Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
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6,500 | 6,880 | Decomposable Submodular Function Minimization
Discrete and Continuous
Alina Ene?
? ?
Huy L. Nguy?n
L?szl? A. V?gh?
Abstract
This paper investigates connections between discrete and continuous approaches for
decomposable submodular function minimization. We provide improved running
time estimates for the state-of-the... | 6880 |@word trial:1 briefly:1 version:8 polynomial:10 norm:18 stronger:1 nd:1 suitably:2 open:1 hu:1 rgb:1 decomposition:4 q1:1 reduction:1 initial:2 series:1 tuned:1 interestingly:1 outperforms:1 current:3 ka:8 com:1 written:2 must:3 subsequent:1 plot:2 update:6 greedy:1 prohibitive:3 plane:1 beginning:1 core:1 provid... |
6,501 | 6,881 | Gauging Variational Inference
Sungsoo Ahn?
Michael Chertkov?
Jinwoo Shin?
?
School of Electrical Engineering,
Korea Advanced Institute of Science and Technology, Daejeon, Korea
?1
Theoretical Division, T-4 & Center for Nonlinear Studies,
Los Alamos National Laboratory, Los Alamos, NM 87545, USA,
?2
Skolkovo Institute ... | 6881 |@word illustrating:1 version:1 middle:2 eliminating:1 polynomial:1 adrian:1 calculus:3 seek:1 accounting:2 ferromagnetism:1 pg:2 pick:1 configuration:7 series:8 liu:2 outperforms:5 current:1 surprising:1 partition:41 informative:1 update:3 v:4 stationary:1 intelligence:1 leaf:1 selected:1 bart:1 provides:5 ire:1 ... |
6,502 | 6,882 | Deep Recurrent Neural Network-Based Identification
of Precursor microRNAs
Seunghyun Park
Electrical and Computer Engineering
Seoul National University
Seoul 08826, Korea
School of Electrical Engineering
Korea University
Seoul 02841, Korea
Seonwoo Min
Electrical and Computer Engineering
Seoul National University
Seoul ... | 6882 |@word snorna:2 cnn:3 eliminating:1 smirnov:2 d2:8 integrative:1 tried:2 moment:1 electronics:1 cyclic:2 contains:2 att:15 score:9 configuration:1 liu:1 past:1 existing:6 freitas:1 current:3 com:1 comparing:1 activation:10 must:1 fn:3 numerical:1 confirming:1 designed:1 plot:1 rd2:1 alone:1 half:2 discovering:2 ud... |
6,503 | 6,883 | Robust Estimation of Neural Signals in Calcium
Imaging
Hakan Inan
Stanford University
inanh@stanford.edu
Murat A. Erdogdu
Microsoft Research
erdogdu@cs.toronto.edu
Mark J. Schnitzer
Stanford University
mschnitz@stanford.edu
Abstract
Calcium imaging is a prominent technology in neuroscience research which allows
for... | 6883 |@word hippocampus:1 seems:1 additively:1 crucially:1 decomposition:1 carry:3 schnitzer:5 reduction:1 initial:5 contains:1 denoting:1 interestingly:1 outperforms:5 existing:3 current:2 ka:1 yet:1 readily:1 gpu:3 subsequent:2 realistic:1 additive:2 cant:1 remove:2 update:1 v:2 greedy:1 guess:1 fpr:3 reciprocal:1 ca... |
6,504 | 6,884 | State Aware Imitation Learning
Yannick Schroecker
College of Computing
Georgia Institute of Technology
yannickschroecker@gatech.edu
Charles Isbell
College of Computing
Georgia Institute of Technology
isbell@cc.gatech.edu
Abstract
Imitation learning is the study of learning how to act given a set of demonstrations
pr... | 6884 |@word trial:1 seems:1 pieter:1 simplifying:3 harder:1 recursively:1 reduction:2 score:11 daniel:1 bootstrapped:1 bilal:3 past:2 outperforms:2 existing:1 current:9 comparing:1 activation:2 guez:1 written:2 john:6 enables:1 motor:1 treating:1 update:15 stationary:8 intelligence:6 generative:4 alone:1 imitate:1 aja:... |
6,505 | 6,885 | Beyond Parity:
Fairness Objectives for Collaborative Filtering
Sirui Yao
Department of Computer Science
Virginia Tech
Blacksburg, VA 24061
ysirui@vt.edu
Bert Huang
Department of Computer Science
Virginia Tech
Blacksburg, VA 24061
bhuang@vt.edu
Abstract
We study fairness in collaborative-filtering recommender systems... | 6885 |@word trial:2 seems:1 proportion:2 norm:1 seek:1 contains:1 score:13 practiced:2 langdon:1 existing:2 current:1 comparing:1 must:1 romance:3 enables:1 hypothesize:1 treating:1 plot:1 discrimination:3 selected:5 fewer:2 item:36 ith:5 smith:1 earnings:1 preference:21 location:1 org:1 five:3 mathematical:2 prove:1 c... |
6,506 | 6,886 | A PAC-Bayesian Analysis of Randomized Learning
with Application to Stochastic Gradient Descent
Ben London
blondon@amazon.com
Amazon
Abstract
We study the generalization error of randomized learning algorithms?focusing
on stochastic gradient descent (SGD)?using a novel combination of PAC-Bayes
and algorithmic stabilit... | 6886 |@word trial:2 private:1 polynomial:1 stronger:3 advantageous:1 suitably:1 unif:2 crucially:1 wexler:1 elisseeff:9 q1:3 sgd:53 thereby:5 accommodate:1 reduction:1 necessity:1 substitution:1 contains:1 series:1 initial:4 denoting:1 document:1 ours:2 tuned:1 past:2 existing:3 current:3 com:1 nt:5 activation:3 yet:4 ... |
6,507 | 6,887 | Fully Decentralized Policies for Multi-Agent Systems:
An Information Theoretic Approach
Roel Dobbe?
Electrical Engineering and Computer Science
University of California, Berkeley
Berkeley, CA 94720
dobbe@eecs.berkeley.edu
David Fridovich-Keil?
Electrical Engineering and Computer Science
University of California, Berk... | 6887 |@word exploitation:1 compression:4 replicate:1 nd:1 open:1 seek:1 linearized:1 simulation:1 decomposition:2 covariance:2 attainable:1 concise:1 shot:1 liu:1 contains:1 series:1 denoting:1 outperforms:3 existing:1 si:9 chu:1 must:1 written:1 john:1 dx:1 predetermined:1 stationary:2 pursued:1 intelligence:2 yokoo:2... |
6,508 | 6,888 | Model-Powered Conditional Independence Test
Rajat Sen1,* , Ananda Theertha Suresh2,* , Karthikeyan Shanmugam3,* , Alexandros G. Dimakis1 , and
Sanjay Shakkottai1
1
The University of Texas at Austin
2
Google, New York
3
IBM Research, Thomas J. Watson Center
Abstract
We consider the problem of non-parametric Conditiona... | 6888 |@word version:2 norm:1 nd:1 r:2 covariance:1 citeseer:1 boundedness:1 harder:1 reduction:5 liu:1 cyclic:1 score:5 chervonenkis:1 daniel:1 bootstrapped:3 rkhs:1 outperforms:2 ka:2 comparing:1 com:1 recovered:1 beygelzimer:2 luis:1 john:4 numerical:1 partition:3 krikamol:1 drop:1 plot:6 aside:3 stationary:1 half:3 ... |
6,509 | 6,889 | Deep Voice 2: Multi-Speaker Neural Text-to-Speech
Sercan ?. Ar?k?
sercanarik@baidu.com
Andrew Gibiansky?
gibianskyandrew@baidu.com
Wei Ping?
pingwei01@baidu.com
Gregory Diamos?
gregdiamos@baidu.com
John Miller?
millerjohn@baidu.com
Jonathan Raiman?
jonathanraiman@baidu.com
Kainan Peng?
pengkainan@baidu.com
Yanqi Zh... | 6889 |@word softsign:21 bn:14 initial:4 ndez:1 contains:2 score:4 liu:1 reynolds:2 com:8 subcomponents:1 comparing:1 activation:3 yet:2 synthesizer:1 john:1 concatenate:1 subsequent:1 remove:1 hypothesize:1 intelligence:1 half:1 generative:4 fewer:1 concat:6 device:1 short:2 location:2 firstly:1 zhang:2 constructed:1 t... |
6,510 | 689 | Spiral Waves in Integrate-and-Fire
Neural Networks
John G. Milton
Department of Neurology
The University of Chicago
Chicago, IL 60637
Po Hsiang Chu
Department of Computer Science
DePaul University
Chicago, IL 60614
Jack D. Cowan
Department of Mathematics
The University of Chicago
Chicago, IL 60637
Abstract
The form... | 689 |@word pulse:1 simulation:7 ld:1 initial:3 series:1 chu:4 readily:2 john:1 regenerating:1 periodically:1 chicago:5 plot:1 shut:1 slowing:1 inspection:1 smith:1 compo:1 math:1 constructed:2 become:2 hopf:3 brain:1 decreasing:1 jm:2 becomes:2 begin:2 provided:3 moreover:1 medium:3 mass:1 differing:1 temporal:4 intern... |
6,511 | 6,890 | Variance-based Regularization with Convex
Objectives
Hongseok Namkoong
Stanford University
hnamk@stanford.edu
John C. Duchi
Stanford University
jduchi@stanford.edu
Abstract
We develop an approach to risk minimization and stochastic optimization that provides a convex surrogate for variance, allowing near-optimal and ... | 6890 |@word mild:1 repository:2 norm:4 suitably:1 hu:5 underperform:1 bn:9 harder:2 substitution:1 contains:1 lichman:1 series:1 chervonenkis:2 document:8 comparing:1 virus:1 protection:1 john:1 numerical:3 partition:2 plot:3 discrimination:1 supx2x:2 provides:5 certificate:2 math:1 org:2 cleavage:4 mathematical:1 c2:7... |
6,512 | 6,891 | Deep Lattice Networks and Partial Monotonic
Functions
Seungil You, David Ding, Kevin Canini, Jan Pfeifer, Maya R. Gupta
Google Research
1600 Amphitheatre Parkway, Mountain View, CA 94043
{siyou,dwding,canini,janpf,mayagupta}@google.com
Abstract
We propose learning deep models that are monotonic with respect to a users... | 6891 |@word repository:1 middle:2 polynomial:8 open:2 d2:1 wtm:5 boundedness:1 initial:4 score:5 daniel:3 existing:1 steiner:1 com:2 trustworthy:1 si:6 activation:2 lang:1 must:2 chu:1 applicant:1 devin:1 numerical:2 shape:2 hypothesize:1 drop:1 interpretable:1 update:2 isard:1 parameterization:1 provides:1 node:2 org:... |
6,513 | 6,892 | Continual Learning with Deep Generative Replay
Hanul Shin
Massachusetts Institute of Technology
SK T-Brain
skyshin@mit.edu
Jung Kwon Lee?, Jaehong Kim?, Jiwon Kim
SK T-Brain
{jklee,xhark,jk}@sktbrain.com
Abstract
Attempts to train a comprehensive artificial intelligence capable of solving multiple
tasks have been im... | 6892 |@word hippocampus:8 solver1:1 risto:1 contrastive:1 incurs:1 thereby:3 moment:1 reduction:1 configuration:2 contains:1 efficacy:1 series:1 hoiem:1 liu:1 seriously:1 tuned:1 ours:1 hyunsoo:1 document:1 past:24 current:8 com:1 comparing:1 recovered:2 activation:1 si:1 yet:2 subsequent:1 plasticity:2 enables:2 drop:... |
6,514 | 6,893 | AIDE: An algorithm for measuring the accuracy of
probabilistic inference algorithms
Marco F. Cusumano-Towner
Probabilistic Computing Project
Massachusetts Institute of Technology
marcoct@mit.edu
Vikash K. Mansinghka
Probabilistic Computing Project
Massachusetts Institute of Technology
vkm@mit.edu
Abstract
Approximat... | 6893 |@word kong:1 briefly:2 middle:2 seems:2 nd:1 adrian:1 simulation:2 dominique:1 contains:3 series:2 daniel:5 ours:1 fa8750:1 outperforms:1 existing:2 subjective:1 bradley:1 comparing:5 nt:5 yet:2 dx:1 written:1 readily:1 diederik:1 john:1 chicago:1 partition:1 predetermined:1 christian:1 asymptote:1 designed:1 plo... |
6,515 | 6,894 | Learning Causal Structures Using Regression
Invariance
AmirEmad Ghassami?? , Saber Salehkaleybar? , Negar Kiyavash?? , Kun Zhang?
