Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
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5,700 | 6,158 | Generating Images with Perceptual Similarity
Metrics based on Deep Networks
Alexey Dosovitskiy and Thomas Brox
University of Freiburg
{dosovits, brox}@cs.uni-freiburg.de
Abstract
We propose a class of loss functions, which we call deep perceptual similarity
metrics (DeePSiM), allowing to generate sharp high resolutio... | 6158 |@word middle:1 version:2 inversion:10 solid:1 initial:1 interestingly:1 deconvolutional:1 existing:2 guadarrama:1 comparing:1 activation:1 yet:1 videolearn:1 visible:2 subsequent:1 realistic:5 visibility:1 designed:1 alone:3 generative:17 provides:1 location:4 attack:1 five:1 along:1 become:2 retrieving:1 consist... |
5,701 | 6,159 | Coin Betting and Parameter-Free Online Learning
Francesco Orabona
Stony Brook University, Stony Brook, NY
francesco@orabona.com
D?avid P?al
Yahoo Research, New York, NY
dpal@yahoo-inc.com
Abstract
In the recent years, a number of parameter-free algorithms have been developed
for online linear optimization over Hilbe... | 6159 |@word version:1 norm:6 seems:1 nd:1 trofimov:5 jacob:1 reduction:6 initial:12 tuned:1 erven:1 existing:2 current:2 com:2 luo:2 yet:1 stony:2 john:1 numerical:2 intelligence:1 instantiate:2 guess:1 warmuth:3 manfred:1 provides:4 boosting:3 math:1 ire:1 mathematical:1 shtarkov:1 become:1 batir:1 prove:7 indeed:1 ra... |
5,702 | 616 | A Method for Learning from Hints
Yaser s. Abu-Mostafa
Departments of Electrical Engineering, Computer Science,
and Computation and Neural Systems
California Institute of Technology
Pasadena, CA 91125
e-mail: yaser@caltech.edu
Abstract
We address the problem of learning an unknown function by
pu tting together several ... | 616 |@word version:1 middle:1 achievable:1 replicate:1 underline:1 pick:2 harder:1 disparity:1 selecting:1 current:1 comparing:1 written:1 numerical:1 selected:2 footing:2 lor:2 along:1 direct:4 become:2 introduce:1 indeed:1 expected:3 automatically:2 actual:2 becomes:3 provided:1 notation:1 underlying:1 what:3 develop... |
5,703 | 6,160 | Temporal Regularized Matrix Factorization for
High-dimensional Time Series Prediction
Hsiang-Fu Yu
University of Texas at Austin
rofuyu@cs.utexas.edu
Nikhil Rao
Technicolor Research
nikhilrao86@gmail.com
Inderjit S. Dhillon
University of Texas at Austin
inderjit@cs.utexas.edu
Abstract
Time series prediction problems... | 6160 |@word repository:1 kondor:1 version:1 norm:4 nd:4 suitably:1 open:1 nicholson:1 covariance:6 decomposition:1 tr:1 inefficiency:1 series:58 contains:1 zij:1 liu:1 denoting:1 past:1 existing:13 outperforms:3 com:1 gmail:1 dx:1 written:3 willinger:2 additive:1 predetermined:1 l2l:10 enables:1 designed:2 interpretabl... |
5,704 | 6,161 | Unsupervised Learning for Physical Interaction
through Video Prediction
Chelsea Finn?
UC Berkeley
cbfinn@eecs.berkeley.edu
Ian Goodfellow
OpenAI
ian@openai.com
Sergey Levine
Google Brain
UC Berkeley
slevine@google.com
Abstract
A core challenge for an agent learning to interact with the world is to predict
how its a... | 6161 |@word illustrating:1 middle:1 cox:1 reused:1 simulation:1 rgb:4 concise:1 initial:3 outperforms:1 existing:4 reaction:1 current:1 com:4 activation:1 must:1 realistic:1 concatenate:1 blur:1 designed:1 interpretable:2 drop:1 stationary:1 generative:2 fewer:1 website:1 beginning:1 core:2 short:2 record:1 location:2 ... |
5,705 | 6,162 | Active Learning from Imperfect Labelers
Songbai Yan
University of California, San Diego
yansongbai@eng.ucsd.edu
Kamalika Chaudhuri
University of California, San Diego
kamalika@cs.ucsd.edu
Tara Javidi
University of California, San Diego
tjavidi@eng.ucsd.edu
Abstract
We study active learning where the labeler can not... | 6162 |@word mild:1 version:3 polynomial:4 stronger:1 c0:2 eng:2 q1:2 harder:1 contains:1 fragment:4 daniel:1 ours:1 existing:1 current:1 comparing:1 od:1 beygelzimer:3 john:2 partition:1 informative:2 drop:1 atlas:1 alone:1 greedy:1 intelligence:2 xk:1 record:1 yuxin:1 provides:2 coarse:1 location:3 allerton:2 zhang:4 ... |
5,706 | 6,163 | Collaborative Recurrent Autoencoder:
Recommend while Learning to Fill in the Blanks
Hao Wang, Xingjian Shi, Dit-Yan Yeung
Hong Kong University of Science and Technology
{hwangaz,xshiab,dyyeung}@cse.ust.hk
Abstract
Hybrid methods that utilize both content and rating information are commonly
used in many recommender sy... | 6163 |@word kong:1 cnn:4 middle:1 version:3 compression:3 bf:2 hu:1 fairer:1 recursively:1 reduction:1 contains:1 score:7 document:3 outperforms:2 existing:4 blank:3 current:1 dx:2 ust:1 e01:1 citeulike:14 kdd:3 shape:1 enables:2 plot:8 interpretable:1 designed:1 drop:2 update:2 alone:1 generative:7 selected:1 item:19 ... |
5,707 | 6,164 | Clustering Signed Networks with the
Geometric Mean of Laplacians
Pedro Mercado1 , Francesco Tudisco2 and Matthias Hein1
1
Saarland University, Saarbr?cken, Germany
2
University of Padua, Padua, Italy
Abstract
Signed networks allow to model positive and negative relationships. We analyze
existing extensions of spectra... | 6164 |@word h:6 repository:1 version:2 briefly:1 proportion:4 justice:1 qsym:11 hu:1 confirms:1 bn:1 decomposition:2 ipm:4 ld:1 liu:2 ours:5 outperforms:3 existing:8 nonmonotone:1 repelling:1 numerical:1 partition:2 informative:8 v:15 mackey:2 generative:1 fewer:1 prohibitive:1 kyk:2 xk:5 ith:1 chiang:1 padua:2 charact... |
5,708 | 6,165 | Dynamic Network Surgery for Efficient DNNs
Yiwen Guo?
Intel Labs China
yiwen.guo@intel.com
Anbang Yao
Intel Labs China
anbang.yao@intel.com
Yurong Chen
Intel Labs China
yurong.chen@intel.com
Abstract
Deep learning has become a ubiquitous technology to improve machine intelligence.
However, most of the existing deep... | 6165 |@word cnn:1 briefly:1 compression:28 seems:1 retraining:5 instruction:1 grey:1 simulation:1 decomposition:4 sgd:3 initial:1 inefficiency:3 liu:3 daniel:1 tuned:1 document:2 ours:3 outperforms:1 existing:3 freitas:2 current:5 com:4 guadarrama:1 surprising:1 activation:4 tackling:1 yet:1 must:1 gpu:2 john:2 subsequ... |
5,709 | 6,166 | On Valid Optimal Assignment Kernels and
Applications to Graph Classification
Nils M. Kriege
Department of Computer Science
TU Dortmund, Germany
nils.kriege@tu-dortmund.de
Pierre-Louis Giscard
Department of Computer Science
University of York, UK
pierre-louis.giscard@york.ac.uk
Richard C. Wilson
Department of Compute... | 6166 |@word kondor:2 middle:1 johansson:2 grey:1 seek:1 decomposition:1 thereby:1 reduction:1 bai:1 initial:2 contains:3 existing:1 assigning:1 yet:2 must:4 reminiscent:1 cruz:1 additive:2 partition:2 numerical:1 kdd:2 shape:1 greedy:1 leaf:13 selected:1 short:1 core:1 provides:2 characterization:1 bijection:3 contribu... |
5,710 | 6,167 | Estimating the class prior and posterior from noisy
positives and unlabeled data
Shantanu Jain, Martha White, Predrag Radivojac
Department of Computer Science
Indiana University, Bloomington, Indiana, USA
{shajain, martha, predrag}@indiana.edu
Abstract
We develop a classification algorithm for estimating posterior di... | 6167 |@word repository:4 briefly:1 version:4 steen:2 proportion:21 thereby:1 mention:1 carry:1 reduction:1 liu:2 contains:4 efficacy:2 score:3 lichman:2 offering:1 past:1 reaction:1 existing:1 recovered:2 comparing:1 spambase:1 mushroom:1 realistic:1 numerical:2 kdd:1 enables:1 designed:1 update:1 unidentifiability:2 v... |
5,711 | 6,168 | Estimating the class prior and posterior from noisy
positives and unlabeled data
Shantanu Jain, Martha White, Predrag Radivojac
Department of Computer Science
Indiana University, Bloomington, Indiana, USA
{shajain, martha, predrag}@indiana.edu
Abstract
We develop a classification algorithm for estimating posterior dis... | 6168 |@word repository:4 briefly:1 version:4 steen:2 proportion:21 thereby:1 mention:1 carry:1 reduction:1 liu:2 contains:4 efficacy:2 score:3 lichman:2 offering:1 past:1 reaction:1 existing:1 recovered:2 comparing:1 spambase:1 mushroom:1 realistic:1 numerical:2 kdd:1 enables:1 designed:1 update:1 unidentifiability:2 v... |
5,712 | 6,169 | Approximate maximum entropy principles via
Goemans-Williamson with applications to provable
variational methods
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... | 6169 |@word version:4 achievable:1 polynomial:6 stronger:1 norm:1 suitably:1 covariance:6 decomposition:1 mention:1 moment:30 configuration:1 contains:1 jaynes:2 surprising:1 si:2 written:2 additive:1 partition:18 dive:1 designed:1 core:1 provides:6 characterization:1 philipp:1 kelner:1 warmup:2 mathematical:1 dn:2 sym... |
5,713 | 617 | ?
a
Statistical Mechanics of Learning In
Large Committee Machine
Holm Schwarze
CONNECT, The Niels Bohr Institute
Blegdamsvej 17, DK-2100 Copenhagen 0, Denmark
John Hertz?
