |
2,647 | 340 | Basis-Function Trees as a Generalization of Local
Variable Selection Methods for Function
Approximation
Terence D. Sanger
Dept. Electrical Engineering and Computer Science
Massachusetts Institute of Technology, E25-534
Cambridge, MA 02139
Abstract
Local variable selection has proven to be a powerful technique for app... | 340 |@word polynomial:6 seems:1 dekker:1 simplifying:1 eng:1 pressure:1 tr:1 united:1 existing:4 current:1 must:1 ikeda:2 john:1 belmont:1 additive:1 realize:1 intelligence:1 fewer:2 leaf:8 cook:1 record:1 provides:4 ire:1 node:3 location:1 traverse:1 direct:1 raibert:1 manner:1 expected:1 behavior:2 elman:1 growing:5 ... |
2,648 | 3,400 | Fast Rates for Regularized Objectives
Karthik Sridharan, Nathan Srebro, Shai Shalev-Shwartz
Toyota Technological Institute ? Chicago
Abstract
We study convergence properties of empirical minimization of a stochastic
strongly convex objective, where the stochastic component is linear. We show
that the value attained b... | 3400 |@word version:1 briefly:1 norm:32 stronger:1 covariance:2 boundedness:2 ecole:1 scovel:2 surprising:2 must:3 written:1 chicago:1 enables:1 plot:4 mcdiarmid:1 zhang:1 learing:1 prove:1 theoretically:1 indeed:2 expected:25 roughly:2 p1:1 nor:1 behavior:5 multi:1 relying:1 equipped:1 becomes:2 begin:1 provided:1 bou... |
2,649 | 3,401 | Near-optimal Regret Bounds for
Reinforcement Learning
Peter Auer
Thomas Jaksch
Ronald Ortner
University of Leoben, Franz-Josef-Strasse 18, 8700 Leoben, Austria
{auer,tjaksch,rortner}@unileoben.ac.at
Abstract
For undiscounted reinforcement learning in Markov decision processes (MDPs)
we consider the total regret of a ... | 3401 |@word version:1 achievable:2 polynomial:4 seems:3 nd:1 d2:2 tr:1 initial:8 contains:1 current:2 ka:2 john:1 ronald:3 additive:1 update:1 fund:1 stationary:4 leaf:1 xk:5 beginning:1 math:1 revisited:1 along:2 c2:2 constructed:1 predecessor:1 katehakis:2 apostolos:1 prove:1 excellence:1 expected:4 discounted:1 litt... |
2,650 | 3,402 | Global Ranking Using Continuous Conditional
Random Fields
1
Tao Qin, 1 Tie-Yan Liu, 2 Xu-Dong Zhang, 2 De-Sheng Wang, 1 Hang Li
1
Microsoft Research Asia, 2 Tsinghua University
1
{taoqin, tyliu, hangli}@microsoft.com
2
{zhangxd, wangdsh ee}@tsinghua.edu.cn
Abstract
This paper studies global ranking problem by learnin... | 3402 |@word trial:2 determinant:1 msr:1 inversion:1 propagate:1 tr:1 liu:7 score:29 tuned:1 document:63 outperforms:3 existing:2 com:1 si:12 chu:1 must:3 john:1 partition:1 happen:1 kdd:2 designed:1 ainen:1 update:1 intelligence:1 item:2 mccallum:1 short:1 math:1 preference:1 zhang:5 five:1 along:1 combine:3 introduce:... |
2,651 | 3,403 | Local Gaussian Process Regression
for Real Time Online Model Learning and Control
Duy Nguyen-Tuong Jan Peters Matthias Seeger
Max Planck Institute for Biological Cybernetics
Spemannstra?e 38, 72076 T?ubingen, Germany
{duy,jan.peters,matthias.seeger}@tuebingen.mpg.de
Abstract
Learning in real-time applications, e.g., ... | 3403 |@word version:1 inversion:2 stronger:1 open:1 simulation:3 covariance:7 decomposition:1 reduction:2 outperforms:2 current:1 realistic:1 enables:1 kyb:2 update:9 intelligence:2 gear:1 xk:1 short:1 core:1 sarcos:11 provides:1 firstly:1 become:2 viable:1 consists:2 combine:2 manner:1 expected:1 roughly:1 mpg:3 frequ... |
2,652 | 3,404 | On Bootstrapping the ROC Curve
Patrice Bertail
CREST (INSEE) & MODAL?X - Universit?e Paris 10
pbertail@u-paris10.fr
St?ephan Cl?emenc?on
Telecom Paristech (TSI) - LTCI UMR Institut Telecom/CNRS 5141
stephan.clemencon@telecom-paristech.fr
Nicolas Vayatis
ENS Cachan & UniverSud - CMLA UMR CNRS 8536
vayatis@cmla.ens-cach... | 3404 |@word h:2 illustrating:1 version:12 briefly:2 norm:6 flach:1 nd:1 relevancy:1 heuristically:1 simulation:5 hsieh:1 mention:1 series:1 score:3 selecting:1 denoting:2 document:2 bootstrapped:1 interestingly:1 outperforms:2 mishra:1 com:1 assigning:1 dx:2 must:1 yet:1 numerical:1 informative:2 shape:1 pertinent:1 tr... |
2,653 | 3,405 | Hierarchical Semi-Markov Conditional Random
Fields for Recursive Sequential Data
Tran The Truyen ? , Dinh Q. Phung ? , Hung H. Bui ? ?, and Svetha Venkatesh ?
?
