Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
6,600 | 6,970 | Influence Maximization with ?-Almost
Submodular Threshold Functions
Qiang Li??, Wei Chen?, Xiaoming Sun??, Jialin Zhang??
?
CAS Key Lab of Network Data Science and Technology,
Institute of Computing Technology, Chinese Academy of Sciences
?
University of Chinese Academy of Sciences
?
Microsoft Research
{liqiang01,sunxi... | 6970 |@word rising:1 polynomial:1 open:1 vldb:2 simulation:1 lakshmanan:4 bicriteria:1 harder:1 reduction:1 venkatasubramanian:1 liu:1 contains:1 score:1 selecting:2 past:1 existing:2 yajun:3 com:1 comparing:1 activation:2 written:3 ronald:1 kdd:3 christian:1 drop:2 designed:1 v:2 greedy:32 selected:2 leaf:2 ubuntu:1 s... |
6,601 | 6,971 | InfoGAIL: Interpretable Imitation Learning from
Visual Demonstrations
Yunzhu Li
MIT
liyunzhu@mit.edu
Jiaming Song
Stanford University
tsong@cs.stanford.edu
Stefano Ermon
Stanford University
ermon@cs.stanford.edu
Abstract
The goal of imitation learning is to mimic expert behavior without access to an
explicit reward ... | 6971 |@word cnn:3 middle:1 open:2 termination:1 d2:1 simulation:3 seek:1 reduction:3 initial:3 series:1 score:2 selecting:1 bc:3 ours:4 interestingly:1 rightmost:1 africa:1 current:4 com:1 recovered:1 yet:1 guez:1 realistic:1 chicago:1 confirming:1 shape:1 remove:3 interpretable:7 update:6 fund:1 generative:17 discover... |
6,602 | 6,972 | Variational Laws of
Visual Attention for Dynamic Scenes
Dario Zanca
DINFO, University of Florence
DIISM, University of Siena
dario.zanca@unifi.it
Marco Gori
DIISM, University of Siena
marco@diism.unisi.it
Abstract
Computational models of visual attention are at the crossroad of disciplines like
cognitive science, co... | 6972 |@word cox:1 version:8 middle:3 seems:2 open:1 calculus:1 cos2:2 brightness:17 boundedness:1 initial:2 born:1 contains:2 configuration:4 selecting:1 score:11 liu:1 interestingly:2 current:3 comparing:1 surprising:1 intriguing:1 attracted:1 written:1 must:1 realistic:1 numerical:2 blur:2 analytic:1 designed:2 stati... |
6,603 | 6,973 | Recursive Sampling for the Nystr?m Method
Cameron Musco
MIT EECS
cnmusco@mit.edu
Christopher Musco
MIT EECS
cpmusco@mit.edu
Abstract
We give the first algorithm for kernel Nystr?m approximation that runs in linear
time in the number of training points and is provably accurate for all kernel matrices,
without dependen... | 6973 |@word trial:3 repository:2 version:1 achievable:1 norm:3 polynomial:1 nd:6 confirms:2 seek:2 r:2 tat:1 decomposition:4 nystr:73 tr:3 recursively:3 reduction:2 liu:1 contains:1 score:45 lichman:1 woodruff:5 daniel:1 dubourg:1 outperforms:1 existing:1 michal:2 surprising:1 yet:1 must:2 john:2 lic13:2 additive:2 sub... |
6,604 | 6,974 | Interpolated Policy Gradient: Merging On-Policy and
Off-Policy Gradient Estimation for Deep
Reinforcement Learning
Shixiang Gu
University of Cambridge
Max Planck Institute
sg717@cam.ac.uk
Richard E. Turner
University of Cambridge
ret26@cam.ac.uk
Timothy Lillicrap
DeepMind
countzero@google.com
Bernhard Sch?lkopf
Max ... | 6974 |@word faculty:1 pieter:5 confirms:1 seek:1 crucially:2 simulation:1 eng:1 harder:1 reduction:5 contains:2 series:1 humanlevel:1 outperforms:3 existing:1 hasselt:1 current:1 com:1 comparing:1 freitas:1 guez:1 john:5 ronald:1 enables:2 christian:1 designed:1 update:26 aside:1 v:2 es:1 aja:1 provides:8 contribute:1 ... |
6,605 | 6,975 | Dynamic Routing Between Capsules
Sara Sabour
Nicholas Frosst
Geoffrey E. Hinton
Google Brain
Toronto
{sasabour, frosst, geoffhinton}@google.com
Abstract
A capsule is a group of neurons whose activity vector represents the instantiation
parameters of a specific type of entity such as an object or an object part. We ... | 6975 |@word trial:1 cnn:2 version:1 replicate:1 logit:2 nd:1 grey:1 accounting:1 image2:1 pick:2 crowding:2 initial:4 inefficiency:2 contains:1 fragment:3 rightmost:1 current:3 com:1 activation:1 assigning:2 diederik:1 written:1 devin:1 shape:3 designed:2 plot:1 v:2 generative:1 leaf:1 intelligence:2 beginning:1 bissac... |
6,606 | 6,976 | Incorporating Side Information by Adaptive
Convolution
Di Kang
Debarun Dhar
Antoni B. Chan
Department of Computer Science
City University of Hong Kong
{dkang5-c, ddhar2-c}@my.cityu.edu.hk, abchan@cityu.edu.hk
Abstract
Computer vision tasks often have side information available that is helpful to
solve the task. For e... | 6976 |@word kong:3 cnn:85 version:1 compression:1 stronger:2 nd:3 disk:7 configuration:2 contains:3 efficacy:1 liu:2 tuned:1 ours:1 interestingly:1 existing:3 current:10 luo:1 activation:5 acnns:2 must:1 gpu:1 subsequent:1 concatenate:1 blur:8 additive:2 drop:1 v:3 half:2 fewer:1 selected:1 intelligence:1 plane:1 short... |
6,607 | 6,977 | Conic Scan-and-Cover algorithms for
nonparametric topic modeling
Mikhail Yurochkin
Department of Statistics
University of Michigan
moonfolk@umich.edu
Aritra Guha
Department of Statistics
University of Michigan
aritra@umich.edu
XuanLong Nguyen
Department of Statistics
University of Michigan
xuanlong@umich.edu
Abstra... | 6977 |@word mild:1 faculty:1 proportion:2 norm:6 suitably:1 open:1 simulation:2 tried:1 contraction:4 accounting:1 pick:1 mention:1 harder:1 moment:2 inefficiency:1 contains:3 series:1 score:3 liu:2 document:33 outperforms:1 recovered:3 com:1 current:1 deteriorating:1 visible:1 subsequent:1 partition:1 shape:1 remove:1... |
6,608 | 6,978 | FALKON: An Optimal Large Scale Kernel Method
Alessandro Rudi ?
INRIA ? Sierra Project-team,
?
Ecole
Normale Sup?erieure, Paris
Luigi Carratino
University of Genoa
Genova, Italy
Lorenzo Rosasco
University of Genoa,
LCSL, IIT & MIT
Abstract
Kernel methods provide a principled way to perform non linear, nonparametric
... | 6978 |@word illustrating:1 version:6 c0:6 km:6 tat:1 decomposition:2 sgd:2 nystr:41 solid:1 tr:1 minmax:1 liu:1 score:11 selecting:1 woodruff:2 ecole:1 denoting:1 interestingly:1 kurt:1 daniel:3 luigi:1 outperforms:3 kx0:1 recovered:1 com:1 err:6 scovel:1 gpu:5 john:2 numerical:1 additive:2 benign:1 analytic:2 christia... |
6,609 | 6,979 | Structured Generative Adversarial Networks
Zhijie Deng? , 2,3 Hao Zhang? , 2 Xiaodan Liang, 2 Luona Yang,
1,2
Shizhen Xu, 1 Jun Zhu? , 3 Eric P. Xing
1
Tsinghua University, 2 Carnegie Mellon University, 3 Petuum Inc.
