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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4,000 | 4,620 | Learning Label Trees for Probabilistic Modelling of
Implicit Feedback
Yee Whye Teh
ywteh@gatsby.ucl.ac.uk
Gatsby Computational Neuroscience Unit
University College London
Andriy Mnih
amnih@gatsby.ucl.ac.uk
Gatsby Computational Neuroscience Unit
University College London
Abstract
User preferences for items can be inf... | 4620 |@word version:1 manageable:1 termination:1 hu:1 bn:5 simplifying:1 reduction:2 liu:1 contains:2 score:7 selecting:3 favouring:1 existing:2 comparing:4 surprising:2 beygelzimer:1 yet:1 written:1 john:1 distant:1 realistic:1 kdd:1 drop:1 treating:3 update:3 n0:2 alone:1 greedy:2 selected:12 prohibitive:1 item:136 l... |
4,001 | 4,621 | Effective Split-Merge Monte Carlo Methods for
Nonparametric Models of Sequential Data
Michael C. Hughes1 , Emily B. Fox2 , and Erik B. Sudderth1
1
Department of Computer Science, Brown University, {mhughes,sudderth}@cs.brown.edu
2
Department of Statistics, University of Washington, ebfox@stat.washington.edu
Abstract
... | 4621 |@word version:1 middle:2 interleave:1 unif:1 open:1 km:14 chopping:1 concise:1 tr:1 initial:1 configuration:3 series:6 contains:2 selecting:5 interestingly:1 outperforms:1 existing:1 ka:27 z2:2 current:3 recovered:4 must:5 subsequent:1 partition:3 enables:1 remove:1 plot:2 update:11 resampling:2 half:3 discoverin... |
4,002 | 4,622 | Tensor Decomposition for Fast Parsing with
Latent-Variable PCFGs
Shay B. Cohen and Michael Collins
Department of Computer Science
Columbia University
New York, NY 10027
scohen,mcollins@cs.columbia.edu
Abstract
We describe an approach to speed-up inference with latent-variable PCFGs, which
have been shown to be highly ... | 4622 |@word version:1 middle:1 norm:3 seems:1 seek:1 decomposition:21 solid:1 recursively:1 initial:1 score:1 charniak:1 existing:2 comparing:1 parsing:17 numerical:1 plot:3 interpretable:1 reranking:1 prohibitive:1 leaf:4 item:1 p7:1 ith:1 coarse:1 node:13 mathematical:1 direct:1 consists:1 prove:2 inside:17 expected:... |
4,003 | 4,623 | Locating Changes in Highly Dependent Data
with Unknown Number of Change Points
Daniil Ryabko
SequeL-INRIA/LIFL-CNRS,
daniil@ryabko.net
Azadeh Khaleghi
SequeL-INRIA/LIFL-CNRS,
Universit?e de Lille, France
azadeh.khaleghi@inria.fr
Abstract
The problem of multiple change point estimation is considered for sequences wit... | 4623 |@word polynomial:2 outlook:1 series:21 exclusively:1 score:18 contains:1 chervonenkis:1 john:1 partition:5 remove:2 designed:1 discrimination:1 stationary:13 selected:1 mccallum:1 core:1 filtered:3 mitigation:2 detecting:2 tahoe:1 simpler:2 zhang:1 mathematical:1 prove:3 introduce:2 inter:1 indeed:1 ra:1 market:1... |
4,004 | 4,624 | Bayesian nonparametric models for ranked data
Yee Whye Teh
Department of Statistics
University of Oxford
Oxford, United Kingdom
y.w.teh@stats.ox.ac.uk
Franc?ois Caron
INRIA
IMB - University of Bordeaux
Talence, France
Francois.Caron@inria.fr
Abstract
We develop a Bayesian nonparametric extension of the popular Plack... | 4624 |@word cox:1 briefly:1 simulation:4 propagate:1 moment:1 generatively:1 series:4 ingersoll:1 united:2 ecole:1 existing:1 bradley:2 yet:2 dx:3 tilted:1 subsequent:1 partition:1 shape:1 analytic:1 enables:1 update:13 stationary:1 generative:3 selected:2 leaf:1 item:50 parameterization:1 xk:21 parametrization:1 ith:7... |
4,005 | 4,625 | Value Pursuit Iteration
Amir-massoud Farahmand?
Doina Precup ?
School of Computer Science, McGill University, Montreal, Canada
Abstract
Value Pursuit Iteration (VPI) is an approximate value iteration algorithm that finds
a close to optimal policy for reinforcement learning problems with large state
spaces. VPI has two... | 4625 |@word mild:2 kgk:2 trial:1 version:2 polynomial:1 norm:18 open:1 decomposition:1 q1:2 recursively:1 initial:4 current:2 comparing:1 attracted:1 john:4 ronald:5 stationary:4 greedy:7 leaf:1 selected:1 amir:5 core:2 short:1 provides:1 multiset:1 completeness:1 mannor:2 dn:13 c2:5 farahmand:9 consists:3 inside:1 man... |
4,006 | 4,626 | Exact and Stable Recovery of Sequences of Signals
with Sparse Increments via Differential
?1-Minimization
Demba Ba1,2 , Behtash Babadi1,2 , Patrick Purdon2 and Emery Brown1,2
1
MIT Department of BCS, Cambridge, MA 02139
2
MGH Department of Anesthesia, Critical Care and Pain Medicine
55 Fruit st, GRJ 4, Boston, MA 02114... | 4626 |@word repository:1 version:1 compression:1 norm:2 simulation:7 propagate:1 decomposition:1 pick:1 delgado:1 series:2 mosher:1 interestingly:1 past:1 outperforms:6 recovered:1 current:1 com:1 surprising:1 yet:2 must:1 numerical:1 fewer:1 prohibitive:1 selected:1 kyk:1 xk:50 record:1 successive:2 compressible:3 unb... |
4,007 | 4,627 | Variational Inference for Crowdsourcing
Qiang Liu
ICS, UC Irvine
qliu1@ics.uci.edu
Jian Peng
TTI-C & CSAIL, MIT
jpeng@csail.mit.edu
Alexander Ihler
ICS, UC Irvine
ihler@ics.uci.edu
Abstract
Crowdsourcing has become a popular paradigm for labeling large datasets. However, it has given rise to the computational task ... | 4627 |@word trial:1 exploitation:1 version:8 eliminating:4 polynomial:1 seems:2 logit:3 p0:5 carry:1 necessity:1 liu:1 configuration:3 karger:10 loeliger:1 interestingly:1 subjective:1 existing:2 current:2 ka:1 assigning:2 john:1 numerical:4 informative:2 enables:1 cheap:1 treating:1 update:14 intelligence:1 generative... |
4,008 | 4,628 | Forward-Backward Activation Algorithm for
Hierarchical Hidden Markov Models
Kei Wakabayashi
Faculty of Library, Information and Media Science
University of Tsukuba, Japan
kwakaba@slis.tsukuba.ac.jp
Takao Miura
Department of Engineering
Hosei University, Japan
miurat@hosei.ac.jp
Abstract
Hierarchical Hidden Markov Mod... | 4628 |@word faculty:1 termination:6 decomposition:3 q1:16 thereby:1 recursively:1 initial:4 contains:2 selecting:1 united:1 document:2 outperforms:1 existing:5 activation:39 must:2 bd:8 enables:5 update:1 resampling:1 stationary:1 intelligence:2 fewer:1 selected:1 beginning:2 characterization:1 provides:1 node:3 detect... |
4,009 | 4,629 | Distributed Probabilistic Learning
for Camera Networks with Missing Data
Vladimir Pavlovic
Department of Computer Science
Rutgers University
vladimir@cs.rutgers.edu
Sejong Yoon
Department of Computer Science
Rutgers University
sjyoon@cs.rutgers.edu
Abstract
Probabilistic approaches to computer vision typically assum... | 4629 |@word version:1 wiesel:2 norm:1 seek:1 covariance:1 tr:1 initial:1 contains:1 series:1 current:1 ij1:4 chu:1 takeo:2 subsequent:3 visible:3 partition:1 shape:1 plot:1 update:1 v:1 generative:3 intelligence:1 device:2 selected:1 plane:1 xk:1 provides:2 node:26 location:2 along:1 direct:1 qualitative:1 ilin:1 combi... |
4,010 | 463 | Illumination and View Position in 3D Visual
Recognition
Amnon Shashua
M.LT. Artificial Intelligence Lab., NE43-737
and Department of Brain and Cognitive Science
Cambridge, MA 02139
Abstract
It is shown that both changes in viewing position and illumination conditions can be compensated for, prior to recognition, usin... | 463 |@word version:1 compression:2 seems:2 duda:1 open:1 closure:1 descnbed:1 brightness:10 selecting:2 recovered:4 incidence:1 yet:2 visible:4 shape:2 alone:4 intelligence:1 leaf:1 provides:1 location:4 along:8 edelman:1 ray:1 manner:1 tomaso:1 nor:1 brain:1 compensating:2 inspired:1 automatically:1 provided:2 bounded... |
4,011 | 4,630 | Compressive Sensing MRI with Wavelet Tree Sparsity
Chen Chen and Junzhou Huang
Department of Computer Science and Engineering
University of Texas at Arlington
cchen@mavs.uta.edu
jzhuang@uta.edu
Abstract
In Compressive Sensing Magnetic Resonance Imaging (CS-MRI), one can reconstruct a MR image with good quality from o... | 4630 |@word version:1 mri:24 compression:1 chakraborty:1 decomposition:1 liu:1 contains:1 amp:11 outperforms:3 existing:5 recovered:3 comparing:1 yet:1 written:2 blur:1 n0:6 fewer:1 website:1 selected:1 asu:1 xk:5 core:1 simpler:4 zhang:6 kingsbury:1 yall1:7 symposium:1 combine:2 introduce:3 manner:1 x0:1 mask:1 rapid:... |
4,012 | 4,631 | On the connections between saliency and tracking
Nuno Vasconcelos
Statistical Visual Computing Laboratory
UC San Diego, La Jolla, CA 92092
nuno@ece.ucsd.edu
Vijay Mahadevan
Yahoo! Labs
Bangalore, India
vmahadev@yahoo-inc.com
Abstract
A model connecting visual tracking and saliency has recently been proposed. This
