Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
6,100 | 6,518 | More Supervision, Less Computation:
Statistical-Computational Tradeoffs in Weakly
Supervised Learning
Xinyang Yi??
Zhaoran Wang?? Zhuoran Yang?? Constantine Caramanis? Han Liu?
?
?
The University of Texas at Austin
Princeton University
?
?
{yixy,constantine}@utexas.edu
{zhaoran,zy6,hanliu}@princeton.edu
{?: equal con... | 6518 |@word version:2 achievable:2 polynomial:6 norm:2 c0:2 seek:1 bn:5 covariance:5 arti:1 reduction:2 liu:4 contains:3 series:2 xinyang:1 interestingly:1 existing:1 cant:1 enables:1 generative:2 intelligence:1 accordingly:2 characterization:1 detecting:5 provides:2 simpler:1 zhang:1 unbounded:2 along:2 constructed:1 ... |
6,101 | 6,519 | Synthesizing the preferred inputs for neurons in
neural networks via deep generator networks
Anh Nguyen
anguyen8@uwyo.edu
Jason Yosinski
jason@geometric.ai
Alexey Dosovitskiy
dosovits@cs.uni-freiburg.de
Thomas Brox
brox@cs.uni-freiburg.de
Jeff Clune
jeffclune@uwyo.edu
Abstract
Deep neural networks (DNNs) have demon... | 6519 |@word cnn:1 version:3 briefly:2 norm:1 nd:1 open:1 rgb:1 harder:2 configuration:1 contains:1 fragment:1 score:2 exclusively:1 liu:1 interestingly:2 deconvolutional:1 s16:1 guadarrama:2 comparing:1 activation:22 videolearn:1 must:2 realistic:10 distant:1 informative:3 blur:3 s21:3 hypothesize:1 designed:2 interpre... |
6,102 | 652 | Computation of Heading Direction From
Optic Flow in Visual Cortex
Markus Lappe?
JosefP. Rauschecker
Laboratory of Neurophysiology, NIMH, Poolesville, MD, U.S.A. and
Max-Planck-Institut fur Biologische Kybernetik, Tiibingen, Germany
Abstract
We have designed a neural network which detects the direction of egomotion ... | 652 |@word neurophysiology:1 version:1 middle:1 proportion:1 seems:1 ruhr:1 simulation:3 contraction:3 excited:2 maes:1 extrastriate:1 contains:3 exclusively:1 tuned:1 denoting:1 rightmost:1 recovered:1 z2:1 nt:1 written:2 mst:2 visible:2 physiol:1 centrifugal:1 designed:1 medial:1 stationary:2 half:2 plane:5 lr:4 comp... |
6,103 | 6,520 | Generating Long-term Trajectories Using Deep
Hierarchical Networks
Stephan Zheng
Caltech
stzheng@caltech.edu
Yisong Yue
Caltech
yyue@caltech.edu
Patrick Lucey
STATS
plucey@stats.com
Abstract
We study the problem of modeling spatiotemporal trajectories over long time
horizons using expert demonstrations. For instance... | 6520 |@word cnn:18 middle:1 nd:1 heuristically:1 simulation:1 bn:5 decomposition:3 initial:2 bai:3 att:2 tist:1 tuned:1 suppressing:1 animated:1 outperforms:1 current:1 com:1 discretization:1 anne:1 must:2 gpu:1 evans:2 realistic:9 ronald:1 christian:1 v:4 stationary:5 generative:1 instantiate:4 half:1 selected:1 imita... |
6,104 | 6,521 | Completely random measures for modelling
block-structured sparse networks
Tue Herlau
Mikkel N. Schmidt Morten M?rup
DTU Compute
Technical University of Denmark
Richard Petersens plads 31,
2800 Lyngby, Denmark
{tuhe,mns,mmor}@dtu.dk
Abstract
Statistical methods for network data often parameterize the edge-probability... | 6521 |@word briefly:1 version:2 middle:1 changyou:1 nd:1 calculus:1 simulation:5 thereby:3 series:2 score:3 selecting:5 daniel:2 ecole:1 existing:1 comparing:1 si:2 yet:1 must:8 written:1 john:1 realize:1 tilted:1 ronald:1 partition:5 plot:4 update:11 pursued:1 generative:2 selected:3 half:1 intelligence:2 hamiltonian:... |
6,105 | 6,522 | Scaled Least Squares Estimator for GLMs
in Large-Scale Problems
Murat A. Erdogdu
Department of Statistics
Stanford University
erdogdu@stanford.edu
Mohsen Bayati
Graduate School of Business
Stanford University
bayati@stanford.edu
Lee H. Dicker
Department of Statistics and Biostatistics
Rutgers University and Amazon ?
... | 6522 |@word briefly:3 version:2 achievable:2 norm:4 nd:5 proportionality:8 covariance:7 mar10:2 initial:2 celebrated:1 contains:1 denoting:2 existing:1 current:3 yet:1 dx:2 written:2 numerical:2 plot:4 designed:1 v:2 aside:1 selected:1 nq:1 provides:1 iterates:1 revisited:1 math:1 simpler:1 ik:1 prove:1 consists:1 nes8... |
6,106 | 6,523 | Data Programming:
Creating Large Training Sets, Quickly
Alexander Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, Christopher R?
Stanford University
{ajratner,cdesa,senwu,dselsam,chrismre}@stanford.edu
Abstract
Large labeled training sets are the critical building blocks of supervised learning
methods and are key e... | 6523 |@word version:2 nd:2 open:1 vldb:1 heuristically:1 seek:1 programmatically:2 mention:9 initial:3 contains:1 score:14 selecting:2 karger:1 daniel:1 tuned:4 ours:1 precluding:1 genetic:1 fa8750:2 document:1 existing:1 bootkrajang:1 com:1 written:2 must:3 realize:1 distant:9 partition:1 enables:1 update:1 alone:1 ge... |
6,107 | 6,524 | A
Appendix: Proof of Theorem 1
We first show that the estimate is unbiased. Indeed, for every i 6= j we can rewrite L(z) as
E? `?(i),?(j) (z). Therefore,
L(z) =
X
1
k2
k
L(z) =
i6=j2[k]
X
1
k2
k
E `?(i),?(j) (z) = E L? (z) ,
?
i6=j2[k]
?
which proves that the multibatch estimate is unbiased.
Next, we turn... | 6524 |@word prof:1 vt:5 rewrite:2 unbiased:2 variance:3 entire:1 quantity:1 i2:2 vp:3 diagonal:3 i1:3 vi2:1 kr:1 entry:1 derivation:1 j6:2 fix:2 according:1 simplicity:1 sampling:2 every:3 index:3 concludes:1 krl:2 expectation:1 k2:5 t2:1 s6:3 off:1 proof:3 partition:1 omit:1 rl:5 appear:1 analogous:1 a2:1 positive:1 s... |
6,108 | 6,525 | Verification Based Solution for Structured MAB
Problems
Zohar Karnin
Yahoo Research
New York, NY 10036
zkarnin@ymail.com
Abstract
We consider the problem of finding the best arm in a stochastic Multi-armed
Bandit (MAB) game and propose a general framework based on verification that
applies to multiple well-motivated g... | 6525 |@word katja:1 exploitation:2 version:7 fabrice:1 soare:1 mention:1 nonexistent:1 initial:1 score:6 existing:3 err:1 com:1 contextual:1 surprising:1 jinbo:1 yet:6 must:3 additive:2 kdd:1 hofmann:2 v:1 intelligence:1 discovering:3 yr:11 selected:2 realizing:1 short:1 provides:6 boosting:1 mannor:3 honda:2 preferenc... |
6,109 | 6,526 | Adaptive Maximization of Pointwise Submodular
Functions With Budget Constraint
Nguyen Viet Cuong1
Huan Xu2
Department of Engineering, University of Cambridge, vcn22@cam.ac.uk
2
Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology,
huan.xu@isye.gatech.edu
1
Abstract
We study the worst-ca... | 6526 |@word exploitation:1 version:3 polynomial:1 underline:1 open:1 p0:2 pick:2 reduction:2 score:1 selecting:3 daniel:1 ours:1 document:1 comparing:2 must:1 kdd:1 shape:1 intelligence:1 greedy:34 half:7 item:33 selected:14 mccallum:1 sys:1 yuxin:1 provides:1 node:2 location:8 along:1 c2:4 natalie:1 focs:1 prove:5 nao... |
6,110 | 6,527 | Conditional Image Generation with
PixelCNN Decoders
A?ron van den Oord
Google DeepMind
avdnoord@google.com
Nal Kalchbrenner
Google DeepMind
nalk@google.com
Lasse Espeholt
Google DeepMind
espeholt@google.com
Alex Graves
Google DeepMind
gravesa@google.com
Oriol Vinyals
Google DeepMind
vinyals@google.com
Koray Kavukcu... | 6527 |@word cnn:1 middle:2 compression:3 nd:2 seek:1 covariance:1 inpainting:1 shot:1 initial:1 contains:1 score:3 jimenez:1 interestingly:1 deconvolutional:2 rightmost:2 outperforms:2 existing:2 current:5 com:6 activation:3 devin:1 realistic:2 uria:1 remove:1 update:1 generative:11 half:2 intelligence:1 ivo:5 short:1 ... |
6,111 | 6,528 | Variational Autoencoder for Deep Learning
of Images, Labels and Captions
Yunchen Pu? , Zhe Gan? , Ricardo Henao? , Xin Yuan? , Chunyuan Li? , Andrew Stevens?
and Lawrence Carin?
?
Department of Electrical and Computer Engineering, Duke University
{yp42, zg27, r.henao, cl319, ajs104, lcarin}@duke.edu
?
