Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
4,600 | 5,162 | Parallel Sampling of DP Mixture
Models using Sub-Clusters Splits
John W. Fisher III?
CSAIL, MIT
fisher@csail.mit.edu
Jason Chang?
CSAIL, MIT
jchang7@csail.mit.edu
Abstract
We present an MCMC sampler for Dirichlet process mixture models that can
be parallelized to achieve significant computational gains. We combine a... | 5162 |@word trial:1 repository:1 version:4 advantageous:1 proportionality:1 simulation:2 solid:1 accommodate:1 initial:5 contains:1 lichman:1 document:6 dpmms:9 pprox:2 current:7 must:2 written:1 john:2 stemming:1 v:1 stationary:4 intelligence:1 leaf:1 selected:1 instantiate:1 core:3 colored:1 blei:3 node:1 complicatio... |
4,601 | 5,163 | Lexical and Hierarchical Topic Regression
Viet-An Nguyen
Computer Science
University of Maryland
College Park, MD
vietan@cs.umd.edu
Jordan Boyd-Graber
iSchool & UMIACS
University of Maryland
College Park, MD
jbg@umiacs.umd.edu
Philip Resnik
Linguistics & UMIACS
University of Maryland
College Park, MD
resnik@umd.edu
... | 5163 |@word private:1 version:4 pcc:8 norm:1 bigram:4 nd:15 seek:1 tried:2 yea:1 outlook:1 initial:1 liu:2 series:1 score:3 contains:4 uncovered:1 document:31 interestingly:1 outperforms:1 existing:5 reaction:1 wd:34 com:1 assigning:5 router:5 readily:1 additive:1 remove:2 fund:2 v:2 infant:2 generative:3 discovering:1... |
4,602 | 5,164 | A Novel Two-Step Method for Cross Language
Representation Learning
Min Xiao and Yuhong Guo
Department of Computer and Information Sciences
Temple University, Philadelphia, PA 19122, USA
{minxiao, yuhong}@temple.edu
Abstract
Cross language text classification is an important learning task in natural language
processin... | 5164 |@word webber:1 norm:4 km:17 covariance:2 decomposition:3 yih:1 liu:1 contains:4 efficacy:2 series:1 document:67 bhattacharyya:1 outperforms:7 recovered:2 comparing:1 egd:3 readily:1 evans:1 shanahan:1 treating:1 update:3 intelligence:1 selected:2 lexicon:3 tagger:1 mathematical:1 constructed:1 become:1 qij:2 prov... |
4,603 | 5,165 | Learning word embeddings efficiently with
noise-contrastive estimation
Koray Kavukcuoglu
DeepMind Technologies
koray@deepmind.com
Andriy Mnih
DeepMind Technologies
andriy@deepmind.com
Abstract
Continuous-valued word embeddings learned by neural language models have recently been shown to capture semantic and syntacti... | 5165 |@word multitask:1 msr:8 version:3 eliminating:1 achievable:1 norm:1 seems:1 hyv:1 ivlbl:11 subscriber:1 contrastive:8 pick:1 yih:1 tr:1 harder:1 reduction:2 initial:1 contains:1 score:16 exclusively:1 lightweight:1 seriously:1 document:1 current:10 com:2 comparing:1 si:2 assigning:1 readily:1 john:1 parsing:1 ena... |
4,604 | 5,166 | Training and Analyzing Deep Recurrent Neural
Networks
Michiel Hermans, Benjamin Schrauwen
Ghent University, ELIS departement
Sint Pietersnieuwstraat 41,
9000 Ghent, Belgium
michiel.hermans@ugent.be
Abstract
Time series often have a temporal hierarchy, with information that is spread out
over multiple time scales. Comm... | 5166 |@word longterm:1 middle:2 compression:3 seems:3 bptt:2 grey:1 confirms:1 simulation:1 propagate:1 jacob:1 sgd:3 solid:1 accommodate:1 recursively:1 initial:3 series:9 initialisation:1 ours:1 past:3 subjective:1 existing:1 current:3 gpu:3 subsequent:2 confirming:1 drop:1 update:11 alone:1 generative:2 selected:1 i... |
4,605 | 5,167 | Extracting regions of interest from biological images
with convolutional sparse block coding
Marius Pachitariu1 , Adam Packer2 , Noah Pettit2 , Henry Dagleish2 ,
Michael Hausser2 and Maneesh Sahani1
1
Gatsby Unit, UCL, UK {marius, maneesh}@gatsby.ucl.ac.uk
2
The Wolfson Institute for Biomedical Research, UCL, UK {a.pa... | 5167 |@word version:1 inversion:1 proportion:3 seems:1 reused:1 open:1 human2:1 simulation:2 decomposition:4 pressure:1 pick:2 thereby:1 schnitzer:1 contains:3 fragment:3 efficacy:1 recovered:5 current:1 activation:7 assigning:1 must:1 visible:3 shape:8 wanted:1 designed:1 update:3 alone:2 generative:15 greedy:2 fewer:... |
4,606 | 5,168 | Mapping cognitive ontologies to and from the brain
Yannick Schwartz, Bertrand Thirion, and Gael Varoquaux
Parietal Team, Inria Saclay Ile-de-France
Saclay, France
firstname.lastname@inria.fr
Abstract
Imaging neuroscience links brain activation maps to behavior and cognition via
correlational studies. Due to the natur... | 5168 |@word fusiform:2 collinearity:1 mri:1 cingulate:1 inversion:3 open:2 instruction:7 confirms:2 mention:1 solid:1 shot:1 harder:1 carry:1 extrastriate:1 reduction:1 celebrated:1 score:5 selecting:1 halchenko:2 genetic:1 interestingly:3 activation:20 yet:1 must:1 shape:2 enables:1 motor:3 haxby:1 drop:1 atlas:2 repr... |
4,607 | 5,169 | Geometric optimisation on positive definite matrices
with application to elliptically contoured distributions
Reshad Hosseini
School of ECE, College of Engineering
University of Tehran, Tehran, Iran
Suvrit Sra
Max Planck Institute for Intelligent Systems
T?ubingen, Germany
Abstract
Hermitian positive definite (hpd) ... | 5169 |@word determinant:1 illustrating:1 version:4 briefly:1 norm:1 pillar:1 stronger:1 suitably:1 wiesel:3 open:1 polynomial:1 d2:6 tried:1 covariance:7 contraction:3 decomposition:1 kent:1 mention:1 tr:4 sepulchre:1 contains:2 cherian:1 selecting:1 ours:2 outperforms:2 mishra:1 elliptical:5 ka:1 nt:1 optim:1 scatter:... |
4,608 | 517 | A Segment-based Automatic Language
Identification System
Yeshwant K. Muthusamy & Ronald A. Cole
Center for Spoken Language Understanding
Oregon Graduate Institute of Science and Technology
Beaverton OR 97006-1999
Abstract
We have developed a four-language automatic language identification system for high-quality spee... | 517 |@word exploitation:1 middle:2 bigram:1 closure:1 cml:1 configuration:1 series:1 score:1 clos:6 com:1 nt:1 activation:1 yet:1 ronald:1 designed:1 plot:1 progressively:2 fewer:1 selected:3 filtered:2 coarse:1 cse:1 toronto:1 successive:3 five:2 consists:1 inter:5 expected:1 rapid:1 voc:10 automatically:1 window:5 pr... |
4,609 | 5,170 | Estimating the Unseen:
Improved Estimators for Entropy and other
Properties
Paul Valiant ?
Brown University
Providence, RI 02912
pvaliant@gmail.com
Gregory Valiant ?
Stanford University
Stanford, CA 94305
valiant@stanford.edu
Abstract
Recently, Valiant and Valiant [1, 2] showed that a class of distributional properti... | 5170 |@word trial:2 briefly:1 clts:1 proportion:1 seems:1 stronger:2 c0:2 unif:3 seek:1 pressure:1 minus:1 moment:1 initial:1 contains:3 fragment:1 genetic:3 ours:1 outperforms:1 past:1 horvitz:2 current:1 com:1 comparing:1 ka:1 gmail:1 yet:2 must:2 mesh:7 shakespeare:2 shape:4 remove:1 malaysia:1 designed:2 plot:6 n0:... |
4,610 | 5,171 | Factorized Asymptotic Bayesian Inference
for Latent Feature Models
Kohei Hayashi??
