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5,800 | 6,248 | Wasserstein Training of
Restricted Boltzmann Machines
Gr?goire Montavon
Technische Universit?t Berlin
Klaus-Robert M?ller?
Technische Universit?t Berlin
gregoire.montavon@tu-berlin.de
klaus-robert.mueller@tu-berlin.de
Marco Cuturi
CREST, ENSAE, Universit? Paris-Saclay
marco.cuturi@ensae.fr
Abstract
Boltzmann machi... | 6248 |@word version:3 stronger:2 villani:1 distribue:1 twelfth:1 heuristically:2 decomposition:2 covariance:1 contrastive:1 mention:1 reduction:1 initial:1 configuration:1 united:1 document:1 interestingly:1 rightmost:2 freitas:1 recovered:1 must:2 readily:1 written:1 visible:2 partition:2 shape:2 plot:5 update:1 gener... |
5,801 | 6,249 | Human Decision-Making under Limited Time
Pedro A. Ortega
Department of Psychology
University of Pennsylvania
Philadelphia, PA 19104
ope@seas.upenn.edu
Alan A. Stocker
Department of Psychology
University of Pennsylvania
Philadelphia, PA 19014
astocker@sas.upenn.edu
Abstract
Subjective expected utility theory assumes ... | 6249 |@word trial:24 determinant:1 cingulate:2 version:8 inversion:1 logit:1 integrative:1 gradual:3 crucially:2 decomposition:14 attainable:2 pick:2 pressure:3 paid:1 thereby:1 united:1 n000141110744:1 subjective:8 savage:1 anterior:2 conjunctive:1 john:1 plot:4 update:1 depict:1 implying:2 cue:2 device:1 accordingly:... |
5,802 | 625 | Visual Motion Computation in Analog
VLSI using Pulses
Rahul Sarpeshkar, Wyeth Bair and Christof Koch
Computation and Neural Systems Program
California Institute of Technology
Pasadena, CA 91125.
Abstract
The real time computation of motion from real images
using a single chip with integrated sensors is a hard problem... | 625 |@word version:3 rising:2 pulse:46 excited:2 harder:1 series:1 t7:1 tuned:3 zerocrossings:2 current:2 com:1 must:1 motor:4 designed:1 progressively:1 v:6 half:4 lr:1 filtered:2 detecting:6 node:1 location:8 height:1 prove:1 resistive:1 behavior:2 inspired:2 correlator:1 window:2 increasing:1 begin:1 provided:1 circ... |
5,803 | 6,250 | Pruning Random Forests for Prediction on a Budget
Feng Nan
Systems Engineering
Boston University
fnan@bu.edu
Joseph Wang
Electrical Engineering
Boston University
joewang@bu.edu
Venkatesh Saligrama
Electrical Engineering
Boston University
srv@bu.edu
Abstract
We propose to prune a random forest (RF) for resource-cons... | 6250 |@word repository:1 middle:1 polynomial:2 nd:1 seek:3 incurs:2 harder:1 reduction:2 initial:1 liu:1 contains:3 sherali:1 offering:1 ours:1 document:6 greedymiser:4 outperforms:5 existing:4 err:2 current:1 z2:2 nt:1 com:1 yet:2 must:2 realize:2 subsequent:2 happen:1 remove:1 plot:2 update:2 depict:1 v:2 greedy:4 le... |
5,804 | 6,251 | Learning Sensor Multiplexing
Design through Back-propagation
Ayan Chakrabarti
Toyota Technological Institute at Chicago
6045 S. Kenwood Ave., Chicago, IL
ayanc@ttic.edu
Abstract
Recent progress on many imaging and vision tasks has been driven by the use of
deep feed-forward neural networks, which are trained by propa... | 6251 |@word version:5 inversion:1 compression:2 seek:2 tried:1 rgb:8 sgd:2 shot:1 carry:2 initial:2 liu:1 contains:1 interestingly:1 outperforms:2 existing:2 past:1 current:1 z2:1 comparing:1 yet:1 gpu:2 chicago:2 visible:2 informative:1 blur:2 periodically:1 cheap:1 designed:1 treating:1 drop:1 update:2 half:1 fewer:1... |
5,805 | 6,252 | Optimal Sparse Linear Encoders and Sparse PCA
Malik Magdon-Ismail
Rensselaer Polytechnic Institute, Troy, NY 12211
magdon@cs.rpi.edu
Christos Boutsidis
New York, NY
christos.boutsidis@gmail.com
Abstract
Principal components analysis (PCA) is the optimal linear encoder of data. Sparse
linear encoders (e.g., sparse PC... | 6252 |@word madelon:1 middle:1 version:3 polynomial:3 norm:4 stronger:1 loading:3 r13:1 underline:1 kbkf:1 open:1 d2:1 calculus:1 seems:1 nscta:1 sammon:1 decomposition:2 p0:1 q1:10 pick:1 tr:5 sepulchre:1 reduction:5 contains:1 dspca:1 ours:1 document:1 existing:4 err:3 ka:1 com:1 current:1 rpi:1 gmail:1 yet:1 must:1 ... |
5,806 | 6,253 | Blazing the trails before beating the path:
Sample-efficient Monte-Carlo planning
Jean-Bastien Grill
Michal Valko
SequeL team, INRIA Lille - Nord Europe, France
jean-bastien.grill@inria.fr
michal.valko@inria.fr
R?mi Munos
Google DeepMind, UK?
munos@google.com
Abstract
You are a robot and you live in a Markov decisi... | 6253 |@word version:2 eliminating:1 polynomial:9 open:4 r:3 pick:2 harder:1 initial:1 contains:1 selecting:2 daniel:1 ours:1 current:1 com:1 michal:2 assigning:1 guez:2 john:2 belmont:1 numerical:1 enables:2 cheap:1 update:1 generative:10 intelligence:4 vanishing:2 aja:1 provides:2 characterization:1 node:94 philipp:1 ... |
5,807 | 6,254 | Domain Separation Networks
Konstantinos Bousmalis?
Google Brain
Mountain View, CA
konstantinos@google.com
Nathan Silberman
Google Research
New York, NY
nsilberman@google.com
George Trigeorgis? ?
Imperial College London
London, UK
g.trigeorgis@imperial.ac.uk
Dilip Krishnan
Google Research
Cambridge, MA
dilipkay@googl... | 6254 |@word private:27 version:3 cnn:5 norm:3 open:2 cleanly:1 covariance:1 brightness:1 lepetit:1 moment:1 initial:1 contains:3 efficacy:1 salzmann:3 ours:4 document:1 outperforms:4 existing:4 bitmap:1 com:5 nt:3 comparing:1 cad:1 si:5 realistic:2 additive:1 blur:1 shape:3 hypothesize:1 remove:1 v:1 alone:1 leaf:1 bis... |
5,808 | 6,255 | Conditional Generative Moment-Matching Networks
Yong Ren, Jialian Li, Yucen Luo, Jun Zhu?
Dept. of Comp. Sci. & Tech., TNList Lab; Center for Bio-Inspired Computing Research
State Key Lab for Intell. Tech. & Systems, Tsinghua University, Beijing, China
{renyong15, luoyc15, jl12}@mails.tsinghua.edu.cn; dcszj@tsinghua.ed... | 6255 |@word cnn:11 version:3 briefly:1 middle:4 norm:4 hu:1 tr:9 harder:1 tnlist:1 ld:4 moment:7 configuration:1 series:2 contains:1 practiced:1 rkhs:9 outperforms:1 contextual:4 od:1 luo:1 comparing:1 exy:1 universality:1 activation:2 written:1 concatenate:1 subsequent:1 hofmann:1 laplacianfaces:1 update:1 v:1 generat... |
5,809 | 6,256 | A Credit Assignment Compiler for Joint Prediction
Kai-Wei Chang
University of Virginia
kw@kwchang.net
He He
University of Maryland
hhe@cs.umd.edu
John Langford
Microsoft Research
jcl@microsoft.com
Hal Daum? III
University of Maryland
me@hal3.name
Stephane Ross
Google
stephaneross@google.com
Abstract
Many machine ... | 6256 |@word middle:3 version:1 tadepalli:2 termination:4 tried:1 accounting:1 sgd:8 yih:1 reduction:4 contains:1 score:1 tuned:4 ours:1 rightmost:1 existing:4 bradley:1 current:7 com:3 comparing:1 beygelzimer:1 si:3 must:2 parsing:8 john:1 exposing:1 hofmann:1 drop:1 designed:1 update:7 v:1 implying:1 half:1 greedy:1 a... |
5,810 | 6,257 | Joint Line Segmentation and Transcription for
End-to-End Handwritten Paragraph Recognition
Th?odore Bluche
A2iA SAS
39 rue de la Bienfaisance
75008 Paris
tb@a2ia.com
Abstract
Offline handwriting recognition systems require cropped text line images for both
training and recognition. On the one hand, the annotation of ... | 6257 |@word torsten:1 version:1 laurence:1 hector:2 open:1 tried:1 mdlstm:20 ndez:1 contains:2 score:2 document:27 interestingly:1 outperforms:2 existing:1 current:2 com:1 comparing:1 michal:2 activation:2 yet:1 subsequent:1 happen:1 romero:2 christian:2 designed:1 interpretable:1 progressively:1 intelligence:1 prohibi... |
5,811 | 6,258 | Incremental Boosting Convolutional Neural Network
for Facial Action Unit Recognition
Shizhong Han, Zibo Meng, Ahmed Shehab Khan, Yan Tong
Department of Computer Science & Engineering, University of South Carolina, Columbia, SC
{han38, mengz, akhan}@email.sc.edu, tongy@cse.sc.edu
Abstract
Recognizing facial action uni... | 6258 |@word multitask:1 cnn:154 open:1 simulation:1 carolina:1 contraction:1 hager:1 liu:6 contains:2 exclusively:1 selecting:2 score:25 tuned:2 outperforms:4 current:7 guadarrama:1 activation:13 attracted:1 shape:2 designed:2 drop:1 update:2 v:2 half:1 selected:19 plane:2 incredible:1 ith:1 short:1 detecting:1 boostin... |
5,812 | 6,259 | Variational Bayes on Monte Carlo Steroids
Aditya Grover, Stefano Ermon
Department of Computer Science
Stanford University
{adityag,ermon}@cs.stanford.edu
Abstract
Variational approaches are often used to approximate intractable posteriors or normalization constants in hierarchical latent variable models. While often ... | 6259 |@word mild:1 version:1 contrastive:1 q1:1 sgd:2 reduction:1 moment:2 configuration:6 score:1 uma:1 document:10 subjective:1 existing:1 activation:1 additive:1 partition:2 visible:3 update:5 hash:4 generative:14 half:1 advancement:2 devising:1 blei:3 node:1 wierstra:1 qualitative:3 consists:4 combine:1 introduce:2... |
5,813 | 626 | A Connectionist Symbol Manipulator
That Discovers the Structure of
Context-Free Languages
Michael C. Mozer and Sreerupa Das
Department of Computer Science &
Institute of Cognitive Science
University of Colorado
Boulder, CO 80309-0430
Abstract
We present a neural net architecture that can discover hierarchical and rec... | 626 |@word briefly:1 version:2 r:1 propagate:1 shading:1 reduction:2 initial:2 contains:2 current:2 activation:1 must:3 readily:1 parsing:2 written:2 numerical:2 designed:2 interpretable:1 half:1 selected:1 item:11 lr:6 compo:1 preference:1 attack:1 height:1 consists:1 theoretically:2 roughly:1 behavior:4 examine:1 con... |
5,814 | 6,260 | x2
x1
100
50
40
OKM
OKM*
OKM*+LPE
OKM*+NPE
80
30
60
20
40
10
20
0
0
0.2
0.4
0.6
F?Score
0.8
1
0
0
OKM*
OKM*+LPE
OKM*+NPE
0.2
0.4
0.6
F?Score
0.8
1
| 6260 |@word lpe:2 score:2 okm:7 x2:1 npe:2 x1:1 |
5,815 | 6,261 | Visual Question Answering with
Question Representation Update (QRU)
Ruiyu Li
Jiaya Jia
The Chinese University of Hong Kong
{ryli,leojia}@cse.cuhk.edu.hk
Abstract
Our method aims at reasoning over natural language questions and visual images.
