Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
3,800 | 4,440 | Identifying Alzheimer?s Disease-Related Brain Regions
from Multi-Modality Neuroimaging Data using Sparse
Composite Linear Discrimination Analysis
Shuai Huang 1, Jing Li1, Jieping Ye 2,3, Kewei Chen 4, Teresa Wu 1, Adam Fleisher 4, Eric
Reiman 4
1
Industrial Engineering, 2Computer Science and Engineering, and 3Center fo... | 4440 |@word multitask:4 determinant:1 mri:36 cingulate:1 hippocampus:6 nd:1 grey:1 simulation:6 seek:3 lobe:6 covariance:7 tr:3 liu:1 series:1 score:3 selecting:1 ours:1 document:1 outperforms:2 existing:4 bradley:1 current:2 com:1 comparing:1 surprising:2 optim:1 activation:1 yet:1 written:1 subsequent:1 enables:2 int... |
3,801 | 4,441 | Generalized Lasso based Approximation of Sparse
Coding for Visual Recognition
Nobuyuki Morioka
The University of New South Wales & NICTA
Sydney, Australia
nmorioka@cse.unsw.edu.au
Shin?ichi Satoh
National Institute of Informatics
Tokyo, Japan
satoh@nii.ac.jp
Abstract
Sparse coding, a method of explaining sensory dat... | 4441 |@word version:1 norm:7 open:1 km:10 seek:1 decomposition:3 q1:2 tr:1 shechtman:1 moment:1 substitution:1 contains:2 initial:3 nii:1 denoting:1 outperforms:2 z2:2 yet:3 attracted:2 written:1 readily:1 partition:2 shape:1 cheap:1 remove:2 drop:1 plot:1 v:1 half:1 selected:1 generative:1 kyk:3 short:1 feedfoward:1 c... |
3,802 | 4,442 | A rational model of causal induction
with continuous causes
Michael D. Pacer
Department of Psychology
University of California, Berkeley
Berkeley, CA 94720
mpacer@berkeley.edu
Thomas L. Griffiths
Department of Psychology
University of California, Berkeley
Berkeley, CA 94720
Tom Griffiths@berkeley.edu
Abstract
Rationa... | 4442 |@word trial:2 proceeded:1 eliminating:1 proportion:1 instruction:1 holyoak:2 simulation:1 accounting:2 paid:1 fifteen:1 accommodate:2 initial:1 series:2 score:2 outperforms:3 existing:1 current:2 comparing:1 must:1 confirming:1 drop:1 designed:5 generative:5 leaf:1 parameterization:7 provides:5 daphne:1 five:2 al... |
3,803 | 4,443 | Algorithms for Hyper-Parameter Optimization
R?emi Bardenet
Laboratoire de Recherche en Informatique
Universit?e Paris-Sud
bardenet@lri.fr
James Bergstra
The Rowland Institute
Harvard University
bergstra@rowland.harvard.edu
Yoshua Bengio
D?ept. d?Informatique et Recherche Op?erationelle
Universit?e de Montr?eal
yoshu... | 4443 |@word trial:28 exploitation:1 version:1 cox:1 nd:1 mockus:1 open:1 zilinskas:1 hyv:1 covariance:2 contrastive:1 tr:1 solid:2 harder:1 initial:3 configuration:15 substitution:1 score:2 uncovered:1 tuned:1 document:1 outperforms:1 existing:1 past:1 current:1 com:2 comparing:2 gmail:1 must:2 gpu:5 readily:1 numerica... |
3,804 | 4,444 | Algorithms and hardness results
for parallel large margin learning
Rocco A. Servedio
Columbia University
rocco@cs.columbia.edu
Philip M. Long
Google
plong@google.com
Abstract
We study the fundamental problem of learning an unknown large-margin halfspace in the context of parallel computation.
Our main positive result... | 4444 |@word h:1 version:2 polynomial:10 nd:2 dekel:1 open:1 d2:4 bn:8 carry:1 initial:4 chervonenkis:1 ours:1 minht:1 existing:1 err:1 kx0:1 com:1 bradley:2 si:2 pothesis:1 must:8 bd:4 john:1 periodically:1 additive:2 drop:1 update:2 bickson:1 alone:1 guess:1 warmuth:1 accepting:1 completeness:1 boosting:39 successive:... |
3,805 | 4,445 | Linear Submodular Bandits
and their Application to Diversified Retrieval
Yisong Yue
iLab, Heinz College
Carnegie Mellon University
yisongyue@cmu.edu
Carlos Guestrin
Machine Learning Department
Carnegie Mellon University
guestrin@cs.cmu.edu
Abstract
Diversified retrieval and online learning are two core research are... | 4445 |@word exploitation:7 version:1 middle:3 briefly:1 km:1 hu:1 simulation:7 covariance:2 incurs:1 reduction:3 karger:1 ours:1 interestingly:1 document:3 outperforms:2 existing:9 past:1 current:1 contextual:5 comparing:2 tackling:1 chu:2 must:5 written:2 subsequent:1 kdd:2 lsbg:42 plot:3 designed:1 v:3 greedy:13 sele... |
3,806 | 4,446 | Efficient Online Learning
via Randomized Rounding
Ohad Shamir
Microsoft Research New England
USA
ohadsh@microsoft.com
Nicol`
o Cesa-Bianchi
DSI, Universit`
a degli Studi di Milano
Italy
nicolo.cesa-bianchi@unimi.it
Abstract
Most online algorithms used in machine learning today are based on variants of mirror descent... | 4446 |@word version:2 achievable:1 polynomial:5 norm:23 seems:3 proportion:1 open:3 seek:1 forecaster:42 p0:1 pick:1 incurs:1 boundedness:1 harder:2 contains:1 ecole:1 com:1 surprising:1 written:1 readily:1 must:1 subsequent:1 kdd:1 prohibitive:1 warmuth:2 vanishing:2 core:1 provides:1 simpler:1 ik:1 viable:1 prove:3 c... |
3,807 | 4,447 | Exploiting spatial overlap to efficiently compute
appearance distances between image windows
Bogdan Alexe
ETH Zurich
Viviana Petrescu
ETH Zurich
Vittorio Ferrari
ETH Zurich
Abstract
We present a computationally efficient technique to compute the distance of highdimensional appearance descriptor vectors between image... | 4447 |@word dalal:2 proportion:1 everingham:1 triggs:2 crucially:1 scg:1 shot:1 reduction:1 initial:3 contains:7 wj2:26 o2:2 current:2 elliptical:1 yet:1 confirming:1 shape:1 gist:10 update:2 hash:5 isard:2 beginning:1 es:1 core:3 detecting:1 location:1 zhang:2 height:1 ijcv:2 inside:1 behavior:2 roughly:2 jegou:1 voc:... |
3,808 | 4,448 | Accelerated Adaptive Markov Chain
for Partition Function Computation?
Stefano Ermon, Carla P. Gomes
Dept. of Computer Science
Cornell University
Ithaca NY 14853, U.S.A.
Ashish Sabharwal
IBM Watson Research Ctr.
Yorktown Heights
NY 10598, U.S.A.
Bart Selman
Dept. of Computer Science
Cornell University
Ithaca NY 14853... | 4448 |@word version:1 pw:9 polynomial:4 norm:1 decomposition:1 simplifying:1 pick:2 thereby:1 initial:4 configuration:25 series:1 selecting:1 interestingly:1 outperforms:3 current:2 si:3 written:1 must:1 dechter:2 numerical:1 partition:39 lengthen:1 analytic:1 remove:1 update:1 bart:1 stationary:4 generative:1 fewer:2 ... |
3,809 | 4,449 | Policy Gradient Coagent Networks
Philip S. Thomas
Department of Computer Science
University of Massachusetts Amherst
Amherst, MA 01002
pthomas@cs.umass.edu
Abstract
We present a novel class of actor-critic algorithms for actors consisting of sets
of interacting modules. We present, analyze theoretically, and empirica... | 4449 |@word mild:1 exploitation:1 advantageous:2 carry:1 initial:1 contains:2 uma:1 selecting:2 hereafter:1 tuned:1 ati:3 existing:2 current:11 activation:1 si:10 written:3 must:4 realistic:1 update:35 stationary:1 intelligence:2 fewer:1 selected:2 greedy:2 parameterization:1 xk:3 beginning:1 ith:3 filtered:1 mitigatio... |
3,810 | 445 | Human and Machine 'Quick Modeling'
Jakob Bernasconi
Asea Brown Boveri Ltd
Corporate Research
CH-5405 Baden,
SWITZERLAND
Karl Gustafson
University of Colorado
Department of Mathematics and
Optoelectronic Computing Center
Boulder, CO 80309
ABSTRACT
We present here an interesting experiment in 'quick modeling' by human... | 445 |@word briefly:1 seems:1 stronger:1 pavel:2 initial:2 comparing:1 informative:1 atlas:3 intelligence:1 selected:1 fewer:1 item:3 beginning:1 short:3 node:1 location:1 height:2 qualitative:1 manner:1 behavior:3 examine:1 provided:1 classifies:5 evolved:1 substantially:2 finding:1 warning:1 unit:3 grant:1 appear:9 em... |
3,811 | 4,450 | Multiclass Boosting: Theory and Algorithms
Mohammad J. Saberian
Statistical Visual Computing Laboratory,
University of California, San Diego
saberian@ucsd.edu
Nuno Vasconcelos
Statistical Visual Computing Laboratory,
University of California, San Diego
nuno@ucsd.edu
Abstract
The problem of multi-class boosting is co... | 4450 |@word duda:1 covariance:1 decomposition:1 frigyik:1 reduction:1 score:1 existing:1 assigning:1 written:1 john:1 additive:1 designed:1 plot:1 update:12 v:7 discrimination:1 greedy:1 intelligence:1 plane:1 sys:1 dover:1 boosting:39 codebook:1 five:1 along:5 consists:1 combine:1 introduce:1 multi:9 ecoc:3 jm:1 incre... |
3,812 | 4,451 | Understanding the Intrinsic Memorability of Images
Phillip Isola
MIT
Devi Parikh
TTI-Chicago
Antonio Torralba
MIT
Aude Oliva
MIT
phillipi@mit.edu
dparikh@ttic.edu
torralba@mit.edu
oliva@mit.edu
Abstract
Artists, advertisers, and photographers are routinely presented with the task of
creating an image that a vi... | 4451 |@word trial:1 version:1 longterm:1 middle:1 polynomial:1 hippocampus:1 open:3 grey:1 concise:2 photographer:3 initial:1 series:1 score:4 contains:2 selecting:4 hoiem:1 subjective:3 past:1 existing:2 current:1 contextual:1 surprising:1 luo:1 parsing:1 chicago:1 visible:6 realistic:1 underly:1 informative:3 shape:2... |
3,813 | 4,452 | Convergence Rates of Inexact Proximal-Gradient
Methods for Convex Optimization
Mark Schmidt
mark.schmidt@inria.fr
Nicolas Le Roux
nicolas@le-roux.name
Francis Bach
francis.bach@ens.fr
INRIA - SIERRA Project Team
?