mo... | 4631 |@word neurophysiology:2 trial:10 version:22 middle:1 replicate:2 approved:1 disk:12 open:1 lobe:2 irb:1 attended:1 initial:2 liu:1 tuned:3 denoting:1 suppressing:1 reynolds:1 current:1 com:1 activation:1 scatter:2 must:4 written:2 designed:2 plot:2 pylyshyn:3 discrimination:5 half:4 cue:1 selected:1 item:1 v:5 is... |
4,013 | 4,632 | Convex Multi-view Subspace Learning
Martha White, Yaoliang Yu, Xinhua Zhang? and Dale Schuurmans
Department of Computing Science, University of Alberta, Edmonton AB T6G 2E8, Canada
{whitem,yaoliang,xinhua2,dale}@cs.ualberta.ca
Abstract
Subspace learning seeks a low dimensional representation of data that enables
accu... | 4632 |@word h:1 version:1 norm:24 seems:1 c0:6 termination:1 grey:1 seek:2 crucially:1 covariance:2 decomposition:1 pick:1 incurs:2 tr:44 accommodate:1 reduction:3 contains:1 salzmann:1 document:2 interestingly:4 outperforms:1 petz:1 bradley:1 current:1 recovered:4 comparing:3 bie:1 must:5 written:1 subsequent:1 ahj:2 ... |
4,014 | 4,633 | A Stochastic Gradient Method with an Exponential
Convergence Rate for Finite Training Sets
Nicolas Le Roux
SIERRA Project-Team
INRIA - ENS
Paris, France
nicolas@le-roux.name
Mark Schmidt
SIERRA Project-Team
INRIA - ENS
Paris, France
mark.schmidt@inria.fr
Francis Bach
SIERRA Project-Team
INRIA - ENS
Paris, France
fra... | 4633 |@word version:1 polynomial:1 stronger:2 norm:3 yi0:1 suitably:2 advantageous:2 termination:3 seek:1 minus:3 n8:2 reduction:1 initial:1 cyclic:1 series:1 liu:1 tuned:2 ati:3 outperforms:1 existing:2 kx0:1 comparing:1 surprising:1 numerical:2 subsequent:1 kdd:3 plot:1 update:5 juditsky:3 v:5 half:2 selected:2 prohi... |
4,015 | 4,634 | Latent Coincidence Analysis: A Hidden
Variable Model for Distance Metric Learning
Matthew Der and Lawrence K. Saul
Department of Computer Science and Engineering
University of California, San Diego
La Jolla, CA 92093
{mfder,saul}@cs.ucsd.edu
Abstract
We describe a latent variable model for supervised dimensionality r... | 4634 |@word kulis:2 repository:1 stronger:1 loading:1 dekker:1 open:1 seek:1 covariance:3 minus:1 solid:1 moment:1 reduction:6 efficacy:1 outperforms:2 current:2 com:2 goldberger:1 yet:2 must:2 wx:1 informative:1 plot:2 update:11 stationary:1 generative:1 discovering:1 instantiate:2 intelligence:2 plane:1 mccallum:1 it... |
4,016 | 4,635 | Accuracy at the Top
Stephen Boyd
Stanford University
Packard 264
Stanford, CA 94305
Corinna Cortes
Google Research
76 Ninth Avenue
New York, NY 10011
boyd@stanford.edu
corinna@google.com
Mehryar Mohri
Courant Institute and Google
251 Mercer Street
New York, NY 10012
Ana Radovanovic
Google Research
76 Ninth Avenue
... | 4635 |@word repository:1 middle:2 version:2 norm:2 stronger:1 nd:1 termination:1 d2:3 seek:2 contraction:1 thereby:1 initial:1 series:2 score:19 document:1 past:1 spambase:2 outperforms:1 com:4 comparing:1 si:1 chu:1 written:2 must:1 kdd:1 hofmann:1 designed:2 n0:2 rd2:1 half:3 prohibitive:1 selected:4 item:16 intellig... |
4,017 | 4,636 | Learning the Dependency Structure of Latent Factors
Yunlong He?
Georgia Institute of Technology
heyunlong@gatech.edu
Yanjun Qi
NEC Labs America
yanjun@nec-labs.com
Haesun Park?
Georgia Institute of Technology
hpark@cc.gatech.edu
Koray Kavukcuoglu
NEC Labs America
koray@nec-labs.com
Abstract
In this paper, we study ... | 4636 |@word version:8 norm:2 hyv:1 confirms:1 covariance:7 hsieh:2 jacob:1 pick:1 concise:3 tr:5 carry:1 liblinear:1 initial:2 contains:2 score:12 series:1 document:5 existing:1 ksk1:2 recovered:1 com:2 comparing:1 si:7 belmont:1 partition:2 wx:1 shape:2 motor:1 remove:1 plot:3 interpretable:2 update:5 v:1 stationary:1... |
4,018 | 4,637 | A Spectral Algorithm for Latent Dirichlet Allocation
Anima Anandkumar
University of California
Irvine, CA
a.anandkumar@uci.edu
Dean P. Foster
University of Pennsylvania
Philadelphia, PA
dean@foster.net
Sham M. Kakade
Microsoft Research
Cambridge, MA
skakade@microsoft.com
Daniel Hsu
Microsoft Research
Cambridge, MA
... | 4637 |@word mild:2 illustrating:1 unaltered:2 version:5 polynomial:2 kintsch:1 laurence:1 nd:1 decomposition:17 covariance:1 franois:1 reduction:1 moment:41 liu:3 efficacy:1 daniel:2 document:26 o2:1 com:2 si:6 p2min:1 must:1 subsequent:1 additive:5 numerical:1 hofmann:1 v:1 isotropic:1 fa9550:1 blei:2 provides:8 simpl... |
4,019 | 4,638 | Relax and Randomize: From Value to Algorithms
Alexander Rakhlin
University of Pennsylvania
Ohad Shamir
Microsoft Research
Karthik Sridharan
University of Pennsylvania
Abstract
We show a principled way of deriving online learning algorithms from a minimax
analysis. Various upper bounds on the minimax value, previousl... | 4638 |@word version:12 manageable:2 norm:16 forecaster:4 linearized:2 nemirovsky:1 q1:4 pick:8 concise:1 solid:2 series:1 chervonenkis:1 tuned:1 prefix:1 past:3 current:1 yet:1 universality:1 written:1 update:4 implying:1 warmuth:2 location:2 successive:1 mcdiarmid:1 simpler:1 org:3 mathematical:1 dn:2 introduce:1 expe... |
4,020 | 4,639 | Learning curves for multi-task Gaussian process
regression
Simon R F Ashton
King?s College London
Department of Mathematics
Strand, London WC2R 2LS, U.K.
Peter Sollich
King?s College London
Department of Mathematics
Strand, London WC2R 2LS, U.K.
peter.sollich@kcl.ac.uk
Abstract
We study the average case performance ... | 4639 |@word middle:3 additively:1 simulation:8 bn:1 covariance:17 decomposition:1 tr:29 solid:4 shot:1 carry:1 reduction:2 initial:5 interestingly:1 nt:3 surprising:1 written:1 e01:6 numerical:3 additive:1 hofmann:1 plot:2 v:1 intelligence:4 prohibitive:1 provides:2 location:2 five:1 height:1 mathematical:1 along:2 bec... |
4,021 | 464 | Information Processing to Create Eye Movements
David A. Robinson
Departments of Ophthalmology
and Biomedical Engineering
The Johns Hopkins University
School of Medicine
Baltimore, MD 21205
ABSTRACT
Because eye muscles never cocontract and do not deal with external
loads, one can write an equation that relates motoneur... | 464 |@word neurophysiology:1 trial:1 carry:1 initial:1 contains:2 yet:1 intriguing:1 must:3 john:1 vor:6 motor:1 nervous:1 plane:1 steepest:1 core:1 oblique:1 record:2 provides:1 location:1 diagnosing:1 burst:1 alert:1 direct:2 differential:1 consists:1 combine:1 pathway:2 inside:1 manner:1 behavior:7 brain:3 integrato... |
4,022 | 4,640 | Best Arm Identification: A Unified Approach to Fixed
Budget and Fixed Confidence
Victor Gabillon
Mohammad Ghavamzadeh
Alessandro Lazaric
INRIA Lille - Nord Europe, Team SequeL
Victor Gabillon, Mohammad Ghavamzadeh & Alessandro Lazaric
Abstract
We study the problem of identifying the best arm(s) in the stochastic mul... | 4640 |@word version:3 open:1 confirms:1 r:5 forecaster:15 kalyanakrishnan:5 tat:1 tr:1 moment:1 series:3 score:2 selecting:3 tuned:2 outperforms:1 existing:5 must:1 shape:1 designed:2 drop:1 progressively:1 update:4 implying:1 intelligence:1 selected:5 parameterization:1 xk:2 beginning:2 provides:2 mannor:1 successive:... |
4,023 | 4,641 | Density-Difference Estimation
Masashi Sugiyama1 Takafumi Kanamori2 Taiji Suzuki3
Marthinus Christoffel du Plessis1 Song Liu1 Ichiro Takeuchi4
1
Tokyo Institute of Technology, Japan 2 Nagoya University, Japan
3
University of Tokyo, Japan 4 Nagoya Institute of Technology, Japan
Abstract
We address the problem of estimat... | 4641 |@word mild:1 norm:3 seems:1 simulation:1 decomposition:1 covariance:1 shot:7 series:8 score:11 nii:1 rkhs:1 yairi:1 si:5 dx:13 plot:1 interpretable:1 v:1 discrimination:1 stationary:1 werwatz:1 record:1 provides:1 detecting:1 simpler:1 sperlich:1 mathematical:2 direct:2 marthinus:1 manner:2 theoretically:1 themse... |
4,024 | 4,642 | Exploration in Model-based Reinforcement Learning
by Empirically Estimating Learning Progress
Manuel Lopes
INRIA
Bordeaux, France
Tobias Lang
FU Berlin
Germany
Marc Toussaint
FU Berlin
Germany
Pierre-Yves Oudeyer
INRIA
Bordeaux, France
Abstract
Formal exploration approaches in model-based reinforcement learning es... | 4642 |@word exploitation:4 polynomial:5 seems:1 grey:1 simulation:1 covariance:1 thereby:1 initial:1 interestingly:1 ala:1 existing:3 current:4 nuttapong:1 manuel:1 lang:2 si:4 ronan:1 happen:1 informative:1 analytic:1 progressively:1 update:1 stationary:10 greedy:6 mental:2 five:1 along:1 become:1 incorrect:2 introduc... |
4,025 | 4,643 | Minimax Multi-Task Learning and a Generalized
Loss-Compositional Paradigm for MTL
Nishant A. Mehta? , Dongryeol Lee?, Alexander G. Gray?
niche@cc.gatech.edu, drselee@gmail.com, agray@cc.gatech.edu
?