?
Department of ECE, University of Illinois at Urbana-Champaign, Urbana, USA.
?
Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, USA.
?
Department of Philo... | 6894 |@word mild:1 cu:4 polynomial:1 open:1 hyv:2 simulation:3 crucially:1 tried:1 covariance:1 pick:1 attended:1 initial:1 contains:5 score:5 selecting:2 series:3 denoting:1 outperforms:2 existing:5 wd:2 written:1 additive:6 realistic:1 designed:1 alone:1 greedy:3 discovering:1 leaf:1 intelligence:7 es:9 coleman:4 dis... |
6,516 | 6,895 | Online Influence Maximization under Independent
Cascade Model with Semi-Bandit Feedback
Zheng Wen
Adobe Research
zwen@adobe.com
Branislav Kveton
Adobe Research
kveton@adobe.com
Michal Valko
SequeL team, INRIA Lille - Nord Europe
michal.valko@inria.fr
Sharan Vaswani
University of British Columbia
sharanv@cs.ubc.ca
A... | 6895 |@word kohli:1 version:2 briefly:2 polynomial:3 norm:1 stronger:1 nd:1 open:1 d2:1 lakshmanan:4 thereby:2 mention:1 recursively:1 contains:1 ours:1 past:5 existing:1 yajun:4 horvitz:1 com:2 michal:4 activation:12 must:1 maniu:2 remove:1 plot:2 update:3 v:2 stationary:1 intelligence:4 beginning:1 reciprocal:2 node:... |
6,517 | 6,896 | Near Minimax Optimal Players for the Finite-Time
3-Expert Prediction Problem
Yasin Abbasi-Yadkori
Adobe Research
Peter L. Bartlett
UC Berkeley
Victor Gabillon
Queensland University of Technology
Abstract
We study minimax strategies for the online prediction problem with expert advice.
It has been conjectured that a ... | 6896 |@word middle:2 version:1 pw:3 seems:2 open:2 simulation:1 crucially:2 queensland:1 simplifying:1 decomposition:1 jacob:2 reduction:1 initial:1 contains:2 exclusively:3 interestingly:3 current:4 surprising:1 luo:1 yet:2 intriguing:1 written:1 john:1 ronald:1 additive:4 subsequent:2 informative:1 designed:2 v:1 sta... |
6,518 | 6,897 | Reinforcement Learning under Model Mismatch
Aurko Roy1 , Huan Xu2 , and Sebastian Pokutta2
1 Google ,? Email: aurkor@google.com
Georgia Institute of Technology, Atlanta, GA, USA.
Email: huan.xu@isye.gatech.edu
2 ISyE, Georgia Institute of Technology, Atlanta, GA, USA.
Email: sebastian.pokutta@isye.gatech.edu
2 ISyE,
A... | 6897 |@word mild:3 version:13 polynomial:2 norm:6 d2:1 simulation:2 contraction:8 p0:1 contains:1 current:1 com:1 must:1 readily:1 john:2 wiewiora:1 drop:1 plot:1 update:6 v:1 greedy:1 prohibitive:1 plane:1 ith:1 characterization:1 uca:6 mannor:4 simpler:1 kv0:1 become:1 ik:3 prove:13 inside:1 indeed:1 expected:4 p1:1 ... |
6,519 | 6,898 | Hierarchical Attentive Recurrent Tracking
Adam R. Kosiorek
Department of Engineering Science
University of Oxford
adamk@robots.ox.ac.uk
Alex Bewley
Department of Engineering Science
University of Oxford
bewley@robots.ox.ac.uk
Ingmar Posner
Department of Engineering Science
University of Oxford
ingmar@robots.ox.ac.uk... | 6898 |@word luk:1 cnn:7 briefly:2 polynomial:1 seems:1 replicate:2 nd:1 hu:1 overwritten:1 pick:1 thereby:3 solid:1 initial:5 ndez:1 foveal:1 score:1 selecting:1 contains:2 initialisation:2 daniel:1 att:4 suppressing:3 ours:1 reynolds:1 favouring:1 current:3 com:1 activation:1 yet:2 intriguing:2 gpu:2 john:1 subsequent... |
6,520 | 6,899 | Tomography of the London Underground:
a Scalable Model for Origin-Destination Data
Nicol? Colombo
Department of Statistical Science
University College London
nicolo.colombo@ucl.ac.uk
Ricardo Silva
The Alan Turing Institute and
Department of Statistical Science
University College London
ricardo.silva@ucl.ac.uk
Soong ... | 6899 |@word middle:1 version:1 nd:1 closure:1 d2:1 simulation:2 r:1 pieter:1 harder:1 recursively:2 moment:6 series:3 score:8 tist:1 daniel:1 document:1 past:2 existing:3 outperforms:2 comparing:1 od:16 analysed:1 si:1 yet:1 router:2 attracted:1 luis:1 duffield:2 diederik:1 numerical:1 shape:2 analytic:1 designed:1 plo... |
6,521 | 69 | 740
SPATIAL ORGANIZATION OF NEURAL NEn~ORKS:
A PROBABILISTIC MODELING APPROACH
A. Stafylopatis
M. Dikaiakos
D. Kontoravdis
National Technical University of Athens, Department of Electrical Engineering, Computer Science Division, 15773 Zographos,
Athens, Greece.
ABSTRACT
The aim of this paper is to explore the spatial ... | 69 |@word seems:1 suitably:3 open:4 grey:1 km:3 simulation:17 r:2 calculus:1 eng:1 brightness:1 initial:1 mag:1 genetic:2 reaction:3 incidence:1 must:1 numerical:3 realistic:1 designed:1 selected:1 nervous:2 compo:1 propagative:4 math:2 node:43 five:2 scie:1 constructed:1 baskett:1 examine:1 udes:1 brain:2 considering:... |
6,522 | 690 | A Fast Stochastic Error-Descent
Algorithm for Supervised Learning and
Optimization
Gert Cauwenberghs
California Institute of Technology
Mail-Code 128-95
Pasadena, CA 91125
E-mail: gert(Qcco. cal tech. edu
Abstract
A parallel stochastic algorithm is investigated for error-descent
learning and optimization in determini... | 690 |@word aircraft:1 trial:1 version:1 simulation:3 simplifying:1 p0:1 attainable:3 solid:1 reduction:1 initial:3 series:1 current:1 activation:1 chu:1 reminiscent:1 i1l:3 numerical:1 j1:1 enables:1 remove:1 update:15 v:1 selected:3 accordingly:1 dembo:3 steepest:3 provides:1 node:1 ron:1 along:1 constructed:2 direct:... |
6,523 | 6,900 | Rotting Bandits
Nir Levine
Electrical Engineering Department
The Technion
Haifa 32000, Israel
levin.nir1@gmail.com
Koby Crammer
Electrical Engineering Department
The Technion
Haifa 32000, Israel
koby@ee.technion.ac.il
Shie Mannor
Electrical Engineering Department
The Technion
Haifa 32000, Israel
shie@ee.technion.ac.... | 6900 |@word trial:1 exploitation:3 middle:1 version:2 leighton:2 seems:1 stronger:1 dekel:1 nd:1 km:2 simulation:5 bn:2 harder:1 wrapper:1 celebrated:1 series:2 liu:2 ours:1 past:5 existing:1 com:1 gmail:1 assigning:1 must:4 benign:1 shape:1 hypothesize:1 plot:1 update:5 v:2 stationary:15 half:2 selected:2 intelligence... |
6,524 | 6,901 | Unbiased estimates for linear regression
via volume sampling
?
Micha? Derezinski
Department of Computer Science
University of California Santa Cruz
mderezin@ucsc.edu
Manfred K. Warmuth
Department of Computer Science
University of California Santa Cruz
manfred@ucsc.edu
Abstract
Given a full rank matrix X with more co... | 6901 |@word determinant:3 version:4 polynomial:3 norm:3 stronger:1 nd:6 suitably:1 open:5 d2:3 seek:1 covariance:2 decomposition:1 pick:1 concise:1 tr:4 moment:1 initial:2 contains:1 score:3 selecting:2 daniel:2 woodruff:2 surprising:1 si:5 yet:1 written:1 luis:2 determinantal:5 cruz:2 informative:1 shape:1 update:2 se... |
6,525 | 6,902 | Approximation Bounds for Hierarchical Clustering:
Average Linkage, Bisecting K-means, and Local
Search
Benjamin Moseley?
Carnegie Mellon University
Pittsburgh, PA 15213, USA
moseleyb@andrew.cmu.edu
Joshua R. Wang?