?National Institute of Informatics
?JST, ERATO, Kawarabayashi Large Graph Project
kohei-h@nii.ac.jp
Ryohei Fujimaki
NEC Laboratories America
rfujimaki@nec-labs.com
Abstract
This paper extends factorized asymptotic Bayesian (FAB) infere... | 5171 |@word mild:1 worsens:1 trial:2 kulis:1 inversion:1 eliminating:1 repository:1 nd:2 simulation:4 linearized:1 arti:4 eld:3 initial:4 series:2 selecting:2 nii:1 existing:1 nally:1 com:1 wd:1 yet:1 must:1 bd:1 cant:1 enables:1 analytic:1 remove:3 update:13 polyphonic:1 stationary:1 intelligence:2 selected:3 warmuth:... |
4,611 | 5,172 | Tracking Time-varying Graphical Structure
David Danks
Carnegie Mellon University
Pittsburgh, PA 15213
ddanks@andrew.cmu.edu
Erich Kummerfeld
Carnegie Mellon University
Pittsburgh, PA 15213
ekummerf@andrew.cmu.edu
Abstract
Structure learning algorithms for graphical models have focused almost exclusively on stable en... | 5172 |@word version:2 nd:1 open:2 simulation:11 covariance:8 dramatic:1 incurs:1 tr:2 reduction:1 series:6 exclusively:1 score:5 interestingly:1 outperforms:1 ramsey:1 current:7 must:7 readily:1 written:1 designed:1 drop:1 update:6 v:2 stationary:9 generative:2 fewer:3 greedy:1 tillman:1 intelligence:1 mccallum:1 prepe... |
4,612 | 5,173 | Sparse Precision Matrix Estimation with Calibration
Tuo Zhao
Department of Computer Science
Johns Hopkins University
Han Liu
Department of Operations Research and Financial Engineering
Princeton University
Abstract
We propose a semiparametric method for estimating sparse precision matrix of
high dimensional elliptica... | 5173 |@word briefly:1 manageable:1 polynomial:1 norm:12 instrumental:1 simulation:3 covariance:17 decomposition:3 pick:1 tr:1 moment:1 liu:5 contains:3 past:1 existing:5 outperforms:6 elliptical:8 adj:2 auritzen:1 john:1 fn:4 numerical:4 plot:1 selected:3 es:1 data2:1 org:1 rc:32 c2:2 direct:1 zkj:1 replication:1 yuan:... |
4,613 | 5,174 | A* Lasso for Learning a Sparse Bayesian Network
Structure for Continuous Variables
Seyoung Kim
Lane Center for Computational Biology
Carnegie Mellon University
Pittsburgh, PA 15213
sssykim@cs.cmu.edu
Jing Xiang
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA 15213
jingx@cs.cmu.edu
Abstract
We ad... | 5174 |@word repository:1 open:10 simulation:2 decomposition:1 elisseeff:1 thereby:1 recursively:1 reduction:1 initial:1 contains:1 score:13 selecting:3 outperforms:2 existing:1 current:2 recovered:1 comparing:1 must:1 john:1 realistic:1 wenjiang:1 predetermined:1 remove:1 hash:1 greedy:2 fewer:2 website:1 selected:1 in... |
4,614 | 5,175 | On model selection consistency of M-estimators with
geometrically decomposable penalties
Jason D. Lee, Yuekai Sun
Institute for Computational and Mathematical Engineering
Stanford University
{jdl17,yuekai}@stanford.edu
Jonathan E. Taylor
Department of Statisticis
Stanford University
jonathan.taylor@stanford.edu
Abstr... | 5175 |@word h:4 multitask:1 determinant:1 norm:22 seek:3 covariance:6 jacob:1 necessity:1 liu:1 contains:1 existing:1 ksk1:1 current:1 must:3 additive:1 statis:6 kyk:5 math:1 mathematical:1 along:1 yuan:1 prove:2 consists:1 combine:1 huber:1 cand:2 nardi:1 decomposed:1 automatically:1 estimating:3 notation:1 bounded:3 ... |
4,615 | 5,176 | A multi-agent control framework for co-adaptation in
brain-computer interfaces
Josh Merel1 , ? Roy Fox2 , Tony Jebara3, Liam Paninski4
Department of Neurobiology and Behavior, 3 Department of Computer Science,
4
Department of Statistics, Columbia University, New York, NY 10027
2
School of Computer Science and Engineer... | 5176 |@word neurophysiology:1 trial:1 version:1 open:3 willing:1 simulation:10 tried:1 covariance:4 accounting:1 eng:3 attainable:1 coadaptation:1 initial:5 jimenez:1 lqr:8 tuned:3 current:9 ka:1 optim:1 follower:1 must:3 realistic:2 plasticity:2 lqg:2 motor:7 plot:4 designed:1 update:54 depict:1 stationary:8 cue:1 hal... |
4,616 | 5,177 | Probabilistic Movement Primitives
Alexandros Paraschos, Christian Daniel, Jan Peters, and Gerhard Neumann
Intelligent Autonomous Systems, Technische Universit?t Darmstadt
Hochschulstr. 10, 64289 Darmstadt, Germany
{paraschos,daniel,peters,neumann}@ias.tu-darmstadt.de
Abstract
Movement Primitives (MP) are a well-establ... | 5177 |@word trial:1 middle:2 advantageous:1 linearized:1 covariance:10 q1:1 versatile:1 shot:9 moment:1 contains:3 lightweight:1 jimenez:1 daniel:3 past:1 existing:2 current:3 activation:10 yet:2 grain:1 shape:1 christian:1 enables:1 motor:6 plot:2 intelligence:1 weighing:1 beginning:1 ith:2 record:1 alexandros:1 param... |
4,617 | 5,178 | Variational Policy Search via Trajectory Optimization
Vladlen Koltun
Stanford University and Adobe Research
vladlen@cs.stanford.edu
Sergey Levine
Stanford University
svlevine@cs.stanford.edu
Abstract
In order to learn effective control policies for dynamical systems, policy search
methods must be able to discover su... | 5178 |@word simulation:2 linearized:3 seek:1 decomposition:4 covariance:4 sgd:4 tr:1 solid:2 harder:1 recursively:3 moment:1 reduction:1 initial:16 score:1 lqr:5 denoting:1 ours:1 interestingly:1 outperforms:2 current:21 must:2 written:1 additive:1 subsequent:1 analytic:1 motor:2 lqg:1 reproducible:1 plot:2 progressive... |
4,618 | 5,179 | Learning Trajectory Preferences for Manipulators
via Iterative Improvement
Ashesh Jain, Brian Wojcik, Thorsten Joachims, Ashutosh Saxena
Department of Computer Science, Cornell University.
{ashesh,bmw75,tj,asaxena}@cs.cornell.edu
Abstract
We consider the problem of learning good trajectories for manipulation tasks. T... | 5179 |@word cylindrical:1 faculty:1 middle:3 norm:1 tadepalli:1 open:1 r:7 pick:2 thereby:1 harder:2 configuration:12 contains:1 score:16 liu:1 liquid:1 kinodynamic:1 cort:1 past:1 existing:1 coactive:3 current:4 contextual:2 o2:1 subjective:1 bradley:1 must:1 ashesh:2 uria:1 happen:1 informative:2 kdd:1 partition:1 lq... |
4,619 | 5,180 | Forgetful Bayes and myopic planning: Human
learning and decision-making in a bandit setting
Angela J. Yu
Department of Cognitive Science
University of California, San Diego
La Jolla, CA 92093
ajyu@ucsd.edu
Shunan Zhang
Department of Cognitive Science
University of California, San Diego
La Jolla, CA 92093
s6zhang@ucsd... | 5180 |@word trial:74 exploitation:8 version:2 proportion:1 open:1 instruction:4 simulation:1 schomaker:1 p0:1 initial:1 selecting:2 past:2 outperforms:1 current:6 discretization:2 comparing:1 distant:1 subsequent:1 informative:1 cheap:1 update:2 stationary:3 greedy:24 generative:1 advancement:1 intelligence:1 parameter... |
4,620 | 5,181 | Context-sensitive active sensing in humans
Sheeraz Ahmad
Department of Computer Science and Engineering
University of California San Diego
9500 Gilman Drive La Jolla, CA 92093
sahmad@cs.ucsd.edu
Angela J. Yu
Department of Cognitive Science
University of California San Diego
9500 Gilman Drive La Jolla, CA 92093
ajyu@uc... | 5181 |@word trial:11 middle:1 briefly:1 achievable:1 simulation:3 p0:5 q1:1 incurs:3 reduction:1 initial:2 configuration:3 contains:3 disparity:1 pt0:1 current:4 comparing:1 nt:2 contextual:1 yet:1 attracted:1 readily:2 najemnik:2 must:1 visible:1 distant:1 informative:1 subsequent:1 motor:2 update:2 greedy:3 intellige... |
4,621 | 5,182 | Bellman Error Based Feature Generation using
Random Projections on Sparse Spaces
Mahdi Milani Fard, Yuri Grinberg, Amir massoud Farahmand, Joelle Pineau, Doina Precup
School of Computer Science
McGill University
Montreal, Canada
{mmilan1,ygrinb,amirf,jpineau,dprecup}@cs.mcgill.ca
Abstract
This paper addresses the pro... | 5182 |@word mild:1 version:4 seems:3 norm:12 d2:3 gradual:1 crucially:1 contraction:3 biconjugate:2 carry:2 reduction:6 initial:1 series:1 mmilan1:1 tuned:1 outperforms:2 past:1 kmk:1 current:3 discretization:1 nt:2 yet:1 john:1 remove:1 drop:1 update:3 stationary:2 intelligence:1 prohibitive:2 guess:1 selected:2 amir:... |
4,622 | 5,183 | Reinforcement Learning in Robust Markov Decision
Processes
Huan Xu
Department of Mechanical Engineering
National University of Singapore
Singapore
mpexuh@nus.edu.sg
Shiau Hong Lim
Department of Mechanical Engineering
National University of Singapore
Singapore
mpelsh@nus.edu.sg
Shie Mannor
Department of Electrical Eng... | 5183 |@word trial:1 briefly:1 middle:1 polynomial:1 norm:1 seek:1 solid:1 mpexuh:1 series:1 exclusively:1 past:1 existing:4 current:2 protection:1 si:2 must:2 readily:1 subsequent:1 numerical:1 happen:2 benign:1 unichain:1 drop:2 stationary:4 s0n:2 beginning:1 vanishing:1 indefinitely:1 provides:2 mannor:14 math:4 mcdi... |
4,623 | 5,184 | Projected Natural Actor-Critic
Philip S. Thomas, William Dabney, Sridhar Mahadevan, and Stephen Giguere
School of Computer Science
University of Massachusetts Amherst
Amherst, MA 01003
{pthomas,wdabney,mahadeva,sgiguere}@cs.umass.edu
Abstract
Natural actor-critics form a popular class of policy search algorithms for ... | 5184 |@word trial:3 middle:3 version:3 norm:9 nd:6 simulation:4 covariance:1 arti:4 solid:1 initial:3 liu:1 uma:1 hereafter:2 selecting:1 punishes:1 tuned:1 ours:1 franklin:2 existing:3 current:3 comparing:1 anterior:1 written:1 must:1 biomechanical:2 realistic:1 numerical:1 cant:1 motor:2 treating:1 plot:1 update:15 s... |
4,624 | 5,185 | (More) Efficient Reinforcement Learning via
Posterior Sampling
Osband, Ian
Stanford University
Stanford, CA 94305
iosband@stanford.edu
Van Roy, Benjamin
Stanford University
Stanford, CA 94305
bvr@stanford.edu
Russo, Daniel
Stanford University
Stanford, CA 94305
djrusso@stanford.edu
Abstract
Most provably-efficient ... | 5185 |@word exploitation:1 dtk:4 version:1 polynomial:4 nd:1 simulation:6 crucially:1 tried:1 automat:1 concise:1 accommodate:1 initial:2 contains:1 selecting:1 daniel:1 outperforms:4 existing:3 past:1 current:2 contextual:1 nt:6 si:1 guez:1 must:1 dx:1 numerical:1 happen:1 designed:1 update:1 stationary:1 intelligence... |
4,625 | 5,186 | Adaptive Step?Size for Policy Gradient Methods
Matteo Pirotta
Dept. Elect., Inf., and Bio.
Politecnico di Milano, ITALY
Marcello Restelli
Dept. Elect., Inf., and Bio.
