Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
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3,100 | 3,809 | Convergent Temporal-Difference Learning with
Arbitrary Smooth Function Approximation
Hamid R. Maei
University of Alberta
Edmonton, AB, Canada
Csaba Szepesv?ari?
University of Alberta
Edmonton, AB, Canada
Shalabh Bhatnagar
Indian Institute of Science
Bangalore, India
Doina Precup
McGill University
Montreal, QC, Canad... | 3809 |@word mild:1 proceeded:1 version:1 norm:1 twelfth:1 open:2 simulation:1 initial:2 contains:1 prefix:1 si:1 written:3 must:1 wiewiora:1 happen:2 shape:1 designed:1 drop:2 update:12 plot:1 stationary:2 pursued:1 intelligence:1 plane:7 provides:2 iterates:2 draft:1 mannor:1 hyperplanes:1 zhang:2 become:2 farahmand:2... |
3,101 | 381 | Using Genetic Algorithms to Improve
Pattern Classification Performance
Eric I. Chang and Richard P. Lippmann
Lincoln Laboratory, MIT
Lexington, MA 02173-9108
Abstract
Genetic algorithms were used to select and create features and to select
reference exemplar patterns for machine vision and speech pattern classificati... | 381 |@word trial:2 eliminating:1 polynomial:3 replicate:1 gradual:1 pressure:1 reduction:1 initial:1 selecting:2 genetic:38 must:1 plot:2 sponsored:1 discrimination:1 half:1 fewer:4 selected:6 intelligence:1 num:1 node:1 simpler:1 five:1 become:3 consists:1 kenney:1 roughly:3 frequently:1 automatically:1 little:1 incre... |
3,102 | 3,810 | Local Rules for Global MAP: When Do They Work ?
Kyomin Jung?
KAIST
Daejeon, Korea
kyomin@kaist.edu
Pushmeet Kohli
Microsoft Research
Cambridge, UK
pkohli@microsoft.com
Devavrat Shah
MIT
Cambridge, MA, USA
devavrat@mit.edu
Abstract
We consider the question of computing Maximum A Posteriori (MAP) assignment
in an arbi... | 3810 |@word kohli:1 trial:2 polynomial:18 simulation:10 mitsubishi:1 pick:2 tr:1 initial:5 series:1 contains:1 selecting:3 ours:3 past:1 imaginary:2 current:2 com:1 surprising:1 plot:4 update:19 intelligence:1 selected:1 short:1 completeness:1 provides:1 node:23 mendel:1 along:1 c2:3 become:1 symposium:2 prove:2 consis... |
3,103 | 3,811 | A General Projection Property for Distribution Families
Yao-Liang Yu
Yuxi Li Dale Schuurmans Csaba Szepesv?ari
Department of Computing Science
University of Alberta
Edmonton, AB, T6G 2E8 Canada
{yaoliang,yuxi,dale,szepesva}@cs.ualberta.ca
Abstract
Surjectivity of linear projections between distribution families with... | 3811 |@word version:2 eliminating:1 closure:1 seek:1 covariance:11 simplifying:1 prominence:1 thereby:1 reduction:1 moment:8 ours:1 interestingly:1 bhattacharyya:1 existing:2 recovered:1 comparing:3 surprising:2 olkin:1 yet:2 must:1 john:1 fund:1 stationary:1 akiko:1 provides:1 mannor:2 simpler:5 zhang:1 mathematical:1... |
3,104 | 3,812 | Streaming k-means approximation
Nir Ailon
Google Research
nailon@google.com
Ragesh Jaiswal?
Columbia University
rjaiswal@gmail.com
Claire Monteleoni?
Columbia University
cmontel@ccls.columbia.edu
Abstract
We provide a clustering algorithm that approximately optimizes the k-means objective, in the one-pass streaming ... | 3812 |@word repository:1 version:2 stronger:1 reused:1 d2:2 km:9 simulation:2 pick:4 tr:3 moment:1 initial:1 uncovered:5 contains:1 selecting:1 ours:1 spambase:3 mishra:1 current:2 com:2 si:3 gmail:1 yet:1 must:4 sergei:2 realistic:1 seeding:14 designed:2 plot:1 v:2 alone:1 implying:1 device:1 item:4 ith:3 revisited:1 ... |
3,105 | 3,813 | Fast subtree kernels on graphs
Nino Shervashidze, Karsten M. Borgwardt
Interdepartmental Bioinformatics Group
Max Planck Institutes T?ubingen, Germany
{nino.shervashidze,karsten.borgwardt}@tuebingen.mpg.de
Abstract
In this article, we propose fast subtree kernels on graphs. On graphs with n nodes
and m edges and maxi... | 3813 |@word kong:1 compression:5 seems:1 flach:1 open:1 recursively:2 reduction:2 initial:1 cyclic:1 united:1 initialisation:1 interestingly:1 prefix:2 kurt:1 outperforms:1 perret:1 current:1 comparing:3 si:17 written:1 concatenate:2 mutagenic:3 hofmann:1 graphlets:2 hash:4 half:1 intelligence:1 warmuth:1 core:1 record... |
3,106 | 3,814 | Learning to Explore and Exploit in POMDPs
Chenghui Cai, Xuejun Liao, and Lawrence Carin
Department of Electrical and Computer Engineering
Duke University
Durham, NC 27708-0291, USA
Abstract
A fundamental objective in reinforcement learning is the maintenance of a proper
balance between exploration and exploitation. T... | 3814 |@word exploitation:28 middle:1 polynomial:2 termination:2 tried:1 incurs:1 minus:7 initial:2 selecting:1 denoting:3 o2:1 existing:1 current:3 yet:1 must:1 happen:1 informative:1 shape:1 noninformative:1 designed:1 drop:1 update:7 v:1 alone:1 intelligence:3 accordingly:1 beginning:1 provides:2 along:1 beta:1 becom... |
3,107 | 3,815 | Conditional Random Fields with High-Order
Features for Sequence Labeling
Dan Wu
Hai Leong Chieu
Nan Ye
Wee Sun Lee
Singapore MIT Alliance
Department of Computer Science DSO National Laboratories
National University of Singapore chaileon@dso.org.sg National University of Singapore
{yenan,leews}@comp.nus.edu.sg
dwu@nus... | 3815 |@word illustrating:1 version:2 polynomial:4 nd:1 kulp:1 asks:1 contains:1 score:7 selecting:1 series:1 bc:5 prefix:6 current:3 z2:13 si:10 parsing:1 partition:3 wx:5 hofmann:1 remove:1 hypothesize:1 alone:2 selected:1 plane:2 mccallum:2 short:1 dissertation:1 record:1 boosting:2 location:1 org:1 zhang:1 become:2 ... |
3,108 | 3,816 | Quanti?cation and the language of thought
Charles Kemp
Department of Psychology
Carnegie Mellon University
ckemp@cmu.edu
Abstract
Many researchers have suggested that the psychological complexity of a concept
is related to the length of its representation in a language of thought. As yet,
however, there are few concre... | 3816 |@word version:1 nd:1 pick:2 concise:1 mention:1 harder:2 accommodate:1 contains:1 fragment:1 score:1 subjective:2 existing:1 current:2 comparing:4 yet:2 written:1 readily:1 cottrell:1 partition:10 cant:1 cheap:1 plot:2 pursued:1 item:34 cult:3 shj:14 colored:1 mental:9 provides:1 gx:1 simpler:2 mathematical:3 alo... |
3,109 | 3,817 | Accelerated Gradient Methods for
Stochastic Optimization and Online Learning
Chonghai Hu?? , James T. Kwok? , Weike Pan?
? Department of Computer Science and Engineering
Hong Kong University of Science and Technology
Clear Water Bay, Kowloon, Hong Kong
? Department of Mathematics, Zhejiang University
Hangzhou, China
h... | 3817 |@word kong:3 version:1 norm:3 d2:5 hu:2 linearized:1 recapitulate:1 sgd:3 boundedness:1 bai:1 initial:1 series:1 outperforms:1 current:1 com:1 exy:2 gmail:1 ust:1 subsequent:1 plot:1 update:16 fewer:1 folos:13 core:1 filtered:1 math:1 cse:1 mathematical:1 introductory:1 introduce:2 theoretically:2 expected:2 inde... |
3,110 | 3,818 | Sufficient Conditions for Agnostic Active Learnable
Liwei Wang
Key Laboratory of Machine Perception, MOE,
School of Electronics Engineering and Computer Science,
Peking University,
wanglw@cis.pku.edu.cn
Abstract
We study pool-based active learning in the presence of noise, i.e. the agnostic setting. Previous works ha... | 3818 |@word briefly:2 version:2 polynomial:5 norm:1 c0:1 open:1 electronics:1 series:1 contains:1 current:1 nt:1 beygelzimer:1 si:8 dx:29 must:4 written:1 fn:2 realistic:1 j1:1 remove:1 fewer:2 num:1 coarse:1 unbounded:3 c2:5 vjk:8 differential:1 tsy04:3 prove:3 introduce:2 x0:11 indeed:1 automatically:1 encouraging:1 ... |
3,111 | 3,819 | Which graphical models are difficult to learn?
Andrea Montanari
Department of Electrical Engineering and
Department of Statistics
Stanford University
montanari@stanford.edu
Jos?e Bento
Department of Electrical Engineering
Stanford University
jbento@stanford.edu
Abstract
We consider the problem of learning the struct... | 3819 |@word version:1 polynomial:3 norm:1 calculus:1 simulation:1 bn:6 pg:1 series:1 score:2 hereafter:1 selecting:1 surprising:1 must:2 portuguese:1 numerical:6 leaf:1 dembo:1 ith:1 core:5 characterization:1 math:4 node:12 contribute:1 unbounded:3 warmup:1 along:1 become:1 yuan:1 prove:4 consists:3 shorthand:1 introdu... |
3,112 | 382 | Shaping the State Space Landscape in Recurrent
Networks
Patrice Y. Simard >I<
Computer Science Dept.
