Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
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3,700 | 4,350 | Shallow vs. Deep Sum-Product Networks
Olivier Delalleau
Department of Computer Science and Operation Research
Universit?e de Montr?eal
delallea@iro.umontreal.ca
Yoshua Bengio
Department of Computer Science and Operation Research
Universit?e de Montr?eal
yoshua.bengio@umontreal.ca
Abstract
We investigate the represent... | 4350 |@word multitask:1 version:1 polynomial:8 open:2 stracuzzi:2 seek:1 contains:8 orponen:2 denoting:1 document:1 comparing:1 nt:4 yet:1 written:7 parsing:1 must:20 additive:3 partition:1 v:4 intelligence:2 fewer:1 greedy:2 item:3 parameterization:2 provides:1 pascanu:1 contribute:1 node:16 five:1 direct:1 predecesso... |
3,701 | 4,351 | On Tracking The Partition Function
Guillaume Desjardins, Aaron Courville, Yoshua Bengio
{desjagui,courvila,bengioy}@iro.umontreal.ca
D?epartement d?informatique et de recherche op?erationnelle
Universit?e de Montr?eal
Abstract
Markov Random Fields (MRFs) have proven very powerful both as density estimators and featur... | 4351 |@word version:2 seems:1 open:2 covariance:4 contrastive:1 q1:1 sgd:1 carry:1 epartement:1 initial:1 configuration:3 series:1 seriously:1 document:1 envision:1 o2:1 freitas:1 comparing:4 z2:2 si:3 yet:1 mushroom:1 gpu:1 visible:4 partition:47 subsequent:1 engendered:1 treating:2 designed:1 update:11 progressively:... |
3,702 | 4,352 | Learning Probabilistic Non-Linear Latent Variable
Models for Tracking Complex Activities
Angela Yao?
ETH Zurich
Juergen Gall
ETH Zurich
Luc Van Gool
ETH Zurich
Raquel Urtasun
TTI Chicago
{yaoa, gall, vangool}@vision.ee.ethz.ch, rurtasun@ttic.edu
Abstract
A common approach for handling the complexity and inherent ... | 4352 |@word dkr:1 norm:1 tried:1 thereby:1 tr:2 reduction:4 initial:6 liu:1 series:4 selecting:1 ours:2 outperforms:8 existing:2 current:1 comparing:2 yet:1 hou:1 additive:2 subsequent:1 chicago:1 update:6 generative:1 guess:1 yr:3 imcrbm:3 node:2 location:5 dn:1 become:1 ijcv:4 manner:1 introduce:1 crbm:5 expected:2 n... |
3,703 | 4,353 | Convergent Bounds on the Euclidean Distance
Yoonho Hwang
Hee-Kap Ahn
Department of Computer Science and Engineering
Pohang University of Science and Technology
POSTECH, Pohang, Gyungbuk, Korea(ROK)
{cypher,heekap}@postech.ac.kr
Abstract
Given a set V of n vectors in d-dimensional space, we provide an efficient method... | 4353 |@word madelon:1 norm:1 vldb:1 reduction:1 exclusively:1 karger:1 ours:1 spambase:1 bitwise:2 current:1 com:1 beygelzimer:1 attracted:1 bd:1 must:1 happen:1 predetermined:1 designed:1 update:2 selected:2 ubuntu:1 plane:5 ruhl:1 ith:2 farther:2 filtered:3 provides:8 zhang:1 mathematical:1 dn:4 become:2 symposium:2 ... |
3,704 | 4,354 | An ideal observer model for identifying
the reference frame of objects
Joseph L. Austerweil
Department of Psychology
University of California, Berkeley
Berkeley, CA 94720
Joseph.Austerweil@gmail.com
Abram L. Friesen
Department of Computer Science and Engineering
University of Washington
Seattle, WA 98195
afriesen@cs.... | 4354 |@word trial:6 norm:1 proportion:1 open:1 instruction:1 confirms:2 covariance:1 brightness:1 solid:1 configuration:2 contains:2 efficacy:1 selecting:1 document:3 existing:1 current:2 com:1 gmail:1 yet:1 assigning:1 tilted:3 realistic:1 partition:8 chicago:1 shape:3 update:1 v:4 cue:9 generative:2 selected:1 item:1... |
3,705 | 4,355 | From Stochastic Nonlinear Integrate-and-Fire to
Generalized Linear Models
Skander Mensi
School of Computer and Communication Sciences and Brain-Mind Institute
Ecole Polytechnique Federale de Lausanne
1015 Lausanne EPFL, SWITZERLAND
skander.mensi@epfl.ch
Richard Naud
School of Computer and Communication Sciences and Bra... | 4355 |@word neurophysiology:3 version:1 middle:1 polynomial:2 wiesel:1 nd:1 pulse:1 moment:1 ecole:3 current:11 comparing:1 must:1 subsequent:1 numerical:2 shape:6 plot:2 half:2 intelligence:1 filtered:1 colored:2 provides:2 psth:9 sigmoidal:1 mathematical:1 differential:2 become:1 qualitative:1 fitting:1 behavior:1 br... |
3,706 | 4,356 | A Machine Learning Approach to Predict Chemical
Reactions
Matthew A. Kayala Pierre Baldi?
Institute of Genomics and Bioinformatics
School of Information and Computer Sciences
University of California, Irvine
Irvine, CA 92697
{mkayala,pfbaldi}@ics.uci.edu
Abstract
Being able to predict the course of arbitrary chemical ... | 4356 |@word mri:1 rising:1 proportion:1 chakraborty:1 nd:1 open:2 simulation:2 recapitulate:2 initial:2 configuration:1 substitution:3 score:1 cyclic:1 series:1 past:1 reaction:141 existing:3 recovered:1 current:1 com:1 surprising:1 neuneier:1 activation:1 must:2 parsing:1 partition:1 hofmann:1 ainen:1 update:1 metabol... |
3,707 | 4,357 | How biased are maximum entropy models?
Jakob H. Macke
Gatsby Computational Neuroscience Unit
University College London, UK
jakob@gatsby.ucl.ac.uk
Iain Murray
School of Informatics
University of Edinburgh, UK
i.murray@ed.ac.uk
Peter E. Latham
Gatsby Computational Neuroscience Unit
University College London, UK
pel@gat... | 4357 |@word determinant:2 version:1 seems:1 nd:1 open:1 hu:1 simulation:8 tkacik:1 solid:1 reduction:2 moment:7 series:1 interestingly:1 si:9 perturbative:5 must:2 written:3 realistic:2 numerical:11 partition:2 plot:1 aside:1 record:1 characterization:3 mathematical:2 along:1 direct:2 become:2 differential:3 symposium:... |
3,708 | 4,358 | Gaussian process modulated renewal processes
Yee Whye Teh
Gatsby Computational Neuroscience Unit
University College London
ywteh@gatsby.ucl.ac.uk
Vinayak Rao
Gatsby Computational Neuroscience Unit
University College London
vrao@gatsby.ucl.ac.uk
Abstract
Renewal processes are generalizations of the Poisson process on... | 4358 |@word neurophysiology:1 cox:2 inversion:1 polynomial:1 simulation:4 covariance:4 subordinating:3 series:2 ours:1 interestingly:1 current:1 discretization:6 incidence:1 comparing:1 elliptical:4 ka:2 must:2 john:1 additive:1 interspike:1 shape:20 noninformative:2 analytic:1 plot:2 resampling:1 stationary:1 generati... |
3,709 | 4,359 | Beating SGD: Learning SVMs in Sublinear Time
Elad Hazan
Tomer Koren
Technion, Israel Institute of Technology
Haifa, Israel 32000
{ehazan@ie,tomerk@cs}.technion.ac.il
Nathan Srebro
Toyota Technological Institute
Chicago, Illinois 60637
nati@ttic.edu
Abstract
We present an optimization approach for linear SVMs based o... | 4359 |@word version:3 norm:13 nd:1 seek:1 unbeatable:2 q1:1 sgd:18 reduction:1 initial:1 contains:1 woodruff:1 tuned:1 current:1 comparing:1 surprising:1 tackling:1 yet:1 must:3 bd:3 chicago:1 update:25 depict:1 v:3 greedy:1 selected:1 mccallum:1 parametrization:1 short:1 node:1 accessed:1 along:2 become:1 differential... |
3,710 | 436 | REMARKS ON INTERPOLATION AND
RECOGNITION USING NEURAL NETS
Eduardo D. Sontag?
SYCON - Center for Systems and Control
Rutgers University
New Brunswick, NJ 08903
Abstract
We consider different types of single-hidden-Iayer feedforward nets: with
or without direct input to output connections, and using either threshold o... | 436 |@word mild:1 version:2 stronger:1 suitably:1 open:5 t_:1 ld:2 chervonenkis:1 denoting:1 comparing:1 activation:6 si:2 yet:2 numerical:1 additive:1 j1:8 partition:1 analytic:2 wanted:2 v:1 plane:1 xk:1 funahashi:2 lr:4 colored:1 math:1 sigmoidal:10 simpler:1 lor:1 direct:14 chester:2 prove:4 inside:1 introduce:1 be... |
3,711 | 4,360 | Co-regularized Multi-view Spectral Clustering
Abhishek Kumar?
Dept. of Computer Science
University of Maryland,
College Park, MD
abhishek@cs.umd.edu
Piyush Rai?
Dept. of Computer Science
University of Utah,
Salt Lake City, UT
piyush@cs.utah.edu
Hal Daum?e III
Dept. of Computer Science
University of Maryland,
College ... | 4360 |@word repository:3 polynomial:1 norm:4 seek:1 crucially:1 covariance:2 tr:16 reduction:2 contains:1 efficacy:1 score:4 eigensolvers:1 document:8 past:3 outperforms:1 written:2 additive:1 subsequent:1 partition:2 informative:3 hofmann:1 plot:3 pursued:1 intelligence:2 weighing:2 concat:1 blei:1 provides:1 node:3 z... |
3,712 | 4,361 | Spatial distance dependent Chinese restaurant
processes for image segmentation
Soumya Ghosh1 , Andrei B. Ungureanu2 , Erik B. Sudderth1 , and David M. Blei3
1
Department of Computer Science, Brown University, {sghosh,sudderth}@cs.brown.edu
2
Morgan Stanley, andrei.b.ungureanu@gmail.com
3
Department of Computer Science... | 4361 |@word version:3 heuristically:1 covariance:3 simplifying:1 brightness:1 accommodate:1 initial:2 configuration:1 series:2 contains:1 tuned:1 outperforms:1 existing:2 elliptical:1 com:1 current:1 comparing:1 gmail:1 scatter:2 must:1 distant:1 partition:13 informative:1 shape:2 remove:2 plot:3 resampling:1 stationar... |
3,713 | 4,362 | Fast and Accurate k-llleans For Large Datasets
Michael Shindler
School of EECS
Oregon State University
shindler@eecs.oregonstate.edu
Alex Wong
Department of Computer Science
UC Los Angeles
alexw@seas.ucla.edu
Adam Meyerson
Google, Inc.