Nordita
Blegdamsvej 17, DK-2100 Copenhagen 0, Denmark
Abstract
We use statistical mechanics to study generalization in large committee machines. Fo... | 617 |@word version:1 simulation:10 solid:6 initial:1 john:1 happen:1 partition:1 shape:1 analytic:3 drop:1 cue:1 ith:1 vanishing:1 location:3 qualitative:2 incorrect:1 specialize:1 behavior:4 mechanic:7 decreasing:2 increasing:1 becomes:4 zippelius:1 quantitative:1 ti:2 exactly:1 unit:26 local:1 limit:8 path:1 twice:1 ... |
5,714 | 6,170 | Privacy Odometers and Filters: Pay-as-you-Go
Composition
Ryan Rogers?
Aaron Roth?
Jonathan Ullman?
Salil Vadhan?
Abstract
In this paper we initiate the study of adaptive composition in differential privacy
when the length of the composition, and the privacy parameters themselves can
be chosen adaptively, as a func... | 6170 |@word private:18 version:5 achievable:1 nd:1 crucially:2 q1:1 incurs:1 asks:1 moment:1 series:2 selecting:1 daniel:2 cort:2 existing:3 must:5 written:1 john:1 additive:1 designed:1 aside:1 selected:2 smith:3 indefinitely:1 caveat:1 characterization:1 kairouz:2 boosting:1 kasiviswanathan:1 simpler:1 along:1 differ... |
5,715 | 6,171 | The Limits of Learning with Missing Data
Brian Bullins
Elad Hazan
Princeton University
Princeton, NJ
{bbullins,ehazan}@cs.princeton.edu
Tomer Koren
Google Brain
Mountain View, CA
tkoren@google.com
Abstract
We study linear regression and classification in a setting where the learning algorithm is allowed to access onl... | 6171 |@word manageable:1 polynomial:3 achievable:2 norm:1 stronger:1 dekel:1 nd:3 d2:34 attainable:4 necessity:1 series:1 com:1 must:1 written:2 designed:1 update:1 intelligence:1 leaf:1 fewer:1 scotland:1 provides:1 along:1 direct:1 prove:11 introduce:1 notably:1 indeed:3 expected:6 p1:1 brain:1 little:1 spain:1 provi... |
5,716 | 6,172 | Image Restoration Using Very Deep Convolutional
Encoder-Decoder Networks with Symmetric Skip
Connections
?
Xiao-Jiao Mao? , Chunhua Shen? , Yu-Bin Yang?
State Key Laboratory for Novel Software Technology, Nanjing University, China
?
School of Computer Science, University of Adelaide, Australia
Abstract
In this paper... | 6172 |@word cnn:4 version:4 eliminating:3 compression:2 norm:1 simulation:1 propagate:2 set5:6 sgd:1 inpainting:3 carry:2 reduction:2 configuration:2 contains:5 liu:2 mag:1 nonlocally:1 deconvolutional:25 outperforms:1 existing:7 guadarrama:1 comparing:1 activation:3 unpooling:1 additive:2 csc:4 bsd100:4 fund:1 rudin:1... |
5,717 | 6,173 | Community Detection on Evolving Graphs
Aris Anagnostopoulos
Sapienza University of Rome
aris@dis.uniroma1.it
Jakub ?acki
?
Sapienza University of Rome
j.lacki@mimuw.edu.pl
Stefano Leonardi
Sapienza University of Rome
leonardi@dis.uniroma1.it
Silvio Lattanzi
Google
silviol@google.com
Mohammad Mahdian
Google
mahdian... | 6173 |@word worsens:2 version:1 stronger:1 nd:1 open:1 simulation:2 simplifying:1 pick:7 bahmani:1 moment:1 score:5 silviol:1 neeman:1 outperforms:1 recovered:1 com:3 current:1 comparing:1 yet:3 crawling:2 issuing:1 partition:2 kdd:2 remove:1 update:1 item:2 accordingly:1 beginning:1 ith:1 smith:1 indefinitely:1 detect... |
5,718 | 6,174 | Convergence guarantees for kernel-based quadrature
rules in misspecified settings
Motonobu Kanagawa? , Bharath K Sriperumbudur? , Kenji Fukumizu?
?
The Institute of Statistical Mathematics, Tokyo 190-8562, Japan
?
Department of Statistics, Pennsylvania State University, University Park, PA 16802, USA
kanagawa@ism.ac.jp... | 6174 |@word version:1 briefly:1 polynomial:1 norm:3 nd:1 c0:1 open:1 simulation:3 covariance:1 q1:1 mention:1 solid:1 series:4 contains:1 rkhs:29 ours:1 scovel:1 si:6 dx:2 written:3 numerical:13 happen:3 enables:2 analytic:2 selected:3 provides:1 hermite:1 mathematical:1 novak:2 constructed:3 prove:2 consists:2 introdu... |
5,719 | 6,175 | An Efficient Streaming Algorithm
for the Submodular Cover Problem
Ashkan Norouzi-Fard ?
Abbas Bazzi ?
ashkan.norouzifard@epfl.ch abbas.bazzi@epfl.ch
Marwa El Halabi ?
marwa.elhalabi@epfl.ch
Ilija Bogunovic ?
Ya-Ping Hsieh ?
Volkan Cevher ?
ilija.bogunovic@epfl.ch
ya-ping.hsieh@epfl.ch
volkan.cevher@epfl.ch
Ab... | 6175 |@word middle:2 version:3 compression:1 laurence:2 seek:3 hsieh:2 kent:1 pick:3 bicriteria:13 selecting:3 daniel:1 current:1 incidence:1 si:4 boldi:3 attracted:1 must:2 takeo:1 crawling:1 sergei:1 numerical:2 informative:2 enables:1 designed:5 update:1 greedy:46 prohibitive:1 selected:5 accordingly:1 cormode:1 vol... |
5,720 | 6,176 | A scaled Bregman theorem with applications
Richard Nock?,?,?
Aditya Krishna Menon?,?
Cheng Soon Ong?,?
?
?
?
Data61, the Australian National University and the University of Sydney
{richard.nock, aditya.menon, chengsoon.ong}@data61.csiro.au
Abstract
Bregman divergences play a central role in the design and analysis o... | 6176 |@word cpe:3 briefly:1 seems:1 norm:21 stronger:1 c0:6 crucially:1 decomposition:1 simplifying:1 invoking:1 pick:2 tr:10 minus:1 reduction:10 ndez:2 initialisation:1 past:1 imaginary:1 current:1 expq:5 yet:3 intriguing:1 written:1 must:4 john:1 periodically:1 happen:1 analytic:1 seeding:9 plot:3 update:10 alone:2 ... |
5,721 | 6,177 | Disease Trajectory Maps
Raman Arora
Dept. of Computer Science
Johns Hopkins University
Baltimore, MD 21218
arora@cs.jhu.edu
Peter Schulam
Dept. of Computer Science
Johns Hopkins University
Baltimore, MD 21218
pschulam@cs.jhu.edu
Abstract
Medical researchers are coming to appreciate that many diseases are in fact com... | 6177 |@word briefly:2 polynomial:2 stronger:1 prognostic:1 uncovers:2 covariance:15 creatinine:1 tr:4 reduction:1 initial:1 series:12 score:4 contains:2 uncovered:2 longitudinal:10 past:1 duong:2 current:1 comparing:2 discretization:1 surprising:2 si:3 scatter:1 must:2 john:3 numerical:1 partition:1 shape:3 analytic:1 ... |
5,722 | 6,178 | 6178 |@word | |
5,723 | 6,179 | On Explore-Then-Commit Strategies
Aur?lien Garivier?
Institut de Math?matiques de Toulouse; UMR5219
Universit? de Toulouse; CNRS
UPS IMT, F-31062 Toulouse Cedex 9, France
aurelien.garivier@math.univ-toulouse.fr
Emilie Kaufmann
Univ. Lille, CNRS, Centrale Lille, Inria SequeL
UMR 9189, CRIStAL - Centre de Recherche en I... | 6179 |@word trial:1 exploitation:7 briefly:1 version:2 achievable:1 interleave:1 seems:1 decomposition:1 pick:1 profit:2 bai:9 liu:2 contains:1 united:1 bs01:1 tuned:2 denoting:1 detc:2 past:3 existing:2 com:1 analysed:2 gmail:1 must:1 ronald:1 subsequent:1 numerical:3 additive:1 shape:1 website:3 ith:3 short:3 recherc... |
5,724 | 618 | Interposing an ontogenic model between
Genetic Algorithms and Neural Networks
Richard K. Belew
rikGcs.ucsd.edu
Cognitive Computer Science Research Group
Computer Science & Engr. Dept. (0014)
University of California - San Diego
La Jolla, CA 92093
Abstract
The relationships between learning, development and evolution ... | 618 |@word polynomial:19 instruction:2 cloned:1 simulation:4 pressure:1 shot:1 initial:9 series:8 genetic:17 seriously:2 tuned:1 ecole:1 past:1 kitano:1 current:1 surprising:1 readily:1 subsequent:1 periodically:1 remove:1 asymptote:1 update:1 half:1 selected:4 nervous:1 ntrain:1 beginning:1 five:2 unbounded:1 mathemat... |
5,725 | 6,180 | Learning Kernels with Random Features
Aman Sinha1
John Duchi1,2
1
Departments of Electrical Engineering and 2 Statistics
Stanford University
{amans,jduchi}@stanford.edu
Abstract
Randomized features provide a computationally efficient way to approximate kernel
machines in machine learning tasks. However, such methods ... | 6180 |@word version:1 polynomial:1 norm:2 nd:1 open:1 covariance:1 p0:15 elisseeff:1 carry:1 contains:1 efficacy:1 selecting:2 rkhs:2 document:1 prefix:2 outperforms:1 current:1 comparing:2 john:2 fn:1 numerical:1 subsequent:1 enables:1 v:12 selected:1 coarse:1 provides:1 characterization:1 along:1 constructed:1 become... |
5,726 | 6,181 | Learning Influence Functions from Incomplete
Observations
Xinran He
Ke Xu
David Kempe
Yan Liu
University of Southern California, Los Angeles, CA 90089
{xinranhe, xuk, dkempe, yanliu.cs}@usc.edu
Abstract
We study the problem of learning influence functions under incomplete observations of node activations. Incomplete o... | 6181 |@word cu:2 version:4 polynomial:2 nd:1 suitably:1 accounting:1 lakshmanan:1 reduction:2 memetracker:2 liu:1 contains:2 initial:3 existing:1 duong:2 activation:33 si:10 crawling:1 must:2 readily:1 assigning:1 fn:1 timestamps:1 kdd:3 sponsored:1 n0:1 parameterization:2 parametrization:1 short:2 core:1 parkes:1 char... |
5,727 | 6,182 | Fast Mixing Markov Chains for Strongly Rayleigh
Measures, DPPs, and Constrained Sampling
Chengtao Li
MIT
ctli@mit.edu
Stefanie Jegelka
MIT
stefje@csail.mit.edu
Suvrit Sra
MIT
suvrit@mit.edu
Abstract
We study probability measures induced by set functions with constraints. Such
measures arise in a variety of real-worl... | 6182 |@word briefly:2 compression:1 polynomial:9 nd:1 unif:1 open:3 closure:1 simulation:2 contraction:1 multicommodity:5 nystr:1 reduction:4 initial:3 contains:2 interestingly:1 existing:1 current:3 comparing:2 si:3 yet:1 must:3 parsing:1 determinantal:9 partition:11 plot:1 stationary:1 greedy:1 selected:1 congestion:... |
5,728 | 6,183 | Search Improves Label for Active Learning
Alina Beygelzimer
Yahoo Research
New York, NY
beygel@yahoo-inc.com
Daniel Hsu
Columbia University
New York, NY
djhsu@cs.columbia.edu
John Langford
Microsoft Research
New York, NY
jcl@microsoft.com
Chicheng Zhang
UC San Diego
La Jolla, CA
chz038@cs.ucsd.edu
Abstract
We inves... | 6183 |@word h:7 exploitation:1 version:30 eliminating:1 stronger:1 advantageous:1 nd:3 termination:1 concise:1 reduction:1 uncovered:1 contains:1 chervonenkis:1 daniel:5 existing:2 err:11 current:5 com:2 beygelzimer:5 si:4 dx:9 must:3 readily:3 john:5 additive:1 cant:1 seeding:1 designed:1 atlas:1 update:1 alone:2 fewe... |
5,729 | 6,184 | End-to-End Kernel Learning with
Supervised Convolutional Kernel Networks
Julien Mairal
Inria?
