Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
3,900 | 4,530 | Latent Graphical Model Selection: Efficient Methods
for Locally Tree-like Graphs
Ragupathyraj Valluvan
UC Irvine
rvalluva@uci.edu
Animashree Anandkumar
UC Irvine
a.anandkumar@uci.edu
Abstract
Graphical model selection refers to the problem of estimating the unknown graph
structure given observations at the nodes in ... | 4530 |@word repository:1 middle:1 polynomial:1 decomposition:1 xtest:4 initial:2 configuration:1 score:9 selecting:1 document:2 existing:1 od:1 mst:5 additive:4 partition:2 enables:1 mulated:1 designed:1 fund:1 v:1 greedy:1 discovering:4 selected:2 vanishing:1 short:4 fa9550:1 blei:1 provides:4 node:84 org:1 phylogenet... |
3,901 | 4,531 | Sparse Approximate Manifolds for
Differential Geometric MCMC
Ben Calderhead?
CoMPLEX
University College London
London, WC1E 6BT, UK
b.calderhead@ucl.ac.uk
M?ty?s A. Sustik
Department of Computer Sciences
University of Texas at Austin
Austin, TX 78712, USA
sustik@cs.utexas.edu
Abstract
One of the enduring challenges i... | 4531 |@word version:1 seems:1 nd:1 simulation:2 covariance:20 kent:1 jacob:1 attainable:1 tr:2 reduction:1 initial:1 necessity:1 series:3 score:3 genetic:1 current:20 must:1 realistic:3 distant:1 enables:3 analytic:3 stationary:11 intelligence:1 selected:1 guess:1 isotropic:2 es:8 haario:1 hamiltonian:3 short:1 provide... |
3,902 | 4,532 | Learning to Discover Social Circles in Ego Networks
Jure Leskovec
Stanford, USA
jure@cs.stanford.edu
Julian McAuley
Stanford, USA
jmcauley@cs.stanford.edu
Abstract
Our personal social networks are big and cluttered, and currently there is no good
way to organize them. Social networking sites allow users to manually ... | 4532 |@word faculty:1 version:1 seems:1 stronger:1 norm:1 grey:1 attended:1 mention:1 mcauley:2 liu:1 contains:2 score:8 series:1 ours:1 document:2 subjective:1 existing:1 current:1 comparing:2 surprising:1 follower:2 must:2 written:1 readily:2 distant:1 visible:1 informative:1 kdd:1 treating:2 concert:1 update:2 inter... |
3,903 | 4,533 | Perceptron Learning of SAT
Matthew B. Blaschko
Center for Visual Computing
Ecole Centrale Paris
matthew.blaschko@inria.fr
Alex Flint
Department of Engineering Science
University of Oxford
alexf@robots.ox.ac.uk
Abstract
Boolean satisfiability (SAT) as a canonical NP-complete decision problem is one
of the most import... | 4533 |@word determinant:1 polynomial:29 termination:1 hu:8 biere:1 q1:1 pick:1 necessity:1 contains:9 score:8 selecting:1 ecole:1 existing:1 current:6 conjunctive:1 must:2 portuguese:1 subsequent:1 benign:1 remove:2 update:18 cue:5 selected:4 cook:2 intelligence:6 core:1 pointer:1 completeness:1 math:1 node:23 boosting... |
3,904 | 4,534 | Truncation-free Stochastic Variational Inference for
Bayesian Nonparametric Models
Chong Wang?
Machine Learning Department
Carnegie Mellon University
chongw@cs.cmu.edu
David M. Blei
Computer Science Department
Princeton Univeristy
blei@cs.princeton.edu
Abstract
We present a truncation-free stochastic variational inf... | 4534 |@word proportion:4 vldb:1 tr:1 configuration:1 contains:2 siebel:1 document:20 ala:1 freitas:1 comparing:2 nt:4 assigning:2 yet:2 must:2 neq:1 kdd:1 remove:1 update:13 aside:1 generative:1 fewer:3 intelligence:5 leaf:1 beginning:1 underestimating:1 blei:11 unbounded:6 mathematical:3 beta:4 incorrect:1 interscienc... |
3,905 | 4,535 | Fast Bayesian Inference for Non-Conjugate
Gaussian Process Regression
Mohammad Emtiyaz Khan, Shakir Mohamed, and Kevin P. Murphy
Department of Computer Science, University of British Columbia
Abstract
We present a new variational inference algorithm for Gaussian process regression with non-conjugate likelihood functi... | 4535 |@word determinant:1 inversion:2 logit:9 tedious:1 vanhatalo:1 covariance:15 tr:2 gnm:1 contains:1 efficacy:1 series:1 denoting:1 existing:9 z2:1 comparing:1 loglik:8 numerical:3 partition:2 gv:6 plot:3 update:17 v:2 intelligence:3 fewer:1 blei:1 provides:1 revisited:1 successive:1 simpler:1 along:1 m22:1 prove:1 ... |
3,906 | 4,536 | A Nonparametric Conjugate Prior Distribution for
the Maximizing Argument of a Noisy Function
Pedro A. Ortega
Max Planck Institute for Intelligent Systems
Max Planck Institute for Biolog. Cybernetics
pedro.ortega@tuebingen.mpg.de
Jordi Grau-Moya
Max Planck Institute for Intelligent Systems
Max Planck Institute for Biol... | 4536 |@word exploitation:1 version:2 mockus:1 open:1 simulation:2 thereby:1 tr:1 solid:4 harder:1 recursively:1 kappen:1 daniel:2 biolog:4 past:2 freitas:1 dx:3 explorative:1 additive:2 numerical:1 entertaining:1 shape:1 plot:1 drop:1 progressively:1 update:2 designed:1 intelligence:1 xk:10 isotropic:1 realizing:1 prov... |
3,907 | 4,537 | Sparse Prediction with the k-Support Norm
Andreas Argyriou
?
Ecole
Centrale Paris
argyrioua@ecp.fr
Rina Foygel
Department of Statistics, Stanford University
rinafb@stanford.edu
Nathan Srebro
Toyota Technological Institute at Chicago
nati@ttic.edu
Abstract
We derive a novel norm that corresponds to the tightest conv... | 4537 |@word repository:1 version:1 norm:84 advantageous:1 hu:1 simulation:2 covariance:1 jacob:1 xtest:1 q1:1 tr:8 series:3 selecting:1 hardy:1 ecole:1 tuned:1 document:1 existing:1 z2:1 must:3 chicago:1 intelligence:1 selected:3 xk:1 core:1 blei:1 provides:2 zhang:1 u2i:2 mathematical:1 replication:1 consists:2 advoca... |
3,908 | 4,538 | A Convex Formulation for Learning Scale-Free
Networks via Submodular Relaxation
Tiberio S. Caetano
NICTA/ANU/University of Sydney
Canberra and Sydney, Australia
tiberio.caetano@nicta.com.au
Aaron J. Defazio
NICTA/Australian National University
Canberra, ACT, Australia
aaron.defazio@anu.edu.au
Abstract
A key problem i... | 4538 |@word middle:1 seems:1 norm:4 adrian:1 covariance:16 decomposition:18 liu:6 series:1 contains:3 tuned:1 interestingly:1 favouring:1 recovered:1 com:1 current:1 optim:1 activation:2 chu:1 must:2 shape:1 designed:1 plot:1 update:5 alone:1 parametrization:1 ith:1 node:17 preference:1 simpler:1 zii:1 yuan:1 excellenc... |
3,909 | 4,539 | A Geometric take on Metric Learning
S?ren Hauberg
MPI for Intelligent Systems
T?ubingen, Germany
Oren Freifeld
Brown University
Providence, US
Michael J. Black
MPI for Intelligent Systems
T?ubingen, Germany
soren.hauberg@tue.mpg.de
freifeld@dam.brown.edu
black@tue.mpg.de
Abstract
Multi-metric learning techniques... | 4539 |@word briefly:1 version:1 polynomial:1 seems:1 norm:1 nd:1 c0:12 open:2 grey:2 km:1 seek:2 calculus:1 covariance:5 pavel:1 kent:1 pick:1 solid:1 reduction:10 initial:5 outperforms:1 current:4 discretization:2 jupp:1 surprising:1 scatter:2 must:3 john:4 numerical:2 shape:20 plot:3 intelligence:2 selected:1 xk:3 sh... |
3,910 | 454 | Gradient Descent: Second-Order Momentum
and Saturating Error
Barak Pearlmutter
Department of Psychology
P.O. Box llA Yale Station
New Haven, CT 06520-7447
pearlmutter-barak@yale.edu
Abstract
=
Batch gradient descent, ~w(t)
-7JdE/dw(t) , conver~es to a minimum
of quadratic form with a time constant no better than '4... | 454 |@word briefly:1 achievable:1 seems:1 termination:1 d2:1 confirms:1 simulation:3 gradual:1 jacob:1 shading:1 initial:1 series:2 past:1 comparing:1 must:3 visible:1 numerical:1 j1:1 subsequent:1 shape:2 plot:1 update:2 progressively:1 v:2 plane:2 location:2 sigmoidal:2 height:1 rc:1 along:1 constructed:2 symposium:1... |
3,911 | 4,540 | Sketch-Based Linear Value Function Approximation
Marc G. Bellemare
University of Alberta
Joel Veness
University of Alberta
Michael Bowling
University of Alberta
mg17@cs.ualberta.ca
veness@cs.ualberta.ca
bowling@cs.ualberta.ca
Abstract
Hashing is a common method to reduce large, potentially infinite feature vecto... | 4540 |@word h:3 mild:1 innovates:2 version:1 trial:5 norm:3 seems:1 nd:1 risto:1 hu:1 seek:1 diuk:1 recursively:1 carry:2 score:16 daniel:1 tuned:1 denoting:1 genetic:1 outperforms:1 comparing:1 must:1 john:1 j1:9 confirming:1 shape:1 drop:1 update:4 hash:33 stationary:3 greedy:3 selected:6 intelligence:5 xk:4 ith:3 sh... |
3,912 | 4,541 | Causal discovery with scale-mixture model for
spatiotemporal variance dependencies
Zhitang Chen* , Kun Zhang? , and Laiwan Chan*
*
Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong
{ztchen,lwchan}@cse.cuhk.edu.hk
?
