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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5,100 | 5,617 | Large Scale Canonical Correlation Analysis with
Iterative Least Squares
Yichao Lu
University of Pennsylvania
yichaolu@wharton.upenn.edu
Dean P. Foster
Yahoo Labs, NYC
dean@foster.net
Abstract
Canonical Correlation Analysis (CCA) is a widely used statistical tool with both
well established theory and favorable perform... | 5617 |@word repository:1 version:3 norm:2 d2:1 decomposition:12 covariance:2 pick:1 reduction:4 initial:1 contains:2 prefix:1 current:1 comparing:2 savage:1 john:2 numerical:4 happen:1 remove:3 drop:1 selected:1 yr:4 xk:1 ith:3 steepest:1 short:1 provides:2 uppsala:1 zhang:2 mathematical:1 c2:2 suspicious:1 consists:5 ... |
5,101 | 5,618 | Cone-constrained Principal Component Analysis
Yash Deshpande
Electrical Engineering
Stanford University
Andrea Montanari
Electrical Engineering and Statistics
Stanford University
Emile Richard
Electrical Engineering
Stanford University
Abstract
Estimating a vector from noisy quadratic observations is a task that ar... | 5618 |@word version:2 polynomial:4 stronger:1 confirms:1 simulation:2 covariance:1 tr:2 reduction:2 initial:1 zij:2 daniel:1 document:1 amp:5 existing:1 surprising:2 attracted:1 must:1 numerical:3 confirming:2 plot:3 maxv:1 vanishing:1 detecting:1 iterates:1 complication:1 provides:1 characterization:1 certificate:1 kv... |
5,102 | 5,619 | Improved Distributed Principal Component Analysis
Vandana Kanchanapally
School of Computer Science
Georgia Institute of Technology
vvandana@gatech.edu
Maria-Florina Balcan
School of Computer Science
Carnegie Mellon University
ninamf@cs.cmu.edu
David Woodruff
Almaden Research Center
IBM Research
dpwoodru@us.ibm.com
... | 5619 |@word h:1 repository:2 version:2 norm:6 c0:6 open:1 widom:1 d2:21 willing:1 heiser:1 decomposition:4 thereby:1 reduction:7 contains:1 lichman:1 woodruff:4 skd:4 fa8750:1 com:1 si:6 srd:1 axk22:2 additive:1 partition:2 numerical:1 shape:1 designed:1 succeeding:1 v:1 half:1 xk:1 fa9550:1 blei:1 provides:1 node:11 l... |
5,103 | 562 | A Neural Network for Motion Detection of
Drift-Balanced Stimuli
Hilary Tunley*
School of Cognitive and Computer Sciences
Sussex University
Brighton, England.
Abstract
This paper briefly describes an artificial neural network for preattentive
visual processing. The network is capable of determiuing image motioll in
a ... | 562 |@word proceeded:1 illustrating:1 middle:2 briefly:1 version:1 vitally:1 km:1 integrative:1 simulation:2 brightness:1 solid:1 initial:2 contains:5 disparity:1 tuned:3 existing:2 current:2 nt:1 surprising:1 activation:1 yet:2 must:1 subsequent:1 realistic:2 blur:1 plasticity:1 girosi:1 motor:17 remove:1 designed:4 i... |
5,104 | 5,620 | Generalized Unsupervised Manifold Alignment
Zhen Cui1,2
Hong Chang1
Shiguang Shan1
Xilin Chen1
Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS),
Institute of Computing Technology, CAS, Beijing, China
2
School of Computer Science and Technology, Huaqiao University, Xiamen, China
{zhen.... | 5620 |@word kulis:1 sgf:28 version:1 briefly:1 norm:2 triggs:1 km:3 hu:1 seek:3 decomposition:1 covariance:1 dramatic:1 tr:11 minus:1 reduction:2 series:1 score:2 contains:2 tuned:1 ours:1 existing:1 current:1 dx:2 subsequent:2 wx:2 analytic:1 remove:1 designed:1 alone:1 discovering:1 selected:2 ith:4 contribute:1 loca... |
5,105 | 5,621 | On Prior Distributions and Approximate Inference
for Structured Variables
Rajiv Khanna
ECE Dept., UT Austin
rajivak@utexas.edu
Oluwasanmi Koyejo
Psychology Dept., Stanford
sanmi@stanford.edu
Russell A. Poldrack
Psychology Dept., Stanford
poldrack@stanford.edu
Joydeep Ghosh
ECE Dept., UT Austin
ghosh@ece.utexas.edu
... | 5621 |@word multitask:1 trial:2 determinant:1 version:1 cingulate:1 polynomial:1 cs0:9 adrian:1 simplifying:1 series:1 reaction:1 recovered:2 comparing:1 anterior:1 jaynes:1 ka:1 si:4 yet:1 dx:6 activation:1 subsequent:1 partition:1 additive:1 oxygenation:1 shape:1 designed:2 interpretable:1 discrimination:1 alone:1 gr... |
5,106 | 5,622 | Real-Time Decoding of an Integrate and Fire Encoder
Shreya Saxena and Munther Dahleh
Department of Electrical Engineering and Computer Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
{ssaxena,dahleh}@mit.edu
Abstract
Neuronal encoding models range from the detailed biophysically-based Hodgkin
Huxley... | 5622 |@word norm:7 simulation:7 incurs:1 solid:1 reduction:2 t7:1 ati:1 past:1 existing:1 outperforms:1 current:4 si:4 written:1 fn:1 realistic:1 subsequent:1 numerical:2 plasticity:1 additive:1 romero:1 device:4 guess:1 accordingly:1 record:3 provides:1 complication:1 along:1 differential:1 introduce:4 indeed:1 uz:1 b... |
5,107 | 5,623 | Spike Frequency Adaptation Implements Anticipative
Tracking in Continuous Attractor Neural Networks
Yuanyuan Mi
State Key Laboratory of Cognitive Neuroscience & Learning,
Beijing Normal University,Beijing 100875,China
miyuanyuan0102@bnu.edu.cn
C. C. Alan Fung,
K. Y. Michael Wong
Department of Physics, The Hong Kong Un... | 5623 |@word neurophysiology:1 kong:3 hippocampus:1 replicate:1 simulation:5 colby:1 idg:1 hereafter:1 interestingly:3 past:1 current:4 anterior:3 si:1 yet:2 dx:6 ust:2 written:4 realize:2 shape:3 enables:1 motor:7 fund:1 v:1 stationary:2 cue:3 accordingly:1 short:1 provides:2 location:4 zhang:3 height:1 along:2 constru... |
5,108 | 5,624 | Analysis of Brain States
from Multi-Region LFP Time-Series
Kyle Ulrich 1 , David E. Carlson 1 , Wenzhao Lian 1 , Jana Schaich Borg 2 ,
Kafui Dzirasa 2 and Lawrence Carin 1
1
Department of Electrical and Computer Engineering
2
Department of Psychiatry and Behavioral Sciences
Duke University, Durham, NC 27708
{kyle.ulri... | 5624 |@word middle:3 version:2 hippocampus:3 proportion:1 unif:3 cola:1 covariance:8 excited:1 decomposition:2 thereby:1 initial:1 series:13 score:1 bradley:1 recovered:4 lang:3 assigning:3 written:1 subsequent:1 opin:1 designed:1 interpretable:1 update:6 v:1 stationary:1 generative:5 implying:1 nervous:2 smith:1 short... |
5,109 | 5,625 | Extracting Latent Structure From Multiple
Interacting Neural Populations
Jo?ao D. Semedo1,2,3 , Amin Zandvakili4 , Adam Kohn4 ,
?
Christian K. Machens3 , ? Byron M. Yu1,5
1
Department of Electrical and Computer Engineering, Carnegie Mellon University
2
Department of Electrical and Computer Engineering, Instituto Super... | 5625 |@word trial:21 private:1 proceeded:1 stronger:1 norm:4 d2:3 covariance:31 q1:2 kerlin:1 reduction:6 exclusively:1 outperforms:1 comparing:5 anne:1 analysed:1 must:2 john:4 informative:1 christian:3 motor:1 remove:1 designed:1 v:1 davi:1 signalling:1 rp1:1 inspection:1 anaesthetised:1 coleman:1 dover:1 smith:1 reg... |
5,110 | 5,626 | Learning convolution filters for inverse covariance
estimation of neural network connectivity
George O. Mohler?
Department of Mathematics and Computer Science
Santa Clara University University
Santa Clara, CA, USA
gmohler@scu.edu
Abstract
We consider the problem of inferring direct neural network connections from Cal... | 5626 |@word private:2 hsieh:1 covariance:52 tr:1 liu:1 series:28 score:23 zij:5 tuned:1 bradley:1 com:1 nt:1 clara:2 must:2 connectomics:6 john:1 subsequent:1 remove:1 plot:2 v:2 alone:2 short:2 core:1 filtered:5 provides:1 location:1 diagnosing:1 along:3 direct:3 become:1 consists:1 sutera:1 introduce:3 manner:1 andre... |
5,111 | 5,627 | On Multiplicative Multitask Feature Learning
Xin Wang? , Jinbo Bi? , Shipeng Yu? , Jiangwen Sun?
?
Dept. of Computer Science & Engineering
Health Services Innovation Center
University of Connecticut
Siemens Healthcare
Storrs, CT 06269
Malvern, PA 19355
wangxin,jinbo,javon@engr.uconn.edu
shipeng.yu@siemens.com
?