Department of Computing, Curtin University of Technology
GPO Box U1987 Perth, WA 6845, Australia
thetruyen.tran@postgrad.curtin.edu.au
{D.Phung,S.Venkatesh}@c... | 3405 |@word middle:2 sri:2 polynomial:2 seems:1 triggs:1 decomposition:3 snack:1 recursively:1 reduction:1 configuration:3 score:2 pub:1 initialisation:4 fa8750:1 rightmost:1 outperforms:1 com:1 contextual:15 surprising:1 attracted:1 parsing:5 must:5 subsequent:1 partition:4 informative:1 numerical:1 happen:1 initialis... |
2,654 | 3,406 | Efficient Inference in Phylogenetic InDel Trees
Alexandre Bouchard-C?ot?e?
Michael I. Jordan??
Dan Klein?
?
Computer Science Division , Department of Statistics?
University of California at Berkeley
Berkeley, CA 94720
{bouchard,jordan,klein}@cs.berkeley.edu
Abstract
Accurate and efficient inference in evolutionary tr... | 3406 |@word middle:1 version:1 polynomial:1 linearized:1 propagate:1 recursively:2 initial:1 substitution:3 contains:1 fragment:1 selecting:1 exclusively:1 score:4 configuration:1 prefix:1 existing:1 current:7 comparing:1 discretization:1 surprising:2 si:2 john:1 realistic:1 resampling:14 stationary:1 generative:1 leaf... |
2,655 | 3,407 | An Empirical Analysis of Domain Adaptation
Algorithms for Genomic Sequence Analysis
Gabriele Schweikert1
Max Planck Institutes
Spemannstr. 35-39, 72070 T?ubingen, Germany
Gabriele.Schweikert@tue.mpg.de
Christian Widmer1
Friedrich Miescher Laboratory
Spemannstr. 39, 72070 T?ubingen, Germany
ZBIT, T?ubingen University
... | 3407 |@word version:2 briefly:2 d2:4 tried:1 pressure:1 tuned:1 rkhs:1 outperforms:1 wd:2 readily:1 distant:2 realistic:2 christian:2 designed:1 drop:1 aside:1 selected:3 website:1 prohibitive:1 core:1 characterization:1 combine:1 manner:2 indeed:1 expected:1 mpg:5 multi:5 little:2 cpu:1 window:1 solver:1 increasing:1 ... |
2,656 | 3,408 | Adaptive Template Matching with
Shift-Invariant Semi-NMF
Jonathan Le Roux
Graduate School of Information
Science and Technology
The University of Tokyo
leroux@hil.t.u-tokyo.ac.jp
Alain de Cheveign?
e
CNRS, Universit?e Paris 5,
and Ecole Normale Sup?erieure
Alain.de.Cheveigne@ens.fr
Lucas C. Parra?
Biomedical Engineer... | 3408 |@word trial:1 version:4 instrumental:1 norm:6 nd:5 pulse:5 simulation:1 decomposition:2 outlook:1 accommodate:1 reduction:1 initial:4 series:1 ecole:1 recovered:8 comparing:1 nt:2 written:2 readily:1 subsequent:1 additive:1 shape:2 drop:1 update:13 n0:1 tone:1 accordingly:1 cult:1 smith:1 gure:1 iterates:1 contri... |
2,657 | 3,409 | Covariance Estimation for High Dimensional Data
Vectors Using the Sparse Matrix Transform
Guangzhi Cao
Charles A. Bouman
School of Electrical and Computer Enigneering
Purdue University
West Lafayette, IN 47907
{gcao, bouman}@purdue.edu
Abstract
Covariance estimation for high dimensional vectors is a classically diffi... | 3409 |@word version:2 seems:2 norm:1 nd:1 sensed:1 covariance:68 decomposition:4 tr:2 contains:1 series:1 selecting:1 pub:1 daniel:1 current:1 ka:1 written:1 visible:1 partition:1 plot:2 grass:14 greedy:5 selected:1 intelligence:2 plane:2 provides:1 five:3 symposium:1 ik:15 pairwise:2 behavior:3 globally:1 little:2 cur... |
2,658 | 341 | Phonetic Classification and Recognition
Using the Multi-Layer Perceptron
Hong C. Leung, James R. Glass,
Michael S. Phillips, and Victor W. Zue
Spoken Language Systems Group
Laboratory for Computer Science
Massachusetts Institute of Technology
Cambridge, Massachusetts 02139, U.S.A.
Abstract
In this paper, we will desc... | 341 |@word bigram:2 covariance:1 substitution:3 contains:4 score:1 current:3 si:25 must:3 designed:1 ith:1 location:1 lor:1 combine:1 manner:2 examine:1 multi:6 automatically:1 begin:2 estimating:1 underlying:1 mass:1 string:1 spoken:4 acoust:1 temporal:1 every:3 classifier:18 milestone:1 utilization:1 unit:12 sd:1 mig... |
2,659 | 3,410 | The Gaussian Process Density Sampler
Ryan Prescott Adams?
Cavendish Laboratory
University of Cambridge
Cambridge CB3 0HE, UK
rpa23@cam.ac.uk
Iain Murray
Dept. of Computer Science
University of Toronto
Toronto, Ontario. M5S 3G4
murray@cs.toronto.edu
David J.C. MacKay
Cavendish Laboratory
University of Cambridge
Cambr... | 3410 |@word trial:2 proportion:1 lenk:2 nd:1 rhesus:2 covariance:5 decomposition:1 phy:1 series:2 past:1 existing:2 current:5 surprising:1 dx:5 must:7 perturbative:2 realize:1 numerical:1 shape:1 enables:1 plot:3 generative:8 discovering:1 leaf:1 selected:1 complementing:1 intelligence:1 accepting:1 provides:3 toronto:... |
2,660 | 3,411 | Skill characterization based on betweenness
? ur
? S?ims?ek?