Politecnico di Milano, ITALY
Luca Bascetta
Dept. Elect., Inf., and Bio.
Politecnico di Milano, ITALY
matteo.pirotta@polimi.it
marcello.restelli@poli... | 5186 |@word mild:1 h:1 version:3 polynomial:5 norm:9 simulation:2 paid:2 initial:3 contains:1 series:2 current:3 must:2 ronald:1 numerical:3 lqg:5 motor:3 designed:1 update:3 stationary:8 intelligence:1 parameterization:2 oldest:1 steepest:1 provides:2 simpler:1 mathematical:2 along:4 s2t:1 specialize:1 introduce:2 exp... |
4,626 | 5,187 | Policy Shaping: Integrating Human Feedback
with Reinforcement Learning
Shane Griffith, Kaushik Subramanian, Jonathan Scholz, Charles L. Isbell, and Andrea Thomaz
College of Computing
Georgia Institute of Technology, Atlanta, GA 30332, USA
{sgriffith7, kausubbu, jkscholz}@gatech.edu,
{isbell, athomaz}@cc.gatech.edu
Ab... | 5187 |@word exploitation:1 version:2 proportion:1 tried:1 initial:1 series:1 tuned:4 past:1 current:2 comparing:1 yet:1 must:1 subsequent:1 shape:1 update:1 intelligence:3 selected:1 advancement:2 beginning:1 smith:1 underestimating:1 characterization:1 provides:2 direct:6 viable:1 consists:2 combine:3 introduce:3 appr... |
4,627 | 5,188 | Optimistic policy iteration and natural actor-critic:
A unifying view and a non-optimality result
Paul Wagner
Department of Information and Computer Science
Aalto University
FI-00076 Aalto, Finland
paul.wagner@aalto.fi
Abstract
Approximate dynamic programming approaches to the reinforcement learning
problem are often... | 5188 |@word mild:1 version:6 norm:1 seems:1 open:2 termination:1 simulation:1 solid:2 reduction:1 contains:1 interestingly:1 existing:3 current:5 must:13 distant:1 shape:1 wellbehaved:1 remove:1 treating:1 gist:1 update:7 overshooting:3 greedy:39 selected:1 parameterization:2 accordingly:1 trapping:1 beginning:1 steepe... |
4,628 | 5,189 | DESPOT: Online POMDP Planning with Regularization
Adhiraj Somani
Nan Ye
David Hsu
Wee Sun Lee
Department of Computer Science
National University of Singapore
adhirajsomani@gmail.com, {yenan,dyhsu,leews}@comp.nus.edu.sg
Abstract
POMDPs provide a principled framework for planning under uncertainty, but are
computationa... | 5189 |@word trial:4 exploitation:1 version:2 achievable:1 stronger:1 suitably:1 simulation:4 r:3 condon:1 accounting:1 dramatic:2 accommodate:1 recursively:3 initial:10 contains:9 series:1 tuned:1 o2:6 outperforms:2 past:1 current:7 com:1 z2:3 comparing:1 gmail:1 must:3 additive:3 visible:1 update:5 greedy:1 leaf:7 sel... |
4,629 | 519 | Adaptive Development of Connectionist Decoders
for Complex Error-Correcting Codes
Sheri L. Gish
Mario Blalull
IBM Rf'search Division
Almaden Research Center
650 Harry Road
San Jose, C A 95120
Abstract
\Ve present. an approach for df'velopment of a decoder for any complex
binary error-correct.ing code- (ECC) via train... | 519 |@word nd:1 bf:1 sepa:1 gish:6 tr:2 complexit:1 err:2 ida:1 comparing:2 activation:1 si:1 must:5 parsing:1 readily:1 import:1 selected:4 patterning:2 node:7 ron:1 hah:1 height:1 istical:2 constructed:1 burst:5 ect:2 prove:1 naor:1 hardness:1 prohlem:1 multi:1 actual:1 becomes:1 provided:1 moreover:1 coder:1 sheri:1... |
4,630 | 5,190 | Approximate Dynamic Programming Finally
Performs Well in the Game of Tetris
Victor Gabillon
INRIA Lille - Nord Europe,
Team SequeL, FRANCE
victor.gabillon@inria.fr
Mohammad Ghavamzadeh?
INRIA Lille - Team SequeL
& Adobe Research
mohammad.ghavamzadeh@inria.fr
Bruno Scherrer
INRIA Nancy - Grand Est,
Team Maia, FRANCE
b... | 5190 |@word seems:1 proportion:1 nd:2 simulation:4 tried:1 arti:1 solid:1 shot:1 initial:4 contains:2 score:33 genetic:1 outperforms:5 current:3 com:1 cant:1 shape:2 remove:3 designed:1 update:3 v:1 generative:3 fewer:4 selected:3 greedy:9 eroded:2 intelligence:1 cult:1 beginning:1 short:2 record:1 revisited:1 location... |
4,631 | 5,191 | Reward Mapping for Transfer in Long-Lived Agents
Xiaoxiao Guo
Computer Science and Eng.
University of Michigan
guoxiao@umich.edu
Satinder Singh
Computer Science and Eng.
University of Michigan
baveja@umich.edu
Richard Lewis
Department of Psychology
University of Michigan
rickl@umich.edu
Abstract
We consider how to ... | 5191 |@word multitask:1 worsens:1 exploitation:1 middle:2 consequential:3 tadepalli:1 nd:1 simulation:1 crucially:1 prasad:1 eng:2 accounting:1 incurs:1 thereby:1 initial:6 liu:1 rightmost:2 o2:4 past:2 current:11 comparing:1 router:3 must:1 ronald:1 subsequent:3 sorg:6 realistic:1 designed:3 plot:2 update:4 rjo:3 cong... |
4,632 | 5,192 | Learning a Deep Compact Image Representation for
Visual Tracking
Naiyan Wang
Dit-Yan Yeung
Department of Computer Science and Engineering
Hong Kong University of Science and Technology
winsty@gmail.com
dyyeung@cse.ust.hk
Abstract
In this paper, we study the challenging problem of tracking the trajectory of a
moving ... | 5192 |@word kong:2 cnn:3 version:3 proportion:1 norm:1 rivlin:1 open:2 decomposition:1 dramatic:1 liu:2 contains:1 fragment:2 tuned:5 ours:1 existing:6 freitas:1 current:3 com:1 babenko:1 si:1 gmail:1 activation:4 ust:1 gpu:3 subsequent:2 additive:1 informative:1 entrance:1 shape:1 blur:1 update:1 fund:1 resampling:1 g... |
4,633 | 5,193 | Learning the Local Statistics of Optical Flow
Dan Rosenbaum1 , Daniel Zoran2 , Yair Weiss1,2
CSE , 2 ELSC , Hebrew University of Jerusalem
{danrsm,daniez,yweiss}@cs.huji.ac.il
1
Abstract
Motivated by recent progress in natural image statistics, we use newly available
datasets with ground truth optical flow to learn t... | 5193 |@word h:13 seems:2 open:1 covariance:8 inpainting:3 celebrated:1 daniel:3 denoting:2 interestingly:2 rightmost:1 past:1 outperforms:4 comparing:5 surprising:2 takeo:1 realistic:1 numerical:1 partition:1 informative:1 v:2 stationary:1 half:1 intelligence:2 website:1 leaf:1 inspection:1 hamiltonian:2 provides:2 cse... |
4,634 | 5,194 | Third-Order Edge Statistics: Contour Continuation,
Curvature, and Cortical Connections
Steven W. Zucker
Computer Science
Yale University
New Haven, CT 06520
zucker@cs.yale.edu
Matthew Lawlor
Applied Mathematics
Yale University
New Haven, CT 06520
matthew.lawlor@yale.edu
Abstract
Association field models have attempt... | 5194 |@word collinearity:1 illustrating:1 middle:1 open:1 d2:1 seek:2 citeseer:1 dramatic:1 franois:1 reduction:3 series:1 tuned:1 discretization:1 surprising:1 visible:1 shape:2 plot:1 half:1 plane:1 isotropic:2 filtered:1 colored:3 provides:1 location:4 preference:1 zhang:1 positing:1 along:6 constructed:1 cooccur:1 ... |
4,635 | 5,195 | What Are the Invariant Occlusive Components of
Image Patches? A Probabilistic Generative Approach
Georgios Exarchakis
Redwood Center for Theoretical Neuroscience,
The University of California, Berkeley, US
exarchakis@berkeley.edu
Zhenwen Dai
University of Sheffield, UK, and
FIAS, Goethe-University Frankfurt, Germany
... | 5195 |@word neurophysiology:1 version:3 inversion:3 briefly:1 seems:1 nd:4 ucke:7 d2:3 hyv:1 hu:1 crucially:1 covariance:2 decomposition:1 solid:1 initial:3 configuration:1 contains:4 exclusively:3 oldenburg:2 score:1 bc:1 si:1 attracted:2 john:1 numerical:5 additive:2 informative:1 confirming:1 shape:3 plot:3 update:3... |
4,636 | 5,196 | Action from Still Image Dataset and Inverse Optimal
Control to Learn Task Specific Visual Scanpaths
Stefan Mathe1,3 and Cristian Sminchisescu2,1
Institute of Mathematics of the Romanian Academy of Science
2
Department of Mathematics, Faculty of Engineering, Lund University
3
Department of Computer Science, University ... | 5196 |@word trial:1 cox:1 faculty:1 dalal:1 everingham:1 triggs:1 open:1 instruction:2 harder:1 initial:2 configuration:3 contains:2 score:7 bc:1 interestingly:1 subjective:1 existing:4 outperforms:2 current:3 contextual:3 z2:1 recovered:1 comparing:1 si:2 yet:2 concatenate:1 partition:2 chicago:1 enables:1 hypothesize... |
4,637 | 5,197 | Action is in the Eye of the Beholder: Eye-gaze Driven
Model for Spatio-Temporal Action Localization
Nataliya Shapovalova?
Michalis Raptis?
?
?
Simon Fraser University
Comcast
{nshapova,mori}@cs.sfu.ca
Leonid Sigal?
Greg Mori?
?
