Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
1,700 | 2,545 | Temporal-Difference Networks
Richard S. Sutton and Brian Tanner
Department of Computing Science
University of Alberta
Edmonton, Alberta, Canada T6G 2E8
{sutton,btanner}@cs.ualberta.ca
Abstract
We introduce a generalization of temporal-difference (TD) learning to
networks of interrelated predictions. Rather than relati... | 2545 |@word version:2 proportion:1 twelfth:1 open:1 seek:1 propagate:1 asks:1 ytn:1 harder:1 moment:2 initial:3 series:1 past:1 o2:1 current:3 comparing:1 must:6 visible:1 pertinent:1 update:3 alone:1 intelligence:1 selected:1 guess:4 accordingly:2 ith:2 provides:1 node:32 clarified:1 successive:2 simpler:1 constructed... |
1,701 | 2,546 | Markov Networks for Detecting
Overlapping Elements in Sequence Data
Joseph Bockhorst
Dept. of Computer Sciences
University of Wisconsin
Madison, WI 53706
joebock@cs.wisc.edu
Mark Craven
Dept. of Biostatistics and Medical Informatics
University of Wisconsin
Madison, WI 53706
craven@biostat.wisc.edu
Abstract
Many sequ... | 2546 |@word q1:1 concise:1 configuration:10 contains:2 score:4 liu:1 current:2 partition:3 plot:1 update:1 generative:1 intelligence:1 mccallum:1 smith:1 filtered:1 detecting:1 provides:1 characterization:1 location:1 along:5 constructed:1 qualitative:2 consists:4 baldi:1 manner:1 inter:2 expected:2 p1:1 discretized:2 ... |
1,702 | 2,547 | Two-Dimensional Linear Discriminant Analysis
Jieping Ye
Department of CSE
University of Minnesota
jieping@cs.umn.edu
Ravi Janardan
Department of CSE
University of Minnesota
janardan@cs.umn.edu
Qi Li
Department of CIS
University of Delaware
qili@cis.udel.edu
Abstract
Linear Discriminant Analysis (LDA) is a well-know... | 2547 |@word compression:1 norm:1 duda:1 nd:1 d2:2 bn:2 decomposition:8 covariance:1 reduction:8 initial:2 contains:4 att:1 daniel:1 document:1 past:1 outperforms:1 com:1 scatter:9 readily:1 sponsored:1 update:1 discrimination:2 rrt:2 v:1 intelligence:3 ith:6 eigenfeatures:1 gpca:1 cse:2 zhang:1 rc:1 become:2 swets:1 be... |
1,703 | 2,548 | Inference, Attention, and Decision
in a Bayesian Neural Architecture
Angela J. Yu
Peter Dayan
Gatsby Computational Neuroscience Unit, UCL
17 Queen Square, London WC1N 3AR, United Kingdom.
feraina@gatsby.ucl.ac.uk
dayan@gatsby.ucl.ac.uk
Abstract
We study the synthesis of neural coding, selective attention and perceptua... | 2548 |@word noradrenergic:2 trial:18 version:2 replicate:1 open:1 simulation:4 thereby:1 solid:1 united:1 precluding:1 reynolds:1 reaction:8 existing:1 current:1 contextual:2 discretization:1 neurophys:1 activation:1 scatter:2 john:1 subsequent:1 additive:2 plot:2 discrimination:8 v:5 cue:27 instantiate:1 half:1 creden... |
1,704 | 2,549 | The Power of Selective Memory:
Self-Bounded Learning of Prediction Suffix Trees
Ofer Dekel Shai Shalev-Shwartz Yoram Singer
School of Computer Science & Engineering
The Hebrew University, Jerusalem 91904, Israel
{oferd,shais,singer}@cs.huji.ac.il
Abstract
Prediction suffix trees (PST) provide a popular and effective ... | 2549 |@word version:2 compression:2 norm:2 dekel:3 p0:2 incurs:1 initial:2 bejerano:2 current:3 comparing:2 shape:1 enables:2 designed:1 update:12 devising:2 beginning:1 short:1 eskin:2 provides:2 infrastructure:1 node:8 ron:2 unbounded:5 shtarkov:1 constructed:2 amnesia:1 prove:5 inside:1 excellence:1 indeed:1 nor:2 g... |
1,705 | 255 | 10
Spence and Pearson
The Computation of Sound Source Elevation
the Barn Owl
.
'In
Clay D. Spence
John C. Pearson
David Sarnoff Research Center
CN5300
Princeton, NJ 08543-5300
ABSTRACT
The midbrain of the barn owl contains a map-like representation of
sound source direction which is used to precisely orient the h... | 255 |@word briefly:1 d2:1 simulation:9 excited:1 pressure:4 contains:1 disparity:2 tuned:11 current:1 anterior:1 must:1 john:1 realistic:2 enables:1 motor:1 half:1 cue:2 iso:1 sigmoidal:4 along:5 direct:1 ouput:1 interaural:4 inter:2 presumed:1 roughly:1 brain:1 terminal:1 actual:1 project:7 matched:1 didn:1 kind:2 dev... |
1,706 | 2,550 | Efficient Kernel Machines Using the
Improved Fast Gauss Transform
Changjiang Yang, Ramani Duraiswami and Larry Davis
Department of Computer Science, Perceptual Interfaces and Reality Laboratory
University of Maryland, College Park, MD 20742
{yangcj,ramani,lsd}@umiacs.umd.edu
Abstract
The computation and memory require... | 2550 |@word trial:1 repository:1 version:1 polynomial:1 nd:1 simulation:3 seek:1 pick:1 nystr:6 solid:1 tr:1 reduction:10 series:5 rkhs:2 fgt:14 si:1 mushroom:3 dx:1 attracted:1 written:1 chicago:2 partition:2 kdd:1 girosi:1 plot:2 maxv:1 greedy:5 prohibitive:1 selected:1 accordingly:1 vanishing:1 hermite:9 five:3 math... |
1,707 | 2,551 | An Auditory Paradigm for
Brain?Computer Interfaces
N. Jeremy Hill1 , T. Navin Lal1 , Karin Bierig1
Niels Birbaumer2 and Bernhard Sch?
olkopf1
1
Max Planck Institute for Biological Cybernetics,
Spemannstra?e 38, 72076 T?
ubingen, Germany.
{jez|navin|bierig|bs}@tuebingen.mpg.de
2
Institute for Medical Psychology and
Be... | 2551 |@word trial:36 beep:14 version:1 proportion:1 norm:1 seems:1 open:1 hyv:1 decomposition:2 thereby:1 harder:1 reduction:1 initial:1 score:1 current:1 readily:1 visible:1 partition:3 happen:1 motor:4 designed:3 olkopf1:1 fewer:1 device:1 tone:1 beginning:1 short:1 record:1 haykin:1 filtered:1 detecting:1 unmixed:1 ... |
1,708 | 2,552 | Intrinsically Motivated Reinforcement Learning
Satinder Singh
Computer Science & Eng.
University of Michigan
baveja@umich.edu
Andrew G. Barto
Dept. of Computer Science
University of Massachusetts
barto@cs.umass.edu
Nuttapong Chentanez
Computer Science & Eng.
University of Michigan
nchentan@umich.edu
Abstract
Psychol... | 2552 |@word neurophysiology:1 briefly:1 instrumental:1 termination:3 simulation:1 eng:2 simplifying:1 arti:1 pressed:2 harder:1 initial:2 contains:2 uma:1 existing:1 current:5 nuttapong:2 activation:1 must:1 visible:1 happen:1 designed:1 update:7 smdp:3 discrimination:1 greedy:4 pursued:1 intelligence:1 scotland:1 shor... |
1,709 | 2,553 | Sampling Methods for Unsupervised Learning
Rob Fergus? & Andrew Zisserman
Dept. of Engineering Science
University of Oxford
Parks Road, Oxford OX1 3PJ, UK.
