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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2,100 | 2,907 | Radial Basis Function Network for Multi-task
Learning
Xuejun Liao
Department of ECE
Duke University
Durham, NC 27708-0291, USA
xjliao@ee.duke.edu
Lawrence Carin
Department of ECE
Duke University
Durham, NC 27708-0291, USA
lcarin@ee.duke.edu
Abstract
We extend radial basis function (RBF) networks to the scenario in w... | 2907 |@word multitask:4 trial:3 determinant:2 cox:1 inversion:1 norm:1 seems:1 bn:1 accounting:1 reduction:1 series:1 score:2 selecting:5 denoting:1 seriously:2 outperforms:1 transferability:2 z2:1 activation:4 must:2 enables:1 designed:2 update:2 implying:1 selected:9 record:1 authority:1 node:20 xnm:2 ik:51 consists:... |
2,101 | 2,908 | Non-iterative Estimation with Perturbed
Gaussian Markov Processes
Yunsong Huang
B. Keith Jenkins
Signal and Image Processing Institute
Department of Electrical Engineering-Systems
University of Southern California
Los Angeles, CA 90089-2564
{yunsongh,jenkins}@sipi.usc.edu
Abstract
We develop an approach for estimation... | 2908 |@word determinant:3 illustrating:1 inversion:1 polynomial:2 norm:1 open:4 simulation:1 covariance:3 invoking:1 q1:3 brightness:1 thereby:2 solid:2 reduction:4 configuration:2 hereafter:1 denoting:2 diagonalized:1 contextual:2 reminiscent:1 readily:1 partition:1 engendered:1 shape:1 implying:1 generative:1 half:1 ... |
2,102 | 2,909 | Laplacian Score for Feature Selection
Xiaofei He1
Deng Cai2
Partha Niyogi1
Department of Computer Science, University of Chicago
{xiaofei, niyogi}@cs.uchicago.edu
2
Department of Computer Science, University of Illinois at Urbana-Champaign
dengcai2@uiuc.edu
1
Abstract
In supervised learning scenarios, feature selecti... | 2909 |@word repository:1 smirnov:1 grey:1 seek:2 brightness:2 tr:1 harder:1 wrapper:5 liu:1 contains:3 score:58 selecting:1 series:1 document:1 existing:2 comparing:1 si:8 must:1 written:2 john:1 chicago:1 remove:2 discrimination:4 intelligence:1 selected:6 lr:8 provides:1 node:6 along:1 introduce:1 expected:1 examine:... |
2,103 | 291 | 818
Smotroff
Dataflow Architectures:
Flexible Platforms for
Neural Network Simulation
Ira G. Smotroff
MITRE-Bedford Neural Network Group
The MITRE Corporation
Bedford, MA 01730
ABSTRACT
Dataflow architectures are general computation engines optimized for
the execution of fme-grain parallel algorithms. Neural networ... | 291 |@word selforganization:1 interleave:1 instruction:9 simulation:20 initial:2 contains:1 must:2 written:1 grain:4 realistic:1 eleven:1 treating:1 device:3 provides:1 location:1 five:4 constructed:1 become:1 prove:1 microchip:1 overhead:5 manner:2 alspector:2 examine:1 simulator:2 automatically:1 actual:1 motorola:1 ... |
2,104 | 2,910 | Policy-Gradient Methods for Planning
Douglas Aberdeen
Statistical Machine Learning, National ICT Australia, Canberra
doug.aberdeen@anu.edu.au
Abstract
Probabilistic temporal planning attempts to find good policies for acting
in domains with concurrent durative tasks, multiple uncertain outcomes,
and limited resources... | 2910 |@word webber:4 trial:1 advantageous:1 simulation:1 seek:1 reduction:1 initial:4 contains:1 daniel:1 existing:1 current:7 surprising:1 must:1 written:1 realistic:1 drop:1 interpretable:1 succeeding:1 update:2 alone:1 intelligence:1 leaf:3 selected:1 fewer:1 inspection:1 meuleau:1 provides:2 node:19 launching:1 zha... |
2,105 | 2,911 | Location-based Activity Recognition
Lin Liao, Dieter Fox, and Henry Kautz
Computer Science & Engineering
University of Washington
Seattle, WA 98195
Abstract
Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract and
label a person?s activi... | 2911 |@word sri:1 propagate:1 pick:1 eld:3 concise:2 thereby:2 pressure:1 contains:1 exibility:1 rightmost:1 outperforms:1 existing:4 comparing:1 contextual:1 si:1 tackling:1 must:3 fn:2 dechter:1 partition:1 cant:29 drop:2 update:3 half:1 leaf:2 instantiate:3 intelligence:5 cult:1 mccallum:1 detecting:1 node:28 locati... |
2,106 | 2,912 | Sensory Adaptation within a Bayesian
Framework for Perception
Alan A. Stocker? and Eero P. Simoncelli
Howard Hughes Medical Institute and
Center for Neural Science
New York University
Abstract
We extend a previously developed Bayesian framework for perception
to account for sensory adaptation. We first note that the ... | 2912 |@word seems:7 wenderoth:1 advantageous:1 open:1 simulation:1 lobe:1 kristjansson:1 configuration:1 ording:1 comparing:1 yet:1 attracted:2 must:2 distant:1 additive:2 visible:1 subsequent:3 informative:1 shape:2 plot:1 discrimination:15 v:2 cue:1 alone:1 smith:1 short:1 provides:2 characterization:1 contribute:1 l... |
2,107 | 2,913 | Consensus Propagation
Ciamac C. Moallemi
Stanford University
Stanford, CA 95014 USA
ciamac@stanford.edu
Benjamin Van Roy
Stanford University
Stanford, CA 95014 USA
bvr@stanford.edu
Abstract
We propose consensus propagation, an asynchronous distributed protocol for averaging numbers across a network. We establish con... | 2913 |@word version:3 norm:4 nd:5 open:2 contraction:1 simplifying:1 q1:1 accommodate:1 carry:1 initial:4 current:1 com:1 surprising:1 must:1 belmont:1 numerical:2 distant:1 enables:1 cis:1 update:4 intelligence:1 guess:2 ith:1 characterization:1 iterates:1 contribute:1 node:37 mathematical:1 along:4 become:2 symposium... |
2,108 | 2,914 | Non-Local Manifold Parzen Windows
Yoshua Bengio, Hugo Larochelle and Pascal Vincent
Dept. IRO, Universit?e de Montr?eal
P.O. Box 6128, Downtown Branch, Montreal, H3C 3J7, Qc, Canada
{bengioy,larocheh,vincentp}@iro.umontreal.ca
Abstract
To escape from the curse of dimensionality, we claim that one can learn
non-local f... | 2914 |@word determinant:1 illustrating:1 version:3 norm:1 tried:1 covariance:15 decomposition:3 simplifying:1 epartement:3 initial:1 contains:1 goldberger:2 si:1 must:2 numerical:1 informative:1 shape:8 s21:1 selected:2 plane:10 isotropic:1 parametrization:1 recherche:3 provides:1 along:1 predecessor:2 scholkopf:2 qual... |
2,109 | 2,915 | Variable KD-Tree Algorithms for Spatial Pattern
Search and Discovery
Jeremy Kubica
Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213
Joseph Masiero
Institute for Astronomy
University of Hawaii
Honolulu, HI 96822
Andrew Moore
Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213
jkubica... | 2915 |@word trial:2 middle:1 seek:1 pick:1 brightness:1 recursively:1 initial:4 configuration:1 series:1 current:9 must:2 john:1 kdd:1 remove:3 drop:1 half:1 leaf:3 short:1 detecting:1 provides:2 node:48 coarse:1 along:3 become:2 consists:3 fitting:1 introduce:1 roughly:2 examine:2 nor:1 multi:4 automatically:1 actual:... |
2,110 | 2,916 | Query By Committee Made Real
Ran Gilad-Bachrach??
Amir Navot?
Naftali Tishby??
? School of Computer Science and Engineering
? Interdisciplinary Center for Neural Computation
The Hebrew University, Jerusalem, Israel.
? Intel Research
Abstract
Training a learning algorithm is a costly task. A major goal of active
learn... | 2916 |@word trial:4 msr:1 version:22 polynomial:1 asks:1 tr:3 solid:1 celebrated:1 selecting:1 prefix:1 outperforms:2 current:1 yet:1 grahm:2 realistic:1 informative:1 enables:2 plot:1 atlas:1 discrimination:1 alone:1 greedy:3 selected:8 stationary:1 v:1 amir:1 half:1 xk:5 short:2 core:1 provides:1 billiard:2 herbrich:... |
2,111 | 2,917 | Neuronal Fiber Delineation in Area of Edema
from Diffusion Weighted MRI
Ofer Pasternak?
