Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
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5,200 | 5,708 | A class of network models recoverable by spectral
clustering
Marina Meil?a
Department of Statistics
University of Washington
Seattle, WA 98195-4322, USA
mmp@stat.washington.edu
Yali Wan
Department of Statistics
University of Washington
Seattle, WA 98195-4322, USA
yaliwan@washington.edu
Abstract
Finding communities i... | 5708 |@word briefly:1 version:7 instrumental:1 c0:5 tedious:1 ci2:1 thereby:1 configuration:3 series:1 denoting:1 ours:3 existing:2 recovered:1 current:2 incidence:1 comparing:1 si:2 yet:2 must:1 numerical:1 partition:7 christian:1 treating:1 interpretable:2 designed:1 stationary:2 generative:1 fewer:1 intelligence:1 x... |
5,201 | 5,709 | Monotone k-Submodular Function Maximization
with Size Constraints
Yuichi Yoshida
National Institute of Informatics, and
Preferred Infrastructure, Inc.
yyoshida@nii.ac.jp
Naoto Ohsaka
The University of Tokyo
ohsaka@is.s.u-tokyo.ac.jp
Abstract
A k-submodular function is a generalization of a submodular function, where ... | 5709 |@word bisubmodularity:2 version:1 polynomial:2 humidity:3 open:1 bn:1 pick:1 versatile:1 n8:1 contains:1 selecting:3 nii:1 document:2 outperforms:2 current:1 activation:1 assigning:2 subsequent:1 partition:1 kdd:3 greedy:33 selected:2 website:1 item:11 xk:12 infrastructure:1 location:4 mathematical:1 lux:1 focs:1... |
5,202 | 571 | Oscillatory Neural Fields for
Globally Optimal Path Planning
Michael Lemmon
Dept. of Electrical Engineering
University of Notre Dame
Notre Dame, Indiana 46556
Abstract
A neural network solution is proposed for solving path planning problems
faced by mobile robots. The proposed network is a two-dimensional sheet
of ne... | 571 |@word trial:1 norm:10 open:1 simulation:11 propagate:1 decomposition:1 thereby:1 initial:5 selecting:1 current:3 discretization:2 surprising:1 tackling:1 yet:1 must:3 realize:1 additive:1 numerical:4 shape:1 selected:2 short:1 chua:2 location:4 yeb:1 preference:1 c6:1 along:1 differential:2 consists:1 resistive:2 ... |
5,203 | 5,710 | Smooth and Strong:
MAP Inference with Linear Convergence
Ofer Meshi
TTI Chicago
Mehrdad Mahdavi
TTI Chicago
Alexander G. Schwing
University of Toronto
Abstract
Maximum a-posteriori (MAP) inference is an important task for many applications. Although the standard formulation gives rise to a hard combinatorial optimiz... | 5710 |@word version:1 norm:6 simplifying:1 decomposition:4 pick:1 solid:2 initial:1 configuration:4 score:9 ours:1 interestingly:1 existing:5 ka:2 current:1 yet:1 universality:1 chicago:2 enables:1 designed:2 update:9 intelligence:2 prohibitive:1 parameterization:2 mccallum:1 steepest:2 smith:1 tarlow:1 provides:1 math... |
5,204 | 5,711 | Stop Wasting My Gradients: Practical SVRG
Reza Babanezhad1 , Mohamed Osama Ahmed1 , Alim Virani2 , Mark Schmidt1
Department of Computer Science
University of British Columbia
1
{rezababa, moahmed, schmidtm}@cs.ubc.ca,2 alim.virani@gmail.com
Jakub Kone?cn?y
School of Mathematics
University of Edinburgh
kubo.konecny@gma... | 5711 |@word inversion:1 advantageous:1 proportion:1 norm:3 bf:6 tedious:1 hu:1 bn:1 decomposition:1 pick:2 minus:2 reduction:4 initial:4 liu:1 contains:1 ati:2 existing:2 current:2 com:2 skipping:2 gmail:2 must:1 written:2 gpu:1 fn:1 plot:1 update:11 half:1 fewer:1 intelligence:1 lr:1 iterates:1 zhang:8 mathematical:2 ... |
5,205 | 5,712 | Spectral Norm Regularization of Orthonormal
Representations for Graph Transduction
Rakesh Shivanna
Google Inc.
Mountain View, CA, USA
rakeshshivanna@google.com
Bibaswan Chatterjee
Dept. of Computer Science & Automation
Indian Institute of Science, Bangalore
bibaswan.chatterjee@csa.iisc.ernet.in
Raman Sankaran, Chira... | 5712 |@word repository:1 norm:9 stronger:1 suitably:1 c0:2 open:2 confirms:1 decomposition:1 ld:1 lichman:1 ecole:1 bhattacharyya:3 pprox:1 outperforms:1 existing:5 kx0:1 current:1 com:1 readily:3 subsequent:1 designed:1 update:1 inspection:1 xk:7 ysp:1 core:1 lr:1 iterates:2 characterization:1 node:7 zhang:3 mathemati... |
5,206 | 5,713 | Differentially Private Learning
of Structured Discrete Distributions
Ilias Diakonikolas?
University of Edinburgh
Moritz Hardt
Google Research
Ludwig Schmidt
MIT
Abstract
We investigate the problem of learning an unknown probability distribution over
a discrete population from random samples. Our goal is to design ef... | 5713 |@word private:60 version:2 briefly:1 polynomial:5 norm:5 eliminating:1 bun:3 crucially:1 asks:1 reduction:1 contains:1 score:1 series:1 daniel:1 ours:1 miklau:1 ka:2 current:1 surprising:1 must:3 refines:2 partition:6 shape:2 enables:1 rote:1 plot:2 ligett:1 v:1 greedy:2 prohibitive:1 selected:1 item:2 complement... |
5,207 | 5,714 | Robust Portfolio Optimization
Fang Han
Department of Biostatistics
Johns Hopkins University
Baltimore, MD 21205
fhan@jhu.edu
Huitong Qiu
Department of Biostatistics
Johns Hopkins University
Baltimore, MD 21205
hqiu7@jhu.edu
Han Liu
Department of Operations Research
and Financial Engineering
Princeton University
Princ... | 5714 |@word determinant:1 wiesel:1 norm:5 loading:6 nd:2 d2:8 simulation:4 covariance:48 contraction:2 harder:2 carry:1 reduction:1 moment:11 liu:1 series:6 bai:2 offering:1 past:1 existing:1 elliptical:12 wd:2 torben:1 scatter:10 john:4 fn:2 shape:1 christian:1 designed:1 drop:1 stationary:6 half:1 selected:2 xk:7 run... |
5,208 | 5,715 | Bayesian Optimization with Exponential Convergence
Kenji Kawaguchi
MIT
Cambridge, MA, 02139
kawaguch@mit.edu
Leslie Pack Kaelbling
MIT
Cambridge, MA, 02139
lpk@csail.mit.edu
Tom?as Lozano-P?erez
MIT
Cambridge, MA, 02139
tlp@csail.mit.edu
Abstract
This paper presents a Bayesian optimization method with exponential c... | 5715 |@word version:1 polynomial:2 advantageous:1 c0:8 open:1 simulation:1 covariance:4 p0:3 recursively:1 ld:2 initial:1 contains:2 selecting:1 existing:1 freitas:5 current:1 nt:1 must:1 written:1 numerical:2 happen:1 partition:1 additive:1 remove:1 tlp:1 update:3 intelligence:3 selected:2 kandasamy:1 accordingly:3 is... |
5,209 | 5,716 | Fast Randomized Kernel Ridge Regression with
Statistical Guarantees?
Ahmed El Alaoui ?
Michael W. Mahoney ?
? Electrical Engineering and Computer Sciences
? Statistics and International Computer Science Institute
University of California, Berkeley, Berkeley, CA 94720.
{elalaoui@eecs,mmahoney@stat}.berkeley.edu
Abstrac... | 5716 |@word trial:1 version:6 inversion:1 polynomial:1 norm:4 stronger:1 open:2 decomposition:4 thereby:1 nystr:19 tr:8 contains:3 score:41 series:1 woodruff:1 rkhs:1 current:1 written:1 john:1 subsequent:1 partition:2 additive:2 sanjiv:1 v:1 intelligence:1 prohibitive:1 accordingly:1 ith:2 coarse:2 provides:2 draft:1 ... |
5,210 | 5,717 | Taming the Wild: A Unified Analysis of
H OGWILD !-Style Algorithms
Christopher De Sa, Ce Zhang, Kunle Olukotun, and Christopher R?e
cdesa@stanford.edu, czhang@cs.wisc.edu,
kunle@stanford.edu, chrismre@stanford.edu
Departments of Electrical Engineering and Computer Science
Stanford University, Stanford, CA 94309
Abstr... | 5717 |@word kong:1 version:12 norm:1 johansson:2 instruction:3 hsieh:1 sgd:40 harder:1 boundedness:1 recursively:1 moment:2 initial:2 liu:2 series:2 fa8750:2 past:1 existing:1 current:3 ka:1 si:2 chu:1 written:3 must:8 john:2 gpu:1 subsequent:1 numerical:1 enables:2 plot:2 update:20 v:1 serialized:2 rku:1 ith:2 pvldb:2... |
5,211 | 5,718 | Beyond Convexity: Stochastic
Quasi-Convex Optimization
Elad Hazan
Princeton University
Kfir Y. Levy
Technion
Shai Shalev-Shwartz
The Hebrew University
ehazan@cs.princeton.edu
kfiryl@tx.technion.ac.il
shais@cs.huji.ac.il
Abstract
Stochastic convex optimization is a basic and well studied primitive in machine
lear... | 5718 |@word private:1 middle:2 version:4 norm:2 open:1 p0:1 sgd:12 series:1 interestingly:1 current:1 comparing:1 surprising:1 luo:1 activation:7 yet:3 bd:8 must:4 takeo:1 enables:1 update:4 discrimination:1 implying:1 intelligence:1 warmuth:1 plane:1 vanishing:1 farther:1 manfred:1 provides:1 characterization:1 pascan... |
5,212 | 5,719 | On the Limitation of Spectral Methods:
From the Gaussian Hidden Clique Problem to
Rank-One Perturbations of Gaussian Tensors
Andrea Montanari
Department of Electrical Engineering and Department of Statistics. Stanford University.
montanari@stanford.edu
Daniel Reichman
Department of Cognitive and Brain Sciences, Univers... | 5719 |@word briefly:1 version:3 faculty:1 polynomial:7 norm:7 nd:2 hu:1 seek:1 p0:16 q1:7 invoking:1 tr:1 arous:1 carry:1 reduction:2 moment:4 zij:2 ktv:12 daniel:2 ours:1 com:1 si:2 gmail:1 intriguing:1 attracted:1 dx:2 must:1 fn:2 j1:1 v:2 isotropic:1 yi1:1 dembo:1 vanishing:1 detecting:3 math:2 location:1 mcdiarmid:... |
5,213 | 572 | Data Analysis using G/SPLINES
David Rogers?