{dzj17,xsz12}@mails.tsinghua.edu.cn, {hao,xiaodan1,luonay1}@cs.cmu.edu,
dcszj@mail.tsinghua.edu.cn, epx... | 6979 |@word mild:1 seems:1 hu:3 pieter:1 tenka:1 pg:37 pick:1 shot:3 carry:1 configuration:1 contains:2 exclusively:3 score:3 liu:2 jimenez:1 document:1 deconvolutional:1 outperforms:5 existing:4 cvae:11 current:1 z2:2 com:1 comparing:1 freitas:1 diederik:2 gpu:2 john:1 devin:1 visible:5 informative:1 confirming:2 shap... |
6,610 | 6,980 | Conservative Contextual Linear Bandits
Abbas Kazerouni
Stanford University
abbask@stanford.edu
Mohammad Ghavamzadeh
DeepMind
ghavamza@google.com
Yasin Abbasi-Yadkori
Adobe Research
abbasiya@adobe.com
Benjamin Van Roy
Stanford University
bvr@stanford.edu
Abstract
Safety is a desirable property that can immensely inc... | 6980 |@word version:2 norm:1 nd:1 d2:4 willing:2 tat:7 simulation:3 confirms:1 incurs:1 harder:1 initial:5 contains:2 existing:1 current:4 contextual:8 com:2 nt:7 chu:2 must:2 written:1 explorative:1 additive:2 happen:1 plot:3 designed:1 update:3 intelligence:2 selected:1 beginning:3 yi1:1 provides:1 along:1 constructe... |
6,611 | 6,981 | Variational Memory Addressing
in Generative Models
J?rg Bornschein Andriy Mnih Daniel Zoran Danilo J. Rezende
{bornschein, amnih, danielzoran, danilor}@google.com
DeepMind, London, UK
Abstract
Aiming to augment generative models with external memory, we interpret the
output of a memory module with stochastic addressin... | 6981 |@word version:4 middle:1 norm:1 retraining:1 confirms:1 pick:3 shot:18 contains:6 series:1 jimenez:3 daniel:1 reynolds:1 existing:1 com:1 surprising:1 diederik:3 john:1 informative:1 confirming:1 enables:1 shanahan:1 treating:2 designed:1 update:7 interpretable:1 sukhbaatar:1 generative:43 selected:4 item:4 ivo:2... |
6,612 | 6,982 | On Tensor Train Rank Minimization: Statistical
Efficiency and Scalable Algorithm
Masaaki Imaizumi
Institute of Statistical Mathematics
RIKEN Center for Advanced Intelligence Project
imaizumi@ism.ac.jp
Takanori Maehara
RIKEN Center for Advanced Intelligence Project
takanori.maehara@riken.jp
Kohei Hayashi
National Instit... | 6982 |@word kgk:1 repository:1 version:3 inversion:1 trial:1 norm:20 stronger:1 trofimov:1 d2:14 vek:3 decomposition:42 citeseer:1 pick:1 initial:7 liu:1 contains:2 kpv:1 series:3 selecting:1 lichman:1 past:2 existing:1 com:1 comparing:1 gmail:1 yet:2 written:2 chu:1 j1:7 shape:1 enables:1 rd2:1 implying:1 intelligence... |
6,613 | 6,983 | Scalable L?evy Process Priors for Spectral Kernel
Learning
Phillip A. Jang
Andrew E. Loeb Matthew B. Davidow
Cornell University
Andrew Gordon Wilson
Abstract
Gaussian processes are rich distributions over functions, with generalization properties determined by a kernel function. When used for long-range extrapolati... | 6983 |@word middle:2 rising:3 stronger:2 grey:3 covariance:26 decomposition:1 accounting:2 series:2 tuned:1 elliptical:1 com:1 scatter:2 must:3 readily:1 additive:1 realistic:1 shape:1 enables:1 interpretable:2 stationary:10 generative:3 isotropic:1 short:3 evy:69 location:7 along:2 direct:2 become:1 qualitative:1 fitt... |
6,614 | 6,984 | Deep Hyperspherical Learning
Weiyang Liu1 , Yan-Ming Zhang2 , Xingguo Li3,1 , Zhiding Yu4 , Bo Dai1 , Tuo Zhao1 , Le Song1
1
Georgia Institute of Technology 2 Institute of Automation, Chinese Academy of Sciences
3
University of Minnesota 4 Carnegie Mellon University
{wyliu,tourzhao}@gatech.edu, ymzhang@nlpr.ia.ac.cn,... | 6984 |@word cnn:30 version:1 cu:2 norm:3 stronger:1 seems:3 bf:2 suitably:1 open:2 kokkinos:1 tried:1 rgb:1 sgd:2 tr:2 recursively:1 liu:5 score:2 ours:1 interestingly:2 outperforms:4 current:3 activation:5 yet:1 written:4 numerical:1 happen:1 supervises:1 shape:2 christian:2 remove:1 drop:1 kv1:1 plot:1 v:6 alone:1 ac... |
6,615 | 6,985 | Learning Deep Structured Multi-Scale Features using
Attention-Gated CRFs for Contour Prediction
Dan Xu1
Wanli Ouyang2 Xavier Alameda-Pineda3 Elisa Ricci4
Xiaogang Wang5 Nicu Sebe1
1
The University of Trento, 2 The University of Sydney, 3 Perception Group, INRIA
4
University of Perugia, 5 The Chinese University of Hong... | 6985 |@word h:12 kong:1 cnn:27 version:2 kohli:1 kokkinos:1 nd:1 open:1 cs0:1 hu:1 confirms:1 rgb:16 decomposition:1 brightness:1 bai:2 liu:3 contains:1 series:1 initial:1 hoiem:1 deconvolutional:1 past:1 existing:1 outperforms:3 current:1 comparing:2 od:9 skipping:1 com:1 guadarrama:1 chu:1 gpu:1 confirming:2 shape:1 ... |
6,616 | 6,986 | On-the-fly Operation Batching
in Dynamic Computation Graphs
Graham Neubig?
Language Technologies Institute
Carnegie Mellon University
gneubig@cs.cmu.edu
Yoav Goldberg?
Computer Science Department
Bar-Ilan University
yogo@cs.biu.ac.il
Chris Dyer
DeepMind
cdyer@google.com
Abstract
Dynamic neural network toolkits such ... | 6986 |@word kong:1 version:2 interleave:1 laurence:1 polynomial:1 suitably:1 open:2 shuicheng:1 pengcheng:1 pick:1 harder:1 initial:2 series:2 lightweight:1 daniel:2 erven:1 existing:3 freitas:1 prioritization:1 comparing:1 com:1 gemm:1 yet:2 must:4 luis:2 gpu:14 parsing:8 devin:1 realistic:1 numerical:1 partition:1 ch... |
6,617 | 6,987 | Nonlinear Acceleration of Stochastic Algorithms
Damien Scieur
INRIA, ENS,
PSL Research University,
Paris France
damien.scieur@inria.fr
Francis Bach
INRIA, ENS,
PSL Research University,
Paris France
francis.bach@inria.fr
Alexandre d?Aspremont
CNRS, ENS,
PSL Research University,
Paris France
aspremon@ens.fr
Abstract
... | 6987 |@word briefly:1 version:9 polynomial:6 norm:5 linearized:5 covariance:2 sgd:51 harder:1 reduction:3 initial:2 kx0:23 current:3 written:1 numerical:5 additive:1 update:2 v:2 intelligence:1 selected:1 fewer:1 xk:3 beginning:3 ith:1 vanishing:1 recherche:1 iterates:13 successive:1 lipchitz:2 zhang:7 mathematical:1 d... |
6,618 | 6,988 | Optimized Pre-Processing for Discrimination
Prevention
Flavio P. Calmon
Harvard University
flavio@seas.harvard.edu
Dennis Wei
IBM Research AI
dwei@us.ibm.com
Karthikeyan Natesan Ramamurthy
IBM Research AI
knatesa@us.ibm.com
Bhanukiran Vinzamuri
IBM Research AI
bhanu.vinzamuri@ibm.com
Kush R. Varshney
IBM Research AI... | 6988 |@word repository:1 norm:1 nd:1 d2:3 seek:3 fairer:2 accounting:1 eng:1 contrastive:1 incurs:1 thereby:1 reduction:5 venkatasubramanian:3 lichman:3 score:6 denoting:1 ours:1 suppressing:1 tuned:1 outperforms:1 existing:3 com:4 protection:1 assigning:1 must:1 applicant:1 stine:1 numerical:2 subsequent:1 additive:1 ... |
6,619 | 6,989 | YASS: Yet Another Spike Sorter
JinHyung Lee1 , David Carlson2 , Hooshmand Shokri1 , Weichi Yao1 , Georges Goetz3 , Espen Hagen4 ,
Eleanor Batty1 , EJ Chichilnisky3 , Gaute Einevoll5 , and Liam Paninski1
1
Columbia University, 2 Duke University, 3 Stanford University, 4 University of Oslo, 5 Norwegian
University of Lif... | 6989 |@word middle:2 achievable:1 johansson:1 vldb:2 zelnik:1 simulation:3 covariance:1 eng:2 citeseer:1 bahmani:1 recursively:1 reduction:2 series:3 efficacy:4 contains:1 outperforms:4 existing:4 nadasdy:1 recovered:3 current:1 comparing:2 skipping:1 com:1 past:1 yet:2 gpu:6 realistic:1 timestamps:2 visible:1 shape:4 ... |
6,620 | 699 | A Practice Strategy for Robot Learning
Control
Terence D. Sanger
Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology, room E25-534
Cambridge, MA 02139
tds@ai.mit.edu
Abstract
"Trajectory Extension Learning" is a new technique for Learning
Control in Robots which assumes that... | 699 |@word trial:5 grey:2 seek:1 solid:2 ivaldi:1 liu:1 initial:3 bootstrapped:1 comparing:1 must:1 subsequent:1 partition:1 motor:2 designed:1 yamada:2 provides:1 simpler:1 direct:3 become:1 differential:1 prove:3 behavior:3 pcx:1 multi:2 brain:1 actual:11 little:1 increasing:1 becomes:1 bounded:2 miyazaki:1 ull:2 wro... |
6,621 | 6,990 | Independence clustering (without a matrix)
Daniil Ryabko
INRIA Lillle,
40 avenue de Halley, Villeneuve d?Ascq, France
daniil@ryabko.net
Abstract
The independence clustering problem is considered in the following formulation:
given a set S of random variables, it is required to find the finest partitioning
{U1 , . . .... | 6990 |@word private:1 version:6 radim:1 polynomial:1 replicate:1 open:4 decomposition:2 harder:1 recursively:4 initial:1 necessity:1 series:22 selecting:2 daniel:1 existing:1 comparing:1 si:11 yet:4 must:2 finest:3 luis:1 grassberger:1 realistic:1 partition:4 informative:1 drop:1 joy:1 v:1 stationary:48 alone:1 kandasa... |
6,622 | 6,991 | Fast amortized inference of neural activity from
calcium imaging data with variational autoencoders
Artur Speiser12 , Jinyao Yan3 , Evan Archer4?, Lars Buesing4?,
Srinivas C. Turaga3? and Jakob H. Macke1??
1
research center caesar, an associate of the Max Planck Society, Bonn, Germany
2
IMPRS Brain and Behavior Bonn/F... | 6991 |@word neurophysiology:1 cnn:8 version:2 inversion:3 norm:3 retraining:1 c0:3 open:1 accounting:1 initial:1 series:2 precluding:1 current:6 discretization:1 comparing:1 si:3 scatter:1 must:2 gpu:3 kiebel:1 additive:1 maaloe:2 enables:2 motor:1 designed:2 plot:1 update:2 drop:2 v:1 extrapolating:1 generative:55 aps... |
6,623 | 6,992 | Adaptive Active Hypothesis Testing under Limited
Information
Fabio Cecchi
Eindhoven University of Technology, Eindhoven, The Netherlands
f.cecchi@tue.nl
Nidhi Hegde
Nokia Bell Labs, Paris-Saclay, France
nidhi.hegde@nokia-bell-labs.com
Abstract
We consider the problem of active sequential hypothesis testing where a Bay... | 6992 |@word trial:2 pw:7 seems:1 d2:2 simulation:7 sensed:1 pick:1 carry:1 moment:1 initial:1 selecting:1 outperforms:4 past:3 existing:2 current:2 com:1 wd:5 comparing:1 must:10 explorative:1 numerical:4 j1:12 informative:2 cheap:1 drop:3 plot:1 update:13 v:1 alone:1 greedy:1 selected:1 provides:2 allerton:2 five:2 ma... |
6,624 | 6,993 | Streaming Weak Submodularity:
Interpreting Neural Networks on the Fly
Ethan R. Elenberg
Department of Electrical
and Computer Engineering
The University of Texas at Austin
elenberg@utexas.edu
Moran Feldman
Department of Mathematics
and Computer Science
Open University of Israel
moranfe@openu.ac.il
Alexandros G. Dimaki... | 6993 |@word mild:1 repository:1 faculty:2 version:1 polynomial:1 stronger:1 laurence:3 retraining:1 open:2 gradual:1 covariance:1 kent:1 bahmani:2 liu:1 contains:3 lichman:2 daniel:1 interestingly:1 bradley:1 com:1 surprising:1 yet:2 partition:1 kdd:2 christian:1 interpretable:5 update:3 greedy:19 selected:1 intelligen... |
6,625 | 6,994 | Successor Features for
Transfer in Reinforcement Learning
Andr? Barreto, Will Dabney, R?mi Munos, Jonathan J. Hunt,
Tom Schaul, David Silver, Hado van Hasselt
{andrebarreto,wdabney,munos,jjhunt,schaul,davidsilver,hado}@google.com
DeepMind
Abstract
Transfer in reinforcement learning refers to the notion that generali... | 6994 |@word multitask:1 version:4 norm:1 stronger:2 pillar:2 seems:1 reused:1 open:2 mehta:3 tadepalli:2 prasad:2 uncovers:1 decomposition:4 pick:1 solid:1 shading:1 moment:1 initial:1 ndez:2 selecting:1 daniel:2 ours:2 interestingly:2 hasselt:1 current:5 com:1 comparing:1 must:5 readily:2 john:2 happen:1 shape:1 remov... |
6,626 | 6,995 | Counterfactual Fairness
Matt Kusner ?