Nokia Bell Labs,... | 6528 |@word kohli:1 cnn:30 version:1 proportion:7 norm:1 tried:1 rgb:1 sgd:1 recursively:1 reduction:1 liu:1 score:1 ours:7 deconvolutional:7 csn:1 com:1 surprising:1 activation:6 assigning:1 gpu:3 unpooling:12 subsequent:1 plot:1 drop:2 grass:1 alone:3 generative:19 fewer:1 selected:1 intelligence:1 short:2 provides:2... |
6,112 | 6,529 | Semiparametric Differential Graph Models
Pan Xu
University of Virginia
px3ds@virginia.edu
Quanquan Gu
University of Virginia
qg5w@virginia.edu
Abstract
In many cases of network analysis, it is more attractive to study how a network
varies under different conditions than an individual static network. We propose
a nove... | 6529 |@word mild:5 trial:2 determinant:1 briefly:1 norm:14 advantageous:1 d2:2 hu:1 simulation:1 covariance:5 eng:1 tr:6 series:1 score:1 genetic:5 denoting:1 nonparanormal:2 outperforms:1 existing:2 elliptical:7 comparing:1 auritzen:1 attracted:1 written:1 john:1 numerical:1 website:1 provides:1 characterization:1 nod... |
6,113 | 653 | Automatic Capacity Tuning
of Very Large VC-dimension Classifiers
I. Guyon
AT&T Bell Labs,
50 Fremont st., 6th floor,
San Francisco, CA 94105
isabelle@neural.att.com
B. Boser?
EECS Department,
University of California,
Berkeley, CA 94720
boser@eecs.berkeley.edu
V. Vapnik
AT&T Bell Labs,
Room 4G-314,
Holmdel, NJ 07733... | 653 |@word eliminating:1 polynomial:19 advantageous:2 duda:1 grey:2 seek:2 shading:2 contains:1 att:2 chervonenkis:1 qth:1 com:2 yet:1 must:2 numerical:2 informative:1 girosi:1 remove:1 half:2 warmuth:1 xk:11 ipi:2 consists:3 theoretically:1 indeed:2 expected:1 multi:1 automatically:3 actual:1 param:1 becomes:2 bounded... |
6,114 | 6,530 | Statistical Inference for Pairwise Graphical Models
Using Score Matching
Ming Yu
mingyu@chicagobooth.edu
Varun Gupta
varun.gupta@chicagobooth.edu
Mladen Kolar?
mladen.kolar@chicagobooth.edu
University of Chicago Booth School of Business
Chicago, IL 60637
Abstract
Probabilistic graphical models have been widely used ... | 6530 |@word briefly:1 faculty:1 norm:1 hyv:8 simulation:2 r:7 bn:1 covariance:2 harder:1 liu:4 contains:1 score:34 series:1 bc:1 existing:1 current:2 luo:1 plcg:1 dx:2 attracted:1 written:1 intriguing:1 chicago:4 partition:3 designed:1 fund:1 eab:18 tscher:2 selected:1 xk:1 provides:1 node:9 contribute:1 p38:1 zhang:3 ... |
6,115 | 6,531 | Testing for Differences in Gaussian Graphical Models:
Applications to Brain Connectivity
Eugene Belilovsky 1,2,3 , Gael Varoquaux2 , Matthew Blaschko3
1
University of Paris-Saclay, 2 INRIA, 3 KU Leuven
{eugene.belilovsky, gael.varoquaux } @inria.fr
matthew.blaschko@esat.kuleuven.be
Abstract
Functional brain networks ... | 6531 |@word multitask:1 trial:1 version:2 norm:3 proportion:1 km:3 d2:2 simulation:2 covariance:10 ld:4 series:1 selecting:2 denoting:1 outperforms:1 craddock:2 comparing:7 yet:1 assigning:1 numerical:1 distant:1 visible:1 m1t:2 remove:1 plot:2 interpretable:1 atlas:2 fund:1 selected:4 fewer:1 smith:1 characterization:... |
6,116 | 6,532 | Tree-Structured Reinforcement Learning for
Sequential Object Localization
Zequn Jie1 , Xiaodan Liang2 , Jiashi Feng1 , Xiaojie Jin1 , Wen Feng Lu1 , Shuicheng Yan1
1
National University of Singapore, Singapore
2
Carnegie Mellon University, USA
Abstract
Existing object proposal algorithms usually search for possible o... | 6532 |@word cnn:27 middle:1 exploitation:4 briefly:1 nd:1 everingham:1 shuicheng:1 attended:6 solid:2 recursively:5 initial:1 uncovered:7 score:1 contains:2 trainval:2 tuned:1 ours:1 past:4 existing:3 outperforms:3 current:21 yet:2 written:1 gpu:1 najemnik:1 john:1 ronan:1 hofmann:1 enables:3 designed:1 update:3 rpn:14... |
6,117 | 6,533 | A Non-generative Framework and Convex
Relaxations for Unsupervised Learning
Elad Hazan
Princeton University
35 Olden Street 08540
ehazan@cs.princeton.edu.
Tengyu Ma
Princeton University
35 Olden Street, NJ 08540
tengyu@cs.princeton.edu.
Abstract
We give a novel formal theoretical framework for unsupervised learning w... | 6533 |@word version:2 polynomial:8 compression:16 norm:17 stronger:1 c0:1 d2:2 r:2 covariance:1 decomposition:4 jafarpour:1 moment:1 contains:2 series:2 daniel:2 document:1 michal:1 john:1 enables:1 rd2:1 joy:1 generative:12 pursued:1 kyk:2 amir:1 huo:1 scotland:1 vanishing:3 short:1 blei:1 completeness:2 toronto:1 sim... |
6,118 | 6,534 | Scaling Factorial Hidden Markov Models:
Stochastic Variational Inference without Messages
Yin Cheng Ng
Dept. of Statistical Science
University College London
y.ng.12@ucl.ac.uk
Pawel Chilinski
Dept. of Computing Science
University College London
ucabchi@ucl.ac.uk
Ricardo Silva
Dept. of Statistical Science
University ... | 6534 |@word repository:1 advantageous:1 open:1 simulation:2 covariance:1 jacob:1 sgd:6 solid:2 reduction:1 initial:1 series:6 lichman:1 jimenez:1 outperforms:2 existing:5 current:1 elliptical:1 tackling:1 diederik:1 written:3 gpu:1 john:3 visible:1 partition:1 plot:2 update:1 aside:1 stationary:2 generative:3 selected:... |
6,119 | 6,535 | Nearly Isometric Embedding by Relaxation
James McQueen
Department of Statistics
University of Washington
Seattle, WA 98195
jmcq@u.washington.edu
Marina Meil?a
Department of Statistics
University of Washington
Seattle, WA 98195
mmp@stat.washington.edu
Dominique Perrault-Joncas
Google
Seattle, WA 98103
dcpjoncas@gmail... | 6535 |@word version:3 briefly:1 polynomial:1 norm:12 advantageous:1 middle:2 nd:1 heuristically:1 dominique:2 r:11 seek:2 gradual:1 covariance:1 shot:1 reduction:5 initial:6 contains:1 series:1 existing:5 reaction:1 current:1 com:1 wd:1 si:1 gmail:1 yet:1 must:2 deniz:1 shape:1 gv:1 hourglass:7 interpretable:1 update:2... |
6,120 | 6,536 | Tracking the Best Expert in Non-stationary Stochastic
Environments
Chen-Yu Wei
Yi-Te Hong
Chi-Jen Lu
Institute of Information Science
Academia Sinica, Taiwan
{bahh723, ted0504, cjlu}@iis.sinica.edu.tw
Abstract
We study the dynamic regret of multi-armed bandit and experts problem in nonstationary stochastic environment... | 6536 |@word trial:1 exploitation:3 achievable:8 norm:1 stronger:2 nd:2 r:1 gradual:1 pick:1 reduction:1 erven:1 existing:3 luo:1 must:2 academia:1 partition:1 update:9 stationary:13 intelligence:1 accordingly:1 beginning:2 chiang:1 characterization:1 provides:1 become:1 prove:7 assaf:2 inside:1 introduce:3 indeed:1 exp... |
6,121 | 6,537 | A Probabilistic Model of Social Decision Making
based on Reward Maximization
Koosha Khalvati
Department of Computer Science
University of Washington
Seattle, WA 98105
koosha@cs.washington.edu
Seongmin A. Park
CNRS UMR 5229
Institut des Sciences Cognitives Marc Jeannerod
Lyon, France
park@isc.cnrs.fr
Jean-Claude Drehe... | 6537 |@word trial:11 version:2 logit:1 integrative:1 simulation:1 koosha:2 accounting:1 initial:6 contains:2 series:1 score:1 denoting:1 past:1 outperforms:1 reaction:1 current:6 comparing:1 existing:1 activation:7 must:3 written:1 shape:1 motor:1 atlas:1 interpretable:4 update:7 v:6 implying:1 half:1 selected:4 intell... |
6,122 | 6,538 | Safe and ef?cient off-policy reinforcement learning
Thomas Stepleton
stepleton@google.com
Google DeepMind
R?emi Munos
munos@google.com
Google DeepMind
Anna Harutyunyan
anna.harutyunyan@vub.ac.be
Vrije Universiteit Brussel
Marc G. Bellemare
bellemare@google.com
Google DeepMind
Abstract
In this work, we take a fresh l... | 6538 |@word mild:1 norm:1 open:2 seek:3 propagate:1 contraction:12 uphold:1 commute:4 arti:3 mention:1 moment:1 reduction:2 score:6 selecting:1 offering:1 interestingly:1 past:2 existing:1 hasselt:1 current:2 com:3 comparing:1 tnot:1 readily:1 john:1 subsequent:1 update:5 greedy:26 intelligence:3 cult:1 short:1 provide... |
6,123 | 6,539 | Hierarchical Object Representation for Open-Ended
Object Category Learning and Recognition
S.Hamidreza Kasaei, Ana Maria Tom?, Lu?s Seabra Lopes
IEETA - Instituto de Engenharia Electr?nica e Telem?tica de Aveiro
University of Aveiro, Averio, 3810-193, Portugal
{seyed.hamidreza, ana, lsl}@ua.pt
Abstract
Most robots la... | 6539 |@word nd:7 open:25 rgb:5 pick:1 initial:1 configuration:5 contains:2 loc:1 selecting:1 daniel:1 document:1 past:1 existing:1 sugato:1 current:3 comparing:3 wd:2 nt:4 must:7 bd:1 visible:1 plasticity:1 shape:10 enables:1 hofmann:1 designed:2 update:4 progressively:1 v:4 fund:1 generative:1 electr:1 selected:8 inte... |
6,124 | 654 | Generic Analog Neural Computation
- The EPSILON Chip
Stepben Cburcber
Dept. of Elee. Engineering
University of Edinburgh
King's Buildings
Edinburgh. EH9 3JL
Donald J. Baxter
Dept of Elec. Engineering
University of Edinburgh
King's Buildings
