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3,300 | 399 | Analog Neural Networks as Decoders
Ruth Erlanson?
Dept. of Electrical Engineering
California Institute of Technology
Pasadena, CA 91125
Yaser Abu-Mostafa
Dept. of Electrical Engineering
California Institute of Technology
Pasadena, CA 91125
Abstract
Analog neural networks with feedback can be used to implement l(Winn... | 399 |@word contain:1 hypercube:1 differ:1 hence:1 spike:2 codewords:5 white:1 alp:1 ll:1 pick:1 width:1 implementing:1 distance:10 simulated:1 decoder:12 initial:1 contains:1 performs:1 l1:1 current:2 ruth:1 majani:4 considered:1 code:30 index:1 must:1 cognition:1 additive:1 partition:1 mostafa:5 abumostafa:1 negative:... |
3,301 | 3,990 | Multitask Learning without Label Correspondences
Novi Quadrianto1 , Alex Smola2 , Tib?erio Caetano1 , S.V.N. Vishwanathan3 , James Petterson1
1 SML-NICTA & RSISE-ANU, Canberra, ACT, Australia
2 Yahoo! Research, Santa Clara, CA, USA
3 Purdue University, West Lafayette, IN, USA
Abstract
We propose an algorithm to perfo... | 3990 |@word multitask:21 version:1 briefly:2 norm:2 seems:1 yi0:1 nd:1 r:1 moment:2 contains:1 tuned:1 ours:1 document:3 interestingly:2 existing:5 com:2 clara:1 yet:1 written:2 readily:3 oldenbourg:1 partition:1 kdd:1 hofmann:2 intelligence:1 fewer:1 accordingly:1 directory:24 mccallum:1 beginning:1 node:4 location:1 ... |
3,302 | 3,991 | Multi-View Active Learning in
the Non-Realizable Case
Wei Wang and Zhi-Hua Zhou
National Key Laboratory for Novel Software Technology
Nanjing University, Nanjing 210093, China
{wangw,zhouzh}@lamda.nju.edu.cn
Abstract
The sample complexity of active learning under the realizability assumption has
been well-studied. The... | 3991 |@word h:28 polynomial:11 c0:4 harder:1 initial:1 existing:1 beygelzimer:1 must:1 hoping:1 atlas:1 ainen:1 greedy:1 fewer:1 coarse:1 provides:1 allerton:1 firstly:1 zhang:1 unbounded:18 c2:11 waived:1 prove:6 consists:1 combine:2 compose:2 theoretically:2 multi:49 inspired:1 zhouzh:1 decreasing:1 muslea:1 zhi:1 co... |
3,303 | 3,992 | Nonparametric Bayesian Policy Priors for
Reinforcement Learning
Finale Doshi-Velez, David Wingate, Nicholas Roy and Joshua Tenenbaum
Massachusetts Institute of Technology
Cambridge, MA 02139
{finale,wingated,nickroy,jbt}@csail.mit.edu
Abstract
We consider reinforcement learning in partially observable domains where t... | 3992 |@word mild:1 trial:2 version:3 polynomial:1 pieter:1 initial:2 selecting:1 daniel:1 current:2 must:1 john:1 realistic:1 analytic:1 motor:2 remove:1 treating:1 designed:1 update:4 rrt:1 stationary:1 fewer:4 intelligence:2 ffm:1 hallway:3 smith:1 accepting:1 blei:1 provides:2 node:14 preference:3 banff:1 simpler:4 ... |
3,304 | 3,993 | Block Variable Selection in Multivariate Regression
and High-dimensional Causal Inference
Aur?elie C. Lozano, Vikas Sindhwani
IBM T.J. Watson Research Center,
1101 Kitchawan Road,
Yorktown Heights NY 10598,USA
{aclozano,vsindhw}@us.ibm.com
Abstract
We consider multivariate regression problems involving high-dimension... | 3993 |@word determinant:1 version:2 polynomial:2 confirms:1 simulation:2 seek:1 covariance:6 jacob:3 thereby:1 tr:6 moment:1 reduction:3 celebrated:1 series:14 score:1 document:1 past:4 existing:1 kmk:1 ka:3 com:1 si:1 bie:1 chu:1 kdd:2 hofmann:1 pertinent:2 interpretable:1 gist:1 alone:2 greedy:3 selected:7 vafa:5 ant... |
3,305 | 3,994 | LSTD with Random Projections
Mohammad Ghavamzadeh, Alessandro Lazaric, Odalric-Ambrym Maillard, R?emi Munos
INRIA Lille - Nord Europe, Team SequeL, France
Abstract
We consider the problem of reinforcement learning in high-dimensional spaces
when the number of features is bigger than the number of samples. In particul... | 3994 |@word version:2 norm:11 nd:4 open:1 d2:1 tat:1 contraction:1 valuefunction:1 asks:1 initial:2 contains:2 selecting:1 ours:1 existing:1 comparing:2 nt:10 worsening:1 dx:3 bd:1 written:2 fn:2 numerical:1 remove:1 drop:1 stationary:14 half:1 greedy:4 intelligence:1 mannor:4 u2i:1 mathematical:1 constructed:1 become:... |
3,306 | 3,995 | A VLSI Implementation of the Adaptive Exponential
Integrate-and-Fire Neuron Model
? Karlheinz Meier,
Sebastian Millner, Andreas Grubl,
Johannes Schemmel and Marc-Olivier Schwartz
Kirchhoff-Institut f?ur Physik
Ruprecht-Karls-Universit?at Heidelberg
smillner@kip.uni-heidelberg.de
Abstract
We describe an accelerated ha... | 3995 |@word neurophysiology:1 version:2 rising:2 physik:1 simulation:19 pulse:2 accounting:1 versatile:1 outlook:1 moment:1 initial:1 contains:1 daniel:1 current:24 com:1 universality:2 must:1 realize:1 numerical:1 realistic:1 ota:5 plasticity:3 enables:1 wanted:1 designed:1 plot:1 update:1 device:7 website:1 parameter... |
3,307 | 3,996 | Heavy-Tailed Process Priors for Selective Shrinkage
Michael I. Jordan
University of California, Berkeley
jordan@cs.berkeley.edu
Fabian L. Wauthier
University of California, Berkeley
flw@cs.berkeley.edu
Abstract
Heavy-tailed distributions are often used to enhance the robustness of regression
and classification metho... | 3996 |@word version:1 stronger:4 c0:5 tedious:1 adrian:1 d2:1 seek:1 covariance:3 tr:3 solid:1 shading:4 carry:1 configuration:1 liu:2 series:1 ours:1 suppressing:1 nonparanormal:1 outperforms:2 comparing:1 chu:1 must:2 readily:1 written:1 john:2 partition:1 tailoring:1 shape:2 plot:5 progressively:2 stationary:1 imita... |
3,308 | 3,997 | Group Sparse Coding with a Laplacian Scale Mixture
Prior
Bruno A. Olshausen
Helen Wills Neuroscience Institute
School of Optometry
University of California, Berkeley
Berkeley, CA 94720
baolshausen@berkeley.edu
Pierre J. Garrigues
IQ Engines, Inc.
Berkeley, CA 94704
pierre.garrigues@gmail.com
Abstract
We propose a cla... | 3997 |@word version:1 compression:1 norm:4 open:1 hyv:2 ks0:1 decomposition:1 jacob:1 harder:1 garrigues:3 configuration:1 series:2 ours:1 outperforms:3 ksk1:2 recovered:1 com:1 si:45 gmail:1 written:2 reminiscent:1 optometry:1 shape:1 plot:5 interpretable:1 update:12 v:1 generative:12 selected:3 fewer:1 provides:1 lsm... |
3,309 | 3,998 | Worst-Case Linear Discriminant Analysis
Yu Zhang and Dit-Yan Yeung
Department of Computer Science and Engineering
Hong Kong University of Science and Technology
{zhangyu,dyyeung}@cse.ust.hk
Abstract
Dimensionality reduction is often needed in many applications due to the high
dimensionality of the data involved. In t... | 3998 |@word kong:2 repository:1 briefly:1 trial:2 kulis:1 norm:1 sammon:2 nd:1 seek:4 covariance:3 tr:48 reduction:23 contains:4 spambase:1 existing:1 ka:1 scatter:25 yet:1 ust:1 realistic:1 subsequent:1 hofmann:1 fund:1 v:2 greedy:4 intelligence:5 xk:2 math:1 cse:1 zhang:2 consists:2 prove:1 kov:1 introduce:2 pairwise... |
3,310 | 3,999 | Active Estimation of F-Measures
Christoph Sawade, Niels Landwehr, and Tobias Scheffer
University of Potsdam
Department of Computer Science
August-Bebel-Strasse 89, 14482 Potsdam, Germany
{sawade, landwehr, scheffer}@cs.uni-potsdam.de
Abstract
We address the problem of estimating the F? -measure of a given model as acc... | 3999 |@word repository:1 instrumental:9 nd:1 decomposition:1 covariance:1 thereby:1 past:1 outperforms:6 current:1 beygelzimer:1 dx:6 readily:1 fn:2 dydx:3 v:1 sawade:4 selected:2 fewer:1 yamada:2 mathematical:1 become:1 introduce:2 privacy:2 expected:3 frequently:1 growing:1 resolve:3 becomes:1 estimating:2 underlying... |
3,311 | 4 | 612
Constrained Differential Optimization
John C. Platt
Alan H. Barr
California Institute of Technology, Pasadena, CA 91125
Abstract
Many optimization models of neural networks need constraints to restrict the space of outputs to
a subspace which satisfies external criteria. Optimizations using energy methods yield "... | 4 |@word collinearity:1 version:1 inversion:1 seems:2 proportionality:1 open:1 closure:1 decomposition:1 harder:1 initial:1 contains:1 existing:1 z2:1 must:6 attracted:2 john:2 numerical:5 stationary:4 alone:1 plane:5 sys:1 location:1 lx:1 height:1 along:2 direct:1 differential:41 become:1 persistent:1 incorrect:1 prov... |
3,312 | 40 | 103
NEURAL NETWORKS FOR TEMPLATE MATCHING:
APPLICATION TO REAL-TIME CLASSIFICATION
OF THE ACTION POTENTIALS OF REAL NEURONS
Yiu-fai Wongt, Jashojiban Banikt and James M. Bower!