College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA
?
GE Global Research, Niskayuna, NY 12309,... | 4643 |@word multitask:1 version:1 norm:14 lenk:2 mehta:1 pick:1 solid:7 harder:2 reduction:3 series:1 score:1 document:1 interestingly:1 outperforms:3 existing:1 com:1 gmail:1 must:1 john:1 hypothesize:1 plot:3 progressively:1 v:5 bart:1 intelligence:1 selected:1 isotropic:1 beginning:1 core:1 short:2 authority:1 node:... |
4,026 | 4,644 | Minimization of Continuous Bethe Approximations:
A Positive Variation
Jason L. Pacheco and Erik B. Sudderth
Department of Computer Science, Brown University, Providence, RI
{pachecoj,sudderth}@cs.brown.edu
Abstract
We develop convergent minimization algorithms for Bethe variational approximations which explicitly cons... | 4644 |@word h:3 trial:3 briefly:3 vi1:3 c0:3 open:1 seek:1 covariance:1 p0:2 tr:1 kappen:1 moment:3 contains:1 series:1 mi0:1 existing:3 must:6 mst:3 subsequent:1 partition:2 plot:1 update:7 v:15 stationary:6 greedy:2 intelligence:5 xk:21 parametrization:2 steepest:1 node:16 unbounded:8 constructed:1 direct:3 consists:... |
4,027 | 4,645 | Stochastic optimization and sparse statistical
recovery: Optimal algorithms for high dimensions
Martin J. Wainwright
Sahand N. Negahban
Alekh Agarwal
Dept. of EECS and Statistics
Dept. of EECS
Microsoft Research
UC Berkeley
MIT
New York NY
alekha@microsoft.com sahandn@mit.edu wainwrig@stat.berkeley.edu
Abstract
We de... | 4645 |@word trial:2 version:9 norm:6 stronger:1 c0:7 unif:1 termination:1 d2:1 hu:1 simulation:5 seek:1 gradual:1 covariance:1 pick:2 sgd:3 initial:7 series:2 exclusively:1 outperforms:1 wainwrig:1 com:1 bd:1 numerical:4 confirming:1 plot:2 update:12 juditsky:5 v:2 ith:2 core:1 provides:1 boosting:1 coarse:1 clarified:... |
4,028 | 4,646 | On the (Non-)existence of Convex, Calibrated
Surrogate Losses for Ranking
Cl?ement Calauz`enes, Nicolas Usunier, Patrick Gallinari
LIP6 - UPMC
4 place Jussieu, 75005 Paris, France
firstname.lastname@lip6.fr
Abstract
We study surrogate losses for learning to rank, in a framework where the rankings
are induced by score... | 4646 |@word polynomial:1 stronger:2 dekel:1 open:2 p0:24 wisniewski:1 liu:3 contains:1 score:16 denoting:1 document:1 past:1 existing:2 err:22 si:4 must:2 numerical:1 enables:1 designed:2 mackey:1 intelligence:1 rudin:1 item:30 reciprocal:3 characterization:9 provides:1 boosting:1 math:1 preference:7 zhang:3 direct:3 p... |
4,029 | 4,647 | GenDeR: A Generic Diversified Ranking Algorithm
Hanghang Tong
IBM T.J. Watson Research
Yorktown Heights, NY 10598
htong@us.ibm.com
Jingrui He
IBM T.J. Watson Research
Yorktown Heights, NY 10598
jingruhe@us.ibm.com
Boleslaw K. Szymanski
Rensselaer Polytechnic Institute
Troy, NY 12180
szymab@rpi.edu
Qiaozhu Mei
Unive... | 4647 |@word kulis:1 version:1 briefly:1 open:1 seek:2 eng:1 paid:1 reduction:1 initial:1 liu:1 score:11 document:7 bhattacharyya:1 outperforms:1 existing:4 current:3 com:3 rpi:1 si:1 kdd:3 enables:1 sponsored:1 update:3 greedy:4 selected:1 reranking:1 directory:1 ith:3 ugander:1 node:8 org:1 height:2 mathematical:1 con... |
4,030 | 4,648 | Entangled Monte Carlo
Seong-Hwan Jun
Liangliang Wang
Alexandre Bouchard-C?ot?e
Department of Statistics
University of British Columbia
{seong.jun, l.wang, bouchard}@stat.ubc.ca
Abstract
We propose a novel method for scalable parallelization of SMC algorithms, Entangled Monte Carlo simulation (EMC). EMC avoids the tr... | 4648 |@word version:4 advantageous:1 grey:1 simulation:17 pick:1 accommodate:1 recursively:1 initial:3 series:2 karger:1 document:1 past:1 existing:1 freitas:1 current:2 readily:1 gpu:1 fn:3 periodically:1 subsequent:1 partition:3 happen:1 cheap:2 wanted:1 update:7 resampling:14 alone:1 greedy:3 leaf:3 instantiate:1 ob... |
4,031 | 4,649 | The Bethe Partition Function of Log-supermodular
Graphical Models
Nicholas Ruozzi
Communication Theory Laboratory
EPFL
Lausanne, Switzerland
nicholas.ruozzi@epfl.ch
Abstract
Sudderth, Wainwright, and Willsky conjectured that the Bethe approximation corresponding to any fixed point of the belief propagation algorithm ... | 4649 |@word version:1 homomorphism:2 moment:1 kappen:1 series:6 contains:1 bc:1 olkin:1 si:1 must:4 keich:1 partition:25 koetter:1 pseudomarginals:1 stationary:3 xk:32 ith:3 provides:2 characterization:1 node:11 allerton:2 direct:1 prove:3 consists:2 interscience:1 pairwise:13 roughly:1 freeman:1 resolve:5 increasing:2... |
4,032 | 465 | Practical Issues in Temporal Difference Learning
Gerald Tesauro
IBM Thomas J. Watson Research Center
P. O. Box 704
Yorktown Heights, NY 10598
tesauro@watson.ibm.com
Abstract
This paper examines whether temporal difference methods for training
connectionist networks, such as Suttons's TO('\) algorithm, can be successf... | 465 |@word seems:3 confirms:1 r:2 simulation:2 assigment:1 harder:1 initial:3 configuration:1 contains:1 series:1 existing:2 current:1 com:1 surprising:1 must:1 designed:2 plot:1 progressively:1 championship:1 v:2 alone:1 half:1 selected:2 intelligence:1 provides:4 contribute:2 location:1 preference:2 attack:1 height:1... |
4,033 | 4,650 | Nonparanormal Belief Propagation (NPNBP)
Gal Elidan
Department of Statistics
Hebrew University
galel@huji.ac.il
Cobi Cario
School of Computer Science and Engineering
Hebrew University
cobi.cario@mail.huji.ac.il
Abstract
The empirical success of the belief propagation approximate inference algorithm
has inspired nume... | 4650 |@word repository:2 middle:1 inversion:6 cortez:2 nonsensical:1 open:1 tried:1 bn:4 covariance:5 q1:1 dramatic:1 carry:3 kappen:2 liu:5 born:1 ours:1 nonparanormal:7 recovered:1 comparing:1 surprising:2 yet:2 readily:1 fn:3 numerical:3 bickson:4 v:6 greedy:1 fx1:4 dun:1 selected:2 intelligence:4 isard:2 xk:9 cbns:... |
4,034 | 4,651 | Learning Probability Measures with Respect to
Optimal Transport Metrics
Guillermo D. Canas?,?
Lorenzo A. Rosasco?,?
? Laboratory for Computational and Statistical Learning - MIT-IIT
? CBCL, McGovern Institute - Massachusetts Institute of Technology
{guilledc,lrosasco}@mit.edu
Abstract
We study the problem of estimati... | 4651 |@word briefly:1 version:1 guillin:1 seems:1 norm:2 villani:4 stronger:4 nd:2 suitably:1 compression:1 decomposition:9 prokhorov:1 thereby:1 harder:2 carry:1 moment:5 celebrated:1 series:6 ktv:1 interestingly:1 past:2 existing:7 surprising:1 must:2 written:2 partition:4 metrizes:1 leaf:1 oldest:1 recherche:1 color... |
4,035 | 4,652 | Continuous Relaxations for Discrete
Hamiltonian Monte Carlo
Yichuan Zhang, Charles Sutton, Amos Storkey
School of Informatics
University of Edinburgh
United Kingdom
Y.Zhang-60@sms.ed.ac.uk,
csutton@inf.ed.ac.uk,
a.storkey@ed.ac.uk
Zoubin Ghahramani
Department of Engineering
University of Cambridge
United Kingdom
zoubi... | 4652 |@word version:7 proportion:1 seems:1 open:3 proportionality:1 git:1 eng:1 covariance:3 evaluating:1 pick:1 series:1 contains:1 united:2 outperforms:1 existing:1 current:3 elliptical:2 si:19 dx:3 written:1 concatenate:1 partition:4 visible:2 distant:1 analytic:1 designed:1 aps:1 intelligence:1 fewer:2 mccallum:3 h... |
4,036 | 4,653 | Multiple Operator-valued Kernel Learning
Hachem Kadri
LIF - CNRS / INRIA Lille - Sequel Project
Universit?e Aix-Marseille
Marseille, France
hachem.kadri@lif.univ-mrs.fr
Alain Rakotomamonjy
LITIS EA 4108
Universit?e de Rouen
St Etienne du Rouvray, France
alain.rakotomamony@insa-rouen.fr
Philippe Preux
INRIA Lille - Se... | 4653 |@word dtk:1 version:1 inversion:1 middle:1 norm:14 polynomial:1 nd:1 km:1 tried:1 decomposition:2 covariance:1 initial:2 series:2 ecole:1 rkhs:7 denoting:1 bhattacharyya:1 past:1 existing:1 optim:1 si:5 universality:1 john:1 analytic:2 selected:1 dinuzzo:1 core:1 filtered:1 weierstrass:1 pillonetto:1 bijection:1 ... |
4,037 | 4,654 | Augmented-SVM: Automatic space partitioning for
combining multiple non-linear dynamics
Ashwini Shukla
ashwini.shukla@epfl.ch
Aude Billard
aude.billard@epfl.ch
Learning Algorithms and Systems Laboratory (LASA)
?