The Alan Turing Institute and
University of Warwick
mkusner@turing.ac.uk
Joshua Loftus ?
New York University
loftus@nyu.edu
Chris Russell ?
The Alan Turing Institute and
University of Surrey
crussell@turing.ac.uk
Ricardo Silva
The Alan Turing Institute and
University College Lon... | 6995 |@word version:1 stronger:1 seems:1 nd:1 bf:1 sex:22 justice:2 adrian:2 willing:1 closure:1 zliobaite:1 attainable:1 substitution:1 series:1 score:4 united:1 punishes:1 contains:1 offering:1 interestingly:1 bilal:3 longitudinal:1 past:1 existing:2 current:1 comparing:1 manuel:2 protection:1 assigning:1 must:8 appl... |
6,627 | 6,996 | Prototypical Networks for Few-shot Learning
Jake Snell
University of Toronto?
Vector Institute
Kevin Swersky
Twitter
Richard Zemel
University of Toronto
Vector Institute
Canadian Institute for Advanced Research
Abstract
We propose Prototypical Networks for the problem of few-shot classification, where
a classifier ... | 6996 |@word kulis:1 cu:1 middle:2 version:3 cnn:3 advantageous:1 stronger:2 retraining:2 seems:1 pieter:1 seek:1 jacob:1 image2:1 concise:1 sgd:3 thereby:1 tr:1 accommodate:1 shot:95 initial:2 configuration:1 contains:2 liu:1 selecting:1 rippel:2 daniel:1 tuned:4 ours:3 jimenez:1 outperforms:1 com:1 goldberger:1 dieder... |
6,628 | 6,997 | Triple Generative Adversarial Nets
Chongxuan Li, Kun Xu, Jun Zhu?, Bo Zhang
Dept. of Comp. Sci. & Tech., TNList Lab, State Key Lab of Intell. Tech. & Sys.,
Center for Bio-Inspired Computing Research, Tsinghua University, Beijing, 100084, China
{licx14, xu-k16}@mails.tsinghua.edu.cn, {dcszj, dcszb}@mail.tsinghua.edu.cn... | 6997 |@word mild:1 version:1 briefly:1 judgement:1 norm:1 heuristically:1 pieter:1 pg:27 citeseer:1 tnlist:1 moment:2 series:1 score:1 jimenez:2 daniel:1 denoting:5 ours:2 document:1 outperforms:4 existing:8 current:1 com:1 diederik:3 john:1 ronald:1 realistic:5 christian:1 treating:1 designed:1 update:4 interpretable:... |
6,629 | 6,998 | Efficient Sublinear-Regret Algorithms for Online
Sparse Linear Regression with Limited Observation
Shinji Ito
NEC Corporation
s-ito@me.jp.nec.com
Hanna Sumita
National Institute of Informatics
sumita@nii.ac.jp
Daisuke Hatano
National Institute of Informatics
hatano@nii.ac.jp
Akihiro Yabe
NEC Corporation
a-yabe@cq.jp... | 6998 |@word mild:2 repository:1 polynomial:13 stronger:3 norm:2 open:1 d2:3 decomposition:1 mention:1 lichman:1 nii:4 ours:2 com:2 dx:4 attracted:1 realize:1 subsequent:1 designed:1 plot:4 bart:1 greedy:6 selected:2 beginning:1 math:1 accessed:1 five:1 incorrect:1 prove:1 consists:1 combine:1 introduce:4 hardness:4 exp... |
6,630 | 6,999 | Mapping distinct timescales of functional interactions
among brain networks
Mali Sundaresan
Centre for Neuroscience
Indian Institute of Science
Bangalore, India 560 012
s.malisundar@gmail.com
Arshed Nabeel
Centre for Neuroscience
Indian Institute of Science
Bangalore, India 560 012
arshed@iisc.ac.in
Devarajan Sridha... | 6999 |@word oostenveld:1 middle:2 briefly:1 seek:3 simulation:22 covariance:2 tr:8 carry:1 configuration:13 series:29 efficacy:1 exclusively:4 contains:1 hemodynamic:13 interestingly:1 past:4 ramsey:1 com:1 anterior:2 surprising:1 lang:9 gmail:1 dx:1 must:1 readily:1 confirming:1 enables:1 webster:2 remove:1 plot:4 atl... |
6,631 | 7 | 377
EXPERIMENTAL DEMONSTRATIONS OF
OPTICAL NEURAL COMPUTERS
Ken Hsu, David Brady, and Demetri Psaltis
Department of Electrical Engineering
California Institute of Technology
Pasadena, CA 91125
ABSTRACT
We describe two expriments in optical neural computing. In the first
a closed optical feedback loop is used to imple... | 7 |@word maz:1 version:1 eng:2 pick:1 tr:1 solid:1 selecting:1 liquid:3 optically:2 must:3 readily:1 exposing:2 designed:1 discrimination:2 half:1 electr:1 device:14 plane:18 trapping:1 ith:9 record:3 supplying:1 sits:1 along:3 eung:1 soffer:1 recognizable:2 diffuser:1 deteriorate:1 behavior:3 p1:2 brain:1 little:1 val... |
6,632 | 70 | 137
On the Power of Neural Networks for
Solving Hard Problems
J ehoshua Bruck
Joseph W. Goodman
Information Systems Laboratory
Departmen t of Electrical Engineering
Stanford University
Stanford, CA 94305
Abstract
This paper deals with a neural network model in which each neuron
performs a threshold logic function. An ... | 70 |@word build:1 implemented:1 implies:4 polynomial:16 hence:3 question:2 symmetric:1 laboratory:1 deal:3 sgn:2 alp:1 uniquely:1 distance:1 mapped:1 sci:1 reduction:1 electronics:1 generalized:1 nx:2 icnn:1 proposition:12 complete:6 performs:1 current:1 hold:2 considered:1 hall:1 great:1 algorithmic:1 setup:4 claim:1 ... |
6,633 | 700 | Neural Network Model Selection Using
Asymptotic Jackknife Estimator and
Cross-Validation Method
Yong Liu
Department of Physics and
Institute for Brain and Neural Systems
Box 1843, Brown University
Providence, RI, 02912
Abstract
Two theorems and a lemma are presented about the use of jackknife estimator and the cross-... | 700 |@word soc:3 auxiliary:1 brown:1 unbiased:3 come:3 true:5 effect:1 regularization:1 nd:1 prof:1 quantity:1 a02:1 parametric:1 fa:1 exploration:1 alp:1 distance:2 link:1 cw:6 criterion:38 liu:5 generalization:2 generalized:2 hereafter:1 selecting:2 stone:10 denoting:1 me:1 summation:3 strictly:1 extension:1 pro:1 ix... |
6,634 | 7,000 | Multi-Armed Bandits with Metric Movement Costs
Tomer Koren
Google Brain
tkoren@google.com
Roi Livni
Princeton University
rlivni@cs.princeton.edu
Yishay Mansour
Tel Aviv University and Google
mansour@cs.tau.ac.il
Abstract
We consider the non-stochastic Multi-Armed Bandit problem in a setting where
there is a fixed an... | 7000 |@word version:3 polynomial:1 achievable:1 seems:1 stronger:1 dekel:3 nd:1 unif:1 contraction:1 automat:1 pick:2 incurs:2 mention:1 recursively:1 reduction:1 selecting:1 united:1 denoting:1 document:1 com:2 discretization:1 surprising:1 assigning:1 john:1 happen:1 designed:1 update:2 fund:1 sundaram:1 stationary:1... |
6,635 | 7,001 | Learning A Structured Optimal Bipartite Graph
for Co-Clustering
1
Feiping Nie1 , Xiaoqian Wang2 , Cheng Deng3 , Heng Huang2?
School of Computer Science, Center for OPTIMAL, Northwestern Polytechnical University, China
2
Department of Electrical and Computer Engineering, University of Pittsburgh, USA
3
School of Elect... | 7001 |@word norm:3 seems:1 km:1 zelnik:1 decomposition:1 initial:1 contains:2 series:1 tuned:1 document:12 existing:2 current:2 com:2 discretization:1 comparing:1 cad:1 gmail:2 written:3 partition:4 blur:1 benign:1 weyl:1 remove:1 depict:2 update:6 intelligence:2 guess:1 metabolism:1 provides:1 node:9 revisited:1 five:... |
6,636 | 7,002 | Learning Low-Dimensional Metrics
Lalit Jain ?
University of Michigan
Ann Arbor, MI 48109
lalitj@umich.edu
Blake Mason ?
University of Wisconsin
Madison, WI 53706
bmason3@wisc.edu
Robert Nowak
University of Wisconsin
Madison, WI 53706
rdnowak@wisc.edu
Abstract
This paper investigates the theoretical foundations of me... | 7002 |@word kgk:3 trial:1 judgement:1 norm:26 km:1 r:1 simulation:1 mention:1 series:1 past:2 outperforms:1 recovered:1 surprising:1 chu:1 must:4 realistic:1 gv:1 hypothesize:1 plot:1 generative:1 selected:1 fewer:1 item:5 intelligence:1 accordingly:1 indicative:1 xk:8 beginning:1 isotropic:5 provides:2 complication:1 ... |
6,637 | 7,003 | The Marginal Value of Adaptive Gradient Methods
in Machine Learning
Ashia C. Wilson] , Rebecca Roelofs] , Mitchell Stern] , Nathan Srebro? , and Benjamin Recht]
{ashia,roelofs,mitchell}@berkeley.edu, nati@ttic.edu, brecht@berkeley.edu
?