Mountain View, CA
awmeyerson@google.com
Abstract
Clustering is a popular problem... | 4362 |@word repository:1 version:4 polynomial:1 compression:1 stronger:1 disk:5 open:1 iki:2 scg:2 paid:1 contains:1 series:1 selecting:2 daniel:1 denoting:1 ours:1 outperforms:1 existing:2 mishra:4 current:3 com:1 comparing:1 must:5 dde:1 sergei:1 realistic:1 ranka:1 kdd:1 christian:6 remove:1 designed:1 drop:1 plot:1... |
3,714 | 4,363 | Scalable Training of Mixture Models via Coresets
Dan Feldman
MIT
Matthew Faulkner
Caltech
Andreas Krause
ETH Zurich
Abstract
How can we train a statistical mixture model on a massive data set? In this paper, we
show how to construct coresets for mixtures of Gaussians and natural generalizations. A
coreset is a weig... | 4363 |@word mild:1 version:2 polynomial:5 norm:1 open:1 d2:2 closure:1 crucially:1 covariance:7 decomposition:1 pick:2 solid:1 moment:1 reduction:3 initial:1 contains:2 existing:1 csn:4 ka:4 si:2 dx:1 must:1 additive:2 partition:1 concatenate:1 subsequent:1 shape:1 remove:2 hypothesize:1 update:3 v:2 spec:3 selected:2 ... |
3,715 | 4,364 | Two is better than one: distinct roles for familiarity
and recollection in retrieving palimpsest memories
Cristina Savin1
cs664@cam.ac.uk
Peter Dayan2
dayan@gatsby.ucl.ac.uk
M?at?e Lengyel1
m.lengyel@eng.cam.ac.uk
1
Computational & Biological Learning Lab, Dept. of Engineering, University of Cambridge, UK
2
Gatsby ... | 4364 |@word version:2 hippocampus:7 anterograde:2 confirms:1 seek:1 simulation:1 lobe:2 eng:1 decomposition:1 paulsen:1 initial:1 cristina:1 efficacy:12 existing:2 current:5 anterior:1 activation:3 yet:1 additive:1 partition:2 informative:1 plasticity:2 shape:4 update:1 medial:2 v:1 stationary:3 generative:1 cue:8 half... |
3,716 | 4,365 | Infinite Latent SVM for Classification and
Multi-task Learning
Jun Zhu? , Ning Chen? , and Eric P. Xing?
Dept. of Computer Science & Tech., TNList Lab, Tsinghua University, Beijing 100084, China
?
Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
dcszj@tsinghua.edu.cn;chenn07@mails.thu.... | 4365 |@word multitask:3 repository:1 briefly:1 loading:1 efh:6 minus:1 harder:1 tnlist:1 moment:1 score:6 existing:3 wd:1 jaynes:1 kdd:1 update:3 n0:3 discrimination:3 stationary:1 discovering:2 website:1 mccallum:2 record:1 rch:1 blei:1 provides:1 authority:1 node:1 hsv:1 zhang:2 unbounded:4 direct:2 beta:2 consists:5... |
3,717 | 4,366 | From Bandits to Experts: On the Value of
Side-Observations
Ohad Shamir
Microsoft Research New England
USA
ohadsh@microsoft.com
Shie Mannor
Department of Electrical Engineering
Technion, Israel
shie@ee.technion.ac.il
Abstract
We consider an adversarial online learning setting where a decision maker can
choose an actio... | 4366 |@word exploitation:1 briefly:1 achievable:1 seems:1 open:1 d2:1 crucially:1 git:6 forecaster:2 decomposition:1 attainable:4 pick:1 reduction:1 cyclic:1 series:1 ours:1 existing:3 current:1 com:1 contextual:2 comparing:1 nt:1 si:5 expq:1 chu:1 must:3 readily:1 partition:14 treating:1 greedy:1 selected:2 provides:5... |
3,718 | 4,367 | On Strategy Stitching in Large Extensive Form
Multiplayer Games
Richard Gibson and Duane Szafron
Department of Computing Science, University of Alberta
Edmonton, Alberta, T6G 2E8, Canada
{rggibson | dszafron}@ualberta.ca
Abstract
Computing a good strategy in a large extensive form game often demands an extraordinary ... | 4367 |@word h:1 private:7 version:3 innovates:1 stronger:3 szafron:7 rayner:1 abou:4 versatile:1 contains:1 score:2 prefix:4 current:1 yet:1 must:2 partition:12 seeding:1 remove:1 aside:1 alone:3 fewer:1 beginning:1 pointer:1 coarse:4 node:2 location:1 earnings:6 org:2 firstly:2 five:1 along:1 constructed:1 supply:1 pa... |
3,719 | 4,368 | Facial Expression Transfer with Input-Output
Temporal Restricted Boltzmann Machines
Matthew D. Zeiler1 , Graham W. Taylor1 , Leonid Sigal2 , Iain Matthews2 , and Rob Fergus1
1
Department of Computer Science, New York University, New York, NY 10012
2
Disney Research, Pittsburgh, PA 15213
Abstract
We present a type of ... | 4368 |@word multitask:1 trial:2 briefly:1 middle:2 seek:2 tried:2 covariance:1 contrastive:3 wjf:3 configuration:4 series:4 generatively:1 selecting:2 pub:1 document:1 interestingly:2 past:3 existing:1 outperforms:4 current:13 contextual:1 com:1 activation:1 yet:1 assigning:1 realistic:1 visible:8 concatenate:1 additiv... |
3,720 | 4,369 | An Unsupervised Decontamination Procedure
For Improving The Reliability Of Human Judgments
Michael C. Mozer,? Benjamin Link,? Harold Pashler?
?
Dept. of Computer Science, University of Colorado
?
Dept. of Psychology, UCSD
Abstract
Psychologists have long been struck by individuals? limitations in expressing their
int... | 4369 |@word trial:47 faculty:1 judgement:1 seems:1 open:1 simulation:5 tried:1 accounting:3 thereby:2 mention:1 solid:1 shot:1 reduction:14 bai:4 liu:2 series:7 selecting:1 united:1 reynolds:2 subjective:3 mumma:3 existing:1 current:7 recovered:10 comparing:1 contextual:3 surprising:1 com:1 yet:1 must:1 olive:1 readily... |
3,721 | 437 | Further Studies of a Model for the
Development and Regeneration
of Eye-Brain Maps
J.D. Cowan & A.E. Friedman
Department of Mathematics, Committee on
Neurobiology, and Brain Research Institute,
The University of Chicago, 5734 S. Univ. Ave.,
Chicago, Illinois 60637
Abstract
We describe a computational model of the deve... | 437 |@word compression:7 simulation:13 lobe:1 fonn:1 innervating:1 carry:1 series:1 fragment:2 existing:1 current:1 nt:3 anne:1 attracted:1 physiol:1 subsequent:2 chicago:3 plasticity:2 occludes:1 occlude:3 half:14 cue:1 cook:2 ith:4 compo:1 provides:1 mathematical:1 along:1 differential:1 rohrer:1 edelman:2 pathway:1 ... |
3,722 | 4,370 | High-Dimensional Graphical Model Selection:
Tractable Graph Families and Necessary Conditions
Anima Anandkumar
Dept. of EECS,
Univ. of California
Irvine, CA, 92697
a.anandkumar@uci.edu
Vincent Y.F. Tan
Dept. of ECE,
Univ. of Wisconsin
Madison, WI, 53706.
