Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
5,400 | 5,889 | Softstar: Heuristic-Guided Probabilistic Inference
Mathew Monfort
Computer Science Department
University of Illinois at Chicago
Chicago, IL 60607
mmonfo2@uic.edu
Brenden M. Lake
Center for Data Science
New York University
New York, NY 10003
brenden@nyu.edu
Patrick Lucey
Disney Research Pittsburgh
Pittsburgh, PA 15232... | 5889 |@word version:1 inversion:2 stronger:2 open:1 simulation:2 seek:1 shot:2 tice:1 initial:2 inefficiency:1 uncovered:6 series:1 contains:2 liu:1 prefix:1 past:1 existing:1 bitmap:3 current:3 com:1 unguided:1 anqi:1 si:4 written:1 must:1 dechter:1 chicago:4 recasting:1 partition:2 update:4 eab:1 greedy:2 half:1 inte... |
5,401 | 589 | Automatic Learning Rate Maximization
by On-Line Estimation of the Hessian's
Eigenvectors
Yann LeCun,l Patrice Y. Simard,l and Barak Pearlmutter 2
1 AT&T Bell Laboratories 101 Crawfords Corner Rd, Holmdel, NJ 07733
2CS&E Dept. Oregon Grad. Inst., 19600 NW vonNeumann Dr, Beaverton, OR 97006
Abstract
We propose a very s... | 589 |@word version:10 eliminating:1 seems:2 tried:1 jacob:2 covariance:1 pick:4 sgd:5 tr:1 initial:4 series:1 tuned:1 rart:1 interestingly:1 outperforms:1 diagonalized:1 current:3 written:1 must:3 john:1 subsequent:1 numerical:1 i1l:1 cheap:2 plot:1 update:3 progressively:1 fewer:1 short:3 toronto:1 along:4 direct:2 ro... |
5,402 | 5,890 | Gradient-free Hamiltonian Monte Carlo
with Efficient Kernel Exponential Families
Heiko Strathmann? Dino Sejdinovic+ Samuel Livingstoneo Zoltan Szabo? Arthur Gretton?
?
Gatsby Unit
University College London
+
Department of Statistics
University of Oxford
o
School of Mathematics
University of Bristol
Abstract
We ... | 5890 |@word trial:1 repository:1 middle:2 version:3 inversion:2 stronger:1 norm:1 nd:1 hyv:3 simulation:9 covariance:7 p0:4 moment:2 contains:3 score:15 lichman:1 tuned:3 rkhs:11 outperforms:3 current:3 com:2 yet:4 dx:6 written:1 distant:1 analytic:1 plot:2 update:9 stationary:1 implying:1 prohibitive:2 fewer:1 intelli... |
5,403 | 5,891 | A Complete Recipe for Stochastic Gradient MCMC
Yi-An Ma, Tianqi Chen, and Emily B. Fox
University of Washington {yianma@u,tqchen@cs,ebfox@stat}.washington.edu
Abstract
Many recent Markov chain Monte Carlo (MCMC) samplers leverage continuous
dynamics to define a transition kernel that efficiently explores a target dis... | 5891 |@word version:3 seek:1 simulation:5 carry:1 inefficiency:1 contains:3 series:1 selecting:1 document:6 interestingly:2 reinvented:1 past:3 existing:3 discretization:1 yet:1 written:5 readily:1 must:2 john:1 periodically:1 distant:1 sdes:3 plot:1 sponsored:1 update:5 resampling:1 stationary:20 implying:1 devising:8... |
5,404 | 5,892 | Barrier Frank-Wolfe for Marginal Inference
Rahul G. Krishnan
Courant Institute
New York University
Simon Lacoste-Julien
INRIA - Sierra Project-Team
?
Ecole
Normale Sup?erieure, Paris
David Sontag
Courant Institute
New York University
Abstract
We introduce a globally-convergent algorithm for optimizing the tree-rewe... | 5892 |@word kohli:1 trial:5 norm:2 suitably:1 open:3 decomposition:2 contraction:7 pick:1 thereby:1 harder:1 initial:1 series:1 ecole:1 denoting:1 frankwolfe:1 interestingly:1 fa8750:1 existing:1 current:1 com:1 si:1 givry:1 subsequent:2 partition:6 informative:1 additive:1 pseudomarginals:4 enables:2 cheap:1 plot:3 up... |
5,405 | 5,893 | Practical and Optimal LSH for Angular Distance
Alexandr Andoni?
Columbia University
Piotr Indyk
MIT
Ilya Razenshteyn
MIT
Thijs Laarhoven
TU Eindhoven
Ludwig Schmidt
MIT
Abstract
We show the existence of a Locality-Sensitive Hashing (LSH) family for the angular distance that yields an approximate Near Neighbor Sear... | 5893 |@word multitask:1 repository:1 version:8 briefly:1 norm:2 proportionality:1 heuristically:1 d2:1 vldb:2 p0:1 reduction:5 contains:1 lichman:1 comparing:1 chazelle:1 attracted:1 john:1 numerical:1 partition:3 razenshteyn:4 kdd:2 analytic:1 moreno:1 plot:2 drop:1 sundaram:1 hash:55 v:1 intelligence:1 hypersphere:1 ... |
5,406 | 5,894 | Principal Differences Analysis: Interpretable
Characterization of Differences between Distributions
Jonas Mueller
CSAIL, MIT
jonasmueller@csail.mit.edu
Tommi Jaakkola
CSAIL, MIT
tommi@csail.mit.edu
Abstract
We introduce principal differences analysis (PDA) for analyzing differences between high-dimensional distributi... | 5894 |@word madelon:2 version:2 polynomial:1 hippocampus:4 norm:2 suitably:1 km:1 seek:2 covariance:6 simplifying:1 pg:1 q1:3 pick:1 asks:1 jacob:1 tr:4 reduction:2 liu:1 contains:1 dspca:1 zij:6 selecting:2 series:2 interestingly:1 envision:1 bradley:1 current:3 comparing:1 ka:1 written:1 must:1 informative:1 designed... |
5,407 | 5,895 | Kullback-Leibler Proximal Variational Inference
Mohammad Emtiyaz Khan?
Ecole Polytechnique F?ed?erale de Lausanne
Lausanne, Switzerland
emtiyaz@gmail.com
Pierre Baqu?e?
Ecole Polytechnique F?ed?erale de Lausanne
Lausanne, Switzerland
pierre.baque@epfl.ch
Pascal Fua
Ecole Polytechnique F?ed?erale de Lausanne
Lausanne, ... | 5895 |@word repository:1 version:1 norm:1 triazine:2 seek:1 linearized:1 covariance:3 simplifying:1 tr:3 initial:1 contains:4 ecole:3 ours:2 existing:5 com:1 loglik:1 gmail:1 john:1 fn:9 numerical:1 partition:1 enables:1 drop:1 plot:3 update:14 v:1 intelligence:1 parameterization:1 akiko:1 blei:3 pascanu:1 node:1 along... |
5,408 | 5,896 | Learning Large-Scale Poisson DAG Models based on
OverDispersion Scoring
Gunwoong Park
Department of Statistics
University of Wisconsin-Madison
Madison, WI 53706
parkg@stat.wisc.edu
Garvesh Raskutti
Department of Statistics
Department of Computer Science
Wisconsin Institute for Discovery, Optimization Group
University... | 5896 |@word mild:1 version:2 polynomial:3 stronger:1 seems:2 proportion:1 c0:7 open:1 relevancy:1 d2:1 hyv:1 simulation:3 harder:1 wrapper:1 liu:1 score:11 selecting:2 existing:2 od:33 assigning:1 must:2 numerical:7 partition:1 additive:1 designed:1 plot:3 generative:1 intelligence:3 greedy:1 xk:6 provides:1 node:42 al... |
5,409 | 5,897 | Streaming Min-Max Hypergraph Partitioning
Jennifer Iglesias?
Carnegie Mellon University
Pittsburgh, PA
jiglesia@andrew.cmu.edu
Dan Alistarh
Microsoft Research
Cambridge, United Kingdom
dan.alistarh@microsoft.com
Milan Vojnovic
Microsoft Research
Cambridge, United Kingdom
milanv@microsoft.com
Abstract
In many applica... | 5897 |@word version:3 polynomial:1 open:1 tr:3 reduction:3 initial:1 contains:1 united:2 neeman:1 document:4 interestingly:1 outperforms:2 recovered:1 com:2 si:4 assigning:2 must:3 partition:35 kdd:4 analytic:1 greedy:39 item:109 provides:5 revisited:1 ron:1 five:1 rc:3 enterprise:1 c2:3 constructed:1 incorrect:1 prove... |
5,410 | 5,898 | Efficient Output Kernel Learning for Multiple Tasks
Pratik Jawanpuria1 , Maksim Lapin2 , Matthias Hein1 and Bernt Schiele2
1
Saarland University, Saarbr?ucken, Germany
2
Max Planck Institute for Informatics, Saarbr?ucken, Germany
Abstract
The paradigm of multi-task learning is that one can achieve better generalizati... | 5898 |@word multitask:4 cnn:2 norm:11 paredes:1 stronger:1 r:27 covariance:1 decomposition:3 jacob:1 tr:1 reduction:1 efficacy:1 score:1 rkhs:5 romera:1 outperforms:3 existing:1 comparing:1 written:2 kdd:1 analytic:3 jawanpuria:3 drop:2 interpretable:1 update:1 mtfl:4 plot:3 stationary:1 v:1 dinuzzo:1 sarcos:2 characte... |
5,411 | 5,899 | Gradient Estimation Using
Stochastic Computation Graphs
1
John Schulman1,2
joschu@eecs.berkeley.edu
Nicolas Heess1
heess@google.com
Theophane Weber1
theophane@google.com
Pieter Abbeel2
pabbeel@eecs.berkeley.edu
Google DeepMind
2
University of California, Berkeley, EECS Department
Abstract
In a variety of probl... | 5899 |@word briefly:1 pieter:1 simulation:1 citeseer:1 recursively:1 reduction:6 configuration:1 series:2 score:9 contains:1 document:1 com:2 comparing:1 exy:1 yet:1 dx:3 must:2 written:1 activation:1 john:1 subsequent:1 numerical:1 analytic:1 enables:1 gv:3 v2s:1 generative:3 intelligence:1 core:1 short:1 blei:1 provi... |
5,412 | 59 | 442
How Neural Nets Work
Alan Lapedes
Robert Farber
Theoretical Division
Los Alamos National Laboratory
Los Alamos, NM 87545
Abstract:
There is presently great interest in the abilities of neural networks to mimic
"qualitative reasoning" by manipulating neural incodings of symbols. Less work
has been performed on usi... | 59 |@word version:1 polynomial:3 seems:2 simulation:1 seek:1 initial:6 configuration:2 series:14 fragment:1 selecting:1 att:1 genetic:1 lapedes:6 past:5 reaction:1 current:1 yet:5 must:2 john:1 numerical:4 wanted:3 remove:2 plot:2 displace:1 mackey:3 half:1 plane:1 steepest:2 provides:5 math:1 detecting:1 sigmoidal:6 h... |
5,413 | 590 | Computing with Almost Optimal Size Neural
Networks
Kai-Yeung Siu
Dept. of Electrical & Compo Engineering
University of California, Irvine
Irvine, CA 92717
V wani Roychowdhury
School of Electrical Engineering
Purdue University
West Lafayette, IN 47907
Thomas Kailath
Information Systems Laboratory
Stanford University
... | 590 |@word polynomial:15 norm:1 reduction:1 celebrated:1 contains:1 ours:1 existing:1 schnitger:2 must:6 hajnal:1 v:1 device:2 compo:5 math:3 attack:1 sigmoidal:29 dn:2 symposium:2 prove:4 symp:4 introduce:2 examine:1 moreover:1 notation:1 circuit:54 bounded:8 interpreted:1 developed:2 turan:1 unified:1 every:3 orlitsk... |
5,414 | 5,900 | Lifted Inference Rules with Constraints
Happy Mittal, Anuj Mahajan
Dept. of Comp. Sci. & Engg.
