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4,100 | 4,710 | Q-MKL: Matrix-induced Regularization in
Multi-Kernel Learning with Applications to
Neuroimaging?
Chris Hinrichs??
?
University of Wisconsin
Madison, WI
Vikas Singh??
?
Jiming Peng?
University of Illinois
?
Sterling C. Johnson??
Geriatric Research Education & Clinical Center
Urbana-Champaign, IL
Wm. S. Middleto... | 4710 |@word mild:1 repository:1 briefly:4 mri:2 achievable:1 norm:35 stronger:1 proportion:1 km:11 additively:1 r:2 seek:1 covariance:6 decomposition:1 contraction:1 elisseeff:1 kwm:7 tr:6 reduction:1 series:2 selecting:2 rkhs:9 interestingly:1 longitudinal:1 outperforms:1 existing:3 current:2 incidence:1 yet:1 dx:2 mu... |
4,101 | 4,711 | Persistent Homology for Learning Densities with
Bounded Support
Florian T. Pokorny
Carl Henrik Ek
Hedvig Kjellstr?om
Danica Kragic ?
Computer Vision and Active Perception Lab, Centre for Autonomous Systems
School of Computer Science and Communication
KTH Royal Institute of Technology, Stockholm, Sweden
{fpokorny, chek... | 4711 |@word mild:1 deformed:1 version:1 norm:4 turlach:1 johansson:1 suitably:1 grey:4 tr:3 carry:1 initial:1 celebrated:1 reynolds:1 current:3 z2:5 dx:6 csc:1 happen:1 enables:3 analytic:1 plot:1 intelligence:1 ith:4 core:1 record:2 provides:1 math:1 location:1 traverse:1 mathematical:2 dn:2 persistent:12 incorrect:1 ... |
4,102 | 4,712 | Timely Object Recognition
Sergey Karayev
UC Berkeley
Tobias Baumgartner
RWTH Aachen University
Mario Fritz
MPI for Informatics
Trevor Darrell
UC Berkeley
Abstract
In a large visual multi-class detection framework, the timeliness of results can be
crucial. Our method for timely multi-class detection aims to give th... | 4712 |@word version:1 dalal:7 compression:1 manageable:1 seems:1 everingham:1 triggs:6 c0:5 open:2 gradual:1 carolina:1 pick:2 asks:1 profit:1 minus:1 recursively:1 initial:2 configuration:2 contains:2 score:14 selecting:4 cyclic:1 hoiem:1 esj:1 existing:1 current:7 com:1 contextual:1 yet:1 tackling:1 must:2 theof:1 wr... |
4,103 | 4,713 | Scaled Gradients on Grassmann Manifolds
for Matrix Completion
Thanh T. Ngo and Yousef Saad
Department of Computer Science and Engineering
University of Minnesota, Twin Cities
Minneapolis, MN 55455
thango@cs.umn.edu, saad@cs.umn.edu
Abstract
This paper describes gradient methods based on a scaled metric on the Grassma... | 4713 |@word multitask:1 milenkovic:1 version:11 norm:4 nd:1 d2:1 decomposition:5 tr:3 sepulchre:1 initial:4 denoting:1 ours:1 ecole:1 outperforms:2 existing:1 diagonalized:1 current:9 si:35 attracted:1 reminiscent:1 must:2 numerical:1 shape:1 treating:1 update:12 grass:8 stationary:1 intelligence:1 selected:1 steepest:... |
4,104 | 4,714 | Unsupervised Template Learning for
Fine-Grained Object Recognition
Shulin Yang
University of Washington, Seattle, WA 98195
yang@cs.washington.edu
Jue Wang
Adobe ATL Labs, Seattle, WA 98103
juewang@adobe.com
Liefeng Bo
ISTC-PC Intel labs, Seattle, WA 98195
liefeng.bo@intel.com
Linda Shapiro
University of Washington, ... | 4714 |@word version:1 tried:1 rgb:2 tr:1 initial:4 contains:5 score:10 selecting:2 outperforms:2 existing:1 current:1 com:2 comparing:2 babenko:1 partition:3 shape:19 christian:1 designed:1 update:4 discrimination:1 cue:6 discovering:2 selected:9 greedy:1 morariu:1 intelligence:1 beginning:1 coarse:1 iterates:2 detecti... |
4,105 | 4,715 | How They Vote:
Issue-Adjusted Models of Legislative Behavior
Sean M. Gerrish?
Department of Computer Science
Princeton University
Princeton, NJ 08540
sgerrish@cs.princeton.edu
David M. Blei
Department of Computer Science
Princeton University
Princeton, NJ 08540
blei@cs.princeton.edu
Abstract
We develop a probabilist... | 4715 |@word cox:3 version:1 nd:1 cha:1 cleanly:1 simulation:1 ld:2 liu:1 series:2 zuk:4 united:2 loc:1 daniel:1 document:7 ours:1 existing:1 err:1 current:1 wd:8 comparing:1 yet:1 bd:20 john:1 ronald:3 cant:1 remove:1 interpretable:2 fund:1 discrimination:1 alone:2 a2d:1 item:7 website:1 affair:3 smith:1 painstaking:1 ... |
4,106 | 4,716 | Topology Constraints in Graphical Models
Marcelo Fiori
Universidad de la
Rep?ublica, Uruguay
mfiori@fing.edu.uy
Pablo Mus?e
Universidad de la
Rep?ublica, Uruguay
pmuse@fing.edu.uy
Guillermo Sapiro
Duke University
Durham, NC 27708
guillermo.sapiro@duke.edu
Abstract
Graphical models are a very useful tool to describe... | 4716 |@word kong:1 illustrating:1 middle:3 version:4 exploitation:1 norm:1 seems:1 covariance:6 recapitulate:1 xtest:2 thereby:1 tr:1 solid:3 minus:1 recursively:1 liu:3 contains:3 series:3 genetic:7 past:2 outperforms:3 existing:2 current:1 fn:1 numerical:3 happen:1 v:2 selected:1 iterates:1 detecting:1 node:32 simple... |
4,107 | 4,717 | Searching for objects driven by context
Bogdan Alexe
BIWI
ETH Zurich
Nicolas Heess
Gatsby Unit
UCL
Yee Whye Teh
Department of Statistics
University of Oxford
Vittorio Ferrari
School of Informatics
University of Edinburgh
Abstract
The dominant visual search paradigm for object class detection is sliding windows. Al... | 4717 |@word version:2 dalal:1 seems:1 everingham:1 triggs:1 d2:1 pick:1 recursively:1 contains:3 score:9 foveal:1 past:9 freitas:1 current:6 comparing:1 surprising:1 must:1 hou:1 najemnik:1 partition:1 informative:1 confirming:1 hofmann:1 enables:1 shape:1 wiewiora:1 designed:3 update:2 v:1 cue:1 fewer:5 selected:1 beg... |
4,108 | 4,718 | Analog readout for optical reservoir computers
A. Smerieri1 , F. Duport1 , Y. Paquot1 , B. Schrauwen2 , M. Haelterman1 , S. Massar3
1
Service OPERA-photonique, Universit? Libre de Bruxelles (U.L.B.), 50 Avenue F. D.
Roosevelt, CP 194/5, B-1050 Bruxelles, Belgium
2
Department of Electronics and Information Systems (E... | 4718 |@word version:1 schurmann:1 middle:1 norm:1 seems:1 extinction:1 open:3 simulation:2 thereby:2 minus:1 electronics:1 liquid:1 tuned:1 amp:1 rightmost:1 o2:3 outperforms:1 recovered:2 comparing:1 current:2 discretization:1 must:3 written:1 john:1 realize:1 subsequent:1 pertinent:1 remove:2 designed:1 plot:1 half:1... |
4,109 | 4,719 | Emergence of Object-Selective Features in
Unsupervised Feature Learning
Adam Coates, Andrej Karpathy, Andrew Y. Ng
Computer Science Department
Stanford University
Stanford, CA 94305
{acoates,karpathy,ang}@cs.stanford.edu
Abstract
Recent work in unsupervised feature learning has focused on the goal of discovering high... | 4719 |@word version:1 briefly:1 middle:1 seems:1 open:1 hyv:3 decomposition:1 garrigues:1 carry:1 reduction:1 contains:2 interestingly:1 deconvolutional:1 existing:5 current:1 activation:2 yet:3 tackling:1 must:1 si:1 reminiscent:1 devin:1 subsequent:1 distant:1 enables:1 designed:1 half:2 discovering:1 selected:2 fewe... |
4,110 | 472 | Competitive Anti-Hebbian Learning of Invariants
Nicol N. Schraudolph
Computer Science & Engr. Dept.
University of California, San Diego
La Jolla, CA 92093-0114
Terrence J. Sejnowski
Computational Neurobiology Laboratory
The Salk Institute for Biological Studies
La Jolla, CA 92186-5800
nici@cs.ucsd.edu
tsejnowski@uc... | 472 |@word autoassociator:2 t_:1 covariance:1 pick:2 tsejnowski:1 catastrophically:1 xiy:3 disparity:27 tuned:4 current:1 nowlan:3 activation:4 intriguing:1 must:1 subsequent:1 happen:1 enables:1 remove:1 discrimination:1 half:5 discovering:1 trapping:1 characterization:1 provides:1 node:12 location:1 hyperplanes:1 fiv... |
4,111 | 4,720 | Multiresolution analysis on the symmetric group
Risi Kondor and Walter Dempsey
Department of Statistics and Department of Computer Science
The University of Chicago
{risi,wdempsey}@uchicago.edu
Abstract
There is no generally accepted way to define wavelets on permutations. We address this issue by introducing the not... | 4720 |@word version:1 kondor:2 polynomial:1 seems:1 d2:1 decomposition:3 mention:1 recursively:3 carry:1 born:1 denoting:1 comparing:1 si:1 yet:1 must:6 written:1 chicago:1 partition:6 j1:8 shape:3 intelligence:3 leaf:1 gribonval:1 math:1 si1:11 cosets:3 mathematical:2 along:1 direct:1 ik:96 inside:1 coifman:3 behavior... |
4,112 | 4,721 | Algorithms for Learning Markov Field Policies
Oliver Kr?omer, Jan Peters
Technische Universit?at Darmstadt
{oli,jan}@robot-learning.de
Abdeslam Boularias
Max Planck Institute for Intelligent Systems
boularias@tuebingen.mpg.de
Abstract
We use a graphical model for representing policies in Markov Decision Processes.
