Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
5,600 | 6,068 | Learning feed-forward one-shot learners
Luca Bertinetto?
University of Oxford
luca@robots.ox.ac.uk
Jo?o F. Henriques?
University of Oxford
joao@robots.ox.ac.uk
Philip H. S. Torr
University of Oxford
philip.torr@eng.ox.ac.uk
Jack Valmadre?
University of Oxford
jvlmdr@robots.ox.ac.uk
Andrea Vedaldi
University of Oxf... | 6068 |@word seems:1 norm:1 fairer:1 eng:1 decomposition:1 prokhorov:1 pick:1 sgd:3 shot:37 hager:1 reduction:1 necessity:1 configuration:2 contains:5 score:3 series:1 initial:3 ours:1 freitas:1 ka:1 activation:3 written:1 must:2 reminiscent:1 gpu:1 dive:1 drop:1 plot:1 update:1 generative:8 selected:1 intelligence:1 pr... |
5,601 | 6,069 | Mixed vine copulas as joint models
of spike counts and local field potentials
Arno Onken
Istituto Italiano di Tecnologia
38068 Rovereto (TN), Italy
arno.onken@iit.it
Stefano Panzeri
Istituto Italiano di Tecnologia
38068 Rovereto (TN), Italy
stefano.panzeri@iit.it
Abstract
Concurrent measurements of neural activity a... | 6069 |@word briefly:2 inversion:2 frigessi:1 simulation:4 decomposition:4 thereby:1 carry:1 selecting:1 longitudinal:1 current:2 wd:1 ka:1 comparing:1 yet:1 dx:1 attracted:1 scatter:2 fn:2 realistic:4 partition:1 shape:2 acar:1 plot:3 v:2 selected:2 indicative:1 xk:2 smith:4 short:1 record:1 lr:10 underestimating:1 pro... |
5,602 | 607 | Recognition-based Segmentation
of On-line Hand-printed Words
M. Schenkel*, H. Weissman, I. Guyon, C. Nohl, D. Henderson
AT&T Bell Laboratories, Holmdel, NJ 07733
* Swiss Federal Institute of Technology, CH-8092 Zurich
Abstract
This paper reports on the performance of two methods for
recognition-based segmentation of ... | 607 |@word private:1 version:2 stronger:1 retraining:1 grey:2 leow:1 mention:2 shading:1 contains:3 score:10 current:1 anne:1 lang:3 yet:1 must:1 written:1 designed:3 v:1 selected:1 device:1 short:1 provides:2 node:4 successive:2 five:2 along:2 dn:11 direct:1 symposium:1 expected:1 simulator:1 multi:1 terminal:1 inspir... |
5,603 | 6,070 | Asynchronous Parallel Greedy Coordinate Descent
Yang You ?, + XiangRu Lian?, + Ji Liu ? Hsiang-Fu Yu ?
Inderjit S. Dhillon ? James Demmel ? Cho-Jui Hsieh ?
+
?
?
equally contributed
University of California, Davis
University of Rochester
?
?
University of Texas, Austin
University of California, Berkeley
youyang@cs.berk... | 6070 |@word mild:2 briefly:1 norm:2 vldb:1 closure:1 overwritten:1 hsieh:7 decomposition:2 pick:4 reduction:1 initial:2 liu:4 cyclic:5 series:1 selecting:5 ours:1 kcr:1 outperforms:1 existing:2 past:1 current:2 com:1 kx0:1 numa:2 surprising:1 si:4 yet:1 written:1 belmont:1 devin:1 partition:7 happen:1 kdd:3 update:31 v... |
5,604 | 6,071 | Generative Shape Models: Joint Text Recognition and
Segmentation with Very Little Training Data
Xinghua Lou, Ken Kansky, Wolfgang Lehrach, CC Laan
Vicarious FPC Inc., San Francisco, USA
xinghua,ken,wolfgang,cc@vicarious.com
Bhaskara Marthi, D. Scott Phoenix, Dileep George
Vicarious FPC Inc., San Francisco, USA
bhaskar... | 6071 |@word kohli:1 cnn:6 version:1 briefly:3 compression:1 stronger:1 outlook:1 bai:1 born:1 contains:4 score:6 selecting:2 liu:1 document:7 suppressing:1 ours:2 mishra:1 current:1 com:3 comparing:1 activation:3 yet:1 must:2 parsing:40 distant:2 blur:7 informative:1 shape:31 hofmann:1 remove:1 designed:2 interpretable... |
5,605 | 6,072 | The Power of Adaptivity in Identifying Statistical
Alternatives
Kevin Jamieson, Daniel Haas, Ben Recht
University of California, Berkeley
Berkeley, CA 94720
{kjamieson,dhaas,brecht}@eecs.berkeley.edu
Abstract
This paper studies the trade-off between two different kinds of pure exploration:
breadth versus depth. We foc... | 6072 |@word exploitation:1 proportion:1 instrumental:1 c0:4 twelfth:1 d2:2 vldb:1 p0:5 pick:3 reduction:1 contains:1 siebel:1 selecting:1 karger:1 daniel:2 fa8750:1 franklin:1 existing:1 com:1 michal:1 surprising:1 stemmed:1 yet:3 dx:1 must:2 written:1 john:1 cis:2 designed:1 interpretable:1 implying:1 accordingly:1 sh... |
5,606 | 6,073 | Designing smoothing functions for improved
worst-case competitive ratio in online optimization
Reza Eghbali
Department of Electrical Engineering
University of Washington
Seattle, WA 98195
eghbali@uw.edu
Maryam Fazel
Department of Electrical Engineering
University of Washington
Seattle, WA 98195
mfazel@uw.edu
Abstrac... | 6073 |@word version:5 norm:3 open:1 mehta:1 jacob:1 psim:3 ftrl:3 contains:1 att:11 existing:1 current:1 discretization:1 wilkens:1 written:1 remove:1 drop:2 update:18 v:1 greedy:3 certificate:1 provides:7 mathematical:2 direct:1 differential:2 symposium:3 prove:1 naor:2 expected:1 roughly:1 decreasing:1 balasubramania... |
5,607 | 6,074 | Proximal Deep Structured Models
Shenlong Wang
University of Toronto
slwang@cs.toronto.edu
Sanja Fidler
University of Toronto
fidler@cs.toronto.edu
Raquel Urtasun
University of Toronto
urtasun@cs.toronto.edu
Abstract
Many problems in real-world applications involve predicting continuous-valued
random variables that ... | 6074 |@word kohli:2 version:2 briefly:2 polynomial:3 norm:8 paredes:1 tried:1 pick:1 initial:1 configuration:5 series:1 disparity:1 ours:5 romera:1 past:1 existing:1 outperforms:3 current:1 reaction:1 activation:5 written:2 gpu:6 numerical:1 partition:1 additive:1 hofmann:1 designed:3 update:2 half:4 isard:1 isotropic:... |
5,608 | 6,075 | Single Pass PCA of Matrix Products
Shanshan Wu
The University of Texas at Austin
shanshan@utexas.edu
Srinadh Bhojanapalli
Toyota Technological Institute at Chicago
srinadh@ttic.edu
Sujay Sanghavi
The University of Texas at Austin
sanghavi@mail.utexas.edu
Alexandros G. Dimakis
The University of Texas at Austin
dimaki... | 6075 |@word repository:2 mr2:3 stronger:1 norm:38 loading:1 disk:3 kbkf:2 simulation:2 tried:1 covariance:2 decomposition:1 reduction:1 liu:2 contains:2 lichman:1 woodruff:3 ours:1 franklin:1 outperforms:5 existing:3 ka:4 com:2 current:1 savage:1 chicago:1 happen:1 numerical:2 benign:1 remove:1 plot:5 designed:1 v:2 ha... |
5,609 | 6,076 | Learning values across many orders of magnitude
Hado van Hasselt
Arthur Guez
Matteo Hessel
Volodymyr Mnih
David Silver
Google DeepMind
Abstract
Most learning algorithms are not invariant to the scale of the signal that is being
approximated. We propose to adaptively normalize the targets used in the learning upda... | 6076 |@word private:1 middle:2 version:3 seems:1 norm:4 nd:1 open:1 calculus:1 seek:1 pick:1 dramatic:1 sgd:27 thereby:6 solid:1 harder:2 moment:2 initial:1 score:8 tuned:2 document:1 bootstrapped:1 existing:1 hasselt:8 current:1 freitas:1 cumulation:1 surprising:1 activation:1 guez:3 written:1 subsequent:1 numerical:1... |
5,610 | 6,077 | Online Bayesian Moment Matching for
Topic Modeling with Unknown Number of Topics
Wei-Shou Hsu and Pascal Poupart
David R. Cheriton School of Computer Science
University of Waterloo
Wateroo, ON N2L 3G1
{wwhsu,ppoupart}@uwaterloo.ca
Abstract
Latent Dirichlet Allocation (LDA) is a very popular model for topic modeling a... | 6077 |@word version:1 middle:2 unif:4 decomposition:4 minus:1 reduction:1 moment:35 substitution:1 contains:4 liu:1 initial:1 daniel:2 document:10 existing:1 recovered:1 com:4 john:2 subsequent:1 shape:1 designed:1 update:13 generative:4 half:2 fewer:1 accordingly:1 inspection:1 xk:3 blei:6 provides:2 simpler:2 five:1 ... |
5,611 | 6,078 | On Mixtures of Markov Chains
Rishi Gupta?
Stanford University
Stanford, CA 94305
rishig@cs.stanford.edu
Ravi Kumar
Google Research
Mountain View, CA 94043
ravi.k53@gmail.com
Sergei Vassilvitskii
Google Research
New York, NY 10011
sergeiv@google.com
Abstract
We study the problem of reconstructing a mixture of Markov ... | 6078 |@word mild:2 version:1 polynomial:3 seems:1 open:1 tried:1 bn:1 decomposition:11 q1:1 pick:1 thereby:1 moment:1 initial:3 series:3 contains:1 outperforms:3 past:2 recovered:1 com:3 nt:1 z2:1 gmail:1 sergei:1 must:1 additive:1 partition:1 plot:1 drop:1 progressively:1 v:2 implying:1 half:3 selected:2 guess:2 ith:4... |
5,612 | 6,079 | High Dimensional Structured Superposition Models
Arindam Banerjee
Dept of Computer Science & Engineering
University of Minnesota, Twin Cities
banerjee@cs.umn.edu
Qilong Gu
Dept of Computer Science & Engineering
University of Minnesota, Twin Cities
guxxx396@cs.umn.edu
Abstract
High dimensional superposition models ch... | 6079 |@word version:3 achievable:1 norm:23 proportion:1 nd:7 hu:1 d2:4 decomposition:6 contains:1 series:2 interestingly:2 existing:3 ksk1:1 current:2 recovered:1 si:1 written:1 must:2 subsequent:1 plot:2 n0:3 v:1 implying:1 mackey:1 instantiate:1 huo:1 characterization:7 c22:1 zhang:1 along:1 c2:14 direct:2 symposium:... |
5,613 | 608 | Summed Weight Neuron Perturbation: An O(N)
Improvement over Weight Perturbation.
