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6,300 | 67 | 602
GENERALIZATION OF BACKPROPAGATION
TO
RECURRENT AND HIGHER ORDER NEURAL NETWORKS
Fernando J. Pineda
Applied Physics Laboratory, Johns Hopkins University
Johns Hopkins Rd., Laurel MD 20707
Abstract
A general method for deriving backpropagation algorithms for networks
with recurrent and higher order networks is intr... | 67 |@word version:1 inversion:2 polynomial:1 tedious:1 r:1 linearized:1 simulation:3 fmite:1 fonn:5 jacqueline:1 moment:1 initial:8 contains:1 lapedes:4 activation:2 dx:3 must:5 luis:1 john:3 numerical:2 visible:5 update:9 stationary:1 tenn:1 alone:1 yr:3 liapunov:2 xk:2 ji2:1 draft:1 location:1 ofbackpropagation:1 mat... |
6,301 | 670 | A Model of Feedback to the Lateral
Geniculate Nucleus
Carlos D. Brody
Computation and Neural Systems Program
California Institute of Technology
Pasadena, CA 91125
Abstract
Simplified models of the lateral geniculate nucles (LGN) and striate cortex illustrate the possibility that feedback to the LG N may
be used for ro... | 670 |@word illustrating:1 briefly:1 wiesel:1 cha:1 grey:1 simulation:2 fortuitous:1 phy:1 contains:1 tuned:7 rightmost:2 activation:2 atop:1 must:2 physiol:1 alone:1 isotropic:1 short:3 farther:2 compo:1 correlat:1 detecting:7 provides:1 location:1 preference:1 along:1 direct:2 pathway:5 combine:1 brain:4 unfolded:1 ac... |
6,302 | 6,700 | SchNet: A continuous-filter convolutional neural
network for modeling quantum interactions
?
K. T. Sch?tt1?, P.-J. Kindermans1 , H. E. Sauceda2 , S. Chmiela1
A. Tkatchenko3 , K.-R. M?ller1,4,5?
1
Machine Learning Group, Technische Universit?t Berlin, Germany
2
Theory Department, Fritz-Haber-Institut der Max-Planck-Ge... | 6700 |@word middle:2 briefly:1 kondor:2 nd:1 simulation:3 sgd:1 initial:2 configuration:1 contains:2 series:3 outperforms:2 o2:3 current:1 activation:2 fn:1 shape:1 enables:1 designed:4 update:3 bart:2 alone:1 generative:1 fewer:1 leaf:1 beginning:1 hamiltonian:1 vanishing:1 short:2 provides:1 location:2 org:1 zhang:1 ... |
6,303 | 6,701 | Active Bias: Training More Accurate Neural
Networks by Emphasizing High Variance Samples
Haw-Shiuan Chang, Erik Learned-Miller, Andrew McCallum
University of Massachusetts, Amherst
140 Governors Dr., Amherst, MA 01003
{hschang,elm,mccallum}@cs.umass.edu
Abstract
Self-paced learning and hard example mining re-weight t... | 6701 |@word trial:5 exploitation:1 version:3 cnn:16 nd:2 tried:2 bn:1 sgd:104 harder:5 reduction:6 initial:2 liu:2 lightweight:3 uma:1 selecting:6 score:1 seriously:1 document:1 fa8750:1 existing:2 freitas:1 current:4 wd:9 nt:3 com:6 informative:2 shape:1 drop:2 sponsored:1 moczulski:1 selected:3 mccallum:5 beginning:3... |
6,304 | 6,702 | Differentiable Learning of Submodular Models
Andreas Krause
Department of Computer Science
ETH Zurich
krausea@ethz.ch
Josip Djolonga
Department of Computer Science
ETH Zurich
josipd@inf.ethz.ch
Abstract
Can we incorporate discrete optimization algorithms within modern machine learning models? For example, is it poss... | 6702 |@word kohli:1 cnn:6 polynomial:5 seems:4 norm:26 open:3 checkable:1 seek:1 rgb:2 pick:1 sgd:1 kijima:1 reduction:1 configuration:3 series:1 score:4 com:1 intriguing:1 must:1 subsequent:1 partition:5 hofmann:1 remove:1 dolhansky:2 fewer:2 selected:1 intelligence:3 xk:1 parametrization:1 certificate:1 characterizat... |
6,305 | 6,703 | Inductive Representation Learning on Large Graphs
William L. Hamilton?
wleif@stanford.edu
Rex Ying?
rexying@stanford.edu
Jure Leskovec
jure@cs.stanford.edu
Department of Computer Science
Stanford University
Stanford, CA, 94305
Abstract
Low-dimensional embeddings of nodes in large graphs have proved extremely
usefu... | 6703 |@word trial:2 repository:1 version:4 proportion:1 tamayo:1 propagate:1 kutzkov:1 sgd:2 reduction:1 contains:6 score:5 daniel:1 document:1 interestingly:1 dubourg:1 outperforms:7 existing:2 current:3 comparing:2 activation:2 si:2 must:2 devin:1 concatenate:1 kdd:4 designed:4 hash:2 alone:1 v:1 advancement:2 concat... |
6,306 | 6,704 | Subset Selection and Summarization
in Sequential Data
Ehsan Elhamifar
Computer and Information Science College
Northeastern University
Boston, MA 02115
eelhami@ccs.neu.edu
M. Clara De Paolis Kaluza
Computer and Information Science College
Northeastern University
Boston, MA 02115
clara@ccs.neu.edu
Abstract
Subset sele... | 6704 |@word polynomial:1 compression:5 duda:1 open:1 instruction:1 seitz:1 decomposition:1 elisseeff:1 pick:1 reduction:1 initial:1 series:4 score:20 selecting:13 loeliger:1 denoting:3 document:6 outperforms:2 existing:3 disaggregation:1 clara:2 assigning:1 chu:1 written:1 determinantal:5 partition:1 informative:1 remo... |
6,307 | 6,705 | Question Asking as Program Generation
Anselm Rothe1
anselm@nyu.edu
1
Brenden M. Lake1,2
brenden@nyu.edu
Todd M. Gureckis1
todd.gureckis@nyu.edu
2
Department of Psychology
Center for Data Science
New York University
Abstract
A hallmark of human intelligence is the ability to ask rich, creative, and revealing
questi... | 6705 |@word polynomial:1 laurence:1 open:4 instruction:1 calculus:1 concise:2 tmg:1 recursively:1 reduction:1 configuration:5 contains:2 score:1 bootstrapped:1 prefix:1 past:1 current:5 com:1 si:7 yet:1 must:2 john:4 numerical:1 partition:2 informative:4 shape:1 wanted:1 remove:1 designed:3 interpretable:1 update:1 dro... |
6,308 | 6,706 | Revisiting Perceptron:
Ef?cient and Label-Optimal Learning of Halfspaces
Songbai Yan
UC San Diego
La Jolla, CA
yansongbai@ucsd.edu
Chicheng Zhang?
Microsoft Research
New York, NY
chicheng.zhang@microsoft.com
Abstract
It has been a long-standing problem to ef?ciently learn a halfspace using as few
labels as possible i... | 6706 |@word polynomial:10 nd:4 dekel:1 open:4 d2:14 hu:7 km:3 prasad:1 arti:2 harder:1 initial:10 liu:2 series:1 chervonenkis:1 daniel:2 ours:2 existing:1 err:13 current:1 com:1 kmk:1 beygelzimer:3 dx:9 must:1 john:5 informative:1 hongyang:3 cheap:1 drop:1 atlas:1 update:3 intelligence:2 selected:1 half:1 isotropic:1 b... |
6,309 | 6,707 | Gradient Descent Can Take Exponential Time to
Escape Saddle Points
Chi Jin
University of California, Berkeley
chijin@berkeley.edu
Simon S. Du
Carnegie Mellon University
ssdu@cs.cmu.edu
Jason D. Lee
University of Southern California
jasonlee@marshall.usc.edu
Michael I. Jordan
University of California, Berkeley
jordan@... | 6707 |@word version:3 polynomial:20 stronger:1 norm:2 nd:8 open:2 unif:1 willing:1 simulation:1 decomposition:2 covariance:2 arti:2 thereby:2 necessity:2 liu:1 daniel:1 xinyang:1 fa8750:1 past:1 existing:1 current:2 luo:2 naman:1 written:2 must:2 readily:1 john:5 analytic:1 plot:2 update:2 stationary:20 intelligence:2 ... |
6,310 | 6,708 | Union of Intersections (UoI) for Interpretable Data
Driven Discovery and Prediction
Kristofer E. Bouchard?
Alejandro F. Bujan?
Shashanka Ubaru?
Prabhat?
Edward F. Chang??
Michael W. Mahoney?
Farbod Roosta-Khorasani?
Antoine M. Snijdersk
Jian-Hua Maok
Sharmodeep Bhattacharyya??
Abstract
The increasing size an... | 6708 |@word trial:1 version:1 compression:7 norm:2 grey:1 seek:1 decomposition:6 thereby:1 solid:1 initial:1 series:1 genetic:3 bootstrapped:1 interestingly:1 bhattacharyya:2 existing:2 ka:1 com:1 gmail:1 scatter:1 numerical:3 plasticity:1 motor:1 plot:4 interpretable:5 resampling:5 v:3 generative:1 selected:4 fewer:2 ... |
6,311 | 6,709 | One-Shot Imitation Learning
Yan Duan?? , Marcin Andrychowicz? , Bradly Stadie?? , Jonathan Ho?? ,
Jonas Schneider? , Ilya Sutskever? , Pieter Abbeel?? , Wojciech Zaremba?
?
Berkeley AI Research Lab, ? OpenAI
?
Work done while at OpenAI
{rockyduan, jonathanho, pabbeel}@eecs.berkeley.edu
{marcin, bstadie, jonas, ilyasu,... | 6709 |@word trial:2 kulis:1 seems:2 proportion:1 replicate:1 open:1 pieter:10 simulation:1 propagate:1 pick:3 versatile:1 shot:16 reduction:1 initial:3 liu:1 configuration:8 contains:1 jimenez:2 bc:3 o2:1 freitas:1 past:2 current:17 com:1 yuxuan:1 cad:1 activation:2 yet:1 diederik:1 must:2 guez:1 readily:2 john:3 devin... |
6,312 | 671 | An Information-Theoretic Approach to
Deciphering the Hippocampal Code
William E. Skaggs
Bruce L. McNaughton
Katalin M. Gothard
Etan J. Markus
Center for Neural Systems, Memory, and Aging
344 Life Sciences North
University of Arizona
Tucson AZ 85724
bill@nsma.arizona.edu
Abstract
Information theory is used to deriv... | 671 |@word trial:2 briefly:1 hippocampus:3 seems:1 methodologically:1 pick:1 thereby:1 moment:1 configuration:1 series:1 interestingly:1 optican:2 current:1 nt:1 dx:2 must:2 distant:1 shape:3 plot:5 designed:2 drop:2 stationary:1 half:6 alone:1 cue:24 short:4 farther:1 provides:1 location:27 mathematical:1 constructed:... |
6,313 | 6,710 | Learning the Morphology of Brain Signals Using
Alpha-Stable Convolutional Sparse Coding
Mainak Jas1 , Tom Dupr? La Tour1 , Umut Sim?