University of Rochester
Rochester, NY 14627
Jean Pierre Raysz
LIUC
Universite de Caen
14032 Caen Cedex
France
Bernard Victorri
ELSAP
Universite de Caen
14032 Caen Cedex
France
Abstract
Fully recurrent (asymmetrical)... | 382 |@word version:1 norm:1 simulation:1 propagate:1 contraction:4 awij:1 discretization:1 activation:8 dx:1 must:1 written:1 visible:8 numerical:1 shape:1 update:2 alone:1 accordingly:1 xk:2 provides:1 five:2 along:1 consists:2 introduce:1 indeed:2 simulator:1 unfolded:1 little:1 increasing:1 notation:1 what:2 eigenve... |
3,113 | 3,820 | Learning models of object structure
Joseph Schlecht
Department of Computer Science
University of Arizona
Kobus Barnard
Department of Computer Science
University of Arizona
schlecht@cs.arizona.edu
kobus@cs.arizona.edu
Abstract
We present an approach for learning stochastic geometric models of object categories from... | 3820 |@word briefly:1 proportion:1 glue:1 triggs:1 open:1 r:16 bn:1 covariance:3 git:1 fifteen:1 solid:1 configuration:3 liu:1 selecting:1 hoiem:1 denoting:1 ours:1 freitas:1 discretization:1 surprising:1 yet:1 must:2 readily:2 parsing:1 numerical:1 informative:2 shape:1 enables:2 treating:1 alone:1 cue:1 generative:5 ... |
3,114 | 3,821 | From PAC-Bayes Bounds to KL Regularization
Pascal Germain, Alexandre Lacasse, Franc?ois Laviolette, Mario Marchand, Sara Shanian
Department of Computer Science and Software Engineering
Laval University, Qu?ebec (QC), Canada
firstname.secondname@ift.ulaval.ca
Abstract
We show that convex KL-regularized objective funct... | 3821 |@word repository:1 version:2 inversion:1 norm:1 c0:2 r:9 tried:1 q1:15 series:3 disparity:1 selecting:3 interestingly:1 outperforms:3 err:1 mushroom:1 yet:1 john:3 numerical:3 remove:1 update:3 half:2 warmuth:1 isotropic:1 short:1 manfred:1 boosting:6 org:1 mathematical:1 consists:3 indeed:2 automatically:1 littl... |
3,115 | 3,822 | Convex Relaxation of Mixture Regression with
Efficient Algorithms
Novi Quadrianto, Tib?erio S. Caetano, John Lim
NICTA - Australian National University
Canberra, Australia
{firstname.lastname}@nicta.com.au
Dale Schuurmans
University of Alberta
Edmonton, Canada
dale@cs.ualberta.ca
Abstract
We develop a convex relaxat... | 3822 |@word multitask:1 version:1 eliminating:1 middle:2 proportion:2 norm:2 crucially:1 prominence:1 jacob:1 tr:34 liu:1 series:1 tuned:1 recovered:3 com:1 comparing:1 bie:1 must:3 written:1 john:1 partition:3 update:1 grass:1 generative:1 pursued:1 ith:3 short:1 iterates:1 recompute:1 mathematical:1 ijcv:1 fitting:1 ... |
3,116 | 3,823 | A Stochastic approximation method for inference
in probabilistic graphical models
Peter Carbonetto
Dept. of Human Genetics
University of Chicago
Chicago, IL, U.S.A.
pcarbone@bsd.uchicago.edu
Matthew King
Firas Hamze
Dept. of Botany
D-Wave Systems
University of British Columbia
Burnaby, B.C., Canada
Vancouver, B.C., C... | 3823 |@word trial:14 version:2 pw:3 proportion:1 simulation:7 decomposition:1 covariance:1 q1:1 moment:2 initial:3 liu:1 series:2 safeguarded:3 genetic:5 document:6 past:1 existing:4 freitas:4 recovered:3 com:1 yet:1 dx:2 attracted:1 written:1 must:1 schierup:2 chicago:2 partition:1 analytic:1 enables:1 plot:1 update:8... |
3,117 | 3,824 | Multi-Label Prediction via Compressed Sensing
Daniel Hsu
UC San Diego
djhsu@cs.ucsd.edu
Sham M. Kakade
TTI-Chicago
sham@tti-c.org
John Langford
Yahoo! Research
jl@hunch.net
Tong Zhang
Rutgers University
tongz@rci.rutgers.edu
Abstract
We consider multi-label prediction problems with large output spaces under the
as... | 3824 |@word mild:1 version:1 compression:14 stronger:1 norm:4 polynomial:2 pick:1 harder:1 reduction:9 contains:3 selecting:2 daniel:1 tuned:1 bradley:1 luo:1 beygelzimer:1 must:1 luis:1 john:1 chicago:1 partition:1 numerical:1 wellbehaved:1 hofmann:1 update:1 v:2 greedy:5 discovering:1 fewer:3 selected:1 half:4 intell... |
3,118 | 3,825 | Solving Stochastic Games
Charles Isbell
College of Computing
Georgia Tech
801 Atlantic Drive
Atlanta, GA 30332-0280
isbell@cc.gatech.edu
Liam Mac Dermed
College of Computing
Georgia Tech
801 Atlantic Drive
Atlanta, GA 30332-0280
liam@cc.gatech.edu
Abstract
Solving multi-agent reinforcement learning problems has prov... | 3825 |@word version:3 middle:1 achievable:10 eliminating:2 polynomial:2 nd:1 coarseness:1 seek:1 contraction:4 deems:1 initial:3 cyclic:3 contains:1 exclusively:1 bc:1 past:2 atlantic:2 yet:1 must:4 evans:1 subsequent:5 predetermined:2 cheap:3 update:1 intelligence:1 device:1 core:1 farther:1 provides:1 successive:1 hy... |
3,119 | 3,826 | Submanifold density estimation
Alexander Gray
College of Computing
Georgia Institute of Technology
agray@cc.gatech.edu
Arkadas Ozakin
Georgia Tech Research Institute
Georgia Insitute of Technology
arkadas.ozakin@gtri.gatech.edu
Abstract
Kernel density estimation is the most widely-used practical method for accurate
... | 3826 |@word worsens:1 determinant:1 polynomial:1 seems:1 mhn:1 covariance:1 citeseer:1 q1:1 pick:1 boundedness:1 reduction:2 initial:1 existing:1 beygelzimer:1 devising:1 kyk:3 plane:1 vanishing:2 oneto:1 completeness:1 contribute:1 clarified:1 mhm:2 mathematical:3 dn:15 direct:1 prove:4 consists:2 mhp:4 manner:1 intri... |
3,120 | 3,827 | Accelerating Bayesian Structural Inference for
Non-Decomposable Gaussian Graphical Models
Baback Moghaddam
Jet Propulsion Laboratory
California Institute of Technology
baback@jpl.nasa.gov
Benjamin M. Marlin
Department of Computer Science
University of British Columbia
bmarlin@cs.ubc.ca
Mohammad Emtiyaz Khan
Departmen... | 3827 |@word determinant:2 advantageous:1 giudici:2 grey:2 d2:4 covariance:5 natsoulis:1 pg:2 accounting:1 pick:1 initial:2 liu:2 series:1 score:43 selecting:1 interestingly:2 past:1 existing:2 current:1 comparing:1 si:1 scatter:5 yet:1 realistic:1 subsequent:1 gv:2 seeding:3 plot:4 update:2 fund:2 v:6 resampling:3 gree... |
3,121 | 3,828 | Explaining human multiple object tracking as
resource-constrained approximate inference in a
dynamic probabilistic model
Edward Vul, Michael C. Frank, and Joshua B. Tenenbaum
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02138
{evul, mcfrank, jbt}@mit.edu
George Alvare... | 3828 |@word trial:6 middle:2 seems:1 covariance:7 harder:5 carry:1 crowding:2 contains:5 series:3 past:1 freitas:1 current:3 nt:1 reali:1 must:18 subsequent:1 enables:1 plot:2 update:4 discrimination:1 resampling:1 generative:1 alone:1 guess:2 pursued:1 pylyshyn:4 cue:1 plane:1 iso:5 short:1 farther:2 underestimating:1... |
3,122 | 3,829 | A Gaussian Tree Approximation for Integer
Least-Squares
Jacob Goldberger
School of Engineering
Bar-Ilan University
goldbej@eng.biu.ac.il
Amir Leshem
School of Engineering
Bar-Ilan University
leshema@eng.biu.ac.il
Abstract
This paper proposes a new algorithm for the linear least squares problem where
the unknown vari... | 3829 |@word version:8 wiesel:1 polynomial:2 norm:1 hu:1 simulation:4 jacob:1 eng:2 covariance:2 configuration:3 liu:10 hereafter:1 mmse:24 outperforms:1 current:1 goldberger:1 written:1 additive:4 zeger:1 partition:1 enables:1 shamai:2 update:3 n0:3 selected:2 leaf:2 amir:1 short:1 ire:1 node:7 qam:1 consists:1 nay:1 i... |
3,123 | 383 | Back Propagation Implementation on the
Adaptive Solutions CNAPS Neurocomputer Chip
Hal McCartor
Adaptive Solutions Inc.