Ozg
Andrew G. Barto
Department of Computer Science
University of Massachusetts
Amherst, MA 01003
{ozgur|barto}@cs.umass.edu
Abstract
We present a characterization of a useful class of skills based on a graphical representation of an agent?s interaction with ... | 3411 |@word trial:4 version:2 proportion:1 disk:2 decomposition:1 innermost:1 pick:3 shading:2 initial:4 configuration:3 uma:1 ours:1 past:1 existing:4 must:1 readily:1 distant:1 partition:1 motor:1 update:1 alone:1 greedy:1 selected:4 betweenness:36 intelligence:3 short:3 fa9550:1 indefinitely:1 colored:1 characteriza... |
2,661 | 3,412 | Cyclizing Clusters via Zeta Function of a Graph
Deli Zhao and Xiaoou Tang
Department of Information Engineering, Chinese University of Hong Kong
Hong Kong, China
{dlzhao,xtang}@ie.cuhk.edu.hk
Abstract
Detecting underlying clusters from large-scale data plays a central role in machine
learning research. In this paper,... | 3412 |@word kong:2 version:2 polynomial:1 proportion:1 norm:2 compression:1 confirms:1 initial:11 cyclic:4 outperforms:1 existing:1 surprising:1 written:5 hou:1 determinantal:2 john:1 partition:1 intelligence:2 selected:1 beginning:1 reciprocal:2 core:3 short:3 detecting:3 provides:1 math:1 toronto:1 preference:1 attac... |
2,662 | 3,413 | Estimating Robust Query Models
with Convex Optimization
Kevyn Collins-Thompson?
Microsoft Research
1 Microsoft Way
Redmond, WA U.S.A. 98052
kevynct@microsoft.com
Abstract
Query expansion is a long-studied approach for improving retrieval effectiveness
by enhancing the user?s original query with additional related wor... | 3413 |@word version:2 polynomial:1 d2:1 seek:2 covariance:1 reduction:1 initial:11 configuration:1 score:4 tuned:1 document:7 past:1 existing:2 horvitz:1 current:7 com:1 subcomponents:1 assigning:1 yet:1 must:3 john:1 designed:1 v:1 greedy:3 selected:5 generative:2 item:2 fewer:2 xk:2 node:3 lavrenko:2 direct:1 ik:1 co... |
2,663 | 3,414 | Efficient Sampling for Gaussian Process Inference
using Control Variables
Michalis K. Titsias, Neil D. Lawrence and Magnus Rattray
School of Computer Science, University of Manchester
Manchester M13 9PL, UK
Abstract
Sampling functions in Gaussian process (GP) models is challenging because of
the highly correlated pos... | 3414 |@word middle:1 seems:1 nd:1 grey:2 confirms:1 simulation:2 covariance:9 tr:1 solid:2 carry:1 initial:1 current:5 discretization:1 activation:2 must:3 written:1 fn:1 numerical:2 informative:4 plot:9 drop:1 resampling:1 stationary:1 intelligence:1 fewer:1 provides:3 location:2 herbrich:1 toronto:1 firstly:2 five:2 ... |
2,664 | 3,415 | Bounds on marginal probability distributions
Joris Mooij
MPI for Biological Cybernetics
T?ubingen, Germany
joris.mooij@tuebingen.mpg.de
Bert Kappen
Department of Biophysics
Radboud University Nijmegen, the Netherlands
b.kappen@science.ru.nl
Abstract
We propose a novel bound on single-variable marginal probability di... | 3415 |@word middle:1 version:1 briefly:1 open:1 cloned:1 propagate:4 bn:13 recursively:1 carry:1 reduction:1 kappen:6 icis:1 ours:1 outperforms:1 existing:3 xnj:2 comparing:1 written:1 partition:4 update:5 intelligence:5 leaf:1 parameterization:1 xk:12 yi1:1 affair:1 math:1 node:31 org:2 become:1 shorthand:2 consists:3... |
2,665 | 3,416 | Fast Computation of Posterior Mode in Multi-Level
Hierarchical Models
Liang Zhang
Department of Statistical Science
Duke University
Durham, NC 27708
lz9@stat.duke.edu
Deepak Agarwal
Yahoo! Research
2821 Mission College Blvd.
Santa Clara, CA 95054
dagarwal@yahoo-inc.com
Abstract
Multi-level hierarchical models provide... | 3416 |@word mild:1 proportion:1 simulation:4 propagate:1 decomposition:1 covariance:2 recursively:2 initial:2 series:5 contains:1 hereafter:1 denoting:1 existing:2 current:4 com:1 clara:1 finest:1 partition:2 informative:1 kdd:1 enables:1 update:4 leaf:15 parametrization:2 ith:6 provides:9 node:52 location:1 zhang:1 al... |
2,666 | 3,417 | Estimation of Information Theoretic Measures for
Continuous Random Variables
Fernando P?erez-Cruz
Princeton University, Electrical Engineering Department
B-311 Engineering Quadrangle, 08544 Princeton (NJ)
fp@princeton.edu
Abstract
We analyze the estimation of information theoretic measures of continuous random variabl... | 3417 |@word seems:1 covariance:1 thereby:2 solid:6 carry:1 contains:2 com:2 dx:3 readily:2 cruz:2 grassberger:1 ministerio:1 partition:2 hofmann:1 kyb:2 moreno:1 plot:9 n0:3 resampling:1 stationary:1 completeness:1 math:1 mathematical:2 differential:15 prove:10 symp:2 inside:1 expected:2 p1:5 nor:1 growing:2 mpg:2 clut... |
2,667 | 3,418 | Exploring Large Feature Spaces
with Hierarchical Multiple Kernel Learning
Francis Bach
?
