Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
5,000 | 5,527 | The Blinded Bandit:
Learning with Adaptive Feedback
Ofer Dekel
Microsoft Research
Elad Hazan
Technion
Tomer Koren
Technion
oferd@microsoft.com
ehazan@ie.technion.ac.il
tomerk@technion.ac.il
Abstract
We study an online learning setting where the player is temporarily deprived of
feedback each time it switches to ... | 5527 |@word exploitation:1 middle:1 version:5 stronger:1 dekel:4 open:1 confirms:1 covariance:1 q1:2 pick:2 asks:1 incurs:6 attainable:1 harder:2 nonexistent:1 past:2 reaction:1 current:1 com:1 nt:9 trustworthy:1 yet:1 must:4 john:1 update:10 v:1 bart:1 leaf:1 directory:1 provides:3 contribute:2 node:2 thermometer:1 fi... |
5,001 | 5,528 | Near-optimal sample compression
for nearest neighbors
Lee-Ad Gottlieb
Department of Computer Science and Mathematics, Ariel University
Ariel, Israel. leead@ariel.ac.il
Aryeh Kontorovich
Computer Science Department, Ben Gurion University
Beer Sheva, Israel. karyeh@cs.bgu.ac.il
Pinhas Nisnevitch
Department of Computer S... | 5528 |@word mild:2 trial:1 repository:1 version:2 compression:13 stronger:1 open:1 seek:1 crucially:1 scg:2 p0:8 invoking:1 thereby:1 reduction:10 initial:1 series:1 woodruff:1 existing:2 err:5 current:1 com:4 comparing:1 beygelzimer:2 ddim:19 gmail:1 yet:3 must:14 assigning:4 dw1:6 refines:1 gurion:1 blur:1 remove:2 v... |
5,002 | 5,529 | Clamping Variables and Approximate Inference
Adrian Weller
Columbia University, New York, NY 10027
adrian@cs.columbia.edu
Tony Jebara
Columbia University, New York, NY 10027
jebara@cs.columbia.edu
Abstract
It was recently proved using graph covers (Ruozzi, 2012) that the Bethe partition
function is upper bounded by ... | 5529 |@word worsens:1 stronger:5 flach:2 adrian:2 r:2 q1:1 pick:3 thereby:1 initial:1 liu:3 series:5 contains:4 configuration:2 score:1 rish:2 current:1 surprising:1 si:8 yet:2 assigning:1 must:4 dx:1 dechter:2 partition:32 pseudomarginals:2 remove:1 plot:3 v:4 stationary:1 greedy:1 selected:2 leaf:1 intelligence:6 xk:... |
5,003 | 553 | Propagation Filters in PDS Networks for
Sequencing and Ambiguity Resolution
Ronald A. Sumida
Michael G. Dyer
Artificial Intelligence Laboratory
Computer Science Department
University of California
Los Angeles, CA, 90024
sumida@cs.ucla.edu
Abstract
We present a Parallel Distributed Semantic (PDS) Network architecture
... | 553 |@word open:13 grey:2 simulation:2 propagate:1 current:1 activation:2 yet:1 must:2 ronald:1 chicago:1 enables:1 intelligence:2 item:4 provides:1 accessed:1 constructed:2 direct:9 become:1 eleventh:1 manner:1 themselves:1 brain:1 inspired:1 automatically:1 resolve:6 nj:2 every:1 act:4 exactly:1 hit:36 control:3 unit... |
5,004 | 5,530 | Advances in Learning Bayesian
Networks of Bounded Treewidth
Denis D. Mau?a
University of S?ao Paulo
S?ao Paulo, Brazil
denis.maua@usp.br
Siqi Nie
Rensselaer Polytechnic Institute
Troy, NY, USA
nies@rpi.edu
Qiang Ji
Rensselaer Polytechnic Institute
Troy, NY, USA
qji@ecse.rpi.edu
Cassio P. de Campos
Queen?s University... | 5530 |@word repository:1 unaltered:1 polynomial:1 seems:1 norm:1 nd:1 dramatic:1 necessity:1 initial:1 contains:1 score:32 selecting:3 hereafter:1 genetic:1 ours:1 outperforms:2 existing:1 imoto:1 rish:1 comparing:1 chordal:6 beygelzimer:1 rpi:2 si:8 yet:2 written:1 mushroom:4 numerical:1 subsequent:1 designed:1 plot:1... |
5,005 | 5,531 | Augur: Data-Parallel Probabilistic Modeling
Jean-Baptiste Tristan1 , Daniel Huang2 , Joseph Tassarotti3 ,
Adam Pocock1 , Stephen J. Green1 , Guy L. Steele, Jr1
1
Oracle Labs {jean.baptiste.tristan, adam.pocock,
stephen.x.green, guy.steele}@oracle.com
2
Harvard University dehuang@fas.harvard.edu
3
Carnegie Mellon Unive... | 5531 |@word repository:2 version:4 polynomial:1 instruction:1 vldb:1 concise:1 thereby:1 harder:1 initial:1 contains:1 lichman:1 selecting:1 zij:6 daniel:1 document:14 existing:1 bradley:1 current:1 com:1 dx:3 must:2 gpu:16 written:2 csc:1 partition:2 enables:2 cheap:1 designed:1 v:2 generative:2 selected:1 intelligenc... |
5,006 | 5,532 | Making Pairwise Binary Graphical Models Attractive
Nicholas Ruozzi
Institute for Data Sciences and Engineering
Columbia University
New York, NY 10027
nr2493@columbia.edu
Tony Jebara
Department of Computer Science
Columbia University
New York, NY 10027
jebara@cs.columbia.edu
Abstract
Computing the partition function ... | 5532 |@word version:3 polynomial:5 homomorphism:2 kappen:2 configuration:2 contains:1 series:2 past:2 olkin:1 john:1 partition:53 koetter:1 j1:2 treating:1 drop:1 update:3 designed:1 plot:1 stationary:1 intelligence:8 selected:2 beginning:1 provides:12 characterization:5 node:10 tahoe:1 five:1 mathematical:2 consists:2... |
5,007 | 5,533 | Mode Estimation for High Dimensional Discrete Tree
Graphical Models
Chao Chen
Department of Computer Science
Rutgers, The State University of New Jersey
Piscataway, NJ 08854-8019
chao.chen.cchen@gmail.com
Han Liu
Department of Operations Research
and Financial Engineering
Princeton University, Princeton, NJ 08544
han... | 5533 |@word briefly:1 middle:2 polynomial:2 d2:1 crucially:1 decomposition:1 mention:1 rivera:1 solid:1 ld:3 carry:1 necessity:2 liu:5 configuration:3 contains:2 series:1 current:1 com:1 gmail:1 written:2 must:1 partition:2 hofmann:1 enables:1 drop:5 intelligence:2 leaf:1 selected:1 xk:1 mccallum:1 characterization:2 p... |
5,008 | 5,534 | Poisson Process Jumping between an Unknown
Number of Rates: Application to Neural Spike Data
Florian Stimberg
Computer Science, TU Berlin
Florian.Stimberg@tu-berlin.de
Andreas Ruttor
Computer Science, TU Berlin
Andreas.Ruttor@tu-berlin.de
Manfred Opper
Computer Science, TU Berlin
Manfred.Opper@tu-berlin.de
Abstract... | 5534 |@word neurophysiology:2 trial:1 cox:1 middle:1 seems:4 mjp:4 bf:3 simulation:2 uncovers:1 liu:1 contains:1 series:2 existing:3 reaction:1 current:4 comparing:1 ka:1 surprising:1 yet:1 assigning:1 john:2 j1:1 shape:1 remove:2 update:1 v:2 generative:2 selected:2 website:1 short:2 manfred:3 sudden:2 characterizatio... |
5,009 | 5,535 | Randomized Experimental Design for Causal Graph
Discovery
Huining Hu
School of Computer Science, McGill University.
huining.hu@mail.mcgill.ca
Zhentao Li
?
LIENS, Ecole
Normale Sup?erieure
zhentao.li@ens.fr
Adrian Vetta
Department of Mathematics and Statistics and School of Computer Science, McGill University.
vetta@... | 5535 |@word version:1 polynomial:1 seems:1 adrian:1 hu:2 simulation:17 q1:2 pick:2 dramatic:2 recursively:1 carry:1 series:2 contains:4 selecting:4 ecole:1 existing:1 chordal:8 surprising:1 si:10 yet:1 must:5 realistic:1 partition:2 shape:11 intelligence:4 selected:4 ith:3 short:1 characterization:9 math:2 completeness... |
5,010 | 5,536 | Transportability from Multiple Environments
with Limited Experiments: Completeness Results
Judea Pearl
Computer Science
UCLA
judea@cs.ucla.edu
Elias Bareinboim
Computer Science
UCLA
eb@cs.ucla.edu
Abstract
This paper addresses the problem of mz-transportability, that is, transferring
causal knowledge collected in se... | 5536 |@word trial:1 illustrating:3 version:2 manageable:1 instrumental:1 nd:2 c0:10 calculus:11 hu:1 nicholson:1 decomposition:2 asks:1 tr:1 recursively:2 reduction:2 contains:2 exclusively:1 series:1 interestingly:2 current:1 z2:28 olkin:1 si:7 intriguing:1 must:3 dx:1 chicago:1 happen:2 remove:1 alone:3 intelligence:... |
5,011 | 5,537 | A Statistical Decision-Theoretic Framework for
Social Choice
Hossein Azari Soufiani?
David C. Parkes ?
Lirong Xia?