Disney Research
mraptis@cable.comcast.com
lsigal@disneyresearch.com
Abstract
We propose... | 5197 |@word cox:1 polynomial:1 norm:1 kokkinos:1 q1:5 initial:1 liu:2 contains:2 score:10 ours:1 outperforms:2 ullah:1 com:2 contextual:1 written:1 supervises:1 enables:1 designed:1 grass:1 v:2 generative:1 selected:2 leaf:1 discovering:1 core:2 record:1 coarse:2 location:5 simpler:1 height:2 along:2 constructed:1 dire... |
4,638 | 5,198 | Higher Order Priors for Joint Intrinsic Image,
Objects, and Attributes Estimation
Vibhav Vineet
Oxford Brookes University, UK
vibhav.vineet@gmail.com
Carsten Rother
TU Dresden, Germany
carsten.rother@tu-dresden.de
Philip H.S. Torr
University of Oxford, UK
philip.torr@eng.ox.ac.uk
Abstract
Many methods have been pro... | 5198 |@word kohli:4 cylindrical:1 version:3 briefly:1 nd:2 everingham:1 propagate:1 rgb:2 eng:1 decomposition:7 jacob:1 textonboost:2 shading:18 initial:1 liu:2 configuration:1 score:4 hoiem:1 ours:4 past:1 existing:2 o2:13 recovered:1 com:1 current:2 si:1 gmail:1 slanted:2 parsing:1 fn:1 informative:1 shape:17 treatin... |
4,639 | 5,199 | Decision Jungles:
Compact and Rich Models for Classification
Jamie Shotton
Sebastian Nowozin
Toby Sharp
John Winn
Microsoft Research
Pushmeet Kohli
Antonio Criminisi
Abstract
Randomized decision trees and forests have a rich history in machine learning and
have seen considerable success in application, perhaps part... | 5199 |@word kohli:3 repository:2 version:1 briefly:1 everingham:1 profit:1 lepetit:1 reduction:5 initial:1 contains:2 score:1 interestingly:1 existing:2 current:5 comparing:1 si:7 yet:2 attracted:1 written:1 must:1 john:2 subsequent:1 partition:1 chicago:1 shape:1 seeding:1 plot:2 ainen:1 grass:9 v:8 greedy:2 leaf:14 f... |
4,640 | 52 | 693
Teaching Artificial Neural Systems to Drive:
Manual Training Techniques for Autonomous Systems
J. F. Shepanski and S. A. Macy
TRW, Inc .
One Space Park, 02/1779
Redondo Beach, CA 90278
Abetract
We have developed a methodology for manually training autononlous control systems
based on artificial neural systems (... | 52 |@word instruction:2 simulation:4 tried:1 solid:2 shot:1 initial:4 configuration:3 cyclic:1 series:1 reaction:1 current:1 deteriorating:1 must:1 readily:1 pertinent:1 afield:1 designed:1 progressively:1 imitate:2 inconvenience:1 short:1 ifx:1 correlat:1 constructed:2 direct:5 driver:3 qualitative:1 consists:1 combin... |
4,641 | 520 | Rule Induction through Integrated Symbolic and
Subsymbolic Processing
Clayton McMillan, Michael C. Mozer, Paul Smolensky
Department of Computer Science and
Institute of Cognitive Science
University of Colorado
Boulder, CO 80309-0430
Abstract
We describe a neural network, called RufeNet, that learns explicit, symbolic ... | 520 |@word version:1 briefly:1 eliminating:1 seems:1 simulation:3 jacob:5 initial:1 contains:1 tram:4 genetic:1 ours:1 rightmost:1 past:2 current:1 nowlan:2 activation:5 assigning:2 conjunctive:2 must:7 grapheme:1 readily:2 periodically:1 enables:1 greedy:1 liberal:1 five:7 along:1 direct:1 consists:3 shorthand:1 combi... |
4,642 | 5,200 | Non-Linear Domain Adaptation with Boosting
Carlos Becker?
C. Mario Christoudias
Pascal Fua
?
CVLab, Ecole
Polytechnique F?ed?erale de Lausanne, Switzerland
firstname.lastname@epfl.ch
Abstract
A common assumption in machine vision is that the training and test samples
are drawn from the same distribution. However, the... | 5200 |@word multitask:3 kulis:2 hippocampus:3 seems:1 tedious:1 seek:4 crucially:1 accounting:1 jacob:1 pick:1 brightness:1 initial:1 contains:1 score:1 salzmann:1 ecole:1 offering:1 outperforms:4 contextual:1 nt:1 yet:1 written:1 informative:1 plot:3 update:1 alone:2 greedy:2 fewer:2 leaf:3 half:2 prohibitive:1 intell... |
4,643 | 5,201 | Modeling Clutter Perception using Parametric
Proto-object Partitioning
Wen-Yu Hua
Department of Statistics
Pennsylvania State University
wxh182@psu.edu
Chen-Ping Yu
Department of Computer Science
Stony Brook University
cheyu@cs.stonybrook.edu
Dimitris Samaras
Department of Computer Science
Stony Brook University
sama... | 5201 |@word middle:1 dalal:1 compression:1 norm:3 scroll:1 triggs:1 confirms:1 r:2 rgb:1 q1:1 accommodate:1 crowding:1 initial:6 contains:3 fragment:8 ala:1 interestingly:1 subjective:1 existing:4 elliptical:1 comparing:1 blank:1 surprising:1 si:2 yet:2 stony:3 must:2 subsequent:2 realistic:1 numerical:1 shape:4 remove... |
4,644 | 5,202 | Mid-level Visual Element Discovery
as Discriminative Mode Seeking
Carl Doersch
Carnegie Mellon University
cdoersch@cs.cmu.edu
Abhinav Gupta
Carnegie Mellon University
abhinavg@cs.cmu.edu
Alexei A. Efros
UC Berkeley
efros@cs.berkeley.edu
Abstract
Recent work on mid-level visual representations aims to capture informa... | 5202 |@word version:2 middle:3 eliminating:1 norm:3 stronger:1 retraining:2 seems:1 seek:2 concise:1 bai:1 configuration:1 liu:1 score:4 selecting:3 hoiem:1 initial:1 ours:3 current:1 assigning:1 must:4 reminiscent:1 informative:2 plot:4 drop:1 update:1 gist:1 v:1 selected:2 guess:6 fewer:1 lamp:1 harvesting:1 provides... |
4,645 | 5,203 | Optimal integration of visual speed across different
spatiotemporal frequency channels
Matja?z Jogan and Alan A. Stocker
Department of Psychology
University of Pennsylvania
Philadelphia, PA 19104
{mjogan,astocker}@sas.upenn.edu
Abstract
How do humans perceive the speed of a coherent motion stimulus that contains
moti... | 5203 |@word neurophysiology:1 trial:6 longterm:1 middle:1 norm:2 accounting:1 configuration:10 contains:3 series:1 disparity:1 tuned:3 bootstrapped:1 n000141110744:1 si:9 written:1 shape:1 treating:1 designed:1 discrimination:19 alone:2 cue:9 selected:1 plane:2 smith:1 provides:1 characterization:3 location:1 simpler:2... |
4,646 | 5,204 | DeViSE: A Deep Visual-Semantic Embedding Model
Andrea Frome*, Greg S. Corrado*, Jonathon Shlens*, Samy Bengio
Jeffrey Dean, Marc?Aurelio Ranzato, Tomas Mikolov
* These authors contributed equally.
{afrome, gcorrado, shlens, bengio, jeff, ranzato?, tmikolov}@google.com
Google, Inc.
Mountain View, CA, USA
Abstract
Mod... | 5204 |@word repository:1 version:2 norm:3 stronger:1 open:2 shot:30 contains:1 score:1 denoting:2 document:2 outperforms:2 current:2 com:1 yet:1 assigning:1 devin:2 blur:1 half:1 fewer:1 selected:1 plane:1 core:8 pointer:1 coarse:2 lexicon:1 honda:1 org:1 along:1 constructed:4 become:2 surprised:1 incorrect:2 gcorrado:... |
4,647 | 5,205 | Visual Concept Learning: Combining Machine Vision
and Bayesian Generalization on Concept Hierarchies
Yangqing Jia1 , Joshua Abbott2 , Joseph Austerweil3 , Thomas Griffiths2 , Trevor Darrell1
1
UC Berkeley EECS 2 Dept of Psychology, UC Berkeley
3
Dept of Cognitive, Linguistics, and Psychological Sciences, Brown Univers... | 5205 |@word trial:2 judgement:2 proportion:2 everingham:1 underperform:1 contains:1 score:11 hoiem:1 tuned:1 ours:1 reassurance:1 existing:9 outperforms:1 current:3 com:1 comparing:1 assigning:3 scatter:1 devin:1 realistic:1 subsequent:1 happen:1 shape:1 plot:3 gist:2 update:2 drop:1 v:1 alone:1 leaf:23 selected:1 xk:1... |
4,648 | 5,206 | Learning invariant representations and applications
to face verification
Qianli Liao, Joel Z Leibo, and Tomaso Poggio
Center for Brains, Minds and Machines
McGovern Institute for Brain Research
Massachusetts Institute of Technology
Cambridge MA 02139
lql@mit.edu, jzleibo@mit.edu, tp@ai.mit.edu
Abstract
One approach to... | 5206 |@word cox:2 version:2 manageable:1 inversion:1 stronger:1 smirnov:2 dalal:1 triggs:2 wiesel:1 simulation:2 tried:1 blender:2 covariance:1 thereby:1 tr:3 accommodate:2 moment:10 series:1 score:1 contains:3 interestingly:1 past:2 current:4 comparing:3 surprising:1 must:1 subsequent:2 blur:1 bmcv:1 depict:3 aside:1 ... |
4,649 | 5,207 | Deep Neural Networks for Object Detection
Christian Szegedy
Alexander Toshev Dumitru Erhan
Google, Inc.