Edinburgh. EH9 3JL
Alister Hamilton
DeptofE~.En~ring
University of Edinburg... | 654 |@word proceeded:1 eliminating:1 pw:2 advantageous:1 pulse:44 simulation:1 thereby:2 electronics:1 current:6 activation:1 must:3 subsequent:1 shape:1 designed:4 plot:2 depict:1 device:3 signalling:1 smith:2 supplying:1 pointer:1 firstly:1 sigmoidal:3 five:1 direct:1 differential:3 supply:5 driver:1 become:1 manner:... |
6,125 | 6,540 | Fast Distributed Submodular Cover:
Public-Private Data Summarization
Baharan Mirzasoleiman
ETH Zurich
Morteza Zadimoghaddam
Google Research
Amin Karbasi
Yale University
Abstract
In this paper, we introduce the public-private framework of data summarization
motivated by privacy concerns in personalized recommender sy... | 6540 |@word private:38 faculty:2 manageable:1 polynomial:1 laurence:1 open:1 d2:1 willing:1 km:1 seitz:1 pick:2 shot:1 carry:1 reduction:3 initial:1 contains:2 score:2 selecting:1 tuned:1 document:2 interestingly:1 existing:2 com:1 si:20 lang:1 sergei:1 determinantal:1 realize:1 john:1 visible:1 partition:2 kdd:2 enabl... |
6,126 | 6,541 | Spatio?Temporal Hilbert Maps for Continuous
Occupancy Representation in Dynamic Environments
Ransalu Senanayake
University of Sydney
rsen4557@uni.sydney.edu.au
Simon O?Callaghan
Data61/CSIRO, Australia
simon.ocallaghan@data61.csiro.au
Lionel Ott
University of Sydney
lionel.ott@sydney.edu.au
Fabio Ramos
University of ... | 6541 |@word unaltered:1 longterm:1 middle:1 polynomial:3 advantageous:1 heuristically:1 r:1 propagate:1 covariance:2 sgd:4 series:2 denoting:1 rkhs:1 past:10 existing:1 outperforms:1 current:1 discretization:1 si:10 written:1 additive:1 shape:2 treating:1 plot:1 update:4 designed:1 v:2 extrapolating:1 intelligence:1 se... |
6,127 | 6,542 | Towards Conceptual Compression
Karol Gregor
Google DeepMind
karolg@google.com
Frederic Besse
Google DeepMind
fbesse@google.com
Ivo Danihelka
Google DeepMind
danihelka@google.com
Danilo Jimenez Rezende
Google DeepMind
danilor@google.com
Daan Wierstra
Google DeepMind
wierstra@google.com
Abstract
We introduce convol... | 6542 |@word version:4 achievable:1 compression:47 kriegeskorte:1 nd:1 d2:1 arjen:1 pressure:1 cleary:1 carry:1 reduction:1 contains:4 jimenez:2 ours:1 outperforms:1 current:3 com:5 z2:1 discretization:5 blank:1 recovered:2 nt:1 yet:1 must:1 diederik:2 john:1 refines:1 subsequent:3 realistic:1 shape:1 pertinent:1 wanted... |
6,128 | 6,543 | Interpretable Nonlinear Dynamic Modeling
of Neural Trajectories
Yuan Zhao and Il Memming Park
Department of Neurobiology and Behavior
Department of Applied Mathematics and Statistics
Institute for Advanced Computational Science
Stony Brook University, NY 11794
{yuan.zhao, memming.park}@stonybrook.edu
Abstract
A centra... | 6543 |@word mild:1 trial:4 longterm:1 nonsensical:1 simulation:1 contraction:1 solid:3 reduction:4 initial:7 series:11 daniel:1 tuned:2 current:5 stony:1 numerical:1 motor:1 plot:2 interpretable:5 discrimination:2 implying:1 half:1 parameterization:2 colored:1 filtered:1 hodgkinhuxley:1 stonybrook:1 provides:1 org:1 un... |
6,129 | 6,544 | Coupled Generative Adversarial Networks
Ming-Yu Liu
Mitsubishi Electric Research Labs (MERL),
mliu@merl.com
Oncel Tuzel
Mitsubishi Electric Research Labs (MERL),
oncel@merl.com
Abstract
We propose coupled generative adversarial network (CoGAN) for learning a joint
distribution of multi-domain images. In contrast to ... | 6544 |@word kohli:1 trial:1 achievable:1 nd:12 d2:4 mitsubishi:2 rgb:2 pg:3 moment:2 reduction:1 liu:2 configuration:1 score:2 contains:6 hoiem:1 salzmann:1 jimenez:2 document:1 existing:4 recovered:1 com:3 luo:1 diederik:3 must:1 john:1 realistic:2 christian:1 hypothesize:1 designed:2 plot:1 update:2 generative:43 gan... |
6,130 | 6,545 | Consistent Kernel Mean Estimation
for Functions of Random Variables
?,?
?
Carl-Johann Simon-Gabriel? , Adam Scibior
, Ilya Tolstikhin, Bernhard Sch?lkopf
Department of Empirical Inference, Max Planck Institute for Intelligent Systems
Spemanstra?e 38, 72076 T?bingen, Germany
?
joint first authors; ? also with: Engineeri... | 6545 |@word version:1 briefly:2 compression:1 polynomial:2 norm:1 yi0:3 c0:5 open:1 d2:1 queensland:1 simplifying:1 asks:1 moment:1 contains:2 series:2 daniel:1 denoting:1 rkhs:7 outperforms:1 sharpley:1 scovel:2 universality:1 must:1 bd:1 numerical:2 krikamol:1 intelligence:1 short:1 core:1 indefinitely:1 provides:4 m... |
6,131 | 6,546 | Multiple-Play Bandits in the Position-Based Model
Paul Lagr?e?
LRI, Universit? Paris Sud
Universit? Paris Saclay
paul.lagree@u-psud.fr
Claire Vernade?
LTCI, CNRS, T?l?com ParisTech
Universit? Paris Saclay
vernade@enst.fr
Olivier Capp?
LTCI, CNRS
T?l?com ParisTech
Universit? Paris Saclay
Abstract
Sequentially learni... | 6546 |@word version:3 nd:2 c0:2 confirms:1 simulation:3 paid:1 necessity:2 contains:2 series:2 denoting:3 bc:3 interestingly:1 past:2 existing:1 ramsey:1 recovered:1 com:4 comparing:1 must:1 written:3 realistic:3 kdd:2 update:1 half:1 selected:2 website:1 item:19 intelligence:1 beginning:1 record:1 filtered:1 provides:... |
6,132 | 6,547 | Reward Augmented Maximum Likelihood
for Neural Structured Prediction
Mohammad Norouzi
Samy Bengio
Zhifeng Chen
Navdeep Jaitly
Mike Schuster
Yonghui Wu
Dale Schuurmans
{mnorouzi, bengio, zhifengc, ndjaitly}@google.com
{schuster, yonghui, schuurmans}@google.com
Google Brain
Abstract
A key problem in structured output pr... | 6547 |@word seems:2 loading:1 logit:2 seek:2 sgd:6 kappen:1 substitution:9 liu:1 score:16 outperforms:1 hasselt:1 com:2 surprising:1 activation:1 yet:1 guez:1 numerical:1 hoping:1 update:1 stationary:2 greedy:1 selected:1 accordingly:1 mccallum:1 smith:1 short:1 provides:1 simpler:1 direct:5 koltun:2 incorrect:2 consis... |
6,133 | 6,548 | Catching heuristics are optimal control policies
Boris Belousov* , Gerhard Neumann* , Constantin A. Rothkopf** , Jan Peters*
**
*
Department of Computer Science, TU Darmstadt
Cognitive Science Center & Department of Psychology, TU Darmstadt
Abstract
Two seemingly contradictory theories attempt to explain how humans m... | 6548 |@word trial:1 c0:3 open:3 simulation:7 covariance:5 lacquaniti:2 tr:4 reduction:1 moment:1 initial:5 o2:2 reaction:13 current:3 com:1 trustworthy:2 interrupted:2 numerical:1 biomechanical:1 motor:9 plot:1 stationary:2 intelligence:1 plane:5 xk:6 compelled:1 parametrization:1 core:1 short:2 record:2 provides:1 loc... |
6,134 | 6,549 | Automated scalable segmentation of neurons from
multispectral images
Uygar S?mb?l
Grossman Center for the Statistics of Mind
and Dept. of Statistics, Columbia University
Douglas Roossien Jr.
University of Michigan Medical School
Fei Chen
MIT Media Lab and McGovern Institute
Nicholas Barry
MIT Media Lab and McGovern... | 6549 |@word briefly:1 faculty:1 version:2 hippocampus:1 rivlin:1 open:2 simulation:7 brightness:1 dramatic:1 briggman:1 deisseroth:1 reduction:2 initial:1 series:1 daniel:1 genetic:2 existing:3 comparing:1 subcomponents:1 si:5 yet:1 must:1 connectomics:3 john:2 additive:1 partition:3 shape:1 enables:1 remove:2 plot:3 d... |
6,135 | 655 | A dynamical model of priming and
repetition blindness
Daphne Bavelier
Laboratory of Neuropsychology
The Salk Institute
La J oHa, CA 92037
Michael I. Jordan
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambridge MA 02139
Abstract
We describe a model of visual word recognition that ... | 655 |@word blindness:18 trial:4 middle:2 briefly:1 stronger:1 interestingly:1 blank:6 current:1 activation:16 si:1 written:1 must:5 distant:1 happen:1 item:4 short:1 filtered:1 hypersphere:2 detecting:2 lexicon:1 daphne:1 height:1 guard:1 c2:5 behavioral:7 manner:2 inter:4 mask:6 kanwisher:4 brain:1 bellman:1 decreasin... |
6,136 | 6,550 | Clustering with Bregman Divergences: an Asymptotic
Analysis
Chaoyue Liu, Mikhail Belkin
Department of Computer Science & Engineering
The Ohio State University
liu.2656@osu.edu, mbelkin@cse.ohio-state.edu
Abstract
Clustering, in particular k-means clustering, is a central topic in data analysis.
Clustering with Bregma... | 6550 |@word multitask:2 kulis:2 version:3 briefly:1 compression:2 norm:8 open:1 pulse:1 mention:1 ld:1 liu:2 configuration:4 contains:1 genetic:1 existing:3 ka:2 z2:6 yet:1 john:1 ranka:1 partition:3 kdd:1 remove:1 plot:3 seeding:2 intelligence:1 quantizer:1 provides:1 cse:1 codebook:2 location:6 ire:1 revisited:1 zhan... |
6,137 | 6,551 | A Comprehensive Linear Speedup Analysis for
Asynchronous Stochastic Parallel Optimization from
Zeroth-Order to First-Order
?
Xiangru Lian* , Huan Zhang? , Cho-Jui Hsieh? , Yijun Huang* , and Ji Liu*
?
