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4,200 | 4,801 | Forging The Graphs: A Low Rank and Positive
Semidefinite Graph Learning Approach
Dijun Luo, Chris Ding, Heng Huang, Feiping Nie
Department of Computer Science and Engineering
The University of Texas at Arlington
dijun.luo@gmail.com, chqding@uta.edu
heng@uta.edu, feipingnie@gmail.com
Abstract
In many graph-based machi... | 4801 |@word kgk:2 repository:1 trial:1 stronger:1 norm:3 d2:3 decomposition:3 tr:1 reduction:3 liu:2 denoting:1 interestingly:2 outperforms:1 com:2 wd:1 luo:4 comparing:1 si:6 gmail:2 written:2 update:2 generative:1 intelligence:1 kyk:6 blei:1 provides:1 math:1 revisited:1 successive:1 zhang:1 mathematical:1 dn:3 c2:3 ... |
4,201 | 4,802 | Semi-Supervised Domain Adaptation with
Non-Parametric Copulas
David Lopez-Paz
MPI for Intelligent Systems
dlopez@tue.mpg.de
Jos?e Miguel Hern?andez-Lobato
University of Cambridge
jmh233@cam.ac.uk
Bernhard Sch?olkopf
MPI for Intelligent Systems
bs@tue.mpg.de
Abstract
A new framework based on the theory of copulas is... | 4802 |@word multitask:1 repository:1 version:2 eliminating:1 middle:2 frigessi:1 nd:3 d2:2 tried:1 decomposition:2 covariance:1 recursively:1 series:9 efficacy:1 contains:1 rkhs:2 past:1 outperforms:2 z2:2 dx:2 must:3 written:1 plot:1 statis:1 generative:1 selected:4 xk:2 sarcos:1 provides:2 node:5 firstly:1 simpler:2 ... |
4,202 | 4,803 | Cost-Sensitive Exploration in
Bayesian Reinforcement Learning
Dongho Kim
Department of Engineering
University of Cambridge, UK
Kee-Eung Kim
Dept of Computer Science
KAIST, Korea
Pascal Poupart
School of Computer Science
University of Waterloo, Canada
dk449@cam.ac.uk
kekim@cs.kaist.ac.kr
ppoupart@cs.uwaterloo.ca
... | 4803 |@word h:4 trial:2 exploitation:2 longterm:1 briefly:1 polynomial:1 version:3 r:1 decomposition:1 brightness:1 recursively:1 initial:3 contains:2 current:5 cmdp:12 assigning:2 must:1 readily:1 numerical:1 analytic:1 update:4 v:1 stationary:1 intelligence:1 provides:2 mannor:1 location:9 eung:1 become:1 symposium:1... |
4,203 | 4,804 | How Prior Probability Influences Decision Making:
A Unifying Probabilistic Model
Abram L. Friesen
University of Washington
afriesen@cs.washington.edu
Yanping Huang
University of Washington
huangyp@cs.washington.edu
Michael N. Shadlen
Columbia University
Howard Hughes Medical Institute
ms4497@columbia.edu
Timothy D.... | 4804 |@word trial:14 illustrating:1 version:1 rising:1 proportion:1 stronger:1 open:1 instruction:1 hu:1 r:9 gradual:1 simulation:2 solid:5 initial:1 selecting:1 denoting:2 subjective:1 reaction:19 current:3 dx:4 must:4 additive:6 partition:1 informative:1 enables:1 motor:1 moreno:1 plot:2 update:2 discrimination:12 im... |
4,204 | 4,805 | A Bayesian Approach for Policy Learning from
Trajectory Preference Queries
Aaron Wilson ?
School of EECS
Oregon State University
Alan Fern ?
School of EECS
Oregon State University
Prasad Tadepalli ?
School of EECS
Oregon State University
Abstract
We consider the problem of learning control policies via trajectory p... | 4805 |@word manageable:1 polynomial:1 judgement:1 stronger:1 tadepalli:1 nd:2 heuristically:2 simulation:3 prasad:1 covariance:2 p0:3 initial:15 configuration:4 selecting:3 past:1 freitas:1 err:1 ka:1 current:4 z2:3 comparing:1 si:2 chu:1 must:8 ronald:1 hofmann:1 remove:1 designed:1 joy:1 generative:1 leaf:2 selected:... |
4,205 | 4,806 | The Perturbed Variation
Maayan Harel
Department of Electrical Engineering
Technion, Haifa, Israel
maayanga@tx.technion.ac.il
Shie Mannor
Department of Electrical Engineering
Technion, Haifa, Israel
shie@ee.technion.ac.il
Abstract
We introduce a new discrepancy score between two distributions that gives an indication ... | 4806 |@word version:3 smirnov:2 nd:5 open:1 vldb:1 simulation:4 rgb:2 brightness:2 eld:1 euclidian:1 initial:2 score:24 zij:8 rkhs:2 interestingly:1 discretization:1 comparing:1 yet:2 dx:1 written:2 nitesimal:1 john:1 partition:7 ideo:1 designed:1 resampling:1 selected:1 accordingly:1 cult:1 hypersphere:1 characterizat... |
4,206 | 4,807 | Multi-task Vector Field Learning
1
2
1
2
1
Binbin Lin
Sen Yang
Chiyuan Zhang
Jieping Ye
Xiaofei He
1
State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058, China
{binbinlinzju, chiyuan.zhang.zju, xiaofeihe}@gmail.com
2
The Biodesign Institute, Arizona State University, Tempe, AZ, 85287
{senyang, jieping.ye}@a... | 4807 |@word multitask:2 trial:2 version:1 norm:3 nd:1 decomposition:1 covariance:1 jacob:1 mention:1 tr:6 liu:2 interestingly:1 outperforms:3 existing:2 com:1 cad:1 gmail:1 written:1 john:1 gv:3 fund:1 n0:5 asu:1 selected:2 plane:1 firstly:1 zhang:5 along:1 x1l:1 constructed:1 differential:8 introduce:3 indeed:1 expect... |
4,207 | 4,808 | Hamming Distance Metric Learning
Mohammad Norouzi?
David J. Fleet?
Ruslan Salakhutdinov?,?
?
Departments of Computer Science and Statistics?
University of Toronto
[norouzi,fleet,rsalakhu]@cs.toronto.edu
Abstract
Motivated by large-scale multimedia applications we propose to learn mappings
from high-dimensional data t... | 4808 |@word kulis:3 version:1 norm:1 bf:1 instruction:1 p0:1 egou:1 incurs:1 euclidian:1 configuration:2 contains:1 score:1 selecting:1 liu:1 document:1 existing:2 ka:2 current:3 com:1 goldberger:1 must:2 gpu:2 numerical:1 shape:1 designed:2 drop:2 update:6 depict:1 hash:25 aside:2 v:2 prohibitive:1 item:14 accordingly... |
4,208 | 4,809 | Semiparametric Principal Component Analysis
Han Liu
Department of Operations Research
and Financial Engineering
Princeton University, NJ 08544
hanliu@princeton.edu
Fang Han
Department of Biostatistics
Johns Hopkins University
Baltimore, MD 21210
fhan@jhsph.edu
Abstract
We propose two new principal component analysis... | 4809 |@word cu:1 middle:1 version:6 averagely:1 norm:4 seems:1 nd:11 stronger:1 c0:7 proportion:1 km:2 d2:4 simulation:2 covariance:18 decomposition:5 tr:1 sepulchre:1 reduction:2 initial:2 liu:6 series:2 dspca:1 score:2 nonparanormal:24 existing:1 recovered:2 current:2 elliptical:1 scatter:2 bd:4 john:1 numerical:2 re... |
4,209 | 481 | The VC-Dimension versus the Statistical
Capacity of Multilayer Networks
Chuanyi Ji "and Demetri Psaltis
Department of Electrical Engineering
California Institute of Technology
Pasadena, CA 91125
Abstract
A general relationship is developed between the VC-dimension and the
statistical lower epsilon-capacity which show... | 481 |@word implemented:1 concept:1 true:2 memorize:1 achievable:4 former:1 assigned:2 quantity:8 occurs:1 d2:2 confirms:1 attribute:1 vc:28 illustrated:1 dependence:1 sgn:3 ll:1 mx:1 explains:1 sand:1 separate:1 oc:1 assign:1 thank:1 capacity:35 contains:1 generalization:17 presynaptic:2 probable:1 complete:1 demonstra... |
4,210 | 4,810 | Smooth-projected Neighborhood Pursuit for
High-dimensional Nonparanormal Graph Estimation
Kathryn Roeder
Department of Statistics
Carnegie Mellon University
Tuo Zhao
Department of Computer Science
Johns Hopkins University
Han Liu
Department of Operations Research and Financial Engineering
Princeton University
Abstr... | 4810 |@word determinant:1 illustrating:1 version:1 norm:13 proportion:2 nd:1 c0:2 smirnov:1 open:1 d2:3 simulation:2 covariance:3 decomposition:1 pressure:1 tr:2 liu:19 contains:4 series:2 document:3 nonparanormal:36 outperforms:3 existing:3 xnj:1 current:1 auritzen:1 john:2 hou:1 numerical:4 additive:2 drop:1 v:3 xk:1... |
4,211 | 4,811 | Label Ranking with Partial Abstention based on
Thresholded Probabilistic Models
?
