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3,600 | 426 | Compact EEPROM-based Weight Functions
A. Kramer, C. K. Sin, R. Chu, and P. K. Ko
Department of Electrical Engineering and Computer Science
University of California at Berkeley
Berkeley, CA 94720
Abstract
We are focusing on the development of a highly compact neural net weight
function based on the use of EEPROM devic... | 426 |@word cox:4 t_:1 etann:1 ld:1 inefficiency:1 existing:1 current:9 chu:4 must:3 designed:1 sponsored:1 nonsaturated:1 prohibitive:1 device:39 beaver:1 iso:1 node:2 firstly:1 mathematical:1 differential:3 manner:1 mask:1 behavior:3 surge:1 actual:1 ua:2 linearity:1 circuit:5 vref:1 developed:4 corporation:1 fabricat... |
3,601 | 4,260 | A More Powerful Two-Sample Test in High
Dimensions using Random Projection
Miles E. Lopes1
Laurent Jacob1
Martin J. Wainwright1,2
1
Departments of Statistics and EECS2
University of California, Berkeley
Berkeley, CA 94720-3860
{mlopes,laurent,wainwrig}@stat.berkeley.edu
Abstract
We consider the hypothesis testing pr... | 4260 |@word illustrating:1 mri:1 middle:1 norm:3 seek:3 simulation:4 bn:3 covariance:18 jacob:3 decomposition:1 thereby:1 minus:1 tr:20 harder:3 reduction:1 initial:1 liu:1 series:1 bai:3 hereafter:1 past:2 wainwrig:1 comparing:3 anne:1 john:2 subsequent:1 realistic:1 benign:1 designed:2 plot:1 interpretable:1 discrimi... |
3,602 | 4,261 | Beyond Spectral Clustering - Tight Relaxations of
Balanced Graph Cuts
Simon Setzer
Saarland University, Saarbr?ucken, Germany
setzer@mia.uni-saarland.de
Matthias Hein
Saarland University, Saarbr?ucken, Germany
hein@cs.uni-saarland.de
Abstract
Spectral clustering is based on the spectral relaxation of the normalized/... | 4261 |@word version:2 norm:1 c0:1 open:1 hu:3 decomposition:3 solid:1 initial:1 liu:1 outperforms:1 lang:1 written:8 fn:1 partition:18 analytic:1 plot:1 v:1 intelligence:2 prize:1 characterization:2 math:1 readability:1 zhang:1 saarland:4 mathematical:2 direct:1 become:1 symposium:1 consists:1 naor:1 symp:1 introduce:1... |
3,603 | 4,262 | Online Learning: Stochastic, Constrained, and
Smoothed Adversaries
Alexander Rakhlin
Department of Statistics
University of Pennsylvania
rakhlin@wharton.upenn.edu
Karthik Sridharan
Toyota Technological Institute at Chicago
karthik@ttic.edu
Ambuj Tewari
Computer Science Department
University of Texas at Austin
ambuj@c... | 4262 |@word version:5 polynomial:2 seems:1 norm:3 approachability:1 unif:2 forecaster:1 crucially:1 prokhorov:1 q1:3 pick:4 series:1 chervonenkis:1 denoting:1 prefix:2 past:4 discretization:1 surprising:1 yet:3 written:7 chicago:1 additive:6 realistic:1 benign:1 half:4 provides:1 certificate:1 hyperplanes:2 shorthand:1... |
3,604 | 4,263 | Inferring Interaction Networks using the IBP applied
to microRNA Target Prediction
Ziv Bar-Joseph
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA, USA
zivbj@cs.cmu.edu
Hai-Son Le
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA, USA
hple@cs.cmu.edu
Abstract
Determining inte... | 4263 |@word mhn:1 c0:2 contains:2 score:9 selecting:2 past:1 recovered:6 yet:1 written:1 additive:2 partition:2 shape:2 plot:2 drop:1 update:2 zik:17 generative:6 selected:2 yr:1 ith:2 short:2 blei:2 provides:1 node:5 preference:2 firstly:1 nonexchangeable:1 unbounded:1 phylogenetic:2 constructed:1 direct:1 beta:3 vjk:... |
3,605 | 4,264 | Analysis and Improvement of
Policy Gradient Estimation
Tingting Zhao, Hirotaka Hachiya, Gang Niu, and Masashi Sugiyama
Tokyo Institute of Technology
{tingting@sg., hachiya@sg., gang@sg., sugiyama@}cs.titech.ac.jp
Abstract
Policy gradient is a useful model-free reinforcement learning approach, but it
tends to suffer f... | 4264 |@word mild:3 trial:3 norm:3 stronger:1 open:1 cos2:1 covariance:3 tr:1 reduction:4 initial:15 series:1 past:1 existing:1 current:1 analytic:1 plot:1 update:5 sehnke:2 intelligence:3 provides:3 five:1 along:1 dn:1 prove:1 consists:1 manner:1 theoretically:6 expected:8 dialog:1 multi:1 discounted:2 td:1 inappropria... |
3,606 | 4,265 | Non-parametric Group Orthogonal Matching Pursuit
for Sparse Learning with Multiple Kernels
Vikas Sindhwani and Aur?elie C. Lozano
IBM T.J. Watson Research Center
Yorktown Heights, NY 10598
{vsindhw,aclozano}@us.ibm.com
Abstract
We consider regularized risk minimization in a large dictionary of Reproducing
kernel Hilb... | 4265 |@word mild:2 h:2 repository:2 version:1 norm:34 stronger:1 k2hk:1 covariance:1 pick:1 carry:1 reduction:2 liu:1 series:2 score:1 selecting:2 tuned:1 rkhs:17 outperforms:1 existing:1 current:1 com:1 recovered:1 tackling:1 bie:1 reminiscent:1 additive:3 kqj:2 numerical:1 designed:1 plot:1 update:1 alone:1 greedy:8 ... |
3,607 | 4,266 | Sparse Inverse Covariance Matrix Estimation
Using Quadratic Approximation
Cho-Jui Hsieh, M?aty?as A. Sustik, Inderjit S. Dhillon, and Pradeep Ravikumar
Department of Computer Science
University of Texas at Austin
Austin, TX 78712 USA
{cjhsieh,sustik,inderjit,pradeepr}@cs.utexas.edu
Abstract
The !1 regularized Gaussia... | 4266 |@word determinant:8 version:1 polynomial:1 norm:3 seek:1 covariance:19 hsieh:2 decomposition:2 tr:18 ipm:8 initial:3 series:1 existing:1 current:1 rish:1 toh:3 written:2 belmont:1 partition:3 plot:1 update:29 stationary:3 greedy:3 implying:3 guess:2 accordingly:1 inspection:1 short:1 moncrief:1 caveat:3 iterates:... |
3,608 | 4,267 | Autonomous Learning of Action Models for Planning
Neville Mehta
Prasad Tadepalli
Alan Fern
School of Electrical Engineering and Computer Science
Oregon State University, Corvallis, OR 97331, USA.
{mehtane,tadepall,afern}@eecs.oregonstate.edu
Abstract
This paper introduces two new frameworks for learning action models... | 4267 |@word version:6 polynomial:52 stronger:1 tadepalli:1 mehta:1 seek:1 prasad:1 innermost:1 pick:1 initial:7 contains:4 current:4 si:2 must:12 cruz:1 informative:13 designed:1 leaf:2 characterization:1 provides:5 node:5 location:1 simpler:1 height:5 dn:1 supply:1 symposium:2 nondeterministic:5 inside:1 introduce:4 h... |
3,609 | 4,268 | Generalization Bounds and Consistency for Latent
Structural Probit and Ramp Loss
Joseph Keshet
TTI-Chicago
jkeshet@ttic.edu
David McAllester
TTI-Chicago
mcallester@ttic.edu
Abstract
We consider latent structural versions of probit loss and ramp loss. We show that
these surrogate loss functions are consistent in the ... | 4268 |@word mild:1 version:2 pw:1 achievable:2 norm:6 seems:3 nd:1 open:4 series:1 score:3 selecting:1 jeopardy:1 must:2 john:1 chicago:2 hofmann:2 update:11 selected:1 isotropic:2 mccallum:1 chiang:1 characterization:1 simpler:1 unbounded:2 mathematical:1 constructed:2 direct:1 become:1 scholkopf:1 prove:9 expected:3 ... |
3,610 | 4,269 | Testing a Bayesian Measure of Representativeness
Using a Large Image Database
Joshua T. Abbott
Department of Psychology
University of California, Berkeley
Berkeley, CA 94720
joshua.abbott@berkeley.edu
Katherine A. Heller
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02... | 4269 |@word seems:1 norm:1 instruction:1 eng:1 mammal:1 series:1 score:13 selecting:1 document:1 outperforms:1 existing:6 xnj:4 subjective:1 comparing:3 yet:1 distant:1 realistic:2 analytic:1 depict:1 generative:1 item:21 smith:1 provides:9 detecting:3 characterization:1 pun:1 direct:1 beta:1 retrieving:1 lopez:1 combi... |
3,611 | 427 | Order Reduction for Dynamical Systems
Describing the Behavior of Complex Neurons
Thomas B. Kepler
Biology Dept.
L. F. Abbott
Physics Dept.
Eve Marder
Biology Dept.
Brandeis University
Waltham, MA 02254
Abstract
We have devised a scheme to reduce the complexity of dynamical
systems belonging to a class that includes... | 427 |@word neurophysiology:1 cu:1 polynomial:1 seems:2 hyperpolarized:3 squid:1 simulation:1 profit:1 solid:4 reduction:16 initial:1 series:2 current:13 recovered:1 activation:1 yet:2 must:5 physiol:2 realistic:7 plot:1 realism:2 farther:1 conscience:1 mental:1 tems:1 kepler:10 location:1 mathematical:1 along:1 direct:... |
3,612 | 4,270 | On the accuracy of `1-filtering of signals with
block-sparse structure
Anatoli Juditsky?
Fatma K?l?nc? Karzan?
Arkadi Nemirovski?
Boris Polyak?