Department of Computer Science, University of Rochester, USA
Department of Electrical and Computer Engineering, Unive... | 6551 |@word version:3 johansson:1 nd:1 hsieh:3 sgd:16 reduction:1 liu:8 contains:1 ours:1 existing:9 com:4 comparing:4 gmail:3 gpu:1 devin:1 subsequent:1 happen:1 kdd:8 interpretable:1 update:4 juditsky:1 chohsieh:1 item:1 rts:8 xk:21 ith:1 smith:1 core:9 short:3 provides:6 completeness:1 complication:1 node:26 bittorf... |
6,138 | 6,552 | Visual Dynamics: Probabilistic Future Frame
Synthesis via Cross Convolutional Networks
Tianfan Xue*1 Jiajun Wu*1 Katherine L. Bouman1 William T. Freeman1,2
1
2
Massachusetts Institute of Technology
Google Research
{tfxue, jiajunwu, klbouman, billf}@mit.edu
Abstract
We study the problem of synthesizing a number of lik... | 6552 |@word version:2 mehta:1 rgb:6 jacob:3 wexler:2 inpainting:1 shot:1 harder:1 carry:1 shechtman:1 liu:4 contains:2 series:1 jimenez:2 tuned:1 ours:4 animated:1 past:1 existing:1 current:2 michal:1 yet:1 diederik:2 must:1 gpu:1 john:1 uria:1 realistic:6 thrust:2 shape:13 nian:1 remove:1 stationary:1 generative:15 se... |
6,139 | 6,553 | Adaptive Averaging in Accelerated Descent Dynamics
Walid Krichene ?
UC Berkeley
Alexandre M. Bayen
UC Berkeley
Peter L. Bartlett
UC Berkeley and QUT
walid@eecs.berkeley.edu
bayen@berkeley.edu
bartlett@cs.berkeley.edu
Abstract
We study accelerated descent dynamics for constrained convex optimization. This
dynamic... | 6553 |@word briefly:1 version:5 polynomial:1 seems:2 nemirovsky:1 concise:1 solid:1 moment:1 initial:2 contains:1 series:3 existing:2 current:2 discretization:6 com:1 written:3 numerical:3 predetermined:1 designed:1 plot:1 update:1 slowing:1 xk:2 vanishing:1 hamiltonian:1 lr:26 provides:2 characterization:1 simpler:1 m... |
6,140 | 6,554 | Finite Sample Prediction and Recovery Bounds
for Ordinal Embedding
Lalit Jain
University of Michigan
Ann Arbor, MI 48109
lalitj@umich.edu
Kevin Jamieson
University of California, Berkeley
Berkeley, CA 94720
kjamieson@berkeley.edu
Robert Nowak
University of Wisconsin
Madison, WI 53706
rdnowak@wisc.edu
Abstract
The go... | 6554 |@word kgk:5 trial:1 version:1 norm:21 open:1 simulation:1 contraction:1 decomposition:1 tr:1 carry:1 moment:1 liu:1 series:2 neeman:1 past:1 recovered:5 dx:3 must:3 numerical:1 weyl:1 hypothesize:1 generative:4 intelligence:1 selected:1 item:12 greedy:1 xk:3 ith:2 fa9550:1 characterization:1 bijection:1 preferenc... |
6,141 | 6,555 | SDP Relaxation with Randomized Rounding for
Energy Disaggregation
Kiarash Shaloudegi
Imperial College London
k.shaloudegi16@imperial.ac.uk
Csaba Szepesv?ri
University of Alberta
szepesva@ualberta.ca
Andr?s Gy?rgy
Imperial College London
a.gyorgy@imperial.ac.uk
Wilsun Xu
University of Alberta
wxu@ualberta.ca
Abstract
... | 6555 |@word innovates:1 version:2 polynomial:2 simulation:1 covariance:2 moment:1 initial:2 series:2 pt0:1 denoting:2 outperforms:2 freitas:1 current:2 disaggregation:19 com:1 comparing:1 assigning:1 dx:3 written:1 chu:1 additive:6 drop:1 plot:1 update:4 v:1 greedy:1 prohibitive:1 half:2 czt:3 xk:6 beginning:1 provides... |
6,142 | 6,556 | Residual Networks Behave Like Ensembles of
Relatively Shallow Networks
Andreas Veit
Michael Wilber
Serge Belongie
Department of Computer Science & Cornell Tech
Cornell University
{av443, mjw285, sjb344}@cornell.edu
Abstract
In this work we propose a novel interpretation of residual networks showing that
they can be s... | 6556 |@word torsten:1 wiesel:1 norm:1 seems:1 yi0:1 open:1 seek:1 propagate:2 dramatic:1 fif:2 recursively:1 carry:2 initial:2 substitution:1 configuration:4 contains:1 selecting:1 liu:2 daniel:1 ours:1 document:1 past:1 surprising:4 activation:1 intriguing:1 written:1 readily:1 subsequent:2 shape:1 christian:3 remove:... |
6,143 | 6,557 | Greedy Feature Construction
Dino Oglic? ?
dino.oglic@uni-bonn.de
?
Institut f?r Informatik III
Universit?t Bonn, Germany
Thomas G?rtner ?
thomas.gaertner@nottingham.ac.uk
?
School of Computer Science
The University of Nottingham, UK
Abstract
We present an effective method for supervised feature construction. The mai... | 6557 |@word kgk:2 repository:1 briefly:1 kulis:2 norm:5 stronger:2 nd:1 c0:2 gfc:3 closure:3 confirms:1 r:1 simulation:1 calculus:1 decomposition:2 hsieh:1 recursively:1 liblinear:1 initial:3 configuration:3 contains:4 exclusively:1 selecting:2 series:1 past:1 existing:3 current:5 comparing:2 nt:1 si:3 written:1 sergei... |
6,144 | 6,558 | Efficient and Robust Spiking Neural Circuit for
Navigation Inspired by Echolocating Bats
Pulkit Tandon, Yash H. Malviya
Indian Institute of Technology, Bombay
pulkit1495,yashmalviya94@gmail.com
Bipin Rajendran
New Jersey Institute of Technology
bipin@njit.edu
Abstract
We demonstrate a spiking neural circuit for azim... | 6558 |@word neurophysiology:1 worsens:1 version:1 proportionality:1 d2:1 simulation:5 r:1 propagate:1 azimuthal:1 incurs:1 mammal:2 thereby:1 n8:1 denoting:1 suppressing:2 past:2 current:1 com:1 gmail:1 readily:1 olive:1 additive:9 realistic:2 periodically:1 plasticity:1 enables:1 motor:1 update:1 discrimination:2 stat... |
6,145 | 6,559 | Sparse Support Recovery with
Non-smooth Loss Functions
Gabriel Peyr?
CNRS, DMA
?cole Normale Sup?rieure
Paris, France 75775
gabriel.peyre@ens.fr
K?vin Degraux
ISPGroup/ICTEAM, FNRS
Universit? catholique de Louvain
Louvain-la-Neuve, Belgium 1348
kevin.degraux@uclouvain.be
Jalal M. Fadili
Normandie Univ, ENSICAEN,
CNRS,... | 6559 |@word mild:1 version:1 middle:1 norm:15 instrumental:1 proportion:1 simulation:4 decomposition:1 tr:1 initial:1 series:1 tuned:1 interestingly:1 existing:1 recovered:1 comparing:1 com:1 must:3 realize:1 numerical:3 additive:3 remove:1 plot:3 progressively:1 ysp:2 transposition:1 certificate:12 provides:2 idi:3 ma... |
6,146 | 656 | Efficient Pattern Recognition Using a
New Transformation Distance
Patrice Simard
Yann Le Cun
John Denker
AT&T Bell Laboratories, 101 Crawford Corner Road, Holmdel, NJ 07724
Abstract
Memory-based classification algorithms such as radial basis functions or K-nearest neighbors typically rely on simple distances (Eucli... | 656 |@word version:2 middle:2 oae:1 norm:2 horizonta:1 imn:1 tried:2 decomposition:1 lpp:3 n8:1 existing:1 nt:1 od:2 surprising:1 must:4 readily:1 john:1 distant:1 analytic:1 progressively:2 aside:1 resampling:1 selected:4 fewer:1 plane:8 beginning:1 filtered:1 postal:1 hyperplanes:1 along:1 incorrect:1 consists:2 inde... |
6,147 | 6,560 | Dual Space Gradient Descent for Online Learning
Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Phung
Centre for Pattern Recognition and Data Analytics
Deakin University, Australia
{trung.l, tu.nguyen, v.nguyen, dinh.phung}@deakin.edu.au
Abstract
One crucial goal in kernel online learning is to bound the model size. Common... | 6560 |@word version:4 middle:2 logit:5 dekel:2 crucially:1 paid:1 sgd:6 initial:1 liu:1 efficacy:1 score:1 existing:2 duong:1 current:1 comparing:4 com:1 tackling:1 dx:1 realize:1 partition:1 enables:1 remove:1 designed:1 v:2 intelligence:3 selected:4 website:1 trung:2 accordingly:1 shifeng:1 core:3 record:1 wth:3 prov... |
6,148 | 6,561 | Improved Dropout for Shallow and Deep Learning
Zhe Li1 , Boqing Gong2 , Tianbao Yang1
The University of Iowa, Iowa city, IA 52245
2
University of Central Florida, Orlando, FL 32816
{zhe-li-1,tianbao-yang}@uiowa.edu
bgong@crcv.ucf.edu
1
Abstract
Dropout has been witnessed with great success in training deep neural net... | 6561 |@word trial:2 nd:1 tried:2 bn:11 covariance:1 sgd:3 tr:14 initial:3 contains:1 document:2 comparing:1 nt:1 com:2 si:1 yet:1 activation:3 bd:4 diederik:2 informative:1 christian:1 drop:1 plot:1 update:3 v:3 selected:3 gbr:1 bissacco:1 zhang:3 mathematical:2 direct:1 overhead:1 baldi:2 introduce:2 theoretically:1 n... |
6,149 | 6,562 | Communication-Optimal Distributed Clustering?
Jiecao Chen
Indiana University
Bloomington, IN 47401
jiecchen@indiana.edu
He Sun
University of Bristol
Bristol, BS8 1UB, UK
h.sun@bristol.ac.uk
David P. Woodruff
IBM Research Almaden
San Jose, CA 95120
dpwoodru@us.ibm.com
Qin Zhang
Indiana University
Bloomington, IN 474... | 6562 |@word shayan:1 illustrating:1 version:5 private:1 stronger:1 rajaraman:1 tat:1 rgb:1 incurs:1 bicriteria:4 recursively:1 reduction:2 moment:1 contains:4 score:5 woodruff:4 fa8750:1 existing:1 com:1 incidence:2 surprising:2 si:4 must:1 written:1 sergei:1 mst:1 additive:1 partition:6 numerical:1 christian:1 seeding... |
6,150 | 6,563 | MoCap-guided Data Augmentation
for 3D Pose Estimation in the Wild
Gr?gory Rogez
Cordelia Schmid
Inria Grenoble Rh?ne-Alpes, Laboratoire Jean Kuntzmann, France
Abstract
This paper addresses the problem of 3D human pose estimation in the wild. A significant challenge is the lack of training data, i.e., 2D images of huma... | 6563 |@word cnn:14 version:1 inversion:1 seems:1 everingham:2 triggs:1 dekker:1 rgb:1 q1:1 lepetit:1 configuration:4 score:3 iqbal:3 selecting:1 ours:5 animated:1 outperforms:5 existing:5 past:1 cad:1 mesh:2 realistic:4 partition:2 concatenate:1 subsequent:1 shape:3 romero:1 moreno:2 drop:3 designed:1 v:1 generative:2 ... |
6,151 | 6,564 | A Bio-inspired Redundant Sensing Architecture
Anh Tuan Nguyen, Jian Xu and Zhi Yang?