{fergus,az
}@robots.ox.ac.uk
Pietro Perona
Dept. Electrical Engineering
California Institute of Technology
Pasadena, CA 91125, USA.
perona@vision.caltech.edu
Ab... | 2553 |@word version:1 covariance:2 solid:6 outperforms:1 existing:1 comparing:1 tackling:1 must:5 subsequent:1 update:2 alone:2 cue:1 selected:1 recompute:1 provides:1 c6:2 along:1 constructed:1 become:1 symposium:1 combine:2 fitting:5 manner:1 introduce:1 mask:1 themselves:1 globally:1 automatically:1 resolve:1 actual... |
1,710 | 2,554 | Active Learning for Anomaly and
Rare-Category Detection
Dan Pelleg and Andrew Moore
School of Computer Science
Carnegie-Mellon University
Pittsburgh, PA 15213 USA
dpelleg@cs.cmu.edu, awm@cs.cmu.edu
Abstract
We introduce a novel active-learning scenario in which a user wants to
work with a learning algorithm to identi... | 2554 |@word repository:1 longterm:1 version:1 interleave:11 seems:2 ambig:15 simulation:1 covariance:2 brightness:1 concise:1 pick:3 shading:1 accommodate:1 configuration:1 series:2 score:3 contains:1 selecting:1 gagliardi:1 undiscovered:1 outperforms:1 existing:1 sugato:1 current:1 yet:1 numerical:1 subsequent:2 reali... |
1,711 | 2,555 | Instance-Ba sed Relevan ce Feedback fo r
Ima ge Retriev al
Giorgio Giacinto and Fabio Roli
Department of Electrical and Electronic Engineering
University of Cagliari
Piazza D?Armi, Cagliari ? Italy 09121
{giacinto,roli}@diee.unica.it
Abstract
High retrieval precision in content-based image retrieval can be
attained b... | 2555 |@word repository:3 version:1 duda:1 vldb:1 fifteen:1 moment:2 initial:1 contains:3 score:12 pub:1 selecting:1 offering:1 ati:1 outperforms:1 com:1 comparing:1 si:1 john:1 kdd:2 shape:1 designed:1 grass:1 intelligence:1 selected:5 accordingly:3 ith:3 core:1 provides:4 vistex:1 zhang:2 dn:1 retrieving:2 qualitative... |
1,712 | 2,556 | Parametric Embedding for Class Visualization
Tomoharu Iwata, Kazumi Saito, Naonori Ueda
NTT Communication Science Laboratories
NTT Corporation
2-4 Hikaridai Seika-Cho Soraku-gun Kyoto, 619-0237 JAPAN
{iwata,saito,ueda}@cslab.kecl.ntt.co.jp
Sean Stromsten, Thomas L. Griffiths, Joshua B. Tenenbaum
Department of Brain an... | 2556 |@word proceeded:1 version:1 norm:1 seems:1 open:1 seek:6 covariance:3 reduction:4 configuration:1 selecting:1 document:3 yet:1 readily:1 visible:2 shape:3 treating:1 plot:4 discrimination:1 generative:2 item:3 directory:2 farther:1 blei:1 provides:2 preference:2 org:1 five:2 mathematical:1 along:2 constructed:1 b... |
1,713 | 2,557 | Conditional Models of Identity Uncertainty
with Application to Noun Coreference
Andrew McCallum?
Department of Computer Science
University of Massachusetts Amherst
Amherst, MA 01003 USA
mccallum@cs.umass.edu
?
Ben Wellner??
The MITRE Corporation
202 Burlington Road
Bedford, MA 01730 USA
wellner@cs.umass.edu
?
Abstra... | 2557 |@word briefly:1 nd:1 mention:44 tr:1 configuration:3 contains:2 uma:2 score:1 celebrated:1 karger:1 charniak:1 document:3 interestingly:1 current:3 assigning:1 yet:2 must:2 parsing:1 partition:14 kdd:1 remove:2 progressively:2 generative:9 selected:2 intelligence:2 mccallum:13 ith:1 record:10 detecting:1 paramete... |
1,714 | 2,558 | Pictorial Structures for Molecular
Modeling: Interpreting Density Maps
Frank DiMaio, Jude Shavlik
Department of Computer Sciences
University of Wisconsin-Madison
{dimaio,shavlik}@cs.wisc.edu
George Phillips
Department of Biochemistry
University of Wisconsin-Madison
phillips@biochem.wisc.edu
Abstract
X-ray crystallogr... | 2558 |@word version:2 eliminating:1 norm:3 seems:2 nd:1 tedious:1 pick:1 euclidian:1 shot:1 reduction:1 initial:3 configuration:17 cyclic:1 score:7 hereafter:1 zij:4 document:1 current:1 must:3 distant:1 visible:1 blur:1 designed:1 depict:1 update:1 alone:1 generative:1 complementing:1 record:1 provides:1 node:8 locati... |
1,715 | 2,559 | Spike Sorting: Bayesian Clustering of
Non-Stationary Data
Aharon Bar-Hillel
Neural Computation Center
The Hebrew University of Jerusalem
aharonbh@cs.huji.ac.il
Adam Spiro
School of Computer Science and Engineering
The Hebrew University of Jerusalem
adams@cs.huji.ac.il
Eran Stark
Department of Physiology
The Hebrew U... | 2559 |@word trial:4 dtk:3 version:1 disk:1 tedious:1 seek:1 pulse:3 covariance:8 eng:2 accounting:1 pick:1 incurs:1 harder:1 reduction:1 initial:2 contains:3 score:24 denoting:2 past:1 nt:1 pothesis:1 import:1 john:2 visible:8 partition:16 happen:1 cpds:4 shape:3 motor:1 remove:1 stationary:8 alone:1 greedy:1 prehensio... |
1,716 | 256 | 810
Nunez and Fortes
Performance of Connectionist Learning Algorithms
on 2-D SIMD Processor Arrays
Fernando J. Nunez* and Jose A.B. Fortes
School of Electrical Engineering
Purdue University
West Lafayette, IN 47907
ABSTRACT
The mapping of the back-propagation and mean field theory
learning algorithms onto a generic... | 256 |@word nd:1 instruction:6 ajj:1 propagate:1 simulation:6 mention:1 tr:1 shading:1 inefficiency:1 series:1 current:1 anne:1 activation:12 must:4 mesh:3 intelligence:1 plane:4 simpler:2 direct:2 cray:1 frequently:1 decomposed:2 motorola:1 subvectors:3 spain:1 what:1 cm:3 developed:1 finding:1 subclass:1 control:3 uni... |
1,717 | 2,560 | Adaptive Manifold Learning
Jing Wang, Zhenyue Zhang
Department of Mathematics
Zhejiang University, Yuquan Campus,
Hangzhou, 310027, P. R. China
wroaring@sohu.com
zyzhang@zju.edu.cn
Hongyuan Zha
Department of Computer Science
Pennsylvania State University
University Park, PA 16802
zha@cse.psu.edu
Abstract
Recently, t... | 2560 |@word eliminating:1 compression:1 nd:1 c0:1 iki:7 contraction:7 pick:1 mention:1 reduction:4 initial:6 selecting:2 denoting:1 com:1 si:3 numerical:1 plot:7 update:1 v:5 half:2 selected:2 xk:1 cse:2 zhang:3 c2:1 consists:2 fitting:2 manner:1 x0:4 globally:1 decreasing:1 campus:1 panel:3 qyi:1 minimizes:2 eigenvect... |
1,718 | 2,561 | Dependent Gaussian Processes
Phillip Boyle and Marcus Frean
School of Mathematical and Computing Sciences
Victoria University of Wellington,
Wellington, New Zealand
{pkboyle,marcus}@mcs.vuw.ac.nz
Abstract
Gaussian processes are usually parameterised in terms of their covariance functions. However, this makes it diffi... | 2561 |@word version:6 d2:2 ci2:2 covariance:20 uphold:1 attainable:1 tr:2 solid:2 phy:1 initial:1 series:30 current:2 si:4 additive:3 cheap:2 stationary:5 intelligence:1 maximised:2 provides:1 toronto:3 c22:5 mathematical:1 along:2 constructed:2 become:3 consists:2 x0:4 expected:1 multi:1 becomes:2 provided:1 begin:1 w... |
1,719 | 2,562 | Edge of Chaos Computation in
Mixed-Mode VLSI - ?A Hard Liquid?
