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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6,000 | 6,428 | The Sound of APALM Clapping: Faster Nonsmooth
Nonconvex Optimization with Stochastic
Asynchronous PALM
Damek Davis and Madeleine Udell
Cornell University
{dsd95,mru8}@cornell.edu
Brent Edmunds
University of California, Los Angeles
brent.edmunds@math.ucla.edu
Abstract
We introduce the Stochastic Asynchronous Proximal... | 6428 |@word mild:1 version:2 norm:1 vldb:1 semicontinuous:1 linearized:4 decomposition:1 hsieh:1 carry:1 reduction:1 liu:3 existing:1 kwjk:1 current:1 written:1 devin:1 numerical:3 enables:1 remove:1 concert:1 update:13 v:4 stationary:2 slowing:1 accordingly:1 xk:22 short:1 core:3 iterates:5 math:1 bittorf:1 zhang:1 un... |
6,001 | 6,429 | Optimistic Bandit Convex Optimization
Mehryar Mohri
Courant Institute and Google
251 Mercer Street
New York, NY 10012
Scott Yang
Courant Institute
251 Mercer Street
New York, NY 10012
mohri@cims.nyu.edu
yangs@cims.nyu.edu
Abstract
1
We introduce the general and powerful scheme of predicting information re-use
in ... | 6429 |@word mild:2 exploitation:1 version:2 achievable:1 polynomial:7 norm:5 dekel:11 c0:13 open:1 d2:8 linearized:1 decomposition:2 incurs:1 reduction:3 ftrl:13 past:1 existing:1 ka:3 current:1 dikin:1 designed:2 update:5 v:1 preemptively:1 greedy:1 selected:2 guess:1 grfp:1 zhang:2 along:1 prove:1 shorthand:1 introdu... |
6,002 | 643 | Extended Regularization Methods for
N onconvergent Model Selection
W. Finnoff, F. Hergert and H.G. Zimmermann
Siemens AG, Corporate Research and Development
Otto-Hahn-Ring 6
8000 Munich 83, Fed. Rep. Germany
Abstract
Many techniques for model selection in the field of neural networks
correspond to well established st... | 643 |@word version:4 seems:1 instrumental:1 nd:1 simulation:3 reduction:1 initial:1 contains:2 comparing:1 activation:2 additive:1 remove:2 designed:1 update:2 precaution:1 alone:1 selected:1 n_o:2 parametrization:2 short:1 detecting:1 constructed:1 consists:2 fitting:2 baldi:1 manner:1 deteriorate:1 frequently:1 brain... |
6,003 | 6,430 | Linear dynamical neural population models through
nonlinear embeddings
Yuanjun Gao? 1 , Evan Archer?12 , Liam Paninski12 , John P. Cunningham12
Department of Statistics1 and Grossman Center2
Columbia University
New York, NY, United States
yg2312@columbia.edu, evan@stat.columbia.edu,
liam@stat.columbia.edu, jpc2181@colu... | 6430 |@word neurophysiology:1 trial:30 private:1 briefly:1 proportion:2 norm:1 busing:1 seek:1 simulation:8 covariance:3 decomposition:1 q1:3 datagenerating:1 thereby:1 carry:1 reduction:13 initial:1 series:1 contains:1 united:1 uncovered:1 ours:1 interestingly:1 outperforms:1 existing:1 recovered:2 comparing:1 nt:2 co... |
6,004 | 6,431 | Improved Error Bounds for Tree Representations of
Metric Spaces
Samir Chowdhury
Department of Mathematics
The Ohio State University
Columbus, OH 43210
chowdhury.57@osu.edu
Facundo M?moli
Department of Mathematics
Department of Computer Science and Engineering
The Ohio State University
Columbus, OH 43210
memoli@math.o... | 6431 |@word polynomial:1 norm:1 open:1 bn:4 kent:1 invoking:3 contains:2 united:1 interestingly:1 past:1 existing:3 bitmap:1 current:1 ddim:1 dx:99 written:2 john:3 realize:1 fn:1 numerical:4 additive:12 partition:6 thrust:1 subsequent:1 noche:1 lemy:1 inspection:1 xk:1 smith:2 realizing:1 characterization:2 math:1 gx:... |
6,005 | 6,432 | Exact Recovery of Hard Thresholding Pursuit
Xiao-Tong Yuan
B-DAT Lab
Nanjing University of Info. Sci.&Tech.
Nanjing, Jiangsu, 210044, China
xtyuan@nuist.edu.cn
Ping Li?? Tong Zhang?
?Depart. of Statistics and ?Depart. of CS
Rutgers University
Piscataway, NJ, 08854, USA
{pingli,tzhang}@stat.rutgers.edu
Abstract
The H... | 6432 |@word mild:1 trial:1 determinant:1 norm:2 replicate:1 nd:1 open:3 gaussion:1 confirms:1 simulation:3 r:4 covariance:2 decomposition:1 bahmani:3 configuration:1 liu:1 ours:2 outperforms:1 existing:2 current:2 comparing:2 recovered:6 numerical:6 subsequent:1 designed:1 update:1 greedy:6 selected:1 intelligence:2 xk... |
6,006 | 6,433 | Spatiotemporal Residual Networks
for Video Action Recognition
Christoph Feichtenhofer
Graz University of Technology
Axel Pinz
Graz University of Technology
Richard P. Wildes
York University, Toronto
feichtenhofer@tugraz.at
axel.pinz@tugraz.at
wildes@cse.yorku.ca
Abstract
Two-stream Convolutional Networks (ConvNe... | 6433 |@word multitask:1 middle:1 advantageous:1 underline:2 wla:1 rgb:5 sgd:3 thereby:2 tr:1 carry:1 moment:2 reduction:2 born:1 series:1 score:4 denoting:1 ours:2 interestingly:2 guadarrama:1 com:1 comparing:3 skipping:2 anne:1 activation:3 yet:3 gpu:1 readily:1 gavves:1 additive:3 shape:1 enables:1 christian:3 design... |
6,007 | 6,434 | Adaptive Smoothed Online Multi-Task Learning
Keerthiram Murugesan?
Carnegie Mellon University
kmuruges@cs.cmu.edu
Hanxiao Liu?
Carnegie Mellon University
hanxiaol@cs.cmu.edu
Jaime Carbonell
Carnegie Mellon University
jgc@cs.cmu.edu
Yiming Yang
Carnegie Mellon University
yiming@cs.cmu.edu
Abstract
This paper addres... | 6434 |@word multitask:8 version:3 manageable:1 middle:2 advantageous:2 norm:1 stronger:1 dekel:4 briefly:1 nemirovsky:1 covariance:2 jacob:1 blender:1 keerthiram:1 moment:1 venkatasubramanian:1 liu:1 contains:1 score:1 ours:2 outperforms:2 existing:5 current:1 transferability:1 attracted:1 john:2 interpretable:1 update... |
6,008 | 6,435 | A Pseudo-Bayesian Algorithm for Robust PCA
Tae-Hyun Oh1
Yasuyuki Matsushita2
In So Kweon1
David Wipf3?
1
Electrical Engineering, KAIST, Daejeon, South Korea
2
Multimedia Engineering, Osaka University, Osaka, Japan
3
Microsoft Research, Beijing, China
thoh.kaist.ac.kr@gmail.com
yasumat@ist.osaka-u.ac.jp
iskweon@kaist.a... | 6435 |@word mild:1 trial:1 determinant:2 version:2 norm:9 stronger:3 replicate:1 seek:1 accounting:1 covariance:1 decomposition:4 tr:8 accommodate:1 liu:2 selecting:1 zij:1 mag:1 egt:3 kweon:3 ours:1 amp:2 outperforms:1 existing:6 recovered:1 optim:1 com:2 yet:1 gmail:1 pcp:16 chu:1 must:4 exposing:1 distant:1 subseque... |
6,009 | 6,436 | SPALS: Fast Alternating Least Squares via Implicit
Leverage Scores Sampling
Dehua Cheng
University of Southern California
dehua.cheng@usc.edu
Ioakeim Perros
Georgia Institute of Technology
perros@gatech.edu
Richard Peng
Georgia Institute of Technology
rpeng@cc.gatech.edu
Yan Liu
University of Southern California
yanli... | 6436 |@word trial:1 version:1 polynomial:2 norm:3 decomposition:41 sgd:3 mcauley:1 reduction:2 initial:1 liu:4 contains:1 score:37 series:1 woodruff:4 tuned:1 ours:1 existing:2 comparing:4 com:1 must:1 numerical:7 subsequent:3 informative:1 kdd:2 enables:1 analytic:1 remove:1 interpretable:1 update:1 v:1 alone:1 fewer:... |
6,010 | 6,437 | Selective inference for group-sparse linear models
Rina Foygel Barber
Department of Statistics
University of Chicago
rina@uchicago.edu
Fan Yang
Department of Statistics
University of Chicago
fyang1@uchicago.edu
Prateek Jain
Microsoft Research India
prajain@microsoft.com
John Lafferty
Depts. of Statistics and Compute... | 6437 |@word trial:5 version:1 polynomial:1 stronger:1 norm:2 open:4 jacob:1 carry:1 initial:2 contains:2 series:3 selecting:1 interestingly:1 past:1 existing:1 com:1 nt:4 dx:1 must:2 john:1 chicago:3 numerical:4 partition:1 enables:2 designed:2 plot:2 update:4 greedy:1 selected:23 core:1 record:1 characterization:1 org... |
6,011 | 6,438 | Accelerating Stochastic Composition Optimization
Mengdi Wang? , Ji Liu? , and Ethan X. Fang
Princeton University, University of Rochester, Pennsylvania State University
mengdiw@princeton.edu, ji.liu.uwisc@gmail.com, xxf13@psu.edu
Abstract
Consider the stochastic composition optimization problem where the objective is ... | 6438 |@word version:1 norm:4 stronger:1 twelfth:1 open:1 r:4 simulation:5 pg:32 liu:12 contains:2 series:1 current:3 com:1 deteriorating:1 surprising:1 gmail:1 must:1 numerical:2 plot:1 update:5 juditsky:1 v:1 intelligence:1 kyk:2 xk:25 short:2 bwt:1 provides:3 iterates:1 zhang:2 mathematical:4 constructed:1 x0:1 expec... |
6,012 | 6,439 | Bayesian optimization under mixed constraints with a
slack-variable augmented Lagrangian
Victor Picheny
MIAT, Universit? de Toulouse, INRA
Castanet-Tolosan, France
victor.picheny@toulouse.inra.fr
Stefan Wild
Argonne National Laboratory
Argonne, IL, USA
wildmcs.anl.gov
Robert B. Gramacy
Virginia Tech
Blacksburg, VA, U... | 6439 |@word mild:1 exploitation:2 version:6 stronger:1 proportion:5 nd:1 mockus:1 open:1 termination:1 simplifying:1 accounting:1 thereby:1 solid:4 accommodate:1 reduction:1 moment:1 ndez:1 contains:2 series:1 initial:6 denoting:1 outperforms:1 freitas:1 current:3 com:1 optim:8 written:2 readily:1 must:1 mesh:1 numeric... |
6,013 | 644 | ?