Abstract
We discuss new methods for the recovery of signals with block-sparse structure,
based on `1 -minimization. Our emphasis is on the efficiently computable error
bou... | 4270 |@word h:1 version:2 seems:1 proportion:1 norm:26 stronger:1 d2:1 covariance:2 p0:1 q1:2 necessity:1 celebrated:1 contains:1 liu:3 denoting:1 tuned:1 subsequent:1 underly:1 partition:1 verifiability:4 juditsky:5 alone:1 greedy:1 rudin:1 certificate:1 math:2 gx:1 org:4 zhang:3 mathematical:2 along:2 c2:1 yuan:1 pro... |
3,613 | 4,271 | Structured sparse coding via lateral inhibition
Karol Gregor
Janelia Farm, HHMI
19700 Helix Drive
Ashburn, VA, 20147
karol.gregor@gmail.com
Arthur Szlam
The City College of New York
Convent Ave and 138th st
New York, NY, 10031
aszlam@courant.nyu.edu
Yann LeCun
New York University
715 Broadway, Floor 12
New York, NY,... | 4271 |@word version:1 norm:1 tried:2 jacob:3 contrastive:1 pick:2 mammal:1 minus:1 harder:1 garrigues:5 configuration:2 contains:1 uma:1 united:1 current:2 com:1 wd:2 activation:3 gmail:1 written:2 distant:1 j1:1 predetermined:1 update:19 greedy:4 generative:1 fewer:1 xk:1 prespecified:2 node:1 location:6 simpler:1 zha... |
3,614 | 4,272 | Regularized Laplacian Estimation and
Fast Eigenvector Approximation
Patrick O. Perry
Information, Operations, and Management Sciences
NYU Stern School of Business
New York, NY 10012
pperry@stern.nyu.edu
Michael W. Mahoney
Department of Mathematics
Stanford University
Stanford, CA 94305
mmahoney@cs.stanford.edu
Abstr... | 4272 |@word determinant:2 version:6 proportion:3 norm:4 stronger:2 proportionality:3 r:1 decomposition:1 tr:17 reduction:1 united:1 interestingly:2 outperforms:1 current:2 discretization:1 lang:2 must:2 mesh:1 realistic:1 partition:1 j1:2 shape:8 plot:4 intelligence:1 fabius:1 provides:1 characterization:1 node:10 lx:5... |
3,615 | 4,273 | Nonnegative dictionary learning in the exponential
noise model for adaptive music signal representation
C?edric F?evotte
CNRS LTCI; T?el?ecom ParisTech
75014, Paris, France
fevotte@telecom-paristech.fr
Onur Dikmen
CNRS LTCI; T?el?ecom ParisTech
75014, Paris, France
dikmen@telecom-paristech.fr
Abstract
In this paper ... | 4273 |@word version:1 polynomial:1 nd:1 plsa:1 seek:2 cml:16 decomposition:4 edric:1 initial:1 configuration:1 document:3 mmse:1 outperforms:1 imaginary:1 current:4 recovered:1 activation:4 readily:1 john:1 stemming:1 fn:3 additive:1 subsequent:1 shape:1 update:14 generative:6 cook:1 inspection:1 scotland:1 sutter:1 sh... |
3,616 | 4,274 | A reinterpretation of the policy oscillation
phenomenon in approximate policy iteration
Paul Wagner
Department of Information and Computer Science
Aalto University School of Science
PO Box 15400, FI-00076 Aalto, Finland
pwagner@cis.hut.fi
Abstract
A majority of approximate dynamic programming approaches to the reinfo... | 4274 |@word mild:1 exploitation:1 version:1 briefly:2 seems:2 suitably:1 open:2 termination:1 simulation:2 gradual:1 attainable:1 concise:1 solid:1 reduction:1 initial:4 cyclic:2 score:7 offering:1 interestingly:1 icga:2 past:1 existing:2 current:4 reminiscent:1 numerical:3 partition:1 visible:1 enables:1 update:13 ove... |
3,617 | 4,275 | Efficient Methods for Overlapping Group Lasso
Lei Yuan
Arizona State University
Tempe, AZ, 85287
Lei.Yuan@asu.edu
Jun Liu
Arizona State University
Tempe, AZ, 85287
j.liu@asu.edu
Jieping Ye
Arizona State University
Tempe, AZ, 85287
jieping.ye@asu.edu
Abstract
The group Lasso is an extension of the Lasso for feature ... | 4275 |@word middle:1 inversion:1 norm:12 simulation:1 jacob:1 pg:1 liu:5 contains:2 series:4 outperforms:1 existing:3 ka:1 surprising:1 si:21 chu:1 written:1 numerical:1 remove:1 plot:1 interpretable:2 half:1 asu:3 xk:1 record:1 completeness:1 math:1 zhang:1 become:1 differential:2 yuan:4 consists:4 prove:3 pathway:9 i... |
3,618 | 4,276 | A Brain-Machine Interface Operating with a
Real-Time Spiking Neural Network Control
Algorithm
Julie Dethier?
Department of Bioengineering
Stanford University, CA 94305
jdethier@stanford.edu
Paul Nuyujukian
Department of Bioengineering
School of Medicine
Stanford University, CA 94305
paul@npl.stanford.edu
Chris Eliasm... | 4276 |@word trial:11 middle:1 version:4 approved:1 open:4 rhesus:3 simulation:9 carolina:1 simplifying:1 thereby:1 tr:1 deisseroth:1 reduction:1 series:1 tuned:1 current:16 assigning:1 must:3 written:3 pioneer:1 additive:1 motor:12 designed:1 plot:2 update:4 v:4 fewer:1 ith:1 realizing:1 core:2 filtered:1 fabricating:1... |
3,619 | 4,277 | Trace Lasso: a trace norm regularization for
correlated designs
?
Edouard
Grave
INRIA, Sierra Project-team
?
Ecole
Normale Sup?erieure, Paris
edouard.grave@inria.fr
Guillaume Obozinski
INRIA, Sierra Project-team
?
Ecole
Normale Sup?erieure, Paris
guillaume.obozinski@inria.fr
Francis Bach
INRIA, Sierra Project-team
?... | 4277 |@word norm:91 km:1 seek:1 covariance:3 decomposition:5 tr:6 initial:2 liu:1 series:6 ecole:3 outperforms:2 existing:2 kmk:2 surprising:1 si:1 john:1 additive:1 partition:4 designed:1 interpretable:1 plot:1 greedy:3 selected:8 guess:1 intelligence:1 blei:1 math:1 zhang:2 direct:1 yuan:1 consists:1 introduce:9 pair... |
3,620 | 4,278 | Learning in Hilbert vs. Banach Spaces: A Measure
Embedding Viewpoint
Bharath K. Sriperumbudur
Gatsby Unit
University College London
Kenji Fukumizu
The Institute of Statistical
Mathematics, Tokyo
Gert R. G. Lanckriet
Dept. of ECE
UC San Diego
bharath@gatsby.ucl.ac.uk
fukumizu@ism.ac.jp
gert@ece.ucsd.edu
Abstract
... | 4278 |@word briefly:1 norm:3 c0:4 checkable:1 open:1 attainable:2 thereby:1 reduction:1 moment:1 seriously:1 rkhs:43 interestingly:1 imaginary:1 existing:1 comparing:1 dx:2 written:1 v:1 characterization:4 provides:2 math:3 zhang:4 prove:1 hermitian:1 introduce:1 classifiability:1 theoretically:1 indeed:1 nor:1 window:... |
3,621 | 4,279 | Anatomically Constrained Decoding of Finger
Flexion from Electrocorticographic Signals
Zuoguan Wang
Department of ECSE
Rensselaer Polytechnic Inst.
Troy, NY 12180
wangz6@rpi.edu
Gerwin Schalk
Wadsworth Center
NYS Dept of Health
Albany, NY, 12201
schalk@wadsworth.org
Qiang Ji
Department of ECSE
Rensselaer Polytechnic... | 4279 |@word trial:1 middle:4 fatourechi:1 propagate:1 simulation:1 eng:4 solid:5 recursively:1 qth:1 existing:3 current:1 discretization:1 rpi:2 readily:1 john:1 deniz:1 subsequent:1 numerical:1 motor:4 update:3 cue:1 selected:1 device:2 isard:1 beginning:2 core:1 farther:1 short:1 filtered:1 provides:1 location:8 org:... |
3,622 | 428 | Navigating through Temporal Difference
Peter Dayan
Centre for Cognitive Science &. Department of Physics
University of Edinburgh
2 Buccleuch Place, Edinburgh EH8 9LW
dayantcns.ed.ac.uk
Abstract
Barto, Sutton and Watkins [2] introduced a grid task as a didactic example of temporal difference planning and asynchronous ... | 428 |@word briefly:1 manageable:1 hippocampus:2 open:1 r:4 pick:1 initial:4 selecting:1 ironing:1 current:1 buckingham:1 must:2 subsequent:1 distant:1 visible:4 cue:9 leaf:1 discovering:1 record:1 provides:1 coarse:6 location:14 traverse:1 c6:2 rc:1 along:3 constructed:1 c2:2 prove:1 ra:1 expected:1 roughly:1 elman:1 p... |
3,623 | 4,280 | An Application of Tree-Structured Expectation
Propagation for Channel Decoding
Pablo M. Olmos? , Luis Salamanca? , Juan J. Murillo-Fuentes? , Fernando P?erez-Cruz?
?
Dept. of Signal Theory and Communications, University of Sevilla
41092 Sevilla Spain
{olmos,salamanca,murillo}@us.es
?