Given a natural language question about an image, our model updates the ques... | 6261 |@word kong:2 cnn:10 open:4 hu:2 shuicheng:1 seek:1 recursively:1 configuration:1 contains:4 score:1 selecting:1 hoiem:1 outperforms:3 activation:2 yet:1 gpu:1 visible:1 blur:1 informative:1 confirming:1 designed:2 drop:2 update:14 sukhbaatar:2 selected:1 ith:3 num:2 provides:1 cse:1 location:6 sits:2 five:1 heigh... |
5,816 | 6,262 | Adaptive Newton Method for Empirical Risk
Minimization to Statistical Accuracy
Aryan Mokhtari?
University of Pennsylvania
aryanm@seas.upenn.edu
Hadi Daneshmand?
ETH Zurich, Switzerland
hadi.daneshmand@inf.ethz.ch
Thomas Hofmann
ETH Zurich, Switzerland
thomas.hofmann@inf.ethz.ch
Aurelien Lucchi
ETH Zurich, Switzerlan... | 6262 |@word mild:1 inversion:5 seems:1 stronger:1 consequential:1 norm:1 reused:1 nd:2 urb:1 citeseer:1 sgd:9 mention:1 reduction:3 initial:8 contains:3 united:2 outperforms:1 current:2 comparing:1 must:1 numerical:4 subsequent:2 kdd:1 hofmann:4 plot:2 update:6 juditsky:1 intelligence:2 website:1 item:1 amir:1 beginnin... |
5,817 | 6,263 | Learning Deep Parsimonious Representations
Renjie Liao1 , Alexander Schwing2 , Richard S. Zemel1,3 , Raquel Urtasun1
University of Toronto1
University of Illinois at Urbana-Champaign2
Canadian Institute for Advanced Research3
{rjliao, zemel, urtasun}@cs.toronto.edu, aschwing@illinois.edu
Abstract
In this paper we aim... | 6263 |@word trial:2 compression:3 norm:1 nd:4 propagate:1 decomposition:1 sgd:1 tr:1 shot:7 initial:1 liu:1 contains:2 score:1 denoting:1 document:1 outperforms:1 current:5 com:1 comparing:1 guadarrama:1 activation:3 assigning:1 partition:1 shape:2 enables:1 remove:1 drop:1 seeding:1 update:10 hash:1 cue:1 selected:1 w... |
5,818 | 6,264 | Dialog-based Language Learning
Jason Weston
Facebook AI Research,
New York.
jase@fb.com
Abstract
A long-term goal of machine learning research is to build an intelligent dialog
agent. Most research in natural language understanding has focused on learning
from fixed training sets of labeled data, with supervision eit... | 6264 |@word open:2 instruction:1 r:1 crucially:1 tried:1 harder:2 carry:1 initial:1 score:1 selecting:1 past:1 existing:2 o2:3 com:1 comparing:1 surprising:2 must:2 parsing:2 john:16 realistic:2 subsequent:3 informative:1 ronan:1 pertinent:1 cracking:1 succeeding:1 v:1 infant:1 intelligence:1 selected:2 guess:1 sukhbaa... |
5,819 | 6,265 | A Sparse Interactive Model for Matrix Completion
with Side Information
Jin Lu
Guannan Liang
Jiangwen Sun
Jinbo Bi
University of Connecticut
Storrs, CT 06269
{jin.lu, guannan.liang, jiangwen.sun, jinbo.bi}@uconn.edu
Abstract
Matrix completion methods can benefit from side information besides the partially observed mat... | 6265 |@word kgk:2 version:2 norm:16 stronger:2 nd:1 d2:4 hu:1 simulation:4 linearized:4 heiser:1 decomposition:2 hsieh:2 liu:2 kpv:1 tuned:1 outperforms:1 existing:5 kmk:1 recovered:5 jinbo:3 reaction:1 written:1 reminiscent:1 informative:4 update:1 rd2:1 selected:1 ith:1 chiang:2 math:1 location:1 simpler:1 zhang:1 ma... |
5,820 | 6,266 | Ancestral Causal Inference
Sara Magliacane
VU Amsterdam & University of Amsterdam
sara.magliacane@gmail.com
Tom Claassen
Radboud University Nijmegen
tomc@cs.ru.nl
Joris M. Mooij
University of Amsterdam
j.m.mooij@uva.nl
Abstract
Constraint-based causal discovery from limited data is a notoriously difficult challenge ... | 6266 |@word mild:3 version:8 middle:1 achievable:1 seems:2 open:1 calculus:1 closure:1 willing:1 crucially:2 cyclic:2 series:2 score:15 contains:1 bootstrapped:16 interestingly:2 outperforms:1 existing:2 ramsey:1 recovered:1 com:2 comparing:1 plcg:19 gmail:1 assigning:1 happen:1 enables:1 plot:4 v:1 greedy:1 discoverin... |
5,821 | 6,267 | Regularized Nonlinear Acceleration
Damien Scieur
INRIA & D.I., UMR 8548,
?cole Normale Sup?rieure, Paris, France.
damien.scieur@inria.fr
Alexandre d?Aspremont
CNRS & D.I., UMR 8548,
?cole Normale Sup?rieure, Paris, France.
aspremon@di.ens.fr
Francis Bach
INRIA & D.I., UMR 8548,
?cole Normale Sup?rieure, Paris, Franc... | 6267 |@word madelon:3 version:3 polynomial:31 norm:7 km:1 recursively:1 moment:1 reduction:1 initial:1 series:3 daniel:1 kx0:15 current:1 ka:6 comparing:1 must:1 written:3 stemming:1 numerical:9 zaid:1 extrapolating:1 interpretable:1 update:1 implying:1 stationary:1 device:1 xk:7 smith:2 recherche:1 ck2:4 num:1 iterate... |
5,822 | 6,268 | MetaGrad: Multiple Learning Rates
in Online Learning
Tim van Erven
Leiden University
tim@timvanerven.nl
Wouter M. Koolen
Centrum Wiskunde & Informatica
wmkoolen@cwi.nl
Abstract
In online convex optimization it is well known that certain subclasses of objective
functions are much easier than arbitrary convex functions... | 6268 |@word briefly:1 version:26 stronger:1 seems:3 norm:5 open:2 mehta:1 d2:11 simulation:2 seek:1 linearized:2 covariance:5 gradual:1 incurs:3 boundedness:1 series:3 contains:2 tuned:1 erven:9 existing:2 past:1 discretization:1 surprising:1 luo:2 intriguing:1 bd:1 tilted:3 partition:2 happen:1 shape:2 drop:1 update:8... |
5,823 | 6,269 | Graphical Time Warping for Joint Alignment of
Multiple Curves
Yizhi Wang
Virginia Tech
yzwang@vt.edu
David J. Miller
Pennsylvania State University
djmiller@engr.psu.edu
Yue Wang
Virginia Tech
yuewang@vt.edu
Kira Poskanzer
University of California, San Francisco
Kira.Poskanzer@ucsf.edu
Lin Tian
University of Califo... | 6269 |@word middle:2 advantageous:1 seems:1 simulation:6 paid:1 fortuitous:1 necessity:1 series:23 contains:1 liquid:1 denoting:1 tuned:1 interestingly:2 batista:1 outperforms:1 existing:3 comparing:1 yet:1 must:1 readily:3 written:1 distant:1 additive:1 subsequent:1 shape:2 designed:1 progressively:1 v:2 intelligence:... |
5,824 | 627 | History-dependent Attractor Neural
Networks
Isaac Meilijson
Eytan Ruppin
School of Mathematical Sciences
Raymond and Beverly Sackler Faculty of Exact Sciences
Tel-A viv University, 69978 Tel-Aviv, Israel.