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6,014 | 6,440 | Avoiding Imposters and Delinquents: Adversarial
Crowdsourcing and Peer Prediction
Jacob Steinhardt
Stanford University
Gregory Valiant
Stanford University
Moses Charikar
Stanford University
Abstract
We consider a crowdsourcing model in which n workers are asked to rate the quality
of n items previously generated by... | 6440 |@word mild:1 version:2 briefly:1 polynomial:1 norm:17 seems:1 judgement:1 open:5 km:6 condon:2 jacob:1 dishonest:3 harder:1 reduction:1 liu:1 score:1 karger:2 neeman:4 bc:5 interestingly:1 ours:2 semirandom:7 imposter:1 tuned:1 recovered:2 ka:2 collude:3 must:3 john:1 partition:1 informative:1 enables:1 wanted:1 ... |
6,015 | 6,441 | Direct Feedback Alignment Provides Learning in
Deep Neural Networks
Arild N?kland
Trondheim, Norway
arild.nokland@gmail.com
Abstract
Artificial neural networks are most commonly trained with the back-propagation
algorithm, where the gradient for learning is provided by back-propagating the error,
layer by layer, from... | 6441 |@word version:1 seems:3 twelfth:1 grey:1 propagate:1 linearized:1 contrastive:2 initial:7 configuration:2 daniel:1 com:1 surprising:1 activation:8 gmail:1 visible:1 distant:1 happen:1 plasticity:1 enables:1 x240:3 christian:1 update:26 intelligence:1 reciprocal:3 steepest:5 short:1 provides:3 relayed:2 org:1 zhan... |
6,016 | 6,442 | Computational and Statistical Tradeoffs in Learning
to Rank
Ashish Khetan and Sewoong Oh
Department of ISE, University of Illinois at Urbana-Champaign
Email: {khetan2,swoh}@illinois.edu
Abstract
For massive and heterogeneous modern data sets, it is of fundamental interest to
provide guarantees on the accuracy of estim... | 6442 |@word illustrating:1 middle:5 achievable:2 logit:1 c0:2 d2:3 willing:2 simulation:1 tr:3 reduction:1 moment:1 offering:2 e2b:1 khetan:2 bradley:1 comparing:1 yet:1 assigning:1 written:2 stemming:1 numerical:3 partition:11 confirming:2 predetermined:1 remove:1 treating:3 item:28 parkes:3 provides:10 node:1 prefere... |
6,017 | 6,443 | Gaussian Processes for Survival Analysis
Tamara Fern?ndez
Department of Statistics,
University of Oxford.
Oxford, UK.
fernandez@stats.ox.ac.uk
Nicol?s Rivera
Department of Informatics,
King?s College London.
London, UK.
nicolas.rivera@kcl.ac.uk
Yee Whye Teh
Department of Statistics,
University of Oxford.
Oxford, UK.... | 6443 |@word trial:3 cox:12 middle:2 version:1 inversion:3 seems:3 proportionality:1 covariance:2 p0:3 rivera:3 harder:1 initial:4 ndez:2 contains:2 score:59 series:1 denoting:1 current:2 elliptical:2 riihim:1 si:1 additive:2 numerical:2 drop:1 treating:1 update:4 stationary:5 generative:2 beginning:3 chile:1 accepting:... |
6,018 | 6,444 | Variational Information Maximization for
Feature Selection
Shuyang Gao
Greg Ver Steeg
Aram Galstyan
University of Southern California, Information Sciences Institute
gaos@usc.edu, gregv@isi.edu, galstyan@isi.edu
Abstract
Feature selection is one of the most fundamental problems in machine learning.
An extensive body o... | 6444 |@word madelon:2 repository:1 version:1 advantageous:1 stronger:2 d2:2 motoda:1 decomposition:8 citeseer:1 elisseeff:1 pick:1 recursively:2 wrapper:3 liu:2 lichman:1 selecting:1 jimenez:1 rightmost:1 outperforms:3 existing:7 spambase:1 current:1 past:2 com:1 written:2 john:3 cruz:1 plot:2 progressively:1 joy:1 gre... |
6,019 | 6,445 | Fast Algorithms for Robust PCA via Gradient
Descent
Xinyang Yi? Dohyung Park? Yudong Chen? Constantine Caramanis?
?
?
The University of Texas at Austin
Cornell University
?
?
{yixy,dhpark,constantine}@utexas.edu
yudong.chen@cornell.edu
Abstract
We consider the problem of Robust PCA in the fully and partially observed ... | 6445 |@word polynomial:1 seems:1 norm:9 c0:2 d2:31 simulation:1 decomposition:8 contraction:2 minming:1 thereby:1 initial:1 liu:3 contains:2 series:2 daniel:2 denoting:1 woodruff:1 xinyang:2 existing:3 ksk1:1 ka:3 recovered:1 luo:1 yet:1 must:1 john:3 numerical:2 plot:3 designed:1 update:2 rd2:6 intelligence:1 prohibit... |
6,020 | 6,446 | Multimodal Residual Learning for Visual QA
Jin-Hwa Kim
Sang-Woo Lee Donghyun Kwak Min-Oh Heo
Seoul National University
{jhkim,slee,dhkwak,moheo}@bi.snu.ac.kr
Jeonghee Kim
Jung-Woo Ha
Naver Labs, Naver Corp.
{jeonghee.kim,jungwoo.ha}@navercorp.com
Byoung-Tak Zhang
Seoul National University & Surromind Robotics
btzhan... | 6446 |@word cnn:4 nd:1 open:10 shuicheng:1 jacob:1 slee:1 mengye:1 denoting:1 outperforms:1 existing:1 com:2 contextual:1 activation:1 yet:2 readily:1 ronald:1 latt:3 christian:1 treating:1 update:2 bart:1 sukhbaatar:1 intelligence:1 selected:2 half:1 num:5 provides:1 contribute:1 successive:1 firstly:1 zhang:3 qualita... |
6,021 | 6,447 | The Power of Optimization from Samples
Eric Balkanski
Harvard University
ericbalkanski@g.harvard.edu
Aviad Rubinstein
University of California, Berkeley
aviad@eecs.berkeley.edu
Yaron Singer
Harvard University
yaron@seas.harvard.edu
Abstract
We consider the problem of optimization from samples of monotone submodular
... | 6447 |@word worsens:1 private:1 polynomial:1 simulation:1 pick:1 contains:2 score:2 document:6 outperforms:1 si:15 must:3 v:2 generative:3 greedy:8 fewer:1 short:1 junta:1 math:1 node:1 unbounded:2 c2:34 focs:1 consists:5 manner:1 tagging:4 expected:7 hardness:3 behavior:3 multi:1 decreasing:1 cardinality:6 increasing:... |
6,022 | 6,448 | Combining Fully Convolutional and Recurrent
Neural Networks for 3D Biomedical Image
Segmentation
Jianxu Chen
University of Notre Dame
jchen16@nd.edu
Yizhe Zhang
University of Notre Dame
yzhang29@nd.edu
Lin Yang
University of Notre Dame
lyang5@nd.edu
Mark Alber
University of Notre Dame
malber@nd.edu
Danny Z. Chen
Un... | 6448 |@word trial:1 cnn:10 briefly:1 illustrating:1 fcns:4 nd:5 bf:2 seek:1 propagate:1 mention:1 minus:1 shot:1 moment:2 initial:1 series:2 score:5 bc:2 ours:3 rightmost:1 outperforms:1 comparing:3 contextual:6 activation:1 yet:1 danny:1 finest:1 gpu:6 subsequent:1 shape:3 alone:2 half:1 fewer:1 selected:5 intelligenc... |
6,023 | 6,449 | Clustering with Same-Cluster Queries
Hassan Ashtiani , Shrinu Kushagra and Shai Ben-David
David R. Cheriton School of Computer Science
University of Waterloo,
Waterloo, Ontario, Canada
{mhzokaei,skushagr,shai}@uwaterloo.ca
Abstract
We propose a framework for Semi-Supervised Active Clustering framework
(SSAC), where t... | 6449 |@word trial:1 kulis:1 version:2 polynomial:11 stronger:1 simulation:2 sheffet:1 asks:4 mention:1 reduction:2 contains:1 ours:1 interestingly:1 sugato:3 current:1 si:20 must:1 seeding:1 aside:1 selected:2 plane:5 location:4 simpler:1 constructed:2 prove:7 consists:3 combine:2 inside:1 yingyu:1 manner:1 introduce:2... |
6,024 | 645 | Using hippocampal 'place cells' for
navigation, exploiting phase coding
Neil Burgess, John O'Keefe and Michael Recce
Department of Anatomy, University College London,
London WC1E 6BT, England.
(e-mail: n.burgess<Ducl.ac . uk)
Abstract
A model of the hippocampus as a central element in rat navigation is presented. Sim... | 645 |@word middle:1 hippocampus:5 seems:1 open:1 cm2:1 simulation:3 crucially:1 excited:1 minus:1 harder:1 moment:1 rearing:1 ranck:1 current:3 activation:1 must:2 john:1 subsequent:2 motor:2 update:1 cue:1 fewer:1 beginning:1 short:1 nearness:1 location:11 successive:1 constructed:1 direct:1 become:1 examine:1 brain:2... |
6,025 | 6,450 | Hardness of Online Sleeping Combinatorial
Optimization Problems
Satyen Kale? ?
Yahoo Research
satyen@satyenkale.com
Chansoo Lee?