Barry Flower and Marwan Jabri
SEDAL
Department of Electrical Engineering
University of Sydney
NSW 2006 Australia
Abstract
The algorithm presented performs gradient descent on the weight space
of an Artificial Neural Network (ANN), using a... | 608 |@word sydney:1 trial:1 effect:1 true:1 implies:1 hence:1 direction:9 equality:1 added:1 correct:5 saved:1 wp:20 simulation:8 stochastic:2 australia:1 ll:1 nsw:1 defmed:1 gradient:18 kth:1 ow:1 require:3 feeding:1 series:4 designate:2 yij:5 qth:2 performs:3 fj:14 current:4 comparing:6 assuming:2 code:2 index:1 acti... |
5,614 | 6,080 | Truncated Variance Reduction: A Unified Approach
to Bayesian Optimization and Level-Set Estimation
Ilija Bogunovic1 , Jonathan Scarlett1 , Andreas Krause2 , Volkan Cevher1
1
Laboratory for Information and Inference Systems (LIONS), EPFL
2
Learning and Adaptive Systems Group, ETH Z?urich
{ilija.bogunovic,jonathan.scarle... | 6080 |@word briefly:1 version:5 suitably:1 seek:4 paid:1 pick:1 mention:1 versatile:1 reduction:7 configuration:2 contains:1 score:6 selecting:1 outperforms:1 existing:4 freitas:2 current:3 com:1 must:1 cis:1 designed:1 plot:3 update:5 drop:1 alone:1 selected:5 plane:1 isotropic:2 sys:5 volkan:2 provides:1 location:2 p... |
5,615 | 6,081 | Convolutional Neural Networks on Graphs
with Fast Localized Spectral Filtering
Micha?l Defferrard
Xavier Bresson
Pierre Vandergheynst
EPFL, Lausanne, Switzerland
{michael.defferrard,xavier.bresson,pierre.vandergheynst}@epfl.ch
Abstract
In this work, we are interested in generalizing convolutional neural networks
(C... | 6081 |@word kulis:1 cnn:13 version:4 polynomial:11 seems:1 open:1 simulation:1 tried:1 decomposition:1 recursively:1 carry:1 reduction:1 initial:3 selecting:1 daniel:1 denoting:1 document:7 outperforms:1 diagonalized:1 com:1 comparing:1 activation:3 dx:1 written:1 gpu:2 pioneer:1 must:4 finest:4 numerical:2 mesh:2 part... |
5,616 | 6,082 | Sampling for Bayesian Program Learning
Kevin Ellis
Brain and Cognitive Sciences
MIT
ellisk@mit.edu
Armando Solar-Lezama
CSAIL
MIT
asolar@csail.mit.edu
Joshua B. Tenenbaum
Brain and Cognitive Sciences
MIT
jbt@mit.edu
Abstract
Towards learning programs from data, we introduce the problem of sampling
programs from pos... | 6082 |@word multitask:1 chakraborty:2 invoking:1 solid:1 shot:2 recursively:1 reduction:1 inefficiency:1 contains:2 paw:1 daniel:1 genetic:2 past:4 freitas:1 comparing:1 superoptimization:1 synthesizer:1 yet:2 written:1 parsing:1 john:1 tilted:2 numerical:1 dechter:1 motor:1 kuldeep:3 v:1 bart:4 intelligence:3 leaf:1 i... |
5,617 | 6,083 | Poisson?Gamma Dynamical Systems
Aaron Schein
College of Information and Computer Sciences
University of Massachusetts Amherst
Amherst, MA 01003
aschein@cs.umass.edu
Mingyuan Zhou
McCombs School of Business
The University of Texas at Austin
Austin, TX 78712
mingyuan.zhou@mccombs.utexas.edu
Hanna Wallach
Microsoft Rese... | 6083 |@word briefly:1 version:1 excited:1 accommodate:1 series:3 uma:2 contains:3 score:6 document:1 comparing:3 com:2 must:1 herring:1 partition:1 noninformative:1 enables:3 shape:3 hypothesize:1 prk:1 interpretable:3 depict:2 resampling:1 stationary:5 generative:1 prohibitive:1 fewer:5 selected:1 tone:1 accordingly:1... |
5,618 | 6,084 | Fast -free Inference of Simulation Models with
Bayesian Conditional Density Estimation
George Papamakarios
School of Informatics
University of Edinburgh
g.papamakarios@ed.ac.uk
Iain Murray
School of Informatics
University of Edinburgh
i.murray@ed.ac.uk
Abstract
Many statistical models can be simulated forwards but ... | 6084 |@word middle:7 version:4 eliminating:1 proportion:1 nd:1 simulation:47 lezaun:1 covariance:4 reduction:1 born:2 series:2 tuned:3 existing:1 reaction:2 com:1 reminiscent:1 realistic:1 informative:2 cheap:1 plot:4 designed:1 v:7 generative:7 fewer:2 discovering:1 intelligence:3 parameterization:1 hamiltonian:2 loca... |
5,619 | 6,085 | Bi-Objective Online Matching and Submodular
Allocations
Hossein Esfandiari
University of Maryland
College Park, MD 20740
hossein@cs.umd.edu
Nitish Korula
Google Research
New York, NY 10011
nitish@google.com
Vahab Mirrokni
Google Research
New York, NY 10011
mirrokni@google.com
Abstract
Online allocation problems have... | 6085 |@word shayan:1 repository:1 version:4 polynomial:1 stronger:1 c0:2 mehta:3 assigment:1 pick:2 paid:1 bicriteria:1 harder:1 bai:1 score:2 interestingly:1 current:2 com:2 wilkens:1 assigning:5 attracted:1 must:4 sergei:1 additive:1 designed:1 sponsored:2 greedy:16 leaf:1 item:73 short:1 math:3 node:10 simpler:1 war... |
5,620 | 6,086 | Learning HMMs with Nonparametric Emissions via
Spectral Decompositions of Continuous Matrices
Kirthevasan Kandasamy?
Carnegie Mellon University
Pittsburgh, PA 15213
kandasamy@cs.cmu.edu
Maruan Al-Shedivat?
Carnegie Mellon University
Pittsburgh, PA 15213
alshedivat@cs.cmu.edu
Eric P. Xing
Carnegie Mellon University
P... | 6086 |@word mild:2 version:1 middle:1 polynomial:12 norm:2 suitably:1 calculus:1 tried:2 decomposition:5 q1:1 pick:1 carry:2 moment:5 initial:2 liu:1 series:7 contains:1 daniel:3 rkhs:2 outperforms:3 existing:2 current:1 com:1 comparing:1 surprising:1 yet:2 john:1 numerical:4 weyl:3 enables:1 interpretable:2 stationary... |
5,621 | 6,087 | Dimension-Free Iteration Complexity of Finite Sum
Optimization Problems
Yossi Arjevani
Weizmann Institute of Science
Rehovot 7610001, Israel
yossi.arjevani@weizmann.ac.il
Ohad Shamir
Weizmann Institute of Science
Rehovot 7610001, Israel
ohad.shamir@weizmann.ac.il
Abstract
Many canonical machine learning problems boi... | 6087 |@word polynomial:14 stronger:1 norm:7 confirms:1 seek:1 nemirovsky:2 thereby:1 carry:1 reduction:2 contains:2 exclusively:1 denoting:1 existing:2 current:4 comparing:1 wd:1 assigning:1 issuing:1 must:2 readily:2 tackling:1 analytic:1 update:3 alone:1 stationary:2 prohibitive:1 accordingly:3 steepest:2 indefinitel... |
5,622 | 6,088 | Adversarial Multiclass Classification:
A Risk Minimization Perspective
Rizal Fathony
Anqi Liu
Kaiser Asif
Brian D. Ziebart
Department of Computer Science
University of Illinois at Chicago
Chicago, IL 60607
{rfatho2, aliu33, kasif2, bziebart}@uic.edu
Abstract
Recently proposed adversarial classification methods have ... | 6088 |@word repository:2 middle:1 polynomial:1 c0:2 open:1 seek:3 moment:1 liu:3 lichman:1 bhattacharyya:1 existing:1 recovered:1 current:1 anqi:2 yet:1 must:2 john:1 realize:1 indistinguishably:2 chicago:2 hofmann:1 enables:2 christian:2 remove:1 treating:1 plot:2 update:1 v:2 greedy:3 selected:1 fewer:3 intelligence:... |
5,623 | 6,089 | Graphons, mergeons, and so on!