? sekli1 , Alexandre Gramfort1,2
1: LTCI, Telecom ParisTech, Universit? Paris-Saclay, Paris, France
2: INRIA, Universit? Paris-Saclay, Saclay, France
Abstract
Neural time-series data co... | 6710 |@word mild:1 trial:17 version:1 inversion:1 middle:1 norm:5 simulation:2 decomposition:2 contraction:1 eng:2 thereby:1 moment:1 liu:1 series:8 contains:1 document:2 deconvolutional:1 existing:1 current:2 recovered:1 activation:13 yet:3 written:2 must:1 csc:68 realistic:1 additive:3 plasticity:1 shape:3 mstep:1 mo... |
6,314 | 6,711 | Integration Methods and Optimization Algorithms
Vincent Roulet
INRIA, ENS,
PSL Research University,
Paris France
vincent.roulet@inria.fr
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
Alexandr... | 6711 |@word version:1 polynomial:11 norm:1 disk:2 seek:3 simulation:1 tat:1 concise:1 initial:5 contains:1 loc:5 interestingly:1 past:1 kx0:1 discretization:4 comparing:1 written:3 must:1 numerical:15 designed:1 xk:54 oldest:1 beginning:1 recherche:1 provides:1 iterates:1 simpler:1 mathematical:2 c2:2 differential:11 i... |
6,315 | 6,712 | Sharpness, Restart and Acceleration
Vincent Roulet
INRIA, ENS
Paris France
vincent.roulet@inria.fr
Alexandre d?Aspremont
CNRS, ENS
Paris France
aspremon@ens.fr
Abstract
The ?ojasiewicz inequality shows that sharpness bounds on the minimum of convex
optimization problems hold almost generically. Sharpness directly co... | 6712 |@word repository:1 dtk:1 version:1 polynomial:2 norm:2 open:2 termination:2 seek:1 crucially:1 linearized:1 recursively:1 initial:4 juditski:3 si:5 bierstone:2 must:1 numerical:1 analytic:2 plot:1 une:1 xk:15 ojasiewicz:11 recherche:1 iterates:1 math:1 revisited:1 rc:1 along:1 mathematical:7 differential:1 initia... |
6,316 | 6,713 | Learning Koopman Invariant Subspaces
for Dynamic Mode Decomposition
Naoya Takeishi? , Yoshinobu Kawahara?,? , Takehisa Yairi?
Department of Aeronautics and Astronautics, The University of Tokyo
?
The Institute of Scientific and Industrial Research, Osaka University
?
RIKEN Center for Advanced Intelligence Project
{tak... | 6713 |@word polynomial:3 norm:1 nd:3 casdagli:1 vogt:1 open:2 d2:1 r:17 decomposition:31 excited:1 sgd:6 mention:1 reduction:1 initial:3 liu:1 series:24 united:2 tuned:1 rightmost:2 past:1 existing:2 current:1 yairi:3 activation:3 must:6 numerical:8 plot:10 interpretable:1 update:3 mackey:1 intelligence:2 selected:1 ge... |
6,317 | 6,714 | Soft-to-Hard Vector Quantization for End-to-End
Learning Compressible Representations
Eirikur Agustsson
ETH Zurich
Fabian Mentzer
ETH Zurich
Michael Tschannen
ETH Zurich
aeirikur@vision.ee.ethz.ch
mentzerf@vision.ee.ethz.ch
michaelt@nari.ee.ethz.ch
Lukas Cavigelli
ETH Zurich
Radu Timofte
ETH Zurich & Merantix
... | 6714 |@word pw:4 compression:55 replicate:1 retraining:1 open:1 simplifying:1 q1:1 sgd:2 thereby:1 solid:1 cleary:1 harder:1 initial:1 electronics:1 contains:1 daniel:2 denoting:1 document:2 ours:6 kurt:1 outperforms:1 current:2 laparra:2 discretization:2 activation:1 assigning:1 diederik:1 guez:1 attracted:1 john:3 re... |
6,318 | 6,715 | Learning spatiotemporal piecewise-geodesic
trajectories from longitudinal manifold-valued data
Juliette Chevallier
CMAP, ?cole polytechnique
juliette.chevallier@polytechnique.edu
Pr St?phane Oudard
Oncology Department
USPC, AP-HP, HEGP
St?phanie Allassonni?re
CRC, Universit? Paris Descartes
stephanie.allassonniere@p... | 6715 |@word trial:2 version:5 seems:3 nd:2 open:2 iki:1 simulation:2 dominique:1 covariance:2 t1r:3 tr:33 solid:2 moment:1 initial:5 series:1 score:13 oldenburg:2 initialisation:2 longitudinal:7 past:1 yet:1 must:3 written:1 interrupted:1 numerical:3 additive:1 j1:15 realistic:1 shape:1 periodically:1 enables:1 asympto... |
6,319 | 6,716 | Improving Regret Bounds for Combinatorial
Semi-Bandits with Probabilistically Triggered Arms
and Its Applications
Qinshi Wang
Princeton University
Princeton, NJ 08544
qinshiw@princeton.edu
Wei Chen
Microsoft Research
Beijing, China
weic@microsoft.com
Abstract
We study combinatorial multi-armed bandit with probabilis... | 6716 |@word exploitation:2 briefly:2 version:6 eliminating:1 norm:14 stronger:2 hu:1 r:10 decomposition:1 lakshmanan:3 harder:2 liu:1 selecting:2 ours:1 prefix:1 past:1 existing:2 yajun:1 current:3 com:1 comparing:6 michal:1 si:5 conjunctive:7 maniu:1 happen:1 kdd:2 remove:6 update:1 greedy:2 selected:4 leaf:1 item:2 i... |
6,320 | 6,717 | Predictive-State Decoders:
Encoding the Future into Recurrent Networks
Arun Venkatraman1? , Nicholas Rhinehart1?, Wen Sun1 ,
Lerrel Pinto , Martial Hebert1 , Byron Boots2 , Kris M. Kitani1 , J. Andrew Bagnell1
1
The Robotics Institute, Carnegie-Mellon University, Pittsburgh, PA
2
School of Interactive Computing, Georg... | 6717 |@word multitask:1 instrumental:2 kokkinos:1 nd:1 bptt:4 open:1 pieter:9 seek:1 propagate:1 simulation:1 r:1 psim:1 recursively:1 reduction:2 moment:4 configuration:1 series:3 daniel:2 lqr:1 precluding:1 rkhs:1 past:3 existing:4 current:5 yet:1 liva:1 must:1 john:4 devin:1 supervises:1 analytic:2 enables:1 hypothe... |
6,321 | 6,718 | Optimistic posterior sampling for reinforcement
learning: worst-case regret bounds
Shipra Agrawal
Columbia University
sa3305@columbia.edu
Randy Jia
Columbia University
rqj2000@columbia.edu
Abstract
We present an algorithm based on posterior sampling (aka Thompson sampling)
that achieves near-optimal worst-case regre... | 6718 |@word h:1 exploitation:2 version:5 polynomial:1 stronger:2 nd:1 open:1 r:8 q1:1 pick:1 initial:3 liu:2 daniel:2 denoting:1 past:1 current:1 contextual:2 john:3 ronald:1 v:2 stationary:1 intelligence:2 beginning:5 ith:1 provides:2 unbounded:1 ucrl2:5 along:2 constructed:1 mathematical:3 katehakis:2 symposium:1 apo... |
6,322 | 6,719 | Mean teachers are better role models:
Weight-averaged consistency targets improve
semi-supervised deep learning results
Antti Tarvainen
The Curious AI Company
tarvaina@cai.fi
Harri Valpola
The Curious AI Company
harri@cai.fi
Abstract
The recently proposed Temporal Ensembling has achieved state-of-the-art results in
s... | 6719 |@word cnn:4 middle:2 version:9 compression:3 seems:1 hu:1 rgb:1 bachman:2 sajjadi:1 accommodate:1 initial:1 liu:1 contains:1 uncovered:1 hoiem:1 daniel:1 ours:1 steiner:1 current:2 optim:1 activation:1 diederik:2 devin:1 additive:1 periodically:1 realistic:1 ronan:1 enables:1 wanted:1 christian:1 drop:1 update:1 ... |
6,323 | 672 | Object-Based Analog VLSI Vision
Circuits
Christof Koch
Computation and Neural Systems
California Institute of Technology
Pasadena, CA
Bimal Mathur, Shih-Chii Liu
Rockwell International Science Center
Thousand Oaks, CA
John G. Harris
MIT Artificial Intelligence Laboratory
Cambridge, MA
Jin Luo, Massimo Sivilotti
Tann... | 672 |@word seal:3 open:3 thereby:1 liu:7 contains:1 series:3 current:7 luo:5 yet:1 must:1 john:1 plot:2 v:1 intelligence:1 half:1 selected:1 compo:1 provides:2 node:8 location:2 oak:1 five:3 along:4 supply:1 consists:1 resistive:4 inside:8 behavior:1 frequently:1 globally:1 automatically:1 little:1 becomes:3 circuit:12... |
6,324 | 6,720 | Matching neural paths: transfer from recognition to
correspondence search
Nikolay Savinov1
1
Lubor Ladicky1
Marc Pollefeys1,2
Department of Computer Science at ETH Zurich, 2 Microsoft
{nikolay.savinov,lubor.ladicky,marc.pollefeys}@inf.ethz.ch
Abstract
Many machine learning tasks require finding per-part correspon... | 6720 |@word cnn:7 version:1 repository:1 polynomial:3 open:1 choy:1 p0:4 dramatic:1 mention:1 initial:1 contains:2 score:3 tuned:1 ours:15 prefix:3 rightmost:2 existing:1 com:1 activation:21 must:1 written:1 john:1 timestamps:2 visible:1 generative:1 colored:1 recompute:1 completeness:3 node:9 provides:1 ron:1 simpler:... |
6,325 | 6,721 | Linearly constrained Gaussian processes
Carl Jidling
Department of Information Technology
Uppsala University, Sweden
carl.jidling@it.uu.se
Niklas Wahlstr?m
Department of Information Technology
Uppsala University, Sweden
niklas.wahlstrom@it.uu.se
Adrian Wills
School of Engineering
University of Newcastle, Australia
a... | 6721 |@word illustrating:2 version:2 norm:1 adrian:2 simulation:1 covariance:37 pick:1 contains:1 efficacy:1 salzmann:1 discretization:1 erms:3 ida:1 intriguing:1 written:5 must:3 john:2 numerical:4 shape:2 remove:2 plot:2 intelligence:2 selected:1 inspection:1 xk:6 short:1 provides:1 math:2 revisited:1 uppsala:3 gx:36... |
6,326 | 6,722 | Fixed-Rank Approximation of a
Positive-Semidefinite Matrix from Streaming Data
Joel A. Tropp
Caltech
Alp Yurtsever
EPFL
Madeleine Udell
Cornell
Volkan Cevher
EPFL
jtropp@caltech.edu alp.yurtsever@epfl.ch mru8@cornell.edu volkan.cevher@epfl.ch
Abstract
Several important applications, such as streaming PCA and semid... | 6722 |@word trial:2 version:2 polynomial:2 norm:17 hu:1 confirms:1 covariance:4 decomposition:6 mention:1 nystr:33 solid:2 reduction:2 substitution:1 series:4 contains:3 selecting:1 woodruff:8 document:1 pprox:1 fa8750:1 existing:1 ketch:4 ka:15 chazelle:1 com:1 dx:1 must:5 fn:29 numerical:11 informative:2 remove:2 des... |
6,327 | 6,723 | Multi-Modal Imitation Learning from Unstructured
Demonstrations using Generative Adversarial Nets
Karol Hausman?? , Yevgen Chebotar??? , Stefan Schaal?? , Gaurav Sukhatme? , Joseph J. Lim?