Univ. of Michigan, Ann Arbor
chansool@umich.edu
D?avid P?al
Yahoo Research
dpal@yahoo-inc.com
Abstract
We show that several online combinatorial optimization problems that admit efficient... | 6450 |@word version:6 polynomial:5 stronger:1 open:9 km:3 carry:1 reduction:7 contains:2 current:2 com:2 michal:1 must:1 benign:1 update:1 implying:1 intelligence:1 warmuth:8 beginning:1 manfred:5 boosting:1 bijection:1 node:6 become:1 symposium:2 prove:6 consists:1 specialize:1 indeed:1 hardness:14 yasin:1 decreasing:... |
6,026 | 6,451 | Learned Region Sparsity and Diversity
Also Predict Visual Attention
Zijun Wei1? ,
Hossein Adeli2? ,
Gregory Zelinsky1,2 ,
Minh Hoai1 ,
Dimitris Samaras1
1. Department of Computer Science 2. Department of Psychology ? Stony Brook University
1.{zijwei, minhhoai, samaras}@cs.stonybrook.edu
2.{hossein.adelijelodar, gr... | 6451 |@word stronger:2 kokkinos:1 everingham:1 attended:2 thereby:1 contains:2 score:27 selecting:1 hereafter:1 trainval:2 offering:1 tuned:2 interestingly:1 outperforms:3 current:2 activation:5 si:1 stony:1 must:1 intriguing:2 indistinguishably:1 cottrell:2 informative:1 blur:3 shape:1 enables:1 plot:4 intelligence:1 ... |
6,027 | 6,452 | Batched Gaussian Process Bandit Optimization via
Determinantal Point Processes
Tarun Kathuria, Amit Deshpande, Pushmeet Kohli
Microsoft Research
t-takat@microsoft.com, amitdesh@microsoft.com, pkohli@microsoft.com
Abstract
Gaussian Process bandit optimization has emerged as a powerful tool for optimizing
noisy black bo... | 6452 |@word kohli:1 determinant:5 version:2 exploitation:4 repository:1 seems:1 simulation:5 crucially:1 covariance:2 pick:1 thereby:1 nystr:1 tr:1 initial:1 contains:1 score:2 selecting:4 series:1 bibtex:4 outperforms:1 existing:3 ka:2 com:4 contextual:1 must:1 determinantal:10 pe1:1 kdd:2 burdick:1 greedy:8 selected:... |
6,028 | 6,453 | Using Social Dynamics to Make Individual Predictions:
Variational Inference with a Stochastic Kinetic Model
Zhen Xu, Wen Dong, and Sargur Srihari
Department of Computer Science and Engineering
University at Buffalo
{zxu8,wendong,srihari}@buffalo.edu
Abstract
Social dynamics is concerned primarily with interactions amo... | 6453 |@word briefly:1 willing:1 simulation:1 fifteen:1 minus:1 moment:1 contains:2 daniel:1 past:2 reaction:7 outperforms:1 current:6 comparing:1 si:1 peyton:1 must:2 written:2 john:1 realistic:1 subsequent:1 christian:1 update:3 discrimination:1 v:1 stationary:1 selected:2 intelligence:1 record:2 manfred:1 santo:1 nod... |
6,029 | 6,454 | A Non-parametric Learning Method for Confidently
Estimating Patient?s Clinical State and Dynamics
William Hoiles
Department of Electrical Engineering
University of California Los Angeles
Los Angeles, CA 90024
whoiles@ucla.edu
Mihaela van der Schaar
Department of Electrical Engineering
University of California Los Ang... | 6454 |@word eliminating:1 polynomial:1 norm:4 km:2 covariance:16 accounting:1 citeseer:1 pressure:2 dramatic:1 tr:1 solid:1 initial:1 contains:7 score:3 selecting:1 series:2 bhattacharyya:2 outperforms:2 reaction:1 current:2 mihaela:2 must:5 written:3 john:1 remove:1 interpretable:1 update:2 prohibitive:1 selected:3 re... |
6,030 | 6,455 | Following the Leader and Fast Rates in Linear
Prediction: Curved Constraint Sets and Other
Regularities
Ruitong Huang
Department of Computing Science
University of Alberta, AB, Canada
ruitong@ualberta.ca
Tor Lattimore
School of Informatics and Computing
Indiana University, IN, USA
tor.lattimore@gmail.com
Andr?s Gy?r... | 6455 |@word innovates:1 version:4 norm:6 seems:1 replicate:1 nd:2 open:1 mehta:1 simulation:1 attainable:2 pick:4 thereby:1 tr:1 ftrl:2 selecting:2 chervonenkis:1 ours:1 erven:2 existing:1 com:1 nt:1 od:1 gmail:1 yet:1 bd:23 fn:1 belmont:1 shape:2 remove:1 update:1 intelligence:1 selected:2 parameterization:1 plane:5 r... |
6,031 | 6,456 | Multi-view Anomaly Detection via Robust
Probabilistic Latent Variable Models
Tomoharu Iwata
NTT Communication Science Laboratories
iwata.tomoharu@lab.ntt.co.jp
Makoto Yamada
Kyoto University
makoto.m.yamada@ieee.org
Abstract
We propose probabilistic latent variable models for multi-view anomaly detection, which is th... | 6456 |@word private:7 nd:1 covariance:1 liu:2 series:2 score:13 disparity:3 tist:1 document:4 existing:4 current:2 wd:14 comparing:1 contextual:1 written:2 romance:2 bd:1 ranka:1 kdd:1 shape:1 enables:1 generative:3 selected:2 pursued:1 item:2 intelligence:2 discovering:1 yamada:3 eskin:1 detecting:4 node:1 org:1 zhang... |
6,032 | 6,457 | CMA-ES with Optimal Covariance Update and
Storage Complexity
Oswin Krause
Dept. of Computer Science
University of Copenhagen
Copenhagen, Denmark
oswin.krause@di.ku.dk
D?dac R. Arbon?s
Dept. of Computer Science
University of Copenhagen
Copenhagen, Denmark
didac@di.ku.dk
Christian Igel
Dept. of Computer Science
Univer... | 6457 |@word trial:7 version:1 briefly:2 norm:2 open:1 d2:6 ajj:1 covariance:30 decomposition:14 initial:1 omidvar:2 att:1 selecting:1 genetic:5 existing:1 comparing:1 numerical:1 shape:2 christian:1 designed:1 plot:1 update:32 drop:1 fund:1 intelligence:2 accordingly:1 beginning:1 smith:2 rosenbrock:6 provides:1 succes... |
6,033 | 6,458 | Large Margin Discriminant Dimensionality
Reduction in Prediction Space
Mohammad Saberian
Netflix
esaberian@netflix.com
Can Xu
Google
canxu@google.com
Jose Costa Pereira
INESCTEC
jose.c.pereira@inesctec.pt
Jian Yang
Yahoo Research
jianyang@yahoo-inc.com
Nuno Vasconcelos
UC San Diego
nvasconcelos@ucsd.edu
Abstract
I... | 6458 |@word kulis:2 middle:3 dekel:1 seek:1 rgb:1 lpp:2 reduction:18 initial:3 liu:1 contains:2 score:1 document:1 current:8 com:3 guadarrama:1 goldberger:1 activation:1 must:1 john:1 distant:1 numerical:2 shape:2 enables:1 gist:1 update:8 v:1 hash:6 half:2 prohibitive:1 selected:2 plane:1 sys:1 steepest:1 short:1 iter... |
6,034 | 6,459 | Ef?cient Globally Convergent Stochastic
Optimization for Canonical Correlation Analysis
Weiran Wang1?
Jialei Wang2?
Dan Garber1
Nathan Srebro1
2
1
Toyota Technological Institute at Chicago
University of Chicago
{weiranwang,dgarber,nati}@ttic.edu
jialei@uchicago.edu
Abstract
We study the stochastic optimization of can... | 6459 |@word version:2 nd:3 reused:1 decomposition:4 covariance:5 concise:1 sgd:8 reduction:3 initial:2 document:1 si:35 dx:4 written:1 readily:1 numerical:1 chicago:2 enables:1 remove:2 plot:1 update:3 v:2 instantiate:2 warmuth:1 beginning:1 provides:3 iterates:7 allerton:2 zhang:2 dn:11 along:1 become:1 consists:2 dou... |
6,035 | 646 | On the Use of Projection Pursuit Constraints for
Training Neural Networks
Nathan Illtl'ator'"
Comput.er Science Department
Tel-Aviv Universit.y
Ramat.-A viv, 69978 ISRAEL
and
Inst.itute for Brain and Neural Systems,
Brown University
nin~math,tau.ac.il
Abstract
\Ve present a novel classifica t.ioll and regression met.... | 646 |@word mild:1 neurophysiology:1 version:2 underst:1 polynomial:2 compression:3 norm:1 nd:1 ivit:1 seitz:1 seek:1 simplifying:1 awij:1 initial:1 erms:1 nowlan:2 lang:1 numerical:1 plasticity:2 nemal:1 ial:3 erat:1 math:1 location:1 ional:4 banff:1 sigmoidal:2 along:1 c2:1 direct:3 become:2 ect:1 edelman:1 combine:1 ... |
6,036 | 6,460 | Dynamic matrix recovery from incomplete
observations under an exact low-rank constraint
Liangbei Xu
Mark A. Davenport
Department of Electrical and Computer Engineering
Georgia Institute of Technology
Atlanta, GA 30318
lxu66@gatech.edu mdav@gatech.edu
Abstract
Low-rank matrix factorizations arise in a wide variety of a... | 6460 |@word trial:2 version:1 briefly:1 norm:13 stronger:1 c0:2 d2:2 simulation:5 decomposition:1 tr:1 reduction:1 liu:1 contains:4 bc:1 outperforms:2 existing:1 ka:3 wd:3 luo:1 visible:1 numerical:1 timestamps:1 kdd:2 treating:1 v:2 item:3 ith:2 prize:1 fa9550:1 math:1 preference:4 c2:2 focs:2 symp:3 n22:2 expected:2 ... |
6,037 | 6,461 | Learning to learn by gradient descent
by gradient descent
Marcin Andrychowicz1 , Misha Denil1 , Sergio G?mez Colmenarejo1 , Matthew W. Hoffman1 ,
David Pfau1 , Tom Schaul1 , Brendan Shillingford1,2 , Nando de Freitas1,2,3
1
Google DeepMind
2
University of Oxford
3
Canadian Institute for Advanced Research
marcin.a... | 6461 |@word version:1 norm:1 bptt:2 gradual:1 decomposition:3 pick:1 sgd:1 solid:3 initial:2 series:1 contains:1 selecting:1 daniel:2 tuned:1 prefix:1 outperforms:3 com:5 optim:1 activation:7 gmail:1 john:1 informative:1 enables:1 designed:5 plot:10 update:19 v:1 intelligence:3 advancement:2 short:2 recherche:1 iterate... |
6,038 | 6,462 | Solving Marginal MAP Problems with NP Oracles
and Parity Constraints
Yexiang Xue
Department of Computer Science
Cornell University
yexiang@cs.cornell.edu
Stefano Ermon
Department of Computer Science
Stanford University
ermon@cs.stanford.edu
Zhiyuan Li?