{szegedy, toshev, dumitru}@google.com
Abstract
Deep Neural Networks (DNNs) have recently shown outstanding performance on
image classification tasks [14]. In this paper we go one step further and address
the probl... | 5207 |@word version:1 dalal:1 compression:1 seems:2 kokkinos:1 everingham:1 triggs:1 d2:1 decomposition:1 harder:1 initial:2 contains:1 score:14 series:1 denoting:1 ours:1 tuned:1 current:2 com:1 surprising:1 yet:1 assigning:1 must:1 parsing:2 john:1 devin:1 subsequent:1 visible:1 shape:2 christian:1 designed:3 interpr... |
4,650 | 5,208 | Fast Template Evaluation with Vector Quantization
David Forsyth
Department of Computer Science
University of Illinois at Urbana-Champaign
daf@illinois.edu
Mohammad Amin Sadeghi
Department of Computer Science
University of Illinois at Urbana-Champaign
msadegh2@illinois.edu
Abstract
Applying linear templates is an int... | 5208 |@word version:10 manageable:1 dalal:2 kokkinos:2 retraining:1 triggs:2 nd:1 instruction:5 thereby:1 contains:2 score:45 hoiem:1 ours:4 existing:2 current:5 comparing:2 assigning:1 scatter:1 must:1 additive:1 cheap:1 motor:1 plot:3 drop:1 update:1 v:2 intelligence:4 postprocess:1 core:6 short:1 provides:2 quantize... |
4,651 | 5,209 | Transfer Learning in a Transductive Setting
Marcus Rohrbach
Sandra Ebert
Bernt Schiele
Max Planck Institute for Informatics, Saarbr?ucken, Germany
{rohrbach,ebert,schiele}@mpi-inf.mpg.de
Abstract
Category models for objects or activities typically rely on supervised learning
requiring sufficiently large training s... | 5209 |@word hierachy:3 kulis:1 middle:1 propagate:2 pick:1 mammal:1 solid:4 shot:37 initial:1 liu:1 score:3 ours:9 document:1 outperforms:1 existing:1 comparing:1 subsequent:1 drop:1 plot:1 v:4 bart:1 leaf:2 website:2 morariu:1 discovering:1 provides:4 node:6 org:1 five:1 constructed:1 direct:15 qualitative:1 consists:... |
4,652 | 521 | Neural Network - Gaussian Mixture Hybrid for
Speech Recognition or Density Estimation
Yoshua Bengio
Dept. Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
Giovanni Flammia
Speech Technology Center,
Aalborg University, Denmark
Renato De Morl
School of Computer Science
McGill Unive... | 521 |@word determinant:8 version:1 briefly:1 proportion:1 decomposition:2 covariance:2 initial:2 substitution:2 contains:1 series:5 tuned:1 comparing:1 z2:1 si:1 dx:2 remove:1 designed:1 update:1 discrimination:1 alone:7 stationary:1 une:1 short:2 simpler:1 along:2 introduce:1 theoretically:1 multi:2 brain:1 integrator... |
4,653 | 5,210 | Reshaping Visual Datasets for Domain Adaptation
Boqing Gong
U. of Southern California
Los Angeles, CA 90089
boqinggo@usc.edu
Kristen Grauman
U. of Texas at Austin
Austin, TX 78701
grauman@cs.utexas.edu
Fei Sha
U. of Southern California
Los Angeles, CA 90089
feisha@usc.edu
Abstract
In visual recognition problems, th... | 5210 |@word kulis:2 version:1 achievable:1 instrumental:1 everingham:1 c0:1 accounting:1 mention:1 liu:2 contains:3 daniel:1 rkhs:1 ours:4 interestingly:1 existing:2 com:1 luo:1 must:2 partition:1 shape:3 eleven:1 plot:1 drop:1 discrimination:2 intelligence:1 discovering:2 selected:1 accordingly:1 chua:1 detecting:1 co... |
4,654 | 5,211 | Heterogeneous-Neighborhood-based Multi-Task
Local Learning Algorithms
Yu Zhang
Department of Computer Science, Hong Kong Baptist University
yuzhang@comp.hkbu.edu.hk
Abstract
All the existing multi-task local learning methods are defined on homogeneous
neighborhood which consists of all data points from only one task.... | 5211 |@word multitask:3 kong:1 trial:4 polynomial:1 norm:1 mtlmnn:3 c0:8 plication:1 ajj:1 jacob:1 tr:13 reduction:1 configuration:1 contains:3 past:1 existing:2 qth:1 current:1 comparing:2 update:7 mtfl:6 stationary:1 intelligence:1 discovering:1 desktop:1 scotland:1 ith:6 record:5 sarcos:1 cse:1 successive:1 c6:6 org... |
4,655 | 5,212 | Learning Feature Selection Dependencies in
Multi-task Learning
Jos?e Miguel Hern?andez-Lobato
Department of Engineering
University of Cambridge
jmh233@cam.ac.uk
Daniel Hern?andez-Lobato
Computer Science Department
Universidad Aut?onoma de Madrid
daniel.hernandez@uam.es
Abstract
A probabilistic model based on the hor... | 5212 |@word multitask:1 oostenveld:1 middle:2 norm:2 nd:1 tedious:1 vogt:1 relevancy:1 d2:1 hu:4 covariance:3 inpainting:1 papoulis:1 garrigues:1 carry:1 initial:1 series:1 daniel:3 document:1 must:2 written:3 readily:2 additive:2 numerical:1 shape:1 dupont:1 update:6 alone:2 greedy:3 fewer:1 half:1 leaf:1 xk:10 balc:1... |
4,656 | 5,213 | Parametric Task Learning
Tatsuya Hongo
Nagoya Institute of Technology
Nagoya, 466-8555, Japan
hongo.mllab.nit@gmail.com
Ichiro Takeuchi
Nagoya Institute of Technology
Nagoya, 466-8555, Japan
takeuchi.ichiro@nitech.ac.jp
Masashi Sugiyama
Tokyo Institute of Technology
Tokyo, 152-8552, Japan
sugi@cs.titech.ac.jp
Shinic... | 5213 |@word version:1 advantageous:1 norm:2 nd:2 seems:1 prognostic:1 turlach:2 tr:13 series:2 longitudinal:1 com:1 nt:10 gmail:1 written:3 numerical:2 plot:3 stationary:2 intelligence:1 metabolism:1 caucasian:1 ith:4 location:1 five:1 mathematical:2 along:1 replication:1 prove:1 introduce:1 inter:1 multi:8 window:2 co... |
4,657 | 5,214 | Direct 0-1 Loss Minimization and Margin
Maximization with Boosting
Shaodan Zhai, Tian Xia, Ming Tan and Shaojun Wang
Kno.e.sis Center
Department of Computer Science and Engineering
Wright State University
{zhai.6,xia.7,tan.6,shaojun.wang}@wright.edu
Abstract
We propose a boosting method, DirectBoost, a greedy coordin... | 5214 |@word worsens:1 repository:2 version:2 hoffgen:1 termination:1 bn:10 q1:2 pick:2 minus:2 reduction:3 score:1 sherali:1 genetic:1 outperforms:1 surprising:1 si:1 written:2 john:1 realistic:1 partition:3 happen:1 additive:1 designed:3 update:9 v:1 discrimination:1 greedy:19 selected:2 half:1 warmuth:2 ith:6 dissert... |
4,658 | 5,215 | Reservoir Boosting : Between Online and Offline
Ensemble Learning
Franc?ois Fleuret
Idiap Research Institute
Martigny, Switzerland
francois.fleuret@idiap.ch
Leonidas Lefakis
Idiap Research Institute
Martigny, Switzerland
leonidas.lefakis@idiap.ch
Abstract
We propose to train an ensemble with the help of a reservoir ... | 5215 |@word version:1 middle:1 dalal:1 triggs:1 dekel:1 seek:1 contraction:1 covariance:6 pick:2 incurs:2 mention:1 reduction:1 substitution:1 contains:1 score:1 selecting:2 series:1 denoting:1 outperforms:4 existing:1 bradley:1 current:1 recovered:1 jaz:1 must:7 parsing:1 john:1 subsequent:1 informative:1 predetermine... |
4,659 | 5,216 | Beyond Pairwise: Provably Fast Algorithms for
Approximate k-Way Similarity Search
Anshumali Shrivastava
Department of Computer Science
Computing and Information Science
Cornell University
Ithaca, NY 14853, USA
anshu@cs.cornell.edu
Ping Li
Department of Statistics & Biostatistics
Department of Computer Science
Rutgers ... | 5216 |@word msr:1 determinant:2 version:3 compression:1 advantageous:1 vldb:2 zelnik:1 jacob:1 mention:1 tr:1 reduction:1 initial:1 contains:1 document:6 bc:5 existing:1 current:2 com:2 determinantal:1 subsequent:1 partition:3 cheap:1 christian:3 plot:1 hash:33 intelligence:4 selected:2 item:1 fa9550:1 provides:2 locat... |
4,660 | 522 | Induction of Multiscale Temporal Structure
Michael C. Moser
Department of Computer Science &:
Institute of Cognitive Science
University of Colorado
Boulder, CO 80309-0430
Abstract
Learning structure in temporally-extended sequences is a difficult computational problem because only a fraction of the relevant informati... | 522 |@word version:6 simulation:5 pick:1 thereby:2 accommodate:1 phy:1 initial:2 responsivity:1 selecting:1 hardy:1 tuned:1 activation:4 must:2 readily:2 drop:1 concert:9 designed:1 selected:3 devising:1 filtered:1 mental:1 detecting:1 revisited:1 five:3 become:2 replication:3 compose:1 burr:2 huber:1 indeed:1 roughly:... |
4,661 | 5,220 | Learning Generative Models with the
Up-Propagation Algorithm
Jong-Hoon Oh and H. Sebastian Seung
Bell Labs, Lucent Technologies
Murray Hill, NJ 07974
fjhoh|seungg@bell-labs.com
Abstract
Up-propagation is an algorithm for inverting and learning neural network
generative models. Sensory input is processed by inverting... | 5220 |@word inversion:12 compression:1 seems:1 propagate:2 tried:2 eld:1 contains:1 existing:1 com:1 activation:3 yet:1 written:2 readily:1 cottrell:1 belmont:1 extensional:1 shape:1 update:2 generative:36 parametrization:1 constructed:1 become:1 consists:1 dan:1 x0:1 mechanic:1 globally:1 encouraging:1 notation:1 xed:... |
4,662 | 5,221 | A Neural Network Based
Head Tracking System
D. D. Lee and H. S. Seung
Bell Laboratories, Lucent Technologies
700 Mountain Ave.
Murray Hill, NJ 07974
fddlee|seungg@bell-labs.com
Abstract
We have constructed an inexpensive, video-based, motorized tracking system that learns to track a head. It uses real time graphical... | 5221 |@word cu:4 version:1 nd:2 c0:2 rgb:3 speechreading:1 maes:1 eld:3 series:1 exclusively:1 past:1 nally:1 com:1 wd:2 nowlan:1 must:1 readily:1 attracted:1 written:1 shape:2 enables:1 motor:3 update:2 depict:1 rpn:1 stationary:1 cue:2 alone:1 device:1 selected:1 cult:1 beginning:1 location:10 sigmoidal:1 rc:1 constr... |
4,663 | 5,222 | Top Rank Optimization in Linear Time
Nan Li1
Rong Jin2
Zhi-Hua Zhou1
National Key Laboratory for Novel Software Technology,
Nanjing University, Nanjing 210023, China
2
Department of Computer Science and Engineering,
Michigan State University, East Lansing, MI 48824
{lin,zhouzh}@lamda.nju.edu.cn rongjin@cse.msu.edu
1
... | 5222 |@word trial:2 version:5 norm:1 hsieh:1 bellevue:1 nystr:2 liblinear:3 liu:2 contains:1 score:3 document:1 spambase:1 existing:6 current:1 com:1 surprising:1 attracted:1 kdd:3 hofmann:1 treating:1 designed:1 update:1 plot:2 v:4 selected:1 rudin:1 lr:12 renshaw:1 completeness:1 boosting:1 cse:1 herbrich:2 preferenc... |
4,664 | 5,223 | SerialRank: Spectral Ranking using Seriation
Fajwel Fogel
?