Felix Sch?
urmann, Karlheinz Meier, Johannes Schemmel
Kirchhoff Institute for Physics
University of Heidelberg
Im Neuenheimer Feld 227, 69120 Heidelberg, Germany
felix.schuermann@kip.uni-heidelberg.de,
WWW home page: http://www.kip.uni-heidelberg.de/vision... | 2562 |@word seems:3 simulation:7 profit:1 outlook:1 shading:2 initial:2 configuration:1 liquid:52 denoting:1 current:3 hohmann:2 activation:2 refresh:1 realize:1 shape:1 plot:4 update:1 device:3 nervous:1 accordingly:1 xk:1 core:1 provides:1 lsm:2 along:3 differential:1 ik:1 psfrag:1 consists:1 combine:1 theoretically:... |
1,720 | 2,563 | Linear Multilayer Independent Component
Analysis for Large Natural Scenes
Yoshitatsu Matsuda ?
Kazunori Yamaguchi Laboratory
Department of General Systems Studies
Graduate School of Arts and Sciences
The University of Tokyo
Japan 153-8902
matsuda@graco.c.u-tokyo.ac.jp
Kazunori Yamaguchi
yamaguch@graco.c.u-tokyo.ac.jp
... | 2563 |@word proceeded:1 tedious:1 hyv:2 initial:1 com:1 yet:1 numerical:4 update:4 generative:1 selected:1 fewer:1 inspection:1 along:1 ica:34 expected:2 equivariant:1 planning:2 decreasing:3 little:1 cpu:2 project:1 xx:1 spain:1 matsuda:7 argmin:1 developed:1 transformation:3 every:1 multidimensional:1 before:1 local:... |
1,721 | 2,564 | Unsupervised Variational Bayesian
Learning of Nonlinear Models
Antti Honkela and Harri Valpola
Neural Networks Research Centre, Helsinki University of Technology
P.O. Box 5400, FI-02015 HUT, Finland
{Antti.Honkela, Harri.Valpola}@hut.fi
http://www.cis.hut.fi/projects/bayes/
Abstract
In this paper we present a framewo... | 2564 |@word version:1 polynomial:1 seems:1 nd:1 open:1 hyv:1 simulation:4 linearized:2 covariance:6 solid:1 kappen:1 moment:1 electronics:1 series:1 existing:1 surprising:1 activation:8 negentropy:1 dx:1 must:1 tot:1 cruz:1 realistic:1 numerical:1 analytic:1 update:7 generative:3 fewer:2 selected:1 haykin:1 provides:1 ... |
1,722 | 2,565 | Instance-Specific Bayesian Model
Averaging f or Classification
Shyam Visweswaran
Center for Biomedical Informatics
Intelligent Systems Program
Pittsburgh, PA 15213
shyam@cbmi.pitt.edu
Gregory F. Cooper
Center for Biomedical Informatics
Intelligent Systems Program
Pittsburgh, PA 15213
gfc@cbmi.pitt.edu
Abstract
Classi... | 2565 |@word trial:2 briefly:1 gfc:1 thereby:1 initial:1 contains:1 score:12 current:7 comparing:1 od:3 discretization:3 si:2 succeeding:1 standalone:1 greedy:1 selected:4 fewer:2 intelligence:2 parameterization:2 flare:1 desktop:1 xk:1 num:1 provides:2 node:27 nom:1 rc:1 constructed:1 predecessor:1 tirri:1 viable:1 des... |
1,723 | 2,566 | Neighbourhood Components Analysis
Jacob Goldberger, Sam Roweis, Geoff Hinton, Ruslan Salakhutdinov
Department of Computer Science, University of Toronto
{jacob,roweis,hinton,rsalakhu}@cs.toronto.edu
Abstract
In this paper we propose a novel method for learning a Mahalanobis
distance measure to be used in the KNN clas... | 2566 |@word repository:1 inversion:2 norm:1 neigbours:1 jacob:2 covariance:3 xtest:1 reap:1 moment:1 reduction:10 score:1 selecting:1 tuned:1 ours:1 goldberger:1 yet:1 scatter:1 must:3 numerical:2 subsequent:1 shape:1 drop:1 fewer:1 toronto:2 attack:1 simpler:1 five:2 height:1 dn:2 ik:2 consists:3 manner:1 pairwise:1 e... |
1,724 | 2,567 | Discriminant Saliency for Visual Recognition
from Cluttered Scenes
Dashan Gao
Nuno Vasconcelos
Department of Electrical and Computer Engineering,
University of California, San Diego
Abstract
Saliency mechanisms play an important role when visual recognition
must be performed in cluttered scenes. We propose a computati... | 2567 |@word version:3 eliminating:1 achievable:1 wiesel:1 stronger:1 nd:1 open:1 tried:1 decomposition:3 initial:1 contains:1 tuned:2 rightmost:1 subjective:1 existing:6 current:3 comparing:2 surprising:1 dx:1 must:1 dct:4 fn:1 shape:3 discrimination:6 v:4 half:2 leaf:2 intelligence:1 dashan:1 inspection:4 xk:3 painsta... |
1,725 | 2,568 | Solitaire: Man Versus Machine
Xiang Yan?
Persi Diaconis?
Paat Rusmevichientong?
Benjamin Van Roy?
?
Stanford University
{xyan,persi.diaconis,bvr}@stanford.edu
?
Cornell University
paatrus@orie.cornell.edu
Abstract
In this paper, we use the rollout method for policy improvement to analyze a version of Klondike sol... | 2568 |@word version:8 seems:1 instruction:1 simulation:4 blade:1 initial:2 configuration:9 score:11 selecting:2 interestingly:1 current:1 surprising:1 yet:1 intriguing:1 must:2 assigning:1 atop:1 embarrassment:1 half:1 fewer:1 intelligence:1 beginning:2 record:2 node:1 club:1 five:3 rollout:27 mathematical:2 become:2 p... |
1,726 | 2,569 | Learning first-order Markov models for control
Pieter Abbeel
Computer Science Department
Stanford University
Stanford, CA 94305
Andrew Y. Ng
Computer Science Department
Stanford University
Stanford, CA 94305
Abstract
First-order Markov models have been successfully applied to many problems, for example in modeling se... | 2569 |@word trial:2 version:3 seems:2 pieter:1 simulation:1 accounting:1 decomposition:1 q1:4 solid:2 recursively:1 initial:7 outperforms:1 lave:1 current:2 john:1 drop:1 plot:1 update:1 v:3 generative:2 intelligence:1 selected:1 iterates:1 node:6 successive:2 simpler:1 rc:1 burst:2 become:1 laub:1 fitting:4 paragraph:... |
1,727 | 257 | 240
Lee
Using A Translation-Invariant Neural Network
To Diagnose Heart Arrhythmia
Susan Ciarrocca Lee
The lohns Hopkins University
Applied Physics Laboratory
Laurel. Maryland 20707
ABSTRACT
Distinctive electrocardiogram (EeG) patterns are created when the heart
is beating normally and when a dangerous arrhythmia is ... | 257 |@word normalized:1 excluded:1 correct:9 laboratory:1 occurs:1 illustrated:1 occupies:1 during:1 separate:2 rhythm:25 maryland:1 series:6 contains:1 pacing:1 seven:1 length:1 nt:2 index:2 cq:1 si:1 normal:1 visually:2 must:4 xixi:1 unfortunately:1 realistic:1 shape:4 vary:2 trace:8 endpoint:1 insensitive:2 occurred... |
1,728 | 2,570 | Constraining a Bayesian Model of Human Visual
Speed Perception
Alan A. Stocker and Eero P. Simoncelli
Howard Hughes Medical Institute,
Center for Neural Science, and Courant Institute of Mathematical Sciences
New York University, U.S.A.
