Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
5,900 | 6,338 | Stein Variational Gradient Descent: A General
Purpose Bayesian Inference Algorithm
Qiang Liu
Dilin Wang
Department of Computer Science
Dartmouth College
Hanover, NH 03755
{qiang.liu, dilin.wang.gr}@dartmouth.edu
Abstract
We propose a general purpose variational inference algorithm that forms a natural
counterpart of ... | 6338 |@word mild:1 trial:3 determinant:1 version:3 inversion:1 norm:2 villani:2 open:1 closure:1 d2:3 simulation:2 covariance:1 p0:8 q1:3 solid:2 carry:1 initial:7 liu:3 ndez:3 score:1 selecting:1 pub:1 series:1 rkhs:9 ours:1 existing:1 freitas:1 current:1 com:3 yet:1 dx:1 subsequent:2 partition:1 designed:1 update:5 m... |
5,901 | 6,339 | Measuring Neural Net Robustness with Constraints
Osbert Bastani
Stanford University
obastani@cs.stanford.edu
Dimitrios Vytiniotis
Microsoft Research
dimitris@microsoft.com
Yani Ioannou
University of Cambridge
yai20@cam.ac.uk
Leonidas Lampropoulos
University of Pennsylvania
llamp@seas.upenn.edu
Aditya V. Nori
Micros... | 6339 |@word moosavi:1 version:2 norm:6 valle:1 shuicheng:1 seek:2 xtest:1 initial:1 contains:1 score:1 tuned:9 document:2 existing:3 guadarrama:1 com:3 surprising:1 activation:4 intriguing:1 must:3 visible:1 subsequent:1 informative:1 christian:2 plot:2 drop:1 n0:1 half:3 fewer:3 devising:2 amir:1 ith:1 core:1 yamada:1... |
5,902 | 634 | Weight Space Probability Densities
in Stochastic Learning:
I. Dynamics and Equilibria
Todd K. Leen and John E. Moody
Department of Computer Science and Engineering
Oregon Graduate Institute of Science & Technology
19600 N.W. von Neumann Dr.
Beaverton, OR 97006-1999
Abstract
The ensemble dynamics of stochastic learnin... | 634 |@word version:1 nd:2 simulation:1 solid:1 moment:1 series:3 current:2 si:2 john:2 realize:1 numerical:1 analytic:2 update:15 stationary:3 isotropic:1 hja:1 haykin:2 lr:1 characterization:1 provides:1 contribute:1 math:1 sigmoidal:1 along:3 differential:1 p8:2 behavior:3 mechanic:1 anisotropy:1 becomes:1 underlying... |
5,903 | 6,340 | Doubly Convolutional Neural Networks
Yu Cheng
IBM T.J. Watson Research Center
Yorktown Heights, NY 10598, USA
chengyu@us.ibm.com
Shuangfei Zhai
Binghamton University
Vestal, NY 13902, USA
szhai2@binghamton.edu
Weining Lu
Tsinghua University
Beijing 10084, China
luwn14@mails.tsinghua.edu.cn
Zhongfei (Mark) Zhang
Bing... | 6340 |@word cnn:25 version:6 compression:1 norm:1 seems:1 shuicheng:1 rgb:1 decomposition:1 reduction:2 configuration:3 contains:1 liu:1 cyclic:1 interestingly:2 outperforms:1 freitas:1 guadarrama:1 com:1 activation:5 gpu:1 readily:2 sanjiv:2 shape:12 christian:2 hongyang:1 designed:1 diogo:1 moczulski:1 v:1 fewer:2 ac... |
5,904 | 6,341 | Multi-armed Bandits:
Competing with Optimal Sequences
Oren Anava
The Voleon Group
Berkeley, CA
oren@voleon.com
Zohar Karnin
Yahoo! Research
New York, NY
zkarnin@yahoo-inc.com
Abstract
We consider sequential decision making problem in the adversarial setting, where
regret is measured with respect to the optimal seque... | 6341 |@word exploitation:14 polynomial:2 stronger:2 norm:2 open:1 willing:1 seek:1 forecaster:1 attainable:2 solid:2 moment:1 necessity:1 contains:2 series:2 ktv:3 tuned:1 ours:2 past:2 existing:1 com:2 yet:4 intriguing:1 must:3 tackling:1 ronald:1 subsequent:1 partition:2 additive:2 benign:1 designed:1 stationary:16 h... |
5,905 | 6,342 | Learning Infinite RBMs with Frank-Wolfe
?
Wei Ping?
Qiang Liu?
Alexander Ihler?
?
Computer Science, UC Irvine
Computer Science, Dartmouth College
{wping,ihler}@ics.uci.edu qliu@cs.dartmouth.edu
Abstract
In this work, we propose an infinite restricted Boltzmann machine (RBM), whose
maximum likelihood estimation (MLE)... | 6342 |@word norm:2 seems:1 r:1 contrastive:3 tr:1 moment:2 liu:1 series:1 configuration:1 contains:1 united:1 document:2 fa8750:1 outperforms:2 existing:2 bradley:2 current:2 freitas:1 beygelzimer:2 luo:1 activation:1 visible:4 partition:5 enables:2 utml:1 remove:1 drop:1 sponsored:1 update:10 generative:1 greedy:11 fe... |
5,906 | 6,343 | Object based Scene Representations using Fisher
Scores of Local Subspace Projections
Mandar Dixit and Nuno Vasconcelos
Department of Electrical and Computer Engineering
University of California, San Diego
{mdixit, nvasconcelos}@ucsd.edu
Abstract
Several works have shown that deep CNNs can be easily transferred across... | 6343 |@word cnn:41 ruiqi:1 loading:5 kokkinos:1 bn:5 covariance:19 harder:1 accommodate:1 reduction:1 initial:1 liu:5 configuration:1 score:28 tuned:1 document:1 outperforms:6 past:1 current:1 comparing:1 activation:2 yet:2 si:5 written:2 additive:1 informative:1 enables:3 x160:4 v:1 generative:5 pursued:1 selected:1 i... |
5,907 | 6,344 | Adaptive optimal training of animal behavior
Ji Hyun Bak1,4 Jung Yoon Choi2,3 Athena Akrami3,5 Ilana Witten2,3 Jonathan W. Pillow2,3
1
Department of Physics, 2 Department of Psychology, Princeton University
3
Princeton Neuroscience Institute, Princeton University
4
School of Computational Sciences, Korea Institute fo... | 6344 |@word trial:39 exploitation:2 achievable:1 stronger:1 nd:1 d2:1 hu:1 gradual:1 seek:4 simulation:3 covariance:3 solid:1 reduction:1 initial:1 ndez:1 series:7 selecting:2 interestingly:1 past:1 existing:2 current:7 comparing:1 recovered:2 yet:2 intriguing:1 written:5 readily:1 subsequent:1 numerical:1 realistic:1 ... |
5,908 | 6,345 | Finding significant combinations of features in the
presence of categorical covariates
Laetitia Papaxanthos? , Felipe Llinares-L?pez? , Dean Bodenham, Karsten Borgwardt
Machine Learning and Computational Biology Lab
D-BSSE, ETH Zurich
*Equally contributing authors.
Abstract
In high-dimensional settings, where the num... | 6345 |@word version:2 proportion:2 stronger:1 open:1 hu:1 confirms:1 attainable:12 invoking:1 recursively:2 reduction:2 contains:2 exclusively:1 efficacy:1 series:1 genetic:9 ours:1 outperforms:1 existing:5 rowan:1 current:5 com:1 must:2 j1:3 enables:1 discovering:2 caucasian:1 accordingly:1 lamp:16 ith:3 short:1 andbo... |
5,909 | 6,346 | Maximal Sparsity with Deep Networks?
Bo Xin1,2
Yizhou Wang1
Wen Gao1
Baoyuan Wang3
David Wipf2
2
3
Peking University
Microsoft Research, Beijing
Microsoft Research, Redmond
{boxin, baoyuanw, davidwip}@microsoft.com {yizhou.wang, wgao}@pku.edu.cn
1
Abstract
The iterations of many sparse estimation algorithms are compr... | 6346 |@word cnn:2 unaltered:1 version:3 interleave:1 norm:9 middle:1 advantageous:1 d2:2 seek:1 propagate:1 crucially:1 contraction:1 decomposition:1 carry:1 reduction:1 substitution:1 selecting:1 mosher:1 tuned:1 ours:3 demarcated:1 existing:12 recovered:3 ka:1 com:1 culprit:1 activation:9 yet:1 reminiscent:3 must:4 r... |
5,910 | 6,347 | Computing and maximizing influence in linear
threshold and triggering models
Justin Khim
Department of Statistics
The Wharton School
University of Pennsylvania
Philadelphia, PA 19104
jkhim@wharton.upenn.edu
Varun Jog
Electrical & Computer Engineering Department
University of Wisconsin - Madison
Madison, WI 53706
vjog... | 6347 |@word achievable:1 norm:1 open:2 km:1 simulation:17 decomposition:1 initial:3 celebrated:1 series:3 selecting:1 existing:1 recovered:1 bta:5 si:5 yet:1 written:1 kdd:1 cis:1 designed:1 succeeding:2 implying:2 greedy:19 selected:5 website:1 beginning:1 record:2 provides:3 node:20 zhang:2 mathematical:5 along:1 sym... |
5,911 | 6,348 | Finite-Dimensional BFRY Priors and Variational
Bayesian Inference for Power Law Models
Juho Lee
POSTECH, Korea
stonecold@postech.ac.kr
Lancelot F. James
HKUST, Hong Kong
lancelot@ust.hk
Seungjin Choi
POSTECH, Korea
seungjin@postech.ac.kr
Abstract
Bayesian nonparametric methods based on the Dirichlet Process (DP), g... | 6348 |@word kong:1 cu:1 middle:1 trial:1 calculus:1 simulation:1 decomposition:1 carry:1 initial:1 series:1 document:4 comparing:1 hkust:2 ust:1 readily:2 must:1 written:7 partition:2 j1:3 enables:1 remove:1 update:1 implying:1 generative:2 blei:4 straddling:1 nrm:3 favaro:1 mathematical:1 along:1 constructed:1 direct:... |
5,912 | 6,349 | DECOrrelated feature space partitioning for
distributed sparse regression
Xiangyu Wang
Dept. of Statistical Science
Duke University
wwrechard@gmail.com
David Dunson
Dept. of Statistical Science
Duke University
dunson@stat.duke.edu
Chenlei Leng
Dept. of Statistics
University of Warwick
C.Leng@warwick.ac.uk
Abstract
... | 6349 |@word repository:1 version:2 complying:1 norm:3 cortez:1 c0:8 simulation:2 decomposition:3 p0:1 accommodate:1 contains:4 score:1 selecting:1 series:4 existing:1 elliptical:1 com:1 comparing:2 surprising:1 z2:1 nicolai:1 gmail:1 anne:1 readily:1 portuguese:1 john:1 numerical:3 partition:13 visible:1 remove:2 desig... |
5,913 | 635 | Topography and Ocular Dominance
with Positive Correlations
Geoffrey J. Goodhill
University of Edinburgh
Centre for Cognitive Science
2 Buccleuch Place
Edinburgh EH8 9LW
SCOTLAND
Abstract
A new computational model that addresses the formation of both topography and ocular dominance is presented. This is motivated by e... | 635 |@word neurophysiology:1 version:1 wiesel:2 stronger:2 termination:1 grey:2 confirms:1 simulation:2 pick:1 series:1 existing:1 assigning:1 atop:1 must:1 nervous:1 postnatal:1 scotland:1 short:1 draft:1 preference:1 firstly:1 mathematical:1 along:1 burst:1 consists:1 manner:1 brain:5 terminal:1 increasing:1 becomes:... |
5,914 | 6,350 | Adaptive Skills Adaptive Partitions (ASAP)
Daniel J. Mankowitz, Timothy A. Mann? and Shie Mannor
The Technion - Israel Institute of Technology,
Haifa, Israel
danielm@tx.technion.ac.il, mann.timothy@acm.org, shie@ee.technion.ac.il
?
