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6,200 | 6,609 | Attentional Pooling for Action Recognition
Rohit Girdhar
Deva Ramanan
The Robotics Institute, Carnegie Mellon University
http://rohitgirdhar.github.io/AttentionalPoolingAction
Abstract
We introduce a simple yet surprisingly powerful model to incorporate attention
in action recognition and human object interaction tas... | 6609 |@word kong:1 cnn:14 version:1 nd:1 everingham:1 chopping:4 tried:1 rgb:16 bn:3 forestry:2 mention:1 tr:1 harder:2 moment:1 initial:4 liu:2 contains:4 score:8 quo:1 ours:11 interestingly:4 batista:1 past:1 existing:5 current:3 contextual:2 com:1 skipping:2 guadarrama:1 yet:2 written:2 reminiscent:1 exposing:1 parm... |
6,201 | 661 | Improving Convergence in Hierarchical
Matching Networks for Object
Recognition
Joachim Utans*
Gene Gindi t
Department of Electrical Engineering
Yale University
P. O. Box 2157 Yale Station
New Haven, CT 06520
Abstract
We are interested in the use of analog neural networks for recognizing visual objects. Objects are de... | 661 |@word simulation:6 tr:1 solid:1 recursively:1 yaleu:2 initial:7 denoting:1 current:1 comparing:1 stony:2 must:8 j1:1 shape:3 designed:2 v:1 alone:1 xk:1 supplying:1 coarse:8 node:1 successive:1 simpler:1 incorrect:1 advocate:1 combine:1 expected:1 behavior:2 freeman:1 actual:2 becomes:1 estimating:1 notation:1 mat... |
6,202 | 6,610 | On the Consistency of Quick Shift
Heinrich Jiang
Google Inc.
1600 Amphitheatre Parkway, Mountain View, CA 94043
heinrich.jiang@gmail.com
Abstract
Quick Shift is a popular mode-seeking and clustering algorithm. We present finite
sample statistical consistency guarantees for Quick Shift on mode and cluster
recovery und... | 6610 |@word mild:7 version:2 stronger:2 rsl:2 bf:4 r:21 pick:1 contains:3 interestingly:2 xinyang:1 current:3 com:1 gmail:1 must:1 john:1 partition:1 kdd:1 designed:1 update:1 v:1 intelligence:4 discovering:1 epanechnikov:1 provides:1 characterization:1 simpler:1 constructed:1 become:3 prove:1 manner:1 x0:72 amphitheat... |
6,203 | 6,611 | Breaking the Nonsmooth Barrier: A Scalable Parallel
Method for Composite Optimization
Fabian Pedregosa
INRIA/ENS?
Paris, France
R?emi Leblond
INRIA/ENS?
Paris, France
Simon Lacoste-Julien
MILA and DIRO
Universit?e de Montr?eal, Canada
Abstract
Due to their simplicity and excellent performance, parallel asynchronous ... | 6611 |@word version:6 norm:3 stronger:1 c0:6 open:2 semicontinuous:1 crucially:1 hsieh:4 arti:1 eld:1 reduction:5 liu:6 contains:1 ecole:1 interestingly:1 outperforms:2 existing:5 past:1 current:2 com:1 leblond:11 si:10 yet:2 dx:1 written:4 grain:1 realistic:1 partition:2 kdd:5 cant:2 designed:1 update:29 greedy:2 sele... |
6,204 | 6,612 | Dual-Agent GANs for Photorealistic and Identity
Preserving Profile Face Synthesis
Jian Zhao1,2?? Lin Xiong3 Karlekar Jayashree3 Jianshu Li1 Fang Zhao1
Zhecan Wang4? Sugiri Pranata3 Shengmei Shen3
Shuicheng Yan1,5 Jiashi Feng1
1
3
National University of Singapore
2
National University of Defense Technology
Panason... | 6612 |@word cnn:2 version:1 middle:1 advantageous:1 proportion:1 tedious:1 annoying:1 shuicheng:1 bn:1 lpp:8 ld:2 liu:3 contains:1 tuned:1 ours:2 franklin:1 outperforms:4 existing:1 com:2 contextual:2 yet:4 parsing:1 refines:2 realistic:3 csc:1 shape:2 designed:3 interpretable:1 alone:1 generative:11 advancement:1 real... |
6,205 | 6,613 | Dilated Recurrent Neural Networks
Shiyu Chang1?, Yang Zhang1?, Wei Han2?, Mo Yu1 , Xiaoxiao Guo1 , Wei Tan1 ,
Xiaodong Cui1 , Michael Witbrock1 , Mark Hasegawa-Johnson2 , Thomas S. Huang2
1
IBM Thomas J. Watson Research Center, Yorktown, NY 10598, USA
2
University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
... | 6613 |@word cnn:26 middle:3 version:3 norm:3 bn:1 harder:1 cyclic:4 contains:2 liu:1 daniel:1 document:1 past:1 existing:2 outperforms:2 com:4 comparing:1 si:2 yet:3 activation:1 must:2 gpu:1 john:1 devin:1 timestamps:4 happen:1 informative:1 subsequent:1 remove:1 designed:1 drop:1 atlas:1 half:1 fewer:6 guess:4 genera... |
6,206 | 6,614 | Hunt For The Unique, Stable, Sparse And Fast
Feature Learning On Graphs
Saurabh Verma
Department of Computer Science
University of Minnesota, Twin Cities
verma@cs.umn.edu
Zhi-Li Zhang
Department of Computer Science
University of Minnesota, Twin Cities
zhang@cs.umn.edu
Abstract
For the purpose of learning on graphs, ... | 6614 |@word kondor:5 inversion:1 polynomial:8 norm:2 loading:1 flach:1 nd:1 open:1 sg2:1 accounting:1 decomposition:3 elisseeff:1 kutzkov:1 sgd:1 mlk:1 accommodate:1 ld:1 reduction:5 substitution:1 series:2 contains:3 tist:1 tuned:1 interestingly:1 outperforms:3 past:1 current:4 comparing:1 com:1 si:1 yet:3 must:5 mesh... |
6,207 | 6,615 | Scalable Generalized Linear Bandits:
Online Computation and Hashing
Kwang-Sung Jun
UW-Madison
kjun@discovery.wisc.edu
Aniruddha Bhargava
UW-Madison
aniruddha@wisc.edu
Robert Nowak
UW-Madison
rdnowak@wisc.edu
Rebecca Willett
UW-Madison
willett@discovery.wisc.edu
Abstract
Generalized Linear Bandits (GLBs), a natural... | 6615 |@word mild:2 trial:3 exploitation:6 version:4 ruiqi:1 polynomial:2 norm:1 katja:1 logit:6 c0:7 dekel:2 d2:2 confirms:2 jingdong:1 q1:1 ld:1 initial:1 series:1 efficacy:1 document:1 ours:2 interestingly:1 past:1 existing:14 comparing:1 contextual:9 chu:3 must:3 readily:1 written:4 bd:4 john:3 sanjiv:1 confirming:1... |
6,208 | 6,616 | Probabilistic Models for Integration Error in the
Assessment of Functional Cardiac Models
Chris. J. Oates1,5 , Steven Niederer2 , Angela Lee2 , Fran?ois-Xavier Briol3 , Mark Girolami4,5
1
Newcastle University, 2 King?s College London, 3 University of Warwick,
4
Imperial College London, 5 Alan Turing Institute
Abstract... | 6616 |@word mri:2 version:1 polynomial:3 simulation:6 crucially:1 seek:1 covariance:4 contraction:4 p0:13 pressure:2 incurs:1 tr:1 initial:5 configuration:1 series:3 precluding:1 interestingly:1 activation:3 dx:40 reminiscent:1 written:1 mesh:1 numerical:31 subsequent:1 partition:1 interpretable:1 generative:1 selected... |
6,209 | 6,617 | Machine Learning with Adversaries:
Byzantine Tolerant Gradient Descent
El Mahdi El Mhamdi?
EPFL, Switzerland
elmahdi.elmhamdi@epfl.ch
Peva Blanchard
EPFL, Switzerland
peva.blanchard@epfl.ch
Rachid Guerraoui
EPFL, Switzerland
rachid.guerraoui@epfl.ch
Julien Stainer
EPFL, Switzerland
julien.stainer@epfl.ch
Abstract
W... | 6617 |@word mild:1 kgk:7 repository:3 version:4 hampson:1 polynomial:1 stronger:2 norm:6 nd:1 bf:6 seems:1 open:2 d2:1 confirms:1 covariance:1 sgd:15 thereby:1 solid:1 reduction:2 moment:8 liu:1 score:8 lichman:1 ours:1 interestingly:2 omniscient:5 outperforms:1 spambase:6 current:2 com:1 collude:1 yet:1 devin:2 realis... |
6,210 | 6,618 | Dynamic Safe Interruptibility for Decentralized
Multi-Agent Reinforcement Learning
El Mahdi El Mhamdi
EPFL, Switzerland
elmahdi.elmhamdi@epfl.ch
Rachid Guerraoui
EPFL, Switzerland
rachid.guerraoui@epfl.ch
Hadrien Hendrikx?
?