Ecole
Normale Sup?erieure, Paris
Abstract
We consider the problem of optimizing the sum of a smooth c... | 4452 |@word version:1 briefly:1 stronger:3 seems:2 norm:6 decomposition:1 jacob:1 moment:1 liu:1 series:2 ecole:1 interestingly:2 kx0:3 luo:1 partition:1 analytic:2 plot:3 xk:29 beginning:1 core:2 iterates:6 provides:1 math:1 org:1 zhang:1 mathematical:1 become:1 fitting:2 introductory:1 pairwise:1 x0:1 expected:1 inde... |
3,814 | 4,453 | Statistical Performance of Convex Tensor
Decomposition
Ryota Tomioka?
Taiji Suzuki?
Department of Mathematical Informatics,
The University of Tokyo
Tokyo 113-8656, Japan
tomioka@mist.i.u-tokyo.ac.jp
s-taiji@stat.t.u-tokyo.ac.jp
Kohei Hayashi?
Graduate School of Information Science,
Nara Institute of Science and Techno... | 4453 |@word polynomial:1 norm:30 c0:5 simulation:1 decomposition:30 reduction:2 liu:1 series:3 interestingly:1 err:1 current:3 additive:1 numerical:3 acar:1 plot:2 drop:1 selected:1 accordingly:1 xk:1 core:2 yamada:1 provides:1 math:1 preference:2 mathematical:2 lathauwer:2 c2:2 become:1 prove:1 consists:1 introduce:2 ... |
3,815 | 4,454 | High-dimensional regression with noisy and missing data:
Provable guarantees with non-convexity
Martin J. Wainwright
Departments of Statistics and EECS
University of California, Berkeley
Berkeley, CA 94720
wainwrig@stat.berkeley.edu
Po-Ling Loh
Department of Statistics
University of California, Berkeley
Berkeley, CA ... | 4454 |@word trial:1 version:2 polynomial:6 norm:15 c0:7 km:1 simulation:5 tat:1 covariance:15 contraction:1 decomposition:1 initial:1 series:3 zij:4 past:1 wainwrig:1 existing:1 john:1 additive:20 realistic:1 drop:1 plot:17 stationary:2 ith:1 iterates:4 provides:2 node:4 org:2 zhang:1 c2:5 become:1 yuan:2 prove:6 short... |
3,816 | 4,455 | k-NN Regression Adapts to Local Intrinsic Dimension
Samory Kpotufe
Max Planck Institute for Intelligent Systems
samory@tuebingen.mpg.de
Abstract
Many nonparametric regressors were recently shown to converge at rates that depend only on the intrinsic dimension of data. These regressors thus escape the
curse of dimensi... | 4455 |@word version:2 polynomial:1 seems:1 c0:14 accounting:1 pick:7 harder:1 reduction:4 series:1 chervonenkis:1 must:1 fn:24 happen:1 implying:1 guess:1 hyperplanes:1 mcdiarmid:1 simpler:1 lipchitz:1 unbounded:1 c2:2 consists:2 combine:1 wild:1 x0:4 hardness:2 expected:1 roughly:2 mpg:1 indeed:1 behavior:4 globally:5... |
3,817 | 4,456 | Unifying Non-Maximum Likelihood Learning
Objectives with Minimum KL Contraction
Siwei Lyu
Computer Science Department
University at Albany, State University of New York
lsw@cs.albany.edu
Abstract
When used to learn high dimensional parametric probabilistic models, the classical maximum likelihood (ML) learning often ... | 4456 |@word cox:1 version:2 nd:1 hyv:5 essay:1 seek:3 contraction:81 contrastive:21 liu:1 score:9 hereafter:1 existing:3 current:2 si:6 yet:1 dx:18 readily:1 ikeda:1 subsequent:1 numerical:2 partition:10 update:7 n0:1 intelligence:2 generative:2 advancement:1 isotropic:1 mccallum:1 provides:1 mathematical:1 constructed... |
3,818 | 4,457 | Shaping Level Sets with Submodular Functions
Francis Bach
INRIA - Sierra Project-team
Laboratoire d?Informatique de l?Ecole Normale Sup?erieure, Paris, France
francis.bach@ens.fr
Abstract
We consider a class of sparsity-inducing regularization terms based on submodular functions. While previous work has focused on non... | 4457 |@word illustrating:3 version:1 middle:3 polynomial:5 norm:19 closure:1 ajj:4 simulation:2 decomposition:2 configuration:1 selecting:2 ecole:1 interestingly:2 existing:1 recovered:2 current:1 adj:1 happen:1 partition:6 j1:2 designed:1 interpretable:1 plot:9 v:1 greedy:6 plane:1 vanishing:1 short:1 recherche:1 iter... |
3,819 | 4,458 | Simultaneous Sampling and Multi-Structure Fitting
with Adaptive Reversible Jump MCMC
Trung Thanh Pham, Tat-Jun Chin, Jin Yu and David Suter
School of Computer Science, The University of Adelaide, South Australia
{trung,tjchin,jin.yu,dsuter}@cs.adelaide.edu.au
Abstract
Multi-structure model fitting has traditionally t... | 4458 |@word instrumental:2 c0:1 km:5 tat:1 propagate:1 initial:3 inefficiency:1 series:1 selecting:1 existing:1 freitas:2 current:2 comparing:1 assigning:1 ws1:6 must:4 readily:2 yet:1 john:1 subsequent:1 ainen:1 progressively:2 update:15 depict:1 stationary:2 implying:1 instantiate:1 assurance:1 item:3 selected:2 trun... |
3,820 | 4,459 | Projection onto A Nonnegative Max-Heap
Jun Liu
Arizona State University
Tempe, AZ 85287, USA
Liang Sun
Arizona State University
Tempe, AZ 85287, USA
Jieping Ye
Arizona State University
Tempe, AZ 85287, USA
j.liu@asu.edu
sun.liang@asu.edu
jieping.ye@asu.edu
Abstract
We consider the problem of computing the Euclid... | 4459 |@word version:1 norm:2 vi1:4 simulation:4 jacob:1 tr:1 recursively:1 liu:5 contains:1 series:1 elaborating:1 existing:1 numerical:1 partition:2 remove:2 plot:21 interpretable:1 treating:1 designed:1 v:1 asu:3 selected:4 leaf:3 plane:1 el1:1 lr:2 provides:3 node:44 firstly:1 zhang:1 constructed:1 direct:2 yuan:1 p... |
3,821 | 446 | Decoding of Neuronal Signals in Visual Pattern
Recognition
Emad N Eskandar
Laboratory of Neuropsychology
National Institute of Mental Health
Bethesda MD 20892 USA
Barry J Richmond
Laboratory of Neuropsychology
National Institute of Mental Health
Bethesda MD 20892 USA
John A Hertz
NORDITA
B1egdamsvej 17
DK-2100 Copen... | 446 |@word trial:9 covariance:1 optican:6 current:8 comparing:1 john:1 subsequent:1 hypothesize:1 discrimination:8 alone:3 v:1 selected:1 half:2 beginning:1 ial:1 mental:2 five:2 pairing:1 pathway:1 behavioral:5 pairwise:1 inter:1 behavior:1 examine:1 begin:1 matched:1 monkey:7 temporal:12 control:1 unit:8 grant:1 medi... |
3,822 | 4,460 | Maximal Cliques that Satisfy Hard Constraints with
Application to Deformable Object Model Learning
Xinggang Wang1? Xiang Bai1
Xingwei Yang2? Wenyu Liu1
Longin Jan Latecki3
1
Dept. of Electronics and Information Engineering, Huazhong Univ. of Science and Technology, China
2
Image Analytics Lab, GE Research, One Res... | 4460 |@word seems:1 recursively:2 carry:1 electronics:1 liu:2 contains:4 score:3 selecting:1 initial:1 bai:2 outperforms:2 current:1 com:2 discretization:1 babenko:1 si:1 gmail:1 yet:1 must:3 follower:4 assigning:3 ronald:1 shape:1 plot:1 depict:1 resampling:1 intelligence:2 selected:9 beginning:1 core:2 short:1 fa9550... |
3,823 | 4,461 | Transfer Learning by Borrowing Examples
for Multiclass Object Detection
Joseph J. Lim
CSAIL, MIT
lim@csail.mit.edu
Ruslan Salakhutdinov
Department of Statistics, University of Toronto
rsalakhu@utstat.toronto.edu
Antonio Torralba
CSAIL, MIT
torralba@csail.mit.edu
Abstract
Despite the recent trend of increasingly lar... | 4461 |@word version:1 dalal:1 norm:5 nd:2 everingham:1 triggs:1 shot:1 contains:5 score:5 series:1 hoiem:1 ours:1 interestingly:1 document:1 subjective:2 existing:1 bookcase:6 current:3 john:1 realistic:1 shape:4 enables:1 v:1 bart:2 generative:5 selected:5 pursued:1 fewer:2 lamp:4 detecting:1 boosting:1 toronto:2 loca... |
3,824 | 4,462 | Orthogonal Matching Pursuit with Replacement
Prateek Jain
Microsoft Research India
Bangalore, INDIA
prajain@microsoft.com
AmbujTewari
The University of Texas at Austin
Austin, TX
ambuj@cs.utexas.edu
Inderjit S. Dhillon
The University of Texas at Austin
Austin, TX
inderjit@cs.utexas.edu
Abstract
In this paper, we c... | 4462 |@word mild:1 milenkovic:1 briefly:2 inversion:1 seems:1 norm:1 heuristically:1 hu:4 llo:1 decomposition:1 fonn:1 incurs:1 initial:1 contains:1 selecting:1 tuned:1 ours:1 o2:1 existing:6 outperforms:2 current:7 com:1 recovered:2 comparing:1 ilxl:1 zll:2 remove:2 designed:1 plot:3 update:1 ouly:2 hash:18 greedy:5 s... |
3,825 | 4,463 | Priors over Recurrent Continuous Time Processes
Ardavan Saeedi
Alexandre Bouchard-C?ot?e
Department of Statistics
University of British Columbia
Abstract
We introduce the Gamma-Exponential Process (GEP), a prior over a large family of continuous time stochastic processes. A hierarchical version of this prior
(HGEP; th... | 4463 |@word trial:1 version:5 instruction:1 p0:2 reduction:2 initial:1 substitution:1 series:13 ours:1 current:5 dx:2 john:1 partition:4 j1:3 informative:1 shape:1 drop:2 plot:1 update:3 resampling:1 alone:1 selected:1 leaf:1 parameterization:2 beginning:2 short:2 blei:1 complication:1 simpler:2 phylogenetic:1 construc... |
3,826 | 4,464 | Quasi-Newton Methods
for Markov Chain Monte Carlo
Yichuan Zhang and Charles Sutton
School of Informatics
University of Edinburgh