1400 N.W. Compton Drive
Suite 340
Beaverton, OR 97006
Abstract
The Adaptive Solutions CN APS architecture chip is a general purpose
neurocomputer chip. It has 64 processors, each with 4 K bytes of l... | 383 |@word implemented:1 c:1 version:1 contain:1 multiplier:1 assigned:1 instruction:2 simulation:1 conditionally:1 during:3 implementing:2 fairfax:1 mapped:1 configuration:2 series:1 demonstrate:1 shifter:1 current:2 accompanying:1 ranging:1 must:2 innovation:1 common:1 sigmoid:1 sigma:1 purpose:4 aps:4 update:5 milli... |
3,124 | 3,830 | Free energy score-space
Alessandro Perina1,3 , Marco Cristani1,2 , Umberto Castellani1
Vittorio Murino1,2 and Nebojsa Jojic3
{alessandro.perina, marco.cristani, umberto.castellani, vittorio.murino}@univr.it
jojic@microsoft.com
1
Department of Computer Science, University of Verona, Italy
2
IIT, Italian Institute of Tec... | 3830 |@word h:1 version:1 kondor:1 achievable:1 verona:1 triggs:1 dekker:1 gish:1 decomposition:3 accounting:1 q1:1 tr:2 kappen:1 configuration:3 contains:1 score:44 genetic:2 interestingly:1 outperforms:3 current:1 com:1 surprising:1 must:1 written:1 finest:2 reminiscent:1 parsing:1 additive:2 visible:1 informative:4 ... |
3,125 | 3,831 | A joint maximum-entropy model for binary neural
population patterns and continuous signals
Sebastian Gerwinn
Philipp Berens
Matthias Bethge
MPI for Biological Cybernetics
and University of T?ubingen
Computational Vision and Neuroscience
Spemannstrasse 41, 72076 T?ubingen, Germany
{firstname.surname}@tuebingen.mpg.de
... | 3831 |@word neurophysiology:1 trial:1 cox:1 middle:2 seems:1 integrative:1 bn:2 covariance:16 tkacik:1 brightness:1 reduction:1 moment:9 contains:1 rightmost:1 current:4 olkin:1 dx:3 partition:2 shape:1 alone:1 half:3 signalling:1 greschner:1 ith:1 short:1 provides:1 node:3 philipp:1 location:5 mathematical:2 different... |
3,126 | 3,832 | Training Factor Graphs with Reinforcement
Learning for Efficient MAP Inference
Michael Wick, Khashayar Rohanimanesh, Sameer Singh, Andrew McCallum
Department of Computer Science
University of Massachusetts Amherst
Amherst, MA 01003
{mwick,khash,sameer,mccallum}@cs.umass.edu
Abstract
Large, relational factor graphs wi... | 3832 |@word h:3 version:2 manageable:1 polynomial:1 briefly:1 pw:2 sri:1 glue:5 contrastive:7 dramatic:1 moment:1 reduction:3 configuration:21 contains:2 uma:1 selecting:1 score:13 daniel:2 initial:3 document:1 bc:1 fa8750:1 current:3 comparing:1 si:1 yet:1 assigning:1 must:5 parsing:1 john:2 partition:1 enables:2 plot... |
3,127 | 3,833 | An Infinite Factor Model Hierarchy
Via a Noisy-Or Mechanism
Aaron C. Courville, Douglas Eck and Yoshua Bengio
Department of Computer Science and Operations Research
University of Montr?eal
Montr?eal, Qu?ebec, Canada
{courvila,eckdoug,bengioy}@iro.umontreal.ca
Abstract
The Indian Buffet Process is a Bayesian nonparamet... | 3833 |@word trial:12 version:5 achievable:1 proportion:1 recursively:1 series:1 njk:3 document:2 interestingly:3 ours:1 yni:2 activation:2 yet:2 assigning:1 written:1 readily:2 audioscrobbler:1 update:1 zik:1 generative:2 intelligence:5 website:1 parametrization:1 num:14 characterization:2 provides:2 daphne:1 unbounded... |
3,128 | 3,834 | The "tree-dependent components" of natural scenes
are edge filters
Daniel Zoran
Interdisciplinary Center for Neural Computation
Hebrew University of Jerusalem
daniez@cs.huji.ac.il
Yair Weiss
School of Computer Science
Hebrew University of Jerusalem
yweiss@cs.huji.ac.il
Abstract
We propose a new model for natural ima... | 3834 |@word neurophysiology:1 middle:1 norm:1 seems:2 nd:1 seek:1 tried:1 decomposition:1 citeseer:1 moment:1 reduction:2 liu:2 initial:1 daniel:1 current:2 dct:4 shape:2 wanted:1 remove:1 plot:2 update:1 intelligence:1 yr:9 math:1 node:14 simpler:1 along:2 beta:1 fitting:1 manner:2 theoretically:1 intricate:1 ica:24 t... |
3,129 | 3,835 | Decoupling Sparsity and Smoothness in the
Discrete Hierarchical Dirichlet Process
Chong Wang
Computer Science Department
Princeton University
David M. Blei
Computer Science Department
Princeton University
chongw@cs.princeton.edu
blei@cs.princeton.edu
Abstract
We present a nonparametric hierarchical Bayesian model o... | 3835 |@word version:2 proportion:9 seems:1 nd:2 decomposition:1 thereby:1 accommodate:1 configuration:1 contains:5 document:20 outperforms:1 current:1 wd:1 scatter:1 must:1 partition:1 kdd:1 motor:1 plot:4 generative:4 selected:2 fewer:1 item:1 ith:1 record:1 blei:4 org:1 simpler:5 unbounded:1 direct:4 beta:5 shorthand... |
3,130 | 3,836 | Sparsistent Learning of Varying-coefficient Models
with Structural Changes
Mladen Kolar, Le Song and Eric P. Xing ?
School of Computer Science, Carnegie Mellon University
{mkolar,lesong,epxing}@cs.cmu.edu
Abstract
To estimate the changing structure of a varying-coefficient varying-structure
(VCVS) model remains an im... | 3836 |@word mild:1 trial:1 version:1 norm:4 open:1 decomposition:1 eng:1 bai:2 configuration:1 series:11 contains:2 selecting:3 tuned:1 bootstrapped:1 longitudinal:1 past:1 existing:2 recovered:1 comparing:2 nicolai:1 reminiscent:1 john:1 numerical:1 partition:12 enables:1 cue:6 selected:2 mccallum:1 detecting:1 boosti... |
3,131 | 3,837 | Optimizing Multi-class Spatio-Spectral Filters via
Bayes Error Estimation for EEG Classification
Wenming Zheng
Research Center for Learning Science
Southeast University
Nanjing, Jiangsu 210096, P.R. China
wenming zheng@seu.edu.cn
Zhouchen Lin
Microsoft Research Asia
Beijing 100190, P.R. China
zhoulin@microsoft.com
A... | 3837 |@word trial:18 covariance:11 decomposition:4 q1:2 solid:2 recursively:1 carry:1 contains:1 hereafter:1 bhattacharyya:1 outperforms:2 com:1 comparing:1 si:8 dx:2 written:2 chu:1 motor:2 update:3 greedy:3 cue:1 ith:8 filtered:1 boosting:1 five:1 become:1 ik:2 consists:1 introduce:1 multi:16 brain:2 xti:11 actual:1 ... |
3,132 | 3,838 | AUC optimization and the two-sample problem
St?ephan Cl?emenc?on
Telecom Paristech (TSI) - LTCI UMR Institut Telecom/CNRS 5141
stephan.clemencon@telecom-paristech.fr
Marine Depecker
Telecom Paristech (TSI) - LTCI UMR Institut Telecom/CNRS 5141
marine.depecker@telecom-paristech.fr
Nicolas Vayatis
ENS Cachan & UniverSud... | 3838 |@word h:10 version:6 middle:1 norm:1 smirnov:1 flach:1 simulation:3 bn:3 covariance:3 pg:1 paid:1 euclidian:1 moment:1 necessity:1 celebrated:1 contains:1 score:8 selecting:1 series:1 denoting:2 assigning:1 dx:17 fn:2 numerical:3 n0:7 v:1 half:2 leaf:1 rudin:1 marine:2 core:1 detecting:3 provides:2 boosting:2 loc... |
3,133 | 3,839 | Correlation Coefficients Are Insufficient
for Analyzing Spike Count Dependencies
Arno Onken
Technische Universit?at Berlin / BCCN Berlin
Franklinstr. 28/29, 10587 Berlin, Germany
aonken@cs.tu-berlin.de
?
Steffen Grunew?
alder
University College London
Gower Street, London WC1E 6BT, UK
steffen@cs.ucl.ac.uk
Klaus Ober... | 3839 |@word proportion:1 nd:1 tedious:1 simulation:2 simplifying:1 thereby:1 solid:1 contains:3 series:1 current:2 yet:1 must:1 realistic:4 shape:2 motor:1 plot:1 alone:1 fx1:2 selected:5 smith:1 short:1 poissonlike:2 provides:3 math:1 tolhurst:1 mef:2 mathematical:3 constructed:2 become:1 profound:1 consists:1 combine... |
3,134 | 384 | Neural Networks Structured for Control
Application to Aircraft Landing
Charles Schley, Yves Chauvin, Van Henkle, Richard Golden
Thomson-CSP, Inc., Palo Alto Research Operations
630 Hansen Way, Suite 250
Palo Alto, CA 94306
Abstract
We present a generic neural network architecture capable of controlling non-linear plant... | 384 |@word aircraft:22 simulation:4 propagate:1 linearized:2 jacob:2 tr:1 initial:1 contains:1 troller:1 subjective:1 written:3 additive:1 numerical:2 designed:3 treating:1 selected:2 flare:10 short:1 provides:3 ames:1 attack:1 sigmoidal:2 five:1 mathematical:1 along:2 constructed:1 consists:1 pathway:1 introduce:1 exp... |
3,135 | 3,840 | Kernels and learning curves for Gaussian process
regression on random graphs
Peter Sollich, Matthew J Urry
King?s College London, Department of Mathematics
London WC2R 2LS, U.K.
{peter.sollich,matthew.urry}@kcl.ac.uk
Camille Coti
INRIA Saclay ?Ile de France, F-91893 Orsay, France
Abstract
We investigate how well Gaus... | 3840 |@word kondor:2 achievable:1 seems:1 nd:1 c0:9 open:1 d2:1 simulation:3 covariance:13 outlook:1 solid:1 initial:1 series:1 score:1 denoting:1 current:1 comparing:1 surprising:1 analysed:1 intriguing:1 must:1 numerical:5 visible:1 shape:3 remove:1 plot:1 v:1 stationary:2 intelligence:1 warmuth:1 accordingly:1 under... |
3,136 | 3,841 | Know Thy Neighbour:
A Normative Theory of Synaptic Depression
Jean-Pascal Pfister
Computational & Biological Learning Lab
Department of Engineering, University of Cambridge
Trumpington Street, Cambridge CB2 1PZ, United Kingdom
jean-pascal.pfister@eng.cam.ac.uk
Peter Dayan
Gatsby Computational Neuroscience Unit, UCL
17... | 3841 |@word polynomial:1 seems:1 suitably:1 simulation:7 eng:2 prominence:1 simplifying:1 accounting:1 covariance:2 thereby:1 solid:1 shading:1 recursively:1 reduction:1 initial:1 contains:2 efficacy:6 united:3 tuned:1 interestingly:1 current:4 comparing:1 must:2 written:3 readily:1 realize:1 numerical:1 additive:1 sub... |
3,137 | 3,842 | Compositionality of optimal control laws
Emanuel Todorov
Applied Mathematics and Computer Science & Engineering
University of Washington
todorov@cs.washington.edu
Abstract
We present a theory of compositionality in stochastic optimal control, showing
how task-optimal controllers can be constructed from certain primit... | 3842 |@word proportion:1 seems:1 nd:2 disk:3 integrative:1 decomposition:1 tr:2 kappen:1 reduction:2 contains:2 existing:1 discretization:4 surprising:2 si:1 yet:8 dx:3 written:2 must:2 belmont:1 realistic:2 numerical:2 happen:1 shape:1 lqg:15 motor:6 fewer:1 nervous:1 plane:1 dover:1 math:1 location:1 mathematical:1 c... |
3,138 | 3,843 | Adaptive Regularization for
Transductive Support Vector Machine
Zenglin Xu ??