julien.mairal@inria.fr
Abstract
In this paper, we introduce a new image representation based on a multilayer kernel
machine. Unlike traditional kernel methods where data representation is decoupled
from the prediction task, ... | 6184 |@word cnn:1 version:1 manageable:1 compression:1 norm:10 seems:1 iki:4 rgb:2 decomposition:1 p0:2 covariance:1 q1:2 set5:3 sgd:2 nystr:3 sepulchre:1 versatile:1 carry:1 initial:3 liu:1 contains:1 rkhs:13 ours:3 document:1 past:2 kx0:1 existing:1 current:2 outperforms:2 yet:2 reminiscent:1 gpu:2 ckns:3 subsequent:... |
5,730 | 6,185 | Bayesian latent structure discovery from
multi-neuron recordings
Scott W. Linderman
Columbia University
swl2133@columbia.edu
Ryan P. Adams
Harvard University and Twitter
rpa@seas.harvard.edu
Jonathan W. Pillow
Princeton University
pillow@princeton.edu
Abstract
Neural circuits contain heterogeneous groups of neurons... | 6185 |@word neurophysiology:1 semitransparent:1 bn:4 covariance:2 pg:1 incurs:1 dramatic:2 harder:1 reduction:1 efficacy:1 recovered:1 com:1 nt:1 current:1 activation:15 tackling:1 written:1 must:3 readily:1 tilted:1 subsequent:1 distant:1 partition:1 confirming:1 designed:1 interpretable:4 update:7 alone:6 generative:... |
5,731 | 6,186 | Unsupervised Learning of Spoken Language with
Visual Context
David Harwath, Antonio Torralba, and James R. Glass
Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02115
{dharwath, torralba, jrg}@csail.mit.edu
Abstract
Humans learn to speak before they can read ... | 6186 |@word cnn:3 version:3 pick:1 sgd:1 paid:1 carry:1 initial:1 wrapper:1 contains:1 score:17 fragment:1 tuned:1 ours:1 document:1 subword:1 past:1 contextual:1 com:2 activation:4 yet:1 must:1 gpu:1 subsequent:1 concatenate:1 informative:1 designed:1 aside:1 motlicek:1 intelligence:2 discovering:1 website:2 selected:... |
5,732 | 6,187 | Feature-distributed sparse regression: a
screen-and-clean approach
Jiyan Yang? Michael W. Mahoney? Michael A. Saunders? Yuekai Sun?
? Stanford University ? University of California at Berkeley ? University of Michigan
jiyan@stanford.edu mmahoney@stat.berkeley.edu
saunders@stanford.edu yuekai@umich.edu
Abstract
Most e... | 6187 |@word trial:1 nd:8 simulation:2 covariance:1 contraction:5 decomposition:1 concise:1 thereby:1 shot:3 harder:1 liu:2 contains:1 series:2 selecting:2 woodruff:2 outperforms:1 existing:3 recovered:1 nt:3 si:14 chu:1 john:1 numerical:1 partition:2 plot:4 operationally:1 selected:6 fewer:2 half:1 accordingly:1 inspec... |
5,733 | 6,188 | Bayesian Optimization with a Finite Budget:
An Approximate Dynamic Programming Approach
Remi R. Lam
Massachusetts Institute of Technology
Cambridge, MA
rlam@mit.edu
Karen E. Willcox
Massachusetts Institute of Technology
Cambridge, MA
kwillcox@mit.edu
David H. Wolpert
Santa Fe Institute
Santa Fe, NM
dhw@santafe.edu
... | 6188 |@word exploitation:6 open:1 seek:2 simulation:7 covariance:1 accounting:1 decomposition:1 recursively:1 reduction:3 initial:10 substitution:1 contains:1 configuration:14 pub:1 ndez:1 outperforms:4 existing:5 past:1 current:2 freitas:1 yet:1 written:2 john:1 fn:3 numerical:2 informative:1 j1:1 cheap:3 designed:1 u... |
5,734 | 6,189 | Kernel Observers: Systems-Theoretic Modeling and
Inference of Spatiotemporally Evolving Processes
Hassan A. Kingravi
Pindrop
Atlanta, GA 30308
hkingravi@pindrop.com
Harshal Maske and Girish Chowdhary
University of Illinois at Urbana Champaign
Urbana, IL 61801
hmaske2@illinois.edu, girishc@illinois.edu
Abstract
We co... | 6189 |@word seems:1 advantageous:1 retraining:1 c0:1 norm:1 km:1 seek:1 propagate:2 covariance:13 decomposition:4 pick:3 incurs:1 thereby:1 nystr:1 accommodate:1 initial:2 cyclic:9 series:10 kingravi:1 selecting:1 salzmann:1 rkhs:8 outperforms:4 existing:1 diagonalized:1 recovered:2 com:1 current:3 ka:2 atlantic:1 john... |
5,735 | 619 | Non-Linear Dimensionality Reduction
David DeMers? & Garrison CottreU t
Dept. of Computer Science & Engr., 0114
Institute for Neural Computation
University of California, San Diego
9500 Gilman Dr.
La Jolla. CA, 92093-0114
Abstract
A method for creating a non-linear encoder-decoder for multidimensional data
with compac... | 619 |@word compression:5 simulation:1 covariance:2 ithere:1 reduction:10 initial:6 series:5 hereafter:1 empath:1 kurt:1 activation:5 yet:1 must:6 cottrell:9 extensional:1 mackey:5 greedy:1 selected:1 provides:1 sigmoidal:1 five:4 unbounded:1 along:2 constructed:1 direct:1 differential:3 consists:1 fitting:1 baldi:2 ins... |
5,736 | 6,190 | A Bandit Framework for Strategic Regression
Yang Liu and Yiling Chen
School of Engineering and Applied Science, Harvard University
{yangl,yiling}@seas.harvard.edu
Abstract
We consider a learner?s problem of acquiring data dynamically for training a regression model, where the training data are collected from strategi... | 6190 |@word private:8 version:6 longterm:1 exploitation:1 stronger:1 adrian:1 covariance:1 minus:2 shot:2 initial:1 liu:1 contains:4 score:3 selecting:4 necessity:1 denoting:1 rightmost:1 past:2 current:1 nt:2 si:6 yet:1 written:2 realistic:1 informative:1 enables:3 designed:1 ligett:1 update:10 v:6 selected:12 short:1... |
5,737 | 6,191 | Spectral Learning of Dynamic Systems from
Nonequilibrium Data
Hao Wu and Frank No?
Department of Mathematics and Computer Science
Freie Universit?t Berlin
Arnimallee 6, 14195 Berlin
{hao.wu,frank.noe}@fu-berlin.de
Abstract
Observable operator models (OOMs) and related models are one of the most important and powerful... | 6191 |@word nd:3 c0:2 d2:4 simulation:14 decomposition:2 covariance:2 initial:4 configuration:1 contains:3 series:1 hereafter:1 prefix:1 existing:1 discretization:1 activation:1 yet:1 dx:2 numerical:2 wiewiora:1 plot:1 rd2:3 stationary:5 selected:3 probi:3 short:4 coarse:2 provides:1 zhang:1 five:2 bowman:1 become:1 di... |
5,738 | 6,192 | What Makes Objects Similar:
A Unified Multi-Metric Learning Approach
Han-Jia Ye
De-Chuan Zhan
Xue-Min Si
Yuan Jiang
Zhi-Hua Zhou
National Key Laboratory for Novel Software Technology,
Nanjing University, Nanjing, 210023, China
{yehj,zhandc,sixm,jiangy,zhouzh}@lamda.nju.edu.cn
Abstract
Linkages are essentially determ... | 6192 |@word trial:1 kulis:1 version:1 norm:11 d2:4 km:3 cml:2 hu:1 covariance:1 decomposition:1 pick:1 tr:9 mcauley:1 denying:1 initial:1 configuration:4 generatively:1 score:11 selecting:1 aple:3 liu:1 denoting:1 tuned:1 imposter:1 existing:1 current:1 recovered:1 si:1 yet:4 assigning:1 must:1 partition:1 shape:3 enab... |
5,739 | 6,193 | Learning and Forecasting Opinion Dynamics in
Social Networks
Abir De?