I.I.T. Delhi, Hauz Khas
New Delhi, 110016, India
happy.mittal@cse.iitd.ac.in,
Vibhav Gogate
Dept. of Comp. Sci.
Univ. of Texas Dallas
Richardson, TX 75080, USA
Parag Singla
Dept. of Comp. Sci. & Engg.
I.I.T. Delhi, Hauz Kha... | 5900 |@word eliminating:1 polynomial:1 closure:2 decomposition:2 eld:2 fif:8 recursively:2 substitution:7 contains:5 series:1 efficacy:1 fa8750:1 existing:9 current:1 com:2 si:2 gmail:1 yet:2 written:5 partition:18 engg:2 drop:1 plot:2 v:5 greedy:2 selected:1 braz:2 intelligence:3 amir:2 mln:12 xk:4 core:2 completeness... |
5,415 | 5,901 | Sparse PCA via Bipartite Matchings
Megasthenis Asteris
The University of Texas at Austin
megas@utexas.edu
Dimitris Papailiopoulos
University of California, Berkeley
dimitrisp@berkeley.edu
Anastasios Kyrillidis
The University of Texas at Austin
anastasios@utexas.edu
Alexandros G. Dimakis
The University of Texas at A... | 5901 |@word repository:1 compression:1 polynomial:8 norm:2 termination:2 seek:1 covariance:6 decomposition:2 sepulchre:1 liu:2 contains:2 series:2 united:1 lichman:1 denoting:1 document:5 outperforms:4 existing:2 ketch:1 ka:2 discretization:1 com:1 written:1 must:1 readily:1 stemming:2 subsequent:3 recasting:2 analytic... |
5,416 | 5,902 | Weighted Theta Functions and Embeddings with
Applications to Max-Cut, Clustering and
Summarization
Fredrik D. Johansson
Computer Science & Engineering
Chalmers University of Technology
G?oteborg, SE-412 96, Sweden
frejohk@chalmers.se
Ankani Chattoraj?
Brain & Cognitive Sciences
University of Rochester
Rochester, NY 14... | 5902 |@word middle:2 version:26 polynomial:1 briefly:1 johansson:1 open:1 crucially:1 decomposition:4 dramatic:1 initial:5 celebrated:2 score:4 document:7 bhattacharyya:4 existing:1 scovel:1 comparing:2 surprising:1 yet:1 scatter:1 must:1 additive:1 partition:1 j1:2 update:2 half:1 greedy:3 guess:1 item:6 electr:1 inst... |
5,417 | 5,903 | Online Rank Elicitation for Plackett-Luce:
A Dueling Bandits Approach
Bal?azs Sz?or?enyi
Technion, Haifa, Israel /
MTA-SZTE Research Group on
Artificial Intelligence, Hungary
szorenyibalazs@gmail.com
?
R?obert Busa-Fekete, Adil Paul, Eyke Hullermeier
Department of Computer Science
University of Paderborn
Paderborn, Ge... | 5903 |@word version:7 eliminating:1 proportion:1 open:1 termination:4 seek:3 pick:1 thereby:1 moment:2 contains:1 score:4 selecting:1 interestingly:1 outperforms:3 subjective:1 bradley:2 current:2 com:1 surprising:1 si:3 gmail:1 yet:4 written:1 tackling:1 must:1 john:1 numerical:1 informative:1 plot:1 drop:1 update:6 r... |
5,418 | 5,904 | Segregated Graphs and Marginals of Chain Graph
Models
Ilya Shpitser
Department of Computer Science
Johns Hopkins University
ilyas@cs.jhu.edu
Abstract
Bayesian networks are a popular representation of asymmetric (for example
causal) relationships between random variables. Markov random fields (MRFs)
are a complementary... | 5904 |@word trial:3 briefly:2 instrumental:1 norm:1 suitably:1 simulation:3 shot:1 carry:4 initial:1 contains:1 exclusively:1 hereafter:1 series:1 amp:1 existing:6 anterior:1 yet:1 assigning:1 john:1 evans:3 partition:1 wanted:1 remove:1 plot:1 intelligence:2 parameterization:2 record:1 characterization:1 parameterizat... |
5,419 | 5,905 | Approximating Sparse PCA from Incomplete Data
Abhisek Kundu ?
Petros Drineas ?
Malik Magdon-Ismail ?
Abstract
We study how well one can recover sparse principal components of a data matrix using a sketch formed from a few of its elements. We show that for a wide
class of optimization problems, if the sketch is clos... | 5905 |@word trial:2 illustrating:1 briefly:1 version:1 repository:1 norm:9 loading:2 polynomial:1 open:1 attainable:1 mention:1 reduction:1 liu:1 contains:1 document:1 reassurance:1 existing:2 outperforms:3 recovered:5 ka:12 rpi:3 must:1 additive:1 numerical:1 happen:1 drop:1 interpretable:1 update:1 maxv:2 greedy:3 se... |
5,420 | 5,906 | Recovering Communities in the General Stochastic
Block Model Without Knowing the Parameters
Emmanuel Abbe
Department of Electrical Engineering and PACM
Princeton University
Princeton, NJ 08540
eabbe@princeton.edu
Colin Sandon
Department of Mathematics
Princeton University
Princeton, NJ 08540
sandon@princeton.edu
Abs... | 5906 |@word trial:1 private:2 version:4 polynomial:2 norm:1 leighton:1 nd:1 proportion:1 open:2 condon:1 p0:3 pick:2 minus:1 initial:2 contains:1 exclusively:1 selecting:1 united:1 denoting:2 neeman:4 bhattacharyya:1 past:1 current:1 comparing:1 yet:1 universality:1 must:3 partition:3 remove:1 drop:1 v:1 selected:2 gue... |
5,421 | 5,907 | Maximum Likelihood Learning With Arbitrary
Treewidth via Fast-Mixing Parameter Sets
Justin Domke
NICTA, Australian National University
justin.domke@nicta.com.au
Abstract
Inference is typically intractable in high-treewidth undirected graphical models,
making maximum likelihood learning a challenge. One way to overcom... | 5907 |@word polynomial:4 norm:8 mehta:1 km:9 hyv:1 simulation:2 decomposition:1 covariance:1 contrastive:7 reduction:2 initial:1 liu:7 configuration:1 score:2 series:2 existing:2 freitas:2 current:4 com:1 comparing:1 must:5 partition:4 drop:1 update:3 stationary:7 implying:1 generative:1 geyer:1 provides:3 node:2 toron... |
5,422 | 5,908 | Testing Closeness With Unequal Sized Samples
Gregory Valiant?
Department of Computer Science
Stanford University
California, CA 94305
valiant@stanford.edu
Bhaswar B. Bhattacharya
Department of Statistics
Stanford University
California, CA 94305
bhaswar@stanford.edu
Abstract
We consider the problem of testing whether... | 5908 |@word trial:2 version:2 briefly:1 polynomial:2 seems:5 smirnov:1 norm:1 c0:2 clts:1 open:1 unif:1 grey:2 jafarpour:3 solid:1 moment:1 venkatasubramanian:1 initial:1 contains:2 efficacy:1 genetic:2 com:1 si:1 intriguing:1 must:1 additive:2 partition:2 designed:1 plot:15 depict:2 stationary:3 half:1 fewer:2 smith:2... |
5,423 | 5,909 | Learning Causal Graphs with Small Interventions
Karthikeyan Shanmugam1 , Murat Kocaoglu2 , Alexandros G. Dimakis3 , Sriram Vishwanath4
Department of Electrical and Computer Engineering
The University of Texas at Austin, USA
1
karthiksh@utexas.edu,2 mkocaoglu@utexas.edu,
3
dimakis@austin.utexas.edu,4 sriram@ece.utexas.e... | 5909 |@word mild:1 polynomial:2 achievable:5 c0:8 open:3 proportionality:1 d2:1 hu:1 simulation:2 recursively:1 initial:3 necessity:1 score:4 comparing:1 chordal:41 si:11 written:1 must:2 additive:1 subsequent:1 remove:1 plot:3 alone:1 intelligence:4 fewer:1 leaf:1 greedy:1 sys:11 alexandros:1 completeness:1 characteri... |
5,424 | 591 | Silicon Auditory Processors
as
Computer Peripherals
.T ollll Lmr,7.Hl'o . .T ollll Wawl'7.Ylwk
CS Division
UC B(~rk('ley
Evans lIall
Bcrl,plpy. Ct\ !H720
lazzaro~cs.berkeley.edu,
johnw~cs.berkeley.edu
M. Mahowald'" ~ Massimo Sivilotti t , Dave Gillcspic t
Califol'lIia lnst,itult' of Technology
Pasadena. CA !) 11:l!)... | 591 |@word agf:2 underst:1 compression:1 nd:2 pulse:4 rol:2 ld:1 moment:3 carry:1 reduction:1 amp:1 cleared:1 usillg:1 com:2 nt:2 aft:1 evans:1 nemal:1 heir:1 designed:1 ilii:1 cue:1 device:1 signalling:1 es:1 ional:4 wir:1 height:1 rc:2 iinit:1 inside:2 ra:1 oscilloscope:1 dist:2 nor:1 ry:3 brain:1 ol:2 window:1 proje... |
5,425 | 5,910 | Regret-Based Pruning in Extensive-Form Games
Tuomas Sandholm
Computer Science Department
Carnegie Mellon University
Pittsburgh, PA 15217
sandholm@cs.cmu.edu
Noam Brown
Computer Science Department
Carnegie Mellon University
Pittsburgh, PA 15217
noamb@cmu.edu
Abstract
Counterfactual Regret Minimization (CFR) is a lead... | 5910 |@word private:1 version:5 polynomial:1 seems:1 proportion:1 szafron:1 rayner:1 pick:1 dramatic:2 carry:1 it1:2 reduction:2 series:1 outperforms:1 current:4 must:2 subsequent:1 happen:1 partition:1 cheap:1 christian:1 drop:2 update:6 intelligence:2 advancement:1 farther:1 provides:2 node:9 revisited:1 traverse:8 c... |
5,426 | 5,911 | Nonparametric von Mises Estimators for Entropies,
Divergences and Mutual Informations
Akshay Krishnamurthy
Microsoft Research, NY
akshaykr@cs.cmu.edu
Kirthevasan Kandasamy
Carnegie Mellon University
kandasamy@cs.cmu.edu
Barnab?as P?oczos, Larry Wasserman
Carnegie Mellon University
bapoczos@cs.cmu.edu, larry@stat.cmu... | 5911 |@word neurophysiology:1 version:5 polynomial:3 stronger:2 seems:1 open:1 calculus:2 simulation:5 dominique:1 q1:5 zolt:1 liu:1 contains:1 series:3 selecting:2 rkhs:2 luigi:1 existing:6 com:1 comparing:1 analysed:1 dx:2 written:1 john:2 cruz:1 numerical:1 plot:2 interpretable:1 v:1 alone:2 kandasamy:4 half:3 selec... |
5,427 | 5,912 | Bounding errors of Expectation-Propagation
Simon Barthelm?