Dept. of Signal Theory and Communi... | 4280 |@word msr:1 pw:3 seems:1 consolider:1 covariance:1 tr:1 solid:7 carry:1 electronics:1 contains:1 daniel:2 outperforms:2 com:1 dx:2 luis:1 tec2009:1 cruz:4 ministerio:1 analytic:1 remove:9 heir:2 plot:8 designed:1 joy:1 intelligence:1 beginning:1 short:4 provides:5 iterates:1 node:35 coarse:1 simpler:1 along:2 bec... |
3,624 | 4,281 | Efficient inference in matrix-variate Gaussian models
with iid observation noise
Oliver Stegle1
Max Planck Institutes
T?ubingen, Germany
stegle@tuebingen.mpg.de
Christoph Lippert1
Max Planck Institutes
T?ubingen, Germany
clippert@tuebingen.mpg.de
Joris Mooij
Institute for Computing and Information Sciences
Radboud Un... | 4281 |@word briefly:2 norm:1 d2:2 simulation:4 covariance:52 accounting:7 simplifying:2 decomposition:2 yuc:4 thereby:2 tr:3 volkswagen:1 reduction:4 liu:1 contains:3 series:1 genetic:1 recovered:3 comparing:2 plcg:3 activation:1 written:1 multioutput:1 drop:1 treating:1 update:2 alone:1 generative:4 selected:2 yr:1 pr... |
3,625 | 4,282 | Directed Graph Embedding: an Algorithm based on
Continuous Limits of Laplacian-type Operators
Marina Meil?a
Department of Statistics
University of Washington
Seattle, WA 98195
mmp@stat.washington.edu
Dominique C. Perrault-Joncas
Department of Statistics
University of Washington
Seattle, WA 98195
dcpj@stat.washington.... | 4282 |@word version:1 briefly:1 pw:1 proportion:1 seems:2 dominique:2 decomposition:1 nsw:1 tr:1 reduction:1 series:1 score:3 united:2 longitudinal:5 recovered:4 surprising:1 si:1 yet:2 must:1 planet:1 subsequent:1 numerical:1 wx:1 hofmann:1 plot:1 alone:3 generative:12 electr:1 plane:1 isotropic:2 vanishing:1 short:1 ... |
3,626 | 4,283 | t-divergence Based Approximate Inference
Nan Ding2 , S.V. N. Vishwanathan1,2 , Yuan Qi2,1
Departments of 1 Statistics and 2 Computer Science
Purdue University
ding10@purdue.edu, vishy@stat.purdue.edu, alanqi@cs.purdue.edu
Abstract
Approximate inference is an important technique for dealing with large, intractable grap... | 4283 |@word deformed:2 version:1 middle:1 nd:4 crucially:1 p0:4 q1:2 eld:3 naudts:10 moment:2 interestingly:1 subjective:1 z2:8 comparing:1 dx:19 reminiscent:1 written:1 john:2 alanqi:1 additive:1 partition:6 cant:1 update:1 warmuth:1 isotropic:1 manfred:1 math:2 boosting:2 org:2 symposium:1 yuan:1 consists:1 p1:20 ins... |
3,627 | 4,284 | Probabilistic amplitude and frequency demodulation
Richard E. Turner?
Computational and Biological Learning Lab, Department of Engineering
University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, UK
ret26@cam.ac.uk
Maneesh Sahani
Gatsby Computational Neuroscience Unit, University College London
Alexandra House... | 4284 |@word kong:1 version:3 norm:6 open:1 crucially:1 covariance:1 decomposition:2 commute:1 solid:3 moment:3 initial:1 liu:1 series:4 contains:2 outperforms:2 existing:5 imaginary:1 delgutte:1 yet:1 must:2 concatenate:1 numerical:1 analytic:2 motor:1 atlas:2 update:6 implying:1 cue:1 device:2 xk:4 isotropic:1 smith:1... |
3,628 | 4,285 | Message-Passing for Approximate MAP Inference
with Latent Variables
Jiarong Jiang
Dept. of Computer Science
University of Maryland, CP
jiarong@umiacs.umd.edu
Piyush Rai
School of Computing
University of Utah
piyush@cs.utah.edu
Hal Daum?e III
Dept. of Computer Science
University of Maryland, CP
hal@umiacs.umd.edu
Abs... | 4285 |@word h:3 worsens:1 version:2 seek:1 pick:1 tr:4 moment:1 liu:1 contains:1 efficacy:1 score:1 uncovered:1 written:1 readily:1 parsing:2 mst:1 subsequent:1 partition:5 hofmann:1 update:4 v:35 greedy:5 fewer:1 generative:1 amir:1 short:1 core:9 provides:3 node:112 readability:1 gx:15 daphne:1 tagger:1 consists:1 da... |
3,629 | 4,286 | Inference in continuous-time change-point models
Florian Stimberg
Computer Science, TU Berlin
flostim@cs.tu-berlin.de
Manfred Opper
Computer Science, TU Berlin
opperm@cs.tu-berlin.de
Andreas Ruttor
Computer Science, TU Berlin
ruttor@cs.tu-berlin.de
Guido Sanguinetti
School of Informatics, University of Edinburgh
gsan... | 4286 |@word version:2 seems:1 open:1 proportionality:1 solid:4 edric:1 initial:4 liu:1 genetic:1 interestingly:1 o2:1 reaction:1 current:2 discretization:1 com:3 surprising:2 activation:5 dx:1 must:2 subsequent:1 numerical:1 sdes:3 opin:1 remove:2 plot:1 interpretable:1 update:1 n0:1 alone:1 generative:1 prohibitive:1 ... |
3,630 | 4,287 | Efficient anomaly detection using
bipartite k-NN graphs
Kumar Sricharan
Department of EECS
University of Michigan
Ann Arbor, MI 48104
kksreddy@umich.edu
Alfred O. Hero III
Department of EECS
University of Michigan
Ann Arbor, MI 48104
hero@umich.edu
Abstract
Learning minimum volume sets of an underlying nominal distri... | 4287 |@word repository:4 briefly:1 proportion:3 disk:1 simulation:2 seek:3 covariance:1 schwabacher:1 liu:2 contains:2 score:2 woodruff:1 smtp:3 dx:4 partition:5 kdd:6 v:2 xk:18 short:1 detecting:2 attack:2 simpler:3 five:1 dn:1 constructed:2 direct:1 clairvoyant:3 prove:1 manner:1 introduce:1 x0:38 detects:2 estimatin... |
3,631 | 4,288 | Variance Reduction in Monte-Carlo Tree Search
Joel Veness
University of Alberta
Marc Lanctot
University of Alberta
Michael Bowling
University of Alberta
veness@cs.ualberta.ca
lanctot@cs.ualberta.ca
bowling@cs.ualberta.ca
Abstract
Monte-Carlo Tree Search (MCTS) has proven to be a powerful, generic planning
techniq... | 4288 |@word mild:1 exploitation:4 version:2 seems:1 nd:1 hu:1 pieter:1 simulation:32 seek:1 tried:1 covariance:2 p0:5 bsm:3 recursively:5 reduction:28 configuration:2 contains:2 efficacy:1 selecting:1 score:5 lightweight:1 icga:1 rightmost:1 outperforms:1 current:10 comparing:1 surprising:2 si:23 jeopardy:1 john:2 subs... |
3,632 | 4,289 | Empirical models of spiking in neural populations
?
Lars Busing
Gatsby Computational Neuroscience Unit
University College London, UK
lars@gatsby.ucl.ac.uk
Jakob H. Macke
Gatsby Computational Neuroscience Unit
University College London, UK
jakob@gatsby.ucl.ac.uk
John P. Cunningham
Department of Engineering
University ... | 4289 |@word trial:17 version:1 briefly:1 wiesel:1 seems:1 nd:1 busing:1 open:1 rhesus:1 covariance:4 simplifying:1 minus:1 initial:2 exclusively:1 score:1 outperforms:3 past:3 current:2 comparing:3 discretization:1 surprising:1 dx:1 must:1 john:1 pioneer:1 realistic:6 plasticity:1 motor:7 glm2:3 update:3 cue:1 signalli... |
3,633 | 429 | Connectionist Music Composition Based on
Melodic and Stylistic Constraints
Michael C. Mozer
Department of Computer Science
and Institute of Cognitive Science
University of Colorado
Boulder, CO 80309-0430
Todd Soukup
Department of Electrical
and Computer Engineering
University of Colorado
Boulder, CO 80309-0425
Abstr... | 429 |@word determinant:1 cu:1 norm:1 d2:1 simulation:6 accommodate:1 necessity:1 initial:2 contains:2 selecting:2 hereafter:1 accompaniment:1 hardy:1 current:3 activation:4 written:1 readily:1 must:1 seeding:1 designed:1 concert:49 half:5 selected:2 item:1 tone:1 short:1 transposition:1 provides:2 five:4 height:5 along... |
3,634 | 4,290 | On Learning Discrete Graphical Models Using
Greedy Methods
Christopher C. Johnson
University of Texas at Asutin
cjohnson@cs.utexas.edu
Ali Jalali
University of Texas at Austin
alij@mail.utexas.edu
Pradeep Ravikumar
University of Texas at Asutin
pradeepr@cs.utexas.edu
Abstract
In this paper, we address the problem o... | 4290 |@word polynomial:2 norm:1 physik:1 d2:4 simulation:3 r:12 bn:1 decomposition:2 covariance:1 liu:1 series:3 score:2 existing:2 recovered:1 yet:2 additive:1 numerical:1 partition:1 remove:3 plot:1 greedy:46 fewer:2 intelligence:2 provides:2 boosting:2 node:19 contribute:1 simpler:1 zhang:6 c2:4 consists:1 specializ... |
3,635 | 4,291 | Improving Topic Coherence with
Regularized Topic Models
David Newman
University of California, Irvine
newman@uci.edu
Edwin V. Bonilla
Wray Buntine
NICTA & Australian National University
{edwin.bonilla, wray.buntine}@nicta.com.au
Abstract
Topic models have the potential to improve search and browsing by extracting
us... | 4291 |@word version:3 inversion:1 judgement:4 pw:4 proportion:3 nd:1 plsa:1 open:1 covariance:3 xtest:3 detective:1 thereby:1 plentiful:1 series:2 score:39 selecting:1 document:17 ours:2 africa:4 outperforms:1 com:3 nt:4 written:3 john:2 numerical:1 hofmann:1 designed:1 interpretable:5 update:5 aside:1 v:3 generative:2... |
3,636 | 4,292 | Clustered Multi-Task Learning Via Alternating
Structure Optimization
Jiayu Zhou, Jianhui Chen, Jieping Ye
Computer Science and Engineering
Arizona State University
Tempe, AZ 85287
{jiayu.zhou, jianhui.chen, jieping.ye}@asu.edu
Abstract
Multi-task learning (MTL) learns multiple related tasks simultaneously to improve
... | 4292 |@word multitask:2 middle:2 norm:3 seek:1 simulation:1 jacob:1 q1:1 tr:36 liu:1 score:1 united:1 tuned:1 interestingly:1 past:1 existing:3 yni:1 numerical:1 weyl:1 analytic:1 plot:3 asu:1 data2:1 record:2 sarcos:3 provides:2 boosting:1 successive:1 org:1 zhang:2 mathematical:3 along:1 constructed:3 direct:4 differ... |
3,637 | 4,293 | Selecting Receptive Fields in Deep Networks
Andrew Y. Ng
Department of Computer Science
Stanford University
Stanford, CA 94305
ang@cs.stanford.edu
Adam Coates
Department of Computer Science
Stanford University
Stanford, CA 94305
acoates@cs.stanford.edu
Abstract
Recent deep learning and unsupervised feature learning ... | 4293 |@word cox:1 briefly:1 norm:1 nd:5 rgb:4 decomposition:1 pick:1 garrigues:1 moment:1 wrapper:1 contains:5 selecting:4 outperforms:1 existing:1 activation:2 yet:2 must:7 concatenate:1 partition:1 j1:1 update:1 v:1 greedy:3 fewer:3 discovering:1 xk:10 ith:1 provides:1 location:3 org:1 zhang:2 constructed:2 become:2 ... |
3,638 | 4,294 | Maximum Margin Multi-Instance Learning
Hua Wang
Computer Science and Engineering
University of Texas at Arlington
huawangcs@gmail.com
Heng Huang
Computer Science and Engineering
University of Texas at Arlington
heng@uta.edu
Farhad Kamangar
Computer Science and Engineering
University of Texas at Arlington
kamangar@ut... | 4294 |@word trial:1 version:3 briefly:1 seems:1 norm:1 advantageous:1 everingham:1 hu:1 confirms:1 ratan:1 thereby:2 shechtman:1 contains:1 outperforms:2 existing:5 freitas:1 com:2 comparing:1 gmail:2 subsequent:1 shape:2 designed:1 characterization:1 boosting:2 contribute:3 lexicon:1 zhang:1 five:1 constructed:2 becom... |
3,639 | 4,295 | Gaussian Process Training with Input Noise
Carl Edward Rasmussen
Department of Engineering
Cambridge University
Cambridge, CB2 1PZ
cer54@cam.ac.uk
Andrew McHutchon
Department of Engineering
Cambridge University
Cambridge, CB2 1PZ
ajm257@cam.ac.uk
Abstract
In standard Gaussian Process regression input locations are a... | 4295 |@word trial:3 middle:1 version:5 d2:1 tried:2 covariance:2 tr:1 solid:3 minus:1 carry:1 moment:3 reduction:1 initial:6 series:9 initialisation:4 outperforms:2 current:7 comparing:2 recovered:2 yet:1 must:3 subsequent:1 happen:1 shawetaylor:1 analytic:2 christian:1 remove:1 designed:3 treating:1 plot:11 alone:1 in... |
3,640 | 4,296 | Efficient Inference in Fully Connected CRFs with
Gaussian Edge Potentials
?