Ecole
Polytechnique, France
hadrien.hendrikx@gmail.com
Alexandre Maurer
EPFL, Switzerland
a... | 6618 |@word achievable:1 norm:1 nd:1 c0:2 open:1 r:2 tried:1 pick:2 moment:1 initial:1 selecting:1 afraid:2 ecole:1 past:1 freitas:1 current:4 com:2 nt:3 gmail:1 yet:3 interrupted:18 realistic:2 remove:2 designed:2 update:11 alone:1 intelligence:6 greedy:8 stationary:1 item:1 short:2 provides:1 org:2 kristjan:1 wierstr... |
6,211 | 6,619 | Interactive Submodular Bandit
1
Lin Chen1,2 , Andreas Krause3 , Amin Karbasi1,2
Department of Electrical Engineering, 2 Yale Institute for Network Science, Yale University
3
Department of Computer Science, ETH Z?rich
{lin.chen, amin.karbasi}@yale.edu, krausea@ethz.ch
Abstract
In many machine learning applications, s... | 6619 |@word private:1 exploitation:1 faculty:1 polynomial:2 norm:17 laurence:1 crucially:1 decomposition:1 citeseer:1 mention:1 mcauley:1 bai:1 contains:1 selecting:5 daniel:4 rkhs:13 ours:2 document:2 outperforms:5 yajun:1 current:2 contextual:17 wd:1 com:1 beygelzimer:1 si:8 yet:1 chu:2 written:1 manuel:1 john:2 mani... |
6,212 | 662 | Mapping Between Neural and Physical
Activities of the Lobster Gastric Mill
Kenji Doya
Mary E. T. Boyle
Allen I. Selverston
Department of Biology
University of California, San Diego
La Jolla, CA 92093-0322
Abstract
A computer model of the musculoskeletal system of the lobster
gastric mill was constructed in order t... | 662 |@word neurophysiology:4 simulation:2 contraction:6 configuration:2 series:1 activation:5 must:1 entrance:1 motor:5 medial:8 update:1 pacemaker:1 nervous:5 plane:1 record:2 beauchamp:1 digestive:1 mathematical:1 constructed:2 differential:1 consists:1 combine:1 behavioral:7 inside:1 pairwise:1 behavior:1 mechanic:1... |
6,213 | 6,620 | Learning to See Physics via Visual De-animation
Jiajun Wu
MIT CSAIL
Erika Lu
University of Oxford
William T. Freeman
MIT CSAIL, Google Research
Pushmeet Kohli
DeepMind
Joshua B. Tenenbaum
MIT CSAIL
Abstract
We introduce a paradigm for understanding physical scenes without human annotations. At the core of our syst... | 6620 |@word kohli:4 inversion:1 open:1 pieter:1 simulation:24 rgb:4 decomposition:1 blender:2 jacob:1 sgd:1 bai:2 liu:1 contains:1 initial:5 jimenez:1 salzmann:2 daniel:1 ours:4 past:1 existing:2 freitas:1 recovered:2 comparing:1 current:1 attracted:1 ronald:1 realistic:2 happen:3 blur:1 ronan:1 shape:3 christian:1 rem... |
6,214 | 6,621 | Label Efficient Learning of Transferable
Representations across Domains and Tasks
Zelun Luo
Stanford University
zelunluo@stanford.edu
Yuliang Zou
Virginia Tech
ylzou@vt.edu
Judy Hoffman
University of California, Berkeley
jhoffman@eecs.berkeley.edu
Li Fei-Fei
Stanford University
feifeili@cs.stanford.edu
Abstract
We... | 6621 |@word cnn:6 compression:1 fcns:1 pieter:1 d2:9 seek:3 propagate:1 rgb:1 vicky:1 shot:11 ld:1 initial:4 liu:4 contains:1 score:7 salzmann:1 tuned:7 ours:5 document:1 outperforms:1 existing:2 current:1 guadarrama:1 nt:3 luo:2 activation:6 diederik:2 written:1 hou:1 subsequent:1 blur:1 christian:1 update:1 depict:1 ... |
6,215 | 6,622 | Decoding with Value Networks for Neural Machine
Translation
Di He1
di_he@pku.edu.cn
Tao Qin4
taoqin@microsoft.com
Hanqing Lu2
hanqinglu@cmu.edu
Liwei Wang1,5
wanglw@cis.pku.edu.cn
Yingce Xia3
xiayingc@mail.ustc.edu.cn
Tie-Yan Liu4
tie-yan.liu@microsoft.com
1
Key Laboratory of Machine Perception, MOE, School of EECS... | 6622 |@word middle:1 briefly:1 open:2 pick:1 sgd:1 liu:10 contains:2 score:21 qatar:1 ours:3 past:1 outperforms:6 current:1 com:3 contextual:1 activation:1 guez:1 attracted:1 gpu:1 concatenate:2 designed:2 plot:1 drop:3 krikun:1 short:6 provides:3 contribute:2 node:1 c2:3 constructed:1 become:1 htx:3 incorrect:1 combin... |
6,216 | 6,623 | Parametric Simplex Method for Sparse Learning
?
Haotian Pang? Robert Vanderbei? Han Liu??
Tuo Zhao?
?
?
Princeton University Tencent AI Lab Northwestern University ? Georgia Tech?
Abstract
High dimensional sparse learning has imposed a great computational challenge to
large scale data analysis. In this paper, we are... | 6623 |@word briefly:1 polynomial:1 norm:8 nd:2 d2:15 hu:2 covariance:6 ipm:2 liu:1 series:1 zij:1 tuned:1 denoting:1 suppressing:1 genetic:1 amp:1 existing:7 blank:1 comparing:1 current:1 recovered:1 si:2 written:2 must:3 bd:2 hou:1 numerical:3 partition:8 remove:1 designed:1 update:3 rd2:1 selected:2 flare:9 according... |
6,217 | 6,624 | Group Sparse Additive Machine
1
Hong Chen1 , Xiaoqian Wang1 , Cheng Deng2 , Heng Huang1?
Department of Electrical and Computer Engineering, University of Pittsburgh, USA
2
School of Electronic Engineering, Xidian University, China
chenh@mail.hzau.edu.cn,xqwang1991@gmail.com
chdeng@mail.xidian.edu.cn,heng.huang@pitt.e... | 6624 |@word mild:3 repository:2 version:1 polynomial:2 norm:12 c0:1 decomposition:2 covariance:1 pick:1 liu:2 contains:1 lichman:1 rkhs:6 outperforms:1 existing:1 current:1 com:1 gmail:1 attracted:1 written:1 john:1 additive:59 informative:2 enables:1 statis:1 half:2 selected:3 kandasamy:1 math:1 zhang:2 unbounded:1 co... |
6,218 | 6,625 | Uprooting and Rerooting Higher-Order Graphical
Models
Mark Rowland?
University of Cambridge
mr504@cam.ac.uk
Adrian Weller?
University of Cambridge and Alan Turing Institute
aw665@cam.ac.uk
Abstract
The idea of uprooting and rerooting graphical models was introduced specifically
for binary pairwise models by Weller [1... | 6625 |@word kohli:1 determinant:1 polynomial:1 stronger:4 advantageous:1 adrian:1 open:2 unif:1 barahona:1 pick:2 harder:1 kappen:1 initial:1 configuration:15 series:2 score:27 selecting:2 sherali:15 recovered:1 comparing:1 surprising:4 yet:2 forbidding:1 must:4 subsequent:2 partition:10 visible:1 plot:3 intelligence:4... |
6,219 | 6,626 | The Unreasonable Effectiveness of Structured
Random Orthogonal Embeddings
Krzysztof Choromanski ?
Google Brain Robotics
kchoro@google.com
Mark Rowland ?
University of Cambridge
mr504@cam.ac.uk
Adrian Weller
University of Cambridge and Alan Turing Institute
aw665@cam.ac.uk
Abstract
We examine a class of embeddings b... | 6626 |@word msr:4 middle:2 version:3 illustrating:1 norm:2 ruiqi:1 adrian:1 open:1 unif:6 km:3 cos2:1 vldb:1 recursively:1 reduction:12 initial:1 contains:1 interestingly:4 kx0:1 com:1 chazelle:3 si:2 yet:2 dx:2 sanjiv:1 razenshteyn:1 shape:1 analytic:1 kdd:1 moreno:1 plot:2 sundaram:2 stationary:1 half:3 fewer:1 selec... |
6,220 | 6,627 | From Parity to Preference-based Notions
of Fairness in Classification
Muhammad Bilal Zafar
MPI-SWS
mzafar@mpi-sws.org
Krishna P. Gummadi
MPI-SWS
gummadi@mpi-sws.org
Isabel Valera
MPI-IS
isabel.valera@tue.mpg.de
Manuel Gomez Rodriguez
MPI-SWS
manuelgr@mpi-sws.org
Adrian Weller
University of Cambridge & Alan Turing In... | 6627 |@word repository:1 version:1 middle:1 norm:3 sex:1 adrian:1 open:2 incurs:2 profit:3 offending:1 reduction:2 venkatasubramanian:2 contains:2 disparity:5 selecting:1 score:1 precluding:1 bilal:1 existing:4 contextual:1 com:1 manuel:1 yet:1 kdd:4 hypothesize:1 drop:2 propublica:4 discrimination:10 v:2 smith:1 core:... |
6,221 | 6,628 | Inferring Generative Model Structure
with Static Analysis
Paroma Varma1 , Bryan He2 , Payal Bajaj2 ,
Nishith Khandwala2 , Imon Banerjee3 , Daniel Rubin3,4 , Christopher R?2
1
Electrical Engineering, 2 Computer Science, 3 Biomedical Data Science, 4 Radiology
Stanford University
{paroma,bryanhe,pabajaj,nishith,imonb,rub... | 6628 |@word cnn:1 seems:1 nd:1 vldb:1 simulation:4 seek:1 programmatically:5 contrastive:2 pick:3 mention:1 configuration:1 score:5 daniel:1 tuned:3 fa8750:3 outperforms:2 existing:1 recovered:1 comparing:2 written:2 must:3 realistic:1 happen:1 distant:4 benign:1 shape:2 treating:1 interpretable:3 sponsored:1 takamatsu... |
6,222 | 6,629 | Structured Embedding Models for Grouped Data
Maja Rudolph
Columbia Univ.
maja@cs.columbia.edu
Francisco Ruiz
Univ. of Cambridge
Columbia Univ.
Susan Athey
Stanford Univ.
David Blei
Columbia Univ.
Abstract
Word embeddings are a powerful approach for analyzing language, and exponential
family embeddings (EFE) extend... | 6629 |@word proportion:2 stronger:1 open:1 uncovers:1 sgd:1 yih:1 harder:1 carry:1 reduction:1 contains:7 document:3 fa8750:1 outperforms:5 existing:1 past:1 com:1 si:4 must:1 john:1 devin:1 enables:1 remove:2 hypothesize:2 intelligence:9 discovering:1 fewer:3 item:11 generative:1 smith:1 blei:4 barkan:2 provides:3 mat... |
6,223 | 663 | A Note on Learning Vector Quantization
Virginia R. de Sa
Dana H. Ballard
Department of Computer Science
University of Rochester
Rochester, NY 14627
Department of Computer Science
University of Rochester
Rochester, NY 14627
Abstract
Vector Quantization is useful for data compression. Competitive Learning which mini... | 663 |@word middle:1 version:2 compression:2 pulse:1 seek:1 simulation:1 barney:1 initial:3 nowlan:2 assigning:1 dx:3 class1:6 xlclass:1 update:2 alit:1 draft:1 quantizer:3 revisited:1 math:1 codebook:25 zii:1 c2:1 consists:1 ra:1 expected:1 formants:1 decreasing:8 little:1 window:26 minimizes:2 classifier:1 grant:2 pos... |
6,224 | 6,630 | A Linear-Time Kernel Goodness-of-Fit Test
Wittawat Jitkrittum
Gatsby Unit, UCL
Wenkai Xu
Gatsby Unit, UCL
Zolt?n Szab??
CMAP, ?cole Polytechnique
wittawatj@gmail.com
wenkaix@gatsby.ucl.ac.uk
zoltan.szabo@polytechnique.edu
Kenji Fukumizu
The Institute of Statistical Mathematics
fukumizu@ism.ac.jp
Arthur Gretton?