Eyke Hullermeier
Mathematics and Computer Science
Philipps-Universit?at Marburg
Marburg, Germany
eyke@mathematik.uni-marburg.de
Weiwei Cheng
Mathematics and Computer Science
Philipps-Universit?at Marburg
Marburg, Germany
cheng@mathematik... | 4811 |@word inversion:2 trotter:1 stronger:2 advantageous:1 dekel:1 tedious:1 closure:1 seek:1 bellevue:1 thereby:1 mention:1 minus:1 liu:1 contains:1 score:1 series:1 interestingly:1 subjective:1 existing:2 bradley:3 comparing:1 yet:1 assigning:1 attracted:1 must:3 chu:1 john:1 partition:1 kdd:1 shape:1 remove:1 aside... |
4,212 | 4,812 | Action-Model Based Multi-agent Plan Recognition
Hankz Hankui Zhuo
Department of Computer Science
Sun Yat-sen University, Guangzhou, China 510006
zhuohank@mail.sysu.edu.cn
Qiang Yang
Huawei Noah?s Ark Research Lab
Core Building 2, Hong Kong Science Park, Shatin, Hong Kong
qyang@cse.ust.hk
Subbarao Kambhampati
Department... | 4812 |@word kong:3 polynomial:2 hector:2 hu:1 seek:1 initial:11 past:1 current:2 comparing:1 chu:1 ust:1 parsing:1 must:2 realistic:1 subsequent:1 partition:8 j1:2 fund:1 intelligence:5 asu:1 device:1 amir:2 plane:1 beginning:1 lamp:1 core:1 provides:2 completeness:3 cse:1 toronto:1 five:3 h4:2 driver:2 advocate:1 comp... |
4,213 | 4,813 | Neurally Plausible Reinforcement Learning of
Working Memory Tasks
Jaldert O. Rombouts, Sander M. Bohte
CWI, Life Sciences
Amsterdam, The Netherlands
{j.o.rombouts, s.m.bohte}@cwi.nl
Pieter R. Roelfsema
Netherlands Institute for Neuroscience
Amsterdam, The Netherlands
p.r.roelfsema@nin.knaw.nl
Abstract
A key function... | 4813 |@word neurophysiology:3 trial:34 version:1 briefly:1 proportion:3 stronger:1 pieter:1 simulation:2 lobe:1 shading:1 carry:1 initial:1 configuration:1 contains:2 series:1 tuned:5 interestingly:2 subjective:2 favouring:2 hasselt:1 current:1 activation:17 yet:1 si:16 visible:1 v0j:1 realistic:1 plasticity:10 shape:2... |
4,214 | 4,814 | Learning visual motion in recurrent neural networks
Marius Pachitariu, Maneesh Sahani
Gatsby Computational Neuroscience Unit
University College London, UK
{marius, maneesh}@gatsby.ucl.ac.uk
Abstract
We present a dynamic nonlinear generative model for visual motion based on a
latent representation of binary-gated Gaus... | 4814 |@word neurophysiology:2 version:2 hippocampus:1 simulation:1 propagate:1 mammal:1 shot:1 exclusively:2 tuned:1 imaginary:1 freitas:1 ka:1 comparing:1 activation:6 yet:1 si:1 must:1 connectomics:1 realistic:3 visible:2 shape:2 motor:1 designed:1 plot:9 treating:1 rpn:1 alone:1 generative:10 selected:2 device:1 gre... |
4,215 | 4,815 | Weighted Likelihood Policy Search
with Model Selection
Tsuyoshi Ueno ?
Japan Science and Technology Agency
ueno@ar.sanken.osaka-u.ac.jp
Takashi Washio
Osaka University
washio@ar.sanken.osaka-u.ac.jp
Kohei Hayashi
University of Tokyo
hayashi.kohei@gmail.com
Yoshinobu Kawahara
Osaka University
kawahara@ar.sanken.osaka-u... | 4815 |@word trial:1 polynomial:4 seems:1 triggs:1 open:2 seek:1 covariance:1 q1:1 kappen:2 moment:1 reduction:5 initial:5 score:4 selecting:1 past:1 bradley:1 current:5 com:2 comparing:2 gmail:1 yet:2 must:1 realize:1 fn:6 enables:1 lqg:2 motor:1 designed:1 update:3 stationary:4 alone:1 generative:3 selected:3 intellig... |
4,216 | 4,816 | Trajectory-Based Short-Sighted Probabilistic
Planning
Felipe W. Trevizan
Manuela M. Veloso
Machine Learning Department
Computer Science Department
Carnegie Mellon University - Pittsburgh, PA
{fwt,mmv}@cs.cmu.edu
Abstract
Probabilistic planning captures the uncertainty of plan execution by probabilistically modeling t... | 4816 |@word h:12 nd:3 heuristically:3 simulation:2 nicholson:1 simplifying:1 initial:10 contains:6 series:1 score:1 outperforms:3 past:1 subsequent:1 partition:2 shape:1 plot:1 update:10 v:1 greedy:3 prohibitive:1 selected:2 intelligence:6 smith:1 short:51 core:1 node:8 contribute:1 location:27 accessed:1 consists:2 in... |
4,217 | 4,817 | Efficient high-dimensional maximum entropy
modeling via symmetric partition functions
J. Andrew Bagnell
The Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213
dbagnell@ri.cmu.edu
Paul Vernaza
The Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213
pvernaza@cmu.edu
Abstract
Maximum entr... | 4817 |@word version:3 briefly:1 norm:1 seek:1 simplifying:1 covariance:2 shading:1 reduction:2 initial:5 substitution:2 series:1 cyclic:1 elaborating:1 existing:1 current:1 comparing:2 discretization:1 recovered:1 contextual:2 jaynes:1 dx:5 written:1 must:2 readily:1 numerical:2 partition:33 enables:5 plot:1 update:6 p... |
4,218 | 4,818 | Parametric Local Metric Learning for Nearest
Neighbor Classification
Adam Woznica
Department of Computer Science
University of Geneva
Switzerland
Adam.Woznica@unige.ch
Jun Wang
Department of Computer Science
University of Geneva
Switzerland
Jun.Wang@unige.ch
Alexandros Kalousis
Department of Business Informatics
Uni... | 4818 |@word kulis:2 polynomial:1 norm:7 mb1:5 zelnik:1 tr:6 zbl:5 contains:1 score:3 outperforms:3 current:1 com:1 remove:1 generative:3 selected:1 xk:8 parametrization:3 alexandros:2 provides:2 boosting:1 cse:1 zhang:2 five:1 along:1 constructed:7 fitting:1 manner:1 x0:5 pairwise:6 indeed:1 expected:3 behavior:1 multi... |
4,219 | 4,819 | MAP Inference in Chains using Column Generation
David Belanger?, Alexandre Passos?, Sebastian Riedel?, Andrew McCallum
Department of Computer Science, University of Massachusetts, Amherst
? Department of Computer Science, University College London
{belanger,apassos,mccallum}@cs.umass.edu, s.riedel@cs.ucl.ac.uk
Abstra... | 4819 |@word mild:1 kohli:1 achievable:1 advantageous:1 twelfth:1 termination:2 decomposition:2 dramatic:1 mcauley:3 reduction:1 initial:3 contains:2 uma:1 score:14 selecting:1 prescriptive:1 ours:1 fa8750:1 ati:2 rightmost:1 outperforms:2 current:5 comparing:2 si:9 assigning:1 written:1 parsing:1 ronald:1 update:4 v:2 ... |
4,220 | 482 | Application of Neural Network Methodology to
the Modelling of the Yield Strength in a Steel
Rolling Plate Mill
Ah Chung Tsoi
Department of Electrical Engineering
University of Queensland,
St Lucia, Queensland 4072,
Australia.
Abstract
In this paper, a tree based neural network viz. MARS (Friedman, 1991) for
the model... | 482 |@word version:1 polynomial:8 km:3 queensland:2 simplifying:1 recursively:1 initial:1 series:4 tuned:2 outperforms:2 past:2 current:1 surprising:1 must:1 readily:1 belmont:1 additive:1 riacs:1 concatenate:1 shape:1 plot:5 update:1 leaf:1 ames:1 consists:2 fitting:5 manner:1 forgetting:1 market:1 examine:1 becomes:1... |
4,221 | 4,820 | Active Learning of Multi-Index Function Models
Hemant Tyagi and Volkan Cevher
LIONS ? EPFL
Abstract
We consider the problem of actively learning multi-index functions of the form
Pk
f (x) = g(Ax) = i=1 gi (aTi x) from point evaluations of f . We assume that
the function f is defined on an `2 -ball in Rd , g is twice ... | 4820 |@word trial:5 faculty:1 norm:3 c0:6 open:1 km:1 d2:2 seek:2 simulation:2 decomposition:3 pick:1 incurs:1 thereby:2 mention:2 carry:1 reduction:4 liu:1 series:3 zij:1 selecting:1 denoting:1 ati:4 recovered:1 bd:1 written:1 lorentz:1 numerical:3 additive:10 realistic:1 enables:2 remove:2 designed:1 plot:1 juditsky:... |
4,222 | 4,821 | On Multilabel Classification and Ranking with
Partial Feedback
Claudio Gentile
DiSTA, Universit`a dell?Insubria, Italy
claudio.gentile@uninsubria.it
Francesco Orabona
TTI Chicago, USA
francesco@orabona.com
Abstract
We present a novel multilabel/ranking algorithm working in partial information
settings. The algorithm ... | 4821 |@word exploitation:7 middle:1 version:8 complying:1 norm:2 seems:1 nd:1 justice:1 c0:3 tried:1 decomposition:1 reduction:1 score:2 selecting:1 ours:1 document:2 fbj:5 past:1 existing:1 coactive:1 com:1 contextual:5 comparing:1 si:9 yet:3 tackling:1 lang:1 chicago:1 j1:3 plot:3 update:6 v:2 half:1 selected:3 leaf:... |
4,223 | 4,822 | Transelliptical Graphical Models
Fang Han
Department of Biostatistics
Johns Hopkins University
Baltimore, MD 21210
fhan@jhsph.edu
Han Liu
Department of Operations Research
and Financial Engineering
Princeton University, NJ 08544
hanliu@princeton.edu
Cun-hui Zhang
Department of Statistics
Rutgers University
Piscatawa... | 4822 |@word determinant:1 middle:1 version:2 norm:1 proportion:1 nd:3 c0:1 simulation:1 covariance:8 decomposition:1 pick:1 incurs:1 moment:2 liu:10 contains:4 score:4 series:2 denoting:1 nonparanormal:25 existing:1 elliptical:22 z2:3 current:1 com:1 luo:1 written:1 john:1 numerical:2 informative:1 intelligence:2 selec... |
4,224 | 4,823 | Calibrated Elastic Regularization in Matrix
Completion
Cun-Hui Zhang
Department of Statistics and Biostatistics
