Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
4,400 | 4,983 | Minimax Theory for High-dimensional Gaussian
Mixtures with Sparse Mean Separation
Martin Azizyan
Machine Learning Department
Carnegie Mellon University
mazizyan@cs.cmu.edu
Aarti Singh
Machine Learning Department
Carnegie Mellon University
aarti@cs.cmu.edu
Larry Wasserman
Department of Statistics
Carnegie Mellon Univ... | 4983 |@word version:1 polynomial:3 nd:10 adrian:1 bn:10 decomposition:2 covariance:3 mention:1 reduction:1 moment:4 contains:1 series:1 daniel:1 ours:1 outperforms:2 existing:2 comparing:1 dx:3 fn:2 numerical:1 dydx:1 intelligence:1 isotropic:5 provides:3 along:1 symposium:3 focs:1 combine:1 x0:2 pairwise:3 ravindran:1... |
4,401 | 4,984 | Cluster Trees on Manifolds
Sivaraman Balakrishnan?
sbalakri@cs.cmu.edu
Srivatsan Narayanan?
srivatsa@cs.cmu.edu
Aarti Singh?
aarti@cs.cmu.edu
Alessandro Rinaldo?
arinaldo@stat.cmu.edu
Larry Wasserman?
larry@stat.cmu.edu
School of Computer Science? and Department of Statistics?
Carnegie Mellon University
In this p... | 4984 |@word mild:4 version:1 middle:2 polynomial:1 norm:1 rsl:17 stronger:1 c0:2 open:1 bn:3 pick:1 recursively:1 contains:6 selecting:1 guez:1 must:6 numerical:1 partition:1 additive:2 shape:1 generative:1 fewer:1 devising:1 plane:2 node:1 successive:1 along:2 constructed:1 c2:5 become:1 walther:2 prove:4 consists:1 i... |
4,402 | 4,985 | Convex Tensor Decomposition via Structured
Schatten Norm Regularization
Ryota Tomioka
Toyota Technological Institute at Chicago
Chicago, IL 60637
tomioka@ttic.edu
Taiji Suzuki
Department of Mathematical
and Computing Sciences
Tokyo Institute of Technology
Tokyo 152-8552, Japan
s-taiji@is.titech.ac.jp
Abstract
We stu... | 4985 |@word middle:1 version:2 norm:73 open:1 simulation:2 decomposition:36 jacob:1 tr:8 solid:1 liu:1 series:5 interestingly:1 current:1 contextual:1 comparing:2 com:1 yet:1 written:3 numerical:3 chicago:2 enables:1 short:1 core:1 yamada:1 lr:7 math:1 zhang:1 mathematical:2 along:4 lathauwer:4 become:1 prove:1 theoret... |
4,403 | 4,986 | Convex Relaxations for Permutation Problems
Fajwel Fogel
?
C.M.A.P., Ecole
Polytechnique,
Palaiseau, France
fogel@cmap.polytechnique.fr
Rodolphe Jenatton
?
CRITEO, Paris & C.M.A.P., Ecole
Polytechnique,
Palaiseau, France
jenatton@cmap.polytechnique.fr
Francis Bach
INRIA, SIERRA Project-Team & D.I.,
?
Ecole
Normale Su... | 4986 |@word cu:5 version:4 briefly:1 polynomial:2 norm:4 mers:3 cloned:2 seek:3 crucially:1 linearized:1 decomposition:2 covariance:4 pick:2 tr:5 moment:1 reduction:3 contains:2 ecole:4 genetic:2 etn:4 recovered:2 comparing:1 current:1 incidence:1 perturbative:1 written:8 must:3 numerical:5 plot:5 joy:1 greedy:1 fewer:... |
4,404 | 4,987 | Solving the multi-way matching problem by
permutation synchronization
Deepti Pachauri,? Risi Kondor? and Vikas Singh??
Dept. of Computer Sciences, University of Wisconsin?Madison
?
Dept. of Biostatistics & Medical Informatics, University of Wisconsin?Madison
?
Dept. of Computer Science and Dept. of Statistics, The Univ... | 4987 |@word version:2 kondor:1 norm:2 heuristically:1 seitz:3 decomposition:3 tr:1 harder:1 shot:3 mcauley:2 initial:1 celebrated:1 series:1 contains:2 rightmost:1 outperforms:1 recovered:2 must:4 written:1 chicago:1 additive:1 visible:2 confirming:1 shape:7 hofmann:1 plot:1 touring:2 progressively:1 v:1 implying:1 lea... |
4,405 | 4,988 | Reflection methods for user-friendly
submodular optimization
Stefanie Jegelka
UC Berkeley
Berkeley, CA, USA
Francis Bach
INRIA - ENS
Paris, France
Suvrit Sra
MPI for Intelligent Systems
T?ubingen, Germany
Abstract
Recently, it has become evident that submodularity naturally captures widely
occurring concepts in mac... | 4988 |@word kohli:1 middle:1 version:3 polynomial:5 norm:3 tedious:1 seek:2 simulation:1 decomposition:13 sgd:12 feasible:1 cyclic:1 document:2 outperforms:1 existing:7 recovered:1 comparing:1 ka:1 com:1 yet:1 written:3 numerical:1 cheap:1 wanted:1 plot:1 greedy:3 fewer:1 leaf:1 yr:1 xk:8 core:4 certificate:1 provides:... |
4,406 | 4,989 | Curvature and Optimal Algorithms for Learning and
Minimizing Submodular Functions
Rishabh Iyer? , Stefanie Jegelka? , Jeff Bilmes?
University of Washington, Dept. of EE, Seattle, U.S.A.
?
University of California, Dept. of EECS, Berkeley, U.S.A.
rkiyer@uw.edu, stefje@eecs.berkeley.edu, bilmes@uw.edu
?
Abstract
We inv... | 4989 |@word kohli:1 version:8 polynomial:18 stronger:2 seems:1 semidifferential:1 open:2 that2:1 decomposition:4 asks:1 solid:1 harder:3 reduction:1 document:2 pna:2 outperforms:1 surprising:1 lang:1 yet:2 written:1 must:1 refines:3 additive:3 subsequent:1 slb:1 visible:1 enables:1 greedy:2 intelligence:2 provides:4 dr... |
4,407 | 499 | Visual Grammars and their Neural Nets
Eric Mjolsness
Department of Computer Science
Yale University
New Haven, CT 06520-2158
Abstract
I exhibit a systematic way to derive neural nets for vision problems. It
involves formulating a vision problem as Bayesian inference or decision
on a comprehensive model of the visual ... | 499 |@word version:3 eliminating:1 nd:1 simulation:1 gradual:1 acknowlegements:1 yaleu:1 configuration:1 imaginary:1 recovered:6 yet:1 numerical:1 remove:1 generative:1 intelligence:1 short:1 coarse:1 location:3 successive:1 simpler:2 relabelling:1 along:1 undetectable:1 qualitative:1 introduce:2 expected:2 intricate:1... |
4,408 | 4,990 | An Approximate, Efficient Solver for LP Rounding
Srikrishna Sridhar1 , Victor Bittorf1 , Ji Liu1 , Ce Zhang1
Christopher R?e2 , Stephen J. Wright1
1
Computer Sciences Department, University of Wisconsin-Madison, Madison, WI 53706
2
Computer Science Department, Stanford University, Stanford, CA 94305
{srikris,vbittorf,j... | 4990 |@word version:2 norm:1 instrumental:1 nd:1 disk:1 vldb:1 seek:1 jacob:1 sgd:3 mention:2 yih:1 shot:1 liu:2 contains:1 score:1 series:1 loeliger:1 denoting:1 ka:2 intriguing:1 numerical:2 shape:1 update:2 intelligence:1 selected:1 ith:2 core:5 iterates:1 node:2 bittorf:1 zhang:1 mathematical:1 along:1 c2:1 symposi... |
4,409 | 4,991 | Hierarchical Modular Optimization of Convolutional
Networks Achieves Representations Similar to
Macaque IT and Human Ventral Stream
Daniel Yamins?
McGovern Institute of Brain Research
Massachusetts Institute of Technology
Cambridge, MA 02139
yamins@mit.edu
Ha Hong?