School of Computer Science
Tel-Aviv University
Tel-Aviv, ISRAEL 69978
oferpas@post.tau.ac.il
Nathan Intrator
School of Computer Science
Tel-Aviv University
nin@post.tau.ac.il
Nir Sochen
Department of Applied Mathematics
Tel-Aviv Un... | 2917 |@word cylindrical:1 faculty:1 mri:24 norm:1 d2:3 calculus:1 pulse:1 decomposition:1 pressure:1 edema:17 reduction:1 initial:4 series:1 realistic:1 shape:1 enables:1 wanted:1 remove:1 fund:1 guess:3 isotropic:7 provides:1 x128:1 mathematical:2 along:1 differential:1 consists:1 fitting:7 assaf:4 acquired:1 expected... |
2,112 | 2,918 | Learning Influence among Interacting
Markov Chains
Dong Zhang
IDIAP Research Institute
CH-1920 Martigny, Switzerland
zhang@idiap.ch
Samy Bengio
IDIAP Research Institute
CH-1920 Martigny, Switzerland
bengio@idiap.ch
Daniel Gatica-Perez
IDIAP Research Institute
CH-1920 Martigny, Switzerland
gatica@idiap.ch
Deb Roy
Massa... | 2918 |@word judgement:2 plsa:10 open:1 initial:2 series:2 contains:1 pub:1 daniel:1 document:3 subjective:1 outperforms:1 current:3 yet:2 follower:1 predetermined:2 hofmann:1 moreno:1 designed:1 treating:1 v:8 intelligence:2 ith:4 leadership:1 dissertation:1 blei:1 provides:3 attack:1 simpler:3 zhang:3 five:1 along:2 s... |
2,113 | 2,919 | Searching for Character Models
Jaety Edwards
Department of Computer Science
UC Berkeley
Berkeley, CA 94720
jaety@cs.berkeley.edu
David Forsyth
Department of Computer Science
UC Berkeley
Berkeley, CA 94720
daf@cs.berkeley.edu
Abstract
We introduce a method to automatically improve character models for a
handwritten s... | 2919 |@word bigram:1 propagate:1 covariance:2 solid:3 incarnation:1 accommodate:1 bck:1 initial:1 configuration:1 series:1 score:12 contains:2 manmatha:1 tuned:3 document:24 past:3 current:7 rath:1 ocurring:1 assigning:1 must:1 ideo:2 half:4 greedy:1 selected:2 beginning:1 provides:1 node:1 location:14 lavrenko:2 heigh... |
2,114 | 292 | Discovering the Structure of a Reactive Environment by Exploration
Discovering the Structure of a Reactive Environment
by Exploration
Michael C. Mozer
Department of Computer Science
and Institute of Cognitive Science
University of Colorado
Boulder, CO 80309-0430
Jonatban Bachrach
DepartmentofCompu~
and Infonnation S... | 292 |@word cu:1 toggling:1 seems:2 r:11 propagate:2 fonn:2 thereby:1 accommodate:2 ld:1 initial:1 contains:1 hereafter:1 current:12 surprising:1 activation:2 must:7 readily:1 subsequent:1 shape:1 designed:1 drop:1 update:46 treating:1 discovering:6 selected:3 fewer:1 beginning:1 record:3 node:18 direct:1 become:2 sympo... |
2,115 | 2,920 | Learning from Data of Variable Quality
Koby Crammer, Michael Kearns, Jennifer Wortman
Computer and Information Science
University of Pennsylvania
Philadelphia, PA 19103
{crammer,mkearns,wortmanj}@cis.upenn.edu
Abstract
We initiate the study of learning from multiple sources of limited data,
each of which may be corru... | 2920 |@word briefly:2 version:1 km:2 simulation:7 moment:1 reduction:1 mkearns:1 contains:4 prefix:5 current:1 yet:1 must:5 subsequent:1 numerical:2 shape:2 selected:1 accordingly:1 ith:2 provides:2 five:1 ik:1 qualitative:1 eleventh:1 introduce:1 upenn:1 expected:1 behavior:2 examine:3 mechanic:1 actual:6 considering:... |
2,116 | 2,921 | Learning Depth from Single Monocular Images
Ashutosh Saxena, Sung H. Chung, and Andrew Y. Ng
Computer Science Department
Stanford University
Stanford, CA 94305
asaxena@stanford.edu,
{codedeft,ang}@cs.stanford.edu
Abstract
We consider the task of depth estimation from a single monocular image. We take a supervised lea... | 2921 |@word seems:1 stronger:1 nd:1 norm:2 r:10 shading:1 initial:1 hoiem:1 ours:1 contextual:2 fn:3 distant:2 additive:1 tilted:1 partition:1 shape:2 motor:2 designed:1 ashutosh:1 grass:2 alone:1 cue:7 leaf:1 intelligence:1 mccallum:1 location:1 corridor:1 lagr:1 combine:1 mask:5 indeed:1 expected:5 behavior:1 themsel... |
2,117 | 2,922 | Learning Topology with the Generative Gaussian
Graph and the EM Algorithm
Micha?el Aupetit
CEA - DASE
BP 12 - 91680
Bruy`eres-le-Ch?atel, France
aupetit@dase.bruyeres.cea.fr
Abstract
Given a set of points and a set of prototypes representing them, how to
create a graph of the prototypes whose topology accounts for tha... | 2922 |@word middle:1 norm:1 open:1 grey:1 reduction:1 initial:3 series:2 exclusively:1 bradley:1 recovered:1 chazelle:1 ida:1 yet:1 must:3 john:2 additive:2 shape:1 n0:5 generative:24 fewer:1 ith:3 core:1 oblique:1 provides:2 quantizer:1 revisited:2 location:2 math:1 simpler:1 along:3 constructed:1 symposium:3 ik:2 qij... |
2,118 | 2,923 | Beyond Pair-Based STDP: a Phenomenogical
Rule for Spike Triplet and Frequency Effects
Jean-Pascal Pfister and Wulfram Gerstner
School of Computer and Communication Sciences
and Brain-Mind Institute,
Ecole Polytechnique F?ed?erale de Lausanne (EPFL), CH-1015 Lausanne
{jean-pascal.pfister, wulfram.gerstner}@epfl.ch
Abs... | 2923 |@word version:3 seems:1 hippocampus:1 pick:1 solid:5 moment:2 configuration:2 contains:1 series:1 ecole:1 past:4 current:3 written:1 must:1 realistic:1 plasticity:5 plot:1 update:5 v:2 dover:1 contribute:1 firstly:1 simpler:1 zhang:1 five:4 mathematical:2 burst:2 along:1 differential:1 pairing:3 dan:3 paragraph:1... |
2,119 | 2,924 | Robust design of biological experiments
Patrick Flaherty
EECS Department
University of California
Berkeley, CA 94720
flaherty@berkeley.edu
Michael I. Jordan
Computer Science and Statistics
University of California
Berkeley, CA 94720
jordan@cs.berkeley.edu
Adam P. Arkin
Bioengineering Department,
LBL, Howard Hughes M... | 2924 |@word determinant:2 norm:1 heuristically:1 grk:3 covariance:7 p0:2 thereby:1 initial:6 series:1 genetic:1 reaction:8 recovered:2 activation:2 must:2 hoboken:1 john:1 realistic:1 additive:1 half:1 selected:1 height:6 mathematical:2 constructed:1 differential:2 pathway:2 wild:5 interscience:1 expected:2 gov:1 littl... |
2,120 | 2,925 | Estimating the ?wrong? Markov random field:
Benefits in the computation-limited setting
Martin J. Wainwright
Department of Statistics, and
Department of Electrical Engineering and Computer Science
UC Berkeley, Berkeley CA 94720
wainwrig@{stat,eecs}.berkeley.edu
Abstract
Consider the problem of joint parameter estimat... | 2925 |@word h:2 trial:2 determinant:2 version:1 middle:1 norm:2 pseudomoment:1 confirms:1 bn:10 kappen:1 initial:4 seriously:1 outperforms:3 wainwrig:1 existing:1 written:1 belmont:1 subsequent:1 additive:1 partition:1 pseudomarginals:7 plot:3 intelligence:4 parameterization:1 accordingly:1 node:4 constructed:1 prove:3... |
2,121 | 2,926 | Multiple Instance Boosting for Object Detection
Paul Viola, John C. Platt, and Cha Zhang
Microsoft Research
1 Microsoft Way
Redmond, WA 98052
{viola,jplatt}@microsoft.com
Abstract
A good image object detection algorithm is accurate, fast, and does not
require exact locations of objects in a training set. We can creat... | 2926 |@word version:1 retraining:3 tedious:1 cha:1 leow:1 harder:1 initial:6 score:6 selecting:1 interestingly:1 com:1 nowlan:4 si:4 yet:1 must:2 john:1 subsequent:2 visible:2 shape:1 hofmann:2 designed:1 alone:1 generative:3 selected:3 greedy:1 detecting:1 boosting:20 location:10 simpler:1 zhang:1 diagnosing:1 along:2... |
2,122 | 2,927 | Scaling Laws in Natural Scenes and the
Inference of 3D Shape
Brian Potetz
Department of Computer Science
Center for the Neural Basis of Cognition
Carnegie Mellon University
Pittsburgh, PA 15213
bpotetz@cs.cmu.edu
Tai Sing Lee
Department of Computer Science
Center for the Neural Basis of Cognition
Carnegie Mellon Univ... | 2927 |@word version:1 achievable:1 stronger:2 grey:2 km:1 confirms:1 covariance:1 decomposition:2 prominence:1 shading:12 reduction:2 uncovered:1 contains:1 series:1 existing:1 imaginary:9 current:2 recovered:1 yet:2 must:3 fn:4 shape:47 extrapolating:1 plot:5 drop:5 v:1 alone:1 cue:13 selected:1 devising:1 half:1 fewe... |
2,123 | 2,928 | Dual-Tree Fast Gauss Transforms
Dongryeol Lee
Computer Science
Carnegie Mellon Univ.
dongryel@cmu.edu
Alexander Gray
Computer Science
Carnegie Mellon Univ.
agray@cs.cmu.edu
Andrew Moore
Computer Science
Carnegie Mellon Univ.