?
University of Southern California, Los Angeles, CA, USA
Max-Planck-Institute for Intelligent Systems, T?bingen, Germany
{hausma... | 6723 |@word multitask:1 c0:2 adrian:1 instruction:1 pieter:7 cha:1 simulation:2 seek:1 covariance:1 p0:4 shot:1 reduction:1 initial:6 series:1 daniel:1 ours:1 hyunsoo:1 com:2 readily:1 john:2 enables:1 christian:1 interpretable:2 update:1 generative:11 leaf:1 imitate:16 imitated:1 accordingly:2 beginning:1 blei:1 detec... |
6,328 | 6,724 | Learning to Inpaint for Image Compression
Mohammad Haris Baig?
Department of Computer Science
Dartmouth College
Hanover, NH
Vladlen Koltun
Intel Labs
Santa Clara, CA
Lorenzo Torresani
Dartmouth College
Hanover, NH
Abstract
We study the design of deep architectures for lossy image compression. We present
two archite... | 6724 |@word cnn:1 middle:1 compression:42 loading:1 kokkinos:1 r:16 propagate:3 thereby:2 inpainting:69 harder:3 reduction:6 initial:3 contains:1 rippel:2 deconvolutional:1 outperforms:2 existing:5 recovered:1 current:3 com:1 laparra:1 guadarrama:1 clara:1 yet:1 diederik:1 must:4 bd:1 subsequent:5 additive:2 pertinent:... |
6,329 | 6,725 | Adaptive Bayesian Sampling with Monte Carlo EM
Anirban Roychowdhury, Srinivasan Parthasarathy
Department of Computer Science and Engineering
The Ohio State University
roychowdhury.7@osu.edu, srini@cse.ohio-state.edu
Abstract
We present a novel technique for learning the mass matrices in samplers obtained
from discret... | 6725 |@word kulis:2 version:2 inversion:1 dalal:1 nd:1 suitably:1 d2:1 simulation:3 covariance:10 sgd:2 thereby:3 initial:1 contains:1 efficacy:1 selecting:1 hereafter:1 series:4 denoting:2 document:7 existing:8 diagonalized:1 current:1 discretization:2 si:2 written:4 numerical:1 remove:2 plot:1 update:24 progressively... |
6,330 | 6,726 | ADMM without a Fixed Penalty Parameter:
Faster Convergence with New Adaptive Penalization
Yi Xu? , Mingrui Liu? , 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, mi... | 6726 |@word mild:1 version:1 polynomial:1 norm:3 nd:1 open:2 semicontinuous:3 linearized:21 covariance:5 decomposition:1 acknowlegements:1 reduction:1 initial:13 liu:6 series:2 tuned:1 interestingly:1 comparing:3 luo:2 tackling:1 chu:1 attracted:1 numerical:1 shape:2 update:11 implying:1 intelligence:1 prohibitive:1 we... |
6,331 | 6,727 | Shape and Material from Sound
Zhoutong Zhang
MIT
Jiajun Wu
MIT
Qiujia Li
University of Cambridge
Zhengjia Huang
ShanghaiTech University
Joshua B. Tenenbaum
MIT
William T. Freeman
MIT, Google Research
Abstract
What can we infer from hearing an object falling onto the ground? Based on
knowledge of the physical worl... | 6727 |@word kohli:1 illustrating:1 middle:1 open:2 simulation:16 excited:1 pressure:3 pick:2 harder:1 initial:5 configuration:2 contains:1 score:1 daniel:1 ours:1 past:7 existing:4 brien:5 current:2 comparing:1 synthesizer:1 familiarized:1 mesh:1 realistic:3 partition:1 informative:2 ronan:1 shape:36 enables:1 treating... |
6,332 | 6,728 | Flexible statistical inference for mechanistic models of
neural dynamics
Jan-Matthis Lueckmann? 1 , Pedro J. Gon?alves? 1 , Giacomo Bassetto1 ,
Kaan ?cal1,2 , Marcel Nonnenmacher1 , Jakob H. Macke?1
1
research center caesar, an associate of the Max Planck Society, Bonn, Germany
2
Mathematical Institute, University of ... | 6728 |@word middle:2 nonsensical:2 open:2 prangle:1 grey:1 simulation:39 lezaun:1 covariance:5 eng:1 pg:7 solid:1 carry:1 reduction:1 initial:1 series:4 genetic:3 current:9 comparing:1 com:1 recovered:1 yet:2 assigning:1 fn:1 numerical:2 partition:1 informative:3 physiol:1 shape:1 enables:2 designed:3 update:1 implying... |
6,333 | 6,729 | Online Prediction with Selfish Experts
Tim Roughgarden
Department of Computer Science
Stanford University
Stanford, CA 94305
tim@cs.stanford.edu
Okke Schrijvers
Department of Computer Science
Stanford University
Stanford, CA 94305
okkes@cs.stanford.edu
Abstract
We consider the problem of binary prediction with expert... | 6729 |@word mild:3 private:2 version:4 stronger:3 seems:2 dekel:2 bf:1 open:2 adrian:1 seek:1 simulation:7 forecaster:1 jacob:1 pick:2 incurs:2 solid:1 harder:2 liu:2 inefficiency:1 score:1 united:1 punishes:1 past:2 outperforms:1 current:1 com:6 savage:2 comparing:1 collude:3 si:2 must:1 john:2 numerical:2 informative... |
6,334 | 673 | A Hybrid Linear/Nonlinear Approach to Channel
Equalization Problems
Wei-Tsih Lee
John Pearson
David Sarnoff Research Center
CN5300
Princeton, NJ 08543
Abstract
Channel equalization problem is an important problem in high-speed
communications. The sequences of symbols transmitted are distorted by
neighboring symbols... | 673 |@word version:2 inversion:7 seems:1 pulse:1 tried:1 ld:1 reduction:1 series:2 current:1 recovered:1 written:4 must:1 john:1 numerical:1 partition:1 v:1 cult:1 ith:1 provides:3 node:13 qam:6 location:1 constructed:4 direct:2 prove:1 combine:2 multi:1 company:1 increasing:6 confused:7 developed:3 nj:1 perfonn:1 xd:1... |
6,335 | 6,730 | Tensor Biclustering
Soheil Feizi
Stanford University
sfeizi@stanford.edu
Hamid Javadi
Stanford University
hrhakim@stanford.edu
David Tse
Stanford University
dntse@stanford.edu
Abstract
Consider a dataset where data is collected on multiple features of multiple individuals over multiple times. This type of data can ... | 6730 |@word trial:1 version:3 polynomial:2 norm:1 km:1 integrative:1 simulation:1 simplifying:1 decomposition:5 covariance:3 p0:6 moment:1 reduction:1 selecting:2 daniel:3 outperforms:3 olded:5 recovered:1 z2:6 com:1 activation:1 written:3 realistic:1 numerical:1 j1:64 drop:1 kv1:2 update:1 generative:1 vanishing:2 pro... |
6,336 | 6,731 | DPSCREEN: Dynamic Personalized Screening
Kartik Ahuja
Electrical and Computer Engineering Department
University of California, Los Angeles
ahujak@ucla.edu
William R. Zame
Economics Department
University of California, Los Angeles
zame@econ.ucla.edu
Mihaela van der Schaar
Engineering Science Department, University of... | 6731 |@word trial:1 cox:3 seems:2 sex:1 pancreatic:6 simulation:3 sensed:1 incurs:1 carry:3 reduction:4 initial:4 score:1 efficacy:1 ours:1 expositional:1 longitudinal:2 past:2 existing:4 current:12 incidence:4 mihaela:2 riskier:1 si:1 must:2 john:1 subsequent:1 chicago:1 informative:1 benign:1 tailoring:1 designed:1 u... |
6,337 | 6,732 | Learning Unknown Markov Decision Processes:
A Thompson Sampling Approach
Yi Ouyang
University of California, Berkeley
ouyangyi@berkeley.edu
Mukul Gagrani
University of Southern California
mgagrani@usc.edu
Ashutosh Nayyar
University of Southern California
ashutosn@usc.edu
Rahul Jain
University of Southern California... | 6732 |@word h:7 exploitation:3 version:2 polynomial:2 seems:1 simulation:4 pick:1 initial:1 liu:1 rightmost:1 outperforms:4 existing:1 current:2 nt:6 must:1 belmont:1 additive:1 numerical:3 ashutosh:2 update:1 resampling:4 stationary:14 v:2 selected:1 beginning:6 ith:2 provides:1 mannor:1 allerton:1 ucrl2:8 katehakis:1... |
6,338 | 6,733 | Testing and Learning on Distributions with
Symmetric Noise Invariance
Ho Chung Leon Law
Department of Statistics
University Of Oxford
hlaw@stats.ox.ac.uk
Christopher Yau
Centre for Computational Biology
University of Birmingham
c.yau@bham.ac.uk
Dino Sejdinovic
Department of Statistics
University Of Oxford
dino.sejdi... | 6733 |@word repository:1 stronger:1 norm:3 proportion:1 consolider:1 k2hk:1 prangle:1 km:1 simulation:4 azimuthal:1 accounting:1 decomposition:1 zolt:2 pick:1 carry:1 uncovered:1 contains:3 series:2 lichman:1 daniel:1 denoting:1 rkhs:1 interestingly:1 comparing:1 surprising:1 activation:1 diederik:1 must:1 written:4 ad... |
6,339 | 6,734 | A Dirichlet Mixture Model of Hawkes Processes for
Event Sequence Clustering
Hongteng Xu?