T... | 4721 |@word kohli:2 trial:1 version:1 middle:2 pieter:1 initial:3 series:1 selecting:1 daniel:1 current:1 contextual:1 si:26 scatter:1 written:1 subsequent:1 partition:2 shape:1 motor:1 update:2 stationary:1 intelligence:2 selected:2 fewer:1 slowing:1 beginning:1 ith:1 iterates:1 philipp:1 herbrich:1 mathematical:1 alo... |
4,113 | 4,722 | Assessing Blinding in Clinical Trials
Ognjen Arandjelovi?c
Deakin University, Australia
Abstract
The interaction between the patient?s expected outcome of an intervention and
the inherent effects of that intervention can have extraordinary effects. Thus in
clinical trials an effort is made to conceal the nature of the... | 4722 |@word trial:78 blindness:5 judgement:2 proportion:8 suitably:1 simulation:2 p0:9 pg:2 deems:3 solid:4 reduction:1 initial:2 born:1 series:4 selecting:1 denoting:1 ours:1 existing:2 current:2 comparing:2 nt:4 worsening:1 yet:3 assigning:1 must:1 readily:4 predetermined:1 plot:8 interpretable:1 progressively:2 half... |
4,114 | 4,723 | Density Propagation and
Improved Bounds on the Partition Function?
Stefano Ermon, Carla P. Gomes
Dept. of Computer Science
Cornell University
Ithaca NY 14853, U.S.A.
Ashish Sabharwal
IBM Watson Research Ctr.
Yorktown Heights
NY 10598, U.S.A.
Bart Selman
Dept. of Computer Science
Cornell University
Ithaca NY 14853, U... | 4723 |@word version:4 polynomial:1 stronger:2 norm:2 open:1 decomposition:9 minus:1 moment:1 initial:5 configuration:24 contains:1 liu:1 current:1 si:3 assigning:1 grain:2 numerical:1 happen:1 additive:1 partition:25 remove:1 treating:1 plot:2 update:14 bart:1 greedy:3 leaf:2 selected:1 intelligence:4 provides:1 coarse... |
4,115 | 4,724 | Adaptive Learning of Smoothing Functions:
Application to Electricity Load Forecasting
Amadou Ba
IBM Research - Ireland
Mulhuddart, Dublin 15
amadouba@ie.ibm.com
Mathieu Sinn
IBM Research - Ireland
Mulhuddart, Dublin 15
mathsinn@ie.ibm.com
Yannig Goude
EDF R&D
Clamart, France
yannig.goude@edf.fr
Pascal Pompey
IBM Re... | 4724 |@word version:1 polynomial:1 stronger:1 decomposition:1 eng:1 abou:2 outlook:1 reduction:1 initial:7 liu:1 series:2 contains:1 score:1 tuned:1 longitudinal:2 outperforms:1 existing:1 current:3 com:3 erms:5 activation:1 hou:1 john:2 additive:30 partition:1 update:5 stationary:5 half:2 xk:26 short:9 chiang:2 gure:2... |
4,116 | 4,725 | Local Supervised Learning through Space
Partitioning
Venkatesh Saligrama
Dept. of Electrical and Computer Engineering
Boston University
Boston, MA 02116
srv@bu.edu
Joseph Wang
Dept. of Electrical and Computer Engineering
Boston University
Boston, MA 02116
joewang@bu.edu
Abstract
We develop a novel approach for super... | 4725 |@word repository:3 middle:1 nd:2 dekel:1 twelfth:1 termination:2 shuicheng:1 seek:2 recursively:1 reduction:1 initial:1 series:1 past:1 existing:3 current:1 surprising:2 yet:1 written:2 subsequent:1 partition:20 v:2 alone:1 greedy:1 fewer:1 implying:1 selected:2 intelligence:3 boosting:12 simpler:4 along:1 constr... |
4,117 | 4,726 | Convergence and Energy Landscape for Cheeger Cut
Clustering
Thomas Laurent
University of California, Riversize
Riverside, CA 92521
laurent@math.ucr.edu
Xavier Bresson
City University of Hong Kong
Hong Kong
xbresson@cityu.edu.hk
David Uminsky
University of San Francisco
San Francisco, CA 94117
duminsky@usfca.edu
Jame... | 4726 |@word kong:3 version:1 stronger:2 norm:1 hu:2 zelnik:1 ipm:22 contains:2 series:1 trinary:3 err:2 si:4 yet:1 must:9 numerical:2 subsequent:1 happen:1 benign:1 remove:1 reproducible:1 n0:2 v:1 alone:2 intelligence:2 s0n:37 steepest:2 fa9550:1 provides:12 math:2 iterates:11 successive:4 simpler:1 mathematical:6 c2:... |
4,118 | 4,727 | Optimal kernel choice for large-scale two-sample tests
Arthur Gretton,1,3 Bharath Sriperumbudur,1 Dino Sejdinovic,1 Heiko Strathmann2
1
Gatsby Unit and 2 CSD, CSML, UCL, UK; 3 MPI for Intelligent Systems, Germany
{arthur.gretton,bharat.sv,dino.sejdinovic,heiko.strathmann}@gmail
Sivaraman Balakrishnan
LTI, CMU, USA
sbal... | 4727 |@word trial:3 version:1 norm:2 d2:2 hu:2 covariance:6 tr:1 boundedness:1 fragment:1 selecting:1 rkhs:7 outperforms:2 exy:1 gmail:1 written:2 must:2 v:7 aside:1 isotropic:2 short:1 accepting:1 mcdiarmid:2 five:3 mathematical:1 c2:2 direct:1 bharat:1 classifiability:1 expected:1 indeed:2 themselves:1 love:1 mouline... |
4,119 | 4,728 | Communication-Efficient Algorithms for
Statistical Optimization
1
Yuchen Zhang1
John C. Duchi1
Martin Wainwright1,2
Department of Electrical Engineering and Computer Science and 2 Department of Statistics
University of California, Berkeley
Berkeley, CA 94720
{yuczhang,jduchi,wainwrig}@eecs.berkeley.edu
Abstract
We s... | 4728 |@word version:4 achievable:3 stronger:1 johansson:2 norm:1 dekel:2 nd:1 open:2 hu:2 simulation:2 bn:2 covariance:1 sgd:5 tr:4 reduction:2 initial:1 contains:1 series:1 wainwrig:1 current:1 comparing:2 com:3 si:3 intriguing:1 must:2 john:1 numerical:1 partition:2 informative:1 kdd:1 plot:8 juditsky:2 resampling:4 ... |
4,120 | 4,729 | Multi-Stage Multi-Task Feature Learning?
?
Pinghua Gong, ? Jieping Ye, ? Changshui Zhang
State Key Laboratory on Intelligent Technology and Systems
Tsinghua National Laboratory for Information Science and Technology (TNList)
Department of Automation, Tsinghua University, Beijing 100084, China
?
Computer Science and En... | 4729 |@word multitask:2 version:2 mri:8 norm:15 nd:6 tnlist:1 moment:1 liu:1 contains:1 score:1 tuned:1 mmse:2 outperforms:1 existing:1 current:1 dx:2 remove:1 treating:1 plot:4 v:4 asu:1 mental:1 provides:3 zhang:12 direct:1 dengcai:1 prove:1 introduce:1 theoretically:3 expected:1 multi:52 globally:2 considering:1 pro... |
4,121 | 473 | Constructing Proofs in Symmetric Networks
Gadi Pinkas
Computer Science Department
Washington University
Campus Box 1045
St. Louis, MO 63130
Abstract
This paper considers the problem of expressing predicate calculus in connectionist networks that are based on energy minimization. Given a firstorder-logic knowledge bas... | 473 |@word km:3 calculus:2 tr:1 accommodate:2 contains:1 activation:2 si:2 must:19 grain:1 dechter:1 visible:1 intelligence:2 ith:1 pointer:1 ron:1 preference:1 constructed:1 become:2 prove:1 dan:1 acquired:1 ra:1 themselves:1 planning:1 chi:1 becomes:1 campus:1 parsimony:1 corporation:1 nj:1 kimura:1 every:7 firstorde... |
4,122 | 4,730 | Deep Learning of Invariant Features via Simulated
Fixations in Video
Will Y. Zou1 , Shenghuo Zhu3 , Andrew Y. Ng2 , Kai Yu3
Department of Electrical Engineering, Stanford University, CA
2
Department of Computer Science, Stanford University, CA
3
NEC Laboratories America, Inc., Cupertino, CA
{wzou, ang}@cs.stanford.edu ... | 4730 |@word repository:2 middle:2 cox:1 norm:2 open:1 simulation:3 decomposition:1 pick:3 carry:2 initial:1 contains:2 selecting:1 com:1 comparing:1 surprising:1 activation:1 reminiscent:1 devin:1 visible:1 plasticity:1 designed:1 plot:4 generative:1 selected:1 fewer:1 plane:1 colored:1 location:4 successive:1 org:1 be... |
4,123 | 4,731 | Clustering Aggregation as
Maximum-Weight Independent Set
Nan Li
Longin Jan Latecki
Department of Computer and Information Sciences
Temple University, Philadelphia, USA
{nan.li,latecki}@temple.edu
Abstract
We formulate clustering aggregation as a special instance of Maximum-Weight
Independent Set (MWIS) problem. For a... | 4731 |@word proportion:4 d2:1 ci2:1 reduction:1 initial:1 contains:3 selecting:3 past:1 existing:1 current:2 assigning:1 partition:9 kdd:1 shape:3 remove:1 update:2 intelligence:1 selected:1 discovering:1 detecting:1 five:1 dn:1 constructed:3 c2:1 symposium:1 consists:3 combine:6 privacy:1 x0:2 pairwise:1 peng:1 p1:2 f... |