Justin Eldridge Mikhail Belkin Yusu Wang
The Ohio State University
{eldridge, mbelkin, yusu}@cse.ohio-state.edu
Abstract
In this work we develop a theory of hierarchical clustering for graphs. Our modeling assumption is that graphs are sampled from a graphon, which is a powerful
and gene... | 6089 |@word version:1 pw:2 stronger:3 norm:4 nd:2 open:2 minus:1 contains:4 deepens:1 janson:2 existing:1 yet:1 assigning:1 must:6 written:1 partition:1 cant:3 christian:1 plot:2 alone:1 half:1 es:2 vanishing:1 olhede:1 gure:1 provides:4 math:1 cse:1 node:43 zhang:1 height:18 mathematical:1 along:3 c2:7 direct:1 ect:2 ... |
5,624 | 609 | Probability Estimation from a Database
Using a Gibbs Energy Model
John W. Miller
Microsoft Research (9/1051)
One Microsoft Way
Redmond, WA 98052
Rodney M. Goodman
Dept. of Electrical Engineering (116-81)
California Institute of Technology
Pasadena, CA 91125
Abstract
We present an algorithm for creating a neural netw... | 609 |@word trial:5 repository:2 version:1 inversion:3 ylp:1 configuration:12 pub:1 selecting:1 surprising:1 written:1 must:1 john:1 remove:1 designed:1 hash:3 stationary:1 intelligence:1 directory:1 scotland:1 record:5 provides:1 quantized:1 zhang:2 mathematical:1 c2:2 cta:1 expected:1 xz:6 cct:1 considering:1 becomes:... |
5,625 | 6,090 | Backprop KF: Learning Discriminative Deterministic
State Estimators
Tuomas Haarnoja, Anurag Ajay, Sergey Levine, Pieter Abbeel
{haarnoja, anuragajay, svlevine, pabbeel}@berkeley.edu
Department of Computer Science, University of California, Berkeley
Abstract
Generative state estimators based on probabilistic filters an... | 6090 |@word bptt:2 disk:11 open:1 pieter:1 rgb:1 covariance:7 arti:1 thereby:1 harder:1 recursively:1 contains:2 tuned:1 ours:3 outperforms:3 past:1 current:2 activation:3 must:4 readily:1 written:1 visible:1 update:3 aside:1 occlude:1 generative:22 leaf:1 fewer:1 half:1 intelligence:1 mccallum:1 short:2 filtered:1 pro... |
5,626 | 6,091 | Operator Variational Inference
Rajesh Ranganath
Princeton University
Jaan Altosaar
Princeton University
Dustin Tran
Columbia University
David M. Blei
Columbia University
Abstract
Variational inference is an umbrella term for algorithms which cast Bayesian inference as optimization. Classically, variational inferen... | 6091 |@word norm:2 hyv:1 seek:4 reduction:1 configuration:1 contains:3 score:8 ndez:2 fa8750:1 existing:1 comparing:2 activation:3 yet:2 written:2 readily:1 must:1 analytic:3 christian:1 remove:1 designed:2 plot:1 update:1 generative:7 half:3 parameterization:1 parametrization:1 core:1 blei:4 parameterizations:1 math:1... |
5,627 | 6,092 | The Multiple Quantile Graphical Model
Alnur Ali
Machine Learning Department
Carnegie Mellon University
alnurali@cmu.edu
J. Zico Kolter
Computer Science Department
Carnegie Mellon University
zkolter@cs.cmu.edu
Ryan J. Tibshirani
Department of Statistics
Carnegie Mellon University
ryantibs@cmu.edu
Abstract
We introdu... | 6092 |@word trial:1 middle:2 dalal:1 d2:1 covariance:10 mention:1 solid:1 initial:1 liu:5 series:2 united:1 rkhs:3 nonparanormal:3 outperforms:2 recovered:4 comparing:2 incidence:4 current:2 nicolai:1 yet:1 chu:2 written:1 john:2 tilted:1 additive:12 j1:2 shape:1 analytic:1 plot:4 update:3 stationary:3 intelligence:2 k... |
5,628 | 6,093 | A Consistent Regularization Approach for Structured
Prediction
Carlo Ciliberto ?,1
cciliber@mit.edu
1
Alessandro Rudi ?,1,2
ale_rudi@mit.edu
Lorenzo Rosasco 1,2
lrosasco@mit.edu
Laboratory for Computational and Statistical Learning - Istituto Italiano di Tecnologia, Genova, Italy &
Massachusetts Institute of Technol... | 6093 |@word mild:1 trial:1 version:1 schoen:1 seems:2 yi0:1 nd:1 dekel:1 open:1 elisseeff:1 ronchetti:1 score:1 ours:2 interestingly:1 outperforms:1 comparing:1 written:2 john:1 hofmann:2 mackey:1 fminunc:1 half:2 intelligence:1 provides:2 preference:1 herbrich:1 org:1 differential:1 viable:1 yuan:1 prove:10 consists:2... |
5,629 | 6,094 | Agnostic Estimation for Misspecified
Phase Retrieval Models
Matey Neykov
Zhaoran Wang
Han Liu
Department of Operations Research and Financial Engineering
Princeton University, Princeton, NJ 08544
{mneykov, zhaoran, hanliu}@princeton.edu
Abstract
The goal of noisy high-dimensional phase retrieval is to estimate an s-sp... | 6094 |@word briefly:2 version:5 polynomial:1 norm:2 c0:9 d2:1 simulation:4 crucially:1 bn:6 covariance:1 decomposition:2 mention:2 tr:1 reduction:4 moment:4 liu:5 selecting:1 tuned:1 outperforms:1 existing:1 ganti:1 z2:3 surprising:1 numerical:4 additive:5 designed:1 half:2 selected:2 cook:1 provides:1 zhang:2 dn:1 c2:... |
5,630 | 6,095 | Lifelong Learning with Weighted Majority Votes
Anastasia Pentina
IST Austria
apentina@ist.ac.at
Ruth Urner
Max Planck Institute for Intelligent Systems
rurner@tuebingen.mpg.de
Abstract
Better understanding of the potential benefits of information transfer and representation learning is an important step towards the ... | 6095 |@word multitask:3 version:1 middle:5 achievable:1 norm:1 replicate:1 paredes:1 open:2 vldb:1 invoking:1 thereby:1 ld:8 reduction:5 series:1 chervonenkis:1 romera:1 past:1 existing:1 current:7 si:10 activation:2 yet:1 intriguing:1 subsequent:2 realistic:1 update:1 intelligence:1 isotropic:2 ruvolo:1 provides:1 boo... |
5,631 | 6,096 | Learning a Probabilistic Latent Space of Object
Shapes via 3D Generative-Adversarial Modeling
Jiajun Wu*
MIT CSAIL
Chengkai Zhang*
MIT CSAIL
William T. Freeman
MIT CSAIL, Google Research
Tianfan Xue
MIT CSAIL
Joshua B. Tenenbaum
MIT CSAIL
Abstract
We study the problem of 3D object generation. We propose a novel f... | 6096 |@word repository:4 cnn:2 choy:2 p0:2 harder:1 carry:3 shechtman:1 bai:2 liu:1 fragment:1 jimenez:1 daniel:4 ours:1 past:1 existing:2 outperforms:3 current:1 bookcase:2 cad:4 activation:3 diederik:2 parsing:1 mesh:2 realistic:5 concatenate:1 informative:3 shape:33 enables:1 designed:1 update:1 v:2 generative:34 al... |
5,632 | 6,097 | Learning Sparse Gaussian Graphical Models with
Overlapping Blocks
1
Mohammad Javad Hosseini1
Su-In Lee1,2
Department of Computer Science & Engineering, University of Washington, Seattle
2
Department of Genome Sciences, University of Washington, Seattle
{hosseini, suinlee}@cs.washington.edu
Abstract
We present a nove... | 6097 |@word stronger:1 prognostic:1 norm:2 tamayo:1 pancreatic:2 covariance:9 hsieh:1 myeloid:3 tr:26 reduction:1 liu:2 contains:1 score:5 selecting:1 interestingly:3 outperforms:3 existing:5 current:1 comparing:1 assigning:2 remove:1 plot:1 interpretable:1 update:2 zik:2 stationary:1 half:1 selected:2 greedy:1 intelli... |
5,633 | 6,098 | Discriminative Gaifman Models
Mathias Niepert
NEC Labs Europe
Heidelberg, Germany
mathias.niepert@neclabs.eu
Abstract
We present discriminative Gaifman models, a novel family of relational machine
learning models. Gaifman models learn feature representations bottom up from
representations of locally connected and boun... | 6098 |@word version:1 nd:1 open:4 duran:1 d2:15 yih:1 substitution:3 liu:5 fragment:3 score:1 existing:5 activation:1 si:2 goldberger:1 written:1 parsing:1 evans:1 numerical:3 academia:1 predetermined:1 treating:1 interpretable:1 intelligence:6 xk:1 mccallum:1 core:1 multiset:2 node:3 location:1 rc:1 dn:14 constructed:... |
5,634 | 6,099 | Professor Forcing: A New Algorithm for Training
Recurrent Networks
Anirudh Goyal?, Alex Lamb? , Ying Zhang, Saizheng Zhang,
Aaron Courville and Yoshua Bengio1
MILA, Universit? de Montr?al, 1 CIFAR
{anirudhgoyal9119, alex6200, ying.zhlisa, saizhenglisa,
aaron.courville, yoshua.umontreal}@gmail.com
Abstract
The Teacher ... | 6099 |@word open:7 seek:1 propagate:1 recursively:1 reduction:2 qatar:1 score:2 ours:1 interestingly:1 document:1 past:1 current:1 com:3 manuel:1 activation:2 gmail:1 guez:1 written:1 visible:1 wanted:1 update:5 v:5 generative:29 half:2 selected:5 intelligence:1 monk:1 inspection:1 short:3 supplying:1 provides:1 ondb:2... |
5,635 | 61 | 402
HOW
THE
PROCESSING
CATFISH TRACKS ITS PREY: AN INTERACTIVE "PIPELINED"
SYSTEM MAY DIRECT FORAGING VIA RETlCULOSPINAL NEURONS.
Jagmeet S. Kanwal
Dept. of Cellular & Structural Biology, Univ. of Colorado, Sch. of
Medicine, 4200 East, Ninth Ave., Denver, CO 80262.
ABSTRACT
Ictalurid catfish use a highly developed... | 61 |@word trial:1 briefly:1 rising:1 seems:1 dekker:1 seek:1 lobe:19 contraction:1 pick:1 carry:1 reduction:1 electronics:1 efficacy:1 longitudinal:1 anterior:1 si:1 yet:4 activation:1 physiol:3 thrust:2 motor:4 precaution:1 half:1 selected:1 stationary:1 nervous:1 accordingly:1 short:1 compo:6 provides:2 along:3 direc... |
5,636 | 610 | Parameterising Feature Sensitive Cell
Formation in Linsker Networks in the
Auditory System
Lance C. Walton
University of Kent at Canterbury
Canterbury
Kent
England
David L. Bisset
University of Kent at Canterbury
Canterbury
Kent
England
Abstract
This paper examines and extends the work of Linsker (1986) on
self orga... | 610 |@word wiesel:2 oncenter:1 hu:1 simulation:4 kent:4 paid:1 mammal:6 genetic:2 reaction:1 ka:2 analysed:1 must:1 written:1 realistic:3 subsequent:2 half:1 intelligence:1 plane:2 core:1 short:2 detecting:1 organising:2 firstly:1 simpler:1 five:1 rc:2 constructed:1 become:1 pathway:3 ra:3 morphology:2 brain:2 multi:1 ... |
5,637 | 6,100 | Active Nearest-Neighbor Learning in Metric Spaces
Aryeh Kontorovich
Department of Computer Science
Ben-Gurion University of the Negev
Beer Sheva 8499000, Israel
Sivan Sabato
Department of Computer Science
Ben-Gurion University of the Negev
Beer Sheva 8499000, Israel
Ruth Urner
Max Planck Institute for Intelligent Sy... | 6100 |@word h:3 faculty:1 version:2 compression:26 seems:1 crucially:3 asks:1 reduction:1 series:1 selecting:2 past:1 err:16 current:1 beygelzimer:1 ddim:3 yet:1 must:1 john:1 numerical:3 partition:2 gurion:2 benign:1 remove:3 designed:1 discrimination:1 half:1 prohibitive:1 fewer:2 selected:7 greedy:1 intelligence:1 m... |
5,638 | 6,101 | Relevant sparse codes with variational information
bottleneck
Matthew Chalk
IST Austria
Am Campus 1
A - 3400 Klosterneuburg, Austria
Olivier Marre
Institut de la Vision
17, Rue Moreau
75012, Paris, France
Gasper Tkacik
IST Austria
Am Campus 1
A - 3400 Klosterneuburg, Austria
Abstract
In many applications, it is desi... | 6101 |@word version:4 compression:2 simulation:4 seek:3 covariance:4 accounting:1 tkacik:2 pressed:1 solid:2 carry:1 phy:1 series:1 interestingly:1 recovered:3 must:1 shape:5 eichhorn:1 hofmann:1 plot:2 update:5 alone:2 generative:1 intelligence:1 greschner:1 ith:3 provides:3 detecting:1 allerton:1 org:1 along:2 constr... |
5,639 | 6,102 | Multistage Campaigning in Social Networks
Mehrdad Farajtabar?