C.M.A.P., Ecole
Polytechnique,
Palaiseau, France
fogel@cmap.polytechnique.fr
Alexandre d?Aspremont
?
CNRS & D.I., Ecole
Normale Sup?erieure
Paris, France
aspremon@ens.fr
Milan Vojnovic
Microsoft Research,
Cambridge, UK
milanv@microsoft.com
Abstract
We descr... | 5223 |@word msr:1 polynomial:2 proportion:4 unif:1 seek:4 minus:2 score:5 united:6 swansea:6 ecole:2 bradley:6 com:1 si:4 numerical:2 j1:5 cheap:1 half:2 selected:1 website:1 item:51 smith:2 boosting:1 preference:9 herbrich:2 rc:5 along:1 mla:1 constructed:1 mathematical:1 symposium:1 consists:4 prove:1 liverpool:6 ins... |
4,665 | 5,224 | Magnitude-sensitive preference formation
Nisheeth Srivastava?
Department of Psychology
University of San Diego
La Jolla, CA 92093
nisheeths@gmail.com
Edward Vul
Department of Psychology
University of San Diego
La Jolla, CA 92093
edwardvul@gmail.com
Paul R Schrater
Dept of Psychology
University of Minnesota
Minneapol... | 5224 |@word trial:4 illustrating:2 version:1 proportion:1 adrian:1 forager:3 willing:3 calculus:1 simulation:1 pick:1 parenthetically:1 thereby:2 reduction:1 initial:4 substitution:1 necessity:1 selecting:1 t7:1 daniel:1 ours:1 past:3 o2:1 existing:1 com:3 comparing:1 gmail:3 yet:1 assigning:1 must:3 john:2 realistic:1... |
4,666 | 5,225 | Learning Mixed Multinomial Logit Model from
Ordinal Data
Sewoong Oh
Dept. of Industrial and Enterprise Systems Engr.
University of Illinois at Urbana-Champaign
Urbana, IL 61801
swoh@illinois.edu
Devavrat Shah
Department of Electrical Engineering
Massachussetts Institute of Technology
Cambridge, MA 02139
devavrat@mit.e... | 5225 |@word briefly:1 version:1 polynomial:2 norm:3 logit:5 nd:8 open:1 km:2 seek:1 crucially:1 decomposition:14 atrix:2 moment:6 initial:1 configuration:1 score:6 selecting:1 past:1 existing:1 bradley:2 current:1 ka:3 realistic:1 numerical:2 mackey:1 implying:2 stationary:4 item:1 kyk:1 affair:1 turnier:1 parkes:3 pro... |
4,667 | 5,226 | Near?Optimal Density Estimation in Near?Linear
Time Using Variable?Width Histograms
Siu-On Chan
Microsoft Research
sochan@gmail.com
Ilias Diakonikolas
University of Edinburgh
ilias.d@ed.ac.uk
Rocco A. Servedio
Columbia University
rocco@cs.columbia.edu
Xiaorui Sun
Columbia University
xiaoruisun@cs.columbia.edu
Abstr... | 5226 |@word mild:1 version:1 briefly:3 achievable:1 stronger:2 norm:1 polynomial:1 closure:1 vldb:1 decomposition:1 p0:9 jafarpour:1 carry:1 moment:1 reduction:4 series:2 contains:4 chervonenkis:1 existing:1 ka:2 com:1 gmail:1 dx:2 must:2 john:1 partition:20 shape:3 enables:1 selected:1 unacceptably:1 oldest:1 provides... |
4,668 | 5,227 | Factoring Variations in Natural Images with
Deep Gaussian Mixture Models
A?aron van den Oord, Benjamin Schrauwen
Electronics and Information Systems department (ELIS), Ghent University
{aaron.vandenoord, benjamin.schrauwen}@ugent.be
Abstract
Generative models can be seen as the swiss army knives of machine learning, a... | 5227 |@word determinant:1 version:2 middle:1 compression:1 norm:1 simulation:2 tried:1 covariance:2 brightness:2 sgd:7 pick:1 shot:1 harder:1 electronics:1 score:1 jimenez:1 daniel:1 tuned:1 ati:1 current:9 rnade:7 gauvain:1 written:2 uria:3 plot:1 interpretable:2 update:2 designed:1 generative:6 fewer:1 intelligence:1... |
4,669 | 5,228 | Robust Kernel Density Estimation by Scaling and
Projection in Hilbert Space
Clayton D. Scott
Deparment of EECS
Univeristy of Michigan
Ann Arbor, MI 48109
clayscot@umich.edu
Robert A. Vandermeulen
Department of EECS
University of Michigan
Ann Arbor, MI 48109
rvdm@umich.edu
Abstract
While robust parameter estimation h... | 5228 |@word version:13 norm:4 proportion:2 essay:1 seek:1 simulation:1 covariance:2 contains:1 outperforms:3 scovel:1 yet:1 dx:3 must:3 john:1 numerical:3 shawetaylor:1 shape:2 noninformative:1 drop:1 n0:3 resampling:1 prohibitive:1 indicative:1 mpm:1 ith:1 math:1 mathematical:1 constructed:3 interscience:1 introduce:4... |
4,670 | 5,229 | Distributed Estimation, Information Loss and
Exponential Families
Qiang Liu
Alexander Ihler
Department of Computer Science, University of California, Irvine
qliu1@uci.edu
ihler@ics.uci.edu
Abstract
Distributed learning of probabilistic models from multiple data repositories
with minimum communication is increasingly ... | 5229 |@word trial:2 repository:6 version:1 wiesel:1 open:2 calculus:1 cos2:1 covariance:1 euclidian:1 moment:3 liu:3 series:1 selecting:1 mag:1 bootstrapped:1 fa8750:1 outperforms:1 bradley:1 ka:3 scatter:1 chu:1 must:1 readily:1 john:4 ronald:1 additive:1 partition:12 numerical:1 plot:1 sponsored:1 n0:1 discrimination... |
4,671 | 523 | LEARNING UNAMBIGUOUS REDUCED
SEQUENCE DESCRIPTIONS
Jiirgen Schmidhuber
Dept. of Computer Science
University of Colorado
Campus Box 430
Boulder, CO 80309, USA
yirgan@cs.colorado.edu
Abstract
Do you want your neural net algorithm to learn sequences? Do not limit yourself to conventional gradient descent (or approximat... | 523 |@word cu:1 version:2 compression:11 stronger:1 bptt:2 decomposition:1 attended:1 tr:2 past:1 existing:1 current:5 activation:5 yet:1 must:2 oldenbourg:1 happen:1 informative:2 motor:1 update:8 pursued:1 fewer:5 device:1 obsolete:1 intelligence:1 beginning:2 ith:1 short:2 compo:2 draft:1 miinchen:1 mathematical:1 a... |
4,672 | 5,230 | Sensory Integration and Density Estimation
Joseph G. Makin and Philip N. Sabes
Center for Integrative Neuroscience/Department of Physiology
University of California, San Francisco
San Francisco, CA 94143-0444 USA
makin, sabes @phy.ucsf.edu
Abstract
The integration of partially redundant information from multiple sens... | 5230 |@word neurophysiology:1 trial:1 stronger:3 norm:1 efh:5 integrative:1 jacob:1 contrastive:3 phy:1 configuration:3 contains:1 interestingly:1 current:1 z2:1 michal:1 yet:1 dx:1 must:3 written:1 john:2 visible:2 distant:1 blur:1 gv:1 designed:1 joy:1 alone:2 generative:9 cue:12 nervous:1 affair:2 smith:1 provides:1... |
4,673 | 5,231 | General Table Completion using a Bayesian
Nonparametric Model
Zoubin Ghahramani
Department of Engineering
University of Cambridge
zoubin@eng.cam.ac.uk
Isabel Valera
Department of Signal Processing
and Communications
University Carlos III in Madrid
ivalera@tsc.uc3m.es
Abstract
Even though heterogeneous databases can ... | 5231 |@word cortez:1 bf:1 consolider:1 simulation:1 eng:1 covariance:2 accommodate:1 mcar:2 contains:7 exclusively:1 bvc:1 genetic:1 outperforms:3 o2:1 chu:1 bd:35 written:1 applicant:1 cruz:2 tec2009:1 ministerio:1 wx:1 kdd:1 shape:1 drop:1 treating:2 update:3 plot:3 s2010:1 generative:2 prohibitive:1 website:1 item:2... |
4,674 | 5,232 | Dependent nonparametric trees for dynamic
hierarchical clustering
Avinava Dubey?? , Qirong Ho?? , Sinead Williamson? , Eric P. Xing?
? Machine Learning Department, Carnegie Mellon University
? Institute for Infocomm Research, A*STAR
?