awm@cs.cmu.edu
Abstract
In previous work we presented an efficient approach to computing ker... | 2928 |@word trial:1 version:1 polynomial:1 simulation:2 decomposition:1 moment:2 celebrated:1 series:17 score:1 fgt:8 existing:1 intriguing:1 must:3 written:3 l2l:3 update:1 intelligence:1 leaf:2 website:1 nq:2 finitedifference:1 node:26 location:1 math:1 hermite:35 five:2 mathematical:1 dn:1 direct:2 incorrect:3 speci... |
2,124 | 2,929 | Correcting sample selection bias in maximum
entropy density estimation
Miroslav Dud??k, Robert E. Schapire
Princeton University
Department of Computer Science
35 Olden St, Princeton, NJ 08544
Steven J. Phillips
AT&T Labs ? Research
180 Park Ave, Florham Park, NJ 07932
phillips@research.att.com
{mdudik,schapire}@prin... | 2929 |@word version:3 c0:4 open:1 nsw:2 mammal:1 dramatic:1 herbarium:2 necessity:1 att:1 tuned:1 pprox:10 outperforms:2 existing:1 africa:1 current:1 com:1 comparing:1 must:1 john:2 fn:1 resampling:1 intelligence:1 record:1 location:5 simpler:1 five:2 unbounded:1 along:1 direct:2 differential:1 prove:2 manner:3 behavi... |
2,125 | 293 | 396
Le Cun, Boser, Denker, Henderson, Howard, Hubbard and Jackel
Handwritten Digit Recognition with a
Back-Propagation Network
Y. Le Cun, B. Boser, J. S. Denker, D. Henderson,
R. E. Howard, W. Hubbard, and L. D. Jackel
AT&T Bell Laboratories, Holmdel, N. J. 07733
ABSTRACT
We present an application of back-propagati... | 293 |@word coprocessor:1 version:1 advantageous:1 substitution:1 contains:2 interestingly:1 yet:1 must:4 readily:1 written:2 reminiscent:1 realistic:1 subsequent:1 alphanumeric:1 shape:2 discernible:1 designed:3 extrapolating:1 half:1 plane:8 ith:1 short:1 detecting:1 postal:3 location:3 h4:4 direct:1 consists:4 themse... |
2,126 | 2,930 | An Analog Visual Pre-Processing Processor
Employing Cyclic Line Access in
Only-Nearest-Neighbor-Interconnects
Architecture
Yusuke Nakashita
Department of Frontier Informatics
School of Frontier Sciences
The University of Tokyo
5-1-5 Kashiwanoha, Kashiwa-shi, Chiba
277-8561, Japan
yusuke@else.k.u-tokyo.ac.jp
Yoshio Mit... | 2930 |@word wiesel:1 overwritten:2 solid:6 carry:4 cyclic:11 configuration:1 current:1 follower:2 must:3 periodically:1 designed:1 half:1 device:2 plane:4 location:1 five:1 c2:2 become:1 supply:1 consists:1 manner:1 themselves:1 becomes:1 linearity:1 matched:1 circuit:14 vref:1 developed:6 fabricated:4 corporation:2 ev... |
2,127 | 2,931 | A Connectionist Model for Constructive
Modal Reasoning
Artur S. d?Avila Garcez
Department of Computing, City University London
London EC1V 0HB, UK
aag@soi.city.ac.uk
Lu??s C. Lamb
Institute of Informatics, Federal University of Rio Grande do Sul
Porto Alegre RS, 91501-970, Brazil
LuisLamb@acm.org
Dov M. Gabbay
Departm... | 2931 |@word illustrating:1 middle:1 nd:1 cml:3 r:1 asks:1 initial:1 necessity:1 denoting:2 current:1 activation:15 must:5 luis:1 refuted:1 intelligence:6 provides:1 org:1 five:1 along:2 constructed:1 h4:4 predecessor:1 prove:3 shorthand:2 introduce:3 inter:1 expected:1 rapid:1 p1:5 increasing:1 becomes:1 nuffield:1 con... |
2,128 | 2,932 | Hyperparameter and Kernel Learning for
Graph Based Semi-Supervised Classification
Ashish Kapoor? , Yuan (Alan) Qi? , Hyungil Ahn? and Rosalind W. Picard?
?
MIT Media Laboratory, Cambridge, MA 02139
{kapoor, hiahn, picard}@media.mit.edu
?
MIT CSAIL, Cambridge, MA 02139
alanqi@csail.mit.edu
Abstract
There have been man... | 2932 |@word kondor:1 polynomial:1 norm:1 advantageous:1 bn:1 decomposition:1 tr:4 initial:3 existing:1 current:1 written:6 alanqi:1 informative:2 remove:1 plot:4 update:1 v:18 half:1 parameterization:1 matrix1:1 provides:4 node:2 constructed:1 become:2 yuan:1 consists:1 little:2 provided:4 notation:1 underlying:1 mediu... |
2,129 | 2,933 | Variational EM Algorithms for
Non-Gaussian Latent Variable Models
J. A. Palmer, D. P. Wipf, K. Kreutz-Delgado, and B. D. Rao
Department of Electrical and Computer Engineering
University of California San Diego, La Jolla, CA 92093
{japalmer,dwipf,kreutz,brao}@ece.ucsd.edu
Abstract
We consider criteria for variational r... | 2933 |@word stronger:1 norm:1 d2:2 closure:1 decomposition:1 p0:1 delgado:2 moment:2 series:1 reynolds:1 past:1 dx:9 update:1 intelligence:4 xk:1 direct:2 become:1 ik:1 consists:1 inside:1 ica:2 decreasing:5 company:1 increasing:4 becomes:1 underlying:1 minimizes:1 berkeley:1 every:2 concave:9 rm:1 ser:1 unit:1 cosh2:1... |
2,130 | 2,934 | Kernelized Infomax Clustering
Felix V. Agakov
Edinburgh University
Edinburgh EH1 2QL, U.K.
felixa@inf.ed.ac.uk
David Barber
IDIAP Research Institute
CH-1920 Martigny Switzerland
david.barber@idiap.ch
Abstract
We propose a simple information-theoretic approach to soft clustering based on maximizing the mutual informa... | 2934 |@word kulis:1 middle:1 inversion:1 norm:1 decomposition:1 reduction:3 initial:3 assigning:1 numerical:2 visible:2 informative:1 kdd:1 update:3 generative:5 intelligence:1 parameterization:1 isotropic:1 parameterizations:1 allerton:1 firstly:1 simpler:1 atj:1 along:1 scholkopf:2 prove:1 fitting:1 indeed:2 nor:1 mu... |
2,131 | 2,935 | Variational Bayesian Stochastic
Complexity of Mixture Models
Kazuho Watanabe?
Department of Computational Intelligence
and Systems Science
Tokyo Institute of Technology
Mail Box:R2-5, 4259 Nagatsuta,
Midori-ku, Yokohama, 226-8503, Japan
kazuho23@pi.titech.ac.jp
Sumio Watanabe
P& I Lab.
Tokyo Institute of Technology
sw... | 2935 |@word determinant:1 proportion:1 p0:5 minus:1 series:1 hereafter:1 clari:2 nally:1 comparing:3 dx:1 enables:1 midori:1 stationary:1 intelligence:1 selected:1 ith:1 provides:1 mef:5 firstly:1 c2:2 become:1 ect:1 prove:1 introduce:1 expected:1 indeed:1 examine:1 little:1 actual:1 provided:1 spain:1 bounded:5 estima... |
2,132 | 2,936 | A Cortically-Plausible Inverse Problem
Solving Method Applied to Recognizing
Static and Kinematic 3D Objects
David W. Arathorn
Center for Computational Biology,
Montana State University
Bozeman, MT 59717
dwa@cns . montana . edu
General Intelligence Corporation
dwa@giclab . com
Abstract
Recent neurophysiological evid... | 2936 |@word cleanly:1 decomposition:2 tr:1 electronics:1 configuration:4 initial:2 com:1 must:2 realistic:1 j1:1 shape:2 motor:5 intelligence:1 generative:5 reciprocal:3 fogassi:1 provides:2 clarified:1 traverse:1 location:1 mathematical:2 along:2 constructed:3 direct:1 become:1 ect:1 incorrect:1 specialize:1 pathway:2... |
2,133 | 2,937 | Inferring Motor Programs from Images of
Handwritten Digits
Geoffrey Hinton and Vinod Nair
Department of Computer Science, University of Toronto
10 King?s College Road, Toronto, M5S 3G5 Canada
{hinton,vnair}@cs.toronto.edu
Abstract
We describe a generative model for handwritten digits that uses two pairs
of opposing s... | 2937 |@word version:2 middle:1 open:2 additively:1 tried:2 jacob:1 pick:1 initial:4 contains:3 score:16 series:1 document:3 current:2 comparing:2 surprising:1 mayraz:1 assigning:1 synthesizer:1 must:1 written:1 john:1 grain:6 subsequent:1 informative:2 shape:2 motor:58 progressively:1 update:1 discrimination:1 dampened... |
2,134 | 2,938 | Combining Graph Laplacians for
Semi?Supervised Learning
Andreas Argyriou,
Mark Herbster,
Massimiliano Pontil
Department of Computer Science
University College London
Gower Street, London WC1E 6BT, England, UK
{a.argyriou, m.herbster, m.pontil}@cs.ucl.ac.uk
Abstract
A foundational problem in semi-supervised learnin... | 2938 |@word version:1 middle:1 kondor:2 norm:10 advantageous:1 nd:1 hu:1 ld:1 contains:1 series:2 selecting:2 rkhs:2 must:1 plot:2 v:18 implying:1 transposition:1 simpler:1 zhang:1 constructed:5 kvk2:1 become:1 ik:3 scholkopf:1 consists:2 specialize:1 combine:1 advocate:1 introduce:1 pairwise:3 indeed:1 expected:1 dist... |
2,135 | 2,939 | Learning Cue-Invariant Visual Responses
Jarmo Hurri
HIIT Basic Research Unit, University of Helsinki
P.O.Box 68, FIN-00014 University of Helsinki, Finland
Abstract
Multiple visual cues are used by the visual system to analyze a scene;
achromatic cues include luminance, texture, contrast and motion. Singlecell recordin... | 2939 |@word neurophysiology:1 version:1 norm:1 simplecell:1 hyv:5 simulation:1 decomposition:3 thereby:3 solid:1 reduction:2 contains:3 rightmost:2 activation:1 conforming:1 john:1 plot:1 implying:1 cue:77 generative:1 filtered:6 location:2 relayed:1 c2:1 become:2 consists:1 inside:1 introduce:3 manner:5 brain:1 detect... |
2,136 | 294 | Generalization Properties of Radial Basis
Functions
Christopher G. Atkeson
Sherif M. Botros
Brain and Cognitive Sciences Department
and the Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139
Abstract
We examine the ability of radial basis functions (RBFs) to generalize. We
com... | 294 |@word cox:1 faculty:1 polynomial:9 norm:4 casdagli:2 tried:1 moment:1 initial:4 series:2 hardy:3 tuned:1 o2:6 od:1 marquardt:1 girosi:5 fund:1 v:1 intelligence:1 selected:2 fewer:1 prespecified:1 compo:1 math:1 location:6 c2:5 become:1 consists:1 fitting:2 expected:2 wl1:1 themselves:1 examine:1 brain:1 ol:1 torqu... |
2,137 | 2,940 | Active Learning For Identifying Function
Threshold Boundaries
Brent Bryan
Center for Automated Learning and Discovery
Carnegie Mellon University
Pittsburgh, PA 15213
bryanba@cs.cmu.edu
Robert C. Nichol
Institute of Cosmology and Gravitation
University of Portsmouth
Portsmouth, PO1 2EG, UK
bob.nichol@port.ac.uk
Christop... | 2940 |@word tadepalli:1 nd:1 simulation:1 covariance:1 pick:3 solid:2 moment:2 reduction:1 initial:1 series:1 score:4 outperforms:1 current:2 comparing:1 si:6 written:1 must:1 fn:2 numerical:1 permeated:1 shape:4 plot:2 depict:1 half:1 chile:2 ith:2 characterization:1 location:5 successive:1 daphne:1 five:2 height:1 be... |
2,138 | 2,941 | Interpolating Between Types and Tokens
by Estimating Power-Law Generators ?