CNRS, Gipsa-lab
simon.barthelme@gipsa-lab.fr
Guillaume Dehaene
University of Geneva
guillaume.dehaene@gmail.com
Abstract
Expectation Propagation is a very popular algorithm for variational inference, but
comes with few theoretical guarantees. In this article... | 5912 |@word version:1 nd:1 open:2 covariance:1 p0:1 carry:1 moment:23 mseeger:1 ours:1 current:1 com:4 surprising:1 gmail:1 yet:2 must:2 dx:2 realistic:1 enables:1 update:2 v:1 intelligence:1 parametrization:1 short:1 manfred:1 characterization:2 provides:1 math:1 org:7 mathematical:1 saarland:1 prove:3 consists:2 intr... |
5,428 | 5,913 | Local Smoothness in Variance Reduced Optimization
Daniel Vainsencher, Han Liu
Dept. of Operations Research & Financial Engineering
Princeton University
Princeton, NJ 08544
{daniel.vainsencher,han.liu}@princeton.edu
Tong Zhang
Dept. of Statistics
Rutgers University
Piscataway, NJ, 08854
tzhang@stat.rutgers.edu
Abstra... | 5913 |@word version:2 proportion:4 norm:1 nd:1 ality:1 sgd:2 thereby:1 harder:1 reduction:3 initial:3 liu:2 daniel:2 ati:1 existing:3 current:7 skipping:1 mushroom:1 realize:2 numerical:1 enables:2 update:8 half:4 fewer:5 indefinitely:1 detecting:1 iterates:1 contribute:2 simpler:1 zhang:8 mathematical:1 become:1 quali... |
5,429 | 5,914 | High Dimensional EM Algorithm:
Statistical Optimization and Asymptotic Normality?
Zhaoran Wang
Princeton University
Quanquan Gu
University of Virginia
Yang Ning
Princeton University
Han Liu
Princeton University
Abstract
We provide a general theory of the expectation-maximization (EM) algorithm for
inferring high di... | 5914 |@word briefly:1 version:12 norm:8 instrumental:1 d2:3 decomposition:1 moment:2 liu:1 series:3 score:21 existing:5 subsequent:1 analytic:1 treating:1 resampling:1 eminent:1 provides:1 characterization:1 along:1 constructed:1 prove:8 introductory:1 introduce:4 manner:2 roughly:2 decomposed:1 spherical:1 r01mh102339... |
5,430 | 5,915 | Associative Memory via a Sparse Recovery Model
Arya Mazumdar
Department of ECE
University of Minnesota Twin Cities
arya@umn.edu
Ankit Singh Rawat?
Computer Science Department
Carnegie Mellon University
asrawat@andrew.cmu.edu
Abstract
An associative memory is a structure learned from a dataset M of vectors (signals)
i... | 5915 |@word briefly:1 version:9 polynomial:3 stronger:1 norm:3 seems:1 hu:1 seek:1 simulation:1 initial:1 selecting:1 ours:1 zurada:2 past:1 recovered:3 com:1 ka:1 must:6 enables:1 analytic:1 remove:1 plot:1 depict:1 selected:1 xk:4 isotropic:2 provides:3 node:12 allerton:2 mathematical:2 along:1 constructed:1 symposiu... |
5,431 | 5,916 | Matrix Completion Under Monotonic Single Index
Models
Ravi Ganti
Wisconsin Institutes for Discovery
UW-Madison
gantimahapat@wisc.edu
Laura Balzano
Electrical Engineering and Computer Sciences
University of Michigan Ann Arbor
girasole@umich.edu
Rebecca Willett
Department of Electrical and Computer Engineering
UW-Madis... | 5916 |@word mild:1 version:3 norm:5 mention:2 harder:1 score:3 pandora:1 daniel:1 ours:1 ganti:1 recovered:1 com:3 weyl:1 plot:2 update:9 progressively:1 bart:2 item:1 iterates:1 provides:2 revisited:1 successive:1 simpler:1 zhang:1 mathematical:2 along:3 direct:1 differential:2 symposium:1 competitiveness:1 incorrect:... |
5,432 | 5,917 | Sparse Linear Programming via
Primal and Dual Augmented Coordinate Descent
Ian E.H. Yen ?
Kai Zhong ? Cho-Jui Hsieh ? Pradeep Ravikumar ? Inderjit S. Dhillon ?
?
University of Texas at Austin
University of California at Davis
{ianyen,pradeepr,inderjit}@cs.utexas.edu zhongkai@ices.utexas.edu
?
chohsieh@ucdavis.edu
?
... | 5917 |@word version:1 advantageous:2 norm:2 open:1 d2:1 hsieh:4 covariance:10 decomposition:2 pick:2 kwm:1 solid:1 harder:1 ipm:10 initial:3 atb:1 series:1 contains:1 ati:3 past:1 existing:4 current:3 luo:2 bello:1 written:1 numerical:4 kdd:2 cheap:1 update:13 chohsieh:1 greedy:1 xk:5 beginning:1 lr:2 iterates:8 provid... |
5,433 | 5,918 | Convergence rates of sub-sampled Newton methods
Murat A. Erdogdu
Department of Statistics
Stanford University
erdogdu@stanford.edu
Andrea Montanari
Department of Statistics
and Electrical Engineering
Stanford University
montanari@stanford.edu
Abstract
We consider the problem of minimizing a sum of n functions via pro... | 5918 |@word h:1 repository:1 version:2 bot10:3 briefly:1 c0:1 crucially:1 decomposition:2 covariance:1 sgd:11 tr:3 mar10:6 initial:4 celebrated:1 crx:1 lichman:1 daniel:2 denoting:1 existing:1 recovered:1 current:6 z2:1 yet:3 tackling:1 written:1 john:2 lic13:4 numerical:2 plot:6 update:8 selected:5 prohibitive:1 vp12:... |
5,434 | 5,919 | Variance Reduced Stochastic Gradient Descent
with Neighbors
Aurelien Lucchi
Department of Computer Science
ETH Zurich, Switzerland
Thomas Hofmann
Department of Computer Science
ETH Zurich, Switzerland
Simon Lacoste-Julien
INRIA - Sierra Project-Team
?
Ecole
Normale Sup?erieure, Paris, France
Brian McWilliams
Departm... | 5919 |@word repository:1 version:2 norm:2 open:1 crucially:2 contraction:3 sgd:29 thereby:1 solid:1 reduction:4 initial:1 contains:3 ecole:1 past:3 outperforms:1 current:3 surprising:1 leblond:1 yet:5 readily:1 realize:1 partition:1 hofmann:1 designed:1 update:28 v:3 kilcher:1 half:1 prohibitive:1 selected:2 nq:5 kyk:2... |
5,435 | 592 | Predicting Complex Behavior
in Sparse Asymmetric Networks
An A. Minai and William B. Levy
Department of Neurosurgery
Box 420. Health Sciences Center
University of Virginia
Charlottesville. V A 22908
Abstract
Recurrent networks of threshold elements have been studied intensively as associative memories and pattern-reco... | 592 |@word proportion:1 km:1 simulation:1 mammal:1 initial:3 series:2 activation:7 must:3 john:1 plot:3 device:3 short:2 provides:2 math:2 location:1 simpler:1 become:1 qualitative:2 consists:1 introduce:2 expected:1 behavior:26 brain:1 increasing:1 becomes:1 underlying:1 kind:3 developed:1 temporal:1 every:1 ti:1 exac... |
5,436 | 5,920 | Non-convex Statistical Optimization for Sparse
Tensor Graphical Model
Wei Sun
Yahoo Labs
Sunnyvale, CA
sunweisurrey@yahoo-inc.com
Zhaoran Wang
Department of Operations Research
and Financial Engineering
Princeton University
Princeton, NJ
zhaoran@princeton.edu
Han Liu
Department of Operations Research
and Financial En... | 5920 |@word determinant:1 middle:1 version:1 norm:29 stronger:1 replicate:1 termination:1 confirms:2 simulation:6 covariance:15 decomposition:5 attainable:1 tr:7 boundedness:1 liu:6 contains:1 series:1 hereafter:1 offering:1 ours:4 outperforms:1 existing:2 kmk:1 com:1 surprising:1 chu:1 numerical:2 informative:1 drop:1... |
5,437 | 5,921 | Convergence Rates of Active Learning
for Maximum Likelihood Estimation
Kamalika Chaudhuri ?
Sham M. Kakade ?
Praneeth Netrapalli ?
Sujay Sanghavi ?
Abstract
An active learner is given a class of models, a large set of unlabeled examples, and
the ability to interactively query labels of a subset of these examples; t... | 5921 |@word mild:1 determinant:1 version:1 polynomial:1 seems:1 open:3 cm2:1 d2:3 covariance:8 tr:22 boundedness:2 series:2 selecting:2 united:1 ours:2 current:1 com:1 beygelzimer:2 written:2 partition:3 remove:1 designed:1 ainen:1 greedy:1 intelligence:1 core:1 provides:3 coarse:3 location:1 tahoe:1 zhang:4 c2:2 fitti... |
5,438 | 5,922 | When are Kalman-Filter Restless Bandits Indexable?