Philipp Kr?ahenbuhl
Computer Science Department
Stanford University
philkr@cs.stanford.edu
Vladlen Koltun
Computer Science Department
Stanford University
vladlen@cs.stanford.edu
Abstract
Most state-of-the-art techniques for multi-class image... | 4296 |@word kohli:6 version:1 everingham:1 triggs:1 propagate:1 decomposition:1 textonboost:3 series:1 contains:2 past:1 outperforms:2 contextual:1 subsequent:1 wiewiora:1 partition:1 predetermined:1 shape:2 enables:1 remove:1 update:7 grass:5 alone:1 fewer:1 mccallum:1 ith:2 smith:1 core:2 nearness:1 provides:1 node:1... |
3,641 | 4,297 | Periodic Finite State Controllers for Efficient
POMDP and DEC-POMDP Planning
Jaakko Peltonen
Aalto University, Department of Information
and Computer Science, Helsinki Institute for
Information Technology HIIT,
P.O. Box 15400, FI-00076 Aalto, Finland
Jaakko.Peltonen@aalto.fi
Joni Pajarinen
Aalto University, Departmen... | 4297 |@word briefly:1 version:2 compression:2 nd:2 r:3 tried:1 simplifying:1 initial:10 contains:1 denoting:2 existing:2 current:11 must:1 written:2 hsvi2:4 periodically:1 happen:1 enables:1 qmdp:1 update:2 intelligence:1 provides:1 node:51 firstly:1 along:1 direct:5 become:2 beta:3 paragraph:1 introduce:12 finitehoriz... |
3,642 | 4,298 | Convergent Fitted Value Iteration
with Linear Function Approximation
Daniel J. Lizotte
David R. Cheriton School of Computer Science
University of Waterloo
Waterloo, ON N2L 3G1 Canada
dlizotte@uwaterloo.ca
Abstract
Fitted value iteration (FVI) with ordinary least squares regression is known to
diverge. We present a ne... | 4298 |@word trial:2 version:2 middle:1 polynomial:1 norm:35 kalyanakrishnan:1 contraction:6 tr:1 initial:3 contains:1 daniel:1 current:1 com:1 si:1 yet:1 guez:1 fvi:26 must:2 written:1 numerical:1 enables:2 remove:1 interpretable:1 implying:1 generative:1 greedy:1 guess:1 half:1 intelligence:1 ith:3 core:1 provides:2 m... |
3,643 | 4,299 | Group Anomaly Detection using Flexible Genre Models
Liang Xiong
Machine Learning Department,
Carnegie Mellon University
lxiong@cs.cmu.edu
Barnab?as P?oczos
Robotics Institute,
Carnegie Mellon University
bapoczos@cs.cmu.edu
Jeff Schneider
Robotics Institute,
Carnegie Mellon University
schneide@cs.cmu.edu
Abstract
An... | 4299 |@word version:1 middle:1 proportion:1 simulation:5 covariance:2 accounting:1 pick:1 serie:1 solid:2 accommodate:1 series:3 contains:3 score:20 daniel:2 document:2 outperforms:1 existing:8 comparing:1 com:1 must:1 john:1 explorative:1 academia:1 kdd:1 hofmann:1 designed:2 update:4 v:1 generative:4 selected:1 half:... |
3,644 | 430 | A competitive modular connectionist architecture
Robert A. Jacobs and Michael I. Jordan
Department of Brain & Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
Abstract
We describe a multi-network, or modular, connectionist architecture that
captures that fact that many tasks have structure... | 430 |@word trial:1 polynomial:1 duda:2 proportion:1 dekker:1 simulation:2 jacob:11 decomposition:11 covariance:1 tr:1 contains:2 troller:1 current:1 nowlan:8 activation:3 si:3 must:1 john:1 partition:1 designed:2 interpretable:1 selected:1 une:1 ith:7 detecting:1 location:2 sigmoidal:1 along:1 consists:2 combine:2 beha... |
3,645 | 4,300 | Budgeted Optimization with Concurrent
Stochastic-Duration Experiments
Javad Azimi, Alan Fern, Xiaoli Z. Fern
School of EECS, Oregon State University
{azimi, afern, xfern}@eecs.oregonstate.edu
Abstract
Budgeted optimization involves optimizing an unknown function that is costly to evaluate by requesting a limited numb... | 4300 |@word cpe:48 pcc:1 nd:1 open:1 simulation:4 bn:3 tr:1 initial:3 selecting:7 genetic:1 outperforms:1 existing:2 freitas:1 current:1 comparing:2 michal:1 surprising:1 must:8 designed:1 fund:1 n0:9 greedy:1 selected:8 device:1 guess:1 fewer:4 beginning:2 rosenbrock:1 short:2 provides:2 location:1 rollout:1 dn:3 alon... |
3,646 | 4,301 | Selective Prediction of Financial Trends with Hidden
Markov Models
Ran El-Yaniv and Dmitry Pidan
Department of Computer Science, Technion
Haifa, 32000 Israel
{rani,pidan}@cs.technion.ac.il
Abstract
Focusing on short term trend prediction in a financial context, we consider the
problem of selective prediction whereby t... | 4301 |@word rani:1 coarseness:4 r:2 tried:2 q1:3 mention:1 profit:1 versatile:1 recursively:4 reduction:1 initial:6 series:3 selecting:1 seriously:1 ours:1 past:1 err:1 soules:1 comparing:1 discretization:1 riskier:1 refines:6 subsequent:2 enables:1 remove:1 progressively:2 update:4 v:2 stationary:2 generative:2 select... |
3,647 | 4,302 | Predicting Dynamic Difficulty
Olana Missura and Thomas G?artner
University of Bonn and Fraunhofer IAIS
Schlo? Birlinghoven
52757 Sankt Augustin, Germany
{olana.missura,thomas.gaertner}@uni-bonn.de
Abstract
Motivated by applications in electronic games as well as teaching systems, we
investigate the problem of dynamic... | 4302 |@word polynomial:1 seems:1 stronger:1 rigged:1 pick:1 recursively:1 contains:2 subjective:1 reaction:1 existing:1 current:2 comparing:2 o2:1 surprising:1 must:1 realistic:2 designed:1 plot:2 update:11 progressively:1 v:4 intelligence:3 half:1 short:3 herbrich:1 become:1 symposium:2 prove:1 artner:4 acquired:1 the... |
3,648 | 4,303 | Learning with the Weighted Trace-norm under
Arbitrary Sampling Distributions
Rina Foygel
Department of Statistics
University of Chicago
rina@uchicago.edu
Ohad Shamir
Microsoft Research New England
ohadsh@microsoft.com
Ruslan Salakhutdinov
Department of Statistics
University of Toronto
rsalakhu@ustat.toronto.edu
Natha... | 4303 |@word seems:1 norm:53 advantageous:1 km:1 r:2 simulation:5 citeseer:1 tr:4 boundedness:1 series:1 contains:2 outperforms:2 com:1 written:1 chicago:2 realistic:1 j1:2 kdd:2 plot:3 aside:2 selected:2 prize:1 provides:2 node:1 toronto:2 location:2 org:1 simpler:1 unbounded:6 prove:2 consists:1 npr:2 introduce:1 theo... |
3,649 | 4,304 | Optimistic Optimization of a Deterministic Function
without the Knowledge of its Smoothness
R?emi Munos
SequeL project, INRIA Lille ? Nord Europe, France
remi.munos@inria.fr
Abstract
We consider a global optimization problem of a deterministic function f in a semimetric space, given a finite budget of n evaluations. ... | 4304 |@word version:1 polynomial:2 norm:3 stronger:1 nd:1 dekker:1 open:1 carolina:1 mention:1 initial:1 contains:2 selecting:3 ours:1 current:3 nt:1 surprising:1 yet:1 must:2 partition:7 shape:1 enables:1 half:1 leaf:13 intelligence:1 provides:4 node:43 teytaud:1 along:1 c2:3 direct:5 symposium:2 ik:3 prove:2 tuy:1 x0... |
3,650 | 4,305 | Spike and Slab Variational Inference for Multi-Task
and Multiple Kernel Learning
Michalis K. Titsias
University of Manchester
mtitsias@gmail.com
Miguel L?azaro-Gredilla
Univ. de Cantabria & Univ. Carlos III de Madrid
miguel@tsc.uc3m.es
Abstract
We introduce a variational Bayesian inference algorithm which can be wid... | 4305 |@word multitask:1 wmf:1 version:1 briefly:1 loading:1 wqm:16 consolider:1 km:6 rgb:1 covariance:13 inpainting:6 tr:1 series:1 outperforms:1 current:1 com:1 gmail:1 written:1 must:1 multioutput:1 partition:4 remove:1 designed:1 hoping:1 update:8 v:1 stationary:4 intelligence:2 selected:1 nq:4 beauchamp:1 firstly:1... |
3,651 | 4,306 | Similarity-based Learning via Data Driven
Embeddings
Prateek Jain
Microsoft Research India
Bangalore, INDIA
prajain@microsoft.com
Purushottam Kar
Indian Institute of Technology
Kanpur, INDIA
purushot@cse.iitk.ac.in
Abstract
We consider the problem of classification using similarity/distance functions over
data. Spec... | 4306 |@word kulis:1 repository:3 seems:1 norm:2 seek:1 hsieh:1 thereby:3 liblinear:2 series:1 selecting:3 offering:1 rkhs:1 outperforms:3 existing:9 past:1 com:1 z2:3 informative:2 landmarked:13 enables:1 remove:1 drop:1 v:10 greedy:1 discovering:1 weighing:5 selected:3 intelligence:1 core:1 provides:1 nitin:1 cse:1 co... |
3,652 | 4,307 | Object Detection with Grammar Models
Ross B. Girshick
Dept. of Computer Science
University of Chicago
Chicago, IL 60637
rbg@cs.uchicago.edu
Pedro F. Felzenszwalb
School of Engineering and
Dept. of Computer Science
Brown University
Providence, RI 02912
pff@brown.edu
David McAllester
TTI-Chicago
Chicago, IL 60637