vtan@wisc.edu
Alan S. Willsky
Dept. of EECS
Massachusetts Inst... | 4370 |@word mild:2 determinant:1 version:1 norm:1 stronger:1 seek:1 covariance:5 harder:1 liu:3 contains:1 karger:1 united:1 recovered:1 surprising:1 john:1 partition:1 limp:1 remove:1 fund:1 greedy:1 vanishing:1 short:6 fa9550:2 loworder:1 parameterizations:1 node:30 characterization:1 allerton:1 mathematical:2 along:... |
3,723 | 4,371 | Information Rates and Optimal Decoding in Large
Neural Populations
Kamiar Rahnama Rad Liam Paninski
Department of Statistics, Columbia University
{kamiar,liam}@stat.columbia.edu
http://www.stat.columbia.edu/?liam/research/pubs/kamiar-ss-info.pdf
Abstract
Many fundamental questions in theoretical neuroscience involve ... | 4371 |@word mild:2 version:4 middle:1 open:1 simulation:3 covariance:13 carry:2 series:1 pub:1 tuned:1 nt:1 written:1 numerical:2 informative:3 motor:1 stationary:1 nervous:1 gear:1 ith:1 short:5 filtered:2 location:1 simpler:3 mathematical:1 along:1 direct:2 become:1 autocorrelation:1 introduce:1 manner:1 inter:1 inde... |
3,724 | 4,372 | Evaluating the inverse decision-making approach to
preference learning
Alan Jern
Department of Psychology
Carnegie Mellon University
ajern@cmu.edu
Christopher G. Lucas
Department of Psychology
Carnegie Mellon University
cglucas@andrew.cmu.edu
Charles Kemp
Department of Psychology
Carnegie Mellon University
ckemp@cmu... | 4372 |@word stronger:3 logit:4 nd:1 solid:4 contains:1 past:1 existing:1 subjective:1 current:2 comparing:1 surprising:2 scatter:1 dx:2 must:3 john:1 fn:1 additive:1 subsequent:1 informative:2 candy:7 evans:1 designed:1 plot:4 fund:1 generative:4 guess:1 item:2 reciprocal:1 smith:2 colored:1 provides:9 location:3 prefe... |
3,725 | 4,373 | Target Neighbor Consistent Feature Weighting
for Nearest Neighbor Classification
Ichiro Takeuchi
Department of Engineering
Nagoya Institute of Technology
takeuchi.ichiro@nitech.ac.jp
Masashi Sugiyama
Department of Computer Science
Tokyo Institute of Technology
sugi@cs.titech.ac.jp
Abstract
We consider feature select... | 4373 |@word trial:1 kulis:1 briefly:1 middle:1 eliminating:1 nd:12 termination:1 tamayo:1 gish:1 cytogenetic:1 cla:1 reduction:1 initial:2 selecting:1 spambase:1 existing:5 current:5 goldberger:1 assigning:1 must:8 written:2 distant:2 numerical:1 update:7 discrimination:1 intelligence:1 selected:5 provides:1 five:1 alo... |
3,726 | 4,374 | On the Completeness of First-Order Knowledge
Compilation for Lifted Probabilistic Inference
Guy Van den Broeck
Department of Computer Science, Katholieke Universiteit Leuven
Celestijnenlaan 200A, B-3001 Heverlee, Belgium
guy.vandenbroeck@cs.kuleuven.be
Abstract
Probabilistic logics are receiving a lot of attention to... | 4374 |@word version:2 briefly:1 polynomial:5 seems:1 adnan:1 closure:1 decomposition:2 dramatic:1 substitution:3 contains:16 existing:4 comparing:2 yet:2 conjunctive:1 written:2 must:4 subsequent:1 remove:2 braz:1 leaf:3 amir:1 mln:6 completeness:11 node:9 mathematical:1 constructed:1 c2:2 consists:2 prove:1 dan:2 darw... |
3,727 | 4,375 | Data Skeletonization via Reeb Graphs
Xiaoyin Ge
Issam Safa
Mikhail Belkin
Yusu Wang
Computer Science and Engineering Department
The Ohio State University
gex,safa,mbelkin,yusu@cse.ohio-state.edu
Abstract
Recovering hidden structure from complex and noisy non-linear data is one of the
most fundamental problems in ... | 4375 |@word version:7 middle:2 stronger:1 open:2 adrian:1 simulation:10 reduction:5 series:2 contains:3 interestingly:1 past:1 existing:4 steiner:1 current:1 com:1 comparing:1 yet:2 assigning:1 written:3 mesh:2 subsequent:1 happen:1 zeger:1 shape:2 remove:2 plot:1 designed:1 treating:1 device:1 website:1 plane:1 beginn... |
3,728 | 4,376 | Differentially Private M-Estimators
Lei, Jing
Department of Statistics
Carnegie Mellon University
Pittsburgh, PA 15213
jinglei@andrew.cmu.edu
Abstract
This paper studies privacy preserving M-estimators using perturbed histograms.
The proposed approach allows the release of a wide class of M-estimators with
both diffe... | 4376 |@word mild:1 trial:1 private:24 version:3 stronger:1 nd:2 d2:3 simulation:1 decomposition:1 ronchetti:2 accommodate:2 moment:1 series:1 contains:1 existing:2 comparing:1 yet:1 dx:9 must:1 john:2 additive:5 partition:1 drop:1 ith:1 smith:2 stahel:1 record:3 provides:1 location:2 attack:1 c2:2 direct:2 differential... |
3,729 | 4,377 | Learning Eigenvectors for Free
Wouter M. Koolen
Royal Holloway and CWI
Wojtek Kot?owski
Centrum Wiskunde & Informatica
Manfred K. Warmuth
UC Santa Cruz
wouter@cs.rhul.ac.uk
kotlowsk@cwi.nl
manfred@cse.ucsc.edu
Abstract
We extend the classical problem of predicting a sequence of outcomes from a finite alphabet to... | 4377 |@word trial:13 version:6 compression:2 achievable:2 seems:2 stronger:1 nd:1 open:4 trofimov:4 calculus:3 decomposition:2 simplifying:1 incurs:4 tr:35 harder:2 reduction:1 bai:4 score:5 past:1 current:1 si:22 yet:1 must:2 john:1 cruz:1 designed:1 update:4 intelligence:1 warmuth:7 smith:1 core:1 manfred:2 recompute... |
3,730 | 4,378 | EigenNet: A Bayesian hybrid of generative and
conditional models for sparse learning
Feng Yan
Computer Science Dept.
Purdue University
West Lafayette, IN 47907, USA
Yuan Qi
Computer Science and Statistics Depts.
Purdue University
West Lafayette, IN 47907, USA
Abstract
For many real-world applications, we often need ... | 4378 |@word mild:1 version:1 hippocampus:1 c0:4 covariance:8 jacob:2 edric:1 initial:1 contains:3 score:4 selecting:4 series:1 wj2:1 genetic:2 tuned:1 outperforms:7 readily:1 informative:1 enables:1 remove:3 update:4 generative:13 selected:4 intelligence:2 runze:1 provides:1 characterization:1 yuan:2 pathway:1 combine:... |
3,731 | 4,379 | Reconstructing Patterns of Information Diffusion
from Incomplete Observations ?
Jon Kleinberg
Department of Computer Science
Cornell University
Ithaca, NY 14853
Flavio Chierichetti
Department of Computer Science
Cornell University
Ithaca, NY 14853
David Liben-Nowell
Department of Computer Science
Carleton College
No... | 4379 |@word mild:2 version:6 briefly:1 addressee:2 extinction:1 open:1 cha:1 simulation:2 asks:1 contains:2 exclusively:1 existing:1 current:1 yet:1 invitation:1 must:6 numerical:1 visible:1 subsequent:1 partition:1 v:1 half:3 leaf:17 pursued:1 item:8 inspection:1 beginning:1 dover:1 provides:2 node:102 zhang:1 unbound... |
3,732 | 438 | Grouping Contours by Iterated Pairing Network
Amnon Shashua
M.I.T. Artificial Intelligence Lab., NE43-737
and Department of Brain and Cognitive Science
Cambridge, MA 02139
Shimon Ullman
Abstract
We describe in this paper a network that performs grouping of image contours. The input to the net are fragments of image ... | 438 |@word collinearity:1 polynomial:1 achievable:2 nd:1 lwk:1 propagate:2 brightness:2 tr:1 contains:1 fragment:6 selecting:3 past:1 must:2 fn:9 partition:1 shape:1 designed:1 update:3 discrimination:2 intelligence:1 selected:1 plane:1 xk:2 provides:1 math:1 node:17 height:1 along:4 pairing:42 consists:1 manner:2 beha... |
3,733 | 4,380 | Expressive Power and Approximation Errors of
Restricted Boltzmann Machines
1
? 1 , Johannes Rauh1 , and Nihat Ay1,2
Guido F. Montufar
Max Planck Institute for Mathematics in the Sciences, Inselstra?e 22 04103 Leipzig, Germany
2
Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501, USA
{montufar,jrauh,n... | 4380 |@word nihat:2 version:1 pw:4 closure:4 decomposition:2 contrastive:3 euclidian:1 initial:1 contains:5 selecting:1 past:1 must:2 written:3 john:1 visible:15 partition:28 j1:1 motor:1 leipzig:2 progressively:1 stationary:1 generative:1 greedy:1 intelligence:1 item:1 zahedi:1 xk:1 short:1 math:3 toronto:1 symposium:... |
3,734 | 4,381 | Randomized Algorithms for Comparison-based
Search
Dominique Tschopp
AWK Group
Bern, Switzerland
dominique.tschopp@gmail.com
Suhas Diggavi
University of California Los Angeles (UCLA)
Los Angeles, CA 90095
suhasdiggavi@ucla.edu
Soheil Mohajer
Princeton University
Princeton, NJ 08544
smohajer@princeton.edu
Payam Delgosh... | 4381 |@word smirnov:1 nd:11 termination:1 willing:1 d2:5 dominique:2 r:5 decomposition:3 invoking:1 asks:2 thereby:1 recursively:1 configuration:2 contains:3 karger:1 ecole:1 existing:3 com:1 si:36 gmail:1 assigning:1 must:2 numerical:2 shape:1 designed:1 hash:1 intelligence:1 ruhl:1 dissertation:1 characterization:6 p... |
3,735 | 4,382 | A Collaborative Mechanism for Crowdsourcing
Prediction Problems
Rafael M. Frongillo
Division of Computer Science
University of California at Berkeley
raf@cs.berkeley.edu
Jacob Abernethy
Division of Computer Science
University of California at Berkeley
jake@cs.berkeley.edu
Abstract
Machine Learning competitions such a... | 4382 |@word private:3 briefly:1 version:2 compression:2 seems:1 norm:2 open:2 adrian:1 jacob:1 paid:4 stateless:1 profit:14 wagering:3 moment:1 initial:6 current:5 com:3 yet:6 must:7 update:7 alone:1 intelligence:1 guess:2 ith:2 prize:10 smith:1 institution:1 provides:3 characterization:1 intellectual:1 contribute:1 pr... |
3,736 | 4,383 | Sequence learning with hidden units
in spiking neural networks
Johanni Brea, Walter Senn and Jean-Pascal Pfister
Department of Physiology
University of Bern
B?uhlplatz 5
CH-3012 Bern, Switzerland
{brea, senn, pfister}@pyl.unibe.ch
Abstract
We consider a statistical framework in which recurrent networks of spiking neu... | 4383 |@word trial:1 version:4 pw:5 contrastive:2 paulsen:1 minus:2 harder:1 cius:1 moment:1 initial:3 series:1 united:1 past:5 recovered:2 soules:1 must:1 written:1 john:1 realistic:4 visible:37 plasticity:7 drop:1 update:1 selected:1 beginning:1 filtered:1 characterization:1 sigmoidal:1 mathematical:2 differential:1 i... |
3,737 | 4,384 | Efficient coding of natural images with a population
of noisy Linear-Nonlinear neurons
Yan Karklin and Eero P. Simoncelli
Howard Hughes Medical Institute and
Center for Neural Science
New York University
New York, NY 10003
{yan.karklin, eero.simoncelli}@nyu.edu
Abstract
Efficient coding provides a powerful principle ... | 4384 |@word neurophysiology:2 trial:2 version:1 norm:2 grey:1 simulation:1 linearized:1 covariance:7 solid:1 shading:1 reduction:1 initial:1 series:2 contains:1 genetic:1 current:2 dx:1 must:3 realistic:6 numerical:3 blur:1 informative:1 shape:3 analytic:1 plot:1 interpretable:1 update:5 generative:1 fewer:2 half:2 met... |
3,738 | 4,385 | Efficient Offline Communication Policies for
Factored Multiagent POMDPs
Matthijs T.J. Spaan
Delft University of Technology
Delft, The Netherlands
m.t.j.spaan@tudelft.nl
Jo?ao V. Messias
Institute for Systems and Robotics
Instituto Superior T?ecnico
Lisbon, Portugal
jmessias@isr.ist.utl.pt
Pedro U. Lima
Institute for... | 4385 |@word illustrating:1 version:1 achievable:4 advantageous:1 bf:4 d2:2 propagate:1 decomposition:1 attainable:1 thereby:1 carry:2 reduction:1 initial:1 contains:3 hereafter:1 selecting:1 daniel:1 existing:1 must:5 bd:1 portuguese:1 realistic:1 partition:3 shlomo:2 update:2 fund:1 v:1 stationary:2 intelligence:5 sel... |
3,739 | 4,386 | Robust Lasso with missing and grossly corrupted
observations
Nam H. Nguyen
Johns Hopkins University
nam@jhu.edu
Nasser M. Nasrabadi
U.S. Army Research Lab
nasser.m.nasrabadi.civ@mail.mil
Trac D. Tran
Johns Hopkins University
trac@jhu.edu
Abstract
This paper studies the problem of accurately recovering a sparse vector... | 4386 |@word trial:1 compression:1 norm:7 seems:1 justice:1 c0:1 proportion:1 seek:2 simulation:5 covariance:11 p0:1 decomposition:1 contains:1 series:2 selecting:1 past:1 recovered:3 current:1 surprising:1 attracted:1 must:2 john:2 numerical:1 civ:1 implying:1 half:1 selected:2 intelligence:1 accordingly:1 sys:1 provid... |
3,740 | 4,387 | Variational Learning for Recurrent Spiking Networks
Danilo Jimenez Rezende
Brain Mind Institute
?