Y.Zhang-60@sms.ed.ac.uk, csutton@inf.ed.ac.uk
Abstract
The performance of Markov chain Monte Carlo methods is often sensitive to the
scaling and correlations between the random variables of ... | 4464 |@word version:3 unif:2 heuristically:1 simulation:3 covariance:7 decomposition:1 p0:1 initial:1 liu:1 exclusively:1 uma:1 initialisation:1 tuned:1 interestingly:1 outperforms:1 current:4 si:3 must:4 numerical:2 remove:1 designed:2 plot:3 update:9 resampling:1 stationary:2 half:1 prohibitive:1 leaf:8 selected:1 in... |
3,827 | 4,465 | Online Submodular Set Cover,
Ranking, and Repeated Active Learning
Jeff Bilmes
Department of Electrical Engineering
University of Washington
bilmes@ee.washington.edu
Andrew Guillory
Department of Computer Science
University of Washington
guillory@cs.washington.edu
Abstract
We propose an online prediction version of ... | 4465 |@word version:19 polynomial:1 stronger:1 seems:1 vi1:2 open:1 tried:2 pick:3 asks:1 dramatic:1 incurs:1 carry:1 reduction:1 contains:1 uncovered:1 selecting:4 ours:1 document:5 interestingly:1 past:1 contextual:4 nt:2 si:21 yet:2 written:1 readily:1 must:2 additive:3 kdd:1 plot:1 ligett:1 update:1 aside:1 greedy:... |
3,828 | 4,466 | How Do Humans Teach:
On Curriculum Learning and Teaching Dimension
Faisal Khan, Xiaojin Zhu, Bilge Mutlu
Department of Computer Sciences, University of Wisconsin?Madison
Madison, WI, 53706 USA. {faisal, jerryzhu, bilge}@cs.wisc.edu
Abstract
We study the empirical strategies that humans follow as they teach a target c... | 4466 |@word trial:2 version:11 norm:2 nd:1 unif:2 seek:1 mitsubishi:2 simplifying:1 irb:1 attainable:1 paid:1 pick:1 accommodate:1 carry:1 moment:1 contains:1 pub:1 selecting:3 subjective:2 current:1 assigning:1 written:1 happen:1 predetermined:1 plasticity:1 wanted:1 plot:5 designed:1 alone:1 cue:1 selected:4 item:2 s... |
3,829 | 4,467 | ICA with Reconstruction Cost for Efficient
Overcomplete Feature Learning
Quoc V. Le, Alexandre Karpenko, Jiquan Ngiam and Andrew Y. Ng
{quocle,akarpenko,jngiam,ang}@cs.stanford.edu
Computer Science Department, Stanford University
Abstract
Independent Components Analysis (ICA) and its variants have been successfully
u... | 4467 |@word seems:1 norm:6 hyv:3 covariance:5 liu:1 contains:1 score:10 ullah:1 luo:1 activation:6 scatter:1 additive:1 realistic:1 subsequent:1 shape:1 enables:2 remove:1 drop:2 plot:1 greedy:1 inspection:1 colored:1 node:6 location:12 toronto:1 zhang:1 mathematical:1 become:2 ik:1 consists:1 wild:1 interscience:1 man... |
3,830 | 4,468 | Inferring spike-timing-dependent plasticity from
spike train data
Ian H. Stevenson and Konrad P. Kording
Department of Physical Medicine and Rehabilitation
Northwestern University
{i-stevenson, kk}@northwestern.edu
Abstract
Synaptic plasticity underlies learning and is thus central for development, memory, and recove... | 4468 |@word neurophysiology:3 briefly:1 hippocampus:1 open:1 simulation:10 covariance:2 simplifying:1 jacob:1 initial:1 configuration:1 npost:7 efficacy:1 past:6 recovered:2 current:1 nt:3 ka:1 written:1 must:1 realistic:1 visible:1 plasticity:13 shape:1 motor:8 drop:1 update:7 aps:1 alone:1 generative:8 stationary:2 s... |
3,831 | 4,469 | Ranking annotators for crowdsourced labeling tasks
Shipeng Yu
Siemens Healthcare, Malvern, PA, USA
shipeng.yu@siemens.com
Vikas C. Raykar
Siemens Healthcare, Malvern, PA, USA
vikas.raykar@siemens.com
Abstract
With the advent of crowdsourcing services it has become quite cheap and reasonably effective to get a datase... | 4469 |@word version:1 briefly:1 judgement:1 middle:1 norm:1 c0:4 simulation:3 paid:2 score:92 hermosillo:1 subjective:1 com:4 assigning:1 written:2 refines:1 j1:2 benign:1 cheap:2 plot:7 intelligence:1 item:2 indicative:1 ruvolo:1 ith:2 short:3 provides:1 contribute:1 along:1 become:2 replication:3 qualitative:1 paragr... |
3,832 | 447 | Neural Control for Rolling Mills: Incorporating
Domain Theories to Overcome Data Deficiency
Martin Roscheisen
Computer Science Dept.
Munich Technical University
8 Munich 40, FRG
Reimar Hofmann
Computer Science Dept.
Edinburgh University
Edinburgh, EH89A, UK
Volker Tresp
Corporate R&D
Siemens AG
8 Munich 83, FRG
Abs... | 447 |@word determinant:1 version:1 duda:1 isil:1 simulation:1 covariance:3 simplifying:1 pressure:1 ipm:1 reduction:2 initial:1 contains:1 series:1 selecting:1 tuned:5 mmse:1 outperforms:1 past:1 recovered:1 discretization:1 nowlan:2 si:4 yet:3 activation:2 written:2 readily:1 additive:1 subsequent:1 hofmann:6 analytic... |
3,833 | 4,470 | Im2Text: Describing Images Using 1 Million
Captioned Photographs
Vicente Ordonez
Girish Kulkarni
Tamara L Berg
Stony Brook University
Stony Brook, NY 11794
{vordonezroma or tlberg}@cs.stonybrook.edu
Abstract
We develop and demonstrate automatic image description methods using a large
captioned photo collection. On... | 4470 |@word kong:2 middle:1 dalal:1 everingham:1 triggs:1 relevancy:2 tried:1 hyponym:1 initial:2 configuration:1 contains:3 score:11 hereafter:1 hoiem:2 document:17 ours:1 past:4 existing:1 subjective:1 freitas:2 activation:2 stony:2 written:4 parsing:4 must:4 visible:1 shape:7 remove:1 gist:3 depict:1 v:3 grass:6 int... |
3,834 | 4,471 | Analytical Results for the Error in Filtering of
Gaussian Processes
Alex Susemihl
Bernstein Center for Computational Neuroscience Berlin,Technische Universit?at Berlin
alex.susemihl@bccn-berlin.de
Ron Meir
Department of Eletrical Engineering, Technion, Haifa
rmeir@ee.technion.ac.il
Manfred Opper
Bernstein Center for Co... | 4471 |@word h:1 inversion:1 simulation:4 seek:1 simplifying:1 covariance:10 celebrated:1 series:2 tuned:2 denoting:1 mmse:17 past:2 current:1 dx:2 written:1 realistic:1 numerical:2 plasticity:1 analytic:2 discernible:1 plot:1 drop:1 stationary:5 short:1 manfred:2 characterization:1 provides:2 contribute:1 ron:4 complic... |
3,835 | 4,472 | TD? : Re-evaluating Complex Backups in Temporal
Difference Learning
George Konidaris??
MIT CSAIL?
Cambridge MA 02139
gdk@csail.mit.edu
Scott Niekum??
Philip S. Thomas??
University of Massachusetts Amherst?
Amherst MA 01003
{sniekum,pthomas}@cs.umass.edu
Abstract
We show that the ?-return target used in the TD(?) fam... | 4472 |@word version:3 eliminating:1 r:29 accounting:1 uma:1 bootstrapped:1 outperforms:3 past:2 current:4 must:1 wiewiora:1 shape:1 update:7 intelligence:3 selected:4 fa9550:1 successive:1 lx:7 five:1 become:3 qualitative:1 prove:1 introduce:1 expected:1 roughly:2 terminal:4 discounted:2 td:65 increasing:2 becomes:1 be... |
3,836 | 4,473 | Hierarchical Matching Pursuit for Image
Classification: Architecture and Fast Algorithms
Liefeng Bo
University of Washington
Seattle WA 98195, USA
Xiaofeng Ren
ISTC-Pervasive Computing Intel Labs
Seattle WA 98195, USA
Dieter Fox
University of Washington
Seattle WA 98195, USA
Abstract
Extracting good representations ... | 4473 |@word kulis:1 middle:1 inversion:2 norm:5 decomposition:6 shechtman:1 contains:1 selecting:2 deconvolutional:5 past:1 outperforms:6 current:3 dx:2 dct:17 visible:1 enables:1 remove:1 gist:3 update:4 discrimination:1 greedy:3 selected:4 website:1 generative:2 xk:3 core:1 codebook:1 location:1 simpler:2 dn:3 ksvd:1... |
3,837 | 4,474 | Learning to Learn with Compound HD Models
Ruslan Salakhutdinov
Department of Statistics, University of Toronto
rsalakhu@utstat.toronto.edu
Joshua B. Tenenbaum
Brain and Cognitive Sciences, MIT
jbt@mit.edu
Antonio Torralba
CSAIL, MIT
torralba@mit.edu
Abstract
We introduce HD (or ?Hierarchical-Deep?) models, a new co... | 4474 |@word arabic:1 middle:1 nd:6 seal:1 open:1 rgb:2 decomposition:1 thereby:1 shot:6 recursively:1 contains:6 score:1 tuned:1 document:11 outperforms:2 existing:2 current:2 nt:2 activation:1 mushroom:1 readily:1 visible:4 partition:8 trout:1 realistic:1 shape:3 treating:1 gist:4 update:1 wlm:2 v:3 alone:1 generative... |
3,838 | 4,475 | Stochastic convex optimization with bandit
feedback
Alekh Agarwal
Department of EECS
UC Berkeley
alekh@cs.berkeley.edu
Dean P. Foster
Department of Statistics
University of Pennysylvania
dean.foster@gmail.com
Sham M. Kakade
Department of Statistics Microsoft Research
University of Pennysylvania
New England
skakade@mi... | 4475 |@word msr:1 exploitation:1 version:5 polynomial:2 suitably:1 dekel:1 pick:1 incurs:3 euclidian:2 solid:1 carry:1 reduction:3 series:2 contains:4 daniel:1 demarcated:1 current:5 com:3 discretization:1 z2:2 gmail:1 yet:1 must:1 update:1 v:1 selected:1 device:4 isotropic:3 beginning:2 vanishing:1 core:1 fa9550:1 cer... |
3,839 | 4,476 | See the Tree Through the Lines:
The Shazoo Algorithm?