Institute of Interdisciplinary Information Sciences
Tsinghua Univ... | 6462 |@word middle:1 polynomial:3 chakraborty:1 replicate:3 nd:1 adnan:3 propagate:1 decomposition:1 pick:1 harder:1 reduction:1 moment:1 configuration:1 series:5 contains:2 liu:2 daniel:3 outperforms:2 comparing:2 conjunctive:1 written:1 dechter:3 plot:1 kuldeep:1 bart:5 aside:1 hash:1 selected:6 intelligence:6 greedy... |
6,039 | 6,463 | PerforatedCNNs: Acceleration through Elimination
of Redundant Convolutions
Michael Figurnov1,2 , Aijan Ibraimova4 , Dmitry Vetrov1,3 , and Pushmeet Kohli5
1
National Research University Higher School of Economics 2 Lomonosov Moscow State University
3
Yandex 4 Skolkovo Institute of Science and Technology 5 Microsoft Re... | 6463 |@word kohli:1 cnn:15 retraining:2 d2:2 rgb:1 decomposition:3 perfo:1 reduction:7 configuration:5 contains:1 tuned:4 ours:1 outperforms:2 existing:1 freitas:1 guadarrama:1 com:6 comparing:1 skipping:2 activation:7 gmail:1 gpu:13 numerical:1 remove:1 moczulski:1 v:1 greedy:3 device:3 fried:1 podoprikhin:1 vanishing... |
6,040 | 6,464 | Learning Deep Embeddings with Histogram Loss
Evgeniya Ustinova and Victor Lempitsky
Skolkovo Institute of Science and Technology (Skoltech)
Moscow, Russia
Abstract
We suggest a loss for learning deep embeddings. The new loss does not introduce
parameters that need to be tuned and results in very good embeddings acros... | 6464 |@word cnn:3 version:2 middle:1 polynomial:1 kokkinos:1 wexler:1 contrastive:6 tr:12 shot:2 moment:1 initial:1 liu:1 series:1 score:2 swansea:1 tuned:4 interestingly:1 outperforms:3 guadarrama:1 comparing:1 com:2 si:3 assigning:1 dx:2 moreno:1 drop:1 intelligence:2 ith:1 node:4 location:1 firstly:1 bowman:1 become... |
6,041 | 6,465 | R-FCN: Object Detection via
Region-based Fully Convolutional Networks
Jifeng Dai
Microsoft Research
Yi Li?
Tsinghua University
Kaiming He
Microsoft Research
Jian Sun
Microsoft Research
Abstract
We present region-based, fully convolutional networks for accurate and efficient
object detection. In contrast to previou... | 6465 |@word cnn:50 fcns:3 kokkinos:1 everingham:1 c0:1 decomposition:1 minus:1 incarnation:1 shot:1 liu:2 series:1 score:34 trainval:9 ours:2 com:2 yet:2 gpu:4 enables:1 lcls:2 hypothesize:1 designed:1 remove:2 drop:1 rpn:19 v:2 aside:1 selected:2 parameterization:1 detecting:1 simpler:1 zhang:4 rc:4 constructed:1 cons... |
6,042 | 6,466 | Bayesian optimization for automated model selection
Gustavo Malkomes,? Chip Schaff,? Roman Garnett
Department of Computer Science and Engineering
Washington University in St. Louis
St. Louis, MO 63130
{luizgustavo, cbschaff, garnett}@wustl.edu
Abstract
Despite the success of kernel-based nonparametric methods, kernel... | 6466 |@word exploitation:3 briefly:2 stronger:1 termination:1 closure:1 seek:2 covariance:14 accounting:1 automl:1 series:1 selecting:3 sobol:1 past:1 freitas:1 must:1 concatenate:1 numerical:1 distant:1 additive:2 treating:1 plot:1 update:2 stationary:1 generative:1 fewer:1 greedy:4 selected:1 intelligence:2 beginning... |
6,043 | 6,467 | Generalization of ERM in Stochastic Convex
Optimization:
The Dimension Strikes Back?
Vitaly Feldman
IBM Research ? Almaden
Abstract
In stochastic convex optimization the goal is to minimize a convex function
.
F (x) = Ef ?D [f (x)] over a convex set K ? Rd where D is some unknown
distribution and each f (?) in the sup... | 6467 |@word briefly:1 version:6 polynomial:1 norm:5 stronger:1 open:2 hu:1 elisseeff:1 thereby:2 boundedness:1 existing:1 current:1 optim:1 yet:1 gv:11 maxv:2 juditsky:1 leaf:1 smith:1 lr:4 district:1 simpler:1 zhang:1 dn:1 differential:1 consists:2 prove:5 interscience:1 privacy:1 huber:1 expected:3 examine:3 moulines... |
6,044 | 6,468 | One-vs-Each Approximation to Softmax for Scalable
Estimation of Probabilities
Michalis K. Titsias
Department of Informatics
Athens University of Economics and Business
mtitsias@aueb.gr
Abstract
The softmax representation of probabilities for categorical variables plays a prominent role in modern machine learning with ... | 6468 |@word repository:1 version:1 norm:6 nd:1 palma:1 crucially:1 jacob:1 citeseer:1 sgd:12 solid:1 carry:1 initial:3 qatar:1 score:13 selecting:1 bibtex:6 blackout:1 interestingly:2 bradley:3 current:1 com:3 surprising:1 must:1 john:1 fn:1 analytic:2 update:3 v:21 stationary:4 half:2 prohibitive:1 alone:1 item:1 para... |
6,045 | 6,469 | Dual Learning for Machine Translation
Di He1,?, Yingce Xia2,? , Tao Qin3 , Liwei Wang1 , Nenghai Yu2 , Tie-Yan Liu3 , Wei-Ying Ma3
1
Key Laboratory of Machine Perception (MOE), School of EECS, Peking University
2
University of Science and Technology of China 3 Microsoft Research
1
{dih,wanglw}@cis.pku.edu.cn; 2 xiayin... | 6469 |@word middle:6 briefly:1 open:2 seek:1 citeseer:1 initial:3 liu:1 contains:4 score:6 qatar:1 document:2 past:1 outperforms:9 recovered:1 com:3 contextual:1 comparing:1 jeopardy:1 gpu:1 subsequent:1 happen:1 informative:1 plot:2 update:6 half:2 accordingly:2 beginning:5 short:3 provides:1 barrault:1 five:1 constru... |
6,046 | 647 | Second order derivatives for network
pruning: Optimal Brain Surgeon
Babak Hassibi* and David G. Stork
Ricoh California Research Center
2882 Sand Hill Road, Suite 115
Menlo Park, CA 94025-7022
stork@crc.ricoh.com
and
* Department of Electrical Engineering
Stanford University
Stanford, CA 94305
Abstract
We investigate ... | 647 |@word eliminating:1 inversion:2 seems:1 retraining:12 hu:2 simulation:2 gradual:1 covariance:3 fonn:1 dramatic:1 thereby:4 solid:1 reduction:3 initial:1 series:1 mag:3 qth:1 recovered:1 com:1 must:1 written:1 subsequent:1 remove:4 unintelligible:1 update:2 tenn:2 monk:8 isotropic:1 plane:1 ith:1 short:1 pointer:1 ... |
6,047 | 6,470 | Efficient Neural Codes under Metabolic Constraints
Zhuo Wang ??
Department of Mathematics
University of Pennsylvania
wangzhuo@nyu.edu
Xue-Xin Wei ??