Ecole
Polytechnique F?ed?erale de Lausanne (EPFL)
Lausanne, Switzerland - 1015
Abstract
Non-linear dynami... | 4654 |@word illustrating:1 advantageous:1 simulation:1 seek:1 decomposition:1 p0:1 pick:1 incurs:1 thereby:1 solid:1 accommodate:2 recursively:1 initial:1 ecole:1 current:3 chu:1 must:4 written:1 mesh:2 realistic:1 subsequent:1 partition:2 motor:2 mulated:1 designed:1 plot:2 progressively:2 v:1 stationary:2 generative:... |
4,038 | 4,655 | Identifiability and Unmixing of Latent Parse Trees
Daniel Hsu
Microsoft Research
Sham M. Kakade
Microsoft Research
Percy Liang
Stanford University
Abstract
This paper explores unsupervised learning of parsing models along two directions.
First, which models are identifiable from infinite data? We use a general tech... | 4655 |@word bigram:1 polynomial:5 open:3 d2:2 decomposition:5 p0:4 pick:1 thereby:1 harder:1 recursively:2 moment:31 initial:3 charniak:1 daniel:1 recovered:2 com:2 yet:1 must:1 parsing:23 written:1 numerical:5 analytic:2 stationary:1 generative:6 xk:2 core:1 unmixed:1 node:11 complication:1 simpler:2 zhang:2 phylogene... |
4,039 | 4,656 | Recognizing Activities by Attribute Dynamics
Weixin Li
Nuno Vasconcelos
Department of Electrical and Computer Engineering
University of California, San Diego
La Jolla, CA 92093, United States
{wel017, nvasconcelos}@ucsd.edu
Abstract
In this work, we consider the problem of modeling the dynamic structure of human acti... | 4656 |@word trial:1 version:1 stronger:1 logit:3 underperform:1 d2:2 tried:1 accounting:1 covariance:1 decomposition:1 gaidon:2 harder:1 hager:1 shechtman:1 bck:1 liu:3 contains:3 score:26 united:1 reduction:1 initial:3 series:2 rkhs:1 ours:1 outperforms:3 recovered:1 contextual:4 comparing:1 skipping:1 blank:1 yet:1 s... |
4,040 | 4,657 | Active Learning of Model Evidence
Using Bayesian Quadrature
Michael A. Osborne
University of Oxford
mosb@robots.ox.ac.uk
Carl E. Rasmussen
University of Cambridge
cer54@cam.ac.uk
David Duvenaud
University of Cambridge
dkd23@cam.ac.uk
Roman Garnett
Carnegie Mellon University
rgarnett@cs.cmu.edu
Stephen J. Roberts
Un... | 4657 |@word trial:1 exploitation:2 seek:1 crucially:1 simulation:1 eng:1 covariance:6 moment:2 score:2 selecting:1 existing:2 current:1 assigning:1 dx:11 must:3 distant:1 numerical:5 partition:2 analytic:5 plot:1 treating:1 alone:1 intelligence:1 discovering:1 selected:1 ith:1 normalising:1 provides:3 detecting:1 locat... |
4,041 | 4,658 | Why MCA? Nonlinear sparse coding with spike-andslab prior for neurally plausible image encoding
Jacquelyn A. Shelton,
Philip Sterne, J?org Bornschein, Abdul-Saboor Sheikh,
Frankfurt Institute for Advanced Studies
Goethe-University Frankfurt, Germany
{shelton, sterne, bornschein, sheikh}@fias.uni-frankfurt.de
?
J?org L... | 4658 |@word neurophysiology:2 middle:1 wiesel:1 polynomial:3 proportion:2 nd:6 ucke:9 arti:5 mammal:1 eld:42 inpainting:2 ld:2 contains:2 selecting:1 denoting:2 activation:3 assigning:1 must:1 additive:1 realistic:3 numerical:1 plasticity:1 shape:7 enables:3 csc:1 plot:2 designed:1 update:1 generative:16 selected:1 par... |
4,042 | 4,659 | Topic-Partitioned Multinetwork Embeddings
Peter Krafft?
CSAIL
MIT
pkrafft@mit.edu
?
Juston Moore? , Bruce Desmarais? , Hanna Wallach?
Department of Computer Science, ? Department of Political Science
University of Massachusetts Amherst
?
{jmoore, wallach}@cs.umass.edu
?
desmarais@polsci.umass.edu
Abstract
We introd... | 4659 |@word version:2 norm:1 twelfth:1 open:3 carolina:1 covariance:2 pg:5 thereby:2 uma:2 score:6 selecting:1 document:4 outperforms:1 existing:4 subjective:1 comparing:3 yet:1 must:1 fn:5 concatenate:1 partition:3 informative:1 noninformative:1 enables:2 remove:1 plot:4 resampling:1 v:5 generative:5 discovering:1 yr:... |
4,043 | 466 | A Contrast Sensitive Silicon Retina with
Reciprocal Synapses
Kwabena A. Boahen
Computation and Neural Systems
California Institute of Technology
Pasadena, CA 91125
Andreas G. Andreou
Electrical and Computer Engineering
Johns Hopkins University
Baltimore, MD 21218
Abstract
The goal of perception is to extract invaria... | 466 |@word maz:1 version:4 proportion:1 stronger:2 open:1 grey:1 linearized:1 thereby:1 offering:1 current:29 readily:1 john:1 gci:2 designed:1 drop:1 plot:1 device:6 accordingly:1 reciprocal:6 node:4 five:1 direct:3 become:1 resistive:2 fitting:1 inter:3 behavior:1 globally:2 vertebrate:2 increasing:10 underlying:2 ci... |
4,044 | 4,660 | Priors for Diversity in Generative
Latent Variable Models
Ryan P. Adams
School of Engineering and Applied Sciences
Harvard University
Cambridge, MA 02138
rpa@seas.harvard.edu
James Y. Zou
School of Engineering and Applied Sciences
Harvard University
Cambridge, MA 02138
jzou@fas.harvard.edu
Abstract
Probabilistic late... | 4660 |@word trial:3 determinant:2 kondor:1 version:1 replicate:2 simulation:1 covariance:1 incurs:1 configuration:3 series:1 pub:1 united:1 document:15 outperforms:2 existing:1 current:1 dx:1 must:2 determinantal:27 happen:1 informative:6 predetermined:1 shape:1 enables:2 christian:1 drop:1 update:2 discrimination:2 ge... |
4,045 | 4,661 | Unsupervised Structure Discovery for Semantic
Analysis of Audio
Bhiksha Raj
Language Technologies Institute
Carnegie Mellon University
Pittsburgh, PA 15213
bhiksha@cs.cmu.edu
Sourish Chaudhuri
Language Technologies Institute
Carnegie Mellon University
Pittsburgh, PA 15213
sourishc@cs.cmu.edu
Abstract
Approaches to a... | 4661 |@word seems:1 nd:3 grey:2 accounting:1 initial:1 liu:1 contains:1 series:1 document:5 envision:1 outperforms:1 current:1 com:3 luo:1 si:3 must:1 parsing:1 import:1 visible:1 numerical:1 interpretable:3 update:5 sundaram:3 alone:2 generative:11 discovering:1 guess:1 beginning:1 short:1 prespecified:1 yamada:1 dete... |
4,046 | 4,662 | Learning Partially Observable Models Using
Temporally Abstract Decision Trees
Erik Talvitie
Department of Mathematics and Computer Science
Franklin & Marshall College
Lancaster, PA 17604
erik.talvitie@fandm.edu
Abstract
This paper introduces timeline trees, which are partial models of partially observable environment... | 4662 |@word trial:8 illustrating:1 version:1 seems:1 twelfth:1 grey:1 tried:1 dealer:5 pressure:1 incurs:1 asks:2 homomorphism:1 initial:1 configuration:1 score:1 selecting:1 franklin:1 past:7 o2:1 existing:1 current:8 must:12 timestamps:26 subsequent:1 informative:4 designed:1 intelligence:6 leaf:10 discovering:1 sele... |
4,047 | 4,663 | Accelerated Training for Matrix-norm
Regularization: A Boosting Approach
Xinhua Zhang?, Yaoliang Yu and Dale Schuurmans
Department of Computing Science, University of Alberta, Edmonton AB T6G 2E8, Canada
{xinhua2,yaoliang,dale}@cs.ualberta.ca
Abstract
Sparse learning models typically combine a smooth loss with a nons... | 4663 |@word multitask:6 version:2 polynomial:1 norm:48 c0:4 seek:2 tried:1 accounting:1 decomposition:1 incurs:1 tr:2 outlook:1 solid:1 recursively:1 carry:2 reduction:1 initial:2 sepulchre:2 contains:1 score:1 selecting:1 denoting:1 tuned:2 interestingly:1 ours:18 movielens10m:2 mishra:2 bradley:1 current:1 recovered:... |
4,048 | 4,664 | Mirror Descent Meets Fixed Share
(and feels no regret)
Nicol? Cesa-Bianchi
Universit? degli Studi di Milano
nicolo.cesa-bianchi@unimi.it
G?bor Lugosi
ICREA & Universitat Pompeu Fabra, Barcelona
gabor.lugosi@upf.edu
Pierre Gaillard
Ecole Normale Sup?rieure?, Paris
pierre.gaillard@ens.fr
Gilles Stoltz
Ecole Normale Sup... | 4664 |@word exploitation:1 norm:4 hec:1 forecaster:24 mention:1 harder:1 ecole:3 tuned:3 denoting:1 past:4 current:3 recovered:1 must:1 designed:2 update:23 greedy:1 warmuth:4 simpler:1 prove:2 introduce:3 manner:1 excellence:1 ra:1 indeed:1 p1:1 examine:1 nor:1 discounted:9 decreasing:1 insist:1 td:1 cardinality:2 inc... |
4,049 | 4,665 | Statistical Consistency of Ranking Methods in A
Rank-Differentiable Probability Space
Yanyan Lan
Institute of Computing Technology
Chinese Academy of Sciences
lanyanyan@ict.ac.cn
Jiafeng Guo
Institute of Computing Technology
Chinese Academy of Sciences
guojiafeng@ict.ac.cn
Xueqi Cheng
Institute of Computing Technolo... | 4665 |@word stronger:7 seems:1 dekel:1 liu:8 score:1 hereafter:2 document:3 existing:4 com:1 surprising:1 yet:2 must:1 happen:1 j1:1 kdd:1 v:2 mackey:1 accordingly:1 xk:18 short:2 renshaw:1 provides:2 boosting:1 preference:8 herbrich:1 zhang:4 constructed:1 become:1 prove:6 introduce:1 g4:1 pairwise:36 expected:6 behav... |
4,050 | 4,666 | Online Regret Bounds for Undiscounted Continuous
Reinforcement Learning
Ronald Ortner??