Department of Biomedical Engineering
University of Minnesota
Minneapolis, MN 55455
?
yang5029@umn.edu
Abstract
Sensing is the process of deriving signals from the environment that allows artificial systems to interact with the physic... | 6564 |@word cu:3 briefly:1 middle:2 polynomial:1 proportion:2 achievable:1 c0:4 simulation:8 mammal:1 incurs:1 solid:1 liu:1 foveal:1 efficacy:1 loeliger:2 suppressing:1 envision:1 comparing:1 yet:1 dx:2 must:1 partition:4 otero:1 shape:1 enables:1 hypothesize:1 designed:4 plot:1 n0:22 msb:3 discrimination:1 device:4 q... |
6,152 | 6,565 | Learning Supervised PageRank with Gradient-Based
and Gradient-Free Optimization Methods
Lev Bogolubsky1,2 , Gleb Gusev1,5 , Andrei Raigorodskii5,2,1,8 , Aleksey Tikhonov1 , Maksim Zhukovskii1,5
Yandex1 , Moscow State University2 , Buryat State University8
{bogolubsky, gleb57, raigorodsky, altsoph, zhukmax}@yandex-team... | 6565 |@word norm:5 seems:1 dekel:1 widom:1 accounting:1 pavel:2 q1:2 liu:3 q32:4 score:4 document:2 outperforms:2 existing:4 kmk:4 current:2 com:1 analysed:1 numerical:2 kdd:1 stationary:10 nq:2 serdyukov:3 xk:21 ecir:1 record:2 weierstrass:1 davison:1 authority:1 node:10 math:2 traverse:1 zhang:1 mathematical:3 consis... |
6,153 | 6,566 | Stochastic Optimization for
Large-scale Optimal Transport
Aude Genevay
CEREMADE, Universit? Paris-Dauphine
INRIA ? Mokaplan project-team
genevay@ceremade.dauphine.fr
Gabriel Peyr?
CNRS and DMA, ?cole Normale Sup?rieure
INRIA ? Mokaplan project-team
gabriel.peyre@ens.fr
Marco Cuturi
CREST, ENSAE
Universit? Paris-Sacla... | 6566 |@word version:1 instrumental:1 villani:1 advantageous:1 norm:6 open:2 hu:1 simulation:1 decomposition:1 p0:1 sgd:23 initial:1 celebrated:1 tuned:1 rkhs:10 document:2 franklin:1 existing:1 current:3 discretization:4 comparing:4 recovered:3 dx:2 written:3 gpu:1 must:1 numerical:3 j1:1 shape:2 plot:11 update:1 judit... |
6,154 | 6,567 | Mistake Bounds for Binary Matrix Completion
Mark Herbster
University College London
Department of Computer Science
London WC1E 6BT, UK
m.herbster@cs.ucl.ac.uk
Stephen Pasteris
University College London
Department of Computer Science
London WC1E 6BT, UK
s.pasteris@cs.ucl.ac.uk
Massimiliano Pontil
Istituto Italiano di ... | 6567 |@word multitask:1 trial:9 polynomial:3 norm:15 seems:1 stronger:1 c0:12 instrumental:1 nd:2 crucially:1 decomposition:3 simplifying:1 q1:1 tr:23 contains:1 document:1 comparing:2 must:1 j1:3 update:3 half:1 fewer:1 warmuth:5 provides:2 mathematical:1 constructed:1 become:3 symposium:2 specialize:1 introduce:2 can... |
6,155 | 6,568 | A Powerful Generative Model Using Random Weights
for the Deep Image Representation
Kun He?, Yan Wang ?
Department of Computer Science and Technology
Huazhong University of Science and Technology, Wuhan 430074, China
brooklet60@hust.edu.cn, yanwang@hust.edu.cn
John Hopcroft
Department of Computer Science
Cornell Univer... | 6568 |@word cnn:12 middle:1 inversion:10 norm:1 seek:1 propagate:1 rgb:1 jacob:1 harder:1 contains:4 score:1 selecting:1 ours:5 deconvolutional:2 existing:1 current:2 com:2 guadarrama:1 activation:10 gpu:1 john:2 recasting:2 blur:1 generative:5 greedy:1 selected:1 leaf:1 reciprocal:1 realism:1 pool2:1 provides:2 kepler... |
6,156 | 6,569 | PAC-Bayesian Theory Meets Bayesian Inference
Pascal Germain? Francis Bach? Alexandre Lacoste? Simon Lacoste-Julien?
?
INRIA Paris - ?cole Normale Sup?rieure, firstname.lastname@inria.fr
?
Google, allac@google.com
Abstract
We exhibit a strong link between frequentist PAC-Bayesian risk bounds and the
Bayesian marginal l... | 6569 |@word polynomial:4 mehta:1 d2:1 decomposition:1 tr:6 ld:17 it1:1 moment:1 ndez:1 contains:1 series:1 selecting:3 initial:1 existing:2 current:1 com:1 comparing:1 contextual:1 assigning:1 john:7 ronald:1 designed:1 generative:1 selected:1 intelligence:1 parameterization:2 amir:1 isotropic:1 beginning:1 inconvenien... |
6,157 | 657 | Optimal Depth Neural Networks for Multiplication
and Related Problems
Kai-Yeung Siu
Dept. of Electrical & Compo Engineering
University of California, Irvine
Irvine, CA 92717
Vwani Roychowdhury
School of Electrical Engineering
Purdue University
West Lafayette, IN 47907
Abstract
An artificial neural network (ANN) is c... | 657 |@word version:1 polynomial:14 seems:2 open:1 calculus:1 must:2 realistic:1 hajnal:2 v:1 devising:1 device:2 nervous:1 compo:4 math:2 sigmoidal:1 unbounded:8 mathematical:1 constructed:3 focs:1 prove:2 symp:3 indeed:2 behavior:1 brain:1 increasing:1 bounded:10 moreover:1 circuit:45 notation:1 mcculloch:1 what:1 tur... |
6,158 | 6,570 | Total Variation Classes Beyond 1d: Minimax Rates,
and the Limitations of Linear Smoothers
Veeranjaneyulu Sadhanala
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA 15213
vsadhana@cs.cmu.edu
Yu-Xiang Wang
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA 15213
yuxiangw@cs.cmu.ed... | 6570 |@word kondor:1 polynomial:1 norm:3 seems:1 suitably:1 open:3 d2:1 seek:2 bn:9 simplifying:1 reduction:4 liu:1 series:2 tuned:3 ours:1 document:1 denoting:1 outperforms:1 current:1 comparing:2 incidence:2 activation:1 yet:1 written:1 must:3 john:1 dct:1 numerical:1 christian:2 acar:1 drop:1 aside:1 intelligence:1 ... |
6,159 | 6,571 | Exponential Family Embeddings
Maja Rudolph
Columbia University
Francisco J. R. Ruiz
Univ. of Cambridge
Columbia University
Stephan Mandt
Columbia University
David M. Blei
Columbia University
Abstract
Word embeddings are a powerful approach for capturing semantic similarity among
terms in a vocabulary. In this pape... | 6571 |@word version:1 loading:5 hu:2 hyv:2 ivlbl:1 seek:1 additively:1 snack:1 contrastive:3 pick:1 sgd:1 yih:1 reduction:3 contains:8 series:1 genetic:1 fa8750:1 past:2 existing:2 outperforms:2 nt:2 gauvain:1 john:1 additive:4 analytic:1 hypothesize:2 remove:1 interpretable:1 aside:1 intelligence:2 fewer:4 item:32 par... |
6,160 | 6,572 | On Regularizing Rademacher Observation Losses
Richard Nock
Data61, The Australian National University & The University of Sydney
richard.nock@data61.csiro.au
Abstract
It has recently been shown that supervised learning linear classifiers with two of
the most popular losses, the logistic and square loss, is equivalent... | 6572 |@word private:3 version:4 briefly:1 achievable:1 norm:2 seems:2 repository:1 nd:1 mehta:1 semicontinuous:1 pick:3 contains:2 lichman:2 hardy:2 existing:1 surprising:1 protection:1 yet:2 readily:1 john:1 shape:1 update:4 v:1 denison:1 warmuth:2 short:2 lr:11 boosting:27 bijection:1 preference:2 shorthand:1 consist... |
6,161 | 6,573 | Binarized Neural Networks
Itay Hubara1 *
itayh@technion.ac.il
Matthieu Courbariaux2 *
matthieu.courbariaux@gmail.com
Ran El-Yaniv1
rani@cs.technion.ac.il
Daniel Soudry3
daniel.soudry@gmail.com
Yoshua Bengio2,4
yoshua.umontreal@gmail.com
(1) Technion, Israel Institute of Technology.
(3) Columbia University.