Isabel Valera?
Niloy Ganguly?
?
Sourangshu Bhattacharya
Manuel Gomez-Rodriguez?
?
IIT Kharagpur
MPI for Software Systems?
{abir.de,niloy,sourangshu}@cse.iitkgp.ernet.in
{ivalera,manuelgr}@mpi-sws.org
Abstract
Social media and social networking sites... | 6193 |@word inversion:1 open:1 hu:5 simulation:18 catastrophically:1 initial:2 contains:2 past:2 nonmonotone:1 current:2 com:2 nt:2 manuel:1 recovered:1 dx:1 readily:2 realistic:1 sdes:2 designed:2 update:4 v:4 generative:1 ivalera:1 realizing:1 core:2 record:1 bvu:6 provides:3 coarse:1 cse:1 node:22 org:1 five:1 dn:4 ... |
5,740 | 6,194 | Generating Videos with Scene Dynamics
Carl Vondrick
MIT
vondrick@mit.edu
Hamed Pirsiavash
UMBC
hpirsiav@umbc.edu
Antonio Torralba
MIT
torralba@mit.edu
Abstract
We capitalize on large amounts of unlabeled video in order to learn a model of
scene dynamics for both video recognition tasks (e.g. action classification) ... | 6194 |@word trial:1 economically:1 version:1 cox:1 replicate:2 open:1 km:1 simulation:2 seek:3 jacob:1 paid:1 sgd:1 inpainting:1 reduction:1 contains:1 tuned:5 interestingly:1 deconvolutional:1 animated:3 past:2 existing:2 outperforms:5 kx0:1 wd:8 places2:1 activation:4 yet:1 diederik:1 must:3 gpu:1 realistic:13 ronan:... |
5,741 | 6,195 | Causal Bandits: Learning Good Interventions via
Causal Inference
Finnian Lattimore
Australian National University and Data61/NICTA
finn.lattimore@gmail.com
Tor Lattimore
Indiana University, Bloomington
tor.lattimore@gmail.com
Mark D. Reid
Australian National University and Data61/NICTA
mark.reid@anu.edu.au
Abstract
... | 6195 |@word exploitation:1 version:1 briefly:1 achievable:1 seems:1 eliminating:2 dekel:1 open:1 hu:3 simulation:1 q1:3 thereby:1 reduction:1 selecting:7 outperforms:1 existing:4 recovered:1 com:3 contextual:6 nt:1 analysed:1 gmail:2 must:2 informative:1 treating:1 bart:2 v:3 half:2 selected:2 greedy:1 stationary:1 xk:... |
5,742 | 6,196 | Optimal Cluster Recovery
in the Labeled Stochastic Block Model
Se-Young Yun
CNLS, Los Alamos National Lab.
Los Alamos, NM 87545
syun@lanl.gov
Alexandre Proutiere
Automatic Control Dept., KTH
Stockholm 100-44, Sweden
alepro@kth.se
Abstract
We consider the problem of community detection or clustering in the labeled
Sto... | 6196 |@word illustrating:1 achievable:1 polynomial:1 proportion:7 cnls:1 nd:9 open:2 decomposition:5 neeman:2 ours:2 interestingly:1 document:1 existing:3 recovered:3 ka:3 assigning:1 attracted:2 must:4 fn:7 partition:7 remove:1 afn:3 item:71 beginning:1 vanishing:3 node:1 successive:1 zhang:3 constructed:1 direct:1 qu... |
5,743 | 6,197 | Multi-step learning and
underlying structure in statistical models
Maia Fraser
Dept. of Mathematics and Statistics
Brain and Mind Research Institute
University of Ottawa
Ottawa, ON K1N 6N5, Canada
mfrase8@uottawa.ca
Abstract
In multi-step learning, where a final learning task is accomplished via a sequence of
intermed... | 6197 |@word mild:1 version:8 middle:1 achievable:2 polynomial:1 norm:1 stronger:1 seek:1 mention:1 tr:1 solid:1 reduction:2 initial:1 generatively:1 chervonenkis:1 bc:3 comparing:1 cumulation:1 z2:4 si:21 additive:1 subsequent:2 partition:2 j1:5 v:1 generative:1 intelligence:1 beginning:1 reciprocal:1 short:1 record:2 ... |
5,744 | 6,198 | Phased Exploration with Greedy Exploitation in
Stochastic Combinatorial Partial Monitoring Games
Sougata Chaudhuri
Department of Statistics
University of Michigan Ann Arbor
sougata@umich.edu
Ambuj Tewari
Department of Statistics and Department of EECS
University of Michigan Ann Arbor
tewaria@umich.edu
Abstract
Parti... | 6198 |@word exploitation:10 polynomial:1 open:1 km:1 crucially:1 incurs:1 boundedness:1 score:3 yajun:1 current:4 must:1 john:2 additive:3 christian:1 designed:1 drop:1 greedy:4 intelligence:2 selected:1 item:28 beginning:1 caveat:1 revisited:1 honda:1 preference:7 simpler:1 along:2 yuan:1 prove:1 combine:6 privacy:1 i... |
5,745 | 6,199 | Near-Optimal Smoothing of Structured Conditional
Probability Matrices
Moein Falahatgar
University of California, San Diego
San Diego, CA, USA
moein@ucsd.edu
Mesrob I. Ohannessian
Toyota Technological Institute at Chicago
Chicago, IL, USA
mesrob@ttic.edu
Alon Orlitsky
University of California, San Diego
San Diego, CA... | 6199 |@word kong:1 version:3 eliminating:1 bigram:8 seems:1 polynomial:1 justice:1 plsa:1 km:10 heuristically:1 seek:1 prasad:1 concise:1 jafarpour:1 reduction:1 initial:1 celebrated:1 necessity:1 ours:1 interestingly:2 past:1 current:2 contextual:1 reminiscent:2 written:1 readily:1 chicago:2 additive:2 hofmann:1 drop:... |
5,746 | 62 | 290
CYCLES: A Simulation Tool for Studying
Cyclic Neural Networks
Michael T. Gately
Texas Instruments Incorporated, Dallas, TX 75265
ABSTRACT
A computer program has been designed and implemented to allow a researcher
to analyze the oscillatory behavior of simulated neural networks with cyclic connectivity. The computer... | 62 |@word exploitation:1 open:2 pulse:1 simulation:3 t_:1 pressure:1 accommodate:1 cyclic:4 series:2 current:1 activation:5 written:1 must:5 shape:3 motor:4 designed:1 update:3 aside:1 short:3 provides:1 node:2 location:1 sigmoidal:2 constructed:1 consists:1 behavior:1 multi:1 brain:2 window:5 stm:2 begin:3 every:2 ti:... |
5,747 | 620 | Connected Letter Recognition with a
Multi-State Time Delay Neural Network
Hermann Hild and Alex Waibel
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213-3891, USA
Abstract
The Multi-State Time Delay Neural Network (MS-TDNN) integrates a nonlinear time alignment procedure (DTW) and the highacc... | 620 |@word contains:1 score:4 punishes:1 bootstrapped:1 activation:4 lang:1 speakerindependent:1 v:2 aside:1 half:1 beginning:1 short:2 toronto:1 along:3 incorrect:3 consists:1 combine:1 multi:10 window:1 becomes:1 classifies:1 kind:1 unspecified:1 string:3 kaufman:1 developed:1 spoken:3 differing:1 bootstrapping:3 pse... |
5,748 | 6,200 | Improved Deep Metric Learning with
Multi-class N-pair Loss Objective
Kihyuk Sohn
NEC Laboratories America, Inc.
ksohn@nec-labs.com
Abstract
Deep metric learning has gained much popularity in recent years, following the
success of deep learning. However, existing frameworks of deep metric learning
based on contrastive... | 6200 |@word mild:1 norm:4 open:2 seek:1 contrastive:7 attainable:1 pick:1 sgd:2 tr:1 reduction:1 bai:1 liu:2 contains:1 efficacy:1 score:8 tuned:1 interestingly:1 outperforms:3 existing:3 guadarrama:1 com:2 si:3 yet:1 goldberger:1 written:2 fn:15 numerical:1 partition:3 confirming:1 designed:1 drop:1 update:7 v:3 alone... |
5,749 | 6,201 | Unsupervised Risk Estimation Using Only
Conditional Independence Structure
Jacob Steinhardt
Stanford University
jsteinhardt@cs.stanford.edu
Percy Liang
Stanford University
pliang@cs.stanford.edu
Abstract
We show how to estimate a model?s test error from unlabeled data, on distributions
very different from the traini... | 6201 |@word mild:1 version:1 polynomial:2 norm:6 johansson:2 stronger:1 instrumental:2 open:2 additively:2 tried:1 jacob:1 p0:10 decomposition:9 asks:1 thereby:1 moment:16 contains:1 score:2 series:1 ours:1 suppressing:1 document:1 intriguing:1 must:1 john:1 realistic:1 partition:1 enables:1 gv:6 remove:1 alone:1 gener... |
5,750 | 6,202 | Hierarchical Question-Image Co-Attention
for Visual Question Answering
Jiasen Lu? , Jianwei Yang? , Dhruv Batra?? , Devi Parikh??
? Virginia Tech, ? Georgia Institute of Technology
{jiasenlu, jw2yang, dbatra, parikh}@vt.edu
Abstract
A number of recent works have proposed attention models for Visual Question
Answering... | 6202 |@word h:2 cnn:10 version:1 briefly:1 bigram:4 norm:3 faculty:2 open:6 hu:1 shuicheng:1 jacob:1 q1:2 attended:7 mengye:1 harder:1 recursively:4 liu:1 contains:2 fragment:1 hoiem:1 ours:1 outperforms:3 com:1 comparing:1 haoyuan:1 activation:2 gpu:1 academia:1 wx:2 christian:1 hypothesize:1 drop:4 interpretable:1 pr... |
5,751 | 6,203 | Understanding the Effective Receptive Field in
Deep Convolutional Neural Networks
Wenjie Luo?
Yujia Li?