Max Planck Institute for Intelligent Systems, T?ubingen, German... | 4541 |@word mild:1 kong:4 trial:2 loading:1 nd:2 hyv:13 confirms:1 covariance:3 series:4 contains:3 sogawa:2 interestingly:1 past:2 reaction:1 err:2 outperforms:1 imoto:1 si:5 additive:1 directlingam:3 plot:1 v:1 intelligence:3 selected:8 parameterization:1 sudden:1 provides:1 cse:1 firstly:1 zhang:8 five:1 mathematica... |
3,913 | 4,542 | Tight Bounds on Profile Redundancy and Distinguishability
Jayadev Acharya
ECE, UCSD
jacharya@ucsd.edu
Hirakendu Das
Yahoo!
hdas@yahoo-inc.com
Alon Orlitsky
ECE & CSE, UCSD
alon@ucsd.edu
Abstract
The minimax KL-divergence of any distribution from all distributions in a collection P has several
practical implications... | 4542 |@word compression:12 seems:2 nd:1 ci2:1 thereby:2 jafarpour:3 moment:1 series:1 contains:1 hardy:1 prefix:3 existing:1 com:1 additive:2 partition:7 progressively:1 n0:5 intelligence:1 xk:1 smith:2 provides:1 multiset:10 cse:1 math:1 zhang:4 mathematical:1 along:2 c2:2 shtarkov:1 symposium:1 consists:3 prove:2 com... |
3,914 | 4,543 | Optimal Regularized Dual Averaging Methods for
Stochastic Optimization
Xi Chen
Machine Learning Department
Carnegie Mellon University
xichen@cs.cmu.edu
?
Qihang Lin
Javier Pena
Tepper School of Business
Carnegie Mellon University
{qihangl,jfp}@andrew.cmu.edu
Abstract
This paper considers a wide spectrum of regulariz... | 4543 |@word version:1 norm:5 dekel:1 hu:1 boundedness:2 ld:2 score:5 pub:1 tuned:1 past:8 existing:2 current:4 john:1 plot:1 drop:1 update:3 juditsky:3 xk:2 short:1 iterates:5 zhang:1 unbounded:1 mathematical:1 prove:1 introductory:1 introduce:1 x0:22 indeed:1 expected:5 os:1 roughly:1 e1n:2 multi:19 inspired:3 decreas... |
3,915 | 4,544 | Learning the Architecture of Sum-Product Networks
Using Clustering on Variables
Dan Ventura
Department of Computer Science
Brigham Young University
Provo, UT 84602
ventura@cs.byu.edu
Aaron Dennis
Department of Computer Science
Brigham Young University
Provo, UT 84602
adennis@byu.edu
Abstract
The sum-product network ... | 4544 |@word middle:2 polynomial:1 c0:1 tedious:1 open:1 adnan:1 tried:1 simplifying:2 recursively:4 initial:7 fragment:2 selecting:3 score:1 dubourg:1 outperforms:1 current:1 comparing:1 si:10 must:3 partition:23 shape:2 update:1 v:1 half:9 leaf:17 fewer:1 intelligence:2 generative:2 inspection:1 short:1 completeness:1... |
3,916 | 4,545 | Imitation Learning by Coaching
Jason Eisner
Department of Computer Science
Johns Hopkins University
Baltimore, MD 21218
jason@cs.jhu.edu
He He Hal Daum? III
Department of Computer Science
University of Maryland
College Park, MD 20740
{hhe,hal}@cs.umd.edu
Abstract
Imitation Learning has been shown to be successful in... | 4545 |@word h:1 achievable:2 pick:1 reduction:6 initial:1 score:8 selecting:3 daniel:1 outperforms:1 past:1 current:6 yet:1 written:2 john:2 nb2:4 enables:1 update:1 selected:4 imitate:4 beginning:1 chiang:2 provides:2 iterates:1 rc:6 direct:4 prove:4 combine:2 busa:1 manner:1 apprenticeship:1 theoretically:1 acquired:... |
3,917 | 4,546 | A Divide-and-Conquer Procedure for Sparse Inverse
Covariance Estimation
Inderjit S. Dhillon
Dept. of Computer Science
University of Texas, Austin
inderjit@cs.utexas.edu
Cho-Jui Hsieh
Dept. of Computer Science
University of Texas, Austin
cjhsieh@cs.utexas.edu
Pradeep Ravikumar
Dept. of Computer Science
University of Te... | 4546 |@word kulis:1 determinant:3 version:1 kbkf:1 tamayo:1 covariance:32 hsieh:2 decomposition:7 thereby:1 tr:3 initial:7 interestingly:2 ka:1 com:1 ncar:1 recovered:2 toh:1 dx:1 bd:8 partition:25 enables:1 cheap:1 plot:1 half:2 intelligence:2 guess:2 indicative:1 ith:1 provides:1 node:13 downing:1 mathematical:1 alon... |
3,918 | 4,547 | Non-parametric Approximate Dynamic
Programming via the Kernel Method
Nikhil Bhat
Graduate School of Business
Columbia University
New York, NY 10027
nbhat15@gsb.columbai.edu
Vivek F. Farias
Sloan School of Management
Massachusetts Institute of Technology
Cambridge, MA 02142
vivekf@mit.edu
Ciamac C. Moallemi
Graduate S... | 4547 |@word version:2 polynomial:3 norm:3 stronger:1 hu:1 r:9 incurs:1 series:1 efficacy:1 selecting:1 denoting:1 precluding:1 offering:1 past:1 existing:1 outperforms:1 recovered:1 must:3 parsing:1 additive:1 numerical:1 wx:4 girosi:1 designed:1 greedy:5 intelligence:1 device:1 indicative:1 dissatisfying:1 mannor:1 gx... |
3,919 | 4,548 | A Simple and Practical Algorithm
for Differentially Private Data Release
Moritz Hardt
IBM Almaden Research
San Jose, CA
mhardt@us.ibm.com
Katrina Ligett?
Caltech
katrina@caltech.edu
Frank McSherry
Microsoft Research SVC
mcsherry@microsoft.com
Abstract
We present a new algorithm for differentially private data releas... | 4548 |@word private:24 version:1 repository:1 polynomial:1 seems:1 achievable:1 proportionality:1 heuristically:1 additively:1 d2:3 vldb:1 solid:3 initial:1 contains:1 score:4 selecting:2 efficacy:1 miklau:6 existing:3 current:2 com:2 ka:3 recovered:1 comparing:1 si:1 must:2 pcp:1 realistic:2 additive:1 informative:3 s... |
3,920 | 4,549 | Multiple Choice Learning:
Learning to Produce Multiple Structured Outputs
Abner Guzman-Rivera
University of Illinois
aguzman5@illinois.edu
Dhruv Batra
Virginia Tech
dbatra@vt.edu
Pushmeet Kohli
Microsoft Research Cambridge
pkohli@microsoft.com
Abstract
We address the problem of generating multiple hypotheses for st... | 4549 |@word kohli:2 version:1 polynomial:2 norm:1 seems:1 everingham:1 confirms:1 rgb:1 simplifying:1 pick:4 rivera:2 reduction:2 initial:3 configuration:3 contains:1 score:10 disparity:2 liu:1 tuned:1 outperforms:4 current:1 com:2 contextual:1 luo:1 assigning:1 must:1 parsing:5 reminiscent:2 confirming:1 hofmann:1 dro... |
3,921 | 455 | Temporal Adaptation
?
In a
Silicon Auditory Nerve
John Lazzaro
CS Division
UC Berkeley
571 Evans Hall
Berkeley, CA 94720
Abstract
Many auditory theorists consider the temporal adaptation of the
auditory nerve a key aspect of speech coding in the auditory periphery. Experiments with models of auditory localization a... | 455 |@word cu:1 middle:2 compression:1 pulse:6 pressure:1 solid:1 liu:2 series:1 tuned:1 existing:1 current:11 delgutte:2 john:1 cruz:2 evans:1 shape:2 designed:3 plot:1 half:1 tone:19 height:2 burst:18 constructed:1 differential:1 sustained:1 combine:1 behavior:1 brain:1 ol:1 lyon:7 begin:2 circuit:33 kiang:5 degradin... |
3,922 | 4,550 | Finite Sample Convergence Rates of Zero-Order
Stochastic Optimization Methods
John C. Duchi1
Michael I. Jordan1,2
Martin J. Wainwright1,2
Andre Wibisono1
1
2
Department of Electrical Engineering and Computer Science and Department of Statistics
University of California, Berkeley
Berkeley, CA USA 94720
{jduchi,jordan,w... | 4550 |@word polynomial:1 norm:23 suitably:1 dekel:1 open:1 simulation:2 moment:2 series:1 renewed:1 interestingly:1 past:1 wainwrig:1 current:1 must:3 john:1 numerical:2 analytic:1 update:4 juditsky:1 leaf:1 warmuth:1 inspection:1 provides:2 characterization:1 org:1 simpler:1 buldygin:1 mathematical:2 c2:4 direct:1 sym... |
3,923 | 4,551 | Inverse Reinforcement Learning
through Structured Classification
Edouard Klein1,2
LORIA ? team ABC
Nancy, France
edouard.klein@supelec.fr
2
1
Matthieu Geist2
Sup?lec ? IMS-MaLIS Research Group
Metz, France
matthieu.geist@supelec.fr
Bilal Piot2,3 , Olivier Pietquin2,3
UMI 2958 (GeorgiaTech-CNRS)
Metz, France
{bilal... | 4551 |@word briefly:2 norm:1 nd:1 pieter:1 r:8 tried:1 contraction:1 decomposition:1 tr:8 harder:1 lorraine:1 series:1 score:16 bilal:2 existing:4 current:1 discretization:1 manuel:1 si:34 written:1 john:1 happen:1 informative:2 designed:1 stationary:3 greedy:3 parameterization:3 short:1 core:1 provides:1 simpler:1 dap... |
3,924 | 4,552 | Bayesian active learning with localized priors
for fast receptive field characterization
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
Active l... | 4552 |@word neurophysiology:6 trial:16 middle:1 inversion:1 achievable:1 version:1 proportionality:1 heuristically:1 simulation:1 seek:1 propagate:1 covariance:15 concise:1 harder:1 carry:1 reduction:1 initial:2 series:1 selecting:6 outperforms:2 existing:2 current:4 elliptical:2 comparing:1 ka:1 yet:3 additive:1 multi... |
3,925 | 4,553 | Learning High-Density Regions for a Generalized
Kolmogorov-Smirnov Test in High-Dimensional Data
Michael Lindenbaoum
Department of Computer Science
Technion ? Israel Institute of Technology
Haifa 32000, Israel
mic@cs.technion.ac.il
Assaf Glazer
Department of Computer Science
Technion ? Israel Institute of Technology
... | 4553 |@word madelon:1 version:13 proportion:3 smirnov:7 open:1 q1:1 venkatasubramanian:1 contains:1 shum:1 document:3 ours:1 outperforms:2 existing:2 current:1 comparing:1 cad:1 dx:1 john:3 fn:8 realistic:1 partition:1 analytic:2 kyb:1 cheap:1 plot:8 v:1 half:3 huo:1 colored:1 hypersphere:3 detecting:3 simpler:4 unboun... |
3,926 | 4,554 | Slice Normalized Dynamic Markov Logic Networks
Tivadar Papai
Henry Kautz
Daniel Stefankovic
Department of Computer Science
University of Rochester
Rochester, NY 14627
{papai,kautz,stefanko}@cs.rochester.edu
Abstract
Markov logic is a widely used tool in statistical relational learning, which uses
a weighted first-ord... | 4554 |@word version:3 stronger:1 accounting:1 tr:1 carry:1 ld:5 initial:1 configuration:1 contains:2 score:6 daniel:3 outperforms:3 assigning:1 john:1 dechter:1 partition:9 utml:1 alone:1 generative:3 instantiate:1 intelligence:7 mln:7 mccallum:2 ith:3 provides:4 node:1 toronto:1 along:1 c2:1 constructed:1 become:4 qua... |
3,927 | 4,555 | Complex Inference in Neural Circuits with
Probabilistic Population Codes and Topic Models
Jeff Beck
Department of Brain and Cognitive Sciences
University of Rochester
jbeck@bcs.rochester.edu
Katherine Heller
Department of Statistical Science
Duke University
kheller@stat.duke.edu
Alexandre Pouget
Department of Neuros... | 4555 |@word proceeded:1 trial:5 version:1 briefly:1 proportion:1 nd:4 simulation:1 covariance:2 daniel:2 seriously:1 document:17 ording:1 current:3 anne:1 must:5 ronald:1 partition:1 shape:1 motor:1 designed:1 update:7 infant:1 cue:2 generative:9 nervous:1 indicative:1 short:4 colored:1 blei:3 provides:3 contribute:1 b... |
3,928 | 4,556 | Learned Prioritization for
Trading Off Accuracy and Speed?