McGovern Institute of Brain Research
Massachusetts I... | 4991 |@word neurophysiology:1 trial:1 cox:3 version:3 judgement:1 fusiform:1 norm:1 kriegeskorte:6 seek:1 solid:1 harder:1 extrastriate:1 series:2 score:3 efficacy:1 contains:1 daniel:1 existing:1 current:1 comparing:1 anterior:2 activation:2 assigning:1 must:1 mesh:1 confirming:1 shape:5 motor:1 opin:1 designed:1 inte... |
4,410 | 4,992 | Bayesian inference for low rank spatiotemporal
neural receptive fields
Jonathan W. Pillow
Center for Perceptual Systems
The University of Texas at Austin
pillow@mail.utexas.edu
Mijung Park
Electrical and Computer Engineering
The University of Texas at Austin
mjpark@mail.utexas.edu
Abstract
The receptive field (RF) o... | 4992 |@word neurophysiology:3 middle:2 nd:3 simulation:1 covariance:14 tr:2 reduction:1 liu:1 nt:6 anqi:1 dx:5 written:1 john:1 informative:2 plot:2 update:1 samplingbased:1 parametrization:1 ith:3 footing:1 characterization:1 tolhurst:1 location:1 consists:2 introduce:3 examine:1 mx0:6 automatically:1 dkx:2 mijung:1 w... |
4,411 | 4,993 | Spectral methods for neural characterization using
generalized quadratic models
Il Memming Park?123 , Evan Archer?13 , Nicholas Priebe14 , & Jonathan W. Pillow123
1. Center for Perceptual Systems, 2. Dept. of Psychology,
3. Division of Statistics & Scientific Computation, 4. Section of Neurobiology,
The University of ... | 4993 |@word neurophysiology:2 trial:1 briefly:1 middle:1 nd:2 dekker:1 grey:1 seek:1 decomposition:5 covariance:11 eng:1 tr:12 reduction:11 moment:15 yxx:4 series:1 selecting:1 contains:1 daniel:1 trinary:2 elliptical:4 yet:3 written:3 numerical:1 earcher:1 informative:3 analytic:1 remove:1 plot:1 v:1 stationary:1 gene... |
4,412 | 4,994 | Fisher-Optimal Neural Population Codes for
High-Dimensional Diffeomorphic Stimulus
Representations
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.upenn.... | 4994 |@word trial:1 illustrating:1 nd:1 simulation:2 r:1 covariance:16 decorrelate:2 attainable:1 tr:9 daniel:1 tuned:1 hardy:1 ka:2 activation:3 si:1 yet:1 additive:1 informative:1 treating:1 stationary:2 half:1 cue:1 plane:2 short:1 filtered:2 characterization:1 sigmoidal:6 zhang:1 height:1 mathematical:1 along:1 dif... |
4,413 | 4,995 | Robust learning of low-dimensional dynamics from
large neural ensembles
David Pfau
Eftychios A. Pnevmatikakis
Liam Paninski
Center for Theoretical Neuroscience
Department of Statistics
Grossman Center for the Statistics of Mind
Columbia University, New York, NY
pfau@neurotheory.columbia.edu
{eftychios,liam}@stat.colu... | 4995 |@word neurophysiology:1 trial:9 illustrating:1 middle:1 inversion:1 norm:34 open:1 decomposition:4 accounting:2 covariance:2 briggman:1 ld:2 reduction:3 liu:3 contains:1 qth:1 imaginary:3 recovered:21 comparing:1 nt:10 ka:1 scatter:1 chu:1 must:1 written:1 multineuron:1 motor:5 drop:1 plot:1 update:2 stationary:3... |
4,414 | 4,996 | Sparse nonnegative deconvolution for compressive
calcium imaging: algorithms and phase transitions
Eftychios A. Pnevmatikakis and Liam Paninski
Department of Statistics, Center for Theoretical Neuroscience
Grossman Center for the Statistics of Mind, Columbia University, New York, NY
{eftychios, liam}@stat.columbia.edu... | 4996 |@word neurophysiology:1 version:3 mri:2 compression:4 norm:5 c0:13 seek:1 sensed:1 simulation:2 covariance:1 solid:2 shot:1 series:2 contains:2 woodruff:1 denoting:1 recovered:1 nt:2 si:11 universality:1 chu:1 written:3 john:1 informative:1 predetermined:2 enables:1 cis:1 plot:2 interpretable:2 discrimination:1 h... |
4,415 | 4,997 | Generalized Method-of-Moments for Rank
Aggregation
Hossein Azari Soufiani
SEAS
Harvard University
azari@fas.harvard.edu
William Z. Chen
Statistics Department
Harvard University
wchen@college.harvard.edu
David C. Parkes
SEAS
Harvard University
parkes@eecs.harvard.edu
Lirong Xia
Computer Science Department
Rensselaer... | 4997 |@word trial:2 inversion:1 judgement:1 seems:1 closure:1 seek:1 ci2:3 pg:9 bellevue:2 moment:8 liu:1 series:2 contains:2 outperforms:3 bradley:3 rpi:2 written:1 kdd:1 designed:1 fund:1 stationary:2 intelligence:2 ith:1 short:2 core:1 imprimerie:1 parkes:3 characterization:1 provides:2 proofness:1 preference:3 succ... |
4,416 | 4,998 | Generalized Random Utility Models with Multiple
Types
Hossein Azari Soufiani
Hansheng Diao
Zhenyu Lai
David C. Parkes
SEAS
Mathematics Department Economics Department
SEAS
Harvard University
Harvard University
Harvard University
Harvard University
azari@fas.harvard.edu
diao@fas.harvard.edu zlai@fas.harvard.edu parkes@... | 4998 |@word mild:1 logit:4 giudici:1 adrian:1 cm2:1 simulation:1 covariance:1 p0:2 bellevue:1 boundedness:1 moment:2 substitution:1 contains:2 series:1 daniel:4 z2:3 chu:2 written:2 must:4 john:2 chicago:1 kdd:1 update:2 stationary:1 generative:1 intelligence:1 metrika:1 accordingly:1 merger:1 ith:1 parkes:4 benkard:1 ... |
4,417 | 4,999 | Speedup Matrix Completion with Side Information:
Application to Multi-Label Learning
Miao Xu1
Rong Jin2
Zhi-Hua Zhou1
1
National Key Laboratory for Novel Software Technology,
Nanjing University, Nanjing 210023, China
2
Department of Computer Science and Engineering,
Michigan State University, East Lansing, MI 48824
{x... | 4999 |@word trial:1 kong:1 briefly:1 norm:7 nd:1 decomposition:1 tr:4 liblinear:1 reduction:2 liu:2 tist:1 outperforms:1 existing:2 current:1 recovered:1 com:3 luo:2 si:1 yet:1 toh:1 kdd:4 eleven:4 update:2 implying:1 prohibitive:1 selected:1 item:1 ith:2 chua:1 cse:1 kasiviswanathan:1 simpler:1 zhang:3 mathematical:1 ... |
4,418 | 5 | 485
TOWARDS AN ORGANIZING PRINCIPLE FOR
A LAYERED PERCEPTUAL NETWORK
Ralph Linsker
IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598
Abstract
An information-theoretic optimization principle is proposed for the development
of each processing stage of a multilayered perceptual network. This principle of
... | 5 |@word cylindrical:1 version:1 wiesel:1 disk:8 heuristically:2 confirms:1 simulation:1 sensed:1 minus:1 moment:1 initial:1 surprising:1 cad:1 yet:1 dx:4 written:2 must:2 realistic:1 happen:1 shape:2 progressively:1 stationary:1 device:3 tone:1 short:1 positionally:1 provides:1 math:1 complication:1 contribute:1 prefe... |
4,419 | 50 | 662
AN ADAPTIVE AND HETERODYNE FILTERING PROCEDURE
FOR THE IMAGING OF MOVING OBJECTS
F. H. Schuling, H. A. K. Mastebroek and W. H. Zaagman
Biophysics Department, Laboratory for General Physics
Westersingel 34, 9718 eM Groningen, The Netherlands
ABSTRACT
Recent experimental work on the stimulus velocity dependent time... | 50 |@word version:2 pulse:1 gradual:2 simulation:11 automat:1 solid:2 initial:1 exclusively:1 tuned:11 moo:1 physiol:1 additive:1 blur:16 hypothesize:1 atlas:1 half:1 device:6 weighing:12 advancement:1 inspection:2 reciprocal:2 realizing:1 compo:1 psth:5 traverse:1 lor:1 height:1 mathematical:1 along:3 differential:7 c... |
4,420 | 500 | Segmentation Circuits Using Constrained
Optimization
John G. Harris'"
MIT AI Lab
545 Technology Sq., Rm 767
Cambridge, MA 02139
Abstract
A novel segmentation algorithm has been developed utilizing an absolutevalue smoothness penalty instead of the more common quadratic regularizer. This functional imposes a piece-wis... | 500 |@word aircraft:1 version:2 lgorithms:1 simulation:5 tr:1 liu:1 mag:1 existing:2 current:1 luo:2 must:5 john:1 additive:2 discernible:1 lue:1 half:1 intelligence:1 device:3 ial:1 dissertation:1 detecting:1 node:2 location:1 ional:1 height:7 constructed:2 prove:1 resistive:7 wild:1 huber:2 terminal:1 decreasing:1 in... |
4,421 | 5,000 | Correlated random features for
fast semi-supervised learning
Brian McWilliams
ETH Z?urich, Switzerland
brian.mcwilliams@inf.ethz.ch
David Balduzzi
ETH Z?urich, Switzerland
david.balduzzi@inf.ethz.ch
Joachim M. Buhmann
ETH Z?urich, Switzerland
jbuhmann@inf.ethz.ch
Abstract
This paper presents Correlated Nystr?om View... | 5000 |@word repository:1 version:2 norm:6 stronger:1 dramatic:1 nystr:39 reduction:6 contains:2 selecting:1 outperforms:6 err:2 comparing:1 si:3 subsequent:1 informative:2 cheap:1 plot:1 v:4 implying:1 selected:1 short:2 core:1 sarcos:5 provides:1 org:1 zhang:1 constructed:3 become:2 consists:1 overhead:1 introduce:1 i... |
4,422 | 5,001 | Manifold-based Similarity Adaptation
for Label Propagation
Masayuki Karasuyama and Hiroshi Mamitsuka
Bioionformatics Center, Institute for Chemical Research, Kyoto University, Japan
{karasuyama,mami}@kuicr.kyoto-u.ac.jp
Abstract
Label propagation is one of the state-of-the-art methods for semi-supervised learning, wh... | 5001 |@word kong:1 repository:1 version:3 zelnik:1 solid:4 reduction:1 initial:4 liu:3 series:1 score:4 selecting:1 tuned:1 document:1 existing:1 mishra:1 comparing:1 yet:1 must:1 subsequent:4 distant:1 numerical:1 designed:1 intelligence:1 parameterization:1 steepest:2 provides:1 node:22 lx:1 kelner:1 zhang:2 along:1 ... |
4,423 | 5,002 | Efficient Supervised Sparse Analysis and Synthesis
Operators
Pablo Sprechmann
Duke University
pablo.sprechmann@duke.edu
Roee Litman
Tel Aviv University
roeelitman@post.tau.ac.il
Tal Ben Yakar
Tel Aviv University
talby10@gmail.com
Alex Bronstein
Tel Aviv University
bron@eng.tau.ac.il