Nicol`o Cesa-Bianchi
DSI, University of Milan, Italy
nicolo.cesa-bianchi@unimi.it
Fabio Vitale
DSI, University of Milan, Italy
fabio.vitale@unimi.it
Giovanni Zappella
Dept. of Mathematics, Univ. of Milan, Italy
giovanni.zappella@unimi.it
Claudio Gentile
DICOM, U... | 4476 |@word version:3 middle:2 briefly:1 norm:1 proportion:1 seems:1 nd:2 grey:1 propagate:2 galeano:1 sparsifies:1 thereby:2 recursively:2 carry:3 reduction:2 initial:1 contains:3 loeliger:1 document:2 interestingly:1 outperforms:3 existing:1 current:2 comparing:1 yet:3 must:1 ybit:6 reminiscent:2 readily:1 mst:4 subs... |
3,840 | 4,477 | Monte Carlo Value Iteration with Macro-Actions
Zhan Wei Lim
David Hsu
Wee Sun Lee
Department of Computer Science, National University of Singapore
Singapore, 117417, Singapore
Abstract
POMDP planning faces two major computational challenges: large state spaces
and long planning horizons. The recently introduced Mon... | 4477 |@word aircraft:2 suitably:1 open:2 termination:6 hu:1 simulation:2 contraction:1 decomposition:1 citeseer:1 pick:1 dramatic:1 recursively:1 carry:1 bai:2 initial:8 denoting:1 current:5 si:17 yet:1 must:1 visibility:1 designed:2 maxv:1 greedy:4 prohibitive:1 selected:2 intelligence:8 xk:2 smith:1 core:1 short:1 me... |
3,841 | 4,478 | Multi-Bandit Best Arm Identification
Victor Gabillon
Mohammad Ghavamzadeh
Alessandro Lazaric
INRIA Lille - Nord Europe, Team SequeL
{victor.gabillon,mohammad.ghavamzadeh,alessandro.lazaric}@inria.fr
S?ebastien Bubeck
Department of Operations Research and Financial Engineering, Princeton University
sbubeck@princeton.ed... | 4478 |@word trial:6 version:7 unif:21 d2:1 km:14 simulation:3 forecaster:6 tat:1 asks:1 incurs:1 harder:1 contains:1 selecting:1 genetic:1 bc:4 tuned:3 past:1 outperforms:1 comparing:1 worsening:1 written:1 additive:1 numerical:3 xmk:3 treating:1 designed:3 progressively:1 update:1 greedy:1 selected:1 accordingly:1 beg... |
3,842 | 4,479 | MAP Inference for
Bayesian Inverse Reinforcement Learning
Jaedeug Choi and Kee-Eung Kim
bDepartment of Computer Science
Korea Advanced Institute of Science and Technology
Daejeon 305-701, Korea
jdchoi@ai.kaist.ac.kr, kekim@cs.kaist.ac.kr
Abstract
The difficulty in inverse reinforcement learning (IRL) arises in choosi... | 4479 |@word h:2 inversion:1 norm:1 open:1 solid:1 initial:3 contains:1 rightmost:1 current:1 comparing:3 wd:1 designed:1 update:1 greedy:2 imitate:1 amir:3 provides:2 location:10 preference:7 mathematical:1 constructed:1 direct:2 eung:1 consists:1 inside:1 manner:1 apprenticeship:6 ra:2 expected:1 behavior:2 planning:2... |
3,843 | 448 | Forward Dynamics Modeling
of Speech Motor Control
Using Physiological Data
Makoto Hirayama
Eric Vatikiotis-Bateson
Mitsuo Kawato
ATR Auditory and Visual Perception Research Laboratories
2 - 2, Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-02, JAPAN
Michael I. Jordan
Department of Brain and Cognitive Sciences
Massach... | 448 |@word version:1 kura:1 closure:1 thereby:1 initial:3 series:3 current:1 anterior:4 activation:1 toh:1 realistic:1 motor:24 medial:1 v:1 patterning:1 yoh:1 honda:1 become:1 symposium:1 visco:3 acquired:2 indeed:1 behavior:3 seika:1 nor:1 examine:1 brain:2 torque:2 chap:1 encouraging:1 provided:2 musculo:1 string:2 ... |
3,844 | 4,480 | Generalised Coupled Tensor Factorisation
Y. Kenan Y?lmaz
A. Taylan Cemgil
Umut S?ims?ekli
Department of Computer Engineering
Bo?gazic?i University, Istanbul, Turkey
kenan@sibnet.com.tr, {taylan.cemgil, umut.simsekli}@boun.edu.tr
Abstract
We derive algorithms for generalised tensor factorisation (GTF) by building upon... | 4480 |@word version:1 briefly:1 nd:1 tedious:1 calculus:1 decomposition:2 contraction:1 tr:2 searle:1 initial:5 configuration:6 score:4 denoting:1 ours:1 current:2 com:1 z2:21 skipping:1 yet:1 dx:2 written:1 readily:1 reminiscent:1 assigning:1 john:1 subsequent:1 partition:1 concatenate:1 shape:1 enables:1 acar:2 updat... |
3,845 | 4,481 | Portmanteau Vocabularies for Multi-Cue Image
Representation
Fahad Shahbaz Khan1 , Joost van de Weijer1 , Andrew D. Bagdanov1,2 , Maria Vanrell1
1
Centre de Visio per Computador, Computer Science Department
1
Universitat Autonoma de Barcelona, Edifci O, Campus UAB (Bellaterra), Barcelona, Spain
2
Media Integration and ... | 4481 |@word middle:1 compression:5 consolider:1 plsa:1 open:1 grey:2 reduction:1 initial:2 configuration:3 contains:8 score:4 interestingly:1 outperforms:4 existing:1 comparing:1 babenko:1 anne:1 si:1 must:2 bd:1 cottrell:1 blur:1 shape:54 eleven:1 plot:2 designed:1 jenson:1 drop:3 discrimination:1 alone:3 cue:78 pursu... |
3,846 | 4,482 | Learning Auto-regressive Models from Sequence and
Non-sequence Data
Jeff Schneider
Robotics Institute
Carnegie Mellon University
schneide@cs.cmu.edu
Tzu-Kuo Huang
Machine Learning Department
Carnegie Mellon University
tzukuoh@cs.cmu.edu
Abstract
Vector Auto-regressive models (VAR) are useful tools for analyzing time... | 4482 |@word wiesel:2 norm:3 covariance:34 noll:1 initial:3 liu:1 series:19 score:10 denoting:1 longitudinal:1 mmse:1 outperforms:2 current:2 ka:6 yet:1 belmont:1 periodically:1 enables:1 plot:2 update:1 stationary:18 half:4 intelligence:2 short:1 regressive:5 along:3 sii:1 direct:1 become:2 qualitative:1 consists:1 com... |
3,847 | 4,483 | Multiple Instance Learning on Structured Data
1
Dan Zhang, 2 Yan Liu, 1 Luo Si, 3 Jian Zhang, 4 Richard D. Lawrence
1. Computer Science Department, Purdue University, West Lafayette, IN 47906
2. Computer Science Department, University of Southern California, Los Angeles, CA 90089
3. Statistics Department, Purdue Univ... | 4483 |@word briefly:1 faculty:4 version:1 flach:1 bpu:5 initial:2 liu:2 contains:2 score:1 tuned:1 document:1 existing:5 current:1 com:2 luo:2 si:2 gmail:1 takeo:1 john:1 kdd:1 hofmann:3 treating:1 sponsored:1 update:2 intelligence:1 selected:2 website:6 nq:11 plane:26 math:1 node:8 location:1 cse:1 zhang:5 daphne:1 he... |
3,848 | 4,484 | Structured Learning for Cell Tracking
Xinghua Lou, Fred A. Hamprecht
Heidelberg Collaboratory for Image Processing (HCI)
Interdisciplinary Center for Scientific Computing (IWR)
University of Heidelberg, Heidelberg 69115, Germany
{xinghua.lou,fred.hamprecht}@iwr.uni-heidelberg.de
Abstract
We study the problem of learn... | 4484 |@word version:1 compression:3 norm:1 nd:2 c0:10 tedious:1 grk:1 profit:2 tr:1 solid:1 mcauley:1 reduction:1 configuration:1 score:4 ours:5 rightmost:1 existing:2 current:3 com:2 nt:8 si:4 must:1 subsequent:2 numerical:1 shape:9 hofmann:2 drop:1 designed:1 update:1 fund:1 v:4 alone:1 selected:1 weighing:1 merger:4... |
3,849 | 4,485 | Action-Gap Phenomenon in Reinforcement Learning
Amir-massoud Farahmand?