Abstract
In this paper, we take a statistical decision-theoretic viewpoint on social choice,
putting a focus on the decision to be made on behalf of a system of agents. In
our framework, we are given a ... | 5537 |@word trial:1 version:1 polynomial:5 stronger:1 nd:1 open:2 d2:2 sheffet:1 jacob:1 pick:1 reduction:2 cyclic:3 contains:1 score:6 selecting:3 siebel:1 bc:1 interestingly:1 subjective:3 outperforms:1 com:1 rpi:2 lang:1 peyton:2 must:2 john:1 nisarg:4 fund:2 v:1 half:2 selected:1 intelligence:1 smith:1 imprimerie:1... |
5,012 | 5,538 | Causal Strategic Inference in Networked
Microfinance Economies
Luis E. Ortiz
Department of Computer Science
Stony Brook University
Stony Brook, NY 11794
leortiz@cs.stonybrook.edu
Mohammad T. Irfan
Department of Computer Science
Bowdoin College
Brunswick, ME 04011
mirfan@bowdoin.edu
Abstract
Performing interventions i... | 5538 |@word polynomial:1 logit:1 seek:1 thereby:1 profit:3 initial:1 celebrated:1 document:1 subjective:1 existing:1 com:2 yet:1 stony:3 must:3 luis:2 written:1 happen:1 analytic:1 remove:1 plot:1 update:1 instantiate:1 shut:1 prize:2 institution:1 stonybrook:1 yunus:4 lending:6 mitigation:1 coarse:1 authority:1 org:2 ... |
5,013 | 5,539 | Depth Map Prediction from a Single Image
using a Multi-Scale Deep Network
David Eigen
deigen@cs.nyu.edu
Christian Puhrsch
cpuhrsch@nyu.edu
Rob Fergus
fergus@cs.nyu.edu
Dept. of Computer Science, Courant Institute, New York University
Abstract
Predicting depth is an essential component in understanding the 3D geomet... | 5539 |@word kohli:1 trial:1 version:1 middle:1 norm:1 seems:1 open:1 seitz:1 tried:1 rgb:13 sgd:1 shot:2 liu:2 contains:2 disparity:1 hoiem:3 tuned:1 existing:1 recovered:1 current:2 comparing:1 activation:2 yet:3 must:1 refines:3 concatenate:3 subsequent:1 visible:1 christian:1 x240:1 remove:1 designed:1 v:2 stationar... |
5,014 | 554 | A Neurocomputer Board Based on the ANNA
Neural Network Chip
Eduard Sackinger, Bernhard E. Boser, and Lawrence D. Jackel
AT&T Bell Laboratories
Crawfords Corner Road, Holmdel, NJ 07733
Abstract
A board is described that contains the ANN A neural-network chip, and a
DSP32C digital signal processor. The ANNA (Analog Neu... | 554 |@word version:2 retraining:1 instruction:15 solid:1 moment:1 contains:5 existing:1 current:1 must:1 readily:1 john:2 written:2 realize:1 periodically:1 alone:2 afn:1 sram:2 supplying:1 provides:2 location:1 height:1 donnie:1 supply:1 combine:2 overhead:1 nay:1 automatically:2 actual:1 bonus:1 circuit:1 barrel:2 co... |
5,015 | 5,540 | Optimal decision-making
with time-varying evidence reliability
Jan Drugowitsch1
Rub?en Moreno-Bote2
Alexandre Pouget1
2
1
Research Unit, Parc Sanitari
D?ept. des Neurosciences Fondamentales
Sant Joan de D?eu and
Universit?e de Gen`eve
University of Barcelona
CH-1211 Gen`eve 4, Switzerland
08950 Barcelona, Spain
jdrugo... | 5540 |@word trial:34 cox:2 rising:1 hu:1 grey:3 simulation:1 nicholson:1 solid:2 harder:2 reduction:1 moment:2 ingersoll:2 series:1 tuned:4 past:1 reaction:2 rowan:1 current:21 com:1 discretization:3 comparing:1 anne:1 gmail:1 dx:4 john:3 realistic:3 numerical:3 informative:1 dive:1 shape:4 moreno:2 drop:4 remove:1 dis... |
5,016 | 5,541 | Optimal Teaching for
Limited-Capacity Human Learners
Xiaojin Zhu
Department of Computer Sciences
University of Wisconsin-Madison
jerryzhu@cs.wisc.edu
Kaustubh Raosaheb Patil
Affective Brain Lab, UCL
& MIT Sloan Neuroeconomics Lab
kaustubh.patil@gmail.com
?ukasz Kope?c
Experimental Psychology
University College London... | 5541 |@word trial:7 proportion:3 accounting:1 paid:2 harder:1 united:1 past:1 outperforms:1 bradley:1 reaction:3 com:1 current:1 gmail:1 mushroom:1 dx:1 must:1 yet:1 drop:1 clumping:1 intelligence:2 selected:1 item:29 ith:2 short:1 provides:1 location:1 preference:1 simpler:1 height:1 mathematical:2 along:1 symposium:1... |
5,017 | 5,542 | Recurrent Models of Visual Attention
Volodymyr Mnih
Nicolas Heess Alex Graves
Google DeepMind
Koray Kavukcuoglu
{vmnih,heess,gravesa,korayk} @ google.com
Abstract
Applying convolutional neural networks to large images is computationally expensive because the amount of computation scales linearly with the number of
... | 5542 |@word cnn:1 instrumental:1 risto:1 tried:1 solid:1 reduction:1 initial:2 contains:2 foveal:1 selecting:1 ours:2 outperforms:5 past:4 freitas:1 current:3 com:1 blank:2 contextual:1 si:1 must:1 gpu:1 john:1 ronald:1 visible:1 distant:1 informative:1 hofmann:1 motor:1 progressively:1 greedy:1 selected:2 fewer:1 half... |
5,018 | 5,543 | Unsupervised learning of an efficient short-term
memory network
Pietro Vertechi
Wieland Brendel ?
Christian K. Machens
Champalimaud Neuroscience Programme
Champalimaud Centre for the Unknown
Lisbon, Portugal
first.last@neuro.fchampalimaud.org
Abstract
Learning in recurrent neural networks has been a topic fraught ... | 5543 |@word version:1 norm:3 open:1 hu:2 integrative:1 simulation:3 pipa:1 covariance:3 thereby:2 cius:1 carry:1 initial:2 configuration:2 past:10 current:2 recovered:1 yet:2 scatter:1 must:4 realistic:2 plasticity:3 shape:1 christian:1 opin:1 designed:2 plot:1 v:1 discrimination:1 beginning:1 short:7 revisited:1 org:1... |
5,019 | 5,544 | Exploiting Linear Structure Within Convolutional
Networks for Efficient Evaluation
Emily Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun and Rob Fergus
Dept. of Computer Science, Courant Institute, New York University
{denton, zaremba, bruna, lecun, fergus} @cs.nyu.edu
Abstract
We present techniques for speeding up t... | 5544 |@word cnn:3 version:5 compression:4 seems:1 norm:5 c0:1 km:3 seek:1 propagate:2 rgb:2 decomposition:20 covariance:7 jacob:1 reduction:6 configuration:2 contains:2 tuned:3 ours:1 deconvolutional:1 freitas:1 current:1 com:1 written:1 gpu:17 devin:1 concatenate:1 designed:1 plot:2 update:1 drop:2 v:4 greedy:1 fewer:... |
5,020 | 5,545 | Unsupervised Deep Haar Scattering on Graphs
Xu Chen1,2 , Xiuyuan Cheng2 , and St?ephane Mallat2
1
2
Department of Electrical Engineering, Princeton University, NJ, USA
?
D?epartement d?Informatique, Ecole
Normale Sup?erieure, Paris, France
Abstract
The classification of high-dimensional data defined on graphs is part... | 5545 |@word polynomial:3 norm:1 proportion:1 bn:18 contraction:2 gabow:1 reduction:6 epartement:1 ecole:1 suppressing:1 recovered:3 must:2 written:2 numerical:3 partition:2 j1:5 progressively:1 greedy:1 selected:3 xk:1 characterization:1 coarse:1 node:4 location:3 zhang:1 along:1 constructed:2 pairing:5 prove:1 introdu... |
5,021 | 5,546 | Multi-View Perceptron: a Deep Model for Learning
Face Identity and View Representations
Zhenyao Zhu1,3
Ping Luo3,1
Xiaogang Wang2,3
Xiaoou Tang1,3
1
Department of Information Engineering, The Chinese University of Hong Kong
2
Department of Electronic Engineering, The Chinese University of Hong Kong
3
Shenzhen Key Lab ... | 5546 |@word kong:2 cnn:5 compression:1 hu:1 seek:2 decomposition:1 liu:2 contains:4 united:1 tuned:1 document:1 outperforms:2 existing:7 current:1 com:1 luo:4 activation:2 gmail:1 intriguing:2 zhu1:1 written:1 gpu:2 parsing:1 shape:1 enables:1 v2s:1 designed:3 update:2 hvs:13 plot:1 implying:1 generative:1 selected:1 i... |
5,022 | 5,547 | Deep Joint Task Learning for Generic Object
Extraction
Xiaolong Wang1,4 , Liliang Zhang1 , Liang Lin1,3?, Zhujin Liang1 , Wangmeng Zuo2
1
Sun Yat-sen University, Guangzhou 510006, China
2
School of Computer Science and Technology, Harbin Institute of Technology, China
3
SYSU-CMU Shunde International Joint Research Inst... | 5547 |@word h:2 version:1 everingham:1 hu:2 dise:1 covariance:1 concise:1 mention:1 solid:1 liu:1 contains:2 score:1 hoiem:1 tuned:3 ours:12 outperforms:1 current:2 luo:1 activation:1 gpu:1 parsing:1 realistic:1 shape:1 treating:1 plot:1 fund:1 update:1 selected:4 accordingly:2 desktop:1 smith:1 location:1 org:1 zhang:... |
5,023 | 5,548 | Discriminative Unsupervised Feature Learning with
Convolutional Neural Networks
Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller and Thomas Brox
Department of Computer Science
University of Freiburg
79110, Freiburg im Breisgau, Germany
{dosovits,springj,riedmiller,brox}@cs.uni-freiburg.de
Abstract
Curre... | 5548 |@word cnn:17 version:4 advantageous:1 stronger:1 nd:1 confirms:1 tried:1 rgb:1 tr:1 initial:2 contains:2 selecting:2 ours:1 outperforms:2 current:5 guadarrama:1 surprising:1 activation:4 assigning:1 videolearn:1 must:2 numerical:1 additive:1 informative:1 cheap:1 hypothesize:1 plot:3 v:2 generative:1 leaf:1 fewer... |
5,024 | 5,549 | Modeling Deep Temporal Dependencies with
Recurrent ?Grammar Cells?