Cluster MMCI
Saarland Univ. & MPI INF
Saarbrucken, Germany
zlxu@mpi-inf.mpg.de
?
Rong Jin
Computer Sci. & Eng.
Michigan State Univ.
East Lansing, MI, U.S.
rongjin@cse.msu.edu
Irwin King?
Michael R. Lyu?
?
Computer Science & Engineering
The... | 3843 |@word kong:3 stronger:2 retraining:2 eng:1 configuration:1 series:3 selecting:3 past:1 existing:2 outperforms:1 current:1 comparing:1 assigning:1 attracted:1 john:1 r01gm079688:1 fn:1 ronan:1 partition:3 kdd:1 enables:1 kyb:2 remove:1 v:2 intelligence:1 selected:4 ith:1 provides:3 cse:2 revisited:1 node:1 org:1 z... |
3,139 | 3,844 | Learning a Small Mixture of Trees?
Daphne Koller
Computer Science Department
Stanford University
koller@cs.stanford.edu
M. Pawan Kumar
Computer Science Department
Stanford University
pawan@cs.stanford.edu
Abstract
The problem of approximating a given probability distribution using a simpler distribution plays an imp... | 3844 |@word trial:1 briefly:2 inversion:1 eliminating:1 everingham:1 suitably:1 tried:1 thereby:5 initial:7 liu:5 contains:1 series:1 recovered:1 current:3 written:2 readily:1 must:1 additive:1 subsequent:1 partition:3 kdd:1 remove:1 drop:1 update:9 cue:1 devising:1 plane:1 ith:2 provides:16 boosting:3 node:1 location:... |
3,140 | 3,845 | Learning Label Embeddings for Nearest-Neighbor
Multi-class Classification with an Application to
Speech Recognition
Natasha Singh-Miller
Massachusetts Institute of Technology
Cambridge, MA
natashas@mit.edu
Michael Collins
Massachusetts Institute of Technology
Cambridge, MA
mcollins@csail.mit.edu
Abstract
We consider... | 3845 |@word proportion:2 closure:2 reduction:2 contains:2 outperforms:2 comparing:1 goldberger:1 dx:1 numerical:1 shape:1 plot:6 alone:1 generative:1 selected:2 intelligence:2 item:2 five:1 along:1 scholkopf:1 consists:5 combine:2 expected:1 p1:1 multi:7 ecoc:20 salakhutdinov:2 automatically:2 little:1 pf:7 considering... |
3,141 | 3,846 | Nonparametric Latent Feature Models
for Link Prediction
Thomas L. Griffiths
Psychology and Cognitive Science
University of California
Berkeley, CA 94720
tom griffiths@berkeley.edu
Kurt T. Miller
EECS
University of California
Berkeley, CA 94720
tadayuki@cs.berkeley.edu
Michael I. Jordan
EECS and Statistics
University ... | 3846 |@word version:1 stronger:1 logit:2 nd:1 adrian:1 seek:1 tried:2 pick:1 configuration:2 contains:5 daniel:1 kurt:1 outperforms:1 existing:2 assigning:1 must:7 distant:1 informative:1 hofmann:1 christian:1 drop:2 interpretable:2 update:4 zik:9 resampling:2 generative:6 intelligence:2 website:1 amir:1 ith:4 yamada:1... |
3,142 | 3,847 | Sparse Metric Learning via Smooth Optimization
Yiming Ying?, Kaizhu Huang?, and Colin Campbell?
?Department of Engineering Mathematics, University of Bristol,
Bristol BS8 1TR, United Kingdom
?National Laboratory of Pattern Recognition, Institute of Automation,
The Chinese Academy of Sciences, 100190 Beijing, China
Ab... | 3847 |@word h:1 kulis:1 repository:1 version:2 briefly:1 norm:20 nd:13 d2:11 km:7 decomposition:3 covariance:2 q1:16 mention:1 tr:23 reduction:7 united:1 tuned:2 outperforms:2 existing:1 ka:1 wd:1 od:1 nt:1 si:4 goldberger:1 written:1 kdd:1 remove:1 designed:1 plot:2 xk:6 location:1 firstly:1 mathematical:1 direct:2 co... |
3,143 | 3,848 | Adaptive Regularization of Weight Vectors
Koby Crammer
Department of
Electrical Enginering
The Technion
Haifa, 32000 Israel
koby@ee.technion.ac.il
Alex Kulesza
Department of Computer
and Information Science
University of Pennsylvania
Philadelphia, PA 19104
kulesza@cis.upenn.edu
Mark Dredze
Human Language Tech.
Cente... | 3848 |@word version:5 bigram:1 norm:1 dekel:1 open:1 blender:1 covariance:3 simplifying:1 tr:1 document:3 outperforms:3 diagonalized:1 current:6 com:1 written:3 must:1 john:2 designed:1 drop:1 update:32 plot:1 v:8 fewer:1 selected:2 haykin:1 sudden:1 contribute:1 herbrich:1 org:1 c2:2 direct:1 incorrect:1 prove:3 combi... |
3,144 | 3,849 | Hierarchical Learning of Dimensional Biases in
Human Categorization
Katherine Heller
Department of Engineering
University of Cambridge
Cambridge CB2 1PZ
heller@gatsby.ucl.ac.uk
Adam Sanborn
Gatsby Computational Neuroscience Unit
University College London
London WC1N 3AR
asanborn@gatsby.ucl.ac.uk
Nick Chater
Cognitive... | 3849 |@word version:4 judgement:1 stronger:2 proportion:1 gradual:1 covariance:22 solid:2 carry:1 initial:1 ecole:1 interestingly:1 past:1 existing:3 current:1 must:5 john:1 exposing:1 additive:1 partition:5 informative:3 shape:6 plot:3 overriding:1 grass:1 newest:1 generative:4 half:2 item:25 isotropic:8 smith:8 menta... |
3,145 | 385 | A Connectionist Learning Control
Architecture for Navigation
Jonathan R. Bachrach
Department of Computer and Information Science
University of Massachusetts
Amherst, MA 01003
Abstract
A novel learning control architecture is used for navigation. A sophisticated test-bed is used to simulate a cylindrical robot with a ... | 385 |@word cylindrical:2 trial:1 version:1 briefly:1 grey:5 jacob:2 usee:1 thereby:1 initial:4 uma:1 tuned:1 existing:1 current:4 repelling:2 nowlan:1 realistic:1 plot:3 designed:1 generative:1 short:1 farther:1 provides:1 location:4 successive:1 simpler:1 belt:2 height:5 consists:3 expected:1 behavior:1 nor:1 simulato... |
3,146 | 3,850 | Ensemble Nystr?om Method
Sanjiv Kumar
Google Research
New York, NY
sanjivk@google.com
Mehryar Mohri
Courant Institute and Google Research
New York, NY
mohri@cs.nyu.edu
Ameet Talwalkar
Courant Institute of Mathematical Sciences
New York, NY
ameet@cs.nyu.edu
Abstract
A crucial technique for scaling kernel methods to ... | 3850 |@word repository:1 manageable:1 norm:23 nd:1 decomposition:1 nystr:78 contains:1 selecting:2 interestingly:1 outperforms:1 com:1 comparing:2 tackling:1 assigning:1 written:2 john:1 sanjiv:1 informative:1 intelligence:1 selected:1 warmuth:1 es:4 ith:2 mcdiarmid:2 zhang:1 five:1 mathematical:1 along:1 sii:1 consist... |
3,147 | 3,851 | Non-Parametric Bayesian Dictionary Learning for
Sparse Image Representations
Mingyuan Zhou, Haojun Chen, John Paisley, Lu Ren, 1 Guillermo Sapiro and Lawrence Carin
Department of Electrical and Computer Engineering
Duke University, Durham, NC 27708-0291, USA
1
Department of Electrical and Computer Engineering
Universi... | 3851 |@word version:3 middle:1 briefly:1 inversion:18 compression:3 d2:4 rgb:1 accounting:1 decomposition:1 thereby:3 inpainting:25 series:2 existing:1 current:2 com:1 si:1 must:2 readily:6 john:1 cruz:1 dct:10 additive:2 partition:2 informative:7 distant:1 analytic:4 designed:1 update:3 zik:3 stationary:1 intelligence... |
3,148 | 3,852 | Semi-supervised Learning using Sparse
Eigenfunction Bases
Kaushik Sinha
Dept. of Computer Science and Engineering
Ohio State University
Columbus, OH 43210
sinhak@cse.ohio-state.edu
Mikhail Belkin
Dept. of Computer Science and Engineering
Ohio State University
Columbus, OH 43210
mbelkin@cse.ohio-state.edu
Abstract
We... | 3852 |@word trial:2 version:1 briefly:1 polynomial:1 norm:2 seek:1 covariance:2 tr:2 series:1 selecting:2 rkhs:1 interestingly:1 outperforms:2 assigning:1 dx:1 must:1 written:1 enables:1 aside:2 alone:1 greedy:1 selected:5 xk:5 ith:5 cse:2 zhang:2 along:1 constructed:3 symposium:1 scholkopf:2 pairwise:2 roughly:1 p1:6 ... |
3,149 | 3,853 | Bayesian Nonparametric Models on Decomposable
Graphs
Franc?ois Caron
INRIA Bordeaux Sud?Ouest
Institut de Math?ematiques de Bordeaux
University of Bordeaux, France
francois.caron@inria.fr
Arnaud Doucet
Departments of Computer Science & Statistics
University of British Columbia, Vancouver, Canada
and The Institute of ... | 3853 |@word briefly:4 version:4 decomposition:2 eng:1 tr:1 wedding:4 existing:1 z2:5 nt:1 reminiscent:1 must:1 partition:28 designed:1 update:1 resampling:1 intelligence:1 leaf:1 item:17 nq:3 blei:1 math:1 c2:2 beta:5 ik:1 doubly:1 yours:1 indeed:2 sud:1 heinz:1 notation:2 moreover:1 what:1 z:2 nj:39 exchangeable:2 enj... |
3,150 | 3,854 | Rethinking LDA: Why Priors Matter
Hanna M. Wallach David Mimno Andrew McCallum
Department of Computer Science
University of Massachusetts Amherst
Amherst, MA 01003
{wallach,mimno,mccallum}@cs.umass.edu
Abstract
Implementations of topic models typically use symmetric Dirichlet priors with
fixed concentration parameter... | 3854 |@word version:1 nd:8 heuristically:1 simulation:1 uma:1 selecting:3 liquid:1 document:52 bc:1 existing:7 current:4 z2:2 nt:13 comparing:2 must:5 partition:12 kdd:1 remove:1 update:1 resampling:4 alone:1 generative:1 selected:3 intelligence:3 mccallum:5 blei:4 provides:4 five:4 along:1 constructed:1 consists:2 cow... |
3,151 | 3,855 | Graph-based Consensus Maximization among
Multiple Supervised and Unsupervised Models
Jing Gao? , Feng Liang? , Wei Fan? , Yizhou Sun? , and Jiawei Han?