tDivision of Engineering and Applied Science
!Division of Biology
California Institute of Technology
Pasadena, CA 91125
ABSTRACT
Much experim... | 40 |@word neurophysiology:1 version:1 simulation:3 simplifying:1 initial:2 configuration:1 document:1 current:3 si:6 yet:1 must:3 readily:1 john:1 realistic:1 multineuron:1 shape:1 designed:3 discrimination:2 implying:1 device:2 nervous:1 assurance:1 accordingly:1 sys:1 record:7 detecting:2 complication:1 location:1 su... |
3,313 | 400 | Note on Learning Rate Schedules for Stochastic
Optimization
Christian Darken and John Moody
Yale University
P.O. Box 2158 Yale Station
New Haven, CT 06520
Email: moody@cs.yale.edu
Abstract
We present and compare learning rate schedules for stochastic gradient
descent, a general algorithm which includes LMS, on-line b... | 400 |@word compression:1 simulation:1 solid:1 loc:1 t7:1 current:2 yet:1 dx:1 must:2 john:1 visible:1 shape:1 christian:1 drop:2 short:2 math:2 location:2 symp:1 introduce:2 uphill:1 expected:1 nor:1 animator:1 automatically:1 minimizes:1 ail:1 guarantee:1 quantitative:1 berkeley:1 preferable:1 stick:1 control:1 unit:1... |
3,314 | 4,000 | Sufficient Conditions for Generating Group Level
Sparsity in a Robust Minimax Framework
Hongbo Zhou and Qiang Cheng
Computer Science department,
Southern Illinois University Carbondale, IL, 62901
hongboz@siu.edu, qcheng@cs.siu.edu
Abstract
Regularization technique has become a principled tool for statistics and machi... | 4000 |@word version:1 eliminating:1 norm:9 ci2:6 decomposition:1 jacob:1 configuration:1 series:2 selecting:2 outperforms:2 xnj:2 surprising:1 must:1 readily:1 john:1 additive:5 designed:2 v:2 intelligence:2 antoniadis:1 ith:4 core:3 short:1 provides:2 math:2 mannor:1 nom:1 simpler:1 zhang:1 five:4 dn:1 become:2 surpri... |
3,315 | 4,001 | Switched Latent Force Models
for Movement Segmentation
1
?
Mauricio A. Alvarez
, Jan Peters 2 , Bernhard Sch?olkopf 2 , Neil D. Lawrence 3,4
School of Computer Science, University of Manchester, Manchester, UK M13 9PL
2
Max Planck Institute for Biological Cybernetics, T?ubingen, Germany 72076
3
School of Computer Scien... | 4001 |@word trial:8 inversion:2 twelfth:1 d2:2 km:3 covariance:35 versatile:1 reduction:1 initial:15 liu:1 series:6 vd0:4 reaction:1 recovered:1 activation:1 must:3 multioutput:1 motor:7 wanted:1 stationary:1 generative:2 selected:1 device:2 a2d:1 intelligence:1 smith:1 sudden:2 yunus:1 location:1 five:1 along:2 c2:4 c... |
3,316 | 4,002 | Evidence-Specific Structures for Rich Tractable CRFs
Carlos Guestrin
Carnegie Mellon University
guestrin@cs.cmu.edu
Anton Chechetka
Carnegie Mellon University
antonc@cs.cmu.edu
Abstract
We present a simple and effective approach to learning tractable conditional random fields with structure that depends on the evide... | 4002 |@word middle:1 faculty:1 version:1 polynomial:2 vldb:1 tried:1 accounting:2 shot:1 moment:1 liu:18 contains:1 score:9 zij:1 plentiful:1 selecting:1 denoting:1 karger:1 existing:5 bradley:1 current:1 contextual:1 written:1 w911nf0810242:1 distant:1 subcomponent:1 plot:1 standalone:2 generative:6 selected:2 intelli... |
3,317 | 4,003 | Towards Holistic Scene Understanding:
Feedback Enabled Cascaded Classification Models
Congcong Li, Adarsh Kowdle, Ashutosh Saxena, Tsuhan Chen
Cornell University, Ithaca, NY.
{cl758,apk64}@cornell.edu,
asaxena@cs.cornell.edu, tsuhan@ece.cornell.edu
Abstract
In many machine learning domains (such as scene understanding... | 4003 |@word multitask:3 briefly:1 dalal:1 everingham:1 triggs:2 open:3 tried:1 pick:1 initial:1 configuration:1 series:1 score:6 hoiem:5 tuned:2 deconvolutional:1 existing:3 contextual:2 z2:2 tackling:1 assigning:5 yet:1 parsing:2 john:1 subsequent:1 shape:1 hofmann:1 voc2006:1 treating:1 designed:5 ashutosh:1 cue:3 mc... |
3,318 | 4,004 | On Herding and the Perceptron Cycling Theorem
Andrew E. Gelfand, Yutian Chen, Max Welling
Department of Computer Science
University of California, Irvine
{agelfand,yutianc,welling}@ics.uci.edu
Laurens van der Maaten
Department of CSE, UC San Diego
PRB Lab, Delft University of Tech.
lvdmaaten@gmail.com
Abstract
The p... | 4004 |@word mild:1 unaltered:1 norm:4 seek:1 contrastive:2 minus:1 boundedness:1 moment:8 configuration:1 contains:3 series:2 denoting:1 document:1 envision:1 existing:1 imaginary:1 com:1 comparing:1 jaynes:1 gmail:1 must:3 visible:1 fertilization:1 treating:1 plot:1 update:23 zik:1 v:1 half:2 selected:3 intelligence:2... |
3,319 | 4,005 | Robust PCA via Outlier Pursuit
Huan Xu
Electrical and Computer Engineering
University of Texas at Austin
huan.xu@mail.utexas.edu
Constantine Caramanis
Electrical and Computer Engineering
University of Texas at Austin
cmcaram@ece.utexas.edu
Sujay Sanghavi
Electrical and Computer Engineering
University of Texas at Aust... | 4005 |@word mild:2 kgk:1 trial:1 version:4 polynomial:1 norm:25 stronger:1 c0:18 km:1 seek:3 decomposition:11 covariance:2 klk:4 reduction:2 series:1 pt0:1 denoting:1 ours:1 existing:4 recovered:2 yet:2 scatter:1 must:1 written:1 john:1 realistic:1 numerical:1 aside:1 v:1 generative:1 accordingly:1 ith:2 certificate:6 ... |
3,320 | 4,006 | Parallelized Stochastic Gradient Descent
Markus Weimer
Yahoo! Labs
Sunnyvale, CA 94089
weimer@yahoo-inc.com
Martin A. Zinkevich
Yahoo! Labs
Sunnyvale, CA 94089
maz@yahoo-inc.com
Lihong Li
Yahoo! Labs
Sunnyvale, CA 94089
lihong@yahoo-inc.com
Alex Smola
Yahoo! Labs
Sunnyvale, CA 94089
smola@yahoo-inc.com
Abstract
Wit... | 4006 |@word briefly:1 maz:1 norm:1 disk:3 suitably:1 contraction:21 dramatic:1 sgd:5 thereby:1 harder:1 ld:4 carry:1 reduction:2 configuration:2 contains:1 initial:3 past:1 current:1 com:4 yet:1 chu:1 must:1 john:1 subsequent:1 partition:1 hofmann:1 plot:2 drop:1 update:3 stationary:8 selected:1 ith:1 short:1 accessed:... |
3,321 | 4,007 | Space-Variant Single-Image Blind Deconvolution
for Removing Camera Shake
Stefan Harmeling, Michael Hirsch, and Bernhard Sch?olkopf
Max Planck Institute for Biological Cybernetics, T?ubingen, Germany
firstname.lastname@tuebingen.mpg.de
Abstract
Modelling camera shake as a space-invariant convolution simplifies the pro... | 4007 |@word middle:2 version:3 briefly:1 norm:1 profit:1 harder:1 mag:1 denoting:1 ours:1 suppressing:1 current:3 ka:1 written:3 gpu:1 must:3 tilted:1 visible:1 informative:1 blur:36 remove:1 update:3 depict:1 plane:4 record:1 filtered:1 location:2 along:2 direct:5 incorrect:1 combine:1 inside:4 introduce:2 divison:1 m... |
3,322 | 4,008 | Object Bank: A High-Level Image Representation for Scene
Classification & Semantic Feature Sparsification
Li-Jia Li*1 , Hao Su*1 , Eric P. Xing2 , Li Fei-Fei1
1 Computer Science Department, Stanford University
2 Machine Learning Department, Carnegie Mellon University
Abstract
Robust low-level image features have been... | 4008 |@word middle:2 dalal:1 compression:19 proportion:1 norm:4 stronger:1 nd:1 triggs:1 r:1 prominence:1 thereby:1 shot:1 harder:1 carry:2 initial:1 series:1 score:1 hoiem:4 offering:1 document:2 envision:1 outperforms:1 existing:1 current:2 comparing:1 nt:1 yet:2 readily:1 indistinguishably:1 subsequent:1 wiewiora:1 ... |
3,323 | 4,009 | Co-regularization Based Semi-supervised Domain Adaptation
Hal Daum?e III
Department of Computer Science
University of Maryland CP, MD, USA
hal@umiacs.umd.edu
Abhishek Kumar
Department of Computer Science
University of Maryland CP, MD, USA
abhishek@umiacs.umd.edu
Avishek Saha
School Of Computing
University of Utah, U... | 4009 |@word h:29 multitask:1 version:2 briefly:2 norm:2 tat:1 blender:1 tr:6 reduction:4 venkatasubramanian:1 electronics:1 contains:1 efficacy:1 necessity:2 rkhs:3 document:1 outperforms:3 existing:3 com:1 written:3 john:4 partition:1 kdd:1 plot:1 chua:1 provides:2 uppsala:1 theodoros:1 constructed:1 shorthand:1 prove... |
3,324 | 401 | Can neural networks do better than the
Vapnik-Chervonenkis bounds?
David Cohn
Dept. of Compo Sci. & Eng.