mcal... | 4307 |@word version:1 dalal:2 seems:1 everingham:2 triggs:2 reused:1 termination:1 decomposition:2 harder:1 accommodate:1 recursively:1 initial:3 score:30 selecting:1 tuned:1 outperforms:2 existing:1 current:1 comparing:1 si:11 assigning:1 dx:1 must:2 parsing:1 activation:1 chicago:5 visible:10 occludes:1 enables:1 hof... |
3,653 | 4,308 | Statistical Tests for Optimization Efficiency
Levi Boyles, Anoop Korattikara, Deva Ramanan, Max Welling
Department of Computer Science
University of California, Irvine
Irvine, CA 92697-3425
{lboyles},{akoratti},{dramanan},{welling}@ics.uci.edu
Abstract
Learning problems, such as logistic regression, are typically for... | 4308 |@word trial:1 middle:3 dalal:2 everingham:1 triggs:1 d2:1 hsieh:1 citeseer:2 delicately:1 sgd:33 liblinear:1 reduction:1 cyclic:1 series:1 score:2 zij:13 tuned:2 ours:1 current:2 z2:1 comparing:2 skipping:2 plot:5 interpretable:2 update:51 drop:1 plane:1 realizing:1 lr:1 accepting:1 provides:2 contribute:1 org:1 ... |
3,654 | 4,309 | Nonstandard Interpretations of Probabilistic
Programs for Efficient Inference
David Wingate
BCS / LIDS, MIT
wingated@mit.edu
Noah D. Goodman
Psychology, Stanford
ngoodman@stanford.edu
?
Andreas Stuhlmuller
BCS, MIT
ast@mit.edu
Jeffrey M. Siskind
ECE, Purdue
qobi@purdue.edu
Abstract
Probabilistic programming langua... | 4309 |@word version:1 polynomial:1 closure:1 simulation:2 accounting:1 dramatic:1 thereby:1 harder:1 initial:1 necessity:1 series:1 aple:1 hereafter:1 lightweight:2 interestingly:1 existing:1 current:2 universality:1 assigning:1 must:8 written:3 mesh:12 distant:1 visible:1 enables:2 designed:1 drop:1 concert:1 grass:1 ... |
3,655 | 431 | A Comparative Study of the Practical
Characteristics of Neural Network and
Conventional Pattern Classifiers
Richard P. Lippmann
Lincoln Laboratory, MIT
Lexington, MA 02173-9108
Kenney Ng
BBN Systems and Technologies
Cambridge, MA 02138
Abstract
Seven different pattern classifiers were implemented on a serial compute... | 431 |@word implemented:2 predicted:2 version:1 polynomial:7 differ:4 guided:2 correct:1 laboratory:1 centered:1 kdtree:2 exhibit:1 width:4 fifteen:1 solid:1 require:4 reduction:1 m:5 f1:2 generalization:1 generalized:1 seven:3 tuned:2 genetic:2 demonstrate:2 theoretic:2 discriminant:1 existing:1 relationship:2 consider... |
3,656 | 4,310 | Boosting with Maximum Adaptive Sampling
Franc?ois Fleuret
Idiap Research Institute
francois.fleuret@idiap.ch
Charles Dubout
Idiap Research Institute
charles.dubout@idiap.ch
Abstract
Classical Boosting algorithms, such as AdaBoost, build a strong classifier without
concern about the computational cost. Some applicati... | 4310 |@word exploitation:3 version:3 hu:5 simulation:2 decomposition:1 q1:7 pick:4 shading:1 reduction:11 configuration:2 series:2 score:1 selecting:1 contains:1 tuned:1 past:2 qth:2 current:2 com:1 nt:1 duffield:1 informative:1 predetermined:1 blur:1 v:1 stationary:2 greedy:5 prohibitive:1 selected:7 alone:1 beginning... |
3,657 | 4,311 | Bayesian Bias Mitigation for Crowdsourcing
Michael I. Jordan
University of California, Berkeley
jordan@cs.berkeley.edu
Fabian L. Wauthier
University of California, Berkeley
flw@cs.berkeley.edu
Abstract
Biased labelers are a systemic problem in crowdsourcing, and a comprehensive
toolbox for handling their responses i... | 4311 |@word norm:1 yi0:13 dekel:4 nd:1 r:2 covariance:1 pick:2 asks:1 mention:1 bellevue:1 reduction:1 contains:1 score:6 selecting:1 bc:1 hermosillo:1 envision:1 subjective:2 outperforms:5 current:2 comparing:1 loglik:1 yet:2 assigning:2 must:1 subsequent:2 realistic:1 informative:1 kdd:2 cheap:1 mislabels:1 treating:... |
3,658 | 4,312 | Generalizing from Several Related Classification
Tasks to a New Unlabeled Sample
Gyemin Lee, Clayton Scott
University of Michigan
{gyemin,clayscot}@umich.edu
Gilles Blanchard
Universit?at Potsdam
blanchard@math.uni-potsdam.de
Abstract
We consider the problem of assigning class labels to an unlabeled test data set,
g... | 4312 |@word multitask:1 middle:1 polynomial:1 norm:2 proportion:1 nd:1 seek:1 decomposition:1 thereby:1 boundedness:2 series:1 rkhs:3 pbx:12 existing:2 comparing:1 nt:12 si:4 assigning:1 scatter:3 written:1 ybit:1 universality:1 yet:1 numerical:1 plot:2 intelligence:2 selected:1 short:1 math:1 boosting:1 mcdiarmid:2 zh... |
3,659 | 4,313 | Hierarchically Supervised Latent Dirichlet Allocation
Adler Perotte
Nicholas Bartlett
No?emie Elhadad
Frank Wood
Columbia University, New York, NY 10027, USA
{ajp9009@dbmi,bartlett@stat,noemie@dbmi,fwood@stat}.columbia.edu
Abstract
We introduce hierarchically supervised latent Dirichlet allocation (HSLDA), a
model f... | 4313 |@word advantageous:1 proportion:2 nd:6 approved:1 open:1 vldb:1 pressure:1 solid:1 accommodate:1 ld:2 reduction:1 generatively:1 series:1 contains:1 document:39 expositional:1 outperforms:3 wd:1 com:4 must:3 numerical:1 shape:1 analytic:1 hypothesize:1 update:1 farkas:1 generative:1 half:1 yr:2 website:2 director... |
3,660 | 4,314 | Extracting Speaker-Specific Information with a
Regularized Siamese Deep Network
Ke Chen and Ahmad Salman
School of Computer Science, The University of Manchester
Manchester M13 9PL, United Kingdom
{chen,salmana}@cs.manchester.ac.uk
Abstract
Speech conveys different yet mixed information ranging from linguistic to
spe... | 4314 |@word version:4 norm:2 seems:1 nd:2 simulation:2 covariance:3 contrastive:5 tr:1 accommodate:1 ld:13 carry:2 reduction:4 initial:1 contains:1 exclusively:2 united:1 score:3 offering:1 ours:2 reynolds:1 outperforms:4 existing:2 comparing:1 goldberger:1 yet:5 tackling:1 scatter:1 visible:1 additive:1 enables:1 drop... |
3,661 | 4,315 | Active dendrites:
adaptation to spike-based communication
Bal?azs B Ujfalussy1,2
M?at?e Lengyel1
ubi@rmki.kfki.hu
m.lengyel@eng.cam.ac.uk
1
Computational & Biological Learning Lab, Dept. of Engineering, University of Cambridge, UK
2
Computational Neuroscience Group, Dept. of Biophysics, MTA KFKI RMKI, Budapest, Hungary... | 4315 |@word version:1 achievable:1 hippocampus:1 proportion:1 hu:1 grey:2 simulation:1 integrative:1 eng:1 covariance:9 innervating:2 reduction:1 moment:3 initial:1 tuned:2 makara:2 interestingly:1 freitas:1 current:3 analysed:1 si:4 must:1 readily:1 subsequent:1 additive:1 informative:1 plasticity:10 shape:1 interspik... |
3,662 | 4,316 | Non-Asymptotic Analysis of Stochastic
Approximation Algorithms for Machine Learning
Eric Moulines
LTCI
Telecom ParisTech, Paris, France
eric.moulines@enst.fr
Francis Bach
INRIA - Sierra Project-team
Ecole Normale Sup?erieure, Paris, France
francis.bach@ens.fr
Abstract
We consider the minimization of a convex objectiv... | 4316 |@word illustrating:1 version:1 inversion:1 norm:5 proportionality:3 simulation:3 nemirovsky:1 covariance:3 sgd:28 tr:1 ld:1 reduction:1 moment:1 initial:6 selecting:2 ecole:1 bc:1 rkhs:1 fn:46 plot:6 juditsky:2 iterates:3 kaxk:1 simpler:4 mathematical:1 h4:6 direct:1 replication:2 introductory:1 introduce:1 forge... |
3,663 | 4,317 | Neural Reconstruction with Approximate
Message Passing (NeuRAMP)
Sundeep Rangan
Polytechnic Institute of New York University
srangan@poly.edu
Alyson K. Fletcher
University of California, Berkeley
alyson@eecs.berkeley.edu
Aniruddha Bhargava
University of Wisconsin Madison
aniruddha@wisc.edu
Lav R. Varshney
IBM Thoma... | 4317 |@word milenkovic:1 version:1 briefly:1 polynomial:5 nd:1 d2:18 hu:2 simulation:5 excited:3 pavel:1 solid:2 reduction:1 initial:3 contains:2 score:4 interestingly:1 outperforms:3 bradley:1 com:1 written:2 bd:1 must:1 john:1 subsequent:3 numerical:1 shape:1 designed:1 plot:2 update:1 v:1 half:1 selected:1 greedy:1 ... |
3,664 | 4,318 | Why The Brain Separates Face Recognition From
Object Recognition
Joel Z Leibo, Jim Mutch and Tomaso Poggio
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambridge MA 02139
jzleibo@mit.edu, jmutch@mit.edu, tp@ai.mit.edu
Abstract
Many studies have uncovered evidence that visual cortex... | 4318 |@word fusiform:3 cox:1 middle:5 inversion:1 wiesel:4 simulation:7 seek:1 blender:4 lobe:2 tr:1 shot:1 extrastriate:3 liu:1 uncovered:1 contains:2 united:1 tuned:3 existing:1 current:1 anterior:6 yet:1 must:10 subsequent:1 shape:2 plot:5 sponsored:1 medial:1 bart:1 intelligence:3 plane:3 lamp:2 location:5 successi... |
3,665 | 4,319 | P I C O D ES: Learning a Compact Code for
Novel-Category Recognition
Alessandro Bergamo, Lorenzo Torresani
Dartmouth College
Hanover, NH, U.S.A.