(*) Ind... | 6573 |@word worsens:1 cnn:2 version:4 rani:1 achievable:1 advantageous:1 seems:2 nd:1 compression:1 open:1 instruction:4 propagate:1 bn:7 tried:1 dramatic:1 sgd:2 solid:1 harder:1 moment:1 liu:2 exclusively:1 daniel:2 ours:1 bitwise:3 com:5 discretization:2 x81:1 activation:32 gmail:3 gpu:15 concatenate:1 enables:1 upd... |
6,162 | 6,574 | Exploiting Tradeoffs for Exact Recovery in
Heterogeneous Stochastic Block Models
Qiyang Han
Department of Statistics
University of Washington
Seattle, WA 98195
royhan@uw.edu
Amin Jalali
Department of Electrical Engineering
University of Washington
Seattle, WA 98195
amjalali@uw.edu
Ioana Dumitriu
Department of Mathema... | 6574 |@word mild:1 briefly:1 achievable:1 proportion:1 norm:5 stronger:2 seems:1 nd:1 open:2 configuration:14 series:2 neeman:1 existing:2 recovered:3 lang:1 yet:1 partition:2 intelligence:1 nq:4 plane:1 characterization:1 provides:4 node:11 certificate:2 detecting:1 zhang:1 mathematical:1 burst:1 along:1 symposium:3 y... |
6,163 | 6,575 | PAC Reinforcement Learning with Rich Observations
Akshay Krishnamurthy
University of Massachusetts, Amherst
Amherst, MA, 01003
akshay@cs.umass.edu
Alekh Agarwal
Microsoft Research
New York, NY 10011
alekha@microsoft.com
John Langford
Microsoft Research
New York, NY 10011
jcl@microsoft.com
Abstract
We propose and stu... | 6575 |@word exploitation:1 version:1 eliminating:1 achievable:1 polynomial:12 stronger:1 open:1 p0:4 invoking:2 concise:1 recursively:3 initial:1 contains:1 uma:1 exclusively:1 prefix:1 existing:1 current:5 com:2 contextual:14 recovered:1 comparing:1 must:7 john:1 realize:1 partition:1 wiewiora:1 enables:2 update:2 gre... |
6,164 | 6,576 | Algorithms and matching lower bounds for
approximately-convex optimization
Yuanzhi Li
Department of Computer Science
Princeton University
Princeton, NJ, 08450
yuanzhil@cs.princeton.edu
Andrej Risteski
Department of Computer Science
Princeton University
Princeton, NJ, 08450
risteski@cs.princeton.edu
Abstract
In recen... | 6576 |@word version:3 briefly:2 polynomial:9 proportion:1 norm:1 dekel:1 open:3 d2:5 additively:1 crucially:1 citeseer:1 pick:2 harder:1 initial:1 series:1 daniel:1 current:2 dx:1 must:2 john:1 additive:1 update:1 core:2 short:1 lce:9 mathematical:1 along:1 symposium:1 prove:6 interscience:2 inside:4 manner:1 indeed:3 ... |
6,165 | 6,577 | Improving PAC Exploration
Using the Median of Means
Jason Pazis
Laboratory for Information and Decision Systems
Massachusetts Institute of Technology
Cambridge, MA 02139, USA
jpazis@mit.edu
Ronald Parr
Department of Computer Science
Duke University
Durham, NC 27708
parr@cs.duke.edu
Jonathan P. How
Aerospace Controls ... | 6577 |@word version:1 polynomial:1 norm:5 c0:2 open:1 km:34 boundedness:1 recursively:1 moment:2 series:1 current:3 yet:2 must:1 readily:1 ronald:4 happen:1 update:5 stationary:4 greedy:2 half:1 selected:1 intelligence:4 underestimating:1 mannor:2 mcdiarmid:3 unbounded:7 mathematical:2 prove:6 shorthand:2 combine:1 int... |
6,166 | 6,578 | Dynamic Filter Networks
Bert De Brabandere1?
ESAT-PSI, KU Leuven, iMinds
Xu Jia1?
ESAT-PSI, KU Leuven, iMinds
Tinne Tuytelaars1
ESAT-PSI, KU Leuven, iMinds
Luc Van Gool1,2
ESAT-PSI, KU Leuven, iMinds
D-ITET, ETH Zurich
1
firstname.lastname@esat.kuleuven.be 2 vangool@vision.ee.ethz.ch
Abstract
In a traditional convo... | 6578 |@word cnn:1 version:2 seems:2 open:1 r:1 propagate:1 carry:1 contains:1 series:1 disparity:1 daniel:1 tuned:1 ours:3 past:1 outperforms:1 current:1 com:1 yet:3 must:1 john:1 unpooling:1 subsequent:1 additive:1 blur:1 displace:1 remove:1 generative:2 instantiate:1 selected:1 short:2 core:1 filtered:3 provides:1 lo... |
6,167 | 6,579 | Gradient-based Sampling: An Adaptive Importance
Sampling for Least-squares
Rong Zhu
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.
rongzhu@amss.ac.cn
Abstract
In modern data analysis, random sampling is an efficient and widely-used strategy
to overcome the computational diffi... | 6579 |@word version:2 proportion:1 norm:1 nd:10 unif:14 d2:3 simulation:4 bn:2 decomposition:4 covariance:2 carry:1 initial:5 series:1 score:4 selecting:1 woodruff:2 outperforms:1 existing:1 si:3 numerical:2 partition:1 informative:1 plot:2 rd2:8 guess:4 ith:2 steepest:1 provides:2 location:1 casp:4 zhang:1 supply:1 sy... |
6,168 | 658 | Perceiving Complex Visual Scenes:
An Oscillator Neural Network Model that
Integrates Selective Attention, Perceptual
Organisation, and Invariant Recognition
Rainer Goebel
Department of Psychology
University of Braunschweig
Spielmannstr. 19
W-3300 Braunschweig, Germany
Abstract
Which processes underly our ability to q... | 658 |@word exploitation:1 open:1 instruction:1 simulation:1 attended:2 extrastriate:1 initial:3 contains:1 att:1 selecting:2 tuned:2 current:3 lang:2 activation:5 must:1 underly:1 shape:4 v:1 cue:3 selected:6 accordingly:1 short:1 filtered:1 location:10 sigmoidal:1 along:1 neisser:3 consists:2 pathway:16 recognizable:1... |
6,169 | 6,580 | Noise-Tolerant Life-Long Matrix Completion via
Adaptive Sampling
Maria-Florina Balcan
Machine Learning Department
Carnegie Mellon University, USA
ninamf@cs.cmu.edu
Hongyang Zhang
Machine Learning Department
Carnegie Mellon University, USA
hongyanz@cs.cmu.edu
Abstract
We study the problem of recovering an incomplete ... | 6580 |@word mild:1 trial:1 version:1 polynomial:1 norm:12 stronger:1 c0:2 km:7 propagate:1 jacob:1 pick:1 mention:2 nystr:1 solid:1 klk:1 initial:1 contains:2 ours:2 outperforms:1 existing:3 kmk:1 current:3 recovered:2 comparing:1 yet:3 must:1 realistic:6 benign:3 hongyang:1 enables:1 remove:1 plot:1 update:1 intellige... |
6,170 | 6,581 | Improved Variational Inference
with Inverse Autoregressive Flow
Diederik P. Kingma
dpkingma@openai.com
Tim Salimans
tim@openai.com
Rafal Jozefowicz
rafal@openai.com
Ilya Sutskever
ilya@openai.com
Xi Chen
peter@openai.com
Max Welling?
M.Welling@uva.nl
Abstract
The framework of normalizing flows provides a general ... | 6581 |@word determinant:10 version:6 compression:1 nd:3 flexiblity:1 covariance:4 initial:5 ours:1 deconvolutional:1 com:6 z2:3 diederik:1 written:1 gpu:1 subsequent:1 cheap:2 update:4 generative:14 half:1 leaf:1 parameterization:2 hamiltonian:4 core:1 colored:1 blei:5 provides:1 zhang:2 wierstra:3 bowman:1 direct:1 co... |
6,171 | 6,582 | Neurons Equipped with Intrinsic Plasticity
Learn Stimulus Intensity Statistics
Travis Monk
Cluster of Excellence Hearing4all
University of Oldenburg
26129 Oldenburg, Germany
travis.monk@uol.de
Cristina Savin
IST Austria
3400 Klosterneuburg
Austria
csavin@ist.ac.at
?
J?org Lucke
Cluster of Excellence Hearing4all
Univ... | 6582 |@word schmuker:1 c0:14 ucke:4 simulation:1 seek:1 accounting:1 dramatic:1 solid:1 carry:1 cristina:1 series:1 oldenburg:4 rightmost:2 past:5 outperforms:1 current:3 comparing:1 activation:1 yet:1 reminiscent:1 attracted:1 numerical:4 realistic:2 subsequent:1 plasticity:25 shape:7 enables:1 drop:1 plot:2 update:6 ... |
6,172 | 6,583 | Dynamic Mode Decomposition with Reproducing
Kernels for Koopman Spectral Analysis
a
Yoshinobu Kawaharaab
The Institute of Scientific and Industrial Research, Osaka University
b
Center for Advanced Integrated Intelligence Research, RIKEN
ykawahara@sanken.osaka-u.ac.jp
Abstract
A spectral analysis of the Koopman opera... | 6583 |@word version:1 briefly:2 polynomial:1 vogt:1 open:1 simulation:1 linearized:1 decomposition:35 p0:4 q1:2 reduction:2 initial:1 score:4 united:1 liquid:1 rkhs:6 existing:1 diagonalized:1 recovered:2 comparing:1 yairi:1 attracted:2 written:1 numerical:2 happen:1 partition:1 intelligence:3 fewer:1 accordingly:1 ham... |
6,173 | 6,584 | Efficient High-Order Interaction-Aware Feature
Selection Based on Conditional Mutual Information
Alexander Shishkin, Anastasia Bezzubtseva, Alexey Drutsa,
Ilia Shishkov, Ekaterina Gladkikh, Gleb Gusev, Pavel Serdyukov
Yandex; 16 Leo Tolstoy St., Moscow 119021, Russia
{sisoid,nstbezz,adrutsa,ishfb,kglad,gleb57,pavser}@y... | 6584 |@word cmi:11 private:1 stronger:1 seems:2 accounting:1 pavel:1 citeseer:1 elisseeff:1 profit:1 reduction:2 wrapper:1 liu:3 contains:1 score:42 selecting:1 outperforms:3 existing:6 current:3 discretization:2 com:1 si:40 yet:1 john:1 fn:1 cheap:1 remove:1 greedy:17 selected:18 half:1 serdyukov:1 provides:1 boosting... |
6,174 | 6,585 | Distributed Flexible Nonlinear Tensor Factorization
Shandian Zhe? , Kai Zhang? , Pengyuan Wang? , Kuang-chih Lee] , Zenglin Xu\ ,
Yuan Qi[ , Zoubin Gharamani?
?
Dept. Computer Science, Purdue University, ? NEC Laboratories America, Princeton NJ,
?