Montanuniversitaet Leoben
8700 Leoben, Austria
rortner@unileoben.ac.at
?
?
Daniil Ryabko?
INRIA Lille-Nord Europe, e? quipe SequeL
59650 Villeneuve d?Ascq, France
daniil@ryabko.net
Abstract
We derive sublinear regret bounds for ... | 4666 |@word exploitation:2 polynomial:2 seems:1 nd:2 open:1 incurs:1 outlook:1 initial:2 selecting:2 denoting:1 phuong:1 discretization:7 si:5 yet:1 written:1 john:1 ronald:6 partition:1 fund:1 generative:2 selected:1 intelligence:3 complementing:1 accordingly:1 ucrl2:6 symposium:2 ik:1 consists:2 combine:1 emma:1 ra:1... |
4,051 | 4,667 | Entropy Estimations Using Correlated Symmetric
Stable Random Projections
Ping Li
Department of Statistical Science
Cornell University
Ithaca, NY 14853
pingli@cornell.edu
Cun-Hui Zhang
Department of Statistics and Biostatistics
Rutgers University
New Brunswick, NJ 08901
czhang@stat.rutgers.edu
Abstract
Methods for eff... | 4667 |@word middle:3 unif:3 widom:1 simulation:1 reduction:2 moment:14 liu:1 united:5 document:1 interestingly:3 bc:1 bhattacharyya:1 comparing:3 com:1 od:6 must:1 aft:1 john:2 plot:1 update:3 v:1 prohibitive:2 selected:2 sudden:1 detecting:3 provides:1 location:2 attack:12 firstly:1 zhang:5 unbounded:1 mathematical:1 ... |
4,052 | 4,668 | Cardinality Restricted Boltzmann Machines
Kevin Swersky
Daniel Tarlow
Ilya Sutskever
Dept. of Computer Science
University of Toronto
[kswersky,dtarlow,ilya]@cs.toronto.edu
Ruslan Salakhutdinov?,?
Richard S. Zemel?
Dept. of Computer Science? and Statistics?
University of Toronto
[rsalakhu,zemel]@cs.toronto.edu
Ryan ... | 4668 |@word proportion:1 seems:1 tried:1 eng:1 covariance:1 contrastive:2 tr:1 kappen:1 initial:1 configuration:7 contains:1 score:3 daniel:1 tuned:1 interestingly:1 freitas:1 current:1 recovered:1 comparing:1 activation:7 assigning:1 collude:1 physiol:1 visible:6 numerical:1 realistic:1 analytic:1 utml:1 interpretable... |
4,053 | 4,669 | Probabilistic Low-Rank Subspace Clustering
S. Derin Babacan
University of Illinois at Urbana-Champaign
Urbana, IL 61801, USA
dbabacan@gmail.com
Shinichi Nakajima
Nikon Corporation
Tokyo, 140-8601, Japan
nakajima.s@nikon.co.jp
Minh N. Do
University of Illinois at Urbana-Champaign
Urbana, IL 61801, USA
minhdo@illinois... | 4669 |@word trial:3 version:2 polynomial:2 norm:4 advantageous:1 nd:3 d2:24 decomposition:1 covariance:4 tr:7 accommodate:1 moment:1 reduction:2 liu:4 contains:4 zimek:1 mag:1 existing:2 recovered:2 com:1 si:3 gmail:1 kdd:2 shape:2 treating:1 update:5 unidentifiability:1 v:1 stationary:1 generative:2 isotropic:1 ith:4 ... |
4,054 | 467 | Network generalization for production:
Learning and producing styled letterforms
Igor Grebert
David G. Stork
541 Cutwater Ln. Ricoh Calif. Research Cen.
Foster City, CA
2882 Sand Hill Rd.# 115
94404
Menlo Park, CA 94025
Ron Keesing
Steve Mims
Dept. Physiology Electrical Engin.
Stanford U.
U. C. S. F.
San Francisc... | 467 |@word fonn:2 reduction:1 symphony:1 tuned:1 ours:1 com:1 surprising:1 yet:1 must:3 chicago:1 shape:2 designed:4 alone:3 ron:3 five:2 constructed:1 direct:1 recognizable:2 indeed:2 expected:1 roughly:1 behavior:1 nor:1 informational:1 decreasing:1 provided:2 project:5 moreover:1 what:1 shuts:1 temporal:1 exactly:2 ... |
4,055 | 4,670 | Rational inference of relative preferences
Paul R Schrater
Dept of Psychology
University of Minnesota
Nisheeth Srivastava
Dept of Computer Science
University of Minnesota
Abstract
Statistical decision theory axiomatically assumes that the relative desirability of
different options that humans perceive is well descri... | 4670 |@word version:2 open:1 mehta:1 seek:2 heretofore:1 uncovers:1 accounting:1 pick:3 pressed:1 epistemic:1 reduction:1 exclusively:1 selecting:1 interestingly:1 past:3 existing:5 o2:3 current:3 si:2 assigning:1 must:6 numerical:2 realistic:1 refuted:1 informative:2 cheap:1 interpretable:2 update:4 half:1 selected:1 ... |
4,056 | 4,671 | Bayesian Probabilistic Co-Subspace Addition
Lei Shi
Baidu.com, Inc
shilei06@baidu.com
Abstract
For modeling data matrices, this paper introduces Probabilistic Co-Subspace Addition (PCSA) model by simultaneously capturing the dependent structures among
both rows and columns. Briefly, PCSA assumes that each entry of a ... | 4671 |@word briefly:1 proportion:8 loading:1 nd:3 norm:1 open:1 d2:48 additively:1 hu:1 covariance:4 pick:1 tr:6 reduction:1 contains:4 score:1 outperforms:1 existing:3 com:2 lang:1 tackling:1 yet:1 assigning:1 additive:1 j1:2 shape:1 designed:3 update:3 rd2:2 generative:5 half:1 item:1 ith:2 smith:1 node:1 toronto:2 o... |
4,057 | 4,672 | Context-Sensitive Decision Forests
for Object Detection
Peter Kontschieder1 Samuel Rota Bul`o2 Antonio Criminisi3
Pushmeet Kohli3 Marcello Pelillo2 Horst Bischof1
1
ICG, Graz University of Technology, Austria
2
DAIS, Universit`a Ca? Foscari Venezia, Italy
3
Microsoft Research Cambridge, UK
Abstract
In this paper we i... | 4672 |@word kohli:2 version:5 c0:9 triggs:1 heuristically:1 tr:10 configuration:2 series:1 contains:3 o2:1 existing:1 current:3 contextual:16 z2:3 tackling:1 wiewiora:1 partition:2 shape:4 plot:4 drop:1 fund:1 cue:1 leaf:18 selected:2 core:1 woodford:1 provides:1 boosting:1 node:50 location:1 detecting:1 along:1 instal... |
4,058 | 4,673 | Ancestor Sampling for Particle Gibbs
Fredrik Lindsten
Div. of Automatic Control
Link?oping University
lindsten@isy.liu.se
Michael I. Jordan
Dept. of EECS and Statistics
University of California, Berkeley
jordan@cs.berkeley.edu
Thomas B. Sch?on
Div. of Automatic Control
Link?oping University
schon@isy.liu.se
Abstrac... | 4673 |@word h:3 middle:1 seems:1 wtm:3 simulation:22 covariance:3 decomposition:1 pg:70 fifteen:1 solid:2 recursively:1 liu:4 series:5 contains:1 dpmms:1 rightmost:1 past:1 existing:1 outperforms:1 current:1 arkk:1 nt:1 tackling:1 assigning:1 must:1 subsequent:1 numerical:3 additive:1 xb1:11 remove:1 plot:2 resampling:... |
4,059 | 4,674 | Feature Clustering for Accelerating
Parallel Coordinate Descent
Chad Scherrer
Independent Consultant
Yakima, WA
chad.scherrer@gmail.com
Ambuj Tewari
Department of Statistics
University of Michigan
Ann Arbor, MI
tewaria@umich.edu
Mahantesh Halappanavar
Pacific Northwest National Laboratory
Richland, WA
mahantesh.halap... | 4674 |@word private:1 middle:1 version:1 norm:6 advantageous:1 confirms:1 simplifying:1 hsieh:1 paid:1 dramatic:1 reduction:1 initial:1 contains:1 series:1 selecting:1 uma:1 document:3 existing:2 bradley:6 current:2 com:2 lang:1 gmail:1 must:2 partition:3 kdd:2 plot:3 update:4 bickson:1 v:1 alone:2 greedy:36 selected:2... |
4,060 | 4,675 | Perfect Dimensionality Recovery
by Variational Bayesian PCA
Shinichi Nakajima
Nikon Corporation
Tokyo, 140-8601, Japan
nakajima.s@nikon.co.jp
Masashi Sugiyama
Tokyo Institute of Technology
Tokyo 152-8552, Japan
sugi@cs.titech.ac.jp
Ryota Tomioka
The University of Tokyo
Tokyo 113-8685, Japan
tomioka@mist.i.u-tokyo.ac.j... | 4675 |@word trial:3 version:1 middle:2 norm:1 cah:5 simulation:2 covariance:5 decomposition:1 bellevue:1 tr:3 solid:2 reduction:1 phy:1 outperforms:1 karoui:1 written:3 numerical:4 realistic:1 kdd:1 analytic:6 hoping:1 stationary:1 half:1 intelligence:1 short:1 simpler:1 ilin:1 prove:1 introduce:2 theoretically:5 indee... |
4,061 | 4,676 | Confusion-Based Online Learning and a
Passive-Aggressive Scheme
Liva Ralaivola
QARMA, Laboratoire d?Informatique Fondamentale de Marseille
Aix-Marseille University, France
liva.ralaivola@lif.univ-mrs.fr
Abstract
This paper provides the first ?to the best of our knowledge? analysis of online
learning algorithms for mu... | 4676 |@word norm:17 dekel:1 d2:8 km:3 simulation:6 simplifying:1 united:1 ka:1 current:1 comparing:1 tackling:1 liva:2 dx:8 must:1 written:1 grain:1 john:1 numerical:5 informative:1 designed:2 drop:1 update:7 aside:1 implying:2 pursued:1 half:1 nq:1 xk:5 provides:6 noncommutative:1 kvk2:1 direct:1 prove:2 advocate:1 in... |
4,062 | 4,677 | Augment-and-Conquer Negative Binomial Processes
Lawrence Carin
Dept. of Electrical and Computer Engineering
Duke University, Durham, NC 27708
lcarin@ee.duke.edu
Mingyuan Zhou
Dept. of Electrical and Computer Engineering
Duke University, Durham, NC 27708
mz1@ee.duke.edu
Abstract
By developing data augmentation method... | 4677 |@word cu:1 middle:1 proportion:5 loading:1 bn:2 p0:9 tr:1 series:1 score:1 united:3 njk:24 denoting:1 document:22 existing:2 comparing:2 yet:1 assigning:1 must:2 readily:1 john:1 partition:4 j1:2 shape:2 update:4 mackey:1 alone:1 selected:2 beginning:1 blei:5 provides:2 evy:3 location:1 five:2 constructed:8 c2:1 ... |
4,063 | 4,678 | Multiclass Learning Approaches:
A Theoretical Comparison with Implications
Amit Daniely
Department of Mathematics
The Hebrew University
Jerusalem, Israel
Sivan Sabato
Microsoft Research
1 Memorial Drive
Cambridge, MA 02142, USA
Shai Shalev-Shwartz
School of CS and Eng.