Raquel Urtasun
Richard Zemel
Department of Computer Science
University of Toronto
{wenjie, yujiali, urtasun, zemel}@cs.toronto.edu
Abstract
We study characteristics of receptive fields of units in deep convolution... | 6203 |@word fusiform:1 cnn:11 version:1 polynomial:1 gradual:1 propagate:4 simplifying:1 harder:1 carry:1 extrastriate:1 initial:4 contains:1 exclusively:2 foveal:2 deconvolutional:1 current:1 comparing:2 luo:1 surprising:2 activation:11 intriguing:1 must:1 shape:7 haxby:1 half:1 cue:1 plane:3 beginning:2 characterizat... |
5,752 | 6,204 | Provable Efficient Online Matrix Completion via
Non-convex Stochastic Gradient Descent
Chi Jin
UC Berkeley
chijin@cs.berkeley.edu
Sham M. Kakade
University of Washington
sham@cs.washington.edu
Praneeth Netrapalli
Microsoft Research India
praneeth@microsoft.com
Abstract
Matrix completion, where we wish to recover a l... | 6204 |@word version:3 pw:1 polynomial:2 seems:1 norm:6 c0:6 unif:3 km:5 d2:29 prasad:1 decomposition:1 citeseer:1 sgd:22 mention:1 initial:9 liu:2 lightweight:1 ours:1 existing:3 kmk:14 current:1 com:2 luo:3 update:22 rd2:3 fewer:1 item:14 ith:3 prize:2 smith:1 iterates:4 provides:2 simpler:2 mathematical:1 along:1 sym... |
5,753 | 6,205 | Swapout: Learning an ensemble of deep architectures
Saurabh Singh, Derek Hoiem, David Forsyth
Department of Computer Science
University of Illinois, Urbana-Champaign
{ss1, dhoiem, daf}@illinois.edu
Abstract
We describe Swapout, a new stochastic training method, that outperforms ResNets
of identical network structure ... | 6205 |@word version:4 achievable:1 nd:1 propagate:1 sgd:1 thereby:1 coadaptation:1 initial:1 configuration:1 liu:2 selecting:1 hoiem:1 ours:8 document:1 outperforms:6 existing:1 current:2 skipping:3 activation:1 yet:1 parsing:1 romero:1 drop:2 plot:2 selected:1 fewer:1 parameterization:1 imitate:1 colored:1 simpler:1 z... |
5,754 | 6,206 | Perspective Transformer Nets: Learning Single-View
3D Object Reconstruction without 3D Supervision
Xinchen Yan1
Jimei Yang2 Ersin Yumer2 Yijie Guo1 Honglak Lee1,3
1
University of Michigan, Ann Arbor
2
Adobe Research
3
Google Brain
{xcyan,guoyijie,honglak}@umich.edu, {jimyang,yumer}@adobe.com
Abstract
Understanding t... | 6206 |@word kohli:1 cnn:13 version:3 repository:1 open:1 choy:2 seitz:1 solid:3 contains:2 disparity:3 score:2 com:1 cad:1 visible:1 shape:48 enables:3 hypothesize:1 drop:1 interpretable:1 n0:2 v:2 generative:3 plane:1 lamp:2 short:1 mental:1 contribute:2 yuting:1 zhang:5 height:1 along:1 kalogerakis:1 consists:2 fully... |
5,755 | 6,207 | Efficient Second Order Online Learning by Sketching
Haipeng Luo
Princeton University, Princeton, NJ USA
haipengl@cs.princeton.edu
Nicol? Cesa-Bianchi
Universit? degli Studi di Milano, Italy
nicolo.cesa-bianchi@unimi.it
Alekh Agarwal
Microsoft Research, New York, NY USA
alekha@microsoft.com
John Langford
Microsoft Res... | 6207 |@word worsens:2 determinant:1 briefly:1 version:4 repository:1 norm:6 seems:1 open:1 d2:1 confirms:1 seek:1 crucially:1 covariance:1 pick:1 sgd:1 incurs:1 mention:1 tr:3 boundedness:1 liu:1 woodruff:2 tuned:2 frankwolfe:1 outperforms:1 diagonalized:1 existing:2 recovered:1 com:2 comparing:1 luo:2 surprising:1 dx:... |
5,756 | 6,208 | R?nyi Divergence Variational Inference
Yingzhen Li
University of Cambridge
Cambridge, CB2 1PZ, UK
yl494@cam.ac.uk
Richard E. Turner
University of Cambridge
Cambridge, CB2 1PZ, UK
ret26@cam.ac.uk
Abstract
This paper introduces the variational R?nyi bound (VR) that extends traditional variational inference to R?nyi?s ... | 6208 |@word mild:2 repository:1 msr:1 open:1 propagate:1 p0:4 thereby:1 tr:1 moment:2 ndez:6 series:2 interestingly:1 erven:1 existing:4 err:1 recovered:1 current:1 com:2 gpu:1 enables:3 update:1 v:1 intelligence:2 selected:2 generative:1 core:2 blei:4 provides:4 location:1 preference:1 zhang:1 wierstra:1 mathematical:... |
5,757 | 6,209 | Hypothesis Testing in Unsupervised Domain
Adaptation with Applications in Alzheimer?s Disease
Hao Henry Zhou?
Sathya N. Ravi?
Vamsi K. Ithapu?
?,?
?
Sterling C. Johnson
Grace Wahba
Vikas Singh?
?
?
William S. Middleton Memorial VA Hospital
University of Wisconsin?Madison
Abstract
Consider samples from two different d... | 6209 |@word trial:2 kulis:1 version:2 simulation:2 seek:2 accounting:1 citeseer:1 asks:1 tr:2 initial:1 united:1 salzmann:1 denoting:2 rkhs:6 interestingly:1 multiuser:1 existing:2 recovered:2 comparing:2 anne:1 must:3 written:1 john:1 numerical:1 blur:1 remove:2 plot:1 interpretable:1 fund:1 discrimination:1 v:10 oper... |
5,758 | 621 | Some Solutions to the Missing Feature Problem
in Vision
Subutai Ahmad
Siemens AG,
Central Research and Development
ZFE ST SN61, Otto-Hahn Ring 6
8000 Miinchen 83, Gennany.
ahmad@icsi.berkeley.edu
Volker Tresp
Siemens AG,
Central Research and Development
ZFE ST SN41, Otto-Hahn Ring 6
8000 Miinchen 83, Gennany.
tresp@in... | 621 |@word duda:2 simulation:1 tried:1 covariance:3 shading:1 score:5 nowlan:3 dx:2 must:4 john:1 realistic:1 numerical:2 plot:1 update:2 alone:1 xk:2 ijb:1 miinchen:2 lx:1 sigmoidal:1 five:4 along:3 direct:1 little:3 encouraging:1 becomes:3 project:1 estimating:1 moreover:1 mass:1 what:1 substantially:1 ag:2 guarantee... |
5,759 | 6,210 | Data driven estimation of Laplace-Beltrami operator
Fr?d?ric Chazal
Inria Saclay
Palaiseau France
frederic.chazal@inria.fr
Ilaria Giulini
Inria Saclay
Palaiseau France
ilaria.giulini@me.com
Bertrand Michel
Ecole Centrale de Nantes
Laboratoire de Math?matiques Jean Leray (UMR 6629 CNRS)
Nantes France
bertrand.michel@... | 6210 |@word version:3 polynomial:1 norm:10 seems:1 bf:1 open:1 fifteen:1 reduction:2 giulini:2 lepskii:2 selecting:5 bs01:1 daniel:1 ecole:1 reaction:1 com:1 written:1 numerical:3 analytic:1 remove:2 selected:7 kkd:5 provides:1 math:1 location:3 mathematical:5 h4:1 become:1 prove:1 introduce:2 coifman:1 indeed:2 nor:1 ... |
5,760 | 6,211 | Supervised Learning with Tensor Networks
E. M. Stoudenmire
Perimeter Institute for Theoretical Physics
Waterloo, Ontario, N2L 2Y5, Canada
David J. Schwab
Department of Physics
Northwestern University, Evanston, IL
Abstract
Tensor networks are approximations of high-order tensors which are efficient to
work with and ... | 6211 |@word version:2 seems:1 norm:1 trofimov:1 crucially:1 tried:1 decomposition:15 contraction:3 uncovers:1 jacob:1 configuration:1 daniel:2 recovered:1 com:2 nt:7 current:2 z2:5 si:1 ws1:1 must:2 realistic:1 shape:1 treating:1 designed:1 update:3 v:2 implying:1 parameterization:1 podoprikhin:1 core:2 provides:1 cont... |
5,761 | 6,212 | Diffusion-Convolutional Neural Networks
James Atwood and Don Towsley
College of Information and Computer Science
University of Massachusetts
Amherst, MA, 01003
{jatwood|towsley}@cs.umass.edu
Abstract
We present diffusion-convolutional neural networks (DCNNs), a new model for
graph-structured data. Through the introduc... | 6212 |@word trial:4 briefly:2 polynomial:4 proportion:5 triggs:1 series:8 uma:1 contains:2 tuned:1 document:1 outperforms:2 existing:2 contextual:2 nt:22 wd:2 z2:1 activation:11 must:1 readily:1 gpu:5 written:1 john:1 visible:3 partition:1 numerical:1 mutagenic:1 designed:2 treating:1 alone:2 parameterization:1 beginni... |
5,762 | 6,213 | Optimal Learning for Multi-pass Stochastic Gradient
Methods
Junhong Lin
LCSL, IIT-MIT, USA
junhong.lin@iit.it
Lorenzo Rosasco
DIBRIS, Univ. Genova, ITALY
LCSL, IIT-MIT, USA
lrosasco@mit.edu
Abstract
We analyze the learning properties of the stochastic gradient method when multiple
passes over the data and mini-batche... | 6213 |@word h:1 trial:2 version:4 polynomial:1 norm:5 seems:1 suitably:1 dekel:1 closure:1 simulation:4 decomposition:10 q1:2 tr:2 nystr:1 harder:1 moment:1 cyclic:1 tuned:1 rkhs:2 document:1 existing:1 comparing:1 numerical:2 subsequent:1 j1:3 juditsky:1 fewer:1 iterates:1 provides:2 readability:1 zhang:2 mathematical... |
5,763 | 6,214 | Path-Normalized Optimization of Recurrent Neural
Networks with ReLU Activations
Behnam Neyshabur?