McCombs School of Business, University of Texas at Austin
akdubey@cs.cmu.edu, hoqiro... | 5232 |@word trial:1 middle:1 proportion:1 seems:1 pressure:7 mammal:1 initial:1 contains:2 efficacy:2 liquid:7 document:17 ours:1 outperforms:1 existing:1 current:2 com:1 recovered:2 virus:8 gmail:1 written:1 must:2 confirming:1 shape:1 simian:3 interpretable:1 stationary:7 half:1 leaf:4 intelligence:1 mccallum:1 core:... |
4,675 | 5,233 | Sparse Bayesian structure learning with dependent
relevance determination prior
Anqi Wu1
Mijung Park2
Oluwasanmi Koyejo3
Jonathan W. Pillow4
1,4
Princeton Neuroscience Institute, Princeton University,
{anqiw, pillow}@princeton.edu
2
The Gatsby Unit, University College London, mijung@gatsby.ucl.ac.uk
3
Department ... | 5233 |@word trial:1 version:2 briefly:1 mri:1 norm:3 stronger:3 covariance:28 hsieh:1 jacob:1 liu:1 series:4 groundwork:1 rightmost:1 anqi:1 written:1 webster:1 drop:1 plot:2 interpretable:1 generative:2 half:2 selected:1 intelligence:3 beginning:1 ith:5 record:1 beauchamp:1 location:4 toronto:1 firstly:1 org:1 zhang:1... |
4,676 | 5,234 | Mondrian Forests: Efficient Online Random Forests
Balaji Lakshminarayanan
Gatsby Unit
University College London
Daniel M. Roy
Department of Engineering
University of Cambridge
Yee Whye Teh
Department of Statistics
University of Oxford
Abstract
Ensembles of randomized decision trees, usually referred to as random for... | 5234 |@word trial:1 version:8 orf:18 contains:1 score:4 daniel:1 denoting:1 dubourg:1 outperforms:2 existing:10 freitas:1 current:1 comparing:2 com:2 lang:1 mushroom:2 realistic:1 periodically:1 partition:7 j1:2 kdd:1 treating:1 plot:2 update:6 v:3 generative:1 prohibitive:1 leaf:32 instantiate:1 greedy:1 fewer:1 ntrai... |
4,677 | 5,235 | Parallel Sampling of HDPs using Sub-Cluster Splits
John W. Fisher III
CSAIL, MIT
fisher@csail.mit.edu
Jason Chang
CSAIL, MIT
jchang7@csail.mit.edu
Abstract
We develop a sampling technique for Hierarchical Dirichlet process models. The
parallel algorithm builds upon [1] by proposing large split and merge moves based
... | 5235 |@word repository:1 version:3 briefly:2 proportion:9 seems:2 initial:3 score:1 selecting:1 lichman:1 denoting:1 document:9 current:2 comparing:1 assigning:1 yet:2 written:1 readily:1 john:1 remove:1 plot:1 resampling:1 stationary:4 generative:2 ith:1 reciprocal:1 core:1 record:1 blei:4 num:7 constructed:1 direct:3... |
4,678 | 5,236 | Localized Data Fusion for Kernel k-Means Clustering
with Application to Cancer Biology
Adam A. Margolin
margolin@ohsu.edu
Department of Biomedical Engineering
Oregon Health & Science University
Portland, OR 97239, USA
Mehmet G?onen
gonen@ohsu.edu
Department of Biomedical Engineering
Oregon Health & Science University... | 5236 |@word repository:1 version:3 prognostic:6 integrative:2 km:19 decomposition:2 covariance:1 tr:14 liu:2 contains:2 united:1 outperforms:1 existing:1 savage:1 com:1 assigning:1 attracted:1 written:2 john:1 concatenate:1 partition:1 informative:3 enables:1 atlas:5 aps:1 intelligence:1 plane:1 characterization:9 org:... |
4,679 | 5,237 | Learning with Fredholm Kernels
Qichao Que Mikhail Belkin Yusu Wang
Department of Computer Science and Engineering
The Ohio State University
Columbus, OH 43210
{que,mbelkin,yusu}@cse.ohio-state.edu
Abstract
In this paper we propose a framework for supervised and semi-supervised learning
based on reformulating the lear... | 5237 |@word version:8 polynomial:1 norm:1 stronger:1 nd:1 hu:1 d2:2 covariance:1 pick:2 reduction:1 contains:4 selecting:1 rkhs:5 suppressing:1 document:2 existing:1 com:1 written:1 john:2 fn:2 informative:2 krikamol:1 designed:1 num:1 provides:4 cse:1 mathematical:2 along:6 c2:3 become:1 prove:1 combine:3 manner:1 int... |
4,680 | 5,238 | Scalable Kernel Methods via Doubly Stochastic Gradients
Bo Dai1 , Bo Xie1 , Niao He1 , Yingyu Liang2 , Anant Raj1 , Maria-Florina Balcan3 , Le Song1
1
Georgia Institute of Technology
{bodai, bxie33, nhe6, araj34}@gatech.edu, lsong@cc.gatech.edu
2
3
Princeton University
Carnegie Mellon University
yingyul@cs.princeton.e... | 5238 |@word msr:1 faculty:1 version:2 polynomial:3 norm:2 additively:1 tried:2 decomposition:1 covariance:2 q1:1 pick:1 sgd:7 incurs:1 nystr:6 tr:1 initial:2 rkhs:20 interestingly:1 prefix:1 document:1 comparing:1 com:1 yet:1 intriguing:1 written:1 readily:2 kft:2 numerical:4 subsequent:2 additive:1 kdd:1 designed:3 ju... |
4,681 | 5,239 | Kernel Mean Estimation via Spectral Filtering
Krikamol Muandet
MPI-IS, T?ubingen
krikamol@tue.mpg.de
Bharath Sriperumbudur
Dept. of Statistics, PSU
bks18@psu.edu
Bernhard Sch?olkopf
MPI-IS, T?ubingen
bs@tue.mpg.de
Abstract
The problem of estimating the kernel mean in a reproducing kernel Hilbert space
(RKHS) is cen... | 5239 |@word version:2 achievable:1 norm:2 suitably:1 open:1 covariance:5 decomposition:3 mention:1 thereby:1 moment:1 reduction:2 contains:1 score:5 rkhs:6 interestingly:2 existing:1 must:2 written:1 tailoring:1 analytic:1 krikamol:2 plot:1 update:3 v:3 implying:2 accordingly:1 isotropic:2 ith:2 reciprocal:1 core:1 din... |
4,682 | 524 | NETWORK MODEL OF STATE-DEPENDENT
SEQUENCING
Jeffrey P. Sutton: Adam N. Mamelak t and J. Allan Hobson
Laboratory of Neurophysiology and Department of Psychiatry
Harvard Medical School
74 Fenwood Road, Boston, MA 02115
Abstract
A network model with temporal sequencing and state-dependent modulatory features is describe... | 524 |@word neurophysiology:1 worsens:1 seems:1 d2:1 simulation:4 simplifying:1 somplinsky:1 hochner:1 initial:2 cyclic:1 physiol:1 plasticity:2 dupont:1 motor:2 plot:3 progressively:1 fund:1 nervous:1 inspection:1 provides:1 location:1 burst:4 loll:1 edelman:1 consists:2 behavioral:1 inter:1 allan:1 rapid:2 behavior:5 ... |
4,683 | 5,240 | Subspace Embeddings for the Polynomial Kernel
Huy L. Nguy?e? n
Simons Institute, UC Berkeley
Berkeley, CA 94720
hlnguyen@cs.princeton.edu
Haim Avron
IBM T.J. Watson Research Center
Yorktown Heights, NY 10598
haimav@us.ibm.com
David P. Woodruff
IBM Almaden Research Center
San Jose, CA 95120
dpwoodru@us.ibm.com
Abstra... | 5240 |@word version:5 polynomial:31 norm:4 stronger:1 nd:1 ount:9 seek:1 decomposition:1 moment:1 reduction:3 contains:2 series:1 woodruff:6 ours:1 ketch:42 ka:5 com:2 nt:10 realistic:1 numerical:3 j1:3 kdd:1 enables:1 hash:6 implying:1 intelligence:1 prohibitive:2 selected:2 fewer:1 kyk:1 item:1 provides:6 boosting:1 ... |
4,684 | 5,241 | Learning the Learning Rate for
Prediction with Expert Advice
Wouter M. Koolen
Queensland University of Technology and UC Berkeley
wouter.koolen@qut.edu.au
Tim van Erven
Leiden University, the Netherlands
tim@timvanerven.nl
?
Peter D. Grunwald
Leiden University and Centrum Wiskunde & Informatica, the Netherlands
pdg@... | 5241 |@word mild:1 trial:2 stronger:2 seems:1 open:1 crucially:1 queensland:1 decomposition:1 incurs:1 contains:1 sah:1 selecting:1 tuned:2 erven:3 existing:1 past:2 current:2 si:1 yet:1 dx:1 must:1 happen:1 succeeding:1 update:3 initialises:1 v:1 half:1 warmuth:1 provides:1 boosting:1 guard:1 overhead:3 introduce:5 ex... |
4,685 | 5,242 | Delay-Tolerant Algorithms for
Asynchronous Distributed Online Learning
Matthew Streeter
Duolingo, Inc.?
Pittsburgh, PA
matt@duolingo.com
H. Brendan McMahan
Google, Inc.
Seattle, WA
mcmahan@google.com
Abstract
We analyze new online gradient descent algorithms for distributed systems with
large delays between gradient... | 5242 |@word version:3 norm:1 dekel:1 open:1 simulation:1 automat:1 incurs:1 sgd:1 tice:1 reduction:1 bck:13 liu:1 contains:1 moment:1 initial:1 daniel:2 tuned:1 past:2 hrafnkelsson:1 current:2 com:2 savage:1 yet:1 must:1 john:5 devin:1 periodically:1 realistic:1 additive:2 kdd:1 cheap:1 remove:1 plot:6 sponsored:2 upda... |
4,686 | 5,243 | Efficient Minimax Strategies for Square Loss Games
Wouter M. Koolen
Queensland University of Technology and UC Berkeley
wouter.koolen@qut.edu.au
Alan Malek
University of California, Berkeley
malek@eecs.berkeley.edu
Peter L. Bartlett
University of California, Berkeley and Queensland University of Technology
peter@ber... | 5243 |@word norm:4 seems:1 seek:1 forecaster:1 queensland:2 decomposition:1 jacob:2 incurs:1 euclidian:1 tr:8 recursively:2 moment:4 past:3 ka:19 current:2 written:1 must:2 half:1 fewer:1 leaf:2 intelligence:2 warmuth:3 xk:1 beginning:1 manfred:3 characterization:3 math:1 zhang:3 prove:1 kov:1 paragraph:1 introduce:1 m... |
4,687 | 5,244 | Online Decision-Making in
General Combinatorial Spaces
Arun Rajkumar
Shivani Agarwal
Department of Computer Science and Automation
Indian Institute of Science, Bangalore 560012, India
{arun r,shivani}@csa.iisc.ernet.in
Abstract
We study online combinatorial decision problems, where one must make sequential decision... | 5244 |@word trial:17 norm:7 c0:1 bf:10 d2:2 decomposition:17 q1:1 incurs:3 kijima:2 contains:3 selecting:3 recovered:1 incidence:1 assigning:1 must:1 additive:2 update:4 prohibitive:1 warmuth:5 manfred:4 characterization:1 boosting:1 location:7 zhang:2 mathematical:2 bowman:3 supply:6 ouput:1 qij:5 incorrect:1 doubly:3... |
4,688 | 5,245 | Model-based Reinforcement Learning
and the Eluder Dimension
Ian Osband
Stanford University
iosband@stanford.edu
Benjamin Van Roy
Stanford University
bvr@stanford.edu
Abstract
We consider the problem of learning to optimize an unknown Markov decision process (MDP). We show that, if the MDP can be parameterized within
... | 5245 |@word exploitation:1 polynomial:3 norm:4 r:1 decomposition:1 attainable:1 concise:1 initial:2 selecting:1 daniel:3 lqr:1 existing:3 discretization:1 si:1 john:1 belmont:1 ronald:2 additive:2 wiewiora:1 remove:1 stationary:1 generative:1 parameterization:1 xk:17 short:1 prediciton:1 predecessor:2 katehakis:1 apost... |
4,689 | 5,246 | Algorithms for CVaR Optimization in MDPs
Yinlam Chow?