]
University of California, Berkeley
Toyota Technological Institute at Chicago
Ab... | 7003 |@word repository:1 version:2 faculty:2 middle:1 norm:7 advantageous:1 pieter:1 r:2 tried:4 bn:1 xtest:6 sgd:29 shading:2 carry:1 initial:10 configuration:4 score:1 charniak:4 tuned:2 interestingly:1 past:1 current:2 com:5 comparing:1 surprising:1 written:3 must:2 parsing:11 john:1 chicago:1 numerical:1 happen:1 c... |
6,638 | 7,004 | Aggressive Sampling for Multi-class to Binary
Reduction with Applications to Text Classification
Bikash Joshi
Univ. Grenoble Alps, LIG
Grenoble, France
bikash.joshi@imag.fr
Massih-Reza Amini
Univ. Grenoble Alps, LIG
Grenoble, France
massih-reza.amini@imag.fr
Franck Iutzeler
Univ. Grenoble Alps, LJK
Grenoble, France
... | 7004 |@word repository:2 proportion:2 seems:1 nd:3 carolina:1 hsieh:1 dramatic:1 tr:1 liblinear:3 reduction:11 liu:1 series:1 score:1 contains:1 daniel:1 tuned:2 document:10 janson:1 com:3 comparing:2 beygelzimer:1 ida:1 exy:1 liva:2 john:6 stemming:1 partition:1 kdd:1 plot:2 v:1 intelligence:1 leaf:1 selected:1 fewer:... |
6,639 | 7,005 | Deconvolutional Paragraph Representation Learning
Yizhe Zhang
Dinghan Shen
Guoyin Wang
Zhe Gan
Ricardo Henao
Lawrence Carin
Department of Electrical & Computer Engineering, Duke University
Abstract
Learning latent representations from long text sequences is an important first step
in many natural language proces... | 7005 |@word cnn:51 briefly:1 norm:4 proportion:3 hu:2 seek:2 accounting:1 pavel:1 recursively:2 carry:1 liu:2 series:1 score:5 fragment:3 jimenez:1 configuration:1 denoting:1 substitution:2 att:1 deconvolutional:23 document:5 ours:3 existing:1 outperforms:3 activation:1 yet:1 diederik:1 written:1 gpu:3 readily:1 john:2... |
6,640 | 7,006 | Random Permutation Online Isotonic Regression
Wojciech Kot?owski
Pozna?n University of Technology
Poland
wkotlowski@cs.put.poznan.pl
Wouter M. Koolen
Centrum Wiskunde & Informatica
Amsterdam, The Netherlands
wmkoolen@cwi.nl
Alan Malek
MIT
Cambridge, MA
amalek@mit.edu
Abstract
We revisit isotonic regression on linea... | 7006 |@word trial:6 version:3 stronger:1 proportion:1 yi0:19 norm:1 open:2 simulation:1 pick:1 incurs:1 thereby:1 harder:2 moment:1 reduction:4 celebrated:1 contains:2 score:1 leeuw:1 interestingly:2 kurt:1 past:5 current:2 surprising:1 yet:3 written:1 john:1 subsequent:1 partition:1 numerical:1 kdd:1 gerchinovitz:1 dr... |
6,641 | 7,007 | A Unified Game-Theoretic Approach to
Multiagent Reinforcement Learning
Marc Lanctot
DeepMind
lanctot@
Karl Tuyls
DeepMind
karltuyls@
Vinicius Zambaldi
DeepMind
vzambaldi@
?
Audrunas
Gruslys
DeepMind
audrunas@
Julien P?rolat
DeepMind
perolat@
David Silver
DeepMind
davidsilver@
Angeliki Lazaridou
DeepMind
angeliki@... | 7007 |@word private:2 interleave:1 stronger:1 nd:1 simulation:6 rgb:1 vicky:1 reduction:5 initial:1 score:3 selecting:1 wako:1 freitas:1 bradley:1 dx:1 refresh:1 periodically:3 happen:1 diogo:1 update:7 stationary:2 discovering:1 selected:1 isotropic:1 beginning:1 serialized:1 symposium:4 expected:6 solver:13 becomes:3... |
6,642 | 7,008 | Inverse Filtering for Hidden Markov Models
Robert Mattila
Department of Automatic Control
KTH Royal Institute of Technology
rmattila@kth.se
Vikram Krishnamurthy
Cornell Tech
Cornell University
vikramk@cornell.edu
Cristian R. Rojas
Department of Automatic Control
KTH Royal Institute of Technology
crro@kth.se
Bo Wahlbe... | 7008 |@word middle:1 version:4 e215:1 norm:1 asks:1 initial:1 cyclic:1 contains:1 recovered:3 goldberger:1 written:1 additive:2 concatenate:1 plot:1 atlas:1 update:6 intelligence:1 selected:1 device:2 xk:6 ith:1 core:1 menell:1 filtered:2 provides:2 detecting:1 characterization:2 codebook:1 preference:3 quantized:1 fiv... |
6,643 | 7,009 | Non-parametric Structured Output Networks
Andreas M. Lehrmann
Disney Research
Pittsburgh, PA 15213
andreas.lehrmann@disneyresearch.com
Leonid Sigal
Disney Research
Pittsburgh, PA 15213
lsigal@disneyresearch.com
Abstract
Deep neural networks (DNNs) and probabilistic graphical models (PGMs) are
the two main tools for ... | 7009 |@word multitask:1 trial:1 illustrating:1 version:1 cnn:2 kokkinos:1 paredes:1 cleanly:1 grey:1 hu:1 propagate:5 mitsubishi:1 decomposition:1 covariance:3 accounting:1 pick:1 thereby:1 initial:1 liu:1 series:2 exclusively:1 contains:1 ours:3 romera:1 outperforms:1 com:2 must:3 written:1 confirming:1 analytic:1 upd... |
6,644 | 701 | Time Warping Invariant Neural Networks
Guo-Zheng Sun, Hsing-Hen Chen and Yee-Chun Lee
Institute for Advanced Computer Studies
and
Laboratory for Plasma Research,
University of Maryland
College Park, MD 20742
Abstract
We proposed a model of Time Warping Invariant Neural Networks (TWINN)
to handle the time warped conti... | 701 |@word deformed:4 version:4 norm:2 efh:2 simulation:1 tried:1 dramatic:1 mention:1 initial:2 liu:2 series:4 score:2 contains:3 mag:1 current:2 lang:1 dx:1 written:2 numerical:8 shape:3 remove:2 plot:1 v:1 selected:1 accordingly:1 short:5 provides:1 contribute:1 mathematical:2 along:4 windowed:2 prove:1 consists:1 i... |
6,645 | 7,010 | Learning Active Learning from Data
Ksenia Konyushkova?
CVLab, EPFL
Lausanne, Switzerland
ksenia.konyushkova@epfl.ch
Sznitman Raphael
ARTORG Center, University of Bern
Bern, Switzerland
raphael.sznitman@artorg.unibe.ch
Pascal Fua
CVLab, EPFL
Lausanne, Switzerland
pascal.fua@epfl.ch
Abstract
In this paper, we suggest ... | 7010 |@word ksenia:4 exploitation:2 version:2 briefly:1 mri:12 proportion:2 everingham:1 r:9 covariance:1 p0:4 pick:1 reduction:14 initial:3 contains:2 score:5 selecting:1 series:1 batista:1 outperforms:5 existing:3 current:2 com:2 luo:1 yet:1 chu:2 must:1 realistic:1 informative:1 shape:1 enables:2 designed:1 plot:2 a... |
6,646 | 7,011 | VAE Learning via Stein Variational Gradient Descent
Yunchen Pu, Zhe Gan, Ricardo Henao, Chunyuan Li, Shaobo Han, Lawrence Carin
Department of Electrical and Computer Engineering, Duke University
{yp42, zg27, r.henao, cl319, shaobo.han, lcarin}@duke.edu
Abstract
A new method for learning variational autoencoders (VAEs... | 7011 |@word cnn:3 loading:1 proportion:2 seek:2 klk:1 liu:3 series:1 score:1 rkhs:4 document:3 deconvolutional:4 outperforms:2 current:1 surprising:1 activation:2 must:3 realize:1 visible:1 analytic:4 update:11 v:2 generative:4 selected:1 intelligence:1 isotropic:2 blei:2 provides:1 zhang:3 five:1 wierstra:2 vi3:1 beta... |
6,647 | 7,012 | Reconstructing perceived faces from brain activations
with deep adversarial neural decoding
Ya?gmur G??l?t?rk*, Umut G??l?*,
Katja Seeliger, Sander Bosch,
Rob van Lier, Marcel van Gerven,
Radboud University, Donders Institute for Brain, Cognition and Behaviour
Nijmegen, the Netherlands
{y.gucluturk, u.guclu}@donders.r... | 7012 |@word katja:1 trial:2 version:1 middle:1 inversion:2 loading:2 approved:1 kriegeskorte:2 mr2:1 judgement:1 open:1 covariance:1 jacob:1 inpainting:2 tr:1 liu:1 contains:1 score:1 denoting:1 dubourg:1 outperforms:1 existing:3 subjective:4 current:1 guadarrama:1 luo:1 activation:6 written:1 gpu:1 kiebel:1 realistic:... |
6,648 | 7,013 | Efficient Use of Limited-Memory Accelerators
for Linear Learning on Heterogeneous Systems
?