School of Computer Science, McGill University
Montreal, Quebec, Canada
Abstract
Many practitioners of reinforcement learning problems have observed that oftentimes the performance of the agent reaches very close to the optimal performance
even th... | 4485 |@word polynomial:1 achievable:1 norm:6 reduction:1 initial:1 configuration:1 series:1 denoting:1 current:2 comparing:1 dx:1 john:1 ronald:2 shlomo:1 enables:1 discrimination:1 stationary:1 greedy:15 half:1 selected:4 amir:4 short:1 provides:1 mannor:2 dn:2 farahmand:8 prove:2 manner:1 apprenticeship:2 ra:1 expect... |
3,850 | 4,486 | Divide-and-Conquer Matrix Factorization
Lester Mackeya
a
Ameet Talwalkara
Michael I. Jordana, b
Department of Electrical Engineering and Computer Science, UC Berkeley
b
Department of Statistics, UC Berkeley
Abstract
This work introduces Divide-Factor-Combine (DFC), a parallel divide-andconquer framework for noisy m... | 4486 |@word mild:1 trial:1 norm:3 proportion:1 c0:4 simulation:3 decomposition:7 eng:1 attainable:1 nystr:8 contains:2 document:1 amp:2 outperforms:1 existing:1 recovered:4 com:1 toh:1 attracted:1 realistic:1 partition:8 plot:1 intelligence:1 item:1 ith:2 prize:1 core:1 vanishing:1 tcp:1 provides:2 detecting:1 math:1 l... |
3,851 | 4,487 | Contextual Gaussian Process Bandit Optimization
Andreas Krause
Cheng Soon Ong
Department of Computer Science, ETH Zurich,
8092 Zurich, Switzerland
krausea@ethz.ch
chengsoon.ong@inf.ethz.ch
Abstract
How should we design experiments to maximize performance of a complex
system, taking into account uncontrollable environ... | 4487 |@word mild:1 trial:5 exploitation:8 multitask:2 achievable:3 norm:3 stronger:3 d2:2 covariance:10 decomposition:1 pick:1 incurs:1 tr:1 reduction:1 contains:2 fragment:1 score:2 daniel:1 document:6 rkhs:3 outperforms:4 past:1 freitas:2 current:1 contextual:38 chu:2 john:2 multioutput:2 additive:6 christian:1 plot:... |
3,852 | 4,488 | Gradient-based kernel method for feature extraction
and variable selection
Kenji Fukumizu
The Institute of Statistical Mathematics
10-3 Midori-cho, Tachikawa, Tokyo 190-8562 Japan
fukumizu@ism.ac.jp
Chenlei Leng
National University of Singapore
6 Science Drive 2, Singapore, 117546
stalc@nus.edu.sg
Abstract
We propose... | 4488 |@word repository:4 inversion:3 polynomial:1 norm:3 open:2 d2:3 km:3 tried:1 covariance:5 decomposition:1 tr:5 reduction:28 initial:1 liu:3 tuned:1 rkhs:4 pna:1 existing:7 z2:2 must:1 written:1 bd:1 realize:1 additive:1 numerical:1 v0j:2 midori:1 v:15 rrt:1 pursued:1 selected:3 prohibitive:1 cook:3 juditsky:1 prov... |
3,853 | 4,489 | Efficient coding provides a direct link between prior
and likelihood in perceptual Bayesian inference
Xue-Xin Wei and Alan A. Stocker?
Departments of Psychology and
Electrical and Systems Engineering
University of Pennsylvania
Philadelphia, PA-19104, U.S.A.
Abstract
A common challenge for Bayesian models of perceptio... | 4489 |@word crucially:1 minus:1 valois:1 n000141110744:1 tuned:8 interestingly:2 current:1 surprising:1 yet:4 must:1 additive:1 informative:1 shape:6 discrimination:1 v:2 cue:3 selected:1 ith:1 oblique:5 provides:6 sigmoidal:1 mathematical:1 along:1 direct:4 transl:1 qualitative:1 prove:1 combine:3 behavioral:2 upenn:1... |
3,854 | 449 | Time-Warping Network:
A Hybrid Framework for Speech Recognition
Roberto Pieraccini
Esther Levin
Enrico Bocchieri
AT&T Bell Laboratories
Speech Research Department
Murray Hill, NJ 00974 USA
ABSTRACT
Recently. much interest has been generated regarding speech
recognition systems based on Hidden Markov Models (HMMs) a... | 449 |@word version:1 covariance:1 fonn:1 initial:1 score:1 selecting:1 subword:3 outperforms:1 activation:14 si:1 scatter:3 j1:2 plot:5 update:1 discrimination:5 tenn:1 selected:1 contribute:2 toronto:1 lx:2 sigmoidal:1 jgj:1 five:1 incorrect:1 consists:1 qualitative:1 combine:1 expected:1 behavior:1 bocchieri:5 multi:... |
3,855 | 4,490 | Learning from the Wisdom of Crowds by Minimax
Entropy
Dengyong Zhou, John C. Platt, Sumit Basu, and Yi Mao
Microsoft Research
1 Microsoft Way, Redmond, WA 98052
{denzho,jplatt,sumitb,yimao}@microsoft.com
Abstract
An important way to make large training sets is to gather noisy labels from crowds
of nonexperts. We prop... | 4490 |@word eliminating:1 judgement:1 norm:5 nd:1 dekel:1 carry:1 contains:4 zij:4 karger:1 daniel:1 horvitz:1 subjective:1 recovered:2 com:3 comparing:2 must:4 written:2 john:1 jkl:7 informative:1 cheap:1 stationary:1 intelligence:1 rudin:1 item:50 ruvolo:1 provides:1 boosting:1 mathematical:1 constructed:1 symposium:... |
3,856 | 4,491 | Efficient Sampling for Bipartite Matching Problems
Richard S. Zemel
University of Toronto
zemel@cs.toronto.edu
Maksims N. Volkovs
University of Toronto
mvolkovs@cs.toronto.edu
Abstract
Bipartite matching problems characterize many situations, ranging from ranking
in information retrieval to correspondence in vision.... | 4491 |@word briefly:1 version:2 polynomial:1 open:1 termination:1 seitz:1 recursively:1 mcauley:1 initial:1 liu:2 contains:1 score:1 selecting:2 document:5 outperforms:2 existing:1 current:2 comparing:1 additive:1 partition:4 tailoring:1 shape:1 designed:2 plot:1 aside:1 generative:3 selected:7 half:4 item:52 core:1 sh... |
3,857 | 4,492 | Learning Halfspaces with the Zero-One Loss:
Time-Accuracy Tradeoffs
Aharon Birnbaum and Shai Shalev-Shwartz
School of Computer Science and Engineering
The Hebrew University
Jerusalem, Israel
Abstract
Given ?, ?, we study the time complexity required to improperly learn a halfspace with misclassification error rate of... | 4492 |@word briefly:1 middle:1 achievable:3 polynomial:25 norm:4 open:3 closure:1 harder:1 ecole:1 rkhs:10 written:1 john:1 additive:1 analytic:1 update:1 intelligence:2 provides:1 boosting:3 sigmoidal:1 zhang:2 direct:1 focs:1 introduce:1 expected:1 hardness:1 behavior:1 indeed:2 roughly:1 brain:1 relying:1 increasing... |
3,858 | 4,493 | FastEx: Hash Clustering with Exponential Families
Amr Ahmed?
Research at Google, Mountain View, CA
amra@google.com
Sujith Ravi
Research at Google, Mountain View, CA
sravi@google.com
Alexander J. Smola
Research at Google, Mountain View, CA
alex@smola.org
Shravan M. Narayanamurthy
Microsoft Research, Bangalore, India
... | 4493 |@word briefly:1 achievable:1 norm:2 advantageous:1 nd:2 disk:2 instruction:2 hu:2 d2:1 vldb:2 invoking:1 accommodate:1 series:1 chervonenkis:1 denoting:1 document:14 past:1 current:1 com:3 comparing:2 surprising:1 beygelzimer:1 gmail:1 must:1 realistic:1 partition:6 update:15 hash:17 item:2 plane:1 scotland:1 cor... |
3,859 | 4,494 | Bayesian Warped Gaussian Processes
Miguel L?azaro-Gredilla
Dept. Signal Processing & Communications
Universidad Carlos III de Madrid - Spain
miguel@tsc.uc3m.es
Abstract
Warped Gaussian processes (WGP) [1] model output observations in regression
tasks as a parametric nonlinear transformation of a Gaussian process (GP)... | 4494 |@word repository:1 middle:1 version:1 seems:2 covariance:7 nystr:1 shading:2 selecting:1 initialisation:1 outperforms:1 existing:1 chu:1 must:2 fn:2 numerical:2 realistic:1 stationary:1 generative:1 selected:1 device:1 intelligence:2 isotropic:1 provides:1 complication:1 location:4 toronto:1 revisited:1 zhang:1 d... |
3,860 | 4,495 | Active Comparison of Prediction Models
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 comparing the risks of two given predictiv... | 4495 |@word version:1 polynomial:1 instrumental:7 seek:1 q1:3 concise:1 incurs:1 thereby:2 liu:1 contains:1 outperforms:1 comparing:9 beygelzimer:3 dx:6 readily:2 designed:1 intelligence:1 sawade:4 selected:2 accordingly:1 inspection:1 provides:1 location:2 preference:1 become:1 shorthand:1 prove:2 pairwise:2 expected:... |
3,861 | 4,496 | Learning Multiple Tasks using Shared Hypotheses
Koby Crammer
Department of Electrical Enginering
The Technion - Israel Institute of Technology
Haifa, 32000 Israel
koby@ee.technion.ac.il
Yishay Mansour
School of Computer Science
Tel Aviv University
Tel - Aviv 69978
mansour@tau.ac.il
Abstract
In this work we consider ... | 4496 |@word mild:1 multitask:3 version:2 middle:2 bigram:1 norm:1 seems:1 stronger:1 blender:1 covariance:4 contains:1 envision:1 outperforms:1 nt:5 yet:10 john:3 realistic:1 partition:3 informative:1 kdd:1 remove:1 plot:5 v:15 bart:1 half:5 isotropic:2 location:1 theodoros:3 zhang:2 shatter:2 direct:1 combine:3 fittin... |
3,862 | 4,497 | Emergence of Object-Selective Features in
Unsupervised Feature Learning
Adam Coates, Andrej Karpathy, Andrew Y. Ng
Computer Science Department
Stanford University
Stanford, CA 94305
{acoates,karpathy,ang}@cs.stanford.edu
Abstract
Recent work in unsupervised feature learning has focused on the goal of discovering high... | 4497 |@word version:1 briefly:1 middle:1 seems:1 open:1 hyv:3 decomposition:1 garrigues:1 carry:1 reduction:1 contains:2 interestingly:1 deconvolutional:1 existing:5 current:1 activation:2 yet:3 tackling:1 must:1 si:1 reminiscent:1 devin:1 subsequent:1 distant:1 enables:1 designed:1 half:2 discovering:1 selected:2 fewe... |
3,863 | 4,498 | Approximate Message Passing with Consistent
Parameter Estimation and Applications to Sparse
Learning
Ulugbek S. Kamilov
EPFL
ulugbek.kamilov@epfl.ch
Sundeep Rangan
Polytechnic Institute of New York University
srangan@poly.edu
Alyson K. Fletcher
University of California, Santa Cruz
afletcher@soe.ucsc.edu
Michael Unse... | 4498 |@word trial:1 version:1 pw:1 termination:1 simulation:2 covariance:4 tr:6 moment:3 initial:3 contains:1 bc:1 amp:10 mmse:2 multiuser:3 outperforms:1 current:1 must:2 attracted:1 realize:1 cruz:1 additive:2 numerical:1 enables:1 plot:3 update:4 selected:2 sys:2 dissertation:1 provides:5 characterization:3 node:4 c... |
3,864 | 4,499 | Structured Learning of Gaussian Graphical Models
Karthik Mohan?, Michael Jae-Yoon Chung?, Seungyeop Han?,
Daniela Witten?, Su-In Lee?, Maryam Fazel?