Roland Memisevic
University of Montreal, Canada
roland.memisevic@umontreal.ca
Vincent Michalski
Goethe University Frankfurt, Germany
vmichals@rz.uni-frankfurt.de
Kishore Konda
Goethe University Frankfurt, Germany
konda.kishorereddy@gmail.com
Abstract... | 5549 |@word faculty:1 version:1 compression:2 bptt:3 confirms:1 seek:1 contrastive:1 sgd:3 thereby:1 reduction:1 initial:1 series:11 contains:2 pub:2 tuned:3 past:1 current:1 com:1 activation:3 gmail:1 subsequent:3 designed:1 plot:1 greedy:1 fewer:1 website:1 intelligence:1 beginning:1 vanishing:2 short:1 detecting:1 n... |
5,025 | 555 | Extracting and Learning an Unknown Grammar with
Recurrent Neural Networks
C.L.Gnes?, C.B. Miller
NEC Research Institute
4 Independence Way
Princeton. NJ. 08540
giles@research.nj.nec.COOl
D. Chen, G.Z. Sun, B.H. Chen, V.C. Lee
*Institute for Advanced Computer Studies
Dept of Physics and Astronomy
University of Maryland... | 555 |@word version:1 seems:2 simulation:2 fmite:1 fonn:1 initial:7 configuration:1 series:1 pub:1 outperforms:3 current:1 comparing:1 surprising:1 lang:1 must:1 readily:1 partition:13 update:2 prohibitive:1 devising:1 selected:1 guess:4 leamed:2 smith:3 lr:1 ofo:1 constructed:3 become:1 ik:1 consists:2 manner:1 expecte... |
5,026 | 5,550 | Convolutional Neural Network Architectures for
Matching Natural Language Sentences
Baotian Hu??
Zhengdong Lu?
Hang Li?
?
Department of Computer Science
& Technology, Harbin Institute of Technology
Shenzhen Graduate School, Xili, China
baotianchina@gmail.com
qingcai.chen@hitsz.edu.cn
Qingcai Chen?
?
Noah?s Ark La... | 5550 |@word kong:1 kondor:1 briefly:1 hu:2 contrastive:1 pick:2 dramatic:1 harder:1 liu:1 contains:3 efficacy:1 score:5 series:1 document:2 interestingly:1 rightmost:1 outperforms:2 existing:1 dole:1 com:5 comparing:1 surprising:2 activation:3 gmail:1 readily:1 parsing:2 happen:2 informative:1 hypothesize:1 designed:3 ... |
5,027 | 5,551 | Deep Recursive Neural Networks
for Compositionality in Language
?
Ozan Irsoy
Department of Computer Science
Cornell University
Ithaca, NY 14853
oirsoy@cs.cornell.edu
Claire Cardie
Department of Computer Science
Cornell University
Ithaca, NY 14853
cardie@cs.cornell.edu
Abstract
Recursive neural networks comprise a cl... | 5551 |@word multitask:1 middle:1 bigram:2 norm:3 seems:1 compression:1 nd:1 open:1 pavel:1 pick:1 harder:1 recursively:4 carry:1 initial:3 score:5 tuned:1 document:1 fa8750:1 outperforms:4 current:1 jaz:1 activation:9 yet:1 parsing:2 john:2 ronan:2 informative:1 hofmann:1 christian:1 drop:1 succeeding:1 update:2 v:1 in... |
5,028 | 5,552 | Algorithm selection by rational metareasoning as a
model of human strategy selection
Falk Lieder
Helen Wills Neuroscience Institute, UC Berkeley
falk.lieder@berkeley.edu
Dillon Plunkett
Department of Psychology, UC Berkeley
dillonplunkett@berkeley.edu
Stuart J. Russell
EECS Department, UC Berkeley
russell@cs.berkeley... | 5552 |@word trial:4 version:1 inversion:5 polynomial:2 proportion:1 nd:1 open:1 instruction:1 simulation:4 paid:1 minus:2 accommodate:1 harder:2 shot:1 series:1 score:17 selecting:4 practiced:2 outperforms:1 existing:5 past:1 savage:2 freitas:1 ka:1 yet:4 fn:9 additive:2 subsequent:1 analytic:1 designed:1 update:1 ries... |
5,029 | 5,553 | A Framework for Testing Identifiability
of Bayesian Models of Perception
Luigi Acerbi1,2
Wei Ji Ma2
Sethu Vijayakumar1
1
School of Informatics, University of Edinburgh, UK
Center for Neural Science & Department of Psychology, New York University, USA
{luigi.acerbi,weijima}@nyu.edu
sethu.vijayakumar@ed.ac.uk
2
Abst... | 5553 |@word trial:7 worsens:1 version:2 stronger:1 suitably:1 seitz:2 crucially:4 moment:4 efficacy:1 ours:1 kurt:2 ording:3 luigi:2 subjective:1 existing:1 recovered:8 comparing:1 current:2 surprising:1 si:8 dx:1 subsequent:3 numerical:1 shape:3 motor:9 remove:1 plot:4 generative:4 cue:5 guess:1 parametrization:1 shor... |
5,030 | 5,554 | Automatic Discovery of Cognitive Skills
to Improve the Prediction of Student Learning
Robert V. Lindsey, Mohammad Khajah, Michael C. Mozer
Department of Computer Science and Institute of Cognitive Science
University of Colorado, Boulder
Abstract
To master a discipline such as algebra or physics, students must acquire... | 5554 |@word trial:9 repository:1 middle:2 stronger:1 proportion:2 open:1 instruction:3 hu:1 simulation:10 unimpressive:1 series:2 score:8 hereafter:1 mastery:5 ecole:1 denoting:1 ours:1 genetic:1 past:1 existing:1 outperforms:2 recovered:4 surprising:2 yet:4 must:5 ikeda:1 refines:1 partition:2 romero:1 designed:3 upda... |
5,031 | 5,555 | Message Passing Inference for Large Scale Graphical
Models with High Order Potentials
Jian Zhang
ETH Zurich
Alexander G. Schwing
University of Toronto
Raquel Urtasun
University of Toronto
jizhang@ethz.ch
aschwing@cs.toronto.edu
urtasun@cs.toronto.edu
Abstract
To keep up with the Big Data challenge, parallelized ... | 5555 |@word kohli:4 version:1 polynomial:1 proportion:1 norm:1 open:1 additively:1 rgb:2 decomposition:11 p0:8 harder:1 configuration:3 series:1 score:10 contains:3 hoiem:1 salzmann:2 denoting:1 ours:8 liu:1 outperforms:1 existing:1 err:24 current:3 si:11 parsing:3 dechter:1 partition:6 update:3 half:1 parameterization... |
5,032 | 5,556 | A Filtering Approach to Stochastic Variational
Inference
Neil M.T. Houlsby ?
Google Research
Zurich, Switzerland
neilhoulsby@google.com
David M. Blei
Department of Statistics
Department of Computer Science
Colombia University
david.blei@colombia.edu
Abstract
Stochastic variational inference (SVI) uses stochastic opti... | 5556 |@word version:1 norm:1 open:1 seek:1 covariance:1 pick:1 moment:4 reduction:2 initial:1 document:9 fa8750:1 outperforms:4 freitas:1 current:9 com:1 must:1 kdd:1 cheap:2 gv:6 analytic:1 treating:1 plot:2 update:40 depict:1 stationary:2 instantiate:1 website:1 item:2 haykin:1 blei:7 location:3 successive:2 simpler:... |
5,033 | 5,557 | Smoothed Gradients for
Stochastic Variational Inference
David Blei
Department of Computer Science
Department of Statistics
Columbia University
david.blei@columbia.edu
Stephan Mandt
Department of Physics
Princeton University
smandt@princeton.edu
Abstract
Stochastic variational inference (SVI) lets us scale up Bayesian... | 5557 |@word repository:3 briefly:1 version:2 middle:4 proportion:2 open:2 carry:1 reduction:6 initial:1 contains:1 document:14 fa8750:1 past:1 current:6 wd:1 si:12 must:1 john:1 subsequent:1 numerical:1 remove:1 plot:3 update:15 aside:1 precaution:2 generative:1 stationary:1 half:1 intelligence:1 blei:8 successive:1 zh... |
5,034 | 5,558 | Analysis of Variational Bayesian Latent Dirichlet
Allocation: Weaker Sparsity than MAP
Shinichi Nakajima
Berlin Big Data Center, TU Berlin
Berlin 10587 Germany
nakajima@tu-berlin.de
Issei Sato
University of Tokyo
Tokyo 113-0033 Japan
sato@r.dl.itc.u-tokyo.ac.jp
Masashi Sugiyama
University of Tokyo
Tokyo 113-0033, Jap... | 5558 |@word achievable:1 stronger:2 plsa:1 hu:1 liu:1 united:1 denoting:1 document:17 current:1 com:1 comparing:1 written:3 numerical:3 informative:1 hofmann:1 analytic:1 update:1 stationary:4 generative:2 item:1 accordingly:1 blei:3 provides:2 node:1 clarified:1 issei:1 consists:1 introduce:1 theoretically:6 expected:... |
5,035 | 5,559 | Decoupled Variational Gaussian Inference
Mohammad Emtiyaz Khan
Ecole Polytechnique F?ed?erale de Lausanne (EPFL), Switzerland
emtiyaz@gmail.com
Abstract
Variational Gaussian (VG) inference methods that optimize a lower bound to the
marginal likelihood are a popular approach for Bayesian inference. A difficulty
remain... | 5559 |@word determinant:1 version:1 logit:2 open:1 vanhatalo:1 linearized:3 covariance:7 thereby:1 tr:2 reduction:1 series:1 ecole:1 existing:5 com:1 gmail:1 must:2 fn:21 visible:2 numerical:1 subsequent:1 enables:1 plot:2 v:3 stationary:3 intelligence:4 parameterization:7 xk:4 volkan:1 blei:1 revisited:1 org:1 mathema... |
5,036 | 556 | Computer Recognition of Wave Location
in Graphical Data by a Neural Network
Donald T. Freeman
School of Medicine
University of Pittsburgh
Pittsburgh. PA 15261
Abstract
Five experiments were performed using several neural network architectures to
identify the location of a wave in the time ordered graphical results fr... | 556 |@word trial:2 version:1 seems:2 proportion:3 meso:1 prognostic:1 simulation:1 eng:2 fonn:2 edema:1 initial:1 series:3 sosa:3 neurophys:1 activation:2 yet:1 must:3 written:1 fn:1 distant:1 uncooperative:1 shape:1 plot:1 medial:1 intelligence:1 selected:5 fewer:3 nervous:3 inspection:1 beginning:3 record:1 valdes:3 ... |
5,037 | 5,560 | Stochastic Variational Inference for Hidden Markov
Models
Nicholas J. Foti? , Jason Xu? , Dillon Laird, and Emily B. Fox
University of Washington
{nfoti@stat,jasonxu@stat,dillonl2@cs,ebfox@stat}.washington.edu
Abstract
Variational inference algorithms have proven successful for Bayesian analysis
in large data settings... | 5560 |@word mild:1 trial:1 version:1 unif:1 integrative:1 seek:1 guarding:1 accounting:1 myeloid:1 initial:1 series:6 contains:2 selecting:1 current:5 comparing:1 k562:1 must:3 written:1 numerical:1 informative:1 enables:1 designed:1 sponsored:1 update:12 juditsky:1 stationary:1 generative:1 prohibitive:1 half:1 select... |
5,038 | 5,561 | Object Localization based on Structural SVM
using Privileged Information
Jan Feyereisl, Suha Kwak?, Jeany Son, Bohyung Han
Dept. of Computer Science and Engineering, POSTECH, Pohang, Korea
thefillm@gmail.com, {mercury3,jeany,bhhan}@postech.ac.kr
Abstract
We propose a structured prediction algorithm for object localiz... | 5561 |@word middle:1 stronger:1 shuicheng:1 minus:1 harder:1 shot:5 contains:2 score:1 hoiem:3 outperforms:1 existing:1 current:2 com:1 luo:1 si:6 gmail:1 yet:1 hofmann:2 shape:1 visibility:1 alone:2 fewer:1 morariu:1 plane:5 es:6 provides:3 location:3 zhang:1 constructed:1 inside:1 introduce:1 mask:5 inspired:1 voc:2 ... |
5,039 | 5,562 | Efficient Inference of Continuous Markov Random
Fields with Polynomial Potentials
Shenlong Wang
University of Toronto
Alexander G. Schwing
University of Toronto
Raquel Urtasun
University of Toronto
slwang@cs.toronto.edu
aschwing@cs.toronto.edu
urtasun@cs.toronto.edu
Abstract
In this paper, we prove that every mu... | 5562 |@word version:2 briefly:1 polynomial:104 underline:1 decomposition:30 shading:13 moment:2 initial:1 configuration:4 salzmann:2 denoting:1 ours:12 outperforms:2 existing:3 current:2 recovered:1 tackling:1 written:1 mesh:8 numerical:1 partition:1 shape:18 hofmann:1 bickson:1 isard:1 provides:1 characterization:1 to... |
5,040 | 5,563 | Structure Regularization for Structured Prediction
Xu Sun??