Ecole
Polytechnique F?ed?erale de Lausanne
1015 Lausanne EPFL, Switzerland
danilo.rezende@epfl.ch
Daan Wierstra
School of Computer and Communication Sciences, Brain Mind Institute
?
Ecole
Polytechnique F?ed?erale de Lausan... | 4387 |@word trial:1 illustrating:1 version:1 stronger:1 open:1 hu:1 confirms:1 simulation:15 covariance:1 citeseer:1 moment:1 efficacy:2 united:2 jimenez:1 daniel:3 ecole:3 ording:3 cleared:1 past:2 blank:1 current:1 dx:1 reminiscent:2 written:3 john:1 visible:1 plasticity:16 shape:2 enables:1 plot:1 update:4 stationar... |
3,741 | 4,388 | Prediction strategies without loss
Rina Panigrahy
Microsoft Research Silicon Valley
Mountain View, CA
rina@microsoft.com
Michael Kapralov
Stanford University
Stanford, CA
kapralov@stanford.edu
Abstract
Consider a sequence of bits where we are trying to predict the next bit from the
previous bits. Assume we are allow... | 4388 |@word repository:1 version:7 norm:1 seems:1 open:2 guarding:1 incurs:3 profit:1 minus:1 solid:1 ours:1 com:1 comparing:1 surprising:1 yet:1 dx:1 shape:4 plot:1 v:1 implying:1 warmuth:2 short:1 provides:1 boosting:5 org:1 simpler:1 mathematical:1 differential:1 focs:1 prove:4 specialize:1 combine:7 market:1 expect... |
3,742 | 4,389 | An Exact Algorithm for F-Measure Maximization
?
Krzysztof Dembczynski
Institute of Computing Science
Pozna?n University of Technology
Pozna?n, 60-695 Poland
kdembczynski@cs.put.poznan.pl
Willem Waegeman
Mathematical Modelling, Statistics
and Bioinformatics, Ghent University
Ghent, 9000 Belgium
willem.waegeman@ugent.b... | 4389 |@word trial:1 repository:1 version:6 pw:2 polynomial:5 seems:4 briefly:1 pcc:14 nd:1 open:1 pick:1 contains:2 score:1 daniel:1 interestingly:2 outperforms:2 existing:4 current:2 surprising:2 luo:1 yet:1 john:2 ronald:1 hofmann:1 plot:2 unacceptably:1 desktop:1 mccallum:2 herbrich:1 zhang:1 five:1 mathematical:1 a... |
3,743 | 439 | The Devil and the Network:
What Sparsity Implies to Robustness and
Memory
Sanjay Biswas and Santosh S. Venkatesh
Department of Electrical Engineering
University of Pennsylvania
Philadelphia, PA 19104
Abstract
Robustness is a commonly bruited property of neural networks; in particular, a folk theorem in neural computa... | 439 |@word polynomial:2 bn:3 invoking:2 carry:1 current:1 surprising:2 tenet:1 partition:2 dupont:1 drop:1 update:1 liapunov:2 indicative:1 beginning:1 short:1 multiset:6 ik:1 consists:1 introduce:1 inter:3 expected:2 examine:1 considering:1 ua:1 notation:1 what:1 substantially:1 guarantee:1 every:1 uk:1 grant:2 appear... |
3,744 | 4,390 | H OGWILD !: A Lock-Free Approach to Parallelizing
Stochastic Gradient Descent
Feng Niu
leonn@cs.wisc.edu
Benjamin Recht
brecht@cs.wisc.edu
Christopher R?e
chrisre@cs.wisc.edu
Stephen J. Wright
swright@cs.wisc.edu
Computer Sciences Department
University of Wisconsin-Madison
Madison, WI 53706
Abstract
Stochastic Grad... | 4390 |@word middle:1 version:4 eliminating:1 achievable:1 norm:2 nd:1 disk:5 dekel:1 instruction:1 vldb:1 sgd:16 mention:2 configuration:1 contains:1 pub:1 fa8750:1 outperforms:4 kx0:2 current:1 comparing:1 com:2 yet:1 must:4 belmont:2 numerical:2 partition:1 kdd:6 designed:1 plot:1 update:11 juditsky:1 alone:1 half:1 ... |
3,745 | 4,391 | Learning unbelievable probabilities
Xaq Pitkow
Department of Brain and Cognitive Science
University of Rochester
Rochester, NY 14607
xaq@neurotheory.columbia.edu
Yashar Ahmadian
Center for Theoretical Neuroscience
Columbia University
New York, NY 10032
ya2005@columbia.edu
Ken D. Miller
Center for Theoretical Neurosci... | 4391 |@word stronger:2 pseudomoment:2 grey:1 simulation:1 covariance:2 contrastive:1 thereby:1 solid:1 kappen:2 moment:6 substitution:1 initial:3 loeliger:1 existing:1 current:1 paramagnetic:1 surprising:1 si:2 yet:6 must:4 happen:1 pseudomarginals:14 remove:1 stationary:4 intelligence:6 selected:2 parameterization:1 m... |
3,746 | 4,392 | Learning a Distance Metric from a Network
Blake Shaw?
Computer Science Dept.
Columbia University
Bert Huang?
Computer Science Dept.
Columbia University
Tony Jebara
Computer Science Dept.
Columbia University
blake@cs.columbia.edu
bert@cs.columbia.edu
jebara@cs.columbia.edu
Abstract
Many real-world networks are de... | 4392 |@word faculty:1 version:1 norm:5 seems:1 d2:3 bn:4 decomposition:1 sgd:3 tr:11 lightweight:1 series:1 zij:1 document:2 interestingly:1 horvitz:1 recovered:1 must:2 written:4 distant:1 kdd:2 remove:2 plot:2 generative:1 intelligence:2 discovering:1 plane:5 xk:4 desktop:1 geyer:1 realizing:1 blei:2 provides:1 node:... |
3,747 | 4,393 | Predicting response time and error rates in visual
search
Bo Chen
Caltech
bchen3@caltech.edu
Vidhya Navalpakkam
Yahoo! Research
nvidhya@yahoo-inc.com
Pietro Perona
Caltech
perona@caltech.edu
Abstract
A model of human visual search is proposed. It predicts both response time (RT)
and error rates (RT) as a function of... | 4393 |@word middle:1 open:2 simulation:4 crucially:1 simplifying:1 minus:1 harder:1 carry:1 valois:2 series:1 tuned:4 existing:1 reaction:1 com:1 comparing:1 discretization:1 anne:1 mushroom:1 yet:1 must:3 happen:1 informative:1 interspike:1 shape:4 motor:1 plot:2 v:10 grass:1 cue:2 discrimination:5 item:10 rts:3 fpr:1... |
3,748 | 4,394 | Kernel Embeddings of Latent Tree Graphical Models
Le Song
College of Computing
Georgia Institute of Technology
lsong@cc.gatech.edu
Ankur P. Parikh
School of Computer Science
Carnegie Mellon University
apparikh@cs.cmu.edu
Eric P. Xing
School of Computer Science
Carnegie Mellon University
epxing@cs.cmu.edu
Abstract
L... | 4394 |@word trial:2 determinant:7 repository:2 proportion:2 nd:1 adrian:1 simulation:1 covariance:15 decomposition:5 pick:1 recursively:1 carry:1 reduction:1 liu:1 series:1 document:1 rkhs:3 nonparanormal:8 outperforms:1 existing:5 diagonalized:1 recovered:2 err:1 yet:1 written:1 subsequent:1 additive:4 plot:1 update:6... |
3,749 | 4,395 | A Model for Temporal Dependencies
in Event Streams
Asela Gunawardana
Microsoft Research
One Microsoft Way
Redmond, WA 98052
aselag@microsoft.com
Christopher Meek
Microsoft Research
One Microsoft Way
Redmond, WA 98052
meek@microsoft.com
Puyang Xu
ECE Dept. & CLSP
Johns Hopkins University
Baltimore, MD 21218
puyangxu@j... | 4395 |@word repository:1 open:1 d2:6 fifteen:1 solid:1 harder:1 recursively:1 carry:2 contains:4 selecting:2 past:8 reaction:1 current:1 com:2 recovered:2 assigning:1 written:3 vere:1 john:3 timestamps:3 christian:3 designed:1 treating:1 comn:1 v:1 greedy:2 leaf:4 website:1 sys:1 filtered:1 provides:1 math:2 node:7 lsm... |
3,750 | 4,396 | Iterative Learning for Reliable Crowdsourcing
Systems
David R. Karger
Sewoong Oh
Devavrat Shah
Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Abstract
Crowdsourcing systems, in which tasks are electronically distributed to numerous
?information piece-workers?, have eme... | 4396 |@word mild:1 version:1 manageable:1 polynomial:1 proportion:1 tedious:1 decomposition:1 paid:2 pick:1 shot:2 harder:1 bck:1 moment:1 initial:1 configuration:3 celebrated:1 series:2 karger:2 zij:1 denoting:1 bc:2 outperforms:1 subjective:2 existing:1 comparing:2 surprising:1 si:13 assigning:3 perror:1 must:3 john:... |
3,751 | 4,397 | A Global Structural EM Algorithm
for a Model of Cancer Progression
Erik Sj?olund
Stockholm Bioinformatics Center
Stockholm University, Sweden
erik.sj?