Xiaojing Ye?
Sahar Harati?
Le Song?
Hongyuan Zha?
Georgia Institute of Technology?
Georgia State University?
Emory University?
mehrdad@gatech.edu
xye@gsu.edu
sahar.harati@emory.edu
{lsong,zha}@cc.gatech.edu
Abstract
We consider the problem of how to opti... | 6102 |@word multitask:1 middle:2 c0:2 open:6 calculus:1 simulation:1 pick:1 initial:1 memetracker:4 contains:2 selecting:1 past:1 existing:1 outperforms:5 current:5 emory:2 ka:1 si:1 must:1 written:1 vere:1 john:1 realistic:1 happen:1 partition:4 timestamps:1 shape:2 designed:1 drop:2 update:1 prk:5 v:2 discovering:2 w... |
5,640 | 6,103 | Coordinate-wise Power Method
1
Qi Lei 1
Kai Zhong 1
Inderjit S. Dhillon 1,2
Institute for Computational Engineering & Sciences 2 Department of Computer Science
University of Texas at Austin
{leiqi, zhongkai}@ices.utexas.edu, inderjit@cs.utexas.edu
Abstract
In this paper, we propose a coordinate-wise version of the p... | 6103 |@word kgk:1 private:2 version:3 loading:7 nd:1 disk:2 open:1 gradual:1 decomposition:2 hsieh:1 incurs:1 sepulchre:1 reduction:1 initial:4 contains:1 selecting:6 ati:4 existing:2 current:3 ka:5 com:4 si:2 partition:1 cheap:1 drop:1 designed:1 update:20 v:3 stationary:2 greedy:15 fewer:1 selected:3 oldest:1 xk:1 co... |
5,641 | 6,104 | Fast learning rates with heavy-tailed losses
1
Vu Dinh1 Lam Si Tung Ho2 Duy Nguyen3 Binh T. Nguyen4
Program in Computational Biology, Fred Hutchinson Cancer Research Center
2
Department of Biostatistics, University of California, Los Angeles
3
Department of Statistics, University of Wisconsin-Madison
4
Department of ... | 6104 |@word polynomial:4 stronger:1 norm:5 c0:4 mehta:6 confirms:1 boundedness:2 ld:1 moment:5 daniel:1 erven:5 existing:2 z2:1 si:3 happen:2 zeger:1 partition:2 enables:2 analytic:1 christian:1 designed:1 v:1 half:1 lr:5 quantizer:3 codebook:2 c6:3 zhang:3 unbounded:13 mathematical:1 c2:16 direct:1 prove:6 manner:1 in... |
5,642 | 6,105 | Guided Policy Search via Approximate Mirror
Descent
William Montgomery
Dept. of Computer Science and Engineering
University of Washington
wmonty@cs.washington.edu
Sergey Levine
Dept. of Computer Science and Engineering
University of Washington
svlevine@cs.washington.edu
Abstract
Guided policy search algorithms can be... | 6105 |@word grey:1 seek:1 linearized:3 r:1 prominence:1 decomposition:1 harder:1 reduction:1 initial:10 lqr:6 current:1 com:1 must:3 informative:1 motor:1 remove:1 reproducible:1 drop:1 update:2 plot:1 intelligence:2 fewer:3 selected:1 parameterization:3 provides:5 simpler:4 zhang:1 wierstra:1 direct:3 fxt:2 koltun:1 p... |
5,643 | 6,106 | Learning Additive Exponential Family Graphical
Models via ?2,1-norm Regularized M-Estimation
Xiao-Tong Yuan? Ping Li?? Tong Zhang? Qingshan Liu? Guangcan Liu?
?B-DAT Lab, Nanjing University of Info. Sci.&Tech.
Nanjing, Jiangsu, 210044, China
?Depart. of Statistics and ?Depart. of Computer Science, Rutgers University
Pi... | 6106 |@word mild:2 determinant:1 version:1 middle:1 norm:15 nd:1 c0:2 simulation:5 covariance:4 moment:1 liu:9 configuration:1 score:13 contains:1 rkhs:1 nonparanormal:16 outperforms:1 existing:2 dx:3 written:2 must:1 additive:13 partition:6 numerical:3 remove:1 ugms:12 treating:1 designed:1 vanishing:1 core:1 fa9550:1... |
5,644 | 6,107 | Observational-Interventional Priors for
Dose-Response Learning
Ricardo Silva
Department of Statistical Science and Centre for Computational Statistics and Machine Learning
University College London
ricardo@stats.ucl.ac.uk
Abstract
Controlled interventions provide the most direct source of information for learning
cau... | 6107 |@word trial:3 middle:2 briefly:1 polynomial:2 seems:1 stronger:4 sex:1 simulation:4 covariance:10 attended:1 solid:1 harder:1 initial:1 liu:1 contains:1 efficacy:1 outperforms:1 ka:6 tackling:1 realistic:2 shape:2 cheap:1 remove:1 designed:1 plot:1 drop:1 update:1 v:1 infant:12 alone:1 selected:1 intelligence:1 c... |
5,645 | 6,108 | Blind Regression: Nonparametric Regression for
Latent Variable Models via Collaborative Filtering
Christina E. Lee
Yihua Li
Devavrat Shah
Dogyoon Song
Laboratory for Information and Decision Systems
Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
{celee, liyihua, devavra... | 6108 |@word mild:3 norm:1 inpainting:1 substitution:1 siebel:1 liu:1 past:1 existing:1 outperforms:1 ganti:1 discretization:1 com:1 varx:2 si:3 additive:3 plot:1 implying:1 item:28 smith:1 provides:3 intellectual:1 preference:1 along:3 symposium:2 prove:2 mui:2 weave:1 manner:1 introduce:2 x0:4 theoretically:1 expected... |
5,646 | 6,109 | SEBOOST ? Boosting Stochastic Learning Using
Subspace Optimization Techniques
Elad Richardson*1 Rom Herskovitz*1 Boris Ginsburg2 Michael Zibulevsky1
1
Technion, Israel Institute of Technology 2 Nvidia INC
{eladrich,mzib}@cs.technion.ac.il {fornoch,boris.ginsburg}@gmail.com
Abstract
We present SEBOOST, a technique for... | 6109 |@word eliminating:1 nemirovsky:1 eng:1 sgd:32 moment:1 denoting:2 interestingly:1 existing:4 current:9 com:3 gmail:1 yet:1 diederik:1 john:1 devin:1 ronan:1 remove:1 update:3 half:1 fewer:1 intelligence:1 xk:16 oldest:2 ith:1 core:3 lr:1 provides:1 boosting:13 location:1 firstly:1 zhang:2 transl:1 manner:1 introd... |
5,647 | 611 | Learning to See Where and What:
Training a Net to Make Saccades and Recognize
Handwritten Characters
Gale Martin, Mosfeq Rashid, David Chapman, and James Pittman
MCC, 3500 Balcones Center Drive, Austin, Texas 78759
ABSTRACT
This paper describes an approach to integrated segmentation and
recognition of hand-printed cha... | 611 |@word version:2 briefly:1 fonn:1 thereby:2 yaleu:1 contains:1 foveal:2 past:1 current:7 activation:2 written:2 must:1 subsequent:1 informative:1 thble:1 enables:1 remove:1 cue:2 half:2 beginning:2 node:12 location:2 successive:2 accessed:2 five:1 height:1 along:7 fixation:1 paragraph:1 expected:1 multi:1 retard:1 ... |
5,648 | 6,110 | Unsupervised Domain Adaptation with Residual
Transfer Networks
Mingsheng Long? , Han Zhu? , Jianmin Wang? , and Michael I. Jordan]
?
KLiss, MOE; TNList; School of Software, Tsinghua University, China
]
University of California, Berkeley, Berkeley, USA
{mingsheng,jimwang}@tsinghua.edu.cn, zhuhan10@gmail.com, jordan@berk... | 6110 |@word kulis:1 cnn:4 middle:1 hu:1 confirms:2 tat:1 sgd:2 tnlist:2 contains:1 efficacy:1 selecting:1 outperforms:4 existing:1 current:1 com:3 transferability:3 nt:9 luo:1 activation:3 gmail:1 ddc:6 must:1 guadarrama:1 enables:2 remove:4 hypothesize:1 designed:2 prohibitive:2 selected:3 short:1 chua:1 provides:1 zh... |
5,649 | 6,111 | Learning What and Where to Draw
Scott Reed1,?
reedscot@google.com
Zeynep Akata2
akata@mpi-inf.mpg.de
Santosh Mohan1
santoshm@umich.edu
Samuel Tenka1
samtenka@umich.edu
Bernt Schiele2
schiele@mpi-inf.mpg.de
Honglak Lee1
honglak@umich.edu
1
2
University of Michigan, Ann Arbor, USA
Max Planck Institute for Inform... | 6111 |@word kohli:1 cnn:3 version:2 advantageous:1 replicate:5 deconvolutions:3 instruction:1 grey:3 additively:3 pg:2 inpainting:1 solid:1 shot:1 configuration:2 series:2 score:3 interestingly:1 deconvolutional:1 existing:1 current:1 com:3 activation:1 must:1 realistic:7 concatenate:1 additive:1 visible:3 shape:1 enab... |
5,650 | 6,112 | Deep Learning without Poor Local Minima
Kenji Kawaguchi
Massachusetts Institute of Technology
kawaguch@mit.edu
Abstract
In this paper, we prove a conjecture published in 1989 and also partially address
an open problem announced at the Conference on Learning Theory (COLT) 2015.