Abstract
We present a methodological framework enabling a detailed description of the performance of Hopfield-like... | 627 |@word trial:1 faculty:1 open:1 simulation:2 moment:1 initial:3 selecting:1 past:1 current:4 activation:14 si:1 dx:1 afl:1 numerical:2 plot:1 update:1 signalling:5 contribute:1 sigmoidal:4 lor:1 mathematical:2 become:2 retrieving:1 consists:1 xji:1 brain:2 actual:2 becomes:1 israel:1 what:1 quantitative:1 every:1 u... |
5,825 | 6,270 | Stochastic Multiple Choice Learning for
Training Diverse Deep Ensembles
Stefan Lee
Virginia Tech
steflee@vt.edu
Senthil Purushwalkam
Carnegie Mellon University
spurushw@andrew.cmu.edu
David Crandall
Indiana University
djcran@indiana.edu
Michael Cogswell
Virginia Tech
cogswell@vt.edu
Viresh Ranjan
Virginia Tech
rvir... | 6270 |@word kohli:2 cnn:14 faculty:3 version:1 middle:1 retraining:5 everingham:1 open:1 surfboard:6 seek:1 propagate:1 prasad:1 pick:2 sgd:4 rivera:5 accommodate:1 reduction:1 initial:1 liu:1 efficacy:3 score:3 selecting:1 tuned:3 past:1 existing:8 outperforms:5 com:2 assigning:1 gpu:2 neuraltalk2:1 distant:1 partitio... |
5,826 | 6,271 | Global Optimality of Local Search
for Low Rank Matrix Recovery
Srinadh Bhojanapalli
srinadh@ttic.edu
Behnam Neyshabur
bneyshabur@ttic.edu
Nathan Srebro
nati@ttic.edu
Toyota Technological Institute at Chicago
Abstract
We show that there are no spurious local minima in the non-convex factorized
parametrization of low-... | 6271 |@word mild:2 polynomial:4 stronger:1 norm:4 seems:3 nd:3 decomposition:3 sgd:5 sepulchre:1 necessity:1 liu:3 ours:2 past:1 ka:3 recovered:1 comparing:1 subsequent:1 chicago:1 cheap:1 hoping:1 plot:2 update:2 implying:1 stationary:6 parametrization:2 matrix1:1 zhang:1 mathematical:3 along:4 symposium:1 yuan:1 comb... |
5,827 | 6,272 | Preference Completion from Partial Rankings
Suriya Gunasekar
University of Texas, Austin, TX, USA
suriya@utexas.edu
Oluwasanmi Koyejo
University of Illinois, Urbana-Champaign, IL, USA
sanmi@illinois.edu
Joydeep Ghosh
University of Texas,Austin, TX, USA
ghosh@ece.utexas.edu
Abstract
We propose a novel and efficient ... | 6272 |@word version:1 pw:1 norm:10 mcrank:1 c0:3 d2:22 km:3 seek:3 grey:1 steck:1 decomposition:1 p0:1 harder:1 initial:1 substitution:2 liu:3 score:33 contains:1 denoting:1 tuned:1 bootstrapped:2 neeman:1 outperforms:2 existing:1 current:2 optim:2 incidence:2 ganti:1 activation:3 numerical:7 partition:1 kdd:1 remove:1... |
5,828 | 6,273 | Fundamental Limits of Budget-Fidelity Trade-off in
Label Crowdsourcing
Farshad Lahouti
Electrical Engineering Department, California Institute of Technology
lahouti@caltech.edu
Babak Hassibi
Electrical Engineering Department, California Institute of Technology
hassibi@caltech.edu
Abstract
Digital crowdsourcing (CS) i... | 6273 |@word achievable:2 compression:3 instrumental:1 dekel:1 tedious:1 seek:1 bn:1 paid:1 accommodate:1 series:1 karger:1 subjective:2 csn:1 current:2 comparing:2 incidence:9 numerical:2 enables:1 remove:1 designed:1 item:22 accordingly:1 ruvolo:1 ith:1 provides:5 completeness:1 contribute:1 node:1 org:1 zhang:1 along... |
5,829 | 6,274 | Generalized Correspondence-LDA Models (GC-LDA)
for Identifying Functional Regions in the Brain
Timothy N. Rubin
SurveyMonkey
Oluwasanmi Koyejo
Univ. of Illinois, Urbana-Champaign
Michael N. Jones
Indiana University
Tal Yarkoni
University of Texas at Austin
Abstract
This paper presents Generalized Correspondence-LD... | 6274 |@word proceeded:1 fusiform:1 middle:2 version:12 loading:1 adrian:1 decomposition:1 fabrice:1 pick:1 reduction:1 liu:1 contains:1 genetic:1 document:18 longitudinal:1 outperforms:1 existing:2 current:4 nt:1 activation:47 assigning:3 distant:2 analytic:3 motor:2 remove:1 atlas:4 interpretable:1 medial:2 toro:1 alo... |
5,830 | 6,275 | Ladder Variational Autoencoders
Casper Kaae S?nderby?
casperkaae@gmail.com
Tapani Raiko?
tapani.raiko@aalto.fi
S?ren Kaae S?nderby?
skaaesonderby@gmail.com
Lars Maal?e?
larsma@dtu.dk
Ole Winther?,?
olwi@dtu.dk
Abstract
Variational autoencoders are powerful models for unsupervised learning. However
deep models with... | 6275 |@word nd:1 d2:1 bn:5 shot:1 harder:1 recursively:5 contains:1 series:1 score:4 tuned:2 current:2 com:6 z2:4 nt:2 comparing:1 surprising:1 gmail:2 subsequent:1 wanted:1 plot:2 progressively:1 v:1 generative:44 parameterization:5 beginning:1 blei:1 provides:6 complication:1 five:6 wierstra:2 dn:2 bowman:1 mathemati... |
5,831 | 6,276 | Select-and-Sample for Spike-and-Slab Sparse Coding
Abdul-Saboor Sheikh
Technical University of Berlin, Germany,
and Cluster of Excellence Hearing4all
University of Oldenburg, Germany,
and SAP Innovation Center Network, Berlin
sheikh.abdulsaboor@gmail.com
J?rg L?cke
Research Center Neurosensory Science
and Cluster of E... | 6276 |@word neurophysiology:1 nd:3 d2:2 seek:1 decomposition:1 arti:2 eld:29 garrigues:1 initial:1 liu:1 contains:1 exclusively:1 oldenburg:2 selecting:2 outperforms:1 recovered:2 com:1 si:18 gmail:1 assigning:1 numerical:1 cant:1 shape:2 plasticity:1 drop:1 interpretable:2 update:1 generative:14 selected:4 half:1 cult... |
5,832 | 6,277 | Refined Lower Bounds for Adversarial Bandits
S?bastien Gerchinovitz
Institut de Math?matiques de Toulouse
Universit? Toulouse 3 Paul Sabatier
Toulouse, 31062, France
sebastien.gerchinovitz@math.univ-toulouse.fr
Tor Lattimore
Department of Computing Science
University of Alberta
Edmonton, Canada
tor.lattimore@gmail.com... | 6277 |@word achievable:1 stronger:1 rigged:1 open:3 q1:8 incurs:1 bs01:1 tuned:2 erven:2 existing:5 com:1 z2:1 surprising:1 comparing:1 allenberg:2 gmail:1 must:5 subsequent:1 informative:1 benign:1 gerchinovitz:2 designed:1 selected:1 beginning:2 ith:1 recherche:1 provides:2 math:4 unbounded:1 symposium:1 prove:11 com... |
5,833 | 6,278 | CRF-CNN: Modeling Structured Information in
Human Pose Estimation
Xiao Chu
The Chinese University of Hong Kong
xchu@ee.cuhk.edu.hk
Wanli Ouyang
The Chinese University of Hong Kong
wlouyang@ee.cuhk.edu.hk
Hongsheng Li
The Chinese University of Hong Kong
hsli@ee.cuhk.edu.hk
Xiaogang Wang
The Chinese University of Hong... | 6278 |@word kong:5 cnn:41 dalal:1 seems:1 paredes:1 everingham:1 triggs:1 heuristically:1 concise:3 configuration:6 contains:3 score:6 liu:1 loeliger:1 ours:3 romera:1 existing:2 activation:1 chu:6 pcp:4 parsing:1 partition:1 drop:1 update:1 greedy:1 leaf:2 half:1 generative:1 ith:4 vanishing:1 colored:1 provides:2 nod... |
5,834 | 6,279 | Natural-Parameter Networks:
A Class of Probabilistic Neural Networks
Hao Wang, Xingjian Shi, Dit-Yan Yeung
Hong Kong University of Science and Technology
{hwangaz,xshiab,dyyeung}@cse.ust.hk
Abstract
Neural networks (NN) have achieved state-of-the-art performance in various applications. Unfortunately in applications w... | 6279 |@word kong:1 middle:2 version:5 seems:1 d2:1 sgd:2 moment:5 ndez:1 lightweight:1 bc:8 document:7 interestingly:1 outperforms:1 existing:1 wd:12 z2:3 od:29 activation:13 tackling:1 dx:7 ust:1 readily:1 bd:8 gpu:1 citeulike:11 subsequent:2 kdd:2 shape:1 enables:2 designed:3 drop:1 update:1 depict:1 treating:1 plot:... |
5,835 | 628 | Unsupervised Discrimination of Clustered Data
via Optimization of Binary Information Gain
Nicol N. Schraudolph
Computer Science & Engr. Dept.
University of California, San Diego
La Jolla, CA 92093-0114
Terrence J. Sejnowski
Computational Neurobiology Laboratory
The Salk Institute for Biological Studies
San Diego, CA ... | 628 |@word eliminating:1 proportion:1 seek:1 covariance:2 solid:1 accommodate:1 tsejnowski:1 barney:3 carry:1 initial:6 substitution:1 existing:1 nowlan:1 scatter:2 must:1 reminiscent:1 happen:1 informative:3 remove:1 plot:5 concert:1 update:1 discrimination:11 half:3 greedy:1 ith:1 provides:2 node:29 preference:1 math... |
5,836 | 6,280 | DeepMath - Deep Sequence Models for Premise
Selection
Alexander A. Alemi ?
Google Inc.
alemi@google.com
Geoffrey Irving ?
Google Inc.
geoffreyi@google.com
Fran?ois Chollet ?
Google Inc.
fchollet@google.com
Christian Szegedy ?
Google Inc.
szegedy@google.com
Niklas Een ?
Google Inc.
een@google.com
Josef Urban ??
Czech... | 6280 |@word repository:1 version:3 cnn:19 stronger:2 calculus:1 underperform:1 heiser:1 propagate:1 essay:1 attainable:1 pick:1 accommodate:1 recursively:1 reduction:1 initial:1 series:2 score:3 pub:1 itp:13 tuned:1 outperforms:2 existing:2 ramsey:1 guadarrama:1 com:7 steiner:1 gmail:1 written:2 parsing:2 gpu:1 devin:1... |
5,837 | 6,281 | Optimizing Affinity-Based Binary Hashing
Using Auxiliary Coordinates
Ramin Raziperchikolaei
EECS, University of California, Merced
rraziperchikolaei@ucmerced.edu
? Carreira-Perpin?
? an
Miguel A.