olund@sbc.su.se
Ali Tofigh
School of Computer Science
McGill Centre for Bioinformatics
McGill University, Canada
ali.tofigh@mcgill.ca
Mattias H?oglund
Department of On... | 4397 |@word proceeded:1 version:7 proportion:1 johansson:1 hu:3 p0:4 cytogenetic:6 thereby:1 initial:1 liu:1 series:1 score:1 genetic:1 outperforms:1 current:1 recovered:14 yet:1 conjunctive:3 written:1 realistic:1 csc:1 numerical:1 cpds:2 mutagenetic:1 v:3 intelligence:1 leaf:3 devising:1 cbns:2 provides:1 detecting:1... |
3,752 | 4,398 | Semi-supervised Regression via
Parallel Field Regularization
Binbin Lin
Chiyuan Zhang
Xiaofei He
State Key Lab of CAD&CG, College of Computer Science, Zhejiang University
Hangzhou 310058, China
{binbinlinzju, chiyuan.zhang.zju, xiaofeihe}@gmail.com
Abstract
This paper studies the problem of semi-supervised learning f... | 4398 |@word middle:2 briefly:1 inversion:2 norm:7 reduction:5 initial:1 contains:1 series:1 united:1 past:1 existing:1 outperforms:2 com:2 cad:1 gmail:1 written:3 john:2 fn:1 gv:1 selected:2 short:1 zhang:2 saarland:1 along:3 dn:6 differential:3 hopf:1 qij:5 introduce:1 pairwise:1 discretized:1 v1t:1 rem:1 elbow:4 beco... |
3,753 | 4,399 | Metric Learning with Multiple Kernels
Jun Wang
Huyen Do
Adam Woznica
Alexandros Kalousis
AI Lab, Department of Informatics
University of Geneva, Switzerland
{Jun.Wang, Huyen.Do, Adam.Woznica, Alexandros.Kalousis}@unige.ch
Abstract
Metric learning has become a very active research field. The most popular
represent... | 4399 |@word kulis:2 repository:1 version:4 polynomial:2 norm:2 km:3 tr:8 initial:1 score:3 outperforms:2 existing:3 comparing:2 written:4 readily:1 half:2 selected:7 instantiate:2 parameterization:1 xk:1 parametrization:9 alexandros:2 matrix1:1 zhang:1 five:1 constructed:1 become:2 ik:1 prove:1 consists:2 combine:2 ins... |
3,754 | 44 | 317
PARTITIONING OF SENSORY DATA BY A CORTICAL NETWORK1
Richard Granger, Jose Ambros-Ingerson, Howard Henry, Gary Lynch
Center for the Neurobiology of Learning and Memory
University of California
Irvine, CA. 91717
SUMMARY
To process sensory data, sensory brain areas must preserve information about both
the similariti... | 44 |@word trial:5 version:2 rising:1 middle:1 stronger:3 hippocampus:6 nd:1 hyperpolarized:2 open:4 termination:1 simulation:19 pulse:14 thereby:2 innervating:1 tr:1 carry:1 reduction:1 initial:6 series:1 efficacy:1 contains:1 exclusively:1 past:1 existing:3 current:11 comparing:1 nt:1 surprising:1 anterior:2 si:2 yet:... |
3,755 | 440 | Unsupervised Classifiers, Mutual Information
and 'Phantom Targets'
David J.e. MacKay
John s. Bridle
Anthony J .R. Heading California Institute of Technology 139-74
Pasadena CA 91125 U.S.A
Defence Research Agency
St. Andrew's Road, Malvern
""orcs. "\VR14 3PS, U.K.
Abstract
We derive criteria for training adaptive clas... | 440 |@word cox:1 briefly:1 nd:1 grey:1 seek:1 propagate:1 covariance:2 initial:1 wd:1 nt:1 surprising:1 activation:2 must:1 john:1 partition:1 drop:1 alone:2 isotropic:1 ith:1 quantizer:1 equi:1 node:1 preference:1 simpler:1 prove:1 recognizable:1 theoretically:1 ra:1 roughly:1 behavior:1 dist:1 encouraging:1 what:1 ki... |
3,756 | 4,400 | Learning Sparse Representations of High
Dimensional Data on Large Scale Dictionaries
Zhen James Xiang Hao Xu Peter J. Ramadge
Department of Electrical Engineering, Princeton University
Princeton, NJ 08544, USA
{zxiang,haoxu,ramadge}@princeton.edu
Abstract
Learning sparse representations on data adaptive dictionaries ... | 4400 |@word compression:1 norm:2 d2:5 hsieh:1 covariance:1 solid:6 liblinear:2 reduction:3 contains:6 selecting:1 groundwork:1 tuned:2 document:1 outperforms:2 existing:4 si:17 attracted:1 must:4 written:1 dct:1 refines:1 informative:2 drop:1 plot:1 update:1 cue:1 half:1 selected:1 leaf:1 fewer:1 intelligence:3 ith:4 p... |
3,757 | 4,401 | On the Analysis of Multi-Channel Neural Spike Data
Bo Chen, David E. Carlson and Lawrence Carin
Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708
{bc69, dec18, lcarin}@duke.edu
Abstract
Nonparametric Bayesian methods are developed for analysis of multi-channel
spike-train data, with ... | 4401 |@word neurophysiology:2 loading:1 hippocampus:2 tried:1 accounting:1 dramatic:1 selecting:1 assigning:1 must:3 readily:1 shape:3 analytic:1 motor:2 update:3 implying:2 generative:2 stationary:2 device:3 filtered:1 blei:2 regressive:1 org:1 unbounded:1 along:2 constructed:1 burst:1 beta:6 ik:1 consists:2 manner:1 ... |
3,758 | 4,402 | RTRMC: A Riemannian trust-region method for
low-rank matrix completion
Nicolas Boumal?
ICTEAM Institute
Universit?e catholique de Louvain
B-1348 Louvain-la-Neuve
nicolas.boumal@uclouvain.be
P.-A. Absil
ICTEAM Institute
Universit?e catholique de Louvain
B-1348 Louvain-la-Neuve
absil@inma.ucl.ac.be
Abstract
We conside... | 4402 |@word milenkovic:2 version:2 norm:3 suitably:1 km:1 seek:1 decomposition:2 pick:1 sepulchre:2 initial:5 score:2 hereafter:1 denoting:1 ecole:1 current:1 written:1 readily:1 numerical:6 grass:1 guess:4 item:3 accordingly:1 propack:5 xk:1 steepest:2 core:1 math:2 successive:1 allerton:4 zhang:1 along:8 differential... |
3,759 | 4,403 | Hashing Algorithms for Large-Scale Learning
Ping Li
Cornell University
pingli@cornell.edu
Anshumali Shrivastava
Cornell University
anshu@cs.cornell.edu
Joshua Moore
Cornell University
jlmo@cs.cornell.edu
Arnd Christian K?onig
Microsoft Research
chrisko@microsoft.com
Abstract
Minwise hashing is a standard technique... | 4403 |@word multitask:1 version:1 achievable:1 loading:3 advantageous:1 logit:12 disk:1 seems:1 compression:1 tried:1 hsieh:3 dramatic:1 sgd:2 thereby:1 solid:3 reduction:2 liblinear:8 contains:1 renewed:1 document:8 interestingly:4 bc:3 com:2 z2:4 comparing:1 surprising:1 si:3 clara:1 crawling:1 written:2 suermondt:1 ... |
3,760 | 4,404 | Neuronal Adaptation for Sampling-Based
Probabilistic Inference in Perceptual Bistability
David P. Reichert, Peggy Seri?s, and Amos J. Storkey
School of Informatics, University of Edinburgh
10 Crichton Street, Edinburgh, EH8 9AB
{d.p.reichert@sms., pseries@inf., a.storkey@} ed.ac.uk
Abstract
It has been argued that pe... | 4404 |@word trial:6 middle:1 version:1 briefly:2 seems:2 norm:2 bf:9 instruction:1 contrastive:3 attended:4 initial:3 contains:1 efficacy:5 suppressing:1 interestingly:1 current:7 activation:6 attracted:1 slanted:1 subsequent:1 visible:4 shape:1 drop:2 plot:1 update:1 v:3 alone:1 generative:2 half:4 cue:1 item:1 intell... |
3,761 | 4,405 | Thinning Measurement Models and
Questionnaire Design
Ricardo Silva
Department of Statistical Science
University College London
Gower Street, London WC1E 6BT
ricardo@stats.ucl.ac.uk
Abstract
Inferring key unobservable features of individuals is an important task in the applied sciences. In particular, an important sou... | 4405 |@word trial:1 determinant:2 version:1 briefly:1 polynomial:2 proportion:2 eliminating:1 stronger:1 replicate:1 nd:1 covariance:5 decomposition:1 pick:1 pressure:4 ipm:8 moment:2 reduction:3 initial:2 contains:1 score:17 united:1 ilps:1 longitudinal:1 existing:2 current:2 z2:1 dx:1 written:1 john:1 determinantal:1... |
3,762 | 4,406 | Higher-Order Correlation Clustering for Image
Segmentation
Sungwoong Kim
Department of EE, KAIST
Daejeon, South Korea
sungwoong.kim01@gmail.com
Sebastian Nowozin
Microsoft Research Cambridge
Cambridge, UK
Sebastian.Nowozin@microsoft.com
Pushmeet Kohli
Microsoft Research Cambridge
Cambridge, UK
pkohli@microsoft.com
... | 4406 |@word kohli:1 judgement:1 polynomial:1 rgb:1 decomposition:1 textonboost:1 contains:1 score:3 hoiem:5 document:1 outperforms:2 com:3 contextual:1 gmail:1 assigning:3 must:1 yep:6 yet:1 distant:1 shape:4 enables:2 hofmann:1 designed:1 cue:3 plane:6 quantized:2 node:13 hsv:1 successive:1 location:3 constructed:1 in... |
3,763 | 4,407 | Non-conjugate Variational Message Passing for
Multinomial and Binary Regression
Thomas P. Minka
Microsoft Research
Cambridge, UK
David A. Knowles
Department of Engineering
University of Cambridge
Abstract
Variational Message Passing (VMP) is an algorithmic implementation of the Variational Bayes (VB) method which ap... | 4407 |@word version:1 briefly:1 seems:1 hu:3 closure:1 infernet:1 moment:1 initial:1 series:1 existing:2 current:4 com:1 dx:3 must:2 written:1 tilted:29 numerical:3 partition:1 designed:1 update:14 stationary:2 exl:2 generative:1 intelligence:2 xk:5 blei:1 provides:1 contribute:1 revisited:1 wkd:1 penalises:1 firstly:1... |
3,764 | 4,408 | Learning to Search Efficiently in High Dimensions
Zhen Li ?