Abstract
It has been demonstrated that basic aspects of human visual motion perce... | 2570 |@word trial:11 seems:2 brightness:1 wellapproximated:1 moment:1 series:1 bootstrapped:1 subjective:3 written:1 additive:1 shape:1 medial:1 discrimination:17 v:3 generative:1 device:1 accordingly:1 provides:1 preference:1 accessed:1 five:3 mathematical:1 c2:5 become:1 qualitative:1 fixation:1 combine:1 swets:1 exp... |
1,729 | 2,571 | Using Machine Learning to Break Visual
Human Interaction Proofs (HIPs)
Kumar Chellapilla
Microsoft Research
One Microsoft Way
Redmond, WA 98052
kumarc@microsoft.com
Patrice Y. Simard
Microsoft Research
One Microsoft Way
Redmond, WA 98052
patrice@microsoft.com
Abstract
Machine learning is often used to automatically s... | 2571 |@word h:2 trial:1 version:3 stronger:2 asks:1 versatile:1 solid:1 harder:5 document:6 current:1 com:10 comparing:1 si:1 yet:3 gmail:2 must:2 clara:1 mesh:4 remove:2 designed:4 concert:1 half:1 intelligence:1 guess:1 devising:1 location:3 attack:15 five:1 along:1 direct:1 become:1 incorrect:1 prove:1 chew:1 market... |
1,730 | 2,572 | Blind one-microphone speech separation:
A spectral learning approach
Francis R. Bach
Computer Science
University of California
Berkeley, CA 94720
fbach@cs.berkeley.edu
Michael I. Jordan
Computer Science and Statistics
University of California
Berkeley, CA 94720
jordan@cs.berkeley.edu
Abstract
We present an algorithm... | 2572 |@word blindness:1 version:1 timefrequency:1 inversion:1 norm:3 proportion:1 open:1 km:1 grey:1 simulation:2 r:1 hyv:1 nystr:1 harder:1 recursively:1 tuned:1 must:2 john:1 numerical:3 partition:4 shape:3 remove:1 designed:1 stationary:1 cue:15 parameterization:1 plane:3 short:1 provides:2 five:1 windowed:1 constru... |
1,731 | 2,573 | Sub-Microwatt Analog VLSI
Support Vector Machine for
Pattern Classification and Sequence Estimation
Shantanu Chakrabartty and Gert Cauwenberghs
Department of Electrical and Computer Engineering
Johns Hopkins University, Baltimore, MD 21218
{shantanu,gert}@jhu.edu
Abstract
An analog system-on-chip for kernel-based pat... | 2573 |@word illustrating:1 version:1 inversion:2 polynomial:2 achievable:1 rising:2 pulse:1 simulation:1 decomposition:1 q1:1 thereby:1 solid:2 score:1 mag:1 loeliger:1 current:17 john:2 designed:1 discrimination:1 v:1 intelligence:2 selected:1 device:4 core:1 detecting:3 node:6 org:1 along:2 m7:4 differential:1 supply... |
1,732 | 2,574 | The Rescorla-Wagner algorithm and Maximum
Likelihood estimation of causal parameters.
Alan Yuille
Department of Statistics
University of California at Los Angeles
Los Angeles, CA 90095
yuille@stat.ucla.edu
Abstract
This paper analyzes generalization of the classic Rescorla-Wagner (RW) learning algorithm and studies t... | 2574 |@word c0:2 holyoak:1 seek:1 covariance:12 initial:2 current:1 surprising:1 plasticity:1 update:12 generative:3 mathematical:2 c2:51 direct:1 ik:1 prove:1 expected:16 growing:1 multi:1 brain:1 becomes:2 provided:6 estimating:3 moreover:1 what:2 unspecified:1 eigenvector:3 unit:1 grant:1 tation:1 fluctuation:8 ap:3... |
1,733 | 2,575 | Comparing Beliefs, Surveys and Random Walks
Erik Aurell
SICS, Swedish Institute of Computer Science
P.O. Box 1263, SE-164 29 Kista, Sweden
and Dept. of Physics,
KTH ? Royal Institute of Technology
AlbaNova ? SCFAB SE-106 91 Stockholm, Sweden
eaurell@sics.se
Uri Gordon and Scott Kirkpatrick
School of Engineering and Co... | 2575 |@word version:2 rising:1 nd:5 proportionality:1 simplifying:1 arti:1 eld:1 asks:1 solid:1 reduction:2 moment:2 series:2 clari:1 comparing:1 paramagnetic:6 surprising:1 must:2 numerical:3 analytic:2 remove:1 asymptote:1 update:6 alone:1 intelligence:1 selected:2 greedy:1 half:2 cult:1 xk:1 short:1 provides:1 trave... |
1,734 | 2,576 | Object Classification from a Single Example
Utilizing Class Relevance Metrics
Michael Fink
Interdisciplinary Center for Neural Computation
The Hebrew University, Jerusalem 91904, Israel
fink@huji.ac.il
Abstract
We describe a framework for learning an object classifier from a single
example. This goal is achieved by e... | 2576 |@word trial:2 version:1 polynomial:2 seems:2 advantageous:1 nd:1 decomposition:1 image2:1 euclidian:1 thereby:1 accommodate:1 current:1 comparing:2 surprising:1 yet:1 john:1 distant:1 shape:19 enables:3 plot:6 v:1 generative:2 selected:7 beaver:1 provides:1 boosting:1 location:11 scholkopf:1 ijcv:1 x0:19 angel:1 ... |
1,735 | 2,577 | Maximum Likelihood Estimation of
Intrinsic Dimension
Elizaveta Levina
Department of Statistics
University of Michigan
Ann Arbor MI 48109-1092
elevina@umich.edu
Peter J. Bickel
Department of Statistics
University of California
Berkeley CA 94720-3860
bickel@stat.berkeley.edu
Abstract
We propose a new method for estimat... | 2577 |@word version:1 proportion:1 seems:1 simulation:5 tried:1 covariance:1 pick:1 tr:1 reduction:6 suppressing:1 outperforms:1 existing:2 yet:1 must:1 grassberger:1 numerical:1 informative:1 plot:3 drop:2 reproducible:1 prohibitive:1 inspection:1 underestimating:1 hypersphere:1 provides:3 dn:3 along:1 become:2 replic... |
1,736 | 2,578 | Co-Training and Expansion: Towards Bridging
Theory and Practice
Maria-Florina Balcan
Computer Science Dept.
Carnegie Mellon Univ.
Pittsburgh, PA 15213
ninamf@cs.cmu.edu
Avrim Blum
Computer Science Dept.
Carnegie Mellon Univ.
Pittsburgh, PA 15213
avrim@cs.cmu.edu
Ke Yang
Computer Science Dept.
Carnegie Mellon Univ.