Jiarong Jiang?
Adam Teichert?
Hal Daum?e III?
Jason Eisner?
?
?
Department of Computer Science
Johns Hopkins University
Baltimore, MD 21218
{teichert,eisner}@jhu.edu
Department of Computer Science
University of Maryland
College Park, MD 20742
{jiarong,ha... | 4556 |@word mild:1 webber:1 exploitation:1 version:1 seems:1 willing:1 heuristically:1 seek:1 simulation:1 pieter:1 pick:1 tr:1 carry:1 reduction:1 takuya:1 initial:7 uncovered:1 score:13 charniak:2 daniel:1 past:4 current:9 ida:4 surprising:1 yet:1 must:1 parsing:29 john:2 shape:1 motor:1 drop:3 designed:2 intelligenc... |
3,929 | 4,557 | Putting Bayes to sleep
Wouter M. Koolen?
Dmitry Adamskiy?
Manfred K. Warmuth?
Abstract
We consider sequential prediction algorithms that are given the predictions from
a set of models as inputs. If the nature of the data is changing over time in that
different models predict well on different segments of the data, ... | 4557 |@word multitask:11 unaltered:1 middle:1 compression:1 stronger:1 disk:1 sex:1 open:3 km:1 forecaster:1 decomposition:1 jacob:2 pick:2 recursively:1 carry:1 venkatasubramanian:1 initial:4 selecting:1 tuned:3 past:14 outperforms:2 existing:1 recovered:3 current:3 nt:1 erven:1 atop:1 intriguing:1 must:1 readily:1 ad... |
3,930 | 4,558 | A Polynomial-time Form of Robust Regression
?
Yaoliang Yu, Ozlem
Aslan and Dale Schuurmans
Department of Computing Science, University of Alberta, Edmonton AB T6G 2E8, Canada
{yaoliang,ozlem,dale}@cs.ualberta.ca
Abstract
Despite the variety of robust regression methods that have been developed, current regression form... | 4558 |@word mild:1 polynomial:15 stronger:2 nd:1 open:1 semicontinuous:1 crucially:1 tried:1 ronchetti:2 tr:1 contains:1 series:1 tuned:1 rkhs:5 reynolds:1 existing:2 current:1 recovered:3 yet:1 must:1 subsequent:1 enables:1 update:1 n0:7 selected:2 ith:1 stahel:1 characterization:2 provides:3 mannor:2 simpler:1 unboun... |
3,931 | 4,559 | Multi-Task Averaging
Sergey Feldman, Maya R. Gupta, and Bela A. Frigyik
Department of Electrical Engineering
University of Washington
Seattle, WA 98103
Abstract
We present a multi-task learning approach to jointly estimate the means of multiple independent data sets. The proposed multi-task averaging (MTA) algorithm
... | 4559 |@word exploitation:1 version:4 inversion:1 norm:1 stronger:1 open:2 km:2 simulation:11 covariance:6 jacob:1 frigyik:1 tr:3 att:1 outperforms:3 past:2 comparing:1 nt:13 surprising:1 crippled:1 must:1 subcomponent:1 enables:1 analytic:1 hypothesize:1 designed:3 v:7 intelligence:1 yr:1 inspection:1 affair:1 ith:1 re... |
3,932 | 456 | Shooting Craps in Search of an Optimal Strategy for
Training Connectionist Pattern Classifiers
J. B. Hampshire IT
and B. V. K. Vijaya Kumar
Department of Electrical & Computer Engineering
Carnegie Mellon University
Pittsbwgh. PA 15213-3890
hamps@speechl.cs.cmu.edu
and
kumar@gauss.ece.cmu.edu
Abstract
We compare t... | 456 |@word version:1 rigged:4 paid:1 thereby:1 dx:1 must:1 thble:1 msb:2 discrimination:5 lr:1 quantized:1 node:1 five:1 mathematical:1 rc:1 differential:19 shooting:1 prove:4 introduce:1 expected:1 themselves:1 elman:1 multi:1 little:1 actual:1 becomes:1 estimating:3 what:1 finding:1 quantitative:1 every:1 um:2 classi... |
3,933 | 4,560 | Semi-supervised Eigenvectors
for Locally-biased Learning
Michael W. Mahoney
Department of Mathematics
Stanford University
Stanford, CA 94305
mmahoney@cs.stanford.edu
Toke Jansen Hansen
Section for Cognitive Systems
DTU Informatics
Technical University of Denmark
tjha@imm.dtu.dk
Abstract
In many applications, one has... | 4560 |@word middle:1 version:5 norm:2 open:1 widom:1 asks:1 bicriteria:1 reduction:2 initial:1 configuration:2 contains:1 ours:1 kahl:1 lang:3 written:1 subsequent:5 partition:2 informative:1 haxby:1 remove:1 plot:3 designed:1 v:20 ith:1 short:1 node:11 location:1 successive:2 revisited:1 along:2 constructed:2 symposiu... |
3,934 | 4,561 | Feature-aware Label Space Dimension Reduction for
Multi-label Classification
Hsuan-Tien Lin
Department of Computer Science
& Information Engineering,
National Taiwan University
htlin@csie.ntu.edu.tw
Yao-Nan Chen
Department of Computer Science
& Information Engineering,
National Taiwan University
r99922008@csie.ntu.edu... | 4561 |@word version:3 compression:3 decomposition:5 elisseeff:2 thereby:1 yea:1 ttn:1 harder:1 tr:4 reduction:21 cyclic:1 contains:8 tuned:1 seriously:1 bibtex:4 outperforms:2 existing:3 comparing:1 luo:1 must:3 readily:1 multioutput:1 numerical:1 wx:15 drop:1 plot:1 designed:2 update:1 intelligence:2 fewer:1 indicativ... |
3,935 | 4,562 | 3D Object Detection and Viewpoint Estimation with a
Deformable 3D Cuboid Model
Sven Dickinson
University of Toronto
sven@cs.toronto.edu
Sanja Fidler
TTI Chicago
fidler@ttic.edu
Raquel Urtasun
TTI Chicago
rurtasun@ttic.edu
Abstract
This paper addresses the problem of category-level 3D object detection. Given
a monocu... | 4562 |@word version:1 middle:3 dalal:1 seems:1 triggs:1 harder:1 lepetit:1 contains:1 score:28 hoiem:2 ours:3 document:1 outperforms:6 current:1 discretization:1 contextual:1 cad:6 luo:1 chicago:2 visible:11 shape:3 enables:3 visibility:8 treating:1 depict:2 fewer:1 accordingly:1 plane:3 vanishing:3 num:3 rescoring:1 t... |
3,936 | 4,563 | Distributed Non-Stochastic Experts
Varun Kanade?
UC Berkeley
vkanade@eecs.berkeley.edu
Zhenming Liu?
Princeton University
zhenming@cs.princeton.edu
Bo?zidar Radunovi?c
Microsoft Research
bozidar@microsoft.com
Abstract
We consider the online distributed non-stochastic experts problem, where the distributed system co... | 4563 |@word version:5 stronger:2 seems:2 dekel:1 open:1 simulation:3 forecaster:7 pick:4 recursively:1 venkatasubramanian:2 liu:4 efficacy:1 woodruff:1 denoting:3 tuned:1 past:2 outperforms:1 current:3 com:1 nt:2 comparing:1 must:8 written:1 additive:3 update:5 v:3 beginning:1 ith:2 record:1 cormode:2 boosting:1 node:1... |
3,937 | 4,564 | On Triangular versus Edge Representations ?