Rutgers University
Piscataway, New Jersey 08854
czhang@stat.rutgers.edu
Tingni Sun
Statistics Department, The Wharton School
University of Pennsylvania
Philadelphia, Pennsylvania 19104
tingni@wharton.upenn.... | 4823 |@word inversion:1 seems:1 proportion:6 norm:16 c0:2 proportionality:1 d2:34 km:24 simulation:6 hu:6 decomposition:3 solid:1 moment:1 series:1 outperforms:1 current:1 com:5 comparing:1 must:1 numerical:2 kdd:1 analytic:1 remove:3 plot:2 implying:1 guess:1 rku:1 provides:6 certificate:4 math:2 simpler:3 zhang:3 k2m... |
4,225 | 4,824 | ImageNet Classification with Deep Convolutional
Neural Networks
Alex Krizhevsky
University of Toronto
kriz@cs.utoronto.ca
Ilya Sutskever
University of Toronto
ilya@cs.utoronto.ca
Geoffrey E. Hinton
University of Toronto
hinton@cs.utoronto.ca
Abstract
We trained a large, deep convolutional neural network to classify ... | 4824 |@word cnn:9 middle:1 version:5 cox:2 seems:1 compression:1 disk:1 termination:1 willing:1 tried:1 rgb:5 covariance:1 brightness:1 solid:1 briggman:1 contains:5 denoting:1 ours:1 interestingly:1 document:2 current:5 com:2 activation:2 written:1 gpu:21 realistic:1 happen:1 plot:1 update:1 half:5 fewer:1 generative:... |
4,226 | 4,825 | Learning from Distributions via Support Measure
Machines
Krikamol Muandet
MPI for Intelligent Systems, T?ubingen
krikamol@tuebingen.mpg.de
Kenji Fukumizu
The Institute of Statistical Mathematics, Tokyo
fukumizu@ism.ac.jp
Francesco Dinuzzo
MPI for Intelligent Systems, T?ubingen
fdinuzzo@tuebingen.mpg.de
Bernhard Sch?... | 4825 |@word kondor:1 polynomial:6 proportion:1 reused:1 plsa:2 mehta:1 km:1 bn:2 covariance:7 tr:3 initial:4 selecting:1 rkhs:6 bhattacharyya:3 outperforms:1 recovered:1 dx:2 additive:2 analytic:2 krikamol:2 christian:1 designed:3 treating:1 plot:2 moreno:1 v:4 generative:2 intelligence:1 accordingly:2 desktop:1 ith:3 ... |
4,227 | 4,826 | Bayesian Nonparametric Modeling of Suicide
Attempts
Isabel Valera
Department of Signal Processing
and Communications
University Carlos III in Madrid
ivalera@tsc.uc3m.es
Francisco J. R. Ruiz
Department of Signal Processing
and Communications
University Carlos III in Madrid
franrruiz@tsc.uc3m.es
Fernando Perez-Cruz
De... | 4826 |@word trial:1 determinant:3 inversion:4 logit:5 nd:20 consolider:1 seek:1 covariance:1 contains:6 united:1 document:3 past:1 current:2 blank:6 written:1 readily:5 bd:35 cruz:2 tec2009:1 ministerio:1 predetermined:1 wanted:3 designed:1 plot:1 update:1 zik:1 alone:1 generative:2 selected:3 s2010:1 malone:1 intellig... |
4,228 | 4,827 | Learning about Canonical Views from Internet Image
Collections
Yair Weiss
Elad Mezuman
Interdisciplinary Center for Neural Computation School of Computer Science and Engineering
Edmond & Lily Safra Center for Brain Sciences Edmond & Lily Safra Center for Brain Sciences
Hebrew University of Jerusalem
Hebrew University o... | 4827 |@word mezuman:2 seitz:2 seek:1 mammal:4 united:1 selecting:1 tuned:1 ours:1 subsequent:1 shape:4 wanted:1 hypothesize:1 remove:1 gist:19 motor:2 intelligence:1 selected:2 discovering:1 revisited:1 location:1 preference:4 simpler:1 height:2 along:2 supply:1 driver:1 edelman:1 indeed:6 brain:2 grade:2 inspired:1 re... |
4,229 | 4,828 | Transelliptical Component Analysis
Han Liu
Department of Operations Research
and Financial Engineering
Princeton University, NJ 08544
hanliu@princeton.edu
Fang Han
Department of Biostatistics
Johns Hopkins University
Baltimore, MD 21210
fhan@jhsph.edu
Abstract
We propose a high dimensional semiparametric scale-invar... | 4828 |@word middle:1 version:7 polynomial:1 norm:4 proportion:3 km:2 simulation:4 covariance:6 decomposition:2 pick:1 tr:1 sepulchre:1 moment:7 liu:3 nonparanormal:4 fbj:1 existing:2 elliptical:50 com:1 john:1 numerical:2 mackey:1 xk:14 fpr:2 earson:2 realizing:2 recherche:1 provides:1 preference:1 firstly:7 zhang:2 kv... |
4,230 | 4,829 | Collaborative Ranking With 17 Parameters
Richard S. Zemel
University of Toronto
zemel@cs.toronto.edu
Maksims N. Volkovs
University of Toronto
mvolkovs@cs.toronto.edu
Abstract
The primary application of collaborate filtering (CF) is to recommend a small set
of items to a user, which entails ranking. Most approaches, ... | 4829 |@word msr:1 version:2 norm:2 retraining:3 open:2 tr:1 reduction:1 liu:3 score:4 selecting:2 tuned:1 document:6 outperforms:3 existing:4 horvitz:1 com:3 kdd:1 hofmann:1 designed:1 drop:1 update:1 selected:2 item:84 shut:1 prize:1 provides:1 toronto:5 preference:29 mathematical:1 along:1 mahieux:1 retrieving:1 cons... |
4,231 | 483 | Modeling Applications with the Focused Gamma Net
Jose C. Principe, Bert de Vries, Jyh-Ming Kuo and Pedro Guedes de Oliveira?
Department of Electrical Engineering
University of Florida, CSE 447
Gainesville, FL 32611
principe@synapse.ee.ufl.edu
*Departamento EletronicalINESC
Universidade de Aveiro
A veiro, Portugal
Ab... | 483 |@word seems:3 bptt:2 integrative:1 gainesville:1 paid:2 versatile:1 initial:1 configuration:2 series:7 past:6 outperforms:2 lang:3 activation:2 mackey:5 selected:4 fewer:1 plane:5 short:4 dissertation:1 ire:1 cse:1 five:1 direct:1 differential:1 hopf:1 become:1 fitting:1 behavior:1 elman:2 aveiro:1 ming:1 becomes:... |
4,232 | 4,830 | Learning Invariant Representations of Molecules for
Atomization Energy Prediction
Gr?goire Montavon1?, Katja Hansen2 , Siamac Fazli1 , Matthias Rupp3 , Franziska Biegler1 ,
Andreas Ziehe1 , Alexandre Tkatchenko2 , O. Anatole von Lilienfeld4 , Klaus-Robert M?ller1,5?
1. Machine Learning Group, TU Berlin
2. Fritz-Haber-... | 4830 |@word katja:1 kondor:1 polynomial:1 norm:3 open:1 simulation:2 pavel:1 reduction:1 initial:3 contains:1 series:1 document:1 reaction:1 comparing:1 activation:1 assigning:1 must:2 sergei:1 john:1 subsequent:1 ronan:1 plot:2 bart:2 intelligence:1 ith:1 hamiltonian:2 leadership:2 harvesting:1 completeness:1 sigmoida... |
4,233 | 4,831 | Angular Quantization-based Binary Codes for
Fast Similarity Search
Yunchao Gong? , Sanjiv Kumar? , Vishal Verma? , Svetlana Lazebnik?
?
Google Research, New York, NY 10011, USA
?
Computer Science Department, University of North Carolina at Chapel Hill, NC 27599, USA
?
Computer Science Department, University of Illinoi... | 4831 |@word kulis:3 version:3 ruiqi:1 compression:1 norm:8 seek:1 carolina:1 decomposition:1 paid:1 tr:4 bai:1 liu:3 contains:5 document:4 ours:1 outperforms:2 past:1 existing:1 bitwise:2 com:1 current:1 yet:1 written:2 sanjiv:1 happen:1 enables:1 designed:2 plot:2 progressively:1 update:2 hash:1 implying:1 fewer:1 cor... |
4,234 | 4,832 | Training sparse natural image models with a fast
Gibbs sampler of an extended state space
Jascha Sohl-Dickstein
Redwood Center
for Theoretical Neuroscience
jascha@berkeley.edu
Lucas Theis
Werner Reichardt Centre
for Integrative Neuroscience
lucas@bethgelab.org
Matthias Bethge
Werner Reichardt Centre
for Integrative ... | 4832 |@word norm:2 integrative:2 simulation:1 decomposition:1 covariance:2 contrastive:1 contains:2 series:2 interestingly:1 current:1 si:10 yet:1 written:1 visible:3 additive:2 partition:1 plot:4 update:3 stationary:2 pursued:1 discovering:1 fewer:1 generative:1 beginning:1 hamiltonian:2 core:1 toronto:1 org:3 constru... |
4,235 | 4,833 | Learning with Partially Absorbing Random Walks
Xiao-Ming Wu1 , Zhenguo Li1 , Anthony Man-Cho So3 , John Wright1 and Shih-Fu Chang1,2
1
Department of Electrical Engineering, Columbia University
2
Department of Computer Science, Columbia University
3
Department of SEEM, The Chinese University of Hong Kong
{xmwu, zgli, j... | 4833 |@word mild:1 kong:1 seems:1 d2:1 confirms:2 simulation:5 propagate:1 ajj:1 tried:1 commute:8 reduction:1 initial:1 contains:1 interestingly:2 existing:4 current:5 si:2 lang:1 must:1 john:1 subsequent:1 enables:1 drop:13 stationary:2 implying:2 selected:1 accordingly:1 beginning:1 provides:1 detecting:2 node:1 mat... |
4,236 | 4,834 | Modelling Reciprocating Relationships
with Hawkes Processes
Charles Blundell
Gatsby Computational Neuroscience Unit
University College London
London, United Kingdom
c.blundell@gatsby.ucl.ac.uk
Katherine A. Heller
Duke University
Durham, NC, USA
kheller@stat.duke.edu
Jeffrey M. Beck
University of Rochester
Rochester, ... | 4834 |@word version:2 norm:1 tat:1 pick:1 series:5 united:1 initialisation:1 daniel:1 prefix:1 outperforms:2 current:1 comparing:1 activation:2 yet:1 must:2 readily:1 john:2 partition:4 shape:1 pertinent:1 christian:1 plot:4 stationary:3 generative:3 half:1 intelligence:5 nq:7 reciprocal:5 ith:1 yamada:1 blei:1 contrib... |
4,237 | 4,835 | Mixability in Statistical Learning
Tim van Erven
Universit?e Paris-Sud, France
tim@timvanerven.nl
?