Celestine Dunner
IBM Research - Zurich
Switzerland
cdu@zurich.ibm.com
Thomas Parnell
IBM Research - Zurich
Switzerland
tpa@zurich.ibm.com
Martin Jaggi
EPFL
Switzerland
martin.jaggi@epfl.ch
Abstract
We propose a generic algor... | 7013 |@word version:3 norm:1 disk:2 hsieh:1 thereby:1 versatile:1 minding:1 initial:1 selecting:2 outperforms:2 existing:6 ka:3 com:2 current:5 nicolai:1 readily:1 refresh:1 gpu:21 informative:1 enables:3 designed:2 plot:3 update:26 v:1 intelligence:2 selected:5 device:1 website:1 indicative:1 beginning:2 steepest:3 sm... |
6,649 | 7,014 | Temporal Coherency based Criteria for Predicting
Video Frames using Deep Multi-stage Generative
Adversarial Networks
Prateep Bhattacharjee1 , Sukhendu Das2
Visualization and Perception Laboratory
Department of Computer Science and Engineering
Indian Institute of Technology Madras, Chennai, India
1
prateepb@cse.iitm.ac.... | 7014 |@word cnn:1 version:4 nd:2 d2:1 simulation:1 contrastive:8 thereby:5 ld:1 reduction:1 configuration:1 series:3 score:12 contains:3 liu:2 tuned:2 document:4 past:4 current:3 luo:1 activation:1 yet:1 unpooling:5 subsequent:2 realistic:1 visibility:2 designed:1 generative:13 intelligence:2 xk:8 short:4 sudden:1 cse:... |
6,650 | 7,015 | Sobolev Training for Neural Networks
Wojciech Marian Czarnecki, Simon Osindero, Max Jaderberg
Grzegorz Swirszcz, and Razvan Pascanu
DeepMind, London, UK
{lejlot,osindero,jaderberg,swirszcz,razp}@google.com
Abstract
At the heart of deep learning we aim to use neural networks as function approximators ? training them t... | 7015 |@word version:2 achievable:1 compression:5 stronger:2 norm:1 polynomial:1 hyv:2 seek:3 bn:1 prokhorov:1 thereby:1 solid:2 harder:1 reduction:1 score:3 interestingly:1 kurt:1 existing:1 com:2 surprising:1 anqi:1 activation:7 dx:7 must:1 guez:1 john:2 devin:1 ronald:1 confirming:1 shape:1 analytic:2 designed:1 plot... |
6,651 | 7,016 | Multi-Information Source Optimization
Matthias Poloczek
Department of Systems and Industrial Engineering
University of Arizona
Tucson, AZ 85721
poloczek@email.arizona.edu
Jialei Wang
Chief Analytics Office
IBM
Armonk, NY 10504
charlie.j.wang@ibm.com
Peter I. Frazier
School of Operations Research and Information Engi... | 7016 |@word repository:1 version:1 inversion:1 c0:1 seek:1 simulation:5 covariance:8 invoking:2 pick:1 thereby:2 profit:1 minus:1 recursively:1 initial:7 configuration:1 contains:1 score:2 selecting:1 ndez:1 interestingly:4 rightmost:3 outperforms:5 xnj:1 com:6 si:2 scatter:1 numerical:4 additive:2 cheap:8 remove:2 des... |
6,652 | 7,017 | Deep Reinforcement Learning
from Human Preferences
Paul F Christiano
OpenAI
paul@openai.com
Miljan Martic
DeepMind
miljanm@google.com
Jan Leike
DeepMind
leike@google.com
Shane Legg
DeepMind
legg@google.com
Tom B Brown
Google Brain?
tombbrown@google.com
Dario Amodei
OpenAI
damodei@openai.com
Abstract
For sophisticate... | 7017 |@word economically:1 seems:2 instruction:1 pick:1 fifteen:1 initial:1 score:6 selecting:1 daniel:2 bilal:1 past:1 bradley:2 current:1 com:6 yet:1 evans:1 happen:1 wanted:1 remove:1 stationary:1 half:2 fewer:1 spaceinvaders:2 beginning:1 short:3 provides:1 draft:1 preference:19 five:2 supply:1 qualitative:2 abadi:... |
6,653 | 7,018 | On the Fine-Grained Complexity of
Empirical Risk Minimization:
Kernel Methods and Neural Networks
Arturs Backurs
CSAIL
MIT
backurs@mit.edu
Piotr Indyk
CSAIL
MIT
indyk@mit.edu
Ludwig Schmidt
CSAIL
MIT
ludwigs@mit.edu
Abstract
Empirical risk minimization (ERM) is ubiquitous in machine learning and underlies most super... | 7018 |@word cnn:1 version:2 achievable:1 polynomial:15 norm:8 bn:5 invoking:1 sgd:2 nystr:3 reduction:10 contains:1 ours:1 past:1 existing:1 comparing:2 nt:4 activation:16 yet:1 conjunctive:1 attracted:1 additive:2 razenshteyn:2 treating:1 complementing:1 core:1 short:1 junta:1 coarse:1 boosting:1 provides:1 certificat... |
6,654 | 7,019 | Policy Gradient With Value Function Approximation
For Collective Multiagent Planning
Duc Thien Nguyen Akshat Kumar Hoong Chuin Lau
School of Information Systems
Singapore Management University
80 Stamford Road, Singapore 178902
{dtnguyen.2014,akshatkumar,hclau}@smu.edu.sg
Abstract
Decentralized (PO)MDPs provide an ex... | 7019 |@word version:1 decomposition:3 thereby:1 profit:2 initial:3 contains:1 series:1 denoting:1 past:1 freitas:1 current:6 nt:36 yet:1 must:1 cruz:1 subsequent:1 confirming:1 update:10 v:1 congestion:5 stationary:1 half:1 intelligence:9 yokoo:1 tcp:1 meuleau:1 provides:4 node:1 location:2 simpler:1 wierstra:1 along:1... |
6,655 | 702 | Modeling Consistency in a Speaker Independent
Continuous Speech Recognition System
Yochai Konig, Nelson Morgan, Chuck Wooters
International Computer Science Institute
1947 Center Street, Suite 600
Berkeley, CA 94704, USA.
Victor Abrash, Michael Cohen, Horacio Franco
SRI International
333 Ravenswood Ave.
Menlo Park, CA... | 702 |@word sri:3 tried:1 score:1 tuned:1 subword:1 existing:1 lang:1 short:1 banff:1 attack:1 along:3 framewise:1 combine:1 multi:1 window:2 totally:1 provided:1 estimating:2 lowest:1 kaufman:1 spoken:1 suite:1 temporal:1 berkeley:1 every:2 unit:10 producing:1 before:1 local:1 limit:2 might:3 chose:1 mateo:2 limited:1 ... |
6,656 | 7,020 | Adversarial Symmetric Variational Autoencoder
Yunchen Pu, Weiyao Wang, Ricardo Henao, Liqun Chen, Zhe Gan, Chunyuan Li
and Lawrence Carin
Department of Electrical and Computer Engineering, Duke University
{yp42, ww109, r.henao, lc267, zg27,cl319, lcarin}@duke.edu
Abstract
A new form of variational autoencoder (VAE) i... | 7020 |@word middle:2 seems:1 cha:1 seek:11 tried:1 liu:3 score:3 offering:1 denoting:1 deconvolutional:4 dx:2 must:3 readily:2 gpu:1 realistic:3 analytic:2 remove:1 designed:4 interpretable:1 generative:28 discovering:1 half:2 intelligence:1 isotropic:2 realism:1 regressive:1 provides:3 boosting:1 zhang:7 wierstra:1 di... |
6,657 | 7,021 | Unified representation of tractography and
diffusion-weighted MRI data using sparse
multidimensional arrays
Cesar F. Caiafa?
Department of Psychological and Brain Sciences
Indiana University (47405) Bloomington, IN, USA
IAR - CCT La Plata, CONICET / CIC-PBA
(1894) V. Elisa, ARGENTINA
ccaiafa@gmail.com
Olaf Sporns
Depa... | 7021 |@word multitask:1 mri:10 briefly:1 compression:10 norm:1 version:1 nd:2 replicate:1 tensorial:3 duda:1 open:1 km:2 pieter:1 decomposition:35 dramatic:1 reduction:2 liu:1 contains:2 series:1 hereafter:4 daniel:4 current:2 com:1 nt:1 discretization:4 comparing:1 torben:2 gmail:1 yet:1 written:3 connectomics:2 john:... |
6,658 | 7,022 | A Minimax Optimal Algorithm for Crowdsourcing
Thomas Bonald
Telecom ParisTech
thomas.bonald@telecom-paristech.fr
Richard Combes
Centrale-Supelec / L2S
richard.combes@supelec.fr
Abstract
We consider the problem of accurately estimating the reliability of workers based
on noisy labels they provide, which is a fundamen... | 7022 |@word eor:6 version:3 seems:4 open:1 covariance:5 jacob:3 attainable:1 reduction:1 initial:2 liu:4 series:1 karger:9 document:1 hermosillo:1 past:1 outperforms:2 subjective:1 recovered:1 must:6 written:1 readily:1 john:1 numerical:5 additive:1 informative:8 partition:1 remove:1 update:1 v:6 half:1 guess:1 ruvolo:... |
6,659 | 7,023 | Estimating Accuracy from Unlabeled Data:
A Probabilistic Logic Approach
Emmanouil A. Platanios
Carnegie Mellon University
Pittsburgh, PA
e.a.platanios@cs.cmu.edu
Hoifung Poon
Microsoft Research
Redmond, WA
hoifung@microsoft.com
Tom M. Mitchell
Carnegie Mellon University
Pittsburgh, PA
tom.mitchell@cs.cmu.edu
Eric H... | 7023 |@word version:2 norm:1 vldb:1 d2:25 pratim:1 accounting:1 mammal:8 minus:3 series:2 horvitz:2 existing:4 outperforms:3 greave:1 com:3 nell:18 guez:1 must:9 dx:1 cruz:1 moreno:3 intelligence:4 advancement:1 mln:1 data2:1 location:2 org:1 constructed:1 direct:1 become:1 yuan:2 consists:4 combine:2 dalvi:1 introduce... |
6,660 | 7,024 | A Decomposition of Forecast Error in
Prediction Markets
Miroslav Dud?k
Microsoft Research, New York, NY
mdudik@microsoft.com
Ryan Rogers
University of Pennsylvania, Philadelphia, PA
rrogers386@gmail.com
S?bastien Lahaie
Google, New York, NY
slahaie@google.com
Jennifer Wortman Vaughan
Microsoft Research, New York, NY
j... | 7024 |@word private:1 version:1 norm:1 willing:1 cleanly:2 simulation:5 crucially:1 uncovers:1 decomposition:8 covariance:1 jacob:2 pick:1 profit:1 thereby:1 solid:1 shading:1 carry:1 initial:2 daniel:1 bc:2 existing:1 current:3 com:4 comparing:2 surprising:1 gmail:1 must:4 written:1 numerical:4 partition:3 shape:1 chr... |
6,661 | 7,025 | Safe Adaptive Importance Sampling
Sebastian U. Stich
EPFL
Anant Raj
Max Planck Institute for Intelligent Systems
sebastian.stich@epfl.ch
anant.raj@tuebingen.mpg.de
Martin Jaggi
EPFL
martin.jaggi@epfl.ch
Abstract
Importance sampling has become an indispensable strategy to speed up optimization algorithms for large... | 7025 |@word norm:4 nd:1 hu:1 hsieh:1 pick:1 sgd:24 thereby:1 tr:11 profit:1 reduction:1 minding:1 cyclic:1 contains:2 liu:1 selecting:2 outperforms:1 existing:1 ndiaye:1 current:1 comparing:1 written:1 readily:1 numerical:2 wenjiang:1 confirming:1 cheap:1 plot:4 update:5 juditsky:1 v:13 intelligence:2 xk:71 beginning:1... |
6,662 | 7,026 | Variational Walkback: Learning a Transition
Operator as a Stochastic Recurrent Net
Anirudh Goyal
MILA, Universit? de Montr?al
anirudhgoyal9119@gmail.com
Surya Ganguli
Stanford University
sganguli@stanford.edu
Nan Rosemary Ke
MILA, ?cole Polytechnique de Montr?al
rosemary.nan.ke@gmail.com
Yoshua Bengio
MILA, Universit... | 7026 |@word illustrating:2 version:3 nd:1 open:4 seek:1 decomposition:3 contrastive:1 thereby:7 inpainting:1 moment:1 hunting:1 substitution:1 series:1 liu:3 configuration:2 efficacy:1 pt0:2 initial:3 interestingly:1 past:4 outperforms:1 current:2 com:5 comparing:1 luo:1 gmail:3 intriguing:2 must:4 assigning:1 visible:... |
6,663 | 7,027 | Polynomial Codes: an Optimal Design for
High-Dimensional Coded Matrix Multiplication
?