Dept. Marketing, University of Georgia at Athens, ] Yahoo! Research,
\
B... | 6585 |@word repository:1 eliminating:1 proportion:1 norm:1 disk:6 tensorial:1 hu:2 r:1 eng:1 decomposition:12 covariance:16 thereby:1 tr:2 outlook:1 contains:7 tuned:1 ours:6 franklin:1 outperforms:5 existing:2 com:3 surprising:1 nell:5 yet:1 chu:2 must:1 numerical:1 additive:3 confirming:1 kdd:1 enables:3 update:8 ele... |
6,175 | 6,586 | Edge-exchangeable graphs and sparsity
Diana Cai
Dept. of Statistics, U. Chicago
Chicago, IL 60637
dcai@uchicago.edu
Trevor Campbell
CSAIL, MIT
Cambridge, MA 02139
tdjc@mit.edu
Tamara Broderick
CSAIL, MIT
Cambridge, MA 02139
tbroderick@csail.mit.edu
Abstract
Many popular network models rely on the assumption of (ver... | 6586 |@word pw:1 seems:1 stronger:1 unif:1 confirms:1 simulation:6 crucially:1 bn:1 thereby:1 recursively:2 moment:2 initial:1 contains:1 denoting:1 janson:1 existing:3 reminiscent:1 chicago:2 partition:1 plot:3 concert:1 v:2 stationary:6 generative:5 fewer:1 intelligence:1 plane:1 olhede:2 characterization:2 multiset:... |
6,176 | 6,587 | Probabilistic Inference with Generating Functions for
Poisson Latent Variable Models
Kevin Winner1 and Daniel Sheldon1,2
{kwinner,sheldon}@cs.umass.edu
1
College of Information and Computer Sciences, University of Massachusetts Amherst
2
Department of Computer Science, Mount Holyoke College
Abstract
Graphical models ... | 6587 |@word version:2 polynomial:13 nd:1 open:2 simulation:1 moment:3 series:7 uma:1 selecting:1 daniel:1 existing:3 atlantic:1 current:2 osh:1 surprising:1 si:5 must:2 realistic:1 partition:1 enables:1 designed:1 bickson:2 n0:1 v:5 intelligence:3 instantiate:1 selected:1 xk:6 provides:1 location:1 org:1 zhang:1 direct... |
6,177 | 6,588 | On Graph Reconstruction via Empirical Risk
Minimization: Fast Learning Rates and Scalability
Guillaume Papa, St?phan Cl?men?on
LTCI, CNRS, T?l?com ParisTech, Universit? Paris-Saclay
75013, Paris, France
first.last@telecom-paristech.fr
Aur?lien Bellet
INRIA
59650 Villeneuve d?Ascq, France
aurelien.bellet@inria.fr
Abs... | 6588 |@word briefly:1 version:6 arcones:2 tensorial:1 dekker:1 bn:5 decomposition:5 accounting:1 thereby:1 moment:2 chervonenkis:1 denoting:2 bc:1 janson:2 horvitz:3 com:2 universality:1 dx:3 must:1 readily:1 numerical:6 kdd:1 discrimination:1 stationary:1 half:1 selected:1 prohibitive:1 fewer:1 provides:3 quantizer:1 ... |
6,178 | 6,589 | Scan Order in Gibbs Sampling: Models in Which it
Matters and Bounds on How Much
Bryan He, Christopher De Sa, Ioannis Mitliagkas, and Christopher R?
Stanford University
{bryanhe,cdesa,imit,chrismre}@stanford.edu
Abstract
Gibbs sampling is a Markov Chain Monte Carlo sampling technique that iteratively
samples variables ... | 6589 |@word mild:3 version:5 polynomial:12 vldb:2 r:4 sgd:3 initial:1 ktv:2 fa8750:2 current:1 comparing:2 surprising:2 si:8 must:6 plot:1 resampling:1 stationary:10 alone:1 selected:7 half:2 intelligence:1 implying:1 mccallum:1 smith:2 completeness:1 noncommutative:1 zbalaban:1 zhang:3 mathematical:3 constructed:2 c2:... |
6,179 | 659 | Assessing and Improving Neural Network
Predictions by the Bootstrap Algorithm
Gerhard Paass
German National Research Center for Computer Science (GMD)
D-5205 Sankt Augustin, Germany
e-mail: paass<Dgmd.de
Abstract
The bootstrap algorithm is a computational intensive procedure to
derive nonparametric confidence interva... | 659 |@word version:3 simulation:5 analoguous:1 moment:1 initial:2 liu:4 series:1 readily:1 belmont:1 fn:10 analytic:3 plot:1 resampling:3 short:1 simpler:1 introductory:1 wild:2 advocate:1 deteriorate:1 pairwise:4 expected:2 estimating:1 underlying:2 interpreted:1 sankt:1 substantially:1 z:1 finding:1 bootstrapping:4 n... |
6,180 | 6,590 | Training and Evaluating Multimodal Word
Embeddings with Large-scale Web Annotated Images
Junhua Mao1
Jiajing Xu2
Yushi Jing2
Alan Yuille1,3
2
3
University of California, Los Angeles
Pinterest Inc.
Johns Hopkins University
mjhustc@ucla.edu, {jiajing,jing}@pinterest.com, alan.l.yuille@gmail.com
1
Abstract
In this paper... | 6590 |@word cnn:10 version:2 repository:1 proportion:1 norm:1 rivlin:1 solan:1 decomposition:1 citeseer:2 initial:1 contains:4 score:12 selecting:1 fragment:1 ours:1 bc:1 outperforms:3 current:3 com:4 comparing:1 surprising:1 gauvain:1 activation:1 gmail:1 anne:1 guadarrama:1 john:1 remove:3 update:1 intelligence:1 sel... |
6,181 | 6,591 | VIME: Variational Information Maximizing
Exploration
Rein Houthooft??? , Xi Chen?? , Yan Duan?? , John Schulman?? , Filip De Turck? , Pieter Abbeel??
?
UC Berkeley, Department of Electrical Engineering and Computer Sciences
?
Ghent University - imec, Department of Information Technology
?
OpenAI
Abstract
Scalable and... | 6591 |@word exploitation:6 middle:1 polynomial:5 compression:10 pieter:1 simulation:1 covariance:1 accommodate:1 reduction:2 initial:2 typology:1 bootstrapped:1 past:1 existing:1 subjective:1 current:1 discretization:5 surprising:1 activation:1 lang:1 guez:1 written:1 john:1 subsequent:1 periodically:1 informative:2 ca... |
6,182 | 6,592 | The Multi-fidelity Multi-armed Bandit
Kirthevasan Kandasamy \ , Gautam Dasarathy ? , Jeff Schneider \ , Barnab?s P?czos \
\
Carnegie Mellon University, ? Rice University
{kandasamy, schneide, bapoczos}@cs.cmu.edu, gautamd@rice.edu
Abstract
We study a variant of the classical stochastic K-armed bandit where observing
... | 6592 |@word trial:1 exploitation:5 briefly:1 version:1 polynomial:1 open:1 simulation:5 forecaster:1 k7:5 attainable:1 concise:1 incurs:2 recursively:1 liu:1 series:1 outperforms:3 past:1 comparing:1 yet:1 partition:4 cheap:3 sponsored:1 n0:1 kandasamy:4 selected:1 short:1 indefinitely:1 provides:1 gautam:2 successive:... |
6,183 | 6,593 | A state-space model of cross-region dynamic
connectivity in MEG/EEG
Ying Yang?
Elissa M. Aminoff? Michael J. Tarr? Robert E. Kass?
Carnegie Mellon University, ? Fordham University
ying.yang.cnbc.cmu@gmail.com, {eaminoff@fordham, michaeltarr@cmu, kass@stat.cmu}.edu
?
Abstract
Cross-region dynamic connectivity, which ... | 6593 |@word neurophysiology:1 trial:21 determinant:1 cox:1 norm:11 simulation:8 covariance:9 eng:1 tr:5 liu:1 series:3 score:5 united:1 mosher:1 bootstrapped:2 past:1 ka:2 com:2 current:7 comparing:1 sosa:1 gmail:1 intriguing:2 yet:1 gqj:2 kiebel:1 mesh:1 shape:1 designed:1 drop:1 stationary:2 selected:2 ith:4 feedfowa... |
6,184 | 6,594 | An Online Sequence-to-Sequence Model Using Partial
Conditioning
Navdeep Jaitly
Google Brain
ndjaitly@google.com
Oriol Vinyals
Google DeepMind
vinyals@google.com
David Sussillo
Google Brain
sussillo@google.com
Ilya Sutskever
Open AI?