Department of Computer Science Stanford University
353 Serra Mall, Stanford, CA 94305, USA
joshua.wang@cs... | 6902 |@word seek:1 recursively:2 score:5 denoting:2 current:3 si:2 must:1 porta:1 partition:3 predetermined:1 analytic:3 wanted:1 intelligence:2 leaf:27 fewer:1 merger:1 scotland:1 pointer:1 characterization:1 node:19 toronto:1 along:1 constructed:2 become:1 symposium:2 consists:1 prove:1 acmsiam:1 theoretically:1 expe... |
6,526 | 6,903 | Adaptive Accelerated Gradient Converging Method
under H?lderian Error Bound Condition
Mingrui Liu, Tianbao Yang
Department of Computer Science
The University of Iowa, Iowa City, IA 52242
mingrui-liu, tianbao-yang@uiowa.edu
Abstract
Recent studies have shown that proximal gradient (PG) method and accelerated
gradient m... | 6903 |@word version:1 polynomial:11 norm:32 nd:1 c0:10 termination:4 semicontinuous:5 pg:46 ld:2 moment:1 initial:6 liu:3 kx0:2 current:2 luo:4 bierstone:1 hou:1 periodically:2 update:8 guess:3 website:1 xk:18 core:2 ojasiewicz:4 caveat:1 math:2 revisited:1 firstly:1 scientifiques:1 zhang:3 along:1 c2:5 pairing:1 consi... |
6,527 | 6,904 | Stein Variational Gradient Descent as Gradient Flow
Qiang Liu
Department of Computer Science
Dartmouth College
Hanover, NH 03755
qiang.liu@dartmouth.edu
Abstract
Stein variational gradient descent (SVGD) is a deterministic sampling algorithm
that iteratively transports a set of particles to approximate given distribut... | 6904 |@word version:1 seems:1 villani:1 stronger:1 norm:7 open:5 simulation:1 pick:1 recursively:2 initial:7 liu:7 series:2 score:1 rkhs:8 existing:1 current:3 nt:4 surprising:1 dx:8 must:1 readily:1 numerical:1 update:11 n0:7 mackey:4 intelligence:3 xk:1 beginning:1 steepest:1 provides:5 location:1 unbounded:2 mathema... |
6,528 | 6,905 | Partial Hard Thresholding: Towards A Principled
Analysis of Support Recovery
Jie Shen
Department of Computer Science
School of Arts and Sciences
Rutgers University
New Jersey, USA
js2007@rutgers.edu
Ping Li
Department of Statistics and Biostatistics
Department of Computer Science
Rutgers University
New Jersey, USA
pi... | 6905 |@word mild:1 trial:3 milenkovic:1 version:1 stronger:2 norm:2 seems:1 open:2 r:12 simulation:2 covariance:4 decomposition:1 pick:10 asks:1 thereby:1 iii1360971:1 bahmani:1 carry:1 initial:1 configuration:1 contains:5 series:2 rightmost:1 current:1 comparing:1 surprising:1 reminiscent:1 numerical:4 realistic:2 par... |
6,529 | 6,906 | Shallow Updates for Deep Reinforcement Learning
Nir Levine?
Dept. of Electrical Engineering
The Technion - Israel Institute of Technology
Israel, Haifa 3200003
levin.nir1@gmail.com
Tom Zahavy?
Dept. of Electrical Engineering
The Technion - Israel Institute of Technology
Israel, Haifa 3200003
tomzahavy@campus.technion... | 6906 |@word h:2 norm:2 retraining:1 open:1 crucially:1 sgd:4 solid:2 configuration:1 contains:2 score:15 daniel:3 interestingly:2 bilal:1 past:1 outperforms:2 hasselt:6 current:11 com:2 freitas:1 surprising:1 si:15 gmail:1 yet:1 activation:2 diederik:1 john:4 guez:2 uria:1 periodically:4 ronald:1 update:17 greedy:1 sel... |
6,530 | 6,907 | LightGBM: A Highly Efficient Gradient Boosting
Decision Tree
Guolin Ke1 , Qi Meng2 , Thomas Finley3 , Taifeng Wang1 ,
Wei Chen1 , Weidong Ma1 , Qiwei Ye1 , Tie-Yan Liu1
1
Microsoft Research 2 Peking University 3 Microsoft Redmond
1
{guolin.ke, taifengw, wche, weima, qiwye, tie-yan.liu}@microsoft.com;
2
qimeng13@pku.edu... | 6907 |@word version:1 polynomial:1 proportion:1 mcrank:1 c0:1 nd:1 open:1 mehta:2 hsieh:2 citeseer:1 dramatic:1 reduction:1 liu:3 contains:3 score:1 jimenez:1 tuned:2 dubourg:1 outperforms:1 existing:1 current:1 com:5 incidence:1 comparing:4 si:2 gpu:3 john:2 luis:1 numerical:1 additive:1 informative:1 kdd:5 ranka:1 dr... |
6,531 | 6,908 | Adversarial Ranking for Language Generation
Kevin Lin?
University of Washington
kvlin@uw.edu
Xiaodong He
Microsoft Research
xiaohe@microsoft.com
Dianqi Li?
University of Washington
dianqili@uw.edu
Zhengyou Zhang
Microsoft Research
zhang@microsoft.com
Ming-Ting Sun
University of Washington
mts@uw.edu
Abstract
Gener... | 6908 |@word cnn:1 norm:1 open:1 simulation:4 pg:9 juliet:2 tianyi:1 carry:1 lantao:1 ndez:1 series:2 score:45 disparity:1 contains:2 liu:2 document:3 ours:1 outperforms:2 existing:4 current:8 com:3 comparing:4 guadarrama:1 written:43 gpu:1 john:1 subsequent:1 realistic:2 informative:2 shakespeare:5 adam:1 christian:1 d... |
6,532 | 6,909 | Regret Minimization in MDPs with Options
without Prior Knowledge
Ronan Fruit
Sequel Team - Inria Lille
ronan.fruit@inria.fr
Matteo Pirotta
Sequel Team - Inria Lille
matteo.pirotta@inria.fr
Alessandro Lazaric
Sequel Team - Inria Lille
alessandro.lazaric@inria.fr
Emma Brunskill
Stanford University
ebrun@cs.stanford.e... | 6909 |@word exploitation:4 version:6 norm:3 underline:1 termination:4 simulation:1 propagate:1 ljo:1 accounting:2 p0:2 innermost:1 decomposition:1 harder:1 reduction:1 initial:4 series:1 contains:3 daniel:4 comparing:1 si:3 john:1 ronald:2 ronan:3 additive:2 numerical:1 remove:2 designed:4 drop:1 smdp:22 stationary:12 ... |
6,533 | 691 | Intersecting regions: The key to combinatorial
structure in hidden unit space
Janet Wiles
Depts of Psychology and
Computer Science,
University of Queensland
QLD 4072 Australia.
janetw@cs.uq.oz.au
Mark Ollila,
Vision Lab, CITRI
Dept of Computer Science,
University of Melbourne,
Vic 3052 Australia
molly@vis.citri.edu.a... | 691 |@word version:1 seems:1 holyoak:1 simulation:7 queensland:1 concise:2 harder:1 current:1 yet:1 shape:14 plot:1 cue:1 fewer:1 item:1 provides:1 location:7 along:2 constructed:1 surprised:1 consists:2 indeed:1 elman:6 themselves:1 multi:2 considering:1 provided:2 interpreted:1 nj:1 temporal:3 ro:1 partitioning:1 uni... |
6,534 | 6,910 | Net-Trim: Convex Pruning of Deep Neural Networks
with Performance Guarantee
Alireza Aghasi?
Institute for Insight
Georgia State University
IBM TJ Watson
aaghasi@gsu.edu
Afshin Abdi
Department of ECE
Georgia Tech
abdi@gatech.edu
Nam Nguyen
IBM TJ Watson
nnguyen@us.ibm.com
Justin Romberg
Department of ECE
Georgia Tech... | 6910 |@word cnn:10 briefly:1 version:4 compression:3 norm:2 johansson:1 retraining:16 seek:2 covariance:1 simplifying:1 sparsifies:1 arous:1 reduction:3 initial:20 contains:1 denoting:2 past:1 recovered:1 com:2 nt:7 nowlan:1 activation:4 yet:1 written:1 gpu:1 pioneer:1 numerical:1 subsequent:5 realistic:1 girosi:1 remo... |
6,535 | 6,911 | Graph Matching via Multiplicative Update Algorithm
Bo Jiang
School of Computer Science
and Technology
Anhui University, China
jiangbo@ahu.edu.cn
Jin Tang
School of Computer Science
and Technology
Anhui University, China
tj@ahu.edu.cn
Yihong Gong
School of Electronic
and Information Engineering
Xi?an Jiaotong Univers... | 6911 |@word version:1 middle:2 open:1 mcauley:2 initial:3 contains:2 score:7 zij:2 renewed:1 kahl:1 outperforms:2 current:2 discretization:2 comparing:1 luo:5 wx:21 update:20 greedy:1 selected:1 assurance:1 intelligence:6 core:2 institution:1 cse:1 node:13 firstly:1 org:1 zhang:2 supply:1 incorrect:1 prove:2 doubly:19 ... |
6,536 | 6,912 | Dynamic Importance Sampling for Anytime Bounds
of the Partition Function
Qi Lou
Computer Science
Univ. of California, Irvine
Irvine, CA 92697, USA
qlou@ics.uci.edu
Rina Dechter
Computer Science
Univ. of California, Irvine
Irvine, CA 92697, USA
dechter@ics.uci.edu
Alexander Ihler
Computer Science
Univ. of California, ... | 6912 |@word version:1 seems:1 chakraborty:2 nd:23 open:7 simulation:1 bn:6 boundedness:1 recursively:1 initial:2 configuration:10 contains:1 series:1 united:1 liu:3 genetic:1 fa8750:1 past:1 current:2 comparing:1 rish:1 si:2 written:1 dechter:7 partition:16 remove:1 sponsored:1 update:3 n0:14 hash:2 intelligence:5 leaf... |
6,537 | 6,913 | Is the Bellman residual a bad proxy?