Peter D. Grunwald
CWI and Leiden University, the Netherlands
pdg@cwi.nl
Mark D. Reid
ANU and NICTA, Australia
Mark.Reid@anu.edu.au
Robert C. Williamson
ANU and NICTA, Australia
Bob.Williamson@anu.edu.au
Abstract
Stat... | 4835 |@word mild:1 version:5 achievable:3 stronger:1 norm:1 nd:1 c0:6 dekel:1 open:1 p0:3 minus:2 contains:6 series:1 erven:2 existing:2 surprising:1 yet:2 written:1 must:4 fn:26 discrimination:1 item:1 recherche:1 provides:1 zhang:8 direct:1 become:1 incorrect:1 specialize:1 prove:1 hellinger:2 introduce:2 excellence:... |
4,238 | 4,836 | Spectral learning of linear dynamics from
generalised-linear observations
with application to neural population data
Lars Buesing? , Jakob H. Macke?,? , Maneesh Sahani
Gatsby Computational Neuroscience Unit
University College London, London, UK
{lars, jakob, maneesh}@gatsby.ucl.ac.uk
Abstract
Latent linear dynamical ... | 4836 |@word trial:20 cox:2 briefly:2 illustrating:1 loading:5 termination:1 simulation:2 rhesus:1 uncovers:1 covariance:18 decomposition:1 eng:1 tr:1 moment:17 initial:6 series:7 initialisation:22 precluding:1 interestingly:1 past:10 current:1 comparing:1 ka:1 must:2 readily:1 additive:1 happen:1 numerical:2 subsequent... |
4,239 | 4,837 | Bayesian nonparametric models for bipartite graphs
Franc?ois Caron
INRIA
IMB - University of Bordeaux
Talence, France
Francois.Caron@inria.fr
Abstract
We develop a novel Bayesian nonparametric model for random bipartite graphs.
The model is based on the theory of completely random measures and is able
to handle a pot... | 4837 |@word cox:1 version:1 proportion:1 suitably:1 willing:1 iki:1 simulation:2 pick:1 mention:1 shot:1 series:2 score:5 zij:15 contains:3 com:2 surprising:1 assigning:1 starring:2 reminiscent:1 vere:1 tilted:2 enables:1 update:6 generative:11 intelligence:2 item:5 short:1 characterization:2 provides:2 node:1 evy:8 lo... |
4,240 | 4,838 | Cocktail Party Processing via Structured Prediction
Yuxuan Wang1 , DeLiang Wang1,2
Department of Computer Science and Engineering
2
Center for Cognitive Science
The Ohio State University
Columbus, OH 43210
{wangyuxu,dwang}@cse.ohio-state.edu
1
Abstract
While human listeners excel at selectively attending to a convers... | 4838 |@word stronger:1 seems:1 cochleagram:3 open:1 hu:2 seek:2 tried:1 kristjansson:1 accounting:1 mysore:2 harder:1 recursively:1 ld:3 reduction:2 contains:2 score:9 denoting:1 document:1 mmse:1 outperforms:7 existing:5 current:1 contextual:3 yuxuan:1 comparing:1 lang:1 written:2 hoboken:1 numerical:2 partition:1 sub... |
4,241 | 4,839 | Slice sampling normalized kernel-weighted
completely random measure mixture models
Sinead A. Williamson
Department of Machine Learning
Carnegie Mellon University
Pittsburgh, PA 15213
sinead@cs.cmu.edu
Nicholas J. Foti
Department of Computer Science
Dartmouth College
Hanover, NH 03755
nfoti@cs.dartmouth.edu
Abstract
... | 4839 |@word version:3 middle:3 proportion:1 seems:1 simulation:1 simplifying:1 pg:3 moment:4 existing:8 comparing:2 surprising:1 written:2 must:1 partition:1 plot:2 stationary:2 instantiate:1 fewer:1 ith:1 record:1 fa9550:1 location:15 evy:6 nrm:4 favaro:1 unbounded:1 blackwellized:2 beta:9 consists:1 prove:1 fitting:1... |
4,242 | 484 | Information Measure Based Skeletonisation
Sowmya Ramachandran
Department of Computer Science
University of Texas at Austin
Austin, TX 78712-1188
Lorien Y. Pratt *
Department of Computer Science
Rutgers University
New Brunswick, NJ 08903
Abstract
Automatic determination of proper neural network topology by trimming
o... | 484 |@word hu:12 simulation:1 pick:4 barney:8 recursively:1 reduction:1 initial:5 configuration:1 score:2 recovered:2 comparing:1 current:1 activation:8 john:3 christian:1 remove:3 drop:1 update:4 fewer:1 dissertation:1 detecting:1 draft:2 hyperplanes:7 sigmoidal:4 along:1 consists:1 brain:1 begin:2 classifies:1 watrou... |
4,243 | 4,840 | Non-linear Metric Learning
Dor Kedem, Stephen Tyree, Kilian Q. Weinberger
Dept. of Comp. Sci. & Engi.
Washington U.
St. Louis, MO 63130
kedem.dor,swtyree,kilian@wustl.edu
Fei Sha
Dept. of Comp. Sci.
U. of Southern California
Los Angeles, CA 90089
feisha@usc.edu
Gert Lanckriet
Dept. of Elec. & Comp. Engineering
U. of... | 4840 |@word collinearity:1 repository:1 version:4 briefly:1 kulis:3 seems:2 replicate:1 open:1 grey:1 additively:2 pavel:1 reduction:9 efficacy:1 ours:1 outperforms:4 existing:2 current:1 contextual:1 babenko:1 goldberger:1 subsequent:1 additive:1 partition:1 designed:2 greedy:1 selected:4 intelligence:2 cook:1 xk:4 st... |
4,244 | 4,841 | The representer theorem for Hilbert spaces: a
necessary and sufficient condition
Francesco Dinuzzo and Bernhard Sch?olkopf
Max Planck Institute for Intelligent Systems
Spemannstrasse 38,72076 T?ubingen
Germany
[fdinuzzo@tuebingen.mpg.de, bs@tuebingen.mpg.de]
Abstract
The representer theorem is a property that lies at... | 4841 |@word cox:1 norm:5 closure:1 semicontinuous:11 cos2:3 necessity:1 contains:1 rkhs:6 recovered:1 written:2 numerical:1 girosi:1 kyk:6 une:1 xk:14 parametrization:1 dinuzzo:1 provides:2 characterization:5 herbrich:1 arctan:1 mathematical:4 along:1 constructed:1 prove:2 fitting:1 ray:1 introduce:1 x0:2 indeed:2 mpg:... |
4,245 | 4,842 | Localizing 3D cuboids in single-view images
Jianxiong Xiao
Bryan C. Russell?
Massachusetts Institute of Technology
?
Antonio Torralba
University of Washington
Abstract
In this paper we seek to detect rectangular cuboids and localize their corners in
uncalibrated single-view images depicting everyday scenes. In co... | 4842 |@word dalal:1 printer:1 everingham:1 triggs:1 seek:2 tried:3 pick:1 dramatic:1 solid:1 initial:4 configuration:4 contains:1 score:8 hoiem:3 existing:3 current:1 wd:2 com:1 marquardt:1 scatter:4 written:1 must:1 parsing:1 visible:1 subsequent:1 informative:2 shape:26 designed:1 plot:6 update:1 v:2 alone:3 cue:2 pl... |
4,246 | 4,843 | Nonparametric Reduced Rank Regression
Rina Foygel?,? , Michael Horrell? , Mathias Drton?,? , John Lafferty?
?
Department of Statistics
Stanford University
?
Department of Statistics
University of Chicago
?
Department of Statistics
University of Washington
Abstract
We propose an approach to multivariate nonparame... | 4843 |@word m1j:2 version:5 norm:23 calculus:1 covariance:6 decomposition:4 tr:7 reduction:1 liu:1 contains:1 series:2 denoting:2 comparing:1 must:1 written:2 john:2 additive:15 chicago:1 camacho:1 plot:1 update:3 stationary:4 implying:1 selected:1 xk:2 dissertation:1 fa9550:1 characterization:1 node:2 five:3 rc:6 cons... |
4,247 | 4,844 | A new metric on the manifold of kernel matrices with
application to matrix geometric means
Suvrit Sra
Max Planck Institute for Intelligent Systems
72076 T?ubigen, Germany
suvrit@tuebingen.mpg.de
Abstract
Symmetric positive definite (spd) matrices pervade numerous scientific disciplines, including machine learning and... | 4844 |@word determinant:2 version:2 polynomial:1 norm:2 open:1 crucially:1 contraction:2 covariance:4 commute:1 mention:2 thereby:1 tr:5 cherian:7 series:1 interestingly:1 petz:2 diagonalized:1 ka:1 optim:1 dx:1 must:6 determinantal:1 numerical:3 analytic:1 drop:1 plot:3 update:1 stationary:4 provides:1 characterizatio... |
4,248 | 4,845 | Clustering by Nonnegative Matrix Factorization
Using Graph Random Walk
Zhirong Yang, Tele Hao, Onur Dikmen, Xi Chen and Erkki Oja
Department of Information and Computer Science
Aalto University, 00076, Finland
{zhirong.yang,tele.hao,onur.dikmen,xi.chen,erkki.oja}@aalto.fi
Abstract
Nonnegative Matrix Factorization (NM... | 4845 |@word kulis:1 briefly:1 version:4 inversion:1 norm:3 zelnik:1 decomposition:3 citeseer:1 tr:8 accommodate:1 initial:4 document:1 past:1 existing:1 ka:2 current:2 assigning:1 additive:1 kdd:1 enables:1 cheap:2 remove:1 update:8 spec:4 selected:3 guess:4 intelligence:3 pnmf:1 farther:2 blei:1 provides:1 iterates:1 ... |
4,249 | 4,846 | Isotropic Hashing
Weihao Kong, Wu-Jun Li
Shanghai Key Laboratory of Scalable Computing and Systems
Department of Computer Science and Engineering, Shanghai Jiao Tong University, China
{kongweihao,liwujun}@cs.sjtu.edu.cn
Abstract
Most existing hashing methods adopt some projection functions to project the original dat... | 4846 |@word kong:3 kulis:3 briefly:2 norm:1 d2:2 vldb:1 covariance:1 decomposition:3 nystr:1 tr:6 carry:3 initial:2 liu:3 contains:3 zij:2 denoting:1 existing:5 comparing:1 si:1 chu:2 must:2 numerical:1 partition:1 kdd:2 shape:1 designed:1 gist:2 hash:5 stationary:3 isotropic:18 ith:1 steepest:1 quantized:3 toronto:1 f... |
4,250 | 4,847 | Super-Bit Locality-Sensitive Hashing
Jianqiu Ji? , Jianmin Li? , Shuicheng Yan? , Bo Zhang? , Qi Tian?