UIUC
zhenli3@uiuc.edu
Huazhong Ning
Liangliang Cao
Google Inc.
huazhong@gooogle.com
IBM T.J. Watson Research Center
liangliang.cao@us.ibm.com
Tong Zhang
Yihong Gong
Thomas S. Huang ?
Rutgers University
tzhang@stat.rutgers.edu
NEC China
ygongca@gmail.... | 4408 |@word kulis:3 pw:8 triggs:1 c0:2 vldb:2 configuration:2 series:1 liu:3 zij:9 indispensible:1 document:1 outperforms:4 existing:1 com:3 comparing:2 gmail:1 must:2 written:1 additive:2 numerical:1 partition:1 kdd:1 shape:1 designed:2 sponsored:1 drop:1 update:2 hash:19 gist:1 reranking:5 selected:6 leaf:10 item:6 c... |
3,765 | 4,409 | The Manifold Tangent Classifier
Salah Rifai, Yann N. Dauphin, Pascal Vincent, Yoshua Bengio, Xavier Muller
Department of Computer Science and Operations Research
University of Montreal
Montreal, H3C 3J7
{rifaisal, dauphiya, vincentp, bengioy, mullerx}@iro.umontreal.ca
Abstract
We combine three important ideas present... | 4409 |@word cnn:4 version:2 norm:2 open:2 rgb:1 decomposition:2 pressure:3 outlook:1 reduction:3 electronics:1 contains:3 tuned:1 document:2 interestingly:1 outperforms:2 activation:1 yet:1 must:3 readily:2 subsequent:1 shape:2 cheap:1 atlas:9 designed:1 update:1 greedy:2 prohibitive:1 selected:3 half:1 generative:1 pl... |
3,766 | 441 | Principled Architecture Selection
for Neural Networks:
Application to Corporate Bond Rating Prediction
John Moody
Department of Computer Science
Yale University
P. O. Box 2158 Yale Station
New Haven, CT 06520
Joachim U tans
Department of Electrical Engineering
Yale University
P. O. Box 2157 Yale Station
New Haven, CT... | 441 |@word version:2 eliminating:1 retraining:2 dekker:2 series:1 selecting:4 subjective:1 si:1 assigning:1 issuing:2 john:3 nur:2 designed:1 update:1 v:2 stationary:1 intelligence:1 selected:2 xk:3 short:1 institution:1 math:2 ron:1 lx:1 ohl:1 sigmoidal:1 five:1 constructed:1 maturity:1 qualitative:1 consists:1 combin... |
3,767 | 4,410 | Optimal Reinforcement Learning
for Gaussian Systems
Philipp Hennig
Max Planck Institute for Intelligent Systems
Department of Empirical Inference
Spemannstra?e 38, 72070 T?ubingen, Germany
phennig@tuebingen.mpg.de
Abstract
The exploration-exploitation trade-off is among the central challenges of reinforcement learnin... | 4410 |@word cylindrical:1 exploitation:14 version:2 polynomial:1 coarseness:1 open:2 heuristically:1 seek:1 crucially:1 covariance:8 incurs:1 tr:2 harder:1 kappen:2 initial:4 hunting:1 uncovered:1 exclusively:1 initialisation:1 interestingly:1 outperforms:1 current:4 marquardt:2 si:3 assigning:2 dx:1 must:2 additive:1 ... |
3,768 | 4,411 | Bayesian Spike-Triggered Covariance Analysis
Il Memming Park
Center for Perceptual Systems
University of Texas at Austin
Austin, TX 78712, USA
memming@austin.utexas.edu
Jonathan W. Pillow
Center for Perceptual Systems
University of Texas at Austin
Austin, TX 78712, USA
pillow@mail.utexas.edu
Abstract
Neurons typicall... | 4411 |@word proportion:1 norm:3 stronger:1 simulation:2 cml:1 seek:1 covariance:11 tr:3 accommodate:2 moment:7 reduction:4 series:1 selecting:2 ording:1 elliptical:8 si:4 yet:2 must:1 written:1 subsequent:1 informative:2 shape:3 drop:2 designed:1 update:5 fewer:1 selected:1 nnsp:1 record:1 colored:1 provides:4 characte... |
3,769 | 4,412 | Greedy Algorithms for Structurally Constrained
High Dimensional Problems
Ambuj Tewarl
Department of Computer Science
University of Texas at Austin
ambuj@cs.utexas.edu
Pradeep Ravikumar
Department of Computer Science
University of Texas at Austin
pradeepr@cs.utexas.edu
Inderjit S. Dhillon
Department of Computer Scien... | 4412 |@word uev:1 cu:1 version:1 polynomial:2 norm:53 stronger:1 tr:1 liu:1 frankwolfe:1 past:1 existing:4 current:2 yet:2 intriguing:1 written:2 attracted:1 ilii:1 greedy:33 discovering:1 denison:1 intelligence:1 short:1 moncrief:1 lr:3 provides:1 boosting:2 iterates:4 llii:1 allerton:1 org:1 zhang:2 nonpararnetric:1 ... |
3,770 | 4,413 | On the Universality of Online Mirror Descent
Nathan Srebro
TTIC
nati@ttic.edu
Karthik Sridharan
TTIC
karthik@ttic.edu
Ambuj Tewari
University of Texas at Austin
ambuj@cs.utexas.edu
Abstract
We show that for a general class of convex online learning problems, Mirror Descent can always
achieve a (nearly) optimal regre... | 4413 |@word multitask:2 version:3 norm:49 seems:1 nd:1 d2:11 covariance:1 decomposition:1 q1:1 pick:3 series:1 current:1 optim:1 universality:3 must:1 fn:6 cant:1 update:2 juditsky:1 warmuth:2 manfred:1 characterization:1 math:1 zhang:1 mathematical:1 dn:2 along:1 become:1 symposium:1 stronglyconvex:1 shorthand:1 consi... |
3,771 | 4,414 | Kernel Bayes? Rule
Kenji Fukumizu
The Institute of Statistical
Mathematics, Tokyo
Le Song
College of Computing
Georgia Institute of Technology
Arthur Gretton
Gatsby Unit, UCL
MPI for Intelligent Systems
fukumizu@ism.ac.jp
lsong@cc.gatech.edu
arthur.gretton@gmail.com
Abstract
A nonparametric kernel-based method f... | 4414 |@word inversion:2 norm:2 advantageous:1 km:1 additively:2 simulation:1 rgb:1 covariance:11 decomposition:2 kbr:60 tr:2 harder:1 boundedness:1 reduction:4 moment:1 selecting:1 tuned:1 rkhs:9 outperforms:3 o2:1 freitas:1 current:1 com:2 gmail:1 dx:2 written:2 realize:1 numerical:1 partition:2 analytic:3 update:5 bi... |
3,772 | 4,415 | Selecting the State-Representation
in Reinforcement Learning
Odalric-Ambrym Maillard
INRIA Lille - Nord Europe
odalricambrym.maillard@gmail.com
R?emi Munos
INRIA Lille - Nord Europe
remi.munos@inria.fr
Daniil Ryabko
INRIA Lille - Nord Europe
daniil@ryabko.net
Abstract
The problem of selecting the right state-repres... | 4415 |@word exploitation:24 polynomial:3 seems:3 c0:4 open:3 rigged:1 decomposition:1 outlook:1 initial:1 contains:1 selecting:5 united:1 past:1 current:5 com:1 discretization:3 gmail:1 yet:2 written:1 bd:15 must:1 john:1 ronald:1 numerical:1 partition:1 additive:1 wiewiora:1 remove:1 designed:1 update:2 intelligence:2... |
3,773 | 4,416 | A blind deconvolution method for neural spike
identification
Chaitanya Ekanadham
Courant Institute
New York University
New York, NY 10012
chaitu@math.nyu.edu
Daniel Tranchina
Courant Institute
New York University
New York, NY 10012
Eero P. Simoncelli
Courant Institute
Center for Neural Science
Howard Hughes Medical I... | 4416 |@word version:4 norm:4 open:2 physik:1 confirms:1 simulation:1 decomposition:3 covariance:1 jacob:1 carry:1 reduction:3 contains:1 series:1 daniel:1 demarcated:1 outperforms:1 nadasdy:1 current:6 ka:1 com:1 assigning:1 john:2 ronald:1 shape:9 remove:1 plot:3 update:2 kristina:1 generative:5 half:1 plane:1 smith:1... |
3,774 | 4,417 | Improved Algorithms for Linear Stochastic Bandits
Yasin Abbasi-Yadkori
D?avid P?al
Csaba Szepesv?ari
abbasiya@ualberta.ca
dpal@google.com
szepesva@ualberta.ca
Dept. of Computing Science
University of Alberta
Dept. of Computing Science
University of Alberta
Dept. of Computing Science
University of Alberta
Abstr... | 4417 |@word exploitation:3 version:2 determinant:4 norm:3 seems:1 nd:2 dekel:2 d2:1 diuk:1 mention:1 moment:1 ours:1 past:3 com:1 contextual:2 surprising:1 chu:3 written:1 stemming:1 additive:1 drop:1 update:3 stationary:1 selected:1 accordingly:1 recompute:2 zhang:1 mathematical:1 dn:1 constructed:2 focs:1 prove:6 lag... |
3,775 | 4,418 | ?-MRF: Capturing Spatial and Semantic Structure in
the Parameters for Scene Understanding
Congcong Li, Ashutosh Saxena, Tsuhan Chen
Cornell University, Ithaca, NY 14853, United States
cl758@cornell.edu, asaxena@cs.cornell.edu, tsuhan@ece.cornell.edu
Abstract
For most scene understanding tasks (such as object detectio... | 4418 |@word multitask:1 middle:2 norm:4 justice:1 confirms:1 mention:1 shot:1 harder:1 garrigues:1 initial:1 score:3 united:1 hoiem:3 ours:1 outperforms:1 current:1 contextual:21 comparing:1 assigning:2 john:1 informative:5 shape:1 designed:1 ashutosh:1 grass:1 alone:2 v:1 ith:2 blei:1 detecting:3 node:7 location:26 zh... |
3,776 | 4,419 | Matrix Completion for
Multi-label Image Classification
Ricardo S. Cabral?,? Fernando De la Torre? Jo?o P. Costeira? , Alexandre Bernardino?