Research Institute for Advanced Computer Science
MS T041-5, NASA/Ames Research Center
Moffett Field, CA 94035
INTERNET: drogerS@riacs.edu
Abstract
G/SPLINES is an algorithm for building functional models of data. It
uses genetic search to discover combinations of basis funct... | 572 |@word illustrating:1 simulation:1 pressure:1 series:1 score:15 selecting:2 tlo:1 genetic:22 com:1 written:1 riacs:2 additive:1 informative:2 noninformative:2 remove:1 plot:10 v:7 fewer:3 selected:3 contribute:1 ames:1 preference:1 five:6 direct:1 become:1 fitting:1 behavior:2 automatically:1 little:2 domestic:1 be... |
5,214 | 5,720 | Regularized EM Algorithms: A Unified Framework
and Statistical Guarantees
Constantine Caramanis
Dept. of Electrical and Computer Engineering
The University of Texas at Austin
constantine@utexas.edu
Xinyang Yi
Dept. of Electrical and Computer Engineering
The University of Texas at Austin
yixy@utexas.edu
Abstract
Laten... | 5720 |@word mild:1 trial:3 version:5 achievable:1 norm:10 stronger:2 c0:2 tedious:1 seek:1 simulation:2 thereby:1 initial:4 liu:1 series:1 tuned:1 xinyang:3 existing:1 si:2 yet:2 must:3 john:1 enables:1 remove:1 designed:1 plot:2 update:2 larization:1 resampling:2 alone:1 rp1:2 isotropic:1 ith:1 lr:1 characterization:1... |
5,215 | 5,721 | Black-box optimization of noisy functions with
unknown smoothness
Jean-Bastien Grill
Michal Valko
SequeL team, INRIA Lille - Nord Europe, France
jean-bastien.grill@inria.fr
michal.valko@inria.fr
R?emi Munos
Google DeepMind, UK?
munos@google.com
Abstract
We study the problem of black-box optimization of a function f ... | 5721 |@word version:3 stronger:4 underline:1 open:2 underperform:1 simulation:1 tried:1 decomposition:2 p0:1 attainable:1 dramatic:1 thereby:1 harder:1 atb:2 contains:2 selecting:1 ecole:1 interestingly:1 existing:2 current:1 com:1 michal:4 surprising:1 yet:3 refines:1 partition:3 drop:1 plot:2 update:2 v:1 half:2 leaf... |
5,216 | 5,722 | Combinatorial Cascading Bandits
Branislav Kveton
Adobe Research
San Jose, CA
kveton@adobe.com
Zheng Wen
Yahoo Labs
Sunnyvale, CA
zhengwen@yahoo-inc.com
Azin Ashkan
Technicolor Research
Los Altos, CA
azin.ashkan@technicolor.com
Csaba Szepesv?ari
Department of Computing Science
University of Alberta
szepesva@cs.ualber... | 5722 |@word polynomial:2 nd:2 simplifying:1 reduction:2 contains:1 prefix:5 animated:5 past:1 existing:1 yajun:1 current:1 com:3 nt:1 surprising:1 smtp:2 written:1 john:1 informative:1 plot:6 update:3 half:1 intelligence:2 item:71 accordingly:1 dover:1 node:3 org:1 zhang:1 along:2 yuan:1 prove:5 shorthand:1 introduce:1... |
5,217 | 5,723 | Adaptive Primal-Dual Splitting Methods for
Statistical Learning and Image Processing
Thomas Goldstein?
Department of Computer Science
University of Maryland
College Park, MD
Min Li?
School of Economics and Management
Southeast University
Nanjing, China
Xiaoming Yuan?
Department of Mathematics
Hong Kong Baptist Univer... | 5723 |@word kong:3 cu:9 mri:1 inversion:1 trial:1 norm:2 k2hk:5 linearized:2 contraction:1 automat:1 contains:2 series:1 discretization:1 surprising:1 dx:3 chu:1 dct:1 numerical:3 enables:2 plot:1 update:5 fund:1 rudin:1 une:1 xk:27 recherche:1 record:1 iterates:8 provides:1 org:1 simpler:3 zhang:3 mathematical:3 direc... |
5,218 | 5,724 | Sum-of-Squares Lower Bounds for Sparse PCA
Tengyu Ma?1 and Avi Wigderson?2
1
Department of Computer Science, Princeton University
2
School of Mathematics, Institute for Advanced Study
Abstract
This paper establishes a statistical versus computational trade-off for solving
a basic high-dimensional machine learning pro... | 5724 |@word version:6 briefly:2 achievable:1 polynomial:24 seems:1 norm:2 stronger:3 nd:1 km:1 calculus:1 palma:1 gish:1 bn:1 covariance:11 decomposition:1 prasad:1 q1:1 pick:1 reduction:4 moment:21 inefficiency:1 contains:3 liu:1 united:1 chervonenkis:1 sherali:2 past:2 existing:2 yet:2 intriguing:1 v:1 intelligence:2... |
5,219 | 5,725 | Online Gradient Boosting
Alina Beygelzimer
Yahoo Labs
New York, NY 10036
beygel@yahoo-inc.com
Elad Hazan
Princeton University
Princeton, NJ 08540
ehazan@cs.princeton.edu
Satyen Kale
Yahoo Labs
New York, NY 10036
satyen@yahoo-inc.com
Haipeng Luo
Princeton University
Princeton, NJ 08540
haipengl@cs.princeton.edu
Abs... | 5725 |@word stronger:1 norm:9 suitably:3 open:2 d2:1 incurs:1 sgd:2 concise:1 ytn:2 ld:2 reduction:6 liu:1 contains:1 efficacy:1 tuned:3 frankwolfe:1 ka:1 com:3 current:2 beygelzimer:3 luo:2 bd:10 john:1 additive:2 christian:1 greedy:5 half:2 kyk:1 short:1 provides:1 boosting:75 sigmoidal:1 simpler:4 zhang:8 along:3 pr... |
5,220 | 5,726 | Regularization-Free Estimation in Trace Regression with
Symmetric Positive Semidefinite Matrices
Matthias Hein
Department of Computer Science
Department of Mathematics
Saarland University
Saarbr?ucken, Germany
hein@cs.uni-saarland.de
Martin Slawski
Ping Li
Department of Statistics & Biostatistics
Department of Compute... | 5726 |@word version:1 seems:2 norm:26 stronger:1 km:4 covariance:12 decomposition:2 invoking:1 q1:1 pick:1 tr:14 iii1360971:1 nystr:1 moment:3 configuration:1 contains:2 series:2 liu:1 denoting:1 tuned:2 past:2 existing:1 com:1 si:3 attracted:1 numerical:1 drop:1 nq:1 complementing:1 accordingly:2 beginning:2 fa9550:1 ... |
5,221 | 5,727 | Convergence Analysis of Prediction Markets via
Randomized Subspace Descent
Rafael Frongillo
Department of Computer Science
University of Colorado, Boulder
raf@colorado.edu
Mark D. Reid
Research School of Computer Science
The Australian National University & NICTA
mark.reid@anu.edu.au
Abstract
Prediction markets are ... | 5727 |@word mild:1 version:1 briefly:1 achievable:1 seems:1 stronger:1 nd:1 open:3 mehta:1 willing:3 closure:1 seek:1 propagate:1 simulation:1 jacob:3 hu:1 incurs:1 thereby:1 carry:1 reduction:1 initial:4 configuration:1 contains:1 inefficiency:1 selecting:1 existing:2 current:5 surprising:1 si:1 yet:1 assigning:1 must... |
5,222 | 5,728 | Accelerated Proximal Gradient Methods for
Nonconvex Programming
Huan Li
Zhouchen Lin B
Key Lab. of Machine Perception (MOE), School of EECS, Peking University, P. R. China
Cooperative Medianet Innovation Center, Shanghai Jiaotong University, P. R. China
lihuanss@pku.edu.cn
zlin@pku.edu.cn
Abstract
Nonconvex and nonsm... | 5728 |@word polynomial:1 norm:8 stronger:2 open:1 termination:1 d2:2 semicontinuous:5 linearized:1 pg:2 q1:2 mention:1 hager:1 reduction:1 celebrated:1 series:1 contains:1 ours:1 existing:1 nonmonotone:14 current:1 kx0:1 written:1 numerical:3 plot:1 gist:10 update:2 v:1 half:1 asu:1 zlin:1 fewer:2 accordingly:3 xk:71 c... |
5,223 | 5,729 | Nearly-Optimal Private LASSO?