School of ECE
Georgia Institute of Technology
hongtengxu313@gmail.com
Hongyuan Zha
College of Computing
Georgia Institute of Technology
zha@cc.gatech.edu
Abstract
How to cluster event sequences generated via different point proc... | 6734 |@word multitask:1 trial:10 version:1 achievable:1 proportion:1 c0:2 rajaraman:1 open:10 initial:1 liu:1 contains:6 series:11 score:5 document:1 outperforms:2 existing:6 freitas:1 current:2 com:2 discretization:1 luo:4 gmail:1 vere:1 determinantal:1 subsequent:1 happen:1 kdd:2 nonnegativeness:1 designed:2 update:8... |
6,340 | 6,735 | Deanonymization in the Bitcoin P2P Network
Giulia Fanti and Pramod Viswanath
Abstract
Recent attacks on Bitcoin?s peer-to-peer (P2P) network demonstrated that its
transaction-flooding protocols, which are used to ensure network consistency,
may enable user deanonymization?the linkage of a user?s IP address with her
ps... | 6735 |@word trial:2 version:1 stronger:1 seems:1 simulation:9 reap:1 pick:1 recursively:1 contains:4 selecting:1 janson:1 o2:1 outperforms:1 savage:1 com:4 virus:1 culprit:2 yet:2 must:1 parsing:1 written:2 realize:1 gpu:1 timestamps:25 enables:1 v:3 leaf:2 selected:1 fewer:1 discovering:1 vanishing:1 core:3 kairouz:1 ... |
6,341 | 6,736 | Accelerated consensus via Min-Sum Splitting
Patrick Rebeschini
Department of Statistics
University of Oxford
patrick.rebeschini@stats.ox.ac.uk
Sekhar Tatikonda
Department of Electrical Engineering
Yale University
sekhar.tatikonda@yale.edu
Abstract
We apply the Min-Sum message-passing protocol to solve the consensus ... | 6736 |@word version:3 polynomial:2 norm:5 johansson:3 seems:1 open:1 d2:7 km:2 r:3 hu:1 tat:1 automat:1 incurs:1 initial:9 liu:1 daniel:1 tuned:1 current:1 chu:1 written:3 john:2 numerical:1 designed:1 update:7 maxv:1 parametrization:2 iterates:1 coarse:1 node:28 math:1 successive:1 allerton:1 location:1 zhang:1 constr... |
6,342 | 6,737 | Generalized Linear Model Regression under
Distance-to-set Penalties
Jason Xu
University of California, Los Angeles
jqxu@ucla.edu
Eric C. Chi
North Carolina State University
eric_chi@ncsu.edu
Kenneth Lange
University of California, Los Angeles
klange@ucla.edu
Abstract
Estimation in generalized linear models (GLM) is ... | 6737 |@word mild:1 trial:2 illustrating:1 briefly:1 version:1 inversion:1 norm:11 seems:2 instrumental:1 proportionality:1 d2:6 seek:3 carolina:1 simulation:1 covariance:1 decomposition:2 invoking:1 mention:1 tr:1 carry:1 reduction:3 initial:1 series:6 score:1 ours:1 rightmost:1 outperforms:1 current:2 comparing:1 marq... |
6,343 | 6,738 | Adaptive stimulus selection for optimizing neural
population responses
Benjamin R. Cowley1,2 , Ryan C. Williamson1,2,5 , Katerina Acar2,6 ,
Matthew A. Smith?,2,7 , Byron M. Yu?,2,3,4
1
Machine Learning Dept., 2 Center for Neural Basis of Cognition, 3 Dept. of Electrical
and Computer Engineering, 4 Dept. of Biomedical ... | 6738 |@word neurophysiology:4 proceeded:1 cnn:55 middle:10 version:2 trial:18 norm:19 simulation:2 r:11 uncovers:2 shading:1 initial:1 liu:1 contains:1 series:1 selecting:3 genetic:3 existing:2 ninit:11 current:4 com:1 nt:5 scatter:14 yet:1 must:1 multineuron:1 subsequent:1 shape:3 plot:2 update:2 medial:1 v:2 alone:1 ... |
6,344 | 6,739 | Nonbacktracking Bounds on the Influence in
Independent Cascade Models
1
Emmanuel Abbe1 2 Sanjeev Kulkarni2 Eun Jee Lee1
Program in Applied and Computational Mathematics 2 The Department of Electrical Engineering
Princeton University
{eabbe, kulkarni, ejlee}@princeton.edu
Abstract
This paper develops upper and lower ... | 6739 |@word trial:1 briefly:1 version:3 instrumental:1 open:15 simulation:22 pulse:1 p0:2 lakshmanan:1 harder:1 recursively:3 initial:6 contains:1 score:4 neeman:1 outperforms:3 virus:1 si:1 yet:1 cis:1 remove:1 v:2 spec:1 selected:1 greedy:1 beginning:2 iterates:1 provides:1 node:45 zhang:2 mathematical:2 along:1 beco... |
6,345 | 674 | Directional-Unit Boltzmann Machines
Richard S. Zemel
Computer Science Dept.
University of Toronto
Toronto, ONT M5S lA4
Christopher K. I. Williams
Computer Science Dept.
University of Toronto
Toronto, ONT M5S lA4
Michael C. Mozer
Computer Science Dept.
University of Colorado
Boulder, CO 80309-0430
Abstract
We present... | 674 |@word version:3 simulation:2 tr:1 initial:1 cyclic:1 configuration:5 contains:2 series:1 current:1 nowlan:1 must:1 numerical:1 update:2 discrimination:1 reciprocal:3 steepest:1 provides:2 complication:1 toronto:6 attack:1 arctan:1 along:2 differential:2 consists:2 combine:2 baldi:2 hermitian:3 manner:2 expected:6 ... |
6,346 | 6,740 | Learning with Feature Evolvable Streams
Bo-Jian Hou Lijun Zhang Zhi-Hua Zhou
National Key Laboratory for Novel Software Technology,
Nanjing University, Nanjing, 210023, China
{houbj,zhanglj,zhouzh}@lamda.nju.edu.cn
Abstract
Learning with streaming data has attracted much attention during the past few years.
Though mo... | 6740 |@word norm:1 nd:1 d2:5 contains:2 series:1 selecting:2 pt0:1 tuned:1 past:2 existing:2 outperforms:2 current:2 recovered:16 ka:1 protection:4 cumulation:1 si:1 yet:2 attracted:2 must:1 hou:2 drop:1 update:11 rd2:6 v:3 fund:1 half:1 intelligence:4 beginning:3 core:2 short:2 record:1 boosting:3 location:1 simpler:1... |
6,347 | 6,741 | Online Convex Optimization with Stochastic
Constraints
Hao Yu, Michael J. Neely, Xiaohan Wei
Department of Electrical Engineering, University of Southern California?
{yuhao,mjneely,xiaohanw}@usc.edu
Abstract
This paper considers online convex optimization (OCO) with stochastic constraints,
which generalizes Zinkevich?... | 6741 |@word norm:1 open:1 d2:11 q1:1 sgd:1 boundedness:2 configuration:1 series:1 selecting:1 existing:1 current:2 com:1 qureshi:3 universality:1 router:3 must:2 john:2 refines:1 numerical:1 happen:1 wellbehaved:1 plot:1 update:5 warmuth:2 beginning:3 iso:1 manfred:2 infrastructure:2 provides:2 mannor:5 simpler:1 unbou... |
6,348 | 6,742 | Max-Margin Invariant Features from Transformed
Unlabeled Data
Dipan K. Pal, Ashwin A. Kannan?, Gautam Arakalgud?, Marios Savvides
Department of Electrical and Computer Engineering
Carnegie Mellon University
Pittsburgh, PA 15213
{dipanp,aalapakk,garakalgud,marioss}@cmu.edu
Abstract
The study of representations invarian... | 6742 |@word briefly:1 version:7 covariance:1 thereby:8 harder:1 shot:3 moment:3 efficacy:1 score:1 tuned:1 rkhs:11 ours:3 past:1 existing:1 outperforms:3 stemmed:1 yet:2 written:1 readily:1 girosi:1 discrimination:2 intelligence:1 plane:1 ith:1 core:2 provides:3 boosting:1 bijection:1 gautam:1 gx:15 zhang:1 along:3 con... |
6,349 | 6,743 | Regularized Modal Regression with Applications in
Cognitive Impairment Prediction
Xiaoqian Wang1 , Hong Chen1 , Weidong Cai2 , Dinggang Shen3 , Heng Huang1?
Department of Electrical and Computer Engineering, University of Pittsburgh, USA
2
School of Information Technologies, University of Sydney, Australia
3
Departmen... | 6743 |@word mild:3 repository:2 version:2 mri:3 polynomial:2 norm:6 hu:1 seitz:1 carolina:1 lobe:6 p0:2 configuration:1 liu:1 score:5 selecting:1 lichman:1 mmse:1 longitudinal:2 outperforms:1 existing:2 com:1 comparing:2 gmail:1 written:1 john:1 informative:4 designed:1 half:4 epanechnikov:5 mental:2 characterization:2... |
6,350 | 6,744 | Translation Synchronization via Truncated Least
Squares
Xiangru Huang?
The University of Texas at Austin
2317 Speedway, Austin, 78712
xrhuang@cs.utexas.edu
Zhenxiao Liang?