?MOE Key Laboratory of Computational Linguistics, Peking University
?School of Electronics Engineering and Computer Science, Peking University
xusun@pku.edu.cn
Abstract
While there are many studies on weight regularization, the study on structure regularizati... | 5563 |@word multitask:1 msr:1 version:4 norm:3 decomposition:7 eng:1 elisseeff:1 sgd:8 reduction:1 initial:1 electronics:1 series:1 score:15 tuned:1 outperforms:1 existing:9 comparing:4 yet:1 must:1 parsing:1 update:2 v:5 reranking:2 mccallum:2 smith:1 record:2 org:1 simpler:3 dn:1 c2:1 yuan:1 introduce:2 pairwise:1 th... |
5,041 | 5,564 | Expectation-Maximization
for Learning Determinantal Point Processes
Jennifer Gillenwater
Computer and Information Science
University of Pennsylvania
jengi@cis.upenn.edu
Alex Kulesza
Computer Science and Engineering
University of Michigan
kulesza@umich.edu
Emily Fox
Statistics
University of Washington
ebfox@stat.wash... | 5564 |@word trial:2 norm:1 seems:1 decomposition:1 covariance:1 nystr:1 moment:5 initial:5 contains:1 document:3 interestingly:1 ka:24 current:2 comparing:1 com:2 yet:1 written:1 determinantal:17 update:10 infant:2 generative:2 selected:2 fewer:2 item:18 parameterization:1 greedy:1 gear:4 intelligence:6 discovering:1 s... |
5,042 | 5,565 | Submodular Attribute Selection for Action
Recognition in Video
Zhuolin Jiang
Noah?s Ark Lab
Huawei Technologies
zhuolin.jiang@huawei.com
Jinging Zheng
UMIACS, University of Maryland
College Park, MD, USA
zjngjng@umiacs.umd.edu
Rama Chellappa
UMIACS, University of Maryland
College Park, MD, USA
rama@umiacs.umd.edu
P.... | 5565 |@word version:1 kokkinos:1 semidifferential:1 decomposition:2 tr:1 shechtman:1 initial:6 liu:7 configuration:1 score:8 selecting:7 contains:3 uncovered:1 backslash:1 document:2 ours:1 hoiem:1 outperforms:6 existing:2 blank:1 com:1 contextual:1 comparing:1 written:1 bd:1 cottrell:1 concatenate:2 partition:1 kdd:1 ... |
5,043 | 5,566 | Scale Adaptive Blind Deblurring
Haichao Zhang
Jianchao Yang
Duke University, NC
hczhang1@gmail.com
Adobe Research, CA
jiayang@adobe.com
Abstract
The presence of noise and small scale structures usually leads to large kernel estimation errors in blind image deblurring empirically, if not a total failure. We
present... | 5566 |@word version:1 middle:1 eliminating:1 advantageous:1 proportion:1 hu:1 decomposition:5 pick:1 shot:1 reduction:1 configuration:2 contains:3 outperforms:1 existing:1 recovered:10 com:2 surprising:3 gmail:1 dx:1 finest:5 subsequent:1 additive:4 blur:46 visible:1 designed:3 plot:1 progressively:1 greedy:2 tone:1 pl... |
5,044 | 5,567 | Shape and Illumination from Shading using the
Generic Viewpoint Assumption
Dilip Krishnan ?
CSAIL, MIT
dilipkay@mit.edu
Daniel Zoran ?
CSAIL, MIT
danielz@mit.edu
William T. Freeman
CSAIL, MIT
billf@mit.edu
Jose Bento
Boston College
jose.bento@bc.edu
Abstract
The Generic Viewpoint Assumption (GVA) states that the p... | 5567 |@word determinant:1 briefly:1 middle:2 seems:2 linearized:6 jacob:1 concise:1 harder:1 shading:41 qatar:1 daniel:1 tuned:1 bc:1 ours:10 current:4 com:1 recovered:1 comparing:1 si:2 yet:1 chu:1 mesh:2 numerical:2 informative:1 koetter:1 shape:47 enables:1 plot:2 update:1 generative:3 fewer:1 intelligence:4 short:2... |
5,045 | 5,568 | Self-Paced Learning with Diversity
Lu Jiang1 , Deyu Meng1,2 , Shoou-I Yu1 , Zhenzhong Lan1 , Shiguang Shan1,3 ,
Alexander G. Hauptmann1
1
School of Computer Science, Carnegie Mellon University
2
School of Mathematics and Statistics, Xi?an Jiaotong University
3
Institute of Computing Technology, Chinese Academy of Scie... | 5568 |@word cox:1 briefly:1 longterm:1 norm:7 seems:2 instrumental:1 confirms:1 gaidon:2 egou:1 concise:1 solid:1 liblinear:1 liu:1 contains:2 series:1 selecting:5 tuned:2 document:1 outperforms:7 existing:3 xnj:1 comparing:1 yet:1 assigning:1 readily:1 parsing:1 realize:1 informative:1 predetermined:1 hypothesize:2 pl... |
5,046 | 5,569 | Hardness of parameter estimation
in graphical models
Guy Bresler1 David Gamarnik2 Devavrat Shah1
Laboratory for Information and Decision Systems
Department of EECS1 and Sloan School of Management2
Massachusetts Institute of Technology
{gbresler,gamarnik,devavrat}@mit.edu
Abstract
We consider the problem of learning th... | 5569 |@word polynomial:17 simplifying:1 mention:1 reduction:13 initial:1 karger:2 pub:1 ours:1 existing:1 si:44 universality:1 must:3 john:1 subsequent:1 partition:18 update:5 congestion:1 intelligence:1 half:1 p7:2 ith:1 core:22 characterization:5 iterates:6 node:7 completeness:2 successive:1 math:1 simpler:1 become:1... |
5,047 | 557 | Multi-Digit Recognition Using A Space
Displacement Neural Network
Ofer Matan*, Christopher J.C. Burges,
Yann Le Cun and John S. Denker
AT&T Bell Laboratories, Holmdel, N. J. 07733
Abstract
We present a feed-forward network architecture for recognizing an unconstrained handwritten multi-digit string. This is an extens... | 557 |@word version:1 seems:1 replicate:2 nd:1 tried:1 leow:2 reduction:1 score:6 current:3 lang:2 activation:1 reminiscent:1 john:1 predetermined:1 selected:1 postal:1 node:2 five:1 height:3 along:1 constructed:1 direct:1 incorrect:1 rapid:1 roughly:1 multi:8 kaufman:1 string:6 developed:1 finding:1 exactly:1 unit:13 a... |
5,048 | 5,570 | Sequential Monte Carlo for Graphical Models
Christian A. Naesseth
Div. of Automatic Control
Link?oping University
Link?oping, Sweden
chran60@isy.liu.se
Fredrik Lindsten
Dept. of Engineering
The University of Cambridge
Cambridge, UK
fsml2@cam.ac.uk
Thomas B. Sch?on
Dept. of Information Technology
Uppsala University
U... | 5570 |@word middle:2 open:1 simulation:3 decomposition:12 q1:1 fifteen:1 thereby:1 solid:1 recursively:1 initial:1 liu:1 series:3 tuned:1 document:6 interestingly:1 freitas:9 current:3 surprising:1 yet:1 dx:1 written:1 ulation:1 subsequent:1 partition:16 numerical:1 christian:1 enables:2 plot:2 update:2 resampling:4 is... |
5,049 | 5,571 | Concavity of reweighted Kikuchi approximation
Po-Ling Loh
Department of Statistics
The Wharton School
University of Pennsylvania
loh@wharton.upenn.edu
Andre Wibisono
Computer Science Division
University of California, Berkeley
wibisono@berkeley.edu
Abstract
We analyze a reweighted version of the Kikuchi approximation... | 5571 |@word h:5 msr:5 version:5 stronger:1 termination:1 barahona:1 simulation:6 tried:1 recursively:1 kappen:1 t2n:1 contains:3 series:2 existing:2 comparing:1 incidence:1 assigning:1 scatter:1 written:3 must:1 partition:17 pseudomarginals:5 designed:1 plot:3 update:7 k15:2 stationary:3 intelligence:5 leaf:1 character... |
5,050 | 5,572 | A Complete Variational Tracker
Ryan Turner
Northrop Grumman Corp.