Guillermo Sapiro
Duke University... | 5002 |@word briefly:1 version:4 norm:2 seems:1 replicate:1 eng:1 decomposition:1 dramatic:1 sgd:1 initial:2 configuration:1 contains:2 score:1 series:1 denoting:1 tuned:2 past:1 outperforms:2 com:1 si:4 gmail:1 john:1 dct:6 fn:2 blur:1 analytic:2 designed:1 polyphonic:7 generative:4 fewer:1 leaf:1 rudin:1 isotropic:1 p... |
4,424 | 5,003 | When in Doubt, SWAP: High-Dimensional
Sparse Recovery from Correlated Measurements
Divyanshu Vats
Rice University
Houston, TX 77251
dvats@rice.edu
Richard Baraniuk
Rice University
Houston, TX 77251
richb@rice.edu
Abstract
We consider the problem of accurately estimating a high-dimensional sparse vector using a small... | 5003 |@word mild:1 trial:1 version:1 mri:1 norm:3 simulation:5 seek:1 covariance:1 solid:1 initial:4 wrapper:2 contains:10 series:2 outperforms:1 recovered:1 surprising:1 si:2 saal:1 numerical:6 shawetaylor:1 remove:1 v:4 greedy:7 selected:3 accordingly:1 fpr:1 location:2 schwab:1 zhang:5 five:1 c2:5 ik:3 prove:2 pathw... |
4,425 | 5,004 | Deep content-based music recommendation
A?aron van den Oord, Sander Dieleman, Benjamin Schrauwen
Electronics and Information Systems department (ELIS), Ghent University
{aaron.vandenoord, sander.dieleman, benjamin.schrauwen}@ugent.be
Abstract
Automatic music recommendation has become an increasingly relevant problem
... | 5004 |@word cnn:3 wmf:12 version:1 armand:1 seems:1 proportion:1 yi0:3 achievable:1 justice:2 hu:3 pulse:1 covariance:1 comprise:1 versatile:1 holy:1 blade:1 initial:2 electronics:1 atb:2 score:6 united:1 series:1 wanna:1 daniel:4 contains:2 interestingly:1 petty:3 com:1 ida:1 bello:1 gpu:2 romance:2 john:3 destiny:1 r... |
4,426 | 5,005 | Probabilistic Low-Rank Matrix Completion with
Adaptive Spectral Regularization Algorithms
Franc?ois Caron
Univ. Oxford, Dept. of Statistics
Oxford, OX1 3TG, UK
Caron@stats.ox.ac.uk
Adrien Todeschini
INRIA - IMB - Univ. Bordeaux
33405 Talence, France
Adrien.Todeschini@inria.fr
Marie Chavent
Univ. Bordeaux - IMB - INR... | 5005 |@word version:1 norm:18 nd:1 suitably:1 zkf:3 d2:2 simulation:1 decomposition:2 hasi:28 liu:1 series:1 zij:4 offering:1 interestingly:1 past:1 outperforms:1 err:4 attracted:1 numerical:1 enables:1 analytic:1 remove:2 interpretable:1 update:1 item:2 de1:3 propack:3 provides:8 contribute:1 node:1 preference:2 org:1... |
4,427 | 5,006 | A Gang of Bandits
Nicol`o Cesa-Bianchi
Universit`a degli Studi di Milano, Italy
Claudio Gentile
University of Insubria, Italy
nicolo.cesa-bianchi@unimi.it
claudio.gentile@uninsubria.it
Giovanni Zappella
Universit`a degli Studi di Milano, Italy
giovanni.zappella@unimi.it
Abstract
Multi-armed bandit problems formal... | 5006 |@word multitask:5 kulis:1 exploitation:4 version:5 inversion:2 compression:1 norm:2 determinant:2 nd:5 ences:1 tried:1 accounting:1 dramatic:3 thereby:1 tr:4 contains:4 denoting:1 rkhs:2 tuned:1 past:2 existing:1 outperforms:2 current:1 contextual:19 yet:1 chu:2 must:1 subsequent:1 additive:1 informative:2 realis... |
4,428 | 5,007 | Contrastive Learning Using Spectral Methods
James Zou
Harvard University
Daniel Hsu
Columbia University
David Parkes
Harvard University
Ryan Adams
Harvard University
Abstract
In many natural settings, the analysis goal is not to characterize a single data set in
isolation, but rather to understand the difference be... | 5007 |@word worsens:1 trial:1 faculty:1 briefly:1 nd:1 open:1 d2:2 seek:1 decomposition:12 contrastive:45 pick:1 wjf:6 moment:24 liu:1 series:1 score:5 daniel:1 document:29 interestingly:1 recovered:1 intriguing:1 import:1 subsequent:1 partition:2 xb1:1 drop:1 interpretable:1 update:3 alone:2 generative:10 half:2 intel... |
4,429 | 5,008 | Fast Determinantal Point Process Sampling with
Application to Clustering
Byungkon Kang ?
Samsung Advanced Institute of Technology
Yongin, Korea
bk05.kang@samsung.com
Abstract
Determinantal Point Process (DPP) has gained much popularity for modeling sets
of diverse items. The gist of DPP is that the probability of cho... | 5008 |@word kulis:1 determinant:18 version:1 inversion:3 cu:11 seems:2 briefly:1 polynomial:1 heuristically:2 decomposition:5 tr:1 initial:11 configuration:3 selecting:2 daniel:1 current:9 com:1 si:3 must:8 determinantal:8 john:1 subsequent:1 partition:5 timestamps:2 kdd:1 remove:1 plot:2 gist:1 update:4 stationary:6 a... |
4,430 | 5,009 | Computing the Stationary Distribution, Locally
Asuman Ozdaglar
LIDS, Department of EECS
Massachusetts Institute of Technology
asuman@mit.edu
Christina E. Lee
LIDS, Department of EECS
Massachusetts Institute of Technology
celee@mit.edu
Devavrat Shah
Department of EECS
Massachusetts Institute of Technology
devavrat@mi... | 5009 |@word version:1 stronger:1 widom:2 vldb:1 termination:3 simulation:6 nemirovsky:1 bahmani:2 recursively:1 initial:1 configuration:1 current:1 must:2 john:2 numerical:1 subsequent:1 predetermined:1 plot:1 update:1 v:2 stationary:22 beginning:4 zmax:12 characterization:2 node:67 mathematical:4 along:1 become:2 prov... |
4,431 | 501 | Optical Implementation of a Self?Organizing
Feature Extractor
Dana Z. Anderson*, Claus Benkert, Verena Hebler, Ju-Seog Jang,
Don Montgomery, and Mark Saffinan.
Joint Institute for Laboratory Astrophysics, University of Colorado and the
Department of Physics, University of Colorado, Boulder Colorado 80309-0440
Abstract... | 501 |@word version:1 simulation:1 pg:1 incidence:1 lang:3 scatter:1 must:3 electr:1 core:2 lr:1 detecting:1 provides:1 severa:1 contribute:1 preference:1 constructed:3 become:5 consists:1 behavioral:1 manner:1 behavior:2 pour:1 correlator:4 becomes:2 provided:2 discover:2 moreover:1 underlying:1 circuit:6 medium:1 begi... |
4,432 | 5,010 | Learning Prices for Repeated Auctions
with Strategic Buyers
Kareem Amin
University of Pennsylvania
akareem@cis.upenn.edu
Afshin Rostamizadeh
Google Research
rostami@google.com
Umar Syed
Google Research
usyed@google.com
Abstract
Inspired by real-time ad exchanges for online display advertising, we consider the
probl... | 5010 |@word mild:1 private:3 exploitation:1 polynomial:1 seems:1 leighton:2 stronger:1 dekel:2 reshef:1 noregret:1 willing:2 crucially:1 p0:1 pressure:1 thereby:1 tr:4 shot:8 reduction:1 selecting:2 offering:5 past:2 existing:1 com:2 surprising:1 si:3 must:2 sponsored:3 discrimination:2 selected:2 leaf:1 item:1 accordi... |
4,433 | 5,011 | Efficient Algorithm for Privately Releasing Smooth
Queries
Ziteng Wang
Key Laboratory of Machine Perception, MOE
School of EECS
Peking University
wangzt@cis.pku.edu.cn
Kai Fan
Key Laboratory of Machine Perception, MOE
School of EECS
Peking University
interfk@hotmail.com
Jiaqi Zhang
Key Laboratory of Machine Percepti... | 5011 |@word private:26 version:1 middle:1 polynomial:31 norm:3 nd:19 asks:1 ld:9 contains:6 series:2 pub:1 miklau:1 kmk:4 com:1 must:2 written:1 lorentz:1 griebel:2 numerical:4 ligett:3 v:1 item:1 smith:1 record:2 provides:2 boosting:1 attack:2 zhang:1 rc:1 differential:20 become:1 focs:2 prove:3 consists:1 naor:1 priv... |
4,434 | 5,012 | (Nearly) Optimal Algorithms for Private Online
Learning in Full-information and Bandit Settings
Adam Smith?
Pennsylvania State University
asmith@cse.psu.edu
Abhradeep Thakurta?
Stanford University and
Microsoft Research Silicon Valley Campus
b-abhrag@microsoft.com
Abstract
We give differentially private algorithms f... | 5012 |@word private:50 version:13 polynomial:2 stronger:3 norm:2 seems:1 dekel:1 nd:1 open:2 seek:3 prasad:2 jacob:1 shot:1 reduction:1 contains:1 daniel:2 existing:1 current:3 com:1 john:1 subsequent:4 remove:1 update:1 leaf:4 isotropic:1 beginning:1 smith:6 lr:1 provides:1 cse:1 node:7 kasiviswanathan:2 org:1 differe... |
4,435 | 5,013 | Local Privacy and Minimax Bounds:
Sharp Rates for Probability Estimation
1
John C. Duchi1
Michael I. Jordan1,2
Martin J. Wainwright1,2
2
Department of Electrical Engineering and Computer Science
Department of Statistics
University of California, Berkeley
{jduchi,jordan,wainwrig}@eecs.berkeley.edu
Abstract
We provide... | 5013 |@word private:31 version:1 cu:3 polynomial:2 achievable:1 stronger:1 proportion:1 suitably:1 norm:1 eliminating:1 km:4 bn:5 attainable:2 paid:1 reduction:1 necessity:1 initial:2 series:5 ktv:2 groundwork:1 interestingly:1 wainwrig:1 current:1 elliptical:2 attainability:1 dx:5 must:2 john:1 additive:2 partition:1 ... |
4,436 | 5,014 | A Stability-based Validation Procedure for
Differentially Private Machine Learning
Kamalika Chaudhuri
Department of Computer Science and Engineering
UC San Diego, La Jolla CA 92093
kamalika@cs.ucsd.edu
Staal Vinterbo
Division of Biomedical Informatics
UC San Diego, La Jolla CA 92093
sav@ucsd.edu
Abstract
Differentia... | 5014 |@word private:106 version:2 repository:2 turlach:1 norm:1 logit:1 open:2 d2:2 citeseer:1 pick:2 mention:1 carry:1 contains:1 score:22 selecting:3 mag:1 miklau:1 past:3 existing:7 pickett:1 dx:5 written:4 partition:1 kdd:3 update:1 n0:2 discrimination:3 v:3 selected:1 discovering:1 pvldb:1 smith:4 core:2 provides:... |
4,437 | 5,015 | Similarity Component Analysis
Soravit Changpinyo?
Dept. of Computer Science
U. of Southern California
Los Angeles, CA 90089
schangpi@usc.edu
Kuan Liu?