Pi... | 2578 |@word version:2 faculty:1 stronger:3 seems:1 heuristically:2 d2:1 propagate:1 solid:1 shot:4 initial:6 plentiful:1 contains:2 document:5 puri:2 current:1 si:14 yet:3 written:1 partition:2 informative:1 wanted:1 drop:5 plot:1 stationary:2 half:1 fewer:1 mccallum:1 ith:1 node:7 simpler:1 si1:12 zhang:1 along:3 c2:7... |
1,737 | 2,579 | Learning Preferences for Multiclass Problems
Fabio Aiolli
Dept. of Computer Science
University of Pisa, Italy
aiolli@di.unipi.it
Alessandro Sperduti
Dept. of Pure and Applied Mathematics
University of Padova, Italy
sperduti@math.unipd.it
Abstract
Many interesting multiclass problems can be cast in the general framew... | 2579 |@word h:1 version:2 duda:1 norm:2 seems:1 dekel:1 r:5 mrk:1 configuration:1 contains:1 score:1 hereafter:1 selecting:1 document:2 interestingly:2 outperforms:1 current:2 comparing:2 yet:1 stemming:1 happen:1 plm:21 remove:1 update:1 intelligence:1 selected:5 math:1 node:6 boosting:1 preference:44 accessed:1 five:... |
1,738 | 258 | 742
DeWeerth and Mead
An Analog VLSI Model of Adaptation
in the Vestibulo-Ocular Reflex
Stephen P. DeWeerth and Carver A. Mead
California Institute of Technology
Pasadena, CA 91125
ABSTRACT
The vestibulo-ocular reflex (VOR) is the primary mechanism that
controls the compensatory eye movements that stabilize retinal ... | 258 |@word neurophysiology:2 version:2 pulse:5 tr:1 current:23 must:4 vor:32 motor:9 designed:4 infant:1 rc:2 along:1 direct:1 differential:15 consists:5 pathway:25 fitting:1 inter:1 rapid:1 behavior:1 lyon:1 circuit:18 vref:1 sivilotti:1 interpreted:1 monkey:7 magnified:1 growth:1 control:7 before:1 aging:1 limit:1 me... |
1,739 | 2,580 | Kernel Projection Machine: a New Tool for
Pattern Recognition?
Gilles Blanchard
Fraunhofer First (IDA),
K?ekul?estr. 7, D-12489 Berlin, Germany
blanchar@first.fhg.de
R?egis Vert
LRI, Universit?e Paris-Sud,
Bat. 490, F-91405 Orsay, France
Masagroup
24 Bd de l?Hopital, F-75005 Paris, France
Regis.Vert@lri.fr
Pascal Ma... | 2580 |@word repository:1 version:5 inversion:1 seems:1 norm:2 k2hk:2 open:1 tried:1 bn:1 solid:1 carry:1 moment:1 reduction:11 liu:1 contains:2 series:1 selecting:5 epartement:2 rkhs:6 interestingly:1 ida:2 surprising:1 dx:3 bd:3 realize:1 numerical:3 v:1 selected:2 device:1 flare:3 math:4 boosting:1 simpler:1 mathemat... |
1,740 | 2,581 | Sparse Coding of Natural Images Using an
Overcomplete Set of Limited Capacity Units
Eizaburo Doi
Center for the Neural Basis of Cognition
Carnegie Mellon University
Pittsburgh, PA 15213
edoi@cnbc.cmu.edu
Michael S. Lewicki
Center for the Neural Basis of Cognition
Computer Science Department
Carnegie Mellon University... | 2581 |@word neurophysiology:1 decomposition:1 covariance:1 tr:2 reduction:4 valois:1 tuned:1 john:1 additive:1 wx:1 shape:1 visibility:1 plot:1 drop:1 generative:1 accordingly:1 coarse:1 provides:1 revisited:1 along:2 become:1 consists:1 introduce:4 cnbc:2 expected:1 ica:16 examine:3 multi:6 decreasing:1 borst:1 consid... |
1,741 | 2,582 | Chemosensory processing in a spiking
model of the olfactory bulb: chemotopic
convergence and center surround
inhibition
Baranidharan Raman and Ricardo Gutierrez-Osuna
Department of Computer Science
Texas A&M University
College Station, TX 77840
{barani,rgutier}@cs.tamu.edu
Abstract
This paper presents a neuromorphic ... | 2582 |@word compression:1 norm:2 nd:1 simulation:1 lobe:3 concise:1 thereby:1 mohm:1 reduction:1 initial:13 efficacy:1 current:4 must:2 tot:1 distant:1 subsequent:2 plasticity:1 device:1 iso:1 ith:1 indefinitely:2 conscience:6 provides:1 node:5 sigmoidal:1 five:1 rc:2 along:3 c2:3 epithelium:1 pathway:4 olfactory:32 ma... |
1,742 | 2,583 | A Method for Inferring Label Sampling
Mechanisms in Semi-Supervised Learning
Saharon Rosset
Data Analytics Research Group
IBM T.J. Watson Research Center
Yorktown Heights, NY 10598
srosset@us.ibm.com
Hui Zou
Department of Statistics
Stanford University
Stanford, CA 94305
hzou@stat.stanford.com
Ji Zhu
Department of St... | 2583 |@word briefly:1 manageable:1 seems:1 logit:4 nd:1 tried:1 decomposition:1 p0:2 moment:5 initial:1 liu:2 contains:1 selecting:2 document:3 rightmost:1 com:2 si:13 john:1 numerical:2 realistic:2 analytic:1 hypothesize:1 remove:1 n0:2 aside:1 v:1 selected:1 guess:1 mccallum:1 caveat:1 math:1 complication:2 simpler:1... |
1,743 | 2,584 | Worst-Case Analysis of Selective Sampling for
Linear-Threshold Algorithms?
Nicol`o Cesa-Bianchi
DSI, University of Milan
cesa-bianchi@dsi.unimi.it
Claudio Gentile
Universit`a dell?Insubria
gentile@dsi.unimi.it
Luca Zaniboni
DTI, University of Milan
zaniboni@dti.unimi.it
Abstract
We provide a worst-case analysis of ... | 2584 |@word trial:17 determinant:1 version:6 norm:1 nd:39 open:2 reduction:1 initial:2 pub:1 document:1 elaborating:1 outperforms:1 current:2 com:1 assigning:1 mesh:1 additive:1 enables:1 atlas:1 update:14 v:3 fewer:4 selected:2 warmuth:3 inspection:1 num:1 dell:1 along:1 direct:2 prove:2 combine:1 symp:1 introduce:1 e... |
1,744 | 2,585 | Hierarchical Clustering of a Mixture Model
Jacob Goldberger Sam Roweis
Department of Computer Science, University of Toronto
{jacob,roweis}@cs.toronto.edu
Abstract
In this paper we propose an efficient algorithm for reducing a large
mixture of Gaussians into a smaller mixture while still preserving the component stru... | 2585 |@word version:9 bn:1 covariance:1 jacob:2 vermaak:1 moment:1 reduction:1 current:1 comparing:2 goldberger:2 must:3 hofmann:1 analytic:1 update:2 resampling:1 alone:1 generative:4 fewer:2 selected:1 yi1:2 toronto:2 allerton:1 five:1 prove:2 consists:2 fitting:1 manner:2 introduce:1 expected:1 multi:1 freeman:1 min... |
1,745 | 2,586 | Variational minimax estimation of discrete
distributions under KL loss
Liam Paninski
Gatsby Computational Neuroscience Unit
University College London
liam@gatsby.ucl.ac.uk
http://www.gatsby.ucl.ac.uk/?liam
Abstract
We develop a family of upper and lower bounds on the worst-case expected KL loss for estimating a discre... | 2586 |@word schurmann:1 polynomial:2 proportion:1 trofimov:1 bn:12 simplifying:1 minus:1 solid:2 harder:1 denoting:1 interestingly:4 comparing:1 surprising:1 must:1 grassberger:1 numerical:3 update:1 location:1 simpler:1 zhang:1 mathematical:1 constructed:1 direct:2 beta:2 manner:1 theoretically:1 indeed:2 expected:5 r... |
1,746 | 2,587 | Integrating Topics and Syntax
Thomas L. Griffiths
gruffydd@mit.edu
Massachusetts Institute of Technology
Cambridge, MA 02139
Mark Steyvers
msteyver@uci.edu
University of California, Irvine
Irvine, CA 92614
David M. Blei
blei@cs.berkeley.edu
University of California, Berkeley
Berkeley, CA 94720
Joshua B. Tenenbaum
jb... | 2587 |@word illustrating:1 seems:1 nd:11 justice:1 cleanly:1 pressure:1 pick:2 solid:1 exclusively:1 slotted:1 liquid:1 selecting:1 document:28 past:1 outperforms:1 recovered:2 z2:1 current:1 ka:1 comparing:1 si:1 must:2 romance:1 partition:1 treating:1 generative:10 fewer:3 discovering:1 cue:1 short:13 blei:3 provides... |
1,747 | 2,588 | Spike-Timing Dependent Plasticity and Mutual
Information Maximization for a Spiking Neuron
Model
Taro Toyoizumi?? ,
Jean-Pascal Pfister?