Steven Bottone
Northrop Grumman Corp.
Bhargav Avasarala
Northrop Grumman Corp.
ryan.turner@ngc.com
steven.bottone@ngc.com
bhargav.avasarala@ngc.com
Abstract
We introduce a novel probabilistic tracking algorithm that incorporates combinatorial data... | 5572 |@word trial:1 version:1 dalal:1 polynomial:1 triggs:1 calculus:1 tried:1 covariance:1 p0:5 decomposition:1 solid:2 moment:1 reduction:1 inefficiency:1 contains:1 series:1 wrapper:1 denoting:2 existing:1 current:2 com:3 nt:32 z2:1 arkk:2 si:13 assigning:1 comparing:1 must:3 additive:1 partition:3 realistic:1 grumm... |
5,051 | 5,573 | Joint Training of a Convolutional Network and a
Graphical Model for Human Pose Estimation
Jonathan Tompson, Arjun Jain, Yann LeCun, Christoph Bregler
New York University
{tompson, ajain, yann, bregler}@cs.nyu.edu
Abstract
This paper proposes a new hybrid architecture that consists of a deep Convolutional Network and ... | 5573 |@word version:1 dalal:1 replicate:2 everingham:4 triggs:1 ankle:5 propagate:1 rgb:3 sgd:2 configuration:2 contains:4 ours:6 outperforms:3 existing:6 activation:3 must:1 parsing:1 gpu:3 subsequent:1 partition:3 numerical:2 shape:3 remove:2 hypothesize:1 v:1 alone:1 generative:1 cue:1 half:1 intelligence:1 coarse:2... |
5,052 | 5,574 | Divide-and-Conquer Learning by Anchoring a
Conical Hull
?
Tianyi Zhou? , Jeff Bilmes? , Carlos Guestrin?
Computer Science & Engineering, ? Electrical Engineering, University of Washington, Seattle
{tianyizh, bilmes, guestrin}@u.washington.edu
Abstract
We reduce a broad class of fundamental machine learning problems,... | 5574 |@word trial:1 version:2 middle:1 polynomial:1 advantageous:1 nd:1 a02:3 decomposition:2 unstably:1 pick:1 tianyi:1 moment:17 liu:2 series:2 selecting:2 document:2 interestingly:1 outperforms:1 existing:1 recovered:2 comparing:1 rpi:1 assigning:2 written:3 enables:2 treating:1 interpretable:3 update:1 sponsored:1 ... |
5,053 | 5,575 | Graphical Models for Recovering Probabilistic
and Causal Queries from Missing Data
Karthika Mohan and Judea Pearl
Cognitive Systems Laboratory
Computer Science Department
University of California, Los Angeles, CA 90024
{karthika,judea}@cs.ucla.edu
Abstract
We address the problem of deciding whether a causal or probab... | 5575 |@word trial:1 sex:2 calculus:2 mcar:3 contains:2 series:2 pub:1 daniel:2 longitudinal:5 recovered:3 com:1 current:1 analysed:1 written:1 refines:1 partition:3 drop:1 depict:1 intelligence:2 weighing:1 guess:1 short:1 record:3 provides:1 completeness:1 node:9 coarse:1 gx:3 alert:1 shorthand:5 darwiche:2 introduce:... |
5,054 | 5,576 | Optimization Methods for Sparse Pseudo-Likelihood
Graphical Model Selection
Onkar Dalal
Stanford University
onkar@alumni.stanford.edu
Sang-Yun Oh
Computational Research Division
Lawrence Berkeley National Lab
syoh@lbl.gov
Kshitij Khare
Department of Statistics
University of Florida
kdkhare@stat.ufl.edu
Bala Rajaratn... | 5576 |@word cox:1 version:2 dalal:2 inversion:1 norm:2 seems:1 c0:4 simulation:2 covariance:11 decomposition:1 simplifying:1 jacob:1 tr:9 initial:9 cyclic:1 contains:1 efficacy:1 series:1 united:1 ours:1 past:2 existing:1 outperforms:1 comparing:1 written:2 numerical:5 plot:1 guess:2 amir:1 slowing:1 ith:1 iterates:1 p... |
5,055 | 5,577 | Learning Chordal Markov Networks
by Dynamic Programming
Kustaa Kangas Teppo Niinim?aki Mikko Koivisto
Helsinki Institute for Information Technology HIIT
Department of Computer Science, University of Helsinki
{jwkangas,tzniinim,mkhkoivi}@cs.helsinki.fi
Abstract
We present an algorithm for finding a chordal Markov netw... | 5577 |@word repository:2 version:1 pw:3 polynomial:1 grey:3 thereby:1 recursively:1 initial:1 liu:1 series:1 score:22 selecting:2 lichman:1 imoto:1 chordal:19 si:25 mushroom:2 forbidding:2 attracted:1 readily:1 must:1 written:2 dechter:1 partition:13 xcj:2 enables:6 intelligence:5 selected:2 malone:2 flare:1 characteri... |
5,056 | 5,578 | Q UIC & D IRTY: A Quadratic Approximation
Approach for Dirty Statistical Models
Cho-Jui Hsieh, Inderjit S. Dhillon, Pradeep Ravikumar
University of Texas at Austin
Austin, TX 78712 USA
{cjhsieh,inderjit,pradeepr}@cs.utexas.edu
Peder A. Olsen
IBM T.J. Watson Research Center
Yorktown Heights, NY 10598 USA
pederao@us.ibm... | 5578 |@word multitask:1 h:1 determinant:3 seems:1 norm:21 open:1 hsieh:6 covariance:11 decomposition:5 pick:1 tr:5 initial:1 cyclic:1 contains:1 document:1 interestingly:1 outperforms:1 ksk1:2 current:9 com:1 ka:1 comparing:1 optim:1 toh:2 forbidding:1 written:5 chu:1 partition:1 designed:1 update:10 xdx:2 greedy:2 acc... |
5,057 | 5,579 | Recursive Inversion Models for Permutations
Marina Meil?a
University of Washington
Seattle, Washington 98195
mmp@stat.washington.edu
Christopher Meek
Microsoft Research
Redmond, Washington 98052
meek@microsoft.com
Abstract
We develop a new exponential family probabilistic model for permutations that
can capture hiera... | 5579 |@word middle:1 inversion:22 polynomial:4 stronger:1 unif:1 squid:1 decomposition:2 innermost:1 recursively:4 series:3 score:9 shrimp:1 outperforms:1 existing:1 current:2 com:1 recovered:1 comparing:1 parsing:2 truct:11 john:2 partition:4 happen:1 kdd:1 enables:1 toro:2 plot:1 generative:2 leaf:4 greedy:2 item:6 i... |
5,058 | 558 | ?
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... | 558 |@word uev:1 t_:3 pbx:1 od:1 bd:2 wx:2 cqr:1 xyu:2 rts:2 mnf:1 gtg:1 uwb:1 gx:2 lx:1 dn:1 vxw:2 h4:2 m7:1 ik:2 p8:1 ra:2 uz:1 ol:1 rem:1 td:1 jm:1 xx:2 qyi:1 sut:1 oax:5 acbed:1 ag:1 nj:4 w8:1 xd:1 rm:2 eha:1 zl:1 j24:1 pwg:2 xv:1 io:1 ak:2 ap:6 kml:1 uwu:2 au:2 nol:1 uy:1 vu:2 j0:1 dhd:1 cpa:1 l:2 d5:1 gsn:1 dw:1 ... |
5,059 | 5,580 | Discovering Structure in High-Dimensional Data
Through Correlation Explanation
Greg Ver Steeg
Information Sciences Institute
University of Southern California
Marina del Rey, CA 90292
gregv@isi.edu
Aram Galstyan
Information Sciences Institute
University of Southern California
Marina del Rey, CA 90292
galstyan@isi.edu
... | 5580 |@word nihat:1 repository:1 version:1 trial:1 compression:1 briefly:1 achievable:1 open:2 heuristically:1 hyv:1 tried:1 decomposition:1 citeseer:1 pick:2 carry:1 reduction:1 electronics:1 liu:1 sah:1 score:1 series:1 lichman:1 daniel:1 contains:1 genetic:1 document:2 dubourg:1 interestingly:1 africa:4 bilal:1 comp... |
5,060 | 5,581 | Coresets for k-Segmentation of Streaming Data
Guy Rosman ? ?
CSAIL, MIT
32 Vassar St., 02139,
Cambridge, MA USA
rosman@csail.mit.edu
Mikhail Volkov ?
CSAIL, MIT
32 Vassar St., 02139,
Cambridge, MA USA
mikhail@csail.mit.edu
Danny Feldman ?
CSAIL, MIT
32 Vassar St., 02139,
Cambridge, MA USA
dannyf@csail.mit.edu
Danie... | 5581 |@word version:1 middle:1 compression:6 norm:1 suitably:1 seek:1 p0:1 bicriteria:1 reduction:6 series:2 contains:1 past:1 existing:1 current:1 comparing:1 tackling:1 danny:1 john:1 additive:2 partition:8 enables:1 plot:4 update:1 v:3 half:1 prohibitive:1 guess:1 plane:1 core:2 provides:7 quantized:1 node:3 locatio... |
5,061 | 5,582 | Approximating Hierarchical MV-sets for Hierarchical
Clustering
Assaf Glazer
Omer Weissbrod
Michael Lindenbaum
Shaul Markovitch
Department of Computer Science, Technion - Israel Institute of Technology
{assafgr,omerw,mic,shaulm}@cs.technion.ac.il
Abstract
The goal of hierarchical clustering is to construct a cluster t... | 5582 |@word briefly:2 polynomial:1 lwk:1 recursively:5 daniel:1 genetic:6 document:3 interestingly:1 trinary:1 past:1 existing:4 comparing:1 manuel:1 olive:7 john:4 numerical:1 partition:6 distant:1 informative:1 kdd:2 remove:4 plot:2 interpretable:1 v:3 half:3 leaf:11 gbr:1 discovering:1 scotland:1 record:1 provides:1... |
5,062 | 5,583 | Tight Continuous Relaxation of the Balanced k-Cut
Problem
Syama Sundar Rangapuram, Pramod Kaushik Mudrakarta and Matthias Hein
Department of Mathematics and Computer Science
Saarland University, Saarbr?ucken
Abstract
Spectral Clustering as a relaxation of the normalized/ratio cut has become one of
the standard graph-b... | 5583 |@word kulis:1 illustrating:1 version:1 briefly:1 repository:1 seems:2 c0:1 propagate:1 contains:1 ours:6 outperforms:3 existing:3 err:18 current:3 assigning:1 must:1 fn:1 visible:1 partition:30 kdd:1 enables:1 update:2 alone:2 greedy:5 spec:3 selected:1 pnmf:3 iterates:1 math:1 c6:1 saarland:1 c2:1 constructed:2 ... |
5,063 | 5,584 | Streaming, Memory Limited Algorithms for
Community Detection
Marc Lelarge ?