University of Washington
Seattle, WA 98195
Gerald Tesauro
IBM Watson Research Center
P.O. Box 704
Yorktown Heights, NY 10598
Abstract
\Ve describe a series of careful llumerical experiments which measure the
aver... | 401 |@word polynomial:13 open:1 simulation:4 sepa:1 linearized:1 eng:1 initial:3 series:1 score:1 chervonenkis:7 comparing:1 must:1 cruz:1 visible:1 numerical:6 shape:1 analytic:1 designed:1 plot:1 v:4 half:1 warmuth:1 compo:1 provides:3 sigmoidal:1 height:1 ucsc:1 fitting:1 manner:1 expected:5 indeed:1 roughly:3 behav... |
3,325 | 4,010 | Learning Efficient Markov Networks
Vibhav Gogate William Austin Webb Pedro Domingos
Department of Computer Science & Engineering
University of Washington
Seattle, WA 98195. USA
{vgogate,webb,pedrod}@cs.washington.edu
Abstract
We present an algorithm for learning high-treewidth Markov networks where inference is still... | 4010 |@word repository:3 version:1 hoffgen:1 polynomial:4 stronger:1 seems:1 twelfth:1 tried:2 decomposition:2 q1:3 pick:1 thereby:1 minus:1 recursively:4 configuration:1 series:1 score:10 selecting:1 united:1 karger:1 liu:1 document:1 fa8750:3 outperforms:2 si:8 suermondt:1 dechter:1 partition:11 greedy:8 leaf:15 sele... |
3,326 | 4,011 | Approximate Inference by Compilation to
Arithmetic Circuits
Daniel Lowd
Department of Computer and Information Science
University of Oregon
Eugene, OR 97403-1202
lowd@cs.uoregon.edu
Pedro Domingos
Department of Computer Science and Engineering
University of Washington
Seattle, WA 98195-2350
pedrod@cs.washington.edu
Ab... | 4011 |@word msr:1 polynomial:5 nd:1 adnan:1 heuristically:1 tried:2 bn:28 simplifying:1 wexler:2 tr:1 initial:1 liu:4 contains:2 score:1 selecting:5 configuration:3 daniel:1 tuned:1 document:1 united:1 fa8750:3 existing:1 conjunctive:1 must:2 written:1 dechter:2 numerical:2 partition:4 realistic:1 cpds:8 kdd:6 wanted:1... |
3,327 | 4,012 | Using body-anchored priors for identifying actions in
single images
Leonid Karlinsky
Michael Dinerstein
Shimon Ullman
Department of Computer Science
Weizmann Institute of Science
Rehovot 76100, Israel
{leonid.karlinsky, michael.dinerstein, shimon.ullman} @weizmann.ac.il
Abstract
This paper presents an approach to th... | 4012 |@word version:2 duda:1 nd:1 everingham:1 simplifying:1 dialing:4 thereby:1 cgc:1 reduction:1 configuration:7 contains:2 score:1 current:5 comparing:2 assigning:2 must:1 readily:1 fn:14 realistic:1 nian:1 remove:1 designed:1 treating:1 drop:2 discrimination:1 generative:4 yr:3 complementing:1 ith:3 smith:1 short:5... |
3,328 | 4,013 | Policy gradients in linearly-solvable MDPs
Emanuel Todorov
Applied Mathematics and Computer Science & Engineering
University of Washington
todorov@cs.washington.edu
Abstract
We present policy gradient results within the framework of linearly-solvable
MDPs. For the first time, compatible function approximators and natu... | 4013 |@word version:5 seems:1 nd:1 open:1 simulation:1 seek:3 covariance:1 incurs:1 kappen:1 exclusively:1 interestingly:1 suppressing:1 current:1 discretization:3 comparing:1 skipping:1 yet:2 must:1 numerical:1 additive:1 lqg:2 treating:1 stationary:13 pursued:1 fewer:1 intelligence:1 parameterization:8 dover:1 compli... |
3,329 | 4,014 | Agnostic Active Learning Without Constraints
Alina Beygelzimer
IBM Research
Hawthorne, NY
beygel@us.ibm.com
Daniel Hsu
Rutgers University &
University of Pennsylvania
djhsu@rci.rutgers.edu
John Langford
Yahoo! Research
New York, NY
jl@yahoo-inc.com
Tong Zhang
Rutgers University
Piscataway, NJ
tongz@rci.rutgers.edu
... | 4014 |@word mild:1 version:15 polynomial:1 achievable:1 c0:21 q1:4 pick:2 whittled:1 initial:2 selecting:1 daniel:1 past:1 existing:1 err:31 current:2 com:2 z2:1 beygelzimer:3 yet:2 readily:1 john:1 subsequent:1 atlas:1 ainen:1 v:2 selected:3 parameterization:2 xk:9 characterization:1 provides:1 node:2 coarse:1 allerto... |
3,330 | 4,015 | Multi-Stage Dantzig Selector
Ji Liu, Peter Wonka, Jieping Ye
Arizona State University
{ji.liu,peter.wonka,jieping.ye}@asu.edu
Abstract
We consider the following sparse signal recovery (or feature selection) problem:
given a design matrix X ? Rn?m (m ? n) and a noisy observation vector
y ? Rn satisfying y = X? ? + ? w... | 4015 |@word middle:1 norm:6 simulation:5 covariance:1 liu:2 contains:1 series:2 selecting:1 past:1 current:2 refines:1 numerical:4 cis:1 remove:1 update:1 greedy:10 asu:1 selected:3 ith:2 record:3 simpler:1 zhang:5 incorrect:1 consists:1 yuan:1 introduce:1 multi:30 decreasing:2 automatically:1 considering:1 increasing:... |
3,331 | 4,016 | Individualized ROI Optimization via
Maximization of Group -wise Consistency of
Structural and Functional Profiles
1, 2*
Kaiming Li, 1Lei Guo, 3Carlos Faraco, 2Dajiang Zhu, 2Fan Deng, 1Tuo Zhang, 1Xi
Jiang, 1Degang Zhang, 1Hanbo Chen, 1Xintao Hu, 3Steve Miller, 2Tianming Liu
1
School of Automation, Northwestern Polytec... | 4016 |@word version:1 norm:1 nd:1 open:2 hu:1 lobe:1 covariance:1 tr:2 initial:5 liu:2 contains:2 configuration:1 series:1 punishes:1 current:2 com:1 activation:11 gmail:1 yet:2 connectomics:3 evans:2 shape:2 atlas:7 designed:1 medial:1 drop:2 v:1 tarokh:1 selected:1 assurance:1 nervous:1 inspection:1 smith:1 precuneus... |
3,332 | 4,017 | New Adaptive Algorithms for Online Classification
Koby Crammer
Department of Electrical Enginering
The Technion
Haifa, 32000 Israel
koby@ee.technion.ac.il
Francesco Orabona
DSI
Universit`a degli Studi di Milano
Milano, 20135 Italy
orabona@dsi.unimi.it
Abstract
We propose a general framework to online learning for cl... | 4017 |@word middle:2 version:10 advantageous:1 seems:1 norm:10 dekel:1 simulation:1 minus:1 tr:2 moment:2 contains:1 series:1 tuned:1 prefix:1 existing:2 current:2 comparing:1 yet:1 kft:5 informative:3 hypothesize:1 designed:1 drop:2 update:17 plot:6 v:2 sponsored:1 half:1 beginning:1 provides:1 bmt:1 completeness:1 u2... |
3,333 | 4,018 | An Approximate Inference Approach to Temporal
Optimization in Optimal Control
Konrad C. Rawlik
School of Informatics
University of Edinburgh
Edinburgh, UK
Marc Toussaint
TU Berlin
Berlin, Germany
Sethu Vijayakumar
School of Informatics
University of Edinburgh
Edinburgh, UK
Abstract
Algorithms based on iterative loc... | 4018 |@word trial:1 middle:1 briefly:1 achievable:1 advantageous:1 proportion:2 nd:1 hu:2 d2:1 simulation:4 covariance:2 tr:8 solid:2 kappen:1 initial:2 series:2 initialisation:1 offering:1 interestingly:1 current:1 discretization:5 tackling:1 dx:3 john:1 realistic:1 additive:1 lqg:3 motor:2 treating:1 plot:1 v:2 stati... |
3,334 | 4,019 | Two-layer Generalization Analysis for Ranking Using
Rademacher Average
Wei Chen?
Chinese Academy of Sciences
chenwei@amss.ac.cn
Tie-Yan Liu
Microsoft Research Asia
tyliu@micorsoft.com
Zhiming Ma
Chinese Academy of Sciences
mazm@amt.ac.cn
Abstract
This paper is concerned with the generalization analysis on learning ... | 4019 |@word repository:1 version:1 proportion:1 seems:1 nd:1 d2:1 q1:5 twolayer:1 liu:7 mi0:2 document:116 existing:5 com:1 z2:4 comparing:1 si:11 kdd:1 listmle:1 remove:1 selected:1 fewer:1 renshaw:1 boosting:1 herbrich:2 preference:1 firstly:1 mcdiarmid:2 zhang:3 constructed:1 c2:2 become:2 prove:6 combine:1 introduc... |
3,335 | 402 | Learning Time-varying Concepts
Anthony Kuh
Dept. of Electrical Eng.
U. of Hawaii at Manoa
Honolulu, HI 96822
kuh@wiliki.eng.hawaii.edu
Thomas Petsche
Siemens Corp. Research
755 College Road East
Princeton, NJ 08540
petsche? learning. siemens.com
Ronald L. Rivest
Lab. for Computer Sci.