Abstract
We consider estimation of multiple high-dimensional Gaussian graphical models corresponding to a single set of nodes under several distinct conditions. We
assume... | 4499 |@word version:2 norm:17 seems:1 unif:3 d2:1 simulation:1 covariance:9 decomposition:2 kent:1 q1:1 hsieh:1 jacob:2 accommodate:1 initial:1 substitution:1 series:3 selecting:1 egfr:3 outperforms:2 aberrant:1 z2:4 com:1 luo:1 chu:1 must:2 stemming:1 plot:1 update:1 intelligence:1 selected:6 ith:1 detecting:3 iterate... |
3,865 | 45 | 397
AN OPTIMIZATION NETWORK FOR MATRIX INVERSION
Ju-Seog Jang, S~ Young Lee, and Sang-Yung Shin
Korea Advanced Institute of Science and Technology,
P.O. Box 150, Cheongryang, Seoul, Korea
ABSTRACT
Inverse matrix calculation can be considered as an optimization. We have
demonstrated that this problem can be rapidly sol... | 45 |@word effect:2 implemented:5 concept:3 multiplier:2 inversion:9 symmetric:1 correct:1 tji:1 realized:1 simulation:1 i2:1 decomposition:2 during:1 gradient:1 elimination:2 aki:1 essence:1 noted:1 steady:2 kth:1 simulated:1 sci:2 initial:6 configuration:3 opt:2 biological:1 complete:1 vnn:1 considered:2 relationship:... |
3,866 | 450 | The Clusteron: Toward a Simple Abstraction for
a Complex Neuron
Bartlett W. Mel
Computation and Neural Systems
Division of Biology
Caltech, 216-76
Pasadena, CA 91125
mel@cns.caltech.edu
Abstract
Are single neocortical neurons as powerful as multi-layered networks? A
recent compartmental modeling study has shown that ... | 450 |@word determinant:1 open:1 simulation:4 shading:1 contains:1 mainen:1 tuned:1 coactive:1 current:4 activation:7 must:1 john:1 physiol:1 numerical:1 asymptote:1 discrimination:2 alone:1 v:2 selected:1 device:1 mental:1 provides:1 location:8 simpler:1 mathematical:1 along:1 direct:3 differential:1 become:1 ouput:1 c... |
3,867 | 4,500 | Dimensionality Dependent PAC-Bayes Margin Bound
Chi Jin
Key Laboratory of Machine Perception, MOE
School of Physics
Peking University
chijin06@gmail.com
Liwei Wang
Key Laboratory of Machine Perception, MOE
School of EECS
Peking University
wanglw@cis.pku.edu.cn
Abstract
Margin is one of the most important concepts in... | 4500 |@word mild:2 repository:2 version:6 polynomial:3 series:4 contains:3 interestingly:1 recovered:1 com:1 comparing:3 magic04:2 gmail:1 mushroom:2 written:1 john:7 plot:1 provides:2 boosting:9 herbrich:1 simpler:1 zhang:1 become:1 prove:3 consists:2 combine:2 interscience:1 theoretically:1 sacrifice:1 chi:1 decreasi... |
3,868 | 4,501 | Simultaneously Leveraging Output and Task
Structures for Multiple-Output Regression
Piyush Rai?
Dept. of Computer Science
University of Texas at Austin
Austin, TX
piyush@cs.utexas.edu
Abhishek Kumar?
Dept. of Computer Science
University of Maryland
College Park, MD
abhishek@cs.umd.edu
Hal Daum?e III
Dept. of Compute... | 4501 |@word multitask:5 version:2 middle:1 norm:2 covariance:60 simplifying:1 tr:31 reduction:2 liu:3 contains:1 series:1 past:1 existing:4 comparing:1 drop:1 plot:4 v:2 alone:1 discovering:1 selected:5 parameterization:1 beginning:1 provides:2 theodoros:1 zhang:2 along:4 become:1 ik:5 introduce:1 roughly:2 behavior:1 ... |
3,869 | 4,502 | High-Order Multi-Task Feature Learning to Identify
Longitudinal Phenotypic Markers for Alzheimer?s
Disease Progression Prediction
Hua Wang, Feiping Nie, Heng Huang,
Department of Computer Science and Engineering,
University of Texas at Arlington, Arlington, TX 76019
{huawangcs, feipingnie}@gmail.com, heng@uta.edu
Jing... | 4502 |@word mild:2 trial:4 version:5 mri:7 middle:4 norm:23 hippocampus:1 km:5 bn:1 lobe:1 thereby:1 tr:42 necessity:1 liu:1 series:1 score:26 ours:3 longitudinal:34 existing:4 com:1 gmail:1 yet:1 chu:1 evans:1 j1:4 enables:1 plot:1 designed:1 update:1 medial:2 selected:5 lr:6 mental:1 provides:1 org:2 firstly:1 zhang:... |
3,870 | 4,503 | The Lov?asz ? function, SVMs and finding large dense
subgraphs
Vinay Jethava ?
Computer Science & Engineering Department,
Chalmers University of Technology
412 96, Goteborg, SWEDEN
jethava@chalmers.se
Chiranjib Bhattacharyya
Department of CSA,
Indian Institute of Science
Bangalore, 560012, INDIA
chiru@csa.iisc.ernet.i... | 4503 |@word version:1 polynomial:3 seems:1 norm:3 dekel:1 open:2 pub:1 bhattacharyya:1 semirandom:1 existing:1 recovered:1 whp:1 nt:10 surprising:1 current:1 scovel:1 gurevich:1 readily:1 partition:1 greedy:1 cue:1 intelligence:1 inspection:1 ith:1 characterization:2 detecting:2 cse:2 node:1 provides:1 ron:1 math:1 mat... |
3,871 | 4,504 | Recovery of Sparse Probability Measures via Convex
Programming
Mert Pilanci and Laurent El Ghaoui
Electrical Engineering and Computer Science
University of California Berkeley
Berkeley, CA 94720
{mert,elghaoui}@eecs.berkeley.edu
Venkat Chandrasekaran
Department of Computing and Mathematical Sciences
California Institu... | 4504 |@word trial:1 version:2 polynomial:1 norm:7 open:1 simulation:1 covariance:1 decomposition:1 pick:1 moment:9 contains:1 denoting:1 interestingly:1 outperforms:2 attracted:1 readily:1 numerical:3 drop:1 depict:1 farkas:1 update:3 warmuth:1 xk:8 probi:1 reciprocal:2 core:1 record:1 allerton:2 mathematical:1 direct:... |
3,872 | 4,505 | Privacy Aware Learning
1
John C. Duchi1
Michael I. Jordan1,2
Martin J. Wainwright1,2
Department of Electrical Engineering and Computer Science, 2 Department of Statistics
University of California, Berkeley
Berkeley, CA USA 94720
{jduchi,jordan,wainwrig}@eecs.berkeley.edu
Abstract
We study statistical risk minimizati... | 4505 |@word private:13 version:8 eliminating:1 polynomial:1 norm:9 justice:1 suitably:1 open:2 willing:1 seek:1 sgd:2 thereby:1 contains:1 series:1 ours:2 past:1 wainwrig:1 current:1 must:13 john:1 subsequent:1 numerical:1 designed:1 ligett:1 juditsky:2 selected:1 leaf:1 smith:4 provides:3 characterization:3 bijection:... |
3,873 | 4,506 | Multiplicative Forests for Continuous-Time Processes
Jeremy C. Weiss
University of Wisconsin
Madison, WI 53706, USA
Sriraam Natarajan
Wake Forest University
Winston Salem, NC 27157, USA
David Page
University of Wisconsin
Madison, WI 53706, USA
jcweiss@cs.wisc.edu
snataraj@wakehealth.edu
page@biostat.wisc.edu
Abs... | 4506 |@word briefly:1 stronger:1 p0:9 pressure:3 thereby:1 initial:4 cyclic:2 series:5 score:4 outperforms:1 current:4 discretization:1 blank:4 comparing:1 si:11 additive:2 partition:57 remove:2 update:8 resampling:1 aside:1 greedy:4 selected:4 fewer:3 generative:2 leaf:9 website:2 accordingly:1 xk:2 intelligence:1 pro... |
3,874 | 4,507 | Dual-Space Analysis of the Sparse Linear Model
David Wipf and Yi Wu
Visual Computing Group, Microsoft Research Asia
davidwipf@gmail.com, jxwuyi@gmail.com
Abstract
Sparse linear (or generalized linear) models combine a standard likelihood function with a sparse prior on the unknown coefficients. These priors can conve... | 4507 |@word trial:6 version:1 compression:1 norm:8 advantageous:2 stronger:2 simulation:3 crucially:1 tried:1 solid:1 accommodate:1 delgado:3 initial:1 series:1 efficacy:1 denoting:1 existing:1 kx0:2 com:2 gmail:2 dx:2 must:3 recasting:1 happen:1 pertinent:1 plot:1 update:7 stationary:1 generative:2 implying:1 fewer:1 ... |
3,875 | 4,508 | Supervised Learning with Similarity Functions
Purushottam Kar
Indian Institute of Technology
Kanpur, INDIA
purushot@cse.iitk.ac.in
Prateek Jain
Microsoft Research Lab
Bangalore, INDIA
prajain@microsoft.com
Abstract
We address the problem of general supervised learning when data can only be accessed through an (indefi... | 4508 |@word repository:3 version:1 polynomial:1 seems:2 nd:1 liblinear:1 reduction:4 offering:1 rkhs:3 existing:6 com:1 yet:1 chu:1 must:2 informative:4 compel:1 landmarked:11 plot:1 interpretable:1 v:1 greedy:2 instantiate:1 weighing:2 selected:1 intelligence:2 theoretician:1 incredible:1 provides:3 equi:2 cse:1 node:... |
3,876 | 4,509 | Query Complexity of Derivative-Free Optimization
Kevin G. Jamieson
University of Wisconsin
Madison, WI 53706, USA
Robert D. Nowak