Inria & ENS
23 Avenue d?Italie, Paris 75013
marc.lelarge@ens.fr
Se-Young. Yun
MSR-Inria
23 Avenue d?Italie, Paris 75013
seyoung.yun@inria.fr
Alexandre Proutiere ?
KTH, EE School / ACL
Osquldasv. 10, Stockholm 100-44, Sweden
alepro@kth.se
Ab... | 5584 |@word msr:2 version:4 proportion:11 seems:1 nd:1 open:2 accommodate:1 initial:2 celebrated:1 neeman:1 ours:1 mishra:1 current:3 surprising:1 assigning:1 attracted:1 must:3 partition:14 remove:3 alone:2 greedy:3 selected:2 guess:1 vanishing:1 recherche:1 detecting:1 node:95 successive:1 direct:1 become:1 symposium... |
5,064 | 5,585 | Computing Nash Equilibria in Generalized
Interdependent Security Games
Hau Chan
Luis E. Ortiz
Department of Computer Science, Stony Brook University
{hauchan,leortiz}@cs.stonybrook.edu
Abstract
We study the computational complexity of computing Nash equilibria in generalized interdependent-security (IDS) games. Like ... | 5585 |@word middle:2 version:2 polynomial:8 seems:1 indiscriminate:1 open:1 simulation:1 anthropological:1 reduction:1 configuration:2 contains:1 united:1 daniel:1 ours:1 interestingly:1 protection:6 si:7 yet:1 stony:1 follower:1 luis:3 must:13 realize:2 assigning:1 realistic:2 partition:6 botton:1 john:1 update:1 inte... |
5,065 | 5,586 | Learning Optimal Commitment to Overcome Insecurity
Avrim Blum
Carnegie Mellon University
Nika Haghtalab
Carnegie Mellon University
Ariel D. Procaccia
Carnegie Mellon University
avrim@cs.cmu.edu
nika@cmu.edu
arielpro@cs.cmu.edu
Abstract
Game-theoretic algorithms for physical security have made an impressive realw... | 5586 |@word mild:1 polynomial:5 nd:2 closure:1 additively:1 crucially:1 p0:2 thereby:1 carry:1 initial:11 inefficiency:3 contains:6 pt0:4 denoting:1 ours:1 undiscovered:1 ksk1:1 si:10 follower:4 must:2 subsequent:1 additive:2 v:1 half:18 discovering:2 intelligence:3 ith:2 provides:3 revisited:1 attack:10 mathematical:1... |
5,066 | 5,587 | Diverse Randomized Agents Vote to Win
Albert Xin Jiang
Trinity University
Leandro Soriano Marcolino
USC
Ariel D. Procaccia
CMU
xjiang@trinity.edu
sorianom@usc.edu
arielpro@cs.cmu.edu
Tuomas Sandholm
CMU
Nisarg Shah
CMU
Milind Tambe
USC
sandholm@cs.cmu.edu
nkshah@cs.cmu.edu
tambe@usc.edu
Abstract
We investi... | 5587 |@word mild:5 version:1 judgement:1 stronger:1 seems:2 open:4 simulation:2 sheffet:1 mention:1 moment:1 liu:1 score:1 leandro:1 selecting:2 united:1 denoting:2 reine:1 outperforms:5 existing:1 discretization:1 surprising:1 must:1 subsequent:1 nisarg:1 designed:2 half:1 instantiate:1 selected:1 item:1 intelligence:... |
5,067 | 5,588 | Fairness in Multi-Agent Sequential Decision-Making
Chongjie Zhang and Julie A. Shah
Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139
{chongjie,julie a shah}@csail.mit.edu
Abstract
We define a fairness solution criterion for multi-agent decision-making
p... | 5588 |@word justice:3 termination:5 willing:1 pulse:6 decomposition:1 shot:1 reduction:1 initial:5 contains:2 envision:1 existing:7 current:5 michal:1 lang:1 diederik:1 must:2 attracted:1 john:3 additive:1 nisarg:1 alone:1 intelligence:6 congestion:3 ith:1 core:1 parkes:1 provides:4 zhang:1 mathematical:1 prove:1 inter... |
5,068 | 5,589 | Repeated Contextual Auctions with Strategic Buyers
Kareem Amin
University of Pennsylvania
akareem@cis.upenn.edu
Afshin Rostamizadeh
Google Research
rostami@google.com
Umar Syed
Google Research
usyed@google.com
Abstract
Motivated by real-time advertising exchanges, we analyze the problem of pricing
inventory in a rep... | 5589 |@word briefly:1 version:2 leighton:1 willing:2 seek:1 covariance:3 paid:1 incurs:1 sgd:2 thereby:2 minus:2 klk:1 shot:2 carry:1 moment:1 necessity:2 selecting:1 cleared:2 existing:1 current:2 contextual:10 com:2 comparing:1 must:2 written:1 subsequent:2 additive:3 designed:2 sponsored:2 update:1 implying:1 greedy... |
5,069 | 559 | Learning How To Teach
or
Selecting Minimal Surface Data
Davi Geiger
Siemens Corporate Research, Inc
755 College Rd. East
Princeton, NJ 08540
USA
Ricardo A. Marques Pereira
Dipartimento di Informatica
Universita di Trento
Via Inama 7, Trento, TN 38100
ITALY
Abstract
Learning a map from an input set to an output set i... | 559 |@word briefly:1 compression:2 norm:2 contains:1 selecting:8 zij:2 yet:1 john:1 partition:2 j1:1 girosi:8 davi:1 selected:13 fewer:1 yr:2 leaf:1 plane:1 compo:1 location:1 differential:1 f3v:1 introduce:3 huber:2 indeed:1 roughly:1 inspired:1 automatically:2 considering:1 becomes:3 provided:1 maximizes:1 mass:1 fin... |
5,070 | 5,590 | Universal Option Models
Hengshuai Yao, Csaba Szepesv?ari, Rich Sutton, Joseph Modayil
Department of Computing Science
University of Alberta
Edmonton, AB, Canada, T6H 4M5
hengshua,szepesva,sutton,jmodayil@cs.ualberta.ca
Shalabh Bhatnagar
Department of Computer Science and Automation
Indian Institute of Science
Bangalor... | 5590 |@word innovates:1 inversion:1 advantageous:1 termination:7 pick:1 initial:5 selecting:3 outperforms:1 existing:1 o2:4 current:2 universality:1 must:4 written:1 sorg:3 enables:1 drop:1 update:4 smdp:2 stationary:4 alone:1 selected:11 fewer:1 intelligence:1 ith:1 short:1 location:8 zhang:2 constructed:5 beta:1 prov... |
5,071 | 5,591 | Semi-Separable Hamiltonian Monte Carlo
for Inference in Bayesian Hierarchical Models
Yichuan Zhang
School of Informatics
University of Edinburgh
Y.Zhang-60@sms.ed.ac.uk
Charles Sutton
School of Informatics
University of Edinburgh
c.sutton@inf.ed.ac.uk
Abstract
Sampling from hierarchical Bayesian models is often diffi... | 5591 |@word repository:2 version:1 cox:2 d2:1 simulation:8 crucially:1 covariance:1 citeseer:3 dramatic:1 accommodate:1 initial:1 series:4 lichman:2 tuned:2 outperforms:1 freitas:2 current:2 discretization:2 yet:1 dx:1 partition:1 nian:1 gv:3 designed:1 update:7 stationary:1 prohibitive:1 leaf:5 es:27 ith:2 hamiltonian... |
5,072 | 5,592 | Bayesian Sampling Using Stochastic Gradient
Thermostats
Nan Ding?
Google Inc.
dingnan@google.com
Changyou Chen
Duke University
cchangyou@gmail.com
Youhan Fang?
Purdue University
yfang@cs.purdue.edu
Robert D. Skeel
Purdue University
skeel@cs.purdue.edu
Ryan Babbush
Google Inc.
babbush@google.com
Hartmut Neven
Google I... | 5592 |@word version:1 briefly:2 changyou:1 nd:4 simulation:1 concise:1 sgd:3 ld:5 contains:6 document:4 existing:2 com:4 discretization:13 comparing:1 gmail:1 must:8 written:2 numerical:4 sdes:5 plot:8 drop:1 update:2 v:3 stationary:3 selected:2 leaf:2 isotropic:1 hamiltonian:6 blei:2 num:2 provides:1 math:1 compressib... |
5,073 | 5,593 | Distributed Variational Inference in Sparse Gaussian
Process Regression and Latent Variable Models
Yarin Gal?
Mark van der Wilk?