Matthieu Geist1 , Bilal Piot2,3 and Olivier Pietquin 2,3
Universit? de Lorraine & CNRS, LIEC, UMR 7360, Metz, F-57070 France
2
Univ. Lille, CNRS, Centrale Lille, Inria, UMR 9189 - CRIStAL, F-59000 Lille, France
3
Now with Google DeepMind, London, United Kingdom
matthieu.geist@univ-... | 6913 |@word seems:1 norm:6 stronger:1 pieter:1 calculus:1 r:8 pg:5 mention:1 harder:1 lorraine:2 initial:5 united:1 bilal:4 current:3 comparing:7 surprising:1 yet:3 john:2 ronald:1 designed:5 greedy:5 selected:1 parameterization:1 core:1 oblique:1 provides:1 boosting:1 parameterizations:1 completeness:1 philipp:1 simpl... |
6,538 | 6,914 | Generalization Properties of Learning with Random
Features
Alessandro Rudi ?
Lorenzo Rosasco
INRIA - Sierra Project-team,
?
Ecole
Normale Sup?erieure, Paris,
75012 Paris, France
alessandro.rudi@inria.fr
University of Genova,
Istituto Italiano di Tecnologia,
Massachusetts Institute of Technology.
lrosasco@mit.edu
Ab... | 6914 |@word polynomial:1 norm:2 seems:1 nd:1 c0:6 advantageous:1 open:1 simulation:2 dramatic:1 nystr:13 tr:1 initial:1 minmax:1 series:1 score:6 woodruff:1 ecole:1 rkhs:12 ours:1 interestingly:1 document:1 recovered:1 numerical:5 additive:1 benign:3 girosi:1 analytic:1 n0:7 v:1 greedy:1 fewer:6 devising:1 intelligence... |
6,539 | 6,915 | Differentially private Bayesian learning on
distributed data
Mikko Heikkil?1
mikko.a.heikkila@helsinki.fi
Samuel Kaski3
samuel.kaski@aalto.fi
Eemil Lagerspetz2
eemil.lagerspetz@helsinki.fi
Kana Shimizu4
shimizu.kana.g@gmail.com
Sasu Tarkoma2
sasu.tarkoma@helsinki.fi
Antti Honkela1,5
antti.honkela@helsinki.fi
1
Hel... | 6915 |@word private:22 version:7 repository:2 proportion:1 cortez:1 nd:1 asks:1 initial:1 series:2 lichman:1 denoting:2 existing:1 current:2 com:2 comparing:3 collude:5 gmail:1 router:1 chu:1 drop:2 update:2 v:2 mitrokotsa:2 intelligence:2 prohibitive:1 device:1 selected:2 decrypted:3 pvldb:1 smith:2 normalising:1 prov... |
6,540 | 6,916 | Learning to Compose Domain-Specific
Transformations for Data Augmentation
Alexander J. Ratner?, Henry R. Ehrenberg?, Zeshan Hussain,
Jared Dunnmon, Christopher R?
Stanford University
{ajratner,henryre,zeshanmh,jdunnmon,chrismre}@cs.stanford.edu
Abstract
Data augmentation is a ubiquitous technique for increasing the s... | 6916 |@word cnn:2 version:1 middle:1 repository:1 norm:1 seems:1 open:1 heuristically:2 crucially:1 excited:1 pg:1 brightness:3 sajjadi:1 mention:2 accommodate:1 reduction:1 initial:1 configuration:1 liu:1 efficacy:1 score:3 heur:3 tuned:3 daniel:2 document:1 fa8750:4 outperforms:2 existing:1 current:1 com:1 realize:1 ... |
6,541 | 6,917 | Wasserstein Learning of Deep Generative Point
Process Models
Shuai Xiao? ? , Mehrdad Farajtabar? Xiaojing Ye? , Junchi Yan?
Xiaokang Yang? , Le Song , Hongyuan Zha
?
Shanghai Jiao Tong University
College of Computing, Georgia Institute of Technology
?
School of Mathematics, Georgia State University
{benjaminforeve... | 6917 |@word multitask:1 cox:2 middle:2 norm:1 villani:1 simulation:1 pg:9 unstably:1 memetracker:2 initial:3 series:2 contains:3 interestingly:1 outperforms:4 subjective:1 past:1 current:3 discretization:1 com:1 surprising:1 manuel:3 activation:2 yet:1 dx:5 written:2 vere:1 john:1 timestamps:1 happen:1 informative:1 wx... |
6,542 | 6,918 | Ensemble Sampling
Xiuyuan Lu
Stanford University
lxy@stanford.edu
Benjamin Van Roy
Stanford University
bvr@stanford.edu
Abstract
Thompson sampling has emerged as an effective heuristic for a broad range of
online decision problems. In its basic form, the algorithm requires computing
and sampling from a posterior dis... | 6918 |@word exploitation:1 version:2 manageable:1 stronger:2 seems:2 nd:1 c0:1 unif:1 open:1 simulation:1 covariance:1 p0:1 pick:1 efficacy:3 selecting:3 ktv:2 tuned:1 bootstrapped:1 past:2 outperforms:2 z2:6 must:1 shawetaylor:1 remove:1 designed:1 plot:4 update:6 greedy:14 selected:10 assurance:2 org:1 wierstra:1 bec... |
6,543 | 6,919 | Character-Level Language Modeling with Recurrent
Highway Hypernetworks
Joseph Suarez
Stanford University
joseph15@stanford.edu
Abstract
We present extensive experimental and theoretical support for the efficacy of recurrent highway networks (RHNs) and recurrent hypernetworks complimentary to
the original works. Where... | 6919 |@word middle:2 norm:11 seems:2 open:1 decomposition:1 incurs:1 minus:1 reduction:1 initial:2 contains:1 efficacy:1 disparity:1 ours:3 existing:1 current:2 com:1 surprising:2 si:15 activation:3 yet:1 must:1 written:1 diederik:1 realistic:1 confirming:1 assuage:1 shape:1 christian:1 drop:3 update:6 precaution:2 lea... |
6,544 | 692 | The Power of Approximating: a
Comparison of Activation Functions
Bhaskar DasGupta
Department of Computer Science
University of Minnesota
Minneapolis, MN 55455-0159
email:
dasgupta~cs.umn.edu
Georg Schnitger
Department of Computer Science
The Pennsylvania State University
University Park, PA 16802
email:
georg~cs.ps... | 692 |@word polynomial:26 norm:2 stronger:1 nd:2 simulation:1 ld:1 series:1 comparing:1 activation:35 schnitger:7 must:1 numerical:1 girosi:2 intelligence:1 funahashi:2 math:1 sigmoidal:6 firstly:1 ipi:1 unbounded:1 predecessor:1 consists:1 symp:1 introduce:1 indeed:1 behavior:1 multi:1 insist:1 provided:2 moreover:5 bo... |
6,545 | 6,920 | Adaptive SVRG Methods under Error Bound
Conditions with Unknown Growth Parameter
Yi Xu? , Qihang Lin? , Tianbao Yang?
Department of Computer Science, The University of Iowa, Iowa City, IA 52242, USA
?
Department of Management Sciences, The University of Iowa, Iowa City, IA 52242, USA
{yi-xu, qihang-lin, tianbao-yang}@u... | 6920 |@word polynomial:2 norm:17 nd:1 c0:12 open:1 termination:1 heuristically:1 semicontinuous:1 r:64 d2:1 t1r:1 pg:1 sgd:1 acknowlegements:1 tr:4 reduction:2 moment:1 liu:5 series:1 initial:14 tuned:1 interestingly:1 current:2 comparing:1 luo:4 tackling:1 must:2 hou:1 designed:1 plot:1 update:7 website:1 beginning:1 ... |
6,546 | 6,921 | Bayesian Compression for Deep Learning
Christos Louizos
University of Amsterdam
TNO Intelligent Imaging
c.louizos@uva.nl
Karen Ullrich
University of Amsterdam
k.ullrich@uva.nl
Max Welling
University of Amsterdam
CIFAR?
m.welling@uva.nl
Abstract
Compression and computational efficiency in deep learning have become a... | 6921 |@word h:1 illustrating:1 version:2 compression:34 annapureddy:1 nd:1 open:1 tried:1 decomposition:2 citeseer:1 sparsifies:1 ld:1 reduction:1 initial:1 liu:1 contains:1 series:2 tuned:1 bc:15 interestingly:1 offering:1 document:1 existing:1 freitas:1 current:1 z2:4 com:1 activation:2 chu:1 must:1 readily:1 gpu:5 d... |
6,547 | 6,922 | Streaming Sparse Gaussian Process Approximations
Thang D. Bui?
Cuong V. Nguyen?
Richard E. Turner
Department of Engineering, University of Cambridge, UK
{tdb40,vcn22,ret26}@cam.ac.uk
Abstract
Sparse pseudo-point approximations for Gaussian process (GP) models provide a
suite of methods that support deployment of GPs ... | 6922 |@word briefly:1 version:4 pillar:1 reused:1 covariance:2 delicately:1 thereby:1 tr:2 solid:2 catastrophically:1 initial:2 ndez:4 series:10 exclusively:1 efficacy:1 contains:2 initialisation:1 kuf:1 interestingly:3 past:2 existing:5 outperforms:2 current:5 recovered:1 z2:5 com:1 comparing:1 must:3 john:1 fn:5 refi... |
6,548 | 6,923 | V EEGAN: Reducing Mode Collapse in GANs using
Implicit Variational Learning
Akash Srivastava
School of Informatics
University of Edinburgh
akash.srivastava@ed.ac.uk
Chris Russell
The Alan Turing Institute
London
crussell@turing.ac.uk
Lazar Valkov
School of Informatics
University of Edinburgh
L.Valkov@sms.ed.ac.uk
Mic... | 6923 |@word version:1 middle:1 replicate:1 simulation:1 jacob:1 p0:24 contrastive:2 sgd:1 series:1 jimenez:1 com:1 diederik:1 dx:5 written:1 must:1 john:1 numerical:1 realistic:1 treating:1 concert:1 update:1 plot:2 designed:1 alone:1 generative:17 joy:1 intelligence:1 item:7 alec:2 isotropic:1 lr:4 blei:1 provides:4 l... |
6,549 | 6,924 | Sparse Embedded k-Means Clustering
?
?
Weiwei Liu?,\,?, Xiaobo Shen?,? , Ivor W. Tsang\
School of Computer Science and Engineering, The University of New South Wales
School of Computer Science and Engineering, Nanyang Technological University
\
Centre for Artificial Intelligence, University of Technology Sydney
{liuw... | 6924 |@word compression:1 norm:8 nd:6 sammon:1 decomposition:4 tr:24 reduction:29 liu:5 score:4 selecting:1 woodruff:1 denoting:1 daniel:1 outperforms:2 com:2 cad:2 njust:1 gmail:1 john:1 partition:1 christian:1 intelligence:2 prohibitive:4 selected:1 greedy:1 xk:2 short:1 provides:2 org:1 constructed:2 dengcai:2 ik:1 ... |
6,550 | 6,925 | Dynamic-Depth Context Tree Weighting
Jo?o V. Messias?