{aleb, lorenzo}@cs.dartmouth.edu
Andrew Fitzgibbon
Microsoft Research
Cambridge, United Kingdom
awf@microsoft.com
Abstract
We introduce P I C O D ES: a very compact image d... | 4319 |@word kulis:2 version:1 proportion:1 disk:1 tried:1 hsieh:1 egou:1 profit:1 accommodate:1 liblinear:2 shechtman:1 reduction:2 united:1 hoiem:2 past:1 existing:2 current:2 com:1 babenko:1 must:5 additive:2 happen:1 subsequent:2 shape:1 wanted:1 remove:1 designed:2 gist:4 drop:1 plot:2 hash:3 update:1 selected:2 ph... |
3,666 | 432 | Rapidly Adapting Artificial Neural Networks for
Autonomous Navigation
Dean A. Pomerleau
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
The ALVINN (Autonomous Land Vehicle In a Neural Network) project addresses
the problem of training artificial neural networks in real time to perf... | 432 |@word middle:1 version:2 compression:1 decomposition:2 jacob:1 fifteen:1 reduction:1 ridden:2 reaction:1 current:7 luo:1 activation:5 lang:1 must:4 hou:1 cottrell:1 realistic:1 predetermined:1 designed:1 update:1 v:3 alone:1 imitate:1 plane:1 short:1 location:2 traverse:1 simpler:1 five:1 along:2 driver:4 consists... |
3,667 | 4,320 | Probabilistic Modeling of Dependencies Among
Visual Short-Term Memory Representations
A. Emin Orhan
Robert A. Jacobs
Department of Brain & Cognitive Sciences
University of Rochester
Rochester, NY 14627
{eorhan,robbie}@bcs.rochester.edu
Abstract
Extensive evidence suggests that items are not encoded independently in v... | 4320 |@word trial:16 proceeded:1 briefly:1 norm:1 replicate:1 seek:1 jacob:1 covariance:34 configuration:19 exclusively:1 interestingly:1 si:10 intriguing:1 written:1 realistic:3 analytic:1 reappeared:2 designed:1 plot:3 moreno:1 aside:1 stationary:7 selected:3 item:60 smith:1 short:5 colored:4 provides:4 location:24 p... |
3,668 | 4,321 | An Empirical Evaluation of Thompson Sampling
Lihong Li
Yahoo! Research
Santa Clara, CA
lihong@yahoo-inc.com
Olivier Chapelle
Yahoo! Research
Santa Clara, CA
chap@yahoo-inc.com
Abstract
Thompson sampling is one of oldest heuristic to address the exploration / exploitation trade-off, but it is surprisingly unpopular i... | 4321 |@word multitask:1 trial:1 exploitation:10 version:5 seems:1 confirms:1 simulation:10 tried:4 covariance:1 initial:2 necessity:1 contains:1 score:2 selecting:1 series:1 interestingly:1 outperforms:1 past:1 com:2 contextual:5 surprising:1 clara:2 si:2 chu:2 john:3 plot:5 drop:1 update:6 sponsored:1 greedy:3 selecte... |
3,669 | 4,322 | Active learning of neural response functions
with Gaussian processes
Mijung Park
Electrical and Computer Engineering
The University of Texas at Austin
mjpark@mail.utexas.edu
Greg Horwitz
Departments of Physiology and Biophysics
The University of Washington
ghorwitz@uw.edu
Jonathan W. Pillow
Departments of Psychology... | 4322 |@word mild:1 trial:11 neurophysiology:2 middle:2 briefly:1 proportion:1 nd:1 simulation:2 rhesus:1 vanhatalo:1 covariance:8 decomposition:1 solid:2 moment:3 reduction:2 contains:1 selecting:6 daniel:1 tuned:1 current:3 ka:1 yet:1 must:2 john:1 numerical:2 shape:1 analytic:2 remove:1 plot:2 ainen:1 update:5 resamp... |
3,670 | 4,323 | Learning large-margin halfspaces
with more malicious noise
Rocco A. Servedio
Columbia University
rocco@cs.columbia.edu
Philip M. Long
Google
plong@google.com
Abstract
We describe a simple algorithm that runs in time poly(n, 1/?, 1/?) and learns an
unknown n-dimensional ?-margin halfspace to accuracy 1 ? ? in theppres... | 4323 |@word version:4 polynomial:1 norm:2 suitably:1 closure:2 simulation:1 crucially:2 bn:4 initial:2 series:1 pt0:2 interestingly:1 omniscient:1 com:1 must:2 written:1 benign:1 mislabels:2 intelligence:1 selected:3 warmuth:1 plane:3 oldest:1 isotropic:1 xk:1 ith:1 short:1 provides:3 boosting:22 multiset:1 math:1 five... |
3,671 | 4,324 | Multiple Instance Filtering
Kamil Wnuk
Stefano Soatto
University of California, Los Angeles
{kwnuk,soatto}@cs.ucla.edu
Abstract
We propose a robust filtering approach based on semi-supervised and multiple instance learning (MIL). We assume that the posterior density would
be unimodal if not for the effect of outliers... | 4324 |@word version:1 norm:1 rivlin:1 open:1 seek:2 propagate:1 ajj:1 covariance:2 solid:2 initial:9 liu:1 contains:1 score:8 selecting:2 fragment:1 ours:1 freitas:1 current:7 nt:3 babenko:1 si:6 tackling:1 must:3 subsequent:1 visible:1 partition:2 additive:1 shape:7 enables:2 hofmann:2 drop:2 update:15 discrimination:... |
3,672 | 4,325 | Lower Bounds for Passive and Active Learning
Maxim Raginsky?
Coordinated Science Laboratory
University of Illinois at Urbana-Champaign
Alexander Rakhlin
Department of Statistics
University of Pennsylvania
Abstract
We develop unified information-theoretic machinery for deriving lower bounds
for passive and active lea... | 4325 |@word version:2 norm:1 open:1 q1:1 mention:2 necessity:1 contains:1 chervonenkis:1 beygelzimer:3 fn:3 informative:1 ainen:2 atlas:1 inspection:1 ith:1 provides:1 math:1 mathematical:1 constructed:2 prove:3 consists:3 introduce:1 notably:1 expected:1 bility:1 globally:1 enumeration:1 considering:1 cardinality:1 be... |
3,673 | 4,326 | Learning Anchor Planes for Classification
Ziming Zhang? L?ubor Ladick?? Philip H.S. Torr? Amir Saffari??
? Department of Computing, Oxford Brookes University, Wheatley, Oxford, OX33 1HX, U.K.
? Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, U.K.