Kunal Talwar
Google Research
kunal@google.com
Abhradeep Thakurta
(Previously) Yahoo! Labs
guhathakurta.abhradeep@gmail.com
Li Zhang
Google Research
liqzhang@google.com
Abstract
We present a nearly optimal differentially private version of the well known
LASSO estimator. Our algorithm pr... | 5729 |@word h:3 private:59 version:13 cox:1 polynomial:9 norm:14 achievable:1 bun:1 open:1 crucially:1 incurs:2 contains:2 series:1 selecting:2 denoting:1 frankwolfe:1 past:1 ksk1:1 ka:2 com:3 protection:2 si:1 gmail:1 must:2 realistic:1 kdd:1 shape:1 greedy:2 discovering:1 item:1 smith:7 record:1 provides:3 defacto:1 ... |
5,224 | 573 | Active Exploration in Dynamic Environments
Sebastian B. Thrun
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
E-mail: thrun@cs.cmu.edu
Knut Moller
University of Bonn
Dept. of Computer Science
ROmerstr. 164
D-5300 Bonn, Germany
Abstract
\Vhenever an agent learns to control an unknown enviro... | 573 |@word exploitation:20 middle:2 cu:1 fixpoints:1 simulation:3 seek:1 asks:1 initial:3 selecting:2 current:1 activation:1 attracted:2 realize:1 subsequent:1 enables:1 atlas:1 progressively:2 stationary:1 selected:1 discovering:1 beginning:1 realizing:1 short:2 five:1 along:1 constructed:2 become:1 combine:2 introduc... |
5,225 | 5,730 | Minimax Time Series Prediction
Alan Malek
UC Berkeley
malek@berkeley.edu
Wouter M. Koolen
Centrum Wiskunde & Informatica
wmkoolen@cwi.nl
Peter L. Bartlett
UC Berkeley & QUT
bartlett@cs.berkeley.edu
Yasin Abbasi-Yadkori
Queensland University of Technology
yasin.abbasiyadkori@qut.edu.au
Abstract
We consider an adversa... | 5730 |@word inversion:2 norm:20 h2t:2 open:3 hu:1 queensland:1 jacob:1 tr:7 versatile:1 shot:2 recursively:5 substitution:1 series:15 tabulate:1 daniel:1 past:7 ka:1 recovered:1 surprising:1 yet:1 intriguing:1 must:3 written:1 drop:1 update:6 stationary:2 half:1 intelligence:3 warmuth:5 xk:1 manfred:5 regressive:1 org:... |
5,226 | 5,731 | Communication Complexity of Distributed
Convex Learning and Optimization
Ohad Shamir
Weizmann Institute of Science
Rehovot 7610001, Israel
ohad.shamir@weizmann.ac.il
Yossi Arjevani
Weizmann Institute of Science
Rehovot 7610001, Israel
yossi.arjevani@weizmann.ac.il
Abstract
We study the fundamental limits to communic... | 5731 |@word mild:3 version:1 stronger:1 seems:1 norm:5 suitably:1 c0:2 open:6 bekkerman:1 d2:4 dekel:1 crucially:1 attainable:6 automat:1 sgd:1 shot:1 necessity:1 woodruff:2 ours:1 existing:3 luo:1 yet:1 chu:1 written:1 must:2 realistic:1 partition:1 numerical:3 hofmann:1 designed:1 alone:1 accordingly:1 smith:1 core:1... |
5,227 | 5,732 | Explore no more: Improved high-probability regret
bounds for non-stochastic bandits
Gergely Neu?
SequeL team
INRIA Lille ? Nord Europe
gergely.neu@gmail.com
Abstract
This work addresses the problem of regret minimization in non-stochastic multiarmed bandit problems, focusing on performance guarantees that hold with hi... | 5732 |@word exploitation:1 version:4 nd:1 open:1 forecaster:1 crucially:1 pick:3 incurs:1 boundedness:2 harder:1 selecting:1 denoting:1 interestingly:2 past:2 existing:1 current:4 com:1 contextual:1 beygelzimer:3 luo:1 gmail:1 written:1 benign:1 drop:1 update:1 half:1 warmuth:4 accordingly:1 rts:1 beginning:1 short:1 p... |
5,228 | 5,733 | A Nonconvex Optimization Framework for Low Rank
Matrix Estimation?
Tuo Zhao
Johns Hopkins University
Zhaoran Wang
Han Liu
Princeton University
Abstract
We study the estimation of low rank matrices via nonconvex optimization. Compared with convex relaxation, nonconvex optimization exhibits superior empirical
performan... | 5733 |@word version:4 pw:5 polynomial:1 norm:5 km:7 hu:1 prasad:1 decomposition:12 contraction:2 invoking:1 recursively:1 initial:1 liu:1 past:1 existing:7 ka:2 luo:1 bd:1 john:1 numerical:1 partition:2 kdd:1 analytic:4 update:5 aside:1 stationary:2 prohibitive:1 prize:2 core:1 c6:2 simpler:1 mathematical:1 c2:2 direct... |
5,229 | 5,734 | Individual Planning in In?nite-Horizon Multiagent
Settings: Inference, Structure and Scalability
Xia Qu
Epic Systems
Verona, WI 53593
quxiapisces@gmail.com
Prashant Doshi
THINC Lab, Dept. of Computer Science
University of Georgia, Athens, GA 30622
pdoshi@cs.uga.edu
Abstract
This paper provides the ?rst formalization ... | 5734 |@word m1j:1 version:1 verona:1 nd:1 seek:4 simulation:1 arti:7 o2i:1 initial:9 cyclic:1 contains:2 series:1 interestingly:1 ati:30 subjective:1 past:1 com:1 nt:15 surprising:1 contextual:1 gmail:1 must:2 john:1 realize:1 shlomo:3 update:6 a1k:1 greedy:20 intelligence:7 beginning:2 provides:3 characterization:1 no... |
5,230 | 5,735 | Randomized Block Krylov Methods for Stronger and
Faster Approximate Singular Value Decomposition
Christopher Musco
Massachusetts Institute of Technology, EECS
Cambridge, MA 02139, USA
cpmusco@mit.edu
Cameron Musco
Massachusetts Institute of Technology, EECS
Cambridge, MA 02139, USA
cnmusco@mit.edu
Abstract
Since bei... | 5735 |@word trial:1 luk:1 version:4 compression:1 stronger:7 norm:36 seems:1 polynomial:14 physik:1 hu:1 confirms:1 seek:1 decomposition:7 mention:1 reduction:1 series:1 woodruff:2 denoting:1 franklin:1 outperforms:2 ka:24 recovered:1 com:3 yet:1 written:1 reminiscent:1 must:3 john:1 numerical:4 subsequent:1 kdd:1 plot... |
5,231 | 5,736 | Minimum Weight Perfect Matching
via Blossom Belief Propagation
Sungsoo Ahn?
Sejun Park?
Michael Chertkov?
Jinwoo Shin?
?
School of Electrical Engineering,
Korea Advanced Institute of Science and Technology, Daejeon, Korea
?
Theoretical Division and Center for Nonlinear Studies,
Los Alamos National Laboratory, Los Ala... | 5736 |@word version:2 polynomial:8 termination:6 closure:1 seek:1 contraction:3 decomposition:12 celebrated:1 contains:2 past:1 remove:4 designed:2 update:15 bickson:1 alone:1 half:8 intelligence:5 yr:1 plane:3 xk:3 provides:1 iterates:2 complication:1 math:1 allerton:2 simpler:1 mathematical:3 constructed:1 c2:1 focs:... |
5,232 | 5,737 | Super-Resolution Off the Grid
Qingqing Huang
MIT,
EECS,
LIDS,
qqh@mit.edu
Sham M. Kakade
University of Washington,
Department of Statistics,
Computer Science & Engineering,
sham@cs.washington.edu
Abstract
Super-resolution is the problem of recovering a superposition of point sources using bandlimited measurements, wh... | 5737 |@word briefly:1 achievable:1 polynomial:5 norm:5 vi1:1 open:1 seek:2 decomposition:16 pick:4 harder:2 moment:1 series:1 ours:1 existing:1 ksk1:1 recovered:1 fn:1 j1:4 v:24 intelligence:1 fewer:1 coarse:3 provides:2 mathematical:4 along:1 constructed:3 direct:1 differential:1 symposium:3 ik:2 prove:1 consists:1 pa... |
5,233 | 5,738 | b-bit Marginal Regression
Ping Li
Department of Statistics and Biostatistics
Department of Computer Science
Rutgers University
pingli@stat.rutgers.edu
Martin Slawski
Department of Statistics and Biostatistics
Department of Computer Science
Rutgers University
martin.slawski@rutgers.edu
Abstract
We consider the problem... | 5738 |@word illustrating:1 briefly:1 version:4 norm:12 proportion:1 proportionality:1 confirms:1 simulation:1 hsieh:1 reduction:1 liblinear:2 celebrated:1 contains:1 configuration:1 liu:1 existing:1 recovered:1 ka:1 comparing:1 yet:1 axk22:1 additive:8 realistic:1 numerical:2 subsequent:1 partition:1 enables:1 depict:1... |
5,234 | 5,739 | LASSO with Non-linear Measurements is Equivalent
to One With Linear Measurements
Ehsan Abbasi
Department of Electrical Engineering
Caltech
eabbasi@caltech.edu
Christos Thrampoulidis,
Department of Electrical Engineering
Caltech
cthrampo@caltech.edu
Babak Hassibi
Department of Electrical Engineering
Caltech
hassibi@ca... | 5739 |@word version:4 norm:13 seems:1 proportionality:5 d2:2 simulation:4 seek:1 q1:1 mention:1 moment:3 reduction:1 celebrated:1 series:4 liu:1 initial:1 daniel:1 interestingly:3 amp:1 ati:7 xinyang:1 kx0:9 si:2 axk22:1 mesh:1 analytic:1 drop:2 fund:1 stationary:1 device:1 accordingly:1 indicative:1 ith:1 core:1 volka... |
5,235 | 574 | 3D Object Recognition Using Unsupervised
Feature Extraction
Nathan Intrator
Center for Neural Science,
Brown University
Providence, RI 02912, USA
Heinrich H. Biilthoff
Dept. of Cognitive Science,
Brown University,
and Center for
Biological Information Processing,
MIT, Cambridge, MA 02139 USA
Josh I. Gold
Center for N... | 574 |@word briefly:1 stronger:2 duda:2 proportion:2 simulation:3 seek:1 mammal:1 mention:1 tr:1 reduction:6 current:1 yet:1 readily:1 john:1 subsequent:1 plasticity:2 designed:2 occlude:1 intelligence:1 plane:3 farther:1 location:1 mathematical:1 along:3 constructed:1 edelman:22 multimodality:1 manner:1 deteriorate:1 i... |
5,236 | 5,740 | Optimal Rates for Random Fourier Features
Bharath K. Sriperumbudur?
Department of Statistics
Pennsylvania State University
University Park, PA 16802, USA
bks18@psu.edu
Zolt?an Szab?o?
Gatsby Unit, CSML, UCL
Sainsbury Wellcome Centre, 25 Howland Street
London - W1T 4JG, UK
zoltan.szabo@gatsby.ucl.ac.uk
Abstract
Kerne... | 5740 |@word kulis:1 private:1 polynomial:1 norm:17 nd:3 open:1 d2:3 hyv:1 bn:3 zolt:1 attainable:1 q1:1 thereby:2 nystr:2 boundedness:4 moment:1 series:1 offering:1 existing:1 current:1 yet:1 dx:3 written:2 additive:3 numerical:1 enables:1 designed:1 hash:1 intelligence:3 fewer:1 ksm:2 vanishing:1 hamiltonian:1 lr:27 p... |
5,237 | 5,741 | Submodular Hamming Metrics
Jennifer Gillenwater? , Rishabh Iyer? , Bethany Lusch? , Rahul Kidambi? , Jeff Bilmes?
?
University of Washington, Dept. of EE, Seattle, U.S.A.
?
University of Washington, Dept. of Applied Math, Seattle, U.S.A.