Kazuyuki Aihara? ?,
Wulfram Gerstner?
? Department of Complexity Science and Engineering,
The University of Tokyo, 153-8505 Tokyo, Japan
? Ecole Polytechnique F?ed?erale de Lausan... | 2588 |@word trial:1 version:1 hu:1 simulation:4 solid:2 reduction:1 contains:1 efficacy:3 wj2:1 ecole:1 suppressing:1 dx:2 numerical:3 interspike:2 plasticity:5 enables:1 shape:2 update:1 stationary:2 vanishing:1 revisited:1 arctan:1 mathematical:1 psfrag:6 autocorrelation:7 introduce:1 manner:1 theoretically:1 indeed:... |
1,748 | 2,589 | Reducing Spike Train Variability:
A Computational Theory Of
Spike-Timing Dependent Plasticity
Sander M. Bohte1,2
S.M.Bohte@cwi.nl
1
Dept. Software Engineering
CWI, Amsterdam, The Netherlands
Michael C. Mozer2
mozer@cs.colorado.edu
2
Dept. of Computer Science
University of Colorado, Boulder, USA
Abstract
Experimental ... | 2589 |@word trial:6 private:1 seems:2 stronger:3 simulation:11 tried:1 r:4 solid:1 reduction:2 efficacy:6 hereafter:1 ours:1 elaborating:1 past:1 current:6 intriguing:1 must:2 readily:1 additive:1 realistic:3 numerical:1 plasticity:16 shape:6 remove:1 plot:1 interpretable:1 update:3 v:1 alone:4 fewer:1 provides:1 revis... |
1,749 | 259 | 168
Lee and Lippmann
Practical Characteristics of Neural Network
and Conventional Pattern Classifiers on
Artificial and Speech Problems*
Yuchun Lee
Digital Equipment Corp.
40 Old Bolton Road,
OGOl-2Ull
Stow, MA 01775-1215
Richard P. Lippmann
Lincoln Laboratory, MIT
Room B-349
Lexington, MA 02173-9108
ABSTRACT
Eigh... | 259 |@word trial:12 middle:2 version:3 simulation:1 tried:1 covariance:1 jacob:1 solid:2 reduction:1 initial:2 contains:1 selecting:2 tuned:2 existing:1 current:1 readily:1 belmont:1 numerical:2 designed:1 sponsored:1 selected:3 short:1 hypersphere:17 quantizer:2 coarse:2 node:10 contribute:1 provides:1 mathematical:1 ... |
1,750 | 2,590 | Log-concavity results on Gaussian process
methods for supervised and unsupervised
learning
Liam Paninski
Gatsby Computational Neuroscience Unit
University College London
liam@gatsby.ucl.ac.uk
http://www.gatsby.ucl.ac.uk/?liam
Abstract
Log-concavity is an important property in the context of optimization,
Laplace appr... | 2590 |@word determinant:1 version:1 cox:1 briefly:1 stronger:1 suitably:1 c0:3 covariance:14 moment:3 series:1 denoting:2 precluding:1 ka:1 comparing:1 additonally:1 must:1 written:3 partition:1 stationary:2 half:2 pursued:1 parameterization:3 isotropic:1 filtered:1 parameterizations:1 contribute:1 math:1 toronto:1 sim... |
1,751 | 2,591 | Detecting Significant Multidimensional Spatial
Clusters
Daniel B. Neill, Andrew W. Moore, Francisco Pereira, and Tom Mitchell
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
{neill,awm,fpereira,t.mitchell}@cs.cmu.edu
Abstract
Assume a uniform, multidimensional grid of bivariate data, where e... | 2591 |@word nd:1 pick:2 tr:1 recursively:4 moment:1 hunting:1 contains:8 score:11 daniel:1 current:3 activation:6 si:10 must:10 tot:1 partition:1 shape:1 wanted:1 infant:1 half:2 discovering:1 leaf:1 selected:1 fewer:1 indicative:1 accordingly:1 core:1 record:3 detecting:4 node:5 location:2 along:1 replication:8 prove:... |
1,752 | 2,592 | Schema Learning: Experience-Based
Construction of Predictive Action Models
Michael P. Holmes
College of Computing
Georgia Institute of Technology
Atlanta, GA 30332-0280
mph@cc.gatech.edu
Charles Lee Isbell, Jr.
College of Computing
Georgia Institute of Technology
Atlanta, GA 30332-0280
isbell@cc.gatech.edu
Abstract
S... | 2592 |@word version:2 solid:1 moment:2 contains:2 exclusively:1 series:3 outperforms:2 current:6 od:3 si:1 activation:4 yet:1 reminiscent:1 must:5 subsequent:1 happen:2 drop:1 succeeding:1 aside:1 implying:1 stationary:1 discovering:2 half:3 item:38 fewer:1 intelligence:2 retroactively:2 mccallum:3 core:5 contribute:1 ... |
1,753 | 2,593 | PAC-Bayes Learning of Conjunctions and
Classification of Gene-Expression Data
Mario Marchand
IFT-GLO, Universit?e Laval
Sainte-Foy (QC) Canada, G1K-7P4
Mario.Marchand@ift.ulaval.ca
Mohak Shah
SITE, University of Ottawa
Ottawa, Ont. Canada,K1N-6N5
mshah@site.uottawa.ca
Abstract
We propose a ?soft greedy? learning alg... | 2593 |@word middle:1 version:3 seems:1 tamayo:1 r:6 ajj:6 gish:1 myeloid:1 recursively:1 wrapper:5 contains:2 err:2 current:1 di2:1 john:2 remove:2 designed:1 eab:1 greedy:13 half:2 selected:1 intelligence:1 nervous:1 provides:4 five:3 constructed:1 become:1 consists:5 ray:38 introduce:1 theoretically:1 indeed:1 expect... |
1,754 | 2,594 | Computing regularization paths
for learning multiple kernels
Francis R. Bach & Romain Thibaux
Computer Science
University of California
Berkeley, CA 94720
{fbach,thibaux}@cs.berkeley.edu
Michael I. Jordan
Computer Science and Statistics
University of California
Berkeley, CA 94720
jordan@cs.berkeley.edu
Abstract
The ... | 2594 |@word repository:1 inversion:1 polynomial:2 norm:22 willing:1 simulation:4 current:1 numerical:9 cheap:1 seeding:1 plot:1 leaf:2 short:1 simpler:1 along:4 become:2 differential:1 fitting:1 notably:2 indeed:2 behavior:7 globally:2 increasing:2 begin:1 provided:1 moreover:1 notation:1 bounded:2 what:2 corporation:1... |
1,755 | 2,595 | Learning Gaussian Process Kernels via
Hierarchical Bayes
Anton Schwaighofer
Fraunhofer FIRST
Intelligent Data Analysis (IDA)
Kekul?estrasse 7, 12489 Berlin
anton@first.fhg.de
Volker Tresp, Kai Yu
Siemens Corporate Technology
Information and Communications
81730 Munich, Germany
{volker.tresp,kai.yu}@siemens.com
Abstr... | 2595 |@word multitask:2 msr:1 proportion:2 covariance:63 nystr:10 tr:1 moment:1 initial:3 contains:1 denoting:1 current:1 ida:1 com:1 comparing:1 yet:2 written:1 readily:1 informative:1 extrapolating:3 update:2 stationary:2 item:10 blei:1 contribute:1 preference:11 zhang:1 fitting:1 excellence:1 expected:2 behavior:1 m... |
1,756 | 2,596 | Matrix Exponentiated Gradient Updates
for On-line Learning and Bregman Projection
Koji Tsuda??, Gunnar R?atsch?? and Manfred K. Warmuth?
?
Max Planck Institute for Biological Cybernetics
Spemannstr. 38, 72076 T?ubingen, Germany
?