Timothy Mann now works at Google Deepmind.
Abstract
We introduce the Adaptive Skills,... | 6350 |@word multitask:2 trial:1 polynomial:6 reused:6 open:1 termination:1 pieter:1 simulation:2 git:1 decomposition:1 pg:5 shot:1 initial:3 bai:3 contains:1 score:2 daniel:3 past:1 existing:1 current:7 com:1 yet:1 must:1 partition:18 shape:2 wanted:1 motor:1 update:11 half:6 fewer:2 intelligence:1 parameterization:1 a... |
5,915 | 6,351 | Sample Complexity of Automated Mechanism Design
Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
{ninamf,sandholm,vitercik}@cs.cmu.edu
Abstract
The design of revenue-maximizing combinatorial auctions, i.e. multi-item auctions
over bundles... | 6351 |@word faculty:1 version:1 polynomial:1 achievable:1 open:1 additively:2 thereby:3 minus:2 versatile:1 denoting:1 ironing:1 michal:1 must:7 written:2 additive:3 designed:2 alone:1 intelligence:3 fewer:1 item:51 beginning:1 ith:1 alexandros:1 characterization:3 boosting:2 preference:1 attack:1 simpler:3 dn:1 supply... |
5,916 | 6,352 | Learning brain regions via large-scale online
structured sparse dictionary-learning
Elvis Dohmatob, Arthur Mensch, Gael Varoquaux, Bertrand Thirion
firstname.lastname@inria.fr
Parietal Team, INRIA / CEA, Neurospin, Universit? Paris-Saclay, France
Abstract
We propose a multivariate online dictionary-learning method fo... | 6352 |@word version:3 norm:2 loading:1 open:1 lobe:1 decomposition:13 eng:1 concise:1 mention:2 boundedness:1 reduction:2 initial:1 configuration:1 contains:1 series:1 selecting:1 score:7 genetic:1 outperforms:1 current:5 activation:5 yet:2 must:3 reminiscent:1 written:1 readily:1 mesh:1 numerical:2 connectomics:1 shap... |
5,917 | 6,353 | Neurally-Guided Procedural Models:
Amortized Inference for Procedural Graphics
Programs using Neural Networks
Daniel Ritchie
Stanford University
Anna Thomas
Stanford University
Pat Hanrahan
Stanford University
Noah D. Goodman
Stanford University
Abstract
Probabilistic inference algorithms such as Sequential Monte ... | 6353 |@word kohli:1 version:1 open:1 seek:1 recursively:2 generatively:2 series:2 score:1 contains:1 jimenez:1 daniel:3 ours:1 past:1 current:9 com:1 unguided:19 surprising:2 yet:1 diederik:2 must:2 shape:17 asymptote:1 designed:1 plot:1 generative:6 fewer:1 leaf:1 geyer:1 painstaking:1 core:3 blei:1 provides:3 coarse:... |
5,918 | 6,354 | Theoretical Comparisons of Positive-Unlabeled
Learning against Positive-Negative Learning
Gang Niu1 Marthinus C. du Plessis1 Tomoya Sakai1 Yao Ma3 Masashi Sugiyama2,1
1
The University of Tokyo, Japan 2 RIKEN, Japan 3 Boston University, USA
{ gang@ms., christo@ms., sakai@ms., yao@ms., sugi@ }k.u-tokyo.ac.jp
Abstract
I... | 6354 |@word mild:3 repository:2 version:1 proportion:1 bpu:11 open:1 decomposition:2 covariance:1 contains:1 ours:1 outperforms:1 spambase:3 past:1 ramsey:1 ida:2 gpu:8 john:1 academia:1 informative:1 kdd:1 depict:2 selected:1 accordingly:2 letouzey:1 denis:2 hyperplanes:1 firstly:1 org:1 zhang:2 five:1 unbounded:1 mcd... |
5,919 | 6,355 | Fairness in Learning: Classic and Contextual Bandits ?
Matthew Joseph
Michael Kearns
Jamie Morgenstern
Aaron Roth
University of Pennsylvania, Department of Computer and Information Science
majos, mkearns, jamiemor, aaroth@cis.upenn.edu
Abstract
We introduce the study of fairness in multi-armed bandit problems. Ou... | 6355 |@word mild:1 version:2 achievable:1 polynomial:6 seems:2 norm:1 closure:1 simulation:1 jacob:1 attainable:5 pick:2 incurs:1 nsw:1 reduction:2 venkatasubramanian:2 mkearns:1 series:2 contains:1 daniel:1 existing:1 contextual:36 nt:4 com:1 beygelzimer:1 chu:1 must:14 import:1 applicant:5 john:3 numerical:1 subseque... |
5,920 | 6,356 | Probabilistic Linear Multistep Methods
Onur Teymur
Department of Mathematics
Imperial College London
o@teymur.uk
Konstantinos Zygalakis
School of Mathematics
University of Edinburgh
k.zygalakis@ed.ac.uk
Ben Calderhead
Department of Mathematics
Imperial College London
b.calderhead@imperial.ac.uk
Abstract
We present a... | 6356 |@word h:13 middle:1 version:4 polynomial:10 briefly:1 nd:1 open:1 seek:1 simulation:1 covariance:5 simplifying:2 decomposition:2 p0:1 concise:1 reduction:1 electronics:1 initial:6 series:3 initialisation:3 current:1 fn:1 numerical:22 visible:1 confirming:1 sdes:1 extrapolating:1 plot:6 intelligence:1 device:2 acc... |
5,921 | 6,357 | High-Rank Matrix Completion and Clustering
under Self-Expressive Models
E. Elhamifar?
College of Computer and Information Science
Northeastern University
Boston, MA 02115
eelhami@ccs.neu.edu
Abstract
We propose efficient algorithms for simultaneous clustering and completion of
incomplete high-dimensional data that li... | 6357 |@word trial:1 version:1 middle:6 compression:1 norm:11 polynomial:2 jacob:1 hsieh:1 inpainting:1 solid:2 tr:1 ld:5 series:1 selecting:6 neeman:1 outperforms:5 existing:3 recovered:3 ganti:1 yet:1 chu:1 written:2 shape:1 remove:2 drop:2 plot:6 mackey:1 intelligence:5 selected:4 guess:1 record:1 chiang:1 provides:2... |
5,922 | 6,358 | Safe Exploration in Finite Markov Decision Processes
with Gaussian Processes
Matteo Turchetta
ETH Zurich
matteotu@ethz.ch
Felix Berkenkamp
ETH Zurich
befelix@ethz.ch
Andreas Krause
ETH Zurich
krausea@ethz.ch
Abstract
In classical reinforcement learning agents accept arbitrary short term loss for long
term gain when ... | 6358 |@word h:1 norm:3 mockus:1 c0:3 open:2 instruction:1 pieter:1 simulation:1 covariance:2 schoellig:2 thereby:2 ld:4 initial:5 ndez:2 contains:3 selecting:1 daniel:1 rkhs:3 s16:1 current:2 com:1 must:6 john:1 planet:1 informative:1 enables:1 burdick:1 plot:1 update:1 greedy:1 prohibitive:1 intelligence:2 randolph:1 ... |
5,923 | 6,359 | Stochastic Gradient MCMC with Stale Gradients
Changyou Chen?
Nan Ding?
Chunyuan Li?
Yizhe Zhang?
Lawrence Carin?
?
Dept. of Electrical and Computer Engineering, Duke University, Durham, NC, USA
?
Google Inc., Venice, CA, USA
?
{cc448,cl319,yz196,lcarin}@duke.edu; ? dingnan@google.com
Abstract
Stochastic gradient MCMC ... | 6359 |@word h:5 cnn:1 version:1 achievable:2 changyou:1 johansson:1 nd:1 d2:4 cipar:1 covariance:1 sgd:4 moment:1 reduction:1 liu:2 denoting:1 interestingly:1 existing:3 guadarrama:1 com:1 comparing:1 must:1 numerical:4 partition:1 kdd:1 cheap:1 analytic:1 designed:1 plot:4 update:4 drop:1 v:5 stationary:2 selected:1 h... |
5,924 | 636 | Remote Sensing Image Analysis via a Texture
Classification Neural Network
Hayit K. Greenspan and Rodney Goodman
Department of Electrical Engineering
California Institute of Technology, 116-81
Pasadena, CA 91125
hayit@electra.micro.caltech.edu
Abstract
In this work we apply a texture classification network to remote s... | 636 |@word version:1 duda:2 nd:1 open:1 decomposition:2 minus:1 reduction:1 initial:8 contains:3 existing:2 current:1 discretization:1 yet:1 john:1 visible:1 informative:3 enables:1 v:1 alone:3 pursued:2 discrimination:2 intelligence:1 filtered:2 provides:1 quantized:2 node:4 detecting:1 become:1 consists:5 combine:1 b... |
5,925 | 6,360 | Learning Transferrable Representations for
Unsupervised Domain Adaptation
Ozan Sener1 ,
Hyun Oh Song1 , Ashutosh Saxena2 , Silvio Savarese1
Stanford University1 Brain of Things2
{ozan,hsong,asaxena,ssilvio}@cs.stanford.edu
Abstract
Supervised learning with large scale labelled datasets and deep layered models has
ca... | 6360 |@word kulis:1 cnn:1 version:3 tedious:1 seek:3 covariance:1 shot:3 initial:2 cyclic:6 series:1 score:2 liu:1 tuned:1 document:1 outperforms:2 existing:4 ddc:1 written:1 subsequent:1 realistic:1 cant:1 remove:1 plot:5 designed:1 ashutosh:1 update:1 intelligence:1 fewer:1 bissacco:1 colored:1 zhang:1 qualitative:2 ... |
5,926 | 6,361 | Deep Submodular Functions: Definitions & Learning
Brian Dolhansky? <bdol@cs.washington.edu>
Dept. of Computer Science and Engineering?