Towards Scalable Modeling of Networks
Qirong Ho
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
qho@cs.cmu.edu
Junming Yin
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
junmingy@cs.cmu.edu
Eric P. Xing
School of Compu... | 4564 |@word trial:2 kondor:1 stronger:1 replicate:1 d2:1 simulation:2 rgb:1 pick:4 reduction:1 contains:1 exclusively:1 precluding:1 document:2 interestingly:1 outperforms:1 current:1 di2:3 comparing:3 recovered:2 si:26 assigning:1 gurevich:1 must:4 underly:1 informative:1 wanted:1 hypothesize:1 plot:1 v:1 implying:1 g... |
3,938 | 4,565 | Near-optimal Differentially Private Principal
Components
Kamalika Chaudhuri
UC San Diego
kchaudhuri@ucsd.edu
Anand D. Sarwate
TTI-Chicago
asarwate@ttic.edu
Kaushik Sinha
UC San Diego
ksinha@cs.ucsd.edu
Abstract
Principal components analysis (PCA) is a standard tool for identifying good lowdimensional approximations ... | 4565 |@word private:51 version:1 seems:1 norm:3 c0:1 mith:4 open:2 termination:1 confirms:1 simulation:1 tried:1 decomposition:4 covariance:2 accounting:1 eng:1 tr:2 moment:4 reduction:7 contains:1 ours:1 past:1 existing:1 outperforms:1 current:1 ka:3 surprising:1 must:5 hou:1 chicago:1 numerical:2 kdd:4 kv1:1 plot:3 v... |
3,939 | 4,566 | Communication/Computation Tradeoffs in
Consensus-Based Distributed Optimization
Konstantinos I. Tsianos, Sean Lawlor, and Michael G. Rabbat
Department of Electrical and Computer Engineering
McGill University, Montr?eal, Canada
{konstantinos.tsianos, sean.lawlor}@mail.mcgill.ca
michael.rabbat@mcgill.ca
Abstract
We stu... | 4566 |@word interleave:1 norm:2 johansson:2 bekkerman:1 proportion:1 d2:2 decomposition:1 contains:2 series:1 selecting:1 existing:1 err:1 comparing:1 must:2 numerical:1 partition:1 cheap:4 drop:1 update:6 progressively:1 stationary:1 fewer:2 selected:1 beginning:1 lr:2 infrastructure:1 caveat:1 node:41 lx:1 mathematic... |
3,940 | 4,567 | Fully Bayesian inference for neural models with
negative-binomial spiking
Jonathan W. Pillow
Center for Perceptual Systems
Department of Psychology
The University of Texas at Austin
pillow@mail.utexas.edu
James G. Scott
Division of Statistics and Scientific Computation
McCombs School of Business
The University of Texa... | 4567 |@word neurophysiology:2 illustrating:2 version:2 briefly:1 loading:3 stronger:1 proportionality:1 teich:1 eng:1 pg:10 dramatic:1 recursively:2 carry:1 moment:3 series:4 uncovered:1 score:2 daniel:1 denoting:1 outperforms:1 current:4 ka:3 yet:2 must:1 john:1 fn:1 numerical:1 shape:6 analytic:2 update:4 implying:1 ... |
3,941 | 4,568 | Dynamic Pruning of Factor Graphs
for Maximum Marginal Prediction
Christoph H. Lampert
IST Austria (Institute of Science and Technology Austria)
Am Campus 1, 3400 Klosterneuburg, Austria
http://www.ist.ac.at/?chl
chl@ist.ac.at
Abstract
We study the problem of maximum marginal prediction (MMP) in probabilistic
graphica... | 4568 |@word mild:1 trial:2 illustrating:1 version:1 kohli:1 achievable:1 seems:1 polynomial:1 bigram:1 open:1 ucke:1 decomposition:1 pick:1 sgd:1 inpainting:7 outlook:1 moment:1 initial:1 configuration:1 liu:1 ours:1 existing:1 current:1 si:1 yet:2 readily:1 partition:1 analytic:2 remove:2 plot:5 n0:1 mccallum:1 es:2 i... |
3,942 | 4,569 | Efficient Monte Carlo Counterfactual Regret
Minimization in Games with Many Player Actions
Richard Gibson, Neil Burch, Marc Lanctot, and Duane Szafron
Department of Computing Science, University of Alberta
Edmonton, Alberta, T6G 2E8, Canada
{rggibson | nburch | lanctot | dszafron}@ualberta.ca
Abstract
Counterfactual ... | 4569 |@word innovates:1 private:3 version:3 manageable:1 szafron:4 d2:4 dealer:1 q1:1 abou:1 initial:2 contains:1 current:8 comparing:1 si:4 must:2 partition:2 eleven:2 remove:1 plot:3 update:3 greedy:1 fewer:1 intelligence:2 provides:1 node:23 traverse:4 tr09:1 firstly:4 daphne:1 five:11 along:1 symposium:1 incorrect:... |
3,943 | 457 | Constant-Time Loading of Shallow 1-Dimensional
Networks
Stephen Judd
Siemens Corporate Research,
755 College Rd. E.,
Princeton, NJ 08540
judd@learning.siemens.com
Abstract
The complexity of learning in shallow I-Dimensional neural networks has
been shown elsewhere to be linear in the size of the network. However,
whe... | 457 |@word trial:1 loading:17 thereby:1 solid:1 harder:1 series:1 tuned:1 current:1 com:1 must:4 shape:2 afield:1 alone:1 ith:2 short:1 core:1 lua:2 complication:2 node:43 unbiological:1 liberal:1 unbounded:1 unacceptable:1 prove:3 manner:1 inter:1 expected:7 themselves:1 examine:1 abscissa:1 resolve:1 cpu:1 little:1 a... |
3,944 | 4,570 | Memorability of Image Regions
Aditya Khosla
Jianxiong Xiao
Antonio Torralba
Aude Oliva
Massachusetts Institute of Technology
{khosla,xiao,torralba,oliva}@csail.mit.edu
Abstract
While long term human visual memory can store a remarkable amount of visual
information, it tends to degrade over time. Recent works have sh... | 4570 |@word trial:1 illustrating:1 version:2 dalal:1 triggs:1 vogt:1 proportionality:1 thereby:1 shechtman:1 initial:1 liu:1 score:22 united:1 offering:1 outperforms:2 current:1 luo:1 yet:2 cottrell:1 visible:1 happen:2 informative:1 shape:9 plot:1 interpretable:4 grass:1 alone:1 half:3 discovering:1 cue:1 intelligence... |
3,945 | 4,571 | Online `1-Dictionary Learning with Application to
Novel Document Detection
Huahua Wang?
University of Minnesota
huwang@cs.umn.edu
Shiva Prasad Kasiviswanathan?
General Electric Global Research
kasivisw@gmail.com
Arindam Banerjee?
University of Minnesota
banerjee@cs.umn.edu
Prem Melville
IBM T.J. Watson Research Cente... | 4571 |@word version:7 polynomial:1 norm:6 open:1 prasad:1 decomposition:2 simplifying:1 pick:2 tr:1 recursively:1 initial:2 substitution:1 zij:2 document:79 past:4 outperforms:1 existing:1 current:2 com:3 nt:9 ka:3 comparing:2 si:2 gmail:1 chu:1 written:1 subsequent:1 plot:7 update:6 v:2 half:1 prohibitive:1 ith:2 filt... |
3,946 | 4,572 | Random function priors for exchangeable arrays with
applications to graphs and relational data
James Robert Lloyd
Department of Engineering
University of Cambridge
Peter Orbanz
Department of Statistics
Columbia University
Zoubin Ghahramani
Department of Engineering
University of Cambridge
Daniel M. Roy
Department o... | 4572 |@word version:1 middle:4 seems:1 proportion:2 stronger:2 suitably:1 open:1 decomposition:3 reduction:2 series:2 daniel:1 outperforms:1 elliptical:3 yet:1 must:1 john:1 visible:1 partition:1 designed:1 interpretable:2 v:1 generative:1 intelligence:2 greedy:1 item:1 yamada:1 blei:1 provides:3 node:3 org:1 simpler:1... |
3,947 | 4,573 | Scalable Inference of Overlapping Communities
Prem Gopalan David Mimno Sean M. Gerrish Michael J. Freedman David M. Blei
{pgopalan,mimno,sgerrish,mfreed,blei}@cs.princeton.edu
Department of Computer Science
Princeton University
Princeton, NJ 08540
Abstract
We develop a scalable algorithm for posterior inference of ov... | 4573 |@word proportion:1 open:1 accounting:1 contains:3 document:1 outperforms:2 current:1 comparing:4 si:1 lang:1 must:3 partition:2 informative:2 kdd:1 update:10 n0:2 aside:1 v:1 generative:1 selected:1 fa9550:1 blei:6 santo:2 detecting:2 iterates:2 node:85 provides:1 mathematical:1 along:1 beta:3 differential:1 cons... |
3,948 | 4,574 | Iterative Thresholding Algorithm for Sparse Inverse
Covariance Estimation
Dominique Guillot
Dept. of Statistics
Stanford University
Stanford, CA 94305
Bala Rajaratnam
Dept. of Statistics
Stanford University
Stanford, CA 94305
Benjamin T. Rolfs
ICME
Stanford University
Stanford, CA 94305
dguillot@stanford.edu
brajar... | 4574 |@word h:5 determinant:1 version:1 inversion:2 norm:3 dominique:1 simulation:1 covariance:30 contraction:4 hsieh:3 decomposition:2 tr:5 reduction:1 initial:6 contains:1 series:1 united:1 comparing:1 com:1 si:1 dx:1 numerical:9 designed:1 interpretable:1 update:1 plot:1 v:1 amir:1 xk:1 core:4 prespecified:1 iterate... |
3,949 | 4,575 | Selective Labeling via Error Bound Minimization
Quanquan Gu? , Tong Zhang? , Chris Ding? , Jiawei Han?
Department of Computer Science, University of Illinois at Urbana-Champaign
?
Department. of Statistics, Rutgers University
?