Dept. of Computer Science
U. of Southern California
Los Angeles, CA 90089
kuanl@usc.edu
Fei Sha
Dept. of Computer Science
U. of Southern California
Los Angeles, CA 90... | 5015 |@word kulis:2 version:1 instrumental:1 advantageous:1 logit:4 stronger:1 seems:1 additively:2 confirms:1 deems:1 liu:1 configuration:2 score:1 contains:1 tuned:1 bc:2 document:7 outperforms:3 existing:1 current:1 comparing:1 recovered:2 goldberger:1 assigning:2 yet:1 written:2 reminiscent:1 indistinguishably:1 sh... |
4,438 | 5,016 | A message-passing algorithm
for multi-agent trajectory planning
Jos?e Bento ?
jbento@disneyresearch.com
Nate Derbinsky
nate.derbinsky@disneyresearch.com
Javier Alonso-Mora
jalonso@disneyresearch.com
Jonathan Yedidia
yedidia@disneyresearch.com
Abstract
We describe a novel approach for computing collision-free global... | 5016 |@word middle:2 version:3 seems:1 open:2 simulation:3 decomposition:1 jacob:1 schoellig:1 solid:2 harder:1 reduction:1 initial:15 configuration:9 kinodynamic:1 daniel:2 ours:2 existing:1 current:1 com:4 yet:1 chu:1 must:2 written:1 john:2 numerical:4 shape:1 update:7 n0:16 alone:1 half:1 deadlock:1 une:1 plane:6 x... |
4,439 | 5,017 | The Power of Asymmetry in Binary Hashing
Behnam Neyshabur
Payman Yadollahpour
Yury Makarychev
Toyota Technological Institute at Chicago
[btavakoli,pyadolla,yury]@ttic.edu
Ruslan Salakhutdinov
Departments of Statistics and Computer Science
University of Toronto
rsalakhu@cs.toronto.edu
Nathan Srebro
Toyota Technologica... | 5017 |@word kulis:1 briefly:1 replicate:1 instruction:1 hu:2 seitz:1 seek:3 nks:3 minus:1 harder:1 liu:2 contains:2 denoting:1 document:1 outperforms:1 ka:4 wd:5 comparing:1 yet:1 chicago:2 cheap:1 gist:2 update:3 hash:55 alone:1 device:1 item:2 payman:1 short:11 shortlist:1 provides:1 toronto:2 five:1 constructed:1 di... |
4,440 | 5,018 | Learning to Prune in Metric and Non-Metric Spaces
Leonid Boytsov
Bilegsaikhan Naidan
Carnegie Mellon University
Norwegian University of Science and Technology
Pittsburgh, PA, USA
Trondheim, Norway
srchvrs@cmu.edu
bileg@idi.ntnu.no
Abstract
Our focus is on approximate nearest neighbor retrieval in metric and no... | 5018 |@word version:4 manageable:1 disk:1 termination:5 instruction:2 vldb:4 seek:1 decomposition:3 jacob:2 versatile:1 recursively:3 reduction:2 configuration:1 contains:2 tuned:1 prefix:5 existing:1 comparing:1 com:1 yet:4 written:1 distant:1 partition:17 shape:1 designed:3 update:1 hash:7 half:2 selected:5 leaf:2 in... |
4,441 | 5,019 | A Deep Architecture for Matching Short Texts
Hang Li
Noah?s Ark Lab
Huawei Technologies Co. Ltd.
Sha Tin, Hong Kong
HangLi.HL@huawei.com
Zhengdong Lu
Noah?s Ark Lab
Huawei Technologies Co. Ltd.
Sha Tin, Hong Kong
Lu.Zhengdong@huawei.com
Abstract
Many machine learning problems can be interpreted as learning for match... | 5019 |@word kong:2 middle:2 version:1 advantageous:1 propagate:1 tried:2 snack:1 p0:6 bellevue:1 harder:3 bai:1 configuration:1 series:1 score:5 exclusively:1 contains:4 liu:1 tuned:1 document:5 current:2 com:5 surprising:1 activation:5 mushroom:1 dx:1 assigning:1 distant:2 informative:1 designed:3 etwork:1 ainen:1 v:1... |
4,442 | 502 | Fault Diagnosis of Antenna Pointing Systems
using Hybrid Neural Network and Signal
Processing Models
Padhraic Smyth, J eft" Mellstrom
Jet Propulsion Laboratory 238-420
California Institute of Technology
Pasadena, CA 91109
Abstract
We describe in this paper a novel application of neural networks to system
health monit... | 502 |@word version:2 proportion:2 replicate:1 seek:1 covariance:1 recursively:1 moment:1 initial:3 series:3 initialisation:1 tachometer:1 past:1 current:2 wd:1 si:1 must:7 shape:2 motor:3 designed:1 plot:1 discrimination:1 generative:2 selected:1 shut:1 gear:1 xk:2 normalising:1 detecting:3 location:2 successive:1 sigm... |
4,443 | 5,020 | On the Representational Efficiency of Restricted
Boltzmann Machines
James Martens?
?
Arkadev Chattopadhyay+
Department of Computer Science
University of Toronto
+
Toniann Pitassi?
Richard Zemel?
School of Technology & Computer Science
Tata Institute of Fundamental Research
{jmartens,toni,zemel}@cs.toronto.edu
a... | 5020 |@word nihat:1 middle:2 version:1 polynomial:11 stronger:2 seems:1 nd:1 suitably:1 open:2 simulation:11 contrastive:3 q1:6 invoking:1 tr:1 harder:2 reduction:1 series:1 contains:2 ours:1 ghj:1 existing:1 freitas:1 comparing:2 surprising:3 activation:21 yet:1 schnitger:1 must:5 written:1 fn:1 realistic:1 visible:2 ... |
4,444 | 5,021 | Distributed Representations of Words and Phrases
and their Compositionality
Tomas Mikolov
Google Inc.
Mountain View
mikolov@google.com
Ilya Sutskever
Google Inc.
Mountain View
ilyasu@google.com
Kai Chen
Google Inc.
Mountain View
kai@google.com
Jeffrey Dean
Google Inc.
Mountain View
jeff@google.com
Greg Corrado
Goo... | 5021 |@word h:6 multitask:1 pw:1 bigram:3 seems:1 open:1 heuristically:1 hyv:1 tried:1 contrastive:6 yih:1 configuration:1 contains:1 score:2 daniel:1 document:1 interestingly:2 task1:1 outperforms:2 existing:1 com:9 yet:1 parsing:1 ronald:1 numerical:1 additive:2 ronan:1 christian:1 vasco:1 intelligence:3 leaf:4 short... |
4,445 | 5,022 | Stochastic Ratio Matching of RBMs for Sparse
High-Dimensional Inputs
Yann N. Dauphin, Yoshua Bengio
D?epartement d?informatique et de recherche op?erationnelle
Universit?e de Montr?eal
Montr?eal, QC H3C 3J7
dauphiya@iro.umontreal.ca,
Yoshua.Bengio@umontreal.ca
Abstract
Sparse high-dimensional data vectors are common ... | 5022 |@word trial:1 version:2 briefly:1 norm:1 nd:1 hyv:7 d2:1 confirms:1 recapitulate:1 contrastive:2 epartement:1 contains:2 score:5 selecting:1 document:5 interestingly:1 freitas:1 current:1 comparing:1 com:1 activation:1 attracted:1 reminiscent:1 must:1 wx:1 confirming:1 update:1 generative:3 selected:2 greedy:1 pa... |
4,446 | 5,023 | Generalized Denoising Auto-Encoders as Generative
Models
Yoshua Bengio, Li Yao, Guillaume Alain, and Pascal Vincent
D?epartement d?informatique et recherche op?erationnelle, Universit?e de Montr?eal
Abstract
Recent work has shown how denoising and contractive autoencoders implicitly
capture the structure of the data-g... | 5023 |@word version:1 norm:1 open:1 hyv:2 git:2 contraction:1 covariance:1 contrastive:2 datagenerating:2 pick:2 reduction:1 epartement:1 score:8 yaoli:1 freitas:1 current:1 recovered:1 com:1 skipping:1 scatter:1 dx:3 attracted:1 gpu:1 partition:1 blur:2 confirming:1 plot:1 stationary:3 generative:7 fewer:1 device:1 se... |
4,447 | 5,024 | Multi-Prediction Deep Boltzmann Machines
Ian J. Goodfellow, Mehdi Mirza, Aaron Courville, Yoshua Bengio
D?epartement d?informatique et de recherche op?erationnelle
Universit?e de Montr?eal
Montr?eal, QC H3C 3J7
{goodfeli,mirzamom,courvila}@iro.umontreal.ca,
Yoshua.Bengio@umontreal.ca
Abstract
We introduce the multi-p... | 5024 |@word seems:1 norm:4 nd:1 twelfth:1 sgd:1 epartement:1 initial:1 configuration:1 contains:3 series:2 score:1 tuned:2 document:1 outperforms:5 existing:1 current:1 si:11 activation:1 must:4 gpu:1 written:1 visible:4 subsequent:1 realistic:1 partition:1 shape:1 cheap:2 remove:1 designed:1 update:6 resampling:1 stat... |
4,448 | 5,025 | Predicting Parameters in Deep Learning
Misha Denil1 Babak Shakibi2 Laurent Dinh3
Marc?Aurelio Ranzato4 Nando de Freitas1,2
1
University of Oxford, United Kingdom
2
University of British Columbia, Canada
3
Universit?e de Montr?eal, Canada
4
Facebook Inc., USA
{misha.denil,nando.de.freitas}@cs.ox.ac.uk
laurent.dinh@umon... | 5025 |@word proceeded:1 bigram:1 seems:1 proportion:5 hyv:1 tried:1 covariance:6 sgd:1 lepetit:1 reduction:5 initial:1 contains:2 score:1 united:1 selecting:3 document:1 elaborating:1 rightmost:1 freitas:2 com:1 comparing:1 activation:3 lang:2 must:4 devin:2 visible:5 ma0:1 enables:1 remove:2 drop:3 half:1 fewer:1 sele... |
4,449 | 5,026 | Learning Stochastic Feedforward Neural Networks
Yichuan Tang
Department of Computer Science
University of Toronto
Toronto, Ontario, Canada.
tang@cs.toronto.edu
Ruslan Salakhutdinov
Department of Computer Science and Statistics
University of Toronto
Toronto, Ontario, Canada.