... | 6630 |@word trial:2 version:1 briefly:1 eliminating:1 norm:4 stronger:1 smirnov:1 c0:4 open:2 d2:5 seek:1 simulation:3 covariance:2 zolt:2 tr:1 harder:1 initial:1 liu:1 series:1 score:1 tuned:1 rkhs:6 com:3 gmail:2 universality:1 written:1 readily:1 john:1 numerical:1 chicago:3 informative:1 wx:1 analytic:10 plot:4 int... |
6,225 | 6,631 | Cortical microcircuits as
gated-recurrent neural networks
Rui Ponte Costa?
Centre for Neural Circuits and Behaviour
Dept. of Physiology, Anatomy and Genetics
University of Oxford, Oxford, UK
rui.costa@cncb.ox.ac.uk
Yannis M. Assael?
Dept. of Computer Science
University of Oxford, Oxford, UK
and DeepMind, London, UK
y... | 6631 |@word h:1 neurophysiology:1 cox:2 version:1 middle:1 hippocampus:3 open:1 pulse:1 kappen:1 initial:1 contains:1 series:1 initialisation:1 bc:1 interestingly:1 past:1 freitas:2 existing:1 current:4 com:1 contextual:4 analysed:2 activation:4 yet:2 must:1 exposing:1 hyperpolarizing:1 subsequent:1 plasticity:13 enabl... |
6,226 | 6,632 | k-Support and Ordered Weighted Sparsity for
Overlapping Groups: Hardness and Algorithms
Cong Han Lim
University of Wisconsin-Madison
clim9@wisc.edu
Stephen J. Wright
University of Wisconsin-Madison
swright@cs.wisc.edu
Abstract
The k-support and OWL norms generalize the `1 norm, providing better prediction
accuracy a... | 6632 |@word multitask:1 trial:1 cox:2 middle:1 version:3 polynomial:1 norm:85 replicate:1 open:1 palma:1 decomposition:24 jacob:1 pick:2 harder:1 configuration:2 contains:1 efficacy:1 series:2 selecting:1 tuned:1 current:1 com:1 incidence:1 si:2 written:1 numerical:2 partition:1 plot:2 update:5 v:1 greedy:1 leaf:6 weig... |
6,227 | 6,633 | A simple model of recognition and recall memory
Nisheeth Srivastava
Computer Science, IIT Kanpur
Kanpur, UP 208016
nsrivast@cse.iitk.ac.in
Edward Vul
Dept of Psychology, UCSD
9500 Gilman Drive La Jolla CA 92093
evul@ucsd.edu
Abstract
We show that several striking differences in memory performance between recognition... | 6633 |@word trial:7 illustrating:1 inversion:1 stronger:3 approved:1 hippocampus:1 extinction:1 termination:2 additively:1 simulation:9 irb:1 attended:1 asks:1 harder:4 contains:1 exclusively:1 uncovered:1 ours:2 reaction:1 current:1 comparing:1 activation:13 assigning:1 atop:1 john:3 realistic:1 engendered:1 wanted:1 ... |
6,228 | 6,634 | On Structured Prediction Theory with Calibrated
Convex Surrogate Losses
Anton Osokin
INRIA/ENS?, Paris, France
HSE?, Moscow, Russia
Francis Bach
INRIA/ENS?, Paris, France
Simon Lacoste-Julien
MILA and DIRO
Universit? de Montr?al, Canada
Abstract
We provide novel theoretical insights on structured prediction in the ... | 6634 |@word msr:1 illustrating:1 version:1 norm:10 c0:2 simplifying:2 pick:1 sgd:1 harder:1 contains:4 score:43 selecting:1 rkhs:8 ours:1 existing:2 dx:3 attracted:1 john:2 refines:1 kpf:1 informative:2 shape:1 hofmann:2 christian:1 update:2 juditsky:1 v:1 implying:4 mackey:1 intelligence:1 xk:2 mccallum:1 smith:2 coar... |
6,229 | 6,635 | Best of Both Worlds: Transferring Knowledge from
Discriminative Learning to a Generative Visual
Dialog Model
Jiasen Lu1?, Anitha Kannan2?, Jianwei Yang1 , Devi Parikh3,1 , Dhruv Batra3,1
1
Georgia Institute of Technology, 2 Curai, 3 Facebook AI Research
{jiasenlu, jw2yang, parikh, dbatra}@gatech.edu
Abstract
We prese... | 6635 |@word cnn:3 stronger:1 norm:2 q1:1 attended:4 mengye:1 ld:3 lantao:1 liu:2 ndez:1 score:14 att:14 ours:13 bilal:1 outperforms:4 existing:1 guadarrama:2 current:5 comparing:5 anne:2 haoyuan:2 com:1 yet:1 z2:4 must:1 john:1 concatenate:1 realistic:1 informative:3 confirming:2 christian:1 enables:2 drop:1 update:4 v... |
6,230 | 6,636 | MaskRNN: Instance Level Video Object
Segmentation
Yuan-Ting Hu
UIUC
ythu2@illinois.edu
Jia-Bin Huang
Virginia Tech
jbhuang@vt.edu
Alexander G. Schwing
UIUC
aschwing@illinois.edu
Abstract
Instance level video object segmentation is an important technique for video editing
and compression. To capture the temporal coh... | 6636 |@word cnn:2 compression:2 nd:2 hu:1 shot:1 configuration:2 contains:5 denoting:1 ours:4 past:1 current:10 assigning:2 gpu:1 refines:1 subsequent:1 shape:5 hvs:1 occlude:1 stationary:1 cue:5 beginning:1 short:3 chua:1 provides:1 detecting:1 location:7 successive:1 org:1 zhang:1 height:1 along:1 brostow:1 yuan:2 co... |
6,231 | 6,637 | Gated Recurrent Convolution Neural Network for
OCR
Jianfeng Wang?
Beijing University of Posts and Telecommunications
Beijing 100876, China
jianfengwang1991@gmail.com
Xiaolin Hu
Tsinghua National Laboratory for Information Science and Technology (TNList)
Department of Computer Science and Technology
Center for Brain-Ins... | 6637 |@word neurophysiology:2 cnn:15 pw:10 wco:1 wiesel:1 bf:1 hu:4 bn:9 concise:1 tnlist:1 bai:2 configuration:4 contains:6 score:1 hereafter:1 liu:3 ours:1 document:2 outperforms:3 existing:2 past:2 blank:2 com:2 comparing:1 mishra:4 sosa:1 babenko:1 gmail:1 written:2 parsing:1 explorative:1 realistic:1 alphanumeric:... |
6,232 | 6,638 | Towards Accurate Binary Convolutional Neural
Network
Xiaofan Lin
Cong Zhao
Wei Pan*
DJI Innovations Inc, Shenzhen, China
{xiaofan.lin, cong.zhao, wei.pan}@dji.com
Abstract
We introduce a novel scheme to train binary convolutional neural networks (CNNs)
? CNNs with weights and activations constrained to {-1,+1} at run-... | 6638 |@word cnn:6 version:1 seems:2 instruction:1 propagate:1 bn:2 teich:1 citeseer:1 reduction:1 electronics:1 configuration:3 contains:3 liu:2 tuned:1 bitwise:12 com:1 luo:1 activation:73 must:2 numerical:1 drop:3 update:1 half:1 leaf:1 device:2 short:1 quantized:6 node:1 zhang:1 five:1 height:1 along:1 symposium:2 i... |
6,233 | 6,639 | Semi-Supervised Learning for Optical Flow
with Generative Adversarial Networks
Wei-Sheng Lai1
Jia-Bin Huang2
Ming-Hsuan Yang1,3
2
3
University of California, Merced
Virginia Tech
Nvidia Research
1
2
{wlai24|mhyang}@ucmerced.edu
jbhuang@vt.edu
1
Abstract
Convolutional neural networks (CNNs) have recently been applied ... | 6639 |@word cnn:11 fcns:1 open:1 brightness:26 inpainting:2 tr:1 ld:2 initial:1 minmax:1 series:1 score:2 liu:1 disparity:1 tuned:3 ours:2 outperforms:3 existing:4 current:1 activation:1 refines:1 realistic:3 blur:1 enables:1 update:5 generative:16 intelligence:2 advancement:1 provides:1 zhang:2 five:1 qualitative:1 ij... |
6,234 | 664 | Forecasting Demand for Electric Power
Jen-Lun Yuan and Terrence L. Fine
School of Electrical Engineering
Cornell University
Ithaca, NY 14853
Abstract
We are developing a forecaster for daily extremes of demand for
electric power encountered in the service area of a large midwestern utility and using this application ... | 664 |@word economically:1 version:2 achievable:1 termination:1 forecaster:2 decomposition:1 covariance:3 weekday:1 thereby:2 reduction:6 initial:3 series:1 denoting:1 past:1 current:4 scatter:3 additive:1 numerical:1 plot:3 atlas:1 v:3 fewer:1 devising:1 plane:1 short:4 node:6 monday:8 mathematical:1 along:1 constructe... |
6,235 | 6,640 | Learning a Multi-View Stereo Machine
Abhishek Kar
UC Berkeley
akar@berkeley.edu
Christian H?ne
UC Berkeley
chaene@berkeley.edu
Jitendra Malik
UC Berkeley
malik@berkeley.edu
Abstract
We present a learnt system for multi-view stereopsis. In contrast to recent learning
based methods for 3D reconstruction, we leverage ... | 6640 |@word cnn:6 version:2 repository:1 replicate:1 choy:2 seitz:2 shading:2 reduction:2 bai:1 liu:1 disparity:9 score:1 ours:1 current:1 comparing:1 yet:2 conforming:2 gpu:2 mesh:2 subsequent:1 realistic:1 visible:1 shape:23 christian:2 enables:1 visibility:2 designed:2 drop:1 depict:1 progressively:2 cue:13 fewer:4 ... |
6,236 | 6,641 | Phase Transitions in the Pooled Data Problem
Jonathan Scarlett and Volkan Cevher
Laboratory for Information and Inference Systems (LIONS)
?cole Polytechnique F?d?rale de Lausanne (EPFL)
{jonathan.scarlett,volkan.cevher}@epfl.ch
Abstract
In this paper, we study the pooled data problem of identifying the labels associat... | 6641 |@word mild:1 version:1 polynomial:1 proportion:4 norm:1 nd:10 open:2 seek:1 pg:6 initial:1 contains:2 series:1 selecting:1 denoting:1 existing:5 nt:9 jaynes:1 must:1 readily:2 written:1 john:1 item:19 accordingly:2 vanishing:1 volkan:2 characterization:3 provides:1 math:1 allerton:1 org:3 simpler:1 unbounded:3 ma... |
6,237 | 6,642 | Universal Style Transfer via Feature Transforms
Yijun Li
UC Merced
yli62@ucmerced.edu
Zhaowen Wang
Adobe Research
zhawang@adobe.com
Chen Fang
Adobe Research
cfang@adobe.com
Xin Lu
Adobe Research
xinl@adobe.com
Jimei Yang
Adobe Research
jimyang@adobe.com
Ming-Hsuan Yang
UC Merced, NVIDIA Research
mhyang@ucmerced.edu
... | 6642 |@word h:2 middle:2 inversion:1 advantageous:1 kokkinos:1 nd:1 open:1 rgb:2 covariance:12 decomposition:2 jacob:1 thereby:1 shot:1 accommodate:1 carry:3 shechtman:2 inefficiency:1 tuned:1 ours:4 subjective:1 existing:7 com:5 yet:2 gpu:1 shape:1 enables:2 remove:1 designed:2 generative:1 accordingly:1 coarse:6 pref... |
6,238 | 6,643 | On the Model Shrinkage Effect of
Gamma Process Edge Partition Models
Iku Ohama??