Morpheus Labs
Oxford, UK
jmessias@morpheuslabs.co.uk
Shimon Whiteson
University of Oxford
Oxford, UK
shimon.whiteson@cs.ox.ac.uk
Abstract
Reinforcement learning (RL) in partially observable settings is challenging because the agent?s observations are not Markov. ... | 6925 |@word version:1 pw:17 compression:3 replicate:1 smirnov:1 cs0:1 instruction:1 d2:51 p0:2 q1:2 cleary:2 recursively:1 carry:1 initial:2 configuration:1 series:6 prefix:1 past:4 existing:3 outperforms:1 current:1 contextual:1 must:8 numerical:1 enables:1 designed:1 treating:2 update:3 n0:1 v:2 stationary:1 implying... |
6,551 | 6,926 | A Regularized Framework for
Sparse and Structured Neural Attention
Vlad Niculae?
Cornell University
Ithaca, NY
vlad@cs.cornell.edu
Mathieu Blondel
NTT Communication Science Laboratories
Kyoto, Japan
mathieu@mblondel.org
Abstract
Modern neural networks are often augmented with an attention mechanism, which
tells the ... | 6926 |@word norm:16 nd:3 open:3 calculus:1 grey:1 carry:1 liu:1 contains:1 score:2 att:3 selecting:1 series:1 tuned:1 ours:1 ati:3 outperforms:2 existing:3 ksk1:1 current:2 reynolds:1 nt:3 surprising:1 activation:1 tackling:2 must:2 gpu:2 visible:1 drop:4 interpretable:5 designed:3 concert:5 kyk:1 contribute:1 org:2 zh... |
6,552 | 6,927 | Multi-output Polynomial Networks
and Factorization Machines
Mathieu Blondel
NTT Communication Science Laboratories
Kyoto, Japan
mathieu@mblondel.org
Takuma Otsuka
NTT Communication Science Laboratories
Kyoto, Japan
otsuka.takuma@lab.ntt.co.jp
Vlad Niculae?
Cornell University
Ithaca, NY
vlad@cs.cornell.edu
Naonori Ued... | 6927 |@word kgk:3 compression:1 polynomial:14 instrumental:1 norm:9 seems:1 interleave:1 open:3 mcrank:7 trofimov:1 linearized:1 wexler:1 decomposition:2 hsieh:1 nystr:4 sepulchre:1 moment:1 reduction:7 liu:1 contains:3 score:2 selecting:3 outperforms:4 existing:2 current:2 comparing:1 surprising:1 activation:7 parsing... |
6,553 | 6,928 | Clustering Billions of Reads for DNA Data Storage
Cyrus Rashtchiana,b Konstantin Makarycheva,c Mikl?s R?cza,d Siena Dumas Anga
Djordje Jevdjica Sergey Yekhanina Luis Cezea,b Karin Straussa
a
Microsoft Research, b CSE at University of Washington,
c
EECS at Northwestern University, d ORFE at Princeton University
Abstra... | 6928 |@word milenkovic:1 version:1 compression:1 chakraborty:2 termination:1 vldb:1 simulation:1 jacob:1 pick:4 fifteen:1 mention:1 reduction:1 substitution:4 series:1 woodruff:1 document:1 prefix:1 outperforms:3 existing:3 current:7 comparing:5 yet:1 must:5 luis:1 partition:4 kdd:2 cheap:2 christian:2 enables:1 plot:4... |
6,554 | 6,929 | Multi-Objective Non-parametric Sequential
Prediction
Guy Uziel
Computer Science Department
Technion - Israel Institute of Technology
guziel@cs.technion.ac.il
Ran El-Yaniv
Computer Science Department
Technion - Israel Institute of Technology
rani@cs.technion.ac.il
Abstract
Online-learning research has mainly been focu... | 6929 |@word mild:1 rani:1 urb:1 open:1 nemirovsky:1 initial:2 series:5 tist:1 past:2 assigning:1 universality:1 fn:2 additive:1 designed:1 update:4 n0:1 stationary:10 implying:1 provides:1 mannor:1 unbounded:1 mathematical:4 constructed:2 reversion:1 ik:9 prove:4 sustained:1 manner:1 x0:13 indeed:1 multi:4 equipped:1 p... |
6,555 | 693 | Integration of Visual and Somatosensory
Information for Preshaping Hand
in Grasping Movements
Yoji Uno
Naohiro Fukumura*
ATR Human Information Processing
Research Laboratories
2-2 Hikaridai, Seika-cho, Soraku-gun,
Kyoto 619-02, Japan
Faculty of Engineering
University of Tokyo
7-3-1 Hongo, Bunkyo-ku,
Tokyo 113, Japa... | 693 |@word faculty:2 compression:2 simulation:1 electronics:1 configuration:24 activation:8 must:1 realize:2 shape:21 motor:5 hypothesize:1 fewer:1 prehension:10 nervous:1 plane:2 ith:4 flexing:2 five:5 c2:2 differential:2 prehensile:18 consists:1 behavioral:2 acquired:4 behavior:1 abscissa:1 seika:2 planning:3 multi:1... |
6,556 | 6,930 | A Universal Analysis of Large-Scale Regularized
Least Squares Solutions
Ashkan Panahi
Department of Electrical and Computer Engineering
North Carolina State University
Raleigh, NC 27606
apanahi@ncsu.edu
Babak Hassibi
Department of Electrical Engineering
California Institute of Technology
Pasadena, CA 91125
hassibi@ca... | 6930 |@word mild:1 trial:2 version:1 briefly:3 norm:15 stronger:1 instruction:1 r:2 carolina:1 decomposition:1 hsieh:1 citeseer:1 carry:1 reduction:1 moment:4 liu:1 series:2 bai:2 ours:3 document:1 amp:2 past:1 existing:1 current:2 karoui:1 universality:13 axk22:1 fn:1 numerical:4 mesh:1 confirming:1 enables:1 ith:1 va... |
6,557 | 6,931 | Deep Sets
Manzil Zaheer1,2 , Satwik Kottur1 , Siamak Ravanbhakhsh1 ,
Barnab?s P?czos1 , Ruslan Salakhutdinov1 , Alexander J Smola1,2
1
2
Carnegie Mellon University
Amazon Web Services
{manzilz,skottur,mravanba,bapoczos,rsalakhu,smola}@cs.cmu.edu
Abstract
We study the problem of designing models for machine learning t... | 6931 |@word repository:1 cnn:4 inversion:1 polynomial:3 seems:1 grey:1 covariance:4 decomposition:2 pick:1 initial:2 liu:1 manmatha:1 score:7 bai:2 contains:3 daniel:1 piotr:1 outperforms:5 existing:2 recovered:1 contextual:1 nt:1 luo:1 com:2 activation:3 yet:1 dx:1 must:2 luis:1 universality:1 scatter:3 mesh:3 sanjiv:... |
6,558 | 6,932 | ExtremeWeather: A large-scale climate dataset for
semi-supervised detection, localization, and
understanding of extreme weather events
Evan Racah1,2 , Christopher Beckham1,3 , Tegan Maharaj1,3 ,
Samira Ebrahimi Kahou4 , Prabhat2 , Christopher Pal1,3
1
MILA, Universit? de Montr?al, evan.racah@umontreal.ca.
2
Lawrence B... | 6932 |@word cnn:9 version:1 mri:1 briefly:1 humidity:1 open:1 grey:1 km:2 simulation:15 rgb:1 blender:1 downloading:1 pressure:3 dramatic:2 shot:2 reduction:1 initial:2 liu:12 configuration:1 score:3 selecting:1 united:1 daniel:1 tuned:1 contains:2 ours:1 deconvolutional:1 animated:1 past:1 existing:2 guadarrama:1 curr... |
6,559 | 6,933 | Process-constrained batch Bayesian Optimisation
Pratibha Vellanki1 , Santu Rana1 , Sunil Gupta1 , David Rubin2
Alessandra Sutti2 , Thomas Dorin2 , Murray Height2 ,Paul Sandars3 , Svetha Venkatesh1
1
Centre for Pattern Recognition and Data Analytics
Deakin University, Geelong, Australia
[pratibha.vellanki, santu.rana, ... | 6933 |@word repository:1 exploitation:1 simulation:1 seek:1 covariance:1 tr:1 configuration:3 series:1 ndez:1 initialisation:1 existing:1 freitas:3 current:1 yet:1 must:1 exposing:1 subsequent:1 benign:1 burdick:1 plot:1 update:2 v:1 intelligence:4 device:4 maximised:1 short:13 location:1 simpler:2 height:1 mathematica... |
6,560 | 6,934 | Bayesian Inference of Individualized Treatment
Effects using Multi-task Gaussian Processes
Ahmed M. Alaa
Electrical Engineering Department
University of California, Los Angeles
ahmedmalaa@ucla.edu
Mihaela van der Schaar
Department of Engineering Science
University of Oxford
mihaela.vanderschaar@eng.ox.ac.uk
Abstra... | 6934 |@word multitask:1 trial:6 version:2 middle:1 inversion:1 advantageous:2 norm:1 instrumental:2 johansson:2 simulation:1 eng:1 covariance:4 contraction:1 citeseer:1 harder:1 moment:1 contains:2 score:8 united:1 envision:1 outperforms:1 current:3 mihaela:2 assigning:1 dx:1 readily:1 stemming:1 additive:1 numerical:1... |
6,561 | 6,935 | Spherical convolutions and their application in
molecular modelling
Wouter Boomsma
Department of Computer Science
University of Copenhagen
wb@di.ku.dk
Jes Frellsen
Department of Computer Science
IT University of Copenhagen
jefr@itu.dk
Abstract
Convolutional neural networks are increasingly used outside the domain of... | 6935 |@word cnn:2 briefly:2 version:1 unaltered:1 illustrating:1 confirms:1 gradual:1 simulation:3 simplifying:1 irb:2 harder:1 reduction:2 initial:2 series:1 score:3 fragment:1 genetic:2 interestingly:2 outperforms:1 existing:2 jupp:2 discretization:1 comparing:1 com:1 activation:2 bd:1 gpu:1 periodically:1 additive:1... |
6,562 | 6,936 | Efficient Optimization for Linear Dynamical Systems
with Applications to Clustering and Sparse Coding
Wenbing Huang1,3 , Mehrtash Harandi2 , Tong Zhang2
Lijie Fan3 , Fuchun Sun3 , Junzhou Huang1
1
Tencent AI Lab. ;
2
Data61, CSIRO and Australian National University, Australia;
3
Department of Computer Science and Tech... | 6936 |@word trial:1 determinant:1 version:3 briefly:1 duda:1 tedious:1 km:1 decomposition:2 tr:2 tnlist:1 initial:2 liu:2 series:1 denoting:2 rkhs:2 interestingly:1 document:1 o2:1 outperforms:4 com:1 comparing:2 assigning:1 written:1 john:1 numerical:3 partition:1 nian:2 enables:1 update:15 grass:16 bart:1 generative:... |
6,563 | 6,937 | On Optimal Generalizability in Parametric Learning
Ahmad Beirami?