University of Washington
Seattle, WA 98105
Jeff Bilmes?? <bilmes@uw.edu>
Dept. of Electrical Engineering?
University of Washington
Seattle, WA 98105
Abstract
We propose and study a n... | 6361 |@word achievable:1 polynomial:1 nd:2 semidifferential:1 open:1 closure:1 seek:1 propagate:1 kent:1 thereby:1 harder:1 recursively:1 bai:1 liu:1 series:1 contains:1 selecting:2 score:1 initial:1 document:4 interestingly:4 past:1 diagonalized:1 current:1 skipping:2 v21:2 activation:4 si:1 yet:1 reminiscent:1 must:1... |
5,927 | 6,362 | Beyond Exchangeability: The Chinese Voting Process
Moontae Lee
Dept. of Computer Science
Cornell University
Ithaca, NY 14853
moontae@cs.cornell.edu
Seok Hyun Jin
Dept. of Computer Science
Cornell University
Ithaca, NY 14853
sj372@cornell.edu
David Mimno
Dept. of Information Science
Cornell University
Ithaca, NY 14853... | 6362 |@word middle:2 eliminating:1 achievable:1 logit:2 nd:1 thereby:1 fif:1 initial:5 configuration:2 contains:1 score:10 selecting:1 fragment:3 afraid:1 liu:1 electronics:1 outperforms:1 existing:7 current:3 contextual:2 comparing:2 com:1 assigning:1 written:1 must:1 subsequent:1 eleven:1 enables:1 drop:1 plot:1 impl... |
5,928 | 6,363 | Tractable Operations for
Arithmetic Circuits of Probabilistic Models
Yujia Shen and Arthur Choi and Adnan Darwiche
Computer Science Department
University of California
Los Angeles, CA 90095
{yujias,aychoi,darwiche}@cs.ucla.edu
Abstract
We consider tractable representations of probability distributions and the polytime... | 6363 |@word version:2 polynomial:1 stronger:2 jointree:4 adnan:1 mention:1 recursively:2 reduction:2 contains:1 denoting:1 interestingly:1 delcher:2 assigning:1 si:3 gaona:1 fn:2 dechter:5 partition:1 acar:2 remove:2 update:1 leaf:5 selected:1 item:2 parameterization:2 provides:2 parameterizations:1 node:23 traverse:2 ... |
5,929 | 6,364 | Optimal Black-Box Reductions
Between Optimization Objectives?
Zeyuan Allen-Zhu
zeyuan@csail.mit.edu
Institute for Advanced Study
& Princeton University
Elad Hazan
ehazan@cs.princeton.edu
Princeton University
Abstract
The diverse world of machine learning applications has given rise to a plethora
of algorithms and op... | 6364 |@word private:1 version:14 polynomial:2 norm:3 termination:2 tat:2 mention:2 solid:4 moment:1 initial:3 reduction:62 necessity:1 tuned:2 ours:2 existing:4 kx0:2 current:1 comparing:2 written:1 interrupted:2 subsequent:1 zaid:1 designed:1 plot:7 update:4 website:1 amir:1 accordingly:2 beginning:2 farther:2 charact... |
5,930 | 6,365 | Achieving the KS threshold in the general stochastic
block model with linearized acyclic belief propagation
Emmanuel Abbe
Applied and Computational Mathematics and EE Dept.
Princeton University
eabbe@princeton.edu
Colin Sandon
Department of Mathematics
Princeton University
sandon@princeton.edu
Abstract
The stochasti... | 6365 |@word version:6 polynomial:1 seems:2 proportion:1 vi1:2 suitably:1 yv0:5 open:2 c0:15 leighton:1 nd:1 linearized:5 decomposition:2 moment:1 initial:5 series:2 united:1 neeman:3 interestingly:1 janson:1 bhattacharyya:1 whp:2 nt:1 assigning:3 partition:5 happen:1 remove:1 update:1 intelligence:1 guess:6 beginning:1... |
5,931 | 6,366 | Yggdrasil: An Optimized System for Training Deep
Decision Trees at Scale
Firas Abuzaid1 , Joseph Bradley2 , Feynman Liang3 , Andrew Feng4 , Lee Yang4 ,
Matei Zaharia1 , Ameet Talwalkar5
1
2
MIT CSAIL, Databricks, 3 University of Cambridge, 4 Yahoo, 5 UCLA
Abstract
Deep distributed decision trees and tree ensembles hav... | 6366 |@word trial:1 private:1 eliminating:1 compression:9 advantageous:1 disk:1 open:3 vldb:2 confirms:1 seek:1 asks:1 recursively:1 reduction:3 configuration:1 contains:1 series:1 liu:1 tuned:2 franklin:1 outperforms:4 existing:2 silvescu:1 bitmap:2 discretization:7 bradley:1 yet:1 dx:1 must:4 planet:5 partition:6 ana... |
5,932 | 6,367 | Deep Neural Networks with Inexact Matching for
Person Re-Identification
Arulkumar Subramaniam
Indian Institute of Technology Madras
Chennai, India 600036
aruls@cse.iitm.ac.in
Moitreya Chatterjee
Indian Institute of Technology Madras
Chennai, India 600036
metro.smiles@gmail.com
Anurag Mittal
Indian Institute of Techno... | 6367 |@word trial:1 cnn:10 kulis:1 polynomial:1 norm:5 nd:1 hu:1 seek:1 jingdong:1 citeseer:1 pick:3 sgd:1 thereby:1 shot:1 liu:1 tuned:1 ours:12 interestingly:3 outperforms:1 existing:6 current:2 com:2 comparing:2 activation:1 gmail:1 yet:3 must:1 gpu:1 subsequent:2 visible:2 christian:1 hypothesize:2 plot:1 farenzena... |
5,933 | 6,368 | Local Similarity-Aware Deep Feature Embedding
Chen Huang
Chen Change Loy
Xiaoou Tang
Department of Information Engineering, The Chinese University of Hong Kong
{chuang,ccloy,xtang}@ie.cuhk.edu.hk
Abstract
Existing deep embedding methods in vision tasks are capable of learning a compact
Euclidean space from images, wh... | 6368 |@word kong:2 cnn:17 middle:1 version:1 dalal:1 compression:1 kokkinos:1 triggs:1 open:5 seek:2 contrastive:6 incurs:3 tr:1 shot:15 necessity:1 liu:2 contains:1 score:32 selecting:1 efficacy:1 initial:1 bc:3 ours:2 amp:1 outperforms:1 existing:5 current:1 contextual:1 si:19 scatter:1 goldberger:1 readily:1 gpu:1 c... |
5,934 | 6,369 | Efficient Nonparametric Smoothness Estimation
Shashank Singh
Carnegie Mellon University
sss1@andrew.cmu.edu
Simon S. Du
Carnegie Mellon University
ssdu@cs.cmu.edu
Barnab?s P?czos
Carnegie Mellon University
bapoczos@cs.cmu.edu
Abstract
Sobolev quantities (norms, inner products, and distances) of probability density
... | 6369 |@word mild:1 sss1:2 polynomial:2 norm:27 seems:1 unif:2 willing:1 simulation:1 decomposition:1 covariance:2 series:4 ours:1 rkhs:1 com:1 comparing:1 dx:8 written:1 fn:3 additive:1 realistic:1 numerical:3 analytic:1 resampling:1 half:1 fewer:1 kandasamy:3 intelligence:1 provides:3 math:2 location:1 unbounded:1 con... |
5,935 | 637 | Weight Space Probability Densities
in Stochastic Learning:
II. Transients and Basin Hopping Times
Genevieve B. Orr and Todd K. Leen
Department of Computer Science and Engineering
Oregon Graduate Institute of Science & Technology
19600 N.W. von Neumann Drive
Beaverton, OR 97006-1999
Abstract
In stochastic learning, we... | 637 |@word nd:1 simulation:3 minus:1 solid:1 initial:3 configuration:1 series:3 john:2 numerical:2 j1:3 christian:1 update:5 stationary:1 leaf:1 selected:1 provides:1 math:2 location:1 height:1 along:1 direct:2 differential:1 become:1 behavior:2 examine:1 decreasing:2 what:1 exactly:1 grant:1 planck:9 before:3 engineer... |
5,936 | 6,370 | Regret of Queueing Bandits
Subhashini Krishnasamy
University of Texas at Austin
Rajat Sen
University of Texas at Austin
Ramesh Johari
Stanford University
Sanjay Shakkottai
University of Texas at Austin
Abstract
We consider a variant of the multiarmed bandit problem where jobs queue for service, and service rates of... | 6370 |@word trial:1 exploitation:2 version:3 cox:1 open:3 simulation:3 pick:1 dramatic:1 paid:1 recursively:1 initial:3 series:1 past:6 current:4 intriguing:1 must:2 enables:1 stationary:1 greedy:1 beginning:5 smith:1 caveat:1 provides:2 coarse:1 characterization:1 revisited:1 mathematical:1 constructed:1 c2:2 become:1... |
5,937 | 6,371 | Online Convex Optimization with Unconstrained
Domains and Losses
Kwabena Boahen
Department of Bioengineering
Stanford University
boahen@stanford.edu
Ashok Cutkosky
Department of Computer Science
Stanford University
ashokc@cs.stanford.edu
Abstract
We propose an online convex optimization algorithm (RESCALEDEXP) that a... | 6371 |@word mild:2 madelon:1 cu:4 repository:1 manageable:1 norm:1 open:3 cleanly:1 hu:1 jacob:3 citeseer:1 sgd:1 thereby:1 recursively:1 olo:5 reduction:1 initial:1 ftrl:9 lichman:1 tist:1 punishes:1 rkhs:1 document:1 existing:1 current:1 diederik:1 must:2 subsequent:1 hofmann:1 plot:1 update:7 v:2 guess:3 website:1 b... |
5,938 | 6,372 | Learning the Number of Neurons in Deep Networks
Jose M. Alvarez?