Tsinghua University
Beijing, China, 100084
liangzx14@mails.tsinghua.edu.cn
Chandrajit Bajaj
The University of Texas at Austin
2317 Speedway, Aust... | 6744 |@word qthat:1 kondor:2 norm:2 seitz:1 scg:1 decomposition:2 pick:1 concise:1 initial:14 series:1 contains:3 score:4 bc:1 outperforms:1 existing:1 current:2 recovered:1 comparing:1 com:1 nt:1 si:4 tackling:1 written:1 cruz:1 shakespeare:1 shape:4 enables:1 greedy:1 half:2 selected:2 directory:1 xk:9 chile:1 provid... |
6,351 | 6,745 | From which world is your graph?
Cheng Li
College of William & Mary
Felix M. F. Wong
Independent Researcher?
Zhenming Liu
College of William & Mary
Varun Kanade
University of Oxford
Abstract
Discovering statistical structure from links is a fundamental problem in the analysis of social networks. Choosing a misspeci... | 6745 |@word mild:1 briefly:1 polynomial:5 stronger:2 proportion:1 nd:1 glue:1 suitably:3 c0:9 adrian:1 closure:1 eng:1 decomposition:1 paid:1 incarnation:1 reduction:3 liu:2 contains:6 score:4 daniel:1 neeman:2 ours:2 document:1 existing:2 err:2 whp:2 surprising:1 lang:2 intriguing:1 follower:2 must:1 john:2 realistic:... |
6,352 | 6,746 | A New Alternating Direction Method for Linear
Programming
Sinong Wang
Department of ECE
The Ohio State University
wang.7691@osu.edu
Ness Shroff
Department of ECE and CSE
The Ohio State University
shroff.11@osu.edu
Abstract
It is well known that, for a linear program (LP) with constraint matrix A ? Rm?n ,
the Alterna... | 6746 |@word mild:1 version:1 advantageous:1 norm:7 seems:1 open:3 simulation:1 linearized:1 tat:1 covariance:4 p0:1 hsieh:1 pick:1 d0k:2 tianyi:1 ipm:3 initial:1 inefficiency:1 contains:1 daniel:1 existing:11 current:7 recovered:1 luo:1 readily:1 numerical:2 subsequent:1 plot:1 designed:1 update:12 amir:1 une:1 xk:32 i... |
6,353 | 6,747 | Regret Analysis for Continuous Dueling Bandit
Wataru Kumagai
Center for Advanced Intelligence Project
RIKEN
1-4-1, Nihonbashi, Chuo, Tokyo 103-0027, Japan
wataru.kumagai@riken.jp
Abstract
The dueling bandit is a learning framework wherein the feedback information in
the learning process is restricted to a noisy compa... | 6747 |@word version:3 polynomial:1 norm:3 stronger:1 logit:1 dekel:1 open:1 d2:3 citeseer:1 pick:1 tr:1 moment:1 reduction:2 contains:1 tuned:1 ours:1 past:5 current:1 dikin:2 must:1 bd:3 numerical:2 enables:1 update:1 intelligence:1 math:1 preference:4 zhang:2 along:1 constructed:1 symposium:1 prove:2 busa:2 introduce... |
6,354 | 6,748 | Best Response Regression
Omer Ben-Porat
Technion - Israel Institute of Technology
Haifa 32000 Israel
omerbp@campus.technion.ac.il
Moshe Tennenholtz
Technion - Israel Institute of Technology
Haifa 32000 Israel
moshet@ie.technion.ac.il
Abstract
In a regression task, a predictor is given a set of instances, along with ... | 6748 |@word briefly:1 polynomial:4 proportion:3 seems:3 dekel:1 willing:1 simulation:3 pick:2 thereby:3 solid:2 recursively:2 ld:4 reduction:1 contains:3 score:2 series:2 chervonenkis:1 interestingly:1 rightmost:1 outperforms:1 current:2 z2:1 com:1 intriguing:1 realistic:1 partition:1 additive:3 designed:1 maxv:1 intel... |
6,355 | 6,749 | TernGrad: Ternary Gradients to Reduce
Communication in Distributed Deep Learning
Wei Wen1 , Cong Xu2 , Feng Yan3 , Chunpeng Wu1 , Yandan Wang4 , Yiran Chen1 , Hai Li1
1
Duke University, 2 Hewlett Packard Labs, 3 University of Nevada ? Reno, 4 University of Pittsburgh
1
{wei.wen, chunpeng.wu, yiran.chen, hai.li}@duke.... | 6749 |@word cnn:1 polynomial:3 seems:1 norm:2 stronger:4 compression:1 heuristically:1 cipar:1 brightness:1 sgd:33 thereby:1 mention:2 nsw:1 reduction:1 initial:1 liu:2 lightweight:1 daniel:1 tuned:1 bradley:1 current:2 com:2 comparing:2 activation:1 diederik:1 danny:1 gpu:5 devin:2 numerical:6 christian:3 drop:2 plot:... |
6,356 | 675 | How Oscillatory Neuronal Responses Reflect
Bistability and Switching of the Hidden
Assembly Dynamics
K. Pawelzik, H.-V. Bauert, J. Deppisch, and T. Geisel
Institut fur Theoretische Physik and SFB 185 Nichtlineare Dynamik
Universitat Frankfurt, Robert-Mayer-Str. 8-10, D-6000 Frankfurt/M. 11, FRG
ttemporary adress:CNS-P... | 675 |@word physik:1 confirms:1 tried:1 activation:1 yet:1 shape:2 designed:1 nichtlineare:1 selected:1 provides:2 successive:1 burst:13 hopf:2 autocorrelation:2 manner:1 inter:2 indeed:1 multi:5 brain:1 weinheim:2 pawelzik:10 str:1 pf:12 increasing:1 unpredictable:1 underlying:6 kind:1 dynamik:1 monkey:1 gottingen:1 te... |
6,357 | 6,750 | Learning Affinity via Spatial Propagation Networks
Sifei Liu
UC Merced, NVIDIA
Guangyu Zhong
Dalian University of Technology
Shalini De Mello
NVIDIA
Ming-Hsuan Yang
UC Merced, NVIDIA
Jinwei Gu
NVIDIA
Jan Kautz
NVIDIA
Abstract
In this paper, we propose spatial propagation networks for learning the affinity matrix fo... | 6750 |@word cnn:27 middle:1 version:3 norm:2 kokkinos:1 everingham:1 paredes:1 seek:1 propagate:2 rgb:5 sgd:5 recursively:1 carry:1 initial:3 liu:7 configuration:2 contains:5 ndez:1 denoting:1 tuned:3 romera:1 existing:1 guadarrama:1 discretization:1 comparing:1 luo:2 must:1 parsing:8 shape:2 enables:2 designed:4 v:2 i... |
6,358 | 6,751 | Linear regression without correspondence
Daniel Hsu
Columbia University
New York, NY
djhsu@cs.columbia.edu
Kevin Shi
Columbia University
New York, NY
kshi@cs.columbia.edu
Xiaorui Sun
Microsoft Research
Redmond, WA
xiaoruisun@cs.columbia.edu
Abstract
This article considers algorithmic and statistical aspects of linea... | 6751 |@word achievable:1 polynomial:5 norm:1 nd:1 c0:3 open:1 bn:1 decomposition:2 arjen:1 pick:2 reduction:13 initial:1 minw2rk:3 daniel:2 woodruff:1 recovered:1 z2:1 written:1 numerical:1 partition:3 klaas:1 generative:1 warmuth:3 beginning:1 short:1 manfred:2 completeness:1 quantized:3 allerton:1 simpler:1 mathemati... |
6,359 | 6,752 | NeuralFDR: Learning Discovery Thresholds
from Hypothesis Features
Fei Xia? ,
Martin J. Zhang?, James Zou? , David Tse?
Stanford University
{feixia,jinye,jamesz,dntse}@stanford.edu
Abstract
As datasets grow richer, an important challenge is to leverage the full features
in the data to maximize the number of useful dis... | 6752 |@word mild:1 version:1 proportion:23 stronger:3 c0:2 unif:3 willing:1 confirms:1 simulation:2 hu:2 accounting:1 ld:1 contains:4 score:1 series:2 genetic:3 ours:1 outperforms:2 bradley:1 com:1 wd:2 assigning:1 must:2 olive:1 john:2 partition:1 informative:2 shape:1 enables:2 pertinent:1 designed:1 interpretable:4 ... |
6,360 | 6,753 | Cost efficient gradient boosting
Sven Peter
Ferran Diego
Heidelberg Collaboratory for Image Processing
Interdisciplinary Center for Scientific Computing
University of Heidelberg
69115 Heidelberg, Germany
Robert Bosch GmbH
Robert-Bosch-Stra?e 200
31139 Hildesheim, Germany
ferran.diegoandilla@de.bosch.com
sven.pete... | 6753 |@word repository:1 briefly:2 compression:2 nd:2 confirms:1 pavel:1 citeseer:1 incurs:1 delgado:1 reduction:3 electronics:1 ndez:1 cristina:1 score:1 lichman:1 liu:1 daniel:2 document:3 greedymiser:2 outperforms:3 current:6 com:3 ka:1 manuel:1 activation:1 yet:4 john:3 ronald:1 subsequent:2 kdd:1 cheap:22 minmin:2... |
6,361 | 6,754 | Probabilistic Rule Realization and Selection
Haizi Yu? ?
Department of Computer Science
University of Illinois at Urbana-Champaign
Urbana, IL 61801
haiziyu7@illinois.edu
Tianxi Li?
Department of Statistics
University of Michigan
Ann Arbor, MI 48109
tianxili@umich.edu
Lav R. Varshney?