Issei Sato?
Takuya Kida?
Hiroki Arimura?
?
?
?
Panasonic Corp., Japan The Univ. of Tokyo, Japan Hokkaido Univ., Japan
ohama.iku@jp.panasonic.com sato@k.u-tokyo.ac.jp {kida,arim}@ist.hokudai.ac.jp
Abstract
The edge partition model (EPM) is... | 6643 |@word version:2 stronger:1 c0:20 hu:1 takuya:1 liu:1 score:1 hereafter:1 com:1 analysed:2 written:1 additive:2 partition:9 j1:1 shape:1 designed:2 update:2 generative:13 discovering:2 selected:1 parameterization:1 geyer:1 yamada:1 colored:1 blei:2 org:3 five:2 c2:4 constructed:1 beta:2 issei:1 introduce:1 manner:... |
6,239 | 6,644 | Pose Guided Person Image Generation
Liqian Ma1
Xu Jia2? Qianru Sun3? Bernt Schiele3 Tinne Tuytelaars2 Luc Van Gool1,4
KU-Leuven/PSI, TRACE (Toyota Res in Europe) 2 KU-Leuven/PSI, IMEC
3
Max Planck Institute for Informatics, Saarland Informatics Campus
4
ETH Zurich
{liqian.ma, xu.jia, tinne.tuytelaars, luc.vangool}@es... | 6644 |@word trial:1 pieter:1 propagate:1 tenka:1 jingdong:1 ld:1 initial:12 liu:2 contains:1 score:6 jimenez:1 daniel:1 denoting:1 ours:3 interestingly:1 outperforms:1 freitas:1 cvae:1 comparing:1 luo:1 diederik:2 connectomics:1 john:2 refines:1 realistic:6 concatenate:3 distant:1 numerical:1 shape:1 uria:1 motor:1 vis... |
6,240 | 6,645 | Inference in Graphical Models
via Semidefinite Programming Hierarchies
Murat A. Erdogdu
Microsoft Research
erdogdu@cs.toronto.edu
Yash Deshpande
MIT and Microsoft Research
yash@mit.edu
Andrea Montanari
Stanford University
montanari@stanford.edu
Abstract
Maximum A posteriori Probability (MAP) inference in graphical m... | 6645 |@word briefly:1 version:2 polynomial:3 norm:1 nd:1 open:2 barahona:3 adrian:1 accounting:2 contraction:2 recursively:1 carry:2 moment:1 reduction:4 contains:1 sherali:4 shum:1 denoting:3 outperforms:3 ka:4 surprising:1 si:1 assigning:1 danny:1 written:5 numerical:4 partition:1 plot:5 update:7 intelligence:2 fewer... |
6,241 | 6,646 | Variable Importance using Decision Trees
Jalil Kazemitabar
UCLA
sjalilk@ucla.edu
Arash A. Amini
UCLA
aaamini@ucla.edu
Adam Bloniarz
UC Berkeley?
adam@stat.berkeley.edu
Ameet Talwalkar
CMU
talwalkar@cmu.edu
Abstract
Decision trees and random forests are well established models that not only offer
good predictive pe... | 6646 |@word mild:1 trial:1 version:8 norm:4 unif:8 simulation:7 r:1 simplifying:1 covariance:1 boundedness:1 reduction:13 liu:1 contains:1 score:7 series:3 ours:1 existing:2 xnj:1 current:1 comparing:1 recovered:1 si:12 yet:1 additive:17 realistic:2 thrust:1 numerical:1 plot:3 greedy:4 leaf:1 selected:2 generative:3 ha... |
6,242 | 6,647 | Preventing Gradient Explosions
in Gated Recurrent Units
Sekitoshi Kanai, Yasuhiro Fujiwara, Sotetsu Iwamura
NTT Software Innovation Center
3-9-11, Midori-cho, Musashino-shi, Tokyo
{kanai.sekitoshi, fujiwara.yasuhiro, iwamura.sotetsu}@lab.ntt.co.jp
Abstract
A gated recurrent unit (GRU) is a successful recurrent neural ... | 6647 |@word trial:1 briefly:1 norm:20 bptt:3 heuristically:1 linearized:2 decomposition:2 jingdong:1 prokhorov:2 pg:1 sgd:10 initial:4 series:4 bppt:1 tuned:1 kurt:2 past:8 outperforms:2 comparing:1 manuel:1 activation:3 diederik:1 must:1 designed:1 update:7 midori:1 polyphonic:5 bart:2 selected:1 vanishing:4 short:4 p... |
6,243 | 6,648 | On the Power of Truncated SVD for General
High-rank Matrix Estimation Problems
Simon S. Du
Carnegie Mellon University
ssdu@cs.cmu.edu
Yining Wang
Carnegie Mellon University
yiningwa@cs.cmu.edu
Aarti Singh
Carnegie Mellon University
aartisingh@cmu.edu
Abstract
? that is close to a general high-rank positive semiWe sh... | 6648 |@word mild:1 private:1 version:1 rising:1 polynomial:5 norm:41 nd:2 r:3 decomposition:8 covariance:21 arti:1 eld:1 asks:1 mention:1 tr:3 nystr:1 liu:2 xinyang:1 existing:7 comparing:1 luo:3 yet:2 attracted:1 must:1 additive:4 cant:1 remove:1 mackey:2 intelligence:1 cult:1 isotropic:1 contribute:1 simpler:1 zhang:... |
6,244 | 6,649 | f -GANs in an Information Geometric Nutshell
Richard Nock?,?,?
Zac Cranko?,?
Aditya Krishna Menon?,?
?,?
Lizhen Qu
Robert C. Williamson?,?
?
?
Data61, the Australian National University and ? the University of Sydney
{firstname.lastname, aditya.menon, bob.williamson}@data61.csiro.au
Abstract
Nowozin et al showed last... | 6649 |@word deformed:10 mild:3 version:2 briefly:2 nd:1 suitably:1 open:2 d2:2 simplifying:1 pick:2 naudts:1 carry:1 moment:1 initial:1 liu:1 series:2 exclusively:1 denoting:2 document:1 current:2 com:1 activation:28 yet:1 tackling:1 distant:1 happen:1 shape:1 drop:1 plot:1 v:3 generative:8 guess:1 warmuth:1 ith:1 shor... |
6,245 | 665 | Generalization Abilities of
Cascade Network Architectures
E. Littmann*
H. Ritter
Department of Information Science
Bielefeld University
D-4800 Bielefeld, FRG
littmann@techfak.uni-bielefeld.de
Department of Information Science
Bielefeld University
D-4800 Bielefeld, FRG
helge@techfak.uni- bielefeld.de
Abstract
In [5]... | 665 |@word inversion:1 covariance:2 paid:1 tr:1 series:7 lapedes:2 meyering:1 comparing:1 must:2 mackey:4 iso:4 node:28 ron:1 differential:2 symposium:1 edelman:1 expected:1 roughly:1 little:1 increasing:1 becomes:2 provided:1 matched:2 kaufman:1 rm:1 control:1 unit:12 grant:1 local:3 limit:1 severely:1 consequence:1 i... |
6,246 | 6,650 | Toward Multimodal Image-to-Image Translation
Jun-Yan Zhu
UC Berkeley
Trevor Darrell
UC Berkeley
Richard Zhang
UC Berkeley
Alexei A. Efros
UC Berkeley
Oliver Wang
Adobe Research
Deepak Pathak
UC Berkeley
Eli Shechtman
Adobe Research
Abstract
Many image-to-image translation problems are ambiguous, as a single input ... | 6650 |@word cnn:1 middle:1 version:3 inversion:1 unpopulated:1 tried:1 propagate:2 asks:1 inpainting:2 shechtman:3 contains:2 score:10 document:1 existing:2 cvae:23 written:1 subsequent:1 concatenate:1 realistic:14 confirming:1 shape:2 interpretable:1 v:3 generative:22 instantiate:1 website:2 intelligence:1 parameteriz... |
6,247 | 6,651 | Mixture-Rank Matrix Approximation
for Collaborative Filtering
Dongsheng Li1
Chao Chen1
Wei Liu2? Tun Lu3,4
Ning Gu3,4
Stephen M. Chu1
1
IBM Research - China
2
Tencent AI Lab, China
3
School of Computer Science, Fudan University, China
4
Shanghai Key Laboratory of Data Science, Fudan University, China
{ldsli, cs... | 6651 |@word private:1 norm:3 km:6 confirms:4 initial:5 contains:1 score:1 series:1 outperforms:4 existing:2 com:1 contextual:1 toh:1 yet:1 chu:2 shakespeare:1 kdd:2 remove:1 update:1 v:1 alone:2 intelligence:2 mackey:1 weighing:1 item:63 accordingly:1 isotropic:1 prize:1 dear:1 boosting:1 location:2 club:1 toronto:1 si... |
6,248 | 6,652 | Non-monotone Continuous DR-submodular
Maximization: Structure and Algorithms
An Bian
ETH Zurich
ybian@inf.ethz.ch
Kfir Y. Levy
ETH Zurich
yehuda.levy@inf.ethz.ch
Andreas Krause
ETH Zurich
krausea@ethz.ch
Joachim M. Buhmann
ETH Zurich
jbuhmann@inf.ethz.ch
Abstract
DR-submodular continuous functions are important obj... | 6652 |@word mild:1 shayan:1 briefly:1 polynomial:5 norm:1 stronger:2 nd:1 laurence:1 open:1 bachman:2 x2p:1 tr:1 ld:2 reduction:4 initial:1 zij:2 daniel:1 document:3 past:1 existing:2 current:1 com:2 yet:3 attracted:1 luis:1 determinantal:4 john:1 partition:1 enables:6 plot:1 update:2 stationary:15 greedy:2 selected:1 ... |
6,249 | 6,653 | Learning with Average Top-k Loss
Yanbo Fan3,4,1 , Siwei Lyu1?, Yiming Ying2 , Bao-Gang Hu3,4
1
Department of Computer Science, University at Albany, SUNY
2
Department of Mathematics and Statistics, University at Albany, SUNY
3
National Laboratory of Pattern Recognition, CASIA
4
University of Chinese Academy of Science... | 6653 |@word madelon:1 repository:1 norm:2 proportion:1 calculus:1 hu:3 wexler:1 accommodate:2 liu:1 tist:1 rkhs:6 spambase:1 existing:2 outperforms:1 yet:2 must:1 subsequent:1 plot:5 fund:1 v:4 intelligence:2 selected:1 rudin:1 monk:5 provides:3 uca:2 completeness:1 dn:1 direct:1 shorthand:1 combine:3 introduce:3 pairw... |
6,250 | 6,654 | Learning multiple visual domains with residual
adapters
Sylvestre-Alvise Rebuffi1
Hakan Bilen1,2
1
Visual Geometry Group
University of Oxford
{srebuffi,hbilen,vedaldi}@robots.ox.ac.uk
Andrea Vedaldi1
2
School of Informatics
University of Edinburgh
Abstract
There is a growing interest in learning data representa... | 6654 |@word aircraft:6 multitask:2 cnn:1 kokkinos:2 rgb:1 bn:11 decomposition:3 shot:2 reduction:2 initial:1 configuration:2 contains:9 liu:3 selecting:1 score:10 hoiem:1 tuned:3 document:1 outperforms:3 existing:1 current:1 wd:1 luo:1 dx:3 reminiscent:1 must:1 written:1 john:1 gavves:1 realistic:1 designed:1 drop:3 v:... |
6,251 | 6,655 | Dykstra?s Algorithm, ADMM, and Coordinate
Descent: Connections, Insights, and Extensions
Ryan J. Tibshirani
Department of Statistics and Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA 15213
ryantibs@stat.cmu.edu
Abstract