? Sony Computer Entertainment Europ... | 4326 |@word middle:2 norm:4 seems:1 everingham:1 lodhi:1 linearized:1 tried:1 decomposition:3 hsieh:1 sgd:1 liblinear:3 contains:4 ours:2 comparing:1 wx:1 hofmann:1 shape:1 remove:1 update:1 v:1 intelligence:1 selected:2 fewer:1 amir:2 plane:22 xk:2 short:2 provides:1 codebook:2 hyperplanes:1 org:2 zhang:6 txk:2 kvk2:1... |
3,674 | 4,327 | Energetically Optimal Action Potentials
Martin Stemmler
BCCN and LMU Munich
Grosshadernerstr. 2,
Planegg, 82125 Germany
Biswa Sengupta, Simon Laughlin, Jeremy Niven
Department of Zoology,
University of Cambridge,
Downing Street, Cambridge CB2 3EJ, UK
Abstract
Most action potentials in the nervous system take on the ... | 4327 |@word determinant:1 rising:1 norm:14 open:3 cm2:18 squid:1 seek:1 pressure:2 solid:1 carry:1 reduction:1 denoting:1 suppressing:1 bilal:1 pna:3 current:66 comparing:1 clements:1 activation:8 yet:6 must:9 written:1 john:1 physiol:4 realistic:1 shape:7 treating:2 drop:1 aps:1 v:1 instantiate:1 nervous:1 signalling:... |
3,675 | 4,328 | Multilinear Subspace Regression: An Orthogonal
Tensor Decomposition Approach
Qibin Zhao 1 , Cesar F. Caiafa 2 , Danilo P. Mandic 3 , Liqing Zhang4 , Tonio Ball 5 , Andreas
Schulze-Bonhage5 , and Andrzej Cichocki1
1
Brain Science Institute, RIKEN, Japan
Instituto Argentino de Radioastronom??a (IAR), CONICET, Argentina
3... | 4328 |@word trial:3 version:1 loading:13 norm:1 open:1 km:9 simulation:5 decomposition:18 covariance:8 q1:2 tr:16 score:1 selecting:1 existing:3 must:1 conforming:1 john:1 subsequent:1 j1:4 acar:1 implying:1 intelligence:1 yi1:1 ith:2 core:7 zhang:2 fujii:1 lathauwer:2 kdk2:1 behavioral:1 introduce:1 pairwise:3 behavio... |
3,676 | 4,329 | Practical Variational Inference for Neural Networks
Alex Graves
Department of Computer Science
University of Toronto, Canada
graves@cs.toronto.edu
Abstract
Variational methods have been previously explored as a tractable approximation
to Bayesian inference for neural networks. However the approaches proposed so
far ha... | 4329 |@word pw:3 compression:6 retraining:3 covariance:1 simplifying:1 pick:2 thereby:1 cleary:1 initial:3 score:1 selecting:1 prefix:1 past:1 existing:1 blank:1 nowlan:2 written:1 must:1 numerical:2 recasting:1 subsequent:1 christian:1 update:3 progressively:1 steepest:2 core:2 short:2 provides:1 plaut:1 revisited:1 t... |
3,677 | 433 | Optimal Filtering in the Salamander Retina
Fred Riekea,l;, W. Geoffrey Owenb and Willialll Bialeka,b,c
Depart.ment.s of Physics a and Molecular and Cell Biologyb
Universit.y of California
Berkeley, California 94720
and
NEC Research Inst.itute C
4 Independence \Vay
Princeton, N e'... .J ersey 08540
Abstract
The dark-a... | 433 |@word version:3 nd:1 inefficiency:1 series:1 contains:1 donner:1 current:22 imat:1 erms:2 si:1 must:3 realize:1 physiol:3 subsequent:2 shape:2 analytic:2 heir:1 fund:1 nervous:1 ial:1 yamada:1 filtered:2 provides:1 denis:1 ional:1 quantit:2 mathematical:1 direct:1 symposium:1 consists:1 cray:1 behavioral:2 fdt:1 p... |
3,678 | 4,330 | Variational Gaussian Process Dynamical Systems
Andreas C. Damianou?
Department of Computer Science
University of Sheffield, UK
andreas.damianou@sheffield.ac.uk
Michalis K. Titsias
School of Computer Science
University of Manchester, UK
mtitsias@gmail.com
Neil D. Lawrence?
Department of Computer Science
University of... | 4330 |@word version:2 nd:3 twelfth:1 km:8 seek:2 covariance:30 decomposition:1 tr:1 reduction:3 initial:1 series:10 outperforms:1 current:1 com:3 rpi:1 gmail:1 dx:4 subsequent:1 informative:1 analytic:2 enables:2 drop:1 extrapolating:1 depict:1 fund:1 stationary:2 generative:2 selected:1 intelligence:4 smith:1 provides... |
3,679 | 4,331 | Unsupervised learning models of primary cortical
receptive fields and receptive field plasticity
Andrew Saxe, Maneesh Bhand, Ritvik Mudur, Bipin Suresh, Andrew Y. Ng
Department of Computer Science
Stanford University
{asaxe, mbhand, rmudur, bipins, ang}@cs.stanford.edu
Abstract
The efficient coding hypothesis holds t... | 4331 |@word version:1 middle:1 proportion:2 replicate:1 justice:1 hyv:1 lobe:1 pressure:1 initial:2 valois:2 contains:5 daniel:1 tuned:2 document:1 rearing:5 ording:1 current:4 surprising:1 must:1 blur:1 plasticity:16 shape:5 remove:1 plot:3 concert:1 v:1 discrimination:1 generative:1 selected:1 half:2 tone:15 destined... |
3,680 | 4,332 | Multi-armed bandits on implicit metric spaces
Aleksandrs Slivkins
Microsoft Research Silicon Valley
Mountain View, CA 94043
slivkins at microsoft.com
Abstract
The multi-armed bandit (MAB) setting is a useful abstraction of many online
learning tasks which focuses on the trade-off between exploration and exploitation.... | 4332 |@word trial:1 exploitation:3 version:9 seems:2 stronger:1 suitably:1 open:1 r:1 crucially:1 simulation:1 contains:1 selecting:2 document:2 interestingly:1 past:1 ka:19 com:1 contextual:2 nt:16 current:1 activation:1 si:14 ronald:1 realistic:1 numerical:4 partition:3 benign:3 shape:1 ligett:1 update:2 aside:1 v:1 ... |
3,681 | 4,333 | Comparative Analysis of Viterbi Training and
Maximum Likelihood Estimation for HMMs
Armen Allahverdyan?
Yerevan Physics Institute
Yerevan, Armenia
aarmen@yerphi.am
Aram Galstyan
USC Information Sciences Institute
Marina del Rey, CA, USA
galstyan@isi.edu
Abstract
We present an asymptotic analysis of Viterbi Training (... | 4333 |@word trial:14 version:1 koopmans:1 norm:3 seek:2 p0:4 q1:6 initial:3 series:1 contains:1 outperforms:1 current:2 recovered:2 si:18 lang:1 parsing:2 pe1:3 n0:1 stationary:2 selected:2 xk:1 steepest:1 realizing:1 hamiltonian:1 smith:1 chiang:1 provides:3 math:3 simpler:2 dn:1 re2:1 consists:1 competitiveness:1 bal... |
3,682 | 4,334 | Sparse Filtering
Jiquan Ngiam, Pang Wei Koh, Zhenghao Chen, Sonia Bhaskar, Andrew Y. Ng
Computer Science Department, Stanford University
{jngiam,pangwei,zhenghao,sbhaskar,ang}@cs.stanford.edu
Abstract
Unsupervised feature learning has been shown to be effective at learning representations that perform well on image, ... | 4334 |@word cox:1 version:2 norm:5 nd:1 hyv:2 rgb:1 covariance:2 exclusively:1 score:1 tuned:1 interestingly:1 existing:1 ksk1:2 current:1 comparing:1 activation:8 must:2 partition:1 remove:1 designed:1 moreno:1 implying:1 greedy:5 half:1 fewer:1 alone:2 ith:2 realizing:1 characterization:1 tolhurst:1 location:1 simple... |
3,683 | 4,335 | Sparse Estimation with Structured Dictionaries
David P. Wipf ?
Visual Computing Group
Microsoft Research Asia
davidwipf@gmail.com
Abstract
In the vast majority of recent work on sparse estimation algorithms, performance
has been evaluated using ideal or quasi-ideal dictionaries (e.g., random Gaussian
or Fourier) char... | 4335 |@word trial:4 illustrating:1 unaltered:1 version:2 proceeded:1 norm:30 determinant:1 heuristically:1 seek:1 simulation:1 covariance:3 mention:1 tr:4 delgado:2 carry:1 series:1 efficacy:1 contains:1 selecting:1 mosher:1 outperforms:1 existing:1 kx0:2 current:1 com:1 recovered:1 surprising:1 comparing:1 gmail:1 dx:... |
3,684 | 4,336 | Dimensionality Reduction
Using the Sparse Linear Model
Todd Zickler
Harvard SEAS
Cambridge, MA 02138
zickler@seas.harvard.edu
Ioannis Gkioulekas
Harvard SEAS
Cambridge, MA 02138
igkiou@seas.harvard.edu
Abstract
We propose an approach for linear unsupervised dimensionality reduction, based
on the sparse linear model ... | 4336 |@word cylindrical:1 cox:2 version:2 norm:4 stronger:1 heuristically:2 hu:1 seek:1 covariance:2 accounting:2 lpp:6 decomposition:1 thereby:1 inpainting:1 harder:1 ld:1 reduction:13 liu:1 exclusively:1 selecting:1 denoting:3 suppressing:1 interestingly:1 outperforms:1 existing:1 recovered:1 written:1 realize:1 info... |
3,685 | 4,337 | Large-Scale Sparse Principal Component Analysis
with Application to Text Data
Youwei Zhang
Department of Electrical Engineering and Computer Sciences
University of California, Berkeley
Berkeley, CA 94720
zyw@eecs.berkeley.edu
Laurent El Ghaoui
Department of Electrical Engineering and Computer Sciences
University of Ca... | 4337 |@word repository:3 manageable:1 polynomial:3 norm:3 loading:1 nd:2 underperform:1 seek:1 ajj:1 covariance:9 simplifying:1 dramatic:2 tr:15 sepulchre:1 harder:1 reduction:3 contains:1 dspca:9 united:1 document:2 ati:4 existing:1 current:2 surprising:2 readily:1 numerical:1 partition:2 enables:1 remove:1 drop:1 int... |
3,686 | 4,338 | Rapid Deformable Object Detection using Dual-Tree
Branch-and-Bound
Iasonas Kokkinos
Center for Visual Computing
Ecole Centrale de Paris
iasonas.kokkinos@ecp.fr
Abstract
In this work we use Branch-and-Bound (BB) to efficiently detect objects with deformable part models. Instead of evaluating the classifier score exhaus... | 4338 |@word kokkinos:4 termination:1 bn:6 simplifying:1 covariance:1 dramatic:1 thereby:2 mention:1 accommodate:2 recursively:1 reduction:1 initial:1 configuration:2 series:1 score:22 contains:1 jimenez:1 ecole:1 denoting:2 o2:1 existing:1 parsing:2 realize:2 shape:2 hofmann:1 remove:1 drop:1 treating:1 plot:3 n0:2 asi... |