Department of Electrical and Comp... | 6754 |@word middle:1 inversion:1 pw:5 norm:14 open:1 termination:2 linearized:1 perpin:1 decomposition:1 q1:1 pick:3 incurs:1 asks:1 mention:1 shot:1 moment:2 necessity:1 reduction:10 initial:1 score:1 selecting:3 interestingly:1 existing:2 err:3 ka:2 current:1 luo:1 lang:1 yet:3 tackling:1 chu:1 finest:1 exposing:1 re... |
6,362 | 6,755 | Nearest-Neighbor Sample Compression:
Efficiency, Consistency, Infinite Dimensions
Aryeh Kontorovich
Department of Computer Science
Ben-Gurion University of the Negev
karyeh@cs.bgu.ac.il
Sivan Sabato
Department of Computer Science
Ben-Gurion University of the Negev
sabatos@bgu.ac.il
Roi Weiss
Department of Computer S... | 6755 |@word h:2 version:2 compression:54 norm:1 yi0:4 open:10 decomposition:1 yjd:1 thereby:1 solid:1 carry:2 reduction:1 shechtman:1 celebrated:1 series:5 substitution:1 daniel:2 tuned:1 prefix:1 err:18 comparing:1 z2:2 surprising:4 ddim:5 beygelzimer:1 yet:2 intriguing:2 must:2 readily:1 john:4 michal:1 numerical:1 p... |
6,363 | 6,756 | A Scale Free Algorithm for Stochastic Bandits with
Bounded Kurtosis
Tor Lattimore?
tor.lattimore@gmail.com
Abstract
Existing strategies for finite-armed stochastic bandits mostly depend on a parameter of scale that must be known in advance. Sometimes this is in the form of a
bound on the payoffs, or the knowledge of a... | 6756 |@word polynomial:1 achievable:2 seems:4 open:2 grey:1 calculus:1 specialises:1 boundedness:1 moment:6 united:1 kurt:4 existing:1 com:1 nt:1 gmail:1 must:1 written:1 partition:1 alone:1 ith:1 location:2 honda:6 mathematical:1 along:1 c2:18 katehakis:11 symposium:1 apostolos:1 indeed:1 expected:1 roughly:1 abbrevia... |
6,364 | 6,757 | Learning Multiple Tasks with Multilinear
Relationship Networks
Mingsheng Long, Zhangjie Cao, Jianmin Wang, Philip S. Yu
School of Software, Tsinghua University, Beijing 100084, China
{mingsheng,jimwang}@tsinghua.edu.cn
caozhangjie14@gmail.com
psyu@uic.edu
Abstract
Deep networks trained on large-scale data can learn... | 6757 |@word multitask:6 determinant:1 cnn:7 stronger:1 paredes:2 chakraborty:1 open:2 d2:24 confirms:2 underperform:1 covariance:48 decomposition:4 jacob:1 sgd:1 versatile:1 tnlist:1 liu:1 contains:3 efficacy:3 selecting:2 tuned:1 suppressing:1 romera:2 outperforms:2 existing:2 com:2 transferability:8 nt:4 comparing:1 ... |
6,365 | 6,758 | Deep Hyperalignment
Muhammad Yousefnezhad, Daoqiang Zhang
College of Computer Science and Technology
Nanjing University of Aeronautics and Astronautics
{myousefnezhad,dqzhang}@nuaa.edu.cn
Abstract
This paper proposes Deep Hyperalignment (DHA) as a regularized, deep extension,
scalable Hyperalignment (HA) method, whic... | 6758 |@word briefly:1 version:1 mri:1 norm:1 open:2 seek:6 simulation:1 r:1 decomposition:4 sgd:3 tr:5 reduction:1 series:2 contains:5 halchenko:1 bc:1 current:3 activation:2 tackling:1 must:7 gpu:1 shape:1 haxby:5 update:3 openfmri:1 fund:1 intelligence:1 selected:3 advancement:1 ubuntu:1 provides:4 location:1 allerto... |
6,366 | 6,759 | Online to Offline Conversions, Universality and
Adaptive Minibatch Sizes
Kfir Y. Levy
Department of Computer Science, ETH Z?rich.
yehuda.levy@inf.ethz.ch
Abstract
We present an approach towards convex optimization that relies on a novel scheme
which converts adaptive online algorithms into offline methods. In the offl... | 6759 |@word version:2 norm:15 dekel:1 open:1 d2:6 seek:1 invoking:4 sgd:14 harder:1 reduction:1 woodruff:1 interestingly:1 past:2 imaginary:4 universality:9 tackling:1 yet:1 written:1 john:1 intriguing:1 diederik:1 numerical:1 zaid:1 depict:1 update:13 v:1 juditsky:1 half:1 core:1 provides:2 zhang:2 mathematical:2 alon... |
6,367 | 676 | Destabilization and Route to Chaos
in Neural Networks
with Random Connectivity
Bernard Doyon
Unite INSERM 230
Service de Neurologie
CHUPurpan
F-31059 Toulouse Cedex, France
Bruno Cessac
Centre d'Etudes et de Recherches
de Toulouse
2, avenue Edouard Belin, BP 4025
F-31055 Toulouse Cedex, France
Mathias Quoy
Centre d'... | 676 |@word proportion:2 seems:1 disk:2 simulation:3 initial:1 born:1 series:1 ecole:1 manuel:1 activation:1 numerical:2 plot:1 stationary:3 selected:1 nervous:1 plane:1 math:2 sigmoidal:1 simpler:1 hopf:12 qualitative:1 cray:1 sustained:1 introduce:1 theoretically:1 expected:1 indeed:1 behavior:6 nor:1 brain:4 freeman:... |
6,368 | 6,760 | Stochastic Optimization with Variance Reduction
for Infinite Datasets with Finite Sum Structure
Alberto Bietti
Inria?
alberto.bietti@inria.fr
Julien Mairal
Inria?
julien.mairal@inria.fr
Abstract
Stochastic optimization algorithms with variance reduction have proven successful
for minimizing large finite sums of funct... | 6760 |@word msr:1 version:1 middle:1 norm:3 seems:1 retraining:1 c0:11 open:1 tried:1 brightness:2 sgd:40 thereby:1 reduction:15 initial:6 contains:2 series:1 selecting:1 genetic:1 ours:2 document:1 tuned:1 outperforms:1 past:1 comparing:1 com:1 yet:1 tackling:1 written:1 tot:12 additive:2 ckns:1 realistic:1 shape:1 ho... |
6,369 | 6,761 | Deep Learning with Topological Signatures
Roland Kwitt
Department of Computer Science
University of Salzburg, Austria
Roland.Kwitt@sbg.ac.at
Christoph Hofer
Department of Computer Science
University of Salzburg, Austria
chofer@cosy.sbg.ac.at
Marc Niethammer
UNC Chapel Hill, NC, USA
mn@cs.unc.edu
Andreas Uhl
Departme... | 6761 |@word briefly:1 c0:4 decomposition:1 p0:2 homomorphism:2 kutzkov:1 sgd:2 bai:2 liu:2 series:1 contains:1 fragment:1 initial:2 ours:4 interestingly:2 outperforms:1 existing:3 steiner:3 current:1 discretization:1 z2:6 com:2 nanda:1 readily:1 realize:1 mesh:1 kdd:1 shape:18 enables:3 designed:1 fund:1 maxv:1 wasserm... |
6,370 | 6,762 | Predicting User Activity Level In Point Processes
With Mass Transport Equation
Yichen Wang? , Xiaojing Ye? , Hongyuan Zha? , Le Song?
?
College of Computing, Georgia Institute of Technology
?
School of Mathematics, Georgia State University
{yichen.wang}@gatech.edu, xye@gsu.edu
{zha,lsong}@cc.gatech.edu
Abstract
Point ... | 6762 |@word multitask:1 seems:1 proportion:6 rajaraman:1 calculus:3 simulation:2 reduction:3 nonexistent:1 substitution:1 contains:9 liu:1 initial:7 past:2 existing:2 outperforms:1 unction:1 follower:1 numerical:1 informative:1 kdd:1 shape:2 enables:1 designed:3 plot:3 update:1 v:9 half:2 advancement:1 item:19 paramete... |
6,371 | 6,763 | Submultiplicative Glivenko-Cantelli and
Uniform Convergence of Revenues
Noga Alon
Tel Aviv University, Israel
and Microsoft Research
nogaa@tau.ac.il
Moshe Babaioff
Microsoft Research
moshe@microsoft.com
Yannai A. Gonczarowski
The Hebrew University of Jerusalem, Israel
and Microsoft Research
yannai@gonch.name
Shay Mo... | 6763 |@word private:2 briefly:1 polynomial:3 stronger:1 willing:2 jacob:1 attainable:1 pick:1 thereby:1 boundedness:3 moment:17 contains:2 series:1 united:1 chervonenkis:2 interestingly:1 envision:2 ironing:1 com:3 gmail:2 fn:13 additive:6 confirming:1 n0:19 item:3 amir:3 yannai:4 short:1 core:1 alexandros:1 characteri... |
6,372 | 6,764 | Deep Dynamic Poisson Factorization Model
Chengyue Gong
Department of Information Management
Peking University
cygong@pku.edu.cn
Win-bin Huang
Department of Information Management
Peking University
huangwb@pku.edu.cn
Abstract
A new model, named as deep dynamic poisson factorization model, is proposed
in this paper fo... | 6764 |@word loading:2 liu:1 series:2 score:1 contains:4 united:5 africa:2 com:2 activation:1 written:1 lauly:1 additive:1 kdd:1 shape:1 interpretable:1 update:3 intelligence:1 fewer:1 greedy:1 generative:1 short:1 core:1 blei:5 five:5 along:1 augmentable:1 beta:1 prove:1 wale:1 fitting:5 combine:1 icews:10 bility:1 gro... |
6,373 | 6,765 | Positive-Unlabeled Learning with
Non-Negative Risk Estimator
Ryuichi Kiryo1,2 Gang Niu1,2 Marthinus C. du Plessis Masashi Sugiyama2,1
1
The University of Tokyo, 7-3-1 Hongo, Tokyo 113-0033, Japan
2
RIKEN, 1-4-1 Nihonbashi, Tokyo 103-0027, Japan
{ kiryo@ms., gang@ms., sugi@ }k.u-tokyo.ac.jp
Abstract
From only positive... | 6765 |@word mild:1 kgk:2 cnn:1 norm:2 advantageous:1 stronger:1 bpu:23 softsign:4 proportion:1 open:1 tried:1 bn:2 contraction:1 rgb:1 p0:18 hsieh:1 sgd:1 solid:1 reduction:4 liu:5 series:1 seriously:1 document:3 existing:2 current:3 com:3 activation:1 yet:2 lang:1 must:2 written:2 gpu:9 realize:1 john:1 partition:1 kd... |
6,374 | 6,766 | Optimal Sample Complexity of M -wise Data for
Top-K Ranking
Minje Jang?
School of Electrical Engineering
KAIST
jmj427@kaist.ac.kr
Sunghyun Kim?