?
University of Illinois at Urbana-Champaign, IL USA
?
IBM TJ Watson Research Center, Hawthorn, NY USA
?
{jinggao3,liangf,sun22,hanj}@illinois.edu, ? weifan@us.ibm.com
... | 3855 |@word multitask:1 version:3 compression:1 norm:2 nd:1 willing:1 seek:2 propagate:1 bn:1 jacob:1 paid:1 electronics:3 initial:8 contains:1 score:4 series:1 outperforms:2 existing:3 current:1 com:1 comparing:1 nowlan:1 must:4 visible:1 partition:4 kdd:1 eleven:2 christian:1 gv:2 designed:1 plot:1 update:3 stationar... |
3,152 | 3,856 | Replicated Softmax: an Undirected Topic Model
Ruslan Salakhutdinov
Brain and Cognitive Sciences and CSAIL
Massachusetts Institute of Technology
rsalakhu@mit.edu
Geoffrey Hinton
Department of Computer Science
University of Toronto
hinton@cs.toronto.edu
Abstract
We introduce a two-layer undirected graphical model, cal... | 3856 |@word proportion:1 p0:5 contrastive:4 twolayer:1 contains:5 series:1 score:1 document:64 outperforms:2 existing:3 stemmed:1 scatter:1 must:1 stemming:1 visible:11 partition:7 hofmann:1 plot:1 designed:1 update:2 v:6 alone:1 generative:4 leaf:2 intelligence:2 mccallum:1 blei:2 provides:1 toronto:2 five:1 stopwords... |
3,153 | 3,857 | Complexity of Decentralized Control: Special Cases
Shlomo Zilberstein
Department of Computer Science
University of Massachusetts
Amherst, MA 01003
shlomo@cs.umass.edu
Martin Allen
Department of Computer Science
Connecticut College
New London, CT 06320
martin.allen@conncoll.edu
Abstract
The worst-case complexity of g... | 3857 |@word briefly:1 version:1 polynomial:2 open:2 calculus:1 seek:1 prasad:1 accounting:1 p0:2 paid:1 subcase:1 harder:1 reduction:10 necessity:1 initial:3 contains:5 uma:2 daniel:2 groundwork:1 ala:1 ati:1 existing:1 hakodate:1 current:1 surprising:1 si:23 must:5 written:2 john:1 additive:3 mundhenk:2 shlomo:9 alone... |
3,154 | 3,858 | Efficie nt M om e nts-base d Per m utation Tests
Chunxiao Zhou
Dept. of Electrical and Computer Eng.
University of Illinois at Urbana-Champaign
Champaign, IL 61820
czhou4@gmail.com
Huixia Judy Wang
Dept. of Statistics
North Carolina State University
Raleigh, NC 27695
wang@stat.ncsu.edu
Yongmei Michelle Wang
Depts. of... | 3858 |@word mri:1 polynomial:2 hippocampus:1 nd:1 dekker:1 hu:1 simulation:4 carolina:1 nicholson:1 eng:1 moment:42 series:4 com:1 nt:2 comparing:2 si:1 gmail:1 john:1 subsequent:1 partition:30 j1:23 shape:6 treating:1 v:1 greedy:2 plane:1 xk:1 node:9 location:5 firstly:1 mathematical:1 constructed:1 direct:1 beta:6 sy... |
3,155 | 3,859 | Maximum likelihood trajectories for continuous-time
Markov chains
Theodore J. Perkins
Ottawa Hospital Research Institute
Ottawa, Ontario, Canada
tperkins@ohri.ca
Abstract
Continuous-time Markov chains are used to model systems in which transitions
between states as well as the time the system spends in each state are ... | 3859 |@word version:2 achievable:2 polynomial:4 seems:1 seek:4 simulation:2 accounting:1 concise:2 initial:16 series:1 score:5 fragment:2 o2:3 reaction:2 existing:1 si:24 written:4 readily:1 must:5 import:1 john:1 periodically:1 numerical:1 happen:1 j1:1 belmont:1 extrapolating:1 stationary:1 leaf:2 inspection:1 dissat... |
3,156 | 386 | c-Entropy and the Complexity of
Feedforward Neural Networks
Robert C. Williamson
Department of Systems Engineering
Research School of Physical Sciences and Engineering
Australian National University
GPO Box 4, Canberra, 2601, Australia
Abstract
We develop a. new feedforward neuralnet.work represent.ation of Lipschitz... | 386 |@word achievable:1 open:2 hu:1 tiw:1 complexit:1 series:1 chervonenkis:3 ecole:1 current:1 comparing:1 erms:1 i1l:1 j1:1 wll:1 alone:1 half:1 warmuth:1 ith:1 funahashi:1 node:8 lx:1 sigmoidal:1 mathematical:1 along:1 combine:1 lcx:1 manner:2 themselves:1 ua:1 totally:1 bounded:3 suffice:1 probabilites:1 ti:1 contr... |
3,157 | 3,860 | Filtering Abstract Senses From Image Search Results
Kate Saenko1,2 and Trevor Darrell2
1
MIT CSAIL, Cambridge, MA
2
UC Berkeley EECS and ICSI, Berkeley, CA
saenko@csail.mit.edu, trevor@eecs.berkeley.edu
Abstract
We propose an unsupervised method that, given a word, automatically selects
non-abstract senses of that wo... | 3860 |@word nd:1 open:1 hyponym:2 downloading:1 tr:1 initial:2 contains:4 cellphone:2 selecting:1 ours:1 document:7 existing:1 issuing:1 remove:1 plot:1 update:1 v:2 reranking:1 selected:4 device:4 mccallum:1 core:14 farther:1 filtered:1 blei:2 harvesting:1 provides:3 detecting:3 node:1 location:1 codebook:2 org:1 zhan... |
3,158 | 3,861 | Constructing Topological Maps using Markov
Random Fields and Loop-Closure Detection
Roy Anati Kostas Daniilidis
GRASP Laboratory
Department of Computer and Information Science
University of Pennsylvania
Philadelphia, PA 19104
{royanati,kostas}@cis.upenn.edu
Abstract
We present a system which constructs a topological ... | 3861 |@word norm:2 open:1 km:4 closure:28 prominence:1 outlook:1 initial:1 substitution:1 contains:2 score:34 outperforms:2 existing:1 must:1 shape:3 remove:3 atlas:3 update:1 maxv:1 generative:1 selected:1 greedy:2 reciprocal:1 short:1 sudden:4 provides:6 detecting:1 node:20 location:18 org:1 five:3 dn:1 constructed:3... |
3,159 | 3,862 | A Data-Driven Approach to Modeling Choice
Vivek F. Farias
Srikanth Jagabathula
Devavrat Shah?