{jengi, rkiyer, herwaldt, rkidambi, bilmes}@uw.edu
Abstract
We show that there i... | 5741 |@word kohli:2 trial:2 version:5 polynomial:1 seems:1 semidifferential:1 open:5 seek:1 paid:1 harder:1 carry:1 initial:2 contains:8 score:12 selecting:2 hoiem:1 document:20 interestingly:1 outperforms:1 existing:2 current:5 yet:2 assigning:1 must:2 written:1 partition:1 seeding:1 greedy:9 fewer:1 item:2 ith:1 comp... |
5,238 | 5,742 | Top-k Multiclass SVM
1
Maksim Lapin,1 Matthias Hein2 and Bernt Schiele1
Max Planck Institute for Informatics, Saarbr?cken, Germany
2
Saarland University, Saarbr?cken, Germany
Abstract
Class ambiguity is typical in image classification problems with a large number
of classes. When classes are difficult to discriminat... | 5742 |@word multitask:1 illustrating:1 version:8 cnn:4 middle:1 contraction:1 decomposition:1 hsieh:1 incurs:1 sgd:1 asks:1 tr:2 liblinear:2 reduction:4 liu:1 score:6 interestingly:1 existing:3 recovered:2 current:1 ka:3 com:1 guadarrama:1 si:2 activation:1 written:1 must:2 numerical:1 partition:1 update:8 v:2 guess:4 ... |
5,239 | 5,743 | Solving Random Quadratic Systems of Equations
Is Nearly as Easy as Solving Linear Systems
Yuxin Chen
Department of Statistics
Stanford University
Stanford, CA 94305
yxchen@stanfor.edu
Emmanuel J. Cand?s
Department of Mathematics and Department of Statistics
Stanford University
Stanford, CA 94305
candes@stanford.edu
... | 5743 |@word trial:1 version:1 seems:1 c0:4 confirms:2 seek:1 accounting:1 covariance:1 incurs:1 tr:3 accommodate:1 carry:1 shechtman:2 reduction:3 initial:6 outperforms:1 current:1 z2:1 luo:1 intriguing:3 refines:1 realistic:2 numerical:10 additive:1 predetermined:1 plot:1 drop:1 update:2 depict:1 v:3 stationary:1 impl... |
5,240 | 5,744 | Sampling from Probabilistic Submodular Models
Alkis Gotovos
ETH Zurich
S. Hamed Hassani
ETH Zurich
Andreas Krause
ETH Zurich
alkisg@inf.ethz.ch
hamed@inf.ethz.ch
krausea@ethz.ch
Abstract
Submodular and supermodular functions have found wide applicability in machine learning, capturing notions such as diversity a... | 5744 |@word mild:1 version:1 briefly:1 polynomial:8 stronger:1 norm:2 laurence:1 unif:2 additively:1 crucially:2 contraction:2 decomposition:1 multicommodity:1 carry:1 contains:2 hereafter:1 daniel:2 document:3 ours:2 past:2 current:1 si:2 written:5 determinantal:4 partition:7 happen:1 remove:5 update:6 maxv:2 depict:1... |
5,241 | 5,745 | Distributionally Robust Logistic Regression
Soroosh Shafieezadeh-Abadeh
Peyman Mohajerin Esfahani
Daniel Kuhn
?
Ecole
Polytechnique F?ed?erale de Lausanne, CH-1015 Lausanne, Switzerland
{soroosh.shafiee,peyman.mohajerin,daniel.kuhn} @epfl.ch
Abstract
This paper proposes a distributionally robust approach to logistic ... | 5745 |@word trial:4 repository:2 guillin:1 norm:8 seems:2 hu:1 confirms:1 seek:1 simulation:6 invoking:1 datagenerating:1 thereby:1 solid:1 moment:2 celebrated:1 contains:3 liu:1 series:1 lichman:1 daniel:2 ecole:1 denoting:1 outperforms:4 existing:3 com:1 si:10 toh:1 must:2 john:2 numerical:1 drop:1 depict:1 v:7 imply... |
5,242 | 5,746 | On some provably correct cases of variational
inference for topic models
Andrej Risteski
Department of Computer Science
Princeton University
Princeton, NJ 08540
risteski@cs.princeton.edu
Pranjal Awasthi
Department of Computer Science
Rutgers University
New Brunswick, NJ 08901
pranjal.awasthi@rutgers.edu
Abstract
Var... | 5746 |@word version:3 briefly:3 polynomial:3 norm:2 proportion:16 seems:2 nd:2 open:1 decomposition:1 pick:2 initial:1 liu:1 contains:3 series:1 denoting:1 document:82 bhattacharyya:1 existing:1 current:6 wd:3 malized:1 written:1 must:2 additive:3 mstep:1 update:39 alone:1 generative:1 intelligence:2 beginning:1 vanish... |
5,243 | 5,747 | Extending Gossip Algorithms to
Distributed Estimation of U -Statistics
Igor Colin, Joseph Salmon, St?ephan Cl?emenc?on
LTCI, CNRS, T?el?ecom ParisTech
Universit?e Paris-Saclay
75013 Paris, France
first.last@telecom-paristech.fr
Aur?elien Bellet
Magnet Team
INRIA Lille - Nord Europe
59650 Villeneuve d?Ascq, France
aure... | 5747 |@word repository:1 version:4 norm:2 c0:2 dekker:1 simulation:1 propagate:2 pick:1 mention:1 solid:2 initial:1 contains:2 score:1 document:1 bc:2 interestingly:1 outperforms:1 existing:1 current:2 yet:3 scatter:7 must:5 bs2:1 john:2 numerical:4 partition:2 christian:1 remove:1 update:9 selected:3 pelckmans:1 xk:9 ... |
5,244 | 5,748 | The Self-Normalized Estimator for Counterfactual
Learning
Thorsten Joachims
Department of Computer Science
Cornell University
tj@cs.cornell.edu
Adith Swaminathan
Department of Computer Science
Cornell University
adith@cs.cornell.edu
Abstract
This paper identifies a severe problem of the counterfactual risk estimator... | 5748 |@word kong:1 repository:1 norm:39 trotter:1 adrian:1 pieter:1 confirms:1 simulation:2 pick:2 catastrophically:1 reduction:1 contains:4 score:3 dubourg:1 outperforms:3 existing:1 past:1 contextual:1 beygelzimer:1 must:1 readily:1 john:7 additive:2 partition:2 chicago:1 kdd:1 drop:1 plot:1 fund:1 stationary:1 selec... |
5,245 | 5,749 | Frank-Wolfe Bayesian Quadrature: Probabilistic
Integration with Theoretical Guarantees
Chris J. Oates
School of Mathematical and Physical Sciences
University of Technology, Sydney
christopher.oates@uts.edu.au
Franc?ois-Xavier Briol
Department of Statistics
University of Warwick
f-x.briol@warwick.ac.uk
Mark Girolami
D... | 5749 |@word version:2 seems:1 norm:1 nd:1 stronger:1 open:3 closure:1 d2:1 simulation:10 propagate:3 seek:1 contraction:10 covariance:1 pick:1 carry:2 initial:1 series:1 selecting:1 renewed:1 rkhs:7 outperforms:1 existing:1 freitas:1 current:1 arkk:3 surprising:2 yet:2 dx:2 written:1 subsequent:3 numerical:36 analytic:... |
5,246 | 575 | Networks with Learned Unit Response Functions
John Moody and Norman Yarvin
Yale Computer Science, 51 Prospect St.
P.O. Box 2158 Yale Station, New Haven, CT 06520-2158
Abstract
Feedforward networks composed of units which compute a sigmoidal function of a weighted sum of their inputs have been much investigated. We
te... | 575 |@word mild:1 version:2 polynomial:28 seems:3 simulation:2 tried:2 covariance:4 decomposition:1 initial:2 series:6 contains:3 allon:1 current:1 wd:1 marquardt:3 analysed:3 perturbative:1 must:4 john:2 additive:1 happen:1 plasticity:1 shape:1 half:2 fewer:2 wiit11:2 scotland:1 location:1 sigmoidal:2 firstly:1 five:4... |
5,247 | 5,750 | Newton-Stein Method:
A Second Order Method for GLMs via Stein?s Lemma
Murat A. Erdogdu
Department of Statistics
Stanford University
erdogdu@stanford.edu
Abstract
We consider the problem of efficiently computing the maximum likelihood estimator in Generalized Linear Models (GLMs) when the number of observations
is much... | 5750 |@word repository:1 briefly:2 bot10:3 manageable:1 norm:1 inversion:3 nd:1 covariance:14 sgd:2 mar10:3 electronics:1 celebrated:1 lichman:1 daniel:1 denoting:1 recovered:1 current:4 comparing:1 yet:2 written:2 john:2 lic13:2 numerical:1 designed:1 plot:7 update:11 selected:3 prohibitive:2 beginning:1 vp12:2 core:1... |
5,248 | 5,751 | Asynchronous Parallel Stochastic Gradient for
Nonconvex Optimization
Xiangru Lian, Yijun Huang, Yuncheng Li, and Ji Liu
Department of Computer Science, University of Rochester
{lianxiangru,huangyj0,raingomm,ji.liu.uwisc}@gmail.com
Abstract
Asynchronous parallel implementations of stochastic gradient (SG) have been
br... | 5751 |@word version:2 achievable:7 norm:2 johansson:1 dekel:2 km:3 hsieh:1 sgd:1 liu:22 cyclic:1 daniel:1 seriously:1 ours:1 existing:4 current:2 com:1 comparing:2 guadarrama:1 gmail:1 written:1 gpu:1 pioneer:1 devin:1 periodically:1 happen:1 numerical:1 update:20 juditsky:1 selected:1 xk:26 ith:2 short:2 core:1 provid... |
5,249 | 5,752 | Distributed Submodular Cover:
Succinctly Summarizing Massive Data
Baharan Mirzasoleiman
ETH Zurich
Amin Karbasi
Yale University
Ashwinkumar Badanidiyuru
Google
Andreas Krause
ETH Zurich
Abstract
How can one find a subset, ideally as small as possible, that well represents a
massive dataset? I.e., its corresponding... | 5752 |@word determinant:1 version:2 briefly:2 agc:1 stronger:1 norm:1 loading:1 disk:1 faculty:1 laurence:1 d2:2 seek:4 rgb:1 incurs:1 lorraine:1 reduction:2 celebrated:1 selecting:3 daniel:2 franklin:1 attracted:1 sergei:2 determinantal:2 john:1 partition:6 kdd:2 v:1 greedy:39 selected:6 fewer:1 item:1 discovering:1 i... |
5,250 | 5,753 | Probabilistic Line Searches
for Stochastic Optimization
Maren Mahsereci and Philipp Hennig
Max Planck Institute for Intelligent Systems
Spemannstra?e 38, 72076 T?ubingen, Germany
[mmahsereci|phennig]@tue.mpg.de
Abstract
In deterministic optimization, line searches are a standard tool ensuring stability
and efficiency... | 5753 |@word illustrating:1 faculty:1 middle:1 polynomial:1 norm:2 eliminating:1 yi0:1 version:1 tedious:1 termination:3 simulation:1 propagate:1 eng:1 covariance:2 sgd:26 tr:3 solid:2 shading:1 papoulis:1 initial:7 lightweight:5 series:1 tuned:2 existing:6 current:2 com:1 arkk:1 si:7 yet:2 must:2 readily:1 written:1 nu... |
5,251 | 5,754 | COEVOLVE: A Joint Point Process Model for
Information Diffusion and Network Co-evolution
Mehrdad Farajtabar?