Christopher Dance and Tomi Silander
Xerox Research Centre Europe
6 chemin de Maupertuis, Meylan, Is`ere, France
{dance,silander}@xrce.xerox.com
Abstract
We study the restless bandit associated with an extremely simple scalar Kalman
filter model in discrete time. Under... | 5922 |@word version:2 briefly:1 polynomial:1 bf:3 open:1 tr:5 series:5 contains:1 interestingly:1 prefix:3 past:1 com:1 olkin:1 yet:2 dx:4 attracted:1 readily:1 john:1 must:2 reminiscent:1 written:1 wx:3 xrce:1 cheap:2 s21:8 alone:1 summarisation:1 xk:2 node:1 clarified:1 gx:2 firstly:1 xnm:8 m22:4 prove:5 consists:2 x... |
5,439 | 5,923 | Policy Gradient for Coherent Risk Measures
Yinlam Chow
Stanford University
ychow@stanford.edu
Aviv Tamar
UC Berkeley
avivt@berkeley.edu
Mohammad Ghavamzadeh
Adobe Research & INRIA
mohammad.ghavamzadeh@inria.fr
Shie Mannor
Technion
shie@ee.technion.ac.il
Abstract
Several authors have recently developed risk-sensitiv... | 5923 |@word mild:1 version:2 simulation:2 decomposition:1 sgd:1 thereby:2 initial:3 celebrated:1 selecting:5 past:1 subjective:1 riskier:1 written:2 john:2 subsequent:1 numerical:5 enables:1 plot:1 update:1 v:2 stationary:3 devising:1 parametrization:1 provides:1 mannor:5 contribute:1 math:1 preference:3 simpler:1 math... |
5,440 | 5,924 | A Dual-Augmented Block Minimization Framework
for Learning with Limited Memory
Ian E.H. Yen ? Shan-Wei Lin ? Shou-De Lin ?
?
University of Texas at Austin
National Taiwan University
ianyen@cs.utexas.edu {r03922067,sdlin}@csie.ntu.edu.tw
?
?
Abstract
In past few years, several techniques have been proposed for traini... | 5924 |@word version:1 norm:8 trofimov:1 hsieh:5 jacob:1 initial:1 series:1 bc:10 past:2 existing:1 current:1 luo:1 universality:1 chu:1 must:1 partition:1 hofmann:1 designed:3 plot:1 update:9 maxv:1 v:1 device:1 beginning:2 smith:2 iterates:3 shou:1 mathematical:2 become:2 yuan:1 consists:1 shorthand:1 overhead:1 polyh... |
5,441 | 5,925 | On the Global Linear Convergence
of Frank-Wolfe Optimization Variants
Simon Lacoste-Julien
INRIA - SIERRA project-team
?
Ecole
Normale Sup?erieure, Paris, France
Martin Jaggi
Dept. of Computer Science
ETH Z?urich, Switzerland
Abstract
The Frank-Wolfe (FW) optimization algorithm has lately re-gained popularity
thanks... | 5925 |@word msr:1 version:3 middle:2 polynomial:2 norm:5 seems:1 open:1 d2:2 decomposition:3 thereby:1 ecole:1 existing:3 hearn:1 current:2 additive:1 numerical:1 happen:1 remove:2 drop:7 update:5 maxv:1 v:1 greedy:1 half:1 website:1 intelligence:1 lkdt:3 accordingly:1 xk:1 beginning:1 core:1 pointer:1 colored:1 iterat... |
5,442 | 5,926 | Quartz: Randomized Dual Coordinate Ascent
with Arbitrary Sampling
Peter Richt?arik
School of Mathematics
The University of Edinburgh
EH9 3FD, United Kingdom
peter.richtarik@ed.ac.uk
Zheng Qu
Department of Mathematics
The University of Hong Kong
Hong Kong
zhengqu@maths.hku.hk
Tong Zhang
Department of Statistics
Rutge... | 5926 |@word kong:2 msr:1 norm:3 nd:2 seek:1 hsieh:1 sgd:3 tr:1 reduction:1 initial:2 united:1 tuned:1 ours:1 past:1 existing:3 outperforms:2 current:2 wd:1 optim:3 assigning:1 attracted:1 numerical:1 hofmann:1 designed:1 update:11 juditsky:1 v:3 beginning:2 smith:1 math:4 simpler:2 zhang:8 five:1 direct:2 prove:1 speci... |
5,443 | 5,927 | A Generalization of Submodular Cover via the
Diminishing Return Property on the Integer Lattice
Tasuku Soma
The University of Tokyo
tasuku soma@mist.i.u-tokyo.ac.jp
Yuichi Yoshida
National Institute of Informatics, and
Preferred Infrastructure, Inc.
yyoshida@nii.ac.jp
Abstract
We consider a generalization of the sub... | 5927 |@word version:4 eliminating:1 polynomial:6 stronger:1 simulation:2 bicriteria:8 reduction:7 initial:1 nii:1 document:3 outperforms:1 existing:1 current:2 com:1 si:9 attracted:1 must:1 pcp:1 kdd:2 update:7 aside:1 greedy:22 short:1 infrastructure:1 detecting:1 multiset:1 draft:1 characterization:1 mathematical:1 b... |
5,444 | 5,928 | A Universal Catalyst for First-Order Optimization
Hongzhou Lin1 , Julien Mairal1 and Zaid Harchaoui1,2
1
2
Inria
NYU
{hongzhou.lin,julien.mairal}@inria.fr
zaid.harchaoui@nyu.edu
Abstract
We introduce a generic scheme for accelerating first-order optimization methods
in the sense of Nesterov, which builds upon a new a... | 5928 |@word msr:1 briefly:1 version:2 norm:4 seems:1 open:1 reduction:1 initial:3 selecting:1 ours:2 interestingly:1 past:3 reaction:1 kx0:2 existing:2 current:1 yet:1 pioneer:1 numerical:1 zaid:2 designed:1 remove:3 update:2 juditsky:2 selected:1 xk:35 iterates:8 provides:4 certificate:5 simpler:1 zhang:4 mathematical... |
5,445 | 5,929 | Fast and Memory Optimal Low-Rank Matrix
Approximation
Se-Young Yun
MSR, Cambridge
seyoung.yun@inria.fr
Marc Lelarge ?
Inria & ENS
marc.lelarge@ens.fr
Alexandre Proutiere ?
KTH, EE School / ACL
alepro@kth.se
Abstract
In this paper, we revisit the problem of constructing a near-optimal rank k approximation of a matri... | 5929 |@word mild:1 msr:2 version:4 norm:8 instrumental:1 zkf:1 km:26 decomposition:6 covariance:2 initial:1 score:1 zij:1 woodruff:3 existing:1 ka:1 si:4 written:1 readily:2 subsequent:2 additive:4 kqj:1 numerical:1 remove:5 treating:1 update:3 alone:1 selected:2 woo14:2 unacceptably:1 vanishing:1 recherche:1 provides:... |
5,446 | 593 | Improving Performance in Neural Networks
Using a Boosting Algorithm
Harris Drucker
AT&T Bell Laboratories
Holmdel, NJ 07733
Robert Schapire
AT&T Bell Laboratories
Murray Hill, NJ 07974
Patrice Simard
AT &T Bell Laboratories
Holmdel, NJ 07733
Abstract
A boosting algorithm converts a learning machine with error rate l... | 593 |@word deformed:12 norm:1 retraining:1 k7:1 paid:1 dramatic:1 thereby:2 score:1 united:3 selecting:1 happen:1 half:3 intelligence:1 selected:1 supplying:1 quantized:1 boosting:17 postal:3 location:3 five:1 along:6 constructed:1 differential:1 supply:1 consists:2 combine:2 inside:1 manner:3 theoretically:1 multi:2 p... |
5,447 | 5,930 | Stochastic Online Greedy Learning with
Semi-bandit Feedbacks
Tian Lin
Tsinghua University
Beijing, China
lintian06@gmail.com
Jian Li
Tsinghua University
Beijing, China
lapordge@gmail.com
Wei Chen
Microsoft Research
Beijing, China
weic@microsoft.com
Abstract
The greedy algorithm is extensively studied in the field of... | 5930 |@word h:5 exploitation:7 polynomial:1 kalyanakrishnan:1 q1:3 accommodate:1 selecting:1 prefix:4 multiuser:1 current:2 com:3 comparing:3 nt:3 si:12 gmail:2 refines:1 subsequent:1 predetermined:2 update:6 greedy:92 selected:7 intelligence:1 beginning:2 prize:3 characterization:2 provides:2 node:2 completeness:1 acc... |
5,448 | 5,931 | Linear Multi-Resource Allocation with Semi-Bandit
Feedback
Koby Crammer
Department of Electrical Engineering
The Technion, Israel
koby@ee.technion.ac.il
Tor Lattimore
Department of Computing Science
University of Alberta, Canada
tor.lattimore@gmail.com
Csaba Szepesv?ari
Department of Computing Science
University of A... | 5931 |@word private:1 version:2 exploitation:1 norm:1 nd:1 d2:1 essay:1 thereby:1 solid:1 accommodate:1 initial:1 plentiful:1 initialisation:1 tuned:1 existing:2 com:1 contextual:2 analysed:1 gmail:1 assigning:1 must:3 bd:3 john:1 subsequent:1 happen:1 shlomo:1 designed:1 plot:1 drop:1 v:1 half:1 intelligence:1 accordi... |
5,449 | 5,932 | Exactness of Approximate MAP Inference in
Continuous MRFs
Nicholas Ruozzi
Department of Computer Science
University of Texas at Dallas
Richardson, TX 75080
Abstract
Computing the MAP assignment in graphical models is generally intractable. As a
result, for discrete graphical models, the MAP problem is often approximat... | 5932 |@word mild:1 version:1 briefly:1 manageable:1 polynomial:4 open:1 closure:10 covariance:2 homomorphism:3 initial:1 series:1 si:2 dx:9 written:2 must:5 partition:1 koetter:1 v:1 intelligence:4 amir:1 xk:2 ith:1 characterization:7 provides:4 node:11 unbounded:2 direct:2 prove:3 x0:1 pairwise:8 lov:4 intricate:1 not... |
5,450 | 5,933 | On the consistency theory of high dimensional
variable screening
Xiangyu Wang
Dept. of Statistical Science
Duke University, USA
xw56@stat.duke.edu
Chenlei Leng
Dept. of Statistics
University of Warwick, UK
C.Leng@warwick.ac.uk
David B. Dunson
Dept. of Statistical Science
Duke University, USA
dunson@stat.duke.edu
Ab... | 5933 |@word version:2 stronger:5 norm:1 c0:24 covariance:7 accommodate:1 reduction:1 necessity:1 contains:1 series:3 existing:1 ka:1 current:1 elliptical:1 si:26 item:1 runze:1 core:1 provides:2 location:1 attack:1 zhang:3 c2:12 ik:1 prove:7 introduce:1 theoretically:2 peng:2 shuheng:1 behavior:1 growing:1 chi:1 inspir... |
5,451 | 5,934 | Finite-Time Analysis of Projected Langevin Monte
Carlo
Ronen Eldan
Weizmann Institute
roneneldan@gmail.com
S?ebastien Bubeck
Microsoft Research
sebubeck@microsoft.com
Joseph Lehec
Universit?e Paris-Dauphine
lehec@ceremade.dauphine.fr
Abstract
We analyze the projected Langevin Monte Carlo (LMC) algorithm, a close cou... | 5934 |@word version:2 polynomial:5 norm:1 seems:2 open:1 calculus:1 km:2 crucially:1 bn:1 covariance:1 sgd:5 kxkk:5 carry:1 contains:2 denoting:1 existing:2 current:1 com:2 discretization:3 dikin:1 nt:2 comparing:1 gmail:1 dx:6 realize:1 numerical:1 hvs:1 stationary:1 xk:13 short:1 record:1 iterates:2 simpler:1 mathema... |
5,452 | 5,935 | Beyond Sub-Gaussian Measurements:
High-Dimensional Structured Estimation with
Sub-Exponential Designs
Vidyashankar Sivakumar
Arindam Banerjee
Department of Computer Science & Engineering
University of Minnesota, Twin Cities
{sivakuma,banerjee}@cs.umn.edu
Pradeep Ravikumar
Department of Computer Science
University of ... | 5935 |@word version:1 norm:58 stronger:3 d2:12 decomposition:1 eng:1 boundedness:1 carry:1 moment:5 initial:1 denoting:1 past:1 existing:4 readily:1 subsequent:3 partition:3 plot:2 v:3 implying:1 isotropic:5 along:3 c2:4 ik:1 prove:2 combine:1 introduce:1 p1:1 frequently:1 examine:1 cardinality:1 increasing:1 precipita... |
5,453 | 5,936 | Less is More: Nystr?om Computational Regularization
Alessandro Rudi?