Abstr... | 5627 |@word multitask:13 norm:31 turlach:1 termination:1 d2:4 simulation:1 decomposition:6 jacob:1 minus:1 initial:1 liu:1 contains:1 score:2 tuned:1 existing:3 current:2 jinbo:3 com:1 comparing:1 additive:2 j1:1 kdd:3 shape:1 enables:1 designed:2 mtfl:17 intelligence:1 selected:5 sarcos:4 provides:2 zhang:3 five:1 pro... |
5,112 | 5,628 | Multitask learning meets tensor factorization: task
imputation via convex optimization
Kishan Wimalawarne
Tokyo Institute of Technology
Meguro-ku, Tokyo, Japan
kishan@sg.cs.titech.ac.jp
Masashi Sugiyama
The University of Tokyo
Bunkyo-ku, Tokyo, Japan
sugi@k.u-tokyo.ac.jp
Ryota Tomioka
TTI-C
Illinois, Chicago, USA
to... | 5628 |@word multitask:26 mild:1 version:3 middle:1 norm:122 paredes:2 d2:11 simulation:1 decomposition:8 tr:6 liu:1 contains:1 romera:2 existing:1 contextual:1 additive:1 chicago:1 drop:2 selected:4 core:1 yamada:1 record:1 authority:1 math:1 simpler:1 zhang:1 trinomial:1 mathematical:1 along:2 c2:6 lathauwer:4 expecte... |
5,113 | 5,629 | Learning Multiple Tasks in Parallel with
a Shared Annotator
Koby Crammer
Department of Electrical Engeneering
The Technion ? Israel Institute of Technology
Haifa, 32000 Israel
koby@ee.technion.ac.il
Haim Cohen
Department of Electrical Engeneering
The Technion ? Israel Institute of Technology
Haifa, 32000 Israel
hcohen... | 5629 |@word multitask:4 exploitation:11 version:7 middle:1 norm:1 dekel:3 tried:3 pick:5 asks:1 thereby:1 harder:7 reduction:8 contains:2 score:7 document:8 ours:1 outperforms:2 past:2 reran:1 contextual:9 comparing:1 yet:13 plm:2 kdd:1 designed:3 update:19 v:44 stationary:1 item:4 kxjt:2 xk:1 mannor:2 triumph:1 prefer... |
5,114 | 563 | A Simple Weight Decay Can Improve
Generalization
Anders Krogh?
The Niels Bohr Institute
Blegdamsvej 17
DK-2100 Copenhagen, Denmark
krogh@cse.ucsc.edu
CONNECT,
John A. Hertz
Nordita
Blegdamsvej 17
DK-2100 Copenhagen, Denmark
hertz@nordita.dk
Abstract
It has been observed in numerical simulations that a weight decay ... | 563 |@word norm:2 seems:1 simulation:4 tried:1 covariance:1 pick:1 dramatic:1 minus:1 contains:1 current:1 written:1 john:1 cruz:2 numerical:3 happen:1 iv1:1 update:1 cse:1 ucsc:1 fitting:3 inside:1 theoretically:1 expected:1 growing:1 brain:1 little:2 actual:2 becomes:3 underlying:1 lowest:1 what:2 kind:3 minimizes:1 ... |
5,115 | 5,630 | Multivariate Regression with Calibration?
Han Liu
Department of Operations Research and Financial Engineering
Princeton University
Lie Wang
Department of Mathematics
Massachusetts Institute of Technology
Tuo Zhao?
Department of Computer Science
Johns Hopkins University
Abstract
We propose a new method named calibrate... | 5630 |@word version:2 norm:14 turlach:1 r13:2 suitably:1 c0:4 hu:3 simulation:6 covariance:4 tr:2 liu:3 series:5 xb0:2 outperforms:8 existing:2 past:1 err:10 wd:1 adj:3 nicolai:1 john:1 numerical:6 enables:1 a1k:2 zik:1 intelligence:1 selected:9 ria:1 core:1 num:1 zhang:3 mathematical:1 direct:1 adk:2 yuan:1 prove:2 fi... |
5,116 | 5,631 | Exclusive Feature Learning on Arbitrary Structures
via `1,2-norm
Deguang Kong1 , Ryohei Fujimaki2 , Ji Liu3 , Feiping Nie1 , Chris Ding1
1
Dept. of Computer Science, University of Texas Arlington, TX, 76019;
2
NEC Laboratories America, Cupertino, CA, 95014;
3
Dept. of Computer Science, University of Rochester, Rochest... | 5631 |@word kong:3 norm:19 hu:1 confirms:2 decomposition:3 jacob:1 pg:1 elisseeff:1 liu:3 series:5 selecting:1 tuned:1 longitudinal:1 outperforms:2 existing:1 current:2 com:4 gmail:2 yet:1 written:2 numerical:1 partition:2 j1:8 kdd:2 shape:3 gv:4 designed:2 greedy:1 selected:8 desktop:1 core:1 record:2 mathematical:1 a... |
5,117 | 5,632 | Flexible Transfer Learning under Support and Model
Shift
Jeff Schneider
Robotics Institute
Carnegie Mellon University
schneide@cs.cmu.edu
Xuezhi Wang
Computer Science Department
Carnegie Mellon University
xuezhiw@cs.cmu.edu
Abstract
Transfer learning algorithms are used when one has sufficient training data for
one s... | 5632 |@word pw:1 nd:1 covariance:11 tr:47 harder:1 venkatasubramanian:2 existing:1 current:1 dx:2 informative:1 krikamol:1 designed:2 intelligence:2 selected:7 beginning:1 parametrization:1 ith:1 location:9 daphne:1 zhang:1 along:1 scholkopf:1 consists:1 combine:1 eleventh:1 tuyen:1 karsten:2 mpg:1 planning:1 growing:1... |
5,118 | 5,633 | Texture Synthesis Using Convolutional Neural
Networks
Leon A. Gatys
Centre for Integrative Neuroscience, University of T?ubingen, Germany
Bernstein Center for Computational Neuroscience, T?ubingen, Germany
Graduate School of Neural Information Processing, University of T?ubingen, Germany
leon.gatys@bethgelab.org
Alexan... | 5633 |@word cnn:2 version:1 seems:1 kriegeskorte:1 nd:1 proportionality:1 integrative:4 decomposition:1 crowding:1 reduction:2 liu:1 contains:1 tuned:1 interestingly:1 envision:1 current:1 com:1 guadarrama:1 surprising:1 activation:6 readily:2 numerical:1 academia:1 resampling:2 stationary:6 generative:1 v:1 discrimina... |
5,119 | 5,634 | Convolutional Neural Networks with Intra-layer
Recurrent Connections for Scene Labeling
Ming Liang
Xiaolin Hu
Bo Zhang
Tsinghua National Laboratory for Information Science and Technology (TNList)
Department of Computer Science and Technology
Center for Brain-Inspired Computing Research (CBICR)
Tsinghua University, Bei... | 5634 |@word cnn:24 briefly:1 seems:1 paredes:1 kokkinos:1 c0:1 bptt:2 hu:2 additively:2 rgb:1 solid:1 tnlist:1 recursively:1 liu:4 contains:2 score:1 seriously:1 deconvolutional:2 romera:1 outperforms:2 current:1 guadarrama:1 parsing:12 finest:1 gpu:11 concatenate:2 predetermined:1 enables:1 treating:1 alone:1 greedy:1... |
5,120 | 5,635 | Grammar as a Foreign Language
Oriol Vinyals?
Google
vinyals@google.com
Terry Koo
Google
terrykoo@google.com
Lukasz Kaiser?
Google
lukaszkaiser@google.com
Slav Petrov
Google
slav@google.com
Ilya Sutskever
Google
ilyasu@google.com
Geoffrey Hinton
Google
geoffhinton@google.com
Abstract
Syntactic constituency parsing ... | 5635 |@word version:1 nd:2 linearized:1 decomposition:1 eng:1 thereby:1 contains:2 score:31 fragment:1 charniak:1 ati:2 existing:2 current:1 com:6 surprising:2 gpu:2 parsing:40 john:4 concatenate:1 ronan:1 subsequent:1 wanted:1 designed:1 interpretable:1 v:1 half:1 selected:1 devising:1 item:1 generative:1 intelligence... |
5,121 | 5,636 | Recursive Training of 2D-3D Convolutional Networks
for Neuronal Boundary Detection
Kisuk Lee, Aleksandar Zlateski
Massachusetts Institute of Technology
{kisuklee,zlateski}@mit.edu
Ashwin Vishwanathan, H. Sebastian Seung
Princeton University
{ashwinv,sseung}@princeton.edu
Abstract
Efforts to automate the reconstructi... | 5636 |@word middle:3 nd:2 d3d:1 ultrathin:2 recursively:4 briggman:2 initial:4 liu:1 contains:2 exclusively:1 score:18 series:1 tuned:1 rightmost:1 outperforms:2 existing:1 current:2 com:1 contextual:3 comparing:1 activation:4 connectomics:5 readily:1 gpu:2 subsequent:1 update:8 fund:1 intelligence:2 fewer:1 plane:2 is... |
5,122 | 5,637 | Generative Image Modeling Using Spatial LSTMs
Matthias Bethge
University of T?ubingen
72076 T?ubingen, Germany
matthias@bethgelab.org
Lucas Theis
University of T?ubingen
72076 T?ubingen, Germany
lucas@bethgelab.org
Abstract
Modeling the distribution of natural images is challenging, partly because of
strong statisti... | 5637 |@word briefly:1 version:3 compression:2 seems:2 nd:3 disk:1 hyv:1 tried:4 bn:3 covariance:2 rgb:2 decomposition:1 thereby:1 inpainting:3 moment:1 reduction:1 renewed:1 outperforms:5 guadarrama:1 comparing:1 discretization:1 rnade:4 activation:1 yet:6 written:1 uria:5 partition:1 cheap:1 christian:1 bmcv:1 treatin... |
5,123 | 5,638 | Faster R-CNN: Towards Real-Time Object Detection
with Region Proposal Networks
Shaoqing Ren? Kaiming He
Ross Girshick
Jian Sun
Microsoft Research
{v-shren, kahe, rbg, jiansun}@microsoft.com
Abstract
State-of-the-art object detection networks depend on region proposal algorithms
to hypothesize object locations. Adv... | 5638 |@word cnn:44 version:1 middle:1 repository:1 everingham:1 sgd:1 minus:1 incarnation:1 contains:1 score:9 trainval:12 tuned:3 ours:1 guadarrama:1 com:4 yet:3 gpu:4 exposing:1 visible:1 enables:2 lcls:2 hypothesize:1 drop:3 plot:1 rpn:78 v:6 alone:3 fewer:6 leaf:1 short:1 provides:2 detecting:1 contribute:2 locatio... |
5,124 | 5,639 | Weakly-supervised Disentangling with
Recurrent Transformations for 3D View Synthesis
Jimei Yang1
Scott Reed2
Ming-Hsuan Yang1
Honglak Lee2
1
University of California, Merced
{jyang44, mhyang}@ucmerced.edu
2
University of Michigan, Ann Arbor
{reedscot, honglak}@umich.edu
Abstract
An important problem for both grap... | 5639 |@word kohli:1 cnn:10 version:2 longterm:1 pick:1 dramatic:1 shot:1 carry:2 series:3 contains:1 tuned:1 ours:1 interestingly:1 existing:1 current:1 comparing:7 guadarrama:1 cad:3 luo:1 must:2 gpu:1 mesh:2 subsequent:1 realistic:1 unpooling:1 informative:1 shape:7 remove:1 drop:2 hypothesize:1 progressively:2 gener... |
5,125 | 564 | Image Segmentation with Networks of Variable Scales
Hans P. Grar
Craig R. Nohl
AT&T Bell Laboratories
Crawfords Comer Road
Holmdel, NJ 07733, USA
Jan Ben
ABSTRACT
We developed a neural net architecture for segmenting complex
images, i.e., to localize two-dimensional geometrical shapes in a scene,
without prior know... | 564 |@word middle:2 open:1 contains:1 document:1 must:1 written:1 janow:1 shape:6 update:1 half:1 fewer:1 guess:1 intelligence:1 sram:1 short:1 detecting:1 provides:4 five:3 height:1 along:1 consists:1 combine:1 inside:1 introduce:1 becomes:1 matched:1 moreover:2 kaufman:1 substantially:1 developed:1 finding:4 transfor... |
5,126 | 5,640 | Exploring Models and Data for Image Question
Answering
Mengye Ren1 , Ryan Kiros1 , Richard S. Zemel1,2
University of Toronto1
Canadian Institute for Advanced Research2
{mren, rkiros, zemel}@cs.toronto.edu
Abstract
This work aims to address the problem of image-based question-answering (QA)
with new models and dataset... | 5640 |@word kohli:1 cnn:12 version:2 middle:1 open:2 tried:1 mengye:1 reduction:3 configuration:1 contains:2 score:2 fragment:1 hoiem:1 tram:1 outperforms:1 existing:2 current:2 com:1 comparing:2 surprising:1 guadarrama:1 conjunctive:1 parsing:2 hypothesize:1 treating:1 designed:2 alone:3 intelligence:1 fewer:1 guess:4... |
5,127 | 5,641 | Are You Talking to a Machine?