Abstract
We visit the following fundamental problem: For a ?generic? model of consumer choice (namely, distributions over preference lists) and a limited
amount of data on how consumers actually make decisions (such as mar... | 3862 |@word illustrating:1 version:2 briefly:1 polynomial:2 logit:4 open:1 seek:2 p0:1 pick:4 contains:1 efficacy:1 offering:2 recovered:1 com:2 must:3 readily:1 realistic:1 partition:4 j1:1 plot:1 generative:5 ajd:13 nq:2 ith:1 vanishing:1 cormode:1 preference:6 mathematical:2 direct:1 become:1 prove:2 fitting:1 polyh... |
3,160 | 3,863 | Modelling Relational Data using Bayesian Clustered
Tensor Factorization
Ilya Sutskever
University of Toronto
ilya@cs.utoronto.ca
Ruslan Salakhutdinov
MIT
rsalakhu@mit.edu
Joshua B. Tenenbaum
MIT
jbt@mit.edu
Abstract
We consider the problem of learning probabilistic models for complex relational
structures between va... | 3863 |@word private:1 middle:1 briefly:1 seal:1 sex:1 d2:7 covariance:20 excited:1 holy:1 reduction:1 initial:1 liu:1 hunting:1 paw:1 murder:1 necessity:1 contains:3 pub:1 genetic:2 o2:3 existing:2 outperforms:2 current:1 comparing:1 virus:1 surprising:1 chu:1 john:1 visible:1 partition:15 shakespeare:1 shape:1 flipper... |
3,161 | 3,864 | Rank-Approximate Nearest Neighbor Search:
Retaining Meaning and Speed in High Dimensions
Parikshit Ram, Dongryeol Lee, Hua Ouyang and Alexander G. Gray
Computational Science and Engineering, Georgia Institute of Technology
Atlanta, GA 30332
{p.ram@,dongryel@cc.,houyang@,agray@cc.}gatech.edu
Abstract
The long-standing... | 3864 |@word mild:1 repository:2 thereby:1 tr:1 reduction:4 phy:2 liu:2 document:1 pprox:14 existing:2 current:1 comparing:1 com:1 beygelzimer:1 stemming:1 subsequent:1 numerical:1 informative:1 hash:2 prohibitive:1 leaf:5 farther:2 papadopoulos:1 hypersphere:2 provides:4 math:1 node:22 five:1 mathematical:1 pairwise:4 ... |
3,162 | 3,865 | Posterior vs. Parameter Sparsity in Latent Variable
Models
Jo?o V. Gra?a
L2 F INESC-ID
Lisboa, Portugal
Kuzman Ganchev
Ben Taskar
University of Pennsylvania
Philadelphia, PA, USA
Fernando Pereira
Google Research
Mountain View, CA, USA
Abstract
We address the problem of learning structured unsupervised models with mo... | 3865 |@word middle:1 norm:6 seems:1 open:2 contrastive:1 pick:1 moment:1 initial:1 contains:1 ours:2 interestingly:1 rightmost:1 outperforms:3 current:3 comparing:2 surprising:1 yet:1 scatter:1 assigning:1 portuguese:4 parsing:3 bd:1 informative:1 confirming:1 plot:3 update:1 v:4 generative:1 half:1 mccallum:4 ith:1 sm... |
3,163 | 3,866 | Learning with Compressible Priors
Volkan Cevher
Rice University
volkan@rice.edu
Abstract
We describe a set of probability distributions, dubbed compressible priors, whose
independent and identically distributed (iid) realizations result in p-compressible
signals. A signal x ? RN is called p-compressible with magnitud... | 3866 |@word compression:2 norm:6 calculus:1 seek:1 simulation:2 decomposition:1 attainable:1 pick:1 mention:1 solid:2 garrigues:1 moment:1 ld:4 reduction:1 contains:1 kx0:3 existing:2 recovered:2 neurophys:1 yet:1 must:4 realize:1 slb:2 numerical:1 shape:2 enables:1 plot:3 v:1 xk:9 scaffold:1 short:1 volkan:2 provides:... |
3,164 | 3,867 | Bayesian Sparse Factor Models and DAGs
Inference and Comparison
Ole Winther
DTU Informatics
Technical University of Denmark
2800 Lyngby, Denmark
Bioinformatics Centre
University of Copenhagen
2200 Copenhagen, Denmark
owi@imm.dtu.dk
Ricardo Henao
DTU Informatics
Technical University of Denmark
2800 Lyngby, Denmark
Bio... | 3867 |@word repository:3 version:1 inversion:1 seems:1 d2:3 hyv:2 tried:2 covariance:1 accounting:1 pick:1 mention:1 tr:3 solid:3 versatile:1 series:3 contains:1 selecting:2 ours:3 existing:4 comparing:3 must:2 written:1 additive:1 shape:1 alone:2 cue:1 prohibitive:1 selected:3 smith:1 core:1 provides:1 node:3 p38:4 dn... |
3,165 | 3,868 | Slow, Decorrelated Features for
Pretraining Complex Cell-like Networks
Yoshua Bengio
University of Montreal
yoshua.bengio@umontreal.ca
James Bergstra
University of Montreal
james.bergstra@umontreal.ca
Abstract
We introduce a new type of neural network activation function based on recent
physiological rate models for... | 3868 |@word neurophysiology:1 version:1 open:1 grey:3 hyv:2 tried:1 covariance:6 decomposition:1 contrastive:1 tr:9 initial:6 contains:1 score:4 tuned:4 ording:4 existing:2 activation:19 must:1 wx:3 blur:1 shape:3 treating:1 generative:3 half:2 fewer:1 short:2 characterization:1 codebook:1 location:1 sigmoidal:5 five:2... |
3,166 | 3,869 | Conditional Neural Fields
Liefeng Bo
Toyota Technological Institute at Chicago
6045 S. Kenwood Ave.
Chicago, IL 60637
liefengbo@gmail.com
Jian Peng
Toyota Technological Institute at Chicago
6045 S. Kenwood Ave.
Chicago, IL 60637
jpengwhu@gmail.com
Jinbo Xu
Toyota Technological Institute at Chicago
6045 S. Kenwood Ave... | 3869 |@word middle:2 norm:1 confirms:1 tried:1 covariance:2 tr:2 liu:3 contains:3 series:1 score:2 tuned:1 past:2 existing:1 outperforms:2 com:3 jinbo:3 gmail:3 chu:1 parsing:1 john:2 chicago:6 hofmann:1 christian:2 designed:1 update:6 generative:2 selected:2 mccallum:1 ith:1 provides:2 judith:1 org:1 daphne:1 zhang:2 ... |
3,167 | 387 | Training Knowledge-Based Neural Networks to
Recognize Genes in DNA Sequences
Michiel O. Noordewier
Computer Science
Rutgers University
New Brunswick, NJ 08903
Geoffrey G. Towell
Computer Sciences
University of Wisconsin
Madison, WI 53706
Jude W. Shavlik
Computer Sciences
University of Wisconsin
Madison, WI 53706
Ab... | 387 |@word briefly:2 solid:3 initial:3 configuration:1 contains:3 selecting:1 genetic:4 lapedes:2 virus:1 must:1 realistic:1 v:1 alone:1 half:1 fewer:1 intelligence:1 provides:3 location:5 initiative:1 incorrect:1 consists:1 combine:1 expected:1 roughly:1 themselves:1 nor:1 brain:1 codon:1 precursor:1 window:2 provided... |
3,168 | 3,870 | Sequential effects reflect parallel learning of multiple
environmental regularities
Matthew H. Wilder? , Matt Jones? , & Michael C. Mozer?
?
Dept. of Computer Science
?
Dept. of Psychology
University of Colorado
Boulder, CO 80309
<wildermh@colorado.edu, mcj@colorado.edu, mozer@colorado.edu>
Abstract
Across a wide ran... | 3870 |@word trial:73 blindness:3 stronger:3 proportion:1 seems:2 instruction:1 r:1 simulation:2 decomposition:1 p0:4 shot:4 moment:1 denoting:1 tuned:1 interestingly:1 rightmost:1 past:5 imaginary:1 subjective:2 current:15 comparing:2 reaction:4 activation:3 additive:2 shape:1 motor:2 remove:1 championship:1 v:5 half:5... |
3,169 | 3,871 | Online Submodular Minimization
Elad Hazan
IBM Almaden Research Center
650 Harry Rd, San Jose, CA 95120
hazan@us.ibm.com
Satyen Kale
Yahoo! Research
4301 Great America Parkway, Santa Clara, CA 95054
skale@yahoo-inc.com
Abstract
We consider an online decision problem over a discrete space in which the loss
function is ... | 3871 |@word exploitation:2 version:4 polynomial:11 norm:1 stronger:1 open:3 bn:7 incurs:1 profit:5 initial:1 celebrated:1 daniel:1 past:1 current:1 com:2 nt:7 define1:1 clara:1 must:1 readily:1 written:1 realistic:1 remove:2 update:2 item:5 warmuth:1 characterization:1 math:1 launching:1 simpler:1 along:1 constructed:2... |
3,170 | 3,872 | 3D Object Recognition with Deep Belief Nets
Vinod Nair and Geoffrey E. Hinton
Department of Computer Science, University of Toronto
10 King?s College Road, Toronto, M5S 3G5 Canada
{vnair,hinton}@cs.toronto.edu
Abstract
We introduce a new type of top-level model for Deep Belief Nets and evaluate it on a 3D object reco... | 3872 |@word version:5 tried:4 contrastive:6 innermost:1 tr:1 initial:1 configuration:4 contains:4 foveal:2 selecting:1 document:1 outperforms:4 current:2 activation:3 yet:2 john:1 additive:1 subsequent:1 visible:8 partition:1 shape:1 utml:1 designed:1 treating:3 update:17 v:3 alone:4 generative:17 half:1 greedy:5 discr... |
3,171 | 3,873 | Factor Modeling for Advertisement Targeting
Ye Chen?
eBay Inc.
yechen1@ebay.com
Michael Kapralov
Stanford University
kapralov@stanford.edu
Dmitry Pavlov?
Yandex Labs
dmitry-pavlov@yandex-team.ru
John F. Canny
University of California, Berkeley
jfc@cs.berkeley.edu
Abstract
We adapt a probabilistic latent variable m... | 3873 |@word polynomial:1 retraining:1 hyv:1 accounting:1 innermost:1 reduction:2 contains:4 score:4 zij:4 document:1 ramsey:1 imaginary:1 com:2 contextual:6 comparing:1 yet:2 readily:1 refresh:3 john:2 fn:1 transcendental:1 stemming:1 concatenate:1 kdd:1 shape:3 hofmann:1 plot:3 sponsored:7 update:2 v:4 generative:4 in... |
3,172 | 3,874 | Efficient Match Kernels between Sets of Features
for Visual Recognition
Cristian Sminchisescu
University of Bonn
sminchisescu.ins.uni-bonn.de
Liefeng Bo
Toyota Technological Institute at Chicago
blf0218@tti-c.org
Abstract
In visual recognition, the images are frequently modeled as unordered collections
of local feat... | 3874 |@word kulis:1 kondor:1 triggs:1 d2:1 closure:2 scg:1 tried:1 decomposition:1 covariance:1 hsieh:1 pick:1 sgd:4 shot:1 shechtman:1 liblinear:2 contains:3 tuned:1 bhattacharyya:4 outperforms:2 existing:1 current:1 recovered:1 bd:1 chicago:1 shape:1 analytic:1 moreno:1 designed:1 plot:2 update:2 isard:1 selected:6 p... |
3,173 | 3,875 | Nonlinear Learning using Local Coordinate Coding
Kai Yu
NEC Laboratories America
kyu@sv.nec-labs.com
Tong Zhang
Rutgers University
tzhang@stat.rutgers.edu
Yihong Gong
NEC Laboratories America
ygong@sv.nec-labs.com
Abstract
This paper introduces a new method for semi-supervised learning on high dimensional nonlinear... | 3875 |@word norm:9 seems:1 pick:3 reduction:3 kx0:1 existing:1 com:2 john:1 remove:2 update:1 v:2 xk:1 provides:1 toronto:1 simpler:2 zhang:1 kvk2:1 direct:3 become:3 welldefined:1 fitting:2 introduce:1 theoretically:1 x0:5 pairwise:1 expected:2 inspired:1 globally:1 voc:1 salakhutdinov:2 curse:3 cardinality:3 becomes:... |
3,174 | 3,876 | Inter-domain Gaussian Processes for
Sparse Inference using Inducing Features
Miguel L?azaro-Gredilla and An??bal R. Figueiras-Vidal
Dep. Signal Processing & Communications
Universidad Carlos III de Madrid, SPAIN
{miguel,arfv}@tsc.uc3m.es
Abstract
We present a general inference framework for inter-domain Gaussian Proc... | 3876 |@word aircraft:1 version:2 briefly:1 inversion:1 advantageous:1 covariance:21 simplifying:1 dramatic:1 nystr:1 harder:1 moment:1 initial:1 selecting:1 initialisation:1 kuf:2 denoting:1 past:1 existing:2 current:1 dx:5 must:3 readily:1 fn:1 informative:1 enables:1 remove:1 plot:9 stationary:2 half:2 selected:4 gre... |
3,175 | 3,877 | Improving Existing Fault Recovery Policies
Guy Shani
Department of Information Systems Engineering
Ben Gurion University, Beer-Sheva, Israel
shanigu@bgu.ac.il
Christopher Meek
Microsoft Research
One Microsoft Way, Redmond, WA
meek@microsoft.com
Abstract
An automated recovery system is a key component in a large data... | 3877 |@word trial:3 disk:1 termination:1 simulation:2 propagate:1 tried:2 q1:2 incurs:1 tr:3 shot:1 reduction:2 initial:4 configuration:1 selecting:1 tuned:1 prefix:1 outperforms:2 existing:13 o2:1 current:4 com:1 comparing:3 surprising:1 soules:1 si:1 yet:3 assigning:3 must:2 john:2 realistic:2 happen:1 gurion:1 cheap... |
3,176 | 3,878 | Group Orthogonal Matching Pursuit for
Variable Selection and Prediction
?