4,124 | 4,732 | Pointwise Tracking the Optimal Regression Function
Ran El-Yaniv and Yair Wiener
Computer Science Department
Technion ? Israel Institute of Technology
{rani,wyair}@{cs,tx}.technion.ac.il
Abstract
This paper examines the possibility of a ?reject option? in the context of least
squares regression. It is shown that using... | 4732 |@word trial:1 repository:1 version:3 rani:1 inversion:1 stronger:1 open:1 solid:2 kxkk:1 reduction:2 substitution:1 denoting:1 outperforms:1 beygelzimer:1 subsequent:2 additive:3 numerical:3 selected:1 devising:1 xk:2 ith:1 provides:1 boosting:2 five:2 unbounded:3 rc:2 x0:16 theoretically:1 indeed:1 expected:1 in... |
4,125 | 4,733 | Fast Resampling Weighted v-Statistics
Chunxiao Zhou
Mark O. Hatfield Clinical Research Center
National Institutes of Health
Bethesda, MD 20892
chunxiao.zhou@nih.gov
Jiseong Park
Dept of Math
George Mason Univ
Fairfax, VA 22030
jiseongp@gmail.com
Yun Fu
Dept of ECE
Northeastern Univ
Boston, MA 02115
yunfu@ece.neu.edu
... | 4733 |@word kondor:1 polynomial:3 nd:1 tedious:1 closure:1 nicholson:1 solid:1 recursively:4 carry:1 moment:14 reduction:6 liu:1 initial:1 existing:4 com:1 comparing:2 gmail:1 john:1 numerical:2 partition:12 resampling:47 greedy:1 intelligence:2 i1d:14 fa9550:1 provides:1 math:2 jdk:1 location:2 zhang:1 mathematical:1 ... |
4,126 | 4,734 | Diffusion Decision Making for Adaptive
k-Nearest Neighbor Classification
Yung-Kyun Noh, Frank Chongwoo Park
Schl. of Mechanical and Aerospace Engineering
Seoul National University
Seoul 151-744, Korea
{nohyung,fcp}@snu.ac.kr
Daniel D. Lee
Dept. of Electrical and Systems Engineering
University of Pennsylvania
Philadel... | 4734 |@word cnn:6 proportionality:1 seek:1 tried:1 concise:1 initial:1 contains:1 exclusively:1 nohyung:1 series:1 daniel:1 offering:1 existing:1 comparing:2 must:1 analytic:2 fuss:1 intelligence:1 fewer:1 papadopoulos:1 hypersphere:9 preference:1 simpler:1 zhang:1 five:11 mathematical:6 dn:11 along:1 introduce:1 theor... |
4,127 | 4,735 | Random Utility Theory for Social Choice
Hossein Azari Soufiani
SEAS, Harvard University
azari@fas.harvard.edu
David C. Parkes
SEAS, Harvard University
parkes@eecs.harvard.edu
Lirong Xia
SEAS, Harvard University
lxia@seas.harvard.edu
Abstract
Random utility theory models an agent?s preferences on alternatives by dra... | 4735 |@word middle:3 version:1 judgement:1 seems:1 logit:1 prominence:1 jacob:1 cyclic:2 series:2 score:6 liu:1 selecting:1 daniel:1 subjective:1 existing:2 bradley:2 comparing:1 lang:1 assigning:1 dx:3 must:2 peyton:1 john:2 numerical:1 partition:3 j1:3 kdd:1 shape:5 nisarg:1 generative:2 selected:1 half:1 parameteriz... |
4,128 | 4,736 | Minimizing Sparse High-Order Energies by
Submodular Vertex-Cover
Andrew Delong
University of Toronto
Olga Veksler
Western University
Anton Osokin
Moscow State University
Yuri Boykov
Western University
andrew.delong@gmail.com
olga@csd.uwo.ca
anton.osokin@gmail.com
yuri@csd.uwo.ca
Abstract
Inference in high-order... | 4736 |@word kohli:4 version:1 achievable:1 polynomial:3 trotter:2 open:1 seek:2 tried:1 thereby:1 reduction:6 initial:1 configuration:2 contains:4 substitution:2 existing:1 com:2 gmail:2 assigning:1 must:4 written:5 reminiscent:1 parsing:3 danny:1 subsequent:2 designed:1 v:1 greedy:4 fewer:1 intelligence:7 plane:1 vani... |
4,129 | 4,737 | Nonparametric Bayesian
Inverse Reinforcement Learning
for Multiple Reward Functions
Jaedeug Choi and Kee-Eung Kim
Department of Computer Science
Korea Advanced Institute of Science and Technology
Daejeon 305-701, Korea
jdchoi@ai.kaist.ac.kr, kekim@cs.kaist.ac.kr
Abstract
We present a nonparametric Bayesian approach t... | 4737 |@word h:1 multitask:1 proportion:1 open:1 pieter:1 simulation:1 fabrice:1 p0:1 initial:1 series:1 score:3 rightmost:1 outperforms:2 current:3 assigning:1 treating:1 designed:1 update:6 generative:1 amir:2 provides:1 preference:7 marivate:1 direct:2 eung:2 driver:3 beta:1 incorrect:1 consists:1 combine:2 apprentic... |
4,130 | 4,738 | Spiking and saturating dendrites differentially
expand single neuron computation capacity.
Mark Humphries
INSERM U960; University of Manchester
29 rue d?Ulm, 75005 Paris; UK
mark.humphries@manchester.ac.uk
Romain Caz?e
INSERM U960, Paris Diderot, Paris 7, ENS
29 rue d?Ulm, 75005 Paris
romain.caze@ens.fr
Boris Gutkin... | 4738 |@word open:1 calculus:1 accounting:1 solid:1 series:1 makara:1 current:1 z2:4 yet:2 conjunctive:2 written:2 must:1 stemming:1 plasticity:3 shape:1 enables:2 implying:1 nervous:1 theoretician:1 short:1 height:1 mathematical:2 c2:5 become:1 prove:3 introduce:2 indeed:2 behavior:1 growing:2 rall:1 decomposed:1 resol... |
4,131 | 4,739 | Clustering Sparse Graphs
Yudong Chen
Department of Electrical and Computer Engineering
The University of Texas at Austin
Austin, TX 78712
ydchen@utexas.edu
Sujay Sanghavi
Department of Electrical and Computer Engineering
The University of Texas at Austin
Austin, TX 78712
sanghavi@mail.utexas.edu
Huan Xu
Mechanical En... | 4739 |@word trial:1 briefly:1 version:4 norm:4 nd:1 c0:2 twelfth:1 km:1 confirms:1 simulation:3 condon:2 decomposition:6 pick:1 mpexuh:1 ours:1 semirandom:1 outperforms:3 existing:8 past:1 must:1 additive:1 partition:22 numerical:1 remove:1 devising:1 nq:3 vanishing:1 provides:6 certificate:4 node:20 simpler:2 ybij:2 r... |
4,132 | 474 | Learning in the Vestibular System:
Simulations of Vestibular Compensation
Using Recurrent Back-Propagation
Thomas J. Anastasio
University of Dlinois
Beckman Institute
405 N. Mathews Ave.
Urbana, II... 61801
Abstract
Vestibular compensation is the process whereby normal functioning is
regained following destruction of... | 474 |@word eliminating:1 retraining:4 heretofore:1 simulation:5 rhesus:1 accounting:1 solid:7 vor:33 subsequent:1 plasticity:3 motor:1 plot:1 medial:3 fewer:1 oldest:1 reciprocal:2 short:1 lr:9 sigmoidal:1 constructed:1 expected:3 behavior:6 themselves:1 brain:2 actual:5 little:1 increasing:1 begin:1 underlying:1 moreo... |
4,133 | 4,740 | Proximal Newton-type methods for convex
optimization
Jason D. Lee? and Yuekai Sun?
Institute for Computational and Mathematical Engineering
Stanford University, Stanford, CA
{jdl17,yuekai}@stanford.edu
Michael A. Saunders
Department of Management Science and Engineering
Stanford University, Stanford, CA
saunders@stanf... | 4740 |@word multitask:1 version:1 norm:3 seems:1 k2hk:1 seek:3 crucially:1 covariance:3 hsieh:1 liblinear:2 outperforms:1 nonmonotone:2 optim:2 cheap:1 drop:1 update:3 ubuntu:1 xk:50 core:2 characterization:1 math:4 tahoe:2 five:1 mathematical:1 supply:1 viable:1 yuan:1 prove:4 x0:1 behavior:1 cand:2 globally:1 cpu:1 s... |
4,134 | 4,741 | Deep Neural Networks Segment Neuronal
Membranes in Electron Microscopy Images
Dan C. Cires?an?
IDSIA
USI-SUPSI
Lugano 6900
dan@idsia.ch
Alessandro Giusti
IDSIA
USI-SUPSI
Lugano 6900
alessandrog@idsia.ch
?
Jurgen
Schmidhuber
IDSIA
USI-SUPSI
Lugano 6900
juergen@idsia.ch
Luca M. Gambardella
IDSIA
USI-SUPSI
Lugano 6900
... | 4741 |@word mild:2 deformed:1 cnn:1 polynomial:2 radim:1 disk:1 open:3 pavel:1 kerlin:1 lepetit:2 briggman:1 liu:3 series:1 score:2 daniel:2 document:4 outperforms:3 existing:1 com:1 contextual:1 activation:5 connectomics:1 gpu:4 john:2 blur:1 shape:3 remove:1 designed:1 davi:1 intelligence:2 fewer:1 nervous:1 merger:1... |
4,135 | 4,742 | To appear in: Neural Information Processing Systems (NIPS),
Lake Tahoe, Nevada. December 3-6, 2012.
Efficient and direct estimation of a neural subunit
model for sensory coding
Brett Vintch
Andrew D. Zaharia
?
J. Anthony Movshon
Eero P. Simoncelli ?