Dataset and Methods for Multilingual Image Question Answering
Haoyuan Gao1
1
Junhua Mao2
Baidu Research
Jie Zhou1 Zhiheng Huang1 Lei Wang1
2
University of California, Los Angeles
Wei Xu1
gaohaoyuan@baidu.com, mjhustc@ucla.edu, {zhoujie01,huangzhiheng,wanglei22,wei.xu}@baidu.com
Abstr... | 5641 |@word cnn:14 version:3 judgement:1 stronger:1 kokkinos:1 instruction:1 idl:3 pick:1 harder:1 initial:4 liu:1 contains:10 score:14 selecting:1 ours:1 current:4 com:6 guadarrama:1 haoyuan:1 activation:6 parsing:1 concatenate:2 remove:3 designed:1 grass:1 half:2 selected:3 guess:2 intelligence:1 beginning:1 vanishin... |
5,128 | 5,642 | Parallel Multi-Dimensional LSTM, With Application
to Fast Biomedical Volumetric Image Segmentation
Marijn F. Stollenga*123 , Wonmin Byeon*1245 , Marcus Liwicki4 , and Juergen Schmidhuber123
*
Shared first authors, both Authors contribruted equally to this work. Corresponding authors:
marijn@idsia.ch, wonmin.byeon@dfki... | 5642 |@word mri:1 inversion:2 tried:1 bn:1 pick:1 fifteen:1 euclidian:1 brightness:2 tr:1 recursively:2 liu:1 contains:1 outperforms:1 err:3 ka:1 com:1 od:1 activation:4 yet:1 gpu:6 shape:1 dive:5 designed:1 update:1 infant:1 intelligence:1 fewer:1 half:1 website:1 plane:6 desktop:1 vanishing:1 short:4 core:1 lr:5 loca... |
5,129 | 5,643 | Learning From Small Samples: An Analysis of
Simple Decision Heuristics
? ur
? S?ims?ek and Marcus Buckmann
Ozg
Center for Adaptive Behavior and Cognition
Max Planck Institute for Human Development
Lentzeallee 94, 14195 Berlin, Germany
{ozgur, buckmann}@mpib-berlin.mpg.de
Abstract
Simple decision heuristics are models ... | 5643 |@word aircraft:1 trial:1 repository:1 version:2 rising:1 norm:1 simulation:2 bn:1 jacob:1 dieckmann:1 asks:1 selecting:1 past:1 lichtenberg:1 comparing:4 si:1 assigning:1 pe1:1 subsequent:1 numerical:1 informative:6 thrust:1 plot:2 drop:1 fund:1 stationary:1 cue:106 alone:1 guess:1 greedy:18 infant:1 intelligence... |
5,130 | 5,644 | 3D Object Proposals for Accurate Object Class
Detection
Xiaozhi Chen1
Kaustav Kundu 2
Huimin Ma1
Yukun Zhu2
Sanja Fidler2
Andrew Berneshawi2
Raquel Urtasun2
2
1
Department of Computer Science
University of Toronto
Department of Electronic Engineering
Tsinghua University
chenxz12@mails.tsinghua.edu.cn, {kkundu... | 5644 |@word cnn:8 achievable:4 everingham:1 closure:1 rgb:15 pick:1 holy:1 configuration:3 contains:2 score:5 ours:21 interestingly:1 batista:2 outperforms:7 existing:6 past:2 current:1 contextual:1 discretization:1 cad:1 skipping:1 visible:1 shape:3 hofmann:1 motor:1 visibility:1 occlude:1 v:1 greedy:1 fewer:1 half:1 ... |
5,131 | 5,645 | The Poisson Gamma Belief Network
Mingyuan Zhou
McCombs School of Business
The University of Texas at Austin
Austin, TX 78712, USA
Yulai Cong
National Laboratory of RSP
Xidian University
Xi?an, Shaanxi, China
Bo Chen
National Laboratory of RSP
Xidian University
Xi?an, Shaanxi, China
Abstract
To infer a multilayer rep... | 5645 |@word trial:4 proportion:1 loading:2 norm:1 c0:6 simulation:1 propagate:3 tried:1 hsieh:1 contrastive:1 liblinear:2 electronics:3 score:1 document:18 outperforms:1 existing:1 comparing:1 com:1 yet:1 lauly:1 visible:1 subsequent:2 j1:1 partition:1 shape:8 remove:1 designed:1 interpretable:1 update:2 v:4 generative... |
5,132 | 5,646 | Semi-Supervised Factored Logistic Regression for
High-Dimensional Neuroimaging Data
Danilo Bzdok, Michael Eickenberg, Olivier Grisel, Bertrand Thirion, Ga?el Varoquaux
INRIA, Parietal team, Saclay, France
CEA, Neurospin, Gif-sur-Yvette, France
firstname.lastname@inria.fr
Abstract
Imaging neuroscience links human behav... | 5646 |@word multitask:1 mild:1 middle:1 compression:2 loading:4 norm:1 advantageous:1 c0:5 open:1 instruction:1 grey:2 confirms:1 decomposition:8 reduction:7 configuration:1 lightweight:1 score:4 halchenko:2 existing:2 current:1 com:1 comparing:1 activation:4 gpu:1 shape:7 toro:1 motor:1 plot:2 interpretable:3 update:1... |
5,133 | 5,647 | BinaryConnect: Training Deep Neural Networks with
binary weights during propagations
Matthieu Courbariaux
?
Ecole
Polytechnique de Montr?eal
matthieu.courbariaux@polymtl.ca
Yoshua Bengio
Universit?e de Montr?eal, CIFAR Senior Fellow
yoshua.bengio@gmail.com
Jean-Pierre David
?
Ecole
Polytechnique de Montr?eal
jean-pi... | 5647 |@word cnn:4 version:6 seems:2 shuicheng:1 bn:1 jacob:1 pick:1 sgd:11 liu:2 daniel:2 ecole:2 ours:1 interestingly:1 document:1 past:1 com:2 discretization:8 luo:1 activation:5 gmail:1 yet:1 must:1 gpu:2 john:1 diederik:1 devin:1 numerical:1 christian:3 hypothesize:1 update:10 v:2 half:2 device:2 math:2 pascanu:3 t... |
5,134 | 5,648 | Learning to Transduce with Unbounded Memory
Edward Grefenstette
Google DeepMind
etg@google.com
Karl Moritz Hermann
Google DeepMind
kmh@google.com
Mustafa Suleyman
Google DeepMind
mustafasul@google.com
Phil Blunsom
Google DeepMind and Oxford University
pblunsom@google.com
Abstract
Recently, strong results have been ... | 5648 |@word illustrating:1 middle:1 version:3 kmh:1 bigram:3 replicate:1 inversion:3 proportion:2 economically:1 unif:2 d2:1 simulation:1 thereby:1 initial:3 substitution:1 configuration:1 score:3 initialisation:1 tuned:3 ours:1 outperforms:1 current:1 com:4 wd:2 comparing:1 si:4 rpi:1 bd:3 must:3 parsing:3 remove:2 de... |
5,135 | 5,649 | Spectral Representations for
Convolutional Neural Networks
Oren Rippel
Department of Mathematics
Massachusetts Institute of Technology
Jasper Snoek
Twitter and Harvard SEAS
jsnoek@seas.harvard.edu
rippel@math.mit.edu
Ryan P. Adams
Twitter and Harvard SEAS
rpa@seas.harvard.edu
Abstract
Discrete Fourier transforms pr... | 5649 |@word coprocessor:1 cnn:10 inversion:1 achievable:1 norm:1 coarseness:1 open:1 shuicheng:1 seek:2 propagate:2 decomposition:2 thereby:2 carry:1 reduction:10 initial:2 configuration:2 series:1 rippel:5 past:1 existing:1 imaginary:1 current:1 com:1 assigning:1 diederik:1 must:2 gpu:1 enables:2 christian:1 plot:1 up... |
5,136 | 565 | Retinogeniculate Development:
The Role of Competition and Correlated Retinal
Activity
Ron Keesing*
David G. Stork
*Ricoh
California
Research Center
Dept. of Physiology
2882 Sand Hill Rd., Suite 115
U.C. San Francisco
San Francisco, CA 94143
Menlo Park, CA 94025
stork@crc.ricoh.com
keesing@phy.ucsf.edu
Carla J. Shatz... | 565 |@word cu:1 cco:1 gradual:1 simulation:10 initial:1 phy:1 series:1 current:1 com:1 neurobio:1 distant:1 plasticity:4 discernible:1 cue:1 provides:1 coarse:4 location:1 ron:1 burst:1 gustafsson:1 rapid:2 behavior:1 disrupts:1 roughly:1 morphology:1 vertebrate:1 moreover:1 what:1 developed:1 finding:1 suite:1 growth:... |
5,137 | 5,650 | A Theory of Decision Making Under Dynamic
Context
Michael Shvartsman
Princeton Neuroscience Institute
Princeton University
Princeton, NJ, 08544
ms44@princeton.edu
Vaibhav Srivastava
Department of Mechanical and Aerospace Engineering
Princeton University
Princeton, NJ, 08544
vaibhavs@princeton.edu
Jonathan D. Cohen
Pr... | 5650 |@word trial:27 middle:1 proportion:3 replicate:1 c0:43 simulation:7 seek:1 p0:24 mention:1 reduction:2 liu:2 series:1 tuned:1 existing:5 reaction:3 current:1 discretization:1 contextual:1 com:1 recovered:1 activation:1 must:1 visible:2 informative:2 wellbehaved:1 analytic:1 shape:1 remove:1 plot:3 concert:1 stroo... |
5,138 | 5,651 | Bidirectional Recurrent Neural Networks as
Generative Models
Mathias Berglund
Aalto University, Finland
Leo K?arkk?ainen
Nokia Labs, Finland
Tapani Raiko
Aalto University, Finland
Akos Vetek
Nokia Labs, Finland
Mikko Honkala
Nokia Labs, Finland
Juha Karhunen
Aalto University, Finland
Abstract
Bidirectional recurrent... | 5651 |@word middle:5 seems:1 open:1 simulation:1 pressed:1 initial:1 series:15 score:3 past:2 arkk:1 od:2 activation:3 yet:1 written:2 gpu:2 uria:1 additive:1 concatenate:1 wx:2 ainen:1 update:2 polyphonic:4 stationary:1 generative:8 prohibitive:1 selected:1 intelligence:2 beginning:2 short:2 record:2 haykin:1 pascanu:... |
5,139 | 5,652 | Recognizing retinal ganglion cells in the dark
Emile Richard
Stanford University
emileric@stanford.edu
Georges Goetz
Stanford University
ggoetz@stanford.edu
E.J. Chichilnisky
Stanford University
ej@stanford.edu
Abstract
Many neural circuits are composed of numerous distinct cell types that perform
different operati... | 5652 |@word neurophysiology:1 middle:5 polynomial:1 seems:2 open:1 thereby:1 initial:1 contains:1 score:1 existing:3 comparing:1 written:1 gpu:2 physiol:1 subsequent:2 concatenate:1 numerical:4 interspike:6 shape:1 enables:1 wanted:1 seeding:1 designed:1 plot:1 opin:1 v:9 pursued:1 fewer:1 guess:1 device:1 intelligence... |
5,140 | 5,653 | A Recurrent Latent Variable Model
for Sequential Data
Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel,
Aaron Courville, Yoshua Bengio?