ilyasu@openai.com
Quoc V. Le
Google Brain
qvl@google.com
Samy Bengio
Google Brain
be... | 6594 |@word proportion:1 seems:1 open:1 decomposition:2 initial:1 configuration:2 relabelled:1 interestingly:1 prefix:2 past:1 freitas:1 current:6 com:6 blank:1 assigning:1 subsequent:2 hoping:1 update:2 bart:1 sukhbaatar:1 ivo:1 provides:1 yeb:1 firstly:1 simpler:1 org:1 windowed:2 kingsbury:1 wierstra:1 transducer:58... |
6,185 | 6,595 | Combinatorial Energy Learning for Image
Segmentation
Jeremy Maitin-Shepard
UC Berkeley Google
jbms@google.com
Peter Li
Google
phli@google.com
Viren Jain
Google
viren@google.com
Michal Januszewski
Google
mjanusz@google.com
Pieter Abbeel
UC Berkeley
pabbeel@cs.berkeley.edu
Abstract
We introduce a new machine learnin... | 6595 |@word h:1 cnn:7 achievable:1 seems:1 kokkinos:1 paredes:1 rivlin:2 pieter:1 r:12 seek:1 lobe:1 pick:1 sgd:2 thereby:1 ultrathin:1 briggman:9 reduction:1 initial:7 configuration:6 contains:1 score:14 fragment:1 lepetit:1 daniel:1 romera:1 outperforms:1 existing:6 current:3 com:4 michal:1 comparing:1 si:10 must:2 c... |
6,186 | 6,596 | Dimensionality Reduction of Massive Sparse Datasets
Using Coresets
Dan Feldman
University of Haifa
Haifa, Israel
dannyf.post@gmail.com
Mikhail Volkov
CSAIL, MIT
Cambridge, MA, USA
mikhail@csail.mit.edu
Daniela Rus
CSAIL, MIT
Cambridge, MA, USA
rus@csail.mit.edu
Abstract
In this paper we present a practical solution ... | 6596 |@word version:1 polynomial:1 compression:1 norm:4 nd:2 c0:2 open:8 pg:1 pick:1 sepulchre:1 recursively:1 reduction:23 contains:2 united:1 woodruff:1 document:10 ours:2 katoh:1 existing:3 current:1 com:2 ka:1 comparing:1 gmail:1 fund:1 update:1 greedy:1 selected:1 item:1 xk:1 record:1 provides:3 kaxk:1 org:1 unbou... |
6,187 | 6,597 | Optimal Binary Classifier Aggregation for General
Losses
Akshay Balsubramani
University of California, San Diego
abalsubr@ucsd.edu
Yoav Freund
University of California, San Diego
yfreund@ucsd.edu
Abstract
We address the problem of aggregating an ensemble of predictors with known loss
bounds in a semi-supervised binar... | 6597 |@word mild:1 version:1 advantageous:1 norm:2 accounting:1 incurs:1 reduction:1 initial:1 score:3 hereafter:1 existing:1 yet:2 written:3 readily:2 plot:1 v:1 accordingly:2 xk:1 realizing:1 boosting:2 revisited:1 constructed:2 predecessor:1 become:1 consists:2 prove:1 introduce:2 hellinger:1 notably:2 indeed:2 expe... |
6,188 | 6,598 | Correlated-PCA: Principal Components? Analysis
when Data and Noise are Correlated
Namrata Vaswani and Han Guo
Iowa State University, Ames, IA, USA
Email: {namrata,hanguo}@iastate.edu
Abstract
Given a matrix of observed data, Principal Components Analysis (PCA) computes
a small number of orthogonal directions that cont... | 6598 |@word version:1 norm:4 c0:3 open:1 riitta:2 km:3 simulation:1 decomposition:5 covariance:8 pick:3 dramatic:1 boundedness:3 reduction:2 ala:1 outperforms:2 existing:2 recovered:1 whp:1 comparing:1 pcp:10 numerical:1 happen:1 partition:7 remove:1 update:2 sys:4 math:2 ames:1 allerton:1 zhang:1 symposium:2 symp:1 he... |
6,189 | 6,599 | An equivalence between high dimensional Bayes
optimal inference and M-estimation
Madhu Advani
Surya Ganguli
Department of Applied Physics, Stanford University
msadvani@stanford.edu and sganguli@stanford.edu
Abstract
When recovering an unknown signal from noisy measurements, the computational
difficulty of performing ... | 6599 |@word mild:1 sgf:1 version:3 achievable:1 open:1 heuristically:3 seek:2 simulation:1 crucially:1 propagate:1 ronchetti:1 thereby:2 minus:3 reduction:1 moment:3 initial:1 series:2 mag:1 interestingly:1 mmse:32 amp:11 outperforms:3 current:2 comparing:3 karoui:1 si:2 yet:1 intriguing:1 must:1 universality:2 additiv... |
6,190 | 66 | 41
ON PROPERTIES OF NETWORKS
OF NEURON-LIKE ELEMENTS
Pierre Baldi? and Santosh S. Venkatesh t
15 December 1987
Abstract
The complexity and computational capacity of multi-layered, feedforward
neural networks is examined. Neural networks for special purpose (structured)
functions are examined from the perspective of c... | 66 |@word private:1 version:1 polynomial:18 open:3 harder:1 cyclic:1 contains:1 configuration:1 comparing:1 yet:1 must:1 readily:1 analytic:1 device:2 hamiltonian:4 ire:1 provides:1 node:2 math:1 contribute:1 hyperplanes:1 firstly:1 unbounded:1 constructed:2 asanuma:1 become:1 focs:1 prove:1 interscience:1 baldi:4 poly... |
6,191 | 660 | Learning Sequential Tasks by
Incrementally Adding Higher Orders
Mark Ring
Department of Computer Sciences, Taylor 2.124
University of Texas at Austin
Austin, Texas 78712
(ring@cs. utexas.edu)
Abstract
An incremental, higher-order, non-recurrent network combines two
properties found to be useful for learning sequentia... | 660 |@word version:1 compression:1 simplifying:1 thereby:1 tr:1 initial:1 contains:1 past:1 current:5 activation:5 must:3 john:1 ronald:1 distant:1 motor:1 wynne:1 alone:1 intelligence:1 item:5 beginning:2 ith:1 record:1 caveat:1 draft:1 node:3 wxy:1 height:1 combine:2 paragraph:1 theoretically:1 behavior:1 elman:4 gro... |
6,192 | 6,600 | Unsupervised Learning of 3D Structure from Images
Danilo Jimenez Rezende*
danilor@google.com
S. M. Ali Eslami*
aeslami@google.com
Peter Battaglia*
peterbattaglia@google.com
Shakir Mohamed*
shakir@google.com
Max Jaderberg*
jaderberg@google.com
Nicolas Heess*
heess@google.com
* Google DeepMind
Abstract
A key goal o... | 6600 |@word kohli:2 repository:1 version:1 middle:8 compression:2 cloned:1 choy:1 simulation:1 pick:1 solid:1 shot:1 accommodate:1 configuration:1 contains:1 score:1 jimenez:6 document:1 outperforms:1 existing:1 com:6 contextual:2 cad:1 yet:1 must:1 mesh:28 shape:13 drop:2 update:1 generative:25 half:3 fried:1 short:2 ... |
6,193 | 6,601 | Local Minimax Complexity of
Stochastic Convex Optimization
Yuancheng Zhu
Wharton Statistics Department
University of Pennsylvania
John Duchi
Department of Statistics
Department of Electrical Engineering
Stanford University
Sabyasachi Chatterjee
Department of Statistics
University of Chicago
John Lafferty
Department of... | 6601 |@word polynomial:5 achievable:1 norm:1 open:1 unif:2 simulation:8 seek:1 nemirovsky:3 sgd:3 solid:1 carry:2 liu:2 contains:1 egt:1 tuned:1 outperforms:2 juditski:2 err:19 current:5 z2:3 comparing:1 must:1 john:3 exposing:1 chicago:2 numerical:4 additive:1 remove:1 designed:1 plot:3 update:1 juditsky:1 half:4 sele... |
6,194 | 6,602 | Error Analysis of Generalized Nystr?m Kernel
Regression
Hong Chen
Computer Science and Engineering
University of Texas at Arlington
Arlington, TX, 76019
chenh@mail.hzau.edu.cn
Haifeng Xia
Mathematics and Statistics
Huazhong Agricultural University
Wuhan 430070,China
haifeng.xia0910@gmail.com
Weidong Cai
School of In... | 6602 |@word mild:2 trial:1 polynomial:5 norm:14 simulation:1 decomposition:4 hsieh:1 nsw:1 nystr:31 boundedness:1 liu:1 contains:1 score:1 woodruff:1 rkhs:4 existing:1 com:1 si:1 gmail:1 v:1 half:1 selected:3 epanechnikov:5 characterization:1 toronto:1 firstly:2 org:3 casp:5 zhang:1 constructed:2 introduce:5 x0:5 expec... |
6,195 | 6,603 | New Liftable Classes for
First-Order Probabilistic Inference
Seyed Mehran Kazemi
The University of British Columbia
smkazemi@cs.ubc.ca
Angelika Kimmig
KU Leuven
angelika.kimmig@cs.kuleuven.be
Guy Van den Broeck
University of California, Los Angeles
guyvdb@cs.ucla.edu
David Poole
The University of British Columbia
po... | 6603 |@word version:1 polynomial:7 open:5 adnan:2 d2:4 essay:1 vldb:1 propagate:1 decomposition:7 p0:12 q1:1 mention:1 harder:1 recursively:5 carry:2 initial:1 born:13 contains:8 denoting:1 past:1 existing:3 current:1 comparing:1 si:5 assigning:1 yet:2 must:4 partition:3 j1:2 remove:3 treating:2 intelligence:3 discover... |
6,196 | 6,604 | C YCLADES:
Conflict-free Asynchronous Machine Learning
Xinghao Pan?, Maximilian Lam?, Stephen Tu?, Dimitris Papailiopoulos?,
Ce Zhang?, Michael I. Jordan?,? Kannan Ramchandran?, Chris Re?, Benjamin Recht??
Abstract
We present C YCLADES, a general framework for parallelizing stochastic optimization algorithms in a shar... | 6604 |@word eliminating:1 achievable:2 nd:1 vldb:1 seek:1 sgd:27 thereby:1 carry:2 reduction:4 liu:1 contains:2 tuned:1 ati:5 outperforms:1 numa:2 bradley:1 savage:1 com:2 surprising:1 si:3 written:1 fn:1 numerical:1 partition:5 devin:1 benign:1 reproducible:1 designed:1 update:76 bickson:1 greedy:1 selected:1 xk:8 ith... |
6,197 | 6,606 | Wider and Deeper, Cheaper and Faster:
Tensorized LSTMs for Sequence Learning
Zhen He1,2 , Shaobing Gao3 , Liang Xiao2 , Daxue Liu2 , Hangen He2 , and David Barber1,4?
1
University College London, 2 National University of Defense Technology, 3 Sichuan University,
4
Alan Turing Institute
Abstract
Long Short-Term Memory... | 6606 |@word cnn:3 version:1 compression:1 seems:1 advantageous:1 paredes:1 r:2 rgb:1 bn:1 thereby:1 shot:1 harder:1 configuration:14 series:1 document:1 romera:1 outperforms:2 freitas:1 current:4 com:1 blank:1 activation:4 gmail:1 intriguing:1 aft:5 gpu:1 danny:1 john:2 ronald:1 concatenate:2 ronan:1 diederik:1 enables... |
6,198 | 6,607 | Concentration of Multilinear Functions of the Ising
Model with Applications to Network Data
Constantinos Daskalakis ?
EECS & CSAIL, MIT
costis@csail.mit.edu
Nishanth Dikkala?
EECS & CSAIL, MIT
nishanthd@csail.mit.edu
Gautam Kamath?
EECS & CSAIL, MIT
g@csail.mit.edu
Abstract
We prove near-tight concentration of meas... | 6607 |@word version:1 polynomial:10 stronger:1 seems:1 nd:4 c0:2 physik:1 closure:1 pieter:1 crucially:1 contraction:4 harder:1 configuration:4 contains:1 efficacy:2 selecting:2 liu:1 daniel:1 ours:1 interestingly:1 z2:2 od:3 si:1 must:1 john:2 maxv:1 mackey:1 stationary:2 greedy:4 half:1 leaf:1 alone:2 detecting:1 pro... |
6,199 | 6,608 | Deep Subspace Clustering Networks
Pan Ji?
University of Adelaide
Tong Zhang?
Australian National University
Mathieu Salzmann
EPFL - CVLab
Hongdong Li
Australian National University
Ian Reid
University of Adelaide
Abstract
We present a novel deep neural network architecture for unsupervised subspace
clustering. Th... | 6608 |@word trial:3 version:1 manageable:1 polynomial:1 norm:11 dalal:1 compression:2 triggs:1 open:1 hu:1 confirms:1 seek:1 jacob:1 pick:1 reduction:2 liu:2 series:1 salzmann:3 tuned:1 ours:1 deconvolutional:2 outperforms:1 err:1 current:1 com:1 activation:4 si:1 devin:1 shape:1 designed:1 depict:1 n0:1 v:1 fewer:2 it... |
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