AIST CBRC, 2-43 Aomi, Koto-ku, Tokyo, 135-0064, Japan
?
Fraunhofer FIRST, Kekul?estr. 7, ... | 2596 |@word trial:2 version:1 seems:1 decomposition:2 incurs:1 tr:38 initial:4 contains:1 current:3 must:1 cruz:1 additive:1 numerical:2 predetermined:1 treating:1 plot:2 update:36 warmuth:4 steepest:1 short:1 manfred:2 boosting:6 cse:1 node:1 successive:1 ucsc:1 constructed:1 qualitative:2 prove:3 introduce:1 excellen... |
1,757 | 2,597 | Coarticulation in Markov Decision
Processes
Khashayar Rohanimanesh
Department of Computer Science
University of Massachusetts
Amherst, MA 01003
khash@cs.umass.edu
Robert Platt
Department of Computer Science
University of Massachusetts
Amherst, MA 01003
rplatt@cs.umass.edu
Sridhar Mahadevan
Department of Computer Scie... | 2597 |@word h:7 trial:2 achievable:2 open:1 termination:1 decomposition:1 incurs:1 contains:1 uma:5 pub:1 outperforms:2 current:4 additive:1 enables:1 smdp:2 intelligence:2 vmin:1 selected:1 beginning:1 location:13 c22:1 rc:2 c2:8 grupen:4 consists:1 manner:1 introduce:4 g4:1 theoretically:1 ascend:13 huber:1 expected:... |
1,758 | 2,598 | Triangle Fixing Algorithms for the Metric
Nearness Problem
Inderjit S. Dhillon
Suvrit Sra
Dept. of Computer Sciences
The Univ. of Texas at Austin
Austin, TX 78712.
{inderjit,suvrit}@cs.utexas.edu
Joel A. Tropp
Dept. of Mathematics
The Univ. of Michigan at Ann Arbor
Ann Arbor, MI, 48109.
jtropp@umich.edu
Abstract
Vari... | 2598 |@word version:1 seems:1 norm:21 open:1 seek:4 r:1 tr:2 carry:1 initial:1 substitution:1 contains:3 series:1 outperforms:1 must:6 written:1 numerical:2 plot:4 atlas:1 update:3 half:1 prohibitive:2 sys:1 ck2:1 nearness:27 provides:1 mathematical:1 along:1 symposium:1 laub:1 dayhoff:1 kdk2:1 manner:1 introduce:3 pai... |
1,759 | 2,599 | Economic Properties of Social Networks
Sham M. Kakade
Michael Kearns
Luis E. Ortiz
Robin Pemantle
Siddharth Suri
University of Pennsylvania
Philadelphia, PA 19104
Abstract
We examine the marriage of recent probabilistic generative models
for social networks with classical frameworks from mathematical economics. We ar... | 2599 |@word mild:1 trial:5 version:4 proportion:3 simulation:5 dramatic:1 solid:2 born:1 contains:2 united:3 ours:1 interestingly:2 rightmost:2 existing:1 s16:1 current:1 yet:2 scatter:1 luis:1 numerical:1 realistic:1 plot:11 v:1 generative:8 provides:3 node:4 club:1 org:1 mathematical:3 along:1 direct:1 supply:1 focs:... |
1,760 | 26 | 387
Neural Net and Traditional Classifiers1
William Y. Huang and Richard P. Lippmann
MIT Lincoln Laboratory
Lexington, MA 02173, USA
Abstract. Previous work on nets with continuous-valued inputs led to generative
procedures to construct convex decision regions with two-layer perceptrons (one hidden
layer) and arbitra... | 26 |@word trial:8 selforganization:1 inversion:1 duda:1 leighton:1 simulation:8 tr:1 barney:1 contains:1 selecting:1 comparing:1 must:3 john:1 shape:1 designed:1 sponsored:1 v:2 generative:2 half:2 selected:2 fewer:3 plane:2 filtered:1 provides:3 node:48 ron:1 hyperplanes:16 sigmoidal:1 simpler:1 along:2 consists:1 bur... |
1,761 | 260 | Discovering High Order Features with Mean Field Modules
Discovering high order features with mean field
modules
Conrad C. Galland and Geoffrey E. Hinton
Physics Dept. and Computer Science Dept.
University of Toronto
Toronto, Canada
M5S lA4
ABSTRACT
A new form of the deterministic Boltzmann machine (DBM) learning proc... | 260 |@word trial:2 concept:1 version:1 true:1 graded:1 seems:1 met:1 hence:2 society:1 bl:1 sweep:2 correct:3 yiyj:1 filter:1 simulation:4 stochastic:6 exploration:2 fa:1 settle:4 pressure:1 during:1 implementing:1 tr:1 uniquely:1 ambiguous:1 objective:3 gradient:2 ow:2 initial:1 microstructure:1 generalization:1 crite... |
1,762 | 2,600 | Following Curved Regularized Optimization
Solution Paths
Saharon Rosset
IBM T.J. Watson Research Center
Yorktown Heights, NY 10598
srosset@us.ibm.com
Abstract
Regularization plays a central role in the analysis of modern data, where
non-regularized fitting is likely to lead to over-fitted models, useless for
both pred... | 2600 |@word mild:1 repository:1 version:1 inversion:1 norm:20 seems:1 tedious:1 tried:1 accounting:1 boundedness:1 series:1 efficacy:1 selecting:3 contains:1 rkhs:1 ala:1 current:2 com:1 karoui:1 numerical:1 remove:1 plot:3 update:2 boosting:6 complication:1 five:4 height:1 mathematical:1 direct:2 prove:4 consists:3 fi... |
1,763 | 2,601 | The Correlated Correspondence Algorithm for
Unsupervised Registration of Nonrigid Surfaces
Dragomir Anguelov1 , Praveen Srinivasan1 , Hoi-Cheung Pang1 ,
Daphne Koller1 , Sebastian Thrun1 , James Davis2 ?
1
Stanford University, Stanford, CA 94305
2
University of California, Santa Cruz, CA 95064
e-mail:{drago,praveens,h... | 2601 |@word deformed:3 eliminating:1 decomposition:3 tr:1 initial:2 configuration:10 contains:3 score:1 denoting:1 existing:1 imaginary:1 current:1 recovered:1 assigning:1 yet:1 must:2 cruz:1 mesh:60 distant:2 shape:12 christian:1 alone:1 cue:1 leaf:1 intelligence:2 isard:1 parameterization:1 coughlan:1 nearness:1 coar... |
1,764 | 2,602 | Maximum Margin Clustering
Linli Xu? ?
James Neufeld? Bryce Larson?
?
University of Waterloo
?
University of Alberta
Dale Schuurmans?
Abstract
We propose a new method for clustering based on finding maximum margin hyperplanes through data. By reformulating the problem in terms
of the implied equivalence relation mat... | 2602 |@word repository:1 seek:1 decomposition:1 reduction:1 renewed:1 interestingly:2 current:1 bie:1 must:2 realize:2 partition:1 spec:1 half:1 hwd:2 flare:1 oldest:1 compelled:1 caveat:1 complication:1 hyperplanes:2 five:1 unbounded:1 constructed:1 focs:1 consists:1 combine:2 manner:1 allan:1 indeed:1 ingenuity:1 alb... |
1,765 | 2,603 | Co-Validation: Using Model Disagreement on
Unlabeled Data to Validate Classification
Algorithms
Omid Madani, David M. Pennock, Gary W. Flake
Yahoo! Research Labs
3rd floor, Pasadena Ave.