Aur?elie C. Lozano, Grzegorz Swirszcz,
Naoki Abe
IBM Watson Research Center,
1101 Kitchawan Road,
Yorktown Heights NY 10598,USA
{aclozano,swirszcz,nabe}@us.ibm.com
Abstract
We consider the problem of variable group selection for least squares re... | 3878 |@word repository:1 version:3 polynomial:2 norm:9 open:1 simulation:2 covariance:1 pick:1 carry:1 moment:1 reduction:1 bai:1 series:3 score:1 selecting:3 ours:1 existing:3 current:1 com:1 comparing:1 must:1 additive:5 subsequent:1 kv1:4 rd2:3 greedy:6 selected:5 half:1 beginning:1 reciprocal:1 provides:1 boosting:... |
3,177 | 3,879 | Statistical Consistency of Top-k Ranking
Fen Xia
Institute of Automation
Chinese Academy of Sciences
fen.xia@ia.ac.cn
Tie-Yan Liu
Microsoft Research Asia
tyliu@microsoft.com
Hang Li
Microsoft Research Asia
hanglig@microsoft.com
Abstract
This paper is concerned with the consistency analysis on listwise ranking metho... | 3879 |@word mild:1 msr:1 version:2 stronger:2 norm:1 mcrank:1 tried:1 tr:1 liu:6 score:10 document:1 outperforms:1 existing:10 com:3 si:2 must:1 j1:6 listmle:24 rudin:1 renshaw:1 boosting:2 preference:1 herbrich:1 zhang:5 become:3 prove:3 introduce:1 theoretically:1 pairwise:4 expected:4 behavior:1 p1:2 multi:2 discoun... |
3,178 | 388 | Integrated Modeling and Control
Based on Reinforcement Learning
and Dynamic Programming
Richard S. Sutton
GTE Laboratories Incorporated
Waltham, MA 02254
Abstract
This is a summary of results with Dyna, a class of architectures for intelligent systems based on approximating dynamic programming methods.
Dyna architect... | 388 |@word trial:16 middle:1 simulation:2 korf:2 tried:4 pick:1 moment:1 initial:1 series:1 selecting:1 reaction:2 existing:1 current:8 yet:1 luis:1 interrupted:1 update:4 alone:2 half:1 intelligence:1 accordingly:1 short:4 record:2 location:3 lx:1 incorrect:1 consists:2 frans:1 manner:1 expected:2 rapid:1 behavior:7 p... |
3,179 | 3,880 | On the Algorithmics and Applications of a
Mixed-norm based Kernel Learning Formulation
G. Dinesh
Dept. of Computer Science & Automation,
Indian Institute of Science, Bangalore.
dinesh@csa.iisc.ernet.in
J. Saketha Nath
Dept. of Computer Science & Engg.,
Indian Institute of Technology, Bombay.
saketh@cse.iitb.ac.in
S. ... | 3880 |@word version:1 faculty:1 eliminating:1 norm:17 minus:1 initial:1 wrapper:2 contains:1 selecting:2 tuned:2 interestingly:1 bhattacharyya:1 outperforms:6 existing:1 kwjk:5 current:2 comparing:1 must:1 readily:1 subsequent:1 numerical:2 j1:1 engg:3 shape:1 drop:1 plot:5 update:6 discrimination:1 alone:2 v:1 selecte... |
3,180 | 3,881 | Efficient Large-Scale Distributed Training of
Conditional Maximum Entropy Models
Gideon Mann
Google
gmann@google.com
Ryan McDonald
Google
ryanmcd@google.com
Mehryar Mohri
Courant Institute and Google
mohri@cims.nyu.edu
Daniel D. Walker?
NLP Lab, Brigham Young University
danl4@cs.byu.edu
Nathan Silberman
Google
nsi... | 3881 |@word multitask:1 kong:1 version:2 briefly:1 pw:5 norm:3 bf:2 disk:5 elisseeff:2 manmatha:1 contains:1 selecting:1 daniel:1 document:1 current:1 com:3 comparing:2 jaynes:2 chu:2 must:2 numerical:1 designed:1 update:7 intelligence:1 prohibitive:1 selected:2 short:1 core:2 affix:1 boosting:1 successive:1 mcdiarmid:... |
3,181 | 3,882 | Dual Averaging Method for Regularized Stochastic
Learning and Online Optimization
Lin Xiao
Microsoft Research, Redmond, WA 98052
lin.xiao@microsoft.com
Abstract
We consider regularized stochastic learning and online optimization problems,
where the objective function is the sum of two convex terms: one is the loss fu... | 3882 |@word msr:1 version:2 norm:3 seems:1 sgd:22 tr:1 solid:2 ipm:9 series:1 document:1 past:1 freitas:1 com:2 comparing:1 written:1 plot:4 update:1 juditsky:1 core:2 banff:1 zhang:2 mathematical:2 direct:1 become:1 indeed:1 blowup:1 roughly:1 inspired:1 alberta:1 considering:1 underlying:2 moreover:1 notation:2 minim... |
3,182 | 3,883 | A Parameter-free Hedging Algorithm
Kamalika Chaudhuri
ITA, UC San Diego
kamalika@soe.ucsd.edu
Yoav Freund
CSE, UC San Diego
yfreund@ucsd.edu
Daniel Hsu
CSE, UC San Diego
djhsu@cs.ucsd.edu
Abstract
We study the problem of decision-theoretic online learning (DTOL). Motivated
by practical applications, we focus on DTO... | 3883 |@word version:2 polynomial:6 norm:1 replicate:1 open:1 crucially:1 bn:3 incurs:6 daniel:1 tuned:2 ours:1 current:2 discretization:2 assigning:1 must:1 reminiscent:1 additive:1 happen:1 plot:1 update:3 half:1 warmuth:3 provides:1 boosting:1 cse:2 direct:1 become:1 replication:11 prove:2 overhead:1 combine:1 introd... |
3,183 | 3,884 | Efficient Bregman Range Search
Lawrence Cayton
Max Planck Institute for Biological Cybernetics
lcayton@tuebingen.mpg.de
Abstract
We develop an algorithm for efficient range search when the notion of dissimilarity is given by a Bregman divergence. The range search task is to return
all points in a potentially large da... | 3884 |@word briefly:1 seems:1 bf:10 open:1 vldb:1 decomposition:7 simplifying:1 recursively:1 liu:2 series:1 contains:4 daniel:1 seriously:1 document:5 interestingly:2 recovered:1 comparing:2 yet:2 must:4 john:1 partition:2 moreno:1 implying:2 leaf:2 warmuth:1 xk:2 core:4 manfred:1 blei:2 provides:6 node:13 zhang:2 mat... |
3,184 | 3,885 | Learning from Neighboring Strokes:
Combining Appearance and Context for
Multi-Domain Sketch Recognition
Tom Y. Ouyang Randall Davis
Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139 USA
{ouyang,davis}@csail.mit.edu
Abstract
We propose a new sketch recogn... | 3885 |@word version:2 middle:1 everingham:1 textonboost:1 initial:2 configuration:1 contains:1 score:1 selecting:1 ours:2 document:1 subjective:1 existing:2 current:3 contextual:1 must:2 parsing:2 subsequent:1 informative:1 shape:4 eleven:1 designed:2 v:1 hash:2 intelligence:4 selected:1 fewer:2 short:1 record:1 detect... |
3,185 | 3,886 | Measuring model complexity with the prior predictive
Wolf Vanpaemel ?
Department of Psychology
University of Leuven
Belgium.
wolf.vanpaemel@psy.kuleuven.be
Abstract
In the last few decades, model complexity has received a lot of press. While many
methods have been proposed that jointly measure a model?s descriptive a... | 3886 |@word trial:2 version:5 judgement:1 proportion:6 seems:2 km:3 additively:1 simulation:5 crucially:1 pick:2 dramatic:1 versatile:1 reduction:1 liu:1 contains:2 selecting:1 subjective:1 existing:5 current:2 comparing:2 ka:1 yet:2 visible:1 additive:2 predetermined:1 enables:2 v:1 alone:3 implying:3 fewer:1 ith:2 fo... |
3,186 | 3,887 | Maximin affinity learning of image segmentation
Srinivas C. Turaga ?