INRIA - Willow Project, Ecole
Normale Sup?erieure
45, rue d?Ulm, 75230 Paris, France
francis.bach@mines.org
Abstract
For supervised and unsupervised learning, positive definite kernels allow to use
large and potentially infinite ... | 3418 |@word repository:2 middle:1 momma:1 polynomial:25 norm:31 advantageous:2 seems:1 hu:1 simulation:6 tried:1 decomposition:7 covariance:4 selecting:4 ecole:1 outperforms:2 existing:1 spambase:3 magic04:2 mushroom:2 attracted:1 must:1 j1:4 maxv:1 greedy:8 selected:12 half:1 recompute:1 boosting:1 node:4 bijection:1 ... |
2,668 | 3,419 | On the asymptotic equivalence between differential
Hebbian and temporal difference learning using a
local third factor
Christoph Kolodziejski1,2 , Bernd Porr3 , Minija Tamosiunaite1,2,4 , Florentin W?rg?tter1,2
1
Bernstein Center for Computational Neuroscience G?ttingen
2
Georg-August University G?ttingen, Department o... | 3419 |@word trial:2 middle:1 rising:2 advantageous:1 open:2 r:3 simulation:1 minus:2 shading:2 initial:1 substitution:1 configuration:1 efficacy:1 electronics:1 existing:1 si:47 yet:2 realistic:2 numerical:1 plasticity:6 shape:8 plot:1 designed:1 update:2 aps:1 leaf:1 beginning:2 scotland:1 short:1 math:1 u2i:2 mathema... |
2,669 | 342 | An Analog VLSI Splining Network
Daniel B. Schwartz and Vijay K. Samalam
GTE Laboratories, Inc.
40 Sylvan Rd.
Waltham, MA 02254
Abstract
We have produced a VLSI circuit capable of learning to approximate arbitrary smooth of a single variable using a technique closely related to
splines. The circuit effectively has 512... | 342 |@word version:1 inversion:3 excited:1 series:4 daniel:1 tuned:4 t7:1 existing:1 current:19 yet:1 assigning:1 follower:2 john:1 shape:4 atlas:2 plot:1 update:2 stationary:2 alone:1 implying:1 parameterization:1 dear:1 sigmoidal:1 simpler:1 direct:1 consists:3 oflocally:1 fitting:1 roughly:1 proliferation:1 frequent... |
2,670 | 3,420 | Automatic online tuning for fast Gaussian summation
Vlad I. Morariu1?, Balaji V. Srinivasan1 , Vikas C. Raykar2 , Ramani Duraiswami1 , and Larry S. Davis1
1
University of Maryland, College Park, MD 20742
2
Siemens Medical Solutions Inc., USA, 912 Monroe Blvd, King of Prussia, PA 19406
morariu@umd.edu, balajiv@umiacs.u... | 3420 |@word briefly:1 polynomial:1 open:1 d2:1 covariance:2 pick:1 incurs:1 tr:2 recursively:1 series:5 score:1 selecting:4 denoting:1 tuned:1 fgt:2 outperforms:1 freitas:3 com:1 lang:3 must:6 klaas:2 remove:1 designed:1 atlas:2 v:1 greedy:1 selected:4 morariu:1 half:1 inspection:1 xk:1 core:1 provides:3 node:2 contrib... |
2,671 | 3,421 | Interpreting the Neural Code with
Formal Concept Analysis
Dominik Endres, Peter F?oldi?ak
School of Psychology,University of St. Andrews
KY16 9JP, UK
{dme2,pf2}@st-andrews.ac.uk
Abstract
We propose a novel application of Formal Concept Analysis (FCA) to neural decoding: instead of just trying to figure out which stim... | 3421 |@word neurophysiology:4 trial:2 version:1 middle:1 briefly:1 duda:1 lobe:2 stsa:4 configuration:2 contains:6 exclusively:2 selecting:1 rightmost:1 com:1 activation:4 assigning:1 dx:1 attracted:1 john:2 subsequent:1 extensional:1 informative:1 discernible:1 designed:1 interpretable:1 generative:2 selected:1 half:2... |
2,672 | 3,422 | A Convex Upper Bound on the Log-Partition Function
for Binary Graphical Models
Laurent El Ghaoui
Department of Electrical Engineering and Computer Science
University of California Berkeley
Berkeley, CA 9470
elghaoui@eecs.berkeley.edu
Assane Gueye
Department of Electrical Engineering and Computer Science
University of ... | 3422 |@word determinant:19 version:1 norm:13 seek:1 crucially:1 tr:3 moment:3 outperforms:2 yet:1 subsequent:1 numerical:4 partition:26 interpretable:2 xk:20 zmax:19 provides:1 constructed:1 differential:2 qij:1 fitting:2 introduce:3 sacrifice:1 inter:1 indeed:2 roughly:3 nor:1 cardinality:16 becomes:2 begin:2 xx:1 bou... |
2,673 | 3,423 | Bounding Performance Loss in Approximate MDP
Homomorphisms
Jonathan J. Taylor
Dept. of Computer Science
University of Toronto
Toronto, Canada, M5S 3G4
jonathan.taylor@utoronto.ca
Doina Precup
School of Computer Science
McGill University
Montreal, Canada, H3A 2A7
dprecup@cs.mcgill.ca
Prakash Panangaden
School of Comp... | 3423 |@word h:3 compression:1 nd:1 heuristically:1 homomorphism:24 pick:1 reduction:1 relabelled:1 denoting:1 interestingly:1 existing:2 surprising:1 si:4 yet:1 must:1 subsequent:1 partition:13 plot:1 drop:1 update:1 greedy:1 selected:1 device:2 intelligence:2 iterates:1 coarse:1 toronto:2 dn:10 constructed:1 become:1 ... |
2,674 | 3,424 | Near-Minimax Recursive Density Estimation
on the Binary Hypercube
Maxim Raginsky
Duke University
Durham, NC 27708
m.raginsky@duke.edu
Svetlana Lazebnik
UNC Chapel Hill
Chapel Hill, NC 27599
lazebnik@cs.unc.edu
Rebecca Willett
Duke University
Durham, NC 27708
willett@duke.edu
Jorge Silva
Duke University
Durham, NC 2... | 3424 |@word cu:10 middle:3 polynomial:2 norm:1 nd:1 suitably:2 d2:1 seek:2 simulation:3 recursively:1 reduction:1 moment:2 series:1 denoting:1 yet:1 bd:9 bs2:5 written:2 happen:1 shape:1 plot:2 v:3 discrimination:1 intelligence:1 prohibitive:2 accordingly:1 core:1 record:1 d2d:1 wth:1 math:1 node:1 zhang:1 five:1 c2:3 ... |
2,675 | 3,425 | Performance analysis for L2 kernel classification
Clayton D. Scott?