Raffaello Camoriano??
Lorenzo Rosasco??
?
Universit`a degli Studi di Genova - DIBRIS, Via Dodecaneso 35, Genova, Italy
?
Istituto Italiano di Tecnologia - iCub Facility, Via Morego 30, Genova, Italy
?
Massachusetts Institute of Technology and Istitut... | 5936 |@word trial:5 briefly:1 version:3 polynomial:2 norm:1 advantageous:1 suitably:1 confirms:1 tat:1 covariance:4 decomposition:2 hsieh:1 nystr:40 tr:1 boundedness:3 moment:1 series:1 score:15 woodruff:1 tuned:1 rkhs:1 interestingly:2 neeman:1 recovered:1 scovel:1 nt:1 si:2 written:2 john:1 update:6 v:1 half:2 select... |
5,454 | 5,937 | Logarithmic Time Online Multiclass prediction
Anna Choromanska
Courant Institute of Mathematical Sciences
New York, NY, USA
achoroma@cims.nyu.edu
John Langford
Microsoft Research
New York, NY, USA
jcl@microsoft.com
Abstract
We study the problem of multiclass classification with an extremely large number
of classes (... | 5937 |@word norm:1 seems:1 nd:1 proportion:1 r:6 tried:1 thereby:3 harder:1 recursively:2 reduction:6 born:1 contains:1 exclusively:2 score:5 liu:1 ours:1 past:5 existing:1 outperforms:1 current:1 com:1 beygelzimer:2 must:2 written:1 john:2 subsequent:1 partition:32 chicago:2 v:3 leaf:29 selected:2 guess:1 ith:1 boosti... |
5,455 | 5,938 | Collaborative Filtering with Graph Information:
Consistency and Scalable Methods
Nikhil Rao
Hsiang-Fu Yu
Pradeep Ravikumar
Inderjit S. Dhillon
{nikhilr, rofuyu, paradeepr, inderjit}@cs.utexas.edu
Department of Computer Science
University of Texas at Austin
Abstract
Low rank matrix completion plays a fundamental ro... | 5938 |@word kong:1 version:1 briefly:1 kondor:1 norm:38 km:1 simulation:1 covariance:3 decomposition:2 jacob:1 sgd:3 yahoomusic:2 tr:9 accommodate:1 reduction:1 liu:1 series:1 contains:1 groundwork:1 ours:1 ka:10 comparing:1 conforming:1 additive:1 thrust:1 kdd:2 cheap:2 update:2 generative:2 selected:1 greedy:4 item:4... |
5,456 | 5,939 | Efficient and Parsimonious Agnostic Active Learning
Tzu-Kuo Huang
Microsoft Research, NYC
Alekh Agarwal
Microsoft Research, NYC
Daniel Hsu
Columbia University
tkhuang@microsoft.com
alekha@microsoft.com
djhsu@cs.columbia.edu
John Langford
Microsoft Research, NYC
Robert E. Schapire
Microsoft Research, NYC
jcl@mi... | 5939 |@word version:2 c0:2 crucially:1 pick:2 thereby:1 daniel:3 tuned:1 outperforms:2 existing:2 err:23 past:1 com:4 horvitz:1 beygelzimer:4 written:2 must:1 john:4 additive:1 numerical:1 drop:1 plot:2 update:3 atlas:1 implying:2 half:2 intelligence:1 short:1 provides:4 multiset:1 coarse:1 zhang:3 c2:6 predecessor:1 p... |
5,457 | 594 | Statistical and Dynamical Interpretation of ISIH
Data from Periodically Stimulated Sensory Neurons
John K. Douglass and Frank Moss
Department of Biology and Department of Physics
University of Missouri at St. Louis
St. Louis, MO 63121
Andre Longtin
Department of Physics
University of Ottawa
Ottawa, Ontario, Canada KIN ... | 594 |@word pulse:1 simulation:8 saccular:1 past:1 must:3 john:1 fn:11 periodically:8 interspike:1 alone:1 sys:1 colored:2 tems:1 height:2 burst:1 constructed:1 transducer:1 sustained:1 behavioral:1 manner:1 indeed:1 behavior:2 frequently:2 mechanic:1 simulator:12 brain:2 integrator:3 actual:1 becomes:1 provided:2 moreo... |
5,458 | 5,940 | Matrix Completion with Noisy Side Information
?
Kai-Yang Chiang? Cho-Jui Hsieh ? Inderjit S. Dhillon ?
?
University of Texas at Austin
University of California at Davis
?
{kychiang,inderjit}@cs.utexas.edu
?
chohsieh@ucdavis.edu
Abstract
We study the matrix completion problem with side information. Side information
h... | 5940 |@word trial:1 kulis:1 version:1 norm:12 d2:4 hsieh:4 attainable:1 liu:1 outperforms:6 recovered:3 current:2 comparing:1 yet:5 mushroom:5 belmont:1 informative:13 kdd:2 drop:1 chohsieh:1 website:1 item:15 core:1 chiang:3 provides:2 math:1 node:1 preference:1 zhang:3 along:1 constructed:1 become:2 combine:1 introdu... |
5,459 | 5,941 | Learning with Symmetric Label Noise: The
Importance of Being Unhinged
Brendan van Rooyen?,?
?
Aditya Krishna Menon?,?
The Australian National University
?
Robert C. Williamson?,?
National ICT Australia
{ brendan.vanrooyen, aditya.menon, bob.williamson }@nicta.com.au
Abstract
Convex potential minimisation is the... | 5941 |@word trial:2 version:1 inversion:1 stronger:3 earnest:1 suitably:1 underperform:1 pg:2 attainable:1 pick:7 boundedness:3 disappointingly:1 ld:9 contains:2 score:5 rkhs:1 interestingly:2 jyv:1 com:1 surprising:1 liva:1 chu:1 john:1 realistic:1 implying:1 pursued:1 device:1 provides:1 draft:1 simpler:1 unbounded:6... |
5,460 | 5,942 | Scalable Semi-Supervised Aggregation of Classifiers
Yoav Freund
UC San Diego
yfreund@cs.ucsd.edu
Akshay Balsubramani
UC San Diego
abalsubr@cs.ucsd.edu
Abstract
We present and empirically evaluate an efficient algorithm that learns to aggregate the predictions of an ensemble of binary classifiers. The algorithm uses ... | 5942 |@word multitask:1 version:1 triggs:1 open:4 scalably:1 seek:1 crucially:1 dramatic:1 sgd:2 mention:1 versatile:1 carry:1 initial:2 liu:1 contains:1 ours:1 com:2 written:1 must:2 john:2 reminiscent:1 partition:1 enables:1 plot:2 v:4 alone:1 generative:1 leaf:10 fewer:1 intelligence:2 greedy:1 warmuth:1 xk:2 prize:... |
5,461 | 5,943 | Spherical Random Features for Polynomial Kernels
Jeffrey Pennington
Felix X. Yu
Sanjiv Kumar
Google Research
{jpennin, felixyu, sanjivk}@google.com
Abstract
Compact explicit feature maps provide a practical framework to scale kernel methods to large-scale learning, but deriving such maps for many types of kernels
... | 5943 |@word polynomial:37 norm:3 nd:1 open:1 d2:1 hsieh:1 carry:1 reduction:1 liblinear:2 celebrated:1 series:1 tuned:1 reine:1 existing:1 com:1 wd:3 z2:2 yet:1 bd:3 must:1 sanjiv:2 additive:3 numerical:1 enables:3 v:1 intelligence:2 prohibitive:1 ith:2 dover:1 characterization:3 provides:3 unbounded:2 mathematical:1 a... |
5,462 | 5,944 | Fast and Guaranteed Tensor Decomposition via
Sketching
Yining Wang, Hsiao-Yu Tung, Alex Smola
Machine Learning Department
Carnegie Mellon University, Pittsburgh, PA 15213
{yiningwa,htung}@cs.cmu.edu
alex@smola.org
Anima Anandkumar
Department of EECS
University of California Irvine
Irvine, CA 92697
a.anandkumar@uci.edu... | 5944 |@word mild:1 private:1 faculty:2 version:2 polynomial:3 norm:3 stronger:1 cu:1 decomposition:33 contraction:12 sgd:1 ld:2 reduction:3 carry:2 liu:1 contains:1 moment:12 selecting:1 initial:1 document:7 outperforms:2 existing:4 ketch:2 recovered:2 wd:1 counterterrorism:1 crawling:1 written:1 subsequent:1 kdd:2 upd... |
5,463 | 5,945 | Teaching Machines to Read and Comprehend
Karl Moritz Hermann? Tom?as? Ko?cisk?y?? Edward Grefenstette?