MIT
Cambridge, MA 02139
rivest@... | 402 |@word polynomial:1 open:1 seek:1 eng:2 pick:2 fonn:1 chervonenkis:4 existing:1 current:5 com:1 si:5 must:7 written:1 cruz:1 ronald:2 realize:1 benign:2 motor:1 remove:1 drop:1 update:2 selected:1 warmuth:4 provides:1 c2:18 direct:1 ucsc:1 symposium:1 focs:1 consists:1 expected:1 actual:1 window:3 cardinality:1 riv... |
3,336 | 4,020 | Over-complete representations on recurrent neural
networks can support persistent percepts
Dmitri B. Chklovskii
Janelia Farm Research Campus
Howard Hughes Medical Institute
Ashburn, VA 20147
mitya@janelia.hhmi.org
Shaul Druckmann
Janelia Farm Research Campus
Howard Hughes Medical Institute
Ashburn, VA 20147
druckmann... | 4020 |@word version:1 middle:4 hippocampus:1 norm:3 seems:1 d2:1 hu:2 simulation:1 decomposition:1 excited:1 garrigues:1 series:1 efficacy:1 offering:1 imaginary:3 current:1 scatter:3 must:2 connectomics:1 realistic:1 numerical:1 shape:1 enables:1 motor:1 plot:5 implying:1 generative:1 plane:4 persistency:1 completenes... |
3,337 | 4,021 | Generalized roof duality and bisubmodular functions
Vladimir Kolmogorov
Department of Computer Science
University College London, UK
v.kolmogorov@cs.ucl.ac.uk
Abstract
Consider a convex relaxation f? of a pseudo-boolean function f . We say that
the relaxation is totally half-integral if f?(x) is a polyhedral function... | 4021 |@word bisubmodularity:4 version:1 middle:1 polynomial:4 trotter:2 mri:1 open:1 reduction:1 existing:1 written:2 v:1 half:29 selected:2 greedy:2 accordingly:2 halfintegral:2 woodford:2 persistency:10 completeness:1 characterization:13 node:17 math:7 mathematical:3 constructed:3 focs:1 prove:8 ijcv:1 polyhedral:3 i... |
3,338 | 4,022 | Latent Variable Models for Predicting File
Dependencies in Large-Scale Software Development
Diane J. Hu1 , Laurens van der Maaten1,2 , Youngmin Cho1 , Lawrence K. Saul1 , Sorin Lerner1
1
Dept. of Computer Science & Engineering, University of California, San Diego
2
Pattern Recognition & Bioinformatics Lab, Delft Unive... | 4022 |@word msr:1 version:4 repository:1 proportion:1 seems:1 nd:4 open:3 tried:1 decomposition:2 contrastive:3 reduction:2 configuration:2 tuned:1 document:2 prefix:1 past:3 existing:1 current:2 rish:1 assigning:1 chu:1 must:5 written:1 remove:1 designed:1 interpretable:1 update:4 stationary:1 generative:1 fewer:1 sel... |
3,339 | 4,023 | Approximate inference in continuous time
Gaussian-Jump processes
Andreas Ruttor
Fakult?at Elektrotechnik und Informatik
Technische Universit?at Berlin
Berlin, Germany
andreas.ruttor@tu-berlin.de
Manfred Opper
Fakult?at Elektrotechnik und Informatik
Technische Universit?at Berlin
Berlin, Germany
opperm@cs.tu-berlin.de... | 4023 |@word briefly:1 version:1 replicate:1 twelfth:1 heuristically:1 egp:2 simulation:4 covariance:5 q1:7 solid:2 harder:1 initial:2 contains:2 series:1 genetic:1 reaction:1 activation:1 dx:2 written:1 must:2 john:1 realistic:1 numerical:3 visible:1 informative:1 christian:1 remove:1 update:2 stationary:1 half:2 prohi... |
3,340 | 4,024 | Variable margin losses for classifier design
Nuno Vasconcelos
Statistical Visual Computing Laboratory,
University of California, San Diego
La Jolla, CA 92039
nuno@ucsd.edu
Hamed Masnadi-Shirazi
Statistical Visual Computing Laboratory,
University of California, San Diego
La Jolla, CA 92039
hmasnadi@ucsd.edu
Abstract
... | 4024 |@word trial:2 briefly:1 prognostic:1 logit:1 c0:2 e2v:2 existing:5 savage:1 surprising:1 yet:1 written:1 john:1 additive:2 shape:2 enables:4 designed:3 reproducible:1 plot:1 greedy:1 inspection:1 characterization:4 boosting:35 provides:1 sigmoidal:11 zhang:1 five:3 along:2 indeed:1 expected:4 behavior:5 themselve... |
3,341 | 4,025 | Throttling Poisson Processes
?
Thomas Vanck
Michael Bruckner
Tobias Scheffer
University of Potsdam
Department of Computer Science
August-Bebel-Strasse 89, 14482 Potsdam, Germany
{uwedick,haider,vanck,mibrueck,scheffer}@cs.uni-potsdam.de
Uwe Dick
Peter Haider
Abstract
We study a setting in which Poisson processes ge... | 4025 |@word norm:1 nd:1 d2:1 crucially:1 initial:2 series:1 score:1 tuned:1 suppressing:5 past:2 outperforms:1 current:7 si:9 assigning:2 must:1 willinger:1 subsequent:1 drop:1 plot:5 stationary:2 selected:1 inspection:1 provides:1 attack:1 rollout:1 become:1 consists:2 ressources:1 deteriorate:1 expected:13 inspired:1... |
3,342 | 4,026 | Random Conic Pursuit for Semidefinite Programming
Ariel Kleiner
Computer Science Division
Univerisity of California
Berkeley, CA 94720
Ali Rahimi
Intel Research Berkeley
Berkeley, CA 94720
ali.rahimi@intel.com
Michael I. Jordan
Computer Science Division
University of California
Berkeley, CA 94720
jordan@cs.berkeley.... | 4026 |@word multitask:1 trial:2 version:1 polynomial:1 advantageous:1 seems:1 seek:1 gish:1 covariance:3 pg:5 thereby:1 tr:9 accommodate:1 shot:1 reduction:1 initial:2 configuration:1 contains:2 efficacy:1 dspca:9 series:1 tuned:1 past:1 existing:1 current:7 com:2 must:1 readily:5 parsing:1 realize:1 fn:1 periodically:... |
3,343 | 4,027 | Label Embedding Trees for Large Multi-Class Tasks
Samy Bengio(1)
Jason Weston(1)
David Grangier(2)
(1)
Google Research, New York, NY
{bengio, jweston}@google.com
(2)
NEC Labs America, Princeton, NJ
{dgrangier}@nec-labs.com
Abstract
Multi-class classification becomes challenging at test time when the number of
cla... | 4027 |@word norm:3 disk:1 dekel:2 jacob:1 mention:1 recursively:1 bai:1 contains:1 score:3 document:11 ours:1 outperforms:2 existing:5 current:1 com:2 beygelzimer:3 must:1 dde:1 bd:1 fn:1 partition:3 remove:1 v:15 implying:1 half:2 prohibitive:1 leaf:6 intelligence:6 record:1 provides:1 node:32 traverse:4 hyperplanes:1... |
3,344 | 4,028 | Efficient Minimization of
Decomposable Submodular Functions
Andreas Krause
California Institute of Technology
Pasadena, CA 91125
krausea@caltech.edu
Peter Stobbe
California Institute of Technology
Pasadena, CA 91125
stobbe@caltech.edu
Abstract
Many combinatorial problems arising in machine learning can be reduced to... | 4028 |@word kohli:2 middle:1 polynomial:6 norm:3 everingham:1 textonboost:7 functions2:1 it1:1 reduction:1 configuration:1 contains:2 score:2 nesta:1 outperforms:2 current:1 activation:1 yet:1 written:3 must:2 additive:1 shape:1 cheap:1 drop:1 designed:1 v:1 greedy:1 half:1 v2r:1 xk:1 core:1 certificate:3 characterizat... |
3,345 | 4,029 | Effects of Synaptic Weight Diffusion on Learning in
Decision Making Networks
Kentaro Katahira1,2,3 , Kazuo Okanoya1,3 and Masato Okada1,2,3
ERATO Okanoya Emotional Information Project, Japan Science Technology Agency
2
Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan
3
RIKE... | 4029 |@word neurophysiology:1 trial:5 version:2 norm:3 seems:1 simulation:8 covariance:13 moment:1 initial:3 efficacy:3 selecting:1 wako:1 past:2 subjective:1 current:1 realize:1 realistic:1 numerical:1 plasticity:5 plot:7 update:7 cue:1 beginning:1 realism:1 short:2 mathematical:1 differential:2 qualitative:1 consists... |
3,346 | 403 | From Speech Recognition to Spoken Language
Understanding: The Development of the MIT
SUMMIT and VOYAGER Systems
Victor Zue, James Glass, David Goodine, Lynette Hirschman,
Hong Leung, Michael Phillips, Joseph Polifroni, and Stephanie Seneff'
Room NE43-601
Spoken Language Systems Group
Laboratory for Computer Science
Ma... | 403 |@word version:3 briefly:2 bigram:1 seek:1 initial:1 configuration:1 contains:1 past:1 current:7 protection:1 yet:1 must:5 realistic:3 matured:1 enables:1 designed:1 selected:1 scotland:1 short:1 provides:1 node:1 lexicon:1 location:6 clarified:1 direct:1 driver:1 incorrect:2 consists:1 inside:1 acquired:1 classifi... |
3,347 | 4,030 | Layered Image Motion with Explicit Occlusions,
Temporal Consistency, and Depth Ordering
Deqing Sun, Erik B. Sudderth, and Michael J. Black
Department of Computer Science, Brown University
{dqsun,sudderth,black}@cs.brown.edu
Abstract
Layered models are a powerful way of describing natural scenes containing
smooth surf... | 4030 |@word kohli:1 wmf:7 version:2 middle:1 decomposition:1 brightness:1 thereby:1 tr:1 initial:4 configuration:1 series:2 selecting:1 existing:1 current:3 assigning:1 must:2 slanted:1 written:2 subsequent:3 realistic:2 partition:3 visible:3 shape:2 enables:2 occludes:1 visibility:1 treating:1 additive:1 occlude:1 gen... |
3,348 | 4,031 | Monte-Carlo Planning in Large POMDPs
Joel Veness
UNSW, Sydney, Australia
jveness@gmail.com
David Silver
MIT, Cambridge, MA 02139
davidstarsilver@gmail.com
Abstract
This paper introduces a Monte-Carlo algorithm for online planning in large
POMDPs. The algorithm combines a Monte-Carlo update of the agent?s
belief stat... | 4031 |@word exploitation:1 middle:1 version:2 achievable:1 simulation:45 r:1 tried:1 initial:4 configuration:1 contains:2 selecting:3 interestingly:1 o2:6 existing:2 ninit:7 current:9 com:2 gmail:2 must:2 lorentz:1 update:15 generative:1 selected:10 greedy:2 intelligence:5 beginning:1 ith:1 smith:1 provides:5 node:15 l... |
3,349 | 4,032 | Tight Sample Complexity of Large-Margin Learning
1
Sivan Sabato1 Nathan Srebro2 Naftali Tishby1
School of Computer Science & Engineering, The Hebrew University, Jerusalem 91904, Israel
2
Toyota Technological Institute at Chicago, Chicago, IL 60637, USA
{sivan sabato,tishby}@cs.huji.ac.il, nati@ttic.edu
Abstract
We o... | 4032 |@word norm:17 covariance:9 tr:1 moment:10 bai:1 series:1 chervonenkis:1 ecole:1 scovel:1 dx:22 must:2 refines:1 chicago:2 analytic:1 v:1 alone:1 generative:4 kyk:1 provides:2 characterization:7 math:1 hyperplanes:1 buldygin:1 zhang:1 unbounded:1 mathematical:1 bd1:4 prove:1 fitting:1 introduce:1 indeed:2 expected... |
3,350 | 4,033 | Boosting Classifier Cascades
Nuno Vasconcelos
Statistical Visual Computing Laboratory,
University of California, San Diego
La Jolla, CA 92039
nuno@ucsd.edu
Mohammad J. Saberian
Statistical Visual Computing Laboratory,
University of California, San Diego
La Jolla, CA 92039
saberian@ucsd.edu
Abstract
The problem of op... | 4033 |@word briefly:1 trialand:1 seek:1 recursively:2 carry:1 initial:6 configuration:6 contains:2 series:1 liu:2 shum:1 denoting:1 rightmost:1 current:2 luo:1 must:4 hou:1 additive:1 designed:3 plot:3 update:4 v:1 half:1 selected:2 intelligence:3 boosting:29 contribute:1 location:1 sochman:1 sigmoidal:1 zhang:2 mathem... |
3,351 | 4,034 | Multiparty Differential Privacy via Aggregation of
Locally Trained Classifiers
Manas A. Pathak
Carnegie Mellon University
Pittsburgh, PA
manasp@cs.cmu.edu
Shantanu Rane
Mitsubishi Electric Research Labs
Cambridge, MA
rane@merl.com
Bhiksha Raj
Carnegie Mellon University
Pittsburgh, PA
bhiksha@cs.cmu.edu
Abstract
As ... | 4034 |@word repository:4 version:1 private:25 norm:2 d2:5 willing:1 additively:3 mitsubishi:1 seek:1 vldb:1 incurs:1 thereby:1 carry:1 contains:1 ours:1 com:1 yet:2 must:2 john:1 realistic:1 additive:16 remove:2 designed:2 drop:1 v:1 implying:3 discovering:1 beginning:1 smith:4 record:2 provides:6 characterization:1 co... |
3,352 | 4,035 | Semi-Supervised Learning with Adversarially
Missing Label Information
Umar Syed
Ben Taskar
Department of Computer and Information Science
University of Pennsylvania
Philadelphia, PA 19104
{usyed,taskar}@cis.upenn.edu
Abstract
We address the problem of semi-supervised learning in an adversarial setting. Instead of ass... | 4035 |@word trial:1 briefly:1 version:2 seems:1 norm:1 thereby:1 boundedness:1 moment:1 contains:2 score:2 selecting:1 tuned:1 yet:1 dx:8 realistic:3 happen:1 informative:1 benign:1 christian:1 designed:3 plot:1 update:1 v:3 intelligence:1 selected:4 website:1 guess:2 amir:1 accordingly:1 warmuth:1 mccallum:1 ith:3 man... |
3,353 | 4,036 | Variational Inference over Combinatorial Spaces
?