Carl E. Rasmussen
University of Cambridge
{yg279,mv310,cer54}@cam.ac.uk
Abstract
Gaussian processes (GPs) are a powerful tool for probabilistic inference over functions. They have been a... | 5593 |@word version:1 briefly:1 inversion:2 nd:2 open:3 km:1 calculus:1 simulation:1 scg:6 nicholson:1 covariance:1 simplifying:1 reduction:2 contains:2 series:2 initialisation:1 tuned:2 outperforms:1 existing:1 comparing:2 com:1 fn:1 happen:1 analytic:1 enables:1 stationary:1 generative:1 prohibitive:1 selected:1 core... |
5,074 | 5,594 | Incremental Local Gaussian Regression
Franziska Meier1
fmeier@usc.edu
1
Philipp Hennig2
phennig@tue.mpg.de
University of Southern California
Los Angeles, CA 90089, USA
2
Stefan Schaal1,2
sschaal@usc.edu
Max Planck Institute for Intelligent Systems
Spemannstra?e 38, T?ubingen, Germany
Abstract
Locally weighted re... | 5594 |@word version:6 polynomial:2 km:2 wtm:2 simulation:2 tried:1 bn:1 tr:1 initial:2 lightweight:1 tuned:1 ours:1 rightmost:1 past:1 existing:3 current:2 nt:1 anne:1 must:1 john:1 distant:1 realistic:2 j1:2 shape:1 cheap:1 treating:1 update:15 stationary:3 generative:6 selected:2 fewer:5 intelligence:3 accordingly:1 ... |
5,075 | 5,595 | Just-In-Time Learning for Fast and Flexible Inference
S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli and John Winn
Microsoft Research
{alie,dtarlow,pkohli,jwinn}@microsoft.com
Abstract
Much of research in machine learning has centered around the search for inference
algorithms that are both general-purpose and effic... | 5595 |@word kohli:2 trial:1 polynomial:5 retraining:1 km:1 seek:1 crucially:1 infernet:1 xout:13 dramatic:1 thereby:3 harder:1 moment:6 series:1 efficacy:1 contains:1 united:1 daniel:6 ours:1 past:1 existing:1 current:1 com:3 comparing:1 parsing:1 john:4 grain:1 periodically:1 realistic:1 shape:2 enables:2 remove:1 des... |
5,076 | 5,596 | Distributed Bayesian Posterior Sampling via
Moment Sharing
Minjie Xu1?, Balaji Lakshminarayanan2 , Yee Whye Teh3 , Jun Zhu1 , and Bo Zhang1
1
State Key Lab of Intelligent Technology and Systems; Tsinghua National TNList Lab
1
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
2
G... | 5596 |@word multitask:1 version:6 suitably:1 simulation:1 covariance:8 p0:12 solid:3 tnlist:1 reduction:2 moment:31 initial:1 contains:4 rkhs:1 ours:1 recovered:1 contextual:1 current:2 surprising:1 zhu1:1 john:3 numerical:1 partition:3 subsequent:1 shape:1 analytic:1 plot:4 update:2 aside:1 intelligence:1 item:1 hamil... |
5,077 | 5,597 | Communication Efficient Distributed Machine
Learning with the Parameter Server
Mu Li?? , David G. Andersen? , Alexander Smola?? , and Kai Yu?
?
Carnegie Mellon University ? Baidu ? Google
{muli, dga}@cs.cmu.edu, alex@smola.org, yukai@baidu.com
Abstract
This paper describes a third-generation parameter server framewor... | 5597 |@word version:1 compression:3 norm:1 replicate:2 disk:1 open:3 cipar:1 vldb:1 hsieh:1 pick:1 carry:1 reduction:1 contains:2 karger:1 ours:2 franklin:2 outperforms:1 existing:1 bradley:1 current:2 com:3 comparing:1 issuing:1 must:1 written:1 devin:1 realistic:1 partition:5 academia:1 kdd:1 designed:1 update:7 bick... |
5,078 | 5,598 | On Model Parallelization and Scheduling Strategies
for Distributed Machine Learning
?Seunghak Lee, ?Jin Kyu Kim, ?Xun Zheng, ?Qirong Ho, ?Garth A. Gibson, ?Eric P. Xing
?School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
?Institute for Infocomm Research
A*STAR
Singapore 138632
seunghak@, jink... | 5598 |@word version:1 bigram:4 advantageous:1 open:1 cipar:1 vldb:1 crucially:1 programmatically:1 hsieh:1 deems:1 pick:1 contains:4 efficacy:1 zij:4 score:1 mastery:1 selecting:1 document:4 united:1 franklin:1 existing:3 bradley:1 current:1 com:1 si:1 gmail:1 scatter:1 must:1 yet:1 exposing:1 devin:2 subsequent:1 part... |
5,079 | 5,599 | Communication-Efficient
Distributed Dual Coordinate Ascent
Martin Jaggi ?
ETH Zurich
Jonathan Terhorst
UC Berkeley
Virginia Smith ?
UC Berkeley
Sanjay Krishnan
UC Berkeley
Martin Tak?ac?
Lehigh University
Thomas Hofmann
ETH Zurich
Michael I. Jordan
UC Berkeley
Abstract
Communication remains the most significant bo... | 5599 |@word version:5 norm:1 stronger:1 dekel:1 open:2 accounting:1 hsieh:3 dramatic:1 sgd:36 liblinear:3 reduction:2 configuration:1 liu:1 selecting:1 deepens:1 daniel:1 interestingly:1 franklin:1 existing:2 bradley:1 ka:3 current:1 comparing:1 yet:4 danny:1 written:1 readily:1 must:1 john:4 hofmann:1 designed:1 updat... |
5,080 | 56 | 701
DISCOVERING STRUCfURE FROM MOTION IN
MONKEY, MAN AND MACHINE
Ralph M. Siegel?
The Salk Institute of Biology, La Jolla, Ca. 92037
ABSTRACT
The ability to obtain three-dimensional structure from visual motion is
important for survival of human and non-human primates. Using a parallel processing model, the current wo... | 56 |@word middle:11 wiesel:1 rhesus:1 r:2 moment:1 current:2 com:1 activation:3 john:1 mst:8 realistic:1 shape:2 designed:1 medial:1 discovering:1 inspection:1 steepest:1 dover:1 caveat:1 detecting:1 location:4 rc:2 along:4 direct:1 qualitative:1 prove:1 indeed:2 roughly:1 examine:1 brain:2 actual:1 begin:1 retinotopic... |
5,081 | 560 | Induction of Finite-State Automata Using
Second-Order Recurrent Networks
Raymond L. Watrous
Siemens Corporate Research
755 College Road East, Princeton, NJ 08540
Gary M. Kuhn
Center for Communications Research, IDA
Thanet Road, Princeton, NJ 08540
Abstract
Second-order recurrent networks that recognize simple finite ... | 560 |@word seems:1 termination:5 offending:1 initial:5 interestingly:1 current:4 ida:1 surprising:1 activation:2 yet:1 partition:7 remove:1 update:1 discrimination:1 selected:2 short:1 record:2 completeness:1 five:1 ladendorf:2 pairing:1 consists:1 elman:3 considering:1 increasing:1 moreover:1 null:3 watrous:7 string:4... |
5,082 | 5,600 | Projective dictionary pair learning for pattern
classification
Shuhang Gu1 , Lei Zhang1 , Wangmeng Zuo2 , Xiangchu Feng3
Dept. of Computing, The Hong Kong Polytechnic University, Hong Kong, China
2
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
3
Dept. of Applied Mathematics, X... | 5600 |@word kong:2 dtk:2 norm:16 open:1 reduction:2 liu:1 existing:2 com:1 si:2 gmail:1 assigning:1 yet:1 readily:1 designed:1 drop:1 update:7 discrimination:12 stationary:1 half:2 intelligence:3 generative:1 desktop:1 xk:31 ith:1 zhang:6 five:1 mathematical:1 become:1 ksvd:13 introduce:2 sacrifice:1 p1:1 inspired:1 re... |
5,083 | 5,601 | Augmentative Message Passing for Traveling
Salesman Problem and Graph Partitioning
Reihaneh Rabbany
Department of Computing Science
University of Alberta
Edmonton, AB T6G 2E8
rabbanyk@ualberta.ca
Siamak Ravanbakhsh
Department of Computing Science
University of Alberta
Edmonton, AB T6G 2E8
mravanba@ualberta.ca
Russel... | 5601 |@word middle:2 polynomial:1 compression:1 seems:1 nd:1 vldb:1 seek:3 venkatasubramanian:1 configuration:1 contains:2 initial:1 current:4 si:2 lang:1 partition:1 j1:1 enables:1 analytic:1 siamak:1 plot:6 update:6 generative:1 selected:1 greedy:1 fewer:1 website:1 cook:1 plane:8 intelligence:1 beginning:1 short:1 t... |
5,084 | 5,602 | Causal Inference through a
Witness Protection Program
Ricardo Silva
Department of Statistical Science and CSML
University College London
ricardo@stats.ucl.ac.uk
Robin Evans
Department of Statistics
University of Oxford
evans@stats.ox.ac.uk
Abstract
One of the most fundamental problems in causal inference is the esti... | 5602 |@word trial:2 instrumental:8 proportion:1 nd:2 sex:1 simulation:1 assigment:1 subcase:1 shot:2 initial:1 substitution:5 contains:1 score:2 series:1 configuration:1 denoting:1 existing:2 current:1 z2:1 protection:5 yet:1 evans:4 numerical:4 happen:1 dechter:1 plot:1 alone:1 intelligence:5 fewer:1 item:1 parameteri... |
5,085 | 5,603 | Biclustering Using Message Passing
Luke O?Connor
Bioinformatics and Integrative Genomics
Harvard University
Cambridge, MA 02138
loconnor@g.harvard.edu
Soheil Feizi
Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Cambridge, MA 02139
sfeizi@mit.edu
Abstract
Biclustering is the analog ... | 5603 |@word proportion:1 integrative:1 simulation:9 initial:4 series:1 fragment:1 score:1 daniel:1 tuned:2 document:4 denoting:1 phuong:1 outperforms:3 existing:3 current:1 si:1 assigning:1 written:1 shape:4 enables:2 update:3 generative:2 greedy:1 selected:2 guess:1 discovering:1 characterization:1 node:11 ron:1 five:... |
5,086 | 5,604 | PAC-Bayesian AUC classification and scoring
James Ridgway?
CREST and CEREMADE University Dauphine
james.ridgway@ensae.fr
Nicolas Chopin
CREST (ENSAE) and HEC Paris
nicolas.chopin@ensae.fr
Pierre Alquier
CREST (ENSAE)
pierre.alquier@ucd.ie
Feng Liang
University of Illinois at Urbana-Champaign
liangf@illinois.edu
Abs... | 5604 |@word mild:2 version:4 seems:2 nd:4 logit:1 d2:5 simulation:2 hec:1 covariance:2 q1:3 mention:1 accommodate:1 moment:3 series:1 score:18 interestingly:1 si:3 numerical:2 subsequent:1 dupont:1 plot:3 update:6 resampling:1 v:4 discrimination:1 leaf:1 intelligence:1 es:1 normalising:4 provides:1 boosting:1 complicat... |
5,087 | 5,605 | Partition-wise Linear Models
Hidekazu Oiwa?