Department. of Computer Science & Engineering, University of Texas at Arlington
qgu3@illinoi... | 4575 |@word version:4 briefly:1 advantageous:1 norm:1 elisseeff:1 incurs:1 tr:14 contains:5 selecting:2 pub:1 tuned:3 outperforms:3 existing:1 current:1 com:1 discretization:3 beygelzimer:2 yet:2 dx:1 john:1 fn:1 informative:3 benign:1 atlas:1 greedy:2 selected:7 half:1 fa9550:1 provides:1 zhang:2 mathematical:1 along:... |
3,950 | 4,576 | A Unifying Perspective of Parametric Policy Search
Methods for Markov Decision Processes
David Barber
Department of Computer Science
University College London
D.Barber@cs.ucl.ac.uk
Thomas Furmston
Department of Computer Science
University College London
T.Furmston@cs.ucl.ac.uk
Abstract
Parametric policy search algori... | 4576 |@word determinant:1 version:1 briefly:1 inversion:4 norm:4 d2:7 simulation:1 linearized:1 boundedness:1 kappen:1 reduction:2 initial:4 series:1 score:2 denoting:1 outperforms:2 existing:1 current:3 comparing:1 com:1 optim:1 written:3 numerical:2 motor:1 plot:7 designed:1 update:17 intelligence:1 selected:2 steepe... |
3,951 | 4,577 | Near-Optimal MAP Inference
for Determinantal Point Processes
Jennifer Gillenwater Alex Kulesza Ben Taskar
Computer and Information Science
University of Pennsylvania
{jengi,kulesza,taskar}@cis.upenn.edu
Abstract
Determinantal point processes (DPPs) have recently been proposed as computationally efficient probabilistic... | 4577 |@word trial:1 version:1 inversion:1 open:1 seek:1 covariance:2 p0:5 tr:1 configuration:4 contains:1 score:2 document:8 frankwolfe:1 past:1 outperforms:2 existing:1 current:2 qth:1 nonmonotone:2 must:4 written:1 determinantal:11 vere:1 additive:1 informative:1 v:6 greedy:29 selected:3 discovering:1 item:5 plane:2 ... |
3,952 | 4,578 | Approximating Concavely Parameterized
Optimization Problems
S?oren Laue
Friedrich-Schiller-Universit?at Jena
Germany
soeren.laue@uni-jena.de
Joachim Giesen
Friedrich-Schiller-Universit?at Jena
Germany
joachim.giesen@uni-jena.de
Jens K. Mueller
Friedrich-Schiller-Universit?at Jena
Germany
jkm@informatik.uni-jena.de
S... | 4578 |@word polynomial:5 norm:1 asks:1 tr:2 atrix:1 contains:2 com:1 mushroom:2 must:1 kdd:1 plot:2 update:1 intelligence:1 website:1 certificate:6 provides:1 node:3 org:1 zhang:1 along:3 c2:11 become:1 symposium:2 artner:1 behavior:1 p1:2 growing:1 decreasing:5 zhi:1 considering:1 increasing:3 solver:1 bounded:4 notat... |
3,953 | 4,579 | A nonparametric variable clustering model
Konstantina Palla?
University of Cambridge
kp376@cam.ac.uk
David A. Knowles?
Stanford University
davidknowles@cs.stanford.edu
Zoubin Ghahramani
University of Cambridge
zoubin@eng.cam.ac.uk
Abstract
Factor analysis models effectively summarise the covariance structure of hig... | 4579 |@word middle:1 loading:8 duda:2 nd:1 d2:10 simulation:2 gish:1 eng:1 covariance:12 decomposition:2 minus:1 xkn:2 analoguous:1 reduction:2 uncovered:1 series:2 denoting:1 outperforms:2 existing:1 current:1 recovered:1 surprising:1 analysed:1 subsequent:1 partition:6 chicago:1 plot:1 interpretable:1 update:2 genera... |
3,954 | 458 | A Connectionist Learning Approach to Analyzing
Linguistic Stress
Prahlad Gupta
Department of Psychology
Carnegie Mellon University
Pittsburgh, PA 15213
David S. Touretzky
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
We use connectionist modeling to develop an analysis of stress... | 458 |@word aircraft:1 version:1 middle:1 proportion:1 simulation:5 llo:1 invoking:1 twolayer:1 autosegmental:1 initial:5 series:1 clash:8 evans:1 thble:1 succeeding:2 v:4 tertiary:3 dissertation:1 characterization:4 provides:4 club:1 five:1 unbounded:2 h4:1 incorrect:1 dan:1 examine:1 decreasing:1 deirdre:1 actual:2 ll... |
3,955 | 4,580 | Hierarchical Optimistic Region Selection driven by
Curiosity
Odalric-Ambrym Maillard
Lehrstuhl f?ur Informationstechnologie
Montanuniversit?at Leoben
Leoben, A-8700, Austria
odalricambrym.maillard@gmail.com
Abstract
This paper aims to take a step forwards making the term ?intrinsic motivation?
from reinforcement learn... | 4580 |@word mild:2 version:1 norm:2 open:1 covariance:2 arti:2 initial:1 contains:2 typology:1 daniel:1 past:1 existing:2 nally:2 current:2 com:1 nt:37 gmail:1 yet:4 written:1 must:1 additive:1 partition:23 happen:1 enables:2 motor:1 remove:3 neurorobotics:1 progressively:1 generative:3 leaf:9 half:1 selected:1 cult:4 ... |
3,956 | 4,581 | Nonparametric Max-Margin Matrix Factorization for
Collaborative Prediction
Minjie Xu, Jun Zhu and Bo Zhang
State Key Laboratory of Intelligent Technology and Systems (LITS)
Tsinghua National Laboratory for Information Science and Technology (TNList)
Department of Computer Science and Technology, Tsinghua University, Be... | 4581 |@word briefly:2 norm:4 nd:1 seek:1 tried:1 simplifying:1 p0:16 ipm:1 tnlist:1 contains:2 selecting:2 outperforms:1 current:1 com:1 gmail:1 intriguing:1 written:1 subsequent:1 partition:9 informative:1 treating:1 update:3 discrimination:6 zik:9 generative:4 fewer:1 selected:1 item:11 intelligence:3 accordingly:6 p... |
3,957 | 4,582 | Learning Networks of Heterogeneous Influence
Nan Du? Le Song? Alex Smola? Ming Yuan?
Georgia Institute of Technology? , Google Research?
dunan@gatech.edu lsong@cc.gatech.edu
alex@smola.org myuan@isye.gatech.edu
Abstract
Information, disease, and influence diffuse over networks of entities in both natural systems and ... | 4582 |@word cnn:1 proportion:1 norm:1 reused:1 grey:1 simulation:1 solid:2 moment:2 memetracker:3 uncovered:1 score:4 outperforms:1 virus:1 lang:1 yet:3 dx:6 written:1 numerical:2 realistic:1 additive:1 happen:3 shape:1 kdd:3 plot:1 update:4 fund:1 v:1 fewer:2 website:1 instantiate:1 core:7 num:12 node:59 location:1 su... |
3,958 | 4,583 | Symmetric Correspondence Topic Models for
Multilingual Text Analysis
Kosuke Fukumasu?
Koji Eguchi?
Eric P. Xing?
Graduate School of System Informatics, Kobe University, Kobe 657-8501, Japan
?
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
?
fukumasu@cs25.scitec.kobe-u.ac.jp, eguchi@p... | 4583 |@word version:2 proportion:6 nd:21 hu:1 carry:1 initial:1 selecting:1 united:2 hereafter:1 document:79 cort:1 outperforms:1 existing:2 si:1 must:2 written:1 john:1 hofmann:1 generative:8 selected:13 intelligence:1 accordingly:3 cult:2 mccallum:1 scotland:2 reciprocal:2 smith:1 blei:3 toronto:1 uppsala:1 org:1 zha... |
3,959 | 4,584 | Structure estimation for discrete graphical models:
Generalized covariance matrices and their inverses
Martin J. Wainwright
Departments of Statistics and EECS
University of California, Berkeley
Berkeley, CA 94720
wainwrig@stat.berkeley.edu
Po-Ling Loh
Department of Statistics
University of California, Berkeley
Berkele... | 4584 |@word trial:2 determinant:3 version:1 inversion:1 norm:1 c0:2 open:2 d2:1 simulation:6 covariance:64 jacob:1 thereby:1 harder:1 carry:1 moment:2 liu:7 configuration:2 contains:1 selecting:1 denoting:1 nonparanormal:3 past:1 wainwrig:1 surprising:2 luo:1 must:1 subsequent:1 partition:2 j1:1 additive:2 plot:1 v:1 p... |
3,960 | 4,585 | Multi-scale Hyper-time Hardware Emulation of
Human Motor Nervous System Based on Spiking
Neurons using FPGA
C. Minos Niu
Department of Biomedical Engineering
University of Southern California
Los Angeles, CA 90089
minos.niu@sangerlab.net
Sirish K. Nandyala
Department of Biomedical Engineering
University of Southern C... | 4585 |@word neurophysiology:5 trial:1 version:1 rising:1 polynomial:1 open:1 pulse:1 soleus:1 contraction:2 thereby:1 initial:1 configuration:1 series:1 efficacy:1 tuned:1 document:1 existing:1 current:7 com:1 comparing:1 si:1 gmail:1 yet:1 must:2 gpu:1 refresh:1 realistic:3 numerical:3 plasticity:3 motor:29 designed:2... |
3,961 | 4,586 | Online Sum-Product Computation over Trees
Fabio Vitale
Department of Computer Science
University of Milan
20135 Milan, Italy
fabio.vitale@unimi.it
Mark Herbster
Stephen Pasteris
Department of Computer Science
University College London
London WC1E 6BT, England, UK
{m.herbster, s.pasteris}@cs.ucl.ac.uk
Abstract
We cons... | 4586 |@word multitask:1 trial:3 version:1 instruction:1 decomposition:21 jacob:1 pick:1 tr:3 recursively:4 contains:2 initialisation:1 denoting:1 loeliger:1 ka:1 subcomponents:1 nt:2 si:1 delcher:1 must:1 update:14 maxv:1 leaf:17 selected:4 normalising:1 provides:1 recompute:1 node:1 clarified:1 successive:1 boosting:1... |
3,962 | 4,587 | Phoneme Classification using Constrained Variational
Gaussian Process Dynamical System
Sungrack Yun
Qualcomm Korea
Seoul, South Korea
sungrack@qualcomm.com
Hyunsin Park
Department of EE, KAIST
Daejeon, South Korea
hs.park@kaist.ac.kr
Sanghyuk Park
Department of EE, KAIST
Daejeon, South Korea
shine0624@kaist.ac.kr
Jo... | 4587 |@word h:1 bigram:1 stronger:1 scg:3 covariance:6 tr:3 reduction:2 series:5 contains:1 score:1 yni:2 xnj:2 outperforms:2 com:2 comparing:3 gmail:1 dx:5 must:2 enables:1 designed:3 intelligence:3 selected:6 jongmin:1 nq:1 fni:1 short:2 core:3 sudden:1 regressive:1 provides:1 constructed:2 prove:1 hci:1 consists:4 i... |
3,963 | 4,588 | Nystr?om Method vs Random Fourier Features:
A Theoretical and Empirical Comparison
Tianbao Yang? , Yu-Feng Li? , Mehrdad Mahdavi\ , Rong Jin\ , Zhi-Hua Zhou?