We study connections between Dykstra?s algorithm for projecting onto an in... | 6655 |@word mild:1 version:18 seems:4 replicate:1 stronger:1 c0:3 adrian:1 confirms:1 seek:2 decomposition:4 jacob:1 cyclic:5 liu:1 series:2 daniel:2 denoting:1 interestingly:1 reinvented:1 existing:3 comparing:1 luo:6 surprising:1 rpi:14 chu:1 must:1 john:2 realize:1 stemming:1 numerical:6 subsequent:1 additive:1 wenj... |
6,252 | 6,656 | Learning Spherical Convolution
for Fast Features from 360? Imagery
Yu-Chuan Su
Kristen Grauman
The University of Texas at Austin
Abstract
While 360? cameras offer tremendous new possibilities in vision, graphics, and
augmented reality, the spherical images they produce make core feature extraction non-trivial. Convol... | 6656 |@word multitask:1 cnn:16 version:2 rising:1 compression:2 replicate:4 everingham:1 hu:2 zelnik:1 azimuthal:1 rgb:2 photographer:1 liu:1 offering:1 ours:1 interestingly:1 document:1 outperforms:5 existing:13 current:2 com:3 comparing:1 guadarrama:1 yet:1 must:1 subsequent:1 romero:1 shape:8 analytic:2 remove:2 plo... |
6,253 | 6,657 | MarrNet: 3D Shape Reconstruction via 2.5D Sketches
Jiajun Wu*
MIT CSAIL
Yifan Wang*
ShanghaiTech University
Tianfan Xue
MIT CSAIL
William T. Freeman
MIT CSAIL, Google Research
Xingyuan Sun
Shanghai Jiao Tong University
Joshua B. Tenenbaum
MIT CSAIL
Abstract
3D object reconstruction from a single image is a highl... | 6657 |@word mild:1 kohli:2 repository:2 middle:1 choy:5 rgb:11 decomposition:1 jacob:1 sgd:1 harder:1 shading:4 contains:3 hoiem:4 jimenez:1 tuned:7 past:2 recovered:2 cad:1 yet:1 dx:10 gpu:1 parsing:1 john:1 realistic:4 happen:1 informative:1 ronan:1 shape:97 ashutosh:1 v:1 generative:3 amir:1 plane:1 davison:2 provid... |
6,254 | 6,658 | Multimodal Learning and Reasoning for Visual
Question Answering
Ilija Ilievski
Integrative Sciences and Engineering
National University of Singapore
ilija.ilievski@u.nus.edu
Jiashi Feng
Electrical and Computer Engineering
National University of Singapore
elefjia@nus.edu.sg
Abstract
Reasoning about entities and their... | 6658 |@word multitask:2 cnn:5 version:2 hu:3 integrative:1 shuicheng:1 jacob:4 contains:4 score:5 hoiem:1 outperforms:1 existing:1 current:4 com:4 haoyuan:1 activation:1 diederik:1 must:1 parsing:1 john:1 ronan:2 enables:3 christian:1 remove:1 designed:1 interpretable:1 intelligence:4 item:1 kyoung:1 short:4 num:4 ment... |
6,255 | 6,659 | Adversarial Surrogate Losses for Ordinal Regression
Rizal Fathony
Mohammad Bashiri
Brian D. Ziebart
Department of Computer Science
University of Illinois at Chicago
Chicago, IL 60607
{rfatho2, mbashi4, bziebart}@uic.edu
Abstract
Ordinal regression seeks class label predictions when the penalty incurred for
mistakes ... | 6659 |@word repository:2 version:2 nd:1 c0:2 triazine:4 seek:4 moment:1 reduction:8 liu:5 contains:3 series:1 lichman:1 undiscovered:1 existing:4 jaynes:1 anqi:2 chu:4 must:1 written:2 joaquim:1 john:1 indistinguishably:2 chicago:2 distant:1 partition:1 shape:1 enables:2 hofmann:1 remove:1 plot:1 intelligence:4 selecte... |
6,256 | 666 | Transient Signal Detection with Neural Networks:
The Search for the Desired Signal
Jose C. Principe and Abir Zahalka
Computational NeuroEngineering Laboratory
Department of Electrical Engineering
University of Florida, CSE 447
Gainesville, FL 32611
principe@synapse.ee.ufl.edu
Abstract
Matched filtering has been one of... | 666 |@word seems:2 gainesville:1 configuration:4 outperforms:1 must:1 readily:2 happen:1 shape:8 analytic:1 extrapolating:1 discrimination:1 stationary:1 fewer:2 selected:1 short:1 lr:1 compo:1 detecting:1 node:5 cse:1 five:1 ladendorf:1 constructed:1 supply:1 consists:3 symp:1 olfactory:2 theoretically:1 nor:1 brain:2... |
6,257 | 6,660 | Hypothesis Transfer Learning via
Transformation Functions
Simon S. Du
Carnegie Mellon University
ssdu@cs.cmu.edu
Jayanth Koushik
Carnegie Mellon University
jayanthkoushik@cmu.edu
Barnab?s P?czos
Carnegie Mellon University
bapoczos@cs.cmu.edu
Aarti Singh
Carnegie Mellon University
aartisingh@cmu.edu
Abstract
We consi... | 6660 |@word neurophysiology:1 trial:3 middle:1 norm:3 proportion:1 nd:1 elisseeff:6 arti:1 shot:1 liu:2 contains:1 rkhs:3 ours:1 fa8750:1 outperforms:1 existing:3 reaction:3 scovel:1 written:1 john:2 sanjiv:1 happen:1 enables:1 krikamol:1 plot:5 stroop:3 medial:1 intelligence:3 greedy:1 cult:1 awg:9 toronto:1 simpler:3... |
6,258 | 6,661 | Controllable Invariance
through Adversarial Feature Learning
Qizhe Xie, Zihang Dai, Yulun Du, Eduard Hovy, Graham Neubig
Language Technologies Institute
Carnegie Mellon University
{qizhex, dzihang, yulund, hovy, gneubig}@cs.cmu.edu
Abstract
Learning meaningful representations that maintain the content necessary for a... | 6661 |@word cnn:1 repository:1 eliminating:3 open:2 pieter:1 seek:1 dramatic:1 shot:1 moment:3 contains:1 score:2 jimenez:1 denoting:1 ours:10 document:1 subword:2 fa8750:1 outperforms:2 existing:1 com:1 comparing:1 diederik:2 dx:3 attracted:1 john:1 enables:1 christian:2 remove:3 drop:1 interpretable:1 stationary:1 ge... |
6,259 | 6,662 | Convergence Analysis of Two-layer Neural Networks
with ReLU Activation
Yuanzhi Li
Computer Science Department
Princeton University
yuanzhil@cs.princeton.edu
Yang Yuan
Computer Science Department
Cornell University
yangyuan@cs.cornell.edu
Abstract
In recent years, stochastic gradient descent (SGD) based techniques has... | 6662 |@word trial:2 version:1 eliminating:1 polynomial:6 seems:1 norm:11 open:2 d2:1 simulation:2 tried:1 decomposition:2 sgd:26 solid:1 arous:2 initial:6 contains:2 kurt:1 err:1 comparing:1 activation:18 diederik:1 john:1 hanie:2 plot:1 update:2 v:1 alone:1 pascanu:3 node:1 hyperplanes:1 sigmoidal:3 org:1 zhang:4 beco... |
6,260 | 6,663 | Doubly Accelerated
Stochastic Variance Reduced Dual Averaging Method
for Regularized Empirical Risk Minimization
Tomoya Murata
NTT DATA Mathematical Systems Inc. , Tokyo, Japan
murata@msi.co.jp
Taiji Suzuki
Department of Mathematical Informatics
Graduate School of Information Science and Technology, The University of ... | 6663 |@word version:1 briefly:1 middle:2 norm:1 seems:1 nd:2 pg:16 unstably:1 pick:2 sgd:5 reduction:10 initial:3 tuned:3 past:1 existing:1 current:2 numerical:4 enables:2 update:3 intelligence:2 xk:13 steepest:1 core:1 zhang:4 mathematical:4 direct:6 become:1 symposium:1 ik:3 yuan:1 prove:1 doubly:3 nlog2:1 introducto... |
6,261 | 6,664 | Langevin Dynamics with Continuous Tempering for
Training Deep Neural Networks
Nanyang Ye
University of Cambridge
Cambridge, United Kingdom
yn272@cam.ac.uk
Zhanxing Zhu
Center for Data Science, Peking University
Beijing Institute of Big Data Research (BIBDR)
Beijing, China
zhanxing.zhu@pku.edu.cn
Rafal K.Mantiuk
Unive... | 6664 |@word polynomial:3 simulation:3 crucially:1 covariance:1 sgd:15 arous:1 configuration:3 united:2 existing:3 current:1 comparing:1 activation:2 numerical:3 sdes:5 designed:2 plot:1 update:4 stationary:6 greedy:1 selected:1 accordingly:1 trapping:4 hamiltonian:5 prize:1 provides:1 completeness:1 pascanu:1 firstly:1... |
6,262 | 6,665 | Efficient Online Linear Optimization
with Approximation Algorithms
Dan Garber
Technion - Israel Institute of Technology
dangar@technion.ac.il
Abstract
We revisit the problem of online linear optimization in case the set of feasible actions is accessible through an approximated linear optimization oracle with a factor
... | 6665 |@word briefly:1 version:4 achievable:1 polynomial:1 seems:3 norm:1 nd:1 d2:6 decomposition:4 jacob:1 q1:2 incurs:3 reduction:2 celebrated:3 etric:1 document:1 current:3 luo:1 yet:1 kft:2 readily:1 must:1 ligett:2 update:1 greedy:1 selected:1 prohibitive:2 instantiate:1 kyk:1 item:1 plane:1 xk:1 vanishing:1 comple... |
6,263 | 6,666 | Geometric Descent Method for
Convex Composite Minimization
Shixiang Chen1 , Shiqian Ma2 , and Wei Liu3
1
Department of SEEM, The Chinese University of Hong Kong, Hong Kong
2
Department of Mathematics, UC Davis, USA
3
Tencent AI Lab, China
Abstract
In this paper, we extend the geometric descent method recently propose... | 6666 |@word kong:2 repository:1 briefly:2 version:2 middle:2 norm:3 stronger:1 c0:3 dekker:4 open:1 tr:4 reduction:1 bai:1 initial:7 contains:2 series:1 past:3 comparing:2 tackling:1 written:1 numerical:9 implying:1 selected:1 xk:77 vanishing:1 ck2:1 successive:1 mathematical:2 differential:1 prove:3 introductory:1 int... |
6,264 | 6,667 | Diffusion Approximations for Online Principal
Component Estimation and Global Convergence
Chris Junchi Li
Mengdi Wang
Princeton University
Department of Operations Research and Financial Engineering, Princeton, NJ 08544
{junchil,mengdiw}@princeton.edu
Tong Zhang
Tencent AI Lab
Shennan Ave, Nanshan District, Shenzhen, ... | 6667 |@word version:1 briefly:1 polynomial:1 norm:1 stronger:1 nd:3 d2:1 simulation:3 covariance:7 decomposition:6 sgd:4 thereby:1 tr:3 initial:8 liu:2 denoting:1 existing:2 written:1 john:1 analytic:1 plot:2 drop:1 update:1 clumping:1 stationary:8 xk:1 beginning:1 short:1 blei:1 provides:3 characterization:6 iterates:... |
6,265 | 6,668 | Avoiding Discrimination through Causal Reasoning
Niki Kilbertus??