Qian Yu? , Mohammad Ali Maddah-Ali? , and A. Salman Avestimehr?
Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA
?
Nokia Bell Labs, Holmdel, NJ, USA
Abstract
We consider a large-scale m... | 7027 |@word inversion:2 achievable:1 polynomial:76 nd:1 km:1 bn:2 k1d:3 interestingly:2 franklin:1 current:1 recovered:2 fn:1 numerical:1 enables:1 designed:4 plot:1 half:6 selected:1 detecting:1 provides:5 node:16 didier:1 allerton:1 along:4 constructed:1 symposium:3 prove:10 overhead:3 xji:1 examine:1 gift:1 project:... |
6,664 | 7,028 | Unsupervised Learning of Disentangled
Representations from Video
Emily Denton
Department of Computer Science
New York University
denton@cs.nyu.edu
Vighnesh Birodkar
Department of Computer Science
New York University
vighneshbirodkar@nyu.edu
Abstract
We present a new model D R N ET that learns disentangled image repr... | 7028 |@word kohli:1 version:4 middle:2 norm:2 cleanly:3 versatile:1 carry:3 initial:1 liu:1 series:1 contains:1 score:5 ours:6 interestingly:1 deconvolutional:1 existing:1 current:4 comparing:2 com:2 shape:1 enables:2 drop:2 stationary:1 generative:6 alone:1 half:1 cue:1 provides:1 simpler:1 zhang:3 wierstra:1 along:1 ... |
6,665 | 7,029 | Federated Multi-Task Learning
Virginia Smith
Stanford
smithv@stanford.edu
Chao-Kai Chiang?
USC
chaokaic@usc.edu
Maziar Sanjabi?
USC
Ameet Talwalkar
CMU
maziarsanjabi@gmail.com talwalkar@cmu.edu
Abstract
Federated learning poses new statistical and systems challenges in training machine
learning models over distri... | 7029 |@word mild:1 trial:1 hampson:1 cox:1 multitask:2 norm:1 vldb:1 hu:1 simulation:3 heiser:1 hsieh:2 covariance:1 jacob:1 sgd:6 dramatic:1 tr:2 initial:1 liu:1 exclusively:1 selecting:1 uma:1 franklin:2 cort:1 outperforms:2 existing:1 current:4 com:4 nt:6 comparing:1 luo:2 gmail:1 must:1 periodically:4 hofmann:1 dro... |
6,666 | 703 | Learning Cellular Automaton Dynamics
with Neural Networks
N H Wulff* and J A Hertz t
CONNECT, the Niels Bohr Institute and Nordita
Blegdamsvej 17, DK-2100 Copenhagen 0, Denmark
Abstract
We have trained networks of E - II units with short-range connections to simulate simple cellular automata that exhibit complex or
c... | 703 |@word version:1 nd:1 instruction:1 cha:1 fairer:1 simulation:1 cla:1 thereby:1 harder:1 initial:11 configuration:8 series:4 contains:1 lapedes:1 si:13 yet:1 activation:3 must:1 subsequent:1 update:3 implying:1 imitate:1 beginning:1 short:4 wolfram:4 constructed:1 qualitative:1 indeed:1 themselves:2 globally:2 decr... |
6,667 | 7,030 | Is Input Sparsity Time Possible for
Kernel Low-Rank Approximation?
Cameron Musco
MIT
cnmusco@mit.edu
David P. Woodruff
Carnegie Mellon University
dwoodruf@cs.cmu.edu
Abstract
Low-rank approximation is a common tool used to accelerate kernel methods: the
? which can be stored
n ? n kernel matrix K is approximated via ... | 7030 |@word version:1 polynomial:16 norm:7 nd:12 c0:3 open:4 km:3 d2:1 seek:1 decomposition:3 accounting:1 asks:1 nystr:7 carry:1 reduction:2 series:1 score:3 contains:2 selecting:1 woodruff:8 ours:1 ati:8 must:4 written:3 bd:1 numerical:1 am15:2 greedy:1 amir:1 ith:1 ws01:2 zhang:2 atj:2 along:3 c2:3 direct:1 dn:1 sym... |
6,668 | 7,031 | The Expxorcist: Nonparametric Graphical Models
Via Conditional Exponential Densities
Arun Sai Suggala ?
Carnegie Mellon University
Pittsburgh, PA 15213
Mladen Kolar ?
University of Chicago
Chicago, IL 60637
Pradeep Ravikumar ?
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
Non-parametric multivariate dens... | 7031 |@word mild:1 faculty:1 version:1 briefly:1 norm:6 polynomial:1 simulation:1 decomposition:7 covariance:2 bai:1 liu:7 score:1 selecting:1 liquid:1 rkhs:6 nonparanormal:14 com:1 jinbo:1 dx:15 written:3 john:3 chicago:4 partition:4 numerical:1 subsequent:1 additive:2 plot:8 fund:1 stationary:3 selected:1 parameteriz... |
6,669 | 7,032 | Improved Graph Laplacian via Geometric
Consistency
Dominique C. Perrault-Joncas
Google, Inc.
dominiquep@google.com
Marina Meil?a
Department of Statistics
University of Washington
mmp2@uw.edu
James McQueen
Amazon
jmcq@amazon.com
Abstract
In all manifold learning algorithms and tasks setting the kernel bandwidth us... | 7032 |@word kondor:1 version:3 inversion:1 norm:3 heuristically:1 d2:2 dominique:1 tried:1 decomposition:1 mention:1 reduction:6 giulini:1 contains:2 exclusively:1 selecting:3 denoting:1 ours:1 interestingly:1 com:2 comparing:1 yet:2 dx:3 must:4 reminiscent:1 john:2 chu:1 numerical:3 subsequent:1 hourglass:3 plot:1 v:1... |
6,670 | 7,033 | Dual Path Networks
Yunpeng Chen1 , Jianan Li1,2 , Huaxin Xiao1,3 , Xiaojie Jin1 , Shuicheng Yan4,1 , Jiashi Feng1
1
National University of Singapore
2
Beijing Institute of Technology
3
National University of Defense Technology
4
Qihoo 360 AI Institute
Abstract
In this work, we present a simple, highly efficient and mo... | 7033 |@word exploitation:1 cnn:3 kokkinos:1 everingham:1 shuicheng:2 reusage:2 sgd:1 f0k:1 liu:1 contains:1 cru:1 trainval:2 existing:2 current:5 dpn:66 skipping:1 yet:1 gpu:8 john:1 concatenate:2 distant:1 enables:5 christian:1 designed:1 drop:1 update:1 hourglass:1 intelligence:1 fewer:2 concat:1 xk:3 kyoung:1 vanish... |
6,671 | 7,034 | Faster and Non-ergodic O(1/K) Stochastic
Alternating Direction Method of Multipliers
Cong Fang
Feng Cheng
Zhouchen Lin?
Key Laboratory of Machine Perception (MOE), School of EECS, Peking University, P. R. China
Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, P. R. China
fangcong@pku.edu.cn
fengch... | 7034 |@word multitask:6 mild:1 faculty:1 inversion:1 seems:1 shuicheng:1 linearized:8 covariance:2 minming:1 solid:1 reduction:4 initial:3 liu:4 ours:2 ati:1 outperforms:1 existing:2 comparing:2 com:1 chu:1 written:3 numerical:2 opg:2 enables:1 zaid:1 update:13 intelligence:3 zlin:3 kyk:1 amir:1 xk:24 tems:1 gx:1 theod... |
6,672 | 7,035 | A Probabilistic Framework for Nonlinearities in
Stochastic Neural Networks
Qinliang Su
Xuejun Liao
Lawrence Carin
Department of Electrical and Computer Engineering
Duke University, Durham, NC, USA
{qs15, xjliao, lcarin}@duke.edu
Abstract
We present a probabilistic framework for nonlinearities, based on doubly truncate... | 7035 |@word mild:1 trial:2 repository:1 changyou:1 norm:2 nd:1 simulation:2 hsieh:1 contrastive:2 sgd:1 liu:1 contains:2 ndez:1 daniel:1 kurt:1 existing:2 err:3 freitas:1 z2:1 comparing:1 michal:1 activation:9 written:1 readily:2 realize:3 additive:1 visible:7 numerical:1 partition:1 christian:1 plot:3 siamak:1 update:... |
6,673 | 7,036 | Distral: Robust Multitask Reinforcement Learning
Yee Whye Teh, Victor Bapst, Wojciech Marian Czarnecki, John Quan,
James Kirkpatrick, Raia Hadsell, Nicolas Heess, Razvan Pascanu
DeepMind
London, UK
Abstract
Most deep reinforcement learning algorithms are data inefficient in complex and
rich environments, limiting thei... | 7036 |@word multitask:22 compression:1 proportion:1 pieter:4 d2:2 r:1 crucially:1 tried:1 sgd:2 harder:1 carry:1 kappen:1 initial:2 series:1 score:2 selecting:1 past:3 existing:1 outperforms:1 hasselt:1 michal:1 chu:1 must:2 reminiscent:3 john:4 guez:1 happen:1 kdd:1 update:5 depict:1 v:4 pursued:1 greedy:7 half:2 inte... |
6,674 | 7,037 | Online Learning of Optimal Bidding Strategy
in Repeated Multi-Commodity Auctions
Sevi Baltaoglu
Cornell University
Ithaca, NY 14850
msb372@cornell.edu
Lang Tong
Cornell University
Ithaca, NY 14850
lt35@cornell.edu
Qing Zhao
Cornell University
Ithaca, NY 14850
qz16@cornell.edu
Abstract
We study the online learning p... | 7037 |@word private:1 version:2 achievable:2 polynomial:7 norm:2 nd:1 rigged:1 incurs:1 profit:8 recursively:2 selecting:1 ours:1 document:1 cleared:9 outperforms:3 existing:1 past:1 current:2 discretization:7 com:4 luo:1 lang:1 yet:1 assigning:1 john:1 sponsored:2 update:2 congestion:2 intelligence:2 website:1 item:1 ... |
6,675 | 7,038 | Trimmed Density Ratio Estimation
Song Liu?