Department of Computer Science and Operations Research
Universit?e de Montr?eal
?
CIFAR Senior Fellow
{firstname.lastname}@umontreal.ca
Abstract
In this paper, we explore th... | 5653 |@word middle:2 version:2 nd:1 covariance:1 recursively:1 contains:5 document:1 com:1 activation:1 must:3 written:1 subsequent:1 enables:1 designed:1 plot:1 update:2 polyphonic:1 generative:6 parameterization:1 inspection:1 beginning:1 fabius:1 short:2 pascanu:1 ondb:5 firstly:1 simpler:2 wierstra:2 guard:1 tokuda... |
5,141 | 5,654 | Deep Knowledge Tracing
Chris Piech? , Jonathan Bassen? , Jonathan Huang?? , Surya Ganguli? ,
Mehran Sahami? , Leonidas Guibas? , Jascha Sohl-Dickstein??
?
Stanford University, ? Khan Academy, ? Google
{piech,jbassen}@cs.stanford.edu, jascha@stanford.edu,
Abstract
Knowledge tracing?where a machine models the knowledge... | 5654 |@word faculty:1 seems:2 norm:1 nd:1 open:3 instruction:1 integrative:1 simulation:1 propagate:1 eng:1 reduction:1 initial:1 substitution:1 series:4 uncovered:1 efficacy:1 tuned:1 cleared:1 past:3 current:4 recovered:1 com:2 comparing:3 contextual:1 activation:1 assigning:2 scatter:3 must:1 john:1 subsequent:1 add... |
5,142 | 5,655 | Deep Temporal Sigmoid Belief Networks
for Sequence Modeling
Zhe Gan, Chunyuan Li, Ricardo Henao, David Carlson and Lawrence Carin
Department of Electrical and Computer Engineering
Duke University, Durham, NC 27708
{zhe.gan, chunyuan.li, r.henao, david.carlson, lcarin}@duke.edu
Abstract
Deep dynamic generative models ... | 5655 |@word trial:1 middle:6 contrastive:2 sgd:2 recursively:1 carry:1 reduction:2 series:9 contains:3 document:3 past:2 current:3 contextual:2 com:1 written:1 readily:4 realistic:1 partition:1 visible:6 designed:1 update:3 polyphonic:8 generative:17 selected:2 fewer:1 intelligence:1 parameterization:1 fabius:1 provide... |
5,143 | 5,656 | Hidden Technical Debt in Machine Learning Systems
D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips
{dsculley,gholt,dgg,edavydov,toddphillips}@google.com
Google, Inc.
Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Franc?ois Crespo, Dan Dennison
{ebner,vchaudhary,mwyoung,jfcrespo,dennison}@googl... | 5656 |@word repository:1 version:4 middle:1 briefly:1 seems:2 glue:9 open:1 closure:2 seek:1 paid:1 pressure:3 pick:2 harder:1 carry:3 reduction:1 initial:1 liu:1 catastrophically:1 score:1 selecting:2 configuration:17 daniel:1 unintended:2 subjective:1 hrafnkelsson:2 existing:1 reaction:1 current:1 contextual:1 surpri... |
5,144 | 5,657 | Statistical Model Criticism
using Kernel Two Sample Tests
James Robert Lloyd
Department of Engineering
University of Cambridge
Zoubin Ghahramani
Department of Engineering
University of Cambridge
Abstract
We propose an exploratory approach to statistical model criticism using maximum
mean discrepancy (MMD) two sample... | 5657 |@word version:2 middle:2 smirnov:1 nd:1 replicate:1 mimick:1 covariance:1 contrastive:1 asks:1 solid:1 reduction:2 series:6 disparity:1 selecting:1 daniel:1 rkhs:3 bradley:1 comparing:2 surprising:3 analysed:1 activation:1 must:1 john:1 visible:2 kdd:1 shape:3 analytic:1 plot:1 update:1 alone:1 generative:6 selec... |
5,145 | 5,658 | Calibrated Structured Prediction
Percy Liang
Department of Computer Science
Stanford University
Stanford, CA 94305
Volodymyr Kuleshov
Department of Computer Science
Stanford University
Stanford, CA 94305
Abstract
In user-facing applications, displaying calibrated confidence measures?
probabilities that correspond to ... | 5658 |@word middle:4 version:1 judgement:1 nd:2 confirms:1 forecaster:25 tried:1 decomposition:3 datagenerating:1 tr:5 series:1 efficacy:1 score:12 interestingly:1 existing:3 current:1 lang:1 must:4 numerical:1 partition:2 informative:3 kdd:1 pseudomarginals:1 drop:1 plot:1 progressively:1 v:3 alone:1 intelligence:1 le... |
5,146 | 5,659 | A Bayesian Framework for Modeling Confidence in
Perceptual Decision Making
Koosha Khalvati, Rajesh P. N. Rao
Department of Computer Science and Engineering
University of Washington
Seattle, WA 98195
{koosha, rao}@cs.washington.edu
Abstract
The degree of confidence in one?s choice or decision is a critical aspect of p... | 5659 |@word trial:46 judgement:1 termination:4 confirms:1 koosha:3 pick:3 solid:4 wagering:12 initial:11 united:1 mainen:2 document:1 reaction:12 current:1 anne:1 must:5 moreno:2 plot:12 update:2 discrimination:8 implying:1 half:1 v:5 guess:7 intelligence:3 mathematical:2 become:1 persistent:1 waived:2 fitting:2 behavi... |
5,147 | 566 | Dual Inhibitory Mechanisms for Definition of
Receptive Field Characteristics in Cat Striate
Cortex
A. B. Bonds
Dept. of Electrical Engineering
Vanderbilt University
Nashville, TN 37235
Abstract
In single cells of the cat striate cortex, lateral inhibition across orientation and/or spatial frequency is found to enhanc... | 566 |@word neurophysiology:3 polynomial:2 norm:1 seems:1 open:3 dramatic:1 solid:2 reduction:4 configuration:1 tuned:2 existing:2 reaction:1 current:1 refines:1 interspike:1 discernible:1 designed:1 v:1 alone:1 half:1 device:1 signalling:2 short:1 burst:29 shapley:2 pathway:2 behavioral:1 manner:1 mask:21 expected:1 ra... |
5,148 | 5,660 | Dependent Multinomial Models Made Easy:
Stick Breaking with the P?olya-Gamma Augmentation
Scott W. Linderman?
Harvard University
Cambridge, MA 02138
swl@seas.harvard.edu
Matthew J. Johnson?
Harvard University
Cambridge, MA 02138
mattjj@csail.mit.edu
Ryan P. Adams
Twitter & Harvard University
Cambridge, MA 02138
rpa@... | 5660 |@word multitask:1 version:1 middle:1 seems:1 logit:1 nd:1 decomposition:1 covariance:3 pg:5 olyagamma:2 dramatic:1 recursively:1 moment:1 born:2 series:5 efficacy:1 united:2 uncovered:1 siebel:1 document:8 outperforms:1 existing:2 elliptical:4 com:1 nt:1 written:1 must:1 john:2 ronan:1 plot:1 interpretable:1 upda... |
5,149 | 5,661 | Scalable Adaptation of State Complexity for
Nonparametric Hidden Markov Models
Michael C. Hughes, William Stephenson, and Erik B. Sudderth
Department of Computer Science, Brown University, Providence, RI 02912
mhughes@cs.brown.edu, wtstephe@gmail.com, sudderth@cs.brown.edu
Abstract
Bayesian nonparametric hidden Marko... | 5661 |@word middle:1 version:1 interleave:1 open:1 km:1 scalably:1 seek:2 memoize:1 splitmerge:1 thereby:1 solid:3 reduction:1 initial:3 series:2 score:3 tuned:1 existing:5 recovered:1 com:1 nt:3 current:5 abundantly:1 si:2 gmail:1 yet:2 assigning:1 must:3 john:2 numerical:1 enables:2 analytic:1 remove:5 plot:1 interpr... |
5,150 | 5,662 | Robust Feature-Sample Linear Discriminant Analysis
for Brain Disorders Diagnosis
Ehsan Adeli-Mosabbeb, Kim-Han Thung, Le An, Feng Shi, Dinggang Shen, for the ADNI?