Center for Neural Science, and
Howard Hughes Medical Institute
Ne... | 4742 |@word neurophysiology:3 trial:5 briefly:1 wiesel:2 open:1 simulation:1 covariance:7 simplifying:1 initial:4 inefficiency:1 contains:3 efficacy:1 loc:1 outperforms:3 current:1 recovered:2 written:1 readily:1 physiol:1 additive:1 partition:1 subsequent:1 informative:2 wellbehaved:1 interpretable:3 v:2 half:1 fewer:... |
4,136 | 4,743 | The Coloured Noise Expansion and Parameter
Estimation of Diffusion Processes
Simo S?arkk?a
Aalto University
Department of Biomedical Engineering
and Computational Science
Rakentajanaukio 2, 02150 Espoo
simo.sarkka@aalto.fi
Simon M.J. Lyons
School of Informatics
University of Edinburgh
10 Crichton Street, Edinburgh, EH... | 4743 |@word open:1 proportionality:2 calculus:2 heuristically:1 simulation:1 decomposition:2 covariance:6 solid:2 ytn:1 initial:7 series:3 disparity:1 zij:1 rightmost:1 past:3 reaction:2 existing:2 current:3 arkk:2 z2:1 atlantic:1 luo:1 must:5 kiebel:1 numerical:7 enables:2 hypothesize:1 designed:1 plot:1 progressively... |
4,137 | 4,744 | A latent factor model for highly multi-relational data
Nicolas Le Roux
INRIA - SIERRA Project Team,
Ecole Normale Sup?erieure, Paris, France
nicolas@le-roux.name
Rodolphe Jenatton
CMAP, UMR CNRS 7641,
Ecole Polytechnique, Palaiseau, France
jenatton@cmap.polytechnique.fr
Antoine Bordes
Heudiasyc, UMR CNRS 7253,
Univer... | 4744 |@word h:2 mild:1 briefly:1 bigram:9 norm:4 advantageous:1 seems:2 stronger:1 open:2 seek:1 accounting:1 decomposition:3 eng:1 mammal:1 thereby:1 carry:1 reduction:1 initial:1 contains:1 score:3 ecole:3 denoting:1 ours:1 interestingly:2 document:1 outperforms:1 existing:1 com:1 si:29 assigning:1 chu:1 written:1 pa... |
4,138 | 4,745 | A Scalable CUR Matrix Decomposition Algorithm:
Lower Time Complexity and Tighter Bound
Shusen Wang and Zhihua Zhang
College of Computer Science & Technology
Zhejiang University
Hangzhou, China 310027
{wss,zhzhang}@zju.edu.cn
Abstract
The CUR matrix decomposition is an important extension of Nystr?om approximation to ... | 4745 |@word mild:1 trial:1 repository:1 briefly:1 faculty:1 polynomial:1 norm:12 proportion:1 loading:4 nd:1 seek:3 decomposition:15 q1:1 mention:1 nystr:2 tr:1 contains:2 selecting:3 document:2 interestingly:1 existing:5 si:4 luis:2 john:1 numerical:1 informative:2 update:1 mackey:1 selected:2 ubuntu:1 provides:1 comp... |
4,139 | 4,746 | MCMC for continuous-time discrete-state systems
Yee Whye Teh
Gatsby Computational Neuroscience Unit
University College London
ywteh@gatsby.ucl.ac.uk
Vinayak Rao
Gatsby Computational Neuroscience Unit
University College London
vrao@gatsby.ucl.ac.uk
Abstract
We propose a simple and novel framework for MCMC inference i... | 4746 |@word mjp:8 confirms:1 simulation:3 tried:1 r:1 propagate:1 initial:2 series:2 united:1 genetic:1 ours:2 interestingly:1 outperforms:4 favouring:1 freitas:2 current:4 discretization:25 comparing:2 si:11 assigning:1 vere:1 additive:1 shape:2 treating:1 plot:4 update:1 resampling:3 stationary:1 generative:3 v:1 int... |
4,140 | 4,747 | Learning with Recursive Perceptual Representations
Oriol Vinyals
UC Berkeley
Berkeley, CA
Li Deng
Microsoft Research
Redmond, WA
Yangqing Jia
UC Berkeley
Berkeley, CA
Trevor Darrell
UC Berkeley
Berkeley, CA
Abstract
Linear Support Vector Machines (SVMs) have become very popular in vision as
part of state-of-the-ar... | 4747 |@word version:2 briefly:1 dalal:1 open:1 additively:1 tr:2 harder:1 recursively:3 shechtman:1 reduction:1 configuration:2 contains:7 score:2 initial:1 tuned:1 outperforms:1 existing:1 current:2 activation:1 subsequent:1 numerical:1 enables:1 designed:1 drop:2 v:3 fewer:1 guess:1 core:3 provides:3 detecting:1 code... |
4,141 | 4,748 | Automatic Feature Induction
for Stagewise Collaborative Filtering
Joonseok Leea , Mingxuan Suna , Seungyeon Kima , Guy Lebanona, b
College of Computing, Georgia Institute of Technology, Atlanta, GA 30332
b
Google Research, Mountain View, CA 94043
{jlee716, msun3, seungyeon.kim}@gatech.edu, lebanon@cc.gatech.edu
a
Abs... | 4748 |@word trial:1 middle:3 seems:1 open:1 contains:1 selecting:2 outperforms:5 horvitz:1 comparing:1 surprising:2 additive:3 informative:1 enables:2 designed:2 aside:1 mackey:1 greedy:2 selected:9 intelligence:6 item:52 kyk:1 prize:1 boosting:4 location:1 preference:1 five:2 constructed:3 combine:5 manner:3 expected:... |
4,142 | 4,749 | From Deformations to Parts:
Motion-based Segmentation of 3D Objects
Soumya Ghosh1 , Erik B. Sudderth1 , Matthew Loper2 , and Michael J. Black2
1
Department of Computer Science, Brown University, {sghosh,sudderth}@cs.brown.edu
2
Perceiving Systems Department, Max Planck Institute for Intelligent Systems,
{mloper,black}... | 4749 |@word deformed:1 calculus:1 km:1 seek:2 covariance:6 pick:1 accommodate:1 bai:1 configuration:1 contains:1 liu:1 animated:1 existing:2 comparing:1 must:5 mesh:114 partition:12 xb1:1 christian:1 shape:11 designed:1 plot:1 n0:13 cue:1 discovering:1 generative:2 fewer:1 selected:2 half:4 blei:2 provides:1 characteri... |
4,143 | 475 | Recurrent Networks and N ARMA Modeling
Jerome Connor
Les E. Atlas
FT-lO
Interactive Systems Design Laboratory
Dept. of Electrical Engineering
University of Washington
Seattle, Washington 98195
Douglas R. Martin
B-317
Dept. of Statistics
University of Washington
Seattle, Washington 98195
Abstract
There exist large c... | 475 |@word thereby:1 initial:1 configuration:1 series:17 lapedes:2 past:7 wd:3 comparing:1 written:2 must:1 distant:2 enables:1 atlas:7 sponsored:2 plot:1 v:2 stationary:2 alone:1 indicative:1 plane:1 xk:2 record:1 provides:1 monday:1 sigmoidal:1 five:1 constructed:2 become:1 midnight:1 xtl:1 nondeterministic:2 inside:... |
4,144 | 4,750 | Learning optimal spike-based representations
Ralph Bourdoukan?
Group for Neural Theory
?
Ecole
Normale Sup?erieure
Paris, France
ralph.bourdoukan@ens.fr
David G.T. Barrett?
Group for Neural Theory
?
Ecole
Normale Sup?erieure
Paris, France
david.barrett@ens.fr
Christian K. Machens
Champalimaud Neuroscience Programme
... | 4750 |@word trial:4 middle:6 manageable:1 seems:1 proportion:3 heterogeneously:1 seek:2 simulation:1 accounting:1 thereby:5 solid:4 reduction:1 initial:3 contains:1 ecole:3 must:5 written:1 realistic:1 interspike:1 plasticity:14 christian:2 drop:2 plot:2 progressively:1 treating:1 greedy:3 selected:1 ith:4 recherche:1 ... |
4,145 | 4,752 | Bayesian Hierarchical Reinforcement Learning
Soumya Ray
Department of EECS
Case Western Reserve University
Cleveland, OH 44106
sray@case.edu
Feng Cao
Department of EECS
Case Western Reserve University
Cleveland, OH 44106
fxc100@case.edu
Abstract
We describe an approach to incorporating Bayesian priors in the MAXQ fr... | 4752 |@word version:4 polynomial:1 replicate:1 tadepalli:2 nd:1 mehta:1 termination:2 simulation:8 propagate:1 prasad:1 simplifying:1 decomposition:7 pick:1 thereby:1 recursively:13 initial:2 contains:1 prefix:1 outperforms:1 current:15 comparing:1 si:2 must:1 ronald:1 visible:1 shape:1 hypothesize:1 treating:2 drop:2 ... |
4,146 | 4,753 | Risk?Aversion in Multi?armed Bandits
Amir Sani
Alessandro Lazaric
R?mi Munos
INRIA Lille - Nord Europe, Team SequeL
{amir.sani,alessandro.lazaric,remi.munos}@inria.fr
Abstract
Stochastic multi?armed bandits solve the Exploration?Exploitation dilemma and
ultimately maximize the expected reward. Nonetheless, in many pr... | 4753 |@word trial:3 exploitation:4 version:3 achievable:1 nd:1 open:3 simulation:4 tat:1 covariance:1 incurs:1 series:1 selecting:1 tuned:1 current:2 surprising:1 must:1 numerical:5 christian:1 designed:2 update:2 v:2 implying:2 amir:3 warmuth:1 beginning:2 vanishing:1 short:1 manfred:1 successive:1 mathematical:1 dire... |
4,147 | 4,754 | Convergence Rate Analysis of MAP Coordinate
Minimization Algorithms
Ofer Meshi ?
Tommi Jaakkola ?
Amir Globerson ?