Yichen Wang?
Manuel Gomez-Rodriguez?
?
?
Shuang Li
Hongyuan Zha
Le Song?
?
Georgia Institute of Technology
MPI for Software Systems?
{mehrdad,yichen.wang,sli370}@gatech.edu
manuelgr@mpi-sws.org
{zha,lsong}@cc.ga... | 5754 |@word proportion:1 nd:2 open:1 closure:1 confirms:1 simulation:7 covariance:9 pick:1 contains:3 longitudinal:1 outperforms:1 current:3 com:1 manuel:1 si:4 follower:3 written:1 readily:1 boldi:1 additive:1 happen:2 romero:1 kdd:6 designed:4 update:2 stationary:2 generative:3 discovering:2 beginning:1 ugander:2 sho... |
5,252 | 5,755 | Linear Response Methods for Accurate Covariance
Estimates from Mean Field Variational Bayes
Ryan Giordano
UC Berkeley
rgiordano@berkeley.edu
Tamara Broderick
MIT
tbroderick@csail.mit.edu
Michael Jordan
UC Berkeley
jordan@cs.berkeley.edu
Abstract
Mean ?eld variational Bayes (MFVB) is a popular posterior approximation... | 5755 |@word mild:1 repository:1 nd:2 open:1 simulation:14 covariance:42 arti:1 eld:15 kappen:1 moment:1 contains:1 series:3 document:1 interestingly:1 past:1 numerical:2 partition:4 shape:1 analytic:1 plot:5 generative:3 prohibitive:1 intelligence:1 parameterization:1 inspection:1 avoids:1 blei:3 provides:2 awry:1 loca... |
5,253 | 5,756 | Latent Bayesian melding for integrating individual
and population models
Mingjun Zhong, Nigel Goddard, Charles Sutton
School of Informatics
University of Edinburgh
United Kingdom
{mzhong,nigel.goddard,csutton}@inf.ed.ac.uk
Abstract
In many statistical problems, a more coarse-grained model may be suitable for
populati... | 5756 |@word version:1 briefly:1 replicate:1 open:1 adrian:2 heuristically:2 simulation:9 reduction:1 moment:7 initial:1 series:2 united:1 itp:1 daniel:1 interestingly:3 disaggregation:25 jaynes:1 si:5 rpi:1 evans:2 additive:4 realistic:2 aps:1 intelligence:1 selected:4 parameterization:2 mccallum:1 affair:1 smith:1 coa... |
5,254 | 5,757 | Rapidly Mixing Gibbs Sampling for a Class of Factor
Graphs Using Hierarchy Width
Christopher De Sa, Ce Zhang, Kunle Olukotun, and Christopher R?e
cdesa@stanford.edu, czhang@cs.wisc.edu,
kunle@stanford.edu, chrismre@stanford.edu
Departments of Electrical Engineering and Computer Science
Stanford University, Stanford, C... | 5757 |@word middle:1 version:4 polynomial:21 seems:1 stronger:2 citeseer:1 recursively:1 necessity:1 celebrated:1 contains:4 score:3 liu:1 disallows:1 fa8750:2 outperforms:1 conjunctive:1 must:1 john:2 fn:2 happen:1 update:1 bart:2 alone:1 intelligence:2 instantiate:1 leaf:1 mln:1 mccallum:1 pvldb:3 colored:1 node:5 lo... |
5,255 | 5,758 | Automatic Variational Inference in Stan
Rajesh Ranganath
Princeton University
rajeshr@cs.princeton.edu
Alp Kucukelbir
Columbia University
alp@cs.columbia.edu
David M. Blei
Columbia University
david.blei@columbia.edu
Andrew Gelman
Columbia University
gelman@stat.columbia.edu
Abstract
Variational inference is a scala... | 5758 |@word proportion:1 paredes:1 tedious:1 seek:1 covariance:1 jacob:1 contains:3 siebel:1 united:1 series:1 daniel:2 fa8750:1 outperforms:1 elliptical:1 recovered:1 diederik:1 must:4 olive:1 john:5 shape:1 analytic:1 christian:1 drop:1 plot:2 depict:1 update:1 bart:1 half:1 generative:2 hamiltonian:2 manfred:1 blei:... |
5,256 | 5,759 | Data Generation as Sequential Decision Making
Philip Bachman
Doina Precup
McGill University, School of Computer Science
phil.bachman@gmail.com
McGill University, School of Computer Science
dprecup@cs.mcgill.ca
Abstract
We connect a broad class of generative models through their shared reliance on
sequential decisi... | 5759 |@word trial:5 pw:1 c0:7 open:1 pieter:1 bachman:4 p0:17 q1:3 pick:2 thereby:1 recursively:3 mcar:9 initial:5 score:9 selecting:2 tuned:1 outperforms:1 existing:5 current:1 com:3 comparing:1 gmail:1 dx:21 written:3 must:1 diederik:2 subsequent:1 visible:1 partition:1 additive:2 update:22 v:2 stationary:3 generativ... |
5,257 | 576 | HARMONET: A Neural Net for Harmonizing
Chorales in the Style of l.S.Bach
Hermann Hild
Johannes Feulner
Wolfram Menzel
hhild@ira.uka.de
johannes@ira.uka.de
menzel@ira.uka.de
Institut fur Logik, Komplexitat und Deduktionssysteme
Am Fasanengarten 5
Universitat Karlsruhe
W-7500 Karlsruhe 1, Germany
Abstract
HARMONET, a s... | 576 |@word middle:1 version:1 manageable:1 inversion:5 decomposition:3 necessity:1 contains:1 accompaniment:4 feulner:5 current:1 yet:1 subsequent:1 treating:1 designed:1 alone:1 vtp:1 beginning:2 dissertation:1 wolfram:1 harmonize:2 along:1 predecessor:1 consists:1 compose:1 heinz:1 decomposed:1 window:9 considering:1... |
5,258 | 5,760 | Stochastic Expectation Propagation
Yingzhen Li
University of Cambridge
Cambridge, CB2 1PZ, UK
yl494@cam.ac.uk
Jos?e Miguel Hern?andez-Lobato
Harvard University
Cambridge, MA 02138 USA
jmh@seas.harvard.edu
Richard E. Turner
University of Cambridge
Cambridge, CB2 1PZ, UK
ret26@cam.ac.uk
Abstract
Expectation propagati... | 5760 |@word repository:2 version:2 faculty:1 briefly:1 advantageous:1 norm:2 msr:2 open:1 plication:1 d2:3 crucially:1 covariance:2 p0:17 g050821:1 tr:2 carry:2 reduction:1 moment:12 contains:1 score:1 series:2 interestingly:1 outperforms:1 existing:1 trueskill:2 current:1 comparing:2 must:4 john:4 fn:24 tilted:6 parti... |
5,259 | 5,761 | Deep learning with Elastic Averaging SGD
Anna Choromanska
Courant Institute, NYU
achoroma@cims.nyu.edu
Sixin Zhang
Courant Institute, NYU
zsx@cims.nyu.edu
Yann LeCun
Center for Data Science, NYU & Facebook AI Research
yann@cims.nyu.edu
Abstract
We study the problem of stochastic optimization for deep learning in th... | 5761 |@word exploitation:3 version:1 achievable:2 bekkerman:1 cipar:1 pick:1 sgd:10 arous:1 initial:2 contains:1 outperforms:1 current:1 com:1 intriguing:1 written:2 gpu:7 chu:1 devin:1 periodically:1 numerical:2 enables:1 analytic:1 update:27 juditsky:1 plane:1 ith:3 provides:2 math:1 node:2 toronto:1 org:1 zhang:4 ma... |
5,260 | 5,762 | Competitive Distribution Estimation:
Why is Good-Turing Good
Ananda Theertha Suresh
UC San Diego
asuresh@ucsd.edu
Alon Orlitsky
UC San Diego
alon@ucsd.edu
Abstract
Estimating distributions over large alphabets is a fundamental machine-learning
tenet. Yet no method is known to estimate all distributions well. For exa... | 5762 |@word trial:2 version:2 polynomial:1 compression:2 trofimov:7 simulation:1 dominique:1 p0:5 incurs:3 jafarpour:5 minmax:1 outperforms:1 comparing:1 yet:3 must:3 john:1 tenet:1 refines:3 subsequent:1 partition:17 designed:16 n0:1 joy:1 half:3 theoretician:1 multiset:8 zhang:1 narayana:1 beta:1 competitiveness:1 pr... |
5,261 | 5,763 | Fast Convergence of Regularized Learning in Games
Vasilis Syrgkanis
Microsoft Research
New York, NY
vasy@microsoft.com
Alekh Agarwal
Microsoft Research
New York, NY
alekha@microsoft.com
Haipeng Luo
Princeton University
Princeton, NJ
haipengl@cs.princeton.edu
Robert E. Schapire
Microsoft Research
New York, NY
schapir... | 5763 |@word middle:1 version:1 achievable:1 norm:6 seems:1 hu:1 git:3 nemirovsky:1 invoking:1 pick:2 thereby:2 minus:1 reduction:2 wrapper:1 ftrl:5 existing:1 com:3 luo:1 si:14 confirming:1 plot:2 ligett:1 update:2 hwit:1 implying:1 instantiate:1 item:10 warmuth:1 ith:1 vanishing:4 record:1 manfred:1 coarse:4 completen... |
5,262 | 5,764 | Interactive Control of Diverse Complex Characters
with Neural Networks
Igor Mordatch, Kendall Lowrey, Galen Andrew, Zoran Popovic, Emanuel Todorov
Department of Computer Science, University of Washington
{mordatch,lowrey,galen,zoran,todorov}@cs.washington.edu
Abstract
We present a method for training recurrent neural ... | 5764 |@word trial:5 r:1 simulation:1 sgd:2 contactinvariant:2 harder:4 moment:2 initial:10 cyclic:4 configuration:2 interestingly:1 imaginary:1 existing:2 current:2 si:1 yet:4 activation:2 must:2 gpu:8 reminiscent:1 realistic:4 additive:1 informative:1 subsequent:1 distant:1 lqg:4 motor:2 numerical:1 designed:3 littled... |
5,263 | 5,765 | The Human Kernel
Andrew Gordon Wilson
CMU
Christoph Dann
CMU
Christopher G. Lucas
University of Edinburgh
Eric P. Xing
CMU
Abstract
Bayesian nonparametric models, such as Gaussian processes, provide a compelling framework for automatic statistical modelling: these models have a high
degree of flexibility, and autom... | 5765 |@word polynomial:5 replicate:1 vitally:1 calculus:1 simulation:1 accounting:1 covariance:21 accommodate:1 series:1 daniel:2 past:4 existing:1 current:1 com:2 comparing:2 surprising:1 must:1 readily:1 john:1 remove:1 extrapolating:1 interpretable:2 progressively:3 plot:1 aside:1 stationary:7 generative:4 alone:1 d... |
5,264 | 5,766 | The Pseudo-Dimension of Near-Optimal Auctions
Jamie Morgenstern?