Sharon Goldwater
Thomas L. Griffiths
Mark Johnson
Department of Cognitive and Linguistic Sciences
Brown University, Providence RI 02912, USA
{sharon goldwater,tom griffiths,mark johnson}@brown.edu
Abstract
Standard statistical models of langu... | 2941 |@word arabic:1 seems:2 proportion:2 tr:1 shading:1 configuration:1 ecole:1 past:3 current:2 stemming:3 partition:2 enables:1 hypothesize:1 discrimination:1 stationary:1 generative:9 item:1 ith:5 affix:1 provides:1 node:1 lexicon:1 sits:1 contribute:1 location:1 preference:1 unbounded:2 consists:1 introduce:1 roug... |
2,139 | 2,942 | Diffusion Maps, Spectral Clustering and
Eigenfunctions of Fokker-Planck Operators
Boaz Nadler? St?ephane Lafon Ronald R. Coifman
Department of Mathematics, Yale University, New Haven, CT 06520.
{boaz.nadler,stephane.lafon,ronald.coifman}@yale.edu
Ioannis G. Kevrekidis
Department of Chemical Engineering and Program in ... | 2942 |@word briefly:1 version:1 middle:1 seems:1 nd:1 hu:1 zelnik:1 simulation:1 commute:1 reduction:6 initial:3 configuration:1 contains:1 reaction:1 wouters:1 yet:1 dx:2 ronald:2 dupont:1 v:1 stationary:2 indicative:1 gear:1 short:1 provides:4 math:2 location:5 org:1 height:1 mathematical:3 combine:1 inside:3 introdu... |
2,140 | 2,943 | Coarse sample complexity bounds for active
learning
Sanjoy Dasgupta
UC San Diego
dasgupta@cs.ucsd.edu
Abstract
We characterize the sample complexity of active learning problems in
terms of a parameter which takes into account the distribution over the
input space, the specific target hypothesis, and the desired accur... | 2943 |@word version:10 achievable:1 seems:2 open:1 paid:1 asks:3 pick:9 whittled:1 carry:2 initial:1 configuration:1 efficacy:1 tuned:1 current:3 yet:2 must:7 john:1 benign:1 remove:1 atlas:1 half:5 greedy:1 short:1 coarse:1 revisited:1 along:1 constructed:2 direct:1 expected:1 roughly:2 buying:1 goldman:1 encouraging:... |
2,141 | 2,944 | Active Learning for Misspecified Models
Masashi Sugiyama
Department of Computer Science, Tokyo Institute of Technology
2-12-1, O-okayama, Meguro-ku, Tokyo, 152-8552, Japan
sugi@cs.titech.ac.jp
Abstract
Active learning is the problem in supervised learning to design the locations of training input points so that the g... | 2944 |@word version:1 seems:1 simulation:3 tr:2 carry:1 okayama:1 outperforms:3 existing:22 surprising:1 dx:10 realistic:1 numerical:3 designed:2 intelligence:1 devising:1 location:6 five:2 theoretically:6 expected:2 planning:1 decomposed:1 totally:3 provided:3 xx:1 estimating:3 fti:2 argmin:2 lowing:1 finding:1 guaran... |
2,142 | 2,945 | Estimation of Intrinsic Dimensionality Using
High-Rate Vector Quantization
Maxim Raginsky and Svetlana Lazebnik
Beckman Institute, University of Illinois
405 N Mathews Ave, Urbana, IL 61801
{maxim,slazebni}@uiuc.edu
Abstract
We introduce a technique for dimensionality estimation based on the notion of quantization di... | 2945 |@word briefly:1 norm:1 disk:1 open:1 heuristically:1 confirms:1 covariance:1 paid:1 solid:1 reduction:4 contains:2 interestingly:2 existing:3 current:1 com:1 reminiscent:1 additive:5 partition:3 shape:2 plot:7 designed:3 v:4 greedy:7 fewer:1 prohibitive:1 selected:1 half:2 accordingly:2 indicative:1 isotropic:2 c... |
2,143 | 2,946 | Prediction and Change Detection
Mark Steyvers
msteyver@uci.edu
University of California, Irvine
Irvine, CA 92697
Scott Brown
scottb@uci.edu
University of California, Irvine
Irvine, CA 92697
Abstract
We measure the ability of human observers to predict the next datum
in a sequence that is generated by a simple statist... | 2946 |@word trial:17 middle:1 simulation:1 dramatic:1 series:2 bootstrapped:1 iple:1 current:3 comparing:2 assigning:1 must:1 readily:2 subsequent:1 realistic:1 wanted:1 plot:1 update:2 v:1 generative:2 guess:1 smith:1 short:3 fa9550:1 location:10 successive:2 simpler:1 five:1 dn:5 introduce:1 market:1 rapid:1 behavior... |
2,144 | 2,947 | Metric Learning by Collapsing Classes
Amir Globerson
School of Computer Science and Engineering,
Interdisciplinary Center for Neural Computation
The Hebrew University Jerusalem, 91904, Israel
gamir@cs.huji.ac.il
Sam Roweis
Machine Learning Group
Department of Computer Science
University of Toronto, Canada
roweis@cs.t... | 2947 |@word mild:1 repository:1 version:5 advantageous:1 norm:1 seek:3 covariance:11 p0:9 decomposition:4 pavel:1 minus:2 accommodate:2 reduction:1 initial:1 denoting:1 outperforms:3 existing:1 current:1 goldberger:1 must:2 additive:1 numerical:1 happen:1 drop:1 v:1 prohibitive:1 amir:1 ith:1 equi:1 toronto:2 location:... |
2,145 | 2,948 | Soft Clustering on Graphs
Kai Yu1 , Shipeng Yu2 , Volker Tresp1
1
Siemens AG, Corporate Technology
2
Institute for Computer Science, University of Munich
kai.yu@siemens.com, volker.tresp@siemens.com
spyu@dbs.informatik.uni-muenchen.de
Abstract
We propose a simple clustering framework on graphs encoding pairwise
data ... | 2948 |@word middle:3 nd:3 gfc:2 recursively:1 initial:2 contains:2 exclusively:1 daniel:1 document:2 interestingly:1 com:2 goldberger:1 must:1 partition:5 happen:1 hofmann:1 shape:2 motor:1 remove:1 treating:1 drop:1 update:6 stationary:3 intelligence:1 short:1 multihop:1 dn:1 along:1 direct:3 prove:3 yu1:1 introduce:1... |
2,146 | 2,949 | A Computational Model of Eye Movements
during Object Class Detection
Wei Zhang?
Hyejin Yang??
Dimitris Samaras?
Gregory J. Zelinsky??
?
Dept. of Computer Science
Dept. of Psychology?
State University of New York at Stony Brook
Stony Brook, NY 11794
{wzhang,samaras}@cs.sunysb.edu?
hjyang@ic.sunysb.edu?
Gregory.Zelinsky... | 2949 |@word trial:8 version:1 gaussion:1 simulation:1 methodologically:1 brightness:1 thereby:1 current:3 comparing:3 stony:3 attracted:1 stemming:1 cottrell:1 realistic:2 zap:1 remove:1 half:1 selected:4 item:1 inspection:1 yamada:1 stonybrook:1 detecting:1 boosting:4 hsv:1 location:4 contribute:1 liberal:1 simpler:1 ... |
2,147 | 295 | Spoken Letter Recognition
Mark Fanty & Ronald Cole
Dept. of Computer Science and Engineering
Oregon Graduate Institute
Beaverton, OR 97006
Abstract
Through the use of neural network classifiers and careful feature selection,
we have achieved high-accuracy speaker-independent spoken letter recognition. For isolated le... | 295 |@word briefly:1 retraining:2 closure:1 minus:2 initial:2 substitution:1 contains:1 score:3 tuned:1 current:1 clos:4 hou:2 ronald:1 subsequent:1 v:1 half:3 selected:2 devising:1 leaf:1 slowing:1 beginning:1 filtered:7 provides:4 node:2 location:5 toronto:1 five:2 consists:1 inside:3 manner:1 notably:1 window:8 beco... |
2,148 | 2,950 | An exploration-exp loitation mod el based
on no rep inep herine and do p amine
activity
Samuel M. McClure* , Mark S. Gilzenrat, and Jonathan D. Cohen
Center for the Study of Brain, Mind, and Behavior
Princeton University
Princeton, NJ 08544
smcclure@princeton.edu; mgilzen@princeton.edu; jdc@princeton.edu
Abstract
We p... | 2950 |@word trial:12 exploitation:3 cingulate:1 noradrenergic:1 integrative:1 simulation:3 initial:1 responsivity:1 selecting:1 reynolds:1 current:2 anterior:1 od:1 activation:1 subsequent:1 periodically:1 motor:2 hypothesize:1 plot:3 update:2 dampened:1 alone:4 greedy:2 core:1 short:4 provides:1 characterization:1 awr... |
2,149 | 2,951 | On Local Rewards and Scaling Distributed
Reinforcement Learning
J. Andrew Bagnell
Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213
Andrew Y. Ng
Computer Science Department
Stanford University
Stanford, CA 94305
dbagnell@ri.cmu.edu
ang@cs.stanford.edu
Abstract
We consider the scaling of the number ... | 2951 |@word trial:19 version:2 stronger:1 tried:1 carry:1 initial:6 configuration:5 si:4 must:3 drop:1 n0:1 pursued:1 intelligence:1 greedy:1 node:9 direct:1 prove:1 notably:1 expected:5 hardness:1 roughly:2 frequently:2 planning:3 multi:8 bellman:4 globally:1 solver:1 considering:1 xx:1 notation:2 bounded:7 estimating... |
2,150 | 2,952 | A Probabilistic Approach for Optimizing
Spectral Clustering
?
Rong Jin? , Chris Ding? , Feng Kang?
Lawrence Berkeley National Laboratory, Berkeley, CA 94720
?