nkilbertus@tue.mpg.de
Moritz Hardt?
hardt@berkeley.edu
Mateo Rojas-Carulla??
mrojas@tue.mpg.de
Dominik Janzing?
janzing@tue.mpg.de
Giambattista Parascandolo??
gparascandolo@tue.mpg.de
Bernhard Sch?olkopf?
bs@tue.mpg.de
?
Max Planck Institute for Int... | 6668 |@word trial:1 middle:1 version:2 manageable:1 judgement:1 instrumental:2 justice:2 tedious:1 sex:1 calculus:1 willing:1 seek:1 p0:10 asks:1 recursively:1 venkatasubramanian:2 substitution:1 score:4 exclusively:1 sendhil:1 envision:1 bilal:3 past:1 existing:4 mishra:1 comparing:2 qureshi:1 manuel:2 yet:2 assigning... |
6,266 | 6,669 | Nonparametric Online Regression
while Learning the Metric
Ilja Kuzborskij
EPFL
Switzerland
ilja.kuzborskij@gmail.com
Nicol`o Cesa-Bianchi
Dipartimento di Informatica
Universit`a degli Studi di Milano
Milano 20135, Italy
nicolo.cesa-bianchi@unimi.it
Abstract
We study algorithms for online nonparametric regression tha... | 6669 |@word mild:2 determinant:3 version:1 norm:5 km:7 bn:1 covariance:1 pick:1 incurs:2 versatile:1 initial:1 contains:1 rkhs:1 past:2 current:3 com:1 gmail:1 bd:1 must:1 subsequent:1 gerchinovitz:2 update:2 discrimination:1 intelligence:1 beginning:1 core:1 provides:1 simpler:1 outerproduct:2 mathematical:1 along:5 c... |
6,267 | 667 | Deriving Receptive Fields Using An
Optimal Encoding Criterion
Ralph Linsker
IBM T. J. Watson Research Center
P. O. Box 218, Yorktown Heights, NY 10598
Abstract
An information-theoretic optimization principle ('infomax') has
previously been used for unsupervised learning of statistical regularities in an input ensembl... | 667 |@word version:1 nd:1 heuristically:2 seek:1 covariance:12 tr:1 carry:1 substitution:1 contains:2 si:3 yet:1 perturbative:1 fn:1 numerical:1 remove:1 plot:3 progressively:1 aside:1 v:5 fewer:2 accordingly:1 provides:1 node:16 location:1 height:1 mathematical:1 along:1 pairwise:3 expected:1 indeed:2 rapid:1 roughly:... |
6,268 | 6,670 | Recycling Privileged Learning
and Distribution Matching for Fairness
Novi Quadrianto?
Predictive Analytics Lab (PAL)
University of Sussex
Brighton, United Kingdom
n.quadrianto@sussex.ac.uk
Viktoriia Sharmanska
Department of Computing
Imperial College London
London, United Kingdom
sharmanska.v@gmail.com
Abstract
Equi... | 6670 |@word compression:2 polynomial:1 fairer:2 p0:2 harder:1 reduction:4 initial:1 venkatasubramanian:2 ndez:3 score:1 united:2 moment:1 daniel:2 contains:1 rkhs:2 sendhil:1 genetic:2 ours:2 bilal:3 existing:6 current:2 comparing:1 contextual:1 manuel:2 com:1 yet:1 gmail:1 must:4 gpu:1 universality:1 john:3 additive:3... |
6,269 | 6,671 | Safe and Nested Subgame Solving for
Imperfect-Information Games
Noam Brown
Computer Science Department
Carnegie Mellon University
Pittsburgh, PA 15217
noamb@cs.cmu.edu
Tuomas Sandholm
Computer Science Department
Carnegie Mellon University
Pittsburgh, PA 15217
sandholm@cs.cmu.edu
Abstract
In imperfect-information gam... | 6671 |@word version:4 proportion:2 szafron:2 vi1:2 stronger:1 tried:1 decomposition:3 pick:1 thereby:2 reduction:2 initial:4 contains:1 past:6 outperforms:1 current:1 guez:1 must:10 john:1 subsequent:2 christian:3 sponsored:1 v:1 alone:1 intelligence:15 guess:6 warmuth:1 libratus:2 prize:1 aja:1 consulting:1 node:41 ma... |
6,270 | 6,672 | Unsupervised Image-to-Image Translation Networks
Ming-Yu Liu,
Thomas Breuel, Jan Kautz
NVIDIA
{mingyul,tbreuel,jkautz}@nvidia.com
Abstract
Unsupervised image-to-image translation aims at learning a joint distribution of
images in different domains by using images from the marginal distributions in
individual domains.... | 6672 |@word trial:1 version:2 nd:1 cha:1 d2:15 eng:1 q1:14 shot:1 harder:3 liu:3 configuration:2 existing:1 current:1 com:3 z2:18 luo:1 realize:1 realistic:4 designed:1 update:3 alone:1 generative:13 gan2:3 bissacco:1 zhang:1 wierstra:1 qualitative:1 consists:3 lopez:1 mingyuliutw:2 wild:1 ex2:1 aitken:1 cheetah:2 mult... |
6,271 | 6,673 | Coded Distributed Computing for Inverse Problems
Yaoqing Yang, Pulkit Grover and Soummya Kar
Carnegie Mellon University
{yyaoqing, pgrover, soummyak}@andrew.cmu.edu
Abstract
Computationally intensive distributed and parallel computing is often bottlenecked
by a small set of slow workers known as stragglers. In this p... | 6673 |@word version:3 inversion:1 polynomial:1 norm:2 replicate:4 nd:1 vi1:1 vldb:2 covariance:4 pick:2 carry:1 mcauley:1 ld:5 liu:2 initial:7 outperforms:1 existing:4 current:1 comparing:1 si:2 assigning:1 yet:1 written:2 fn:2 additive:1 j1:3 designed:2 selected:1 xk:1 short:2 iterates:1 provides:2 node:4 preference:1... |
6,272 | 6,674 | A Screening Rule for `1-Regularized
Ising Model Estimation
Zhaobin Kuang1 , Sinong Geng2 , David Page3
University of Wisconsin
zkuang@wisc.edu1 , sgeng2@wisc.edu2 , page@biostat.wisc.edu3
Abstract
We discover a screening rule for `1 -regularized Ising model estimation. The simple
closed-form screening rule is a neces... | 6674 |@word trial:4 rising:1 polynomial:1 stronger:1 eliminating:1 nd:5 twelfth:1 checkable:1 prominence:1 covariance:8 hsieh:2 attainable:1 pick:1 contrastive:1 moment:10 necessity:1 liu:17 contains:1 safeguarded:1 karger:2 initial:1 configuration:1 series:3 current:1 comparing:1 luo:2 yet:1 readily:2 stemming:1 parti... |
6,273 | 6,675 | Improved Dynamic Regret for Non-degenerate
Functions
Lijun Zhang? , Tianbao Yang? , Jinfeng Yi? , Rong Jin? , Zhi-Hua Zhou?
National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
?
Department of Computer Science, The University of Iowa, Iowa City, USA
?