beirami@seas.harvard.edu
Meisam Razaviyayn?
razaviya@usc.edu
Shahin Shahrampour?
shahin@seas.harvard.edu
Vahid Tarokh?
vahid@seas.harvard.edu
Abstract
We consider the parametric learning problem, where the objective of the learner is
determined by a... | 6937 |@word mri:1 version:2 hu:2 confirms:1 carry:1 liu:1 configuration:2 series:1 selecting:1 tuned:1 renewed:1 existing:1 bradley:1 freitas:1 yet:1 readily:1 john:1 ronald:1 numerical:4 designed:1 plot:1 v:3 tarokh:1 half:1 fewer:1 selected:2 cook:1 plane:1 provides:2 characterization:2 contribute:1 detecting:1 gauta... |
6,564 | 6,938 | Near Optimal Sketching of Low-Rank Tensor Regression
Jarvis Haupt1
jdhaupt@umn.edu
1
Xingguo Li1,2
lixx1661@umn.edu
David P. Woodruff 3
dwoodruf@cs.cmu.edu ?
2
University of Minnesota
Georgia Institute of Technology
3
Carnegie Mellon University
Abstract
We study the least squares regression problem
min
?2Rp1 ????... | 6938 |@word multitask:2 mild:2 trial:3 version:5 mri:3 norm:3 paredes:1 nd:2 open:1 d2:2 simulation:2 propagate:1 bn:4 decomposition:23 e2v:1 dirksen:2 yasuo:1 reduction:13 cyclic:2 liu:3 score:11 woodruff:5 daniel:1 interestingly:1 longitudinal:1 romera:1 amp:1 existing:1 ka:14 z2:1 written:1 axk22:1 numerical:6 kv1:2... |
6,565 | 6,939 | Tractability in Structured Probability Spaces
Arthur Choi
University of California
Los Angeles, CA 90095
aychoi@cs.ucla.edu
Yujia Shen
University of California
Los Angeles, CA 90095
yujias@cs.ucla.edu
Adnan Darwiche
University of California
Los Angeles, CA 90095
darwiche@cs.ucla.edu
Abstract
Recently, the Probabili... | 6939 |@word mild:1 polynomial:6 adnan:1 bn:1 decomposition:3 pick:1 mention:1 minus:1 accommodate:1 recursively:1 contains:1 denoting:1 horvitz:1 bitwise:1 current:6 com:1 yet:1 router:1 must:1 partition:3 remove:1 half:3 leaf:1 intelligence:3 item:3 parameterization:2 accepting:1 filtered:1 characterization:1 paramete... |
6,566 | 694 | Kohonen Feature Maps and Growing
Cell Structures a Performance Comparison
Bernd Fritzke
International Computer Science Institute
1947 Center Street, Suite 600
Berkeley, CA 94704-1105, USA
Abstract
A performance comparison of two self-organizing networks, the Kohonen Feature Map and the recently proposed Growing Cell ... | 694 |@word version:5 briefly:1 compression:2 norm:1 simulation:8 reduction:1 series:1 tuned:1 current:1 comparing:1 assigning:1 numerical:1 realistic:3 partition:2 half:1 direct:1 consists:1 inside:1 manner:1 roughly:1 growing:24 automatically:3 pawelzik:2 increasing:1 becomes:1 underlying:2 suite:1 berkeley:1 every:9 ... |
6,567 | 6,940 | Model-based Bayesian inference of neural activity
and connectivity from all-optical interrogation of a
neural circuit
Laurence Aitchison
University of Cambridge
Cambridge, CB2 1PZ, UK
laurence.aitchison@gmail.com
Adam Packer
University College London
London, WC1E 6BT, UK
adampacker@gmail.com
Lloyd Russell
University C... | 6940 |@word neurophysiology:1 trial:4 stronger:1 laurence:2 norm:1 bf:1 replicate:1 rivlin:1 pulse:1 propagate:1 seek:1 covariance:3 simulation:1 dramatic:1 deisseroth:1 initial:2 bc:1 hirtz:1 past:4 steiner:1 com:4 virus:1 activation:2 gmail:4 yet:1 written:2 gpu:2 must:1 connectomics:1 devin:1 wll:3 designed:2 interp... |
6,568 | 6,941 | Gaussian process based nonlinear latent structure
discovery in multivariate spike train data
Anqi Wu, Nicholas A. Roy, Stephen Keeley, & Jonathan W. Pillow
Princeton Neuroscience Institute
Princeton University
Abstract
A large body of recent work focuses on methods for extracting low-dimensional
latent structure from... | 6941 |@word trial:8 illustrating:1 hippocampus:1 nd:4 busing:1 seek:2 simulation:4 linearized:1 covariance:17 tr:1 reduction:1 contains:4 daniel:1 ala:5 outperforms:3 current:1 anqi:1 si:2 dx:2 written:2 must:2 john:2 numerical:1 shape:1 enables:1 analytic:1 motor:1 fyhn:1 update:1 generative:1 selected:1 discovering:1... |
6,569 | 6,942 | Neural system identification for large populations
separating ?what? and ?where?
6
David A. Klindt * 1-3 , Alexander S. Ecker * 1,2,4,6 , Thomas Euler 1-3 , Matthias Bethge 1,2,4-6
*
Authors contributed equally
1
Centre for Integrative Neuroscience, University of T?bingen, Germany
2
Bernstein Center for Computational... | 6942 |@word neurophysiology:1 cnn:28 torsten:1 wiesel:1 kriegeskorte:1 integrative:2 seek:1 simulation:2 bn:1 covariance:2 solid:2 initial:5 contains:1 daniel:2 ours:2 interestingly:1 outperforms:3 current:3 com:2 recovered:2 activation:3 gmail:1 scatter:1 yet:1 gpu:2 diederik:1 realistic:1 subsequent:1 shape:1 christi... |
6,570 | 6,943 | Certified Defenses for Data Poisoning Attacks
Jacob Steinhardt?
Stanford University
jsteinha@stanford.edu
Pang Wei Koh?
Stanford University
pangwei@cs.stanford.edu
Percy Liang
Stanford University
pliang@cs.stanford.edu
Abstract
Machine learning systems trained on user-provided data are susceptible to data
poisoning ... | 6943 |@word version:1 stronger:1 seems:3 indiscriminate:1 open:3 seek:2 jacob:1 covariance:3 incurs:2 solid:3 liu:3 efficacy:1 tram:2 daniel:1 document:2 interestingly:1 past:2 existing:3 mishra:1 imposter:1 transferability:1 current:1 scaffolding:1 intriguing:1 must:2 john:1 subsequent:1 realistic:1 numerical:1 confir... |
6,571 | 6,944 | Eigen-Distortions of Hierarchical Representations
Alexander Berardino
Center for Neural Science
New York University
agb313@nyu.edu
Johannes Ball?
Center for Neural Science
New York University?
johannes.balle@nyu.edu
Valero Laparra
Image Processing Laboratory
Universitat de Val?ncia
valero.laparra@uv.es
Eero Simonce... | 6944 |@word trial:2 cnn:18 version:4 compression:1 kriegeskorte:2 proportion:1 necessity:2 contains:1 score:1 tuned:1 rightmost:1 current:1 laparra:7 comparing:6 rpi:1 diederik:1 intriguing:1 visible:2 subsequent:1 additive:1 realistic:1 enables:1 christian:1 designed:2 plot:1 discrimination:11 lydia:1 fewer:1 paramete... |
6,572 | 6,945 | Limitations on Variance-Reduction and Acceleration
Schemes for Finite Sum Optimization
Yossi Arjevani
Department of Computer Science and Applied Mathematics
Weizmann Institute of Science
Rehovot 7610001, Israel
yossi.arjevani@weizmann.ac.il
Abstract
We study the conditions under which one is able to efficiently apply... | 6945 |@word version:1 polynomial:3 norm:2 seems:1 nd:2 seek:1 crucially:1 attainable:4 sgd:1 thereby:1 moment:1 reduction:13 denoting:1 wd:1 must:9 readily:1 john:1 additive:2 enables:1 analytic:2 zaid:1 designed:2 update:5 joy:1 stationary:8 implying:1 intelligence:1 guess:1 xk:7 steepest:2 iterates:1 accessed:1 zhang... |
6,573 | 6,946 | Unsupervised Sequence Classification using
Sequential Output Statistics
Yu Liu ? , Jianshu Chen ? , and Li Deng?
?
Microsoft Research, Redmond, WA 98052, USA?
jianshuc@microsoft.com
?
Citadel LLC, Seattle/Chicago, USA?