Data61 @ CSIRO
Canberra, ACT 2601, Australia
jose.alvarez@data61.csiro.au
Mathieu Salzmann
CVLab, EPFL
CH-1015 Lausanne, Switzerland
mathieu.salzmann@epfl.ch
Abstract
Nowadays, the number of layers and of neurons in each layer of a deep network
are typ... | 6372 |@word kohli:3 trial:1 version:1 eliminating:1 norm:3 rgb:2 decomposition:1 simplifying:1 reduction:14 initial:14 liu:5 contains:1 configuration:1 selecting:1 series:1 salzmann:2 tuned:1 ours:15 interestingly:1 rog:1 past:2 existing:1 freitas:1 current:2 optim:1 places2:4 yet:2 bello:3 written:1 gpu:2 john:1 subse... |
5,939 | 6,373 | k ?-Nearest Neighbors: From Global to Local
Oren Anava
The Voleon Group
oren@voleon.com
Kfir Y. Levy
ETH Zurich
yehuda.levy@inf.ethz.ch
Abstract
The weighted k-nearest neighbors algorithm is one of the most fundamental nonparametric methods in pattern recognition and machine learning. The question of
setting the opti... | 6373 |@word kulis:2 repository:1 version:2 polynomial:2 seems:2 nd:3 open:1 essay:1 seek:1 motoda:1 profit:1 ld:2 liu:1 series:1 genetic:1 document:1 interestingly:1 outperforms:1 existing:1 current:1 com:1 written:1 enables:1 designed:2 update:1 discrimination:2 greedy:2 half:3 website:1 xk:1 core:1 epanechnikov:1 pro... |
5,940 | 6,374 | Equality of Opportunity in Supervised Learning
Moritz Hardt
Google
m@mrtz.org
Eric Price?
UT Austin
ecprice@cs.utexas.edu
Nathan Srebro
TTI-Chicago
nati@ttic.edu
Abstract
We propose a criterion for discrimination against a specified sensitive attribute in
supervised learning, where the goal is to predict some targe... | 6374 |@word middle:1 achievable:3 justice:1 checkable:1 zliobaite:1 p0:4 pick:7 concise:1 asks:1 profit:12 denying:1 score:39 united:1 seriously:1 sendhil:1 envision:1 bilal:1 subjective:1 existing:4 manuel:1 must:1 yep:12 applicant:1 realize:1 john:2 chicago:1 subsequent:1 remove:1 plot:2 interpretable:1 discriminatio... |
5,941 | 6,375 | Interaction Screening: Efficient and Sample-Optimal
Learning of Ising Models
Marc Vuffray1 , Sidhant Misra2 , Andrey Y. Lokhov1,3 , and Michael Chertkov1,3,4
1
Theoretical Division T-4, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
Theoretical Division T-5, Los Alamos National Laboratory, Los Alamos, NM 8... | 6375 |@word trial:1 achievable:1 polynomial:3 norm:1 d2:3 simulation:1 seek:1 covariance:4 mention:1 harder:1 kappen:1 liu:2 contains:1 configuration:4 selecting:1 recovered:1 surprising:1 guez:1 additive:2 numerical:3 partition:2 camacho:1 enables:2 plot:2 designed:1 greedy:1 guess:1 iso:13 provides:2 node:17 simpler:... |
5,942 | 6,376 | General Tensor Spectral Co-clustering
for Higher-Order Data
Tao Wu
Purdue University
wu577@purdue.edu
Austin R. Benson
Stanford University
arbenson@stanford.edu
David F. Gleich
Purdue University
dgleich@purdue.edu
Abstract
Spectral clustering and co-clustering are well-known techniques in data analysis,
and recent ... | 6376 |@word trial:2 illustrating:1 version:1 briefly:1 decomposition:3 pick:1 recursively:2 initial:2 celebrated:1 contains:2 score:2 efficacy:2 selecting:2 united:2 liu:1 document:1 interestingly:1 outperforms:1 com:2 luo:1 yet:1 assigning:1 must:2 numerical:1 partition:11 kdd:4 moreno:1 designed:2 concert:1 stationar... |
5,943 | 6,377 | Fast and Flexible Monotonic Functions with
Ensembles of Lattices
K. Canini, A. Cotter, M. R. Gupta, M. Milani Fard, J. Pfeifer
Google Inc.
1600 Amphitheatre Parkway, Mountain View, CA 94043
{canini,acotter,mayagupta,janpf,mmilanifard}@google.com
Abstract
For many machine learning problems, there are some inputs that a... | 6377 |@word repository:2 norm:1 hu:1 sgd:1 delgado:1 score:1 selecting:5 daniel:1 past:1 com:1 comparing:1 must:1 realistic:1 informative:1 enables:2 remove:1 plot:1 interpretable:1 hypothesize:1 v:5 greedy:1 leaf:3 selected:1 fewer:1 intelligence:1 oldest:1 ith:1 provides:2 sigmoidal:1 zhang:1 along:1 constructed:1 be... |
5,944 | 6,378 | Learning Parametric Sparse Models for Image
Super-Resolution
Yongbo Li, Weisheng Dong?, Xuemei Xie, Guangming Shi1 , Xin Li2 , Donglai Xu3
State Key Lab. of ISN, School of Electronic Engineering, Xidian University, China
1
Key Lab. of IPIU (Chinese Ministry of Education), Xidian University, China
2
Lane Dep. of CSEE, ... | 6378 |@word version:1 norm:1 linearized:1 eng:1 set5:2 initial:1 nonlocally:1 suppressing:1 outperforms:3 existing:3 subjective:2 current:3 recovered:6 surprising:1 activation:1 additive:1 blur:5 bsd100:4 remove:1 update:2 fund:1 intelligence:2 selected:1 lr:54 zhang:3 lowresolution:1 consists:1 combine:1 aliasing:1 bm... |
5,945 | 6,379 | Composing graphical models with neural networks
for structured representations and fast inference
Matthew James Johnson
Harvard University
mattjj@seas.harvard.edu
Alexander B. Wiltschko
Harvard University, Twitter
awiltsch@fas.harvard.edu
David Duvenaud
Harvard University
dduvenaud@seas.harvard.edu
Sandeep R. Datta
H... | 6379 |@word illustrating:1 middle:1 reused:1 bun:2 covariance:1 harder:1 generatively:1 series:2 comparing:1 z2:2 cxn:1 com:1 yet:1 diederik:1 must:2 readily:1 parsing:2 john:2 shape:3 enables:1 interpretable:4 update:8 generative:7 intelligence:1 parameterization:1 ivo:1 short:2 blei:1 provides:3 daphne:1 mathematical... |
5,946 | 638 | Network Structuring And Training Using
Rule-based Knowledge
Volker Tresp
Siemens AG
Central Research
Otto-Hahn-Ring 6
8000 Munchen 83, Germany
Jiirgen Hollatz*
Institut fur Informatik
TV Munchen
ArcisstraBe 21
8000 Munchen 2, Germany
Subutai Ahmad
Siemens AG
Central Research
Otto-Hahn-Ring 6
8000 Munchen 83, Germany... | 638 |@word polynomial:1 proportion:1 nd:1 yisi:1 jacob:2 concise:1 reduction:2 initial:5 tuned:1 interestingly:1 subjective:1 existing:1 ninit:2 neuneier:1 nowlan:2 si:8 conjunct:1 dx:1 subsequent:1 additive:1 partition:1 hofmann:4 remove:2 xlclass:1 alone:1 intelligence:1 xk:2 beginning:2 provides:2 miinchen:1 mathema... |
5,947 | 6,380 | Mutual information for symmetric rank-one matrix
estimation: A proof of the replica formula
Jean Barbier, Mohamad Dia and Nicolas Macris
Laboratoire de Th?orie des Communications, Facult? Informatique et Communications,
Ecole Polytechnique F?d?rale de Lausanne, 1015, Suisse.