Lasse Espeholt? Will Kay? Mustafa Suleyman? Phil Blunsom??
?
Google DeepMind ? University of Oxford
{kmh,tkocisky,etg,lespeholt,wkay,mustafasul,pblunsom}@google.com
Abstract
Teaching machines to read natural language... | 5945 |@word cnn:9 version:6 kmh:1 wqm:1 replicate:1 open:1 seek:2 propagate:3 pavel:1 pick:2 attended:1 harder:2 initial:2 contains:1 efficacy:1 score:2 tuned:2 document:55 past:2 favouring:2 com:3 contextual:1 surprising:1 yet:1 must:3 readily:1 parsing:7 ronan:1 candy:2 designed:1 sukhbaatar:1 generative:1 intelligen... |
5,464 | 5,946 | Saliency, Scale and Information:
Towards a Unifying Theory
Neil D.B. Bruce
Department of Computer Science
University of Manitoba
bruce@cs.umanitoba.ca
Shafin Rahman
Department of Computer Science
University of Manitoba
shafin109@gmail.com
Abstract
In this paper we present a definition for visual saliency grounded in... | 5946 |@word neurophysiology:1 middle:1 ixx:1 seems:1 stronger:1 norm:1 advantageous:1 hu:1 zelnik:1 rgb:2 jacob:1 contrastive:1 carry:2 shiota:1 exclusively:2 selecting:1 score:2 tuned:1 existing:2 com:1 contextual:1 comparing:1 gmail:1 yet:1 reminiscent:1 malized:1 hou:3 mesh:9 cottrell:1 blur:6 plot:1 concert:1 v:1 a... |
5,465 | 5,947 | Semi-Supervised Learning with Ladder Networks
Antti Rasmus and Harri Valpola
The Curious AI Company, Finland
Mikko Honkala
Nokia Labs, Finland
Mathias Berglund and Tapani Raiko
Aalto University, Finland & The Curious AI Company, Finland
Abstract
We combine supervised learning with unsupervised learning in deep neura... | 5947 |@word cnn:8 version:4 norm:1 seems:1 simulation:1 bn:3 rgb:1 pressure:1 dramatic:1 initial:1 series:1 exclusively:1 jimenez:1 tuned:1 ours:2 document:1 deconvolutional:1 outperforms:1 existing:3 com:1 zpre:4 activation:4 diederik:3 additive:1 happen:1 ronan:1 shape:3 christian:3 designed:1 atlas:1 update:2 convpo... |
5,466 | 5,948 | Enforcing balance allows local supervised learning in
spiking recurrent networks
Sophie Deneve
Group For Neural Theory, ENS Paris
Rue dUlm, 29, Paris, France
sophie.deneve@ens.fr
Ralph Bourdoukan
Group For Neural Theory, ENS Paris
Rue dUlm, 29, Paris, France
ralph.bourdoukan@gmail.com
Abstract
To predict sensory inp... | 5948 |@word neurophysiology:1 version:1 briefly:1 hyperpolarized:2 grey:1 simulation:2 linearized:1 covariance:1 solid:1 kappen:1 initial:4 configuration:1 liquid:1 current:12 com:1 gmail:1 yet:1 must:2 scatter:2 realistic:1 plasticity:9 motor:7 plot:6 update:2 greedy:1 accordingly:2 short:2 filtered:3 characterization... |
5,467 | 5,949 | Semi-supervised Sequence Learning
Andrew M. Dai
Google Inc.
adai@google.com
Quoc V. Le
Google Inc.
qvl@google.com
Abstract
We present two approaches to use unlabeled data to improve Sequence Learning
with recurrent networks. The first approach is to predict what comes next in a
sequence, which is a language model in... | 5949 |@word cnn:3 bigram:3 tried:2 harder:1 mcauley:1 carry:1 wanna:1 document:26 outperforms:1 current:1 com:3 lang:1 yet:2 gpu:1 parsing:1 stemming:1 devin:1 subsequent:1 hypothesize:1 remove:1 v:1 short:6 pascanu:1 toronto:1 zhang:6 five:1 accessed:1 become:3 consists:1 behavioral:1 paragraph:5 ontology:1 roughly:1 ... |
5,468 | 595 | Learning Spatio-Temporal Planning from
a Dynamic Programming Teacher:
Feed-Forward N eurocontrol for Moving
Obstacle A voidance
Gerald Fahner *
Department of Neuroinformatics
University of Bonn
Romerstr. 164
W -5300 Bonn 1, Germany
Rolf Eckmiller
Department of Neuroinformatics
University of Bonn
Romerstr. 164
W-5300 B... | 595 |@word version:1 simulation:3 llo:2 accounting:1 carry:1 configuration:1 contains:1 kinodynamic:4 series:1 current:1 incidence:3 must:1 john:1 realize:1 stemming:1 subsequent:1 partition:1 distant:1 rward:1 motor:14 aside:1 device:1 short:3 firstly:2 unbounded:1 rc:1 along:1 expected:3 xji:1 behavior:2 planning:23 ... |
5,469 | 5,950 | Skip-Thought Vectors
Ryan Kiros 1 , Yukun Zhu 1 , Ruslan Salakhutdinov 1,2 , Richard S. Zemel 1,2
Antonio Torralba 3 , Raquel Urtasun 1 , Sanja Fidler 1
University of Toronto 1
Canadian Institute for Advanced Research 2
Massachusetts Institute of Technology 3
Abstract
We describe an approach for unsupervised learning... | 5950 |@word cnn:1 version:1 middle:1 bigram:2 norm:1 scroll:1 annoying:1 open:1 jacob:1 contrastive:1 pressure:1 pick:1 hunting:4 contains:1 score:15 jimenez:1 tuned:2 ours:1 document:1 existing:4 com:2 si:20 yet:2 activation:1 must:1 diederik:1 written:1 romance:1 concatenate:1 wx:1 update:4 bart:2 alone:1 smith:1 sho... |
5,470 | 5,951 | Learning to Linearize Under Uncertainty
1
Ross Goroshin?1 Michael Mathieu?1 Yann LeCun1,2
Dept. of Computer Science, Courant Institute of Mathematical Science, New York, NY
2
Facebook AI Research, New York, NY
{goroshin,mathieu,yann}@cs.nyu.edu
Abstract
Training deep feature hierarchies to solve supervised learning ... | 5951 |@word version:2 seems:1 linearized:9 propagate:4 thereby:1 mag:3 renewed:1 deconvolutional:1 past:1 current:1 activation:7 must:1 gpu:1 readily:1 ronan:1 realistic:1 blur:1 update:2 alone:1 generative:4 selected:1 half:1 plane:1 parametrization:1 short:1 parameterizations:2 location:2 mathematical:1 consists:1 no... |
5,471 | 5,952 | Synaptic Sampling: A Bayesian Approach to
Neural Network Plasticity and Rewiring
David Kappel1
Stefan Habenschuss1
Robert Legenstein
Wolfgang Maass
Institute for Theoretical Computer Science
Graz University of Technology
A-8010 Graz, Austria
[kappel, habenschuss, legi, maass]@igi.tugraz.at
Abstract
We reexamine in t... | 5952 |@word trial:3 illustrating:1 middle:2 briefly:1 version:1 advantageous:1 kura:1 open:1 simulation:5 carry:2 initial:1 efficacy:6 exclusively:1 tuned:1 existing:2 current:12 written:5 readily:1 additive:1 plasticity:21 shape:1 enables:3 plot:1 update:3 stationary:10 generative:4 leaf:1 provides:5 zhang:1 mathemati... |
5,472 | 5,953 | Natural Neural Networks
Guillaume Desjardins, Karen Simonyan, Razvan Pascanu, Koray Kavukcuoglu
{gdesjardins,simonyan,razp,korayk}@google.com
Google DeepMind, London
Abstract
We introduce Natural Neural Networks, a novel family of algorithms that speed up
convergence by adapting their internal representation during tr... | 5953 |@word middle:1 version:5 compression:2 seems:1 c0:1 km:2 confirms:2 crucially:1 bn:6 covariance:8 decomposition:4 sgd:13 incarnation:1 solid:1 analoguous:1 reduction:1 initial:6 configuration:1 liu:1 daniel:1 document:1 interestingly:1 envision:1 outperforms:2 current:1 com:1 surprising:1 activation:9 must:1 gpu:... |
5,473 | 5,954 | Convolutional Networks on Graphs
for Learning Molecular Fingerprints
David Duvenaud? , Dougal Maclaurin?, Jorge Aguilera-Iparraguirre
Rafael G?omez-Bombarelli, Timothy Hirzel, Al?an Aspuru-Guzik, Ryan P. Adams
Harvard University
Abstract
We introduce a convolutional neural network that operates directly on graphs.