Department of Radiology and BRIC
University of North Carolina at Chapel Hill, NC, 27599, USA
{eadeli,khthung,le_an,fengshi,dgshen}@med.unc.edu
Abstract
A ... | 5662 |@word mild:2 mri:7 middle:2 norm:11 d2:5 seek:1 carolina:1 decomposition:1 covariance:1 tr:21 initial:1 liu:1 contains:2 ours:1 outperforms:3 existing:1 current:1 neurobio:1 activation:1 yet:1 motor:1 e22:1 designed:1 atlas:2 update:7 medial:1 v:6 generative:1 selected:4 device:2 nervous:1 weighing:1 accordingly:... |
5,151 | 5,663 | Learning spatiotemporal trajectories from
manifold-valued longitudinal data
Jean-Baptiste Schiratti2,1 , St?ephanie Allassonni`ere2 , Olivier Colliot1 , Stanley Durrleman1
1
ARAMIS Lab, INRIA Paris, Inserm U1127, CNRS UMR 7225, Sorbonne Universit?es,
UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle e... | 5663 |@word mild:1 version:4 middle:1 seems:1 logit:2 open:1 hyv:1 simulation:7 simplifying:1 p0:26 commute:1 initial:1 series:2 score:7 ecole:1 longitudinal:16 current:2 ida:1 si:3 written:1 john:1 mesh:1 zeger:1 shape:7 seeding:1 plot:3 stationary:1 metabolism:1 item:5 parameterization:1 parametrization:1 ith:2 hamil... |
5,152 | 5,664 | Hessian-free Optimization for Learning
Deep Multidimensional Recurrent Neural Networks
Minhyung Cho
Chandra Shekhar Dhir
Jaehyung Lee
Applied Research Korea, Gracenote Inc.
{mhyung.cho,shekhardhir}@gmail.com
jaehyung.lee@kaist.ac.kr
Abstract
Multidimensional recurrent neural networks (MDRNNs) have shown a remarkable ... | 5664 |@word arabic:3 sgd:8 tif:2 reduction:1 initial:2 substitution:1 contains:3 liu:1 interestingly:1 blank:1 com:1 contextual:1 marquardt:1 activation:2 gmail:1 written:7 romero:1 christian:1 gv:4 remove:2 update:1 half:1 selected:2 beginning:1 core:2 short:1 provides:1 pascanu:1 org:1 five:3 unbounded:1 along:2 cons... |
5,153 | 5,665 | Scalable Inference for Gaussian Process Models with
Black-Box Likelihoods
Edwin V. Bonilla
The University of New South Wales
e.bonilla@unsw.edu.au
Amir Dezfouli
The University of New South Wales
akdezfuli@gmail.com
Abstract
We propose a sparse method for scalable automated variational inference (AVI) in
a large class... | 5665 |@word cox:6 middle:2 version:1 inversion:2 proportion:1 faculty:1 repository:1 covariance:10 decomposition:2 tr:1 solid:1 shading:1 harder:2 edric:1 carry:1 contains:1 lichman:1 daniel:1 ours:1 interestingly:2 outperforms:1 elliptical:3 com:1 lgcp:2 anne:1 gmail:1 yet:2 attracted:1 realize:1 fn:22 multioutput:1 i... |
5,154 | 5,666 | Variational Dropout and
the Local Reparameterization Trick
?
Diederik P. Kingma? , Tim Salimans? and Max Welling??
?
Machine Learning Group, University of Amsterdam
?
Algoritmica
University of California, Irvine, and the Canadian Institute for Advanced Research (CIFAR)
D.P.Kingma@uva.nl, salimans.tim@gmail.com, M.Wel... | 5666 |@word version:4 seems:1 nd:1 crucially:1 covariance:5 harder:1 ld:7 reduction:1 initial:1 contains:1 ours:1 current:2 com:1 comparing:1 activation:8 diederik:2 gmail:1 yet:3 si:4 written:1 gpu:2 happen:1 designed:1 update:1 generative:1 fewer:2 device:1 parameterizations:1 pascanu:1 math:1 toronto:1 simpler:2 zha... |
5,155 | 5,667 | Infinite Factorial Dynamical Model
Isabel Valera?
Max Planck Institute for
Software Systems
ivalera@mpi-sws.org
Francisco J. R. Ruiz?
Department of Computer Science
Columbia University
f.ruiz@columbia.edu
Fernando Perez-Cruz
Universidad Carlos III de Madrid, and
Bell Labs, Alcatel-Lucent
fernandop@ieee.org
Lennart Sv... | 5667 |@word briefly:1 pcc:2 open:1 mibp:7 r:3 propagate:2 simulation:2 covariance:1 p0:4 xout:2 recursively:1 ld:1 initial:7 metre:7 series:4 contains:1 amp:4 multiuser:8 existing:2 outperforms:5 current:6 disaggregation:10 com:1 ts2:1 nt:6 si:1 recovered:3 arkk:1 guez:1 cruz:1 casi:1 additive:3 realistic:1 ministerio:... |
5,156 | 5,668 | Variational Information Maximisation for
Intrinsically Motivated Reinforcement Learning
Shakir Mohamed and Danilo J. Rezende
Google DeepMind, London
{shakir, danilor}@google.com
Abstract
The mutual information is a core statistical quantity that has applications in all areas of machine learning, whether this is in tra... | 5668 |@word selforganization:1 manageable:2 open:2 termination:1 d2:2 seek:2 simulation:2 harder:1 configuration:2 series:1 daniel:1 past:1 existing:2 current:6 com:1 comparing:2 activation:1 scatter:1 must:6 john:1 realistic:2 subsequent:1 entrance:1 cheap:1 plot:1 designed:2 update:1 v:1 generative:3 leaf:1 greedy:2 ... |
5,157 | 5,669 | Copula variational inference
Dustin Tran
Harvard University
David M. Blei
Columbia University
Edoardo M. Airoldi
Harvard University
Abstract
We develop a general variational inference method that preserves dependency
among the latent variables. Our method uses copulas to augment the families of
distributions used i... | 5669 |@word version:1 eliminating:1 inversion:1 proportion:1 logit:1 unif:2 d2:1 simulation:1 propagate:1 decomposition:1 covariance:9 kappen:1 moment:1 series:4 score:2 selecting:1 fa8750:1 outperforms:3 existing:1 elliptical:1 must:1 john:1 partition:1 enables:2 plot:2 update:4 sont:1 intelligence:2 generative:1 hami... |
5,158 | 567 | Merging Constrained Optimisation with
Deterministic Annealing to "Solve"
Combinatorially Hard Problems
Paul Stolorz?
Santa Fe Institute
1660 Old Pecos Trail, Suite A
Santa Fe, NM 87501
ABSTRACT
Several parallel analogue algorithms, based upon mean field theory (MFT)
approximations to an underlying statistical mechani... | 567 |@word eliminating:1 inversion:1 seems:1 nd:1 suitably:1 sex:2 simulation:3 seek:2 r:1 crucially:1 dramatic:1 solid:1 contains:1 series:1 offering:1 analysed:1 tackling:2 yet:2 must:4 written:1 numerical:4 additive:1 girosi:1 update:1 alone:1 prohibitive:1 inspection:1 vanishing:3 compo:1 location:1 firstly:1 simpl... |
5,159 | 5,670 | Fast Second-Order Stochastic Backpropagation for
Variational Inference
Kai Fan
Duke University
kai.fan@stat.duke.edu
Ziteng Wang?
HKUST?
wangzt2012@gmail.com
Jeffrey Beck
Duke University
jeff.beck@duke.edu
Katherine Heller
Duke University
kheller@gmail.com
James T. Kwok
HKUST
jamesk@cse.ust.hk
Abstract
We propose ... | 5670 |@word kong:2 version:3 seems:1 nd:23 covariance:5 decomposition:1 sgd:6 reduction:1 initial:1 substitution:1 series:1 jimenez:2 seriously:1 tuned:3 com:2 hkust:2 activation:2 gmail:2 diederik:2 ust:1 gpu:2 readily:1 john:2 numerical:2 designed:2 interpretable:1 update:2 generative:12 fewer:1 device:1 parameteriza... |
5,160 | 5,671 | Rethinking LDA: Moment Matching for Discrete ICA
Anastasia Podosinnikova
Francis Bach
Simon Lacoste-Julien
?
INRIA - Ecole
normale sup?erieure Paris
Abstract
We consider moment matching techniques for estimation in latent Dirichlet allocation (LDA). By drawing explicit links between LDA and discrete versions of
indep... | 5671 |@word msr:1 repository:1 version:1 proportion:1 norm:2 nd:1 c0:12 hyv:1 covariance:3 decomposition:1 moment:45 liu:2 contains:1 ecole:1 document:42 interestingly:1 outperforms:1 existing:2 err:1 current:2 discretization:1 comparing:2 recovered:1 com:1 additive:1 happen:1 numerical:2 shape:1 plot:1 update:1 genera... |
5,161 | 5,672 | Model-Based Relative Entropy Stochastic Search
Abbas Abdolmaleki1,2,3 , Rudolf Lioutikov4 , Nuno Lau1 , Luis Paulo Reis2,3 ,
Jan Peters4,6 , and Gerhard Neumann5
1: IEETA, University of Aveiro, Aveiro, Portugal
2: DSI, University of Minho, Braga, Portugal
3: LIACC, University of Porto, Porto, Portugal
4: IAS, 5: CLAS,... | 5672 |@word trial:2 exploitation:3 version:2 inversion:1 open:1 simulation:1 tried:1 covariance:5 reduction:7 initial:1 genetic:1 outperforms:8 existing:1 elliptical:2 current:2 contextual:1 yet:3 written:2 luis:1 bd:1 subsequent:2 shape:1 analytic:1 motor:2 update:15 intelligence:2 plane:1 rosenbrock:4 provides:1 mann... |
5,162 | 5,673 | Supervised Learning for Dynamical System Learning
Ahmed Hefny ?
Carnegie Mellon University
Pittsburgh, PA 15213
ahefny@cs.cmu.edu
Carlton Downey ?
Carnegie Mellon University
Pittsburgh, PA 15213
cmdowney@cs.cmu.edu
Geoffrey J. Gordon ?