Michigan State University, East Lansing , MI 48824
Abstract
Spectral clustering enjoys its success in both data clustering and semisupervised learning. But, m... | 2952 |@word trial:1 repository:1 advantageous:2 nd:1 q1:1 contains:2 document:5 past:1 existing:2 outperforms:1 current:3 comparing:3 si:10 attracted:1 numerical:1 happen:1 kdd:1 hofmann:2 designed:1 generative:1 intelligence:2 blei:2 provides:1 math:1 banff:1 five:3 constructed:2 introduce:1 lansing:1 acquired:1 exami... |
2,151 | 2,953 | Preconditioner Approximations for
Probabilistic Graphical Models
Pradeep Ravikumar John Lafferty
School of Computer Science
Carnegie Mellon University
Abstract
We present a family of approximation techniques for probabilistic graphical models, based on the use of graphical preconditioners developed in
the scientific ... | 2953 |@word version:1 norm:1 stronger:3 heuristically:1 simulation:1 carry:1 moment:1 series:1 existing:1 steiner:3 readily:1 john:1 partition:15 plot:2 update:1 intelligence:2 selected:1 provides:2 node:9 direct:1 pairwise:4 uiuc:1 freeman:1 actual:1 increasing:3 begin:2 estimating:1 notation:3 underlying:2 what:1 min... |
2,152 | 2,954 | Message passing for task redistribution on
sparse graphs
K. Y. Michael Wong
Hong Kong U. of Science & Technology
Clear Water Bay, Hong Kong, China
phkywong@ust.hk
David Saad
NCRG, Aston University
Birmingham B4 7ET, UK
D.Saad@aston.ac.uk
Zhuo Gao
Hong Kong U. of Science & Technology, Clear Water Bay, Hong Kong, Chin... | 2954 |@word kong:5 simulation:4 thereby:1 accommodate:1 carry:1 initial:1 configuration:3 current:27 nt:2 ust:1 numerical:3 realistic:1 partition:4 predetermined:1 enables:1 drop:1 interpretable:1 update:2 congestion:1 ith:1 provides:2 node:58 become:2 descendant:5 consists:3 introduce:1 manner:1 indeed:1 mechanic:2 di... |
2,153 | 2,955 | CMOL CrossNets: Possible Neuromorphic
Nanoelectronic Circuits
Jung Hoon Lee
Xiaolong Ma
Konstantin K. Likharev
Stony Brook University
Stony Brook, NY 11794-3800
klikharev@notes.cc.sunysb.edu
Abstract
Hybrid ?CMOL? integrated circuits, combining CMOS subsystem
with nanowire crossbars and simple two-terminal nanodevic... | 2955 |@word version:1 manageable:1 c0:1 open:5 cm2:8 termination:3 r:2 reduction:1 electronics:3 initial:1 necessity:1 current:4 si:1 stony:2 import:2 nanoscale:2 realistic:2 shape:1 pertinent:1 reproducible:1 half:4 selected:4 device:14 patterning:2 short:2 provides:1 location:1 simpler:2 along:1 direct:1 differential... |
2,154 | 2,956 | Goal-Based Imitation as Probabilistic Inference
over Graphical Models
Deepak Verma
Deptt of CSE, Univ. of Washington,
Seattle WA- 98195-2350
deepak@cs.washington.edu
Rajesh P. N. Rao
Deptt of CSE, Univ. of Washington,
Seattle WA- 98195-2350
rao@cs.washington.edu
Abstract
Humans are extremely adept at learning new sk... | 2956 |@word trial:2 exploitation:1 nd:1 open:1 seek:1 pick:1 solid:1 initial:3 series:1 selecting:2 denoting:1 interestingly:1 past:2 o2:1 current:12 yet:1 dumbbell:1 readily:1 rizzolatti:1 realistic:2 visible:3 motor:2 update:4 infant:10 stationary:1 selected:1 imitate:10 ith:1 fogassi:1 short:2 record:2 colored:1 men... |
2,155 | 2,957 | Learning Dense 3D Correspondence
?
Florian Steinke? , Bernhard Sch?olkopf? , Volker Blanz+
Max Planck Institute for Biological Cybernetics, 72076 T?ubingen, Germany
{steinke, bs}@tuebingen.mpg.de
+
Universit?at Siegen, 57068 Siegen, Germany
blanz@mpi-sb.mpg.de
Abstract
Establishing correspondence between distinct ob... | 2957 |@word deformed:3 cylindrical:1 version:1 norm:2 nd:1 covariance:3 decomposition:1 liu:1 rkhs:1 o2:2 existing:2 outperforms:1 comparing:1 com:1 intriguing:1 readily:1 john:1 mesh:2 numerical:1 shape:11 designed:1 implying:1 intelligence:1 selected:1 parameterization:2 iso:1 vbr:1 coarse:2 parameterizations:1 math:... |
2,156 | 2,958 | Effects of Stress and Genotype on Meta-parameter
Dynamics in Reinforcement Learning
Gediminas Luk?sys1,2
gediminas.luksys@epfl.ch
Denis Sheynikhovich1
denis.sheynikhovich@epfl.ch
? 1
J?er?emie Knusel
jeremie.knuesel@epfl.ch
Carmen Sandi2
carmen.sandi@epfl.ch
Wulfram Gerstner1
wulfram.gerstner@epfl.ch
1
Laboratory o... | 2958 |@word luk:1 exploitation:13 version:1 trial:15 noradrenergic:1 seems:1 stronger:1 retraining:2 hippocampus:2 open:3 integrative:1 simulation:2 dba:13 initial:7 responsivity:1 liquid:1 genetic:9 ecole:1 subjective:3 current:2 exposing:1 subsequent:1 numerical:1 plasticity:2 update:4 medial:1 v:2 half:6 selected:1 ... |
2,157 | 2,959 | PAC-Bayes Bounds for the Risk of the Majority Vote
and the Variance of the Gibbs Classifier
Alexandre Lacasse, Franc?ois Laviolette and Mario Marchand
D?epartement IFT-GLO
Universit?e Laval
Qu?ebec, Canada
Firstname.Secondname@ift.ulaval.ca
Pascal Germain
D?epartement IFT-GLO
Universit?e Laval Qu?ebec, Canada
Pascal.Ge... | 2959 |@word version:1 compression:1 seems:3 suitably:2 open:1 r:8 covariance:4 q1:7 moment:9 epartement:2 contains:1 outperforms:1 mushroom:4 must:2 john:2 numerical:1 v:4 half:5 intelligence:1 provides:3 boosting:3 trinomial:1 direct:2 prove:1 consists:1 inside:1 pairwise:3 expected:5 indeed:6 p1:2 actual:1 considerin... |
2,158 | 296 | How Receptive Field Parameters Affect Neural
Learning
Bartlett W. Mel
CNS Program
Caltech, 216-76
Pasadena, CA 91125
Stephen M. Omohundro
ICSI
1947 Center St., Suite 600
Berkeley, CA 94704
Abstract
We identify the three principle factors affecting the performance of learning by networks with localized units: unit no... | 296 |@word version:2 reduction:2 contains:1 tuned:4 current:3 recovered:1 must:3 visible:1 additive:1 shape:2 motor:1 implying:1 half:2 fewer:1 ith:1 coarse:2 contribute:2 direct:1 consists:1 fitting:1 baldi:3 expected:1 themselves:1 increasing:5 mitigated:1 matched:4 kind:3 suite:1 berkeley:1 act:1 unit:28 persists:1 ... |
2,159 | 2,960 | Balanced Graph Matching
Timothee Cour, Praveen Srinivasan and Jianbo Shi
Department of Computer and Information Science
University of Pennsylvania
Philadelphia, PA 19104
{timothee,psrin,jshi}@seas.upenn.edu
Abstract
Graph matching is a fundamental problem in Computer Vision and Machine
Learning. We present two contri... | 2960 |@word trial:4 version:2 middle:3 norm:8 proportion:1 weq:3 seek:2 decomposition:1 dramatic:1 carry:1 reduction:4 bck:1 moment:1 series:2 score:9 disparity:1 initial:2 offering:1 existing:3 discretization:5 comparing:1 yet:1 informative:5 christian:1 drop:1 plot:2 v:9 greedy:1 intelligence:2 plane:1 colored:1 prov... |
2,160 | 2,961 | Active learning for misspecified
generalized linear models
Francis R. Bach
Centre de Morphologie Math?ematique
Ecole des Mines de Paris
Fontainebleau, France
francis.bach@mines.org
Abstract
Active learning refers to algorithmic frameworks aimed at selecting training data
points in order to reduce the number of requir... | 2961 |@word determinant:1 illustrating:1 version:1 middle:4 advantageous:2 norm:1 seems:1 simulation:4 decomposition:1 p0:32 tr:5 moment:1 reduction:5 score:1 selecting:2 ecole:2 outperforms:1 current:4 comparing:1 yet:1 dx:2 readily:4 grain:1 realistic:5 partition:2 enables:1 update:1 v:2 selected:6 parameterization:1... |
2,161 | 2,962 | Non-rigid point set registration: Coherent Point Drift
? Carreira-Perpin?
? an
Andriy Myronenko
Xubo Song
Miguel A.
Department of Computer Science and Electrical Engineering
OGI School of Science and Engineering
Oregon Health and Science University
Beaverton, OR, USA, 97006
{myron, xubosong, miguel}@csee.ogi.edu
Abstr... | 2962 |@word middle:1 norm:1 gradual:1 perpin:1 tried:1 decomposition:2 covariance:1 tr:2 moment:1 initial:7 denoting:1 outperforms:1 current:2 recovered:2 must:1 girosi:1 shape:2 drop:1 update:2 plane:1 isotropic:1 parametrization:1 filtered:1 coarse:1 math:1 location:3 mathematical:2 registering:1 direct:1 differentia... |
2,162 | 2,963 | AdaBoost is Consistent
Peter L. Bartlett
Department of Statistics and Computer Science Division
University of California, Berkeley
Mikhail Traskin
Department of Statistics
University of California, Berkeley
bartlett@stat.berkeley.edu
mtraskin@stat.berkeley.edu
Abstract
The risk, or probability of error, of the clas... | 2963 |@word wenxin:2 version:4 norm:3 yi0:1 logit:2 contraction:1 chervonenkis:1 dx:1 half:1 provides:1 boosting:15 node:2 successive:1 hyperplanes:1 mcdiarmid:1 zhang:2 c2:4 lessening:1 prove:2 introduce:1 behavior:1 terminal:2 decreasing:2 provided:1 begin:2 underlying:2 notation:2 bounded:2 linearity:1 what:1 findin... |
2,163 | 2,964 | A Switched Gaussian Process for Estimating
Disparity and Segmentation in Binocular Stereo
Oliver Williams
Microsoft Research Ltd.