Department of EECS
University of Michigan
Ann Arbor, MI, USA
clayscot@umich.edu
JooSeuk Kim
Department of EECS
University of Michigan
Ann Arbor, MI, USA
stannum@umich.edu
Abstract
We provide statistical performance guarantees for a recently introduce... | 3425 |@word norm:1 turlach:1 meinicke:1 d2:2 decomposition:2 existing:2 dx:19 must:1 luis:1 deniz:1 kdb:4 treating:1 discrimination:1 intelligence:2 dissertation:1 boosting:1 dn:2 prove:2 specialize:1 introduce:1 torbj:1 window:1 pf:1 increasing:1 becomes:2 provided:2 estimating:2 bounded:2 xx:2 guarantee:6 universit:1... |
2,676 | 3,426 | An Efficient Sequential Monte Carlo Algorithm for
Coalescent Clustering
?
Dilan G?orur
Gatsby Unit
University College London
Yee Whye Teh
Gatsby Unit
University College London
dilan@gatsby.ucl.ac.uk
ywteh@gatsby.ucl.ac.uk
Abstract
We propose an efficient sequential Monte Carlo inference scheme for the recently
pro... | 3426 |@word briefly:1 reused:1 open:1 termination:1 essay:1 tried:1 covariance:1 p0:1 pick:2 tr:1 solid:1 initial:1 liu:1 ours:2 past:2 existing:3 nepali:3 current:1 xlr:5 written:1 romance:18 portuguese:3 subsequent:2 entrance:2 informative:2 remove:1 drop:2 update:2 resampling:12 greedy:1 discovering:2 leaf:2 item:3 ... |
2,677 | 3,427 | Regularized Learning with Networks of Features
Ted Sandler, Partha Pratim Talukdar, and Lyle H. Ungar
Department of Computer & Information Science, University of Pennsylvania
{tsandler,partha,ungar}@cis.upenn.edu
John Blitzer
Department of Computer Science, U.C. Berkeley
blitzer@cs.berkeley.edu
Abstract
For many supe... | 3427 |@word trial:1 bigram:2 norm:6 pratim:1 tried:1 covariance:15 decomposition:2 blender:1 harder:1 reduction:3 electronics:4 loc:1 score:7 series:1 denoting:2 document:13 interestingly:1 outperforms:5 com:1 written:3 john:1 distant:1 informative:1 node:1 lexicon:1 appliance:3 zhang:1 five:3 rc:1 along:1 constructed:... |
2,678 | 3,428 | Supervised Bipartite Graph Inference
Yoshihiro Yamanishi
Mines ParisTech CBIO
Institut Curie, INSERM U900,
35 rue Saint-Honore, Fontainebleau, F-77300 France
yoshihiro.yamanishi@ensmp.fr
Abstract
We formulate the problem of bipartite graph inference as a supervised learning
problem, and propose a new method to solve ... | 3428 |@word norm:6 seems:1 hu:5 simulation:2 euclidian:2 recursively:2 initial:1 series:1 score:8 outperforms:1 recovered:1 comparing:1 must:1 written:3 girosi:1 gv:13 plot:1 v:20 selected:1 smith:3 node:2 gautam:1 five:1 differential:2 fitting:1 pathway:1 guenther:1 roughly:1 nor:1 encouraging:1 becomes:1 project:1 du... |
2,679 | 3,429 | Using Bayesian Dynamical Systems for
Motion Template Libraries
Silvia Chiappa, Jens Kober, Jan Peters
Max-Planck Institute for Biological Cybernetics
Spemannstra?e 38, 72076 T?bingen, Germany
{silvia.chiappa,jens.kober,jan.peters}@tuebingen.mpg.de
Abstract
Motor primitives or motion templates have become an important... | 3429 |@word briefly:1 middle:3 simulation:4 covariance:2 hochner:1 jacob:1 initial:3 configuration:2 series:20 contains:1 selecting:1 past:1 current:2 subsequent:1 realistic:2 predetermined:1 enables:1 motor:15 designed:3 plot:2 update:9 generative:13 selected:2 website:1 intelligence:1 parametrization:3 smith:1 short:... |
2,680 | 343 | Extensions of a Theory of Networks for
Approximation and Learning: Outliers and
Negative Examples
Federico Girosi
AI Lab. M.I.T.