rsalakhu@cs.toronto.edu
Abstract
Multilaye... | 5026 |@word proportion:1 seek:1 propagate:1 covariance:1 contrastive:2 xtest:2 thereby:1 tr:1 configuration:2 daniel:1 comparing:1 activation:3 must:2 john:1 occl:3 partition:5 shape:1 wanted:1 hypothesize:1 plot:3 update:5 half:1 generative:5 selected:6 intelligence:2 isotropic:1 colored:1 tarlow:1 provides:2 node:19 ... |
4,450 | 5,027 | Zero-Shot Learning Through Cross-Modal Transfer
Richard Socher, Milind Ganjoo, Christopher D. Manning, Andrew Y. Ng
Computer Science Department, Stanford University, Stanford, CA 94305, USA
richard@socher.org, {mganjoo, manning}@stanford.edu, ang@cs.stanford.edu
Abstract
This work introduces a model that can recogniz... | 5027 |@word multitask:1 version:1 briefly:2 justice:1 confirms:1 seek:1 rgb:2 covariance:1 blender:1 harder:1 shot:47 zimek:1 paw:1 hoiem:2 ours:1 document:1 fa8750:1 existing:1 comparing:2 assigning:1 remove:1 designed:2 plot:1 hypothesize:1 drop:1 bart:1 discrimination:1 intelligence:1 selected:2 fewer:1 plane:1 segw... |
4,451 | 5,028 | Reasoning With Neural Tensor Networks
for Knowledge Base Completion
Richard Socher?, Danqi Chen*, Christopher D. Manning, Andrew Y. Ng
Computer Science Department, Stanford University, Stanford, CA 94305, USA
richard@socher.org, {danqi,manning}@stanford.edu, ang@cs.stanford.edu
Abstract
Knowledge bases are an importa... | 5028 |@word multitask:1 version:1 open:4 vldb:1 contrastive:1 pick:1 dramatic:1 tr:2 harder:3 born:1 score:12 united:1 ours:2 document:1 fa8750:1 outperforms:4 existing:5 com:1 readily:3 parsing:3 realize:1 evans:1 enables:2 remove:1 intelligence:1 device:1 core:1 institution:3 provides:2 node:1 revisited:1 location:4 ... |
4,452 | 5,029 | Discriminative Transfer Learning with
Tree-based Priors
Nitish Srivastava
Department of Computer Science
University of Toronto
nitish@cs.toronto.edu
Ruslan Salakhutdinov
Department of Computer Science and Statistics
University of Toronto
rsalakhu@cs.toronto.edu
Abstract
High capacity classifiers, such as deep neural ... | 5029 |@word multitask:1 norm:1 covariance:1 paid:1 sgd:3 shot:1 initial:4 cyclic:1 contains:4 united:1 existing:2 activation:1 universality:1 assigning:1 written:1 partition:1 trout:1 pertinent:1 hypothesize:1 designed:1 treating:1 plot:2 bart:3 alone:1 generative:3 discovering:2 leaf:5 v:2 device:2 website:1 fewer:1 i... |
4,453 | 503 | Refining PIn Controllers using Neural Networks
Gary M. Scott
Department of Chemical Engineering
1415 Johnson Drive
University of Wisconsin
Madison, WI 53706
Jude W. Shavlik
Department of Computer Sciences
1210 W. Dayton Street
University of Wisconsin
Madison, WI 53706
W. Harmon Ray
Department of Chemical Engineering... | 503 |@word trial:1 wcb:1 trialand:1 wco:1 jacob:2 initial:6 configuration:1 tuned:1 past:2 existing:3 current:1 activation:3 written:2 interrupted:1 periodically:1 subsequent:1 intelligence:1 fewer:1 ysp:1 sigmoidal:1 mathematical:1 ray:8 manner:1 rapid:1 behavior:1 examine:1 td:3 company:1 actual:2 becomes:1 begin:1 p... |
4,454 | 5,030 | Adaptive Multi-Column Deep Neural Networks
with Application to Robust Image Denoising
Forest Agostinelli
Michael R. Anderson
Honglak Lee
Division of Computer Science and Engineering
University of Michigan
Ann Arbor, MI 48109, USA
{agostifo,mrander,honglak}@umich.edu
Abstract
Stacked sparse denoising autoencoders (SSD... | 5030 |@word faculty:1 version:4 blu:1 open:1 simulation:1 tried:1 inpainting:1 reduction:2 electronics:1 initial:1 series:2 efficacy:4 tuned:1 ours:1 suppressing:1 document:1 outperforms:2 current:1 com:1 activation:10 must:3 john:1 additive:1 concatenate:1 wx:1 remove:2 designed:1 alone:2 stationary:2 greedy:2 selecte... |
4,455 | 5,031 | Top-Down Regularization of Deep Belief Networks
Hanlin Goh?, Nicolas Thome, Matthieu Cord
Laboratoire d?Informatique de Paris 6
UPMC ? Sorbonne Universit?es, Paris, France
{Firstname.Lastname}@lip6.fr
Joo-Hwee Lim?
Institute for Infocomm Research
A*STAR, Singapore
joohwee@i2r.a-star.edu.sg
Abstract
Designing a princ... | 5031 |@word trial:2 version:1 stronger:1 retraining:1 valle:1 gradual:2 contrastive:9 reduction:1 initial:2 configuration:1 contains:2 score:2 selecting:2 tuned:1 document:1 outperforms:2 existing:6 current:5 z2:9 activation:17 realize:1 partition:1 wx:2 enables:2 update:10 standalone:1 discrimination:1 greedy:7 genera... |
4,456 | 5,032 | Adaptive dropout for training deep neural networks
Lei Jimmy Ba Brendan Frey
Department of Electrical and Computer Engineering
University of Toronto
jimmy, frey@psi.utoronto.ca
Abstract
Recently, it was shown that deep neural networks can perform very well if the
activities of hidden units are regularized during lear... | 5032 |@word tried:1 recapitulate:1 contrastive:1 citeseer:1 configuration:4 contains:1 tuned:4 interestingly:2 outperforms:3 err:2 current:3 activation:6 written:1 gpu:3 concatenate:1 partition:1 enables:1 remove:1 drop:1 designed:1 update:5 generative:1 greedy:2 half:1 intelligence:1 plane:2 vanishing:1 provides:2 tor... |
4,457 | 5,033 | Stochastic Optimization of PCA with Capped MSG
Raman Arora
TTI-Chicago
Chicago, IL USA
arora@ttic.edu
Andrew Cotter
TTI-Chicago
Chicago, IL USA
cotter@ttic.edu
Nathan Srebro
Technion, Haifa, Israel
and TTI-Chicago
nati@ttic.edu
Abstract
We study PCA as a stochastic optimization problem and propose a novel stochasti... | 5033 |@word norm:7 km:5 d2:6 ks0:1 decomposition:2 ality:1 covariance:4 sgd:11 tr:4 moment:2 contains:1 vd0:1 current:2 comparing:1 surprising:1 si:4 universality:1 must:2 john:2 chicago:5 displace:1 treating:1 plot:5 update:25 drop:1 juditsky:1 warmuth:18 accordingly:1 parameterization:1 inspection:1 beginning:1 param... |
4,458 | 5,034 | Variance Reduction for
Stochastic Gradient Optimization
Chong Wang Xi Chen? Alex Smola Eric P. Xing
Carnegie Mellon University, University of California, Berkeley?
{chongw,xichen,epxing}@cs.cmu.edu alex@smola.org
Abstract
Stochastic gradient optimization is a class of widely used algorithms for training
machine learn... | 5034 |@word version:1 polynomial:1 norm:1 proportion:2 nd:2 unif:1 seek:1 simulation:2 covariance:2 sgd:1 mention:1 minus:3 thereby:1 tr:9 solid:1 ld:2 moment:10 reduction:35 contains:5 tuned:1 document:17 outperforms:1 wd:3 comparing:1 must:3 john:1 plot:1 update:7 juditsky:1 generative:1 selected:1 leaf:1 website:2 h... |
4,459 | 5,035 | Memory Limited, Streaming PCA
Constantine Caramanis
Dept. of Electrical and Computer Engineering
The University of Texas at Austin
constantine@utexas.edu
Ioannis Mitliagkas
Dept. of Electrical and Computer Engineering
The University of Texas at Austin
ioannis@utexas.edu
Prateek Jain
Microsoft Research
Bangalore, Ind... | 5035 |@word trial:1 version:5 norm:6 c0:2 simulation:1 seek:1 covariance:21 decomposition:6 tr:2 reduction:3 initial:3 plentiful:1 series:1 woodruff:3 tuned:1 renewed:1 interestingly:1 document:3 past:1 recovered:3 com:1 current:3 yet:2 must:1 numerical:1 enables:1 plot:1 update:4 half:1 prohibitive:2 generative:2 gues... |
4,460 | 5,036 | Near-Optimal Entrywise Sampling for Data Matrices
Dimitris Achlioptas
UC Santa Cruz
optas@cs.ucsc.edu
Zohar Karnin
Yahoo Labs
zkarnin@ymail.com
Edo Liberty
Yahoo Labs
edo.liberty@ymail.com
Abstract
We consider the problem of selecting non-zero entries of a matrix A in order to
produce a sparse sketch of it, B, that ... | 5036 |@word mild:1 version:1 norm:15 stronger:1 nd:6 seems:1 seek:2 pick:1 dramatic:2 harder:1 reduction:1 contains:1 fragment:1 selecting:1 document:2 interestingly:1 com:2 must:1 readily:2 cruz:1 subsequent:1 informative:2 plot:3 update:1 isard:1 selected:2 item:6 isotropic:1 ith:2 math:1 location:4 compressible:1 pr... |
4,461 | 5,037 | Large Scale Distributed Sparse Precision Estimation
Huahua Wang, Arindam Banerjee
Dept. of Computer Science & Engg, University of Minnesota, Twin Cities
{huwang,banerjee}@cs.umn.edu
Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon
Dept. of Computer Science, University of Texas, Austin
{cjhsieh,pradeepr,inderjit}@... | 5037 |@word determinant:1 version:1 polynomial:1 norm:6 open:1 strong:2 vldb:1 tamayo:1 propagate:1 hsieh:3 covariance:24 decomposition:1 cleary:1 cyclic:7 contains:3 disparity:1 liu:4 denoting:1 existing:4 ncar:1 luo:1 toh:1 yet:1 chu:1 written:2 devin:1 engg:1 designed:1 plot:1 update:5 bickson:1 flare:6 accordingly:... |
4,462 | 5,038 | Optimistic Concurrency Control for
Distributed Unsupervised Learning
Xinghao Pan1 Joseph Gonzalez1 Stefanie Jegelka1 Tamara Broderick1,2 Michael I. Jordan1,2
1
Department of Electrical Engineering and Computer Science, and 2 Department of Statistics
University of California, Berkeley
Berkeley, CA USA 94720
{xinghao,jeg... | 5038 |@word kulis:2 version:3 proportion:2 nd:2 open:1 d2:3 vldb:2 simplifying:2 invoking:1 thereby:1 bahmani:1 ours:1 fa8750:1 franklin:1 existing:5 mishra:1 current:1 assigning:1 danny:1 must:3 john:2 partition:1 kdd:1 plot:1 update:5 bickson:1 zik:8 half:1 fewer:1 intelligence:3 core:1 accepting:1 colored:1 blei:1 p... |
4,463 | 5,039 | Distributed Submodular Maximization:
Identifying Representative Elements in Massive Data
Baharan Mirzasoleiman
ETH Zurich
Amin Karbasi
ETH Zurich
Rik Sarkar
University of Edinburgh
Andreas Krause
ETH Zurich
Abstract
Many large-scale machine learning problems (such as clustering, non-parametric
learning, kernel mac... | 5039 |@word faculty:1 manageable:2 agc:4 inversion:1 stronger:1 norm:2 disk:1 suitably:1 laurence:1 version:1 km:2 seek:2 rgb:1 covariance:2 pick:1 lorraine:1 selecting:5 daniel:3 document:2 interestingly:1 outperforms:4 si:3 yet:2 assigning:3 written:1 e01:2 fvi:1 lang:1 chu:1 subsequent:1 partition:16 informative:1 r... |
4,464 | 504 | Green's Function Method for Fast On-line Learning
Algorithm of Recurrent Neural Networks
Guo-Zheng Sun, Hsing-Hen Chen and Yee-Chun Lee
Institute for Advanced Computer Studies
and
Laboratory for Plasma Research,
University of Maryland
College Park, MD 20742
Abstract
The two well known learning algorithms of recurrent... | 504 |@word briefly:1 version:1 annoying:1 simulation:3 tried:1 decomposition:1 tr:1 initial:1 series:2 selecting:1 current:1 cumulation:1 si:2 yet:2 dx:7 written:1 numerical:13 shape:2 update:7 accordingly:1 math:1 mathematical:1 along:1 constructed:3 differential:3 become:1 viable:1 introduce:2 manner:1 behavior:1 cpu... |
4,465 | 5,040 | Simultaneous Rectification and Alignment via Robust
Recovery of Low-rank Tensors
Xiaoqin Zhang, Di Wang
Institute of Intelligent System and Decision
Wenzhou University
zhangxiaoqinnan@gmail.com, wangdi@wzu.edu.cn
Zhengyuan Zhou
Department of Electrical Engineering
Stanford University
zyzhou@stanford.edu
Yi Ma
Visual ... | 5040 |@word deformed:2 cox:1 version:3 briefly:1 norm:14 tensorial:9 simulation:2 linearized:3 decomposition:14 eng:1 liu:2 contains:3 ours:1 existing:2 com:2 comparing:3 gmail:1 yet:1 written:1 realize:2 additive:1 remove:2 v:2 intelligence:3 fewer:1 accordingly:2 core:1 yamada:1 lr:2 location:3 clarified:1 zhang:2 al... |
4,466 | 5,041 | Phase Retrieval using Alternating Minimization
Praneeth Netrapalli
Department of ECE
The University of Texas at Austin
Austin, TX 78712
praneethn@utexas.edu
Prateek Jain
Microsoft Research India
Bangalore, India
prajain@microsoft.com
Sujay Sanghavi
Department of ECE
The University of Texas at Austin
Austin, TX 78712... | 5041 |@word trial:1 ia2:1 briefly:1 version:2 norm:4 stronger:1 open:2 phasecut:15 pick:2 incurs:1 harder:2 marchesini:1 shechtman:1 reduction:1 initial:3 zij:3 outperforms:2 kx0:2 existing:1 recovered:1 com:1 whp:4 z2:1 attracted:1 partition:1 update:2 resampling:1 plane:1 ith:1 record:1 provides:1 successive:1 mathem... |
4,467 | 5,042 | Machine Teaching for Bayesian Learners
in the Exponential Family
Xiaojin Zhu
Department of Computer Sciences, University of Wisconsin-Madison
Madison, WI, USA 53706
jerryzhu@cs.wisc.edu
Abstract
What if there is a teacher who knows the learning goal and wants to design good
training data for a machine learner? We pro... | 5042 |@word version:3 compression:1 nd:1 d2:1 simulation:2 covariance:1 p0:7 pick:2 tr:2 solid:1 harder:2 initial:4 quo:1 document:5 comparing:1 collude:1 yet:1 written:1 must:2 readily:1 sorg:1 partition:2 plasticity:1 s21:1 designed:2 plot:1 update:3 overshooting:1 alone:1 generative:1 fewer:1 intelligence:1 item:20 ... |
4,468 | 5,043 | Analyzing Hogwild Parallel Gaussian Gibbs Sampling
Matthew J. Johnson
EECS, MIT
mattjj@mit.edu
James Saunderson
EECS, MIT
jamess@mit.edu
Alan S. Willsky
EECS, MIT
willsky@mit.edu
Abstract
Sampling inference methods are computationally difficult to scale for many models in part because global dependencies can reduce... | 5043 |@word norm:1 bekkerman:2 disk:1 linearized:1 covariance:24 pick:1 sgd:2 thereby:1 moment:2 initial:1 liu:2 contains:1 series:2 tist:1 current:1 com:1 comparing:1 surprising:1 written:5 must:2 numerical:3 partition:10 shape:1 plot:3 update:33 stationary:4 half:2 intelligence:1 yi1:1 fa9550:1 colored:1 provides:9 i... |
4,469 | 5,044 | Flexible sampling of discrete data correlations
without the marginal distributions
Ricardo Silva
Department of Statistical Science and CSML
University College London
ricardo@stats.ucl.ac.uk
Alfredo Kalaitzis
Department of Statistical Science and CSML
University College London
a.kalaitzis@ucl.ac.uk
Abstract
Learning ... | 5044 |@word mild:1 briefly:1 duda:1 loading:2 seems:1 simulation:6 decomposition:2 covariance:3 pick:2 thereby:1 reduction:3 initial:3 liu:1 series:1 past:1 current:1 elliptical:3 must:3 partition:2 shape:2 remove:1 plot:3 update:2 alone:1 generative:1 parameterization:2 complementing:1 plane:2 xk:4 desktop:1 hamiltoni... |
4,470 | 5,045 | Auxiliary-variable Exact Hamiltonian Monte
Carlo Samplers for Binary Distributions
Ari Pakman and Liam Paninski
Department of Statistics
Center for Theoretical Neuroscience
Grossman Center for the Statistics of Mind
Columbia University
New York, NY, 10027
Abstract
We present a new approach to sample from generic bina... | 5045 |@word middle:1 nd:1 simulation:2 covariance:1 outlook:1 initial:6 ours:1 interestingly:1 si:29 john:1 interrupted:1 numerical:1 plot:4 hamiltonian:11 sudden:1 beauchamp:1 successive:4 firstly:1 zhang:1 height:1 along:3 differential:1 consists:1 inside:1 introduce:1 expected:1 indeed:1 behavior:1 mechanic:1 landau... |
4,471 | 5,046 | Wavelets on Graphs via Deep Learning
Raif M. Rustamov & Leonidas Guibas
Computer Science Department, Stanford University
{rustamov,guibas}@stanford.edu
Abstract
An increasing number of applications require processing of signals defined on
weighted graphs. While wavelets provide a flexible tool for signal processing i... | 5046 |@word kolaczyk:1 briefly:1 norm:2 proportion:1 decomposition:2 moment:5 lightweight:1 pub:1 mag:1 interestingly:1 existing:1 recovered:1 ka:1 discretization:1 must:3 finest:3 mesh:1 distant:1 partition:6 update:18 alone:1 greedy:4 half:4 intelligence:2 vanishing:4 gribonval:1 fa9550:1 provides:4 coarse:1 location... |
4,472 | 5,047 | Stochastic blockmodel approximation of a graphon:
Theory and consistent estimation
Edoardo M. Airoldi
Dept. Statistics
Harvard University
Thiago B. Costa
SEAS, and Dept. Statistics
Harvard University
Stanley H. Chan
SEAS, and Dept. Statistics
Harvard University
Abstract
Non-parametric approaches for analyzing netwo... | 5047 |@word sba:26 trial:4 middle:2 seek:1 simulation:1 decomposition:2 pick:2 moment:1 series:2 ours:2 janson:1 outperforms:2 com:1 dx:2 iv1:1 informative:1 plot:1 update:2 greedy:3 half:2 olhede:1 blei:1 equi:1 node:3 symposium:1 ik:1 theoretically:1 lov:2 indeed:1 expected:4 behavior:2 growing:6 usvt:14 globally:2 i... |
4,473 | 5,048 | Bayesian Hierarchical Community Discovery
Charles Blundell?