University of Bristol
song.liu@bristol.ac.uk
Taiji Suzuki
University of Tokyo,
Sakigake (PRESTO), JST,
AIP, RIKEN,
taiji@mist.i.u-tokyo.ac.jp
Akiko Takeda
The Institute of Statistical Mathematics,
AIP, RIKEN,
atakeda@ism.ac.jp
Kenji Fukumizu
The Institute of Statistical Mat... | 7038 |@word mild:5 version:1 briefly:1 norm:1 proportion:3 covariance:1 pick:1 tr:8 liu:5 series:1 selecting:1 interestingly:2 rightmost:1 assigning:3 dx:3 written:1 gpu:1 subsequent:1 drop:1 plot:2 update:1 designed:1 generative:4 selected:1 guess:1 nq:10 parameterization:1 accordingly:1 intelligence:1 akiko:2 fried:1... |
6,676 | 7,039 | Training recurrent networks to generate hypotheses
about how the brain solves hard navigation problems
Ingmar Kanitscheider & Ila Fiete
Department of Neuroscience
The University of Texas
Austin, TX 78712
ikanitscheider, ilafiete @mail.clm.utexas.edu
Abstract
Self-localization during navigation with noisy sensors in a... | 7039 |@word trial:22 middle:2 version:1 fiete:2 wco:1 hippocampus:8 retraining:1 bf:1 seems:2 open:2 covariance:2 initial:4 liu:1 contains:1 efficacy:1 daniel:1 tuned:2 bc:1 interestingly:1 past:1 outperforms:1 err:2 current:1 existing:1 contextual:1 si:6 yet:3 assigning:1 must:9 activation:7 john:4 diederik:1 distant:... |
6,677 | 704 | Biologically Plausible Local Learning Rules for
the Adaptation of the Vestibulo-Ocular Reflex
Olivier Coenen*
Terrence J. Sejnowski
Computational Neurobiology Laboratory
Howard Hughes Medical Institute
The Salk Institute
P.O.Box 85800
San Diego, CA 92186-5800
Stephen G. Lisberger
Department of Physiology
W.M. Keck... | 704 |@word neurophysiology:2 version:1 longterm:1 proportion:4 nd:1 integrative:1 simulation:6 minus:1 carry:1 initial:1 bd:1 vor:30 physiol:2 subsequent:1 plasticity:3 motor:4 drop:1 v:4 selected:1 nervous:1 short:3 provides:1 math:1 node:13 constructed:1 become:2 pairing:1 indeed:1 expected:2 rapid:3 behavior:1 integ... |
6,678 | 7,040 | Visual Interaction Networks: Learning a Physics
Simulator from Video
Nicholas Watters, Andrea Tacchetti, Th?ophane Weber
Razvan Pascanu, Peter Battaglia, Daniel Zoran
DeepMind
London, United Kingdom
{nwatters, atacchet, theophane,
razp, peterbattaglia, danielzoran}@google.com
Abstract
From just a glance, humans can ma... | 7040 |@word cnn:6 version:1 norm:1 open:1 pieter:1 seitz:1 simulation:12 citeseer:1 solid:1 accommodate:2 initial:1 united:1 jimenez:1 daniel:1 document:1 bhattacharyya:1 outperforms:4 current:1 com:1 comparing:1 discretization:1 diederik:1 must:5 visible:2 realistic:2 shape:2 hypothesize:1 interpretable:1 update:1 hal... |
6,679 | 7,041 | Reconstruct & Crush Network
Erin? Merdivan1,2 , Mohammad Reza Loghmani3 and Matthieu Geist4
1
AIT Austrian Institute of Technology GmbH, Vienna, Austria
2
LORIA (Univ. Lorraine & CNRS), CentraleSup?lec, Univ. Paris-Saclay, 57070 Metz, France
3
Vision4Robotics lab, ACIN, TU Wien, Vienna, Austria
4
Universit? de Lorraine... | 7041 |@word cnn:11 replicate:1 sex:1 decomposition:1 asks:1 mcauley:1 lorraine:3 necessity:1 configuration:1 contains:6 score:7 series:1 liu:4 ours:2 activation:3 assigning:1 written:1 kdd:1 designed:1 update:1 generative:4 selected:3 advancement:1 provides:1 location:1 denis:1 zhang:1 c2:1 specialize:1 consists:1 beha... |
6,680 | 7,042 | Streaming Robust Submodular Maximization:
A Partitioned Thresholding Approach
Slobodan Mitrovi?c?
EPFL
Ilija Bogunovic?
EPFL
Ashkan Norouzi-Fard?
EPFL
Jakub Tarnawski?
EPFL
Volkan Cevher?
EPFL
Abstract
We study the classical problem of maximizing a monotone submodular function
subject to a cardinality constraint ... | 7042 |@word version:4 hsieh:1 mcauley:1 contains:4 score:2 document:2 outperforms:4 existing:1 must:1 realistic:1 numerical:3 partition:27 remove:2 plot:1 alone:1 greedy:19 half:5 discovering:2 item:1 ieve:20 intelligence:1 sys:1 short:1 prespecified:1 volkan:2 provides:4 node:6 location:2 preference:1 mathematical:1 a... |
6,681 | 7,043 | Simple Strategies for Recovering Inner Products from
Coarsely Quantized Random Projections
Ping Li
Baidu Research, and
Rutgers University
pingli98@gmail.com
Martin Slawski
Department of Statistics
George Mason University
mslawsk3@gmu.edu
Abstract
Random projections have been increasingly adopted for a diverse set of... | 7043 |@word mild:1 repository:1 version:1 middle:1 compression:4 norm:18 simulation:1 scg:1 q1:1 solid:1 reduction:9 celebrated:1 series:1 contains:1 interestingly:1 mishra:1 com:1 comparing:2 chazelle:1 gmail:1 subsequent:3 numerical:3 kdd:2 plot:7 korolova:1 v:3 selected:3 accordingly:1 vanishing:1 coarse:3 quantized... |
6,682 | 7,044 | Discovering Potential Correlations via
Hypercontractivity
Hyeji Kim1? Weihao Gao1? Sreeram Kannan2? Sewoong Oh1? Pramod Viswanath1?
University of Illinois at Urbana Champaign1 and University of Washington2
{hyejikim,wgao9}@illinois.edu,ksreeram@uw.edu,{swoh,pramodv}@illinois.edu
Abstract
Discovering a correlation from... | 7044 |@word cmi:3 faculty:1 version:4 reshef:3 replicate:1 bekkerman:1 open:2 unif:3 seek:1 initial:1 celebrated:1 series:7 score:1 bibliographic:1 genetic:5 erkip:1 existing:8 recovered:1 com:1 comparing:1 surprising:1 si:2 scatter:2 numerical:4 additive:3 informative:3 visible:2 remove:1 plot:2 v:5 resampling:3 disco... |
6,683 | 7,045 | Doubly Stochastic Variational Inference
for Deep Gaussian Processes
Hugh Salimbeni
Imperial College London and PROWLER.io
hrs13@ic.ac.uk
Marc Peter Deisenroth
Imperial College London and PROWLER.io
m.deisenroth@imperial.ac.uk
Abstract
Gaussian processes (GPs) are a good choice for function approximation as they are
... | 7045 |@word faculty:1 version:1 compression:1 open:1 hu:1 seek:2 crucially:2 simulation:1 covariance:10 propagate:2 idl:1 pick:1 recursively:2 reduction:1 initial:1 ndez:8 series:3 contains:1 efficacy:1 rippel:1 ours:1 existing:1 steiner:1 com:2 surprising:1 activation:1 written:2 readily:1 must:3 gpu:2 devin:1 concate... |
6,684 | 7,046 | Ranking Data with Continuous Labels
through Oriented Recursive Partitions
Stephan Cl?emenc?on
Mastane Achab
LTCI, T?el?ecom ParisTech, Universit?e Paris-Saclay
75013 Paris, France
first.last@telecom-paristech.fr
Abstract
We formulate a supervised learning problem, referred to as continuous ranking,
where a continuous... | 7046 |@word h:4 version:5 briefly:2 norm:1 c0:2 pick:1 euclidian:1 initial:1 score:3 fragment:1 denoting:3 ours:1 current:1 discretization:4 comparing:1 dx:6 must:2 written:1 subsequent:1 numerical:5 partition:3 progressively:1 v:1 pursued:2 leaf:3 half:2 selected:1 problemspecific:1 characterization:2 provides:2 compl... |
6,685 | 7,047 | Scalable Model Selection for Belief Networks
Zhao Song? , Yusuke Muraoka? , Ryohei Fujimaki? , Lawrence Carin?
?
Department of ECE, Duke University
Durham, NC 27708, USA
{zhao.song, lcarin}@duke.edu
?