The Hebrew University
Jerusalem, Israel
Abstra... | 4678 |@word middle:2 briefly:1 achievable:3 stronger:1 seems:1 d2:1 eng:1 attainable:1 ld:2 reduction:9 contains:8 document:1 ala:1 rightmost:1 outperforms:1 err:8 current:1 comparing:2 beygelzimer:4 surprising:2 tackling:1 yet:1 must:1 john:1 ronald:1 partition:1 v:4 intelligence:1 leaf:13 guess:1 selected:1 completen... |
4,064 | 4,679 | Efficient Reinforcement Learning for High
Dimensional Linear Quadratic Systems
Adel Javanmard
Stanford University
Stanford, CA 94305
adelj@stanford.edu
Morteza Ibrahimi
Stanford University
Stanford, CA 94305
ibrahimi@stanford.edu
Benjamin Van Roy
Stanford University
Stanford, CA 94305
bvr@stanford.edu
Abstract
We s... | 4679 |@word h:3 kgk:2 version:1 norm:3 km:1 covariance:1 decomposition:4 q1:1 profit:1 initial:4 celebrated:1 series:1 lqr:1 past:1 current:1 comparing:1 dx:1 written:1 n0:2 v:1 stationary:1 beginning:3 ith:1 provides:1 revisited:1 lx:1 constructed:1 lk2:4 differential:3 direct:1 c2:9 prove:5 interdependence:2 javanmar... |
4,065 | 468 | Operators and curried functions:
Training and analysis of simple recurrent networks
Anthony Bloesch,
Janet Wiles
Depts of Psychology and Computer Science,
University of Queensland
QLD 4072 Australia.
janetw@CS.uq.oz.au
Dept of Computer Science,
University of Queensland,
QLD 4072 Australia
anthonyb@cs.uq.oz.au
Abstr... | 468 |@word seems:1 simulation:12 queensland:2 carry:1 initial:2 denoting:2 current:5 analysed:1 activation:9 must:2 john:1 subsequent:3 selected:1 item:1 simpler:1 five:2 combine:1 elman:6 themselves:1 considering:1 provided:3 string:1 developed:1 interpreter:1 finding:1 temporal:1 every:4 act:4 ro:4 unit:38 grant:1 co... |
4,066 | 4,680 | Analyzing 3D Objects in Cluttered Images
Mohsen Hejrati
UC Irvine
Deva Ramanan
UC Irvine
shejrati@ics.uci.edu
dramanan@ics.uci.edu
Abstract
We present an approach to detecting and analyzing the 3D configuration of objects
in real-world images with heavy occlusion and clutter. We focus on the application
of finding... | 4680 |@word determinant:1 middle:1 dalal:1 everingham:2 triggs:1 crucially:1 covariance:2 simplifying:1 initial:1 configuration:5 liu:3 score:8 contains:1 tuned:3 outperforms:3 existing:1 cad:1 dx:1 must:2 readily:1 written:2 refines:1 visible:9 shape:62 visibility:16 designed:2 treating:1 drop:1 rrt:1 half:1 fewer:1 p... |
4,067 | 4,681 | A mechanistic model of early sensory
processing based on subtracting sparse
representations
Shaul Druckmann*
Tao Hu*
Dmitri B. Chklovskii
* - Equal contribution
Janelia Farm Research Campus
{druckmanns, hut, mitya}@janelia.hhmi.org
Abstract
Early stages of sensory systems face the challenge of compressing
information... | 4681 |@word version:2 middle:1 compression:3 norm:1 advantageous:1 hu:1 seek:1 linearized:9 simplifying:1 thereby:2 solid:1 initial:1 substitution:1 series:6 contains:1 existing:1 current:1 activation:3 yet:1 must:4 physiol:1 informative:1 shape:1 opin:1 drop:1 plot:1 progressively:3 update:3 implying:1 alone:1 fewer:1... |
4,068 | 4,682 | Multiresolution Gaussian Processes
David B. Dunson
Dept of Statistical Science, Duke University
dunson@stat.duke.edu
Emily B. Fox
Dept of Statistics, University of Washington
ebfox@stat.washington.edu
Abstract
We propose a multiresolution Gaussian process to capture long-range, nonMarkovian dependencies while allowin... | 4682 |@word trial:35 middle:1 interleave:1 c0:1 seek:1 simulation:2 lobe:4 covariance:17 accommodate:1 recursively:1 generatively:1 series:8 selecting:1 current:1 activation:1 written:1 readily:2 additive:5 partition:63 tailoring:1 blur:1 shape:2 enables:3 plot:3 stationary:4 generative:2 selected:4 fewer:1 greedy:1 le... |
4,069 | 4,683 | Multimodal Learning with Deep Boltzmann Machines
Nitish Srivastava
Department of Computer Science
University of Toronto
nitish@cs.toronto.edu
Ruslan Salakhutdinov
Department of Statistics and Computer Science
University of Toronto
rsalakhu@cs.toronto.edu
Abstract
A Deep Boltzmann Machine is described for learning a ... | 4683 |@word middle:4 briefly:1 open:1 accounting:1 contrastive:2 bellevue:1 thereby:1 harder:1 carry:1 configuration:1 contains:2 selecting:1 document:3 interestingly:1 outperforms:2 current:1 activation:1 must:3 written:1 visible:13 shape:1 motor:1 remove:1 designed:1 gist:1 update:2 v:1 alone:1 generative:6 greedy:1 ... |
4,070 | 4,684 | Generalization Bounds for Domain Adaptation
Chao Zhang1 , Lei Zhang2 , Jieping Ye1,3
Center for Evolutionary Medicine and Informatics, The Biodesign Institute,
and 3 Computer Science and Engineering, Arizona State University, Tempe, USA
{czhan117,jieping.ye}@asu.edu
2
School of Computer Science and Technology,
Nanjing... | 4684 |@word briefly:2 norm:5 blender:1 initial:1 denoting:1 existing:2 com:1 nt:3 njust:1 john:2 numerical:6 designed:1 sponsored:1 intelligence:1 asu:1 warmuth:1 along:1 prove:1 introduce:2 expected:5 behavior:5 decreasing:1 little:1 becomes:1 provided:4 moreover:5 bounded:5 notation:1 kind:7 minimizes:4 differing:1 f... |
4,071 | 4,685 | Regularized Off-Policy TD-Learning
Bo Liu, Sridhar Mahadevan
Computer Science Department
University of Massachusetts
Amherst, MA 01003
{boliu, mahadeva}@cs.umass.edu
Ji Liu
Computer Science Department
University of Wisconsin
Madison, WI 53706
ji-liu@cs.wisc.edu
Abstract
We present a novel l1 regularized off-policy c... | 4685 |@word middle:1 norm:1 d2:1 mention:1 tr:1 harder:1 boundedness:1 moment:1 initial:1 liu:4 uma:1 att:1 denoting:1 rightmost:1 existing:1 comparing:1 si:3 yet:2 written:1 john:1 wiewiora:1 enables:1 drop:1 update:6 juditsky:2 smdp:1 stationary:1 intelligence:2 selected:1 greedy:1 xk:1 fa9550:1 successive:1 org:1 ma... |
4,072 | 4,686 | Image Denoising and Inpainting with Deep Neural
Networks
Junyuan Xie, Linli Xu, Enhong Chen1
School of Computer Science and Technology
University of Science and Technology of China
eric.jy.xie@gmail.com, linlixu@ustc.edu.cn, cheneh@ustc.edu.cn
Abstract
We present a novel approach to low-level vision problems that comb... | 4686 |@word briefly:1 version:3 norm:1 advantageous:1 blu:1 grey:1 inpainting:46 harder:3 delgado:1 series:1 score:1 tuned:1 existing:1 com:1 comparing:1 surprising:1 activation:7 gmail:1 must:1 additive:3 realistic:2 numerical:1 remove:4 designed:2 fund:2 v:1 greedy:1 selected:1 intelligence:1 colored:1 provides:1 loc... |
4,073 | 4,687 | Large Scale Distributed Deep Networks
Jeffrey Dean, Greg S. Corrado, Rajat Monga, Kai Chen,
Matthieu Devin, Quoc V. Le, Mark Z. Mao, Marc?Aurelio Ranzato,
Andrew Senior, Paul Tucker, Ke Yang, Andrew Y. Ng
{jeff, gcorrado}@google.com
Google Inc., Mountain View, CA
Abstract
Recent work in unsupervised feature learning ... | 4687 |@word multitask:1 version:1 vldb:1 sgd:51 asks:1 moment:1 reduction:2 configuration:5 existing:1 current:2 com:1 surprising:2 activation:1 assigning:1 yet:1 must:1 gpu:14 john:1 devin:2 periodically:1 partition:10 timestamps:1 enables:2 designed:5 plot:1 update:6 bickson:1 v:1 alone:1 hash:1 instantiate:1 prohibi... |
4,074 | 4,688 | Semi-Crowdsourced Clustering: Generalizing Crowd
Labeling by Robust Distance Metric Learning
Jinfeng Yi? , Rong Jin? , Anil K. Jain? , Shaili Jain\ , Tianbao Yang?
?
Michigan State University, East Lansing, MI 48824, USA
\
Yale University, New Haven, CT 06520, USA
?
Machine Learning Lab, GE Global Research, San Ramon,... | 4688 |@word trial:1 kulis:1 version:1 norm:3 nd:2 everingham:1 tried:1 bn:2 jacob:1 asks:1 liu:4 contains:2 tist:1 seriously:1 document:1 outperforms:2 past:1 o2:1 recovered:2 com:1 existing:1 sugato:1 contextual:1 goldberger:1 john:1 partition:1 joy:1 v:1 intelligence:2 selected:1 short:2 provides:1 completeness:1 cse... |
4,075 | 4,689 | Selecting Diverse Features via Spectral
Regularization
Abhimanyu Das?