Computer and Information Science
University of Pennsylvania
Philadelphia, PA
jamiemor@cis.upenn.edu
Tim Roughgarden
Stanford University
Palo Alto, CA
tim@cs.stanford.edu
Abstract
This paper develops a general approach, rooted in statistical learning the... | 5766 |@word version:1 polynomial:8 stronger:1 nd:1 c0:1 open:2 seek:1 pick:1 paid:1 thereby:2 fif:1 minus:1 reduction:1 contains:2 selecting:1 chervonenkis:1 ours:4 past:2 ironing:1 err:6 comparing:1 nt:22 si:9 yet:2 must:5 fn:1 additive:2 partition:1 chicago:1 v:1 implying:1 selected:1 fewer:1 item:25 short:1 org:1 al... |
5,265 | 5,767 | High-dimensional neural spike train analysis with
generalized count linear dynamical systems
Lars Buesing
Department of Statistics
Columbia University
New York, NY 10027
lars@stat.columbia.edu
Yuanjun Gao
Department of Statistics
Columbia University
New York, NY 10027
yg2312@columbia.edu
Krishna V. Shenoy
Department ... | 5767 |@word neurophysiology:1 trial:10 briefly:2 middle:6 loading:1 norm:1 seems:1 rhesus:1 covariance:9 q1:4 concise:1 thereby:1 tr:1 reduction:8 initial:1 series:1 offering:1 denoting:2 outperforms:2 current:3 com:1 recovered:1 yet:1 must:2 john:1 visible:1 informative:1 shape:1 motor:6 remove:1 drop:1 update:1 imply... |
5,266 | 5,768 | Measuring Sample Quality with Stein?s Method
Jackson Gorham
Department of Statistics
Stanford University
Lester Mackey
Department of Statistics
Stanford University
Abstract
To improve the efficiency of Monte Carlo estimation, practitioners are turning to
biased Markov chain Monte Carlo procedures that trade off asymp... | 5768 |@word mild:1 version:1 middle:3 norm:8 open:1 unif:5 km:2 seek:1 scg:1 simulation:1 attainable:1 accommodate:1 ipm:6 moment:3 reduction:2 score:2 selecting:1 sobol:2 rightmost:1 recovered:3 comparing:3 trustworthy:1 com:1 dx:3 readily:1 plot:5 designed:2 n0:4 mackey:1 alone:1 stationary:3 selected:3 greedy:2 fewe... |
5,267 | 5,769 | Biologically Inspired Dynamic Textures
for Probing Motion Perception
Andrew Isaac Meso
Institut de Neurosciences de la Timone
UMR 7289 CNRS/Aix-Marseille Universit?e
13385 Marseille Cedex 05, FRANCE
andrew.meso@univ-amu.fr
Jonathan Vacher
CNRS UNIC and Ceremade
Univ. Paris-Dauphine
75775 Paris Cedex 16, FRANCE
vacher... | 5769 |@word neurophysiology:1 trial:6 middle:2 judgement:2 stronger:1 meso:2 grey:3 seitz:1 seek:1 simulation:1 lup:2 covariance:8 accounting:2 shot:2 moment:1 subjective:2 discretization:2 z2:12 comparing:1 written:1 gpu:1 realistic:1 distant:1 subsequent:1 numerical:2 shape:1 enables:1 discrimination:2 stationary:10 ... |
5,268 | 577 | Reverse TDNN: An Architecture for Trajectory
Generation
Patrice Simard
AT &T Bell Laboratories
101 Crawford Corner Rd
Holmdel, NJ 07733
Yann Le Cun
AT&T Bell Laboratories
101 Crawford Corner Rd
Holmdel, NJ 07733
Abstract
The backpropagation algorithm can be used for both recognition and generation of time trajectori... | 577 |@word version:2 middle:2 seems:1 simulation:1 tried:4 gradual:1 dramatic:2 mention:1 reduction:1 initial:2 series:2 discretization:2 surprising:1 lang:3 activation:6 yet:1 written:3 must:1 visible:1 numerical:1 wx:3 motor:3 designed:2 update:2 progressively:2 aside:1 v:1 half:1 tjw:1 short:1 record:1 lr:1 provides... |
5,269 | 5,770 | Large-Scale Bayesian Multi-Label Learning via
Topic-Based Label Embeddings
Piyush Rai?? , Changwei Hu? , Ricardo Henao? , Lawrence Carin?
?
?
CSE Dept, IIT Kanpur
ECE Dept, Duke University
piyush@cse.iitk.ac.in, {ch237,r.henao,lcarin}@duke.edu
Abstract
We present a scalable Bayesian multi-label learning model based o... | 5770 |@word multitask:1 kong:1 version:1 inversion:1 proportion:1 seems:2 hu:2 decomposition:1 covariance:1 pg:4 olyagamma:2 thereby:1 mlk:2 reduction:2 raajay:1 score:3 daniel:1 bibtex:5 document:6 interestingly:1 current:3 comparing:3 protection:1 written:1 readily:1 john:1 belmont:1 realistic:1 partition:1 kdd:3 che... |
5,270 | 5,771 | Closed-form Estimators for High-dimensional
Generalized Linear Models
Eunho Yang
IBM T.J. Watson Research Center
eunhyang@us.ibm.com
Aur?elie C. Lozano
IBM T.J. Watson Research Center
aclozano@us.ibm.com
Pradeep Ravikumar
University of Texas at Austin
pradeepr@cs.utexas.edu
Abstract
We propose a class of closed-form... | 5771 |@word mild:1 lognp:6 determinant:1 trial:1 norm:3 stronger:1 suitably:2 c0:10 open:1 simulation:4 covariance:15 p0:10 moment:9 initial:1 liu:1 series:1 daniel:2 denoting:1 tuned:1 com:2 surprising:1 yet:1 written:5 partition:6 analytic:1 stationary:3 instantiate:1 selected:2 parameterization:1 accordingly:2 sys:2... |
5,271 | 5,772 | Learning Stationary Time Series using Gaussian
Processes with Nonparametric Kernels
Felipe Tobar
ftobar@dim.uchile.cl
Center for Mathematical Modeling
Universidad de Chile
Thang D. Bui
tdb40@cam.ac.uk
Department of Engineering
University of Cambridge
Richard E. Turner
ret26@cam.ac.uk
Department of Engineering
Univer... | 5772 |@word version:2 simulation:1 crucially:1 lobe:2 covariance:25 tr:4 moment:3 initial:1 series:14 contains:1 initialisation:1 rearing:1 existing:1 current:1 recovered:1 nt:3 imat:5 analysed:1 must:1 written:1 numerical:1 additive:2 partition:2 shape:1 analytic:4 designed:2 stationary:5 generative:6 intelligence:2 c... |
5,272 | 5,773 | Deep Generative Image Models using a
Laplacian Pyramid of Adversarial Networks
Emily Denton?
Dept. of Computer Science
Courant Institute
New York University
Soumith Chintala?
Arthur Szlam
Facebook AI Research
New York
Rob Fergus
Abstract
In this paper we introduce a generative parametric model capable of producing
... | 5773 |@word briefly:1 version:5 manageable:1 middle:1 d2:1 rgb:1 pick:1 sgd:1 inpainting:1 initial:2 series:1 score:1 tuned:1 rightmost:1 existing:1 current:1 z2:3 surprising:1 assigning:1 readily:1 subsequent:5 realistic:4 blur:2 shape:4 designed:1 plot:1 update:1 v:2 generative:36 selected:1 intelligence:1 inspection... |
5,273 | 5,774 | Shepard Convolutional Neural Networks
Jimmy SJ. Ren?
SenseTime Group Limited
rensijie@sensetime.com
Li Xu
SenseTime Group Limited
xuli@sensetime.com
Qiong Yan
SenseTime Group Limited
yanqiong@sensetime.com
Wenxiu Sun
SenseTime Group Limited
sunwenxiu@sensetime.com
Abstract
Deep learning has recently been introduce... | 5774 |@word trial:1 cnn:20 version:2 stronger:2 norm:1 set5:2 inpainting:17 carry:1 configuration:1 contains:1 liu:2 document:1 outperforms:1 past:1 current:3 com:4 activation:1 written:1 enables:1 drop:1 designed:1 generative:1 selected:2 ith:1 node:2 five:1 ksvd:2 consists:1 downscaled:1 mask:17 rapid:1 multi:2 bm3d:... |
5,274 | 5,775 | Learning Structured Output Representation
using Deep Conditional Generative Models
Kihyuk Sohn??
Xinchen Yan?
Honglak Lee?
?
NEC Laboratories America, Inc.
?