Cambridge, UK
omcw2@cam.ac.uk
Abstract
This paper describes a Gaussian process framework for inferring pixel-wise
disparity and bi-layer segmentation of a scene given a stereo pair of image... | 2964 |@word grey:1 rgb:1 covariance:10 initial:1 contains:2 disparity:33 score:1 selecting:1 current:1 com:2 assigning:1 yet:1 must:3 bd:1 readily:1 visible:5 informative:2 treating:2 mislabelled:2 update:1 plot:1 alone:1 greedy:4 selected:4 fewer:1 intelligence:1 ith:3 short:2 location:16 firstly:1 ladendorf:1 along:2... |
2,164 | 2,965 | Hidden Markov Dirichlet Process: Modeling Genetic
Recombination in Open Ancestral Space
Eric P. Xing
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
epxing@cs.cmu.edu
Kyung-Ah Sohn
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
ksohn@cs.cmu.edu
Abstract
We prese... | 2965 |@word repository:1 version:1 proportion:1 open:8 thereby:1 solid:1 accommodate:1 ld:7 initial:4 configuration:4 contains:4 liu:4 score:7 uncovered:1 genetic:26 interestingly:1 existing:1 current:2 recovered:4 yet:1 written:1 must:1 readily:1 subsequent:1 partition:2 remove:1 treating:1 plot:5 update:2 aside:1 sta... |
2,165 | 2,966 | Comparative Gene Prediction using Conditional
Random Fields
Jade P. Vinson? ?
jpvinson@broad.mit.edu
David DeCaprio?
daved@broad.mit.edu
Stacey Luoma
sluoma@broad.mit.edu
Matthew D. Pearson
mdp@broad.mit.edu
James E. Galagan
jgalag@broad.mit.edu
The Broad Institute of MIT and Harvard
Cambridge, MA 02142
Abstract
... | 2966 |@word yi0:1 open:3 seek:1 korf:1 kulp:1 covariance:1 serafim:1 pavel:1 tr:1 solid:1 initial:3 generatively:2 selecting:2 daniel:1 existing:3 current:2 com:1 comparing:2 parsing:2 john:1 distant:1 partition:1 hofmann:1 siepel:2 generative:10 instantiate:1 selected:3 indicative:1 mccallum:3 beginning:1 core:1 recor... |
2,166 | 2,967 | Large Margin Multi-channel Analog-to-Digital
Conversion with Applications to Neural Prosthesis
Amit Gore and Shantanu Chakrabartty
Department of Electrical and Computer Engineering
Michigan State University
East Lansing, MI 48823
{goreamit,shantanu}@egr.msu.edu
Abstract
A key challenge in designing analog-to-digital ... | 2967 |@word illustrating:4 briefly:1 eliminating:2 compression:6 norm:7 open:1 cm2:1 ona:1 pulse:3 eng:1 initial:2 configuration:1 series:2 contains:1 nordhausen:1 written:1 additive:1 periodically:1 candy:1 girosi:1 update:12 stationary:4 shut:1 nervous:2 core:1 characterization:1 direct:1 become:1 consists:1 shantanu... |
2,167 | 2,968 | Automated Hierarchy Discovery for Planning in
Partially Observable Environments
Laurent Charlin & Pascal Poupart
David R. Cheriton School of Computer Science
Faculty of Mathematics
University of Waterloo
Waterloo, Ontario
{lcharlin,ppoupart}@cs.uwaterloo.ca
Romy Shioda
Dept of Combinatorics and Optimization
Faculty o... | 2968 |@word trial:1 version:2 faculty:2 advantageous:1 termination:6 tried:1 decomposition:5 simplifying:1 tr:1 solid:1 recursively:2 vno:1 initial:2 past:3 existing:1 mishra:1 nt:4 must:3 written:1 reminiscent:1 update:1 n0:48 intelligence:4 discovering:5 fewer:2 parameterization:1 beginning:1 meuleau:1 num:2 provides... |
2,168 | 2,969 | Recursive Attribute Factoring
David Cohn
Google Inc.,
1600 Amphitheatre Parkway
Mountain View, CA 94043
cohn@google.com
Deepak Verma
Dept. of CSE, Univ. of Washington,
Seattle WA- 98195-2350
deepak@cs.washington.edu
Karl Pfleger
Google Inc.,
1600 Amphitheatre Parkway
Mountain View, CA 94043
kpfleger@google.com
Abst... | 2969 |@word faculty:1 version:5 manageable:1 seems:1 plsa:3 cleanly:1 propagate:3 carry:1 reduction:1 contains:2 pfleger:1 daniel:2 document:85 outperforms:1 existing:1 current:2 com:2 surprising:4 yet:1 must:1 written:1 realize:1 informative:1 hofmann:2 remove:1 update:1 alone:4 generative:1 prohibitive:1 half:1 stati... |
2,169 | 297 | Spherical Units as Dynamic Consequential Regions:
Implications for Attention, Competition and Categorization
Mark A. Gluck
Stephen Jose Hanson*
Learning and Knowledge
Acquisition Group
Siemens Corporate Research
Princeton, NJ 08540
Center for Molecular &
Behavioral Neuroscience
Rutgers University
Newark, NJ 07102
A... | 297 |@word trial:1 version:4 proportion:1 consequential:20 open:1 hu:2 contraction:1 tr:1 solid:2 series:1 tuned:1 current:1 nowlan:2 activation:3 yet:1 must:1 shape:6 drop:1 concert:1 v:2 cue:13 item:2 shj:2 record:1 probablity:1 provides:1 hypersphere:1 node:4 location:5 successive:1 attack:1 arctan:4 lx:4 hyperplane... |
2,170 | 2,970 | Sparse Kernel Orthonormalized PLS for feature
extraction in large data sets
Jer?onimo Arenas-Garc??a, Kaare Brandt Petersen and Lars Kai Hansen
Informatics and Mathematical Modelling
Technical University of Denmark
DK-2800 Kongens Lyngby, Denmark
{jag,kbp,lkh}@imm.dtu.dk
Abstract
In this paper we are presenting a nov... | 2970 |@word collinearity:1 exploitation:1 version:2 inversion:2 repository:1 norm:1 seems:1 momma:1 johansson:1 simulation:2 covariance:3 jacob:1 tr:5 minus:1 reduction:2 contains:1 selecting:1 rkhs:1 existing:1 current:1 comparing:2 activation:2 written:1 must:1 john:1 evans:1 numerical:1 partition:1 informative:1 chi... |
2,171 | 2,971 | Learning to Rank with Nonsmooth Cost Functions
Christopher J.C. Burges
Microsoft Research
One Microsoft Way
Redmond, WA 98052, USA
Robert Ragno
Microsoft Research
One Microsoft Way
Redmond, WA 98052, USA
Quoc Viet Le
Statistical Machine
Learning Program
NICTA, ACT 2601, Australia
cburges@microsoft.com
rragno@micro... | 2971 |@word msr:1 version:1 polynomial:2 open:2 d2:6 calculus:1 tried:2 twolayer:1 tr:1 initial:1 score:19 document:51 outperforms:1 com:2 si:13 activation:2 attracted:1 must:3 stemming:1 additive:1 j1:2 hofmann:1 remove:1 drop:1 plot:2 update:3 designed:1 alone:1 item:6 nq:2 reciprocal:3 renshaw:1 provides:3 boosting:... |
2,172 | 2,972 | Learning from Multiple Sources
Koby Crammer, Michael Kearns, Jennifer Wortman
Department of Computer and Information Science
University of Pennsylvania
Philadelphia, PA 19104
Abstract
We consider the problem of learning accurate models from multiple sources of
?nearby? data. Given distinct samples from multiple data ... | 2972 |@word briefly:1 version:2 stronger:1 advantageous:1 bf:1 p0:2 contains:2 disparity:5 zij:1 prefix:3 current:1 si:3 distant:4 subsequent:1 additive:1 plot:1 website:1 short:1 provides:2 mathematical:1 along:1 direct:2 prove:1 paragraph:1 manner:1 introduce:4 pairwise:1 indeed:1 expected:26 behavior:2 p1:14 examine... |
2,173 | 2,973 | Fast Computation of Graph Kernels
S.V. N. Vishwanathan
svn.vishwanathan@nicta.com.au
Statistical Machine Learning, National ICT Australia,
Locked Bag 8001, Canberra ACT 2601, Australia
Research School of Information Sciences & Engineering
Australian National University, Canberra ACT 0200, Australia
Karsten M. Borgwar... | 2973 |@word polynomial:1 norm:1 flach:1 disk:1 open:1 calculus:2 p0:13 mention:1 outlook:1 reduction:1 initial:5 series:3 rkhs:9 outperforms:1 existing:1 com:2 comparing:1 si:1 yet:2 written:4 readily:2 john:1 numerical:3 cheap:1 designed:1 n0:5 warmuth:1 tertiary:1 filtered:1 node:10 mathematical:1 dn:1 direct:9 ik:2 ... |
2,174 | 2,974 | Single Channel Speech Separation
Using Factorial Dynamics
John R. Hershey
Trausti Kristjansson
Steven Rennie
Peder A. Olsen
IBM Thomas J. Watson Research Center
Yorktown Heights, NY 10598
Abstract
Human listeners have the extraordinary ability to hear and recognize speech even
when more than one person is talking... | 2974 |@word version:1 seems:2 d2:2 kristjansson:4 covariance:5 decomposition:1 carry:2 contains:1 mmse:4 outperforms:2 com:1 si:1 must:1 readily:1 john:2 realistic:1 half:1 selected:1 cue:1 short:3 provides:2 node:1 simpler:2 height:1 dn:2 direct:1 consists:3 combine:2 expected:4 bocchieri:1 multi:2 little:1 provided:2... |
2,175 | 2,975 | Hierarchical Dirichlet Processes with Random Effects
Seyoung Kim
Department of Computer Science
University of California, Irvine
Irvine, CA 92697-3435
sykim@ics.uci.edu
Padhraic Smyth
Department of Computer Science
University of California, Irvine
Irvine, CA 92697-3435
smyth@ics.uci.edu
Abstract
Data sets involving m... | 2975 |@word briefly:1 version:1 middle:1 proportion:12 simulation:3 covariance:1 score:3 existing:1 activation:40 assigning:1 yet:1 written:2 additive:2 update:1 generative:2 half:1 item:2 ith:1 blei:1 provides:1 detecting:2 location:4 toronto:1 height:3 direct:1 frequently:1 multi:1 brain:11 freeman:1 automatically:1 ... |
2,176 | 2,976 | Learning to parse images of articulated bodies
Deva Ramanan
Toyota Technological Institute at Chicago
Chicago, IL 60637
ramanan@tti-c.org
Abstract
We consider the machine vision task of pose estimation from static images, specifically for the case of articulated objects. This problem is hard because of the large
numb... | 2976 |@word kohli:1 version:3 middle:3 seems:2 stronger:1 nd:1 tried:1 initial:7 configuration:4 liu:1 score:4 tuned:2 surprising:1 luo:1 si:2 must:1 readily:1 parsing:10 visible:1 numerical:1 chicago:2 partition:1 shape:2 grass:1 cue:5 selected:1 intelligence:1 hallucinate:1 record:1 location:10 org:1 zhang:1 prove:2 ... |
2,177 | 2,977 | Online Clustering of Moving Hyperplanes
Ren?e Vidal
Center for Imaging Science, Department of Biomedical Engineering, Johns Hopkins University
308B Clark Hall, 3400 N. Charles St., Baltimore, MD 21218, USA
rvidal@cis.jhu.edu
Abstract
We propose a recursive algorithm for clustering trajectories lying in multiple movin... | 2977 |@word middle:2 version:1 compression:1 polynomial:32 nd:2 ckd:2 seek:4 imn:5 bn:2 covariance:2 rgb:3 thereby:1 recursively:1 initial:3 series:1 past:1 existing:3 dpn:1 assigning:1 must:5 written:1 john:1 designed:1 update:7 stationary:1 intelligence:1 kkd:2 plane:4 xk:1 reappears:3 ith:1 smith:1 gpca:16 provides:... |
2,178 | 2,978 | Mutagenetic tree Fisher kernel improves prediction of
HIV drug resistance from viral genotype
Tobias Sing
Department of Computational Biology
Max Planck Institute for Informatics
Saarbr?ucken, Germany
tobias.sing@mpi-sb.mpg.de
Niko Beerenwinkel?