With no unrealistic assumption, we first... | 6112 |@word version:1 polynomial:1 norm:1 open:10 decomposition:1 arous:2 reduction:1 contains:3 kurt:1 past:1 comparing:1 activation:9 yet:3 dx:15 must:2 ronald:1 realistic:2 happen:1 intelligence:2 greedy:1 fewer:1 accordingly:1 beginning:1 hamiltonian:1 provides:1 pascanu:1 node:1 org:1 simpler:1 zhang:1 mathematica... |
5,651 | 6,113 | Learning to Poke by Poking: Experiential Learning of
Intuitive Physics
Pulkit Agrawal?
Ashvin Nair?
Pieter Abbeel
Jitendra Malik
Sergey Levine
Berkeley Artificial Intelligence Research Laboratory (BAIR)
University of California Berkeley
{pulkitag,anair17,pabbeel,malik,svlevine}@berkeley.edu
Abstract
We investigate a... | 6113 |@word trial:1 cnn:2 middle:1 pieter:3 simulation:8 r:1 rgb:1 harder:3 initial:14 configuration:5 series:3 selecting:1 daniel:1 ours:2 outperforms:5 current:10 wd:4 discretization:1 surprising:2 manuel:1 must:1 readily:1 takeo:1 planet:1 informative:1 enables:1 displace:8 designed:2 depict:1 ashutosh:1 infant:3 in... |
5,652 | 6,114 | Weight Normalization: A Simple Reparameterization
to Accelerate Training of Deep Neural Networks
Tim Salimans
OpenAI
tim@openai.com
Diederik P. Kingma
OpenAI
dpkingma@openai.com
Abstract
We present weight normalization: a reparameterization of the weight vectors
in a neural network that decouples the length of those... | 6114 |@word cnn:7 version:2 norm:21 nd:1 bn:2 covariance:5 pick:1 thereby:2 initial:2 liu:1 score:7 tuned:1 ours:1 interestingly:1 cvae:1 current:2 com:5 activation:11 diederik:1 gpu:1 subsequent:1 additive:2 cheap:1 enables:1 hypothesize:1 plot:1 update:5 convpool:2 juditsky:1 generative:10 fewer:1 selected:1 intellig... |
5,653 | 6,115 | Linear-Memory and Decomposition-Invariant
Linearly Convergent Conditional Gradient Algorithm
for Structured Polytopes
Dan Garber
Toyota Technological Institute at Chicago
dgarber@ttic.edu
Ofer Meshi
Google
meshi@google.com
Abstract
Recently, several works have shown that natural modifications of the classical
conditi... | 6115 |@word armand:1 version:1 eliminating:1 polynomial:1 norm:6 briefly:1 seems:1 middle:2 nd:1 d2:13 tried:1 decomposition:31 concise:1 reduction:2 minding:1 frankwolfe:1 past:1 outperforms:1 err:1 current:8 com:1 luo:1 must:1 readily:2 written:2 chicago:1 numerical:1 update:3 aside:1 v:1 amir:2 xk:2 paulin:1 iterate... |
5,654 | 6,116 | Proximal Stochastic Methods for Nonsmooth
Nonconvex Finite-Sum Optimization
Sashank J. Reddi
Carnegie Mellon University
sjakkamr@cs.cmu.edu
Suvrit Sra
Massachusetts Institute of Technology
suvrit@mit.edu
Barnab?s P?czos
Carnegie Mellon University
bapoczos@cs.cmu.edu
Alexander J. Smola
Carnegie Mellon University
alex... | 6116 |@word version:4 stronger:1 norm:1 nd:3 open:1 pick:2 sgd:4 reduction:7 initial:2 liu:2 hereafter:1 selecting:1 ours:2 comparing:1 afflict:1 must:2 written:1 john:1 realistic:1 stationary:7 prohibitive:1 xk:9 ojasiewicz:3 provides:2 math:1 allerton:2 org:1 simpler:1 zhang:4 mathematical:2 become:1 yuan:1 prove:3 f... |
5,655 | 6,117 | Bayesian Optimization with
Robust Bayesian Neural Networks
Jost Tobias Springenberg Aaron Klein Stefan Falkner Frank Hutter
Department of Computer Science
University of Freiburg
{springj,kleinaa,sfalkner,fh}@cs.uni-freiburg.de
Abstract
Bayesian optimization is a prominent method for optimizing expensive-to-evaluate
b... | 6117 |@word exploitation:1 version:4 repository:2 changyou:2 hu:2 confirms:1 crucially:3 covariance:1 sgd:6 thereby:1 reduction:2 initial:4 automl:1 substitution:1 efficacy:1 ndez:3 contains:1 configuration:3 tuned:5 rippel:1 interestingly:1 series:1 existing:1 freitas:1 current:2 com:1 recovered:1 yet:1 readily:1 peri... |
5,656 | 6,118 | Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations
Kirthevasan Kandasamy \ , Gautam Dasarathy ? , Junier Oliva \ ,
Jeff Schneider \ , Barnab?s P?czos \
\
Carnegie Mellon University, ? Rice University
{kandasamy, joliva, schneide, bapoczos}@cs.cmu.edu, gautamd@rice.edu
Abstract
In many scientific and... | 6118 |@word trial:4 exploitation:1 worsens:1 stronger:1 mockus:1 simulation:8 tried:1 covariance:1 pick:1 solid:4 configuration:3 series:1 score:2 contains:2 efficacy:2 initialisation:1 genetic:1 ours:1 interestingly:2 tuned:1 selecting:1 outperforms:3 freitas:2 com:1 optim:1 analysed:1 yet:1 john:1 explorative:1 addit... |
5,657 | 6,119 | Maximizing Influence in an Ising Network:
A Mean-Field Optimal Solution
Christopher W. Lynn
Department of Physics and Astronomy
University of Pennsylvania
chlynn@sas.upenn.edu
Daniel D. Lee
Department of Electrical and Systems Engineering
University of Pennsylvania
ddlee@seas.upenn.edu
Abstract
Influence maximizatio... | 6119 |@word simulation:4 contraction:1 simplifying:1 incurs:1 initial:1 configuration:1 series:1 daniel:1 interestingly:1 outperforms:1 current:1 comparing:1 must:1 numerical:4 kdd:3 analytic:2 treating:1 plot:6 intelligence:1 selected:1 provides:1 node:31 along:1 prove:1 upenn:2 expected:1 proliferation:1 mechanic:3 g... |
5,658 | 612 | Combining Neural and Symbolic
Learning to Revise Probabilistic Rule
Bases
J. Jeffrey Mahoney and Raymond J. Mooney
Dept. of Computer Sciences
University of Texas
Austin, TX 78712
mahoney@cs.utexas.edu, mooney@cs.utexas.edu
Abstract
This paper describes RAPTURE - a system for revising probabilistic knowledge bases that... | 612 |@word trial:3 repository:1 version:1 seek:1 deems:1 solid:1 initial:6 contains:1 existing:1 current:3 comparing:1 ginsberg:3 michal:1 activation:3 must:1 blur:1 enables:1 fertilization:1 plot:1 designed:1 alone:2 intelligence:3 rulebase:1 beginning:1 short:1 supplying:1 node:21 location:1 contribute:1 ames:1 diagn... |
5,659 | 6,120 | An urn model for majority voting
in classification ensembles
Victor Soto
Computer Science Department
Columbia University
New York, NY, USA
vsoto@cs.columbia.edu
Alberto Su?rez and Gonzalo Mart?nez-Mu?oz
Computer Science Department
Universidad Aut?noma de Madrid
Madrid, Spain
{gonzalo.martinez,alberto.suarez}@uam.es
A... | 6120 |@word repository:2 simulation:1 solid:1 delgado:1 moment:2 initial:1 ndez:2 contains:2 series:1 current:2 com:1 noma:1 mushroom:3 chu:3 readily:1 casi:1 subsequent:1 ministerio:1 partition:2 kdd:1 shape:1 plot:2 update:1 intelligence:6 record:1 colored:1 tumer:1 provides:2 recompute:1 s2013:1 boosting:3 zhang:1 m... |
5,660 | 6,121 | Dense Associative Memory for Pattern Recognition
Dmitry Krotov
Simons Center for Systems Biology
Institute for Advanced Study
Princeton, USA
krotov@ias.edu
John J. Hopfield
Princeton Neuroscience Institute
Princeton University
Princeton, USA
hopfield@princeton.edu
Abstract
A model of associative memory is studied, wh... | 6121 |@word version:1 polynomial:15 proportion:1 open:1 moment:1 initial:4 configuration:9 contains:1 ours:1 interestingly:1 document:1 current:1 comparing:1 activation:23 perror:2 dx:1 yet:2 must:1 john:1 written:1 universality:1 visible:12 subsequent:1 numerical:2 remove:1 update:21 alone:1 cue:1 generative:1 selecte... |
5,661 | 6,122 | Cooperative Graphical Models
Josip Djolonga
Dept. of Computer Science, ETH Z?urich
josipd@inf.ethz.ch
Stefanie Jegelka
CSAIL, MIT
stefje@mit.edu
Sebastian Tschiatschek
Dept. of Computer Science, ETH Z?urich
stschia@inf.ethz.ch
Andreas Krause
Dept. of Computer Science, ETH Z?urich
krausea@inf.ethz.ch
Abstract
We st... | 6122 |@word kohli:2 determinant:1 faculty:1 briefly:1 polynomial:3 norm:2 seems:3 semidifferential:1 open:3 linearized:5 pick:1 configuration:6 contains:1 efficacy:2 ours:1 ala:1 existing:3 current:3 com:1 yet:1 chu:1 written:1 subsequent:1 partition:17 shape:1 pseudomarginals:1 update:2 k15:4 intelligence:1 mccallum:1... |
5,662 | 6,123 | Finite-Sample Analysis of Fixed-k Nearest Neighbor
Density Functional Estimators
Shashank Singh
Statistics & Machine Learning Departments
Carnegie Mellon University
sss1@andrew.cmu.edu
Barnab?s P?czos
Machine Learning Departments
Carnegie Mellon University