Department of Psychology
University of Pennsylvania
weixxpku@gmail.com
Alan A. Stocker
Department of Psychology
University of Pennsylvania
astocker@sas.upenn.edu
Daniel... | 6470 |@word mild:1 trial:2 achievable:1 seems:1 grey:1 seek:1 solid:4 reduction:3 configuration:1 series:1 daniel:3 interestingly:3 current:4 com:1 nt:3 gmail:1 yet:1 must:2 readily:1 john:1 additive:3 numerical:2 informative:2 shape:3 bart:1 stationary:2 half:1 cue:1 metabolism:1 parameterization:1 short:4 characteriz... |
6,048 | 6,471 | Stochastic Variance Reduction Methods
for Saddle-Point Problems
P. Balamurugan
INRIA - Ecole Normale Sup?rieure, Paris
balamurugan.palaniappan@inria.fr
Francis Bach
INRIA - Ecole Normale Sup?rieure, Paris
francis.bach@ens.fr
Abstract
We consider convex-concave saddle-point problems where the objective functions
may b... | 6471 |@word middle:1 version:1 norm:8 stronger:1 nd:7 unif:3 subcase:1 reduction:10 woodruff:2 ecole:2 existing:7 written:1 readily:1 refresh:2 numerical:1 plot:2 update:11 resampling:3 v:2 selected:1 fewer:1 xk:2 isotropic:1 provides:1 math:2 herbrich:1 simpler:1 zhang:3 mathematical:1 dn:1 ik:3 prove:1 introductory:1... |
6,049 | 6,472 | Simple and Efficient Weighted Minwise Hashing
Anshumali Shrivastava
Department of Computer Science
Rice University
Houston, TX, 77005
anshumali@rice.edu
Abstract
Weighted minwise hashing (WMH) is one of the fundamental subroutine, required by many celebrated approximation algorithms, commonly
adopted in industrial pr... | 6472 |@word msr:1 briefly:1 manageable:2 eliminating:1 proportion:2 seems:2 advantageous:1 loading:1 faculty:1 compression:1 dalal:1 triggs:1 rajaraman:1 scg:1 rgb:1 tr:1 reduction:2 necessity:1 celebrated:2 series:1 document:3 existing:9 nally:1 comparing:2 surprising:3 clara:1 realistic:1 kdd:1 cheap:1 designed:1 plo... |
6,050 | 6,473 | Incremental Variational Sparse Gaussian Process
Regression
Ching-An Cheng
Institute for Robotics and Intelligent Machines
Georgia Institute of Technology
Atlanta, GA 30332
cacheng@gatech.edu
Byron Boots
Institute for Robotics and Intelligent Machines
Georgia Institute of Technology
Atlanta, GA 30332
bboots@cc.gatech.... | 6473 |@word determinant:1 version:1 illustrating:1 nd:1 heuristically:1 linearized:2 covariance:12 tr:2 solid:1 moment:1 precluding:1 rkhs:11 recovered:1 written:3 john:1 multioutput:1 numerical:2 subsequent:1 partition:1 j1:3 christian:1 designed:1 update:12 juditsky:1 intelligence:5 selected:3 isotropic:1 parametriza... |
6,051 | 6,474 | Combining Adversarial Guarantees and
Stochastic Fast Rates in Online Learning
Wouter M. Koolen
Centrum Wiskunde & Informatica
Science Park 123, 1098 XG
Amsterdam, the Netherlands
wmkoolen@cwi.nl
Peter Gr?nwald
CWI and Leiden University
pdg@cwi.nl
Tim van Erven
Leiden University
Niels Bohrweg 1, 2333 CA
Leiden, the N... | 6474 |@word illustrating:2 version:1 norm:2 mehta:2 d2:1 gradual:1 crucially:1 linearized:1 incurs:1 harder:1 interestingly:1 erven:16 z2:1 luo:2 worsening:1 surprising:1 yet:4 must:1 readily:1 gerchinovitz:1 succeeding:1 v:1 selected:1 plane:1 chiang:2 provides:3 characterization:1 boosting:1 along:1 c2:7 prove:3 doub... |
6,052 | 6,475 | A scalable end-to-end Gaussian process adapter for
irregularly sampled time series classification
Steven Cheng-Xian Li
Benjamin Marlin
College of Information and Computer Sciences
University of Massachusetts Amherst
Amherst, MA 01003
{cxl,marlin}@cs.umass.edu
Abstract
We present a general framework for classification... | 6475 |@word middle:3 norm:1 vi1:1 nd:5 scalably:1 covariance:13 decomposition:1 recursively:1 carry:1 reduction:1 liu:1 series:55 uma:1 contains:2 daniel:1 ours:1 interestingly:1 rightmost:1 outperforms:2 existing:1 ka:4 com:1 si:5 diederik:1 written:1 must:1 numerical:3 partition:1 wx:5 kdd:2 enables:2 drop:2 plot:6 u... |
6,053 | 6,476 | Inference by Reparameterization in Neural
Population Codes
Rajkumar V. Raju
Department of ECE
Rice University
Houston, TX 77005
rv12@rice.edu
Xaq Pitkow
Dept. of Neuroscience, Dept. of ECE
Baylor College of Medicine, Rice University
Houston, TX 77005
xaq@rice.edu
Abstract
Behavioral experiments on humans and animals ... | 6476 |@word trial:4 cingulate:1 version:2 polynomial:1 nd:1 simulation:4 covariance:3 ttn:1 att:1 past:2 ka:1 anterior:1 dx:2 must:4 readily:1 written:1 mst:2 distant:1 subsequent:1 pseudomarginals:11 motor:1 gv:3 treating:1 update:17 intelligence:2 leaf:1 parameterization:1 ith:1 filtered:1 provides:2 node:20 five:2 m... |
6,054 | 6,477 | Understanding Probabilistic Sparse
Gaussian Process Approximations
Matthias Bauer??
Mark van der Wilk?
Carl Edward Rasmussen?
?
Department of Engineering, University of Cambridge, Cambridge, UK
?
Max Planck Institute for Intelligent Systems, T?ubingen, Germany
{msb55, mv310, cer54}@cam.ac.uk
Abstract
Good sparse appr... | 6477 |@word worsens:2 briefly:1 inversion:2 twelfth:1 calculus:1 grey:1 covariance:10 tr:1 harder:1 initial:6 configuration:7 contains:1 series:3 initialisation:1 kuf:1 existing:1 recovered:1 must:1 fn:1 numerical:1 happen:1 remove:3 plot:1 update:1 clumping:6 intelligence:7 prohibitive:1 fewer:2 selected:1 greedy:1 is... |
6,055 | 6,478 | Fast and Provably Good Seedings for k-Means
Olivier Bachem
Department of Computer Science
ETH Zurich
olivier.bachem@inf.ethz.ch
Mario Lucic
Department of Computer Science
ETH Zurich
lucic@inf.ethz.ch
S. Hamed Hassani
Department of Computer Science
ETH Zurich
hamed@inf.ethz.ch
Andreas Krause
Department of Computer Sc... | 6478 |@word stronger:1 nd:3 unif:1 vldb:1 d2:15 bahmani:2 initial:6 kingravi:1 selecting:1 ktv:1 daniel:1 outperforms:6 csn:7 yet:2 dx:2 sergei:2 additive:1 subsequent:1 kdd:6 seeding:29 stationary:1 half:2 selected:1 obsolete:2 intelligence:1 record:1 hypersphere:1 quantizer:2 completeness:1 provides:2 firstly:1 sympo... |
6,056 | 6,479 | Optimal spectral transportation with application to
music transcription
R?mi Flamary
Universit? C?te d?Azur, CNRS, OCA
remi.flamary@unice.fr
Nicolas Courty
Universit? de Bretagne Sud, CNRS, IRISA
courty@univ-ubs.fr
C?dric F?votte
CNRS, IRIT, Toulouse
cedric.fevotte@irit.fr
Valentin Emiya
Aix-Marseille Universit?, CNR... | 6479 |@word middle:1 eliminating:1 villani:2 plsa:1 iki:1 km:2 decomposition:6 simplifying:1 contains:2 score:1 daniel:2 denoting:1 tuned:1 outperforms:1 thre:1 com:1 activation:2 negentropy:1 must:2 realistic:1 subsequent:1 additive:1 shape:1 hofmann:2 remove:1 designed:1 plot:3 sampl:1 polyphonic:2 discrimination:2 h... |
6,057 | 648 | On-Line Estimation of the Optimal Value
Function: HJB-Estimators
James K. Peterson
Department of Mathematical Sciences
Martin Hall Box 341907
Clemson University
Clemson, SC 29634-1907
email: petersonOmath. clemson. edu
Abstract
In this paper, we discuss on-line estimation strategies that model
the optimal value functi... | 648 |@word automat:1 reduction:1 initial:10 series:1 zij:1 expositional:1 must:8 mesh:2 drop:1 update:3 alone:1 argm:1 dissertation:1 coarse:5 characterization:1 math:1 successive:1 ofo:1 mathematical:2 differential:2 corridor:7 supply:2 ooj:1 hjb:17 manner:1 introduce:1 ra:1 indeed:1 roughly:1 dist:3 planning:2 discre... |
6,058 | 6,480 | Coevolutionary Latent Feature Processes for
Continuous-Time User-Item Interactions
Yichen Wang? , Nan Du? , Rakshit Trivedi? , Le Song?
?
Google Research
?
College of Computing, Georgia Institute of Technology
{yichen.wang, rstrivedi}@gatech.edu, dunan@google.com
lsong@cc.gatech.edu
Abstract
Matching users to the righ... | 6480 |@word cox:2 norm:5 proportion:1 flach:2 simulation:1 jacob:1 moment:1 liu:1 contains:7 series:1 past:5 outperforms:2 err:4 current:1 com:2 comparing:1 nell:1 attracted:1 romance:2 happen:1 informative:1 kdd:4 shape:1 designed:5 drop:1 update:2 v:3 selected:2 website:2 item:107 desktop:1 xk:8 parametrization:1 sho... |
6,059 | 6,481 | Nested Mini-Batch K-Means
Franc?ois Fleuret
Idiap Research Institue & EPFL
francois.fleuret@idiap.ch
James Newling
Idiap Research Institue & EPFL
james.newling@idiap.ch
Abstract
A new algorithm is proposed which accelerates the mini-batch k-means algorithm
of Sculley (2010) by using the distance bounding approach of ... | 6481 |@word eliminating:1 compression:1 proportion:1 nd:1 reused:4 closure:2 curtail:1 thereby:1 initial:3 contains:2 kingravi:1 initialisation:5 ours:3 dubourg:1 existing:1 current:1 comparing:3 com:1 must:3 written:2 subsequent:1 partition:2 kdd:1 enables:1 remove:2 drop:2 plot:1 update:14 pursued:1 selected:2 intell... |
6,060 | 6,482 | Blind Attacks on Machine Learners
Alex Beatson
Department of Computer Science
Princeton University
abeatson@princeton.edu
Zhaoran Wang
Department of Operations Research
and Financial Engineering
Princeton University
zhaoran@princeton.edu
Han Liu
Department of Operations Research
and Financial Engineering
Princeton Un... | 6482 |@word private:5 briefly:2 polynomial:1 proportion:2 nd:1 pick:6 accommodate:1 shot:1 carry:4 reduction:5 liu:2 omniscient:1 counterterrorism:2 current:1 comparing:2 si:1 yet:2 tackling:1 must:3 s2max:2 intelligence:4 advancement:2 ith:1 provides:6 completeness:2 attack:59 firstly:2 direct:3 differential:7 symposi... |
6,061 | 6,483 | Minimax Estimation of Maximum Mean Discrepancy
with Radial Kernels
Ilya Tolstikhin
Department of Empirical Inference
MPI for Intelligent Systems
T?bingen 72076, Germany
ilya@tuebingen.mpg.de
Bharath K. Sriperumbudur
Department of Statistics
Pennsylvania State University
University Park, PA 16802, USA
bks18@psu.edu
Be... | 6483 |@word version:1 norm:2 seems:1 nd:1 open:2 cm2:1 covariance:2 p0:11 q1:5 thereby:3 mention:1 ipm:2 moment:2 rkhs:6 interestingly:1 past:1 existing:1 universality:1 nt1:3 fn:3 tailoring:1 intelligence:1 kyk:2 beginning:1 provides:5 math:1 c22:1 zhang:1 dn:2 constructed:2 c2:24 lopez:1 consists:1 prove:1 introduce:... |
6,062 | 6,484 | Fast recovery from a union of subspaces
Chinmay Hegde
Iowa State University
Piotr Indyk
MIT
Ludwig Schmidt
MIT
Abstract
We address the problem of recovering a high-dimensional but structured vector
from linear observations in a general setting where the vector can come from an
arbitrary union of subspaces. This setu... | 6484 |@word torsten:1 version:2 polynomial:3 compression:2 norm:6 stronger:1 d2:11 vldb:2 decomposition:4 accounting:1 incurs:1 initial:1 liu:1 contains:3 ours:1 pprox:4 past:2 ka:1 written:1 must:1 john:1 additive:2 numerical:1 enables:2 update:1 v:1 greedy:1 instantiate:5 item:1 propack:7 cormode:1 volkan:2 iterates:... |
6,063 | 6,485 | Structured Prediction Theory Based on
Factor Graph Complexity
Corinna Cortes
Google Research
New York, NY 10011
Vitaly Kuznetsov
Google Research
New York, NY 10011
corinna@google.com
vitaly@cims.nyu.edu
Mehryar Mohrii
Courant Institute and Google
New York, NY 10012
Scott Yang
Courant Institute
New York, NY 10012
... | 6485 |@word mild:1 version:2 eliminating:1 norm:5 tadepalli:1 nd:1 r:4 crucially:1 seek:1 decomposition:11 contraction:5 reduction:1 substitution:1 series:3 score:1 past:1 existing:5 com:1 yet:2 must:1 parsing:4 additive:5 hofmann:2 enables:1 designed:1 fewer:1 mccallum:1 grfp:1 provides:3 boosting:3 m3n:4 node:7 simpl... |
6,064 | 6,486 | Coresets for Scalable Bayesian Logistic Regression
Jonathan H. Huggins
Trevor Campbell
Tamara Broderick
Computer Science and Artificial Intelligence Laboratory, MIT
{jhuggins@, tdjc@, tbroderick@csail.}mit.edu
Abstract
The use of Bayesian methods in large-scale data settings is attractive because of
the rich hierarch... | 6486 |@word version:1 polynomial:2 norm:1 d2:1 seek:1 crucially:2 q1:1 thereby:1 tr:3 boundedness:1 reduction:1 moment:1 efficacy:1 score:1 genetic:2 document:2 past:1 existing:3 must:3 kqj:1 informative:1 wanted:1 seeding:1 plot:2 drop:1 n0:3 intelligence:5 generative:3 haario:1 hamiltonian:1 blei:1 location:1 org:5 z... |
6,065 | 6,487 | Universal Correspondence Network
Christopher B. Choy
Stanford University
chrischoy@ai.stanford.edu
JunYoung Gwak
Stanford University
jgwak@ai.stanford.edu
Silvio Savarese
Stanford University
ssilvio@stanford.edu
Manmohan Chandraker
NEC Laboratories America, Inc.