Th... | 5954 |@word multitask:2 trial:1 nd:1 vogt:1 open:1 scalably:1 simulation:1 propagate:1 initial:3 configuration:1 series:2 efficacy:3 fragment:15 cristina:1 existing:3 current:1 com:2 contextual:1 activation:3 diederik:1 must:1 written:1 john:2 luis:1 concatenate:1 shape:2 christian:1 webster:1 remove:1 designed:2 inter... |
5,474 | 5,955 | Convolutional LSTM Network: A Machine Learning
Approach for Precipitation Nowcasting
Xingjian Shi Zhourong Chen Hao Wang Dit-Yan Yeung
Department of Computer Science and Engineering
Hong Kong University of Science and Technology
{xshiab,zchenbb,hwangaz,dyyeung}@cse.ust.hk
Wai-kin Wong Wang-chun Woo
Hong Kong Observato... | 5955 |@word kong:8 version:1 wco:2 bf:2 bptt:1 disk:3 open:1 simulation:2 initial:1 configuration:1 series:2 contains:4 score:3 bc:2 cleared:1 outperforms:3 existing:1 past:3 current:3 com:1 guadarrama:1 ust:1 written:1 gpu:2 numerical:1 periodically:1 concatenate:1 partition:1 blur:1 remove:1 update:1 occlude:1 genera... |
5,475 | 5,956 | Scheduled Sampling for Sequence Prediction with
Recurrent Neural Networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, Noam Shazeer
Google Research
Mountain View, CA, USA
{bengio,vinyals,ndjaitly,noam}@google.com
Abstract
Recurrent Neural Networks can be trained to produce sequences of tokens given
some input, as exemp... | 5956 |@word version:1 open:1 tried:1 propagate:1 pick:1 harder:1 reduction:1 configuration:7 series:1 score:4 selecting:1 past:3 current:10 com:1 contextual:1 guadarrama:1 tackling:1 attracted:1 parsing:10 additive:2 ldc93s1:1 realistic:1 hypothesize:1 v:2 motlicek:1 intelligence:2 discovering:1 mccallum:1 beginning:1 ... |
5,476 | 5,957 | Mind the Gap: A Generative Approach to
Interpretable Feature Selection and Extraction
Been Kim
Julie Shah
Massachusetts Institute of Technology
Cambridge, MA 02139
{beenkim, julie a shah}@csail.mit.edu
Finale Doshi-Velez
Harvard University
Cambridge, MA 02138
finale@seas.harvard.edu
Abstract
We present the Mind the ... | 5957 |@word repository:1 middle:1 version:2 seek:1 chili:3 contrastive:2 mammal:2 minus:1 ld:38 reduction:1 initial:1 configuration:1 contains:3 liu:2 selecting:1 score:1 lichman:1 series:1 document:1 wrapper:1 outperforms:1 existing:1 freitas:1 recovered:1 current:1 assigning:2 yet:1 must:3 readily:2 determinantal:4 a... |
5,477 | 5,958 | Max-Margin Deep Generative Models
Chongxuan Li? , Jun Zhu? , Tianlin Shi? , Bo Zhang?
?
Dept. of Comp. Sci. & Tech., State Key Lab of Intell. Tech. & Sys., TNList Lab,
Center for Bio-Inspired Computing Research, Tsinghua University, Beijing, 100084, China
?
Dept. of Comp. Sci., Stanford University, Stanford, CA 94305,... | 5958 |@word mild:1 cnn:11 stronger:1 calculus:1 covariance:1 p0:5 sgd:1 harder:1 tnlist:2 reduction:3 series:1 ours:1 document:1 outperforms:2 com:2 activation:4 gmail:1 must:1 written:2 fn:11 unpooling:3 concatenate:1 periodically:1 hofmann:2 drop:1 update:3 discrimination:2 generative:48 plane:1 sys:1 bissacco:1 pasc... |
5,478 | 5,959 | Cross-Domain Matching for Bag-of-Words Data
via Kernel Embeddings of Latent Distributions
Yuya Yoshikawa?
Nara Institute of Science and Technology
Nara, 630-0192, Japan
yoshikawa.yuya.yl9@is.naist.jp
Tomoharu Iwata
NTT Communication Science Laboratories
Kyoto, 619-0237, Japan
iwata.tomoharu@lab.ntt.co.jp
Hiroshi Sawa... | 5959 |@word polynomial:2 covariance:2 decomposition:1 citeseer:1 anthrax:3 moment:4 liu:2 document:38 rkhs:7 outperforms:1 existing:3 com:1 si:2 written:1 john:2 krikamol:2 intelligence:5 generative:2 isard:1 ith:9 dinuzzo:1 yamada:3 multiset:2 zhang:1 yoshikawa:4 stopwords:1 five:4 mathematical:1 c2:1 consists:1 intro... |
5,479 | 596 | Learning to categorize objects using
temporal coherence
Suzanna Becker?
The Rotman Research Institute
Baycrest Center
3560 Bathurst St.
Toronto, Ontario, M6A 2E1
Abstract
The invariance of an objects' identity as it transformed over time
provides a powerful cue for perceptual learning. We present an unsupervised lear... | 596 |@word version:1 simulation:2 initial:1 disparity:1 tuned:2 current:2 lang:2 activation:2 assigning:1 must:5 shape:3 infant:4 cue:4 selected:1 half:1 stereoacuity:2 ith:5 short:2 filtered:1 detecting:2 provides:2 toronto:1 location:2 successive:5 five:1 burst:1 become:1 introduce:1 pairwise:1 rapid:1 multi:2 global... |
5,480 | 5,960 | A Gaussian Process Model of Quasar
Spectral Energy Distributions
Andrew Miller? , Albert Wu
School of Engineering and Applied Sciences
Harvard University
acm@seas.harvard.edu, awu@college.harvard.edu
Jeffrey Regier, Jon McAuliffe
Department of Statistics
University of California, Berkeley
{jeff, jon}@stat.berkeley.edu... | 5960 |@word middle:1 version:2 pw:1 proportion:1 nd:1 c0:3 twelfth:1 simulation:1 decomposition:2 attainable:1 brightness:2 dramatic:1 carry:2 daniel:2 denoting:1 outperforms:1 existing:1 lang:2 fn:16 distant:1 alam:1 pertinent:1 analytic:1 interpretable:1 depict:1 generative:4 leaf:1 spec:6 parameterization:1 inspecti... |
5,481 | 5,961 | Neural Adaptive Sequential Monte Carlo
Shixiang Gu??
Zoubin Ghahramani?
Richard E. Turner?
University of Cambridge, Department of Engineering, Cambridge UK
?
MPI for Intelligent Systems, T?ubingen, Germany
sg717@cam.ac.uk, zoubin@eng.cam.ac.uk, ret26@cam.ac.uk
?
Abstract
Sequential Monte Carlo (SMC), or particle filt... | 5961 |@word middle:2 briefly:2 advantageous:1 proportion:1 nd:1 open:2 dz1:1 simulation:1 eng:1 covariance:1 attainable:1 thereby:1 g050821:1 carry:1 moment:1 initial:1 series:5 contains:2 score:1 tuned:2 interestingly:1 outperforms:3 existing:2 freitas:2 current:4 comparing:1 contextual:1 si:2 must:1 analytic:1 enable... |
5,482 | 5,962 | Convolutional Spike-triggered Covariance Analysis
for Neural Subunit Models
Anqi Wu1
Il Memming Park2
Jonathan W. Pillow1
Princeton Neuroscience Institute, Princeton University
{anqiw, pillow}@princeton.edu
Department of Neurobiology and Behavior, Stony Brook University
memming.park@stonybrook.edu
1
2
Abstract
Sub... | 5962 |@word cnn:1 version:1 briefly:1 polynomial:2 middle:1 nd:1 km:12 simulation:1 covariance:9 decomposition:12 tr:2 reduction:3 moment:16 contains:1 document:1 outperforms:2 diagonalized:1 ka:4 anqi:1 yet:2 stony:1 written:1 bd:1 must:1 john:1 physiol:1 informative:1 shawetaylor:1 interpretable:1 generative:1 fewer:... |
5,483 | 5,963 | Rectified Factor Networks
Djork-Arn?e Clevert, Andreas Mayr, Thomas Unterthiner and Sepp Hochreiter
Institute of Bioinformatics, Johannes Kepler University, Linz, Austria
{okko,mayr,unterthiner,hochreit}@bioinf.jku.at
Abstract
We propose rectified factor networks (RFNs) to efficiently construct very sparse,
non-linear... | 5963 |@word polynomial:1 loading:1 norm:6 hyv:1 d2:1 covariance:17 automat:1 thereby:1 tr:13 delgado:1 contains:3 tabulate:1 jku:2 affymetrix:1 current:1 optim:1 dx:1 must:1 gpu:4 visible:3 subsequent:1 numerical:1 shape:2 hochreit:1 update:8 v:1 generative:9 selected:3 greedy:1 short:1 kepler:1 toronto:1 h4:1 mapk:2 c... |
5,484 | 5,964 | Embed to Control: A Locally Linear Latent
Dynamics Model for Control from Raw Images
Manuel Watter?
Jost Tobias Springenberg?
Joschka Boedecker
University of Freiburg, Germany
{watterm,springj,jboedeck}@cs.uni-freiburg.de
Martin Riedmiller
Google DeepMind
London, UK
riedmiller@google.com
Abstract
We introduce Embed ... | 5964 |@word version:2 briefly:1 achievable:1 seems:1 nd:1 fixpoints:1 seek:1 linearized:2 crucially:1 covariance:3 contraction:1 sgd:1 xgoal:1 kappen:1 moment:1 contains:2 att:1 series:1 deconvolutional:1 current:3 com:1 nt:1 manuel:1 discretization:2 activation:1 recovered:1 z2:2 must:4 shape:1 lqg:1 motor:1 remove:1 ... |
5,485 | 5,965 | Bayesian Dark Knowledge
Anoop Korattikara, Vivek Rathod, Kevin Murphy
Google Research
{kbanoop, rathodv, kpmurphy}@google.com
Max Welling
University of Amsterdam
m.welling@uva.nl
Abstract
We consider the problem of Bayesian parameter estimation for deep neural networks, which is important in problem settings where w... | 5965 |@word trial:3 version:2 compression:1 seems:1 open:1 propagate:2 incurs:1 sgd:30 initial:3 configuration:2 contains:1 score:3 past:1 guadarrama:1 com:1 comparing:1 contextual:1 activation:3 diederik:1 dx:1 intriguing:1 romero:1 kdd:1 christian:1 update:3 generative:1 hamiltonian:3 num:1 location:1 org:1 simpler:1... |
5,486 | 5,966 | GP Kernels for Cross-Spectrum Analysis
1
Kyle Ulrich, 3 David E. Carlson, 2 Kafui Dzirasa, 1 Lawrence Carin
Department of Electrical and Computer Engineering, Duke University
2
Department of Psychiatry and Behavioral Sciences, Duke University
3
Department of Statistics, Columbia University
{kyle.ulrich, kafui.dzirasa... | 5966 |@word multitask:1 determinant:1 version:3 pw:1 inversion:2 hippocampus:3 c0:10 simulation:1 covariance:27 pick:1 inpainting:1 solid:1 tr:3 initial:1 series:8 contains:1 interestingly:1 com:1 gmail:1 multioutput:1 additive:1 subsequent:1 numerical:1 extrapolating:1 interpretable:1 plot:1 stationary:5 implying:1 di... |
5,487 | 5,967 | End-to-end Learning of LDA by Mirror-Descent Back
Propagation over a Deep Architecture
Jianshu Chen? , Ji He? , Yelong Shen? , Lin Xiao? , Xiaodong He? , Jianfeng Gao? ,
Xinying Song? and Li Deng?
?
Microsoft Research, Redmond, WA 98052, USA,
{jianshuc,yeshen,lin.xiao,xiaohe,jfgao,xinson,deng}@microsoft.com
?