NEC Data Science Research Laboratories
Cupertino, CA 95014, USA
{ymuraoka, rfujimaki}@nec-labs.com
Abstract
We prop... | 7047 |@word trial:1 determinant:4 version:1 eliminating:1 proportion:1 cml:1 hsieh:1 dramatic:1 sgd:2 concise:1 reduction:2 initial:3 liu:2 contains:3 score:1 selecting:1 mag:1 salzmann:3 tuned:1 document:5 interestingly:1 past:1 outperforms:1 com:2 nt:7 deteriorating:1 must:1 gpu:1 stemming:1 visible:4 enables:4 remov... |
6,686 | 7,048 | Targeting EEG/LFP Synchrony with Neural Nets
Yitong Li1 , Michael Murias2 , Samantha Major2 , Geraldine Dawson2 , Kafui Dzirasa2 ,
Lawrence Carin1 and David E. Carlson3,4
1
Department of Electrical and Computer Engineering, Duke University
Departments of Psychiatry and Behavioral Sciences, Duke University
3
Department... | 7048 |@word mild:1 trial:10 cnn:10 version:3 inversion:1 oostenveld:1 neurophysiology:2 approved:1 c0:2 open:5 azimuthal:1 covariance:4 excited:1 reduction:1 initial:1 liu:3 contains:1 series:6 score:3 genetic:1 bc:2 outperforms:1 existing:1 current:2 comparing:1 rish:1 mari:1 written:2 gpu:1 additive:1 kdd:1 shape:1 m... |
6,687 | 7,049 | Near-Optimal Edge Evaluation in Explicit
Generalized Binomial Graphs
Sanjiban Choudhury
The Robotics Institute
Carnegie Mellon University
sanjiban@cmu.edu
Shervin Javdani
The Robotics Institute
Carnegie Mellon University
sjavdani@cmu.edu
Siddhartha Srinivasa
The Robotics Institute
Carnegie Mellon University
siddh@cs... | 7049 |@word trial:1 version:6 open:2 heuristically:1 simulation:1 hec:2 pick:1 dramatic:1 configuration:5 contains:2 efficacy:2 selecting:1 disparity:3 exclusively:1 daniel:2 ours:1 kinodynamic:1 outperforms:3 existing:2 com:2 si:12 assigning:1 yet:1 must:1 john:1 ronald:1 realistic:1 sanjiv:1 informative:2 enables:2 c... |
6,688 | 705 | A Recurrent Neural Network for
Generation of Ocular Saccades
Lina L.E. Massone
Department of Physiology
Department of Electrical Engineering and Computer Scienc~
Northwestern University
303 E. Chicago Avenue, Chicago, 1160611
Abstract
This paper presents a neural network able to control saccadic
movements. The input ... | 705 |@word neurophysiology:3 version:1 simulation:4 t1r:2 initial:1 configuration:1 selecting:1 bc:1 current:1 activation:1 must:3 i1l:1 chicago:2 motor:14 medial:1 selected:1 shut:1 nervous:1 scienc:1 plane:1 oblique:4 location:1 burst:11 along:2 alert:1 become:1 differential:1 fixation:11 sustained:1 rostral:3 orbita... |
6,689 | 7,050 | Non-Stationary Spectral Kernels
Sami Remes
Markus Heinonen
Samuel Kaski
sami.remes@aalto.fi
markus.o.heinonen@aalto.fi
samuel.kaski@aalto.fi
Helsinki Institute for Information Technology HIIT
Department of Computer Science, Aalto University
Abstract
We propose non-stationary spectral kernels for Gaussian process regr... | 7050 |@word middle:2 logit:4 covariance:14 decomposition:6 bsm:3 accommodate:1 liu:1 series:8 contains:1 initialisation:1 com:1 si:10 yet:2 written:1 fn:2 realistic:1 distant:1 numerical:1 informative:1 cheap:1 remove:2 plot:1 interpretable:1 stationary:58 half:1 ksm:1 hamiltonian:1 short:1 geospatial:1 provides:1 ire:... |
6,690 | 7,051 | Overcoming Catastrophic Forgetting by
Incremental Moment Matching
Sang-Woo Lee1 , Jin-Hwa Kim1 , Jaehyun Jun1 , Jung-Woo Ha2 , and Byoung-Tak Zhang1,3
Seoul National University1
Clova AI Research, NAVER Corp2
Surromind Robotics3
{slee,jhkim,jhjun}@bi.snu.ac.kr jungwoo.ha@navercorp.com
btzhang@bi.snu.ac.kr
Abstract
Ca... | 7051 |@word cnn:1 middle:2 norm:2 nd:1 d2:1 covariance:8 jacob:1 simplifying:1 q1:8 slee:1 sgd:5 tr:1 moment:18 initial:1 series:2 hoiem:1 tuned:11 outperforms:1 com:1 goldberger:1 diederik:1 john:1 shape:1 christian:1 hypothesize:1 drop:26 update:2 alone:3 intelligence:2 selected:1 weighing:5 xk:5 ith:2 pascanu:3 node... |
6,691 | 7,052 | Balancing information exposure in social networks
Kiran Garimella
Aalto University & HIIT
Helsinki, Finland
kiran.garimella@aalto.fi
Aristides Gionis
Aalto University & HIIT
Helsinki, Finland
aristides.gionis@aalto.fi
Nikos Parotsidis
University of Rome Tor Vergata
Rome, Italy
nikos.parotsidis@uniroma2.it
Nikolaj T... | 7052 |@word version:1 stronger:1 underline:1 simulation:2 propagate:4 q1:1 lakshmanan:1 initial:7 contains:2 score:4 selecting:3 lightweight:1 ours:1 outperforms:2 existing:5 past:1 current:1 clash:1 surprising:1 activation:1 yet:1 follower:4 additive:3 realistic:3 partition:1 kdd:2 drop:1 plot:1 v:2 greedy:20 selected... |
6,692 | 7,053 | SafetyNets: Verifiable Execution of Deep Neural
Networks on an Untrusted Cloud
Zahra Ghodsi, Tianyu Gu, Siddharth Garg
New York University
{zg451, tg1553, sg175}@nyu.edu
Abstract
Inference using deep neural networks is often outsourced to the cloud since it is
a computationally demanding task. However, this raises a f... | 7053 |@word cnn:15 version:2 polynomial:7 norm:1 nd:1 seek:4 bn:1 q1:5 pick:5 incurs:1 asks:1 mention:1 recursively:1 configuration:1 offering:1 ours:1 existing:2 err:5 activation:36 si:2 must:5 fn:1 enables:3 verifiability:1 succeeding:1 drop:1 n0:1 plot:1 designed:1 intelligence:1 selected:1 assurance:1 short:1 kbyte... |
6,693 | 7,054 | Query Complexity of Clustering with
Side Information
Arya Mazumdar and Barna Saha
College of Information and Computer Sciences
University of Massachusetts Amherst
Amherst, MA 01003
{arya,barna}@cs.umass.edu
Abstract
Suppose, we are given a set of n elements to be clustered into k (unknown) clusters,
and an oracle/expe... | 7054 |@word middle:2 version:3 polynomial:2 achievable:1 stronger:2 seems:1 faculty:1 open:4 hu:3 vldb:1 tried:1 dramatic:1 reduction:2 contains:1 uma:2 selecting:4 bibliographic:1 score:3 denoting:1 karger:1 interestingly:1 neeman:1 franklin:2 existing:5 ka:1 current:4 yet:5 intriguing:1 must:18 subsequent:1 partition... |
6,694 | 7,055 | QMDP-Net: Deep Learning for Planning under
Partial Observability
Peter Karkus1,2
1
David Hsu1,2
Wee Sun Lee2
NUS Graduate School for Integrative Sciences and Engineering
2
School of Computing
National University of Singapore
{karkus, dyhsu, leews}@comp.nus.edu.sg
Abstract
This paper introduces the QMDP-net, a neura... | 7055 |@word cnn:9 version:1 briefly:1 repository:2 stronger:1 integrative:1 simulation:2 seek:1 pick:1 dramatic:1 recursively:1 bai:1 initial:13 contains:4 interestingly:3 outperforms:1 past:3 o2:3 current:6 com:1 comparing:1 hasselt:1 activation:1 guez:2 must:4 devin:1 distant:2 shape:2 enables:1 qmdp:94 designed:2 up... |
6,695 | 7,056 | Robust Optimization for Non-Convex Objectives
Robert Chen
Computer Science
Harvard University
Brendan Lucier
Microsoft Research
New England
Yaron Singer
Computer Science
Harvard University
Vasilis Syrgkanis
Microsoft Research
New England
Abstract
We consider robust optimization problems, where the goal is to optim... | 7056 |@word mild:1 version:10 polynomial:3 nd:7 seek:1 wexler:2 jacob:1 pick:8 sgd:1 asks:2 reduction:7 initial:1 selecting:1 ours:3 outperforms:4 current:1 activation:1 must:2 john:4 additive:4 kdd:1 update:3 greedy:7 selected:1 parameterization:1 vanishing:1 provides:1 node:3 simpler:1 mathematical:1 scur:11 expected... |
6,696 | 7,057 | Thy Friend is My Friend: Iterative Collaborative
Filtering for Sparse Matrix Estimation
Christian Borgs
Jennifer Chayes
Christina E. Lee
borgs@microsoft.com
jchayes@microsoft.com
celee@mit.edu
Microsoft Research New England
One Memorial Drive, Cambridge MA, 02142
Devavrat Shah
devavrat@mit.edu
Massachusetts Institute ... | 7057 |@word mild:1 private:1 polynomial:5 seems:2 stronger:1 d2:19 decomposition:1 pg:1 tr:1 ld:1 contains:2 selecting:1 daniel:2 neeman:2 ours:3 janson:1 existing:1 com:3 comparing:10 whp:2 yet:1 must:2 additive:2 subsequent:1 informative:1 partition:1 christian:8 remove:1 generative:2 fewer:2 item:2 smith:2 olhede:1 ... |
6,697 | 7,058 | Adaptive Classification for Prediction Under a Budget
Venkatesh Saligrama
Electrical Engineering
Boston University
Boston, MA 02215
srv@bu.edu
Feng Nan
Systems Engineering
Boston University
Boston, MA 02215
fnan@bu.edu
Abstract
We propose a novel adaptive approximation approach for test-time resourceconstrained pred... | 7058 |@word repository:2 norm:2 nd:1 dekel:2 lgorithms:1 palma:1 confirms:1 jacob:1 thereby:2 solid:1 recursively:2 carry:1 reduction:5 initial:3 liu:1 exclusively:1 selecting:1 ap1:1 daniel:1 ours:1 interestingly:1 document:3 greedymiser:2 outperforms:5 existing:4 yet:3 must:2 readily:2 subsequent:1 partition:3 minmin... |
6,698 | 7,059 | Convergence rates of a partition based Bayesian
multivariate density estimation method
Linxi Liu ?
Department of Statistics
Columbia University
ll3098@columbia.edu
Dangna Li
ICME
Stanford University
dangna@stanford.edu
Wing Hung Wong
Department of Statistics
Stanford University
whwong@stanford.edu
Abstract
We study... | 7059 |@word mild:1 middle:2 compression:2 simulation:1 contraction:2 p0:2 recursively:1 moment:1 initial:2 liu:1 series:4 selecting:1 past:1 existing:1 current:1 luo:1 john:1 partition:41 informative:1 plot:14 v:3 implying:1 item:1 jonge:1 location:2 judith:3 five:1 mathematical:1 along:3 become:1 beta:1 introduce:1 he... |
6,699 | 706 | Synchronization and Grammatical Inference
in an Oscillating Elman Net
Bill Baird
Dept Mathematics,
U .C.Berkeley,
Berkeley, Ca. 94720,
baird@math.berkeley.edu
Todd Troyer
Dept Mathematics,
U .C.Berkeley,
Berkeley, Ca. 94720
Frank Eeckman
Lawrence Livermore
National Laboratory,
P.O. Box 808 (L-426),
Livermore, Ca. 94... | 706 |@word polynomial:1 open:1 simulation:1 thereby:1 sychronization:1 initial:3 cordinates:1 contains:1 od:1 analysed:1 activation:2 must:3 tilted:1 additive:1 underly:1 enables:1 motor:6 designed:3 leaf:1 device:1 beginning:1 math:1 node:1 location:1 quantized:2 mathematical:2 constructed:6 differential:3 beta:1 ood:... |
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