University of Wisconsin
Madison, WI 53706, USA
Benjamin Recht
University of Wisconsin
Madison, WI 53706, USA
kgjamieson@wisc.edu
nowak@engr.wisc.edu
brecht@cs.wisc.edu
Abstract
This pa... | 4509 |@word polynomial:2 dekel:1 open:2 simulation:2 nemirovsky:1 simplifying:1 p0:3 pick:1 mention:1 initial:3 configuration:1 selecting:2 tuned:1 lang:1 must:2 john:1 exposing:1 additive:2 numerical:1 shape:1 ainen:1 juditsky:1 xk:46 ith:1 dover:1 core:1 provides:1 location:2 mathematical:1 c2:4 differential:1 prove:... |
3,877 | 451 | Neural Network Diagnosis of Avascular Necrosis
from Magnetic Resonance Images
Armando Manduca
Dept. of Physiology and Biophysics
Mayo Clinic
Rochester, MN 55905
Paul Christy
Dept. of Diagnostic Radiology
Mayo Clinic
Rochester, MN 55905
Richard Ehman
Dept. of Diagnostic Radiology
Mayo Clinic
Rochester, MN 55905
Abst... | 451 |@word trial:1 middle:1 mri:3 gradual:1 initial:1 configuration:2 yet:1 intriguing:1 readily:2 visible:1 shape:1 wanted:1 aside:1 half:3 selected:3 fewer:1 accordingly:1 cse:1 node:10 traverse:1 x128:3 five:3 supply:2 fullyconnected:1 expected:1 multi:1 little:1 actual:1 encouraging:1 ehman:4 interpreted:1 develope... |
3,878 | 4,510 | Reducing statistical time-series problems to binary
classification
J?er?emie Mary
SequeL-INRIA/LIFL-CNRS,
Universit?e de Lille, France
Jeremie.Mary@inria.fr
Daniil Ryabko
SequeL-INRIA/LIFL-CNRS,
Universit?e de Lille, France
daniil@ryabko.net
Abstract
We show how binary classification methods developed to work on i.i.... | 4510 |@word mild:1 polynomial:2 compression:2 seems:1 smirnov:1 stronger:4 norm:1 vldb:1 covariance:1 reduction:3 necessity:1 series:41 chervonenkis:2 beygelzimer:1 john:1 fn:3 realistic:1 n0:7 discrimination:1 stationary:25 selected:1 xk:4 mental:1 detecting:1 math:1 hyperplanes:1 simpler:1 mathematical:1 constructed:... |
3,879 | 4,511 | On Lifting the Gibbs Sampling Algorithm
Vibhav Gogate
Department of Computer Science
The University of Texas at Dallas
Richardson, TX, 75080, USA
vgogate@hlt.utdallas.edu
Deepak Venugopal
Department of Computer Science
The University of Texas at Dallas
Richardson, TX, 75080, USA
dxv021000@utdallas.edu
Abstract
First... | 4511 |@word kong:2 polynomial:4 heuristically:1 simulation:1 prominence:1 covariance:1 thereby:1 mention:1 recursively:1 substitution:1 contains:3 liu:2 document:1 existing:4 current:3 comparing:2 chicago:1 partition:5 drop:1 update:3 stationary:3 greedy:1 instantiate:2 selected:3 intelligence:4 braz:1 mln:39 xk:8 num:... |
3,880 | 4,512 | Affine Independent Variational Inference
Edward Challis
David Barber
Department of Computer Science
University College London, UK
{edward.challis,david.barber}@cs.ucl.ac.uk
Abstract
We consider inference in a broad class of non-conjugate probabilistic models
based on minimising the Kullback-Leibler divergence between... | 4512 |@word proportion:1 stronger:1 open:1 d2:3 lup:1 covariance:2 decomposition:1 delgado:1 moment:2 bai:6 existing:1 current:1 wd:4 readily:2 fn:12 numerical:4 subsequent:1 partition:2 confirming:1 additive:1 enables:1 analytic:3 plot:3 bickson:1 intelligence:3 core:1 blei:1 provides:1 iterates:1 complication:1 revis... |
3,881 | 4,513 | Predicting Action Content On-Line and in
Real Time before Action Onset ? an
Intracranial Human Study
Shengxuan Ye
California Institute of Technology
Pasadena, CA
sye@caltech.edu
Uri Maoz
California Institute of Technology
Pasadena, CA
urim@caltech.edu
Ian Ross
Huntington Hospital
Pasadena, CA
ianrossmd@aol.com
Adam ... | 4513 |@word neurophysiology:3 trial:41 determinant:1 version:2 cingulate:1 hippocampus:1 approved:1 proportion:1 norm:1 instruction:2 pulse:1 r:5 lobe:1 pressed:2 bai:2 score:4 subjective:1 past:2 current:5 com:1 anterior:1 si:1 router:1 realistic:2 motor:5 wanted:2 drop:11 medial:1 libet:5 half:2 selected:1 guess:1 to... |
3,882 | 4,514 | Risk Aversion in Markov Decision Processes
via Near-Optimal Chernoff Bounds
Pieter Abbeel
Department of Computer Science
University of California at Berkeley
Berkeley CA 94720, USA
pabbeel@cs.berkeley.edu
Teodor Mihai Moldovan
Department of Computer Science
University of California at Berkeley
Berkeley CA 94720, USA
... | 4514 |@word version:1 seems:2 reused:1 willing:1 pieter:1 incurs:1 minus:14 minmax:10 past:1 lave:1 com:1 john:2 additive:1 subsequent:1 enables:1 analytic:1 jfk:2 plot:2 treating:1 remove:1 hash:1 v:1 fewer:1 assurance:1 parametrization:2 colored:1 ps0:1 provides:2 mannor:3 successive:1 mathematical:1 become:1 consist... |
3,883 | 4,515 | Strategic Impatience in Go/NoGo versus
Forced-Choice Decision-Making
Angela J. Yu
Cognitive Science Department
University of California, San Diego
La Jolla, CA, 92093
ajyu@ucsd.edu
Pradeep Shenoy
Cognitive Science Department
University of California, San Diego
La Jolla, CA, 92093
pshenoy@ucsd.edu
Abstract
Two-altern... | 4515 |@word neurophysiology:1 trial:28 illustrating:1 briefly:1 eliminating:3 judgement:1 version:4 simulation:6 accounting:1 incurs:1 solid:2 moment:2 initial:4 contains:1 series:1 cherian:1 existing:1 current:4 activation:1 must:4 shape:1 motor:2 hypothesize:2 discrimination:2 generative:1 fewer:2 rts:1 beginning:1 d... |
3,884 | 4,516 | Discriminative Learning of Sum-Product Networks
Robert Gens
Pedro Domingos
Department of Computer Science and Engineering
University of Washington
Seattle, WA 98195-2350, U.S.A.
{rcg,pedrod}@cs.washington.edu
Abstract
Sum-product networks are a new deep architecture that can perform fast, exact inference on high-tree... | 4516 |@word polynomial:6 nd:1 twelfth:1 hyv:1 memoize:1 rgb:1 covariance:1 accommodate:1 harder:1 generatively:3 series:1 united:1 selecting:1 document:1 fa8750:1 past:1 existing:1 si:1 yet:1 partition:5 informative:1 blur:1 hypothesize:1 update:8 v:2 generative:13 fewer:2 leaf:2 prohibitive:1 item:4 half:1 intelligenc... |
3,885 | 4,517 | Meta-Gaussian Information Bottleneck
M?elanie Rey
Department of Mathematics and Computer Science
University of Basel
melanie.rey@unibas.ch
Volker Roth
Department of Mathematics and Computer Science
University of Basel
volker.roth@unibas.ch
Abstract
We present a reformulation of the information bottleneck (IB) problem ... | 4517 |@word determinant:1 version:1 repository:1 compression:22 nd:3 open:4 simulation:1 tried:1 covariance:10 reduction:1 contains:3 score:9 series:1 outperforms:2 unibas:2 current:1 z2:1 dx:2 must:2 numerical:1 informative:2 zik:6 selected:3 fx1:1 short:2 provides:1 allerton:1 direct:2 differential:1 beta:4 consists:... |
3,886 | 4,518 | Factoring nonnegative matrices with linear programs
Victor Bittorf
bittorf@cs.wisc.edu
Benjamin Recht
brecht@cs.wisc.edu
Computer Sciences
University of Wisconsin
Christopher R?e
chrisre@cs.wisc.edu
Joel A. Tropp
Computing and Mathematical Sciences
California Institute of Technology
tropp@cms.caltech.edu
Abstract
... | 4518 |@word middle:2 version:4 polynomial:2 norm:8 stronger:1 nd:1 d2:2 seek:1 decomposition:5 pick:1 sgd:2 tr:5 reduction:2 configuration:2 contains:4 series:1 selecting:2 document:2 outperforms:1 comparing:1 com:3 toh:1 must:4 belmont:1 numerical:2 hofmann:1 remove:1 drop:1 plot:3 update:4 polyphonic:1 prohibitive:1 ... |
3,887 | 4,519 | Adaptive Strati?ed Sampling for Monte-Carlo
integration of Differentiable functions
Alexandra Carpentier
Statistical Laboratory, CMS
Wilberforce Road, Cambridge
CB3 0WB UK
a.carpentier@statslab.cam.ac.uk
R?emi Munos
INRIA Lille - Nord Europe
40, avenue Halley
59000 Villeneuve d?ascq, France
remi.munos@inria.fr
Abstra... | 4519 |@word exploitation:1 version:1 proportion:5 stronger:2 simulation:1 harder:1 ld:2 initial:2 contains:1 nally:2 tackling:1 dx:20 numerical:2 partition:26 shape:4 enables:1 etor:1 designed:1 accordingly:1 cult:1 xk:8 beginning:1 provides:3 math:1 wkd:1 become:1 prove:7 consists:1 advocate:1 interscience:1 introduce... |
3,888 | 452 | Obstacle Avoidance through Reinforcement
Learning
Tony J. Prescott and John E. W. Maybew
Artificial Intelligence and Vision Research Unit.