Alexandre Bouchard-C?ot?e?
Michael I. Jordan?,?
?
Computer Science Division Department of Statistics
University of California at Berkeley
Abstract
Since the discovery of sophisticated fully polynomial randomized algorithms for a range of
#P problems [1, 2, 3], theore... | 4036 |@word illustrating:1 polynomial:5 logit:17 pseudomoment:1 km:2 calculus:1 simulation:1 serafim:1 decomposition:6 eld:1 mention:1 moment:4 substitution:1 score:3 mi0:3 daniel:1 outperforms:2 current:1 written:1 parsing:4 reminiscent:1 must:2 john:2 partition:29 enables:2 afield:1 plot:1 siepel:1 update:31 n0:4 bar... |
3,354 | 4,037 | Improving the Asymptotic Performance of Markov
Chain Monte-Carlo by Inserting Vortices
Faustino Gomez
IDSIA
Galleria 2, Manno CH-6928, Switzerland
tino@idsia.ch
Yi Sun
IDSIA
Galleria 2, Manno CH-6928, Switzerland
yi@idsia.ch
?
Jurgen
Schmidhuber
IDSIA
Galleria 2, Manno CH-6928, Switzerland
juergen@idsia.ch
Abstract... | 4037 |@word h:4 confirms:2 simulation:1 r:2 covariance:1 reduction:1 necessity:2 liu:1 contains:2 initial:1 selecting:1 freitas:1 current:4 comparing:4 yet:1 written:5 must:7 j1:3 plot:1 progressively:1 stationary:12 half:1 fewer:1 leaf:2 item:1 provides:1 node:3 toronto:2 firstly:1 along:2 constructed:4 prove:2 hermit... |
3,355 | 4,038 | Adaptive Multi-Task Lasso: with Application to
eQTL Detection
Seunghak Lee, Jun Zhu and Eric P. Xing
School of Computer Science, Carnegie Mellon University
{seunghak,junzhu,epxing}@cs.cmu.edu
Abstract
To understand the relationship between genomic variations among population and
complex diseases, it is essential to d... | 4038 |@word multitask:1 snorna:3 version:1 briefly:1 norm:8 stronger:1 open:1 simulation:4 gradual:1 covariance:1 pick:1 contains:2 score:11 series:1 united:1 genetic:5 interestingly:1 outperforms:2 existing:1 current:1 comparing:2 surprising:1 mahoudeaux:1 treating:1 interpretable:1 update:3 plot:2 v:1 half:1 selected... |
3,356 | 4,039 | Random Projection Trees Revisited
Aman Dhesi?
Department of Computer Science
Princeton University
Princeton, New Jersey, USA.
adhesi@princeton.edu
Purushottam Kar
Department of Computer Science and Engineering
Indian Institute of Technology
Kanpur, Uttar Pradesh, INDIA.
purushot@cse.iitk.ac.in
Abstract
The Random Pr... | 4039 |@word version:2 norm:1 seems:1 nd:1 open:1 covariance:13 pick:2 thereby:1 reduction:12 contains:10 document:1 animated:1 existing:1 assigning:1 must:2 john:2 christian:1 generative:1 plane:4 provides:1 revisited:1 cse:1 node:4 contribute:1 c6:2 zhang:1 purushot:1 mathematical:1 c2:2 become:2 symposium:2 descendan... |
3,357 | 404 | Design and Implementation of a High Speed
CMAC Neural Network Using Programmable
CMOS Logic Cell Arrays
W. Thomas Miller, III, Brian A. Box, and Erich C. Whitney
Department of Electrical and Computer Engineering
Kingsbury Hall
University of New Hampshire
Durham, New Hampshire 03824
James M. Glynn
Shenandoah Systems Co... | 404 |@word implemented:2 version:2 hypercube:1 nd:1 laboratory:1 receptive:12 primary:1 traditional:1 excited:4 adjacent:1 during:5 width:1 virtual:4 september:1 accommodate:1 recursively:1 card:3 xilinx:2 maryland:1 series:3 generalization:2 unh:1 asme:1 brian:1 adjusted:2 extension:1 performs:2 dedicated:1 motion:1 s... |
3,358 | 4,040 | Generative Local Metric Learning for
Nearest Neighbor Classification
Yung-Kyun Noh1,2
Byoung-Tak Zhang2
Daniel D. Lee1
GRASP Lab, University of Pennsylvania, Philadelphia, PA 19104, USA
2
Biointelligence Lab, Seoul National University, Seoul 151-742, Korea
1
nohyung@seas.upenn.edu, btzhang@snu.ac.kr, ddlee@seas.upen... | 4040 |@word kulis:1 determinant:1 briefly:1 duda:1 nd:1 d2:2 decomposition:1 covariance:5 q1:1 tr:11 reduction:15 contains:2 nohyung:1 daniel:1 bhattacharyya:2 outperforms:2 comparing:2 ida:1 com:1 goldberger:1 dx:11 john:1 cruz:1 v:2 generative:37 intelligence:4 website:1 isotropic:1 mccallum:1 hypersphere:2 provides:... |
3,359 | 4,041 | Learning from Candidate Labeling Sets
Francesco Orabona
DSI, Universit`a degli Studi di Milano
orabona@dsi.unimi.it
Luo Jie
Idiap Research Institute and EPF Lausanne
jluo@idiap.ch
Abstract
In many real world applications we do not have access to fully-labeled training
data, but only to a list of possible labels. Thi... | 4041 |@word version:1 norm:1 underline:1 liblinear:2 initial:1 contains:1 outperforms:2 existing:2 freitas:1 current:1 z2:1 luo:1 assigning:2 must:1 visible:1 additive:1 hofmann:3 cheap:1 designed:1 treating:2 plot:1 drop:1 v:2 sponsored:2 intelligence:1 selected:1 website:2 xk:4 blei:1 provides:2 zhang:2 direct:1 beco... |
3,360 | 4,042 | Universal Consistency of Multi-Class
Support Vector Classification
Tobias Glasmachers
Dalle Molle Institute for Artificial Intelligence (IDSIA), 6928 Manno-Lugano, Switzerland
tobias@idsia.ch
Abstract
Steinwart was the first to prove universal consistency of support vector machine
classification. His proof analyzed t... | 4042 |@word stronger:1 suitably:1 open:2 liu:1 contains:1 series:1 dx:3 written:1 fn:2 partition:13 drop:1 intelligence:2 half:1 nq:3 p7:2 short:1 provides:1 contribute:1 simpler:1 direct:1 symposium:1 prove:3 combine:4 introduce:2 pairwise:1 x0:2 p8:2 indeed:1 p1:2 xz:3 multi:22 cardinality:3 notation:2 moreover:2 bou... |
3,361 | 4,043 | Learning To Count Objects in Images
Andrew Zisserman
Visual Geometry Group
University of Oxford
Victor Lempitsky
Visual Geometry Group
University of Oxford
Abstract
We propose a new supervised learning framework for visual object counting tasks, such
as estimating the number of cells in a microscopic image or the nu... | 4043 |@word kohli:1 kong:1 middle:4 smirnov:4 nd:1 everingham:1 triggs:1 open:1 tried:1 covariance:1 pick:1 versatile:1 harder:1 reduction:1 liu:1 series:1 fragment:1 tuned:1 outperforms:3 current:1 comparing:1 com:1 assigning:1 gpu:1 additive:1 realistic:1 blur:1 shape:2 discernible:1 plot:1 designed:1 alone:1 generat... |
3,362 | 4,044 | Subgraph Detection Using Eigenvector L1 Norms
Nadya T. Bliss
Lincoln Laboratory
Massachusetts Institute of Technology
Lexington, MA 02420
nt@ll.mit.edu
Benjamin A. Miller
Lincoln Laboratory
Massachusetts Institute of Technology
Lexington, MA 02420
bamiller@ll.mit.edu
Patrick J. Wolfe
Statistics and Information Scien... | 4044 |@word trial:1 version:1 briefly:1 compression:1 norm:27 stronger:1 proportion:1 d2:1 confirms:1 simulation:6 decomposition:1 minus:1 efficacy:2 selecting:1 united:1 past:1 comparing:3 nt:1 od:1 lang:1 visible:1 kdd:3 plot:2 drop:1 sponsored:1 v:18 implying:1 selected:2 cook:2 inspection:1 plane:1 ith:1 filtered:1... |
3,363 | 4,045 | Robust Clustering as Ensembles of Affinity Relations
1
Hairong Liu1 , Longin Jan Latecki2 , Shuicheng Yan1
Department of Electrical and Computer Engineering, National University of Singapore, Singapore
2
Department of Computer and Information Sciences, Temple University, Philadelphia, USA
lhrbss@gmail.com,latecki@tem... | 4045 |@word deformed:1 trial:2 determinant:1 version:2 kulis:1 stronger:1 norm:2 seems:1 d2:1 shuicheng:1 zelnik:1 tried:1 solid:3 xv1:1 configuration:1 contains:3 seriously:1 outperforms:2 existing:2 com:1 si:2 gmail:1 must:6 partition:5 informative:1 noninformative:2 shape:7 drop:2 update:6 intelligence:4 selected:2 ... |
3,364 | 4,046 | An analysis on negative curvature induced by
singularity in multi-layer neural-network learning
Eiji Mizutani
Department of Industrial Management
Taiwan Univ. of Science & Technology
eiji@mail.ntust.edu.tw
Stuart Dreyfus
Industrial Engineering & Operations Research
University of California, Berkeley
dreyfus@ieor.berk... | 4046 |@word middle:1 version:1 norm:2 d2:6 confirms:1 simulation:2 jacob:1 p0:5 attainable:1 pick:1 thereby:1 solid:7 initial:5 configuration:3 ev1:2 hereafter:1 existing:1 yet:2 must:2 readily:2 numerical:6 distant:1 shape:1 plot:2 update:2 stationary:14 alone:1 intelligence:1 slowing:1 plane:1 steepest:4 realizing:1 ... |
3,365 | 4,047 | Feature Set Embedding for Incomplete Data
Iain Melvin
NEC Labs America
Princeton, NJ
iain@nec-labs.com
David Grangier
NEC Labs America