EECS, University of California, Merced
mcarreira-perpinan@ucmerced.edu
Abstract
In supervised binary hashing, one wants t... | 6281 |@word mild:1 kulis:2 version:3 briefly:1 disk:1 perpin:1 tried:1 crucially:1 hsieh:1 solid:1 harder:1 liblinear:2 reduction:6 liu:2 contains:2 initial:1 interestingly:1 outperforms:4 existing:2 current:3 comparing:1 must:2 written:2 concatenate:1 numerical:1 shape:1 designed:1 gist:2 progressively:1 plot:3 hash:9... |
5,838 | 6,282 | Stochastic Gradient Geodesic MCMC Methods
?
Chang Liu? , Jun Zhu? , Yang Song??
Dept. of Comp. Sci. & Tech., TNList Lab; Center for Bio-Inspired Computing Research
?
State Key Lab for Intell. Tech. & Systems, Tsinghua University, Beijing, China
?
Dept. of Physics, Tsinghua University, Beijing, China
{chang-li14@mails... | 6282 |@word briefly:1 version:4 changyou:3 advantageous:1 norm:1 nd:13 proportion:1 open:1 scalably:1 km:2 simulation:4 covariance:2 tnlist:1 liu:1 series:1 score:1 salzmann:1 ours:2 document:4 existing:1 jupp:1 yet:1 dx:1 john:3 numerical:1 shape:1 analytic:1 sdes:1 enables:1 kv1:1 stationary:4 generative:2 intelligen... |
5,839 | 6,283 | Deconvolving Feedback Loops
in Recommender Systems
Ayan Sinha
Purdue University
sinhayan@mit.edu
David F. Gleich
Purdue University
dgleich@purdue.edu
Karthik Ramani
Purdue University
ramani@purdue.edu
Abstract
Collaborative filtering is a popular technique to infer users? preferences on new
content based on the col... | 6283 |@word exploitation:3 briefly:2 version:2 adomavicius:1 seems:1 norm:3 open:1 hu:2 r:52 simulation:1 decomposition:5 eng:1 mcauley:1 initial:1 series:4 score:38 exclusively:1 interestingly:1 current:1 contextual:2 com:1 surprising:1 si:3 scatter:4 assigning:1 rpi:2 chu:1 subsequent:2 thrust:1 kdd:3 enables:3 asymp... |
5,840 | 6,284 | Latent Attention For If-Then Program Synthesis
Xinyun Chen?
Shanghai Jiao Tong University
Chang Liu
Richard Shin
UC Berkeley
Dawn Song
Mingcheng Chen?
UIUC
Abstract
Automatic translation from natural language descriptions into programs is a longstanding challenging problem. In this work, we consider a simple yet ... | 6284 |@word middle:1 norm:5 instruction:1 shot:10 liu:1 contains:3 denoting:1 document:1 fa8750:1 outperforms:3 existing:6 past:1 com:9 unction:4 yet:1 parsing:8 concatenate:1 numerical:1 subsequent:1 informative:1 designed:2 update:1 sukhbaatar:1 fewer:1 website:3 device:1 kushman:1 short:2 provides:3 along:3 transduc... |
5,841 | 6,285 | A Multi-step Inertial Forward?Backward Splitting
Method for Non-convex Optimization
Jingwei Liang and Jalal M. Fadili
Normandie Univ, ENSICAEN, CNRS, GREYC
{Jingwei.Liang,Jalal.Fadili}@greyc.ensicaen.fr
Gabriel Peyr?
CNRS, DMA, ENS Paris
Gabriel.Peyre@ens.fr
Abstract
We propose a multi-step inertial Forward?Backward... | 6285 |@word version:1 norm:10 nd:2 open:1 calculus:1 decomposition:1 solid:3 boundedness:2 initial:1 outperforms:1 existing:2 comparing:1 discretization:1 optim:1 yet:1 pcp:4 written:1 numerical:5 additive:2 plot:2 xk:36 short:1 ojasiewicz:9 iterates:5 characterization:1 zhang:1 mathematical:5 along:3 become:1 differen... |
5,842 | 6,286 | Barzilai-Borwein Step Size for Stochastic Gradient
Descent
Conghui Tan
The Chinese University of Hong Kong
chtan@se.cuhk.edu.hk
Shiqian Ma
The Chinese University of Hong Kong
sqma@se.cuhk.edu.hk
Yu-Hong Dai
Chinese Academy of Sciences, Beijing, China
dyh@lsec.cc.ac.cn
Yuqiu Qian
The University of Hong Kong
qyq79@con... | 6286 |@word kong:4 briefly:1 norm:2 pick:2 sgd:66 solid:5 recursively:1 hager:1 reduction:2 initial:7 cyclic:1 efficacy:1 tuned:13 past:1 existing:2 outperforms:1 current:1 must:1 numerical:15 update:4 fund:1 selected:1 xk:14 lr:4 iterates:3 zhang:4 mathematical:1 along:2 prove:5 manner:1 introduce:1 x0:5 frequently:1 ... |
5,843 | 6,287 | Pairwise Choice Markov Chains
Stephen Ragain
Management Science & Engineering
Stanford University
Stanford, CA 94305
sragain@stanford.edu
Johan Ugander
Management Science & Engineering
Stanford University
Stanford, CA 94305
jugander@stanford.edu
Abstract
As datasets capturing human choices grow in richness and scale... | 6287 |@word kohli:1 exploitation:1 faculty:1 version:1 seems:1 norm:1 logit:10 proportion:1 instrumental:1 open:4 sheffet:1 commute:1 asks:1 blade:1 reduction:2 necessity:1 substitution:1 contains:3 score:2 cyclic:4 selecting:3 efficacy:1 symphony:2 outperforms:2 past:1 bradley:4 mishra:1 com:1 assigning:1 must:1 serge... |
5,844 | 6,288 | Split LBI: An Iterative Regularization Path with
Structural Sparsity
Chendi Huang1 , Xinwei Sun1 , Jiechao Xiong1 , Yuan Yao2,1
Peking University, 2 Hong Kong University of Science and Technology
{cdhuang, sxwxiaoxiaohehe, xiongjiechao}@pku.edu.cn, yuany@ust.hk
1
Abstract
An iterative regularization path with structu... | 6288 |@word h:1 kong:1 repository:1 version:2 norm:1 d2:2 linearized:2 rgb:1 decomposition:1 solid:2 initial:1 liu:1 series:2 score:1 denoting:3 outperforms:1 africa:2 recovered:1 comparing:1 osh:4 si:1 yet:1 ust:1 shape:1 drop:3 championship:3 alone:1 intelligence:1 rudin:1 xk:3 short:1 provides:1 coarse:1 boosting:1 ... |
5,845 | 6,289 | An Ensemble Diversity Approach
to Supervised Binary Hashing
? Carreira-Perpin?
? an
Miguel A.
EECS, University of California, Merced
mcarreira-perpinan@ucmerced.edu
Ramin Raziperchikolaei
EECS, University of California, Merced
rraziperchikolaei@ucmerced.edu
Abstract
Binary hashing is a well-known approach for fast ap... | 6289 |@word kulis:1 polynomial:3 proportion:2 seems:3 disk:1 open:1 confirms:1 perpin:1 bn:6 decomposition:3 hsieh:1 liblinear:2 bai:1 liu:2 contains:3 series:2 selecting:2 reduction:5 initial:1 document:1 bootstrapped:3 ours:1 outperforms:2 wd:1 yet:1 written:3 john:1 partition:1 happen:1 shape:1 designed:1 gist:1 upd... |
5,846 | 629 | Hidden Markov Models in Molecular
Biology: New Algorithms and
Applications
Yves Chauvin t
Net-ID, Inc.
8, Cathy Place
Menlo Park, CA 94305
Pierre Baldi ?
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, CA 91109
Tim H lmkapiller
Division of Biology
California Institute of Technology
Marcella A.... | 629 |@word private:1 briefly:2 mammal:1 phosphorylation:1 initial:2 fragment:1 egfr:5 terminus:1 ala:1 current:1 virus:1 si:1 must:1 cruz:1 remove:1 treating:1 plot:1 update:2 selected:1 beginning:1 tertiary:1 characterization:1 phylogenetic:2 mathematical:1 dn:1 constructed:1 ucsc:1 consists:2 baldi:10 recognizable:1 ... |
5,847 | 6,290 | Measuring the reliability of MCMC inference with
bidirectional Monte Carlo
Roger B. Grosse
Department of Computer Science
University of Toronto
Siddharth Ancha
Department of Computer Science
University of Toronto
Daniel M. Roy
Department of Statistics
University of Toronto
Abstract
Markov chain Monte Carlo (MCMC)... | 6290 |@word mild:1 version:2 simulation:2 moment:3 initial:6 configuration:1 lightweight:1 series:2 uncovered:1 daniel:1 disallows:1 tuned:1 subjective:3 wd:20 comparing:1 com:1 must:1 written:1 dechter:1 realistic:2 partition:6 informative:2 update:5 mackey:2 intelligence:1 leaf:2 generative:1 isotropic:1 realizing:1 ... |
5,848 | 6,291 | Unsupervised Learning from Noisy Networks with
Applications to Hi-C Data
Bo Wang?1 , Junjie Zhu2 , Oana Ursu3 , Armin Pourshafeie4 , Serafim Batzoglou1 and Anshul Kundaje3,1
2
1
Department of Computer Science, Stanford University
Department of Electrical Engineering, Stanford University
3
Department of Genetics, Stanf... | 6291 |@word kong:1 version:1 norm:2 dekker:3 sex:1 termination:1 hu:1 zelnik:1 seek:2 serafim:1 uncovers:1 tr:8 mcauley:1 initial:2 liu:2 contains:1 score:1 selecting:1 uncovered:1 genetic:1 existing:1 current:1 com:1 od:1 si:5 activation:1 numerical:1 partition:2 treating:1 drop:5 update:2 aside:1 implying:1 selected:... |
5,849 | 6,292 | Linear Contextual Bandits with Knapsacks
Shipra Agrawal?
Nikhil R. Devanur?