DeepMind Technologies
charles@deepmind.com
Yee Whye Teh
Department of Statistics,
University of Oxford
y.w.teh@stats.ox.ac.uk
Abstract
We propose an efficient Bayesian nonparametric model for discovering hierarchical community structure in social networks. ... | 5048 |@word bosco:12 version:2 grey:2 r:4 decomposition:1 elisseeff:1 pick:1 recursively:1 initial:1 liu:1 score:6 disparity:1 mainen:1 lapedes:1 kurt:1 existing:1 current:4 com:1 nt:1 luo:1 written:1 john:12 partition:25 girosi:1 greedy:11 discovering:5 leaf:18 generative:2 intelligence:2 merger:3 monk:1 beginning:1 i... |
4,474 | 5,049 | Nonparametric Multi-group Membership Model
for Dynamic Networks
Jure Leskovec
Stanford University
Stanford, CA 94305
jure@cs.stanford.edu
Myunghwan Kim
Stanford University
Stanford, CA 94305
mykim@stanford.edu
Relational data?like graphs, networks, and matrices?is often dynamic, where the relational structure evolve... | 5049 |@word briefly:1 faculty:1 version:1 reused:1 pick:1 volkswagen:1 born:10 contains:1 series:2 score:4 interestingly:1 longitudinal:1 outperforms:2 existing:5 current:1 com:1 comparing:1 assigning:1 yet:2 realize:1 kdd:1 remove:3 plot:1 update:8 zik:21 generative:1 selected:1 parametrization:1 core:2 blei:2 provide... |
4,475 | 505 | Markov Random Fields Can Bridge Levels of
Abstraction
Paul R. Cooper
Institute for the Learning Sciences
Northwestern University
Evanston, IL
cooper@ils.nwu.edu
Peter N. Prokopowicz
Institute for the Learning Sciences
Northwestern U ni versity
Evanston, IL
prokopowicz@ils.nwu.edu
Abstract
Network vision systems must... | 505 |@word exploitation:1 middle:2 simplifying:1 configuration:20 existing:2 current:1 must:4 luis:1 written:1 remove:1 designed:1 intelligence:2 leaf:1 plane:1 short:1 provides:2 consulting:1 node:3 rc:1 constructed:3 combine:1 vide:1 andrea:1 roughly:1 simulator:1 detects:1 versity:1 provided:1 kaufman:1 interpreted:... |
4,476 | 5,050 | Universal models for binary spike patterns using
centered Dirichlet processes
Il Memming Park123 , Evan Archer24 , Kenneth Latimer12 , Jonathan W. Pillow1234
1. Institue for Neuroscience, 2. Center for Perceptual Systems, 3. Department of Psychology
4. Division of Statistics & Scientific Computation
The University of T... | 5050 |@word nd:2 hu:1 simulation:1 seek:2 universality:1 yet:1 written:3 scatter:4 partition:1 earcher:1 plot:5 alone:2 selected:1 tone:2 record:1 blei:1 completeness:1 provides:1 boosting:1 node:3 mathematical:1 along:1 constructed:1 nnk:1 combine:2 fitting:5 manner:1 introduce:2 pairwise:2 theoretically:2 expected:1 ... |
4,477 | 5,051 | A Determinantal Point Process Latent Variable
Model for Inhibition in Neural Spiking Data
Jasper Snoek?
Harvard University
jsnoek@seas.harvard.edu
Ryan P. Adams
Harvard University
rpa@seas.harvard.edu
Richard S. Zemel
University of Toronto
zemel@cs.toronto.edu
Abstract
Point processes are popular models of neural s... | 5051 |@word neurophysiology:1 determinant:5 middle:2 hippocampus:8 open:1 covariance:4 thereby:1 nystr:1 configuration:3 series:2 contains:1 hereafter:1 interestingly:1 past:3 current:2 activation:1 determinantal:15 realistic:1 distant:3 enables:2 designed:1 interpretable:1 n0:3 stationary:1 generative:1 intelligence:1... |
4,478 | 5,052 | Neural representation of action sequences: how far
can a simple snippet-matching model take us?
Cheston Tan
Institute for Infocomm Research
Singapore
cheston@mit.edu
Jedediah M. Singer
Boston Children?s Hospital
Boston, MA 02115
jedediah.singer@childrens.harvard.edu
Thomas Serre
David Sheinberg
Brown University
Prov... | 5052 |@word neurophysiology:3 trial:4 mri:1 johansson:1 rhesus:1 fairer:1 simplifying:1 accounting:1 stateless:1 minus:1 shading:1 initial:1 contains:4 series:1 efficacy:1 united:2 interestingly:3 existing:1 blank:1 current:5 anterior:1 mst:2 realistic:3 predetermined:1 haxby:2 plot:2 medial:1 v:4 alone:3 cue:1 intelli... |
4,479 | 5,053 | Firing rate predictions in optimal balanced networks
Sophie Den`eve
Group for Neural Theory
?
Ecole
Normale Sup?erieure
Paris, France
sophie.deneve@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... | 5053 |@word middle:7 wiesel:1 proportion:2 nd:2 adrian:2 hu:1 simulation:6 accounting:1 thereby:2 initial:1 configuration:1 series:2 ecole:2 biolog:1 si:2 written:3 must:8 reminiscent:2 plasticity:1 shape:6 christian:2 treating:1 plot:2 alone:1 short:1 recherche:1 penalises:1 org:1 sigmoidal:1 mathematical:1 along:2 be... |
4,480 | 5,054 | Perfect Associative Learning with
Spike-Timing-Dependent Plasticity
Maren Westkott
Institute of Theoretical Physics
University of Bremen
28359 Bremen, Germany
maren@neuro.uni-bremen.de
Christian Albers
Institute of Theoretical Physics
University of Bremen
28359 Bremen, Germany
calbers@neuro.uni-bremen.de
Klaus Pawel... | 5054 |@word neurophysiology:1 trial:3 version:1 middle:1 longterm:1 underline:1 pulse:2 crucially:1 simulation:3 thereby:1 initial:2 liu:1 interestingly:1 past:1 current:5 yet:1 ust:23 written:1 realize:2 underly:1 realistic:4 subsequent:1 plasticity:29 shape:1 christian:1 enables:1 drop:3 concert:4 selected:1 inspecti... |
4,481 | 5,055 | Reciprocally Coupled Local Estimators Implement
Bayesian Information Integration Distributively
Wen-hao Zhang1,2,3 , Si Wu1
State Key Laboratory of Cognitive Neuroscience and Learning, and
IDG/McGovern Institute for Brain Research, Beijing Normal University, China.
2
Institute of Neuroscience, Chinese Academy of Scien... | 5055 |@word mild:2 trial:1 neurophysiology:1 middle:1 disk:2 dz1:1 simulation:3 simplifying:1 jacob:1 solid:1 carry:2 initial:1 idg:1 tuned:3 interestingly:1 current:1 z2:7 ka:1 si:1 written:1 realize:1 wll:1 shape:5 enables:1 medial:2 wlm:4 stationary:5 cue:59 half:1 implying:2 vtp:1 reciprocal:28 realizing:1 vanishin... |
4,482 | 5,056 | Multisensory Encoding, Decoding, and Identification
Yevgeniy B. Slutskiy?
Department of Electrical Engineering
Columbia University
New York, NY 10027
ys2146@columbia.edu
Aurel A. Lazar
Department of Electrical Engineering
Columbia University
New York, NY 10027
aurel@ee.columbia.edu
Abstract
We investigate a spiking ... | 5056 |@word trial:13 middle:6 polynomial:2 open:1 u11:6 q1:4 carry:1 daniel:1 denoting:3 rkhs:5 interestingly:1 ording:2 existing:1 imaginary:1 current:11 recovered:3 comparing:2 written:4 informative:2 motor:1 drop:2 v:1 cue:1 intelligence:1 ith:2 el1:10 fa9550:1 tems:4 traverse:2 simpler:1 mathematical:2 dn:1 constru... |
4,483 | 5,057 | Recurrent networks of coupled Winner-Take-All
oscillators for solving constraint satisfaction problems
?
Hesham Mostafa, Lorenz K. Muller,
and Giacomo Indiveri
Institute for Neuroinformatics
University of Zurich and ETH Zurich
{hesham,lorenz,giacomo}@ini.uzh.ch
Abstract
We present a recurrent neuronal network, modele... | 5057 |@word trial:5 oostenveld:1 middle:6 stronger:3 mehta:1 simulation:4 pulse:1 solid:1 carry:1 initial:2 configuration:4 contains:1 liu:1 current:5 surprising:1 john:1 realize:1 ashesh:1 periodically:2 realistic:1 plasticity:5 analytic:1 plot:4 greedy:2 selected:2 device:1 leaf:1 beginning:1 smith:1 short:1 tertiary... |
4,484 | 5,058 | Capacity of strong attractor patterns to model
behavioural and cognitive prototypes
Abbas Edalat
Department of Computing
Imperial College London
London SW72RH, UK
ae@ic.ac.uk
Abstract
We solve the mean field equations for a stochastic Hopfield network with temperature (noise) in the presence of strong, i.e., multiply... | 5058 |@word d2:11 confirms:1 simulation:2 seek:2 ferromagnetism:1 p0:13 solid:1 harder:2 initial:1 configuration:4 series:1 past:1 yni:4 z2:3 comparing:1 surprising:1 si:6 yet:1 dx:1 activation:1 john:2 partition:3 j1:2 enables:1 drop:1 stationary:1 xk:1 smith:2 provides:3 math:1 node:5 firstly:1 simpler:1 zii:1 mathem... |
4,485 | 5,059 | Compete to Compute
Rupesh Kumar Srivastava, Jonathan Masci, Sohrob Kazerounian,
Faustino Gomez, J?rgen Schmidhuber
IDSIA, USI-SUPSI
Manno?Lugano, Switzerland
{rupesh, jonathan, sohrob, tino, juergen}@idsia.ch
Abstract
Local competition among neighboring neurons is common in biological neural networks (NNs). In this p... | 5059 |@word cnn:6 bigram:1 seems:1 norm:1 suitably:1 reused:1 risto:1 heuristically:1 tried:1 propagate:1 blender:1 tr:1 initial:1 liu:1 contains:1 score:1 selecting:2 electronics:2 document:3 anne:1 activation:24 yet:1 gpu:1 john:4 subsequent:2 partition:1 enables:2 utml:1 hypothesize:1 v:1 half:1 selected:1 device:1 ... |
4,486 | 506 | Principles of Risk Minimization
for Learning Theory
V. Vapnik
AT &T Bell Laboratories
Holmdel, NJ 07733, USA
Abstract
Learning is posed as a problem of function estimation, for which two principles of solution are considered: empirical risk minimization and structural
risk minimization. These two principles are appli... | 506 |@word private:4 dramatic:1 contains:2 selecting:1 chervonenkis:1 csn:1 yet:1 written:1 offunctions:1 selected:3 provides:5 postal:1 five:1 mathematical:2 shatter:1 constructed:2 c2:3 become:1 introduce:1 theoretically:1 expected:3 actual:5 considering:2 provided:2 maximizes:1 what:1 minimizes:2 developed:2 transfo... |
4,487 | 5,060 | RNADE: The real-valued neural autoregressive
density-estimator
Benigno Uria and Iain Murray
School of Informatics
University of Edinburgh
{b.uria,i.murray}@ed.ac.uk
Hugo Larochelle
D?epartement d?informatique
Universit?e de Sherbrooke
hugo.larochelle@usherbrooke.ca
Abstract
We introduce RNADE, a new model for joint ... | 5060 |@word repository:1 version:1 compression:1 seems:2 covariance:5 brightness:3 tr:2 inpainting:1 solid:1 minus:1 epartement:1 score:1 selecting:1 lichman:1 daniel:2 outperforms:1 existing:1 rnade:74 activation:4 must:3 bd:6 john:1 lauly:1 uria:2 visible:3 cheap:1 plot:1 update:2 generative:1 fewer:2 selected:1 webs... |
4,488 | 5,061 | Real-Time Inference for a Gamma Process
Model of Neural Spiking
David Carlson, 2 Vinayak Rao, 2 Joshua Vogelstein, 1 Lawrence Carin
1
Electrical and Computer Engineering Department, Duke University
2
Statistics Department, Duke University
{dec18,lcarin}@duke.edu, {var11,jovo}@stat.duke.edu
1
Abstract
With simultaneous... | 5061 |@word neurophysiology:1 version:1 seems:3 hippocampus:2 nd:1 calculus:1 simulation:1 crucially:1 lobe:1 accounting:4 simplifying:1 covariance:5 pick:1 solid:1 recursively:1 moment:1 series:3 efficacy:1 contains:1 denoting:1 outperforms:1 assigning:2 must:1 readily:1 numerical:1 informative:1 plasticity:1 shape:16... |
4,489 | 5,062 | Transportability from Multiple Environments
with Limited Experiments
Elias Bareinboim?