Department of Mathematics
University of California
Berkeley, CA 94720
A... | 2978 |@word exploitation:1 kondor:1 briefly:2 advantageous:1 replicate:2 underline:1 additively:1 pressure:2 solid:3 score:3 genetic:9 denoting:1 interestingly:1 existing:1 current:1 virus:4 ddc:2 attracted:1 aft:1 mutagenetic:27 designed:1 update:1 generative:9 selected:2 accordingly:1 schapiro:2 problemspecific:1 pro... |
2,179 | 2,979 | Efficient sparse coding algorithms
Honglak Lee
Alexis Battle
Rajat Raina
Computer Science Department
Stanford University
Stanford, CA 94305
Andrew Y. Ng
Abstract
Sparse coding provides a class of algorithms for finding succinct representations
of stimuli; given only unlabeled input data, it discovers basis function... | 2979 |@word neurophysiology:2 trial:1 version:1 norm:3 pieter:1 hyv:1 bn:1 covariance:1 decomposition:1 tice:1 reduction:1 initial:2 series:1 contains:1 current:4 activation:4 si:1 written:3 must:5 readily:1 refines:1 remove:1 update:4 v:1 generative:1 discovering:1 guess:4 fewer:1 selected:1 cavanaugh:1 indefinitely:1... |
2,180 | 298 | Language Induction by Phase Transition
in Dynamical Recognizers
Jordan B. Pollack
Laboratory for AI Research
The Ohio State University
Columbus,OH 43210
pollack@cis.ohio-state.edu
Abstract
A higher order recurrent neural network architecture learns to recognize and
generate languages after being "trained" on categori... | 298 |@word eliminating:1 termination:2 awijk:1 calculus:1 awij:2 electronics:1 configuration:1 contains:3 initial:5 longitudinal:1 lapedes:1 current:2 universality:2 import:1 grassberger:2 treating:1 generative:2 intelligence:3 nervous:1 wolfram:2 accepting:1 preference:1 ofo:1 simpler:1 mathematical:5 along:1 construc... |
2,181 | 2,980 | Conditional Random Sampling: A Sketch-based
Sampling Technique for Sparse Data
Ping Li
Department of Statistics
Stanford University
Stanford, CA 94305
pingli@stat.stanford.edu
Kenneth W. Church
Microsoft Research
One Microsoft Way
Redmond, WA 98052
church@microsoft.com
Trevor J. Hastie
Department. of Statistics
Stanfo... | 2980 |@word groupwise:1 version:1 compression:1 norm:4 nd:1 seek:1 simulation:1 decomposition:1 tr:1 solid:2 moment:2 reduction:8 document:6 outperforms:7 com:1 comparing:2 kdd:2 designed:1 kv1:1 plot:3 ith:2 provides:2 org:2 mathematical:1 become:1 focs:1 consists:3 combine:2 introduce:1 theoretically:1 pairwise:11 ro... |
2,182 | 2,981 | Chained Boosting
Christian R. Shelton
University of California
Riverside CA 92521
cshelton@cs.ucr.edu
Wesley Huie
University of California
Riverside CA 92521
whuie@cs.ucr.edu
Kin Fai Kan
University of California
Riverside CA 92521
kkan@cs.ucr.edu
Abstract
We describe a method to learn to make sequential stopping de... | 2981 |@word h:1 version:5 proportion:1 advantageous:1 termination:1 pick:1 brightness:1 thereby:1 minus:1 solid:1 reduction:2 initial:1 series:7 score:2 selecting:1 contains:1 envision:1 current:3 skipping:1 yet:1 tackling:1 must:4 christian:1 plot:1 update:1 generative:1 xk:3 detecting:1 boosting:12 coarse:1 ron:1 soc... |
2,183 | 2,982 | Shifting, One-Inclusion Mistake Bounds and
Tight Multiclass Expected Risk Bounds
Benjamin I. P. Rubinstein
Computer Science Division
University of California, Berkeley
Berkeley, CA 94720-1776, U.S.A.
benr@cs.berkeley.edu
Peter L. Bartlett
Computer Science Division and
Department of Statistics
University of California... | 2982 |@word compression:6 nd:4 open:2 d2:1 pick:1 solid:2 contains:3 pub:1 chervonenkis:1 offering:1 denoting:1 written:1 must:3 enables:1 maxv:1 v:14 alone:1 implying:1 selected:2 half:3 warmuth:8 manfred:1 characterization:1 cse:1 shatter:4 along:2 dn:4 vs0:1 direct:1 ucsc:1 prove:3 pairwise:1 expected:10 nor:1 multi... |
2,184 | 2,983 | Analysis of Representations for Domain Adaptation
Shai Ben-David
School of Computer Science
University of Waterloo
shai@cs.uwaterloo.ca
John Blitzer, Koby Crammer, and Fernando Pereira
Department of Computer and Information Science
University of Pennsylvania
{blitzer, crammer, pereira}@cis.upenn.edu
Abstract
Discrim... | 2983 |@word briefly:2 seems:1 vldb:1 heuristically:1 mention:1 plentiful:1 charniak:1 chervonenkis:1 document:2 err:2 current:1 yet:1 written:1 parsing:2 john:2 subsequent:1 realistic:2 enables:1 designed:1 plot:6 v:6 reranking:1 smith:1 blei:1 provides:2 detecting:1 zhang:1 tagger:2 focs:2 consists:3 prove:1 theoretic... |
2,185 | 2,984 | Online Classification for Complex Problems Using
Simultaneous Projections
1
Yonatan Amit1 Shai Shalev-Shwartz1 Yoram Singer1,2
School of Computer Sci. & Eng., The Hebrew University, Jerusalem 91904, Israel
2
Google Inc. 1600 Amphitheatre Pkwy, Mountain View, CA 94043, USA
{mitmit,shais,singer}@cs.huji.ac.il
Abstract... | 2984 |@word trial:26 version:1 norm:5 dekel:1 eng:1 minus:1 score:2 denoting:1 outperforms:2 current:1 written:1 additive:1 hofmann:1 analytic:3 plot:1 update:13 devising:2 weighing:1 warmuth:2 infrastructure:1 provides:1 boosting:2 constructed:1 direct:1 symposium:1 incorrect:1 consists:3 prove:1 fitting:1 combine:2 i... |
2,186 | 2,985 | Reducing Calibration Time For Brain-Computer
Interfaces: A Clustering Approach
Matthias Krauledat1,2, Michael Schr?der2 , Benjamin Blankertz2 , Klaus-Robert M?ller1,2
1 Technical
University Berlin, Str. des 17. Juni 135, 10 623 Berlin, Germany
Fraunhofer FIRST.IDA, Kekul?str. 7, 12 489 Berlin, Germany
{kraulem,schroe... | 2985 |@word trial:26 cox:2 duda:1 nd:1 r13:1 mimick:1 decomposition:2 covariance:1 eng:10 profit:1 solid:2 initial:2 series:1 exclusively:3 selecting:1 interestingly:1 outperforms:2 existing:2 current:4 ida:1 yet:1 must:1 visible:2 realistic:1 motor:10 reproducible:1 discrimination:2 v:3 pursued:1 cue:2 half:5 device:1... |
2,187 | 2,986 | Context dependent amplification of both rate and
event-correlation in a VLSI network of spiking
neurons
Elisabetta Chicca, Giacomo Indiveri and Rodney J. Douglas
Institute of Neuroinformatics
University - ETH Zurich
Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
chicca,giacomo,rjd@ini.phys.ethz.ch
Abstract
Coope... | 2986 |@word unaltered:1 open:2 pulse:2 somplinsky:1 carry:1 electronics:1 liu:2 series:1 contains:1 jimenez:1 past:1 current:1 activation:1 guez:1 realize:1 moreno:1 plot:3 designed:1 intelligence:1 selected:2 device:4 infrastructure:2 five:2 mathematical:3 along:1 differential:1 symposium:3 consists:1 pairwise:1 behav... |
2,188 | 2,987 | Optimal Single-Class Classification Strategies
Ran El-Yaniv
Department of Computer Science
Technion- Israel Institute of Technology
Technion, Israel 32000
rani@cs.technion.ac.il
Mordechai Nisenson
Department of Computer Science
Technion - Israel Institute of Technology
Technion, Israel 32000
motin@cs.technion.ac.il
... | 2987 |@word rani:1 achievable:5 seems:1 open:1 relevancy:1 closure:1 willing:1 q1:5 omniscient:4 existing:1 scovel:1 comparing:1 si:2 must:9 numerical:2 distant:2 partition:1 mordechai:1 depict:1 v:1 generative:2 selected:3 leaf:2 intelligence:1 accordingly:1 mpm:1 provides:1 c2:5 prove:2 qi1:1 introduce:1 indeed:2 p1:... |
2,189 | 2,988 | Large Scale Hidden Semi-Markov SVMs
Gunnar R?atsch?