Toyota Technological Institute at Chicago
Yuhuai Wu?
University of Toronto
bneyshabur@ttic.edu
ywu@cs.toronto.edu
Ruslan Salakhutdinov
Carnegie Mellon University
Nathan Srebro
Toyota Technological Institute at Chicago... | 6214 |@word version:3 briefly:1 compression:1 seems:1 norm:12 stronger:1 hu:6 confirms:1 seek:1 tried:1 p0:1 sgd:37 harder:1 boundedness:1 recursively:2 contains:1 efficacy:1 fa8750:1 subword:1 outperforms:1 current:1 activation:23 diederik:1 written:2 fn:3 chicago:2 distant:1 partition:1 enables:1 cheap:1 asymptote:2 ... |
5,764 | 6,215 | On Multiplicative Integration with
Recurrent Neural Networks
Yuhuai Wu1,? , Saizheng Zhang2,? , Ying Zhang2 , Yoshua Bengio2,4 and Ruslan Salakhutdinov3,4
1
University of Toronto, 2 MILA, Universit? de Montr?al, 3 Carnegie Mellon University, 4 CIFAR
ywu@cs.toronto.edu,2 {firstname.lastname}@umontreal.ca,rsalakhu@cs.cmu... | 6215 |@word cnn:1 version:1 middle:3 repository:1 norm:11 propagate:1 tried:1 bn:12 git:1 decomposition:3 yih:1 initial:1 ndez:1 contains:1 daniel:1 ours:26 interestingly:1 subword:1 outperforms:6 existing:3 err:1 current:1 comparing:1 com:3 amjad:1 activation:14 diederik:1 gpu:1 additive:16 partition:1 wx:11 remove:1 ... |
5,765 | 6,216 | Minimizing Regret on Reflexive Banach Spaces and
Nash Equilibria in Continuous Zero-Sum Games
Maximilian Balandat, Walid Krichene, Claire Tomlin, Alexandre Bayen
Electrical Engineering and Computer Sciences, UC Berkeley
[balandat,walid,tomlin]@eecs.berkeley.edu, bayen@berkeley.edu
Abstract
We study a general adversar... | 6216 |@word mild:2 norm:3 approachability:2 c0:16 suitably:1 open:1 nd:1 hu:2 semicontinuous:6 seek:1 minus:1 interestingly:1 past:3 existing:1 si:10 universality:1 dx:1 realize:1 numerical:2 update:4 vanishing:1 characterization:1 provides:1 unbounded:3 mathematical:3 pairing:1 s2t:3 prove:5 shapley:2 introduce:2 x0:1... |
5,766 | 6,217 | Density Estimation via Discrepancy Based
Adaptive Sequential Partition
Dangna Li
ICME,
Stanford University
Stanford, CA 94305
dangna@stanford.edu
Kun Yang
Google
Mountain View, CA 94043
kunyang@stanford.edu
Wing Hung Wong
Department of Statistics
Stanford University
Stanford, CA 94305
whwong@stanford.edu
Abstract
G... | 6217 |@word repository:1 middle:1 compression:2 proportion:1 simulation:3 p0:1 concise:3 recursively:1 moment:1 initial:2 liu:2 hardy:1 past:1 existing:4 comparing:1 luo:2 assigning:1 dx:9 bd:3 readily:1 attracted:1 john:1 sergei:1 numerical:2 partition:32 christian:1 seeding:1 plot:2 greedy:1 beginning:1 runze:1 ith:1... |
5,767 | 6,218 | How Deep is the Feature Analysis underlying Rapid
Visual Categorization?
Sven Eberhardt?
Jonah Cader?
Thomas Serre
Department of Cognitive Linguistic & Psychological Sciences
Brown Institute for Brain Sciences
Brown University
Providence, RI 02818
{sven2,jonah_cader,thomas_serre}@brown.edu
Abstract
Rapid categorizati... | 6218 |@word trial:10 version:1 faculty:1 polynomial:1 briefly:1 approved:1 nd:1 confirms:1 r:1 prominence:1 irb:1 mammal:3 bai:1 initial:1 score:14 united:1 tuned:5 bootstrapped:1 interestingly:1 dubourg:1 past:4 reaction:3 outperforms:1 current:1 com:2 guadarrama:1 comparing:1 familiarized:1 enables:1 motor:3 drop:2 p... |
5,768 | 6,219 | Constraints Based Convex Belief Propagation
Yaniv Tenzer
Department of Statistics
The Hebrew University
Alexander Schwing
Department of Electrical and Computer Engineering
University of Illinois at Urbana-Champaign
Kevin Gimpel
Toyota Technological Institute at Chicago
Tamir Hazan
Faculty of Industrial Engineering ... | 6219 |@word kohli:3 version:1 faculty:1 bigram:1 everingham:1 open:1 p0:2 textonboost:1 contains:3 series:1 score:2 tuned:1 outperforms:3 existing:2 past:1 must:5 parsing:1 chicago:1 partition:2 remove:1 update:14 fewer:1 selected:1 xk:1 smith:2 tarlow:1 node:5 cbp:28 preference:2 org:1 unbounded:1 along:1 bertoldi:1 c... |
5,769 | 622 | Information, prediction, and query by
committee
Yoav Freund
Computer and Information Sciences
University of California, Santa Cruz
yoavQcse.ucsc.edu
Eli Shamir
Institute of Computer Science
Hebrew University, Jerusalem
sharnirQcs.huji.ac.il
H. Sebastian Seung
AT &T Bell Laboratories
Murray Hill, New Jersey
seungQphys... | 622 |@word version:28 polynomial:1 seems:2 stronger:2 open:3 cal90:3 pick:1 contains:1 att:1 selecting:1 com:1 dx:1 aft:1 must:2 bd:2 cruz:1 realistic:1 informative:2 shape:1 enables:1 cheap:1 designed:1 atlas:1 alone:1 fewer:1 warmuth:1 plane:1 accepting:1 filtered:1 manfred:1 provides:1 math:1 hyperplanes:1 along:4 u... |
5,770 | 6,220 | Multivariate tests of association based on univariate
tests
Ruth Heller
Department of Statistics and Operations Research
Tel-Aviv University
Tel-Aviv, Israel 6997801
ruheller@gmail.com
Yair Heller
heller.yair@gmail.com
Abstract
For testing two vector random variables for independence, we propose testing
whether the d... | 6220 |@word mild:1 norm:4 smirnov:5 open:1 simulation:5 bn:2 covariance:3 carry:5 score:13 selecting:2 rkhs:1 interestingly:1 existing:2 current:1 com:2 comparing:6 si:4 gmail:2 yet:1 bd:3 partition:10 informative:2 tailoring:1 analytic:3 interpretable:2 braz:1 discovering:1 selected:2 kyk:4 inspection:1 xk:1 ith:1 mat... |
5,771 | 6,221 | Memory-Efficient Backpropagation Through Time
?
Audrunas
Gruslys
Google DeepMind
audrunas@google.com
R?mi Munos
Google DeepMind
munos@google.com
Marc Lanctot
Google DeepMind
lanctot@google.com
Ivo Danihelka
Google DeepMind
danihelka@google.com
Alex Graves
Google DeepMind
gravesa@google.com
Abstract
We propose a n... | 6221 |@word version:1 bptt:28 reused:2 grey:1 cloned:1 d2:4 propagate:1 q1:3 recursively:1 reduction:1 initial:4 plentiful:3 contains:2 document:1 reynolds:1 outperforms:4 current:1 com:5 written:1 gpu:1 john:1 ronald:1 numerical:2 plot:5 v:1 intelligence:1 device:2 ivo:3 beginning:1 core:33 short:3 firstly:2 zhang:1 u... |
5,772 | 6,222 | Brains on Beats
Umut G??l?
Radboud University, Donders Institute for
Brain, Cognition and Behaviour
Nijmegen, the Netherlands
u.guclu@donders.ru.nl
Jordy Thielen
Radboud University, Donders Institute for
Brain, Cognition and Behaviour
Nijmegen, the Netherlands
j.thielen@psych.ru.nl
Michael Hanke?
Otto-von-Guericke Un... | 6222 |@word trial:4 mri:1 hippocampus:1 kriegeskorte:3 bn:2 tr:1 initial:3 contains:2 existing:1 current:1 comparing:3 anterior:8 com:1 yet:1 subsequent:1 distant:1 remove:1 designed:1 fund:1 selected:1 plane:1 inspection:3 ith:1 pool2:1 core:2 short:2 filtered:2 coarse:1 provides:1 location:5 traverse:2 preference:1 f... |
5,773 | 6,223 | Identification and Overidentification of
Linear Structural Equation Models
Bryant Chen
University of California, Los Angeles
Computer Science Department
Los Angeles, CA, 90095-1596, USA
Abstract
In this paper, we address the problems of identifying linear structural equation
models and discovering the constraints the... | 6223 |@word polynomial:1 instrumental:1 nd:1 twelfth:1 calculus:2 d2:3 covariance:7 decomposition:20 q1:1 recursively:6 contains:2 pub:3 denoting:4 existing:4 z2:2 si:1 dx:1 must:2 dechter:2 partition:1 enables:2 remove:2 designed:2 implying:1 half:25 discovering:3 leaf:1 de1:1 v1r:1 intelligence:14 node:26 org:1 simpl... |
5,774 | 6,224 | Assortment Optimization Under the Mallows model
Antoine D?sir
IEOR Department
Columbia University
antoine@ieor.columbia.edu
Vineet Goyal
IEOR Department
Columbia University
vgoyal@ieor.columbia.edu
Srikanth Jagabathula
IOMS Department
NYU Stern School of Business
sjagabat@stern.nyu.edu
Danny Segev
Department of Stat... | 6224 |@word version:1 polynomial:1 logit:5 simulation:4 dramatic:1 profit:5 versatile:1 carry:1 initial:1 substitution:5 contains:3 ndez:1 series:1 existing:5 com:1 danny:1 must:2 written:2 john:2 numerical:3 j1:1 designed:1 v:2 implying:1 selected:2 guess:2 item:4 xk:3 core:1 record:1 provides:3 bijection:1 location:7... |
5,775 | 6,225 | Variational Inference in
Mixed Probabilistic Submodular Models
Josip Djolonga
Sebastian Tschiatschek
Andreas Krause
Department of Computer Science, ETH Z?urich
{josipd,tschiats,krausea}@inf.ethz.ch
Abstract
We consider the problem of variational inference in probabilistic models with both
log-submodular and log-superm... | 6225 |@word faculty:1 polynomial:4 norm:1 hyv:1 seek:1 decomposition:2 contrastive:3 pick:1 thereby:1 minus:2 celebrated:1 contains:2 series:2 outperforms:1 current:1 written:3 determinantal:3 realistic:1 partition:7 enables:1 update:2 greedy:3 instantiate:1 selected:1 item:42 intelligence:4 gear:2 reciprocal:3 woodfor... |
5,776 | 6,226 | The Product Cut
Xavier Bresson
Nanyang Technological University
Singapore
xavier.bresson@ntu.edu.sg
Thomas Laurent
Loyola Marymount University
Los Angeles
tlaurent@lmu.edu
Arthur Szlam
Facebook AI Research
New York
aszlam@fb.com
James H. von Brecht
California State University, Long Beach
Long Beach
james.vonbrecht@c... | 6226 |@word kulis:1 version:5 stronger:3 termination:1 bn:13 citeseer:5 invoking:1 dramatic:1 solid:1 contains:3 selecting:4 daniel:2 current:3 com:2 comparing:1 lang:1 yet:1 assigning:1 must:1 subsequent:1 partition:55 depict:1 n0:5 stationary:1 greedy:1 selected:2 intelligence:2 ith:2 vanishing:1 record:1 provides:8 ... |
5,777 | 6,227 | An algorithm for 1 nearest neighbor search via
monotonic embedding
Xinan Wang?