UCLA
Sanghack Lee?
Penn State University
Vasant Honavar
Penn State University
Judea Pearl
UCLA
Abstract
This paper considers the problem of transferring experimental findings learned
from multiple heterogeneous domains to a targ... | 5062 |@word trial:1 illustrating:3 version:3 manageable:1 instrumental:1 nd:2 c0:10 calculus:14 hu:1 dz1:1 d2:7 nicholson:1 decomposition:1 q1:3 tr:4 reduction:1 contains:1 exclusively:1 united:1 interestingly:3 freitas:1 z2:102 olkin:1 si:6 tackling:1 yet:1 assigning:1 must:1 dx:7 visible:1 happen:1 chicago:1 update:1... |
4,490 | 5,063 | Causal Inference on Time Series using Restricted
Structural Equation Models
Jonas Peters?
Seminar for Statistics
ETH Z?urich, Switzerland
Dominik Janzing
MPI for Intelligent Systems
T?ubingen, Germany
Bernhard Sch?olkopf
MPI for Intelligent Systems
T?ubingen, Germany
peters@math.ethz.ch
janzing@tuebingen.mpg.de
b... | 5063 |@word repository:1 version:1 proportion:1 nd:3 open:1 hyv:3 d2:1 r:1 covariance:1 reduction:1 series:60 contains:10 past:2 existing:2 ramsey:1 recovered:2 nt:3 surprising:1 activation:1 yet:1 chu:5 john:1 additive:20 happen:1 oxygenation:1 webster:1 remove:2 drop:2 half:1 fewer:1 discovering:1 intelligence:1 acco... |
4,491 | 5,064 | Discovering Hidden Variables in Noisy-Or Networks
using Quartet Tests
Yacine Jernite, Yoni Halpern, David Sontag
Courant Institute of Mathematical Sciences
New York University
{halpern, jernite, dsontag}@cs.nyu.edu
Abstract
We give a polynomial-time algorithm for provably learning the structure and parameters of bipa... | 5064 |@word determinant:1 polynomial:11 stronger:1 tarsus:3 seek:1 r:1 decomposition:4 p0:1 moment:28 liu:1 daniel:4 kurt:1 o2:1 existing:1 horvitz:1 p2min:1 must:1 john:2 additive:2 remove:3 generative:1 discovering:5 intelligence:1 randolph:1 core:3 filtered:1 node:1 zhang:1 daphne:1 mathematical:2 prove:2 consists:2... |
4,492 | 5,065 | Learning Hidden Markov Models from Non-sequence
Data via Tensor Decomposition
Jeff Schneider
Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213
schneide@cs.cmu.edu
Tzu-Kuo Huang
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA 15213
tzukuoh@cs.cmu.edu
Abstract
Learning dynamic mo... | 5065 |@word version:1 briefly:1 polynomial:1 proportion:1 stronger:2 norm:2 bigram:1 d2:3 simulation:4 decomposition:23 covariance:1 boundedness:1 carry:1 moment:17 initial:19 configuration:1 contains:2 liu:1 denoting:1 ours:1 interestingly:2 document:3 existing:3 surprising:1 si:4 yet:1 subsequent:1 j1:2 confirming:1 ... |
4,493 | 5,066 | Learning Efficient Random Maximum A-Posteriori
Predictors with Non-Decomposable Loss Functions
Tamir Hazan
University of Haifa
Subhransu Maji
TTI Chicago
Joseph Keshet
Bar-Ilan university
Tommi Jaakkola
CSAIL, MIT
Abstract
In this work we develop efficient methods for learning random MAP predictors for
structured ... | 5066 |@word kohli:1 middle:1 additively:1 r:18 covariance:2 decomposition:1 harder:1 moment:8 initial:2 hoiem:1 daniel:1 tuned:1 interestingly:2 outperforms:1 past:1 john:3 chicago:1 additive:3 partition:1 update:1 intelligence:2 devising:1 yr:34 tarlow:2 completeness:1 provides:1 along:2 constructed:1 direct:2 incorre... |
4,494 | 5,067 | Variational Planning for Graph-based MDPs
Qiang Cheng?
Qiang Liu?
Feng Chen?
Alexander Ihler?
Department of Automation, Tsinghua University
?
Department of Computer Science, University of California, Irvine
?
{cheng-q09@mails., chenfeng@mail.}tsinghua.edu.cn
?
{qliu1@,ihler@ics.}uci.edu
?
Abstract
Markov Decision Pr... | 5067 |@word mild:1 version:1 seems:1 hu:1 d2:4 profit:1 shot:1 moment:3 initial:1 liu:14 contains:1 outperforms:2 existing:2 fvi:19 gaona:1 ronald:3 additive:4 update:3 fund:1 stationary:4 intelligence:11 half:1 ith:1 provides:1 node:13 simpler:1 daphne:3 dn:1 along:1 interscience:1 introduce:1 commenting:1 abelardo:1 ... |
4,495 | 5,068 | Integrated Non-Factorized Variational Inference
Shaobo Han
Duke University
Durham, NC 27708
shaobo.han@duke.edu
Xuejun Liao
Duke University
Durham, NC 27708
xjliao@duke.edu
Lawrence Carin
Duke University
Durham, NC 27708
lcarin@duke.edu
Abstract
We present a non-factorized variational method for full posterior infe... | 5068 |@word determinant:1 briefly:1 manageable:1 achievable:2 norm:5 economically:1 sex:1 seek:1 simulation:1 covariance:1 tr:14 gamerman:1 shot:1 moment:1 series:4 recovered:1 discretization:3 ka:1 dx:10 john:1 numerical:6 additive:1 kdd:1 analytic:1 enables:3 remove:2 plot:1 interpretable:1 update:3 pursued:1 intelli... |
4,496 | 5,069 | Global Solver and Its Efficient Approximation for
Variational Bayesian Low-rank Subspace Clustering
Shinichi Nakajima
Nikon Corporation
Tokyo, 140-8601 Japan
nakajima.s@nikon.co.jp
Akiko Takeda
The University of Tokyo
Tokyo, 113-8685 Japan
takeda@mist.i.u-tokyo.ac.jp
S. Derin Babacan
Google Inc.
Mountain View, CA 94... | 5069 |@word trial:3 version:1 inversion:1 polynomial:21 norm:2 nd:1 palma:1 covariance:2 decomposition:1 attainable:2 tr:6 reduction:1 liu:3 tuned:1 outperforms:1 com:1 attainability:1 gmail:1 dx:1 written:7 fn:1 numerical:1 additive:1 enables:1 analytic:1 update:1 stationary:19 intelligence:1 prohibitive:2 selected:4 ... |
4,497 | 507 | Dynamically-Adaptive Winner-Take-All Networks
Treat E. Laale
Artif1cia1IntcUigeoce Laboratory
Computer Science Department
Univmity of California. Los Angeles. CA 90024
Abstract
Winner-Take-All (WTA) networks. in which inhibitory interconnections are used to determine the most highly-activated of a pool of unilS.
are ... | 507 |@word trial:1 middle:3 version:1 rising:2 grey:1 seek:1 simulation:1 pressure:4 shading:1 initial:16 tuned:1 suppressing:2 lave:1 current:1 wd:5 activation:75 must:1 plot:5 v:1 tenn:2 accordingly:1 short:1 provides:4 node:3 five:4 mathematical:1 direct:1 symposium:1 expected:1 rapid:1 themselves:1 simulator:1 ol:1... |
4,498 | 5,070 | Learning to Pass Expectation Propagation Messages
Nicolas Heess?
Gatsby Unit, UCL
Daniel Tarlow
Microsoft Research
John Winn
Microsoft Research
Abstract
Expectation Propagation (EP) is a popular approximate posterior inference algorithm that often provides a fast and accurate alternative to sampling-based
methods. H... | 5070 |@word kohli:1 version:4 middle:1 open:1 gradual:1 propagate:1 decomposition:1 infernet:1 xout:22 harder:2 initial:2 daniel:1 ours:1 rightmost:1 existing:3 trueskill:1 recovered:2 current:2 com:1 yet:1 dx:3 must:3 john:1 additive:1 happen:1 midway:1 shape:3 analytic:6 wanted:2 plot:11 update:13 v:2 intelligence:2 ... |
4,499 | 5,071 | Translating Embeddings for Modeling
Multi-relational Data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Dur?an
Universit?e de Technologie de Compi`egne ? CNRS
Heudiasyc UMR 7253
Compi`egne, France
{bordesan, nusunier, agarciad}@utc.fr
Jason Weston, Oksana Yakhnenko
Google
111 8th avenue
New York, NY, USA
{jweston, o... | 5071 |@word version:3 proportion:1 norm:6 seems:1 open:1 mention:1 configuration:1 contains:1 score:7 series:1 selecting:1 etric:1 born:1 ours:1 outperforms:2 existing:1 atlantic:1 current:1 com:4 comparing:2 universality:1 yet:1 must:1 luis:1 evans:1 kdd:1 wanted:2 remove:1 designed:3 update:1 intelligence:3 fewer:1 s... |
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