Electronics and Telecommunications Research Institute
Daejeon, Korea
koishkim@etri.re.kr
Changho Suh
School of Electrical Engineering
KAIST
chsuh@kaist.ac.kr
Sewoong Oh
Indu... | 6766 |@word trial:4 version:3 instrumental:1 norm:3 logit:1 c0:2 heuristically:2 simulation:5 pick:1 solid:1 recursively:1 moment:1 reduction:3 electronics:1 series:1 score:13 janson:2 horvitz:1 bradley:3 si:9 yet:2 numerical:8 partition:2 happen:1 drop:1 alone:3 stationary:2 item:47 beginning:1 reciprocal:1 vanishing:... |
6,375 | 6,767 | Reliable Decision Support using
Counterfactual Models
Suchi Saria
Department of Computer Science
Johns Hopkins University
Baltimore, MD 21211
ssaria@cs.jhu.edu
Peter Schulam
Department of Computer Science
Johns Hopkins University
Baltimore, MD 21211
pschulam@cs.jhu.edu
Abstract
Making a good decision involves conside... | 6767 |@word multitask:1 trial:1 longterm:1 polynomial:1 johansson:2 nd:1 grey:3 additively:1 simulation:1 hu:1 accounting:1 decomposition:1 covariance:7 creatinine:14 solid:1 initial:3 series:10 contains:2 score:14 ours:1 interestingly:1 longitudinal:3 past:1 reaction:1 horvitz:1 com:1 nt:10 vere:3 must:4 john:2 writte... |
6,376 | 6,768 | QSGD: Communication-Efficient SGD
via Gradient Quantization and Encoding
Dan Alistarh
IST Austria & ETH Zurich
dan.alistarh@ist.ac.at
Demjan Grubic
ETH Zurich & Google
demjangrubic@gmail.com
Ryota Tomioka
Microsoft Research
ryoto@microsoft.com
Jerry Z. Li
MIT
jerryzli@mit.edu
Milan Vojnovic
London School of Econom... | 6768 |@word private:1 faculty:1 msr:1 compression:9 norm:1 version:9 proportion:1 bekkerman:1 briefly:1 open:2 achievable:1 bn:1 sgd:42 tr:1 solid:1 recursively:1 reduction:10 initial:3 liu:2 moment:10 contains:3 daniel:1 tuned:1 denoting:1 kurt:1 prefix:1 past:2 outperforms:1 current:5 com:4 luo:1 yet:1 gmail:1 must:2... |
6,377 | 6,769 | Convergent Block Coordinate Descent for Training
Tikhonov Regularized Deep Neural Networks
Ziming Zhang and Matthew Brand
Mitsubishi Electric Research Laboratories (MERL)
Cambridge, MA 02139-1955
{zzhang, brand}@merl.com
Abstract
By lifting the ReLU function into a higher dimensional space, we develop a smooth
multi-... | 6769 |@word trial:1 polynomial:1 norm:3 seems:2 nd:2 reused:1 mitsubishi:1 propagate:1 decomposition:1 sgd:16 arous:1 initial:3 contains:1 ours:1 guadarrama:1 com:2 comparing:1 luo:1 activation:6 dx:2 chu:1 gpu:1 numerical:1 oberman:1 update:2 v:4 stationary:8 half:1 lky:2 parameterization:1 accordingly:3 xk:7 vanishin... |
6,378 | 677 | A Formal Model of the Insect Olfactory
Macroglomerulus: Simulations and
Analytical Results.
Christiane Linster
David Marsan
ESPCI, Laboratoire d'Electronique
10, Rue Vauquelin
75005 Paris, France
Claudine Masson
Laboratoire de Neurobiologie Comparee des
Invertebrees
INRA/CNRS (URA 1190)
91140 Bures sur Yvette, France
... | 677 |@word middle:1 sex:2 simulation:17 lobe:3 excited:2 mammal:1 reaction:1 john:2 physiol:1 designed:2 discrimination:2 nq:1 recherche:1 compo:2 direct:1 differential:2 consists:1 pathway:2 olfactory:20 indeed:1 behavior:3 p1:1 brain:2 pf:1 ua:1 project:1 bounded:1 transformation:1 oscillates:1 classifier:2 unit:2 gr... |
6,379 | 6,770 | Train longer, generalize better: closing the
generalization gap in large batch training of neural
networks
Elad Hoffer1?,
Itay Hubara?,
Daniel Soudry2
1
Technion - Israel Institute of Technology, Haifa, Israel
2
Columbia University, New York, New York, USA
{elad.hoffer, itayhubara, daniel.soudry}@gmail.com
Abstract
Ba... | 6770 |@word briefly:1 seems:2 norm:7 open:2 heuristically:1 bn:7 covariance:6 decomposition:1 incurs:1 sgd:18 arous:1 initial:13 daniel:2 tuned:1 document:1 interestingly:1 current:4 com:2 comparing:1 activation:1 gmail:1 yet:6 attracted:1 must:1 scatter:1 realistic:1 numerical:1 subsequent:1 partition:1 shape:1 enable... |
6,380 | 6,771 | Flexpoint: An Adaptive Numerical Format for
Efficient Training of Deep Neural Networks
Urs K?ster* , Tristan Webb* , Xin Wang* , Marcel Nassar* , Arjun Bansal, William Constable,
Oguz Elibol, Stewart Hall, Luke Hornof, Amir Khosrowshahi, Carey Kloss, Ruby Pai,
Naveen Rao
Artificial Intelligence Products Group, Intel Co... | 6771 |@word trial:1 version:1 eliminating:2 annapureddy:1 tensorial:1 open:1 reused:1 simulation:1 accommodate:1 shading:1 pub:1 daniel:1 existing:2 bitwise:1 current:1 com:2 activation:14 yet:1 written:1 gpu:5 must:2 readily:1 numerical:18 periodically:1 additive:1 enables:1 designed:5 drop:1 update:10 yinda:1 preempt... |
6,381 | 6,772 | Model evidence from nonequilibrium simulations
Michael Habeck
Statistical Inverse Problems in Biophysics, Max Planck Institute for Biophysical Chemistry &
Institute for Mathematical Stochastics, University of G?ttingen, 37077 G?ttingen, Germany
email mhabeck@gwdg.de
Abstract
The marginal likelihood, or model evidence... | 6772 |@word version:3 seems:3 confirms:1 simulation:54 p0:5 contrastive:2 pick:1 minus:1 carry:1 initial:5 configuration:3 contains:1 liu:1 document:1 ka:1 com:1 jaynes:1 yet:1 dx:2 realistic:1 partition:2 kpf:1 visible:3 heir:1 update:1 stationary:6 generative:1 selected:1 guess:1 intelligence:2 xk:16 geyer:1 core:1 s... |
6,382 | 6,773 | Minimal Exploration
in Structured Stochastic Bandits
Richard Combes
Centrale-Supelec / L2S
richard.combes@supelec.fr
Stefan Magureanu
KTH, EE School / ACL
magur@kth.se
Alexandre Proutiere
KTH, EE School / ACL
alepro@kth.se
Abstract
This paper introduces and addresses a wide class of stochastic bandit problems
where ... | 6773 |@word trial:1 exploitation:6 version:1 briefly:1 simplifying:1 attainable:1 pick:1 necessity:1 selecting:1 outperforms:2 existing:5 comparing:1 optim:1 contextual:1 yet:3 must:3 numerical:5 update:1 intelligence:1 selected:8 metrika:1 recherche:1 colored:1 mannor:2 complication:1 revisited:1 honda:2 math:1 simple... |
6,383 | 6,774 | Learned D-AMP: Principled Neural Network based
Compressive Image Recovery
Christopher A. Metzler
Rice University
chris.metzler@rice.edu
Ali Mousavi
Rice University
ali.mousavi@rice.edu
Richard G. Baraniuk
Rice University
richb@rice.edu
Abstract
Compressive image recovery is a challenging problem that requires fast ... | 6774 |@word cnn:1 briefly:1 version:1 mri:3 compression:2 norm:1 blu:1 d2:2 seek:1 sensed:1 born:1 contains:2 selecting:1 mag:2 tuned:1 amp:43 mmse:5 outperforms:2 existing:1 comparing:1 discretization:2 com:1 yet:1 must:4 gpu:1 axk22:1 realistic:1 additive:1 enables:2 remove:1 designed:10 interpretable:2 update:2 poly... |
6,384 | 6,775 | Deliberation Networks: Sequence Generation
Beyond One-Pass Decoding ?
1
Yingce Xia, 2 Fei Tian, 3 Lijun Wu, 1 Jianxin Lin, 2 Tao Qin, 1 Nenghai Yu, 2 Tie-Yan Liu
1
University of Science and Technology of China, Hefei, China
2
3
Microsoft Research, Beijing, China
Sun Yat-sen University, Guangzhou, China
1
yingce.xia@g... | 6775 |@word cnn:1 briefly:1 middle:7 seems:1 d2:14 paid:1 sgd:2 carry:1 initial:1 liu:10 series:1 score:11 contains:2 configuration:1 document:1 subword:1 past:1 outperforms:3 com:3 contextual:5 gmail:1 parmar:1 gpu:1 refines:4 concatenate:1 nian:1 remove:1 update:1 generative:1 selected:1 intelligence:2 krikun:1 short... |
6,385 | 6,776 | Adaptive Clustering through Semidefinite
Programming
Martin Royer
Laboratoire de Math?matiques d?Orsay, Univ. Paris-Sud, CNRS,
Universit? Paris-Saclay,
91405 Orsay, France
martin.royer@math.u-psud.fr
Abstract
We analyze the clustering problem through a flexible probabilistic model that
aims to identify an optimal par... | 6776 |@word version:4 polynomial:1 achievable:2 norm:7 c0:2 open:1 simulation:2 bn:2 tr:9 reduction:1 configuration:1 score:1 mixon:1 recovered:1 comparing:2 written:1 numerical:2 partition:12 isotropic:4 detecting:1 math:2 node:1 provides:1 c6:2 c2:2 shorthand:1 introduce:4 peng:2 homoscedasticity:1 indeed:3 roughly:1... |
6,386 | 6,777 | Log-normality and Skewness of Estimated
State/Action Values in Reinforcement Learning
Liangpeng Zhang1,2 , Ke Tang3,1 , and Xin Yao3,2
1
School of Computer Science and Technology,
University of Science and Technology of China
2
University of Birmingham, U.K.
3
Shenzhen Key Lab of Computational Intelligence,
Department... | 6777 |@word illustrating:1 stronger:1 smirnov:1 nd:2 d2:1 pieter:1 citeseer:1 solid:1 recursively:1 moment:1 initial:1 selecting:1 daniel:1 hasselt:3 current:2 comparing:1 analysed:1 si:24 yet:1 guez:2 john:3 timestamps:1 happen:1 informative:1 realistic:1 predetermined:1 cheap:1 update:1 overshooting:1 stationary:1 in... |
6,387 | 6,778 | Repeated Inverse Reinforcement Learning
Kareem Amin?
Google Research
New York, NY 10011
kamin@google.com
Nan Jiang?