Institute of Computational & Mathematical Engineering, Stanford University
Mohammad Ghavamzadeh?
Adobe Research & INRIA Lille - Team SequeL
Abstract
In many sequential decision-making problems we may want to manage risk by
minimizing some measure of variability in... | 5246 |@word briefly:1 open:1 prasad:1 p0:6 pg:12 incurs:1 mention:1 moment:1 initial:5 contains:4 janson:2 current:6 written:4 john:1 numerical:1 update:31 v:1 stationary:1 intelligence:1 xk:34 mannor:2 mathematical:4 along:1 differential:1 prove:2 consists:1 inside:1 manner:1 finitehorizon:1 x0:72 expected:8 spsa:14 t... |
4,690 | 5,247 | Sparse Multi-Task Reinforcement Learning
Daniele Calandriello ?
Alessandro Lazaric?
Team SequeL
INRIA Lille ? Nord Europe, France
Marcello Restelli?
DEIB
Politecnico di Milano, Italy
Abstract
In multi-task reinforcement learning (MTRL), the objective is to simultaneously
learn multiple tasks and exploit their simila... | 5247 |@word multitask:1 version:1 norm:11 stronger:1 tadepalli:1 confirms:1 simulation:1 dealer:6 decomposition:2 jacob:1 dramatic:1 reduction:1 initial:1 contains:1 selecting:1 rkhs:2 existing:2 comparing:1 nt:3 surprising:1 worsening:1 yet:1 dx:4 must:1 numerical:4 designed:2 mtfl:1 implying:2 generative:2 greedy:4 l... |
4,691 | 5,248 | Probabilistic Differential Dynamic Programming
Yunpeng Pan and Evangelos A. Theodorou
Daniel Guggenheim School of Aerospace Engineering
Institute for Robotics and Intelligent Machines
Georgia Institute of Technology
Atlanta, GA 30332
ypan37@gatech.edu, evangelos.theodorou@ae.gatech.edu
Abstract
We present a data-driv... | 5248 |@word luk:1 trial:2 version:2 eliminating:1 open:1 propagate:1 covariance:7 tr:1 solid:3 xgoal:5 ld:3 reduction:1 moment:2 initial:3 daniel:1 document:1 outperforms:2 existing:1 hasselt:2 dx:10 written:1 numerical:3 analytic:3 lqg:1 motor:1 update:2 intelligence:1 parameterization:4 xk:40 parameterizations:1 firs... |
4,692 | 5,249 | Weighted importance sampling for off-policy learning
with linear function approximation
A. Rupam Mahmood, Hado van Hasselt, Richard S. Sutton
Reinforcement Learning and Artificial Intelligence Laboratory
University of Alberta
Edmonton, Alberta, Canada T6G 1S2
{ashique,vanhasse,sutton}@cs.ualberta.ca
Abstract
Importanc... | 5249 |@word innovates:2 middle:1 version:1 norm:1 termination:2 simulation:1 recursively:1 carry:1 initial:3 liu:2 rightmost:1 existing:2 hasselt:2 si:6 yet:1 written:2 happen:1 update:11 intelligence:2 offpolicy:2 xk:10 dissertation:1 provides:1 gx:1 lx:4 provisional:5 constructed:1 become:2 ik:1 prove:1 shorthand:2 s... |
4,693 | 525 | A Network of Localized Linear Discriminants
Martin S. Glassman
Siemens Corporate Research
755 College Road East
Princeton, NJ 08540
msg@siemens.siemens.com
Abstract
The localized linear discriminant network (LLDN) has been designed to address
classification problems containing relatively closely spaced data from diff... | 525 |@word illustrating:1 version:2 advantageous:1 retraining:1 d2:2 grey:1 harder:1 accommodate:1 initial:1 tuned:1 com:1 yet:1 grain:1 shape:1 remove:1 designed:1 plot:2 update:2 discrimination:5 v:1 plane:2 beginning:1 short:1 coarse:1 sigmoidal:1 along:4 constructed:1 ouput:1 combine:1 introduce:1 roughly:2 growing... |
4,694 | 5,250 | A Representation Theory for Ranking Functions
Harsh Pareek, Pradeep Ravikumar
Department of Computer Science
University of Texas at Austin
{harshp,pradeepr}@cs.utexas.edu
Abstract
This paper presents a representation theory for permutation-valued functions,
which in their general form can also be called listwise rank... | 5250 |@word version:1 norm:5 open:2 closure:1 decomposition:25 pick:2 mention:1 boundedness:1 initial:1 configuration:1 series:1 score:17 contains:3 liu:4 document:34 err:1 recovered:1 savage:2 jaynes:1 written:3 john:1 realistic:1 j1:1 analytic:6 ainen:1 reranking:7 website:1 item:1 xk:1 reciprocal:2 ith:2 smith:1 yam... |
4,695 | 5,251 | Near-Optimal-Sample Estimators for Spherical
Gaussian Mixtures
Jayadev Acharya?
MIT
jayadev@mit.edu
Ashkan Jafarpour, Alon Orlitsky, Ananda Theertha Suresh
UC San Diego
{ashkan, alon, asuresh}@ucsd.edu
Abstract
Many important distributions are high dimensional, and often they can be modeled
as Gaussian mixtures. We d... | 5251 |@word clts:1 polynomial:7 norm:4 d2:1 seek:1 covariance:5 decomposition:1 k7:1 mention:1 jafarpour:2 recursively:1 moment:1 necessity:1 contains:1 selecting:1 document:3 reynolds:1 past:3 existing:1 recovered:1 current:1 od:1 remove:1 xk:4 beginning:1 ith:1 smith:1 core:1 completeness:2 multiset:1 provides:1 coar... |
4,696 | 5,252 | Tighten after Relax: Minimax-Optimal Sparse PCA
in Polynomial Time
Zhaoran Wang
Huanran Lu
Han Liu
Department of Operations Research and Financial Engineering
Princeton University
Princeton, NJ 08540
{zhaoran,huanranl,hanliu}@princeton.edu
Abstract
We provide statistical and computational analysis of sparse Principal... | 5252 |@word mild:1 version:3 polynomial:3 norm:8 d2:1 covariance:17 decomposition:6 incurs:1 tr:4 sepulchre:1 initial:4 liu:4 dspca:2 hereafter:3 o2:1 existing:4 outperforms:1 tackling:1 chu:1 john:1 numerical:3 analytic:3 update:1 mackey:1 greedy:3 intelligence:1 nq:2 accordingly:1 provides:1 allerton:2 zhang:2 along:... |
4,697 | 5,253 | Consistency of weighted majority votes
Daniel Berend Computer Science Department and Mathematics Department
Ben Gurion University
Beer Sheva, Israel berend@cs.bgu.ac.il
Aryeh Kontorovich
Computer Science Department Ben Gurion University
Beer Sheva, Israel karyeh@cs.bgu.ac.il
Abstract
We revisit from a statistical lea... | 5253 |@word mild:1 version:2 norm:1 open:5 mezuman:1 simplifying:1 eng:1 contraction:1 invoking:1 necessity:1 series:1 ktv:4 daniel:1 ours:1 recovered:1 si:5 goldberger:2 must:2 readily:1 happen:1 gurion:2 update:1 v:2 half:1 warmuth:2 ith:4 record:1 consulting:1 boosting:3 math:1 direct:1 aryeh:1 beta:3 incorrect:1 pr... |
4,698 | 5,254 | Quantized Estimation of Gaussian Sequence Models
in Euclidean Balls
Yuancheng Zhu
John Lafferty
Department of Statistics
University of Chicago
Abstract
A central result in statistical theory is Pinsker?s theorem, which characterizes the
minimax rate in the normal means model of nonparametric estimation. In this
pape... | 5254 |@word briefly:1 compression:5 polynomial:1 norm:1 simulation:5 bn:12 decomposition:1 incurs:1 series:1 denoting:4 written:1 john:4 planet:1 chicago:1 confirming:1 drop:1 treating:1 prohibitive:1 selected:1 fa9550:1 characterization:1 quantized:23 node:1 kepler:2 codebook:7 nussbaum:1 zhang:2 five:1 height:1 dn:1 ... |
4,699 | 5,255 | On the Convergence Rate of Decomposable
Submodular Function Minimization
Robert Nishihara, Stefanie Jegelka, Michael I. Jordan
Electrical Engineering and Computer Science
University of California
Berkeley, CA 94720
{rkn,stefje,jordan}@eecs.berkeley.edu
Abstract
Submodular functions describe a variety of discrete probl... | 5255 |@word kohli:1 version:2 polynomial:3 norm:2 open:1 cos2:1 decomposition:4 pick:1 thereby:1 harder:1 cyclic:3 siebel:1 fa8750:1 existing:1 current:1 ka:1 written:1 must:1 cruz:1 numerical:2 partition:2 enables:1 update:1 knyazev:1 leaf:1 yr:3 intelligence:1 xk:3 characterization:2 math:2 unbounded:1 mathematical:2... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.