Li.Deng@citadel.com
Abstract
We consider learning a sequence classifier without labeled data by usi... | 6946 |@word version:1 proportion:1 open:1 seek:2 simulation:1 evaluating:1 attainable:1 sgd:10 ytn:16 harder:1 liu:2 substitution:2 contains:1 unintended:1 document:2 interestingly:1 ours:1 current:4 com:4 comparing:4 diederik:1 dx:5 devin:1 chicago:1 additive:1 partition:2 plm:37 plot:1 designed:1 update:1 generative:... |
6,574 | 6,947 | Subset Selection under Noise
Chao Qian1
Jing-Cheng Shi2
Yang Yu2
Ke Tang3,1
Zhi-Hua Zhou2
1
Anhui Province Key Lab of Big Data Analysis and Application, USTC, China
2
National Key Lab for Novel Software Technology, Nanjing University, China
3
Shenzhen Key Lab of Computational Intelligence, SUSTech, China
chaoqian@ustc.... | 6947 |@word nkb:2 polynomial:4 nd:1 lakshmanan:1 contains:3 selecting:3 pub:1 outperforms:1 current:1 comparing:5 z2:3 com:1 si:5 must:2 additive:17 subsequent:1 realistic:2 kdd:3 plot:1 maxv:1 v:2 intelligence:1 greedy:47 selected:7 item:14 xk:1 node:6 firstly:2 mathematical:1 prove:7 behavioral:1 introduce:2 x0:23 th... |
6,575 | 6,948 | Collecting Telemetry Data Privately
Bolin Ding, Janardhan Kulkarni, Sergey Yekhanin
Microsoft Research
{bolind, jakul, yekhanin}@microsoft.com
Abstract
The collection and analysis of telemetry data from user?s devices is routinely
performed by many software companies. Telemetry collection leads to improved
user exper... | 6948 |@word private:31 version:6 eliminating:1 stronger:2 advantageous:1 nd:4 willing:1 memoize:3 invoking:1 pick:2 weekday:2 pihur:2 carry:1 bai:1 tuned:1 existing:2 current:1 com:1 discretization:7 protection:8 exposing:1 partition:1 j1:6 shape:1 korolova:2 update:2 maxv:1 device:4 smith:6 kairouz:1 defacto:1 provide... |
6,576 | 6,949 | Concrete Dropout
Yarin Gal
yarin.gal@eng.cam.ac.uk
University of Cambridge
and Alan Turing Institute, London
Jiri Hron
jh2084@cam.ac.uk
University of Cambridge
Alex Kendall
agk34@cam.ac.uk
University of Cambridge
Abstract
Dropout is used as a practical tool to obtain uncertainty estimates in large vision
models and... | 6949 |@word exploitation:3 middle:3 manageable:1 repository:2 seems:2 nd:1 unif:1 open:1 underperform:1 pieter:1 seek:1 simulation:1 eng:1 jacob:1 epistemic:23 reduction:2 initial:1 configuration:2 ndez:2 score:5 efficacy:1 liu:2 initialisation:3 tuned:8 daniel:1 interestingly:1 lichman:1 jimenez:1 rowan:1 existing:2 c... |
6,577 | 695 | Some Estimates of Necessary Number of
Connections and Hidden Units for
Feed-Forward Networks
Adam Kowalczyk
Telecom Australia, Research Laboratories
770 Blackburn Road, Clayton, Vic. 3168, Australia
(a.kowalczyk@trl.oz.au)
Abstract
The feed-forward networks with fixed hidden units (FllU-networks)
are compared against... | 695 |@word determinant:1 pw:1 rising:1 loading:2 norm:1 nd:1 stronger:2 achievable:2 open:4 hu:8 closure:1 q1:2 necessity:2 contains:2 past:1 activation:1 must:4 girosi:1 aside:1 fewer:1 device:2 selected:3 vanishing:2 short:1 constructed:1 differential:1 director:1 consists:1 manner:1 introduce:1 ra:2 decreasing:1 td:... |
6,578 | 6,950 | Adaptive Batch Size for Safe Policy Gradients
Matteo Papini
DEIB
Politecnico di Milano, Italy
Matteo Pirotta
SequeL Team
Inria Lille, France
Marcello Restelli
DEIB
Politecnico di Milano, Italy
matteo.papini@polimi.it
matteo.pirotta@inria.fr
marcello.restelli@polimi.it
Abstract
Policy gradient methods are among t... | 6950 |@word h:1 trial:1 version:4 norm:3 d2:1 pieter:1 simulation:4 paid:2 reduction:1 initial:7 series:1 existing:1 current:2 worsening:3 must:1 john:2 ronald:1 realistic:1 numerical:2 lqg:2 christian:1 motor:2 update:22 n0:2 overshooting:1 stationary:4 greedy:1 selected:1 intelligence:3 sehnke:1 ivo:1 parameterizatio... |
6,579 | 6,951 | A Disentangled Recognition and Nonlinear Dynamics
Model for Unsupervised Learning
Marco Fraccaro??
Simon Kamronn ??
Ulrich Paquet?
?
Technical University of Denmark
?
DeepMind
Ole Winther?
Abstract
This paper takes a step towards temporal reasoning in a dynamically changing video,
not in the pixel space that consti... | 6951 |@word cnn:2 middle:3 simulation:1 covariance:3 solid:1 recursively:1 initial:5 contains:1 series:2 outperforms:3 past:2 steiner:1 current:2 com:2 yet:1 flunkert:1 reminiscent:1 readily:1 devin:1 visible:1 realistic:1 haxby:1 opin:1 plot:3 interpretable:1 generative:11 intelligence:1 isard:1 parameterization:1 pla... |
6,580 | 6,952 | PASS-GLM: polynomial approximate sufficient
statistics for scalable Bayesian GLM inference
Jonathan H. Huggins
CSAIL, MIT
jhuggins@mit.edu
Ryan P. Adams
Google Brain and Princeton
rpa@princeton.edu
Tamara Broderick
CSAIL, MIT
tbroderick@csail.mit.edu
Abstract
Generalized linear models (GLMs)?such as logistic regres... | 6952 |@word faculty:1 version:3 manageable:1 polynomial:22 norm:2 logit:15 nd:5 unif:1 seek:1 covariance:1 sgd:14 thereby:2 ld:14 moment:2 series:2 efficacy:1 ours:1 document:1 outperforms:1 comparing:1 od:4 com:1 yet:1 must:2 numerical:1 partition:1 interpretable:1 update:1 v:1 intelligence:4 fewer:1 hamiltonian:1 cor... |
6,581 | 6,953 | Bayesian GAN
Yunus Saatchi
Uber AI Labs
Andrew Gordon Wilson
Cornell University
Abstract
Generative adversarial networks (GANs) can implicitly learn rich distributions over
images, audio, and data which are hard to model with an explicit likelihood. We
present a practical Bayesian formulation for unsupervised and se... | 6953 |@word briefly:2 nd:5 hu:2 rgb:2 pavel:1 sgd:3 mention:1 thereby:1 harder:1 accommodate:2 carry:1 reduction:1 contains:2 deconvolutional:1 outperforms:2 com:1 discretization:1 activation:2 reminiscent:2 must:1 import:1 gpu:3 written:1 shape:1 plot:1 interpretable:3 update:8 discrimination:4 v:1 generative:14 pursu... |
6,582 | 6,954 | Off-policy evaluation for slate recommendation
Adith Swaminathan
Microsoft Research, Redmond
adswamin@microsoft.com
Alekh Agarwal
Microsoft Research, New York
alekha@microsoft.com
Akshay Krishnamurthy
University of Massachusetts, Amherst
akshay@cs.umass.edu
Miroslav Dud?k
Microsoft Research, New York
mdudik@microsof... | 6954 |@word katja:1 trial:1 exploitation:1 middle:5 judgement:1 norm:2 stronger:1 suitably:1 open:1 additively:1 crucially:1 covariance:1 decomposition:1 pick:1 harder:1 liu:2 contains:2 uma:1 score:3 daniel:1 tuned:3 document:14 past:2 outperforms:3 err:15 horvitz:1 com:7 contextual:16 luo:1 si:10 chu:3 written:1 john... |
6,583 | 6,955 | A multi-agent reinforcement learning model of
common-pool resource appropriation
Julien Perolat?
DeepMind
London, UK
perolat@google.com
Charles Beattie
DeepMind
London, UK
cbeattie@google.com
Joel Z. Leibo?
DeepMind
London, UK
jzl@google.com
Karl Tuyls
University of Liverpool
Liverpool, UK
karltuyls@google.com
Vinici... | 6955 |@word katja:1 trial:5 exploitation:2 private:2 open:3 grey:1 hu:1 simulation:1 tat:2 eng:1 kent:1 pressure:1 profit:1 harder:1 initial:3 configuration:1 plentiful:1 score:2 necessity:1 united:1 prescriptive:1 daniel:1 interestingly:1 rightmost:1 past:1 existing:1 atlantic:1 current:1 com:6 nt:1 outperforms:1 scat... |
6,584 | 6,956 | On the Optimization Landscape of Tensor
Decompositions
Rong Ge
Duke University
rongge@cs.duke.edu
Tengyu Ma
Facebook AI Research
tengyuma@cs.stanford.edu
Abstract
Non-convex optimization with local search heuristics has been widely used in
machine learning, achieving many state-of-art results. It becomes increasingl... | 6956 |@word determinant:3 version:3 polynomial:11 norm:3 stronger:1 nd:2 seems:1 open:4 d2:6 bn:1 decomposition:15 pg:10 sgd:1 tr:6 sepulchre:1 arous:3 moment:2 liu:1 contains:8 daniel:4 existing:1 current:1 intriguing:1 written:1 dx:2 john:1 hmr16:2 interpretable:1 v:1 guess:6 xk:1 podoprikhin:1 vanishing:1 characteri... |
6,585 | 6,957 | High-Order Attention Models for Visual Question
Answering
Idan Schwartz
Department of Computer Science
Technion
idansc@cs.technion.ac.il
Alexander G. Schwing
Department of Electrical and Computer Engineering
University of Illinois at Urbana-Champaign
aschwing@illinois.edu
Tamir Hazan
Department of Industrial Enginee... | 6957 |@word cnn:1 armand:1 polynomial:1 seems:1 nd:1 d2:1 jacob:2 q1:1 attended:13 concise:2 series:2 hoiem:1 document:1 ours:4 past:1 existing:2 com:2 contextual:1 gmail:1 yet:3 gpu:1 concatenate:1 ronan:1 enables:1 rd2:1 device:4 nq:10 item:1 kyoung:1 short:2 num:1 provides:3 location:1 zhang:3 along:2 c2:5 direct:1 ... |
6,586 | 6,958 | Sparse convolutional coding for neuronal assembly
detection
Sven Peter1,?
Elke Kirschbaum1,?