UC San Diego
xinan@ucsd.edu
Sanjoy Dasgupta
UC San Diego
dasgupta@cs.ucsd.edu
Abstract
Fast algorithms for nearest neighbor (NN) search have in large part focused on 2
distance. Here we develop an approach for 1 distance that begins wit... | 6227 |@word compression:1 knd:2 norm:3 nd:9 reused:1 vldb:1 jacob:1 covariance:1 pg:8 tr:2 reduction:4 liu:1 contains:2 document:4 interestingly:1 existing:1 current:1 chazelle:1 guadarrama:1 beygelzimer:1 additive:2 partition:4 subsequent:3 j1:1 shape:3 remove:1 hash:4 prohibitive:1 xk:4 blei:1 math:1 location:1 org:1... |
5,778 | 6,228 | Man is to Computer Programmer as Woman is to
Homemaker? Debiasing Word Embeddings
Tolga Bolukbasi1 , Kai-Wei Chang2 , James Zou2 , Venkatesh Saligrama1,2 , Adam Kalai2
1
Boston University, 8 Saint Mary?s Street, Boston, MA
Microsoft Research New England, 1 Memorial Drive, Cambridge, MA
tolgab@bu.edu, kw@kwchang.net, j... | 6228 |@word msr:1 version:1 middle:1 briefly:1 rivlin:1 d2:1 confirms:2 gradual:3 solan:1 seek:1 zliobaite:1 excited:1 thereby:1 yih:1 initial:1 venkatasubramanian:1 contains:2 score:3 uncovered:1 occupational:1 document:2 ours:1 subjective:1 err:1 contextual:1 com:3 surprising:1 gmail:1 written:1 parsing:2 john:2 happ... |
5,779 | 6,229 | Estimating the Size of a Large Network and its
Communities from a Random Sample
1
Lin Chen1,2 , Amin Karbasi1,2 , Forrest W. Crawford2,3
Department of Electrical Engineering, 2 Yale Institute for Network Science,
3
Department of Biostatistics, Yale University
{lin.chen, amin.karbasi, forrest.crawford}@yale.edu
Abstr... | 6229 |@word briefly:2 faculty:2 version:2 sex:4 termination:2 confirms:2 pulse:6 bn:4 pick:2 moment:5 initial:3 contains:1 united:1 outperforms:2 medi:1 crawling:1 must:9 partition:7 plot:1 update:2 maxv:4 intelligence:2 selected:3 guess:3 half:1 accordingly:1 record:2 provides:1 characterization:1 node:10 org:1 zhang:... |
5,780 | 623 | Performance Through Consistency:
MS-TDNN's for Large Vocabulary
Continuous Speech Recognition
Joe Tebelskis and Alex Waibel
School of Computf'f Science
Carnegie MeHon University
Pittsburgh, PA 15213
Abstract
Connectionist Rpeech recognition systems are often handicapped by
an inconsistency between training and testin... | 623 |@word version:2 ivit:1 retraining:1 tif:1 series:1 score:2 bootstrapped:2 outperforms:3 existing:1 current:2 activation:7 yet:5 must:4 subsequent:1 designed:1 discrimination:5 half:1 ria:1 successive:1 sigmoidal:3 simpler:1 along:1 become:1 incorrect:2 consists:1 behavior:1 frequently:1 multi:4 compensating:1 td:1... |
5,781 | 6,230 | Attend, Infer, Repeat:
Fast Scene Understanding with Generative Models
S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa,
David Szepesvari, Koray Kavukcuoglu, Geoffrey E. Hinton
{aeslami,heess,theophane,tassa,dsz,korayk,geoffhinton}@google.com
Google DeepMind, London, UK
Abstract
We present a framework for... | 6230 |@word kohli:2 cnn:1 middle:3 nd:1 grey:1 pieter:1 crucially:2 propagate:1 pressure:1 contains:4 score:2 daniel:1 interestingly:1 existing:1 com:1 z2:3 diederik:1 readily:1 john:1 mesh:1 visible:1 shape:1 treating:1 interpretable:7 plot:1 v:1 generative:25 intelligence:1 item:1 parameterization:1 ivo:1 amir:1 prov... |
5,782 | 6,231 | A Probabilistic Framework for Deep Learning
Ankit B. Patel
Baylor College of Medicine, Rice University
ankitp@bcm.edu,abp4@rice.edu
Tan Nguyen
Rice University
mn15@rice.edu
Richard G. Baraniuk
Rice University
richb@rice.edu
Abstract
We develop a probabilistic framework for deep learning based on the Deep Rendering M... | 6231 |@word middle:2 version:2 rgb:2 harder:1 recursively:1 moment:1 reduction:2 configuration:9 contains:1 initial:3 document:1 prefix:1 outperforms:3 past:2 comparing:1 activation:1 yet:1 must:2 enables:4 designed:2 drop:1 progressively:1 update:2 discrimination:1 interpretable:1 generative:21 implying:1 v:5 intellig... |
5,783 | 6,232 | Learning Treewidth-Bounded Bayesian Networks
with Thousands of Variables
Mauro Scanagatta
IDSIA? , SUPSI? , USI?
Lugano, Switzerland
mauro@idsia.ch
Giorgio Corani
IDSIA? , SUPSI? , USI?
Lugano, Switzerland
giorgio@idsia.ch
Cassio P. de Campos
Queen?s University Belfast
Northern Ireland, UK
c.decampos@qub.ac.uk
Marco... | 6232 |@word polynomial:2 seems:1 decomposition:3 minus:1 moment:1 necessity:1 initial:6 contains:4 score:47 outperforms:1 existing:1 current:1 recovered:1 worsening:2 yet:9 mushroom:1 subsequent:1 informative:3 designed:1 update:1 greedy:1 selected:1 leaf:2 prohibitive:1 malone:1 intelligence:5 plane:1 provides:2 node:... |
5,784 | 6,233 | Hierarchical Deep Reinforcement Learning:
Integrating Temporal Abstraction and
Intrinsic Motivation
Tejas D. Kulkarni?
DeepMind, London
tejasdkulkarni@gmail.com
Karthik R. Narasimhan?
CSAIL, MIT
karthikn@mit.edu
Ardavan Saeedi
CSAIL, MIT
ardavans@mit.edu
Joshua B. Tenenbaum
BCS, MIT
jbt@mit.edu
Abstract
Learning go... | 6233 |@word middle:2 version:1 nd:1 open:4 termination:1 d2:6 decomposition:3 diuk:2 q1:16 pick:2 harder:1 initial:1 configuration:1 series:1 score:3 typology:1 genetic:1 document:4 bootstrapped:1 existing:4 current:5 com:1 gmail:1 scatter:1 guez:1 numerical:1 subsequent:1 periodically:1 sorg:2 enables:2 plot:6 neuroro... |
5,785 | 6,234 | Confusions over Time: An Interpretable Bayesian
Model to Characterize Trends in Decision Making
Himabindu Lakkaraju
Department of Computer Science
Stanford University
himalv@cs.stanford.edu
Jure Leskovec
Department of Computer Science
Stanford University
jure@cs.stanford.edu
Abstract
We propose Confusions over Time ... | 6234 |@word mild:2 judgement:1 stronger:1 approved:1 dekel:1 bn:1 p0:1 carry:1 liu:2 contains:1 renewed:1 outperforms:2 past:1 horvitz:1 nt:4 yet:2 written:1 readily:2 additive:1 kdd:3 dive:1 cheap:1 designed:1 interpretable:15 update:3 generative:5 selected:1 intelligence:1 item:64 rudin:2 inspection:1 ruvolo:1 cavana... |
5,786 | 6,235 | Kernel Bayesian Inference with
Posterior Regularization
Yang Song? , Jun Zhu??, Yong Ren?