3,687 | 4,339 | Agnostic Selective Classification
Ran El-Yaniv and Yair Wiener
Computer Science Department
Technion ? Israel Institute of Technology
{rani,wyair}@{cs,tx}.technion.ac.il
Abstract
For a learning problem whose associated excess loss class is (?, B)-Bernstein, we
show that it is theoretically possible to track the same c... | 4339 |@word repository:1 version:2 rani:1 achievable:1 stronger:2 open:1 heuristically:2 tamayo:1 incurs:1 solid:2 reduction:1 contains:1 selecting:1 united:1 outperforms:1 err:1 beygelzimer:2 must:3 numerical:2 realistic:1 depict:1 selected:1 characterization:1 provides:1 location:1 zhang:1 height:2 rc:4 along:1 mathe... |
3,688 | 434 | Direct memory access using two cues: Finding
the intersection of sets in a connectionist model
Janet Wiles, Michael S. Humphreys, John D. Bain and Simon Dennis
Departments of Psychology and Computer Science
University of Queensland QLD 4072 Australia
email: janet@psych.psy.uq.oz.au
Abstract
For lack of alternative mo... | 434 |@word trial:3 stronger:1 simulation:7 queensland:2 initial:1 contains:1 analysed:1 activation:4 john:1 additive:4 enables:1 alone:1 cue:86 selected:2 short:1 filtered:1 preference:2 successive:1 c2:16 direct:8 viable:1 retrieving:1 expected:2 themselves:2 wallace:2 nor:1 multi:1 provided:2 underlying:1 linearity:1... |
3,689 | 4,340 | Active Classification based on Value of Classifier
Daphne Koller
Department of Computer Science
Stanford University
Stanford, CA 94305
koller@cs.stanford.edu
Tianshi Gao
Department of Electrical Engineering
Stanford University
Stanford, CA 94305
tianshig@stanford.edu
Abstract
Modern classification tasks usually invol... | 4340 |@word repository:4 twelfth:1 tried:1 pick:2 reduction:1 configuration:1 score:4 selecting:5 ours:3 existing:2 current:6 nt:4 written:1 informative:3 cpds:1 shape:1 cheap:3 drop:1 gist:4 treating:1 v:21 intelligence:1 selected:7 fewer:1 beginning:2 filtered:1 provides:1 boosting:6 node:3 simpler:1 daphne:1 mathema... |
3,690 | 4,341 | Bayesian Partitioning of Large-Scale Distance Data
David Adametz
Volker Roth
Department of Computer Science & Mathematics
University of Basel
Basel, Switzerland
{david.adametz,volker.roth}@unibas.ch
Abstract
A Bayesian approach to partitioning distance matrices is presented. It is inspired
by the Translation-invari... | 4341 |@word determinant:4 briefly:1 version:2 nkb:1 advantageous:1 proportion:1 seems:1 norm:1 vogt:1 d2:7 bn:3 covariance:11 methodologically:1 decomposition:2 frigyik:1 mention:1 thereby:1 tr:13 recursively:1 reduction:2 initial:3 cyclic:1 series:1 contains:5 selecting:2 substitution:1 score:1 rightmost:2 existing:2 ... |
3,691 | 4,342 | Noise Thresholds for Spectral Clustering
Sivaraman Balakrishnan
Min Xu
Akshay Krishnamurthy
Aarti Singh
School of Computer Science, Carnegie Mellon University
{sbalakri,minx,akshaykr,aarti}@cs.cmu.edu
Abstract
Although spectral clustering has enjoyed considerable empirical success in machine learning, its theoreti... | 4342 |@word h:19 illustrating:1 determinant:1 norm:5 stronger:2 nd:1 c0:1 suitably:1 open:2 r:1 simulation:2 bn:2 prasad:1 accommodate:1 recursively:1 ld:1 score:4 genetic:2 outperforms:2 existing:1 yet:2 must:2 readily:1 john:1 additive:1 partition:3 subsequent:1 shape:2 pertinent:1 plot:1 interpretable:1 leaf:1 isotr... |
3,692 | 4,343 | The Fast Convergence of Boosting
Matus Telgarsky
Department of Computer Science and Engineering
University of California, San Diego
9500 Gilman Drive, La Jolla, CA 92093-0404
mtelgars@cs.ucsd.edu
Abstract
This manuscript considers the convergence rate of boosting under a large class of
losses, including the exponentia... | 4343 |@word version:3 briefly:1 stronger:1 norm:1 adrian:1 heretofore:1 crucially:2 seek:2 decomposition:2 attainable:10 mention:1 recursively:1 initial:2 contains:8 daniel:1 interestingly:3 past:1 current:3 ka:7 surprising:2 luo:1 attainability:10 subsequent:1 additive:1 pertinent:1 update:2 farkas:1 rudin:1 warmuth:3... |
3,693 | 4,344 | Global Solution of Fully-Observed
Variational Bayesian Matrix Factorization
is Column-Wise Independent
Shinichi Nakajima
Nikon Corporation
Tokyo, 140-8601, Japan
nakajima.s@nikon.co.jp
Masashi Sugiyama
Tokyo Institute of Technology
Tokyo 152-8552, Japan
sugi@cs.titech.ac.jp
Derin Babacan
University of Illinois at Ur... | 4344 |@word trial:5 repository:2 norm:1 stronger:5 seems:1 cah:12 nd:2 decomposition:3 covariance:4 arti:8 bellevue:1 tr:8 solid:1 reduction:1 initial:2 series:1 current:2 written:6 kdd:1 analytic:30 update:2 implying:1 selected:1 accordingly:2 short:2 provides:1 preference:1 org:1 mathematical:1 along:1 c2:2 become:1 ... |
3,694 | 4,345 | Structure Learning for Optimization
Shulin (Lynn) Yang
Department of Computer Science
University of Washington
Seattle, WA 98195
yang@cs.washington.edu
Ali Rahimi
Red Bow Labs
Berkeley, CA 94704
ali@redbowlabs.com
Abstract
We describe a family of global optimization procedures that automatically decompose optimizatio... | 4345 |@word trial:1 stronger:1 advantageous:1 nd:1 glue:1 tedious:1 seek:1 ci2:1 simplifying:1 thereby:1 recursively:2 carry:1 initial:1 contains:1 score:6 tuned:1 ours:1 document:3 amp:1 outperforms:1 existing:5 recovered:6 com:1 comparing:1 discretization:1 yet:2 readily:2 numerical:2 shape:1 plot:1 gist:3 v:1 fewer:... |
3,695 | 4,346 | Select and Sample ? A Model of Efficient
Neural Inference and Learning
Jacquelyn A. Shelton, J?org Bornschein, Abdul-Saboor Sheikh
Frankfurt Institute for Advanced Studies
Goethe-University Frankfurt, Germany
{shelton,bornschein,sheikh}@fias.uni-frankfurt.de
Pietro Berkes
Volen Center for Complex Systems
Brandeis Uni... | 4346 |@word trial:1 toggling:1 version:1 nd:1 ucke:4 covariance:1 thereby:1 reduction:2 initial:1 contains:5 series:1 selecting:2 denoting:2 current:5 comparing:1 si:1 yet:1 subsequent:1 csc:1 numerical:2 plasticity:1 update:3 v:1 alone:6 pursued:1 generative:8 selected:13 contribute:1 org:2 become:1 manner:1 theoretic... |
3,696 | 4,347 | Large-Scale Category Structure Aware Image
Categorization
Bin Zhao
School of Computer Science
Carnegie Mellon University
Li Fei-Fei
Computer Science Department
Stanford University
Eric P. Xing
School of Computer Science
Carnegie Mellon University
binzhao@cs.cmu.edu
feifeili@cs.stanford.edu
epxing@cs.cmu.edu
Abst... | 4347 |@word multitask:2 briefly:1 norm:4 jacob:1 tr:1 shot:1 shechtman:1 reduction:1 initial:1 contains:1 score:1 series:1 genetic:1 document:1 outperforms:2 existing:1 comparing:2 stemmed:1 chu:1 olive:1 pioneer:1 realistic:1 informative:2 shape:2 enables:1 gv:7 designed:2 treating:1 bart:1 generative:2 leaf:4 advance... |
3,697 | 4,348 | On fast approximate submodular minimization
Stefanie Jegelka? , Hui Lin? , Jeff Bilmes?
Max Planck Institute for Intelligent Systems, Tuebingen, Germany
?
University of Washington, Dept. of EE, Seattle, U.S.A.
jegelka@tuebingen.mgp.de,{hlin,bilmes}@ee.washington.edu
?
Abstract
We are motivated by an application to ex... | 4348 |@word kohli:1 version:3 polynomial:12 norm:15 open:1 decomposition:4 pick:6 carry:1 moment:1 contains:1 series:1 selecting:1 err:1 current:1 si:1 must:4 subsequent:1 additive:2 partition:8 benign:2 selected:1 leaf:1 item:1 xk:7 node:21 simpler:1 mathematical:2 constructed:2 combine:4 introduce:3 magnanti:1 x0:1 l... |
3,698 | 4,349 | Sparse Recovery with Brownian Sensing
Alexandra Carpentier
INRIA Lille
Alexandra.carpentier@inria.fr
Odalric-Ambrym Maillard
INRIA Lille
odalricambrym.maillard@gmail.com
R?emi Munos
INRIA Lille
remi.munos@inria.fr
Abstract
We consider the problem of recovering the parameter ? ? RK of a sparse function
f (i.e. the n... | 4349 |@word trial:1 illustrating:2 polynomial:1 norm:5 nd:6 c0:2 covariance:6 decomposition:1 eld:1 initial:3 contains:1 series:1 selecting:1 outperforms:1 existing:3 com:1 gmail:1 dx:2 numerical:9 nian:1 enables:2 selected:1 cult:1 parametrization:3 short:1 provides:2 bijection:1 unbounded:1 mathematical:1 along:15 dn... |
3,699 | 435 | On Stochastic Complexity and Admissible
Models for Neural Network Classifiers
Padhraic Smyth
Communications Systems Research
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, CA 91109
Abstract
Given some training data how should we choose a particular network classifier from a family of networks ... | 435 |@word briefly:1 eliminating:1 achievable:1 compression:1 suitably:1 seek:1 gish:2 mention:1 cytology:1 selecting:2 past:2 existing:1 subjective:1 od:1 activation:2 yet:1 must:1 fn:1 benign:1 alone:1 greedy:2 ith:2 short:1 node:4 become:1 consists:1 manner:4 introduce:1 indeed:1 cand:1 examine:1 frequently:1 wallac... |
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