meshi@cs.huji.ac.il
tommi@csail.mit.edu
gamir@cs.huji.ac.il
Abstract
Finding maximum a posteriori (MAP) assignments in graphical models is an important task in many applications. Since the problem i... | 4754 |@word version:2 norm:2 advantageous:1 stronger:2 c0:2 seek:2 decomposition:6 euclidian:1 solid:1 harder:1 cyclic:3 configuration:1 bhattacharyya:1 outperforms:1 current:1 comparing:1 si:21 written:1 must:1 additive:1 cheap:1 drop:1 update:26 v:1 implying:1 greedy:22 intelligence:2 amir:1 ctu:1 smith:1 core:1 cave... |
4,148 | 4,755 | Approximating Equilibria in Sequential Auctions with
Incomplete Information and Multi-Unit Demand
Jiacui Li
Department of Applied Math/Economics
Brown University
Providence, RI 02912
jiacui li@alumni.brown.edu
Amy Greenwald and Eric Sodomka
Department of Computer Science
Brown University
Providence, RI 02912
{amy,sod... | 4755 |@word private:5 economically:1 fatima:2 termination:1 simulation:6 heretofore:1 pulse:1 paid:2 profit:2 minus:1 shot:3 initial:4 liu:2 contains:1 denoting:1 past:3 existing:3 com:1 comparing:1 si:13 must:2 additive:1 subsequent:2 happen:1 confirming:1 remove:1 plot:3 update:2 intelligence:4 item:1 provides:3 math... |
4,149 | 4,756 | Symbolic Dynamic Programming for Continuous
State and Observation POMDPs
Zahra Zamani
ANU & NICTA
Canberra, Australia
Scott Sanner
NICTA & ANU
Canberra, Australia
zahra.zamani@anu.edu.au
scott.sanner@nicta.com.au
Pascal Poupart
U. of Waterloo
Waterloo, Canada
Kristian Kersting
Fraunhofer IAIS & U. of Bonn
Bonn, Ge... | 4756 |@word h:1 version:3 polynomial:1 open:14 crucially:1 pressure:12 solid:1 recursively:1 delgado:1 carry:1 initial:3 substitution:4 contains:1 o2:8 com:2 must:4 porta:1 numerical:1 partition:25 subsequent:1 v:2 intelligence:5 scotland:1 smith:1 short:1 provides:3 node:2 toronto:1 karina:1 along:1 direct:1 eung:1 ds... |
4,150 | 4,757 | Online allocation and homogeneous partitioning for
piecewise constant mean-approximation
Odalric Ambrym Maillard
Montanuniversit?at Leoben
Franz-Josef Strasse 18
A-8700 Leoben, Austria
Alexandra Carpentier
Statistical Laboratory, CMS
Wilberforce Road, Cambridge
CB3 0WB UK
odalricambrym.maillard@gmail.com
a.carpentie... | 4757 |@word exploitation:1 version:2 norm:3 stronger:2 nd:1 proportion:1 d2:2 decomposition:2 covariance:1 shot:2 moment:3 initial:2 past:1 existing:1 com:1 gmail:1 dx:7 written:6 must:1 yet:4 fn:3 numerical:2 partition:25 enables:1 accordingly:1 cult:4 beginning:1 mental:2 provides:4 argmax1:1 mathematical:2 direct:1 ... |
4,151 | 4,758 | Natural Images, Gaussian Mixtures and Dead Leaves
Daniel Zoran
Yair Weiss
Interdisciplinary Center for Neural Computation
School of Computer Science and Engineering
Hebrew University of Jerusalem
Israel
http : //www . cs . hu j i . ac .i l/ daniez
Hebrew University of Jerusalem
Israel
yweiss@cs . huj i. ac . i l
... | 4758 |@word version:1 seems:2 nd:1 hu:1 seek:1 covariance:18 decorrelate:1 pick:1 minus:1 reduction:1 configuration:2 score:1 daniel:1 denoting:1 interestingly:1 outperforms:6 subjective:1 current:4 comparing:3 surprising:5 yet:1 written:1 numerical:1 visible:2 partition:1 shape:4 stationary:4 generative:7 leaf:35 plan... |
4,152 | 4,759 | A lattice filter model of the visual pathway
Karol Gregor
Dmitri B. Chklovskii
Janelia Farm Research Campus, HHMI
19700 Helix Drive, Ashburn, VA
{gregork, mitya}@janelia.hhmi.org
Abstract
Early stages of visual processing are thought to decorrelate, or whiten, the incoming temporally varying signals. Motivated by the... | 4759 |@word neurophysiology:5 version:1 briefly:1 polynomial:2 compression:4 stronger:2 seems:2 hu:1 decorrelate:2 paulsen:1 mammal:2 rivera:1 minus:2 schnitzer:1 reduction:3 initial:1 born:1 series:4 contains:2 efficacy:1 offering:1 interestingly:4 past:5 existing:1 current:5 comparing:1 nt:2 yet:1 dx:4 reminiscent:1 ... |
4,153 | 476 | Generalization Performance in PARSEC-A
Structured Connectionist Parsing Architecture
Ajay N. Jain?
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213-3890
ABSTRACT
This paper presents PARSEC-a system for generating connectionist
parsing networks from example parses. PARSEC is not based on form... | 476 |@word version:5 alliant:1 yea:1 series:1 current:1 com:1 surprising:1 lang:1 parsing:23 distant:1 alphanumeric:2 device:1 prize:2 lr:6 toronto:1 location:1 lexicon:1 parsec:65 along:1 constructed:2 incorrect:2 combine:1 ascribed:1 acquired:1 elman:2 dialog:4 multi:4 jimukyoku:2 automatically:1 actual:1 overwhelmin... |
4,154 | 4,760 | Semantic Kernel Forests from Multiple Taxonomies
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
When learning features for c... | 4760 |@word briefly:1 dalal:1 norm:1 seal:3 triggs:1 hu:3 seek:1 r:1 accounting:1 attainable:1 incurs:1 thereby:1 accommodate:1 initial:2 series:1 contains:1 tuned:2 genetic:1 biolog:1 past:1 outperforms:2 regarding:1 current:1 bmr:1 comparing:1 si:1 yet:3 stemming:2 ctn:5 distant:1 happen:1 partition:1 informative:2 s... |
4,155 | 4,761 | Human memory search as a random walk
in a semantic network
Joseph L. Austerweil
Department of Psychology
University of California, Berkeley
Berkeley, CA 94720
joseph.austerweil@gmail.com
Joshua T. Abbott
Department of Psychology
University of California, Berkeley
Berkeley, CA 94720
joshua.abbott@berkeley.edu
Thomas L... | 4761 |@word mild:1 repository:1 longterm:1 kintsch:1 middle:1 stronger:2 seems:1 norm:1 forager:1 simulation:11 decomposition:1 prominence:3 accounting:1 tr:1 carry:1 initial:1 contains:1 fragment:1 reaction:1 existing:1 current:3 com:1 activation:1 gmail:1 intriguing:1 must:1 additive:4 predetermined:1 drop:1 stationa... |
4,156 | 4,762 | Tractable Objectives for Robust Policy Optimization
Katherine Chen
University of Alberta
Michael Bowling
University of Alberta
kchen4@ualberta.ca
bowling@cs.ualberta.ca
Abstract
Robust policy optimization acknowledges that risk-aversion plays a vital role in
real-world decision-making. When faced with uncertainty a... | 4762 |@word trial:5 innovates:1 version:1 polynomial:6 szafron:1 unif:1 seek:1 dramatic:1 wisniewski:1 carry:1 initial:2 contains:1 daniel:1 past:5 existing:2 subjective:1 current:6 must:3 john:1 informative:2 shape:2 remove:1 plot:2 drop:1 update:2 stationary:3 intelligence:2 selected:2 guess:1 leaf:1 advancement:1 ac... |
4,157 | 4,763 | Nonconvex Penalization Using Laplace Exponents
and Concave Conjugates
Zhihua Zhang and Bojun Tu
College of Computer Science & Technology
Zhejiang University
Hangzhou, China 310027
{zhzhang, tubojun}@zju.edu.cn
Abstract
In this paper we study sparsity-inducing nonconvex penalty functions using L?evy
processes. We defi... | 4763 |@word repository:1 norm:2 proportion:2 triazine:2 calculus:1 simulation:1 covariance:1 kent:1 elisseeff:1 delgado:1 garrigues:1 initial:1 liu:1 series:2 interestingly:1 bradley:1 readily:1 plot:1 intelligence:1 selected:1 accordingly:2 provides:2 evy:13 compressible:1 zhang:6 along:1 constructed:1 direct:2 beta:1... |
4,158 | 4,764 | Multiclass Learning with Simplex Coding
Youssef Mroueh],? , Tomaso Poggio],? , Lorenzo Rosasco],? Jean-Jacques E. Slotine?
] - CBCL, McGovern Institute, MIT;? -LCSL, MIT- IIT; ? - ME, BCS, MIT
ymroueh, lrosasco,jjs@mit.edu tp@ai.mit.edu
Abstract
In this paper we discuss a novel framework for multiclass learning, defi... | 4764 |@word cox:1 version:1 seems:1 norm:1 c0:4 r:3 decomposition:2 mention:1 moment:1 liu:1 series:1 rkhs:2 interestingly:3 recovered:1 com:1 nt:3 yet:1 written:2 must:1 fn:3 numerical:2 shape:1 hofmann:1 treating:1 v:2 half:2 fewer:1 cy0:4 intelligence:2 hypersphere:1 boosting:7 math:1 zhang:2 c2:4 zickler:1 symposiu... |
4,159 | 4,765 | Globally Convergent Dual MAP LP Relaxation
Solvers using Fenchel-Young Margins
Tamir Hazan
TTI Chicago
tamir@ttic.edu
Alexander G. Schwing
ETH Zurich
aschwing@inf.ethz.ch
Raquel Urtasun
TTI Chicago
rurtasun@ttic.edu
Marc Pollefeys
ETH Zurich
pomarc@inf.ethz.ch
Abstract
While finding the exact solution for the MAP ... | 4765 |@word kohli:1 version:1 norm:2 decomposition:4 configuration:1 denoting:1 interestingly:1 outperforms:1 existing:2 current:1 comparing:1 recovered:1 com:1 written:1 parsing:1 chicago:2 partition:1 designed:1 update:6 stationary:5 steepest:18 smith:1 tarlow:1 provides:7 characterization:2 certificate:1 detecting:1... |
4,160 | 4,766 | Fast Variational Inference in the
Conjugate Exponential Family
James Hensman?
Department of Computer Science
The University of Sheffield
james.hensman@sheffield.ac.uk
Magnus Rattray
Faculty of Life Science
The University of Manchester
magnus.rattray@manchester.ac.uk
Neil D. Lawrence?