MIT
Kevin L. Briggman
Max-Planck Insitute for Medical Research
Moritz Helmstaedter
Max-Planck Insitute for Medical Research
Winfried Denk
Max-Planck Insitute for Medical Research
H. Sebastian Seung
MIT, HHMI
Abstract
Images can be segmented by fi... | 3887 |@word version:1 inversion:1 paid:1 incurs:3 dramatic:1 pick:5 brightness:1 carry:1 briggman:3 outperforms:1 comparing:1 contextual:1 si:14 intriguing:1 must:2 parsing:2 written:1 visible:1 partition:1 opin:2 designed:1 plot:1 update:3 v:1 intelligence:3 cue:1 merger:8 smith:1 short:2 colored:1 detecting:1 boostin... |
3,187 | 3,888 | Slow Learners are Fast
John Langford, Alexander J. Smola, Martin Zinkevich
Machine Learning, Yahoo! Labs and Australian National University
4401 Great America Pky, Santa Clara, 95051 CA
{jl, maz, smola}@yahoo-inc.com
Abstract
Online learning algorithms have impressive convergence properties when it comes
to risk minim... | 3888 |@word multitask:1 version:2 maz:1 achievable:1 norm:1 stronger:1 proportion:1 disk:4 simulation:1 tried:1 decomposition:2 covariance:1 dramatic:1 sgd:1 thereby:1 harder:3 reduction:1 initial:1 configuration:2 score:1 past:1 current:3 com:1 surprising:1 clara:1 tackling:1 chu:1 written:2 john:2 fn:1 subsequent:4 h... |
3,188 | 3,889 | Nonparametric Greedy Algorithms for the Sparse
Learning Problem
Han Liu and Xi Chen
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
This paper studies the forward greedy strategy in sparse nonparametric regression. For additive models, we propose an algorithm called additive forward... | 3889 |@word mild:1 trial:3 repository:1 version:4 polynomial:1 norm:4 twelfth:1 km:4 covariance:1 decomposition:1 recursively:1 liu:5 contains:2 score:2 selecting:1 series:2 current:3 comparing:1 john:4 ronald:1 additive:31 numerical:3 wx:3 plot:1 greedy:21 selected:4 intelligence:1 xk:1 boosting:2 firstly:1 zhang:6 fi... |
3,189 | 389 | Lg DEPTH ESTIMATION AND RIPPLE FIRE
CHARACTERIZA TION USING
ARTIFICIAL NEURAL NETWORKS
John L. Perry and Douglas R. Baumgardt
ENSCO, Inc.
Signal Analysis and Systems Division
5400 Port Royal Road
Springfield, Virginia 22151
(703) 321-9000, perry@dewey.css.gov
Abstract
This srudy has demonstrated how artificial neural ... | 389 |@word pcc:8 simulation:2 propagate:1 bn:1 tr:1 shot:16 selecting:1 transfonn:1 past:1 current:1 comparing:1 activation:12 si:5 written:2 john:1 wanted:4 designed:1 discrimination:6 v:3 xk:1 characterization:9 node:25 sigmoidal:1 five:1 lor:1 symposium:1 incorrect:1 consists:1 recognizable:1 blast:3 theoretically:1... |
3,190 | 3,890 | CUR from a Sparse Optimization Viewpoint
Jacob Bien?
Department of Statistics
Stanford University
Stanford, CA 94305
Ya Xu?
Department of Statistics
Stanford University
Stanford, CA 94305
Michael W. Mahoney
Department of Mathematics
Stanford University
Stanford, CA 94305
jbien@stanford.edu
yax.stanford@gmail.com
... | 3890 |@word trial:1 version:1 norm:5 seems:1 tamayo:1 seek:2 simulation:5 jacob:3 decomposition:16 covariance:2 asks:1 thereby:2 nystr:2 solid:1 initial:1 series:2 score:6 selecting:1 tuned:1 existing:1 err:15 com:1 comparing:1 si:11 gmail:1 written:1 subsequent:1 informative:1 plot:7 interpretable:3 selected:1 xk:3 it... |
3,191 | 3,891 | Getting lost in space: Large sample analysis of the
commute distance
Ulrike von Luxburg
Agnes Radl
Max Planck Institute for Biological Cybernetics, T?ubingen, Germany
{ulrike.luxburg,agnes.radl}@tuebingen.mpg.de
Matthias Hein
Saarland University, Saarbr?ucken, Germany
hein@cs.uni-sb.de
Abstract
The commute distance be... | 3891 |@word mild:1 inversion:1 stronger:1 seems:1 simulation:2 commute:74 profit:1 solid:3 disappointingly:1 reduction:2 contains:2 series:2 denoting:1 recovered:1 surprising:2 wouters:1 written:1 visible:1 dupont:1 remove:1 plot:11 prohibitive:1 node:3 readability:1 c6:2 simpler:1 saarland:1 dn:2 c2:2 mathematical:3 b... |
3,192 | 3,892 | Auto-Regressive HMM Inference with Incomplete
Data for Short-Horizon Wind Forecasting
Joseph Bockhorst
EE and Computer Science
University of Wisconsin-Milwaukee, USA
Chris Barber
EE and Computer Science
University of Wisconsin-Milwaukee, USA
Paul Roebber
Atmospheric Science
University of Wisconsin-Milwaukee, USA
Ab... | 3892 |@word multitask:1 proportion:2 advantageous:1 proportionality:1 uncovers:1 covariance:3 q1:2 initial:1 configuration:1 series:1 contains:1 united:3 selecting:1 offering:1 current:3 must:1 numerical:2 shape:1 enables:1 camacho:1 arrayed:1 drop:1 plot:4 stationary:1 intelligence:1 prohibitive:2 selected:2 leaf:1 wa... |
3,193 | 3,893 | Learning via Gaussian Herding
Koby Crammer
Department of Electrical Enginering
The Technion
Haifa, 32000 Israel
koby@ee.technion.ac.il
Daniel D. Lee
Dept. of Electrical and Systems Engineering
University of Pennsylvania
Philadelphia, PA 19104
ddlee@seas.upenn.edu
Abstract
We introduce a new family of online learning... | 3893 |@word version:1 norm:2 dekel:1 seek:1 covariance:17 commute:1 tr:7 solid:2 initial:1 contains:2 att:1 daniel:1 document:3 interestingly:1 outperforms:8 current:10 yet:2 written:1 additive:1 analytic:1 plot:2 designed:2 update:38 drop:4 v:1 isotropic:1 ith:1 five:10 c2:3 become:1 fitting:1 combine:1 introduce:1 up... |
3,194 | 3,894 | Smoothness, Low-Noise and Fast Rates
Ambuj Tewari
ambuj@cs.utexas.edu
Computer Science Dept., University of Texas at Austin
Nathan Srebro
Karthik Sridharan
nati@ttic.edu
karthik@ttic.edu
Toyota Technological Institute at Chicago
Abstract
?
? HR2n + HL? Rn for ERM with an H-smooth loss
We establish an excess risk... | 3894 |@word version:1 achievable:1 norm:29 nd:2 unif:1 d2:4 covariance:1 elisseeff:2 pick:1 boundedness:1 moment:1 chervonenkis:1 ecole:1 err:7 scovel:1 comparing:1 chicago:1 update:2 alone:1 core:1 caveat:1 provides:3 zhang:1 along:1 direct:1 become:1 focs:1 prove:3 inside:2 expected:6 roughly:1 behavior:4 nor:1 cardi... |
3,195 | 3,895 | Gated Softmax Classification
Christopher Zach
Department of Computer Science
ETH Zurich
Switzerland
chzach@inf.ethz.ch
Roland Memisevic
Department of Computer Science
ETH Zurich
Switzerland
roland.memisevic@gmail.com
Marc Pollefeys
Department of Computer Science
ETH Zurich
Switzerland
marc.pollefeys@inf.ethz.ch
Geof... | 3895 |@word version:1 polynomial:1 seems:1 logit:1 tried:2 set5:1 xtest:2 contrastive:2 contains:2 score:14 daniel:1 interestingly:4 outperforms:1 com:2 comparing:1 activation:2 gmail:1 written:5 reminiscent:1 ronan:1 additive:1 shape:2 plot:1 v:2 alone:1 indicative:1 accordingly:1 oldest:1 short:1 provides:2 coarse:1 ... |
3,196 | 3,896 | Online Classification with Specificity Constraints
Shie Mannor
Department of Electrical Engineering
Technion - Israel Institute of Technology
Haifa, 32000, Israel
shie@ee.technion.ac.il
Andrey Bernstein
Department of Electrical Engineering
Technion - Israel Institute of Technology
Haifa, 32000, Israel
andreyb@tx.tech... | 3896 |@word briefly:1 version:2 polynomial:5 seems:1 approachability:5 dekel:1 noregret:1 bn:13 attainable:12 pick:1 minus:2 initial:1 outperforms:2 existing:1 z2:1 attainability:2 must:1 fn:7 additive:1 treating:1 stationary:1 implying:1 half:2 warmuth:1 vanishing:3 mannor:3 contribute:1 shorthand:1 prove:1 combine:3 ... |
3,197 | 3,897 | Probabilistic Inference and Differential Privacy
Frank McSherry
Microsoft Research
Mountain View, CA 94043
mcsherry@microsoft.com
Oliver Williams
Microsoft Research
Mountain View, CA 94043
olliew@microsoft.com
Abstract
We identify and investigate a strong connection between probabilistic inference
and differential p... | 3897 |@word trial:1 private:30 briefly:1 repository:2 reused:1 cm2:8 seek:1 covariance:1 configuration:2 contains:2 existing:5 com:2 protection:1 dx:12 must:5 shape:2 plot:1 alone:1 generative:3 fewer:1 accordingly:1 nent:1 smith:2 record:14 colored:1 hypersphere:1 parameterizations:1 node:1 attack:1 simpler:1 dn:1 dir... |
3,198 | 3,898 | Basis Construction from Power Series Expansions of
Value Functions
Bo Liu
Department of Computer Science
University of Massachusetts
Amherst, MA 01003
boliu@cs.umass.edu
Sridhar Mahadevan
Department of Computer Science
University of Massachusetts
Amherst, MA 01003
mahadeva@cs.umass.edu
Abstract
This paper explores li... | 3898 |@word version:1 middle:2 polynomial:5 km:2 decomposition:3 initial:3 liu:2 series:27 uma:2 ap1:1 past:1 existing:1 written:2 numerical:2 subsequent:2 plot:2 progressively:2 v:4 stationary:2 intelligence:1 plane:1 xk:1 short:1 fa9550:1 indefinitely:1 provides:6 successive:4 mathematical:1 interscience:1 expected:4... |
3,199 | 3,899 | Exact inference and learning for cumulative
distribution functions on loopy graphs
Jim C. Huang, Nebojsa Jojic and Christopher Meek
Microsoft Research
One Microsoft Way, Redmond, WA 98052
Abstract
Many problem domains including climatology and epidemiology require models
that can capture both heavy-tailed statistics a... | 3899 |@word version:1 eliminating:1 covariance:2 decomposition:3 recursively:5 liu:1 series:6 contains:2 hardy:1 nonparanormal:7 existing:3 comparing:2 partition:3 enables:1 plot:1 designed:1 update:3 nebojsa:1 intelligence:2 leaf:4 fewer:1 wolfram:1 caveat:1 node:38 location:1 toronto:1 simpler:1 constructed:2 become:... |
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