?
Machine Learning Lab, GE Global Research, San Ramon, CA 94583
\
Michigan State University, East Lansing, MI 48824
?
National Key Laboratory for Novel Software ... | 4588 |@word trial:1 version:1 polynomial:1 norm:4 nd:1 dekel:1 decomposition:3 nystr:39 carry:1 tist:1 outperforms:1 com:2 comparing:1 fn:6 numerical:1 designed:1 plot:1 drop:1 v:3 selected:2 guess:1 website:1 rudin:1 vbr:1 zhang:1 constructed:1 c2:2 interscience:1 introduce:2 lansing:1 theoretically:1 expected:1 zhouz... |
3,964 | 4,589 | Repulsive Mixtures
Vinayak Rao
Gatsby Computational Neuroscience Unit
University College London
vrao@gatsby.ucl.ac.uk
Francesca Petralia
Department of Statistical Science
Duke University
fp12@duke.edu
David B. Dunson
Department of Statistical Science
Duke University
dunson@stat.duke.edu
Abstract
Discrete mixtures a... | 4589 |@word norm:1 seek:1 contraction:1 p0:9 decomposition:1 q1:2 solid:3 contains:1 exclusively:1 selecting:1 outperforms:1 existing:1 vere:1 plot:3 interpretable:1 fewer:2 characterization:1 provides:3 location:13 guard:1 c2:4 constructed:1 become:3 symposium:1 consists:1 fitting:3 thinned:1 pairwise:7 theoretically:... |
3,965 | 459 | Kernel Regression and
Backpropagation Training with Noise
Petri Koistinen and Lasse Holmstrom
Rolf Nevanlinna Institute, University of Helsinki
Teollisuuskatu 23, SF-0051O Helsinki, Finland
Abstract
One method proposed for improving the generalization capability of a feedforward network trained with the backpropagati... | 459 |@word mild:1 seems:1 xkn:3 ld:2 initial:1 denoting:1 tuned:1 marquardt:1 activation:1 fn:2 additive:6 discrimination:1 selected:2 xk:2 yi1:1 node:1 lx:7 c2:2 fitting:1 deteriorate:1 expected:3 begin:1 provided:1 estimating:1 interpreted:1 minimizes:2 pseudo:1 classifier:5 ser:1 control:1 unit:4 appear:1 local:1 te... |
3,966 | 4,590 | Kernel Latent SVM for Visual Recognition
Yang Wang
Department of Computer Science
University of Manitoba
ywang@cs.umanitoba.ca
Weilong Yang
School of Computing Science
Simon Fraser University
wya16@sfu.ca
Greg Mori
School of Computing Science
Simon Fraser University
mori@cs.sfu.ca
Arash Vahdat
School of Computing S... | 4590 |@word version:2 dalal:1 triggs:1 open:1 mammal:3 initial:3 contains:2 outperforms:4 current:1 z2:14 yet:2 additive:1 hofmann:1 shape:2 update:1 v:2 alone:1 half:2 intelligence:1 provides:3 quantized:2 location:22 toronto:1 simpler:1 five:10 along:1 c2:5 consists:2 combine:4 compose:1 introduce:2 pairwise:1 x0:13 ... |
3,967 | 4,591 | Multilabel Classification using Bayesian Compressed
Sensing
Ashish Kapoor? , Prateek Jain? and Raajay Viswanathan?
?
Microsoft Research, Redmond, USA
?
Microsoft Research, Bangalore, INDIA
{akapoor, prajain, t-rviswa}@microsoft.com
Abstract
In this paper, we present a Bayesian framework for multilabel classification ... | 4591 |@word version:1 inversion:2 compression:1 zelnik:1 seek:9 propagate:1 infogain:1 asks:1 thereby:2 harder:1 liblinear:1 reduction:2 configuration:2 raajay:1 efficacy:1 selecting:1 initial:1 tabulate:1 outperforms:3 existing:2 recovered:2 com:1 luo:1 numerical:1 partition:1 informative:7 thrust:1 hofmann:1 enables:... |
3,968 | 4,592 | Kernel Hyperalignment
Alexander Lorbert & Peter J. Ramadge
Department of Electrical Engineering
Princeton University
Abstract
We offer a regularized, kernel extension of the multi-set, orthogonal Procrustes
problem, or hyperalignment. Our new method, called Kernel Hyperalignment,
expands the scope of hyperalignment t... | 4592 |@word r:1 seek:2 decomposition:2 q1:1 tr:5 solid:1 harder:1 reduction:2 series:7 halchenko:1 selecting:2 rkhs:1 hemodynamic:1 envision:1 outperforms:1 current:5 comparing:1 ka:2 must:3 partition:2 shape:2 enables:2 haxby:4 hofmann:1 plot:1 atlas:1 prohibitive:1 selected:1 accordingly:1 plane:3 xk:2 smith:1 short:... |
3,969 | 4,593 | Homeostatic plasticity in Bayesian spiking networks as
Expectation Maximization with posterior constraints
Stefan Habenschuss? , Johannes Bill? , Bernhard Nessler
Institute for Theoretical Computer Science, Graz University of Technology
{habenschuss,bill,nessler}@igi.tugraz.at
Abstract
Recent spiking network models o... | 4593 |@word neurophysiology:1 trial:2 version:3 eliminating:1 proportion:1 norm:1 ucke:1 simulation:7 decomposition:4 thereby:1 versatile:1 carry:2 configuration:3 contains:1 document:1 ording:1 existing:1 current:2 recovered:1 activation:10 yet:1 must:3 reminiscent:2 written:1 realistic:4 distant:1 plasticity:39 enabl... |
3,970 | 4,594 | Controlled Recognition Bounds for Visual Learning
and Exploration
Vasiliy Karasev1
1
Alessandro Chiuso2
University of California, Los Angeles
2
Stefano Soatto1
University of Padova
Abstract
We describe the tradeoff between the performance in a visual recognition problem
and the control authority that the agent can... | 4594 |@word cu:1 middle:2 compression:1 seems:1 c0:4 simulation:2 covariance:3 initial:2 configuration:1 contains:2 past:1 current:3 nt:6 yet:1 must:4 written:1 additive:3 visible:4 realistic:1 informative:2 noninformative:1 enables:1 burdick:1 hypothesize:1 drop:1 greedy:2 half:1 intelligence:3 inspection:1 indefinite... |
3,971 | 4,595 | Waveform Driven Plasticity in BiFeO3 Memristive
Devices: Model and Implementation
Christian Mayr, Paul Staerke, Johannes Partzsch, Rene Schueffny
Institute of Circuits and Systems
TU Dresden, Dresden, Germany
{christian.mayr,johannes.partzsch,rene.schueffny}@tu-dresden.de
Love Cederstroem
Zentrum Mikroelektronik Dresd... | 4595 |@word neurophysiology:1 rising:2 hippocampus:2 seems:3 replicate:2 open:1 d2:1 grey:1 pulse:16 simulation:3 overwritten:1 solid:1 carry:1 current:12 com:1 pickett:1 si:1 yet:1 dx:4 nanoscale:3 realistic:3 visible:1 additive:1 plasticity:41 christian:2 enables:2 plot:1 drop:1 device:69 short:12 lr:2 chua:5 org:5 z... |
3,972 | 4,596 | Fused sparsity and robust estimation for linear
models with unknown variance
Arnak S. Dalalyan
ENSAE-CREST-GENES
92245 MALAKOFF Cedex, FRANCE
arnak.dalalyan@ensae.fr
Yin Chen
University Paris Est, LIGM
77455 Marne-la-Valle, FRANCE
yin.chen@eleves.enpc.fr
Abstract
In this paper, we develop a novel approach to the prob... | 4596 |@word trial:1 norm:5 instrumental:1 valle:1 suitably:2 open:1 km:5 paid:1 mention:1 carry:2 contains:2 exclusively:1 ka:2 enpc:1 comparing:2 optim:1 written:2 readily:1 john:1 additive:1 eleven:1 strecha:2 designed:1 vanishing:1 short:1 math:4 evy:2 simpler:1 daphne:1 zhang:2 along:1 kvk2:1 ik:1 qualitative:2 pro... |
3,973 | 4,597 | A Conditional Multinomial Mixture Model for
Superset Label Learning
Thomas G. Dietterich
EECS, Oregon State University
Corvallis, OR 97331
tgd@cs.orst.edu
Li-Ping Liu
EECS, Oregon State University
Corvallis, OR 97331
liuli@eecs.oregonstate.edu
Abstract
In the superset label learning problem (SLL), each training inst... | 4597 |@word repository:1 version:2 confirms:1 seek:2 pick:1 liu:2 contains:5 score:1 outperforms:1 existing:1 ida:1 must:3 written:1 numerical:1 informative:2 kdd:2 update:2 v:1 generative:1 instantiate:1 fewer:1 selected:1 intelligence:1 short:1 provides:1 detecting:1 node:3 iterates:1 coarse:1 zhang:1 beta:1 become:1... |
3,974 | 4,598 | A Linear Time Active Learning Algorithm
for Link Classification?