?
?
Carnegie Mellon University,
ISR - Instituto Superior T?cnico,
Pittsburgh, PA
Lisboa, Portugal
rscabral@cmu.edu, ftorre@cs.cmu.edu, {jpc,alex}@isr.ist.utl.pt
Abstract
Recently... | 4419 |@word multitask:1 version:2 norm:11 open:1 d2:2 ratan:1 contraction:3 decomposition:1 pg:1 textonboost:1 incurs:1 tr:5 initial:3 manmatha:1 contains:1 series:1 zij:20 disparity:1 score:2 liu:1 ours:1 outperforms:2 existing:2 ka:1 comparing:1 toh:1 portuguese:1 concatenate:1 numerical:1 shape:2 remove:1 gist:3 pro... |
3,777 | 442 | Splines, Rational Functions and Neural Networks
Robert C. Willialnson
Department of Systems Engineering
Australian National University
Canberra, 2601
Australia
Peter L. Bartlett
Department of Electrical Engineering
University of Queensland
Queensland, 4072
Australia
Abstract
Connections between spline approximation,... | 442 |@word polynomial:6 compression:1 achievable:6 norm:1 open:2 queensland:2 decomposition:2 electronics:1 substitution:1 series:1 efficacy:1 complexit:1 dx:3 written:1 cruz:1 partition:4 girosi:1 parametrization:1 lr:10 chua:1 lx:3 c2:1 direct:4 ucsc:1 prove:1 multi:1 little:1 what:3 tic:1 whilst:1 partitioning:2 con... |
3,778 | 4,420 | Nonlinear Inverse Reinforcement Learning with
Gaussian Processes
Zoran Popovi?c
University of Washington
zoran@cs.washington.edu
Sergey Levine
Stanford University
svlevine@cs.stanford.edu
Vladlen Koltun
Stanford University
vladlen@cs.stanford.edu
Abstract
We present a probabilistic algorithm for nonlinear inverse re... | 4420 |@word version:1 nd:1 covariance:5 thereby:1 tr:4 shading:1 score:2 selecting:1 rightmost:1 bradley:1 current:4 si:3 yet:1 dx:2 written:1 must:1 v:1 stationary:2 intelligence:3 selected:1 fewer:1 amir:1 provides:1 mannor:1 boosting:1 five:1 along:1 constructed:1 become:5 koltun:2 ik:1 combine:1 fitting:1 apprentic... |
3,779 | 4,421 | A Convergence Analysis of Log-Linear Training
Hermann Ney
Computer Science Department
RWTH Aachen University
52056 Aachen, Germany
ney@cs.rwth-aachen.de
Simon Wiesler
Computer Science Department
RWTH Aachen University
52056 Aachen, Germany
wiesler@cs.rwth-aachen.de
Abstract
Log-linear models are widely used probabil... | 4421 |@word polynomial:3 stronger:1 seems:1 norm:7 c0:2 nd:1 termination:5 d2:7 covariance:7 minus:1 harder:1 liu:1 contains:3 pub:1 ours:1 interestingly:2 outperforms:4 written:1 parsing:2 numerical:2 weyl:3 analytic:1 enables:1 kyb:1 generative:4 intelligence:1 mccallum:3 steepest:5 iterates:2 characterization:1 math... |
3,780 | 4,422 | Uniqueness of Belief Propagation on Signed Graphs
Yusuke Watanabe?
The Institute of Statistical Mathematics
10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan
watay@ism.ac.jp
Abstract
While loopy Belief Propagation (LBP) has been utilized in a wide variety of applications with empirical success, it comes with few theor... | 4422 |@word determinant:3 inversion:1 closure:1 bn:1 contraction:10 minus:5 kappen:2 reduction:18 cyclic:2 terminus:1 ue1:3 existing:3 current:1 com:1 yet:1 assigning:1 must:4 ikeda:1 pseudomarginals:2 update:3 midori:1 stationary:3 intelligence:1 item:1 reciprocal:2 provides:1 math:1 node:1 characterization:1 five:2 d... |
3,781 | 4,423 | Inductive reasoning about chimeric creatures
Charles Kemp
Department of Psychology
Carnegie Mellon University
ckemp@cmu.edu
Abstract
Given one feature of a novel animal, humans readily make inferences about other
features of the animal. For example, winged creatures often fly, and creatures that
eat fish often live i... | 4423 |@word version:5 proportion:2 seems:1 squid:1 grey:2 mammal:1 score:1 ridden:1 slotted:1 past:1 comparing:2 surprising:1 must:5 readily:1 herring:1 grain:1 numerical:1 partition:1 trout:1 enables:1 designed:1 plot:3 fund:1 grass:1 alone:1 selected:1 beaver:1 rehder:2 smith:1 mental:4 node:12 location:1 five:1 phyl... |
3,782 | 4,424 | On Causal Discovery with
Cyclic Additive Noise Models
Joris M. Mooij
Radboud University
Nijmegen, The Netherlands
j.mooij@cs.ru.nl
Tom Heskes
Radboud University
Nijmegen, The Netherlands
t.heskes@cs.ru.nl
Dominik Janzing
Max Planck Institute for Intelligent Systems
T?ubingen, Germany
dominik.janzing@tuebingen.mpg.de
... | 4424 |@word determinant:4 version:1 stronger:1 seems:2 norm:1 nd:1 twelfth:1 open:1 hyv:1 simplifying:1 contraction:1 covariance:2 harder:1 initial:2 cyclic:28 series:2 contains:1 hereafter:1 ramsey:1 current:2 discretization:1 surprising:1 yet:1 must:3 written:3 john:1 additive:23 realistic:1 happen:1 remove:2 alone:1... |
3,783 | 4,425 | Nearest Neighbor based Greedy Coordinate Descent
Inderjit S. Dhillon
Department of Computer Science
University of Texas at Austin
inderjit@cs.utexas.edu
Pradeep Raviknmar
Department of Computer Science
University of Texas at Austin
pradeepr@cs.utexas.edu
Ambuj Tewari
Department of Computer Science
University of Texa... | 4425 |@word trial:1 version:2 polynomial:1 norm:2 reused:1 open:2 simulation:2 pick:4 incurs:1 harder:1 reduction:4 initial:1 cyclic:16 selecting:1 tuned:1 renewed:2 allon:1 past:2 current:1 chazelle:2 comparing:1 od:1 written:2 additive:4 partition:4 distant:1 fertilization:1 plot:8 update:11 ouly:1 stationary:1 greed... |
3,784 | 4,426 | Structural equations and divisive normalization for
energy-dependent component analysis
Jun-ichiro Hirayama
Dept. of Systems Science
Graduate School of of Informatics
Kyoto University
611-0011 Uji, Kyoto, Japan
Aapo Hyv?arinen
Dept. of Mathematics and Statistics
Dept. of Computer Science and HIIT
University of Helsin... | 4426 |@word briefly:1 eliminating:1 norm:3 seems:4 termination:1 hyv:10 d2:1 simulation:3 riitta:1 recapitulate:1 decomposition:1 solid:1 garrigues:1 moment:2 reduction:1 cyclic:9 series:1 score:1 initial:1 tuned:1 interestingly:3 ramsey:1 laparra:2 anterior:1 si:17 written:1 john:1 subsequent:1 additive:2 wx:5 enables... |
3,785 | 4,427 | Active Ranking using Pairwise Comparisons
Kevin G. Jamieson
University of Wisconsin
Madison, WI 53706, USA
Robert D. Nowak
University of Wisconsin
Madison, WI 53706, USA
kgjamieson@wisc.edu
nowak@engr.wisc.edu
Abstract
This paper examines the problem of ranking a collection of objects using pairwise
comparisons (ra... | 4427 |@word mild:1 cox:2 version:2 nd:1 open:1 simulation:1 citeseer:1 q1:4 pick:1 idl:1 thereby:1 solid:1 initial:1 inefficiency:2 contains:1 selecting:4 existing:1 si:1 chu:1 must:5 john:1 partition:20 informative:3 atlas:1 alone:1 generative:1 fewer:2 selected:6 renshaw:1 num:1 provides:3 characterization:2 boosting... |
3,786 | 4,428 | Active Learning Ranking from Pairwise Preferences
with Almost Optimal Query Complexity
Nir Ailon?