University of Michigan, Ann Arbor
ksohn@nec-labs.com, {xcyan,honglak}@umich.edu
Abstract
Supervised deep learning has been successfully applied to many recognit... | 5775 |@word cnn:15 covariance:1 sgd:2 tr:1 reduction:1 initial:1 liu:1 contains:1 score:1 document:1 outperforms:1 existing:1 cvae:38 com:1 written:4 gpu:1 parsing:2 realistic:4 partition:1 shape:6 update:1 generative:21 half:1 weighing:1 guess:2 tarlow:1 coarse:1 location:1 zhang:2 height:2 narayana:1 along:1 wierstra... |
5,275 | 5,776 | Expressing an Image Stream with a Sequence of
Natural Sentences
Cesc Chunseong Park
Gunhee Kim
Seoul National University, Seoul, Korea
{park.chunseong,gunhee}@snu.ac.kr
https://github.com/cesc-park/CRCN
Abstract
We propose an approach for retrieving a sequence of natural sentences for an
image stream. Since general us... | 5776 |@word cnn:18 manageable:1 compression:1 bf:2 bptt:1 decomposition:1 contrastive:1 sgd:2 recursively:3 moment:2 series:2 score:12 fragment:2 ours:3 document:1 outperforms:4 existing:3 o2:1 current:1 com:1 comparing:2 guadarrama:1 activation:6 yet:1 crawling:1 must:1 i1l:3 concatenate:2 informative:1 drop:1 gist:1 ... |
5,276 | 5,777 | V ISALOGY: Answering Visual Analogy Questions
C. Lawrence Zitnick
Microsoft Research
larryz@microsoft.com
Fereshteh Sadeghi
University of Washington
fsadeghi@cs.washington.edu
Ali Farhadi
University of Washington, The Allen Institute for AI
ali@cs.washington.edu
Abstract
In this paper, we study the problem of answe... | 5777 |@word cnn:1 middle:5 c0:3 open:2 holyoak:2 jacob:1 p0:2 contrastive:3 homomorphism:1 sgd:1 asks:1 yih:1 contains:1 hoiem:1 tuned:2 ours:18 outperforms:2 current:3 com:1 comparing:1 cad:2 blank:1 activation:1 guadarrama:1 must:1 creat:1 john:1 remove:2 plot:2 v:2 half:1 discovering:5 selected:3 website:1 according... |
5,277 | 5,778 | Bidirectional Recurrent Convolutional Networks
for Multi-Frame Super-Resolution
Yan Huang1
Wei Wang1
Liang Wang1,2
Center for Research on Intelligent Perception and Computing
National Laboratory of Pattern Recognition
2
Center for Excellence in Brain Science and Intelligence Technology
Institute of Automation, Chinese... | 5778 |@word cnn:9 longterm:1 version:2 c0:1 propagate:1 catastrophically:1 configuration:1 contains:2 liu:2 ours:1 existing:4 current:6 contextual:3 com:2 comparing:1 activation:1 blur:2 cheap:1 v:2 intelligence:4 generative:1 ith:1 successive:2 along:1 lowresolution:1 qualitative:3 ksvd:1 combine:2 webbase:1 introduce... |
5,278 | 5,779 | SubmodBoxes: Near-Optimal Search for a Set of
Diverse Object Proposals
Qing Sun
Virginia Tech
Dhruv Batra
Virginia Tech
sunqing@vt.edu
https://mlp.ece.vt.edu/
Abstract
This paper formulates the search for a set of bounding boxes (as needed in object
proposal generation) as a monotone submodular maximization proble... | 5779 |@word cnn:1 briefly:1 middle:1 achievable:1 interleave:1 dalal:1 everingham:2 triggs:1 r:1 crucially:2 prasad:2 q1:1 pick:1 mention:1 inefficiency:1 contains:3 score:13 selecting:1 liu:1 tuned:1 document:2 suppressing:6 interestingly:2 outperforms:3 existing:2 current:1 contextual:3 si:7 must:2 written:2 gpu:1 kd... |
5,279 | 578 | A Comparison of Projection Pursuit and Neural
Network Regression Modeling
Jellq-Nellg Hwang, Hang Li,
Information Processing Laboratory
Dept. of Elect. Engr., FT-lO
University of Washington
Seattle WA 98195
Martin Maechler, R. Douglas Martin, Jim Schimert
Department of Statistics
Mail Stop: GN-22
University of Washing... | 578 |@word version:2 eliminating:1 nd:2 simulation:10 tried:2 thereby:1 moment:1 tuned:1 interestingly:1 comparing:1 activation:1 subsequent:1 additive:1 numerical:1 pertinent:1 remove:2 designed:1 update:4 half:1 fewer:1 ith:1 prespecified:1 caveat:1 provides:1 node:1 ron:1 location:1 contribute:1 sigmoidal:5 five:2 m... |
5,280 | 5,780 | Galileo: Perceiving Physical Object Properties by
Integrating a Physics Engine with Deep Learning
Jiajun Wu?
EECS, MIT
jiajunwu@mit.edu
Joseph J. Lim
EECS, MIT
lim@csail.mit.edu
Ilker Yildirim?
BCS MIT, The Rockefeller University
ilkery@mit.edu
William T. Freeman
EECS, MIT
billf@mit.edu
Joshua B. Tenenbaum
BCS, MIT
... | 5780 |@word open:1 pieter:1 seek:1 crucially:2 simulation:19 llo:1 carry:1 moment:1 initial:1 born:1 contains:1 score:1 liquid:1 document:1 interestingly:2 current:1 yet:2 john:1 takeo:1 realistic:5 happen:1 shape:18 enables:1 designed:1 gist:1 update:1 drop:1 v:9 infant:5 generative:15 cue:1 guess:1 item:1 intelligenc... |
5,281 | 5,781 | Learning visual biases from human imagination
Carl Vondrick
Hamed Pirsiavash?
Aude Oliva Antonio Torralba
Massachusetts Institute of Technology ?University of Maryland, Baltimore County
{vondrick,oliva,torralba}@mit.edu hpirsiav@umbc.edu
Abstract
Although the human visual system can recognize many concepts under chal... | 5781 |@word trial:5 cnn:17 kulis:1 inversion:4 dalal:1 norm:2 advantageous:1 seems:1 triggs:1 everingham:2 mezuman:1 seek:1 tried:1 rgb:4 covariance:2 paid:1 egou:1 concise:1 shot:1 liu:1 united:4 current:1 com:1 babenko:1 surprising:1 must:1 shape:7 enables:1 remove:1 hypothesize:2 interpretable:1 plot:1 discriminatio... |
5,282 | 5,782 | Character-level Convolutional Networks for Text
Classification?
Xiang Zhang
Junbo Zhao
Yann LeCun
Courant Institute of Mathematical Sciences, New York University
719 Broadway, 12th Floor, New York, NY 10003
{xiang, junbo.zhao, yann}@cs.nyu.edu
Abstract
This article offers an empirical exploration on the use of charact... | 5782 |@word version:4 seems:2 norm:1 open:1 tried:1 sgd:1 outlook:1 mcauley:1 initial:1 configuration:2 series:1 contains:6 selecting:2 score:1 qatar:1 document:4 freitas:1 blank:1 comparing:2 com:1 lang:1 distant:1 treating:1 designed:1 plot:3 alone:1 intelligence:1 selected:1 short:6 quantized:1 pascanu:1 org:2 zhang... |
5,283 | 5,783 | Winner-Take-All Autoencoders
Alireza Makhzani, Brendan Frey
University of Toronto
makhzani, frey@psi.toronto.edu
Abstract
In this paper, we propose a winner-take-all method for learning hierarchical sparse
representations in an unsupervised fashion. We first introduce fully-connected
winner-take-all autoencoders which... | 5783 |@word cnn:3 version:1 decomposition:2 contrastive:1 dramatic:1 necessity:1 selecting:1 tuned:1 interestingly:1 deconvolutional:10 outperforms:1 activation:8 subsequent:1 visible:1 shape:1 update:1 half:1 selected:1 generative:2 intelligence:3 bissacco:1 contribute:2 toronto:7 location:7 sigmoidal:1 direct:3 consi... |
5,284 | 5,784 | Learning both Weights and Connections for Efficient
Neural Networks
Jeff Pool
NVIDIA
jpool@nvidia.com
Song Han
Stanford University
songhan@stanford.edu
William J. Dally
Stanford University
NVIDIA
dally@stanford.edu
John Tran
NVIDIA
johntran@nvidia.com
Abstract
Neural networks are both computationally intensive and ... | 5784 |@word kohli:2 multitask:1 cnn:1 compression:4 retraining:30 shuicheng:1 gradual:1 propagate:1 pavel:1 solid:4 harder:1 reduction:6 initial:3 liu:3 document:1 outperforms:1 freitas:2 com:2 comparing:3 activation:3 must:1 written:2 john:4 gpu:1 realize:1 ronan:1 informative:1 plasticity:1 christian:1 remove:2 desig... |
5,285 | 5,785 | Unsupervised Learning by Program Synthesis
Kevin Ellis
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
ellisk@mit.edu
Armando Solar-Lezama
MIT CSAIL
Massachusetts Institute of Technology
asolar@csail.mit.edu
Joshua B. Tenenbaum
Department of Brain and Cognitive Sciences
Massachusetts... | 5785 |@word briefly:1 compression:1 seek:2 q1:2 shot:1 reduction:1 initial:3 wrapper:1 contains:7 daniel:1 genetic:2 document:1 past:11 existing:1 z2:1 comparing:2 superoptimization:1 yet:1 written:3 must:3 parsing:2 realize:1 stemming:1 additive:1 john:2 shape:29 piepenbrock:1 treating:2 interpretable:1 designed:2 prk... |
5,286 | 5,786 | Deep Poisson Factor Modeling
Ricardo Henao, Zhe Gan, James Lu and Lawrence Carin
Department of Electrical and Computer Engineering
Duke University, Durham, NC 27708
{r.henao,zhe.gan,james.lu,lcarin}@duke.edu
Abstract
We propose a new deep architecture for topic modeling, based on Poisson Factor Analysis (PFA) modules... | 5786 |@word trial:1 version:2 proportion:1 loading:2 advantageous:1 cipar:1 seek:1 bn:4 contrastive:2 xkn:2 accommodate:1 reduction:1 initial:1 series:1 denoting:1 document:30 ours:1 outperforms:3 activation:2 written:1 readily:2 lauly:1 additive:2 partition:1 xmk:1 maaloe:1 christian:3 interpretable:2 update:10 discri... |
5,287 | 5,787 | Tensorizing Neural Networks
Alexander Novikov1,4
Dmitry Podoprikhin1
Anton Osokin2
Dmitry Vetrov1,3
1
Skolkovo Institute of Science and Technology, Moscow, Russia
2
INRIA, SIERRA project-team, Paris, France
3
National Research University Higher School of Economics, Moscow, Russia
4
Institute of Numerical Mathematics o... | 5787 |@word cnn:2 version:2 eliminating:1 compression:17 norm:1 msr:1 nd:1 d2:2 seek:1 tried:1 decomposition:16 solid:2 necessity:1 liu:1 contains:1 daniel:1 tuned:1 prefix:1 outperforms:1 existing:1 freitas:2 current:2 com:4 surprising:1 gmail:1 readily:1 gpu:4 numerical:2 j1:9 shape:2 drop:1 plot:1 update:1 moczulski... |
5,288 | 5,788 | Training Restricted Boltzmann Machines via the
Thouless-Anderson-Palmer Free Energy
Marylou Gabri?e
Eric W. Tramel
Florent Krzakala
Laboratoire de Physique Statistique, UMR 8550 CNRS
?