?
State Key Laboratory of Intelligent Technology and Systems,
Tsinghua National Laboratory for Information Science and Technology (TNList),
Department of Computer Science and Technology,
Tsinghua University, Beijing 1... | 4847 |@word kulis:4 faculty:1 version:3 norm:3 seems:1 proportion:2 shuicheng:2 pick:1 egou:1 tnlist:1 reduction:6 liu:2 contains:3 document:2 outperforms:3 bitwise:1 current:1 si:1 yet:3 numerical:1 sanjiv:3 informative:3 kdd:1 christian:2 plot:1 hash:10 half:2 intelligence:1 isotropic:5 provides:8 zhang:1 symposium:4... |
4,251 | 4,848 | Learning Image Descriptors with the Boosting-Trick
Tomasz Trzcinski, Mario Christoudias, Vincent Lepetit and Pascal Fua
CVLab, EPFL, Lausanne, Switzerland
firstname.lastname@epfl.ch
Abstract
In this paper we apply boosting to learn complex non-linear local visual feature
representations, drawing inspiration from its ... | 4848 |@word kulis:4 illustrating:2 version:1 dalal:1 compression:1 norm:1 triggs:1 open:1 lepetit:2 reduction:2 configuration:5 liu:1 offering:2 tuned:1 interestingly:3 psdboost:1 past:1 outperforms:3 wd:1 comparing:2 bd:1 strecha:3 designed:4 plot:3 update:1 drop:1 hash:2 v:1 greedy:2 selected:5 website:1 half:1 param... |
4,252 | 4,849 | Learning with Target Prior
Siwei Lyu
Computer Science, Univ. at Albany, SUNY
Albany, NY 12222
lsw@cs.albany.edu
Zuoguan Wang
Dept. of ECSE, Rensselaer Polytechnic Inst.
Troy, NY 12180
wangz6@rpi.edu
Qiang Ji
Dept. of ECSE, Rensselaer Polytechnic Inst.
Troy, NY 12180
jiq@rpi.edu
Gerwin Schalk
Wadsworth Center, NYS D... | 4849 |@word trial:11 private:1 sgplvm:10 proportion:2 tedious:1 open:1 seek:1 fatourechi:1 eng:2 contrastive:2 solid:1 reduction:1 moment:2 pub:1 salzmann:1 daniel:1 outperforms:1 existing:3 current:4 recovered:1 rpi:2 john:1 numerical:2 visible:3 ronan:1 shape:1 motor:1 remove:2 update:2 cue:1 generative:1 mccallum:2 ... |
4,253 | 485 | A Weighted Probabilistic Neural Network
David Montana
Bolt Beranek and Newman Inc.
10 Moulton Street
Cambridge, MA 02138
Abstract
The Probabilistic Neural Network (PNN) algorithm represents the likelihood function of a given class as the sum of identical, isotropic Gaussians.
In practice, PNN is often an excellent pa... | 485 |@word norm:1 d2:1 covariance:29 initial:2 series:1 score:1 genetic:14 marquardt:1 yet:1 designed:1 depict:2 intelligence:1 isotropic:4 xk:1 ith:1 short:2 provides:1 node:1 contribute:2 five:3 constructed:1 initiative:1 incorrect:1 consists:1 sacrifice:1 indeed:2 roughly:2 globally:1 increasing:1 classifies:1 what:... |
4,254 | 4,850 | A Marginalized Particle Gaussian Process Regression
Yali Wang and Brahim Chaib-draa
Department of Computer Science
Laval University
Quebec, Quebec G1V0A6
{wang,chaib}@damas.ift.ulaval.ca
Abstract
We present a novel marginalized particle Gaussian process (MPGP) regression,
which provides a fast, accurate online Bayesi... | 4850 |@word seems:3 suitably:1 simulation:1 propagate:1 covariance:10 mention:1 recursively:1 initial:1 liu:1 contains:1 outperforms:2 freitas:2 current:4 ka:1 nt:1 additive:2 plot:1 update:3 resampling:2 stationary:3 selected:2 website:1 beginning:1 data2:2 provides:3 location:2 toronto:1 firstly:5 ssm:7 bayesfilters:... |
4,255 | 4,851 | Learning Mixtures of Tree Graphical Models
Daniel Hsu
Microsoft Research New England
dahsu@microsoft.com
Animashree Anandkumar
UC Irvine
a.anandkumar@uci.edu
Furong Huang
UC Irvine
furongh@uci.edu
Sham M. Kakade
Microsoft Research New England
skakade@microsoft.com
Abstract
We consider unsupervised estimation of mix... | 4851 |@word briefly:1 polynomial:3 norm:1 d2:1 bn:4 decomposition:13 moment:2 initial:1 liu:6 series:2 configuration:2 score:1 daniel:1 com:2 j1:1 fund:1 greedy:1 fa9550:2 provides:4 node:44 complication:1 mathematical:1 along:2 direct:1 symposium:2 prove:1 fitting:1 pairwise:10 hardness:1 indeed:1 roughly:1 frequently... |
4,256 | 4,852 | Link Prediction in Graphs with Autoregressive
Features
Emile Richard
CMLA UMR CNRS 8536,
ENS Cachan, France
St?phane Ga?ffas
CMAP - Ecole Polytechnique
& LSTA - Universit? Paris 6
Nicolas Vayatis
CMLA UMR CNRS 8536,
ENS Cachan, France
Abstract
In the paper, we consider the problem of link prediction in time-evolving... | 4852 |@word multitask:1 mild:1 version:3 norm:16 seems:1 km:4 simulation:1 decomposition:1 tr:1 series:5 uncovered:2 contains:2 score:1 ecole:1 document:1 longitudinal:1 ka:1 z2:4 nt:23 current:1 must:1 realistic:1 numerical:2 j1:2 designed:1 plot:1 generative:1 intelligence:1 regressive:1 provides:1 node:8 successive:... |
4,257 | 4,853 | Small-Variance Asymptotics for Exponential Family
Dirichlet Process Mixture Models
Ke Jiang, Brian Kulis
Department of CSE
The Ohio State University
{jiangk,kulis}@cse.ohio-state.edu
Michael I. Jordan
Departments of EECS and Statistics
University of California at Berkeley
jordan@cs.berkeley.edu
Abstract
Sampling and ... | 4853 |@word kulis:3 repository:1 briefly:4 version:7 trial:1 c0:2 open:4 crucially:1 covariance:10 contains:1 exclusively:1 selecting:3 zij:2 score:1 series:1 document:5 outperforms:2 existing:6 past:1 current:1 comparing:1 activation:1 dx:1 written:2 partition:5 motor:1 update:10 fund:1 half:1 fewer:2 intelligence:1 w... |
4,258 | 4,854 | Transferring Expectations in Model-based
Reinforcement Learning
Trung Thanh Nguyen, Tomi Silander, Tze-Yun Leong
School of Computing
National University of Singapore
Singapore, 117417
{nttrung, silander, leongty}@comp.nus.edu.sg
Abstract
We study how to automatically select and adapt multiple abstractions or represen... | 4854 |@word trial:1 version:5 polynomial:1 norm:1 tadepalli:1 reused:1 calculus:2 d2:2 decomposition:3 diuk:1 pick:3 fifteen:2 homomorphism:1 series:1 efficacy:1 score:16 selecting:4 past:1 existing:2 outperforms:3 current:5 savage:1 cmdp:3 si:5 written:1 remove:1 update:4 v:2 stationary:3 generative:2 obsolete:1 greed... |
4,259 | 4,855 | Majorization for CRFs and Latent Likelihoods
Anna Choromanska
Department of Electrical Engineering
Columbia University
aec2163@columbia.edu
Tony Jebara
Department of Computer Science
Columbia University
jebara@cs.columbia.edu
Abstract
The partition function plays a key role in probabilistic modeling including condit... | 4855 |@word multitask:1 version:3 manageable:1 inversion:2 proportion:1 norm:1 termination:2 heiser:1 decomposition:4 incarnation:1 versatile:1 configuration:11 contains:7 series:2 leeuw:1 outperforms:1 current:4 comparing:1 recovered:1 skipping:1 si:10 yet:2 must:3 written:2 parsing:1 realistic:1 partition:27 hofmann:... |
4,260 | 4,856 | Probabilistic Event Cascades for Alzheimer?s disease
Daniel Alexander
University College London
d.alexander@cs.ucl.ac.uk
Jonathan Huang
Stanford University
jhuang11@stanford.edu
Abstract
Accurate and detailed models of neurodegenerative disease progression are
crucially important for reliable early diagnosis and the ... | 4856 |@word mild:2 trial:1 mri:1 inversion:1 kapil:1 hippocampus:2 proportion:1 open:1 crucially:1 tried:1 decomposition:1 pg:4 bellevue:1 harder:1 carry:1 reduction:1 initial:2 series:1 score:9 karger:1 daniel:2 document:1 outperforms:1 current:4 comparing:1 surprising:2 yet:1 must:3 john:6 ronald:4 realistic:1 partit... |
4,261 | 4,857 | Scalable Influence Estimation in
Continuous-Time Diffusion Networks
Nan Du?
Le Song?
Manuel Gomez-Rodriguez?
Hongyuan Zha?
?