Carnegie Mellon University
Pittsburgh, PA 15213
ggordon@cs.cmu.edu
Abstract
Rece... | 5673 |@word repository:1 middle:1 instrumental:12 proportion:1 closure:1 covariance:10 decomposition:1 xtest:5 q1:2 pick:1 thereby:3 solid:1 harder:1 reduction:1 moment:11 initial:7 series:2 contains:1 daniel:2 past:4 existing:5 current:1 soules:1 must:2 john:3 remove:1 designed:1 v:2 spec:7 intelligence:3 short:1 core... |
5,163 | 5,674 | Expectation Particle Belief Propagation
Thibaut Lienart, Yee Whye Teh, Arnaud Doucet
Department of Statistics
University of Oxford
Oxford, UK
{lienart,teh,doucet}@stats.ox.ac.uk
Abstract
We propose an original particle-based implementation of the Loopy Belief Propagation (LPB) algorithm for pairwise Markov Random Fie... | 5674 |@word msr:1 illustrating:1 version:2 underperform:1 simulation:6 pick:1 tr:2 moment:3 selecting:2 shum:1 daniel:1 outperforms:2 current:4 comparing:1 com:1 recovered:1 must:2 written:1 john:1 tilted:4 mesh:4 christian:1 update:12 isard:1 selected:1 rudin:1 randolph:1 short:4 core:2 provides:5 math:1 node:41 unbou... |
5,164 | 5,675 | Embedding Inference
for Structured Multilabel Prediction
Farzaneh Mirzazadeh Siamak Ravanbakhsh
University of Alberta
Nan Ding
Google
Dale Schuurmans
University of Alberta
{mirzazad,mravanba}@ualberta.ca
dingnan@google.com
daes@ualberta.ca
Abstract
A key bottleneck in structured output prediction is the need for ... | 5675 |@word eliminating:1 norm:2 nd:1 hu:1 accounting:1 sepulchre:1 shot:1 initial:1 configuration:2 substitution:1 score:31 document:3 rightmost:2 jyv:1 ka:3 com:2 assigning:1 must:12 written:1 subsequent:1 happen:2 tailoring:1 hofmann:2 shape:1 mirzazadeh:2 cis:1 siamak:1 depict:1 implying:4 generative:1 greedy:1 lea... |
5,165 | 5,676 | Tractable Learning for Complex Probability Queries
Jessa Bekker, Jesse Davis
KU Leuven, Belgium
{jessa.bekker,jesse.davis}@cs.kuleuven.be
Arthur Choi, Adnan Darwiche, Guy Van den Broeck
University of California, Los Angeles
{aychoi,darwiche,guyvdb}@cs.ucla.edu
Abstract
Tractable learning aims to learn probabilistic m... | 5676 |@word version:1 polynomial:2 norm:1 adnan:1 tried:1 q1:2 carry:1 initial:2 liu:1 contains:2 score:12 daniel:1 document:1 existing:3 current:1 comparing:1 stemmed:2 schnitger:1 conjunctive:4 written:1 must:1 determinantal:2 partition:5 fund:1 v:2 greedy:3 leaf:1 selected:4 generative:1 core:1 num:1 detecting:1 com... |
5,166 | 5,677 | Double or Nothing: Multiplicative
Incentive Mechanisms for Crowdsourcing
Nihar B. Shah
University of California, Berkeley
nihar@eecs.berkeley.edu
Dengyong Zhou
Microsoft Research
dengyong.zhou@microsoft.com
Abstract
Crowdsourcing has gained immense popularity in machine learning applications
for obtaining large amoun... | 5677 |@word mild:2 trial:1 version:2 proportion:1 stronger:1 adrian:1 simulation:8 arjen:1 pg:4 paid:5 thereby:2 reduction:3 liu:2 score:7 karger:1 daniel:1 hermosillo:1 interestingly:2 outperforms:1 existing:1 com:2 surprising:1 must:4 john:2 subsequent:1 additive:9 j1:1 visible:1 cheap:1 wanted:1 selected:2 leaf:1 we... |
5,167 | 5,678 | Local Expectation Gradients for Black Box
Variational Inference
Michalis K. Titsias
Athens University of Economics and Business
mtitsias@aueb.gr
Miguel L?azaro-Gredilla
Vicarious
miguel@vicarious.com
Abstract
We introduce local expectation gradients which is a general purpose stochastic
variational inference algorith... | 5678 |@word version:1 polynomial:1 covariance:2 reduction:9 contains:1 series:1 score:2 jimenez:1 denoting:1 outperforms:1 current:2 com:1 wd:4 si:1 diederik:1 dx:2 written:5 john:2 ronald:1 subsequent:1 numerical:4 informative:1 update:8 intelligence:1 generative:1 mccallum:1 blei:5 provides:4 firstly:1 blackwellized:... |
5,168 | 5,679 | Learning with a Wasserstein Loss
Charlie Frogner? Chiyuan Zhang?
Center for Brains, Minds and Machines
Massachusetts Institute of Technology
frogner@mit.edu, chiyuan@mit.edu
Mauricio Araya-Polo
Shell International E & P, Inc.
Mauricio.Araya@shell.com
Hossein Mobahi
CSAIL
Massachusetts Institute of Technology
hmobahi@c... | 5679 |@word version:1 briefly:1 achievable:1 norm:2 kokkinos:1 villani:1 adrian:1 jacob:1 harder:1 edric:1 score:1 selecting:1 daniel:1 com:1 comparing:2 john:2 numerical:3 shape:1 enables:1 plot:1 v:1 alone:1 bart:1 prohibitive:1 selected:1 rudin:1 plane:1 provides:2 math:2 location:1 club:1 org:2 simpler:1 zhang:1 al... |
5,169 | 568 | Networks with Learned Unit Response Functions
John Moody and Norman Yarvin
Yale Computer Science, 51 Prospect St.
P.O. Box 2158 Yale Station, New Haven, CT 06520-2158
Abstract
Feedforward networks composed of units which compute a sigmoidal function of a weighted sum of their inputs have been much investigated. We
te... | 568 |@word mild:1 polynomial:28 seems:3 tried:2 series:5 contains:3 allon:1 marquardt:3 must:2 john:2 half:2 fewer:2 wiit11:2 location:1 sigmoidal:2 five:4 along:2 huber:1 torque:4 little:4 elbow:1 increasing:2 provided:1 bounded:1 anx:3 substantially:2 every:2 scaled:3 unit:42 grant:2 producing:1 before:1 studied:1 co... |
5,170 | 5,680 | Principal Geodesic Analysis for Probability Measures
under the Optimal Transport Metric
Vivien Seguy
Graduate School of Informatics
Kyoto University
vivien.seguy@iip.ist.i.kyoto-u.ac.jp
Marco Cuturi
Graduate School of Informatics
Kyoto University
mcuturi@i.kyoto-u.ac.jp
Abstract
Given a family of probability measure... | 5680 |@word briefly:1 middle:1 proportion:1 villani:4 adrian:1 rgb:2 decomposition:1 edric:1 carry:3 reduction:3 ati:1 luigi:1 existing:1 recovered:2 written:2 john:1 numerical:2 oberman:1 shape:6 plot:3 kv1:1 depict:1 update:4 v:1 intelligence:1 parameterization:3 accordingly:1 short:1 core:1 volkan:1 location:9 compr... |
5,171 | 5,681 | Fast and Accurate Inference of Plackett?Luce Models
Lucas Maystre
EPFL
lucas.maystre@epfl.ch
Matthias Grossglauser
EPFL
matthias.grossglauser@epfl.ch
Abstract
We show that the maximum-likelihood (ML) estimate of models derived from
Luce?s choice axiom (e.g., the Plackett?Luce model) can be expressed as the
stationary... | 5681 |@word ksenia:1 kong:1 version:1 inversion:1 briefly:1 logit:2 open:1 contraction:1 incurs:1 mention:1 accommodate:1 moment:3 initial:1 necessity:1 series:1 score:1 interestingly:2 outperforms:3 past:1 bradley:11 current:1 com:1 erms:7 surprising:1 yet:2 reminiscent:1 readily:1 numerical:1 partition:2 enables:2 an... |
5,172 | 5,682 | BACK S HIFT :
Learning causal cyclic graphs from
unknown shift interventions
Dominik Rothenh?ausler?
Seminar f?ur Statistik
ETH Z?urich, Switzerland
rothenhaeusler@stat.math.ethz.ch
Jonas Peters
Max Planck Institute for Intelligent Systems
T?ubingen, Germany
jonas.peters@tuebingen.mpg.de
Christina Heinze?
Seminar f?... | 5682 |@word version:4 seems:1 nd:3 hyv:1 simulation:1 nicholson:1 covariance:17 moment:1 cyclic:14 series:8 selecting:1 sogawa:1 ramsey:1 recovered:1 com:2 nicolai:1 plcg:5 yet:1 must:1 john:1 numerical:2 visible:1 additive:2 directlingam:1 alone:2 greedy:2 discovering:2 selected:2 intelligence:6 short:1 characterizati... |
5,173 | 5,683 | Learning with Relaxed Supervision
Percy Liang
Stanford University
pliang@cs.stanford.edu
Jacob Steinhardt
Stanford University
jsteinhardt@cs.stanford.edu
Abstract
For weakly-supervised problems with deterministic constraints between the latent
variables and observed output, learning necessitates performing inference... | 5683 |@word faculty:1 norm:1 advantageous:1 c0:9 instruction:1 jacob:1 decomposition:1 covariance:3 q1:1 pressure:3 simplifying:1 mention:1 pick:2 tr:1 initial:1 substitution:2 contains:2 past:1 current:2 z2:1 clash:1 surprising:1 conjunctive:3 must:1 parsing:7 written:1 john:1 additive:1 numerical:1 distant:1 plot:3 u... |
5,174 | 5,684 | M -Statistic for Kernel Change-Point Detection
Shuang Li, Yao Xie
H. Milton Stewart School of
Industrial and Systems Engineering
Georgian Institute of Technology
sli370@gatech.edu
yao.xie@isye.gatech.edu
Hanjun Dai, Le Song
Computational Science and Engineering
College of Computing
Georgia Institute of Technology
hanj... | 5684 |@word version:1 briefly:1 c0:2 vldb:1 simulation:10 covariance:4 recursively:1 moment:2 reduction:1 liu:1 series:3 contains:1 nii:2 denoting:1 rkhs:1 past:1 existing:2 current:1 comparing:1 john:1 numerical:4 partition:2 enables:1 remove:1 designed:1 plot:1 update:2 intelligence:1 fewer:1 accordingly:1 oldest:1 s... |
5,175 | 5,685 | Fast Two-Sample Testing with Analytic
Representations of Probability Measures
Kacper Chwialkowski
Gatsby Computational Neuroscience Unit, UCL
kacper.chwialkowski@gmail.com
Dino Sejdinovic
Dept of Statistics, University of Oxford
dino.sejdinovic@gmail.com
Aaditya Ramdas
Dept. of EECS and Statistics, UC Berkeley
aramdas... | 5685 |@word repository:1 inversion:1 stronger:1 norm:4 smirnov:1 d2:10 simulation:4 linearized:1 bn:2 covariance:5 azimuthal:1 boundedness:1 carry:1 lichman:1 wj2:1 rkhs:10 outperforms:1 imaginary:1 current:1 com:3 scovel:1 gmail:3 yet:2 written:2 must:1 universality:1 informative:1 j1:1 analytic:27 plot:1 v:8 selected... |
5,176 | 5,686 | Adversarial Prediction Games for Multivariate Losses
Hong Wang
Wei Xing
Kaiser Asif
Brian D. Ziebart
Department of Computer Science
University of Illinois at Chicago
Chicago, IL 60607
{hwang27, wxing3, kasif2, bziebart}@uic.edu
Abstract
Multivariate loss functions are used to assess performance in many modern predict... | 5686 |@word repository:1 version:1 middle:1 inversion:2 polynomial:2 hoffgen:1 adrian:1 relevancy:2 seek:2 moment:1 initial:1 liu:3 score:31 lichman:1 document:7 existing:1 contextual:3 comparing:1 must:5 john:1 chicago:2 additive:5 hofmann:1 enables:1 discrimination:1 v:1 greedy:3 half:1 intelligence:2 item:18 nent:1 ... |
5,177 | 5,687 | Regressive Virtual Metric Learning
Micha?el Perrot, and Amaury Habrard
Universit?e de Lyon, Universit?e Jean Monnet de Saint-Etienne,
Laboratoire Hubert Curien, CNRS, UMR5516, F-42000, Saint-Etienne, France.