bapoczos@cs.cmu.edu
Abstract
We provide finite-sample analys... | 6123 |@word mild:1 neurophysiology:1 sss1:1 version:1 norm:1 open:1 bn:2 decomposition:1 zolt:1 liu:1 series:2 selecting:1 ours:1 rkhs:1 bradley:1 dx:4 cruz:1 evans:1 numerical:1 additive:2 kandasamy:2 intelligence:2 guess:1 vanishing:1 boosting:2 complication:2 math:1 org:2 unbounded:4 along:1 direct:1 aryeh:1 symposi... |
5,663 | 6,124 | Achieving Budget-optimality with Adaptive Schemes
in Crowdsourcing
Ashish Khetan and Sewoong Oh
Department of ISE, University of Illinois at Urbana-Champaign
Email: {khetan2,swoh}@illinois.edu
Abstract
Adaptive schemes, where tasks are assigned based on the data collected thus far, are
widely used in practical crowdso... | 6124 |@word version:6 achievable:3 nd:1 tedious:1 willing:1 simulation:1 crucially:2 solid:1 initial:1 configuration:1 liu:2 karger:3 khetan:1 subjective:1 existing:3 comparing:3 assigning:3 john:1 numerical:2 additive:1 subsequent:2 half:2 fewer:1 accordingly:2 ruvolo:1 beginning:1 caveat:1 characterization:1 provides... |
5,664 | 6,125 | Improved Techniques for Training GANs
Tim Salimans
tim@openai.com
Ian Goodfellow
ian@openai.com
Wojciech Zaremba
woj@openai.com
Alec Radford
alec@openai.com
Vicki Cheung
vicki@openai.com
Xi Chen
peter@openai.com
Abstract
We present a variety of new architectural features and training procedures that we
apply to ... | 6125 |@word cnn:1 seems:1 norm:1 logit:1 heuristically:1 seek:1 bn:3 pg:1 incurs:2 moment:1 series:2 score:16 contains:1 kweon:1 daniel:3 jimenez:1 ours:1 interestingly:1 past:1 subjective:1 current:2 com:9 comparing:1 surprising:1 activation:1 yet:1 diederik:2 intriguing:1 gpu:1 realistic:2 concatenate:1 shape:1 chris... |
5,665 | 6,126 | Robust k-means: a Theoretical Revisit
Alexandros Georgogiannis
School of Electrical and Computer Engineering
Technical University of Crete, Greece
alexandrosgeorgogiannis at gmail.com
Abstract
Over the last years, many variations of the quadratic k-means clustering procedure
have been proposed, all aiming to robustify... | 6126 |@word mild:1 version:2 polynomial:2 norm:5 proportion:1 suitably:1 ronchetti:1 initial:4 configuration:1 contains:2 chervonenkis:1 com:1 gmail:1 dx:1 must:1 luis:1 john:1 partition:1 cheap:1 christian:1 remove:1 drop:1 designed:1 update:1 plot:2 discrimination:1 implying:2 half:1 antoniadis:1 accordingly:2 stahel... |
5,666 | 6,127 | Stochastic Three-Composite Convex Minimization
? and Volkan Cevher
Alp Yurtsever, B`a? ng C?ng Vu,
Laboratory for Information and Inference Systems (LIONS)
?cole Polytechnique F?d?rale de Lausanne, Switzerland
alp.yurtsever@epfl.ch, bang.vu@epfl.ch, volkan.cevher@epfl.ch
Abstract
We propose a stochastic optimization m... | 6127 |@word mild:2 repository:1 advantageous:1 norm:1 open:1 d2:1 semicontinuous:3 simulation:3 seek:1 hu:1 decomposition:1 solid:1 initial:2 liu:1 contains:2 lichman:1 selecting:1 tuned:1 ours:1 ati:1 existing:1 bd:1 numerical:6 partition:2 cheap:1 fama:2 update:1 xk:1 ith:1 short:1 volkan:2 characterization:2 provide... |
5,667 | 6,128 | Normalized Spectral Map Synchronization
Yanyao Shen
UT Austin
Austin, TX 78712
shenyanyao@utexas.edu
Qixing Huang
TTI Chicago and UT Austin
Austin, TX 78712
huangqx@cs.utexas.edu
Nathan Srebro
TTI Chicago
Chicago, IL 60637
nati@ttic.edu
Sujay Sanghavi
UT Austin
Austin, TX 78712
sanghavi@mail.utexas.edu
Abstract
Es... | 6128 |@word kondor:2 dalal:1 norm:6 triggs:1 decomposition:1 harder:1 moment:1 initial:6 liu:1 contains:1 series:1 existing:2 recovered:3 comparing:1 chazelle:1 surprising:1 si:8 yet:6 chicago:3 shape:7 enables:1 rrt:2 spec:4 advancement:1 guess:1 nq:2 xk:3 provides:2 org:2 dell:1 along:3 constructed:3 become:1 symposi... |
5,668 | 6,129 | Reconstructing Parameters of Spreading Models
from Partial Observations
Andrey Y. Lokhov
Center for Nonlinear Studies and Theoretical Division T-4
Los Alamos National Laboratory, Los Alamos, NM 87545, USA
lokhov@lanl.gov
Abstract
Spreading processes are often modelled as a stochastic dynamics occurring on top
of a giv... | 6129 |@word repository:1 version:2 polynomial:1 norm:1 nd:1 simulation:2 r:22 harder:1 carry:1 initial:11 contains:1 series:1 blackout:1 past:3 existing:1 outperforms:1 recovered:1 current:1 com:1 surprising:1 activation:13 si:5 scatter:2 attracted:1 written:1 realistic:2 subsequent:1 numerical:3 timestamps:1 moreno:1 ... |
5,669 | 613 | Bayesian Learning
via Stochastic Dynamics
Radford M. Neal
Department of Computer Science
University of Toronto
Toronto, Ontario, Canada M5S lA4
Abstract
The attempt to find a single "optimal" weight vector in conventional network training can lead to overfitting and poor generalization. Bayesian methods avoid this, w... | 613 |@word interleave:1 seems:1 proportion:1 nd:1 rno:1 simulation:4 t_:1 pressure:1 tr:1 minus:1 solid:2 fif:1 ld:2 must:5 subsequent:1 predetermined:1 v:1 stationary:5 leaf:2 selected:1 hamiltonian:4 toronto:3 sigmoidal:1 simpler:1 notably:1 discretized:1 actual:1 considering:1 becomes:1 begin:1 minimizes:1 differing... |
5,670 | 6,130 | Probing the Compositionality of Intuitive Functions
Eric Schulz
University College London
e.schulz@cs.ucl.ac.uk
Joshua B. Tenenbaum
MIT
jbt@mit.edu
Maarten Speekenbrink
University College London
m.speekenbrink@ucl.ac.uk
David Duvenaud
University of Toronto
duvenaud@cs.toronto.edu
Samuel J. Gershman
Harvard Univers... | 6130 |@word trial:8 judgement:6 proportion:10 reshef:1 covariance:5 eng:1 xtest:1 paid:1 thereby:1 shot:2 harder:1 past:2 qth:1 subjective:2 current:2 written:1 wanted:1 remove:1 plot:2 interpretable:1 v:3 stationary:3 intelligence:1 discovering:1 item:1 parametrization:2 mental:2 characterization:1 parameterizations:1... |
5,671 | 6,131 | A Bayesian method for reducing bias in neural
representational similarity analysis
Ming Bo Cai
Princeton Neuroscience Institute
Princeton University
Princeton, NJ 08544
mcai@princeton.edu
Nicolas W. Schuck
Princeton Neuroscience Institute
Princeton University
Princeton, NJ 08544
nschuck@princeton.edu
Jonathan W. Pil... | 6131 |@word determinant:2 cox:1 kriegeskorte:5 stronger:1 proportion:1 confirms:1 pulse:1 simulation:4 covariance:56 decomposition:1 accounting:1 tr:1 series:3 halchenko:1 denoting:1 interestingly:1 rightmost:1 schuck:2 existing:1 reaction:1 recovered:13 com:1 comparing:5 nt:4 current:1 si:15 activation:3 anterior:1 at... |
5,672 | 6,132 | Average-case hardness of RIP certification
Tengyao Wang
Centre for Mathematical Sciences
Cambridge, CB3 0WB, United Kingdom
t.wang@statslab.cam.ac.uk
Quentin Berthet
Centre for Mathematical Sciences
Cambridge, CB3 0WB, United Kingdom
q.berthet@statslab.cam.ac.uk
Yaniv Plan
1986 Mathematics Road
Vancouver BC V6T 1Z2,... | 6132 |@word milenkovic:2 version:1 polynomial:15 proportion:2 norm:4 open:2 p0:2 pick:1 reduction:5 contains:2 united:2 denoting:1 bc:1 ours:1 interestingly:1 mixon:3 existing:1 z2:1 must:1 subsequent:1 juditsky:2 greedy:1 leaf:2 short:1 detecting:5 math:2 completeness:1 location:1 zhang:2 mathematical:5 constructed:3 ... |
5,673 | 6,133 | Learning in Games: Robustness of Fast Convergence
Dylan J. Foster?
Zhiyuan Li?
Thodoris Lykouris?
Karthik Sridharan?
?va Tardos?
Abstract
We show that learning algorithms satisfying a low approximate regret property
experience fast convergence to approximate optimality in a large class of repeated
games. Our prope... | 6133 |@word private:1 version:5 faculty:1 rani:1 stronger:1 d2:1 simulation:1 forecaster:1 simplifying:1 jacob:2 pick:1 prescriptive:1 erven:1 current:1 comparing:1 dikin:1 luo:2 si:10 allenberg:1 realistic:2 additive:2 subsequent:4 informative:1 enables:1 ligett:1 update:3 hwit:3 congestion:7 implying:2 greedy:1 item:... |
5,674 | 6,134 | Stochastic Structured Prediction
under Bandit Feedback
Artem Sokolov,?, Julia Kreutzer? , Christopher Lo?,? , Stefan Riezler?,?
?
Computational Linguistics & ? IWR, Heidelberg University, Germany
{sokolov,kreutzer,riezler}@cl.uni-heidelberg.de
?