University of Sheffield. S 10 2TN. England.
Abstract
A method is described for generating plan-like. reflexive. obstacle
avoidance behaviour in a mobile robot. The experiments rep... | 452 |@word open:1 simulation:9 attainable:1 tr:1 initial:2 configuration:1 selecting:1 denoting:1 reaction:2 existing:1 current:5 nt:2 must:1 john:1 enables:1 update:1 stationary:1 intelligence:1 deadlock:1 slowing:1 realism:1 short:3 coarse:2 math:1 location:1 five:3 mathematical:1 along:1 consists:3 ray:8 acquired:6 ... |
3,889 | 4,520 | Mandatory Leaf Node Prediction in
Hierarchical Multilabel Classification
Wei Bi
James T. Kwok
Department of Computer Science and Engineering
Hong Kong University of Science and Technology
Clear Water Bay, Hong Kong
{weibi,jamesk}@cse.ust.hk
Abstract
In hierarchical classification, the prediction paths may be required ... | 4520 |@word multitask:3 kong:3 version:1 stronger:2 c0:1 heuristically:1 pick:2 recursively:1 initial:1 contains:4 denoting:1 document:3 longitudinal:1 outperforms:2 existing:3 freitas:2 ust:1 written:1 must:1 remove:1 designed:1 update:3 v:1 implying:1 greedy:5 leaf:38 pursued:1 selected:2 fewer:1 accordingly:1 intell... |
3,890 | 4,521 | Bayesian estimation of discrete entropy with mixtures
of stick-breaking priors
Evan Archer?124 , Il Memming Park?234 , & Jonathan W. Pillow234
1. Institute for Computational and Engineering Sciences
2. Center for Perceptual Systems, 3. Dept. of Psychology,
4. Division of Statistics & Scientific Computation
The Universi... | 4521 |@word illustrating:1 proportion:1 grey:1 simulation:1 ld:2 moment:5 series:1 horvitz:1 ka:1 written:3 must:1 grassberger:1 numerical:1 informative:1 noninformative:1 analytic:5 compution:1 plot:1 fewer:2 selected:1 leaf:1 affair:1 sys:4 short:1 provides:4 revisited:1 mathematical:2 direct:2 beta:4 qualitative:1 m... |
3,891 | 4,522 | Practical Bayesian Optimization of Machine
Learning Algorithms
Jasper Snoek
Department of Computer Science
University of Toronto
jasper@cs.toronto.edu
Hugo Larochelle
Department of Computer Science
University of Sherbrooke
hugo.larochelle@usherbrooke.edu
Ryan P. Adams
School of Engineering and Applied Sciences
Harva... | 4522 |@word economically:1 version:2 faculty:1 briefly:2 exploitation:2 mockus:1 d2:1 zilinskas:1 covariance:19 pick:1 configuration:3 series:1 quo:1 selecting:2 tuned:1 document:5 outperforms:2 existing:1 freitas:2 current:4 com:2 danny:1 must:7 john:1 enables:1 plot:1 update:1 alone:1 greedy:1 fewer:1 half:1 prohibit... |
3,892 | 4,523 | A quasi-Newton proximal splitting method
S. Becker?
M.J. Fadili?
Abstract
A new result in convex analysis on the calculation of proximity operators in certain scaled norms is derived. We describe efficient implementations of the proximity calculation for a useful class of functions; the implementations exploit the
p... | 4523 |@word version:5 inversion:1 polynomial:1 norm:11 nd:1 open:1 calculus:2 ipm:3 hager:1 hereafter:1 tuned:1 nonmonotone:1 recovered:1 optim:2 hearn:1 yet:1 tackling:1 written:1 must:4 luis:1 numerical:6 designed:2 update:11 kyk:1 xk:22 eminent:1 provides:2 math:5 simpler:2 zhang:2 along:2 differential:1 ray:1 manne... |
3,893 | 4,524 | A provably efficient simplex algorithm for
classification
Elad Hazan ?
Technion - Israel Inst. of Tech.
Haifa, 32000
ehazan@ie.technion.ac.il
Zohar Karnin
Yahoo! Research
Haifa
zkarnin@ymail.com
Abstract
We present a simplex algorithm for linear programming in a linear classification
formulation. The paramount comple... | 4524 |@word version:1 polynomial:20 norm:23 seems:1 nd:3 solver1:1 km:3 hu:1 bn:1 mention:1 reduction:3 initial:2 contains:3 woodruff:1 daniel:2 current:1 com:1 ka:2 yet:1 assigning:1 written:2 must:7 john:1 additive:4 partition:1 happen:1 designed:1 alone:1 plane:6 xk:1 provides:2 math:2 kelner:2 simpler:1 five:1 math... |
3,894 | 4,525 | Patient Risk Stratification for Hospital-Associated
C. diff as a Time-Series Classification Task
Jenna Wiens
jwiens@mit.edu
John V. Guttag
guttag@mit.edu
Eric Horvitz
horvitz@microsoft.com
Abstract
A patient?s risk for adverse events is affected by temporal processes including the
nature and timing of diagnostic an... | 4525 |@word nd:1 thereby:1 initial:2 series:38 score:7 selecting:1 contains:1 horvitz:4 outperforms:1 existing:1 current:16 com:1 incidence:1 yet:2 must:1 john:1 fn:1 concatenate:1 dupont:1 hypothesize:2 remove:3 exploded:1 n0:1 alone:1 half:1 selected:1 twostate:1 fpr:1 short:1 complication:1 location:3 hospitalized:6... |
3,895 | 4,526 | Deep Spatio-Temporal Architectures and Learning
for Protein Structure Prediction
Pietro Di Lena, Ken Nagata, Pierre Baldi
Department of Computer Science, Institute for Genomics and Bioinformatics
University of California, Irvine
{pdilena,knagata,pfbaldi}@[ics.]uci.edu
Abstract
Residue-residue contact prediction is a f... | 4526 |@word repository:1 private:1 achievable:1 seems:2 advantageous:1 nd:1 simulation:3 r:1 hsieh:1 dramatic:1 shot:2 initial:1 series:1 selecting:1 tuned:1 past:1 current:2 recovered:1 parsing:1 refines:2 informative:1 enables:1 opin:1 designed:1 progressively:1 greedy:1 selected:1 vanishing:4 short:3 provides:3 coar... |
3,896 | 4,527 | Synchronization can Control Regularization in
Neural Systems via Correlated Noise Processes
Jake Bouvrie
Department of Mathematics
Duke University
Durham, NC 27708
jvb@math.duke.edu
Jean-Jacques Slotine
Nonlinear Systems Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02138
jjs@mit.edu
Abstract
To lea... | 4527 |@word trial:2 middle:2 stronger:1 nd:1 open:1 grey:1 simulation:4 pancreatic:2 covariance:6 contraction:1 elisseeff:1 dramatic:1 sgd:1 tr:7 accommodate:1 moment:1 reduction:4 liu:1 initial:2 ording:1 dx:2 must:3 readily:1 written:1 additive:3 visible:1 wx:1 confirming:1 sdes:2 plot:9 drop:2 rinzel:1 discriminatio... |
3,897 | 4,528 | Classi?cation Calibration Dimension for
General Multiclass Losses
Harish G. Ramaswamy Shivani Agarwal
Department of Computer Science and Automation
Indian Institute of Science, Bangalore 560012, India
{harish gurup,shivani}@csa.iisc.ernet.in
Abstract
We study consistency properties of surrogate loss functions for gene... | 4528 |@word wenxin:1 illustrating:1 version:1 nd:2 r:1 q1:7 arti:1 liu:1 contains:1 score:1 document:6 brien:1 z2:2 nt:1 surprising:1 written:2 john:1 cant:1 mackey:1 half:1 intelligence:1 cult:1 farther:1 boosting:1 preference:3 zhang:4 daphne:1 incorrect:1 prove:1 consists:1 introduce:1 pairwise:7 indeed:1 expected:2... |
3,898 | 4,529 | Interpreting prediction markets: a stochastic
approach
Nicol?
as Della Penna
Research School of Computer Science
The Australian National University
me@nikete.com
Rafael M. Frongillo
Computer Science Divison
University of California, Berkeley
raf@cs.berkeley.edu
Mark D. Reid
Research School of Computer Science
The Au... | 4529 |@word middle:2 version:5 seems:1 open:2 seek:1 crucially:1 simulation:7 initial:1 contains:1 offering:1 existing:2 current:5 com:1 surprising:1 analysed:2 beygelzimer:1 must:5 plot:1 update:10 stationary:8 nq:1 accordingly:1 provides:1 supply:1 prove:1 manner:1 divison:1 excellence:1 theoretically:1 expected:5 in... |
3,899 | 453 | Experimental Evaluation of Learning in a Neural Microsystem
Joshua Alspector Anthony Jayakumar Stephan Lunat
Bellcore
Morristown, NJ 07962-1910
Abstract
We report learning measurements from a system composed of a cascadable
learning chip, data generators and analyzers for training pattern presentation,
and an X-windo... | 453 |@word version:1 sharpens:1 simulation:4 electronics:1 contains:1 current:7 activation:2 yet:1 chu:1 visible:1 designed:2 plot:4 update:1 accordingly:1 short:1 node:1 accessed:1 lor:1 along:1 constructed:1 beta:1 supply:2 replication:11 microchip:3 behavior:1 alspector:17 dist:1 roughly:4 simulator:2 window:3 lll:3... |
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