AI Foundations Lab, IBM Thomas ... | 6675 |@word polynomial:1 achievable:1 norm:1 nd:1 open:2 gradual:1 bellevue:1 incurs:1 past:1 current:3 com:2 z2:1 dikin:1 must:1 realize:1 update:4 stationary:2 intelligence:1 warmuth:2 chiang:2 successive:2 zhang:2 height:1 along:1 differential:1 clairvoyant:1 prove:2 naor:1 introductory:1 redefine:2 polyhedral:1 int... |
6,274 | 6,676 | Learning Efficient Object Detection Models with
Knowledge Distillation
Guobin Chen1,2 Wongun Choi1 Xiang Yu1 Tony Han2 Manmohan Chandraker1,3
1
2
3
NEC Labs America
University of Missouri
University of California, San Diego
Abstract
Despite significant accuracy improvement in convolutional neural networks (CNN)
based... | 6676 |@word cnn:16 middle:1 compression:14 stronger:1 norm:2 loading:1 seems:1 everingham:1 r:4 rgb:1 decomposition:5 thereby:3 harder:3 shot:1 configuration:1 lightweight:2 score:2 trainval:2 liu:2 tuned:2 interestingly:1 outperforms:1 freitas:1 blank:1 activation:1 written:1 gpu:3 subsequent:1 romero:3 informative:1 ... |
6,275 | 6,677 | One-Sided Unsupervised Domain Mapping
Sagie Benaim1 and Lior Wolf1,2
1
The Blavatnik School of Computer Science , Tel Aviv University, Israel
2
Facebook AI Research
Abstract
In unsupervised domain mapping, the learner is given two unmatched datasets
A and B. The goal is to learn a mapping GAB that translates a sample... | 6677 |@word cnn:1 version:2 fcns:1 seems:3 norm:1 cha:1 rgb:3 d0k:5 initial:1 configuration:1 liu:3 score:6 ours:1 hyunsoo:1 deconvolutional:2 outperforms:3 existing:2 com:1 comparing:2 luo:2 activation:5 numerical:3 realistic:2 distant:1 happen:1 shape:1 christian:1 remove:1 v:4 generative:9 selected:1 half:6 alec:1 b... |
6,276 | 6,678 | Deep Mean-Shift Priors for Image Restoration
Siavash A. Bigdeli
University of Bern
bigdeli@inf.unibe.ch
Meiguang Jin
University of Bern
jin@inf.unibe.ch
Paolo Favaro
University of Bern
favaro@inf.unibe.ch
Matthias Zwicker
University of Bern, and University of Maryland, College Park
zwicker@cs.umd.edu
Abstract
In t... | 6678 |@word cnn:2 version:1 seems:1 underline:1 rgb:1 p0:5 set5:2 initial:1 substitution:1 daniel:3 ours:11 interestingly:1 reaction:1 com:1 blur:2 remove:1 designed:2 update:4 half:3 selected:1 intelligence:5 rudin:1 kyoung:1 core:1 provides:1 location:1 simpler:1 zhang:9 favaro:5 hazirbas:1 constructed:1 qualitative:... |
6,277 | 6,679 | Greedy Algorithms for Cone Constrained
Optimization with Convergence Guarantees
Francesco Locatello
MPI for Intelligent Systems - ETH Zurich
Michael Tschannen
ETH Zurich
locatelf@ethz.ch
michaelt@nari.ee.ethz.ch
Gunnar R?tsch
ETH Zurich
Martin Jaggi
EPFL
raetsch@inf.ethz.ch
martin.jaggi@epfl.ch
Abstract
Greedy... | 6679 |@word repository:1 version:1 mri:2 briefly:2 norm:13 nd:1 open:1 crucially:2 hsieh:1 decomposition:2 kz1:1 thereby:2 boundedness:1 reduction:1 initial:1 cyclic:1 cristina:1 electronics:1 selecting:1 contains:1 ap1:1 kahles:1 daniel:5 document:1 amp:10 hyunsoo:1 frankwolfe:1 existing:5 kx0:2 ka:7 current:2 z2:2 lu... |
6,278 | 668 | Single-iteration Threshold Hamming
Networks
Eytan Ruppin
Isaac Meilijson
Moshe Sipper
School of Mathematical Sciences
Raymond and Beverly Sackler Faculty of Exact Sciences
Tel Aviv University, 69978 Tel Aviv, Israel
Abstract
We analyze in detail the performance of a Hamming network classifying inputs that are distor... | 668 |@word effect:1 predicted:2 version:2 faculty:1 rk2:1 proportion:2 hence:1 society:1 moshe:1 correct:5 laboratory:1 eng:1 tr:1 bin:3 subnet:9 distance:2 berlin:1 rat:1 initial:1 capacity:4 selecting:1 opt:1 tuned:1 renewed:1 biological:1 evident:1 tn:1 performs:2 fj:2 practically:1 activation:2 normal:4 dx:1 exp:11... |
6,279 | 6,680 | A New Theory for Matrix Completion
Guangcan Liu?
Qingshan Liu?
Xiao-Tong Yuan?
School of Information & Control, Nanjing University of Information Science & Technology
NO 219 Ningliu Road, Nanjing, Jiangsu, China, 210044
{gcliu,qsliu,xtyuan}@nuist.edu.cn
Abstract
Prevalent matrix completion theories reply on an ass... | 6680 |@word mild:3 trial:2 middle:1 polynomial:1 norm:17 km:3 shuicheng:3 seek:2 simulation:2 theran:1 decomposition:1 thereby:1 klk:2 necessity:1 liu:13 series:1 configuration:1 selecting:1 hereafter:1 contains:1 seriously:1 initial:2 interestingly:3 bc:1 daniel:1 existing:4 recovered:1 comparing:4 luo:1 must:1 john:1... |
6,280 | 6,681 | Robust Hypothesis Test for Nonlinear Effect
with Gaussian Processes
Jeremiah Zhe Liu, Brent Coull
Department of Biostatistics
Harvard University
Cambridge, MA 02138
{zhl112@mail, bcoull@hsph}.harvard.edu
Abstract
This work constructs a hypothesis test for detecting whether an data-generating
function h : Rp ? R belong... | 6681 |@word trial:1 version:1 polynomial:4 norm:2 open:1 km:1 hu:1 simulation:7 covariance:1 decomposition:2 elisseeff:2 tr:4 solid:4 carry:1 moment:1 liu:2 contains:2 score:3 selecting:1 genetic:1 rkhs:7 intake:2 yet:2 subsequent:2 additive:2 partition:1 selected:3 rp1:3 ith:1 record:1 detecting:4 zhang:1 unbiasedly:1... |
6,281 | 6,682 | Lower bounds on the robustness to adversarial
perturbations
Jonathan Peck1,2 , Joris Roels2,3 , Bart Goossens3 , and Yvan Saeys1,2
1
Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, 9000, Belgium
2
Data Mining and Modeling for Biomedicine, VIB Inflammation Research Center, G... | 6682 |@word moosavi:1 cnn:1 eliminating:1 norm:18 nd:2 c0:2 rgb:1 reduction:1 liu:1 series:1 score:1 bc:3 document:1 guadarrama:1 comparing:1 com:1 protection:1 activation:1 intriguing:1 must:2 written:2 drop:1 designed:1 bart:1 intelligence:1 oldest:1 krkf:3 characterization:5 provides:2 location:1 toronto:1 tahoe:1 b... |
6,282 | 6,683 | Minimizing a Submodular Function from Samples
Eric Balkanski
Harvard University
ericbalkanski@g.harvard.edu
Yaron Singer
Harvard University
yaron@seas.harvard.edu
Abstract
In this paper we consider the problem of minimizing a submodular function from
training data. Submodular functions can be efficiently minimized an... | 6683 |@word private:1 polynomial:9 seems:2 norm:1 nd:1 seek:3 tat:1 cla:1 contains:3 united:1 surprising:1 si:17 must:2 additive:7 partition:4 drop:2 selected:1 tahoe:1 mathematical:1 constructed:6 direct:1 symposium:2 prove:1 combine:2 introduce:1 pairwise:1 indeed:1 hardness:3 behavior:1 multi:3 pitfall:1 paclearnabl... |
6,283 | 6,684 | Introspective Classification with Convolutional Nets
Long Jin
UC San Diego
longjin@ucsd.edu
Justin Lazarow
UC San Diego
jlazarow@ucsd.edu
Zhuowen Tu
UC San Diego
ztu@ucsd.edu
Abstract
We propose introspective convolutional networks (ICN) that emphasize the importance of having convolutional neural networks empowere... | 6684 |@word cnn:52 seems:1 logit:1 simulation:1 contrastive:2 sgd:20 carry:5 reduction:1 initial:4 liu:2 series:1 ours:8 past:2 existing:3 outperforms:2 current:2 icn:69 comparing:1 contextual:1 com:2 stemmed:2 activation:1 dx:1 partition:1 informative:1 grumman:1 designed:2 plot:1 progressively:4 update:5 v:21 generat... |
6,284 | 6,685 | Label Distribution Learning Forests
Wei Shen1,2 , Kai Zhao1 , Yilu Guo1 , Alan Yuille2
Key Laboratory of Specialty Fiber Optics and Optical Access Networks,
Shanghai Institute for Advanced Communication and Data Science,
School of Communication and Information Engineering, Shanghai University
2
Department of Computer ... | 6685 |@word version:1 eng:1 contains:1 ours:6 longitudinal:1 past:1 existing:3 current:4 com:1 guadarrama:1 comparing:1 gmail:1 assigning:1 john:1 hou:2 numerical:1 additive:1 partition:1 shape:1 enables:2 update:18 v:2 alone:6 greedy:1 leaf:31 selected:1 cook:1 smith:1 short:1 boosting:3 node:57 cse:1 zhang:3 rc:1 con... |
6,285 | 6,686 | Unsupervised learning of object frames by dense
equivariant image labelling
James Thewlis1
Hakan Bilen2
1
Andrea Vedaldi1
2
Visual Geometry Group
University of Oxford
{jdt,vedaldi}@robots.ox.ac.uk
School of Informatics
University of Edinburgh
hbilen@ed.ac.uk
Abstract
One of the key challenges of visual perceptio... | 6686 |@word cnn:8 middle:4 version:1 dalal:1 norm:1 triggs:1 seitz:1 shuicheng:1 rgb:1 decomposition:2 jacob:1 brightness:1 inpainting:2 harder:1 liu:3 score:2 daniel:1 interestingly:1 animated:2 existing:1 com:1 luo:3 must:7 gavves:1 subsequent:1 realistic:1 shape:2 enables:1 remove:1 plot:1 localise:1 generative:2 se... |
6,286 | 6,687 | Compression-aware Training of Deep Networks
Mathieu Salzmann
EPFL - CVLab
Lausanne, Switzerland
mathieu.salzmann@epfl.ch
Jose M. Alvarez
Toyota Research Institute
Los Altos, CA 94022
jose.alvarez@tri.global
Abstract
In recent years, great progress has been made in a variety of application domains
thanks to the devel... | 6687 |@word kohli:1 compression:34 stronger:1 seems:1 norm:2 hu:1 seek:4 accounting:3 decomposition:3 simplifying:1 sgd:1 incurs:1 reduction:6 initial:5 configuration:3 liu:2 salzmann:3 tuned:4 ours:4 interestingly:2 existing:4 freitas:1 current:3 activation:5 yet:1 guez:1 written:2 gpu:2 numerical:1 alphanumeric:1 rem... |
6,287 | 6,688 | Multiscale Semi-Markov Dynamics for
Intracortical Brain-Computer Interfaces
Daniel J. Milstein ?
daniel_milstein@alumni.brown.edu
John D. Simeral ? ?
john_simeral@brown.edu
Jason L. Pacheco ?