Abstract
We consider the linear contextual bandit problem with resource consumption, in
addition to reward generation. In each round, the outcome of pulling an arm is
a reward as well as a vector of resource consumptions. The expected values... | 6292 |@word trial:2 exploitation:2 version:1 norm:3 open:1 km:11 pick:5 profit:1 harder:1 reduction:1 liu:1 pt0:8 k1d:1 ours:1 current:1 contextual:27 com:1 comparing:1 wilkens:1 tackling:1 chu:1 midway:1 enables:1 sponsored:1 update:5 half:1 beginning:1 core:1 provides:1 along:4 constructed:2 become:1 focs:1 prove:2 c... |
5,850 | 6,293 | Variance Reduction in Stochastic Gradient
Langevin Dynamics
Avinava Dubey? , Sashank J. Reddi? , Barnab?as P?oczos, Alexander J. Smola, Eric P. Xing
Department of Machine Learning
Carnegie-Mellon University
Pittsburgh, PA 15213
{akdubey, sjakkamr, bapoczos, alex, epxing}@cs.cmu.edu
Sinead A. Williamson
IROM/Statistics ... | 6293 |@word repository:1 version:2 changyou:2 norm:1 h2t:5 pick:2 thereby:1 ld:13 reduction:14 initial:2 series:1 selecting:1 rightmost:1 outperforms:1 current:4 discretization:1 nt:3 numerical:1 cant:2 cheap:1 designed:3 plot:4 update:8 stationary:1 selected:2 item:1 complementing:1 slowing:1 accordingly:1 experiment3... |
5,851 | 6,294 | Safe Policy Improvement by Minimizing Robust
Baseline Regret
Marek Petrik
University of New Hampshire
mpetrik@cs.unh.edu
Mohammad Ghavamzadeh
Adobe Research & INRIA Lille
ghavamza@adobe.com
Yinlam Chow
Stanford University
ychow@stanford.edu
Abstract
An important problem in sequential decision-making under uncertain... | 6294 |@word briefly:1 polynomial:3 norm:1 p0:3 profit:1 solid:1 initial:1 contains:1 outperforms:1 existing:3 current:4 com:1 yet:1 readily:2 realistic:1 happen:1 stationary:1 intelligence:2 website:1 provides:2 mannor:2 node:3 simpler:2 along:1 constructed:3 prove:5 consists:1 combine:1 manner:1 introduce:1 x0:4 indee... |
5,852 | 6,295 | Can Active Memory Replace Attention?
?ukasz Kaiser
Google Brain
lukaszkaiser@google.com
Samy Bengio
Google Brain
bengio@google.com
Abstract
Several mechanisms to focus attention of a neural network on selected parts of its
input or memory have been used successfully in deep learning models in recent
years. Attention... | 6295 |@word compression:3 seems:1 norm:1 open:1 d2:1 tried:1 p0:2 pick:1 versatile:1 shot:1 harder:1 carry:1 substitution:1 contains:1 score:12 selecting:1 jimenez:3 liu:3 tuned:1 o2:2 existing:2 steiner:1 com:3 culprit:1 diederik:1 gpu:29 parsing:2 ronald:1 subsequent:1 devin:1 shape:7 remove:1 designed:2 plot:2 depic... |
5,853 | 6,296 | Kronecker Determinantal Point Processes
Zelda Mariet
Massachusetts Institute of Technology
Cambridge, MA 02139
zelda@csail.mit.edu
Suvrit Sra
Massachusetts Institute of Technology
Cambridge, MA 02139
suvrit@mit.edu
Abstract
Determinantal Point Processes (DPPs) are probabilistic models over all subsets
a ground set of... | 6296 |@word determinant:2 polynomial:1 seems:1 confirms:1 prominence:1 decomposition:3 covariance:1 profit:1 nystr:3 tr:4 initial:4 liu:1 contains:1 series:1 document:2 precluding:1 ka:3 si:4 assigning:1 bd:2 must:1 determinantal:20 realistic:1 partition:2 happen:1 subsequent:1 enables:3 drop:1 update:25 v:1 greedy:1 i... |
5,854 | 6,297 | Consistent Estimation of Functions of Data Missing
Non-Monotonically and Not at Random
Ilya Shpitser
Department of Computer Science
Johns Hopkins University
ilyas@cs.jhu.edu
Abstract
Missing records are a perennial problem in analysis of complex data of all types,
when the target of inference is some function of the ... | 6297 |@word mild:4 version:3 briefly:1 open:1 simulation:4 mcar:2 configuration:2 series:2 daniel:1 ours:1 longitudinal:3 horvitz:2 existing:3 nonmonotone:1 yet:1 john:1 subsequent:1 realistic:1 benign:1 resampling:2 aside:1 intelligence:1 parameterization:4 mccallum:1 record:5 characterization:1 parameterizations:3 no... |
5,855 | 6,298 | Scaling Memory-Augmented Neural Networks with
Sparse Reads and Writes
Jack W Rae?
jwrae
Jonathan J Hunt?
jjhunt
Greg Wayne
gregwayne
Tim Harley
tharley
Alex Graves
gravesa
Ivo Danihelka
danihelka
Andrew Senior
andrewsenior
Timothy P Lillicrap
countzero
Google DeepMind
@google.com
Abstract
Neural networks augme... | 6298 |@word armand:1 version:2 compression:1 bptt:4 open:1 tried:1 jacob:1 tr:5 shot:3 series:1 att:1 contains:3 daniel:1 reynolds:2 existing:1 current:1 com:1 comparing:1 surprising:1 si:2 written:2 must:1 john:1 additive:1 subsequent:1 ronan:1 designed:1 update:3 progressively:1 hash:2 sukhbaatar:1 cue:1 prohibitive:... |
5,856 | 6,299 | Breaking the Bandwidth Barrier:
Geometrical Adaptive Entropy Estimation
Weihao Gao?, Sewoong Oh?, and Pramod Viswanath?
University of Illinois at Urbana-Champaign
Urbana, IL 61801
{wgao9,swoh,pramodv}@illinois.edu
Abstract
Estimators of information theoretic measures such as entropy and mutual information are a basic... | 6299 |@word mild:1 determinant:1 briefly:1 polynomial:5 reshef:2 heuristically:2 hu:1 simulation:3 covariance:2 substitution:1 series:1 liu:1 renewed:1 past:1 existing:2 outperforms:5 ka:1 universality:1 dx:1 readily:1 grassberger:3 fn:2 numerical:7 additive:1 informative:1 remove:1 intelligence:2 kandasamy:1 discoveri... |
5,857 | 63 | 1
CONNECTIVITY VERSUS ENTROPY
Yaser S. Abu-Mostafa
California Institute of Technology
Pasadena, CA 91125
ABSTRACT
How does the connectivity of a neural network (number of synapses per
neuron) relate to the complexity of the problems it can handle (measured by
the entropy)? Switching theory would suggest no relation a... | 63 |@word implemented:1 concept:1 version:1 normalized:1 hence:10 restate:1 diagonal:6 alp:1 during:1 implementing:1 everything:1 accommodate:1 oa:1 ln2:1 hill:1 biological:1 complete:1 hold:3 length:2 lof:1 purposely:2 must:6 written:1 equivalently:1 mostafa:4 substituting:2 iv1:1 relate:1 a2:1 veal:2 designed:3 jl:2 ... |
5,858 | 630 | Attractor Neural Networks with Local
Inhibition: from Statistical Physics to a
Digital Programmable Integrated Circuit
E. Pasero
Dipartimento di Elettronica
Politecnico di Torino
1-10129 Torino, Italy
R. Zecchina
Dipartimento di Fisica Teorica e INFN
Universita. di Torino
1-10125 Torino, Italy
Abstract
Networks with... | 630 |@word luk:1 version:4 seems:2 jijsj:1 simulation:2 r:1 pulse:1 reduction:1 phy:1 configuration:5 initial:2 hereafter:1 selecting:1 ala:1 past:1 existing:1 clash:1 od:1 si:1 activation:2 must:2 additive:1 numerical:3 partition:1 stationary:3 selected:2 device:8 short:2 provides:1 rc:1 become:2 inside:1 indeed:1 exp... |
5,859 | 6,300 | Examples are not Enough, Learn to Criticize!
Criticism for Interpretability
Been Kim?
Allen Institute for AI
beenkim@csail.mit.edu
Rajiv Khanna
UT Austin
rajivak@utexas.edu
Oluwasanmi Koyejo
UIUC
sanmi@illinois.edu
Abstract
Example-based explanations are widely used in the effort to improve the interpretability of h... | 6300 |@word determinant:3 polynomial:1 seitz:1 asks:2 reduction:1 contains:2 score:3 selecting:6 offering:1 ours:2 rkhs:2 document:3 tuned:1 subjective:2 existing:2 genetic:1 comparing:1 surprising:1 assigning:1 must:2 written:3 additive:2 kdd:2 remove:1 designed:2 gist:1 interpretable:6 v:1 alone:2 greedy:9 selected:8... |
5,860 | 6,301 | Large-Scale Price Optimization via Network Flow
Shinji Ito
NEC Corporation
s-ito@me.jp.nec.com
Ryohei Fujimaki
NEC Corporation
rfujimaki@nec-labs.com
Abstract
This paper deals with price optimization, which is to find the best pricing strategy
that maximizes revenue or profit, on the basis of demand forecasting mode... | 6301 |@word mild:1 middle:1 polynomial:4 norm:1 km:2 simulation:4 git:4 decomposition:1 profit:17 contains:1 pub:1 prescriptive:5 ours:1 existing:4 com:3 written:1 additive:3 kdd:1 cheap:1 enables:1 update:2 half:1 selected:1 intelligence:5 accordingly:1 core:2 oblique:1 provides:1 org:1 treed:1 dn:1 constructed:1 ryoh... |
5,861 | 6,302 | Low-Rank Regression with Tensor Responses
Guillaume Rabusseau and Hachem Kadri
Aix Marseille Univ, CNRS, LIF, Marseille, France
{firstname.lastname}@lif.univ-mrs.fr
Abstract
This paper proposes an efficient algorithm (HOLRR) to handle regression
tasks where the outputs have a tensor structure. We formulate the regres... | 6302 |@word h:1 multitask:3 trial:2 version:4 middle:1 polynomial:2 norm:3 paredes:1 d2:9 rgb:2 decomposition:11 invoking:1 solid:1 reduction:1 liu:2 series:1 contains:3 rkhs:1 ours:1 romera:1 outperforms:2 existing:1 comparing:1 written:3 readily:2 additive:1 numerical:2 kdd:1 analytic:1 designed:1 update:1 rd2:1 v:2 ... |
5,862 | 6,303 | Architectural Complexity Measures of
Recurrent Neural Networks
Saizheng Zhang1,?, Yuhuai Wu2,? , Tong Che4 , Zhouhan Lin1 ,
Roland Memisevic1,5 , Ruslan Salakhutdinov3,5 and Yoshua Bengio1,5
1
MILA, Universit? de Montr?al, 2 University of Toronto, 3 Carnegie Mellon University,
4
Institut des Hautes ?tudes Scientifiques... | 6303 |@word mild:4 repository:1 version:1 nchen:1 compression:1 seems:1 nd:1 km:2 p0:2 paid:1 solid:1 recursively:1 carry:2 necessity:1 cyclic:11 series:1 contains:2 subword:1 outperforms:1 current:2 comparing:1 com:1 surprising:1 skipping:2 activation:2 diederik:1 amjad:1 must:1 plot:1 v:1 bart:1 intelligence:1 beginn... |
5,863 | 6,304 | Convolutional Neural Fabrics
Shreyas Saxena
Jakob Verbeek
INRIA Grenoble ? Laboratoire Jean Kuntzmann
Abstract
Despite the success of CNNs, selecting the optimal architecture for a given task
remains an open problem. Instead of aiming to select a single optimal architecture,
we propose a ?fabric? that embeds an expone... | 6304 |@word cnn:11 middle:1 version:3 advantageous:1 kokkinos:1 tedious:1 open:1 heuristically:1 hu:1 propagate:1 sgd:1 accommodate:1 necessity:1 liu:3 contains:5 ndez:1 selecting:1 hoiem:1 ours:7 document:1 deconvolutional:2 past:1 recovered:2 activation:19 reminiscent:1 finest:1 parsing:1 shape:2 progressively:2 v:1 ... |
5,864 | 6,305 | Linear Feature Encoding for Reinforcement Learning
Zhao Song, Ronald Parr? , Xuejun Liao, Lawrence Carin
Department of Electrical and Computer Engineering
?