firstname.lastname@epfl.ch
Florent Krzakala... | 6380 |@word version:2 briefly:1 polynomial:4 norm:1 open:2 calculus:1 r:55 covariance:1 p0:20 arous:1 harder:1 moment:1 zij:3 ecole:2 interestingly:1 mmse:36 amp:91 rightmost:2 existing:1 com:2 analysed:2 si:12 gmail:2 universality:3 dx:1 attracted:1 readily:1 must:7 reminiscent:1 additive:3 partition:1 shape:1 analyti... |
5,948 | 6,381 | A Communication-Efficient Parallel Algorithm for
Decision Tree
Qi Meng1,?, Guolin Ke2,? , Taifeng Wang2 , Wei Chen2 , Qiwei Ye2 ,
Zhi-Ming Ma3 , Tie-Yan Liu2
1
Peking University 2 Microsoft Research
3
Chinese Academy of Mathematics and Systems Science
1
qimeng13@pku.edu.cn; 2 {Guolin.Ke, taifengw, wche, qiwye, tie-yan.... | 6381 |@word private:2 version:1 rightchild:1 briefly:1 open:1 mehta:2 vldb:2 citeseer:1 solid:1 reduction:4 liu:1 contains:3 score:4 selecting:2 series:1 outperforms:3 existing:2 sugato:1 current:1 com:1 ganti:1 must:2 john:1 grain:1 planet:1 numerical:4 partition:4 informative:6 kdd:2 dive:1 ranka:1 drop:3 interpretab... |
5,949 | 6,382 | Leveraging Sparsity for Efficient
Submodular Data Summarization
Erik M. Lindgren, Shanshan Wu, Alexandros G. Dimakis
The University of Texas at Austin
Department of Electrical and Computer Engineering
erikml@utexas.edu, shanshan@utexas.edu, dimakis@austin.utexas.edu
Abstract
The facility location problem is widely use... | 6382 |@word trial:1 private:1 version:2 vldb:1 ci2:1 nemirovsky:1 detective:1 reduction:1 hunting:1 liu:1 exclusively:1 selecting:1 document:2 franklin:1 bradley:1 current:2 com:1 anne:1 beygelzimer:1 gpu:2 numerical:1 razenshteyn:1 kdd:2 civ:5 plot:3 interpretable:3 remove:1 hash:7 v:2 greedy:47 selected:1 item:3 cult... |
5,950 | 6,383 | Unifying Count-Based Exploration and Intrinsic Motivation
Marc G. Bellemare
bellemare@google.com
Sriram Srinivasan
srsrinivasan@google.com
Georg Ostrovski
ostrovski@google.com
Tom Schaul
schaul@google.com
David Saxton
saxton@google.com
R?emi Munos
munos@google.com
Google DeepMind
London, United Kingdom
Abstract... | 6383 |@word trial:2 private:1 version:1 compression:3 proportion:1 seems:1 polynomial:1 confirms:1 simulation:1 dramatic:1 recursively:1 carry:1 initial:1 score:12 united:1 outperforms:1 existing:2 hasselt:2 past:1 com:6 lang:1 yet:3 guez:1 must:6 john:1 enables:3 depict:1 n0:1 aside:1 stationary:1 greedy:3 generative:... |
5,951 | 6,384 | Causal meets Submodular: Subset Selection with
Directed Information
Costas J. Spanos
Department of EECS
UC Berkeley
spanos@berkeley.edu
Yuxun Zhou
Department of EECS
UC Berekely
yxzhou@berkeley.edu
Abstract
We study causal subset selection with Directed Information as the measure of
prediction causality. Two typical ... | 6384 |@word briefly:2 achievable:1 polynomial:1 seems:1 stronger:2 nd:1 d2:5 decomposition:1 reduction:2 series:4 score:1 contains:2 document:1 past:2 existing:3 current:1 recovered:1 comparing:2 si:5 yet:1 written:2 j1:2 remove:1 stationary:2 greedy:39 selected:6 accordingly:1 coleman:2 short:1 provides:4 seasonalitie... |
5,952 | 6,385 | Matching Networks for One Shot Learning
Oriol Vinyals
Google DeepMind
vinyals@google.com
Charles Blundell
Google DeepMind
cblundell@google.com
Koray Kavukcuoglu
Google DeepMind
korayk@google.com
Timothy Lillicrap
Google DeepMind
countzero@google.com
Daan Wierstra
Google DeepMind
wierstra@google.com
Abstract
Learn... | 6385 |@word trial:2 cnn:4 version:2 briefly:1 crucially:1 tried:1 jacob:1 pick:1 harder:2 shot:51 liu:1 contains:1 series:1 ours:1 interestingly:1 outperforms:1 current:1 com:5 comparing:2 contextual:1 surprising:1 virus:1 yet:2 protection:1 must:1 activation:1 fn:3 hypothesize:1 update:3 hash:1 intelligence:1 leaf:1 s... |
5,953 | 6,386 | Learning Bayesian networks
with ancestral constraints
Eunice Yuh-Jie Chen and Yujia Shen and Arthur Choi and Adnan Darwiche
Computer Science Department
University of California
Los Angeles, CA 90095
{eyjchen,yujias,aychoi,darwiche}@cs.ucla.edu
Abstract
We consider the problem of learning Bayesian networks optimally, w... | 6386 |@word mild:1 eliminating:2 proportion:2 adnan:1 bn:11 p0:1 dramatic:1 accommodate:2 reduction:1 contains:11 score:35 efficacy:1 selecting:1 ilps:1 genetic:1 ours:1 interestingly:1 omniscient:1 existing:4 com:1 yet:1 must:3 greedy:1 discovering:1 malone:9 leaf:4 selected:1 intelligence:11 provides:1 node:11 along:... |
5,954 | 6,387 | CliqueCNN: Deep Unsupervised Exemplar Learning
Miguel A. Bautista? , Artsiom Sanakoyeu? , Ekaterina Sutter, Bj?rn Ommer
Heidelberg Collaboratory for Image Processing
IWR, Heidelberg University, Germany
firstname.lastname@iwr.uni-heidelberg.de
Abstract
Exemplar learning is a powerful paradigm for discovering visual si... | 6387 |@word cnn:50 version:2 briefly:1 norm:1 everingham:1 seek:1 covariance:1 pick:1 sgd:9 tr:5 initial:5 contains:2 score:3 selecting:2 jimenez:1 tuned:1 ours:5 outperforms:1 com:1 yet:1 assigning:1 pcp:3 parsing:2 gpu:1 distant:6 shape:1 remove:2 designed:1 plot:2 update:3 depict:1 half:1 discovering:2 selected:2 fe... |
5,955 | 6,388 | Deep Learning Models of the Retinal Response to
Natural Scenes
Lane T. McIntosh?1 , Niru Maheswaranathan?1 , Aran Nayebi1 ,
Surya Ganguli2,3 , Stephen A. Baccus3
1
Neurosciences PhD Program, 2 Department of Applied Physics, 3 Neurobiology Department
Stanford University
{lmcintosh, nirum, anayebi, sganguli, baccus}@stan... | 6388 |@word neurophysiology:1 trial:2 cnn:22 nd:1 underperform:2 hyv:1 simplifying:1 decomposition:2 thereby:1 series:1 daniel:1 amp:1 surprising:1 activation:4 diederik:1 readily:1 realistic:1 plasticity:1 shape:3 enables:2 plot:1 designed:1 bart:1 stationary:1 selected:3 fewer:2 signalling:1 greschner:1 short:2 insti... |
5,956 | 6,389 | On Robustness of Kernel Clustering
Bowei Yan
Department of Statistics and Data Sciences
University of Texas at Austin
Purnamrita Sarkar
Department of Statistics and Data Sciences
University of Texas at Austin
Abstract
Clustering is an important unsupervised learning problem in machine learning
and statistics. Among ... | 6389 |@word kulis:1 version:2 norm:12 c0:2 suitably:1 km:4 d2:2 covariance:1 decomposition:4 sheffet:1 carry:1 liu:1 mixon:1 existing:1 kx0:3 recovered:1 comparing:1 karoui:1 scatter:1 numerical:2 partition:1 happen:1 kdd:1 drop:1 n0:4 zik:1 v:4 half:3 intelligence:2 pelckmans:1 isotropic:2 xk:2 ith:2 vanishing:1 recor... |
5,957 | 639 | STIMULUS ENCODING BY
MULTIDIMENSIONAL RECEPTIVE FIELDS
IN SINGLE CELLS AND CELL POPULATIONS
IN VI OF AWAKE MONKEY
Edward Stern
Center for Neural Computation
and Department of Neurobiology
Life Sciences Institute
Hebrew University
Jerusalem, Israel
Eilon Vaadia
Center for Neural Computation
and Physiology Department
Ha... | 639 |@word middle:2 seems:1 ruhr:1 shading:1 carry:1 tuned:5 must:5 physiol:2 realistic:1 shape:2 short:1 location:2 psth:8 height:1 along:4 alert:1 fixation:1 sustained:4 shapley:3 manner:1 expected:2 indeed:1 behavior:1 abscissa:1 themselves:1 multi:4 window:3 considering:1 linearity:2 spitzer:4 israel:6 monkey:4 tem... |
5,958 | 6,390 | CNNpack: Packing Convolutional Neural Networks
in the Frequency Domain
Yunhe Wang1,3 , Chang Xu2 , Shan You1,3 , Dacheng Tao2 , Chao Xu1,3
Key Laboratory of Machine Perception (MOE), School of EECS, Peking University
2
Centre for Quantum Computation and Intelligent Systems,
School of Software, University of Technology... | 6390 |@word cnn:10 version:1 private:4 compression:64 proportion:1 d2:4 r:7 decomposition:3 thereby:1 reduction:2 electronics:1 liu:3 contains:2 document:1 err:4 guadarrama:1 contextual:1 activation:1 must:2 gpu:1 written:1 john:1 dct:37 csc:1 j1:11 enables:1 treating:1 drop:1 device:6 accordingly:3 desktop:1 fni:1 num... |
5,959 | 6,391 | Generative Adversarial Imitation Learning
Jonathan Ho
OpenAI
hoj@openai.com
Stefano Ermon
Stanford University
ermon@cs.stanford.edu
Abstract
Consider learning a policy from example expert behavior, without interaction with
the expert or access to a reinforcement signal. One approach is to recover the
expert?s cost f... | 6391 |@word trial:1 nd:1 seek:1 r:14 crucially:1 p0:3 incurs:1 thereby:1 outlook:1 shading:1 reduction:2 initial:1 ours:1 interestingly:2 outperforms:1 existing:3 recovered:4 com:1 current:1 yet:1 must:2 written:1 john:2 designed:2 treating:1 update:1 stationary:1 generative:10 instantiate:1 imitate:2 accordingly:1 cha... |
5,960 | 6,392 | Feature selection in functional data classification with
recursive maxima hunting
Jos?e L. Torrecilla
Computer Science Department
Universidad Aut?onoma de Madrid
28049 Madrid, Spain
joseluis.torrecilla@uam.es
Alberto Su?arez
Computer Science Department
Universidad Aut?onoma de Madrid
28049 Madrid, Spain
alberto.suare... | 6392 |@word norm:2 simulation:3 llo:2 covariance:2 p0:3 galeano:4 absorbance:1 moment:1 initial:1 liu:2 wrapper:2 reduction:13 selecting:2 hunting:20 nt:3 must:1 readily:2 casi:1 subsequent:2 plot:7 interpretable:3 discrimination:2 v:2 half:2 selected:25 ntrain:3 short:1 record:1 provides:1 s2013:1 height:1 competitive... |