Leigh R. Hochberg ? ? ?
pachecoj@mit.edu
leigh_hochberg@brown.edu
Beata Jarosiewicz k ? ??
beataj@stanford.edu
Erik B. S... | 6688 |@word trial:10 open:1 tried:1 covariance:6 eng:1 thereby:1 accommodate:1 initial:1 configuration:7 series:1 liu:1 selecting:1 united:1 daniel:1 tuned:5 bc:2 past:1 existing:1 reaction:2 current:3 ka:14 discretization:1 yet:1 written:1 must:2 john:1 enables:1 motor:10 designed:4 plot:2 update:1 intelligence:2 cue:... |
6,288 | 6,689 | PredRNN: Recurrent Neural Networks for Predictive
Learning using Spatiotemporal LSTMs
Yunbo Wang
School of Software
Tsinghua University
wangyb15@mails.tsinghua.edu.cn
Jianmin Wang
School of Software
Tsinghua University
jimwang@tsinghua.edu.cn
Mingsheng Long?
School of Software
Tsinghua University
mingsheng@tsinghua.e... | 6689 |@word trial:1 cnn:3 version:1 wmf:1 wco:3 cox:1 bf:3 open:1 tried:1 rgb:4 tnlist:1 recursively:3 reduction:1 contains:2 score:2 past:1 existing:1 outperforms:3 current:7 guadarrama:1 anne:1 activation:1 devin:1 concatenate:1 happen:1 blur:3 subsequent:1 shape:4 visibility:1 designed:1 update:1 fund:1 occlude:1 ge... |
6,289 | 669 | Hidden Markov Model Induction by Bayesian
Model Merging
Andreas Stolcke*'**
*Computer Science Division
University of California
Berkeley, CA 94720
stolcke@icsi.berkeley.edu
Stephen Omohundro"
**International Computer Science Institute
1947 Center Street, Suite 600
Berkeley, CA 94704
om@icsi.berkeley.edu
Abstract
Thi... | 669 |@word trial:4 version:1 briefly:1 seems:1 replicate:1 accounting:2 tr:2 accommodate:1 carry:2 initial:12 contains:1 series:1 bc:7 interestingly:1 soules:2 current:2 must:1 cruz:1 shape:2 drop:1 cfo:1 update:3 alone:1 greedy:2 fewer:2 intelligence:1 smith:2 provides:1 complication:1 simpler:2 mathematical:1 along:6... |
6,290 | 6,690 | Detrended Partial Cross Correlation
for Brain Connectivity Analysis
Jaime S Ide?
Yale University
New Haven, CT 06519
jaime.ide@yale.edu
Fabio A Cappabianco
Federal University of Sao Paulo
S.J. dos Campos, 12231, Brazil
cappabianco@unifesp.br
Fabio A Faria
Federal University of Sao Paulo
S.J. dos Campos, 12231, Brazi... | 6690 |@word middle:1 version:1 mri:2 polynomial:1 approved:1 open:1 hu:2 simulation:5 seek:2 covariance:5 fifteen:1 tr:2 shot:1 reduction:4 initial:1 series:18 interestingly:1 dubourg:1 past:1 reaction:1 current:3 ka:1 activation:7 connectomics:2 realistic:2 additive:1 oxygenation:1 analytic:1 motor:5 hypothesize:3 atl... |
6,291 | 6,691 | Contrastive Learning for Image Captioning
Bo Dai
Dahua Lin
Department of Information Engineering, The Chinese University of Hong Kong
db014@ie.cuhk.edu.hk
dhlin@ie.cuhk.edu.hk
Abstract
Image captioning, a popular topic in computer vision, has achieved substantial
progress in recent years. However, the distinctivenes... | 6691 |@word kong:3 cnn:1 compression:1 stronger:7 hyv:1 jacob:1 contrastive:12 pg:2 mention:1 ytn:1 initial:1 liu:1 contains:4 score:1 att:2 ours:3 suppressing:1 existing:1 guadarrama:1 comparing:2 nt:5 luo:1 written:1 readily:2 john:1 neuraltalk2:9 numerical:1 partition:1 periodically:2 designed:1 fund:1 alone:1 gener... |
6,292 | 6,692 | Safe Model-based Reinforcement Learning with
Stability Guarantees
Felix Berkenkamp
Department of Computer Science
ETH Zurich
befelix@inf.ethz.ch
Matteo Turchetta
Department of Computer Science,
ETH Zurich
matteotu@inf.ethz.ch
Angela P. Schoellig
Institute for Aerospace Studies
University of Toronto
schoellig@utias.ut... | 6692 |@word middle:1 norm:3 mockus:1 c0:3 open:1 pieter:2 simulation:1 linearized:1 covariance:1 jacob:1 schoellig:5 evaluating:2 pick:1 sgd:1 thereby:1 initial:16 ndez:1 series:1 contains:2 selecting:2 daniel:2 rkhs:1 steiner:1 current:8 discretization:7 com:1 anne:1 activation:1 intriguing:1 must:1 john:2 devin:1 aca... |
6,293 | 6,693 | Online Multiclass Boosting
Young Hun Jung
Jack Goetz
Department of Statistics
University of Michigan
Ann Arbor, MI 48109
{yhjung, jrgoetz, tewaria}@umich.edu
Ambuj Tewari
Abstract
Recent work has extended the theoretical analysis of boosting algorithms to multiclass problems and to online settings. However, the mul... | 6693 |@word repository:1 version:3 polynomial:1 stronger:1 norm:1 cochleagram:1 open:1 hu:2 pick:2 incurs:1 mention:1 tr:1 solid:1 series:1 contains:1 past:1 subjective:1 existing:1 current:1 outperforms:1 com:1 beygelzimer:12 luo:1 si:14 yet:1 mushroom:1 enables:1 update:2 v:1 intelligence:3 guess:4 warmuth:2 ith:2 sh... |
6,294 | 6,694 | Matching on Balanced Nonlinear Representations for
Treatment Effects Estimation
Yun Fu
Northeastern University
Boston, MA
yunfu@ece.neu.edu
Sheng Li
Adobe Research
San Jose, CA
sheli@adobe.com
Abstract
Estimating treatment effects from observational data is challenging due to the
missing counterfactuals. Matching is ... | 6694 |@word exploitation:1 version:1 prognostic:1 nd:1 johansson:2 essay:1 seek:1 simulation:1 lpp:7 tr:6 klk:1 carry:1 reduction:2 hunting:1 liu:1 contains:6 score:17 att:16 selecting:1 series:2 genetic:2 rkhs:4 document:4 ours:1 daniel:1 past:1 existing:5 outperforms:1 current:1 com:1 comparing:1 nt:7 protection:1 sc... |
6,295 | 6,695 | Learning Overcomplete HMMs
Vatsal Sharan
Stanford University
vsharan@stanford.edu
Sham Kakade
University of Washington
sham@cs.washington.edu
Percy Liang
Stanford University
pliang@cs.stanford.edu
Gregory Valiant
Stanford University
valiant@stanford.edu
Abstract
We study the problem of learning overcomplete HMMs?t... | 6695 |@word mild:2 trial:1 koopmans:1 polynomial:22 seems:4 norm:3 stronger:1 open:5 d2:2 simulation:2 crucially:1 decomposition:15 p0:2 pick:1 recursively:1 carry:2 reduction:1 moment:27 cyclic:2 contains:1 initial:6 necessity:1 denoting:1 document:2 ours:3 past:3 recovered:1 surprising:1 must:4 written:1 additive:1 d... |
6,296 | 6,696 | GP CaKe: Effective brain connectivity with causal
kernels
Luca Ambrogioni
Radboud University
l.ambrogioni@donders.ru.nl
Max Hinne
Radboud University
m.hinne@donders.ru.nl
Marcel A. J. van Gerven
Radboud University
m.vangerven@donders.ru.nl
Eric Maris
Radboud University
e.maris@donders.ru.nl
Abstract
A fundamental g... | 6696 |@word trial:2 mri:1 version:1 polynomial:2 coombes:1 d2:2 simulation:5 propagate:1 covariance:24 accounting:1 decomposition:5 thereby:1 moment:1 series:12 efficacy:1 selecting:1 outperforms:1 imaginary:1 recovered:2 mari:4 discretization:1 current:2 scaffolding:1 must:1 written:1 connectomics:1 kiebel:1 realistic... |
6,297 | 6,697 | Decoupling ?when to update? from ?how to update?
Eran Malach
School of Computer Science
The Hebrew University, Israel
eran.malach@mail.huji.ac.il
Shai Shalev-Shwartz
School of Computer Science
The Hebrew University, Israel
shais@cs.huji.ac.il
Abstract
Deep learning requires data. A useful approach to obtain data is t... | 6697 |@word version:6 seems:2 open:1 jacob:2 palso:1 sgd:1 mention:1 tr:1 moment:1 initial:4 configuration:1 contains:1 liu:1 daniel:1 past:1 existing:2 outperforms:1 current:2 comparing:2 com:2 bootkrajang:2 goldberger:2 tackling:1 must:3 realistic:1 happen:2 klaas:1 christian:1 atlas:2 mislabelled:1 update:57 sukhbaa... |
6,298 | 6,698 | Self-Normalizing Neural Networks
G?nter Klambauer
Thomas Unterthiner
Andreas Mayr
Sepp Hochreiter
LIT AI Lab & Institute of Bioinformatics,
Johannes Kepler University Linz
A-4040 Linz, Austria
{klambauer,unterthiner,mayr,hochreit}@bioinf.jku.at
Abstract
Deep Learning has revolutionized vision via convolutional neu... | 6698 |@word mild:1 arabic:1 repository:3 version:1 cnn:1 norm:6 stronger:3 nd:1 contraction:5 mammal:1 sgd:2 thereby:1 delgado:1 moment:4 initial:1 configuration:1 ndez:1 series:1 jku:1 bradley:1 activation:51 readily:1 subsequent:1 shape:1 enables:1 hochreit:1 update:1 selected:3 device:1 proficient:1 marine:1 vanishi... |
6,299 | 6,699 | Learning to Pivot with Adversarial Networks
Gilles Louppe
New York University
g.louppe@nyu.edu
Michael Kagan
SLAC National Accelerator Laboratory
makagan@slac.stanford.edu
Kyle Cranmer
New York University
kyle.cranmer@nyu.edu
Abstract
Several techniques for domain adaptation have been proposed to account for
differ... | 6699 |@word illustrating:1 middle:2 stronger:1 open:2 simulation:1 propagate:1 eng:1 thereby:1 carry:1 venkatasubramanian:1 phy:2 initial:1 score:6 efficacy:1 pub:2 salzmann:1 interestingly:1 com:1 activation:8 written:1 evans:2 shape:1 plot:3 atlas:5 update:2 generative:4 selected:1 accordingly:1 lr:24 characterizatio... |
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