{sven.peter,elke.kirschbaum}@iwr.uni-heidelberg.de
Martin Both2
mboth@physiologie.uni-heidelberg.de
Brandon K. Harvey3
bharvey@mail.nih.gov
Lee A. Campbell3
lee.campbell@nih.gov
Conor Heins3,4,?
conor.heins@ds.mpg.de
Daniel D... | 6958 |@word neurophysiology:2 trial:3 repository:1 norm:5 approved:1 hippocampus:1 open:1 barahona:1 grey:1 decomposition:1 recursively:1 electronics:1 initial:2 contains:2 series:2 daniel:2 outperforms:3 past:1 existing:1 current:4 com:2 subcomponents:1 virus:1 activation:11 si:17 yet:1 bello:1 underly:1 additive:2 nu... |
6,587 | 6,959 | Quantifying how much sensory information in a
neural code is relevant for behavior
Giuseppe Pica1,2
giuseppe.pica@iit.it
Houman Safaai1,3
houman_safaai@hms.harvard.edu
Tommaso Fellin2,6
tommaso.fellin@iit.it
Eugenio Piasini1
eugenio.piasini@iit.it
Caroline A. Runyan3,4
runyan@pitt.edu
Christoph Kayser7,8
christoph.ka... | 6959 |@word h:1 trial:28 illustrating:1 faculty:1 seems:1 coarseness:1 proportion:2 seal:1 simulation:4 decomposition:9 jacob:1 harder:1 carry:11 liu:1 contains:1 interestingly:1 existing:2 comparing:2 si:14 yet:2 must:1 saal:1 additive:1 numerical:1 informative:2 alam:1 shape:1 enables:2 motor:4 opin:1 cracking:1 disc... |
6,588 | 696 | Rational Parametrizations of Neural
Networks
Uwe Helmke
Department of Mathematics
University of Regensburg
Regensburg 8400 Germany
Robert C. Williamson
Department of Systems Engineering
Australian National University
Canberra 2601 Australia
Abstract
A connection is drawn between rational functions, the realization
th... | 696 |@word aircraft:1 cu:4 version:2 stronger:1 open:2 q1:2 electronics:1 substitution:1 contains:2 series:1 z2:1 activation:2 b01:1 attracted:1 written:1 john:1 analytic:8 plane:3 parametrization:3 lr:5 cheney:1 sigmoidal:3 mathematical:2 c2:2 ik:9 company:2 becomes:1 what:1 kind:1 textbook:1 q2:1 transformation:1 edu... |
6,589 | 6,960 | Geometric Matrix Completion with Recurrent
Multi-Graph Neural Networks
Federico Monti
Universit? della Svizzera italiana
Lugano, Switzerland
federico.monti@usi.ch
Michael M. Bronstein
Universit? della Svizzera italiana
Lugano, Switzerland
michael.bronstein@usi.ch
Xavier Bresson
School of Computer Science and Enginee... | 6960 |@word cnn:13 faculty:1 version:2 polynomial:10 norm:4 hu:1 propagate:1 yahoomusic:5 incurs:3 solid:1 reduction:2 initial:1 configuration:1 liu:1 score:10 offering:1 document:1 rightmost:2 outperforms:3 wd:1 com:1 yet:1 dx:4 j1:1 kdd:2 shape:4 moreno:1 progressively:2 update:4 depict:1 stationary:4 intelligence:1 ... |
6,590 | 6,961 | Reducing Reparameterization Gradient Variance
Andrew C. Miller?
Harvard University
acm@seas.harvard.edu
Nicholas J. Foti
University of Washington
nfoti@uw.edu
Alexander D?Amour
UC Berkeley
alexdamour@berkeley.edu
Ryan P. Adams
Google Brain and Princeton University
rpa@princeton.edu
Abstract
Optimization with noisy... | 6961 |@word version:1 norm:4 nd:1 seek:2 crucially:1 covariance:2 dramatic:1 sgd:1 solid:2 reduction:20 initial:1 score:20 efficacy:1 jimenez:1 ours:1 existing:1 com:3 yet:1 diederik:2 must:3 written:1 john:2 devin:1 numerical:1 cheap:4 remove:1 plot:1 update:1 generative:6 beginning:1 blei:7 caveat:1 iterates:3 locati... |
6,591 | 6,962 | Visual Reference Resolution using Attention Memory
for Visual Dialog
Paul Hongsuck Seo?
Andreas Lehrmann?
Bohyung Han?
Leonid Sigal?
?
?
POSTECH
Disney Research
{hsseo, bhhan}@postech.ac.kr {andreas.lehrmann, lsigal}@disneyresearch.com
Abstract
Visual dialog is a task of answering a series of inter-dependent question... | 6962 |@word cnn:3 middle:2 version:3 stronger:1 seems:1 hu:1 attended:2 mention:1 initial:3 series:3 att:10 score:1 contains:4 document:1 past:2 outperforms:2 existing:1 current:23 com:1 yet:1 fn:3 subsequent:1 designed:3 drop:1 update:1 hash:1 alone:1 v:1 fewer:3 selected:2 generative:1 sukhbaatar:1 accordingly:1 reci... |
6,592 | 6,963 | Joint distribution optimal transportation for domain
adaptation
Nicolas Courty?
Universit? de Bretagne Sud,
IRISA, UMR 6074, CNRS,
courty@univ-ubs.fr
R?mi Flamary?
Universit? C?te d?Azur,
Lagrange, UMR 7293 , CNRS, OCA
remi.flamary@unice.fr
Amaury Habrard
Univ Lyon, UJM-Saint-Etienne, CNRS,
Lab. Hubert Curien UMR 55... | 6963 |@word kulis:1 illustrating:1 middle:2 version:7 compression:1 norm:4 briefly:1 proportion:1 c0:1 bigram:1 open:1 villani:1 km:1 seek:1 propagate:1 accommodate:1 electronics:7 liu:1 contains:3 score:1 salzmann:1 rkhs:3 interestingly:2 past:1 existing:1 luigi:1 com:1 nt:6 activation:2 yet:1 tackling:1 must:2 si:1 n... |
6,593 | 6,964 | Multiresolution Kernel Approximation for
Gaussian Process Regression
Yi Ding? , Risi Kondor?? , Jonathan Eskreis-Winkler?
Department of Computer Science, ? Department of Statistics
The University of Chicago, Chicago, IL, 60637
{dingy,risi,eskreiswinkler}@uchicago.edu
?
Abstract
Gaussian process regression generally ... | 6964 |@word kulis:1 determinant:3 version:2 kondor:3 compression:19 norm:1 polynomial:1 d2:1 simulation:1 covariance:2 hsieh:1 decomposition:3 abou:1 q1:9 pick:1 evaluating:1 nystr:20 solid:1 recursively:1 efficacy:1 woodruff:1 offering:1 reinvented:1 outperforms:1 existing:1 si:2 yet:1 forbidding:1 must:3 bd:1 john:1 ... |
6,594 | 6,965 | Collapsed variational Bayes for Markov jump
processes
Jiangwei Pan??
Department of Computer Science
Duke University
panjiangwei@gmail.com
Boqian Zhang?
Department of Statistics
Purdue University
zhan1977@purdue.edu
Vinayak Rao
Department of Statistics
Purdue University
varao@purdue.edu
Abstract
Markov jump processe... | 6965 |@word middle:6 nd:1 mjp:34 calculus:1 simulation:2 splitmerge:1 thereby:1 carry:1 initial:3 liu:1 series:2 united:1 ours:1 reaction:1 current:2 com:1 discretization:11 nt:4 gmail:1 partition:1 plot:1 update:4 v:1 generative:2 greedy:2 website:1 half:2 fewer:1 intelligence:1 ith:1 short:2 record:1 provides:1 locat... |
6,595 | 6,966 | Universal consistency and minimax rates for online
Mondrian Forests
Jaouad Mourtada
Centre de Math?matiques Appliqu?es
?cole Polytechnique, Palaiseau, France
jaouad.mourtada@polytechnique.edu
St?phane Ga?ffas
Centre de Math?matiques Appliqu?es
?cole Polytechnique,Palaiseau, France
st?phane.gaiffas@polytechnique.edu
E... | 6966 |@word version:3 proportion:1 nd:1 c0:2 bn:4 simplifying:1 jacob:1 recursively:2 carry:1 reduction:1 contains:4 score:1 daniel:6 tuned:3 ecole:1 dubourg:1 past:1 outperforms:2 freitas:2 surprising:1 realistic:1 partition:36 informative:2 enables:1 christian:1 designed:1 update:5 discrimination:1 alone:1 intelligen... |
6,596 | 6,967 | Welfare Guarantees from Data
Darrell Hoy
University of Maryland
darrell.hoy@gmail.com
Denis Nekipelov
University of Virginia
denis@virginia.edu
Vasilis Syrgkanis
Microsoft Research
vasy@microsoft.com
Abstract
Analysis of efficiency of outcomes in game theoretic settings has been a main item
of study at the intersect... | 6967 |@word private:15 version:1 inversion:3 achievable:1 polynomial:2 vi1:1 calculus:1 seek:1 bn:3 invoking:3 thereby:2 inefficiency:8 score:9 existing:1 com:2 si:5 gmail:1 assigning:1 must:1 refines:1 happen:1 benign:1 eleven:3 sponsored:6 v:1 item:9 characterization:1 provides:2 denis:2 simpler:1 unbounded:1 along:1... |
6,597 | 6,968 | Diving into the shallows: a computational perspective
on large-scale shallow learning
Siyuan Ma
Mikhail Belkin
Department of Computer Science and Engineering
The Ohio State University
{masi, mbelkin}@cse.ohio-state.edu
Abstract
Remarkable recent success of deep neural networks has not been easy to analyze
theoretical... | 6968 |@word version:3 manageable:1 polynomial:8 norm:7 seems:2 nd:1 km:1 covariance:10 hsieh:1 decomposition:2 incurs:1 sgd:20 nystr:2 reduction:2 initial:2 liu:3 series:3 daniel:2 woodruff:1 rkhs:8 interestingly:1 outperforms:1 com:1 naman:1 yet:1 diederik:1 reminiscent:1 must:4 gpu:9 written:3 readily:1 periodically:... |
6,598 | 6,969 | End-to-End Differentiable Proving
Tim Rockt?schel
University of Oxford
tim.rocktaschel@cs.ox.ac.uk
Sebastian Riedel
University College London & Bloomsbury AI
s.riedel@cs.ucl.ac.uk
Abstract
We introduce neural networks for end-to-end differentiable proving of queries to
knowledge bases by operating on dense vector rep... | 6969 |@word armand:1 briefly:1 stronger:1 nd:7 open:1 hu:1 seek:1 pratim:2 jacob:1 decomposition:1 evaluating:1 thereby:4 yih:2 recursively:5 carry:1 initial:1 cyclic:1 qatar:2 score:22 united:2 liu:1 daniel:1 substitution:20 contains:4 document:1 past:1 freitas:2 steiner:1 recovered:1 current:1 comparing:1 outperforms... |
6,599 | 697 | Analog Cochlear Model for Multiresolution
Speech Analysis
Weimin
Liu~
Andreas G. Andreou and Moise H. Goldstein, Jr.
Department of Electrical and Computer Engineering
The Johns Hopkins University, Baltimore, Maryland 21218 USA
Abstract
This paper discusses the parameterization of speech by an analog cochlear
model.... | 697 |@word illustrating:1 middle:1 compression:1 advantageous:1 simulation:6 searle:2 liu:6 pub:1 seriously:1 tuned:3 yet:1 john:2 subsequent:2 additive:1 realistic:2 j1:1 plot:1 stationary:1 pursued:1 parameterization:2 tone:11 plane:1 smith:2 short:3 dissertation:2 location:1 ipi:9 direct:1 become:2 ik:1 consists:3 a... |
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