Cambridge, MA 02139
Tomaso Poggio
Al Lab. M.LT.
Cambridge, MA 021:39
Bruno Caprile
I.R.S.T .
Povo, Italy, 38050
Abstract
Learning an input-output mapping from a set of examples can be re... | 343 |@word cox:1 f32:4 polynomial:1 norm:1 dekker:1 t_:3 cla:2 noll:1 configuration:1 etric:1 denoting:1 ka:2 must:1 written:1 attracted:1 girosi:12 intelligence:1 selected:1 unbounded:1 differential:1 consists:3 inter:1 ra:3 tomaso:1 p1:1 bility:1 dist:2 multi:1 td:1 considering:1 increasing:1 becomes:3 maximizes:1 nu... |
2,681 | 3,430 | Multiscale Random Fields with Application to
Contour Grouping
Longin Jan Latecki
Dept. of Computer and Info. Sciences
Temple University, Philadelphia, USA
latecki@temple.edu
ChengEn Lu
Dept. of Electronics and Info. Eng.
Huazhong Univ. of Sci. and Tech., China
luchengen@gmail.com
Marc Sobel
Statistics Dept.
Temple U... | 3430 |@word briefly:1 r:1 eng:2 decomposition:5 initial:1 configuration:3 contains:1 fragment:2 selecting:1 series:1 fevrier:1 bai:2 electronics:2 existing:2 com:2 si:3 gmail:2 assigning:1 finest:1 visible:1 partition:2 informative:2 shape:28 discrimination:1 cue:2 intelligence:2 generative:1 xk:13 hallucinate:1 provid... |
2,682 | 3,431 | An Homotopy Algorithm for the Lasso with Online
Observations
Pierre J. Garrigues
Department of EECS
Redwood Center for Theoretical Neuroscience
University of California
Berkeley, CA 94720
garrigue@eecs.berkeley.edu
Laurent El Ghaoui
Department of EECS
University of California
Berkeley, CA 94720
elghaoui@eecs.berkeley.... | 3431 |@word inversion:1 compression:1 turlach:1 norm:4 advantageous:1 simulation:2 decomposition:1 ipm:2 garrigues:1 contains:1 series:1 err:2 current:5 comparing:1 attracted:1 numerical:3 partition:2 remove:1 plot:1 interpretable:1 update:13 selected:2 ith:2 become:1 manner:1 introduce:4 indeed:2 cand:1 examine:1 v1t:... |
2,683 | 3,432 | High-dimensional support union recovery in
multivariate regression
Guillaume Obozinski
Department of Statistics
UC Berkeley
gobo@stat.berkeley.edu
Martin J. Wainwright
Department of Statistics
Dept. of Electrical Engineering and Computer Science
UC Berkeley
wainwright@stat.berkeley.edu
Michael I. Jordan
Department o... | 3432 |@word version:1 norm:19 seems:2 suitably:1 willing:1 km:2 simulation:5 r:3 confirms:1 covariance:5 decomposition:2 thereby:1 epartement:1 reduction:1 liu:2 denoting:1 ecole:1 current:1 z2:2 must:1 additive:1 partition:1 analytic:1 drop:1 designed:1 plot:2 v:4 ith:2 core:1 provides:1 location:1 zhang:1 along:2 con... |
2,684 | 3,433 | Extended Grassmann Kernels for
Subspace-Based Learning
Daniel D. Lee
GRASP Laboratory
University of Pennsylvania
Philadelphia, PA 19104
ddlee@seas.upenn.edu
Jihun Hamm
GRASP Laboratory
University of Pennsylvania
Philadelphia, PA 19104
jhham@seas.upenn.edu
Abstract
Subspace-based learning problems involve data whose ... | 3433 |@word trial:1 kondor:3 inversion:1 polynomial:3 yi0:10 covariance:2 tr:18 series:1 daniel:2 rkhs:1 bc:5 bhattacharyya:19 interestingly:1 yet:1 dx:3 jkl:7 john:1 realize:1 shape:1 nian:1 designed:1 treating:2 smith:1 short:1 caveat:1 toronto:1 firstly:2 along:1 become:2 chiuso:1 edelman:1 consists:3 doubly:1 helli... |
2,685 | 3,434 | Probabilistic detection of short events, with
application to critical care monitoring
Norm Aleks
U.C. Berkeley
norm@cs.berkeley.edu
Diane Morabito
U.C. San Francisco
morabitod@
neurosurg.ucsf.edu
Stuart Russell
U.C. Berkeley
russell@cs.berkeley.edu
Kristan Staudenmayer
Stanford University
kristans@
stanford.edu
Mic... | 3434 |@word h:2 middle:1 polynomial:1 norm:2 seems:1 nd:1 c0:1 open:4 sensed:2 pressure:53 harder:1 series:1 ours:2 existing:1 current:5 timer:1 must:2 suermondt:1 fn:9 oxygenation:1 enables:1 drop:1 generative:2 fewer:1 device:1 half:2 intelligence:3 beginning:4 smith:1 short:2 record:1 detecting:2 provides:1 node:3 u... |
2,686 | 3,435 | Shared Segmentation of Natural Scenes
Using Dependent Pitman-Yor Processes
Erik B. Sudderth and Michael I. Jordan
Electrical Engineering & Computer Science, University of California, Berkeley
sudderth@cs.berkeley.edu, jordan@cs.berkeley.edu
Abstract
We develop a statistical framework for the simultaneous, unsupervise... | 3435 |@word seems:1 proportion:16 stronger:1 triggs:1 open:1 zelnik:1 simulation:1 covariance:7 brightness:1 thereby:2 moment:1 contains:1 fa8750:1 current:1 z2:1 scaffolding:1 scatter:2 refines:1 realistic:1 partition:15 informative:1 shape:3 plot:4 update:2 occlude:1 cue:6 discovering:2 pursued:1 generative:3 farther... |
2,687 | 3,436 | Model Selection in Gaussian Graphical Models:
High-Dimensional Consistency of ?1-regularized MLE
Pradeep Ravikumar? , Garvesh Raskutti? , Martin J. Wainwright?? and Bin Yu??