Ecole
Normale Sup?erieure & Universit?e Pierre et Marie Curie
75005 Paris, France
{marylou.gabrie, eric.tramel}@lps.ens.fr, florent.kr... | 5788 |@word trial:1 middle:1 version:2 seems:2 nd:1 accounting:1 contrastive:5 decorrelate:1 kappen:1 reduction:1 configuration:2 series:1 necessity:1 ecole:1 document:1 interestingly:3 subjective:1 reaction:1 freitas:1 recovered:1 com:1 comparing:1 surprising:1 si:5 yet:1 guez:1 written:2 must:4 visible:17 partition:1... |
5,289 | 5,789 | The Brain Uses Reliability of Stimulus Information
when Making Perceptual Decisions
Sebastian Bitzer1
sebastian.bitzer@tu-dresden.de
1
Stefan J. Kiebel1
stefan.kiebel@tu-dresden.de
Department of Psychology, Technische Universit?at Dresden, 01062 Dresden, Germany
Abstract
In simple perceptual decisions the brain has... | 5789 |@word trial:22 briefly:1 middle:2 proportion:6 replicate:3 seitz:1 crucially:1 contains:1 series:1 reaction:8 current:3 comparing:1 anne:4 yet:1 kiebel:2 must:5 written:1 john:1 subsequent:1 distant:1 plot:3 discrimination:1 generative:5 selected:1 alec:1 short:4 caveat:2 provides:1 mental:1 institution:1 clarifi... |
5,290 | 579 | Multimodular Architecture for Remote Sensing
Operations.
Sylvie Thiria(1,2)
Carlos Mejia(l)
Fouad Badran(1,2)
Michel Crepon(3)
(1) Laboratoire de Recherche en Informatique
Universite de Paris Sud, B 490 - 91405 ORSAY Cedex France
(2)
(3)
CEDRIC, Conservatoire National des Arts et Metiers
292 rue Saint Martin - ... | 579 |@word middle:1 briefly:1 inversion:2 simulation:2 hannonic:1 fonn:1 carry:1 series:1 atlantic:1 recovered:1 contextual:1 incidence:5 com:1 marquardt:1 si:2 yet:1 erms:2 must:1 realize:1 numerical:1 enables:1 designed:1 selected:1 device:1 recherche:1 supplying:1 provides:1 successive:4 along:1 consists:4 prove:1 i... |
5,291 | 5,790 | Unlocking neural population non-stationarity
using a hierarchical dynamics model
Mijung Park1 , Gergo Bohner1 , Jakob H. Macke2
1 Gatsby Computational Neuroscience Unit, University College London
2
Research Center caesar, an associate of the Max Planck Society, Bonn
Max Planck Institute for Biological Cybernetics,
Ber... | 5790 |@word neurophysiology:1 trial:81 middle:2 loading:1 replicate:1 hippocampus:1 open:1 simulation:1 covariance:21 eng:1 tr:1 solid:1 initial:3 score:1 recovered:3 z2:3 comparing:1 current:2 ka:1 numerical:1 additive:1 plasticity:5 shape:1 wanted:1 opin:3 plot:1 update:6 stationary:23 half:1 ith:2 smith:1 short:6 es... |
5,292 | 5,791 | Deeply Learning the Messages in Message
Passing Inference
Guosheng Lin, Chunhua Shen, Ian Reid, Anton van den Hengel
The University of Adelaide, Australia; and Australian Centre for Robotic Vision
E-mail: {guosheng.lin,chunhua.shen,ian.reid,anton.vandenhengel}@adelaide.edu.au
Abstract
Deep structured output learning ... | 5791 |@word cnn:35 kokkinos:1 paredes:1 everingham:1 sgd:4 accommodate:1 recursively:2 configuration:1 contains:3 score:2 liu:2 ours:5 romera:1 contextual:2 comparing:1 attracted:1 readily:1 written:3 concatenate:3 partition:3 zpf:2 alone:1 intelligence:1 fewer:2 mccallum:1 potted:1 node:33 org:10 simpler:1 constructed... |
5,293 | 5,792 | Efficient Learning of Continuous-Time Hidden
Markov Models for Disease Progression
Yu-Ying Liu, Shuang Li, Fuxin Li, Le Song, and James M. Rehg
College of Computing
Georgia Institute of Technology
Atlanta, GA
Abstract
The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progr... | 5792 |@word blindness:2 cox:1 version:1 achievable:1 norm:2 hippocampus:4 yv0:3 unif:14 simulation:4 r:1 initial:3 liu:2 contains:2 score:1 longitudinal:4 outperforms:2 existing:1 diagonalized:1 current:5 past:1 yet:1 dx:4 written:1 must:1 numerical:2 kdd:1 shape:1 analytic:1 update:1 fund:1 fewer:1 guess:1 leaf:1 sele... |
5,294 | 5,793 | The Population Posterior
and Bayesian Modeling on Streams
James McInerney
Columbia University
james@cs.columbia.edu
Rajesh Ranganath
Princeton University
rajeshr@cs.princeton.edu
David Blei
Columbia University
david.blei@columbia.edu
Abstract
Many modern data analysis problems involve inferences from streaming data... | 5793 |@word proportion:1 nd:1 accommodate:2 series:2 contains:2 siebel:1 document:8 fa8750:1 outperforms:3 existing:3 current:4 comparing:1 surprising:1 yet:1 dx:1 written:2 john:3 tenet:1 additive:1 enables:2 treating:1 plot:1 update:8 half:3 selected:2 intelligence:4 item:1 mccallum:1 ith:1 smith:1 blei:7 provides:2 ... |
5,295 | 5,794 | Probabilistic Curve Learning: Coulomb Repulsion
and the Electrostatic Gaussian Process
David Dunson
Department of Statistics
Duke University
Durham, NC, USA, 27705
dunson@stat.duke.edu
Ye Wang
Department of Statistics
Duke University
Durham, NC, USA, 27705
eric.ye.wang@duke.edu
Abstract
Learning of low dimensional st... | 5794 |@word middle:4 seems:1 stronger:1 unif:1 d2:1 seek:1 simulation:8 scg:2 covariance:2 p0:6 rgb:1 dramatic:2 inpainting:2 solid:1 shading:5 reduction:4 necessity:1 initial:6 series:1 contains:1 tuned:1 outperforms:2 existing:1 current:2 blank:1 comparing:1 yet:1 determinantal:2 analytic:1 generative:2 selected:1 ha... |
5,296 | 5,795 | Preconditioned Spectral Descent for Deep Learning
David E. Carlson,1 Edo Collins,2 Ya-Ping Hsieh,2 Lawrence Carin,3 Volkan Cevher2
1
Department of Statistics, Columbia University
2
Laboratory for Information and Inference Systems (LIONS), EPFL
3
Department of Electrical and Computer Engineering, Duke University
Abstr... | 5795 |@word h:1 cnn:3 middle:5 norm:31 open:1 propagate:1 rgb:1 hsieh:2 decomposition:1 contrastive:3 dramatic:1 sgd:13 multicommodity:1 arous:1 wellapproximated:1 reduction:1 configuration:3 outperforms:1 current:2 written:1 visible:1 wx:1 shape:4 cheap:3 update:8 generative:2 half:1 xk:25 volkan:1 iterates:3 provides... |
5,297 | 5,796 | Learning Continuous Control Policies by
Stochastic Value Gradients
Nicolas Heess? , Greg Wayne? , David Silver, Timothy Lillicrap, Yuval Tassa, Tom Erez
Google DeepMind
{heess, gregwayne, davidsilver, countzero, tassa, etom}@google.com
?
These authors contributed equally.
Abstract
We present a unified framework for l... | 5796 |@word multitask:1 trial:1 version:4 middle:4 briefly:1 termination:1 heuristically:1 simulation:3 r:5 p0:1 recursively:1 initial:1 configuration:3 contains:1 past:1 existing:1 hasselt:1 current:3 com:1 freitas:1 activation:1 guez:1 must:3 written:1 readily:1 john:2 subsequent:1 additive:1 analytic:2 enables:1 mot... |
5,298 | 5,797 | Path-SGD: Path-Normalized Optimization in
Deep Neural Networks
Behnam Neyshabur
Toyota Technological Institute at Chicago
bneyshabur@ttic.edu
Ruslan Salakhutdinov
Departments of Statistics and Computer Science
University of Toronto
rsalakhu@cs.toronto.edu
Nathan Srebro
Toyota Technological Institute at Chicago
nati@... | 5797 |@word middle:2 norm:28 seems:2 open:1 seek:1 tried:1 sgd:39 initial:1 document:1 current:1 activation:7 universality:1 written:2 john:1 numerical:1 chicago:2 christian:1 plot:5 update:28 steepest:10 bissacco:1 node:9 toronto:3 revisited:1 zhang:1 rc:1 along:1 c2:2 prove:1 expected:2 indeed:2 roughly:2 bneyshabur:... |
5,299 | 5,798 | Learning with Group Invariant Features:
A Kernel Perspective.
Youssef Mroueh
IBM Watson Group
mroueh@us.ibm.com
Stephen Voinea?
CBMM, MIT.
voinea@mit.edu
?Co-first author
Tomaso Poggio
CBMM, MIT .
tp@ai.mit.edu
Abstract
We analyze in this paper a random feature map based on a theory of invariance
(I-theory) introdu... | 5798 |@word version:1 norm:6 seek:2 r:1 tidigits:5 pg:1 attainable:1 nystr:1 reduction:4 contains:1 series:1 rkhs:5 document:1 outperforms:1 com:1 dx:5 fn:9 numerical:2 girosi:1 dupont:1 wanted:1 designed:1 plot:3 half:1 inconvenience:1 core:9 hypersphere:1 gx:13 allerton:1 zhang:1 c2:3 interscience:1 introduce:1 pairw... |
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