Department of Computer Science
T... | 4766 |@word version:2 faculty:1 eliminating:1 inversion:1 proportion:5 reused:1 proportionality:1 covariance:1 initial:4 efficacy:1 initialisation:1 document:4 ours:1 existing:2 current:2 z2:2 si:2 dx:6 written:1 readily:1 realize:1 eleven:1 enables:1 analytic:1 update:13 intelligence:2 fewer:1 selected:2 steepest:10 r... |
4,161 | 4,767 | Efficient Bayes-Adaptive Reinforcement Learning
using Sample-Based Search
Arthur Guez
David Silver
Peter Dayan
aguez@gatsby.ucl.ac.uk
d.silver@cs.ucl.ac.uk
dayan@gatsby.ucl.ac.uk
Abstract
Bayesian model-based reinforcement learning is a formally elegant approach to
learning optimal behaviour under model uncertai... | 4767 |@word h:45 exploitation:3 version:2 polynomial:2 seems:1 nd:1 suitably:1 simulation:30 r:6 pressure:1 brightness:1 minus:1 bourgine:1 tuned:2 existing:5 current:7 guez:1 written:1 readily:1 must:1 stemming:1 numerical:1 sorg:2 designed:1 update:8 resampling:2 implying:1 greedy:3 generative:3 leaf:2 selected:4 vmi... |
4,162 | 4,768 | Exponential Concentration for Mutual Information
Estimation with Application to Forests
John Lafferty
Department of Computer Science
Department of Statistics
University of Chicago, IL 60637
lafferty@galton.uchicago.edu
Han Liu
Department of Operations Research
and Financial Engineering
Princeton University, NJ 08544
... | 4768 |@word norm:1 nd:2 decomposition:1 contraction:1 paid:2 reduction:1 liu:8 series:2 pbh:13 ours:1 yet:1 dx:26 john:3 grassberger:1 chicago:1 partition:1 n0:2 joy:1 xk:9 vanishing:2 short:1 fa9550:1 provides:2 node:2 herbrich:1 mcdiarmid:1 unbounded:1 mathematical:3 c2:4 h4:1 differential:1 prove:3 advocate:1 expect... |
4,163 | 4,769 | Learning to Align from Scratch
Gary B. Huang1
Marwan A. Mattar1 Honglak Lee2 Erik Learned-Miller1
1
University of Massachusetts, Amherst, MA
{gbhuang,mmattar,elm}@cs.umass.edu
2
University of Michigan, Ann Arbor, MI
honglak@eecs.umich.edu
Abstract
Unsupervised joint alignment of images has been demonstrated to improv... | 4769 |@word multitask:1 cox:3 version:1 mri:1 norm:5 everingham:1 d2:1 hyv:1 gradual:1 decomposition:1 crbms:3 contrastive:2 thereby:1 shot:1 bai:1 configuration:2 liu:2 uma:1 score:8 tuned:2 deconvolutional:1 existing:1 current:1 activation:7 must:2 mesh:1 subsequent:1 visible:11 periodically:1 shape:2 designed:1 gene... |
4,164 | 477 | A Parallel Analog CCD/CMOS Signal Processor
Charles F. Neugebauer
Amnon Yariv
Department of Applied Physics
California Institute of Technology
Pasadena, CA 91125
Abstract
A CCO based signal processing IC that computes a fully parallel single
quadrant vector-matrix multiplication has been designed and fabricated with... | 477 |@word version:1 loading:3 cco:5 pulse:1 q1:1 fonn:1 solid:1 initial:1 configuration:1 contains:2 optically:1 selecting:1 offering:1 amp:1 current:3 activation:1 must:1 refresh:2 periodically:1 designed:1 device:11 chiang:2 num:1 successive:1 x128:1 along:1 c2:2 driver:1 qij:2 consists:1 overhead:1 manner:2 lov:1 b... |
4,165 | 4,770 | Dynamical And-Or Graph Learning for Object Shape
Modeling and Detection
Liang Lin?
Sun Yat-Sen University
Guangzhou, P.R. China 510006
linliang@ieee.org
Xiaolong Wang
Sun Yat-Sen University
Guangzhou, P.R. China 510006
dragonwxl123@gmail.com
Abstract
This paper studies a novel discriminative part-based model to repre... | 4770 |@word unaltered:1 version:1 middle:1 decomposition:1 accounting:1 p0:15 brightness:1 solid:1 reduction:1 initial:3 configuration:4 contains:3 fragment:10 selecting:1 score:2 fevrier:1 nonexistent:1 outperforms:3 existing:1 current:2 com:1 contextual:1 comparing:1 ka:1 activation:5 gmail:1 dx:2 parsing:5 partition... |
4,166 | 4,771 | Identification of Recurrent Patterns in the Activation
of Brain Networks
Firdaus Janoos? Weichang Li Niranjan Subrahmanya
ExxonMobil Corporate Strategic Research
Annandale, NJ 08801
? M?orocz William M. Wells (III)
Istv?an A.
Harvard Medical School
Boston, MA 02115
Abstract
Identifying patterns from the neuroimaging ... | 4771 |@word mri:3 open:1 essay:1 pearlson:1 accounting:1 decomposition:1 jacob:1 eng:1 homomorphism:1 fifteen:1 thereby:2 tr:2 pressure:1 shot:1 reduction:1 configuration:1 series:6 selecting:2 tuned:1 existing:1 rish:1 comparing:2 com:1 nt:1 activation:8 chu:1 nt1:2 numerical:1 distant:2 partition:1 enables:1 motor:7 ... |
4,167 | 4,772 | Scaling MPE Inference for Constrained Continuous
Markov Random Fields with Consensus Optimization
Stephen H. Bach
University of Maryland, College Park
College Park, MD 20742
bach@cs.umd.edu
Matthias Broecheler
Aurelius LLC
matthias@thinkaurelius.com
Lise Getoor
University of Maryland, College Park
College Park, MD 2... | 4772 |@word mild:2 version:1 c0:38 crucially:1 decomposition:4 dramatic:1 wrapper:2 substitution:1 denoting:2 current:2 com:2 dx:1 must:1 written:1 chu:1 numerical:1 partition:1 drop:1 exploded:2 update:12 intelligence:3 une:1 inspection:8 xk:9 smith:1 core:1 recherche:1 iterates:1 math:1 node:1 preference:8 liberal:1 ... |
4,168 | 4,773 | Convolutional-Recursive Deep Learning
for 3D Object Classification
Richard Socher, Brody Huval, Bharath Bhat, Christopher D. Manning, Andrew Y. Ng
Computer Science Department, Stanford University, Stanford, CA 94305, USA
richard@socher.org, {brodyh,bbhat,manning}@stanford.edu, ang@cs.stanford.edu
Abstract
Recent adva... | 4773 |@word cnn:36 version:1 briefly:2 cox:1 nd:1 glue:2 grey:1 hyv:1 propagate:1 rgb:34 recursively:2 cellphone:2 ours:2 document:1 fa8750:1 outperforms:2 kleenex:4 current:1 comparing:1 yet:1 mushroom:6 gpu:2 parsing:3 devin:1 concatenate:1 shape:5 designed:4 cue:1 leaf:2 fewer:1 intelligence:1 es:1 record:2 colored:... |
4,169 | 4,774 | A Generative Model
for Parts-based Object Segmentation
S. M. Ali Eslami
School of Informatics
University of Edinburgh
s.m.eslami@sms.ed.ac.uk
Christopher K. I. Williams
School of Informatics
University of Edinburgh
ckiw@inf.ed.ac.uk
Abstract
The Shape Boltzmann Machine (SBM) [1] has recently been introduced as a stat... | 4774 |@word h:1 trial:2 kohli:1 version:1 stronger:1 proportion:1 everingham:1 rgb:1 covariance:2 xtest:1 configuration:1 score:3 ours:2 existing:2 si:8 parsing:3 john:8 visible:3 realistic:2 shape:58 seeding:1 update:1 nebojsa:3 generative:14 selected:2 realism:2 lr:1 provides:1 diffs:1 evaluator:1 constructed:1 c2:1 ... |
4,170 | 4,775 | A P300 BCI for the Masses: Prior Information
Enables Instant Unsupervised Spelling
Pieter-Jan Kindermans, Hannes Verschore, David Verstraeten and Benjamin Schrauwen
Ghent University, Electronics and Information Systems
Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium
PieterJan.Kindermans@UGent.be
Abstract
The usabilit... | 4775 |@word neurophysiology:1 trial:1 version:2 middle:1 eliminating:1 unaltered:1 bigram:1 tedious:1 pieter:1 lobe:1 initial:3 electronics:1 contains:5 series:1 selecting:1 outperforms:2 current:2 surprising:1 realistic:2 enables:1 drop:2 update:8 fund:1 discrimination:1 half:1 fewer:1 selected:4 website:1 short:2 men... |
4,171 | 4,776 | Delay Compensation with Dynamical Synapses
C. C. Alan Fung, K. Y. Michael Wong
Hong Kong University of Science and Technology, Hong Kong, China
alanfung@ust.hk, phkywong@ust.hk
Si Wu
State Key Laboratory of Cognitive Neuroscience and Learning,
Beijing Normal University, Beijing 100875, China
wusi@bnu.edu.cn
Abstract
... | 4776 |@word kong:3 cu:1 polynomial:1 hippocampus:2 confirms:1 simulation:3 propagate:1 p0:6 solid:2 series:2 efficacy:2 interestingly:1 ranck:1 current:2 anterior:4 si:1 conjunctive:2 ust:2 dx:2 numerical:1 motor:5 plot:2 drop:1 medial:1 stationary:4 cue:1 implying:2 plane:1 short:2 mental:1 provides:1 location:1 succe... |
4,172 | 4,777 | Learning Manifolds with K-Means and K-Flats
Guillermo D. Canas?,?
Tomaso Poggio?,?
Lorenzo A. Rosasco?,?
? Laboratory for Computational and Statistical Learning - MIT-IIT
? CBCL, McGovern Institute - Massachusetts Institute of Technology
guilledc@mit.edu tp@ai.mit.edu lrosasco@mit.edu
Abstract
We study the problem of... | 4777 |@word kulis:1 version:2 compression:1 polynomial:1 norm:2 nd:1 open:1 simulation:1 crucially:3 decomposition:2 attainable:2 reduction:4 series:1 interestingly:2 past:1 brien:1 bradley:1 com:1 surprising:1 yet:1 dx:2 must:3 sergei:1 guez:1 fn:8 numerical:1 partition:3 mesh:1 kdd:1 seeding:2 n0:8 greedy:1 plane:1 v... |
4,173 | 4,778 | One Permutation Hashing
Ping Li
Department of Statistical Science
Cornell University
Art B Owen
Department of Statistics
Stanford University
Cun-Hui Zhang
Department of Statistics
Rutgers University
Abstract
Minwise hashing is a standard procedure in the context of search, for efficiently
estimating set similaritie... | 4778 |@word multitask:1 version:1 briefly:1 achievable:1 loading:1 mmds:1 logit:12 norm:1 hsieh:1 sgd:2 mention:1 reduction:2 liblinear:2 document:3 interestingly:1 bc:3 outperforms:4 current:2 imat:3 com:1 surprising:2 si:1 clara:1 written:1 gpu:1 must:1 realize:1 john:2 realistic:2 concatenate:3 kdd:3 christian:4 des... |
4,174 | 4,779 | The variational hierarchical EM algorithm for
clustering hidden Markov models
Emanuele Coviello
ECE Dept., UC San Diego
ecoviell@ucsd.edu
Antoni B. Chan
CS Dept., CityU of Hong Kong
abchan@cityu.edu.hk
Gert R.G. Lanckriet
ECE Dept., UC San Diego
gert@ece.ucsd.edu
Abstract
In this paper, we derive a novel algorithm ... | 4779 |@word kong:2 trial:1 kondor:1 version:3 proportion:1 covariance:3 citeseer:1 shot:1 initial:1 series:7 score:2 efficacy:2 zij:4 bhattacharyya:2 outperforms:3 existing:1 current:2 assigning:1 visible:1 partition:1 j1:2 moreno:1 designed:2 interpretable:1 v:3 stationary:3 generative:4 fewer:2 intelligence:1 accordi... |
4,175 | 478 | A comparison between a neural network model for
the formation of brain maps and experimental data
K. Obermayer
Beckman-Institute
University of Illinois
Urbana, IL 61801
K. Schulten
Beckman-Institute
University of Illinois
Urbana, IL 61801
G.G. Blasdel
Harvard Medical School
Harvard University
Boston, MA 02115
Abstr... | 478 |@word cylindrical:1 wiesel:2 proportion:1 simulation:7 reduction:1 initial:1 contains:1 si:1 import:1 must:3 shape:2 v:5 selected:4 plane:1 isotropic:1 iso:10 lr:1 compo:1 location:14 preference:22 five:4 along:7 direct:1 ik:1 consists:1 autocorrelation:1 indeed:1 wier:1 brain:4 decreasing:1 anisotropy:1 jm:3 reti... |
4,176 | 4,780 | To appear in: Neural Information Processing Systems (NIPS),
Lake Tahoe, Nevada. December 3-6, 2012.