Microsoft Research
Mountain View
abhidas@microsoft.com
Anirban Dasgupta
Yahoo! Labs
Sunnyvale
anirban@yahoo-inc.com
Ravi Kumar?
Google
Mountain View
ravi.k53@gmail.com
Abstract
We study the problem of diverse feature selection in linear regressio... | 4689 |@word determinant:2 version:3 norm:7 seems:1 cs0:2 simulation:1 decomposition:1 covariance:6 pick:1 tr:2 ld:6 carry:2 selecting:5 nonmonotone:1 clash:2 com:4 bradley:1 si:4 gmail:1 additive:2 plot:3 bickson:1 greedy:27 selected:17 spec:4 ith:1 provides:1 firstly:1 bioinform:1 zhang:1 become:1 differential:8 beta:... |
4,076 | 469 | Statistical Reliability of a Blowfly
Movement-Sensitive Neuron
Rob de Ruyter van Steveninck ..
Biophysics Group,
Rijksuniversiteit Groningen,
Groningen, The Netherlands
William Bialek
NEe Research Institute
4 Independence Way,
Princeton, N J 08540
Abstract
We develop a model-independent method for characterizing the... | 469 |@word judgement:1 seems:1 covariance:1 solid:1 shot:2 electronics:1 discretization:1 comparing:2 surprising:1 yet:1 conforming:1 physiol:4 subsequent:1 partition:1 update:1 discrimination:5 nervous:9 short:2 sudden:1 tolhurst:2 successive:3 direct:1 qualitative:2 consists:1 wild:1 pathway:1 behavioral:2 swets:2 in... |
4,077 | 4,690 | Towards a learning-theoretic analysis of
spike-timing dependent plasticity
David Balduzzi
MPI for Intelligent Systems, T?ubingen, Germany
ETH Zurich, Switzerland
david.balduzzi@inf.ethz.ch
Michel Besserve
MPI for Intelligent Systems and MPI for Biological Cybernetics
T?ubingen, Germany
michel.besserve@tuebingen.mpg.de
... | 4690 |@word h:1 worsens:2 trial:8 version:1 advantageous:1 stronger:1 suitably:1 seek:1 simulation:1 bn:2 incurs:1 thereby:1 minus:1 recursively:1 carry:1 initial:1 contains:3 efficacy:15 interestingly:2 outperforms:1 current:1 exposing:1 realistic:3 subsequent:2 plasticity:18 enables:1 update:6 half:1 complementing:1 ... |
4,078 | 4,691 | Shifting Weights: Adapting Object Detectors from
Image to Video
Kevin Tang1
Vignesh Ramanathan2
Li Fei-Fei1
Daphne Koller1
1
Computer Science Department, Stanford University, Stanford, CA 94305
2
Department of Electrical Engineering, Stanford University, Stanford, CA 94305
{kdtang,vigneshr,feifeili,koller}@cs.stanford.... | 4691 |@word kulis:2 illustrating:1 middle:1 dalal:1 norm:4 everingham:1 triggs:1 seek:2 hsieh:1 harder:1 shechtman:1 liblinear:2 liu:1 contains:1 score:25 initial:2 salzmann:1 outperforms:1 blank:1 contextual:2 nt:3 comparing:1 luo:1 assigning:1 must:1 realistic:1 happen:1 shape:2 remove:2 hypothesize:1 update:2 half:1... |
4,079 | 4,692 | Submodular-Bregman and the Lov?asz-Bregman
Divergences with Applications
Jeff Bilmes
Department of Electrical Engineering
University of Washington
bilmes@uw.edu
Rishabh Iyer
Department of Electrical Engineering
University of Washington
rkiyer@u.washington.edu
Abstract
We introduce a class of discrete divergences on ... | 4692 |@word briefly:1 version:3 polynomial:1 bf:2 pick:4 frigyik:1 tr:1 contains:1 score:4 interestingly:4 past:1 si:3 yet:1 readily:1 john:1 partition:1 plot:2 update:2 greedy:3 intelligence:1 warmuth:2 scotland:1 nearness:1 provides:2 characterization:2 math:4 gx:19 preference:2 unbounded:2 mathematical:3 direct:2 sy... |
4,080 | 4,693 | Robustness and risk-sensitivity in Markov decision
processes
Takayuki Osogami
IBM Research - Tokyo
5-6-52 Toyosu, Koto-ku, Tokyo, Japan
osogami@jp.ibm.com
Abstract
We uncover relations between robust MDPs and risk-sensitive MDPs. The objective of a robust MDP is to minimize a function, such as the expectation of cumu... | 4693 |@word nd:1 c0:9 p0:17 q1:8 minus:1 recursively:3 moment:1 existing:2 com:1 si:29 yet:1 hoboken:2 readily:1 john:1 intelligence:2 mannor:5 preference:1 mathematical:3 along:3 become:2 prove:3 introduce:1 expected:17 p1:4 xz:2 mechanic:1 bellman:7 discounted:1 becomes:2 unrelated:1 mass:13 what:3 minimizes:4 q2:3 d... |
4,081 | 4,694 | Neuronal Spike Generation Mechanism as an
Oversampling, Noise -shaping A-to-D Converter
Dmitri B. Chklovskii
Janelia Farm Research Campus
Howard Hughes Medical Institute
mitya@janelia.hhmi.org
Daniel Soudry
Department of Electrical Engineering
Technion
daniel.soudry@gmail.com
Abstract
We test the hypothesis that the... | 4694 |@word version:3 unaltered:1 advantageous:1 stronger:2 norm:1 hu:1 confirms:1 gradual:1 propagate:1 linearized:1 simulation:3 grey:1 decomposition:1 invoking:1 solid:2 carry:1 reduction:3 electronics:1 contains:2 series:1 mainen:1 daniel:2 interestingly:1 existing:3 current:47 com:2 surprising:1 gmail:1 yet:3 must... |
4,082 | 4,695 | Coding efficiency and detectability of rate fluctuations
with non-Poisson neuronal firing
Shinsuke Koyama?
Department of Statistical Modeling
The Institute of Statistical Mathematics
10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan
skoyama@ism.ac.jp
Abstract
Statistical features of neuronal spike trains are known to ... | 4695 |@word neurophysiology:1 trial:3 cox:1 open:1 simulation:5 covariance:1 jacob:1 solid:6 carry:1 moment:7 initial:1 series:1 discretization:2 si:2 dx:3 ikeda:1 numerical:6 alam:1 interspike:3 shape:5 plot:1 update:1 midori:1 discrimination:1 stationary:4 parameterization:1 parametrization:1 ith:3 funahashi:1 provid... |
4,083 | 4,696 | Mixing Properties of Conditional Markov Chains
with Unbounded Feature Functions
Mathieu Sinn
IBM Research - Ireland
Mulhuddart, Dublin 15
mathsinn@ie.ibm.com
Bei Chen
McMaster University
Hamilton, Ontario, Canada
bei.chen@math.mcmaster.ca
Abstract
Conditional Markov Chains (also known as Linear-Chain Conditional Rand... | 4696 |@word version:4 open:1 covariance:5 versatile:1 outlook:1 moment:2 com:1 nt:5 written:3 parsing:1 must:2 hofmann:1 stationary:4 intelligence:2 accordingly:1 mccallum:2 ith:1 math:1 unbounded:7 c2:5 become:1 incorrect:1 prove:1 shorthand:1 introduce:6 x0:3 expected:3 decreasing:1 underlying:1 bounded:4 notation:6 ... |
4,084 | 4,697 | Spectral Learning of General Weighted Automata
via Constrained Matrix Completion
Borja Balle
Universitat Polit`ecnica de Catalunya
Mehryar Mohri
Courant Institute and Google Research
bballe@lsi.upc.edu
mohri@cims.nyu.edu
Abstract
Many tasks in text and speech processing and computational biology require estimating... | 4697 |@word mild:1 version:4 compression:1 seems:2 norm:15 suitably:1 open:1 seek:1 r:2 decomposition:5 elisseeff:1 tr:1 moment:2 initial:1 liu:1 contains:2 series:3 prefix:2 existing:3 kmk:1 current:1 assigning:1 must:1 written:1 parsing:1 subsequent:1 partition:1 chicago:1 designed:1 pursued:1 half:1 parametrization:... |
4,085 | 4,698 | Efficient Spike-Coding with Multiplicative
Adaptation in a Spike Response Model
Sander M. Bohte
CWI, Life Sciences
Amsterdam, The Netherlands
S.M.Bohte@cwi.nl
Abstract
Neural adaptation underlies the ability of neurons to maximize encoded information over a wide dynamic range of input stimuli. Recent spiking neuron m... | 4698 |@word neurophysiology:2 version:5 open:2 grey:4 simulation:8 electrosensory:1 accounting:2 solid:7 reduction:1 initial:2 series:1 tuned:1 past:1 current:18 realistic:1 additive:25 plasticity:3 shape:1 discernible:1 v:1 greedy:3 accordingly:1 short:1 filtered:12 provides:2 differential:3 fitting:1 manner:1 rapid:1... |
4,086 | 4,699 | On the Sample Complexity of Robust PCA
Matthew Coudron
Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Cambridge, MA 02139
mcoudron@mit.edu
Gilad Lerman
School of Mathematics
University of Minnesota
Minneapolis, MN 55455
lerman@umn.edu
Abstract
We estimate the rate of ... | 4699 |@word determinant:1 version:10 norm:13 c0:8 open:1 d2:5 covariance:35 decomposition:3 q1:5 attended:1 ronchetti:1 tr:5 ld:2 moment:1 reduction:2 series:4 contains:1 recovered:2 must:2 john:2 fn:11 numerical:1 remove:1 short:1 node:1 clarified:1 hyperplanes:1 zhang:11 mathematical:4 dn:1 c2:4 direct:3 constructed:... |
4,087 | 47 | 249
HIERARCHICAL LEARNING CONTROL AN APPROACH WITH NEURON-LIKE ASSOCIATIVE MEMORIES
E. Ersu
ISRA Systemtechnik GmbH, Schofferstr. 15, D-6100 Darmstadt, FRG
H. Tolle
TH Darmstadt, Institut fur Regelungstechnik,
Schlo~graben 1, D-6100 Darmstadt, FRG
ABSTRACT
Advances in brain theory need two complementary approaches:
An... | 47 |@word cylindrical:1 cu:1 seems:2 nd:1 suitably:1 calculus:1 simulation:6 outlook:2 necessity:1 series:1 reaction:2 existing:1 si:1 must:1 realize:1 explorative:1 seelen:2 motor:4 wanted:1 half:2 device:2 guess:1 nervous:2 accordingly:1 beginning:1 tolle:7 short:3 characterization:2 math:1 complication:1 ron:1 first... |
4,088 | 470 | Repeat Until Bored: A Pattern Selection Strategy
Paul W. Munro
Depamnent of Information Science
University of Pittsburgh
Pittsburgh, PA 15260
ABSTRACT
An alternative to the typical technique of selecting training examples
independently from a fixed distribution is fonnulated and analyzed, in
which the current example... | 470 |@word trial:8 seems:1 simulation:3 dramatic:1 tr:1 initial:2 selecting:2 current:2 lang:2 activation:1 must:2 fonnulated:1 interrupted:1 mesh:3 atlas:2 update:1 stationary:1 half:2 selected:4 discovering:1 item:2 intelligence:1 dissertation:1 indefinitely:1 five:1 along:1 beta:1 overhead:1 behavioral:1 expected:1 ... |
4,089 | 4,700 | Collaborative Gaussian Processes for
Preference Learning
Jose Miguel Hern?andez-Lobato ?