Georgia Institute of Technology
MPI for Intelligent Systems?
dunan@gatech.edu
lsong@cc.gatech.edu
manuelgr@tue.mpg.de
zha@cc.gatech.edu
Abstract
If a piece of information is released from a medi... | 4857 |@word faculty:1 closure:1 vldb:1 seek:1 lakshmanan:1 harder:1 memetracker:2 configuration:1 contains:3 initial:1 selecting:5 interestingly:1 outperforms:2 yajun:2 current:4 discretization:1 manuel:6 must:2 numerical:1 kdd:7 shape:1 designed:1 v:8 greedy:12 selected:10 generative:1 intelligence:1 core:6 short:1 pr... |
4,262 | 4,858 | Adaptive Anonymity via b-Matching
Krzysztof Choromanski
Columbia University
kmc2178@columbia.edu
Tony Jebara
Columbia University
tj2008@columbia.edu
Kui Tang
Columbia University
kt2384@columbia.edu
Abstract
The adaptive anonymity problem is formalized where each individual shares their
data along with an integer va... | 4858 |@word private:1 version:2 polynomial:2 nd:2 vldb:1 seek:1 bn:12 gabow:1 reduction:1 venkatasubramanian:1 initial:2 contains:4 offering:1 interestingly:2 recovered:2 protection:6 yet:2 must:1 numerical:1 partition:2 kdd:2 drop:1 plot:1 intelligence:1 fewer:2 beginning:1 pvldb:1 record:6 cormode:2 provides:3 contri... |
4,263 | 4,859 | Exact and Stable Recovery of Pairwise Interaction
Tensors
Shouyuan Chen
Michael R. Lyu Irwin King
The Chinese University of Hong Kong
{sychen,lyu,king}@cse.cuhk.edu.hk
Zenglin Xu
Purdue University
xu218@purdue.edu
Abstract
Tensor completion from incomplete observations is a problem of significant practical interest. ... | 4859 |@word kong:2 version:4 norm:10 c0:8 tensorial:1 kbkf:2 simulation:7 decomposition:6 concise:1 configuration:1 series:1 exclusively:1 selecting:2 contains:1 daniel:1 liu:2 leandro:1 interestingly:1 outperforms:2 existing:3 kmk:1 recovered:9 subsequent:1 enables:1 remove:1 designed:1 acar:1 bart:1 kyk:4 xk:7 propac... |
4,264 | 486 | A Computational Mechanism To Account For
Averaged Modified Hand Trajectories
Ealan A. Henis*and Tamar Flash
Department of Applied Mathematics and Computer Science
The Weizmann Institute of Science
Rehovot 76100, Israel
Abstract
Using the double-step target displacement paradigm the mechanisms underlying arm trajector... | 486 |@word trial:10 middle:1 version:14 schoen:1 gradual:1 simulation:1 accounting:3 initial:19 configuration:1 extrapersonal:1 score:1 united:1 reaction:4 current:1 marquardt:3 activation:1 must:1 subsequent:1 motor:9 v:2 stationary:1 indicative:1 short:2 provides:2 location:35 successive:1 simpler:1 five:2 mathematic... |
4,265 | 4,860 | Matrix factorization with Binary Components
Martin Slawski, Matthias Hein and Pavlo Lutsik
Saarland University
{ms,hein}@cs.uni-saarland.de, p.lutsik@mx.uni-saarland.de
Abstract
Motivated by an application in computational biology, we consider low-rank matrix factorization with {0, 1}-constraints on one of the factor... | 4860 |@word trial:1 version:1 middle:3 seems:1 stronger:2 proportion:6 integrative:1 crucially:1 decomposition:5 pick:3 tr:1 reduction:11 contains:5 selecting:2 hottopixx:6 rightmost:1 outperforms:1 recovered:1 com:1 must:4 written:1 john:1 subsequent:1 additive:2 kdd:1 plot:2 ainen:1 update:1 v:2 alone:1 selected:2 fi... |
4,266 | 4,861 | On the Complexity and Approximation of
Binary Evidence in Lifted Inference
Guy Van den Broeck and Adnan Darwiche
Computer Science Department
University of California, Los Angeles
{guyvdb,darwiche}@cs.ucla.edu
Abstract
Lifted inference algorithms exploit symmetries in probabilistic models to speed
up inference. They s... | 4861 |@word polynomial:8 seems:1 nd:1 adnan:1 open:2 decomposition:7 p0:2 q1:8 reduction:6 contains:2 pub:1 outperforms:3 existing:1 yet:2 attracted:1 must:2 partition:1 remove:2 plot:2 interpretable:2 ainen:2 bart:1 generative:2 half:1 fewer:1 braz:4 amir:1 mln:12 indicative:1 intelligence:10 beginning:1 manfred:1 con... |
4,267 | 4,862 | Unsupervised Spectral Learning of FSTs
Rapha?el Bailly
Xavier Carreras
Ariadna Quattoni
Universitat Politecnica de Catalunya
Barcelona, 08034
rbailly,carreras,aquattoni@lsi.upc.edu
Abstract
Finite-State Transducers (FST) are a standard tool for modeling paired inputoutput sequences and are used in numerous applicatio... | 4862 |@word polynomial:3 norm:9 open:2 closure:1 d2:2 confirms:1 decomposition:1 contains:1 prefix:18 recovered:1 yet:1 must:2 john:1 fn:1 realistic:1 alone:1 core:1 denis:1 simpler:1 zhang:1 transducer:10 consists:1 prove:1 fitting:1 combine:1 introduce:1 theoretically:1 ra:9 pkdd:1 planning:1 morphology:1 considering... |
4,268 | 4,863 | On Decomposing the Proximal Map
Yaoliang Yu
Department of Computing Science, University of Alberta, Edmonton AB T6G 2E8, Canada
yaoliang@cs.ualberta.ca
Abstract
The proximal map is the key step in gradient-type algorithms, which have become prevalent in large-scale high-dimensional problems. For simple functions
this... | 4863 |@word norm:22 stronger:1 suitably:1 simulation:1 decomposition:20 pg:47 pick:1 incurs:1 harder:1 liu:1 contains:3 series:1 lucet:2 ktv:2 interestingly:3 current:1 comparing:1 must:3 john:1 subsequent:1 kpf:1 additive:3 partition:1 cheap:1 enables:1 designed:1 update:1 intelligence:1 item:1 kyk:1 accordingly:1 ami... |
4,269 | 4,864 | Non-Uniform Camera Shake Removal Using a
Spatially-Adaptive Sparse Penalty
?
Haichao Zhang?? and David Wipf ?
School of Computer Science, Northwestern Polytechnical University, Xi?an, China
?
Department of Electrical and Computer Engineering, Duke University, USA
?
Visual Computing Group, Microsoft Research Asia, Bei... | 4864 |@word determinant:1 version:1 briefly:1 middle:1 proportion:1 advantageous:1 norm:15 stronger:1 open:1 hu:1 delgado:1 wellapproximated:1 initial:1 contains:5 uncovered:2 selecting:1 efficacy:1 existing:4 recovered:1 com:2 gmail:2 dx:1 yet:2 must:1 realistic:1 partition:1 blur:52 subsequent:1 shape:3 designed:1 fe... |
4,270 | 4,865 | Provable Subspace Clustering:
When LRR meets SSC
Yu-Xiang Wang
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213 USA
yuxiangw@cs.cmu.edu
Huan Xu
Dept. of Mech. Engineering
National Univ. of Singapore
Singapore, 117576
mpexuh@nus.edu.sg
Chenlei Leng
Department of Statistics
University of Warwi... | 4865 |@word mild:1 version:4 briefly:1 norm:19 stronger:1 seems:1 nd:1 tedious:1 simulation:1 linearized:1 pick:1 mpexuh:1 liu:5 contains:3 comparing:2 yet:2 chu:1 concatenate:1 numerical:7 shape:1 plot:1 drop:1 update:3 alone:1 intelligence:4 leaf:2 xk:1 ith:1 gpca:3 certificate:2 favaro:1 mathematical:2 c2:7 become:2... |
4,271 | 4,866 | Matrix Completion From any Given Set of
Observations
Troy Lee
Nanyang Technological University and
Centre for Quantum Technologies
troyjlee@gmail.com
Adi Shraibman
Department of Computer Science
Tel Aviv-Yaffo Academic College
adi.shribman@gmail.com
Abstract
In the matrix completion problem the aim is to recover an u... | 4866 |@word version:3 polynomial:1 norm:44 mention:1 tr:3 initial:12 ka:5 com:2 si:2 gmail:2 must:1 prize:1 simpler:1 dn:1 constructed:1 qij:7 prove:3 introduce:1 expected:2 globally:1 becomes:1 bounded:3 notation:1 moreover:1 lowest:2 what:4 minimizes:1 shraibman:4 finding:1 unobserved:1 guarantee:7 every:5 exactly:3 ... |
4,272 | 4,867 | Convex Two-Layer Modeling
?
Ozlem
Aslan
Hao Cheng
Dale Schuurmans
Department of Computing Science, University of Alberta
Edmonton, AB T6G 2E8, Canada
{ozlem,hcheng2,dale}@cs.ualberta.ca
Xinhua Zhang
Machine Learning Research Group
National ICT Australia and ANU
xinhua.zhang@anu.edu.au
Abstract
Latent variable predict... | 4867 |@word pw:1 polynomial:2 proportion:2 norm:1 heuristically:1 seek:1 bn:4 decomposition:1 tr:18 accommodate:2 moment:3 reduction:2 contains:1 score:1 afraid:1 nii:7 outperforms:1 freitas:1 bradley:1 current:3 recovered:1 nt:8 yet:1 chu:1 must:6 readily:1 written:1 devin:1 subsequent:1 realistic:1 j1:1 update:1 n0:5... |
4,273 | 4,868 | Reconciling ?priors? & ?priors? without prejudice?
R?emi Gribonval ?
Inria
Centre Inria Rennes - Bretagne Atlantique
remi.gribonval@inria.fr
Pierre Machart
Inria
Centre Inria Rennes - Bretagne Atlantique
pierre.machart@inria.fr
Abstract
There are two major routes to address linear inverse problems. Whereas
regulariz... | 4868 |@word norm:5 nd:1 tedious:1 additively:4 covariance:4 decomposition:1 hsieh:1 commute:1 carry:1 quo:2 interestingly:1 mmse:20 existing:3 z2:7 profusion:1 liva:1 attracted:1 written:1 fn:1 additive:6 alone:1 stationary:8 gribonval:5 short:1 core:2 anthoine:1 colored:1 volkan:1 characterization:1 readability:2 comp... |
4,274 | 4,869 | Robust Sparse Principal Component Regression
under the High Dimensional Elliptical Model
Han Liu
Department of Operations Research
and Financial Engineering
Princeton University
Princeton, NJ 08544
hanliu@princeton.edu
Fang Han
Department of Biostatistics
Johns Hopkins University
Baltimore, MD 21210
fhan@jhsph.edu
A... | 4869 |@word mild:1 collinearity:4 version:5 averagely:2 norm:4 smirnov:1 nd:2 simulation:3 bn:3 covariance:12 pick:1 tr:3 solid:1 reduction:1 liu:9 outperforms:1 existing:1 elliptical:26 com:1 si:2 scatter:2 john:2 numerical:1 plot:6 treating:2 v:2 half:1 selected:7 cook:2 greedy:1 accordingly:4 ud2:1 xk:1 lr:4 provide... |
4,275 | 487 | Simulation of Optimal Movements Using the
Minimum-Muscle-Tension-Change Model.