Hierarchical spike coding of sound
Yan Karklin?
Howard Hughes Medical Institute,
Center for Neural Science
New York University
yan.karklin@nyu.edu
Chaitanya Ekanadham?
Courant Institute of Mathematical Sciences
New Yo... | 4780 |@word middle:3 bf:1 decomposition:1 pressure:4 accommodate:1 configuration:1 contains:3 ording:1 outperforms:1 current:1 activation:1 si:3 must:1 additive:1 interspike:1 enables:1 update:1 stationary:2 generative:4 fewer:1 smith:2 gribonval:1 colored:3 coarse:8 math:1 provides:3 location:1 recompute:1 tahoe:1 zha... |
4,177 | 4,781 | Volume Regularization for Binary Classification
Tal Wagner?
Faculty of Mathematics and Computer Science
Weizmann Institute of Science
Rehovot, 76100, Israel
tal.wagner@gmail.com
Koby Crammer
Department of Electrical Enginering
The Technion - Israel Institute of Technology
Haifa, 32000 Israel
koby@ee.technion.ac.il
A... | 4781 |@word version:7 middle:1 faculty:1 polynomial:1 norm:2 d2:1 seek:1 tried:1 blender:1 covariance:1 pick:2 sgd:1 electronics:1 contains:2 tuned:1 interestingly:1 bhattacharyya:2 outperforms:5 com:1 comparing:1 gmail:1 yet:4 assigning:1 written:1 additive:1 partition:1 kdd:1 shape:1 plot:2 designed:1 update:3 v:6 al... |
4,178 | 4,782 | Scalable imputation of genetic data with a discrete
fragmentation-coagulation process
Lloyd T. Elliott
Gatsby Computational Neuroscience Unit
University College London
17 Queen Square
London WC1N 3AR, U.K.
elliott@gatsby.ucl.ac.uk
Yee Whye Teh
Department of Statistics
University of Oxford
1 South Parks Road
Oxford OX... | 4782 |@word version:4 proportion:3 nd:1 mjp:2 multipoint:1 simulation:2 bn:1 thereby:1 initial:1 substitution:1 series:1 fragment:3 genetic:18 existing:1 current:1 must:2 subsequent:1 partition:40 plot:1 designed:1 update:11 aside:1 implying:2 generative:2 intelligence:1 item:3 short:1 eskin:1 provides:2 location:27 si... |
4,179 | 4,783 | Optimal Neural Tuning Curves for Arbitrary
Stimulus Distributions: Discrimax, Infomax and
Minimum Lp Loss
Alan A. Stocker
Department of Psychology
University of Pennsylvania
Philadelphia, PA 19104
astocker@sas.upenn.edu
Zhuo Wang
Department of Mathematics
University of Pennsylvania
Philadelphia, PA 19104
wangzhuo@sas... | 4783 |@word h:1 polynomial:1 norm:6 simulation:3 tried:1 attainable:1 solid:1 boundedness:1 carry:1 reduction:1 moment:14 daniel:1 tuned:2 si:1 written:2 numerical:4 partition:1 informative:2 predetermined:1 shape:3 plot:1 v:1 short:2 sigmoidal:6 zhang:1 mathematical:2 dn:1 direct:1 differential:1 prove:1 introduce:1 m... |
4,180 | 4,784 | Factorial LDA:
Sparse Multi-Dimensional Text Models
Michael J. Paul and Mark Dredze
Human Language Technology Center of Excellence (HLTCOE)
Center for Language and Speech Processing (CLSP)
Johns Hopkins University
Baltimore, MD 21218
{mpaul,mdredze}@cs.jhu.edu
Abstract
Latent variable models can be enriched with a mu... | 4784 |@word trial:1 version:1 nonsensical:1 plsa:1 instruction:1 contrastive:1 thereby:1 accommodate:1 initial:1 contains:1 score:3 series:1 united:1 tuned:1 document:34 existing:1 current:1 z2:2 must:2 written:3 john:1 parsing:4 additive:3 hofmann:1 enables:1 remove:4 interpretable:2 update:1 v:3 alone:1 generative:5 ... |
4,181 | 4,785 | Scalable nonconvex inexact proximal splitting
Suvrit Sra
Max Planck Institute for Intelligent Systems
72076 T?ubigen, Germany
suvrit@tuebingen.mpg.de
Abstract
We study a class of large-scale, nonsmooth, and nonconvex optimization problems. In particular, we focus on nonconvex problems with composite objectives.
This ... | 4785 |@word version:3 briefly:1 advantageous:1 stronger:3 norm:7 seems:1 open:1 hu:1 simulation:1 simplifying:1 pg:4 invoking:1 delgado:1 initial:1 liu:1 series:1 tuned:1 nonmonotone:1 comparing:1 must:2 written:1 john:1 realistic:1 numerical:2 plot:4 update:1 stationary:6 intelligence:1 instantiate:1 selected:1 xk:171... |
4,182 | 4,786 | The Time-Marginalized Coalescent Prior for
Hierarchical Clustering
Max Welling
Department of Computer Science
University of California, Irvine
Irvine, CA 92617
welling@uci.edu
Levi Boyles
Department of Computer Science
University of California, Irvine
Irvine, CA 92617
lboyles@uci.edu
Abstract
We introduce a new prio... | 4786 |@word sri:2 version:2 middle:3 proportion:2 norm:2 covariance:2 pick:1 tr:1 genetic:1 rightmost:1 existing:2 current:1 si:5 yet:3 assigning:1 must:2 written:2 rpi:5 plot:1 update:2 generative:2 leaf:13 intelligence:1 ntrain:1 ith:1 blei:1 provides:1 node:32 location:4 toronto:1 simpler:2 constructed:5 direct:2 be... |
4,183 | 4,787 | Discriminatively Trained Sparse Code Gradients
for Contour Detection
Xiaofeng Ren and Liefeng Bo
Intel Science and Technology Center for Pervasive Computing, Intel Labs
Seattle, WA 98195, USA
{xiaofeng.ren,liefeng.bo}@intel.com
Abstract
Finding contours in natural images is a fundamental problem that serves as the
bas... | 4787 |@word middle:1 version:6 seems:2 kokkinos:2 norm:3 everingham:1 open:1 seek:1 scg:28 rgb:31 prasad:2 tried:1 decomposition:2 hsieh:1 brightness:3 pick:2 textonboost:1 liblinear:2 contains:1 outperforms:2 existing:1 current:2 com:1 comparing:6 od:5 yet:1 intriguing:1 readily:2 shape:2 remove:1 designed:7 v:8 alone... |
4,184 | 4,788 | Joint Modeling of a Matrix with Associated Text
via Latent Binary Features
Lawrence Carin
Duke University
lcarin@duke.edu
XianXing Zhang
Duke University
xianxing.zhang@duke.edu
Abstract
A new methodology is developed for joint analysis of a matrix and accompanying
documents, with the documents associated with the ma... | 4788 |@word briefly:1 proportion:1 open:2 decomposition:12 covariance:1 thereby:1 yea:5 liu:1 loc:1 score:1 selecting:1 denoting:1 document:38 existing:1 written:2 readily:1 partition:1 informative:3 kdd:1 enables:1 remove:2 designed:1 interpretable:1 drop:1 selected:3 website:2 fewer:1 beginning:1 ith:2 blei:6 provide... |
4,185 | 4,789 | Proper losses for learning from partial labels
? Cid-Sueiro
Jesus
Department of Signal Theory and Communications
Universidad Carlos III de Madrid
Legans-Madrid, 28911 Spain
jcid@tsc.uc3m.es
Abstract
This paper discusses the problem of calibrating posterior class probabilities from
partially labelled data. Each instan... | 4789 |@word version:1 nd:1 contains:7 savage:6 z2:6 comparing:1 nt:1 guez:1 attracted:1 written:1 must:4 kdd:1 designed:1 xk:1 transposition:1 provides:1 constructed:1 direct:1 incorrect:1 consists:2 qij:1 inside:1 excellence:1 ra:1 expected:5 eck:2 little:1 becomes:1 spain:1 provided:2 estimating:6 notation:1 moreover... |
4,186 | 479 | Stationarity of Synaptic Coupling Strength Between
Neurons with Nonstationary Discharge Properties
Mark R. Sydorenko and Eric D. Young
Dept. of Biomedical Engineering & Center for Hearing Sciences
The Johns Hopkins School of Medicine
720 Rutland Avenue
Baltimore. Maryland 21205
Abstract
Based on a general non-station... | 479 |@word mild:1 trial:2 bf:1 simulation:5 gradual:1 moment:9 series:1 efficacy:19 dff:2 current:1 neurophys:3 john:1 interspike:5 designed:1 aps:2 stationary:12 pursued:1 tone:9 reciprocal:1 smith:1 short:3 record:1 location:1 successive:1 windowed:3 burst:4 correlograms:2 constructed:2 supply:1 qij:1 inside:2 manner... |
4,187 | 4,790 | Coupling Nonparametric Mixtures via
Latent Dirichlet Processes
John Fisher
MIT CSAIL
fisher@csail.mit.edu
Dahua Lin
MIT CSAIL
dhlin@mit.edu
Abstract
Mixture distributions are often used to model complex data. In this paper, we develop a new method that jointly estimates mixture models over multiple data sets
by explo... | 4790 |@word h:17 trial:1 version:3 hu:1 d2:1 covariance:1 accounting:1 configuration:4 series:1 q32:1 contains:4 document:8 outperforms:1 existing:2 current:1 comparing:1 nt:5 must:1 john:3 periodically:1 distant:2 hofmann:1 update:7 implying:1 generative:1 instantiate:1 devising:1 fewer:1 half:2 accordingly:1 selected... |
4,188 | 4,791 | Projection Retrieval for Classification