manu@nec-labs.com
Abstract
We present a deep learnin... | 6487 |@word cnn:12 version:1 dalal:1 stronger:1 advantageous:1 everingham:1 triggs:1 open:1 choy:2 decomposition:2 jacob:2 contrastive:17 pick:1 tr:2 lepetit:2 reduction:1 configuration:1 series:2 disparity:1 liu:2 ours:19 outperforms:4 guadarrama:1 com:1 activation:10 yet:1 si:2 gpu:2 shape:10 enables:1 designed:4 plo... |
6,066 | 6,488 | Protein contact prediction from amino acid
co-evolution using convolutional networks for
graph-valued images
Vladimir Golkov1 , Marcin J. Skwark2 , Antonij Golkov3 , Alexey Dosovitskiy4 ,
Thomas Brox4 , Jens Meiler2 , and Daniel Cremers1
1
Technical University of Munich, Germany
2
Vanderbilt University, Nashville, TN,... | 6488 |@word mri:1 version:1 compression:1 norm:2 stronger:1 open:1 simulation:3 covariance:2 pressure:6 arous:1 reduction:1 configuration:2 contains:1 series:2 score:1 exclusively:1 daniel:2 uncovered:1 united:2 envision:1 outperforms:2 reaction:1 existing:1 recovered:1 com:1 current:1 videolearn:1 scatter:1 diederik:1... |
6,067 | 6,489 | Single-Image Depth Perception in the Wild
Weifeng Chen
Zhao Fu
Dawei Yang
Jia Deng
University of Michigan, Ann Arbor
{wfchen,zhaofu,ydawei,jiadeng}@umich.edu
Abstract
This paper studies single-image depth perception in the wild, i.e., recovering depth
from a single image taken in unconstrained settings. We introduce ... | 6489 |@word kohli:1 version:2 middle:1 judgement:1 seems:1 rgb:17 jacob:1 decomposition:1 pick:1 incurs:1 shading:2 harder:1 liu:7 series:2 exclusively:1 hoiem:2 salzmann:1 tuned:1 ours:4 outperforms:5 existing:6 lichtenberg:1 current:4 comparing:2 recovered:1 yet:5 must:1 shape:4 remove:2 hourglass:3 zik:2 cue:2 websi... |
6,068 | 649 | Learning Fuzzy Rule-Based Neural
Networks for Control
Charles M. Higgins and Rodney M. Goodman
Department of Electrical Engineering, 116-81
California Institute of Technology
Pasadena, CA 91125
Abstract
A three-step method for function approximation with a fuzzy system is proposed. First, the membership functions and ... | 649 |@word compression:10 loading:2 simulation:1 gradual:1 simplifying:1 decomposition:1 initial:5 contains:4 xiy:1 hereafter:1 existing:1 current:1 must:2 numerical:1 partition:1 remove:1 designed:1 v:1 fewer:1 timo:2 node:8 successive:1 mathematical:1 constructed:5 direct:1 yuhas:4 combine:1 manner:1 automatically:1 ... |
6,069 | 6,490 | On statistical learning via the lens of compression
Ofir David
Department of Mathematics
Technion - Israel Institute of Technology
ofirdav@tx.technion.ac.il
Shay Moran
Department of Computer Science
Technion - Israel Institute of Technology
shaymrn@cs.technion.ac.il
Amir Yehudayoff
Department of Mathematics
Technion -... | 6490 |@word version:9 polynomial:2 compression:113 open:3 ld:12 zij:1 chervonenkis:6 ramsey:4 com:1 z2:1 gmail:1 must:1 john:1 ligett:1 cue:1 selected:1 amir:3 warmuth:8 core:1 manfred:5 provides:1 boosting:3 characterization:1 compressible:1 herbrich:1 c2:2 aryeh:1 prove:2 consists:1 theoretically:1 indeed:1 hardness:... |
6,070 | 6,491 | Robust Spectral Detection of Global Structures in the
Data by Learning a Regularization
Pan Zhang
Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China
panzhang@itp.ac.cn
Abstract
Spectral methods are popular in detecting global structures in the given data that
can be represented as a ... | 6491 |@word illustrating:1 norm:1 tried:1 decomposition:2 asks:1 carry:1 initial:1 contains:3 selecting:1 itp:1 denoting:3 neeman:2 suppressing:1 outperforms:3 existing:3 si:5 written:2 numerical:3 partition:11 informative:8 remove:1 plot:5 update:1 v:1 generative:1 selected:3 fewer:1 item:5 short:1 detecting:3 complet... |
6,071 | 6,492 | Quantized Random Projections and Non-Linear
Estimation of Cosine Similarity
Ping Li
Rutgers University
Michael Mitzenmacher
Harvard University
Martin Slawski
Rutgers University
pingli@stat.rutgers.edu
michaelm@eecs.harvard.edu
martin.slawski@rutgers.edu
Abstract
Random projections constitute a simple, yet effecti... | 6492 |@word repository:2 version:1 briefly:1 compression:1 norm:8 inversion:4 manageable:1 faculty:1 decomposition:1 jacob:1 attainable:2 thereby:1 tr:15 moment:1 reduction:5 celebrated:1 series:1 zij:1 tabulate:2 interestingly:1 kx0:1 comparing:1 yet:1 subsequent:1 numerical:1 kdd:2 treating:1 v:7 accordingly:4 vanish... |
6,072 | 6,493 | Adaptive Concentration Inequalities
for Sequential Decision Problems
Shengjia Zhao
Tsinghua University
zhaosj12@stanford.edu
Enze Zhou
Tsinghua University
zhouez_thu_12@126.com
Ashish Sabharwal
Allen Institute for AI
AshishS@allenai.org
Stefano Ermon
Stanford University
ermon@cs.stanford.edu
Abstract
A key challen... | 6493 |@word version:1 simulation:2 bn:5 kalyanakrishnan:1 necessity:1 configuration:1 ours:1 interestingly:1 existing:2 com:1 comparing:1 must:2 readily:1 fn:1 subsequent:2 analytic:1 remove:2 plot:6 drop:2 v:1 intelligence:1 half:1 fewer:1 provides:1 math:2 mannor:2 org:5 simpler:1 constructed:1 symposium:1 prove:1 wa... |
6,073 | 6,494 | Threshold Learning for Optimal Decision Making
Nathan F. Lepora
Department of Engineering Mathematics, University of Bristol, UK
n.lepora@bristol.ac.uk
Abstract
Decision making under uncertainty is commonly modelled as a process of competitive stochastic evidence accumulation to threshold (the drift-diffusion model).... | 6494 |@word neurophysiology:1 trial:58 exploitation:1 version:1 hu:2 simulation:1 gradual:1 covariance:2 recursively:1 initial:1 substitution:1 selecting:1 past:2 freitas:1 com:1 written:1 must:1 john:1 plasticity:1 shape:1 motor:2 moreno:1 plot:3 designed:1 update:2 v:4 discrimination:4 fewer:3 beginning:1 proficient:... |
6,074 | 6,495 | Sorting out typicality with the inverse moment matrix
SOS polynomial
Jean-Bernard Lasserre
LAAS-CNRS & IMT
Universit? de Toulouse
31400 Toulouse, France
lasserre@laas.fr
Edouard Pauwels
IRIT & IMT
Universit? Toulouse 3 Paul Sabatier
31400 Toulouse, France
edouard.pauwels@irit.fr
Abstract
We study a surprising phenom... | 6495 |@word repository:1 inversion:4 polynomial:80 proportion:3 nd:2 r:6 simulation:1 accounting:1 covariance:1 decomposition:1 independant:1 dishonest:1 ld:2 moment:31 contains:1 score:9 series:1 lichman:1 ours:2 existing:2 current:1 surprising:2 smtp:2 written:1 determinantal:1 numerical:4 additive:1 kdd:2 shape:17 p... |
6,075 | 6,496 | Sublinear Time Orthogonal Tensor Decomposition?
Zhao Song?
David P. Woodruff?
Huan Zhang?
Dept. of Computer Science, University of Texas, Austin, USA
?
IBM Almaden Research Center, San Jose, USA
?