Menashe Dornay*
Yoji Uno"
Mitsuo Kawato*
Ryoji Suzuki**
?Cognitive Processes Department, ATR Auditory and Visual Perception Research
Laboratories, Sanpeidani, Inuidani, Seika-Cho, Soraku-Gun, Kyoto 619-02 Japan.
??Department of Mathemat... | 487 |@word middle:1 faculty:1 open:1 gradual:1 simulation:11 rhesus:1 tried:1 tr:1 moment:1 ivaldi:2 configuration:1 initial:4 t7:8 past:1 current:3 must:7 john:1 numerical:3 realistic:5 motor:3 update:1 device:1 nervous:6 plane:1 location:1 mathematical:2 along:1 become:1 symposium:2 ray:2 behavioral:3 expected:1 inde... |
4,276 | 4,870 | Structured Learning via Logistic Regression
Justin Domke
NICTA and The Australian National University
justin.domke@nicta.com.au
Abstract
A successful approach to structured learning is to write the learning objective as
a joint function of linear parameters and inference messages, and iterate between
updates to each.... | 4870 |@word kohli:2 dalal:1 polynomial:1 triggs:2 textonboost:1 solid:1 configuration:2 contains:2 current:4 com:1 written:2 parsing:1 john:2 must:2 sanjiv:1 realistic:1 pseudomarginals:3 update:12 aside:1 greedy:1 selected:1 leaf:3 amir:2 xk:19 smith:1 iterates:1 boosting:12 node:3 location:1 daphne:2 consists:1 ijcv:... |
4,277 | 4,871 | Correlations strike back (again): the case of
associative memory retrieval
Cristina Savin1
cs664@cam.ac.uk
Peter Dayan2
dayan@gatsby.ucl.ac.uk
M?at?e Lengyel1
m.lengyel@eng.cam.ac.uk
1
Computational & Biological Learning Lab, Dept. Engineering, University of Cambridge, UK
2
Gatsby Computational Neuroscience Unit, U... | 4871 |@word worsens:1 middle:2 version:3 compression:1 seems:1 hippocampus:1 nd:1 additively:1 overwritten:1 eng:1 covariance:22 accounting:1 postsynaptically:2 paulsen:1 dramatic:1 fortuitous:1 accommodate:2 catastrophically:1 moment:1 initial:1 configuration:1 cristina:1 exclusively:2 efficacy:2 interestingly:1 past:... |
4,278 | 4,872 | A memory frontier for complex synapses
Subhaneil Lahiri and Surya Ganguli
Department of Applied Physics, Stanford University, Stanford CA
sulahiri@stanford.edu, sganguli@stanford.edu
Abstract
An incredible gulf separates theoretical models of synapses, often described solely
by a single scalar value denoting the size... | 4872 |@word version:1 achievable:9 hippocampus:2 open:3 hu:1 crucially:1 simplifying:1 decomposition:2 pick:1 incurs:1 thereby:3 solid:2 initial:12 contains:1 efficacy:7 denoting:2 tuned:1 past:1 must:6 written:1 subsequent:7 numerical:4 plasticity:8 analytic:1 designed:3 alone:1 stationary:1 implying:1 device:1 leaf:1... |
4,279 | 4,873 | Bayesian entropy estimation for binary spike train
data using parametric prior knowledge
Evan Archer13 , Il Memming Park123 , Jonathan W. Pillow123
1. Center for Perceptual Systems, 2. Dept. of Psychology,
3. Division of Statistics & Scientific Computation
The University of Texas at Austin
{memming@austin., earcher@, ... | 4873 |@word briefly:1 proportion:2 simulation:2 bn:1 carry:1 synergistically:1 series:2 contains:2 selecting:1 liu:1 outperforms:1 existing:1 ka:2 com:1 grassberger:1 indistinguishably:1 multineuron:1 numerical:1 informative:1 earcher:1 remove:1 designed:2 alone:1 xk:2 ith:2 short:1 record:1 provides:3 characterization... |
4,280 | 4,874 | Inferring neural population dynamics from multiple
partial recordings of the same neural circuit
Srinivas C. Turaga?1,2 , Lars Buesing1 , Adam M. Packer2 , Henry Dalgleish2 , Noah Pettit2 , Michael
H?ausser2 and Jakob H. Macke3,4
1
2
Gatsby Computational Neuroscience Unit, University College London
Wolfson Institute f... | 4874 |@word trial:4 hampson:1 middle:2 open:1 simulation:3 covariance:3 initial:1 series:2 interestingly:1 ording:1 current:2 comparing:1 recovered:3 scatter:2 intriguing:1 realistic:1 shape:1 wanted:1 plot:2 drop:2 update:2 v:1 stationary:1 half:3 selected:1 signalling:1 xk:1 parametrization:2 filtered:1 provides:2 to... |
4,281 | 4,875 | Noise-Enhanced Associative Memories
Amin Karbasi
Swiss Federal Institute of Technology Zurich
amin.karbasi@inf.ethz.ch
Amir Hesam Salavati
Ecole Polytechnique Federale de Lausanne
hesam.salavati@epfl.ch
Amin Shokrollahi
Ecole Polytechnique Federale de Lausanne
amin.shokrollahi@epfl.ch
Lav R. Varshney
IBM Thomas J. W... | 4875 |@word version:3 briefly:1 eliminating:2 polynomial:1 hippocampus:5 open:1 simulation:5 contraction:1 p0:4 invoking:1 pick:1 thereby:1 solid:1 electronics:1 initial:1 series:1 ecole:2 interestingly:1 outperforms:2 current:3 surprising:1 si:3 fn:1 additive:1 opin:1 remove:1 designed:2 extrapolating:1 update:11 v:1 ... |
4,282 | 4,876 | Demixing odors ? fast inference in olfaction
?
Agnieszka Grabska-Barwinska
Gatsby Computational Neuroscience Unit
UCL
agnieszka@gatsby.ucl.ac.uk
Jeff Beck
Duke University
jeff@gatsby.ucl.ac.uk
Peter E. Latham
Gatsby Computational Neuroscience Unit
UCL
pel@gatsby.ucl.ac.uk
Alexandre Pouget
University of Geneva
Alexa... | 4876 |@word seek:1 simulation:2 p0:1 solid:1 initial:3 mainen:1 denoting:1 past:1 blank:1 anterior:1 activation:2 dx:1 must:2 written:1 physiol:1 realistic:5 plasticity:1 plot:6 drop:9 update:3 v:1 generative:4 fewer:1 reciprocal:2 record:1 colored:2 location:1 differential:2 ik:1 consists:2 behavioral:3 olfactory:34 i... |
4,283 | 4,877 | Recurrent linear models of
simultaneously-recorded neural populations
Marius Pachitariu, Biljana Petreska, Maneesh Sahani
Gatsby Computational Neuroscience Unit
University College London, UK
{marius,biljana,maneesh}@gatsby.ucl.ac.uk
Abstract
Population neural recordings with long-range temporal structure are often be... | 4877 |@word trial:8 inversion:1 achievable:1 loading:3 suitably:1 simulation:2 rhesus:1 covariance:14 decomposition:1 harder:1 recursively:1 series:1 past:2 imaginary:5 recovered:2 si:1 must:3 readily:1 written:1 attracted:1 realistic:1 partition:2 motor:4 plot:1 concert:1 stationary:3 generative:5 discovering:2 prohib... |
4,284 | 4,878 | Understanding Dropout
Peter Sadowski
Department of Computer Science
University of California, Irvine
Irvine, CA 92697
pjsadows@ics.uci.edu
Pierre Baldi
Department of Computer Science
University of California, Irvine
Irvine, CA 92697
pfbaldi@uci.edu
Abstract
Dropout is a relatively new algorithm for training neural n... | 4878 |@word confirms:1 additively:1 simulation:5 configuration:1 contains:1 exclusively:1 o2:1 od:4 si:2 activation:2 must:1 gpu:1 intelligence:2 beginning:1 short:3 provides:6 math:1 pascanu:1 toronto:2 sigmoidal:5 org:1 mathematical:3 along:1 prove:2 baldi:2 introduce:1 expected:1 roughly:1 p1:3 examine:1 behavior:1 ... |
4,285 | 4,879 | Annealing Between Distributions by
Averaging Moments
Roger Grosse
Comp. Sci. & AI Lab
MIT
Cambridge, MA 02139
Chris J. Maddison
Dept. of Computer Science
University of Toronto
Toronto, ON M5S 3G4
Ruslan Salakhutdinov
Depts. of Statistics and Comp. Sci.,
University of Toronto
Toronto, ON M5S 3G4, Canada
Abstract
Man... | 4879 |@word d2:2 simulation:1 covariance:2 p0:3 contrastive:3 decomposition:1 reduction:1 moment:46 configuration:2 series:1 initial:13 document:1 outperforms:1 blank:3 comparing:2 surprising:1 activation:4 must:3 john:1 numerical:1 partition:18 visible:6 distant:1 shape:1 cheap:5 update:4 generative:3 leaf:2 fewer:2 i... |
4,286 | 488 | Bayesian Model Comparison and Backprop Nets
David J.C. MacKay?
Computation and Neural Systems
California Institute of Technology 139-14
Pasadena CA 91125
mackayGras.phy.cam.ac.uk
Abstract
The Bayesian model comparison framework is reviewed, and the Bayesian
Occam's razor is explained. This framework can be applied t... | 488 |@word determinant:1 polynomial:2 nd:1 wla:2 covariance:2 abou:1 solid:1 phy:1 tuned:1 subjective:1 current:1 comparing:1 yet:1 v:2 smith:1 normalising:2 location:1 preference:1 penalises:3 simpler:2 height:2 c2:4 become:1 fitting:5 indeed:1 expected:2 embody:1 decomposed:1 automatically:2 estimating:1 matched:1 wh... |
4,287 | 4,880 | A simple example of Dirichlet process mixture
inconsistency for the number of components
Jeffrey W. Miller
Division of Applied Mathematics
Brown University
Providence, RI 02912
jeffrey miller@brown.edu
Matthew T. Harrison
Division of Applied Mathematics
Brown University
Providence, RI 02912
matthew harrison@brown.edu... | 4880 |@word seems:1 open:1 carolina:1 p0:15 series:2 genetic:1 interestingly:1 realistic:1 partition:5 pertinent:1 plot:1 core:1 detecting:1 characterization:1 location:1 x1p:1 become:2 prove:2 paragraph:1 introduce:1 behavior:1 multi:1 project:1 estimating:1 bounded:1 suffice:1 mass:4 what:2 sivaganesan:1 guarantee:1 ... |
4,288 | 4,881 | Approximate Bayesian Image Interpretation using
Generative Probabilistic Graphics Programs
Vikash K. Mansinghka?