Nicol`
o Cesa-Bianchi
Dipartimento di Informatica
Universit`
a degli Studi di Milano, Italy
Claudio Gentile
Dipartimento di Scienze Teoriche ed Applicate
Universit`a dell?Insubria, Italy
Giovanni Zappella
Dipartimento di Matematica
Universit`a degli Stud... | 4598 |@word seems:1 yi0:2 open:1 decomposition:2 harder:3 recursively:1 carry:1 initial:3 contains:5 series:1 outperforms:1 comparing:1 assigning:1 yet:1 must:1 hou:1 partition:1 iacono:1 remove:1 designed:1 plot:1 leaf:2 website:1 selected:3 reciprocal:1 short:2 chiang:1 iterates:1 node:31 org:1 simpler:1 dell:1 heigh... |
3,975 | 4,599 | On-line Reinforcement Learning Using Incremental
Kernel-Based Stochastic Factorization
Andr?e M. S. Barreto
School of Computer Science
McGill University
Montreal, Canada
amsb@cs.mcgill.ca
Doina Precup
School of Computer Science
McGill University
Montreal, Canada
dprecup@cs.mcgill.ca
Joelle Pineau
School of Computer S... | 4599 |@word version:10 manageable:1 norm:2 simulation:1 recursively:1 initial:1 configuration:1 precluding:1 ka:1 yet:1 ws1:1 must:3 attracted:1 dx:1 john:1 partition:1 drop:1 update:14 stationary:1 greedy:1 intelligence:3 weighing:1 amir:1 accordingly:1 ria:1 beginning:1 realizing:1 short:1 hinged:1 provides:3 recompu... |
3,976 | 46 | 432
Performance Measures for Associative Memories
that Learn and Forget
Anthony /(uh
Department of Electrical Engineering
University of Hawaii at Manoa
Honolulu HI, 96822
ABSTRACT
Recently, many modifications to the McCulloch/Pitts model have been proposed
where both learning and forgetting occur. Given that the netw... | 46 |@word trial:2 briefly:1 version:2 achievable:2 calculus:1 gradual:3 simulation:5 eng:1 paid:1 initial:6 efficacy:8 existing:1 recovered:3 nt:1 activation:11 si:2 must:4 refresh:1 periodically:1 limp:1 plasticity:11 pertinent:1 plot:1 update:9 stationary:1 selected:1 nervous:1 beginning:1 short:1 indefinitely:1 prov... |
3,977 | 460 | MODELS WANTED: MUST FIT DIMENSIONS
OF SLEEP AND DREAMING*
J. Allan Hohson, Adam N. Mamelak t and Jeffrey P. Sutton t
Laboratory of Neurophysiology and Department of Psychiatry
Harvard Medical School
74 Fenwood Road, Boston, MA 02115
Abstract
During waking and sleep, the brain and mind undergo a tightly linked and
pre... | 460 |@word neurophysiology:2 noradrenergic:1 hippocampus:1 integrative:1 simulation:2 simplifying:1 dramatic:1 minus:1 solid:1 moment:2 phy:1 cyclic:1 efficacy:1 existing:1 activation:7 yet:1 must:4 ulation:1 physiol:1 realistic:1 subsequent:1 plasticity:2 dupont:1 wanted:4 plot:2 fund:1 cue:1 tone:1 reciprocal:3 short... |
3,978 | 4,600 | Compressive neural representation of sparse,
high-dimensional probabilities
xaq pitkow
Department of Brain and Cognitive Sciences
University of Rochester
Rochester, NY 14607
xpitkow@bcs.rochester.edu
Abstract
This paper shows how sparse, high-dimensional probability distributions could
be represented by neurons with ... | 4600 |@word trial:3 version:2 compression:7 norm:5 c0:3 km:1 ks0:1 simulation:2 accounting:1 tkacik:1 jafarpour:1 harder:1 reduction:3 cyclic:2 mag:1 interestingly:1 kx0:1 comparing:1 scatter:1 dx:1 gurevich:1 readily:2 visible:6 informative:1 plot:1 half:2 fewer:3 selected:2 ith:2 colored:1 provides:1 math:1 node:1 lo... |
3,979 | 4,601 | Newton-Like Methods for Sparse Inverse Covariance
Estimation
Figen Oztoprak
Sabanci University
figen@sabanciuniv.edu
Peder A. Olsen
IBM, T. J. Watson Research Center
pederao@us.ibm.com
Jorge Nocedal
Northwestern University
nocedal@eecs.northwestern.edu
Steven J. Rennie
IBM, T. J. Watson Research Center
sjrennie@us.ib... | 4601 |@word trial:2 version:2 advantageous:2 norm:5 bf:1 termination:1 covariance:27 p0:10 hsieh:1 natsoulis:1 citeseer:1 ipm:2 initial:1 series:1 zij:1 current:9 com:3 rish:2 toh:1 chu:1 must:2 numerical:5 kpf:1 greedy:2 xk:3 beginning:1 steepest:3 matrix1:1 five:1 mathematical:3 along:3 fitting:1 manner:1 x0:6 alm:13... |
3,980 | 4,602 | Bayesian Pedigree Analysis using Measure
Factorization
Bonnie Kirkpatrick
Computer Science Department
University of British Columbia
bbkirk@cs.ubc.ca
Alexandre Bouchard-C?ot?e
Statistics Department
University of British Columbia
bouchard@stat.ubc.ca
Abstract
Pedigrees, or family trees, are directed graphs used to ide... | 4602 |@word h:1 determinant:1 version:1 advantageous:1 replicate:2 sex:1 simulation:2 wexler:1 accommodate:1 kappen:2 reduction:1 cyclic:1 contains:2 score:4 series:1 moment:2 initial:1 genetic:13 ours:2 outperforms:1 existing:3 current:3 yet:1 must:2 readily:1 john:1 partition:1 designed:1 alone:1 generative:3 leaf:1 ... |
3,981 | 4,603 | Provable ICA with Unknown Gaussian Noise, with
Implications for Gaussian Mixtures and Autoencoders
Sanjeev Arora?
Rong Ge?
Ankur Moitra ?
Sushant Sachdeva?
Abstract
We present a new algorithm for Independent Component Analysis (ICA) which
has provable performance guarantees. In particular, suppose we are given sam... | 4603 |@word version:2 polynomial:11 seems:3 norm:5 nd:1 suitably:1 yi0:1 stronger:1 open:1 covariance:10 decomposition:2 pick:1 concise:1 tr:1 recursively:1 carry:1 moment:8 liu:1 series:1 interestingly:1 current:2 recovered:1 ka:2 dx:1 reminiscent:1 bd:1 tenet:1 cruz:2 additive:8 visible:1 analytic:1 utml:1 v:1 beginn... |
3,982 | 4,604 | Minimizing Uncertainty in Pipelines?
Nilesh Dalvi
Facebook, Inc.
nileshd@fb.com
Aditya Parameswaran
Stanford University
adityagp@cs.stanford.edu
Vibhor Rastogi
Google, Inc.
vibhor.rastogi@gmail.com
Abstract
In this paper, we consider the problem of debugging large pipelines by human
labeling. We represent the execu... | 4604 |@word version:5 polynomial:11 stronger:1 open:4 widom:1 d2:5 vldb:1 adnan:1 q1:3 pick:10 ld:1 reduction:9 wrapper:1 icis:1 selecting:1 daniel:1 existing:1 rish:1 com:2 surprising:1 beygelzimer:4 gmail:1 issuing:1 john:5 evans:1 subsequent:2 informative:1 kdd:1 drop:1 update:2 alone:1 half:1 leaf:27 selected:1 ite... |
3,983 | 4,605 | Learning as MAP Inference in Discrete
Graphical Models
James Petterson
NICTA/ANU
Canberra, Australia
james.petterson@nicta.com.au
Xianghang Liu
NICTA/UNSW
Sydney, Australia
xianghang.liu@nicta.com.au
Tiberio S. Caetano
NICTA/ANU/University of Sydney
Canberra and Sydney, Australia
tiberio.caetano@nicta.com.au
Abstra... | 4605 |@word repository:2 polynomial:2 norm:7 seems:1 c0:5 open:7 willing:1 decomposition:1 attainable:1 mcauley:1 liu:2 configuration:1 series:1 selecting:1 existing:1 current:1 com:3 discretization:5 yet:1 chu:1 must:1 additive:3 underly:1 informative:3 shawetaylor:1 plot:2 v:2 pursued:1 intelligence:1 parameterizatio... |
3,984 | 4,606 | Truly Nonparametric Online Variational Inference
for Hierarchical Dirichlet Processes
Michael Bryant and Erik B. Sudderth
Department of Computer Science, Brown University, Providence, RI
mbryantj@gmail.com, sudderth@cs.brown.edu
Abstract
Variational methods provide a computationally scalable alternative to Monte Carlo... | 4606 |@word trial:1 middle:1 pw:1 tried:1 covariance:3 accounting:1 minus:1 initial:2 contains:1 wj2:5 document:31 existing:1 com:1 activation:8 gmail:1 plot:3 update:20 selected:2 accepting:1 blei:6 provides:1 simpler:1 unbounded:1 direct:3 become:2 consists:3 prove:1 behavioral:3 inside:1 theoretically:1 notably:1 up... |
3,985 | 4,607 | Fiedler Random Fields: A Large-Scale Spectral
Approach to Statistical Network Modeling
Mikaela Keller?
Marc Tommasi?
INRIA Lille ? Nord Europe
40 avenue Halley ? B?at A ? Park Plaza
59650 Villeneuve d?Ascq (France)
{antonino.freno, mikaela.keller, marc.tommasi}@inria.fr
Antonino Freno
Abstract
Statistical models for ... | 4607 |@word kolaczyk:1 repository:1 stronger:1 smirnov:1 twelfth:1 tried:1 decomposition:3 contrastive:5 bai:1 configuration:7 contains:1 series:1 offering:1 tuned:1 interestingly:2 michal:1 activation:1 liva:1 must:1 numerical:1 happen:1 partition:3 informative:1 shape:5 analytic:1 realistic:1 kdd:1 designed:1 plot:1 ... |
3,986 | 4,608 | A systematic approach to extracting semantic
information from functional MRI data
Francisco Pereira
Siemens Corporation, Corporate Technology
Princeton, NJ 08540
francisco.pereira@gmail.com
Matthew Botvinick
Princeton Neuroscience Institute and Department of Psychology
Princeton University
Princeton NJ 08540
matthewb@p... | 4608 |@word trial:6 mri:2 kriegeskorte:1 lobe:1 covariance:2 decomposition:1 accommodate:1 harder:1 series:4 contains:3 blank:1 com:1 trustworthy:1 rish:1 torben:1 activation:26 gmail:1 kiebel:1 john:2 visible:2 partition:1 informative:6 oxygenation:1 shape:2 cant:1 haxby:3 kdd:1 designed:1 interpretable:1 progressivel... |
3,987 | 4,609 | Bayesian models for Large-scale Hierarchical
Classification
Siddharth Gopal
Bing Bai
Yiming Yang
Alexandru Niculescu-Mizil
sgopal1@andrew.cmu.edu yiming@cs.cmu.edu
{bing,alex}@nec-labs.com
Carnegie Mellon University
NEC Laboratories America, Princeton
Abstract
A challenging problem in hierarchical classificatio... | 4609 |@word msr:1 version:1 inversion:3 interleave:1 seems:2 logit:1 open:2 seek:1 tried:2 covariance:16 mammal:6 thereby:3 tr:1 harder:1 accommodate:1 recursively:2 bai:1 liu:2 score:1 bc:1 document:1 subjective:1 ka:1 com:1 comparing:1 luo:1 yet:1 distant:1 partition:1 informative:2 shape:1 enables:3 hofmann:2 remove... |
3,988 | 461 | Recognizing Overlapping Hand-Printed Characters by
Centered-Object Integrated Segmentation and Recognition
Gale L. Martin- & Mosfeq Rashid
MCC
Austin, Thxas 78759 USA
Abstract
This paper describes an approach, called centered object integrated segmentation and recognition (COISR). for integrating object segmentation a... | 461 |@word middle:1 version:10 thchnical:2 seems:1 nd:1 descnbed:2 leow:1 tr:1 carry:1 contains:1 score:1 blank:4 current:3 comparing:2 activation:7 parsing:1 john:3 subsequent:1 kheng:1 shape:2 enables:1 remove:1 half:3 cue:1 pointer:1 provides:1 node:22 location:1 five:2 height:4 along:3 consists:4 inter:2 behavior:1... |
3,989 | 4,610 | A Better Way to Pretrain Deep Boltzmann Machines
Geoffrey Hinton
Department of Computer Science
University of Toronto
hinton@cs.toronto.edu
Ruslan Salakhutdinov
Department of Statistics and Computer Science
University of Toronto
rsalakhu@cs.toronto.edu
Abstract
We describe how the pretraining algorithm for Deep Bolt... | 4610 |@word briefly:1 nd:6 contrastive:5 initial:4 generatively:1 contains:5 series:1 tuned:1 existing:5 current:1 visible:7 partition:2 treating:1 wlm:1 aside:1 generative:15 greedy:3 half:6 intelligence:2 plane:1 toronto:4 five:1 direct:1 consists:1 combine:1 compose:1 introduce:1 pairwise:1 expected:1 salakhutdinov:... |
3,990 | 4,611 | Gradient Weights help Nonparametric Regressors
Samory Kpotufe?