Departmen... | 5967 |@word polynomial:1 proportion:3 c0:1 triggs:1 open:1 adrian:1 seek:2 propagate:1 blender:1 nemirovsky:1 mcauley:1 reduction:1 electronics:1 contains:1 score:10 selecting:1 mag:1 uma:1 tuned:1 document:27 outperforms:6 silvescu:1 com:2 wd:50 written:1 plot:2 designed:1 update:1 interpretable:1 alone:1 generative:9... |
5,488 | 5,968 | Particle Gibbs for Infinite Hidden Markov Models
Nilesh Tripuraneni*
University of Cambridge
nt357@cam.ac.uk
Shixiang Gu*
University of Cambridge
MPI for Intelligent Systems
sg717@cam.ac.uk
Hong Ge
University of Cambridge
hg344@cam.ac.uk
Zoubin Ghahramani
University of Cambridge
zoubin@eng.cam.ac.uk
Abstract
Infini... | 5968 |@word middle:2 eliminating:1 seems:2 eng:1 pg:49 q1:2 ytn:3 initial:5 series:7 denoting:1 rightmost:2 outperforms:1 existing:2 current:1 comparing:4 assigning:1 must:2 subsequent:1 informative:1 j1:1 plot:4 resampling:7 stationary:1 v:13 leaf:1 selected:1 colored:2 blei:1 recompute:1 completeness:2 provides:1 fir... |
5,489 | 5,969 | Sparse Local Embeddings for Extreme Multi-label
Classification
Kush Bhatia? , Himanshu Jain? , Purushottam Kar?? , Manik Varma? , and Prateek Jain?
?
Microsoft Research, India
?
Indian Institute of Technology Delhi, India
?
Indian Institute of Technology Kanpur, India
{t-kushb,prajain,manik}@microsoft.com
himanshu.j689... | 5969 |@word compression:1 termination:1 hu:1 seek:2 ality:1 decomposition:5 q1:2 arti:1 incurs:1 shot:1 reduction:4 contains:2 bibtex:2 document:3 outperforms:5 existing:4 ka:1 com:2 comparing:1 gmail:1 tackling:1 additive:1 partition:5 kdd:2 cis:1 plot:2 designed:1 update:6 drop:1 v:7 alone:1 generative:1 prohibitive:... |
5,490 | 597 | Q-Learning with Hidden-Unit Restarting
Charles W. Anderson
Department of Computer Science
Colorado State University
Fort Collins, CO 80523
Abstract
Platt's resource-allocation network (RAN) (Platt, 1991a, 1991b)
is modified for a reinforcement-learning paradigm and to "restart"
existing hidden units rather than addin... | 597 |@word version:1 faculty:1 advantageous:1 tried:1 jacob:2 initial:1 series:1 tuned:1 genetic:1 past:1 existing:2 current:8 activation:1 yet:1 must:2 j1:2 drop:1 update:1 mackey:1 selected:2 plane:1 ith:1 smith:2 hinged:1 along:1 symposium:1 consists:1 combine:1 roughly:1 planning:1 f3h:2 considering:1 becomes:1 pro... |
5,491 | 5,970 | Robust Spectral Inference for Joint Stochastic Matrix
Factorization
David Mimno
Dept. of Information Science
Cornell University
Ithaca, NY 14850
mimno@cornell.edu
Moontae Lee, David Bindel
Dept. of Computer Science
Cornell University
Ithaca, NY 14850
{moontae,bindel}@cs.cornell.edu
Abstract
Spectral inference provide... | 5970 |@word trial:1 version:1 middle:2 inversion:2 eliminating:1 tensorial:1 open:1 adrian:2 d2:1 hu:1 seek:1 tried:1 covariance:1 decomposition:1 pick:1 tianyi:1 recursively:1 moment:1 initial:2 liu:1 contains:5 score:1 selecting:1 united:1 daniel:1 document:9 outperforms:1 recovered:2 z2:9 com:1 must:2 subsequent:1 n... |
5,492 | 5,971 | Space-Time Local Embeddings
Ke Sun1? Jun Wang2
Alexandros Kalousis3,1
St?ephane Marchand-Maillet1
1
Viper Group, Computer Vision and Multimedia Laboratory, University of Geneva
sunk.edu@gmail.com, Stephane.Marchand-Maillet@unige.ch, and 2 Expedia,
Switzerland, jwang1@expedia.com, and 3 Business Informatics Department,... | 5971 |@word mild:1 judgement:1 norm:3 profit:1 garrigues:1 reduction:9 liu:1 fragment:1 bradley:1 repelling:2 dx:2 cue:1 tone:1 zhang:2 symposium:1 excise:1 baldi:1 manner:1 tart:1 indeed:1 roughly:1 panic:1 globally:1 unfolded:1 becomes:1 what:1 unified:1 transformation:1 growth:1 shed:1 exactly:1 grant:1 attend:1 loc... |
5,493 | 5,972 | A Fast, Universal Algorithm
to Learn Parametric Nonlinear Embeddings
? Carreira-Perpin?
? an
Miguel A.
EECS, University of California, Merced
Max Vladymyrov
UC Merced and Yahoo Labs
http://eecs.ucmerced.edu
maxv@yahoo-inc.com
Abstract
Nonlinear embedding algorithms such as stochastic neighbor embedding do dimension... | 5972 |@word mild:1 version:1 middle:1 seems:1 sammon:2 seek:2 perpin:1 bn:1 simulation:1 nystr:1 solid:1 harder:1 carry:1 ld:1 reduction:5 initial:2 series:1 existing:7 current:3 com:1 z2:1 goldberger:1 must:2 gpu:2 realize:2 numerical:3 subsequent:1 plot:1 progressively:1 maxv:1 hash:2 intelligence:1 beginning:2 provi... |
5,494 | 5,973 | Bayesian Manifold Learning:
The Locally Linear Latent Variable Model
Mijung Park, Wittawat Jitkrittum, Ahmad Qamar?,
Zolt?an Szab?o, Lars Buesing?, Maneesh Sahani
Gatsby Computational Neuroscience Unit
University College London
{mijung, wittawat, zoltan.szabo}@gatsby.ucl.ac.uk
atqamar@gmail.com, lbuesing@google.com, m... | 5973 |@word version:2 briefly:1 norm:3 seems:1 grey:1 crucially:1 bn:1 covariance:1 zolt:1 q1:1 tr:2 solid:1 shading:1 nystr:1 reduction:6 initial:1 initialisation:1 favouring:1 existing:1 outperforms:1 current:3 com:3 recovered:2 gmail:1 dx:10 must:1 written:3 concatenate:1 happen:1 shape:1 update:1 maxv:1 xdx:1 gener... |
5,495 | 5,974 | Local Causal Discovery of Direct Causes and Effects
Tian Gao
Qiang Ji
Department of ECSE
Rensselaer Polytechnic Institute, Troy, NY 12180
{gaot, jiq}@rpi.edu
Abstract
We focus on the discovery and identification of direct causes and effects of a target
variable in a causal network. State-of-the-art causal learning al... | 5974 |@word nd:1 seek:2 propagate:1 elisseeff:2 minus:3 recursively:2 initial:3 contains:5 score:5 outperforms:1 existing:7 current:1 rpi:1 must:3 happen:4 kdd:1 enables:1 designed:1 update:1 unshielded:1 alone:1 greedy:1 infant:1 intelligence:4 wimberly:1 record:1 completeness:3 node:81 daphne:1 zhang:1 along:1 c2:7 d... |
5,496 | 5,975 | Discriminative Robust Transformation Learning
Jiaji Huang
Qiang Qiu
Guillermo Sapiro
Robert Calderbank
Department of Electrical Engineering, Duke University
Durham, NC 27708
{jiaji.huang,qiang.qiu,guillermo.sapiro,robert.calderbank}@duke.edu
Abstract
This paper proposes a framework for learning features that are ... | 5975 |@word compression:1 norm:2 yi0:1 hu:1 seek:1 covariance:1 paid:1 thereby:1 reduction:1 initial:1 contains:1 denoting:1 humanlevel:1 outperforms:1 z2:1 comparing:1 goldberger:1 partition:5 discrimination:11 implying:1 half:1 intelligence:1 pool2:1 provides:4 mannor:1 revisited:1 prove:1 consists:1 wild:4 theoretic... |
5,497 | 5,976 | Max-Margin Majority Voting for
Learning from Crowds
Tian Tian, Jun Zhu
Department of Computer Science & Technology; Center for Bio-Inspired Computing Research
Tsinghua National Lab for Information Science & Technology
State Key Lab of Intelligent Technology & Systems; Tsinghua University, Beijing 100084, China
tiant13... | 5976 |@word version:3 inversion:1 seems:1 p0:9 initial:1 liu:3 contains:1 score:9 njk:3 selecting:1 karger:1 existing:1 comparing:2 si:1 assigning:2 cheap:1 designed:1 update:1 discrimination:1 v:1 generative:14 selected:1 item:10 plane:1 ruvolo:1 core:1 provides:1 contribute:1 tems:1 zhang:3 initiative:2 consists:3 pa... |
5,498 | 5,977 | M-Best-Diverse Labelings
for Submodular Energies and Beyond
Alexander Kirillov1
Dmitrij Schlesinger1
Dmitry Vetrov2
1
Carsten Rother
Bogdan Savchynskyy1
1
2
TU Dresden, Dresden, Germany
Skoltech, Moscow, Russia
alexander.kirillov@tu-dresden.de
Abstract
We consider the problem of finding M best diverse solutions of en... | 5977 |@word kohli:3 determinant:1 version:1 underline:1 everingham:1 yv0:1 flach:1 confirms:1 prasad:1 rivera:4 initial:6 configuration:9 contains:2 tuned:2 outperforms:2 existing:1 current:1 yet:1 determinantal:1 numerical:1 premachandran:1 greedy:3 selected:1 item:1 accordingly:1 plane:1 tarlow:2 provides:1 node:27 r... |
5,499 | 5,978 | Covariance-Controlled Adaptive Langevin
Thermostat for Large-Scale Bayesian Sampling
Xiaocheng Shang?
University of Edinburgh
x.shang@ed.ac.uk
Zhanxing Zhu?
University of Edinburgh
zhanxing.zhu@ed.ac.uk
Benedict Leimkuhler
University of Edinburgh
b.leimkuhler@ed.ac.uk
Amos J. Storkey
University of Edinburgh
a.stork... | 5978 |@word middle:1 briefly:1 trotter:1 nd:8 simulation:4 covariance:33 p0:1 accommodate:1 configuration:1 existing:2 current:2 discretization:1 written:2 must:1 numerical:8 sdes:2 designed:2 plot:2 update:1 stationary:3 selected:4 device:2 iso:1 reciprocal:1 hamiltonian:3 mathematical:2 along:2 direct:2 differential:... |
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