1,2
, Tejas D. Kulkarni?
1,2
, Yura N. Perov1,2,3 , and Joshua B. Tenenbaum1,2
1
Computer Science and Artificial Intelligence Laboratory, MIT
2
Department of Brain and Cognitive Sciences, MIT
3
Institu... | 4881 |@word illustrating:1 inversion:1 retraining:1 rgb:3 decomposition:2 initial:2 configuration:1 contains:1 efficacy:2 series:2 hoiem:2 daniel:2 document:1 existing:4 comparing:1 manuel:1 si:11 assigning:1 dx:1 written:4 parsing:4 john:1 visible:1 alphanumeric:1 blur:16 enables:1 generative:24 intelligence:2 cue:2 w... |
4,289 | 4,882 | Dropout Training as Adaptive Regularization
Stefan Wager? , Sida Wang? , and Percy Liang?
Departments of Statistics? and Computer Science?
Stanford University, Stanford, CA-94305
swager@stanford.edu, {sidaw, pliang}@cs.stanford.edu
Abstract
Dropout and other feature noising schemes control overfitting by artificially ... | 4882 |@word version:1 bigram:3 triggs:1 simulation:5 linearized:4 sgd:9 tr:1 solid:1 contains:2 united:1 tuned:1 document:5 outperforms:2 comparing:1 deteriorating:1 yet:2 intriguing:1 must:1 written:1 john:2 additive:11 partition:3 shape:1 christian:1 enables:1 minmin:1 designed:2 drop:3 update:2 interpretable:1 v:1 g... |
4,290 | 4,883 | Stochastic Gradient Riemannian Langevin Dynamics
on the Probability Simplex
Yee Whye Teh
Department of Statistics
University of Oxford
y.w.teh@stats.ox.ac.uk
Sam Patterson
Gatsby Computational Neuroscience Unit
University College London
spatterson@gatsby.ucl.ac.uk
Abstract
In this paper we investigate the use of Lan... | 4883 |@word version:1 proportion:1 replicate:1 nd:3 kent:1 tr:2 ld:4 configuration:1 contains:1 document:27 current:1 wd:9 comparing:1 surprising:1 written:1 must:2 remove:1 update:17 stationary:1 generative:2 leaf:1 half:1 item:4 ntrain:1 intelligence:1 isotropic:1 es:1 steepest:2 hamiltonian:3 blei:3 firstly:1 access... |
4,291 | 4,884 | Restricting exchangeable nonparametric distributions
Sinead A. Williamson
University of Texas at Austin
Steven N. MacEachern
The Ohio State University
Eric P. Xing
Carnegie Mellon University
Abstract
Distributions over matrices with exchangeable rows and infinitely many columns
are useful in constructing nonparamet... | 4884 |@word trial:2 seems:1 replicate:1 nd:1 proportion:1 liu:2 series:1 zij:2 selecting:1 ecole:1 document:8 existing:2 si:6 assigning:1 written:1 must:2 remove:1 designed:3 interpretable:6 hypothesize:1 zik:19 stationary:1 half:1 selected:1 intelligence:4 isotropic:1 ith:1 contribute:1 location:2 direct:2 beta:15 ik:... |
4,292 | 4,885 | Approximate inference in latent Gaussian-Markov
models from continuous time observations
Botond Cseke1
Manfred Opper2
School of Informatics
University of Edinburgh, U.K.
{bcseke,gsanguin}@inf.ed.ac.uk
1
Guido Sanguinetti1
Computer Science
TU Berlin, Germany
manfred.opper@tu-berlin.de
2
Abstract
We propose an approx... | 4885 |@word neurophysiology:1 cox:3 version:1 inversion:1 calculus:1 grey:1 covariance:5 p0:8 kappen:2 moment:12 initial:2 series:3 att:1 interestingly:1 elliptical:1 arkk:2 dx:1 written:3 tilted:1 realistic:1 partition:1 sdes:1 plot:1 update:17 intelligence:2 accordingly:1 smith:4 manfred:2 provides:3 org:1 direct:2 b... |
4,293 | 4,886 | Bayesian inference as iterated random functions with
applications to sequential inference in graphical
models
XuanLong Nguyen
Department of Statistics
University of Michigan
Ann Arbor, Michigan 48109
xuanlong@umich.edu
Arash A. Amini
Department of Statistics
University of Michigan
Ann Arbor, Michigan 48109
aaamini@umi... | 4886 |@word private:2 version:1 briefly:1 polynomial:1 seems:1 norm:9 nd:1 middle:1 d2:1 simulation:3 contraction:1 recursively:3 initial:3 precluding:1 existing:1 xnj:2 enables:1 update:7 n0:8 lky:1 xk:3 provides:2 detecting:1 node:11 successive:1 viable:1 prove:1 introduce:2 manner:1 x0:13 expected:3 behavior:2 growi... |
4,294 | 4,887 | Optimizing Instructional Policies
Robert V. Lindsey? , Michael C. Mozer? , William J. Huggins? , Harold Pashler?
?
Department of Computer Science, University of Colorado, Boulder
? Department of Psychology, University of California, San Diego
Abstract
Psychologists are interested in developing instructional policies t... | 4887 |@word trial:25 exploitation:22 middle:1 proportion:1 norm:3 seems:1 disk:4 nd:1 advantageous:1 instruction:4 grey:4 simulation:1 covariance:2 paid:1 pressure:1 solid:1 shading:2 accommodate:1 series:2 score:3 selecting:4 rightmost:1 past:1 elliptical:2 current:7 unction:1 comparing:1 intriguing:1 must:3 readily:3... |
4,295 | 4,888 | Linear Decision Rule as Aspiration
for Simple Decision Heuristics
? ur
? S?ims?ek
Ozg
Center for Adaptive Behavior and Cognition
Max Planck Institute for Human Development
Lentzeallee 94, 14195 Berlin, Germany
ozgur@mpib-berlin.mpg.de
Abstract
Several attempts to understand the success of simple decision heuristics ha... | 4888 |@word repository:1 version:3 proportion:10 consequential:1 simplifying:1 decomposition:1 asks:1 contains:2 series:1 comparing:2 si:1 assigning:1 written:1 subsequent:2 cue:21 fewer:2 selected:4 xk:7 dawes:2 location:1 five:1 mathematical:2 along:1 differential:1 replication:1 consists:1 fitting:1 manner:1 expecte... |
4,296 | 4,889 | Scoring Workers in Crowdsourcing: How Many
Control Questions are Enough?
Qiang Liu
Dept. of Computer Science
Univ. of California, Irvine
qliu1@uci.edu
Mark Steyvers
Dept. of Cognitive Sciences
Univ. of California, Irvine
mark.steyvers@uci.edu
Alexander Ihler
Dept. of Computer Science
Univ. of California, Irvine
ihle... | 4889 |@word trial:2 version:1 seems:1 simulation:1 jacob:2 tr:7 liu:3 configuration:1 score:5 karger:4 att:2 selecting:1 interestingly:1 past:2 existing:1 current:1 nt:42 readily:1 john:1 refines:1 subsequent:1 shlomo:1 analytic:2 hoping:1 update:1 unidentifiability:2 v:4 intelligence:2 leaf:2 fewer:4 item:95 selected:... |
4,297 | 489 | Estimating Average-Case Learning Curves
Using Bayesian, Statistical Physics and
VC Dimension Methods
David Haussler
University of California
Santa Cruz, California
Michael Kearns?
AT&T Bell Laboratories
Murray Hill, New Jersey
Manfred Opper
Institut fur Theoretische Physik
Universita.t Giessen, Germany
Robert Schap... | 489 |@word trial:3 briefly:1 version:6 physik:1 decomposition:1 mkearns:1 att:1 series:1 chervonenkis:4 current:1 com:1 cruz:2 partition:7 wanted:1 warmuth:2 beginning:1 short:1 manfred:1 provides:2 characterization:1 ron:1 ucsc:1 direct:1 prove:1 expected:7 roughly:2 behavior:3 examine:1 mechanic:1 multi:3 gyorgi:1 es... |
4,298 | 4,890 | Bayesian Inference and Online Experimental Design
for Mapping Neural Microcircuits
Ben Shababo ?
Department of Biological Sciences
Columbia University, New York, NY 10027
bms2156@columbia.edu
Brooks Paige ?
Department of Engineering Science
University of Oxford, Oxford OX1 3PJ, UK
brooks@robots.ox.ac.uk
Ari Pakman
D... | 4890 |@word trial:34 stronger:1 seems:1 hu:1 simulation:1 deisseroth:1 series:1 contains:1 optically:1 tuned:2 denoting:1 interestingly:1 genetic:1 hirtz:2 current:6 comparing:1 yet:2 must:7 connectomics:1 realistic:4 informative:2 shape:1 designed:2 plot:2 update:2 extrapolating:1 greedy:2 selected:1 fewer:1 shababo:1... |
4,299 | 4,891 | Sparse Overlapping Sets Lasso for Multitask
Learning and its Application to fMRI Analysis
Nikhil S. Rao?
nrao2@wisc.edu
Christopher R. Cox#
crcox@wisc.edu
Robert D. Nowak?
nowak@ece.wisc.edu
?
Timothy T. Rogers#
ttrogers@wisc.edu
Department of Electrical and Computer Engineering, # Department of Psychology
Univers... | 4891 |@word multitask:10 trial:4 cox:1 groupwise:1 judgement:1 norm:19 proportion:3 bn:1 decomposition:5 jacob:2 carry:1 reduction:1 series:2 exclusively:1 selecting:1 daniel:1 denoting:1 past:1 outperforms:2 existing:1 bradley:1 z2:1 rish:1 must:1 additive:1 kdd:1 shape:1 christian:1 cis:1 interpretable:1 half:1 selec... |
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