Princeton, NJ
dgrangier@nec-labs.com
Abstract
We present a new learning strategy for classification problems in which train and/or
test data suffer from missing features. In previous... | 4047 |@word repository:1 kondor:1 polynomial:2 advantageous:1 seems:2 dekel:3 minus:1 versatile:1 accommodate:1 initial:2 contains:1 selecting:4 batista:1 document:2 bhattacharyya:1 com:2 surprising:1 assigning:2 confirming:1 kdd:1 enables:2 shape:1 designed:1 plot:1 v:2 cue:1 generative:5 intelligence:1 parameterizati... |
3,366 | 4,048 | Online Markov Decision Processes under Bandit
Feedback
Gergely Neu??
Andr?as Gy?orgy
?
?
Department of Computer Science and
Information Theory, Budapest University of
Technology and Economics, Hungary
neu.gergely@gmail.com
Machine Learning Research Group
MTA SZTAKI Institute for Computer
Science and Control, Hunga... | 4048 |@word version:2 seems:1 norm:1 tedious:1 hu:2 decomposition:1 p0:3 initial:1 selecting:1 ours:1 past:1 current:5 com:1 gmail:1 must:2 subsequent:1 fund:1 stationary:15 short:2 provides:2 mannor:4 prove:4 interscience:1 introduce:1 excellence:1 x0:13 sublinearly:1 expected:14 ingenuity:1 bellman:4 alberta:2 become... |
3,367 | 4,049 | Learning invariant features using
the Transformed Indian Buffet Process
Thomas L. Griffiths
Department of Psychology
University of California, Berkeley
Berkeley, CA 94720
Tom Griffiths@berkeley.edu
Joseph L. Austerweil
Department of Psychology
University of California, Berkeley
Berkeley, CA 94720
Joseph.Austerweil@gma... | 4049 |@word judgement:1 seems:1 nd:1 open:1 d2:3 confirms:1 pick:2 fifteen:1 contains:1 tabulate:1 current:2 com:1 surprising:1 activation:2 gmail:1 yet:2 chicago:1 realistic:1 shape:11 hoping:1 generative:4 intelligence:2 record:1 provides:2 location:12 daphne:1 five:1 unbounded:1 along:1 consists:1 behavioral:5 intro... |
3,368 | 405 | SEXNET: A NEURAL NETWORK
IDENTIFIES SEX FROM HUMAN FACES
B.A. Golomb, D.T. Lawrence, and T.J. Sejnowski
The Salk Institute
10010 N. Torrey Pines Rd.
La Jolla, CA 92037
Abstract
Sex identification in animals has biological importance. Humans are good
at making this determination visually, but machines have not matched... | 405 |@word build:1 sex:8 r:1 human:10 traditional:1 ambiguous:1 unable:1 override:1 biological:1 image:1 visually:1 lawrence:1 must:1 visible:1 pine:1 physical:1 favorably:1 cue:3 ai:1 rd:1 makeup:2 etc:1 female:2 jolla:1 certain:2 indeed:1 pattern:2 hidden:1 recognized:1 matched:1 golomb:1 hairstyle:1 animal:2 determi... |
3,369 | 4,050 | Accounting for network effects in neuronal responses
using L1 regularized point process models
Ryan C. Kelly?
Computer Science Department
Center for the Neural Basis of Cognition
Carnegie Mellon University
Pittsburgh, PA 15213
rkelly@cs.cmu.edu
Matthew A. Smith
University of Pittsburgh
Center for the Neural Basis of ... | 4050 |@word trial:14 briefly:1 version:1 hyperpolarized:1 mee:1 integrative:1 linearized:1 accounting:2 eng:1 dramatic:1 carry:1 series:2 contains:1 fragment:1 past:2 ka:3 current:3 emory:1 michal:1 scatter:1 written:1 john:2 distant:1 plasticity:1 shape:1 motor:1 plot:2 discrimination:1 alone:3 device:1 greschner:1 si... |
3,370 | 4,051 | Inferring Stimulus Selectivity from the Spatial
Structure of Neural Network Dynamics
Kanaka Rajan
Lewis-Sigler Institute for Integrative Genomics
Carl Icahn Laboratories # 262, Princeton University
Princeton NJ 08544 USA
krajan@princeton.edu
L. F. Abbott
Department of Neuroscience
Department of Physiology and Cellular... | 4051 |@word h:1 wiesel:1 stronger:1 integrative:1 d2:3 simulation:2 recursively:1 suppressing:1 comparing:3 activation:2 must:1 pioneer:1 physiol:1 additive:2 subsequent:2 shape:2 plot:1 drop:1 progressively:2 designed:1 plane:1 ith:1 dissertation:1 provides:2 math:1 location:1 traverse:1 along:2 borg:1 director:1 cons... |
3,371 | 4,052 | A Computational Decision Theory
for Interactive Assistants
Prasad Tadepalli
School of EECS
Oregon State University
Corvallis, OR 97331
tadepall@eecs.oregonstate.edu
Alan Fern
School of EECS
Oregon State University
Corvallis, OR 97331
afern@eecs.oregonstate.edu
Abstract
We study several classes of interactive assistan... | 4052 |@word h:2 version:2 polynomial:5 tadepalli:2 suitably:1 open:5 prasad:1 asks:1 reduction:2 initial:8 contains:2 daniel:1 omniscient:6 prefix:1 current:5 si:3 yet:1 must:1 subsequent:1 mundhenk:1 unchanging:1 half:1 selected:3 cook:1 leaf:5 fewer:1 intelligence:1 desktop:4 beginning:2 short:3 provides:2 certificat... |
3,372 | 4,053 | Probabilistic Belief Revision with Structural
Constraints
Peter B. Jones
MIT Lincoln Laboratory
Lexington, MA 02420
jonep@ll.mit.edu
Venkatesh Saligrama
Dept. of ECE
Boston University
Boston, MA 02215
srv@bu.edu
Sanjoy K. Mitter
Dept. of EECS
MIT
Cambridge, MA 02139
mitter@mit.edu
Abstract
Experts (human or computer... | 4053 |@word briefly:1 stronger:1 nd:2 simulation:1 simplifying:1 p0:7 recursively:1 initial:3 substitution:1 contains:1 united:1 daniel:1 denoting:1 subjective:3 z2:1 comparing:1 must:3 additive:1 partition:6 happen:1 informative:1 treating:1 sponsored:1 update:4 joy:1 intelligence:1 ith:1 accepting:1 revisited:1 attac... |
3,373 | 4,054 | Energy Disaggregation via Discriminative
Sparse Coding
J. Zico Kolter
Computer Science and
Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139
kolter@csail.mit.edu
Siddarth Batra, Andrew Y. Ng
Computer Science Department
Stanford University
Stanford, CA 94305
{sidbatra,ang}@cs... | 4054 |@word cox:1 version:1 briefly:1 achievable:1 norm:4 seems:1 adrian:1 seek:1 crucially:1 mention:1 reduction:1 electronics:3 bai:1 contains:4 series:1 united:1 reynolds:1 past:1 existing:1 err:2 current:2 disaggregation:60 outperforms:1 bradley:1 luo:1 activation:16 readily:2 concatenate:1 informative:1 shape:1 pl... |
3,374 | 4,055 | Learning Networks of
Stochastic Differential Equations
Morteza Ibrahimi
Department of Electrical Engineering
Stanford University
Stanford, CA 94305
ibrahimi@stanford.edu
Jos?e Bento
Department of Electrical Engineering
Stanford University
Stanford, CA 94305
jbento@stanford.edu
Andrea Montanari
Department of Electric... | 4055 |@word mild:1 kgk:1 version:2 polynomial:3 norm:3 stronger:1 confirms:1 simulation:2 bn:6 covariance:6 initial:2 configuration:5 contains:1 series:3 selecting:1 reaction:5 existing:1 current:1 recovered:3 dx:1 portuguese:1 numerical:3 additive:1 subsequent:1 enables:2 plot:3 n0:4 v:6 stationary:8 record:1 provides... |
3,375 | 4,056 | Phoneme Recognition with Large Hierarchical
Reservoirs
Fabian Triefenbach
Azarakhsh Jalalvand
Benjamin Schrauwen
Jean-Pierre Martens
Department of Electronics and Information Systems
Ghent University
Sint-Pietersnieuwstraat 41, 9000 Gent, Belgium
fabian.triefenbach@elis.ugent.be
Abstract
Automatic speech recognitio... | 4056 |@word inversion:1 bigram:4 norm:3 seems:1 bptt:1 open:1 closure:4 tried:1 contrastive:1 attainable:1 mention:1 harder:2 reduction:1 electronics:1 substitution:1 series:1 contains:1 configuration:1 liquid:1 denoting:1 renewed:1 tuned:1 past:1 si:1 activation:5 must:3 subsequent:4 happen:1 designed:1 drop:1 plot:2 ... |
3,376 | 4,057 | Infinite Relational Modeling of Functional
Connectivity in Resting State fMRI
Morten M?rup
Section for Cognitive Systems
DTU Informatics
Technical University of Denmark
mm@imm.dtu.dk
Kristoffer Hougaard Madsen
Danish Research Centre for Magnetic Resonance
Copenhagen University Hospital Hvidovre
khm@drcmr.dk
Anne Mar... | 4057 |@word version:1 mri:9 stronger:2 nd:1 r:3 pulse:1 pearlson:1 fifteen:1 thereby:1 series:2 score:7 united:2 current:2 comparing:1 rish:1 anne:1 activation:2 intriguing:1 readily:1 oxygenation:2 motor:8 remove:1 reproducible:2 treating:1 discrimination:2 v:2 generative:1 selected:5 martinot:2 nervous:1 intelligence... |
3,377 | 4,058 | A Bayesian Framework for Figure-Ground
Interpretation
Vicky Froyen
Center for Cognitive Science
Rutgers University, Piscataway, NJ 08854
Laboratory of Experimental Psychology
University of Leuven (K.U. Leuven), Belgium
vicky.froyen@eden.rutgers.edu
?