Department of Mathematics, Tufts University, Boston, MA, USA
chris.aa.lo... | 6134 |@word multitask:1 exploitation:2 version:1 pw:17 judgement:2 norm:11 advantageous:1 stronger:1 dekel:1 simulation:1 boundedness:1 series:1 score:8 selecting:1 skd:1 current:1 com:1 contextual:4 surprising:1 gmail:1 chu:1 parsing:1 numerical:6 kdd:1 drop:1 update:9 greedy:1 selected:2 prohibitive:1 smith:1 boostin... |
5,675 | 6,135 | The Multiscale Laplacian Graph Kernel
Risi Kondor
Department of Computer Science
Department of Statistics
University of Chicago
Chicago, IL 60637
risi@cs.uchicago.edu
Horace Pan
Department of Computer Science
University of Chicago
Chicago, IL 60637
hopan@uchicago.edu
Abstract
Many real world graphs, such as the grap... | 6135 |@word middle:1 kondor:5 johansson:1 twelfth:1 open:1 motoda:1 linearized:1 covariance:1 q1:4 concise:1 nystr:4 recursively:5 efficacy:2 rkhs:2 bhattacharyya:3 kurt:2 existing:1 comparing:2 si:3 dx:1 must:4 chicago:5 happen:1 informative:2 mutagenic:1 shape:4 christian:1 drop:1 graphlets:1 hash:1 intelligence:1 le... |
5,676 | 6,136 | Learning Bound for Parameter Transfer Learning
Wataru Kumagai
Faculty of Engineering
Kanagawa University
kumagai@kanagawa-u.ac.jp
Abstract
We consider a transfer-learning problem by using the parameter transfer approach,
where a suitable parameter of feature mapping is learned through one task and applied to another ... | 6136 |@word h:1 multitask:1 faculty:1 briefly:1 norm:7 paredes:1 suitably:1 mehta:6 r:2 bn:13 citeseer:1 thereby:1 accommodate:1 reduction:1 plentiful:1 tuned:1 romera:1 existing:1 luo:1 generative:1 intelligence:1 indicative:1 provides:1 mannor:1 clarified:1 c2:1 become:1 consists:3 prove:2 introduce:3 expected:5 cons... |
5,677 | 6,137 | Combinatorial semi-bandit with known covariance
R?my Degenne
LMPA, Universit? Paris Diderot
CMLA, ENS Paris-Saclay
degenne@cmla.ens-cachan.fr
Vianney Perchet
CMLA, ENS Paris-Saclay
CRITEO Research, Paris
perchet@normalesup.org
Abstract
The combinatorial stochastic semi-bandit problem is an extension of the classical
... | 6137 |@word cu:1 seems:1 gaspard:1 nd:1 covariance:9 decomposition:1 existing:1 yajun:1 comparing:1 nt:22 must:3 john:1 happen:5 update:1 intelligence:1 selected:1 ith:1 provides:1 math:1 revisited:1 honda:1 successive:3 org:1 yuan:1 prove:3 introduce:3 inter:1 indeed:1 expected:4 multi:6 yasin:1 inspired:2 information... |
5,678 | 6,138 | Automatic Neuron Detection in Calcium Imaging
Data Using Convolutional Networks
Noah J. Apthorpe1? Alexander J. Riordan2? Rob E. Aguilar1 Jan Homann2
Yi Gu2 David W. Tank2 H. Sebastian Seung12
1
Computer Science Department 2 Princeton Neuroscience Institute
Princeton University
{apthorpe, ariordan, dwtank, sseung}@pri... | 6138 |@word neurophysiology:1 manageable:1 houweling:1 approved:1 nd:1 disk:1 open:1 prasad:1 brightness:1 sgd:4 schnitzer:1 deisseroth:1 initial:4 series:13 score:13 daniel:3 ours:1 subjective:1 outperforms:1 com:1 activation:1 yet:1 must:1 readily:1 john:1 fn:2 subsequent:1 visible:3 shape:2 motor:1 designed:3 medial... |
5,679 | 6,139 | Supervised Word Mover?s Distance
Gao Huang? , Chuan Guo?
Cornell University
{gh349,cg563}@cornell.edu
Yu Sun, Kilian Q. Weinberger
Cornell University
{ys646,kqw4}@cornell.edu
Matt J. Kusner?
Alan Turing Institute, University of Warwick
mkusner@turing.ac.uk
Fei Sha
University of California, Los Angeles
feisha@cs.ucla.... | 6139 |@word multitask:1 kulis:1 version:3 seems:1 nd:1 d2:2 bn:2 decomposition:1 pick:1 reduction:1 initial:5 liu:2 contains:2 document:77 outperforms:4 ka:3 com:2 goldberger:2 must:1 cheap:1 plot:1 update:2 generative:1 prohibitive:1 selected:1 desktop:1 xk:1 ith:2 farther:1 blei:2 completeness:1 c6:1 zhang:1 five:1 d... |
5,680 | 614 | Explanation-Based Neural Network Learning
for Robot Control
Tom M. Mitchell
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
E-mail: mitchell@cs.cmu.edu
Sebastian B. Thrun
University of Bonn
Institut fUr Infonnatik III
ROmerstr. 164, D-5300 Bonn, Germany
thrnn@uran.informatik.uni-bonn.de
Ab... | 614 |@word illustrating:1 version:1 open:2 grey:1 minus:1 recursively:1 initial:3 si:1 must:2 john:1 ronald:1 subsequent:1 partition:1 shape:4 sponsored:1 update:1 greedy:2 fewer:3 selected:1 underestimating:1 ebnn:35 location:2 five:1 constructed:1 become:1 consists:1 combine:2 fitting:2 introduce:1 acquired:1 expecte... |
5,681 | 6,140 | Graph Clustering: Block-models and model free
results
Marina Meil?a
Department of Statistics
University of Washington
Seattle, WA 98195-4322, USA
mmp@stat.washington.edu
Yali Wan
Department of Statistics
University of Washington
Seattle, WA 98195-4322, USA
yaliwan@washington.edu
Abstract
Clustering graphs under the S... | 6140 |@word norm:6 stronger:3 nd:2 c0:8 decomposition:3 arti:1 harder:1 ld:1 contains:2 daniel:1 bc:1 existing:6 current:1 comparing:2 incidence:1 must:1 informative:2 cant:1 christian:1 zik:1 stationary:1 v:1 instantiate:1 fewer:1 intelligence:1 directory:1 santo:1 node:17 preference:2 liberal:1 simpler:1 mathematical... |
5,682 | 6,141 | An Architecture for Deep, Hierarchical Generative
Models
Philip Bachman
phil.bachman@maluuba.com
Maluuba Research
Abstract
We present an architecture which lets us train deep, directed generative models
with many layers of latent variables. We include deterministic paths between all
latent variables and the generated... | 6141 |@word version:2 compression:1 seems:1 stronger:1 propagate:1 bachman:5 crucially:1 paid:1 inpainting:2 shot:1 initial:2 liu:1 lightweight:3 score:1 existing:1 current:5 com:4 luo:1 yet:1 intriguing:1 written:1 must:1 gpu:1 exposing:1 subsequent:2 concatenate:1 visible:1 shape:3 enables:1 remove:1 designed:1 plot:... |
5,683 | 6,142 | Data Poisoning Attacks on Factorization-Based
Collaborative Filtering
Bo Li ?
Vanderbilt University
bo.li.2@vanderbilt.edu
Aarti Singh
Carnegie Mellon University
aarti@cs.cmu.edu
Yining Wang ?
Carnegie Mellon University
ynwang.yining@gmail.com
Yevgeniy Vorobeychik
Vanderbilt University
yevgeniy.vorobeychik@vanderbilt... | 6142 |@word version:2 seems:1 norm:21 open:1 km:1 decomposition:2 p0:3 arjen:1 pavel:1 pick:1 harder:2 substitution:1 contains:2 score:2 selecting:1 daniel:1 fa8750:1 existing:3 kmk:2 current:1 com:1 recovered:1 gmail:1 enables:1 plot:3 update:3 poisoned:1 intelligence:1 selected:1 item:34 rav:1 ith:4 characterization:... |
5,684 | 6,143 | DISCO Nets: DISsimilarity COefficient Networks
Diane Bouchacourt
University of Oxford
diane@robots.ox.ac.uk
M. Pawan Kumar
University of Oxford
pawan@robots.ox.ac.uk
Sebastian Nowozin
Microsoft Research Cambridge
sebastian.nowozin@microsoft.com
Abstract
We present a new type of probabilistic model which we call DIS... | 6143 |@word cnn:1 middle:2 version:1 norm:4 replicate:1 tedious:1 grey:2 covariance:2 q1:4 acknowlegements:1 tr:4 lepetit:2 moment:2 contains:3 score:5 interestingly:1 existing:5 com:1 z2:1 must:2 subsequent:1 partition:1 premachandran:2 designed:1 update:2 discrimination:1 generative:13 half:1 intelligence:1 maximised... |
5,685 | 6,144 | Higher-Order Factorization Machines
Mathieu Blondel, Akinori Fujino, Naonori Ueda
NTT Communication Science Laboratories
Japan
Masakazu Ishihata
Hokkaido University
Japan
Abstract
Factorization machines (FMs) are a supervised learning approach that can use
second-order feature combinations even when the data is very ... | 6144 |@word briefly:1 polynomial:17 advantageous:1 d2:1 km:2 decomposition:2 recursively:1 cyclic:1 contains:2 score:1 liu:1 tist:1 interestingly:1 current:1 ixj:2 yet:2 j1:5 remove:1 update:5 stationary:1 intelligence:1 item:1 xk:2 node:4 org:1 simpler:1 constructed:1 become:1 abadi:1 shorthand:1 combine:1 compose:1 i... |
5,686 | 6,145 | A Multi-Batch L-BFGS Method for Machine
Learning
Albert S. Berahas
Northwestern University
Evanston, IL
albertberahas@u.northwestern.edu
Jorge Nocedal
Northwestern University
Evanston, IL
j-nocedal@northwestern.edu
Martin Tak??c
Lehigh University
Bethlehem, PA
takac.mt@gmail.com
Abstract
The question of how to paral... | 6145 |@word version:1 norm:2 seek:2 sgd:17 mention:2 solid:2 reduction:1 initial:2 contains:1 selecting:2 o2:1 current:1 com:1 si:3 gmail:1 assigning:1 must:1 devin:1 numerical:6 predetermined:1 update:9 oldest:2 beginning:3 iterates:3 provides:1 node:27 simpler:1 zhang:2 mathematical:3 along:2 become:1 consists:1 over... |
5,687 | 6,146 | SoundNet: Learning Sound
Representations from Unlabeled Video
Yusuf Aytar?
MIT
yusuf@csail.mit.edu
Carl Vondrick?