Department of Engineering
University of Cambridge
Neil Houlsby ?
Department of Engineering
University of Cambridge
Zoubin Ghahramani
Department of Engineering
University of Cambridge
Ferenc Husz?ar
Department of Engineering
Uni... | 4700 |@word version:5 briefly:1 judgement:3 seems:1 nd:1 seek:2 covariance:14 p0:3 reduction:4 phy:1 contains:3 series:2 tuned:1 ours:1 multiuser:2 outperforms:4 existing:1 current:2 wd:1 freitas:1 dx:1 must:2 chu:1 refines:2 informative:4 plot:3 update:3 n0:7 generative:1 prohibitive:1 selected:4 item:27 fewer:1 intel... |
4,090 | 4,701 | Iterative Ranking from Pair-wise Comparisons
Sewoong Oh
Department of IESE
University of Illinois at Urbana Champaign
swoh@illinois.edu
Sahand Negahban
Department of EECS
Massachusetts Institute of Technology
sahandn@mit.edu
Devavrat Shah
Department of EECS
Massachusetts Institute of Technology
devavrat@mit.edu
Abs... | 4701 |@word msr:2 version:5 norm:4 logit:2 relevancy:1 simulation:1 p0:3 harder:1 initial:2 celebrated:1 score:35 efficacy:2 selecting:1 interestingly:2 outperforms:3 trueskill:3 bradley:5 assigning:1 must:1 numerical:3 realistic:1 analytic:5 resampling:1 stationary:28 mackey:1 discovering:2 selected:2 item:34 short:1 ... |
4,091 | 4,702 | Probabilistic n-Choose-k Models for Classification
and Ranking
Kevin Swersky
Daniel Tarlow
Dept. of Computer Science
University of Toronto
[kswersky,dtarlow]@cs.toronto.edu
Richard S. Zemel
Dept. of Computer Science
University of Toronto
zemel@cs.toronto.edu
Ryan P. Adams
School of Eng. and Appl. Sciences
Harvard Uni... | 4702 |@word kohli:1 version:2 proportion:1 instruction:1 eng:1 configuration:1 series:1 score:5 contains:1 liu:3 daniel:1 document:2 outperforms:1 freitas:1 recovered:1 surprising:1 yet:1 assigning:1 written:1 wx:1 remove:1 treating:1 hypothesize:1 generative:3 intelligence:4 item:6 mccallum:1 lr:7 tarlow:3 provides:1 ... |
4,092 | 4,703 | The topographic unsupervised learning of
natural sounds in the auditory cortex
Hiroki Terashima
The University of Tokyo / JSPS
Tokyo, Japan
teratti@teratti.jp
Masato Okada
The University of Tokyo / RIKEN BSI
Tokyo, Japan
okada@k.u-tokyo.ac.jp
Abstract
The computational modelling of the primary auditory cortex (A1) h... | 4703 |@word neurophysiology:2 wiesel:1 grey:1 hyv:3 simulation:2 mammal:1 initial:1 contains:1 series:1 ati:1 subjective:1 existing:1 surprising:1 si:11 yet:2 must:3 evans:1 distant:21 plasticity:1 discernible:1 designed:2 depict:1 medial:1 half:1 selected:2 generative:1 tone:5 inspection:3 ith:1 sutter:1 short:2 smith... |
4,093 | 4,704 | Deep Representations and Codes for Image
Auto-Annotation
Csaba Szepesv?ari
Department of Computing Science
University of Alberta
Edmonton, AB, Canada
szepesva@ualberta.ca
Ryan Kiros
Department of Computing Science
University of Alberta
Edmonton, AB, Canada
rkiros@ualberta.ca
Abstract
The task of image auto-annotation... | 4704 |@word innovates:1 proportion:1 nd:5 hu:1 tried:1 rgb:4 covariance:2 brightness:1 reduction:2 initial:1 liu:1 manmatha:1 selecting:1 document:1 outperforms:1 existing:6 bitwise:1 nt:5 activation:3 assigning:2 subsequent:1 partition:1 additive:1 shape:1 remove:2 hypothesize:2 gist:1 update:1 generative:1 selected:1... |
4,094 | 4,705 | Finding Exemplars from Pairwise Dissimilarities
via Simultaneous Sparse Recovery
Ehsan Elhamifar
EECS Department
University of California, Berkeley
Guillermo Sapiro
ECE, CS Department
Duke University
Ren?e Vidal
Center for Imaging Science
Johns Hopkins University
Abstract
Given pairwise dissimilarities between data... | 4705 |@word duda:1 norm:3 stronger:1 tr:2 shot:2 reduction:1 selecting:10 zij:14 document:4 subjective:1 comparing:1 z2:1 assigning:1 must:1 john:1 partition:3 informative:1 remove:1 plot:3 greedy:1 selected:4 half:1 intelligence:4 provides:1 location:2 zii:1 dn:4 become:1 ik:2 consists:3 symp:1 inside:1 interscience:1... |
4,095 | 4,706 | Locally Uniform Comparison Image Descriptor
Andrew Ziegler? Eric Christiansen David Kriegman Serge Belongie
Department of Computer Science and Engineering, University of California, San Diego
amz@gatech.edu, {echristiansen, kriegman, sjb}@cs.ucsd.edu
Abstract
Keypoint matching between pairs of images using popular de... | 4706 |@word version:5 underst:1 norm:1 smirnov:1 nd:3 open:1 instruction:3 rgb:9 lepetit:1 reduction:4 liu:1 series:3 score:1 ecole:1 skd:6 interestingly:1 kurt:1 outperforms:1 existing:2 current:2 com:2 comparing:1 written:1 gpu:2 john:2 blur:7 remove:1 designed:1 plot:5 strecha:1 discrimination:1 intelligence:1 devic... |
4,096 | 4,707 | Expectation Propagation in
Gaussian Process Dynamical Systems
Marc Peter Deisenroth?
Department of Computer Science
Technische Universit?at Darmstadt, Germany
Shakir Mohamed?
Department of Computer Science
University of British Columbia, Canada
Abstract
Rich and complex time-series data, such as those generated from... | 4707 |@word trial:9 version:1 norm:1 covariance:14 moment:26 initial:2 series:6 existing:8 current:1 z2:1 si:1 must:3 written:1 refines:1 subsequent:1 numerical:3 additive:1 partition:10 informative:1 analytic:1 motor:2 update:20 intelligence:4 fewer:2 dover:1 provides:1 node:1 location:1 arctan:1 org:1 bayesfilters:1 ... |
4,097 | 4,708 | Graphical Gaussian Vector for Image Categorization
Tatsuya Harada
The University of Tokyo/JST PRESTO
7-3-1 Hongo Bunkyo-ku, Tokyo Japan
harada@isi.imi.i.u-tokyo.ac.jp
Yasuo Kuniyoshi
The University of Tokyo
7-3-1 Hongo Bunkyo-ku, Tokyo Japan
kuniyosh@isi.imi.i.u-tokyo.ac.jp
Abstract
This paper proposes a novel image... | 4708 |@word trial:4 compression:1 norm:18 nd:2 c0:22 dekel:1 d2:1 r:3 covariance:3 fifteen:2 shechtman:1 yasuo:1 score:6 comparing:4 z2:5 assigning:1 dx:1 shape:1 generative:2 selected:4 parameterization:1 xk:3 provides:1 quantized:1 codebook:4 node:1 jkj:1 zhang:3 mathematical:2 c2:4 direct:1 consists:1 ijcv:1 inter:1... |
4,098 | 4,709 | No-Regret Algorithms for Unconstrained
Online Convex Optimization
Matthew Streeter
Duolingo, Inc.?
Pittsburgh, PA 15232
matt@duolingo.com
H. Brendan McMahan
Google, Inc.
Seattle, WA 98103
mcmahan@google.com
Abstract
Some of the most compelling applications of online convex optimization, including online prediction a... | 4709 |@word version:1 norm:2 stronger:1 open:1 accounting:1 jacob:2 q1:1 minus:1 moment:1 reduction:2 initial:4 contains:2 series:1 selecting:1 tuned:1 past:1 existing:1 current:1 com:2 must:2 john:1 realistic:1 selected:3 guess:5 leaf:1 warmuth:2 ith:2 manfred:1 provides:1 direct:1 prove:5 consists:1 khk:1 introduce:2... |
4,099 | 471 | CCD Neural Network Processors for Pattern
Recognition
Alice M. Chiang
Michael L. Chuang
Jeffrey R. LaFranchise
MIT Lincoln Laboratory
244 Wood Street
Lexington, MA 02173
Abstract
A CCD-based processor that we call the NNC2 is presented. The NNC2
implements a fully connected 192-input, 32-output two-layer network a... | 471 |@word version:1 briefly:1 cco:2 cm2:1 simulation:1 sensed:1 mitsubishi:1 twolayer:2 asks:1 etann:2 solid:2 contains:2 efficacy:1 refresh:2 realize:1 j1:1 designed:2 concert:1 hts:1 selected:1 device:14 chiang:10 pointer:2 node:8 sigmoidal:4 windowed:1 constructed:1 c2:1 prove:1 consists:2 isscc:4 indeed:1 window:3... |
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