{michael.perrot,amaury.habrard}@univ-st-etienne.fr
Abstract
We are interested in supervised metric learning of... | 5687 |@word kulis:2 repository:2 version:8 inversion:1 illustrating:1 seems:2 norm:4 villani:1 tedious:1 open:2 d2:4 decomposition:2 jacob:1 elisseeff:2 harder:1 edric:1 reduction:3 series:2 lichman:1 selecting:3 outperforms:1 existing:2 goldberger:1 must:6 written:1 john:1 partition:2 aside:1 intelligence:2 prohibitiv... |
5,178 | 5,688 | Halting in Random Walk Kernels
Karsten M. Borgwardt
D-BSSE, ETH Z?urich
Basel, Switzerland
karsten.borgwardt@bsse.ethz.ch
Mahito Sugiyama
ISIR, Osaka University, Japan
JST, PRESTO
mahito@ar.sanken.osaka-u.ac.jp
Abstract
Random walk kernels measure graph similarity by counting matching walks in
two graphs. In their m... | 5688 |@word kondor:1 instrumental:2 flach:1 open:1 confirms:2 isir:1 thereby:1 initial:1 series:5 score:8 interestingly:1 perret:1 comparing:1 happen:1 remove:1 plot:1 drop:2 core:1 caveat:1 five:2 mehlhorn:1 along:1 schweitzer:1 direct:3 become:2 prove:1 artner:1 introduce:2 pairwise:1 theoretically:4 hardness:1 karst... |
5,179 | 5,689 | Rate-Agnostic (Causal) Structure Learning
David Danks
Carnegie-Mellon University
Pittsburgh, PA
ddanks@cmu.edu
Sergey Plis
The Mind Research Network,
Albuquerque, NM
s.m.plis@gmail.com
Vince Calhoun
The Mind Research Network
ECE Dept., University of New Mexico
Albuquerque, NM
vcalhoun@mrn.org
Cynthia Freeman
The Mi... | 5689 |@word version:1 proportion:1 open:1 cleanly:1 simulation:2 propagate:1 minus:1 harder:1 tice:1 cyclic:2 series:13 exclusively:1 contains:1 interestingly:3 current:2 com:2 si:5 gmail:2 yet:1 must:3 analytic:1 enables:1 remove:3 plot:5 fund:1 intelligence:4 fewer:1 discovering:1 greedy:1 inspection:1 core:4 regress... |
5,180 | 569 | The Efficient Learning of Multiple Task
Sequences
Satinder P. Singh
Department of Computer Science
University of Massachusetts
Amherst, MA 01003
Abstract
I present a modular network architecture and a learning algorithm based
on incremental dynamic programming that allows a single learning agent
to learn to solve mul... | 569 |@word trial:5 middle:1 simulation:4 decomposition:13 jacob:6 rightmost:1 current:5 nowlan:1 si:3 assigning:2 informative:1 update:2 alone:1 greedy:1 discovering:2 selected:2 short:1 location:4 traverse:1 simpler:3 constructed:1 c2:1 prove:1 consists:1 compose:2 combine:2 overhead:1 expected:3 coa:2 simulator:1 dec... |
5,181 | 5,690 | Online Prediction at the Limit of Zero Temperature
Mark Herbster
Stephen Pasteris
Department of Computer Science
University College London
London WC1E 6BT, England, UK
{m.herbster,s.pasteris}@cs.ucl.ac.uk
Shaona Ghosh
ECS
University of Southampton
Southampton, UK SO17 1BJ
ghosh.shaona@gmail.com
Abstract
We design an... | 5690 |@word h:1 trial:4 seems:2 norm:3 stronger:1 nd:3 simplifying:2 pg:4 initial:1 contains:3 selecting:1 tuned:2 current:1 com:1 gmail:1 must:1 john:2 partition:4 frievald:1 enables:2 update:1 half:1 selected:1 warmuth:1 beginning:1 smith:1 manfred:1 provides:1 multiset:1 math:1 firstly:1 five:2 rc:1 along:2 construc... |
5,182 | 5,691 | Lifted Symmetry Detection and
Breaking for MAP Inference
Tim Kopp
University of Rochester
Rochester, NY
Parag Singla
I.I.T. Delhi
Hauz Khas, New Delhi
Henry Kautz
University of Rochester
Rochester, NY
tkopp@cs.rochester.edu
parags@cse.iitd.ac.in
kautz@cs.rochester.edu
Abstract
Symmetry breaking is a technique fo... | 5691 |@word briefly:1 version:2 polynomial:6 open:3 adnan:1 tried:3 harder:1 reduction:2 contains:1 efficacy:1 daniel:2 outperforms:1 ginsberg:1 z2:1 com:1 comparing:1 anne:1 written:2 partition:20 enables:1 remove:1 update:1 hash:1 vasco:1 greedy:1 braz:2 intelligence:4 amir:2 mln:2 plane:1 ith:1 colored:1 detecting:2... |
5,183 | 5,692 | Bandits with Unobserved Confounders:
A Causal Approach
Andrew Forney?
Department of Computer Science
University of California, Los Angeles
forns@cs.ucla.edu
Elias Bareinboim?
Department of Computer Science
Purdue University
eb@purdue.edu
Judea Pearl
Department of Computer Science
University of California, Los Angele... | 5692 |@word trial:6 exploitation:2 judgement:1 rigged:1 open:1 simulation:10 accounting:1 attainable:2 datagenerating:1 q1:3 profit:2 paid:1 solid:1 carry:1 efficacy:4 pub:1 daniel:1 bc:1 reaction:1 current:3 contextual:4 comparing:3 com:1 written:2 must:1 john:2 tenet:1 realize:1 realistic:3 fn:1 shawetaylor:1 numeric... |
5,184 | 5,693 | Sample Complexity Bounds for Iterative Stochastic
Policy Optimization
Marin Kobilarov
Department of Mechanical Engineering
Johns Hopkins University
Baltimore, MD 21218
marin@jhu.edu
Abstract
This paper is concerned with robustness analysis of decision making under uncertainty. We consider a class of iterative stochas... | 5693 |@word norm:1 pieter:1 zilinskas:1 seborg:1 linearized:1 covariance:1 moment:5 initial:7 zij:3 lqr:1 past:2 existing:2 current:1 contextual:1 optim:1 dx:1 must:4 john:1 numerical:1 christian:2 plot:1 update:2 lydia:1 xk:13 wolfram:1 manfred:1 certificate:1 location:1 philipp:1 unbounded:5 along:1 direct:1 prove:1 ... |
5,185 | 5,694 | Basis Refinement Strategies for Linear Value
Function Approximation in MDPs
Gheorghe Comanici
School of Computer Science
McGill University
Montreal, Canada
gcoman@cs.mcgill.ca
Doina Precup
School of Computer Science
McGill University
Montreal, Canada
dprecup@cs.mcgill.ca
Prakash Panangaden
School of Computer Science... | 5694 |@word briefly:1 villani:4 open:1 contraction:4 homomorphism:1 initial:2 contains:1 past:2 existing:5 current:3 comparing:1 discretization:1 si:6 activation:1 must:4 refines:3 partition:7 designed:3 intelligence:1 selected:1 detecting:1 provides:7 mannor:1 mathematical:3 constructed:1 symposium:1 prove:1 behaviora... |
5,186 | 5,695 | Probabilistic Variational Bounds
for Graphical Models
Qiang Liu
Computer Science
Dartmouth College
qliu@cs.dartmouth.edu
John Fisher III
CSAIL
MIT
fisher@csail.mit.edu
Alexander Ihler
Computer Science
Univ. of California, Irvine
ihler@ics.uci.edu
Abstract
Variational algorithms such as tree-reweighted belief propag... | 5695 |@word version:1 essay:1 simulation:2 wexler:2 bn:8 decomposition:1 boundedness:2 harder:2 initial:4 liu:5 contains:1 configuration:1 united:1 series:1 fa8750:1 outperforms:3 existing:1 past:1 freitas:1 rish:1 must:1 readily:1 john:1 refines:2 dechter:6 partition:18 enables:3 drop:1 generative:1 selected:2 leaf:1 ... |
5,187 | 5,696 | On the Convergence of Stochastic Gradient MCMC
Algorithms with High-Order Integrators
Changyou Chen?
Nan Ding?
Lawrence Carin?
Dept. of Electrical and Computer Engineering, Duke University, Durham, NC, USA
?
Google Inc., Venice, CA, USA
cchangyou@gmail.com; dingnan@google.com; lcarin@duke.edu
?