Dept. of Electrical and Computer Engineering, University of California, Davis, USA
zhaos@utexas.edu, dpwoodru@us.ibm.com, ... | 6496 |@word h:1 mild:1 repository:1 version:6 mri:1 polynomial:1 norm:33 proportion:1 private:1 c0:5 hu:3 seek:1 decomposition:23 contraction:10 weekday:1 moment:1 initial:3 liu:2 selecting:1 woodruff:3 ours:1 fa8750:1 pprox:12 existing:3 current:1 com:3 si:7 kdd:2 cheap:1 remove:2 update:1 generative:1 core:1 short:1 ... |
6,076 | 6,497 | Neural Universal Discrete Denoiser
Taesup Moon
DGIST
Daegu, Korea 42988
tsmoon@dgist.ac.kr
Seonwoo Min, Byunghan Lee, Sungroh Yoon
Seoul National University
Seoul, Korea 08826
{mswzeus, styxkr, sryoon}@snu.ac.kr
Abstract
We present a new framework of applying deep neural networks (DNN) to devise a
universal discrete ... | 6497 |@word faculty:1 version:1 evaluating:1 sgd:4 inpainting:1 substitution:3 selecting:1 tuned:1 outperforms:2 activation:2 enables:1 plot:1 drop:1 update:1 fund:1 v:1 half:1 device:1 core:1 short:1 pascanu:1 node:3 location:7 lx:1 toronto:1 firstly:1 simpler:1 bioinform:1 along:1 become:2 consists:1 inside:1 deterio... |
6,077 | 6,498 | Online and Differentially-Private Tensor Decomposition
Animashree Anandkumar
Department of EECS
University of California, Irvine
a.anandkumar@uci.edu
Yining Wang
Machine Learning Department
Carnegie Mellon University
yiningwa@cs.cmu.edu
Abstract
Tensor decomposition is an important tool for big data analysis. In this... | 6498 |@word private:22 faculty:1 version:1 polynomial:2 norm:8 sharpens:1 stronger:1 c0:2 open:2 simulation:4 decomposition:47 sgd:6 moment:8 liu:1 series:1 document:1 existing:3 recovered:1 current:1 protection:1 written:2 must:2 numerical:1 additive:1 j1:2 subsequent:1 designed:1 update:2 implying:1 fa9550:1 boosting... |
6,078 | 6,499 | Crowdsourced Clustering: Querying Edges vs
Triangles
Ramya Korlakai Vinayak
Department of Electrical Engineering
Caltech, Pasadena
ramya@caltech.edu
Babak Hassibi
Department of Electrical Engineering
Caltech, Pasadena
hassibi@systems.caltech.edu
Abstract
We consider the task of clustering items using answers from no... | 6499 |@word norm:5 km:1 condon:1 simulation:3 bn:1 pick:1 tr:1 klk:1 configuration:19 contains:1 liu:2 karger:2 series:1 daniel:1 hermosillo:1 outperforms:2 err:5 recovered:2 ksk1:1 comparing:1 manuel:1 anne:1 si:4 luis:1 john:1 planet:3 partition:3 shape:1 cheap:1 designed:1 drop:2 v:2 generative:4 intelligence:1 item... |
6,079 | 65 | 348
Minkowski-r Back-Propaaation: Learnine in Connectionist
Models with Non-Euclidian Error Silllais
Stephen Jose Hanson and David J. Burr
Bell Communications Research
Morristown, New Jersey 07960
Abstract
Many connectionist learning models are implemented using a gradient descent
in a least squares error function of ... | 65 |@word illustrating:1 compression:1 seems:2 simulation:2 euclidian:6 moment:1 reduction:4 emn:1 recovered:5 activation:5 dx:1 mesh:3 partition:2 shape:12 update:3 discrimination:1 tenn:1 plane:4 lr:1 simpler:2 five:1 differential:1 replication:1 burr:2 expected:3 roughly:1 examine:1 decreasing:2 increasing:3 underly... |
6,080 | 650 | Diffusion Approximations for the
Constant Learning Rate
Backpropagation Algorithm and
Resistence to Local Minima
William Finnoff
Siemens AG, Corporate Research and Development
Otto-Hahn-Ring 6
8000 Munich 83, Fed. Rep. Germany
Abstract
In this paper we discuss the asymptotic properties of the most commonly used varian... | 650 |@word version:3 tedious:1 covariance:3 boundedness:1 exclusively:1 denoting:1 document:1 activation:2 additive:1 update:4 xk:2 ifx:1 provides:1 mathematical:1 constructed:1 differential:3 combine:2 indeed:1 expected:4 ra:1 themselves:1 decreasing:1 actual:1 considering:1 notation:2 bounded:1 developed:1 ag:1 every... |
6,081 | 6,500 | Linear Relaxations for Finding Diverse Elements in
Metric Spaces
Aditya Bhaskara
University of Utah
bhaskara@cs.utah.edu
Mehrdad Ghadiri
Sharif University of Technology
ghadiri@ce.sharif.edu
Vahab Mirrokni
Google Research
mirrokni@google.com
Ola Svensson
EPFL
ola.svensson@epfl.ch
Abstract
Choosing a diverse subset ... | 6500 |@word madelon:1 cu:7 repository:1 polynomial:2 nd:5 open:2 vldb:1 pick:3 concise:1 mention:1 reduction:3 liu:1 contains:4 lichman:1 selecting:2 score:1 freitas:1 nonmonotone:1 com:1 comparing:3 yet:1 written:2 partition:2 happen:1 cant:1 kdd:1 remove:3 drop:1 treating:1 update:1 v:1 greedy:9 selected:2 half:1 ite... |
6,082 | 6,501 | Deep Exploration via Bootstrapped DQN
Ian Osband1,2 , Charles Blundell2 , Alexander Pritzel2 , Benjamin Van Roy1
1
Stanford University, 2 Google DeepMind
{iosband, cblundell, apritzel}@google.com, bvr@stanford.edu
Abstract
Efficient exploration remains a major challenge for reinforcement learning
(RL). Common ditherin... | 6501 |@word exploitation:3 version:1 pieter:1 crucially:1 propagate:2 q1:3 pick:1 carry:1 initial:5 series:1 efficacy:1 selecting:1 score:2 daniel:1 tuned:1 bootstrapped:64 ours:1 rightmost:1 outperforms:2 existing:1 bradley:2 hasselt:1 com:1 freitas:1 guez:2 must:5 john:1 ronald:1 realistic:1 informative:5 enables:1 d... |
6,083 | 6,502 | SURGE: Surface Regularized Geometry Estimation
from a Single Image
Peng Wang1 Xiaohui Shen2 Bryan Russell2 Scott Cohen2 Brian Price2 Alan Yuille3
1
University of California, Los Angeles
2
Adobe Research
3
Johns Hopkins University
Abstract
This paper introduces an approach to regularize 2.5D surface normal and de... | 6502 |@word kohli:1 cnn:16 kokkinos:2 paredes:1 nd:1 seek:1 propagate:6 rgb:8 ndez:1 contains:1 liu:1 hoiem:3 ours:3 romera:1 outperforms:1 existing:1 lichtenberg:1 current:1 guadarrama:1 written:1 readily:1 john:1 designed:1 drop:3 v:1 alone:1 cue:2 inspection:1 plane:36 vanishing:1 coarse:1 revisited:1 location:2 fir... |
6,084 | 6,503 | A Locally Adaptive Normal Distribution
Georgios Arvanitidis, Lars Kai Hansen and S?ren Hauberg
Technical University of Denmark, Lyngby, Denmark
DTU Compute, Section for Cognitive Systems
{gear,lkai,sohau}@dtu.dk
Abstract
The multivariate normal density is a monotonic function of the distance to the mean,
and its elli... | 6503 |@word trial:1 e215:1 nd:1 open:2 hu:1 covariance:20 thereby:1 reduction:3 initial:2 selecting:1 ours:1 outperforms:1 contextual:1 goldberger:1 yet:1 intriguing:1 written:1 numerical:1 shape:1 enables:1 moreno:1 v:7 generative:5 half:1 intelligence:4 tone:1 gear:1 isotropic:1 merger:1 steepest:1 short:1 provides:1... |
6,085 | 6,504 | Learning Structured Sparsity in Deep Neural
Networks
Wei Wen
University of Pittsburgh
wew57@pitt.edu
Chunpeng Wu
University of Pittsburgh
chw127@pitt.edu
Yiran Chen
University of Pittsburgh
yic52@pitt.edu
Yandan Wang
University of Pittsburgh
yaw46@pitt.edu
Hai Li
University of Pittsburgh
hal66@pitt.edu
Abstract
Hi... | 6504 |@word cnn:1 middle:2 version:1 compression:4 norm:10 averagely:1 pg:1 solid:1 reduction:6 necessity:1 liu:4 configuration:1 series:1 offering:1 tuned:3 document:1 freitas:1 err:1 recovered:1 com:1 comparing:1 guadarrama:1 gemm:7 activation:1 written:1 gpu:16 john:2 grain:2 shape:33 enables:1 christian:2 remove:4 ... |
6,086 | 6,505 | Fast Active Set Methods for
Online Spike Inference from Calcium Imaging
1
Johannes Friedrich1,2 , Liam Paninski1
Grossman Center and Department of Statistics, Columbia University, New York, NY
2
Janelia Research Campus, Ashburn, VA
j.friedrich@columbia.edu, liam@stat.columbia.edu
Abstract
Fluorescent calcium indicat... | 6505 |@word neurophysiology:1 faculty:1 version:3 polynomial:3 c0:15 disk:1 proportionality:1 r:8 holy:1 solid:1 deisseroth:2 initial:2 series:6 contains:1 optically:2 daniel:1 denoting:1 outperforms:1 ksk1:3 current:4 optim:1 skipping:1 chu:2 john:1 numerical:2 realistic:2 confirming:2 enables:2 remove:1 succeeding:1 ... |
6,087 | 6,506 | NESTT: A Nonconvex Primal-Dual Splitting Method
for Distributed and Stochastic Optimization
Davood Hajinezhad, Mingyi Hong ?
Tuo Zhao?
Zhaoran Wang?
Abstract
We study a stochastic and distributed algorithm for nonconvex problems whose
objective consists of a sum of N nonconvex Li /N -smooth functions, plus a nonsmo... | 6506 |@word mild:1 version:4 norm:1 seems:1 logit:1 confirms:1 covariance:1 q1:2 pick:4 sgd:7 reduction:2 initial:2 liu:2 past:2 existing:2 surprising:1 luo:3 activation:1 chu:1 hajinezhad:3 written:1 additive:1 numerical:1 designed:4 update:8 stationary:11 lky:1 selected:3 half:1 antoniadis:1 ith:1 characterization:1 ... |
6,088 | 6,507 | LazySVD: Even Faster SVD Decomposition
Yet Without Agonizing Pain?