Department of Statistics? , Department of EECS? ,
University of California, Berkeley
{pradeepr,garveshr,wainwright,binyu}@stat.berkeley.edu
Abstr... | 3436 |@word trial:1 determinant:12 version:2 polynomial:1 norm:18 d2:3 grey:1 simulation:5 covariance:22 dramatic:1 zij:1 current:1 must:1 plot:8 alone:1 accordingly:1 xk:1 core:1 provides:2 node:18 clarified:1 location:1 mathematical:1 along:1 c2:2 constructed:2 differential:6 yuan:1 prove:2 consists:2 manner:1 expect... |
2,688 | 3,437 | Empirical performance maximization
for linear rank statistics
St?ephan Cl?emenc?on
Telecom Paristech (TSI) - LTCI UMR Institut Telecom/CNRS 5141
stephan.clemencon@telecom-paristech.fr
Nicolas Vayatis
ENS Cachan & UniverSud - CMLA UMR CNRS 8536
vayatis@cmla.ens-cachan.fr
Abstract
The ROC curve is known to be the golden... | 3437 |@word h:12 version:1 pw:1 norm:5 proportion:2 seems:1 heuristically:1 bn:25 decomposition:6 pick:1 carry:1 celebrated:1 contains:1 score:11 denoting:1 savage:1 yet:1 john:1 additive:1 subsequent:1 discrimination:1 v:1 leaf:1 device:1 rudin:1 xk:6 provides:3 math:3 revisited:1 herbrich:1 simpler:1 zhang:1 mathemat... |
2,689 | 3,438 | On the Reliability of Clustering Stability in the Large
Sample Regime - Supplementary Material
Ohad Shamir? and Naftali Tishby??
? School of Computer Science and Engineering
? Interdisciplinary Center for Neural Computation
The Hebrew University
Jerusalem 91904, Israel
{ohadsh,tishby}@cs.huji.ac.il
A
Exact Formulati... | 3438 |@word mild:1 version:2 norm:3 stronger:3 calculus:1 willing:1 covariance:3 pick:1 score:2 denoting:1 surprising:1 si:22 dx:31 must:1 written:3 dydx:4 characterization:1 hyperplanes:2 attack:1 si1:2 mathematical:1 along:2 become:2 prove:8 consists:1 inside:6 manner:2 x0:5 indeed:1 expected:6 behavior:1 themselves:... |
2,690 | 3,439 | Bio-inspired Real Time Sensory Map Realignment in
a Robotic Barn Owl
Juan Huo, Zhijun Yang and Alan Murray
DTC, School of Informatics, Schoolf of Electronics & Engineering
The University of Edinburgh
Edinburgh, UK
{J.Huo, Zhijun.Yang, Alan.Murray}@ed.ac.uk
Abstract
The visual and auditory map alignment in the Superior... | 3439 |@word blindness:3 determinant:1 instruction:1 simulation:3 excited:1 gertler:1 initial:2 electronics:1 disparity:1 attracted:1 physiol:1 plasticity:5 motor:1 newest:1 cue:4 selected:1 website:1 nervous:1 intelligence:1 huo:4 smith:3 provides:3 location:3 accessed:1 mathematical:2 edelman:1 pathway:21 interaural:1... |
2,691 | 344 | Neural Network Application to Diagnostics and
Control of Vehicle Control Systems
Kenneth A. Marko
Research Staff
Ford Motor Company
Dearborn, Michigan 48121
ABSTRACT
Diagnosis of faults in complex, real-time control systems is a
complicated task that has resisted solution by traditional methods. We
have shown that ne... | 344 |@word briefly:2 seems:1 tedious:1 simulation:3 linearized:1 attainable:1 thereby:2 initial:2 exclusively:1 tuned:1 existing:3 current:1 stemmed:1 must:5 readily:3 numerical:1 predetermined:1 analytic:2 motor:1 remove:1 selected:1 beginning:1 provides:1 complication:2 node:2 mathematical:1 constructed:2 direct:1 be... |
2,692 | 3,440 | Kernel Measures of Independence for non-iid Data
Xinhua Zhang
NICTA and Australian National University
Canberra, Australia
xinhua.zhang@anu.edu.au
Le Song?