Artur Dubrawski
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
awd@cs.cmu.edu
Madalina Fiterau
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA 15213
mfiterau@cs.cmu.edu
Abstract
In many applications, classification systems ofte... | 4791 |@word multitask:1 version:1 polynomial:1 norm:1 proportion:3 hu:1 confirms:1 gradual:1 p0:6 attainable:1 pick:2 concise:1 carry:1 reduction:1 initial:1 liu:1 contains:2 zij:6 selecting:2 tuned:1 recovered:7 contextual:1 assigning:1 must:4 readily:1 subsequent:1 informative:22 kdd:1 wynne:1 unintelligible:1 design... |
4,189 | 4,792 | Fusion with Diffusion for Robust Visual Tracking
Yu Zhou1?, Xiang Bai1 , Wenyu Liu1 , Longin Jan Latecki2
1
Dept. of Electronics and Information Engineering, Huazhong Univ. of Science and Technology, P. R. China
2
Dept. of Computer and Information Sciences, Temple Univ., Philadelphia, USA
{zhouyu.hust,xiang.bai}@gmai... | 4792 |@word kondor:1 dalal:1 smirnov:1 rivlin:1 triggs:1 propagate:2 decomposition:1 accommodate:1 bai:4 liu:4 electronics:1 fragment:2 score:3 bhattacharyya:1 outperforms:4 current:2 com:1 babenko:1 coke:5 contextual:1 gmail:1 shape:4 plot:5 ainen:1 cue:4 selected:1 intelligence:7 boosting:3 node:3 location:14 constru... |
4,190 | 4,793 | Co-Regularized Hashing for Multimodal Data
Yi Zhen and Dit-Yan Yeung
Department of Computer Science and Engineering
Hong Kong University of Science and Technology
Clear Water Bay, Kowloon, Hong Kong
{yzhen,dyyeung}@cse.ust.hk
Abstract
Hashing-based methods provide a very promising approach to large-scale similarity s... | 4793 |@word kong:3 kulis:2 briefly:4 norm:1 d2:1 shuicheng:1 seek:1 tat:1 bn:1 fabrice:1 set5:1 initial:1 liu:3 document:9 past:2 existing:1 outperforms:3 current:1 luo:2 si:1 ust:1 attracted:1 john:1 realize:1 sanjiv:2 wx:19 kdd:1 shape:1 remove:1 plot:1 update:1 fund:1 hash:40 v:19 spec:1 chua:1 blei:1 boosting:6 cse... |
4,191 | 4,794 | Max-Margin Structured Output Regression for
Spatio-Temporal Action Localization
Du Tran and Junsong Yuan
School of Electrical and Electronic Engineering
Nanyang Technological University, Singapore
trandu@gmail.com, jsyuan@ntu.edu.sg
Abstract
Structured output learning has been successfully applied to object localizat... | 4794 |@word dalal:2 triggs:2 hu:2 pick:1 harder:2 configuration:1 liu:1 score:11 ours:5 interestingly:1 outperforms:4 ullah:1 current:3 com:1 comparing:2 contextual:1 gmail:1 parsing:2 hofmann:2 shape:1 dive:4 treating:1 plot:3 update:1 drop:1 greedy:1 selected:1 cue:1 half:1 intelligence:2 plane:3 provides:2 detecting... |
4,192 | 4,795 | Dip-means: an incremental clustering method for
estimating the number of clusters
Argyris Kalogeratos
Department of Computer Science
University of Ioannina
Ioannina, Greece 45110
akaloger@cs.uoi.gr
Aristidis Likas
Department of Computer Science
University of Ioannina
Ioannina, Greece 45110
arly@cs.uoi.gr
Abstract
Le... | 4795 |@word trial:1 repository:1 version:1 kulis:1 polynomial:1 seems:1 smirnov:1 hu:1 zelnik:1 seek:1 bn:1 covariance:3 pg:16 initial:3 liu:1 series:1 contains:5 score:12 wrapper:2 ka:1 comparing:2 must:1 written:1 fn:10 numerical:1 partition:5 shape:3 enables:1 designed:2 plot:2 fund:2 resampling:1 intelligence:1 sel... |
4,193 | 4,796 | Wavelet based multi-scale shape features on arbitrary
surfaces for cortical thickness discrimination
Won Hwa Kim??? Deepti Pachauri? Charles Hatt?
Moo K. Chung? Sterling C. Johnson?? Vikas Singh????
?
Dept. of Computer Sciences, University of Wisconsin, Madison, WI
Dept. of Biostatistics & Med. Informatics, University ... | 4796 |@word mild:2 mri:1 briefly:2 compression:3 seems:1 stronger:1 cingulate:1 middle:2 polynomial:4 nd:1 pulse:1 lobe:3 decomposition:1 pick:1 carry:1 reduction:1 celebrated:1 series:4 interestingly:1 mmse:1 longitudinal:1 existing:3 recovered:1 comparing:2 anterior:1 surprising:1 current:1 dx:1 moo:1 must:1 gpu:1 me... |
4,194 | 4,797 | Stochastic Gradient Descent with
Only One Projection
Mehrdad Mahdavi? , Tianbao Yang? , Rong Jin? , Shenghuo Zhu? , and Jinfeng Yi?
?
?
Dept. of Computer Science and Engineering, Michigan State University, MI, USA
?
Machine Learning Lab, GE Global Research, CA, USA
?
NEC Laboratories America, CA, USA
{mahdavim,rong... | 4797 |@word mild:3 briefly:1 polynomial:3 norm:7 stronger:1 nd:1 linearized:1 decomposition:1 sgd:28 celebrated:1 lightweight:1 series:1 liu:1 existing:1 current:1 com:2 designed:1 update:6 juditsky:1 greedy:4 provides:1 math:2 complication:1 c22:4 zhang:3 c2:4 consists:1 prove:1 introductory:1 introduce:2 theoreticall... |
4,195 | 4,798 | Ensemble weighted kernel estimators
for multivariate entropy estimation
Kumar Sricharan, Alfred O. Hero III
Department of EECS
University of Michigan
Ann Arbor, MI 48104
{kksreddy,hero}@umich.edu
Abstract
The problem of estimation of entropy functionals of probability densities
has received much attention in the info... | 4798 |@word mri:1 achievable:1 compression:1 proportion:1 norm:1 proportionality:3 simulation:4 seek:1 covariance:1 series:1 suppressing:2 omniscient:3 outperforms:2 dx:2 must:3 plot:3 ainen:1 discrimination:3 v:1 selected:1 quantizer:1 boosting:2 provides:3 mathematical:2 constructed:2 c2:4 beta:2 differential:1 speci... |
4,196 | 4,799 | Visual Recognition using Embedded Feature
Selection for Curvature Self-Similarity
Angela Eigenstetter
HCI & IWR, University of Heidelberg
aeigenst@iwr.uni-heidelberg.de
Bj?orn Ommer
HCI & IWR, University of Heidelberg
ommer@uni-heidelberg.de
Abstract
Category-level object detection has a crucial need for informative... | 4799 |@word version:2 dalal:1 compression:2 norm:6 yi0:3 underline:1 triggs:1 open:1 everingham:1 d2:1 p0:1 elisseeff:2 brightness:2 arti:1 shechtman:2 initial:2 reduction:11 contains:2 zuk:1 wrapper:2 ours:1 outperforms:1 bradley:1 current:1 comparing:1 concatenate:1 informative:2 shape:10 hofmann:1 designed:1 fund:1 ... |
4,197 | 48 | 174
A Neural Network Classifier Based on Coding Theory
Tzt-Dar Chlueh and Rodney Goodman
eanrornla Instltute of Technology. Pasadena. eanromla 91125
ABSTRACT
The new neural network classifier we propose transforms the
classification problem into the coding theory problem of decoding a noisy
codeword. An input vector ... | 48 |@word trial:2 polynomial:1 confirms:1 seek:1 simulation:8 bn:5 pick:2 cyclic:1 score:2 recovered:1 blank:1 nt:1 comparing:1 si:2 yet:1 must:1 fn:2 fewer:1 item:1 complementing:1 accordingly:2 xk:1 iterates:1 five:1 constructed:1 consists:1 themselves:1 automatically:1 td:1 provided:2 matched:2 moreover:1 circuit:2 ... |
4,198 | 480 | JANUS: Speech-to-Speech Translation Using
Connectionist and Non-Connectionist Techniques
Alex Waibel? Ajay N. Jain t
Arthur McNair Joe Tebelskis
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Louise OsterhoItz
Computational Linguistics Program
Carnegie Mellon University
Hiroaki Saito
Ott... | 480 |@word version:5 eliminating:1 bigram:1 alliant:1 score:1 synthesizer:1 written:6 parsing:16 must:9 designed:1 alone:1 device:2 prize:1 lr:11 parsec:24 along:1 direct:1 inter:1 notably:1 behavior:1 planning:1 dialog:5 multi:5 jimukyoku:3 automatically:3 increasing:1 provided:1 medium:1 what:1 string:1 developed:3 s... |
4,199 | 4,800 | On the Use of Non-Stationary Policies for Stationary
Infinite-Horizon Markov Decision Processes
Boris Lesner
Inria, Villers-l`es-Nancy, F-54600, France
boris.lesner@inria.fr
Bruno Scherrer
Inria, Villers-l`es-Nancy, F-54600, France
bruno.scherrer@inria.fr
Abstract
We consider infinite-horizon stationary ?-discounted ... | 4800 |@word version:3 norm:9 contraction:1 kappen:1 initial:2 interestingly:1 written:2 pioneer:1 stationary:59 greedy:7 newest:1 intelligence:5 oldest:1 beginning:1 iterates:1 mannor:1 simpler:2 farahmand:2 prove:6 consists:2 introduce:2 expected:1 indeed:2 planning:1 growing:2 bellman:5 discounted:6 actual:1 consider... |
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