Max Planck Institute for Intelligent Systems
samory@tuebingen.mpg.de
Abdeslam Boularias
Max Planck Institute for Intelligent Systems
boularias@tuebingen.mpg.de
Abstract
In regression problems over Rd , the unknown function f often varies more in
some coor... | 4611 |@word mild:1 aircraft:2 repository:2 kulis:1 polynomial:1 norm:7 accounting:1 harder:1 contains:2 score:1 tuned:1 current:1 beygelzimer:1 yet:3 luis:1 fn:28 chicago:1 shape:1 v:2 half:2 ith:2 sarcos:9 math:1 contribute:1 org:1 along:3 c2:2 prove:3 inside:1 introduce:1 x0:9 inter:2 mosci:1 behavior:1 mpg:2 nor:1 t... |
3,991 | 4,612 | Multi-criteria Anomaly Detection using
Pareto Depth Analysis
Ko-Jen Hsiao, Kevin S. Xu, Jeff Calder, and Alfred O. Hero III
University of Michigan, Ann Arbor, MI, USA 48109
{coolmark,xukevin,jcalder,hero}@umich.edu
Abstract
We consider the problem of identifying patterns in a data set that exhibit anomalous behavior,... | 4612 |@word nd:1 open:3 simulation:2 attainable:3 bai:1 contains:3 score:11 selecting:3 daniel:1 neighbors1:1 outperforms:2 comparing:2 yet:1 must:1 written:1 numerical:1 shape:3 designed:1 intelligence:2 selected:2 item:18 ith:2 eskin:2 detecting:2 math:1 mathematical:3 along:5 constructed:1 dn:2 barndorff:1 consists:... |
3,992 | 4,613 | A Neural Autoregressive Topic Model
Stanislas Lauly
D?epartement d?informatique
Universit?e de Sherbrooke
stanislas.lauly@usherbrooke.ca
Hugo Larochelle
D?epartement d?informatique
Universit?e de Sherbrooke
hugo.larochelle@usherbrooke.ca
Abstract
We describe a new model for learning meaningful representations of tex... | 4613 |@word version:3 advantageous:1 confirms:1 tried:1 jacob:1 contrastive:3 epartement:2 document:68 outperforms:3 activation:3 must:2 lauly:2 additive:2 realistic:1 partition:1 confirming:1 christian:1 remove:2 update:4 aside:2 generative:16 leaf:6 half:1 selected:2 intelligence:3 inspection:2 ith:2 colored:1 blei:2... |
3,993 | 4,614 | Monte Carlo Methods for Maximum Margin
Supervised Topic Models
Qixia Jiang?? , Jun Zhu?? , Maosong Sun? , and Eric P. Xing??
Department of Computer Science & Technology, Tsinghua National TNList Lab,
?
State Key Lab of Intelligent Tech. & Sys., Tsinghua University, Beijing 100084, China
?
School of Computer Science, Ca... | 4614 |@word chakraborty:1 proportion:1 nd:8 stronger:1 seek:1 simulation:1 p0:14 thereby:1 tnlist:1 reduction:1 moment:1 contains:1 exclusively:1 score:1 uma:1 seriously:1 document:19 maosong:1 existing:2 wd:5 comparing:1 must:1 written:1 j1:1 designed:1 update:5 discrimination:2 generative:2 discovering:5 plane:2 sys:... |
3,994 | 4,615 | Matrix reconstruction with the local max norm
Rina Foygel
Department of Statistics
Stanford University
rinafb@stanford.edu
Nathan Srebro
Toyota Technological Institute at Chicago
nati@ttic.edu
Ruslan Salakhutdinov
Dept. of Statistics and Dept. of Computer Science University of Toronto
rsalakhu@utstat.toronto.edu
Abs... | 4615 |@word trial:3 version:4 norm:188 nd:1 that2:1 simulation:3 decomposition:2 citeseer:1 mention:1 tr:4 existing:10 comparing:4 written:1 chicago:1 kdd:1 plot:2 designed:1 alone:1 half:2 selected:3 ith:1 prize:2 provides:1 node:1 toronto:2 location:3 org:1 five:1 unbounded:1 u2i:1 prove:3 fitting:2 introduce:4 theor... |
3,995 | 4,616 | Bandit Algorithms boost motor-task selection for
Brain Computer Interfaces
Joan Fruitet
INRIA, Sophia Antipolis
2004 Route des Lucioles
06560 Sophia Antipolis, France
joan.fruitet@inria.fr
Alexandra Carpentier
Statistical Laboratory, CMS
Wilberforce Road, Cambridge
CB3 0WB UK
a.carpentier@statslab.cam.ac.uk
R?emi Mun... | 4616 |@word neurophysiology:2 trial:1 exploitation:4 illustrating:1 eliminating:1 seems:1 proportion:2 nd:9 heterogeneously:1 open:1 grey:1 arti:2 eld:2 thereby:1 pressed:1 reduction:1 contains:1 selecting:2 chervonenkis:1 tuned:1 ours:1 outperforms:1 imaginary:38 comparing:1 activation:1 yet:2 must:2 subsequent:1 nume... |
3,996 | 4,617 | Graphical Models via Generalized Linear Models
Pradeep Ravikumar
Department of Computer Science
University of Texas at Austin
pradeepr@cs.utexas.edu
Eunho Yang
Department of Computer Science
University of Texas at Austin
eunho@cs.utexas.edu
Zhandong Liu
Department of Pediatrics-Neurology
Baylor College of Medicine
z... | 4617 |@word trial:1 version:1 seems:1 suitably:1 c0:1 hu:2 integrative:1 simulation:1 moment:4 initial:1 liu:5 configuration:1 series:1 united:1 egfr:1 tuned:1 genetic:1 interestingly:2 suppressing:1 nonparanormal:1 reynolds:1 recovered:3 tackling:1 must:1 john:1 subsequent:1 partition:5 plot:1 atlas:3 fund:1 v:2 websi... |
3,997 | 4,618 | CPRL ? An Extension of Compressive Sensing to the
Phase Retrieval Problem
Henrik Ohlsson
Division of Automatic Control, Department of Electrical Engineering,
Link?oping University, Sweden.
Department of Electrical Engineering and Computer Sciences
University of California at Berkeley, CA, USA
ohlsson@eecs.berkeley.edu
... | 4618 |@word version:3 briefly:1 middle:6 norm:7 termination:1 seek:1 simulation:3 bn:1 decomposition:6 excited:1 tr:12 solid:1 shot:1 shechtman:2 marchesini:2 liu:2 series:1 past:1 existing:2 outperforms:2 ka:1 recovered:7 com:1 steiner:1 chu:1 written:1 must:1 nanoscale:1 axk22:1 numerical:7 enables:2 plot:5 update:3 ... |
3,998 | 4,619 | 3D Social Saliency from Head-mounted Cameras
Hyun Soo Park
Carnegie Mellon University
hyunsoop@cs.cmu.edu
Eakta Jain
Texas Instruments
e-jain@ti.com
Yaser Sheikh
Carnegie Mellon University
yaser@cs.cmu.edu
Abstract
A gaze concurrence is a point in 3D where the gaze directions of two or more
people intersect. It is ... | 4619 |@word neurophysiology:1 middle:3 judgement:1 seitz:1 seek:1 p0:14 solid:2 lepetit:1 ld:4 initial:2 configuration:1 selecting:1 subjective:2 recovered:3 com:2 current:5 written:4 must:4 takeo:1 visible:3 blur:1 occludes:1 enables:3 moreno:1 farenzena:1 mounting:1 stationary:2 cue:2 half:4 device:1 website:1 takama... |
3,999 | 462 | Against Edges: Function Approximation with
Multiple Support Maps
Trevor Darrell and Alex Pentland
Vision and Modeling Group, The Media Lab
Massachusetts Institute of Technology
E15-388, 20 Ames Street
Cambridge MA, 02139
Abstract
Networks for reconstructing a sparse or noisy function often use an edge
field to segmen... | 462 |@word version:1 manageable:1 polynomial:1 nd:2 open:2 willing:1 solid:2 initial:4 selecting:1 recovered:5 luo:1 si:2 dx:2 must:1 john:1 visible:1 realistic:1 girosi:2 shape:4 plot:3 update:1 stationary:1 leaf:2 fewer:1 plane:2 detecting:1 node:4 ames:1 successive:2 x128:1 along:1 direct:2 resistive:4 fitting:1 exp... |
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