Technion, Haifa, Israel nailon@cs.technion.ac.il
Abstract
Given a set V of n elements we wish to linearly order them using pairwise
preference labels which may be non-transitive (due to irrationality or arbitrary
noise).... | 4428 |@word cu:3 version:3 manageable:1 achievable:1 polynomial:2 middle:1 nd:1 c0:1 open:2 seek:3 decomposition:16 accounting:1 mention:1 thereby:1 moment:1 reduction:3 liu:1 contains:2 score:1 selecting:1 series:1 daniel:1 denoting:1 document:1 ours:1 chervonenkis:1 current:1 beygelzimer:3 si:1 yet:1 written:1 must:1... |
3,787 | 4,429 | Efficient Learning of Generalized Linear and Single
Index Models with Isotonic Regression
Sham M. Kakade
Microsoft Research and Wharton, U Penn
skakade@microsoft.com
Adam Tauman Kalai
Microsoft Research
adum@microsoft.com
Ohad Shamir
Microsoft Research
ohadsh@microsoft.com
Varun Kanade
SEAS, Harvard University
vkana... | 4429 |@word mild:1 version:2 middle:1 polynomial:1 achievable:1 norm:2 nd:1 harder:2 contains:1 minht:2 existing:1 err:10 com:3 z2:1 bd:6 must:2 additive:1 plot:2 designed:1 update:5 juditsky:1 v:1 half:1 iso:5 provides:2 node:4 org:1 simpler:1 along:5 constructed:1 direct:2 symposium:1 descendant:1 prove:1 focs:1 fitt... |
3,788 | 443 | Connectionist Optimisation of Tied Mixture
Hidden Markov Models
Steve Renals
Nelson Morgan
ICSI
Berkeley CA 94704
USA
Herve Bourlard
L&H Speech products
leper B-9800
Belgium
Horacio Franco
Michael Cohen
SRI International
Menlo Park CA 94025
USA
Abstract
Issues relating to the estimation of hidden Markov model (HMM)... | 443 |@word cox:2 sri:1 covariance:2 initial:1 contains:1 current:1 must:3 numerical:1 additive:1 discrimination:1 steepest:1 normalising:2 codebook:1 toronto:2 lexicon:2 sigmoidal:1 simpler:1 constructed:1 combine:2 theoretically:1 ra:1 frequently:1 multi:3 globally:1 decomposed:1 resolve:2 baker:2 interpreted:1 fuzzy:... |
3,789 | 4,430 | A concave regularization technique
for sparse mixture models
Martin Larsson
School of Operations Research and Information Engineering
Cornell University
mol23@cornell.edu
Johan Ugander
Center for Applied Mathematics
Cornell University
jhu5@cornell.edu
Abstract
Latent variable mixture models are a powerful tool for e... | 4430 |@word version:1 briefly:1 norm:4 plsa:37 checkable:2 open:2 seek:1 mention:1 ld:15 contains:3 exclusively:1 document:14 xz0:2 current:2 com:1 surprising:2 dx:1 must:5 written:1 readily:2 intriguing:1 additive:1 realistic:2 hofmann:1 update:3 stationary:22 intelligence:1 parameterization:1 xk:12 beginning:1 ugande... |
3,790 | 4,431 | Hierarchical Topic Modeling for Analysis of
Time-Evolving Personal Choices
XianXing Zhang
Duke University
xianxing.zhang@duke.edu
David B. Dunson
Duke University
dunson@stat.duke.edu
Lawrence Carin
Duke University
lcarin@ee.duke.edu
Abstract
The nested Chinese restaurant process is extended to design a nonparametri... | 4431 |@word version:1 faculty:1 proportion:4 calculus:3 seek:3 simulation:1 pick:1 thereby:1 holy:1 shot:1 recursively:1 uncovered:2 contains:1 pub:2 denoting:2 document:5 existing:2 current:1 readily:1 applicant:1 subsequent:1 hypothesize:1 drop:1 statis:1 update:3 bart:1 generative:2 selected:7 leaf:1 item:3 device:1... |
3,791 | 4,432 | Better Mini-Batch Algorithms
via Accelerated Gradient Methods
Andrew Cotter
Toyota Technological Institute at Chicago
cotter@ttic.edu
Ohad Shamir
Microsoft Research, NE
ohadsh@microsoft.com
Nathan Srebro
Toyota Technological Institute at Chicago
nati@ttic.edu
Karthik Sridharan
Toyota Technological Institute at Chica... | 4432 |@word version:1 briefly:1 pw:3 polynomial:1 norm:2 advantageous:1 dekel:2 open:1 d2:6 bn:7 sgd:19 solid:1 recursively:1 initial:1 outperforms:1 existing:1 com:1 bd:2 refines:1 chicago:3 plot:2 update:4 juditsky:1 v:1 half:1 selected:1 math:2 mathematical:1 ect:2 ectively:1 overhead:1 theoretically:2 indeed:2 expe... |
3,792 | 4,433 | Co-Training for Domain Adaptation
Minmin Chen, Kilian Q. Weinberger
Department of Computer Science and Engineering
Washington University in St. Louis
St. Louis, MO 63130
mc15,kilian@wustl.edu
John C. Blitzer
Google Research
1600 Amphitheatre Parkway
Mountain View, CA 94043
blitzer@google.com
Abstract
Domain adaptati... | 4433 |@word multitask:2 kulis:1 cu:8 version:1 middle:1 bigram:3 pcc:4 stronger:1 plsa:1 hu:2 seek:4 blender:1 decomposition:4 pick:1 accommodate:1 reduction:1 electronics:7 initial:1 contains:2 score:4 selecting:2 liu:1 charniak:1 outperforms:1 existing:1 current:4 com:3 surprising:1 must:4 john:1 kdd:1 minmin:1 rote:... |
3,793 | 4,434 | Linearized Alternating Direction Method with
Adaptive Penalty for Low-Rank Representation
Zhouchen Lin
Visual Computing Group
Microsoft Research Asia
Risheng Liu
Zhixun Su
School of Mathematical Sciences
Dalian University of Technology
Abstract
Many machine learning and signal processing problems can be formulated a... | 4434 |@word version:3 inversion:3 norm:13 advantageous:1 linearized:9 decomposition:1 eng:1 dramatic:1 liu:4 substitution:1 existing:2 diagonalized:1 current:1 si:3 yet:1 chu:1 toh:1 readily:1 csc:1 numerical:3 update:16 fund:2 propack:7 xk:49 short:1 core:1 successive:3 zhang:1 favaro:1 mathematical:1 constructed:1 yu... |
3,794 | 4,435 | Transfer from Multiple MDPs
Alessandro Lazaric
INRIA Lille - Nord Europe, Team SequeL, France
alessandro.lazaric@inria.fr
Marcello Restelli
Department of Electronics and Informatics, Politecnico di Milano, Italy
restelli@elet.polimi.it
Abstract
Transfer reinforcement learning (RL) methods leverage on the experience c... | 4435 |@word norm:4 proportion:12 nd:1 open:2 d2:1 confirms:1 r:7 propagate:1 tat:1 attainable:1 initial:3 electronics:1 selecting:1 outperforms:1 dx:2 written:1 must:1 john:1 plot:6 drop:1 generative:2 greedy:1 selected:1 accordingly:1 beginning:1 provides:2 readability:1 direct:2 m7:3 prove:1 introduce:5 excellence:1 ... |
3,795 | 4,436 | Continuous-Time Regression Models for
Longitudinal Networks
Duy Q. Vu
Department of Statistics
Pennsylvania State University
University Park, PA 16802
dqv100@stat.psu.edu
Arthur U. Asuncion?
Department of Computer Science
University of California, Irvine
Irvine, CA 92697
asuncion@ics.uci.edu
David R. Hunter
Departme... | 4436 |@word cox:24 briefly:1 version:3 nd:2 cha:1 closure:2 tried:2 decomposition:1 covariance:3 reduction:1 initial:1 series:1 score:2 denoting:1 interestingly:1 longitudinal:16 past:2 outperforms:2 current:4 yet:1 must:2 written:1 vere:1 additive:15 subsequent:1 timestamps:1 enables:1 acar:1 plot:2 update:4 discoveri... |
3,796 | 4,437 | A Reinforcement Learning Theory for Homeostatic
Regulation
Mehdi Keramati
Group for Neural Theory, LNC, ENS
Paris, France
mohammadmahdi.keramati@ens.fr
Boris Gutkin
Group for Neural Theory, LNC, ENS
Paris, France
boris.gutkin@ens.fr
Abstract
Reinforcement learning models address animal?s behavioral adaptation to its... | 4437 |@word trial:1 instrumental:6 seems:2 extinction:2 sex:1 d2:1 seek:2 sensed:1 simulation:3 q1:1 euclidian:1 carry:1 reduction:17 initial:1 series:2 contains:1 united:1 interestingly:1 past:1 current:5 contextual:1 intake:2 written:2 must:2 physiol:2 shape:1 designed:1 update:6 cue:5 accordingly:1 iso:1 smith:1 pro... |
3,797 | 4,438 | SpaRCS: Recovering Low-Rank and Sparse Matrices
from Compressive Measurements
Andrew E. Waters, Aswin C. Sankaranarayanan, Richard G. Baraniuk
Rice University
{andrew.e.waters, saswin, richb}@rice.edu
Abstract
We consider the problem of recovering a matrix M that is the sum of a low-rank
matrix L and a sparse matrix S... | 4438 |@word version:4 compression:3 norm:4 km:1 simulation:1 decomposition:9 klk:3 series:1 efficacy:1 contains:1 mag:1 existing:1 kmk:1 current:1 ka:2 recovered:5 ksk1:1 com:1 yet:1 must:3 john:1 chicago:1 enables:2 plot:6 update:2 greedy:8 prohibitive:1 device:1 merger:1 realizing:1 fa9550:1 provides:2 characterizati... |
3,798 | 4,439 | Generalized Beta Mixtures of Gaussians
Artin Armagan
Dept. of Statistical Science
Duke University
Durham, NC 27708
artin@stat.duke.edu
David B. Dunson
Dept. of Statistical Science
Duke University
Durham, NC 27708
dunson@stat.duke.edu
Merlise Clyde
Dept. of Statistical Science
Duke University
Durham, NC 27708
clyde@st... | 4439 |@word h:1 advantageous:1 stronger:2 c0:4 accounting:1 pick:1 solid:1 moment:3 initial:1 contains:1 series:2 tuned:1 ka:2 comparing:1 yet:4 must:1 shape:3 treating:2 update:1 half:5 intelligence:2 guess:1 provides:1 beauchamp:1 unbounded:1 mathematical:1 along:1 direct:1 beta:14 prove:1 consists:1 recognizable:1 m... |
3,799 | 444 | Locomotion in a Lower Vertebrate:
Studies of the Cellular Basis of Rhythmogenesis
and Oscillator Coupling
James T. Buchanan
Department of Biology
Marquette University
Milwaukee, WI 53233
Abstract
To test whether the known connectivies of neurons in the lamprey spinal
cord are sufficient to account for locomotor rhyth... | 444 |@word nd:1 termination:1 pulse:1 propagate:1 simulation:3 phy:1 current:5 must:1 bd:1 motor:3 plot:1 v:1 half:2 nervous:1 reciprocal:2 compo:2 contribute:1 successive:1 simpler:1 five:1 burst:5 along:1 buchanan:15 lagging:1 manner:1 behavior:1 multi:2 morphology:1 brain:1 vertebrate:3 project:1 provided:1 cens:1 c... |
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