Department of Computer Science
Duke University, Durham, NC 27708, USA
Abstract
Feature construction is of vital importance in reinforcement learning, as the qual... | 6305 |@word h:1 version:3 norm:2 termination:4 km:4 seek:1 simulation:2 tried:1 decomposition:1 dealer:1 thereby:1 wrapper:1 outperforms:2 existing:3 current:2 ka:1 surprising:1 si:6 reminiscent:1 must:2 written:1 john:1 ronald:1 numerical:1 enables:1 plot:1 update:5 aside:1 v:1 greedy:4 selected:2 half:1 shut:1 accord... |
5,865 | 6,306 | Online ICA: Understanding Global Dynamics of
Nonconvex Optimization via Diffusion Processes
Chris Junchi Li
Zhaoran Wang
Han Liu
Department of Operations Research and Financial Engineering, Princeton University
{junchil, zhaoran, hanliu}@princeton.edu
Abstract
Solving statistical learning problems often involves nonco... | 6306 |@word polynomial:1 norm:1 c0:2 calculus:1 d2:1 decomposition:8 sgd:33 v2o:2 moment:1 initial:7 liu:12 luo:1 written:1 john:1 distant:1 informative:1 analytic:4 drop:1 update:3 clumping:1 stationary:8 intelligence:1 accordingly:1 xk:1 short:1 blei:1 characterization:5 coarse:1 provides:3 iterates:6 traverse:5 loca... |
5,866 | 6,307 | The Parallel Knowledge Gradient Method
for Batch Bayesian Optimization
Jian Wu, Peter I. Frazier
Cornell University
Ithaca, NY, 14853
{jw926, pf98}@cornell.edu
Abstract
In many applications of black-box optimization, one can evaluate multiple points
simultaneously, e.g. when evaluating the performances of several dif... | 6307 |@word cnn:5 briefly:1 version:2 exploitation:1 open:2 multipoint:1 underperform:1 zilinskas:1 simulation:1 covariance:2 dramatic:1 initial:5 configuration:1 ndez:1 liu:2 zij:10 tuned:1 ours:1 reine:1 past:1 outperforms:2 com:2 z2:3 discretization:2 comparing:1 must:3 additive:1 numerical:2 burdick:1 designed:1 up... |
5,867 | 6,308 | Anchor-Free Correlated Topic Modeling:
Identifiability and Algorithm
Kejun Huang?
Xiao Fu?
Nicholas D. Sidiropoulos
Department of Electrical and Computer Engineering
University of Minnesota
Minneapolis, MN 55455, USA
huang663@umn.edu xfu@umn.edu nikos@ece.umn.edu
Abstract
In topic modeling, many algorithms that guara... | 6308 |@word mild:1 trial:2 determinant:5 version:3 manageable:1 polynomial:2 norm:1 middle:1 justice:1 simulation:1 tried:1 decomposition:10 eng:1 covariance:1 pick:3 thereby:1 moment:1 liu:3 contains:1 lightweight:1 score:3 series:2 document:22 suppressing:1 hottopixx:1 existing:1 surprising:1 yet:1 attracted:1 must:1... |
5,868 | 6,309 | A Constant-Factor Bi-Criteria Approximation
Guarantee for k-means++
Dennis Wei
IBM Research
Yorktown Heights, NY 10598, USA
dwei@us.ibm.com
Abstract
This paper studies the k-means++ algorithm for clustering as well as the class of D`
sampling algorithms to which k-means++ belongs. It is shown that for any constant
fac... | 6309 |@word cu:28 version:1 polynomial:4 stronger:1 d2:1 confirms:1 hu:5 recursively:1 initial:2 uncovered:12 series:1 selecting:5 katoh:1 existing:5 current:3 com:1 discretization:1 assigning:1 must:1 numerical:1 subsequent:1 asymptote:1 seeding:1 update:1 pursued:1 selected:4 half:1 leaf:1 fewer:1 provides:3 location... |
5,869 | 631 | Input Reconstruction Reliability Estimation
Dean A. Pomerleau
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
This paper describes a technique called Input Reconstruction Reliability Estimation
(IRRE) for determining the response reliability of a restricted class of multi-layer
per... | 631 |@word hippocampus:1 jacob:4 eng:1 solid:3 exclusively:1 selecting:2 existing:1 current:3 nowlan:1 activation:8 cottrell:4 subsequent:1 update:1 intelligence:2 fewer:1 inspection:1 provides:2 location:2 sigmoidal:1 rc:1 along:3 incorrect:2 baldi:4 expected:1 frequently:2 planning:1 multi:2 automatically:1 actual:11... |
5,870 | 6,310 | Phased LSTM: Accelerating Recurrent Network
Training for Long or Event-based Sequences
Daniel Neil, Michael Pfeiffer, and Shih-Chii Liu
Institute of Neuroinformatics
University of Zurich and ETH Zurich
Zurich, Switzerland 8057
{dneil, pfeiffer, shih}@ini.uzh.ch
Abstract
Recurrent Neural Networks (RNNs) have become th... | 6310 |@word cnn:5 middle:2 version:3 manageable:1 wco:2 retraining:1 bf:2 c0:2 open:13 linearized:1 bn:16 thereby:4 shading:1 carry:1 reduction:1 initial:2 liu:3 cyclic:1 configuration:1 contains:1 daniel:1 bc:2 outperforms:1 past:2 current:1 comparing:1 com:1 surprising:1 activation:1 must:1 gpu:1 fn:2 plasticity:2 dr... |
5,871 | 6,311 | Iterative Refinement of the Approximate Posterior for
Directed Belief Networks
R Devon Hjelm
University of New Mexico and the Mind Research Network
dhjelm@mrn.org
Kyunghyun Cho
Courant Institute & Center for Data Science, New York University
kyunghyun.cho@nyu.edu
Junyoung Chung
University of Montreal
junyoung.chung@umo... | 6311 |@word middle:1 faculty:1 version:5 consequential:1 nd:1 adrian:1 r:1 simulation:1 sgd:1 reduction:2 initial:6 configuration:6 series:1 jimenez:1 tuned:1 fa8750:1 existing:1 freitas:1 current:1 com:2 comparing:2 si:2 yet:1 diederik:2 must:1 written:1 gpu:1 refines:1 shape:1 update:5 resampling:2 nebojsa:1 generati... |
5,872 | 6,312 | Mapping Estimation for Discrete Optimal Transport
Micha?el Perrot
Univ Lyon, UJM-Saint-Etienne, CNRS,
Lab. Hubert Curien UMR 5516, F-42023
michael.perrot@univ-st-etienne.fr
R?emi Flamary
Universit?e C?ote d?Azur,
Lagrange, UMR 7293 , CNRS, OCA
remi.flamary@unice.fr
Nicolas Courty
Universit?e de Bretagne Sud,
IRISA, U... | 6312 |@word mild:1 trial:2 version:8 norm:1 seems:1 nd:1 villani:1 open:3 seek:1 propagate:1 xtest:1 tr:1 initial:1 tuned:1 ours:1 amp:1 comparing:1 nt:4 activation:1 yet:1 must:1 written:1 import:1 readily:1 numerical:1 oberman:1 interpretable:1 half:2 prohibitive:1 realizing:1 short:3 regressive:1 provides:1 recomput... |
5,873 | 6,313 | Bayesian Intermittent Demand Forecasting for Large
Inventories
Matthias Seeger, David Salinas, Valentin Flunkert
Amazon Development Center Germany
Krausenstrasse 38
10115 Berlin
matthis@amazon.de, dsalina@amazon.de, flunkert@amazon.de
Abstract
We present a scalable and robust Bayesian method for demand forecasting in ... | 6313 |@word inversion:1 seems:1 norm:1 nd:1 proportion:1 zit:4 forecaster:2 crucially:1 p0:2 pick:1 harder:1 reduction:4 series:13 contains:3 tuned:2 ours:1 franklin:1 outperforms:5 current:2 comparing:2 z2:1 yet:4 flunkert:2 written:1 john:2 additive:1 happen:1 enables:1 drop:2 treating:1 plot:1 v:1 intelligence:2 ite... |
5,874 | 6,314 | Convex Two-Layer Modeling with Latent Structure
Vignesh Ganapathiraman? ,
Xinhua Zhang? ,
Yaoliang Yu? ,
Junfeng Wen]
?
University of Illinois at Chicago, Chicago, IL, USA
?
University of Waterloo, Waterloo, ON, Canada, ] University of Alberta, Edmonton, AB, Canada
{vganap2, zhangx}@uic.edu, yaoliang.yu@uwaterloo.ca, ... | 6314 |@word version:1 polynomial:2 seems:1 norm:6 yi0:2 open:1 p0:1 contrastive:3 q1:2 sgd:1 tr:11 inpainting:6 accommodate:1 initial:1 score:3 mi0:1 tuned:2 past:1 current:1 com:1 recovered:1 gmail:1 scatter:1 written:1 parsing:2 chicago:2 partition:3 subsequent:2 kdd:1 remove:1 designed:2 drop:1 gist:1 maxv:1 aside:1... |
5,875 | 6,315 | Deep Learning Games
Dale Schuurmans?