Abstract
Recent advan... | 5696 |@word mild:2 version:2 briefly:2 changyou:1 norm:1 trotter:1 nd:9 suitably:1 tr:1 ld:1 initial:2 document:2 interestingly:1 outperforms:1 existing:1 com:2 gmail:1 written:1 readily:4 numerical:17 happen:3 informative:1 sdes:4 plot:2 update:1 stationary:6 selected:2 beginning:1 hamiltonian:4 blei:2 completeness:1 ... |
5,188 | 5,697 | An Active Learning Framework using Sparse-Graph
Codes for Sparse Polynomials and Graph Sketching
Kannan Ramchandran?
UC Berkeley
kannanr@berkeley.edu
Xiao Li
UC Berkeley
xiaoli@berkeley.edu
Abstract
Let f : {?1, 1}n ? R be an n-variate polynomial consisting of 2n monomials,
in which only s 2n coefficients are non-... | 5697 |@word trial:2 faculty:1 polynomial:28 nd:1 d2:2 reduction:1 contains:3 existing:4 recovered:1 written:2 fn:4 numerical:1 additive:3 partition:2 remove:3 drop:1 hash:2 half:1 selected:2 fewer:1 intelligence:1 core:1 junta:1 record:1 provides:1 intellectual:1 node:37 allerton:2 constructed:1 symposium:4 consists:3 ... |
5,189 | 5,698 | Discrete R?enyi Classifiers
Meisam Razaviyayn?
meisamr@stanford.edu
Farzan Farnia?
farnia@stanford.edu
David Tse?
dntse@stanford.edu
Abstract
Consider the binary classification problem of predicting a target variable Y from
a discrete feature vector X = (X1 , . . . , Xd ). When the probability distribution
P(X, Y )... | 5698 |@word repository:2 mezuman:1 xtest:1 pick:2 moment:2 selecting:2 hereafter:1 bhattacharyya:1 erkip:1 outperforms:1 existing:2 comparing:1 jaynes:1 chu:1 numerical:3 v:1 greedy:2 selected:1 intelligence:2 mpm:1 xk:1 core:1 short:1 provides:2 math:1 firstly:1 five:2 mathematical:2 prove:1 fitting:3 combine:1 inside... |
5,190 | 5,699 | GAP Safe screening rules for sparse multi-task and
multi-class models
Eugene Ndiaye Olivier Fercoq Alexandre Gramfort Joseph Salmon
LTCI, CNRS, T?el?ecom ParisTech, Universit?e Paris-Saclay
Paris, 75013, France
firstname.lastname@telecom-paristech.fr
Abstract
High dimensional regression benefits from sparsity promotin... | 5699 |@word multitask:2 unaltered:1 norm:14 gaspard:1 bf:2 confirms:1 hsieh:1 palso:1 liblinear:2 liu:1 contains:1 series:1 dubourg:1 ndiaye:1 existing:2 current:2 recovered:1 optim:1 surprising:1 written:2 fn:1 happen:1 shape:1 cheap:1 remove:1 ainen:1 stationary:1 beginning:1 short:1 gribonval:2 gpx:2 provides:1 hype... |
5,191 | 570 | A Self-Organizing Integrated Segmentation And
Recognition Neural Net
Jim Keeler *
MCC
3500 West Balcones Center Drive
Austin, TX 78729
David E. Rumelhart
Psychology Department
Stanford University
Stanford, CA 94305
Abstract
We present a neural network algorithm that simultaneously performs segmentation and recogniti... | 570 |@word grey:3 leow:3 contains:3 document:1 com:2 lang:2 yet:1 activation:5 must:1 parsing:2 john:1 kheng:1 net1:1 discrimination:1 location:3 sigmoidal:3 consists:1 indeed:1 detects:1 little:1 becomes:2 project:4 insure:1 didn:1 kind:1 nation:1 act:1 wrong:1 unit:26 normally:1 segmenting:2 before:1 local:4 approxim... |
5,192 | 5,700 | Decomposition Bounds for Marginal MAP
?
Wei Ping?
Qiang Liu?
Alexander Ihler?
?
Computer Science, UC Irvine
Computer Science, Dartmouth College
{wping,ihler}@ics.uci.edu qliu@cs.dartmouth.edu
Abstract
Marginal MAP inference involves making MAP predictions in systems defined
with latent variables or missing informati... | 5700 |@word illustrating:1 pw:2 norm:1 jointree:1 open:1 heuristically:1 d2:1 tried:1 bn:4 decomposition:32 wexler:1 attainable:1 harder:1 moment:4 liu:5 configuration:3 score:2 selecting:1 united:1 hardy:1 genetic:1 fa8750:1 outperforms:1 existing:2 rish:1 comparing:1 dechter:4 partition:9 enables:1 plot:3 sponsored:1... |
5,193 | 5,701 | Anytime Influence Bounds and the Explosive
Behavior of Continuous-Time Diffusion Networks
1
Kevin Scaman1
R?emi Lemonnier1,2
Nicolas Vayatis1
CMLA, ENS Cachan, CNRS, Universit?e Paris- Saclay, France, 2 1000mercis, Paris, France
{scaman, lemonnier, vayatis}@cmla.ens-cachan.fr
Abstract
The paper studies transition ph... | 5701 |@word pnij:1 version:1 nd:1 simulation:2 propagate:1 simplifying:1 initial:2 series:2 denoting:1 janson:1 existing:1 yajun:2 comparing:1 virus:2 manuel:5 si:6 subsequent:2 numerical:1 n0:25 intelligence:1 beginning:1 randolph:1 provides:4 node:25 simpler:1 mathematical:1 along:5 direct:1 become:1 natalie:1 prove:... |
5,194 | 5,702 | Estimating Mixture Models via Mixtures of
Polynomials
Sida I. Wang Arun Tejasvi Chaganty Percy Liang
Computer Science Department, Stanford University, Stanford, CA, 94305
{sidaw,chaganty,pliang}@cs.stanford.edu
Abstract
Mixture modeling is a general technique for making any simple model more expressive through weighte... | 5702 |@word faculty:1 polynomial:44 proportion:2 norm:2 nd:1 open:1 simulation:2 seek:1 r:2 covariance:4 crucially:1 decomposition:4 pressed:1 tr:2 moment:93 liu:1 series:3 contains:2 existing:1 current:1 com:1 tackling:1 written:1 john:1 fn:14 numerical:2 treating:1 update:3 xk:1 smith:1 short:1 provides:1 detecting:1... |
5,195 | 5,703 | Robust Gaussian Graphical Modeling with the
Trimmed Graphical Lasso
Aur?elie C. Lozano
IBM T.J. Watson Research Center
aclozano@us.ibm.com
Eunho Yang
IBM T.J. Watson Research Center
eunhyang@us.ibm.com
Abstract
Gaussian Graphical Models (GGMs) are popular tools for studying network structures. However, many modern a... | 5703 |@word mild:1 determinant:3 version:1 briefly:1 norm:10 physik:1 seek:1 simulation:3 covariance:4 p0:3 hsieh:1 paid:1 pick:1 tr:1 liu:1 series:2 score:2 united:1 genetic:1 existing:1 recovered:1 com:2 ka:2 activation:1 assigning:2 written:1 partition:1 pertinent:1 update:3 depict:1 stationary:1 metabolism:1 sys:3 ... |
5,196 | 5,704 | Matrix Completion from Fewer Entries:
Spectral Detectability and Rank Estimation
Alaa Saade1 and Florent Krzakala1,2
Laboratoire de Physique Statistique, CNRS & ?cole Normale Sup?rieure, Paris, France.
2
Sorbonne Universit?s, Universit? Pierre et Marie Curie Paris 06, F-75005, Paris, France
1
Lenka Zdeborov?
Institu... | 5704 |@word version:2 briefly:1 achievable:1 norm:1 open:1 simulation:1 linearized:1 covariance:1 decomposition:3 tr:5 kappen:1 moment:1 liu:2 initial:13 neeman:1 interestingly:1 outperforms:1 existing:4 paramagnetic:6 si:7 yet:1 numerical:3 informative:2 plot:3 stationary:1 fewer:2 detecting:2 provides:4 node:1 locati... |
5,197 | 5,705 | Robust PCA with compressed data
Wooseok Ha
University of Chicago
haywse@uchicago.edu
Rina Foygel Barber
University of Chicago
rina@uchicago.edu
Abstract
The robust principal component analysis (RPCA) problem seeks to separate lowrank trends from sparse outliers within a data matrix, that is, to approximate a n?d
matr... | 5705 |@word multitask:1 trial:3 version:5 compression:50 norm:9 proportion:4 nd:15 c0:5 km:6 seek:3 simulation:6 decomposition:14 tianyi:1 reduction:1 initial:1 series:1 contains:1 selecting:2 zij:1 denoting:1 existing:3 ksk1:1 zpre:9 john:4 chicago:2 additive:4 realistic:2 numerical:1 prohibitive:1 nent:1 ith:1 provid... |
5,198 | 5,706 | Mixed Robust/Average Submodular Partitioning:
Fast Algorithms, Guarantees, and Applications
Kai Wei1
Rishabh Iyer1
Shengjie Wang2
Wenruo Bai1
Jeff Bilmes1
1
Department of Electrical Engineering, University of Washington
2
Department of Computer Science, University of Washington
{kaiwei, rkiyer, wangsj, wrbai, bi... | 5706 |@word version:5 polynomial:2 semidifferential:1 closure:1 pick:2 asks:2 thereby:2 harder:1 bai:1 initial:1 efficacy:3 denoting:1 past:1 existing:6 outperforms:2 current:1 si:3 chu:1 readily:1 slb:19 additive:2 partition:44 seeding:1 plot:1 sponsored:1 v:1 greedy:19 item:6 rch:1 provides:2 recompute:1 node:2 locat... |
5,199 | 5,707 | Subspace Clustering with Irrelevant Features via
Robust Dantzig Selector
Chao Qu
Department of Mechanical Engineering
National University of Singapore
Huan Xu
Department of Mechanical Engineering
National University of Singapore
A0117143@u.nus.edu
mpexuh@nus.edu.sg
Abstract
This paper considers the subspace cluste... | 5707 |@word version:1 compression:1 norm:3 c0:2 simulation:4 simplifying:1 harder:1 mpexuh:1 celebrated:1 contains:3 liu:1 series:1 z2:1 yet:2 john:2 numerical:6 additive:1 generative:1 selected:2 intelligence:2 provides:1 certificate:3 mannor:1 gpca:2 c2:14 symposium:1 psf:1 expected:1 mask:1 indeed:3 cand:1 examine:1... |
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