Dept. of Physics, Tsinghua University, Beijing, China
Dept. of Comp. Sci. & Tech., TNList Lab; Center for Bio-Inspired Computing Research
State Key Lab for Intell. Tech. & Systems, Tsinghua University, Beijing, China
yangsong@cs.... | 6235 |@word kondor:1 version:1 inversion:1 norm:5 open:3 covariance:6 citeseer:1 invoking:2 kbr:7 tnlist:1 moment:1 contains:1 efficacy:2 rkhs:22 document:1 com:1 exy:1 written:1 enables:1 plot:1 generative:1 half:1 intelligence:1 plane:1 xk:2 provides:3 characterization:2 arctan:1 zhang:1 height:1 mathematical:1 direc... |
5,787 | 6,236 | Maximization of
Approximately Submodular Functions
Thibaut Horel
Harvard University
thorel@seas.harvard.edu
Yaron Singer
Harvard University
yaron@seas.harvard.edu
Abstract
We study the problem of maximizing a function that is approximately submodular
under a cardinality constraint. Approximate submodularity implicit... | 6236 |@word exploitation:1 briefly:1 version:5 polynomial:1 stronger:2 norm:1 additively:1 bn:2 selecting:1 document:2 si:26 written:1 realize:1 additive:7 informative:1 kdd:1 v:2 greedy:14 fewer:1 intelligence:1 provides:1 mathematical:1 constructed:1 direct:1 symposium:1 persistent:1 prove:1 introduce:1 manner:1 inde... |
5,788 | 6,237 | Eliciting Categorical Data for Optimal Aggregation
Chien-Ju Ho
Cornell University
ch624@cornell.edu
Rafael Frongillo
CU Boulder
raf@colorado.edu
Yiling Chen
Harvard University
yiling@seas.harvard.edu
Abstract
Models for collecting and aggregating categorical data on crowdsourcing platforms typically fall into two br... | 6237 |@word economically:1 version:2 cu:1 private:5 open:2 seek:1 simulation:5 series:1 score:7 karger:2 tuned:1 subjective:2 existing:4 outperforms:1 savage:1 com:1 must:2 partition:37 informative:1 kdd:1 enables:2 designed:3 intelligence:3 leaf:1 selected:1 ruvolo:1 accepting:1 parkes:2 characterization:1 allerton:1 ... |
5,789 | 6,238 | Globally Optimal Training of Generalized
Polynomial Neural Networks with Nonlinear
Spectral Methods
A. Gautier, Q. Nguyen and M. Hein
Department of Mathematics and Computer Science
Saarland Informatics Campus, Saarland University, Germany
Abstract
The optimization problem behind neural networks is highly non-convex.
... | 6238 |@word cu:7 briefly:1 pw:14 polynomial:3 norm:1 version:1 tedious:1 decomposition:1 p0:1 contraction:2 pick:2 sgd:13 minus:1 arous:1 series:1 score:3 kpv:1 outperforms:1 current:1 activation:3 yet:1 john:1 belmont:1 gv:1 selected:1 provides:1 characterization:2 readability:1 sigmoidal:1 nussbaum:1 saarland:2 mathe... |
5,790 | 6,239 | Joint quantile regression in vector-valued RKHSs
Maxime Sangnier Olivier Fercoq Florence d?Alch?e-Buc
LTCI, CNRS, T?el?ecom ParisTech
Universit?e Paris-Saclay
75013, Paris, France
{maxime.sangnier, olivier.fercoq, florence.dalche}
@telecom-paristech.fr
Abstract
Addressing the will to give a more complete picture than... | 6239 |@word repository:1 version:1 inversion:2 seems:2 norm:4 nd:7 closure:1 contraction:2 tr:4 born:1 series:1 liu:3 denoting:1 rkhs:9 tuned:1 ours:1 outperforms:1 current:1 recovered:1 comparing:2 elliptical:1 yet:3 tackling:1 written:1 numerical:5 additive:1 enables:1 mtfl:5 update:3 v:11 intelligence:1 utterly:1 it... |
5,791 | 624 | Analogy--Watershed or Waterloo?
Structural alignment and the development of
connectionist models of analogy
Dedre Gentner
Department of Psychology
Northwestern University
2029 Sheridan Rd.
Evanston, IL 60208
Arthur B. Markman
Department of Psychology
Northwestern University
2029 Sheridan Rd.
Evanston, IL 60208
ABSTRA... | 624 |@word middle:1 underline:2 open:2 holyoak:3 q1:1 wisniewski:1 configuration:12 series:1 united:3 interestingly:1 current:1 comparing:2 activation:2 yet:1 intriguing:1 must:4 readily:1 planet:4 chicago:2 sponsored:1 mounting:1 pylyshyn:2 implying:2 intelligence:3 selected:2 item:14 along:1 expected:1 behavior:1 elm... |
5,792 | 6,240 | Mixed Linear Regression with Multiple Components
Kai Zhong 1
Prateek Jain 2
Inderjit S. Dhillon 3
2
University of Texas at Austin
Microsoft Research India
2
zhongkai@ices.utexas.edu,
prajain@microsoft.com
3
inderjit@cs.utexas.edu
1,3
1
Abstract
In this paper, we study the mixed linear regression (MLR) problem, where ... | 6240 |@word trial:3 proportion:3 seems:2 norm:5 open:2 d2:2 decomposition:2 moment:9 initial:6 liu:1 series:2 daniel:5 ours:2 interestingly:2 xinyang:2 existing:2 err:2 current:1 com:1 od:3 comparing:1 recovered:1 si:2 numerical:2 partition:3 hanie:1 kdd:1 update:1 resampling:6 v:1 greedy:2 prohibitive:1 amir:1 provide... |
5,793 | 6,241 | A Theoretically Grounded Application of Dropout in
Recurrent Neural Networks
Yarin Gal
University of Cambridge
{yg279,zg201}@cam.ac.uk
Zoubin Ghahramani
Abstract
Recurrent neural networks (RNNs) stand at the forefront of many recent developments in deep learning. Yet a major difficulty with these models is their ten... | 6241 |@word trial:1 seems:6 replicate:1 suitably:1 underperform:1 additively:1 reduction:2 inefficiency:1 series:2 score:1 qatar:1 jimenez:1 offering:1 tuned:1 outperforms:1 existing:8 past:3 current:1 comparing:2 com:1 yet:4 intriguing:1 diederik:2 gpu:4 remove:1 drop:6 plot:3 alone:8 generative:1 short:1 math:1 toron... |
5,794 | 6,242 | A primal-dual method for conic constrained
distributed optimization problems
Necdet Serhat Aybat
Department of Industrial Engineering
Penn State University
University Park, PA 16802
nsa10@psu.edu
Erfan Yazdandoost Hamedani
Department of Industrial Engineering
Penn State University
University Park, PA 16802
evy5047@ps... | 6242 |@word private:6 briefly:1 norm:3 open:1 iki:1 km:1 seek:1 decomposition:1 covariance:1 contraction:1 thereby:1 accommodate:1 initial:3 ours:1 cort:1 incidence:1 si:6 dx:8 written:3 numerical:2 synchronicity:1 partition:1 sundaram:1 selected:1 xk:55 beginning:1 stest:2 completeness:1 iterates:3 node:32 provides:1 ... |
5,795 | 6,243 | Infinite Hidden Semi-Markov Modulated Interaction
Point Process
Peng Lin?? , Bang Zhang? , Ting Guo? , Yang Wang? , Fang Chen?
Data61 CSIRO, Australian Technology Park, 13 Garden Street, Eveleigh NSW 2015, Australia
?
School of Computer Science and Engineering, The University of New South Wales, Australia
{peng.lin, ba... | 6243 |@word briefly:1 simulation:1 bn:4 simplifying:1 pg:5 nsw:1 thereby:1 initial:4 series:2 denoting:1 past:2 existing:1 outperforms:1 current:3 disaggregation:3 si:10 yet:1 tackling:3 must:1 readily:2 loglik:2 vere:1 partition:1 designed:1 update:3 depict:1 resampling:9 generative:2 intelligence:3 s0n:2 selected:1 p... |
5,796 | 6,244 | High resolution neural connectivity from incomplete
tracing data using nonnegative spline regression
Kameron Decker Harris
Applied Mathematics, U. of Washington
kamdh@uw.edu
Stefan Mihalas
Allen Institute for Brain Science
Applied Mathematics, U. of Washington
stefanm@alleninstitute.org
Eric Shea-Brown
Applied Mathe... | 6244 |@word briefly:1 version:10 wiesel:1 compression:2 norm:6 anterograde:3 proportionality:1 seek:2 sensed:1 covariance:1 decomposition:1 kerlin:1 briggman:1 necessity:2 efficacy:1 existing:1 current:3 com:2 virus:3 discretization:2 surprising:1 hohmann:1 must:1 connectomics:1 dydx:1 atlas:6 designed:3 depict:2 media... |
5,797 | 6,245 | Without-Replacement Sampling
for Stochastic Gradient Methods
Ohad Shamir
Department of Computer Science and Applied Mathematics
Weizmann Institute of Science
Rehovot, Israel
ohad.shamir@weizmann.ac.il
Abstract
Stochastic gradient methods for machine learning and optimization problems are
usually analyzed assuming data... | 6245 |@word mild:1 version:1 advantageous:1 norm:3 c0:2 dekel:1 open:2 r:2 crucially:1 sgd:1 reduction:1 prefix:1 past:2 existing:3 current:1 si:2 yet:3 readily:1 stemming:1 numerical:2 hofmann:1 cheap:2 designed:2 update:4 v:1 intelligence:2 leaf:1 instantiate:1 beginning:3 smith:1 iterates:1 draft:1 noncommutative:1 ... |
5,798 | 6,246 | Orthogonal Random Features
Felix Xinnan Yu Ananda Theertha Suresh Krzysztof Choromanski
Daniel Holtmann-Rice Sanjiv Kumar
Google Research, New York
{felixyu, theertha, kchoro, dhr, sanjivk}@google.com
Abstract
We present an intriguing discovery related to Random Fourier Features: in Gaussian
kernel approximation, repl... | 6246 |@word version:2 compression:1 polynomial:3 norm:6 open:1 d2:8 seek:1 korf:12 orf:55 decomposition:2 simulation:4 simplifying:1 thereby:1 nystr:1 reduction:2 daniel:1 interestingly:1 existing:1 com:1 wd:4 z2:10 chazelle:1 si:2 intriguing:3 written:3 must:1 john:1 sanjiv:1 additive:3 wx:2 concatenate:1 razenshteyn:... |
5,799 | 6,247 | A Minimax Approach to Supervised Learning
Farzan Farnia?
farnia@stanford.edu
David Tse?
dntse@stanford.edu
Abstract
Given a task of predicting Y from X, a loss function L, and a set of probability
distributions ? on (X, Y ), what is the optimal decision rule minimizing the worstcase expected loss over ?? In this pap... | 6247 |@word repository:1 version:10 norm:3 mezuman:1 seek:1 prasad:1 decomposition:1 jacob:1 boundedness:1 carry:1 reduction:1 moment:6 liu:1 series:2 tuned:1 interestingly:1 mmse:1 bhattacharyya:1 jaynes:1 yet:1 john:2 numerical:3 partition:2 enables:1 joy:1 v:2 intelligence:1 selected:1 amir:2 mpm:2 ith:3 eqx:2 provi... |
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