Google
daes@ualberta.ca
Martin Zinkevich
Google
martinz@google.com
Abstract
We investigate a reduction of supervised learning to game playing that reveals new
connections and learning methods. For convex one-layer problems, we demonstrate
an equivalence between global minimizers ... | 6315 |@word nd:1 diametrically:1 simplifying:2 sgd:6 accommodate:1 shot:2 recursively:1 reduction:12 uncovered:1 contains:1 selecting:1 series:1 denoting:1 tuned:3 document:1 outperforms:1 current:3 com:1 surprising:2 luo:1 activation:8 si:1 must:3 chicago:2 hajnal:1 gv:5 plot:1 update:5 intelligence:3 warmuth:2 accord... |
5,876 | 6,316 | Satisfying Real-world Goals with Dataset Constraints
Gabriel Goh
Dept. of Mathematics
UC Davis
Davis, CA 95616
ggoh@math.ucdavis.edu
Andrew Cotter, Maya Gupta
Google Inc.
1600 Amphitheatre Parkway
Mountain View, CA 94043
acotter@google.com
mayagupta@google.com
Michael Friedlander
Dept. of Computer Science
University... | 6316 |@word version:1 proportion:5 d2:9 seek:3 covariance:1 hsieh:1 sgd:1 liblinear:2 initial:4 series:1 score:1 contains:3 disparity:1 outperforms:1 current:2 com:3 must:3 written:1 fn:3 partition:1 informative:1 enables:1 hypothesize:1 plot:2 maxv:2 v:3 intelligence:1 selected:1 plane:7 mccallum:2 ith:1 math:1 hyperp... |
5,877 | 6,317 | ?Congruent? and ?Opposite? Neurons: Sisters for
Multisensory Integration and Segregation
Wen-Hao Zhang1,2 ? , He Wang1 , K. Y. Michael Wong1 , Si Wu2
wenhaoz@ust.hk, hwangaa@connect.ust.hk, phkywong@ust.hk, wusi@bnu.edu.cn
1
Department of Physics, Hong Kong University of Science and Technology, Hong Kong.
2
State Key ... | 6317 |@word kong:3 integrative:1 carry:2 disparity:21 idg:1 hereafter:1 interestingly:2 past:2 current:2 recovered:1 comparing:2 si:12 ust:3 written:2 realize:1 ronald:1 informative:2 shape:1 hypothesize:1 medial:5 n0:3 v:5 implying:2 cue:110 half:2 discrimination:1 reciprocal:13 short:1 provides:1 zhang:3 height:2 dir... |
5,878 | 6,318 | Synthesis of MCMC and Belief Propagation
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... | 6318 |@word mild:1 trial:2 determinant:1 version:3 polynomial:15 calculus:4 decomposition:2 pick:2 harder:1 reduction:1 series:21 outperforms:4 freitas:1 current:3 z2:37 comparing:1 com:1 partition:11 remove:1 plot:1 update:4 stationary:1 intelligence:4 selected:2 advancement:1 core:3 provides:8 iterates:1 completeness... |
5,879 | 6,319 | Reshaped Wirtinger Flow for
Solving Quadratic System of Equations
Huishuai Zhang
Department of EECS
Syracuse University
Syracuse, NY 13244
hzhan23@syr.edu
Yingbin Liang
Department of EECS
Syracuse University
Syracuse, NY 13244
yliang06@syr.edu
Abstract
We study the problem of recovering a vector x ? Rn from its magn... | 6319 |@word trial:7 version:2 advantageous:1 norm:2 yi0:1 c0:4 moment:1 shechtman:2 reduction:1 contains:1 initial:2 document:1 ati:31 outperforms:3 recovered:1 z2:2 comparing:1 activation:1 written:1 readily:1 numerical:4 designed:4 plot:1 update:6 intelligence:1 fewer:1 plane:1 xk:5 core:1 fa9550:1 characterization:1... |
5,880 | 632 | Analog VLSI Implementation of
Multi-dimensional Gradient Descent
David B. Kirk, Douglas Kerns, Kurt Fleischer, Alan H. Barr
California Institute of Technology
Beckman Institute 350-74
Pasadena, CA 91125
E-mail: dkIDegg.gg . cal tech. edu
Abstract
We describe an analog VLSI implementation of a multi-dimensional
gradien... | 632 |@word version:1 additively:2 simulation:2 tr:2 initial:1 document:1 kurt:2 yet:1 chu:1 written:1 john:1 realize:1 designed:1 pacemaker:1 dembo:3 filtered:3 provides:4 node:1 along:1 uyi:1 microchip:1 yuhas:1 combine:1 alspector:10 multi:15 integrator:2 chi:2 decomposed:1 decreasing:1 automatically:1 actual:1 provi... |
5,881 | 6,320 | Efficient state-space modularization for planning:
theory, behavioral and neural signatures
Daniel McNamee, Daniel Wolpert, M?t? Lengyel
Computational and Biological Learning Lab
Department of Engineering
University of Cambridge
Cambridge CB2 1PZ, United Kingdom
{d.mcnamee|wolpert|m.lengyel}@eng.cam.ac.uk
Abstract
Ev... | 6320 |@word version:1 inversion:1 compression:21 seems:1 hippocampus:1 nd:1 termination:3 grey:1 integrative:1 simulation:3 r:1 eng:1 decomposition:2 pg:4 pressure:1 thereby:2 initial:1 series:2 fragment:1 united:1 score:2 daniel:2 mag:2 elaborating:1 task1:1 current:2 contextual:1 od:2 si:6 scatter:1 activation:1 real... |
5,882 | 6,321 | RETAIN: An Interpretable Predictive Model for
Healthcare using Reverse Time Attention Mechanism
Edward Choi? , Mohammad Taha Bahadori? , Joshua A. Kulas? ,
Andy Schuetz? , Walter F. Stewart? , Jimeng Sun?
?
?
Georgia Institute of Technology
Sutter Health
{mp2893,bahadori,jkulas3}@gatech.edu,
{schueta1,stewarwf}@sutterh... | 6321 |@word h:1 mild:1 version:1 hu:1 confirms:1 r:1 bn:8 liu:1 series:1 score:1 offering:1 genetic:1 past:6 outperforms:1 existing:1 medi:2 com:2 comparing:1 yet:1 dx:2 must:1 gpu:1 timestamps:3 benign:2 kdd:2 remove:1 drop:1 interpretable:13 v:1 alone:1 stationary:2 selected:3 affair:1 sutter:2 vanishing:1 short:1 re... |
5,883 | 6,322 | Exponential expressivity in deep neural networks
through transient chaos
Ben Poole1 , Subhaneil Lahiri1 , Maithra Raghu2 , Jascha Sohl-Dickstein2 , Surya Ganguli1
1
Stanford University, 2 Google Brain
{benpoole,sulahiri,sganguli}@stanford.edu, {maithra,jaschasd}@google.com
Abstract
We combine Riemannian geometry with... | 6322 |@word cox:1 polynomial:2 norm:1 nd:1 open:1 dz1:1 simulation:7 propagate:4 covariance:1 thereby:3 solid:4 moment:1 initial:3 uncovered:1 com:2 wd:1 z2:3 activation:1 bd:1 john:1 numerical:1 partition:1 enables:3 remove:1 update:1 v:1 alone:1 intelligence:1 parameterization:2 plane:6 footing:1 provides:2 pascanu:1... |
5,884 | 6,323 | Learnable Visual Markers
Oleg Grinchuk1 , Vadim Lebedev1,2 , and Victor Lempitsky1
1
Skolkovo Institute of Science and Technology, Moscow, Russia
2
Yandex, Moscow, Russia
Abstract
We propose a new approach to designing visual markers (analogous to QR-codes,
markers for augmented reality, and robotic fiducial tags) ba... | 6323 |@word briefly:1 inversion:1 printer:1 tried:3 bn:1 invoking:1 minus:1 reduction:1 contains:1 series:1 optically:1 tuned:1 interestingly:1 deconvolutional:1 rightmost:1 existing:2 recovered:2 activation:1 synthesizer:29 subsequent:1 blur:7 designed:3 update:1 generative:3 half:1 device:2 core:1 provides:1 location... |
5,885 | 6,324 | Local Maxima in the Likelihood of Gaussian Mixture
Models: Structural Results and Algorithmic
Consequences
Chi Jin
UC Berkeley
chijin@cs.berkeley.edu
Yuchen Zhang
UC Berkeley
yuczhang@berkeley.edu
Martin J. Wainwright
UC Berkeley
wainwrig@berkeley.edu
Sivaraman Balakrishnan
Carnegie Mellon University
siva@stat.cmu.... | 6324 |@word version:1 polynomial:2 achievable:2 norm:2 suitably:1 open:8 covariance:4 decomposition:2 thereby:1 carry:1 moment:3 initial:5 liu:3 configuration:6 contains:1 selecting:2 series:1 daniel:1 necessity:2 wainwrig:1 current:1 surprising:1 yet:1 written:1 must:3 john:1 transcendental:1 benign:1 plot:2 update:10... |
5,886 | 6,325 | Hierarchical Clustering via Spreading Metrics
Aurko Roy1 and Sebastian Pokutta2
1
College of Computing, Georgia Institute of Technology, Atlanta, GA, USA.
Email: aurko@gatech.edu
2
ISyE, Georgia Institute of Technology, Atlanta, GA, USA.