Friedrich Miescher Laboratoy, Max Planck Society
Spemannstr. 39, 72070 T?ubingen, Germany
Gunnar.Raetsch@tuebingen.mpg.de
S?oren Sonnenburg
Fraunhofer FIRST.IDA
Kekul?estr. 7, 12489 Berlin, Germany
sonne@first.fhg.de
Abstract
We describe Hidden Semi-Markov Support ... | 2988 |@word proceeded:1 version:4 seems:2 nd:1 mers:1 open:1 closure:1 korf:1 kulp:1 decomposition:1 simplifying:1 contains:1 score:8 tuned:2 outperforms:1 existing:1 current:2 ida:1 wd:1 nt:2 written:3 parsing:1 interrupted:1 partition:1 hofmann:3 drop:1 half:1 fewer:1 website:1 selected:1 intelligence:1 plane:1 posit... |
2,190 | 2,989 | Convergence of Laplacian Eigenmaps
Mikhail Belkin
Department of Computer Science
Ohio State University
Columbus, OH 43210
mbelkin@cse.ohio-state.edu
Partha Niyogi
Department of Computer Science
The University of Chicago
Hyde Park, Chicago, IL 60637.
niyogi@cs.uchicago.edu
Abstract
Geometrically based methods for var... | 2989 |@word version:3 stronger:1 norm:1 kf2:3 tr:1 boundedness:2 reduction:3 initial:1 series:2 attracted:1 readily:1 written:3 chicago:3 ith:9 core:1 provides:1 cse:1 differential:3 prove:3 consists:1 coifman:2 x0:6 expected:1 indeed:2 warner:1 increasing:1 provided:1 underlying:4 bounded:2 lowest:1 what:2 eigenvector... |
2,191 | 299 | Development and Spatial Structure of Cortical
Feature Maps: A Model Study
K. 0 berulayer
Beckman-Institute
University of Illinois
Urbana, IL 61801
H. Ritter
Technische Fakultiit
Universitiit Bielefeld
D-4800 Bielefeld
K. Schulten
Beckman-Insti t u te
University of Illinois
Urbana, IL 61801
Abstract
Feature selecti... | 299 |@word wiesel:1 nd:1 hu:1 d2:2 simulation:1 crucially:1 brightness:2 contains:1 tuned:1 interestingly:1 cort:2 diagonalized:1 must:2 import:1 visible:1 numerical:1 stationary:7 half:1 selected:2 yr:1 plane:3 isotropic:2 ial:1 prespecified:1 compo:1 location:6 preference:6 along:4 become:5 autocorrelation:3 rapid:1 ... |
2,192 | 2,990 | Sample complexity of policy search with known
dynamics
Peter L. Bartlett
Divison of Computer Science and Department of Statistics
University of California, Berkeley
Berkeley, CA 94720-1776
bartlett@cs.berkeley.edu
Ambuj Tewari
Division of Computer Science
University of California, Berkeley
Berkeley, CA 94720-1776
amb... | 2990 |@word version:1 polynomial:1 stronger:4 simulation:7 pick:1 mention:1 boundedness:3 carry:1 initial:6 series:1 chervonenkis:1 denoting:1 ours:2 fa8750:1 past:1 current:1 yet:1 reminiscent:1 written:2 additive:1 plot:1 alone:1 implying:1 selected:1 intelligence:1 indicative:1 iterates:1 node:1 bijection:1 unbounde... |
2,193 | 2,991 | Fast Iterative Kernel PCA
Nicol N. Schraudolph
?
Simon Gunter
S.V. N. Vishwanathan
{nic.schraudolph,simon.guenter,svn.vishwanathan}@nicta.com.au
Statistical Machine Learning, National ICT Australia
Locked Bag 8001, Canberra ACT 2601, Australia
Research School of Information Sciences & Engineering
Australian Nationa... | 2991 |@word version:1 norm:3 nd:1 suitably:2 calculus:1 covariance:1 thereby:1 reduction:1 initial:1 tuned:3 rkhs:8 outperforms:2 current:4 com:1 comparing:1 must:2 plot:1 update:22 v:1 stationary:1 intelligence:1 greedy:1 isotropic:1 reciprocal:4 ith:2 provides:2 mathematical:1 along:1 differential:4 overhead:1 introd... |
2,194 | 2,992 | Training Conditional Random Fields for Maximum
Labelwise Accuracy
Samuel S. Gross
Computer Science Department
Stanford University
Stanford, CA, USA
ssgross@cs.stanford.edu
Chuong B. Do
Computer Science Department
Stanford University
Stanford, CA, USA
chuongdo@cs.stanford.edu
Olga Russakovsky
Computer Science Departme... | 2992 |@word briefly:1 pw:21 proportion:2 seek:2 serafim:2 simulation:3 decomposition:1 initial:2 interestingly:1 outperforms:3 existing:1 current:1 assigning:1 must:1 parsing:9 written:1 numerical:2 partition:1 hofmann:2 analytic:1 designed:2 v:2 generative:7 guess:1 plane:1 mccallum:1 provides:1 sigmoidal:1 height:1 d... |
2,195 | 2,993 | Bayesian Policy Gradient Algorithms
Mohammad Ghavamzadeh
Yaakov Engel
Department of Computing Science, University of Alberta
Edmonton, Alberta, Canada T6E 4Y8
{mgh,yaki}@cs.ualberta.ca
Abstract
Policy gradient methods are reinforcement learning algorithms that adapt a parameterized policy by following a performance g... | 2993 |@word version:1 briefly:1 polynomial:1 termination:1 km:6 gptd:1 covariance:8 p0:5 pg:5 mention:1 moment:4 initial:8 inefficiency:1 score:3 lqr:5 subjective:2 outperforms:1 current:3 dx:5 must:1 subsequent:1 partition:2 treating:1 update:18 stationary:2 generative:1 selected:1 ith:1 provides:1 recompute:1 mannor:... |
2,196 | 2,994 | Modeling General and Specific Aspects of Documents
with a Probabilistic Topic Model
Chaitanya Chemudugunta, Padhraic Smyth
Department of Computer Science
University of California, Irvine
Irvine, CA 92697-3435, USA
{chandra,smyth}@ics.uci.edu
Mark Steyvers
Department of Cognitive Sciences
University of California, Irv... | 2994 |@word illustrating:1 nd:13 vogt:2 csx:12 lowfrequency:1 paid:2 mention:3 profit:1 reduction:1 initial:2 contains:2 score:11 selecting:1 fragment:1 document:97 existing:1 wd:9 com:1 comparing:1 assigning:1 written:1 john:2 cottrell:2 evans:1 hofmann:2 interpretable:1 cwd:2 update:1 generative:2 half:1 pursued:1 it... |
2,197 | 2,995 | An Information Theoretic Framework for
Eukaryotic Gradient Sensing
Joseph M. Kimmel? and Richard M. Salter?
joekimmel@uchicago.edu, rms@cs.oberlin.edu
Computer Science Program
Oberlin College
Oberlin, Ohio 44074
Peter J. Thomas?
peter.j.thomas@case.edu
Departments of Mathematics, Biology and Cognitive Science
Case Wes... | 2995 |@word cylindrical:1 heuristically:1 simulation:14 invoking:1 shading:1 harder:1 carry:1 reduction:4 phy:1 configuration:1 series:1 initial:2 reaction:4 existing:1 current:2 jupp:1 activation:1 must:2 john:2 mesh:1 chicago:1 subsequent:1 partition:1 realistic:3 informative:1 analytic:4 drop:1 v:1 stationary:1 half... |
2,198 | 2,996 | Distributed Inference in Dynamical Systems
Stanislav Funiak Carlos Guestrin
Carnegie Mellon University
Mark Paskin
Google
Rahul Sukthankar
Intel Research
Abstract
We present a robust distributed algorithm for approximate probabilistic inference
in dynamical systems, such as sensor networks and teams of mobile robot... | 2996 |@word version:1 eliminating:1 propagate:3 crucially:1 decomposition:2 covariance:3 pick:1 recursively:1 carry:3 initial:3 liu:2 contains:1 selecting:2 omniscient:1 past:2 existing:1 current:5 comparing:2 nt:12 si:4 yet:1 dx:1 must:4 written:2 interrupted:2 periodically:1 partition:25 informative:3 wx:3 numerical:... |
2,199 | 2,997 | Manifold Denoising
Matthias Hein
Markus Maier
Max Planck Institute for Biological Cybernetics
T?ubingen, Germany
{first.last}@tuebingen.mpg.de
Abstract
We consider the problem of denoising a noisily sampled submanifold M in Rd ,
where the submanifold M is a priori unknown and we are only given a noisy point
sample. T... | 2997 |@word determinant:1 nd:1 carry:1 reduction:5 configuration:2 existing:1 current:1 grassberger:1 mesh:1 kyb:1 v:2 generative:2 selected:1 isotropic:2 provides:1 math:2 recompute:1 mathematical:1 along:1 c2:1 differential:5 qualitative:1 fitting:1 introduce:1 expected:1 mpg:2 dist:2 resolve:1 equipped:1 considering... |
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