Zeyuan Allen-Zhu
zeyuan@csail.mit.edu
Institute for Advanced Study
& Princeton University
Yuanzhi Li
yuanzhil@cs.princeton.edu
Princeton University
Abstract
We study k-SVD that is to obtain the first k singular vectors of a matrix A.
Recently, a few ... | 6507 |@word version:6 inversion:5 knd:8 compression:1 norm:18 nd:3 polynomial:4 stronger:2 open:5 cleanly:1 km:1 tried:2 decomposition:4 incurs:1 reduction:6 liu:1 woodruff:1 denoting:1 ours:1 outperforms:4 kmk:4 ka:20 comparing:1 yet:1 numerical:3 plot:4 update:2 v:25 fewer:1 website:2 selected:1 short:1 core:3 provid... |
6,089 | 6,508 | Statistical Inference for Cluster Trees
Jisu Kim
Department of Statistics
Carnegie Mellon University
Pittsburgh, USA
jisuk1@andrew.cmu.edu
Yen-Chi Chen
Department of Statistics
University of Washington
Seattle, USA
yenchic@uw.edu
Alessandro Rinaldo
Department of Statistics
Carnegie Mellon University
Pittsburgh, USA
a... | 6508 |@word mild:2 eliminating:1 norm:1 open:2 simulation:3 crucially:1 p0:14 pick:2 concise:1 solid:6 contains:4 efficacy:1 pbh:2 existing:1 comparing:1 john:2 dtq:1 shape:3 remove:1 interpretable:2 generative:1 leaf:15 fewer:1 smith:1 provides:1 node:3 complication:1 firstly:1 simpler:4 phylogenetic:2 height:7 along:... |
6,090 | 6,509 | Deep Learning for Predicting
Human Strategic Behavior
Jason Hartford, James R. Wright, Kevin Leyton-Brown
Department of Computer Science
University of British Columbia
{jasonhar, jrwright, kevinlb}@cs.ubc.ca
Abstract
Predicting the behavior of human participants in strategic settings is an important
problem in many do... | 6509 |@word middle:3 version:5 proportion:3 nd:1 seek:1 simplifying:1 paid:1 sgd:1 thereby:1 recursively:1 initial:1 configuration:4 selecting:1 tuned:2 ours:2 offering:1 denoting:3 interestingly:1 outperforms:1 existing:5 current:2 si:2 activation:2 guez:1 must:4 john:1 subsequent:3 drop:1 update:1 aside:1 implying:1 ... |
6,091 | 651 | Memory-based Reinforcement Learning: Efficient
Computation with Prioritized Sweeping
Andrew W. Moore
awm@ai.mit.edu
NE43-759 MIT AI Lab.
545 Technology Square
Cambridge MA 02139
Christopher G. At:iteson
cga@ai.mit.edu
NE43-771 MIT AI Lab.
545 Technology Square
Cambridge MA 02139
Abstract
We present a new algorithm, ... | 651 |@word trial:1 version:1 tr:1 initial:1 tuned:2 existing:1 current:1 subsequent:1 remove:1 designed:1 alone:1 intelligence:1 prohibitive:1 fewer:1 record:1 draft:1 quantized:1 nom:1 five:1 rc:1 along:2 predecessor:5 qij:2 combine:1 peng:3 forgetting:1 themselves:1 examine:1 planning:1 terminal:3 globally:1 td:8 lit... |
6,092 | 6,510 | Depth from a Single Image by Harmonizing
Overcomplete Local Network Predictions
Ayan Chakrabarti
TTI-Chicago
Chicago, IL
ayanc@ttic.edu
Jingyu Shao
Dept. of Statistics, UCLA?
Los Angeles, CA
shaojy15@ucla.edu
Gregory Shakhnarovich
TTI-Chicago
Chicago, IL
gregory@ttic.edu
Abstract
A single color image can contain ma... | 6510 |@word kohli:1 version:1 norm:1 replicate:1 rgb:4 decomposition:1 sgd:1 shading:3 carry:2 initial:1 liu:3 contains:2 efficacy:1 disparity:1 hoiem:1 interestingly:1 current:2 z2:1 activation:3 gpu:1 chicago:5 informative:2 shape:1 cheap:1 cue:11 fewer:1 intelligence:1 parameterization:1 accordingly:1 plane:2 recipr... |
6,093 | 6,511 | Combinatorial Multi-Armed Bandit with General
Reward Functions
Wei Chen?
Wei Hu?
Fu Li?
Jian Li?
Yu Liu?
Pinyan Luk
Abstract
In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB)
framework that allows a general nonlinear reward function, whose expected value
may not depend only on the mea... | 6511 |@word luk:1 exploitation:1 version:2 private:1 polynomial:3 laurence:1 hu:1 d2:2 r:3 jacob:1 profit:1 boundedness:1 liu:2 contains:1 celebrated:1 selecting:1 daniel:1 past:1 existing:4 yajun:1 com:4 discretization:5 si:4 gmail:3 conjunctive:1 attracted:1 john:1 underly:1 numerical:1 partition:1 enables:1 remove:1... |
6,094 | 6,512 | LightRNN: Memory and Computation-Efficient
Recurrent Neural Networks
1
Xiang Li1
Tao Qin2 Jian Yang1 Tie-Yan Liu2
Nanjing University of Science and Technology 2 Microsoft Research Asia
1
implusdream@gmail.com 1 csjyang@njust.edu.cn
2
{taoqin, tie-yan.liu}@microsoft.com
Abstract
Recurrent neural networks (RNNs) have ... | 6512 |@word luk:1 msr:1 norm:1 hu:1 heuristically:1 tried:1 pavel:1 citeseer:1 solid:1 tnlist:1 reduction:2 initial:1 liu:2 contains:2 score:1 pub:1 blackout:3 document:2 outperforms:2 existing:2 com:5 comparing:1 activation:1 gmail:1 njust:1 gpu:8 partition:1 xcj:2 drop:1 fund:1 v:1 half:1 leaf:4 device:7 yr:1 data2:1... |
6,095 | 6,513 | Contextual semibandits via supervised learning oracles
Akshay Krishnamurthy?
akshay@cs.umass.edu
?
Alekh Agarwal?
alekha@microsoft.com
College of Information and Computer Sciences
University of Massachusetts, Amherst, MA
Miroslav Dud?k?
mdudik@microsoft.com
?
Microsoft Research
New York, NY
Abstract
We study an on... | 6513 |@word trial:1 exploitation:7 version:1 seems:1 norm:1 nd:3 suitably:1 c0:3 unif:2 open:1 termination:1 crucially:1 harder:1 moment:3 reduction:2 contains:1 uma:1 exclusively:1 selecting:2 tuned:3 document:7 longitudinal:1 outperforms:3 existing:6 contextual:33 com:4 discretization:1 chu:3 must:3 benign:1 enables:... |
6,096 | 6,514 | Stochastic Gradient Richardson-Romberg
Markov Chain Monte Carlo
?
Alain Durmus1 , Umut S?ims?ekli1 , Eric
Moulines2 , Roland Badeau1 , Ga?el Richard1
1: LTCI, CNRS, T?el?ecom ParisTech, Universit?e Paris-Saclay, 75013, Paris, France
?
2: Centre de Math?ematiques Appliqu?ees, UMR 7641, Ecole
Polytechnique, France
Abstr... | 6514 |@word mild:3 polynomial:1 norm:1 confirms:2 covariance:4 sgd:3 moment:1 initial:2 liu:1 contains:3 ecole:1 document:8 current:1 discretization:4 z2:1 yet:1 dx:1 additive:1 numerical:6 kdd:1 sdes:4 drop:1 update:2 stationary:1 generative:1 selected:1 intelligence:1 accordingly:4 xk:1 hamiltonian:4 core:2 provides:... |
6,097 | 6,515 | Riemannian SVRG: Fast Stochastic Optimization on
Riemannian Manifolds
Hongyi Zhang
Sashank J. Reddi
Suvrit Sra
MIT
Carnegie Mellon University
MIT
Abstract
We study optimization of finite sums of geodesically smooth functions on Riemannian manifolds. Although variance reduction techniques for optimizing finite-sum... | 6515 |@word briefly:1 version:1 middle:1 norm:4 trigonometry:1 stronger:1 seems:2 advantageous:1 nd:1 wiesel:2 hu:2 d2:2 simulation:2 bn:1 covariance:4 pick:1 sgd:2 sepulchre:1 reduction:11 liu:1 cherian:1 offering:1 bc:1 existing:2 mishra:1 elliptical:1 comparing:1 yet:1 john:1 numerical:1 analytic:1 plot:1 update:4 s... |
6,098 | 6,516 | Tensor Switching Networks
Chuan-Yung Tsai?, Andrew Saxe?, David Cox
Center for Brain Science, Harvard University, Cambridge, MA 02138
{chuanyungtsai,asaxe,davidcox}@fas.harvard.edu
Abstract
We present a novel neural network algorithm, the Tensor Switching (TS) network,
which generalizes the Rectified Linear Unit (ReL... | 6516 |@word cox:1 briefly:1 cnn:10 compression:6 advantageous:1 middle:1 hu:1 propagate:1 lobe:1 contraction:11 pick:1 sgd:1 reduction:4 bai:1 liu:2 rippel:1 tuned:2 interestingly:2 rightmost:1 current:1 comparing:1 com:1 activation:24 tackling:1 written:1 readily:1 must:3 gpu:4 wx:4 confirming:1 shape:2 remove:1 plot:... |
6,099 | 6,517 | The non-convex Burer?Monteiro approach works
on smooth semide?nite programs
Vladislav Voroninski?
Department of Mathematics
Massachusetts Institute of Technology
vvlad@math.mit.edu
Nicolas Boumal?
Department of Mathematics
Princeton University
nboumal@math.princeton.edu
Afonso S. Bandeira
Department of Mathematics an... | 6517 |@word version:1 polynomial:2 norm:7 stronger:1 nd:1 open:1 linearized:1 jacob:1 tr:5 sepulchre:3 initial:1 celebrated:1 interestingly:1 mishra:1 recovered:1 z2:2 com:1 toh:1 must:4 numerical:2 informative:1 benign:1 guess:1 caveat:3 provides:1 math:2 node:2 firstly:1 org:1 zhang:1 mathematical:7 consists:1 naor:2... |
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