Satinder Singh
Computer Science & Engineering,
University of Michigan, Ann Arbor, MI 48104
{nanjiang,baveja}@umich.edu
Abstract
We introduce a novel repeated Inverse Reinforcement Learning problem: the... | 6778 |@word private:1 version:4 polynomial:1 stronger:2 norm:1 unif:1 d2:3 pieter:4 jacob:1 deems:1 reduction:1 initial:13 inefficiency:1 contains:2 score:2 daniel:1 existing:1 err:1 current:1 com:1 comparing:1 yet:1 must:1 john:2 realize:1 ronald:1 subsequent:1 additive:1 remove:1 update:6 unidentifiability:5 greedy:1... |
6,388 | 6,779 | The Numerics of GANs
Lars Mescheder
Autonomous Vision Group
MPI T?bingen
lars.mescheder@tuebingen.mpg.de
Sebastian Nowozin
Machine Intelligence and Perception Group
Microsoft Research
sebastian.nowozin@microsoft.com
Andreas Geiger
Autonomous Vision Group
MPI T?bingen
andreas.geiger@tuebingen.mpg.de
Abstract
In this ... | 6779 |@word norm:2 open:3 p0:4 nsw:3 thereby:1 inpainting:1 score:3 selecting:1 imaginary:11 current:2 com:2 contextual:1 john:2 devin:1 realistic:1 numerical:3 informative:1 enables:1 christian:1 update:1 depict:2 stationary:8 intelligence:1 generative:14 half:1 alec:2 accordingly:1 plane:1 iterates:2 pascanu:1 revisi... |
6,389 | 678 | Physiologically Based Speech Synthesis
~akoto
Hirayanaa
t ATR Human Information Processing Research Laboratories
2-2, Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-02 Japan
Eric Vatikiotis-Bateson
tATR Auditory and Visual Perception Research Laboratories
Kiyoshi Hondat
Yasuharu Koiket
~itsuo
Kawatot*
Abstract
This ... | 678 |@word kura:1 blade:1 initial:1 anterior:1 wakita:2 synthesizer:1 yet:2 toh:1 numerical:1 subsequent:2 shape:5 motor:13 medial:1 v:1 device:1 yoh:1 record:1 honda:3 along:1 skilled:1 direct:1 emma:1 introduce:2 acquired:2 ra:1 behavior:6 seika:1 torque:1 encouraging:1 window:1 considering:1 increasing:1 begin:1 pro... |
6,390 | 6,780 | Practical Bayesian Optimization for Model Fitting
with Bayesian Adaptive Direct Search
Luigi Acerbi?
Center for Neural Science
New York University
luigi.acerbi@nyu.edu
Wei Ji Ma
Center for Neural Science & Dept. of Psychology
New York University
weijima@nyu.edu
Abstract
Computational models in fields such as computa... | 6780 |@word exploitation:1 version:5 middle:5 briefly:2 nd:4 mockus:1 zilinskas:1 calculus:1 simulation:5 seek:1 crucially:3 covariance:11 accounting:3 pick:1 initial:7 configuration:5 series:2 pub:3 sobol:2 tuned:1 genetic:2 interestingly:1 luigi:3 existing:1 ninit:2 freitas:3 current:12 outperforms:3 subjective:1 com... |
6,391 | 6,781 | Learning Chordal Markov Networks
via Branch and Bound
Kari Rantanen
HIIT, Dept. Comp. Sci.,
University of Helsinki
Antti Hyttinen
HIIT, Dept. Comp. Sci.,
University of Helsinki
Matti J?rvisalo
HIIT, Dept. Comp. Sci.,
University of Helsinki
Abstract
We present a new algorithmic approach for the task of finding a chor... | 6781 |@word middle:2 version:2 wiesel:1 polynomial:1 nd:2 giudici:1 heuristically:1 tried:1 bn:16 covariance:1 thereby:3 solid:1 versatile:1 initial:1 contains:4 score:22 past:1 current:10 comparing:1 chordal:28 john:1 tarantola:1 enables:1 update:2 fund:1 v:3 newest:1 intelligence:3 fewer:1 malone:2 selected:1 beginni... |
6,392 | 6,782 | Revenue Optimization with Approximate Bid
Predictions
? Medina
Andr?es Munoz
Google Research
76 9th Ave
New York, NY 10011
Sergei Vassilvitskii
Google Research
76 9th Ave
New York, NY 10011
Abstract
In the context of advertising auctions, finding good reserve prices is a notoriously
challenging learning problem. This... | 6782 |@word version:1 achievable:2 polynomial:5 leighton:3 twelfth:1 willing:2 attainable:1 reduction:3 celebrated:1 contains:2 selecting:1 outperforms:2 current:1 surprising:2 si:3 sergei:2 must:2 readily:1 tenet:1 refines:1 adexchange:1 partition:23 shape:2 designed:1 plot:1 half:1 item:5 desktop:1 beginning:1 stest:... |
6,393 | 6,783 | Solving Most Systems of Random
Quadratic Equations
Gang Wang?,?
?
Georgios B. Giannakis?
Yousef Saad?
Jie Chen?
Key Lab of Intell. Contr. and Decision of Complex Syst., Beijing Inst. of Technology
?
Digital Tech. Center & Dept. of Electrical and Computer Eng., Univ. of Minnesota
?
Department of Computer Science an... | 6783 |@word trial:4 phasemax:2 polynomial:1 norm:5 suitably:1 c0:2 confirms:1 rgb:1 eng:1 attainable:1 incurs:1 carry:1 shechtman:1 initial:5 series:1 efficacy:2 hereafter:1 score:2 mag:1 outperforms:1 existing:1 past:1 current:3 recovered:3 com:1 optim:1 tackling:1 yet:1 readily:2 additive:3 numerical:12 benign:1 pert... |
6,394 | 6,784 | Unsupervised Learning of Disentangled and
Interpretable Representations from Sequential Data
Wei-Ning Hsu, Yu Zhang, and James Glass
Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139, USA
{wnhsu,yzhang87,glass}@csail.mit.edu
Abstract
We present a factori... | 6784 |@word kohli:1 middle:2 hu:1 pieter:1 grey:1 covariance:2 eng:1 configuration:3 contains:8 jimenez:2 outperforms:2 z2:81 com:1 lang:1 yet:1 diederik:4 john:2 distant:1 subsequent:2 designed:2 interpretable:10 drop:1 update:1 alone:1 intelligence:2 generative:17 alec:1 isotropic:2 fabius:1 short:5 regressive:2 prov... |
6,395 | 6,785 | Lookahead Bayesian Optimization
with Inequality Constraints
Remi R. Lam
Massachusetts Institute of Technology
Cambridge, MA
rlam@mit.edu
Karen E. Willcox
Massachusetts Institute of Technology
Cambridge, MA
kwillcox@mit.edu
Abstract
We consider the task of optimizing an objective function subject to inequality
constr... | 6785 |@word exploitation:4 illustrating:1 seems:1 nd:1 mockus:1 zilinskas:1 seek:1 simulation:10 thereby:1 shading:1 recursively:1 reduction:5 initial:2 ndez:3 series:1 disparity:1 past:1 existing:1 current:2 com:1 surprising:1 yet:1 must:1 john:1 fn:1 numerical:6 subsequent:1 cheap:4 analytic:1 designed:2 update:1 dep... |
6,396 | 6,786 | Hierarchical Methods of Moments
Matteo Ruffini ?
Universitat Polit?cnica
de Catalunya
Guillaume Rabusseau ?
McGill University
Borja Balle ?
Amazon Research
Cambridge
Abstract
Spectral methods of moments provide a powerful tool for learning the parameters
of latent variable models. Despite their theoretical appeal, t... | 6786 |@word polynomial:2 stronger:1 proportion:1 open:1 d2:1 decomposition:13 recursively:3 moment:30 initial:1 liu:1 contains:3 series:1 daniel:6 genetic:1 document:11 outperforms:1 existing:6 diagonalized:1 recovered:2 com:1 comparing:2 written:1 john:1 numerical:2 partition:2 confirming:1 kdd:1 remove:2 designed:1 h... |
6,397 | 6,787 | Interpretable and Globally Optimal Prediction for
Textual Grounding using Image Concepts
Raymond A. Yeh,
Jinjun Xiong? ,
Minh N. Do,
Wen-mei W. Hwu,
Alexander G. Schwing
Department of Electrical Engineering, University of Illinois at Urbana-Champaign
?
IBM Thomas J. Watson Research Center
yeh17@illinois.edu, jinj... | 6787 |@word kong:2 norm:1 kokkinos:1 underline:1 everingham:1 stronger:1 open:1 termination:1 hu:4 r:1 decomposition:4 mention:1 recursively:1 necessity:1 configuration:4 contains:2 score:26 cellphone:2 liu:1 initial:3 denoting:2 ours:6 tuned:3 fragment:1 outperforms:1 existing:3 current:1 com:1 guadarrama:2 contextual... |
6,398 | 6,788 | Revisit Fuzzy Neural Network:
Demystifying Batch Normalization and ReLU with
Generalized Hamming Network
Lixin Fan
lixin.fan@nokia.com
Nokia Technologies
Tampere, Finland
Abstract
We revisit fuzzy neural network with a cornerstone notion of generalized hamming distance, which provides a novel and theoretically justif... | 6788 |@word kulis:1 cnn:4 middle:2 briefly:1 nd:1 open:1 closure:2 calculus:2 hu:1 bn:13 prominence:1 thres:4 lepetit:1 reduction:1 celebrated:3 liu:2 series:1 wj2:1 kurt:1 existing:1 current:1 com:3 manuel:1 analysed:1 activation:12 yet:2 intriguing:1 must:2 readily:2 john:1 luis:1 diederik:2 chu:1 confirming:1 christ... |
6,399 | 6,789 | Speeding Up Latent Variable Gaussian Graphical
Model Estimation via Nonconvex Optimization
Pan Xu
Department of Computer Science
University of Virginia
Charlottesville, VA 22904
px3ds@virginia.edu
Jian Ma
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
jianma@cs.cmu.edu
Quanquan Gu
Departme... | 6789 |@word determinant:7 briefly:1 inversion:2 norm:25 bf:3 c0:5 d2:7 confirms:1 r:5 covariance:11 decomposition:9 contraction:1 tr:4 initial:3 configuration:1 liu:9 score:3 tuned:2 ours:3 xinyang:1 nonparanormal:1 outperforms:1 existing:3 nicolai:1 luo:1 toh:1 yet:1 written:1 bd:3 john:3 numerical:2 designed:1 plot:1... |
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