5,961 | 6,393 | Integrated Perception with Recurrent Multi-Task
Neural Networks
Hakan Bilen
Andrea Vedaldi
Visual Geometry Group, University of Oxford
{hbilen,vedaldi}@robots.ox.ac.uk
Abstract
Modern discriminative predictors have been shown to match natural intelligences in
specific perceptual tasks in image classification, object a... | 6393 |@word multitask:2 cnn:6 compression:1 retraining:1 everingham:1 rgb:1 sgd:1 initial:4 configuration:3 contains:3 score:1 series:1 liu:2 ndez:1 tuned:1 ours:3 outperforms:3 existing:1 current:1 luo:1 activation:2 tackling:1 yet:1 written:2 reminiscent:1 parsing:1 blur:1 remove:1 drop:1 progressively:2 update:14 in... |
5,962 | 6,394 | Structured Matrix Recovery via the Generalized
Dantzig Selector
Sheng Chen
Arindam Banerjee
Dept. of Computer Science & Engineering
University of Minnesota, Twin Cities
{shengc,banerjee}@cs.umn.edu
Abstract
In recent years, structured matrix recovery problems have gained considerable
attention for its real world appl... | 6394 |@word briefly:1 version:1 norm:83 suitably:1 c0:9 decomposition:1 dirksen:3 paid:1 tr:5 boundedness:1 reduction:2 contains:2 series:1 zuk:1 existing:2 mesh:1 additive:1 drop:1 isotropic:3 provides:1 characterization:2 complication:1 zhang:2 zii:2 u2i:1 along:3 c2:13 constructed:1 consists:2 shorthand:2 introduce:... |
5,963 | 6,395 | Unsupervised Feature Extraction by
Time-Contrastive Learning and Nonlinear ICA
Aapo Hyv?rinen1,2 and Hiroshi Morioka1
1
Department of Computer Science and HIIT
University of Helsinki, Finland
2
Gatsby Computational Neuroscience Unit
University College London, UK
Abstract
Nonlinear independent component analysis (I... | 6395 |@word middle:1 version:1 seems:4 stronger:1 hyv:7 confirms:1 simulation:4 contrastive:9 solid:1 reduction:1 initial:2 series:8 exclusively:3 seriously:2 zurada:1 interestingly:1 existing:1 recovered:1 urgently:1 si:33 yet:1 activation:2 must:8 written:1 enables:1 discrimination:2 stationary:4 generative:11 half:2... |
5,964 | 6,396 | Joint M-Best-Diverse Labelings as a Parametric
Submodular Minimization
Alexander Kirillov1 Alexander Shekhovtsov2 Carsten Rother1 Bogdan Savchynskyy1
1
2
TU Dresden, Dresden, Germany
TU Graz, Graz, Austria
alexander.kirillov@tu-dresden.de
Abstract
We consider the problem of jointly inferring the M -best diverse labeli... | 6396 |@word kohli:4 determinant:1 version:1 briefly:1 cnn:1 everingham:1 yv0:1 r:1 prasad:1 rgb:1 contrastive:1 pick:1 rivera:4 outlook:1 reduction:2 configuration:10 score:2 tuned:1 ours:1 interestingly:1 outperforms:2 existing:1 comparing:2 yet:2 written:1 must:1 determinantal:1 subsequent:1 premachandran:1 alone:1 g... |
5,965 | 6,397 | Faster Projection-free Convex Optimization over the
Spectrahedron
Dan Garber
Toyota Technological Institute at Chicago
dgarber@ttic.edu
Abstract
Minimizing a convex function over the spectrahedron, i.e., the set of all d ? d
positive semidefinite matrices with unit trace, is an important optimization task
with many ap... | 6397 |@word briefly:1 achievable:1 norm:13 nd:3 d2:11 decomposition:23 pick:2 tr:2 reduction:1 current:4 surprising:1 written:1 chicago:1 cheap:3 update:7 maxv:1 aside:1 v:1 greedy:1 prohibitive:1 half:1 short:1 provides:1 iterates:1 boosting:1 zhang:1 along:1 bd1:1 symposium:1 dan:5 polyhedral:2 x0:2 peng:1 indeed:5 e... |
5,966 | 6,398 | Learning Multiagent Communication
with Backpropagation
Sainbayar Sukhbaatar
Dept. of Computer Science
Courant Institute, New York University
sainbar@cs.nyu.edu
Arthur Szlam
Facebook AI Research
New York
aszlam@fb.com
Rob Fergus
Facebook AI Research
New York
robfergus@fb.com
Abstract
Many tasks in AI require the coll... | 6398 |@word trial:1 version:7 middle:6 norm:2 cah:1 nd:2 bf:1 iki:2 simulation:4 propagate:2 simplifying:5 incurs:1 amaps:1 fif:3 versatile:1 harder:1 carry:2 initial:1 bai:1 contains:1 daniel:1 ours:1 outperforms:3 freitas:1 err:1 current:4 com:2 nt:1 si:2 theof:3 must:14 srd:1 gpu:1 written:14 guez:1 visible:2 happen... |
5,967 | 6,399 | InfoGAN: Interpretable Representation Learning by
Information Maximizing Generative Adversarial Nets
Xi Chen?? , Yan Duan?? , Rein Houthooft?? , John Schulman?? , Ilya Sutskever? , Pieter Abbeel??
? UC Berkeley, Department of Electrical Engineering and Computer Sciences
? OpenAI
Abstract
This paper describes InfoGAN,... | 6399 |@word kohli:1 version:1 nd:1 c0:4 unif:3 pieter:1 simulation:1 pg:8 shot:1 reduction:1 liu:1 contains:2 existing:2 luo:2 cad:1 intriguing:1 must:1 john:1 numerical:1 shape:5 remove:2 treating:1 interpretable:10 plot:1 generative:21 discovering:2 guess:1 intelligence:1 core:1 toronto:2 org:1 zhang:1 five:2 c2:7 di... |
5,968 | 64 | 592
A Trellis-Structured Neural Network*
Thomas Petsche t and Bradley W. Dickinson
Princeton University, Department of Electrical Engineering
Princeton, N J 08544
Abstract
We have developed a neural network which consists of cooperatively interconnected Grossberg on-center off-surround subnets and which can be used t... | 64 |@word cu:1 version:3 seems:1 oncenter:1 simulation:18 decomposition:2 initial:2 contains:7 pub:1 bradley:1 discretization:1 activation:1 si:4 written:3 additive:1 designed:4 fewer:1 item:1 xk:1 beginning:1 sys:1 ith:3 short:1 provides:1 node:14 tvo:1 rc:1 burst:3 differential:1 consists:2 prove:3 behavior:3 themsel... |
5,969 | 640 | Hybrid Circuits of Interacting Computer Model
and Biological Neurons
Sylvie Renaud-LeMassonDepartment of Physics
Brandeis University
Waltham. MA 02254
Gwendal LeMasson'
Department of Biology
Brandeis University
Waltham. MA 02254
Eve Marder
Department of Biology
Brandeis University
Waltham. MA 02254
L.F. Abbott
Depa... | 640 |@word neurophysiology:4 polynomial:1 configuration:1 contains:2 current:16 anterior:1 activation:1 must:1 periodically:1 realistic:4 plot:1 reciprocal:3 record:1 provides:3 contribute:1 mathematical:1 burst:5 differential:3 consists:1 fitting:1 behavior:1 actual:1 increasing:3 moreover:1 underlying:1 circuit:8 wha... |
5,970 | 6,400 | Improved Regret Bounds for Oracle-Based
Adversarial Contextual Bandits
Vasilis Syrgkanis
Microsoft Research
vasy@microsoft.com
Haipeng Luo
Microsoft Research
haipeng@microsoft.com
Akshay Krishnamurthy
University of Massachusetts, Amherst
akshay@cs.umass.edu
Robert E. Schapire
Microsoft Research
schapire@microsoft.co... | 6400 |@word briefly:1 version:1 achievable:1 open:3 rigged:1 q1:4 pick:1 incurs:2 invoking:1 minus:1 uma:1 daniel:2 existing:1 contextual:19 com:3 luo:1 john:3 subsequent:1 enables:1 update:1 greedy:2 intelligence:1 warmuth:1 beginning:2 manfred:1 boosting:1 simpler:1 zhang:3 focs:1 shorthand:1 prove:1 inside:1 theoret... |
5,971 | 6,401 | Quantum Perceptron Models
Nathan Wiebe
Microsoft Research
Redmond WA, 98052
nawiebe@microsoft.com
Ashish Kapoor
Microsoft Research
Redmond WA, 98052
akapoor@microsoft.com
Krysta M Svore
Microsoft Research
Redmond WA, 98052
ksvore@microsoft.com
Abstract
We demonstrate how quantum computation can provide non-trivial ... | 6401 |@word version:36 seek:1 pick:2 minus:1 carry:1 reduction:2 initial:2 born:2 daniel:1 document:1 blank:1 com:3 current:3 jaz:1 must:2 john:1 enables:1 depict:2 update:3 v:1 half:4 intelligence:1 devising:1 item:3 plane:1 provides:4 sits:1 hyperplanes:9 herbrich:2 accessed:3 constructed:1 direct:2 symposium:1 consi... |
5,972 | 6,402 | Parameter Learning
for Log-supermodular Distributions
Tatiana Shpakova
INRIA - ?cole Normale Sup?rieure Paris
tatiana.shpakova@inria.fr
Francis Bach
INRIA - ?cole Normale Sup?rieure Paris
francis.bach@inria.fr
Abstract
We consider log-supermodular models on binary variables, which are probabilistic
models with negat... | 6402 |@word kohli:2 polynomial:2 stronger:1 norm:3 proportion:1 contrastive:1 configuration:1 hoiem:1 daniel:1 denoting:1 document:2 existing:3 yet:1 written:1 readily:1 partition:23 remove:1 juditsky:1 intelligence:2 ntrain:1 mccallum:1 tarlow:1 iterates:1 consulting:1 node:1 simpler:1 zhang:1 mathematical:1 consists:... |
5,973 | 6,403 | Exploiting the Structure:
Stochastic Gradient Methods Using Raw Clusters?
Zeyuan Allen-Zhu?
Princeton University / IAS
zeyuan@csail.mit.edu
Yang Yuan?
Cornell University
yangyuan@cs.cornell.edu
Karthik Sridharan
Cornell University
sridharan@cs.cornell.edu
Abstract
The amount of data available in the world is growin... | 6403 |@word version:3 norm:6 stronger:1 nd:7 tat:1 bn:1 sgd:4 reduction:3 initial:1 contains:1 denoting:1 past:1 outperforms:4 current:1 comparing:1 yet:2 partition:4 razenshteyn:1 hofmann:6 zaid:1 designed:1 plot:6 update:3 v:2 selected:3 guess:5 website:1 accordingly:1 beginning:3 record:1 provides:1 completeness:1 c... |
5,974 | 6,404 | Can Peripheral Representations Improve Clutter
Metrics on Complex Scenes?