Email: sebastian.pokutta@isye.gatech.edu
Abstract
We study the cost function for... | 6325 |@word repository:1 version:1 polynomial:10 leighton:2 open:2 decomposition:3 citeseer:1 multicommodity:2 recursively:1 contains:1 series:1 lichman:1 daniel:1 existing:1 err:2 assigning:1 must:2 partition:2 drop:1 n0:4 leaf:13 characterization:8 provides:1 node:3 org:1 dn:1 constructed:1 symposium:7 descendant:2 p... |
5,887 | 6,326 | Fast and accurate spike sorting of high-channel count
probes with KiloSort
Marius Pachitariu1 , Nick Steinmetz1 , Shabnam Kadir1
Matteo Carandini1 and Kenneth Harris1
1
UCL, UK {ucgtmpa, }@ucl.ac.uk
Abstract
New silicon technology is enabling large-scale electrophysiological recordings in
vivo from hundreds to thousa... | 6326 |@word neurophysiology:2 private:4 achievable:2 norm:3 bf:1 mehta:1 covariance:5 decomposition:6 reduction:1 bai:1 initial:1 score:13 daniel:2 past:2 current:3 com:1 analysed:1 assigning:1 must:2 gpu:3 distant:1 shape:2 remove:2 update:3 v:3 discrimination:1 generative:3 greedy:2 selected:1 fewer:1 intelligence:1 ... |
5,888 | 6,327 | Full-Capacity Unitary Recurrent Neural Networks
Scott Wisdom1? , Thomas Powers1? , John R. Hershey2 , Jonathan Le Roux2 , and Les Atlas1
1
Department of Electrical Engineering, University of Washington
{swisdom, tcpowers, atlas}@uw.edu
2
Mitsubishi Electric Research Laboratories (MERL)
{hershey, leroux}@merl.com
Abst... | 6327 |@word trial:1 timefrequency:1 norm:1 replicate:1 open:1 d2:1 confirms:1 mitsubishi:1 decomposition:1 covariance:1 thereby:1 tr:1 initial:1 series:1 score:1 past:3 imaginary:2 current:1 com:3 z2:2 blank:4 activation:2 must:3 john:1 atlas:2 drop:1 update:3 designed:1 half:2 fewer:1 guess:1 parameterization:16 begin... |
5,889 | 6,328 | The Generalized Reparameterization Gradient
Francisco J. R. Ruiz
University of Cambridge
Columbia University
Michalis K. Titsias
Athens University of
Economics and Business
David M. Blei
Columbia University
Abstract
The reparameterization gradient has become a widely used method to obtain Monte
Carlo gradients to o... | 6328 |@word determinant:1 version:1 seems:2 logit:10 simulation:2 covariance:1 moment:3 reduction:3 score:9 document:2 fa8750:1 outperforms:4 com:1 must:1 written:2 john:1 shape:7 analytic:2 christian:1 update:1 aside:1 intelligence:5 generative:1 device:1 inspection:1 short:1 blei:7 provides:4 completeness:1 ire:1 loc... |
5,890 | 6,329 | ?Short-Dot?: Computing Large Linear Transforms
Distributedly Using Coded Short Dot Products
Sanghamitra Dutta
Carnegie Mellon University
sanghamd@andrew.cmu.edu
Viveck Cadambe
Pennsylvania State University
viveck@engr.psu.edu
Pulkit Grover
Carnegie Mellon University
pgrover@andrew.cmu.edu
Abstract
Faced with satura... | 6329 |@word inversion:1 grey:1 km:1 decomposition:2 jacob:1 atrix:1 reduction:1 electronics:1 cyclic:1 liu:1 exclusively:1 necessity:1 outperforms:1 existing:3 com:1 nt:1 si:4 scatter:1 written:1 must:1 nanoscale:1 partition:3 predetermined:1 designed:2 plot:1 sponsored:1 selected:1 device:1 short:54 provides:2 complet... |
5,891 | 633 | Planar Hidden Markov Modeling:
from Speech to Optical Character Recognition
Esther Levin and Roberto Pieraccini
AIT Bell Laboratories
600 Mountain Ave.
Murray Hill, NJ 07974
Abstract
We propose in this paper a statistical model (planar hidden Markov model PHMM) describing statistical properties of images. The model g... | 633 |@word trial:1 eliminating:2 polynomial:4 grey:1 tr:2 harder:2 ld:1 initial:1 contains:1 document:1 current:1 assigning:1 yet:1 must:2 written:5 blur:1 drop:1 update:1 selected:1 yr:9 parametrization:1 sudden:1 coarse:1 successive:1 direct:1 become:1 introduce:1 deteriorate:1 behavior:3 decreasing:1 goldman:1 incre... |
5,892 | 6,330 | Optimal Architectures in a Solvable Model of Deep
Networks
Jonathan Kadmon
The Racah Institute of Physics and ELSC
The Hebrew University, Israel
jonathan.kadmon@mail.huji.ac.il
Haim Sompolinsky
The Racah Institute of Physics and ELSC
The Hebrew University, Israel
and
Center for Brain Science
Harvard University
Abstra... | 6330 |@word neurophysiology:1 version:5 simulation:4 propagate:2 q1:2 thereby:1 solid:1 carry:2 initial:14 series:1 denoting:1 interestingly:1 suppressing:1 kurt:1 past:2 comparing:1 nt:7 mushroom:1 dx:1 numerical:1 subsequent:1 realistic:3 plasticity:1 update:1 n0:11 implying:1 mccallum:1 realism:1 completeness:1 node... |
5,893 | 6,331 | Robustness of classifiers:
from adversarial to random noise
Alhussein Fawzi?, Seyed-Mohsen Moosavi-Dezfooli?, Pascal Frossard
?cole Polytechnique F?d?rale de Lausanne
Lausanne, Switzerland
{alhussein.fawzi, seyed.moosavi, pascal.frossard} at epfl.ch
Abstract
Several recent works have shown that state-of-the-art classi... | 6331 |@word moosavi:3 dkr:2 version:1 norm:4 seems:1 valle:1 open:2 q1:1 contains:1 mag:2 interestingly:1 bhattacharyya:1 suppressing:1 luo:1 activation:1 intriguing:1 written:2 bd:1 gpu:1 numerical:1 distant:1 remove:1 designed:1 plot:1 plane:2 yamada:1 provides:2 mannor:2 kingsbury:1 along:2 c2:2 mathematical:1 diffe... |
5,894 | 6,332 | Geometric Dirichlet Means algorithm
for topic inference
Mikhail Yurochkin
Department of Statistics
University of Michigan
moonfolk@umich.edu
XuanLong Nguyen
Department of Statistics
University of Michigan
xuanlong@umich.edu
Abstract
We propose a geometric algorithm for topic learning and inference that is built on
t... | 6332 |@word kulis:4 version:3 proportion:8 seems:1 nd:1 simulation:5 contraction:3 decomposition:1 accounting:1 pick:1 carry:2 moment:1 inefficiency:2 contains:1 liu:2 series:1 tuned:2 document:52 outperforms:4 dx:3 reminiscent:1 must:1 realistic:1 partition:1 hofmann:3 enables:1 aside:1 generative:1 intelligence:1 acc... |
5,895 | 6,333 | Regularization With Stochastic Transformations and
Perturbations for Deep Semi-Supervised Learning
Mehdi Sajjadi
Mehran Javanmardi
Tolga Tasdizen
Department of Electrical and Computer Engineering
University of Utah
{mehdi, mehran, tolga}@sci.utah.edu
Abstract
Effective convolutional neural networks are trained on ... | 6333 |@word version:4 norm:1 tried:2 rgb:1 decomposition:1 citeseer:2 pick:1 sajjadi:2 liu:1 contains:9 tuned:1 document:1 com:1 activation:1 must:2 readily:1 cheap:1 v:4 generative:2 fewer:1 intelligence:1 bissacco:1 provides:1 node:2 location:1 simpler:1 zhang:2 five:9 along:2 c2:7 replication:3 consists:4 combine:2 ... |
5,896 | 6,334 | Flexible Models for Microclustering with
Application to Entity Resolution
Giacomo Zanella?
Department of Decision Sciences
Bocconi University
Brenda Betancourt?
Department of Statistical Science
Duke University
giacomo.zanella@unibocconi.it
bb222@stat.duke.edu
Hanna Wallach
Microsoft Research
hanna@dirichlet.net
... | 6334 |@word proportion:2 sex:1 contains:2 uma:1 series:1 united:1 longitudinal:1 existing:1 err:1 current:1 z2:2 must:1 john:1 realistic:2 partition:31 informative:1 shape:1 pertinent:1 plot:1 generative:1 intelligence:1 parameterization:1 accordingly:1 prize:1 record:38 five:2 mathematical:1 along:1 constructed:2 comm... |
5,897 | 6,335 | Deep Alternative Neural Network: Exploring
Contexts as Early as Possible for Action Recognition
Jinzhuo Wang, Wenmin Wang, Xiongtao Chen, Ronggang Wang, Wen Gao?
School of Electronics and Computer Engineering, Peking University
?
School of Electronics Engineering and Computer Science, Peking University
jzwang@pku.edu.... | 6335 |@word torsten:2 cnn:7 version:1 wiesel:2 laurence:1 bptt:1 km:1 hu:2 rgb:4 sgd:1 initial:1 configuration:4 contains:4 fragment:4 score:5 liu:1 electronics:2 tuned:2 ours:2 outperforms:2 existing:1 current:6 comparing:1 guadarrama:1 anne:1 parsing:1 ronan:2 christian:1 designed:1 selected:1 amir:1 beginning:1 ith:... |
5,898 | 6,336 | Boosting with Abstention
Corinna Cortes
Google Research
New York, NY 10011
Giulia DeSalvo
Courant Institute
New York, NY 10012
Mehryar Mohri
Courant Institute and Google
New York, NY 10012
corinna@google.com
desalvo@cims.nyu.edu
mohri@cims.nyu.edu
Abstract
We present a new boosting algorithm for the key scenario ... | 6336 |@word repository:1 version:2 middle:1 polynomial:1 dubuisson:1 d2:3 incurs:1 thereby:1 configuration:1 series:3 contains:1 document:1 dubourg:1 outperforms:1 com:1 comparing:1 z2:7 luo:1 assigning:1 tackling:1 numerical:2 partition:1 plot:4 designed:1 update:1 selected:2 guess:1 leaf:1 provides:2 boosting:16 iter... |
5,899 | 6,337 | Dueling Bandits: Beyond Condorcet Winners to
General Tournament Solutions
Siddartha Ramamohan
Indian Institute of Science
Bangalore 560012, India
Arun Rajkumar
Xerox Research
Bangalore 560103, India
Shivani Agarwal
University of Pennsylvania
Philadelphia, PA 19104, USA
siddartha.yr@csa.iisc.ernet.in
arun_r@csa.iisc... | 6337 |@word katja:1 trial:9 exploitation:1 middle:2 open:1 atul:1 decomposition:1 fabrice:1 initial:1 uncovered:18 score:1 contains:2 disparity:1 existing:1 savage:5 contextual:2 current:2 must:3 j1:2 hofmann:1 ramamohan:1 designed:1 update:3 intelligence:1 selected:1 yr:1 instantiate:1 guess:1 website:1 record:1 color... |
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