Jacob Feldman
Center for Cognitive Science
Rutgers University, Pisc... | 4058 |@word neurophysiology:2 trial:1 briefly:3 stronger:1 seems:1 proportion:1 c0:3 simulation:2 propagate:4 jacob:2 simplifying:1 vicky:2 reduction:1 initial:1 configuration:4 subjective:1 past:1 current:2 surprising:1 yet:2 distant:1 shape:36 praeger:1 strecha:1 medial:5 depict:1 cue:19 fewer:1 farther:2 coarse:1 no... |
3,378 | 4,059 | Functional Geometry Alignment
and Localization of Brain Areas
Georg Langs, Polina Golland
Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology
Cambridge, MA 02139, USA
langs@csail.mit.edu, polina@csail.mit.edu
Yanmei Tie, Laura Rigolo, Alexandra J. Golby
Department of Neurosurgery, Br... | 4059 |@word mri:1 norm:1 grey:1 seek:1 perpin:1 lobe:2 decomposition:2 commute:1 tr:1 initial:2 substitution:1 contains:1 configuration:1 selecting:1 series:1 daniel:1 outperforms:1 activation:16 ronald:1 distant:1 subsequent:1 informative:1 shape:5 enables:1 haxby:2 plot:2 drop:2 atlas:1 n0:1 v:6 intelligence:2 greedy... |
3,379 | 406 | Distributed Recursive Structure Processing
Geraldine Legendre
Yoshiro Miyata
Department of
Optoelectronic
Linguistics
Computing Systems Center
University of Colorado
Boulder, CO 80309-0430?
Paul Smolensky
Department of
Computer Science
Abstract
Harmonic grammar (Legendre, et al., 1990) is a connectionist theory of l... | 406 |@word tensorial:1 calculus:1 simulation:2 r:1 decomposition:9 contraction:1 rol:1 recursively:2 contains:2 existing:1 current:1 contextual:1 si:3 assigning:1 activation:1 written:3 numerical:1 cheap:1 v:1 pylyshyn:2 intelligence:1 fewer:1 node:4 location:3 complication:1 accessed:1 constructed:3 direct:1 attested:... |
3,380 | 4,060 | Regularized estimation of image statistics
by Score Matching
Diederik P. Kingma
Department of Information and Computing Sciences
Universiteit Utrecht
d.p.kingma@students.uu.nl
Yann LeCun
Courant Institute of Mathematical Sciences
New York University
yann@cs.nyu.edu
Abstract
Score Matching is a recently-proposed crite... | 4060 |@word middle:1 version:5 proportionality:1 hyv:8 contrastive:4 initial:1 score:21 pub:1 tuned:1 document:1 suppressing:1 activation:4 diederik:1 assigning:1 dx:2 readily:1 must:1 subsequent:4 enables:1 remove:2 update:1 intelligence:2 steepest:1 vanishing:1 quantized:3 node:11 mathematical:1 become:1 qualitative:... |
3,381 | 4,061 | Layer-wise analysis of deep networks with Gaussian
kernels
Gr?egoire Montavon
Machine Learning Group
TU Berlin
Mikio L. Braun
Machine Learning Group
TU Berlin
?
Klaus-Robert Muller
Machine Learning Group
TU Berlin
gmontavon@cs.tu-berlin.de
mikio@cs.tu-berlin.de
krm@cs.tu-berlin.de
Abstract
Deep networks can poten... | 4061 |@word multitask:1 cnn:19 middle:1 wiesel:2 seems:1 propagate:1 solid:2 reduction:2 initial:1 selecting:1 offering:1 document:1 ala:1 outperforms:1 transferability:1 must:3 subsequent:3 ronan:2 distant:1 partition:1 informative:1 plot:1 progressively:2 discrimination:4 greedy:2 selected:1 node:3 successive:1 consi... |
3,382 | 4,062 | Spectral Regularization for Support Estimation
Ernesto De Vito
DSA, Univ. di Genova, and
INFN, Sezione di Genova, Italy
Lorenzo Rosasco
CBCL - MIT, - USA, and
IIT, Italy
devito@dima.ungie.it
lrosasco@mit.edu
Alessandro Toigo
Politec. di Milano, Dept. of Math., and
INFN, Sezione di Milano, Italy
toigo@ge.infn.it
A... | 4062 |@word mild:2 trial:2 version:2 briefly:1 norm:2 open:1 closure:1 calculus:1 decomposition:2 mention:2 reduction:3 initial:1 rkhs:10 interestingly:2 recovered:1 scovel:1 dx:10 fn:17 numerical:1 analytic:1 drop:1 v:6 parameterization:1 short:2 provides:3 math:2 complication:1 characterization:3 boosting:1 hyperplan... |
3,383 | 4,063 | Global Analytic Solution
for Variational Bayesian Matrix Factorization
Shinichi Nakajima
Nikon Corporation
Tokyo, 140-8601, Japan
nakajima.s@nikon.co.jp
Masashi Sugiyama
Tokyo Institute of Technology
Tokyo 152-8552, Japan
sugi@cs.titech.ac.jp
Ryota Tomioka
The University of Tokyo
Tokyo 113-8685, Japan
tomioka@mist.i... | 4063 |@word trial:2 determinant:1 repository:2 advantageous:2 norm:8 nd:7 cah:15 stronger:1 decomposition:4 covariance:2 arti:7 tr:2 reduction:1 initial:3 series:2 current:1 com:1 attracted:1 evans:1 numerical:1 additive:1 kdd:2 analytic:26 update:2 stationary:4 intelligence:1 accordingly:1 gure:1 provides:1 mathematic... |
3,384 | 4,064 | Exploiting weakly-labeled Web images to improve
object classification: a domain adaptation approach
Alessandro Bergamo
Lorenzo Torresani
Computer Science Department
Dartmouth College
Hanover, NH 03755, U.S.A.
{aleb, lorenzo}@cs.dartmouth.edu
Abstract
Most current image categorization methods require large collections... | 4064 |@word h:2 worsens:1 kulis:1 version:2 everingham:1 relevancy:3 tried:1 covariance:3 shot:1 contains:5 exclusively:3 selecting:2 score:1 tuned:2 ours:1 subjective:1 current:3 nt:15 yet:4 subsequent:1 designed:2 drop:1 plot:1 v:1 selected:3 item:1 harvesting:1 classeme:2 boosting:1 shooting:1 consists:1 combine:2 i... |
3,385 | 4,065 | Divisive Normalization: Justification and
Effectiveness as Efficient Coding Transform
Siwei Lyu ?