Arturo Deza
Dynamical Neuroscience
Institute for Collaborative Biotechnologies
UC Santa Barbara, CA, USA
deza@dyns.ucsb.edu
Miguel P. Eckstein
Psychological and Brain Sciences
Institute for Collaborative Biotechnologies
UC Santa Barbara, CA, US... | 6404 |@word trial:6 version:5 judgement:3 stronger:3 seems:1 norm:8 seek:1 cos2:1 hsieh:1 harder:1 crowding:9 initial:1 configuration:1 foveal:3 score:39 liu:1 ours:1 outperforms:1 current:4 com:1 comparing:1 yet:2 must:1 mordechai:1 shape:1 remove:1 sponsored:1 v:9 congestion:63 half:4 intelligence:1 item:1 isotropic:... |
5,975 | 6,405 | GAP Safe Screening Rules for Sparse-Group Lasso
Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
LTCI, CNRS, T?l?com ParisTech
Universit? Paris-Saclay
75013 Paris, France
first.last@telecom-paristech.fr
Abstract
For statistical learning in high dimension, sparse regularizations have proven useful
to b... | 6405 |@word norm:43 proportion:1 humidity:1 bf:1 r:1 crucially:1 egp:2 decomposition:1 p0:1 pressure:2 pick:1 reduction:4 contains:1 series:1 hereafter:1 denoting:1 bc:2 outperforms:1 ndiaye:2 current:1 com:3 ncar:3 optim:1 yet:1 written:1 aft:2 numerical:1 partition:1 remove:1 prk:3 selected:1 kyk:3 gribonval:2 charac... |
5,976 | 6,406 | Deep ADMM-Net for Compressive Sensing MRI
Yan Yang
Xi?an Jiaotong University
yangyan92@stu.xjtu.edu.cn
Jian Sun
Xi?an Jiaotong University
jiansun@mail.xjtu.edu.cn
Huibin Li
Xi?an Jiaotong University
huibinli@mail.xjtu.edu.cn
Zongben Xu
Xi?an Jiaotong University
zbxu@mail.xjtu.edu.cn
Abstract
Compressive Sensing (C... | 6406 |@word briefly:1 mri:42 norm:4 solid:1 liu:1 ours:1 document:1 outperforms:1 current:1 z2:1 luo:1 yet:1 scatter:2 chu:1 john:3 hou:1 dct:6 ronald:1 designed:1 plot:2 update:9 selected:1 desktop:1 core:1 node:7 successive:1 zhang:1 mathematical:1 along:1 constructed:1 consists:2 overhead:1 introduce:1 expected:1 ra... |
5,977 | 6,407 | Homotopy Smoothing for Non-Smooth Problems
with Lower Complexity than O(1/)
Yi Xu??, Yan Yan??, Qihang Lin\ , Tianbao Yang )?
Department of Computer Science, University of Iowa, Iowa City, IA 52242
?
QCIS, University of Technology Sydney, NSW 2007, Australia
\
Department of Management Sciences, University of Iowa, Io... | 6407 |@word mild:2 norm:11 seems:1 d2:5 semicontinuous:1 unbeatable:1 decomposition:2 nsw:1 acknowlegements:1 initial:1 series:2 nesta:1 tuned:1 ours:1 bc:1 comparing:1 optim:5 luo:1 written:2 bd:1 designed:1 update:6 xk:2 ojasiewicz:6 certificate:1 math:6 scientifiques:1 zhang:3 mathematical:2 consists:2 polyhedral:2 ... |
5,978 | 6,408 | Learning from Small Sample Sets by Combining
Unsupervised Meta-Training with CNNs
Yu-Xiong Wang
Martial Hebert
Robotics Institute, Carnegie Mellon University
{yuxiongw, hebert}@cs.cmu.edu
Abstract
This work explores CNNs for the recognition of novel categories from few examples. Inspired by the transferability proper... | 6408 |@word kulis:1 cnn:75 middle:6 norm:1 c0:3 r:1 crucially:1 hsieh:1 shot:5 ld:2 liblinear:2 initial:4 contains:2 score:1 selecting:2 hoiem:1 tuned:5 ours:2 outperforms:2 existing:1 current:2 transferability:10 com:1 guadarrama:1 activation:41 si:2 realistic:1 partition:3 concatenate:1 seeding:1 drop:1 update:5 hash... |
5,979 | 6,409 | Learning under uncertainty: a comparison between
R-W and Bayesian approach
He Huang
Laureate Institute for Brain Research
Tulsa, OK, 74133
crane081@gmail.com
Martin Paulus
Laureate Institute for Brain Research
Tulsa, OK, 74133
mpaulus@laureateinstitute.org
Abstract
Accurately differentiating between what are truly un... | 6409 |@word trial:21 simulation:13 p0:2 substitution:1 outperforms:1 reaction:1 current:5 com:1 comparing:4 surprising:2 gmail:1 shape:17 drop:1 update:5 v:4 denison:1 record:1 revisited:1 location:10 preference:1 org:1 zhang:1 mathematical:1 profound:1 behavioral:14 expected:6 behavior:18 frequently:1 examine:8 brain:... |
5,980 | 641 | Discriminability-Based Transfer between
Neural Networks
L. Y. Pratt
Department of Mathematical and Computer Sciences
Colorado School of Mines
Golden, CO 80401
lpratt@mines.colorado.edu
Abstract
Previously, we have introduced the idea of neural network transfer,
where learning on a target problem is sped up by using th... | 641 |@word h:2 illustrating:1 nificantly:1 seems:1 proportion:1 proportionality:1 seek:1 simulation:1 solid:1 initial:7 contains:1 score:4 pub:1 activation:1 si:1 must:1 aft:1 john:2 update:6 v:1 intelligence:3 fewer:2 accordingly:1 smith:1 detecting:2 hyperplanes:14 mathematical:1 along:2 direct:1 forgetting:1 multi:1... |
5,981 | 6,410 | A Non-convex One-Pass Framework for Generalized
Factorization Machine and Rank-One Matrix Sensing
Ming Lin
University of Michigan
linmin@umich.edu
Jieping Ye
University of Michigan
jpye@umich.edu
Abstract
We develop an efficient alternating framework for learning a generalized version of
Factorization Machine (gFM) ... | 6410 |@word version:5 achievable:1 polynomial:4 norm:25 km:22 d2:8 decomposition:3 covariance:1 tr:12 recursively:1 moment:1 initial:1 liu:2 existing:1 kmk:4 ka:3 com:1 nt:1 current:1 comparing:1 additive:1 numerical:1 blur:1 remove:2 update:5 knyazev:2 yuxin:1 davison:1 zhang:8 along:1 constructed:1 retrieving:3 consi... |
5,982 | 6,411 | Adaptive Neural Compilation
Rudy Bunel?
University of Oxford
rudy@robots.ox.ac.uk
Pushmeet Kohli
Microsoft Research
pkohli@microsoft.com
Alban Desmaison?
University of Oxford
alban@robots.ox.ac.uk
Philip H.S. Torr
University of Oxford
philip.torr@eng.ox.ac.uk
M. Pawan Kumar
University of Oxford
pawan@robots.ox.ac.u... | 6411 |@word kohli:1 armand:2 version:13 briefly:1 advantageous:1 open:1 instruction:34 eng:1 simplifying:1 thereby:3 initial:7 contains:3 score:1 selecting:1 series:1 initialisation:7 tuned:1 envision:1 ati:2 existing:2 freitas:4 recovered:1 com:2 superoptimization:3 current:2 diederik:1 written:9 must:1 ronald:1 subse... |
5,983 | 6,412 | On the Recursive Teaching Dimension
of VC Classes
Yu Cheng
Department of Computer Science
University of Southern California
yu.cheng.1@usc.edu
Xi Chen
Department of Computer Science
Columbia University
xichen@cs.columbia.edu
Bo Tang
Department of Computer Science
Oxford University
tangbonk1@gmail.com
Abstract
The re... | 6412 |@word behw89:2 briefly:1 compression:6 stronger:1 underline:2 c0:26 open:4 biere:1 pick:2 recursively:1 contains:2 chervonenkis:2 jku:1 com:1 gmail:1 conjunctive:1 must:9 cruz:1 realistic:1 partition:1 remove:3 selected:1 fewer:1 warmuth:7 beginning:2 d2d:3 five:2 mathematical:1 along:1 c2:11 symposium:2 prove:9 ... |
5,984 | 6,413 | Showing versus Doing: Teaching by Demonstration
Mark K Ho
Department of Cognitive, Linguistic, and Psychological Sciences
Brown University
Providence, RI 02912
mark_ho@brown.edu
Michael L. Littman
Department of Computer Science
Brown University
Providence, RI 02912
mlittman@cs.brown.edu
Fiery Cushman
Department of Psy... | 6413 |@word trial:7 proportion:1 initial:2 configuration:1 comparing:1 si:5 yet:1 must:1 visible:1 analytic:1 wanted:2 motor:1 treating:1 v:2 stationary:1 pursued:1 cue:1 cook:1 intelligence:3 imitate:1 amir:1 menell:1 colored:11 grfp:1 provides:2 revisited:2 location:8 marivate:1 simpler:1 qualitative:3 fitting:1 beha... |
5,985 | 6,414 | Strategic Attentive Writer for Learning
Macro-Actions
Alexander (Sasha) Vezhnevets, Volodymyr Mnih, John Agapiou,
Simon Osindero, Alex Graves, Oriol Vinyals, Koray Kavukcuoglu
Google DeepMind
{vezhnick,vmnih,jagapiou,osindero,gravesa,vinyals,korayk}@google.com
Abstract
We present a novel deep recurrent neural network ... | 6414 |@word cnn:5 version:3 open:1 termination:3 pieter:2 additively:1 overwritten:1 propagate:1 decomposition:2 pick:1 thereby:6 harder:2 reduction:1 moment:1 configuration:1 contains:2 score:12 initial:1 jimenez:1 daniel:1 reaction:1 current:4 com:2 surprising:1 activation:1 yet:1 tackling:1 diederik:1 john:2 ronald:... |
5,986 | 6,415 | Online Pricing with Strategic and Patient Buyers
Michal Feldman
Tel-Aviv University and MSR Herzliya
michal.feldman@cs.tau.ac.il
Roi Livni?
Princeton University
rlivni@cs.princeton.edu
Yishay Mansour?
Tel-Aviv University
mansour@tau.ac.il
Tomer Koren?
Google Brain
tkoren@google.com
Aviv Zohar?