School of Computer Science
Carnegie Mellon University, Pittsburgh, USA
lesong@cs.cmu.edu
Arthur Gretton
MPI T?ubingen for Biological Cybernetics
T?ubingen, German... | 3440 |@word mild:2 version:2 briefly:1 norm:2 tedious:1 decomposition:4 covariance:3 decorrelate:1 thereby:1 tr:8 carry:1 moment:1 reduction:2 configuration:1 series:16 contains:2 united:1 rkhs:8 past:1 existing:1 recovered:1 comparing:2 ida:1 clara:1 mesh:4 subsequent:1 hofmann:1 plot:1 stationary:2 yr:1 core:1 colore... |
2,693 | 3,441 | Scalable Algorithms for String Kernels with Inexact
Matching
Pavel P. Kuksa, Pai-Hsi Huang, Vladimir Pavlovic
Department of Computer Science,
Rutgers University, Piscataway, NJ 08854
{pkuksa,paihuang,vladimir}@cs.rutgers.edu
Abstract
We present a new family of linear time algorithms for string comparison with
mismatc... | 3441 |@word ixx:1 open:2 mers:30 pavel:3 elisseeff:1 pick:1 substitution:1 contains:2 score:3 document:3 past:1 existing:5 current:1 attracted:1 readily:2 john:2 remove:1 plot:1 generative:1 leaf:2 data2:1 short:1 eskin:2 detecting:2 node:2 lexicon:1 simpler:1 melvin:1 direct:1 become:1 symposium:1 viable:1 interscienc... |
2,694 | 3,442 | Supervised Exponential Family Principal Component
Analysis via Convex Optimization
Yuhong Guo
Computer Sciences Laboratory
Australian National University
yuhongguo.cs@gmail.com
Abstract
Recently, supervised dimensionality reduction has been gaining attention, owing
to the realization that data labels are often availa... | 3442 |@word version:1 tedious:1 decomposition:1 p0:4 tr:20 reduction:30 initial:2 substitution:1 outperforms:2 existing:1 recovered:2 com:1 current:1 rish:1 gmail:1 dx:1 partition:1 kdd:1 drop:1 designed:1 update:5 discovering:2 selected:1 colored:9 provides:2 math:1 allerton:1 five:2 constructed:1 become:1 scholkopf:1... |
2,695 | 3,443 | B reak in g Aud i o CAPTCHAs
Jennifer Tam
Computer Science Department
Carnegie Mellon University
5000 Forbes Ave, Pittsburgh 15217
jdtam@cs.cmu.edu
Jiri Simsa
Computer Science Department
Carnegie Mellon University
5000 Forbes Ave, Pittsburgh 15217
jsimsa@cs.cmu.edu
Sean Hyde
Electrical and Computer Engineering
Carneg... | 3443 |@word cu:1 faculty:1 version:4 inversion:1 proportion:1 twelfth:1 mention:1 euclidian:1 harder:2 initial:1 contains:1 selecting:2 existing:1 current:7 com:4 contextual:1 gmail:2 yet:1 must:3 luis:2 creat:1 designed:3 spec:1 selected:1 half:5 fewer:1 accordingly:3 erat:1 location:4 universi:1 attack:3 five:4 regis... |
2,696 | 3,444 | A Transductive Bound for the Voted Classifier with an
Application to Semi-supervised Learning
Massih R. Amini
Laboratoire d?Informatique de Paris 6
Universit?e Pierre et Marie Curie, Paris, France
massih-reza.amini@lip6.fr
Franc?ois Laviolette
Universit?e Laval
Qu?ebec (QC), Canada
francois.laviolette@ift.ulaval.ca
... | 3444 |@word trial:3 repository:2 compression:1 bn:2 q1:1 carry:1 initial:3 outperforms:1 current:1 od:1 assigning:1 dx:1 must:2 informative:1 kyb:1 stationary:1 intelligence:2 accordingly:2 pointer:1 provides:1 boosting:1 prove:2 x0:77 indeed:3 mpg:1 examine:1 automatically:2 reorganizing:1 considering:2 becomes:2 prov... |
2,697 | 3,445 | Regularized Policy Iteration
Amir-massoud Farahmand1 , Mohammad Ghavamzadeh2 , Csaba Szepesv?ari1 , Shie Mannor3
1
Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
2
INRIA Lille - Nord Europe, Team SequeL, France
3
Department of ECE, McGill University, Canada - Department of EE, Techni... | 3445 |@word version:2 polynomial:1 norm:11 seems:1 twelfth:1 open:1 hu:1 r:1 simplifying:1 valuefunction:1 boundedness:1 harder:1 carry:1 initial:2 series:1 contains:1 rkhs:4 bc:2 existing:1 current:1 comparing:1 written:3 must:2 realistic:1 enables:1 fund:1 update:1 stationary:3 greedy:7 leaf:1 selected:3 generative:1... |
2,698 | 3,446 | Online Metric Learning and Fast Similarity Search
Prateek Jain, Brian Kulis, Inderjit S. Dhillon, and Kristen Grauman
Department of Computer Sciences
University of Texas at Austin
Austin, TX 78712
{pjain,kulis,inderjit,grauman}@cs.utexas.edu
Abstract
Metric learning algorithms can provide useful distance functions fo... | 3446 |@word kulis:4 briefly:1 version:4 interleave:1 norm:1 stronger:2 nd:2 vldb:1 seitz:1 decomposition:1 incurs:1 tr:1 accommodate:1 initial:1 series:2 contains:1 outperforms:7 existing:14 past:1 current:5 ka:2 must:10 gv:1 plot:8 drop:1 update:47 hash:32 prohibitive:1 selected:3 item:1 warmuth:1 recompute:1 provides... |
2,699 | 3,447 | Resolution Limits of Sparse Coding in
High Dimensions?
Alyson K. Fletcher,? Sundeep Rangan,? and Vivek K Goyal?
Abstract
This paper addresses the problem of sparsity pattern detection for unknown ksparse n-dimensional signals observed through m noisy, random linear measurements. Sparsity pattern recovery arises in a n... | 3447 |@word trial:1 compression:1 seems:1 stronger:2 itrue:12 open:2 seek:1 simulation:4 decomposition:1 eng:1 electronics:1 mag:1 ecole:1 com:1 comparing:4 must:4 numerical:2 additive:1 plot:1 succeeding:1 drop:1 v:1 tarokh:2 greedy:1 fewer:1 sys:1 provides:4 detecting:2 math:1 location:1 allerton:1 simpler:2 zhang:1 ... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.