MIT
vondrick@mit.edu
Antonio Torralba
MIT
torralba@mit.edu
Abstract
We learn rich natural sound representations by capitalizing on large amounts of
unlabeled sound data collected in the wild. We leverag... | 6146 |@word economically:2 version:2 cnn:3 pw:2 stronger:1 open:1 seek:1 tried:1 rgb:1 jacob:1 downloading:1 pick:2 configuration:4 contains:3 series:2 score:2 daniel:3 document:1 interestingly:2 outperforms:3 existing:5 comparing:1 places2:2 activation:2 yet:3 diederik:1 must:1 gpu:1 bello:1 devin:1 informative:1 enab... |
5,688 | 6,147 | Towards Unifying Hamiltonian Monte Carlo
and Slice Sampling
Yizhe Zhang, Xiangyu Wang, Changyou Chen, Ricardo Henao, Kai Fan, Lawrence Carin
Duke University
Durham, NC, 27708
{yz196,xw56,changyou.chen, ricardo.henao, kf96 , lcarin} @duke.edu
Abstract
We unify slice sampling and Hamiltonian Monte Carlo (HMC) sampling, ... | 6147 |@word repository:2 version:1 changyou:2 seems:4 hyv:1 confirms:1 seek:2 simulation:2 p0:38 doeblin:1 ld:1 initial:6 series:1 lichman:1 selecting:2 interestingly:3 outperforms:1 elliptical:2 discretization:2 z2:4 comparing:1 current:1 yet:1 dx:4 diederik:1 john:1 numerical:21 enables:1 analytic:20 christian:1 drop... |
5,689 | 6,148 | Interpretable Distribution Features
with Maximum Testing Power
Wittawat Jitkrittum, Zolt?n Szab?, Kacper Chwialkowski, Arthur Gretton
wittawatj@gmail.com
zoltan.szabo.m@gmail.com
kacper.chwialkowski@gmail.com
arthur.gretton@gmail.com
Gatsby Unit, University College London
Abstract
Two semimetrics on probability distr... | 6148 |@word trial:9 version:2 norm:4 proportion:1 simulation:1 covariance:3 zolt:1 tr:16 initial:1 contains:1 series:1 afraid:1 rkhs:4 document:1 outperforms:1 com:5 exy:3 gmail:4 stemmed:1 must:1 universality:1 stemming:1 j1:1 predetermined:1 informative:5 analytic:9 confirming:1 remove:1 plot:6 interpretable:10 drop:... |
5,690 | 6,149 | Threshold Bandit, With and Without Censored
Feedback
Jacob Abernethy
Department of Computer Science
University of Michigan
Ann Arbor, MI 48109
jabernet@umich.edu
Kareem Amin
Department of Computer Science
University of Michigan
Ann Arbor, MI 48109
amkareem@umich.edu
Ruihao Zhu
AeroAstro&CSAIL
MIT
Cambridge, MA 02139
... | 6149 |@word exploitation:1 version:1 stronger:1 d2:4 km:2 crucially:1 jacob:3 simplifying:1 paid:1 minus:1 harder:1 offload:1 cyclic:3 selecting:1 denoting:1 past:2 existing:2 current:1 optim:3 surprising:1 must:2 written:1 john:2 fn:1 ronald:1 remove:1 sponsored:1 bart:2 v:1 intelligence:1 selected:1 beginning:1 ith:2... |
5,691 | 615 | Neural Network On-Line Learning Control
of Spacecraft Smart Structures
Dr. Christopher Bowman
Ball Aerospace Systems Group
P.O. Box 1062
Boulder. CO 80306
Abstract
The overall goal is to reduce spacecraft weight. volume, and cost by online adaptive non-linear control of flexible structural components. The
objective o... | 615 |@word trial:1 version:1 inversion:1 simulation:1 pulse:1 bn:1 jacob:2 necessity:1 synergistically:1 lightweight:1 score:5 initial:4 current:6 activation:1 numerical:1 shape:1 remove:1 update:1 slowing:1 provides:3 five:1 bowman:6 unacceptable:1 direct:8 symposium:1 incorrect:1 spacecraft:6 behavior:2 elman:1 actua... |
5,692 | 6,150 | Learning from Rational Behavior:
Predicting Solutions to Unknown Linear Programs
Shahin Jabbari, Ryan Rogers, Aaron Roth, Zhiwei Steven Wu
University of Pennsylvania
{jabbari@cis, ryrogers@sas, aaroth@cis, wuzhiwei@cis}.upenn.edu
Abstract
We define and study the problem of predicting the solution to a linear program (... | 6150 |@word collinearity:1 economically:1 compression:1 polynomial:7 stronger:1 norm:1 open:2 d2:1 profit:2 necessity:3 contains:6 q1e:2 recovered:1 current:2 must:7 parsing:1 written:10 remove:1 update:18 intelligence:1 selected:1 reranking:1 accordingly:1 record:1 preference:15 hyperplanes:6 along:2 symposium:1 incor... |
5,693 | 6,151 | A Forward Model at Purkinje Cell Synapses
Facilitates Cerebellar Anticipatory Control
Ivan Herreros-Alonso
SPECS lab
Universitat Pompeu Fabra
Barcelona, Spain
ivan.herreros@upf.edu
Xerxes D. Arsiwalla
SPECS lab
Universitat Pompeu Fabra
Barcelona, Spain
Paul F.M.J. Verschure
SPECS, UPF
Catalan Institution of Research... | 6151 |@word neurophysiology:1 trial:34 worsens:1 version:1 norm:1 closure:2 simulation:4 simplifying:2 thereby:1 carry:1 initial:1 substitution:1 series:1 efficacy:3 contains:5 exclusively:1 united:1 tuned:1 interestingly:1 past:3 reaction:2 current:10 contextual:1 incidence:1 must:3 olive:2 plasticity:5 motor:17 updat... |
5,694 | 6,152 | Learning Tree Structured Potential Games
Vikas K. Garg
CSAIL, MIT
vgarg@csail.mit.edu
Tommi Jaakkola
CSAIL, MIT
tommi@csail.mit.edu
Abstract
Many real phenomena, including behaviors, involve strategic interactions that can
be learned from data. We focus on learning tree structured potential games where
equilibria ar... | 6152 |@word briefly:1 version:1 norm:1 justice:9 open:1 termination:1 decomposition:18 initial:1 configuration:23 efficacy:1 score:2 united:1 yni:2 bradley:1 current:1 recovered:4 chu:1 written:1 parsing:3 must:1 subsequent:1 realistic:1 hofmann:1 enables:1 designed:2 treating:1 update:5 intelligence:1 fewer:1 selected... |
5,695 | 6,153 | Estimating Nonlinear Neural Response Functions
using GP Priors and Kronecker Methods
Cristina Savin
IST Austria
Klosterneuburg, AT 3400
csavin@ist.ac.at
Gasper Tka?cik
IST Austria
Klosterneuburg, AT 3400
tkacik@ist.ac.at
Abstract
Jointly characterizing neural responses in terms of several external variables
promises... | 6153 |@word trial:1 cox:1 version:1 determinant:1 middle:1 hippocampus:7 coarseness:3 nd:2 open:7 covariance:16 simplifying:1 tkacik:2 decomposition:2 cristina:1 series:1 denoting:1 rightmost:1 past:1 recovered:1 discretization:4 comparing:1 yet:1 readily:1 realistic:1 subsequent:1 plasticity:1 designed:1 medial:2 v:3 ... |
5,696 | 6,154 | A Simple Practical Accelerated Method for Finite
Sums
Aaron Defazio
Ambiata, Sydney Australia
Abstract
We describe a novel optimization method for finite sums (such as empirical risk
minimization problems) building on the recently introduced SAGA method. Our
method achieves an accelerated convergence rate on strongly ... | 6154 |@word repository:1 version:2 replicate:1 open:1 decomposition:2 pick:2 sgd:3 reduction:1 initial:1 cyclic:1 existing:1 current:1 com:1 mushroom:1 written:1 must:1 hofmann:2 wanted:1 zaid:1 treating:1 designed:1 update:2 plot:2 selected:1 guess:1 website:1 xk:38 short:2 simpler:2 zhang:10 mathematical:1 profound:1... |
5,697 | 6,155 | Active Learning with Oracle Epiphany
Tzu-Kuo Huang ?
Uber Advanced Technologies Group
Pittsburgh, PA 15201
Ara Vartanian
University of Wisconsin?Madison
Madison, WI 53706
Saleema Amershi
Microsoft Research
Redmond, WA 98052
Lihong Li
Microsoft Research
Redmond, WA 98052
Xiaojin Zhu
University of Wisconsin?Madison
Ma... | 6155 |@word worsens:1 trial:5 version:21 seems:1 stronger:1 open:1 termination:1 additively:1 simulation:1 seek:1 pick:1 dramatic:1 incurs:1 minus:1 accommodate:2 contains:1 daniel:4 document:5 interestingly:1 omniscient:2 existing:2 err:28 current:4 imaginary:1 beygelzimer:3 yet:2 must:4 john:4 realistic:2 subsequent:... |
5,698 | 6,156 | ?-risk: a New Surrogate Risk for Learning
from Weakly Labeled Data
Valentina Zantedeschi?
R?mi Emonet
Marc Sebban
firstname.lastname@univ-st-etienne.fr
Univ Lyon, UJM-Saint-Etienne, CNRS, Institut d Optique Graduate School,
Laboratoire Hubert Curien UMR 5516, F-42023, SAINT-ETIENNE, France
Abstract
During the past ... | 6156 |@word repository:2 version:3 proportion:14 norm:1 tried:1 paid:1 rivera:1 initial:1 contains:1 lichman:1 tuned:2 past:2 outperforms:1 current:2 com:1 chu:1 intelligence:2 instantiate:2 selected:1 beginning:1 provides:1 boosting:3 org:2 c2:4 direct:2 become:2 symposium:1 consists:1 fitting:2 combine:1 privacy:1 in... |
5,699 | 6,157 | Double Thompson Sampling for Dueling Bandits
Huasen Wu
University of California, Davis
hswu@ucdavis.edu
Xin Liu
University of California, Davis
xinliu@ucdavis.edu
Abstract
In this paper, we propose a Double Thompson Sampling (D-TS) algorithm for
dueling bandit problems. As its name suggests, D-TS selects both the fi... | 6157 |@word exploitation:1 version:3 simulation:1 liu:2 substitution:5 score:10 selecting:1 nii:5 document:2 interestingly:1 outperforms:1 existing:7 savage:5 comparing:10 current:1 com:2 attracted:1 periodically:1 enables:3 designed:1 update:3 stationary:1 intelligence:2 selected:6 trapping:2 provides:3 mannor:2 honda... |
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