Computer Science Department
University at Albany, State University of New York
Albany, NY 12222, USA
Abstract
Divisive normalization (DN) has been advocated as an effective nonlinear efficient coding transform for natura... | 4065 |@word determinant:3 inversion:1 compression:3 norm:1 seems:1 nd:1 confirms:1 linearized:1 covariance:3 solid:7 reduction:8 initial:1 series:2 hereafter:1 interestingly:1 current:2 si:2 yet:2 dx:3 written:1 visible:1 subsequent:1 numerical:1 shape:7 remove:2 plot:6 drop:1 kyk:7 tone:1 isotropic:12 xk:6 ith:2 filte... |
3,386 | 4,066 | Evaluating neuronal codes for inference using Fisher
information
Ralf M. Haefner? and Matthias Bethge
Centre for Integrative Neuroscience, University of T?ubingen,
Bernstein Center for Computational Neuroscience, T?ubingen,
Max Planck Institute for Biological Cybernetics
Spemannstr. 41, 72076 T?ubingen, Germany
Abstra... | 4066 |@word c0:3 a02:2 integrative:1 rhesus:1 covariance:2 thereby:2 tr:1 solid:1 carry:3 initial:1 disparity:91 tuned:4 interestingly:1 o2:3 existing:1 com:1 comparing:2 gmail:1 yet:1 dx:2 readily:1 additive:1 realistic:1 shape:2 treating:1 plot:1 v:3 discrimination:2 cue:4 steepest:1 core:1 short:1 contribute:1 heigh... |
3,387 | 4,067 | Lifted Inference Seen from the Other Side : The
Tractable Features
Abhay Jha Vibhav Gogate Alexandra Meliou Dan Suciu
Computer Science & Engineering
University of Washington
Washington, WA 98195
{abhaykj,vgogate,ameli,suciu}@cs.washington.edu
Abstract
Lifted Inference algorithms for representations that combine first-... | 4067 |@word version:1 inversion:2 polynomial:16 open:1 closure:2 recursively:2 substitution:3 contains:3 series:1 daniel:1 existing:8 z2:2 incidence:1 conjunctive:3 must:3 written:4 dechter:1 partition:10 enables:1 remove:2 drop:1 v:3 intelligence:10 braz:1 amir:1 mln:20 completeness:1 bijection:1 simpler:5 zhang:1 unb... |
3,388 | 4,068 | Rates of convergence for the cluster tree
Kamalika Chaudhuri
UC San Diego
kchaudhuri@ucsd.edu
Sanjoy Dasgupta
UC San Diego
dasgupta@cs.ucsd.edu
Abstract
For a density f on Rd , a high-density cluster is any connected component of {x :
f (x) ? ?}, for some ? > 0. The set of all high-density clusters form a hierarchy
... | 4068 |@word mild:1 cylindrical:1 repository:1 version:2 open:4 pick:5 mention:1 reduction:1 contains:6 exclusively:1 series:1 ours:1 interestingly:1 comparing:1 si:5 yet:2 dx:1 must:5 bd:10 fn:8 numerical:1 partition:2 intelligence:1 leaf:1 fewer:1 parametrization:1 node:1 location:2 successive:1 hyperplanes:1 along:9 ... |
3,389 | 4,069 | Direct Loss Minimization for Structured Prediction
David McAllester
TTI-Chicago
mcallester@ttic.edu
Tamir Hazan
TTI-Chicago
tamir@ttic.edu
Joseph Keshet
TTI-Chicago
jkeshet@ttic.edu
Abstract
In discriminative machine learning one is interested in training a system to optimize a certain desired measure of performance... | 4069 |@word mild:1 version:4 briefly:1 polynomial:1 seems:3 norm:2 nonsensical:1 open:3 minus:1 contains:1 score:8 selecting:1 outperforms:1 current:1 must:1 written:2 chicago:3 hofmann:1 update:20 selected:2 ydirect:15 chiang:1 provides:1 node:1 unbounded:1 along:1 direct:10 become:1 viable:1 prove:2 consists:1 inside... |
3,390 | 407 | Convergence of a Neural Network Classifier
John S. Baras
Systems Research Center
University of Maryland
College Park, Maryland 20705
Anthony La Vigna
Systems Research Center
University of Maryland
College Park, Maryland 20705
Abstract
In this paper, we prove that the vectors in the LVQ learning algorithm
converge. W... | 407 |@word effect:2 true:1 duda:2 lyapunov:3 concentrate:1 hence:5 assigned:2 move:1 correct:1 occurs:2 simulation:1 stochastic:4 during:3 require:1 maryland:5 coincides:1 berlin:1 initial:3 vigna:2 majority:7 argue:1 complete:1 occuring:1 performs:1 o2:2 past:2 strictly:2 hold:2 correction:1 od:1 ttern:1 around:1 prio... |
3,391 | 4,070 | Identifying Dendritic Processing
Yevgeniy B. Slutskiy?
Department of Electrical Engineering
Columbia University
New York, NY 10027
ys2146@columbia.edu
Aurel A. Lazar
Department of Electrical Engineering
Columbia University
New York, NY 10027
aurel@ee.columbia.edu
Abstract
In system identification both the input and ... | 4070 |@word middle:2 covariance:1 q1:3 slee:1 carry:1 reduction:1 daniel:2 denoting:1 rkhs:1 current:1 recovered:1 surprising:1 written:1 numerical:1 v:4 record:1 characterization:2 rc:2 mathematical:1 c2:1 olfactory:3 introduce:1 roughly:1 window:13 increasing:2 becomes:2 provided:4 spain:1 bounded:8 vertebrate:1 circ... |
3,392 | 4,071 | Dynamic Infinite Relational Model
for Time-varying Relational Data Analysis
Katsuhiko Ishiguro Tomoharu Iwata
Naonori Ueda
NTT Communication Science Laboratories
Kyoto, 619-0237 Japan
{ishiguro,iwata,ueda}@cslab.kecl.ntt.co.jp
Joshua Tenenbaum
MIT
Boston, MA.
jbt@mit.edu
Abstract
We propose a new probabilistic model... | 4071 |@word inversion:1 twelfth:1 gradual:1 accounting:1 liu:1 series:6 contains:1 united:1 bibliographic:1 imoto:2 z2:1 comparing:1 yet:1 happen:1 partition:3 matured:1 enables:4 cfo:1 update:1 stationary:2 generative:3 selected:1 website:1 item:1 intelligence:1 merger:2 cult:2 ith:1 yamada:1 sudden:2 infrastructure:1... |
3,393 | 4,072 | Estimation of R?enyi Entropy and Mutual Information
Based on Generalized Nearest-Neighbor Graphs
Barnab?as P?oczos
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA, USA
poczos@ualberta.ca
D?avid P?al
Department of Computing Science
University of Alberta
Edmonton, AB, Canada
dpal@cs.ualberta.ca
Cs... | 4072 |@word neurophysiology:1 version:1 advantageous:1 seems:1 open:7 hyv:2 covariance:1 contains:2 series:2 ours:1 fbj:1 xnj:1 z2:1 comparing:2 si:2 lang:1 john:1 grassberger:1 mst:5 additive:1 numerical:4 thrust:1 show1:1 wx:1 greedy:1 intelligence:1 provides:1 boosting:2 node:2 org:1 unbounded:1 direct:1 differentia... |
3,394 | 4,073 | Near?Optimal Bayesian Active Learning
with Noisy Observations
Daniel Golovin
Caltech
Andreas Krause
Caltech
Debajyoti Ray
Caltech
Abstract
We tackle the fundamental problem of Bayesian active learning with noise, where
we need to adaptively select from a number of expensive tests in order to identify
an unknown hyp... | 4073 |@word uev:1 version:11 approved:1 termination:3 pick:2 incurs:3 infogain:6 carry:1 moment:1 reduction:4 contains:1 selecting:3 daniel:3 denoting:1 interestingly:1 outperforms:3 existing:3 rish:1 beygelzimer:2 p2min:1 must:6 john:2 additive:1 plot:1 greedy:12 selected:1 weighing:1 fewer:1 intelligence:2 probablity... |
3,395 | 4,074 | The Multidimensional Wisdom of Crowds
Peter Welinder1 Steve Branson2 Serge Belongie2 Pietro Perona1
1
California Institute of Technology, 2 University of California, San Diego
{welinder,perona}@caltech.edu
{sbranson,sjb}@cs.ucsd.edu
Abstract
Distributing labeling tasks among hundreds or thousands of annotators is an ... | 4074 |@word trial:2 version:3 proportion:1 tedious:1 instruction:1 tried:1 covariance:1 paid:1 carry:2 series:1 selecting:1 united:1 wj2:1 interestingly:1 past:1 subjective:1 current:2 z2:3 assigning:1 written:1 john:1 kdd:1 shape:2 cheap:1 remove:1 plot:1 discrimination:2 v:1 generative:2 selected:2 website:1 weighing... |
3,396 | 4,075 | Identifying graph-structured activation patterns in
networks
James Sharpnack
Machine Learning Department, Statistics Department
Carnegie Mellon University
Pittsburgh, PA 15213
jsharpna@andrew.cmu.edu
Aarti Singh
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA 15213
aartisingh@cmu.edu
Abstract
We... | 4075 |@word eex:1 version:2 kondor:1 stronger:3 norm:1 nd:3 simulation:1 covariance:3 decomposition:2 pg:7 pick:1 accommodate:1 reduction:2 series:1 recovered:1 whp:1 activation:22 dx:3 must:1 luis:1 j1:3 opg:1 farkas:1 congestion:1 implying:1 intelligence:2 xk:4 ith:2 provides:1 detecting:2 node:24 lx:16 zhang:1 mathe... |
3,397 | 4,076 | Linear readout from a neural population
with partial correlation data
Adrien Wohrer(1) , Ranulfo Romo(2) , Christian Machens(1)
(1)
Group for Neural Theory
Laboratoire de Neurosciences Cognitives
?
Ecole
Normale Suprieure
75005 Paris, France
{adrien.wohrer,christian.machens}@ens.fr
(2)
Instituto de Fisiolog??a Celul... | 4076 |@word trial:16 seems:1 norm:1 stronger:1 pulse:1 simulation:1 simplifying:1 covariance:3 pick:2 reduction:1 moment:5 configuration:1 series:1 ecole:1 bc:1 comparing:1 si:5 yet:1 must:7 written:1 christian:2 motor:1 discrimination:7 v:1 generative:1 half:1 reciprocal:1 indefinitely:1 provides:3 location:1 successi... |
3,398 | 4,077 | Optimal learning rates
for Kernel Conjugate Gradient regression
Nicole Kr?amer
Weierstrass Institute
Mohrenstr. 39, 10117 Berlin, Germany
nicole.kraemer@wias-berlin.de
Gilles Blanchard
Mathematics Institute, University of Potsdam
Am neuen Palais 10, 14469 Potsdam
blanchard@math.uni-potsdam.de
Abstract
We prove rates... | 4077 |@word h:1 collinearity:1 version:3 polynomial:4 norm:13 stronger:1 nd:1 km:7 d2:4 hu:1 decomposition:1 recapitulate:1 covariance:3 attainable:2 tr:2 boundedness:1 reduction:2 series:1 lepskii:1 neeman:1 current:1 scovel:1 exy:2 readily:1 numerical:1 update:3 intelligence:1 parametrization:1 footing:1 dissertation... |
3,399 | 4,078 | Identifying Patients at Risk of Major Adverse
Cardiovascular Events Using Symbolic Mismatch
Zeeshan Syed
University of Michigan
Ann Arbor, MI 48109
zhs@eecs.umich.edu
John Guttag
Massachusetts Institute of Technology
Cambridge, MA 02139
guttag@csail.mit.edu
Abstract
Cardiovascular disease is the leading cause of dea... | 4078 |@word trial:6 cox:1 compression:1 advantageous:1 open:2 vldb:1 decomposition:1 eng:1 pressure:2 harder:1 initial:1 series:15 score:6 united:1 efficacy:1 symphony:2 denoting:1 reynolds:1 past:3 existing:10 current:3 comparing:4 must:1 john:1 subsequent:1 partition:2 hypothesize:1 atlas:1 device:1 short:2 sudden:1 ... |
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