Hebrew University of Je... | 6415 |@word msr:1 briefly:3 private:1 leighton:4 stronger:2 dekel:3 open:1 willing:3 crucially:1 incurs:2 thereby:1 shot:1 reduction:11 configuration:1 pt0:1 offering:2 ours:1 bc:1 existing:1 current:3 com:1 michal:2 must:1 readily:1 additive:3 subsequent:1 drop:1 update:1 alone:1 half:7 selected:1 guess:1 item:15 acco... |
5,987 | 6,416 | FPNN: Field Probing Neural Networks for 3D Data
Yangyan Li1,2
1
S?ren Pirk1
Hao Su1
Stanford University, USA
Charles R. Qi1
2
Leonidas J. Guibas1
Shandong University, China
Abstract
Building discriminative representations for 3D data has been an important task in
computer graphics and computer vision research. ... | 6416 |@word cnn:8 seems:1 stronger:1 disk:1 open:1 confirms:1 bn:5 decomposition:1 rgb:1 concise:1 sgd:1 thereby:1 bai:2 contains:3 daniel:3 tuned:1 document:1 past:1 existing:1 outperforms:1 current:2 discretization:1 com:1 surprising:1 guadarrama:1 chazelle:1 assigning:2 intriguing:1 parsing:1 gpu:4 mesh:5 distant:4 ... |
5,988 | 6,417 | Recovery Guarantee of Non-negative Matrix
Factorization via Alternating Updates
Yuanzhi Li, Yingyu Liang, Andrej Risteski
Computer Science Department at Princeton University
35 Olden St, Princeton, NJ 08540
{yuanzhil, yingyul, risteski}@cs.princeton.edu
Abstract
Non-negative matrix factorization is a popular tool for... | 6417 |@word mild:7 version:1 norm:8 proportion:2 nd:1 cleanly:1 crucially:1 decomposition:2 reduction:2 moment:3 liu:1 contains:3 daniel:3 tuned:1 ours:1 document:1 bhattacharyya:1 existing:2 kmk:2 current:3 recovered:2 ka:8 surprising:1 com:1 activation:1 yet:1 remove:3 designed:1 interpretable:1 update:14 generative:... |
5,989 | 6,418 | Interaction Networks for Learning about Objects,
Relations and Physics
Anonymous Author(s)
Affiliation
Address
email
Abstract
Reasoning about objects, relations, and physics is central to human intelligence, and
a key goal of artificial intelligence. Here we introduce the interaction network, a
model which can reason... | 6418 |@word cnn:1 middle:1 pw:1 grey:1 simulation:17 r:5 crucially:1 initial:2 configuration:4 contains:1 o2:7 freitas:1 current:3 assigning:1 dx:3 must:2 subsequent:1 realistic:3 happen:1 blur:1 shape:3 intelligence:5 half:2 selected:4 fewer:1 generative:1 core:1 colored:1 mental:3 coarse:1 provides:1 node:2 rollout:2... |
5,990 | 6,419 | Minimax Optimal Alternating Minimization
for Kernel Nonparametric Tensor Learning
?,?
Taiji Suzuki? , Heishiro Kanagawa?
Department of Mathematical and Computing Science, Tokyo Institute of Technology
?
PRESTO, Japan Science and Technology Agency
?
Center for Advanced Integrated Intelligence Research, RIKEN
s-taiji@i... | 6419 |@word multitask:10 mild:1 polynomial:1 norm:10 stronger:1 achievable:1 paredes:1 nd:1 decomposition:9 p0:6 eng:1 contraction:1 commute:1 boundedness:2 recursively:1 initial:8 liu:4 contains:1 score:2 hereafter:1 tuned:1 rkhs:11 amp:29 romera:1 sharpley:1 existing:4 scovel:1 contextual:1 si:2 yet:1 dx:1 numerical:... |
5,991 | 642 | Global Regularization of Inverse Kinematics for Redundant
Manipulators
David DeMers
Dept. of Computer Science & Engr.
Institute for Neural Computation
University of California, San Diego
La Jolla. CA 92093-0114
Kenneth Kreutz-Delgado
Dept. of Electrical & Computer Engr.
Institute for Neural Computation
University o... | 642 |@word inversion:2 achievable:1 seek:1 delgado:7 initial:3 configuration:6 cyclic:2 daniel:1 nt:2 john:1 numerical:1 partition:4 burdick:6 motor:1 designed:1 infant:1 selected:1 parameterization:11 inspection:1 plane:1 parameterizations:2 node:3 location:21 mathematical:1 along:1 constructed:1 direct:7 differential... |
5,992 | 6,420 | Cooperative Inverse Reinforcement Learning
Dylan Hadfield-Menell?
Anca Dragan
Pieter Abbeel
Stuart Russell
Electrical Engineering and Computer Science
University of California at Berkeley
Berkeley, CA 94709
Abstract
For an autonomous system to be helpful to humans and to pose no unwarranted
risks, it needs to ali... | 6420 |@word h:2 private:3 middle:4 proportion:1 norm:3 tadepalli:2 instruction:1 pieter:1 crucially:1 simplifying:1 p0:4 pick:2 profit:1 reduction:4 initial:7 existing:1 current:2 must:1 exposing:1 realistic:1 subsequent:1 informative:1 enables:1 hoping:2 plot:1 update:3 v:2 intelligence:1 selected:2 fewer:1 item:3 ami... |
5,993 | 6,421 | Bayesian Optimization for Probabilistic Programs
?
Tom Rainforth? Tuan Anh Le? Jan-Willem van de Meent?
Michael A. Osborne? Frank Wood?
?
Department of Engineering Science, University of Oxford
College of Computer and Information Science, Northeastern University
{twgr,tuananh,mosb,fwood}@robots.ox.ac.uk, j.vandemeen... | 6421 |@word exploitation:1 middle:3 km:2 simulation:7 covariance:2 incurs:5 solid:7 shading:4 carry:5 initial:4 configuration:2 series:3 contains:5 lightweight:6 uncovered:5 rippel:1 fa8750:1 existing:10 freitas:2 current:2 com:2 optim:2 assigning:1 dx:1 must:11 written:1 subsequent:1 analytic:2 remove:1 plot:7 designe... |
5,994 | 6,422 | Dual Decomposed Learning with Factorwise Oracles
for Structural SVMs of Large Output Domain
Ian E.H. Yen ? Xiangru Huang ?
Pradeep Ravikumar ?
?
Carnegie Mellon University
Kai Zhong ? Ruohan Zhang ?
Inderjit S. Dhillon ?
?
University of Texas at Austin
Abstract
Many applications of machine learning involve structure... | 6422 |@word version:3 polynomial:1 bigram:5 norm:1 yja:1 decomposition:6 hsieh:2 liblinear:1 minding:1 series:1 score:6 contains:2 tuned:1 outperforms:1 existing:1 current:3 com:1 luo:1 written:2 parsing:2 realize:1 partition:2 hofmann:1 treating:1 drop:5 update:4 plot:2 greedy:15 prohibitive:3 half:1 plane:2 directory... |
5,995 | 6,423 | A Unified Approach for Learning the Parameters of
Sum-Product Networks
Han Zhao
Machine Learning Dept.
Carnegie Mellon University
han.zhao@cs.cmu.edu
Pascal Poupart
School of Computer Science
University of Waterloo
ppoupart@uwaterloo.ca
Geoff Gordon
Machine Learning Dept.
Carnegie Mellon University
ggordon@cs.cmu.edu... | 6423 |@word version:1 polynomial:16 seems:1 propagate:1 recursively:2 initial:2 contains:1 score:3 interestingly:1 outperforms:1 current:3 wd:12 fvi:5 numerical:1 additive:2 kdd:2 shape:1 update:14 stationary:3 generative:2 leaf:3 fewer:1 intelligence:3 greedy:1 warmuth:1 chiang:1 provides:1 math:1 node:27 height:1 dir... |
5,996 | 6,424 | A Posteriori Error Bounds for Joint Matrix
Decomposition Problems
Nicol? Colombo
Department of Statistical Science
University College London
nicolo.colombo@ucl.ac.uk
Nikos Vlassis
Adobe Research
San Jose, CA
vlassis@adobe.com
Abstract
Joint matrix triangularization is often used for estimating the joint eigenstructu... | 6424 |@word mild:1 collinearity:1 determinant:2 version:2 polynomial:1 norm:7 nd:2 d2:1 decomposition:31 sepulchre:1 boundedness:1 moment:2 reduction:1 contains:1 past:1 existing:2 com:1 numerical:1 remove:1 v:2 intelligence:1 ith:1 simpler:1 lathauwer:2 symposium:1 pairing:1 transducer:1 consists:3 prove:2 expected:3 ... |
5,997 | 6,425 | Structured Sparse Regression via Greedy
Hard-thresholding
Prateek Jain
Microsoft Research India
Nikhil Rao
Technicolor
Inderjit Dhillon
UT Austin
Abstract
Several learning applications require solving high-dimensional regression problems
where the relevant features belong to a small number of (overlapping) groups. F... | 6425 |@word multitask:2 cox:1 briefly:1 middle:1 stronger:2 norm:4 laurence:1 c0:1 r:8 decomposition:1 covariance:1 jacob:1 harder:1 n8:1 contains:1 backslash:1 outperforms:1 existing:12 clash:1 additive:2 plot:3 v:1 greedy:25 fewer:1 selected:3 intelligence:1 volkan:3 iterates:1 completeness:1 coarse:1 allerton:2 simp... |
5,998 | 6,426 | Stochastic Variational Deep Kernel Learning
Andrew Gordon Wilson*
Cornell University
Zhiting Hu*
CMU
Ruslan Salakhutdinov
CMU
Eric P. Xing
CMU
Abstract
Deep kernel learning combines the non-parametric flexibility of kernel methods
with the inductive biases of deep learning architectures. We propose a novel deep
ke... | 6426 |@word repository:1 cnn:10 hu:2 covariance:13 decomposition:2 dramatic:1 sgd:1 incurs:1 shot:1 carry:1 ld:2 series:2 selecting:1 fa8750:1 outperforms:3 guadarrama:1 comparing:1 surprising:1 activation:1 tackling:2 must:1 gpu:2 additive:21 enables:2 interpretable:2 update:3 v:4 alone:13 intelligence:7 prohibitive:2... |
5,999 | 6,427 | Toward Deeper Understanding of Neural Networks: The Power
of Initialization and a Dual View on Expressivity
Amit Daniely
Google Brain
Roy Frostig?
Google Brain
Yoram Singer
Google Brain
Abstract
We develop a general duality between neural networks and compositional kernel
Hilbert spaces. We introduce the notion of ... | 6427 |@word h:7 version:1 polynomial:11 norm:14 stronger:1 nd:6 twelfth:1 covariance:1 sgd:1 concise:1 arous:1 boundedness:1 recursively:1 ld:4 initial:2 selecting:1 ours:1 nally:1 current:1 activation:40 dx:1 numerical:1 intelligence:1 ith:2 lr:1 lrc:1 node:28 sigmoidal:1 zhang:1 hermite:2 mathematical:1 along:1 sympo... |
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