Graduate School of Information Science and Technology
The University of Tokyo
hidekazu.oiwa@gmail.com
Ryohei Fujimaki
NEC Laboratories America
rfujimaki@nec-labs.com
Abstract
Region-specific linear models are widely used in practical applications because
of their non-linear... | 5605 |@word version:5 norm:5 d2:1 decomposition:9 jacob:1 initial:1 selecting:1 ours:6 existing:1 current:1 com:3 ka:3 gmail:1 assigning:1 written:2 yet:1 john:1 partition:76 enables:2 minmin:1 designed:1 interpretable:2 update:2 discrimination:1 parameterization:1 amir:2 plane:1 core:1 provides:1 node:1 hyperplanes:1 ... |
5,088 | 5,606 | Learning Shuffle Ideals
Under Restricted Distributions
Dongqu Chen
Department of Computer Science
Yale University
dongqu.chen@yale.edu
Abstract
The class of shuffle ideals is a fundamental sub-family of regular languages. The
shuffle ideal generated by a string set U is the collection of all strings containing
some st... | 5606 |@word repository:1 version:1 polynomial:8 open:1 harder:1 initial:3 series:3 contains:1 lichman:1 recovered:4 si:5 ctn:2 additive:1 moreno:1 greedy:7 intelligence:1 warmuth:1 accepting:1 provides:1 mathematical:1 c2:1 direct:1 transducer:3 prove:4 weinstein:1 inside:1 introduce:1 x0:11 little:1 snn:1 unpredictabl... |
5,089 | 5,607 | Extracting Certainty from Uncertainty: Transductive
Pairwise Classification from Pairwise Similarities
Tianbao Yang? , Rong Jin?\
The University of Iowa, Iowa City, IA 52242
?
Michigan State University, East Lansing, MI 48824
\
Alibaba Group, Hangzhou 311121, China
tianbao-yang@uiowa.edu, rongjin@msu.edu
?
Abstract
In... | 5607 |@word mild:1 kgk:1 trial:1 mr2:1 kulis:2 norm:2 zkf:1 km:2 r:1 citeseer:4 nystr:3 contains:1 score:2 zij:1 woodruff:1 recovered:7 yet:1 realistic:1 predetermined:1 v:2 short:1 provides:1 node:2 toronto:1 preference:4 simpler:1 zhang:1 dn:12 constructed:1 consists:3 prove:3 introduce:4 lansing:1 pairwise:92 roughl... |
5,090 | 5,608 | Incremental Clustering: The Case for Extra Clusters
Margareta Ackerman
Florida State University
600 W College Ave, Tallahassee, FL 32306
mackerman@fsu.edu
Sanjoy Dasgupta
UC San Diego
9500 Gilman Dr, La Jolla, CA 92093
dasgupta@eng.ucsd.edu
Abstract
The explosion in the amount of data available for analysis often ne... | 5608 |@word stronger:2 norm:1 rigged:1 d2:6 eng:1 pick:6 initial:1 configuration:10 contains:8 interestingly:1 mishra:1 recovered:1 yet:2 must:4 readily:3 partition:14 j1:1 enables:1 remove:1 alone:1 intelligence:2 leaf:3 fewer:1 core:8 detecting:8 characterization:1 node:2 mathematical:2 c2:3 become:1 symposium:3 prov... |
5,091 | 5,609 | Parallel Successive Convex Approximation for
Nonsmooth Nonconvex Optimization
Meisam Razaviyayn?
meisamr@stanford.edu
Mingyi Hong?
mingyi@iastate.edu
Zhi-Quan Luo?
luozq@umn.edu
Jong-Shi Pang?
jongship@usc.edu
Abstract
Consider the problem of minimizing the sum of a smooth (possibly non-convex)
and a convex (possi... | 5609 |@word norm:1 cyclic:26 liu:1 selecting:1 existing:6 bradley:1 current:2 luo:4 must:3 numerical:7 partition:1 cheap:1 update:15 bickson:1 lky:2 greedy:6 stationary:4 implying:1 core:4 short:1 dissertation:1 fa9550:1 iterates:3 node:3 successive:8 bittorf:1 mathematical:2 become:1 symposium:1 transceiver:1 focs:1 o... |
5,092 | 561 | A Neural Network for Motion Detection of
Drift-Balanced Stimuli
Hilary Tunley*
School of Cognitive and Computer Sciences
Sussex University
Brighton, England.
Abstract
This paper briefly describes an artificial neural network for preattentive
visual processing. The network is capable of determiuing image motioll in
a ... | 561 |@word illustrating:1 briefly:1 vitally:1 km:1 brightness:1 contains:1 disparity:1 tuned:1 existing:1 current:1 nt:1 activation:1 yet:1 blur:1 girosi:1 remove:1 nervous:1 xk:1 scotland:1 detecting:1 successive:1 differential:1 qualitative:1 consists:2 autocorrelation:1 ol:1 actual:2 increasing:1 underlying:1 what:1... |
5,093 | 5,610 | Stochastic Proximal Gradient Descent with
Acceleration Techniques
Atsushi Nitanda
NTT DATA Mathematical Systems Inc.
1F Shinanomachi Rengakan, 35,
Shinanomachi, Shinjuku-ku, Tokyo,
160-0016, Japan
nitanda@msi.co.jp
Abstract
Proximal gradient descent (PGD) and stochastic proximal gradient descent
(SPGD) are popular me... | 5610 |@word nd:1 dekel:1 bn:2 pick:2 reduction:10 initial:1 ati:1 numerical:1 update:3 kyk:8 xk:51 beginning:3 core:1 provides:1 zhang:9 mathematical:1 ik:6 prove:3 introductory:1 indeed:1 multi:2 becomes:1 notation:1 moreover:2 developed:1 sag:2 exactly:1 k2:34 omit:1 before:1 positive:1 chose:1 co:1 bi:3 range:1 test... |
5,094 | 5,611 | Beyond the Birkhoff Polytope:
Convex Relaxations for Vector Permutation Problems
Stephen J. Wright
Department of Computer Sciences
University of Wisconsin - Madison
Madison, WI 53706
swright@cs.wisc.edu
Cong Han Lim
Department of Computer Sciences
University of Wisconsin - Madison
Madison, WI 53706
conghan@cs.wisc.edu... | 5611 |@word briefly:1 version:7 norm:5 stronger:1 nd:1 seek:2 linearized:1 bn:2 decomposition:1 covariance:4 xout:5 reduction:1 contains:3 etn:1 elliptical:1 comparing:1 current:2 com:2 dx:2 must:1 written:1 numerical:1 remove:1 plot:4 progressively:1 selected:1 discovering:1 de1:1 xk:12 ith:3 core:1 fa9550:1 provides:... |
5,095 | 5,612 | Bregman Alternating Direction Method of Multipliers
Huahua Wang,
Arindam Banerjee
Dept of Computer Science & Engg, University of Minnesota, Twin Cities
{huwang,banerjee}@cs.umn.edu
Abstract
The mirror descent algorithm (MDA) generalizes gradient descent by using a
Bregman divergence to replace squared Euclidean dista... | 5612 |@word version:4 norm:5 linearized:2 decomposition:1 tr:1 initial:1 contains:3 zij:3 interestingly:1 existing:1 ka:2 com:2 luo:1 chu:1 gpu:7 numerical:1 engg:1 plot:3 update:43 v:2 short:1 core:1 provides:2 node:7 zhang:1 mathematical:1 along:1 yuan:1 consists:1 prove:1 kiwiel:1 introduce:2 x0:4 pairwise:2 rapid:1... |
5,096 | 5,613 | Multi-Step Stochastic ADMM in High Dimensions:
Applications to Sparse Optimization
and Matrix Decomposition
Hanie Sedghi
Univ. of Southern California
Los Angeles, CA 90089
hsedghi@usc.edu
Anima Anandkumar
University of California
Irvine, CA 92697
a.anandkumar@uci.edu
Edmond Jonckheere
Univ. of Southern California
Lo... | 5613 |@word faculty:1 version:8 norm:15 unif:1 d2:6 km:4 decomposition:25 covariance:1 tr:1 klk:4 carry:1 reduction:1 initial:8 pt0:3 daniel:1 document:1 outperforms:1 existing:1 ksk1:2 nt:2 luo:3 si:1 chu:1 hanie:2 tailoring:1 update:22 v:1 fewer:1 kyk:1 xk:13 beginning:1 core:1 provides:2 zhang:1 accessed:1 symposium... |
5,097 | 5,614 | Accelerated Mini-batch Randomized Block
Coordinate Descent Method
?
Tuo Zhao??? Mo Yu?? Yiming Wang? Raman Arora? Han Liu?
Johns Hopkins University ? Harbin Institute of Technology ? Princeton University
{tour,myu25,freewym,arora}@jhu.edu,hanliu@princeton.edu
Abstract
We consider regularized empirical risk minimizat... | 5614 |@word norm:2 suitably:1 simulation:1 covariance:1 reduction:11 initial:2 liu:2 contains:5 cyclic:1 series:3 tuned:2 ours:2 past:1 existing:11 outperforms:4 deteriorating:1 john:3 additive:1 periodically:1 numerical:4 partition:3 update:2 selected:5 amir:1 accordingly:1 beginning:2 core:1 iterates:3 characterizati... |
5,098 | 5,615 | Sparse PCA with Oracle Property
Zhaoran Wang
Department of Operations Research
and Financial Engineering
Princeton University
Princeton, NJ 08544, USA
zhaoran@princeton.edu
Quanquan Gu
Department of Operations Research
and Financial Engineering
Princeton University
Princeton, NJ 08544, USA
qgu@princeton.edu
Han Liu
... | 5615 |@word version:2 polynomial:1 norm:13 stronger:2 simulation:1 covariance:23 decomposition:3 p0:1 tr:2 sepulchre:1 reduction:1 liu:4 contains:2 tuned:1 past:1 existing:5 surprising:1 numerical:3 update:1 intelligence:1 fpr:4 provides:2 zhang:3 constructed:1 direct:1 yuan:2 prove:5 consists:1 shorthand:2 introduce:4... |
5,099 | 5,616 | A statistical model for tensor PCA
Emile Richard
Electrical Engineering
Stanford University
Andrea Montanari
Statistics & Electrical Engineering
Stanford University
Abstract
We consider the Principal Component Analysis problem for large tensors of arbitrary order k under a single-spike (or rank-one plus noise) model.... | 5616 |@word version:3 polynomial:2 norm:8 stronger:1 nd:1 suitably:1 paredes:1 confirms:1 simulation:1 covariance:3 decomposition:4 arous:2 recursively:1 moment:1 bai:1 liu:1 initial:3 amp:19 romera:1 outperforms:1 recovered:1 nt:1 numerical:2 j1:2 drop:1 plot:1 stationary:1 greedy:1 intelligence:1 nq:1 yi1:1 fa9550:2 ... |
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