Pasadena, CA 91103
{madani|pennockd|flakeg}@yahoo-inc.com
Abstract
In the context of binary classification, we define disagreement ... | 2603 |@word trial:5 repository:1 polynomial:6 seems:2 underline:1 dise:12 solid:1 plentiful:3 configuration:2 selecting:1 document:7 prefix:1 outperforms:1 current:1 com:2 comparing:2 lang:2 yet:1 dx:5 readily:2 stemming:1 realistic:1 partition:1 informative:2 chicago:1 cheap:2 christian:1 plot:2 aside:1 alone:1 half:5... |
1,766 | 2,604 | Assignment of Multiplicative Mixtures in
Natural Images
Odelia Schwartz
HHMI and Salk Institute
La Jolla, CA 92014
odelia@salk.edu
Terrence J. Sejnowski
HHMI and Salk Institute
La Jolla, CA 92014
terry@salk.edu
Peter Dayan
GCNU, UCL
17 Queen Square, London
dayan@gatsby.ucl.ac.uk
Abstract
In the analysis of natural ... | 2604 |@word version:1 compression:2 advantageous:1 hyv:4 simulation:1 covariance:1 reduction:1 configuration:3 efficacy:1 ording:1 current:1 comparing:1 si:1 shape:3 plot:2 update:1 v:1 generative:9 leaf:1 iso:1 prespecified:1 characterization:1 location:1 preference:1 along:2 become:2 symposium:1 ica:3 roughly:1 behav... |
1,767 | 2,605 | Semi-supervised Learning via Gaussian
Processes
Neil D. Lawrence
Department of Computer Science
University of Sheffield
Sheffield, S1 4DP, U.K.
neil@dcs.shef.ac.uk
Michael I. Jordan
Computer Science and Statistics
University of California
Berkeley, CA 94720, U.S.A.
jordan@cs.berkeley.edu
Abstract
We present a probabi... | 2605 |@word proportion:2 seek:1 covariance:2 fifteen:1 solid:3 moment:1 contains:1 selecting:1 current:1 must:3 readily:1 john:2 fn:34 informative:3 enables:1 treating:1 plot:2 update:2 v:1 generative:1 short:1 node:2 revisited:1 location:4 herbrich:1 simpler:1 qualitative:2 manner:2 introduce:3 indeed:1 inspired:2 lit... |
1,768 | 2,606 | Generalization Error and Algorithmic
Convergence of Median Boosting
Bal?azs K?egl
Department of Computer Science and Operations Research, University of Montreal
CP 6128 succ. Centre-Ville, Montr?eal, Canada H3C 3J7
kegl@iro.umontreal.ca
Abstract
We have recently proposed an extension of A DA B OOST to regression
that... | 2606 |@word achievable:5 agressive:1 minmax:1 contains:4 must:2 happen:1 selected:1 warmuth:1 math:1 boosting:12 dn:25 c2:2 along:1 shatter:1 psfrag:2 consists:1 underfitting:3 manner:1 x0:3 actual:1 cardinality:1 becomes:1 confused:1 classifies:1 underlying:1 notation:4 moreover:1 bounded:1 provided:1 minimizes:2 guar... |
1,769 | 2,607 | Large-Scale Prediction of Disulphide Bond
Connectivity
Pierre Baldi Jianlin Cheng
Schoolof Information and Computer Science
University of California, Irvine
Irvine, CA 92697-3425
{pfbaldi,jianlinc}@ics.uci.edu
Alessandro Vullo
Computer Science Department
University College Dublin
Dublin, Ireland
alessandro.vullo@ucd.... | 2607 |@word mri:1 version:2 simulation:2 gabow:5 pick:2 contains:3 score:2 series:1 exclusively:1 systemwide:1 outperforms:1 existing:2 current:2 contextual:2 must:3 deposited:1 fn:2 distant:1 alone:1 greedy:5 selected:2 parameterization:1 plane:16 short:1 filtered:3 provides:1 math:1 node:6 location:2 mathematical:1 p... |
1,770 | 2,608 | Parallel Support Vector Machines:
The Cascade SVM
Hans Peter Graf, Eric Cosatto,
Leon Bottou, Igor Durdanovic, Vladimir Vapnik
NEC Laboratories
4 Independence Way, Princeton, NJ 08540
{hpg, cosatto, leonb, igord, vlad}@nec-labs.com
Abstract
We describe an algorithm for support vector machines (SVM) that
can be paralle... | 2608 |@word illustrating:1 middle:1 eliminating:2 nd:1 d2:3 simulation:1 q1:2 solid:2 contains:4 pub:1 com:2 mari:1 yet:2 subsequent:1 happen:1 girosi:1 leipzig:1 alone:1 intelligence:1 selected:2 core:1 filtered:1 provides:3 idi:1 become:1 consists:2 shorthand:1 overhead:1 combine:1 indeed:2 multi:1 globally:1 td:9 ac... |
1,771 | 2,609 | Density Level Detection is Classification
Ingo Steinwart, Don Hush and Clint Scovel
Modeling, Algorithms and Informatics Group, CCS-3
Los Alamos National Laboratory
{ingo,dhush,jcs}@lanl.gov
Abstract
We show that anomaly detection can be interpreted as a binary classification problem. Using this interpretation we pro... | 2609 |@word version:3 sex:1 open:1 seek:1 euclidian:2 tr:4 contains:1 rkhs:1 scovel:4 yet:2 fn:11 realistic:1 plot:2 discrimination:1 provides:1 coarse:4 detecting:1 mathematical:2 psfrag:2 shorthand:3 prove:1 consists:4 introduce:2 x0:6 expected:1 behavior:1 inspired:1 automatically:1 gov:2 little:1 begin:3 estimating... |
1,772 | 261 | 566
Atlas, Cohn and Ladner
Training Connectionist Networks with
Queries and Selective Sampling
Les Atlas
Dept. of E.E.
David Cohn
Dept. of C.S. & E.
Richard Ladner
Dept. of C.S. & E.
M.A. El-Sharkawi, R.J. Marks II, M.E. Aggoune, and D.C. Park
Dept. of E.E.
University of Washington, Seattle, WA 98195
ABSTRACT
"S... | 261 |@word version:8 loading:2 simulation:2 asks:2 accommodate:1 initial:2 configuration:8 contains:3 exclusively:1 selecting:1 chervonenkis:1 current:3 conjunctive:1 ronald:1 atlas:6 plot:2 drop:1 v:2 warmuth:1 manfred:1 node:2 sigmoidal:1 height:1 along:1 c2:3 ucsc:4 become:1 symposium:1 consists:2 inside:4 expected:... |
1,773 | 2,610 | Semi-supervised Learning with Penalized
Probabilistic Clustering
Zhengdong Lu and Todd K. Leen
Department of Computer Science and Engineering
OGI School of Science and Engineering , OHSU
Beaverton, OR 97006
{zhengdon,tleen}@cse.ogi.edu
Abstract
While clustering is usually an unsupervised operation, there are circumst... | 2610 |@word relevancy:2 closure:1 covariance:1 pick:4 tr:1 solid:2 configuration:1 series:2 z2:4 assigning:1 must:2 numerical:1 cheap:1 hoping:1 seeding:1 update:1 half:1 prohibitive:1 leaf:1 item:2 fewer:1 ith:1 colored:1 cse:1 toronto:1 preference:22 five:1 c2:1 become:1 fitting:1 combine:1 introduce:1 manner:1 pairw... |
1,774 | 2,611 | Implicit Wiener Series for Higher-Order Image
Analysis
Matthias O. Franz
Bernhard Sch?olkopf
Max-Planck-Institut f?ur biologische Kybernetik
Spemannstr. 38, D-72076 T?ubingen, Germany
mof;bs@tuebingen.mpg.de
Abstract
The computation of classical higher-order statistics such as higher-order
moments or spectra is diffi... | 2611 |@word briefly:1 inversion:1 polynomial:5 norm:1 seems:1 nd:4 proportion:1 open:1 d2:1 grey:1 moment:4 series:30 contains:4 rkhs:10 interestingly:1 recovered:2 discretization:1 written:1 readily:1 must:5 fn:7 drop:2 alone:1 accordingly:1 ith:1 contribute:1 five:1 kinh:1 direct:1 become:2 xnm:1 consists:1 prove:1 a... |
1,775 | 2,612 | Discrete profile alignment via constrained
information bottleneck
Sean O?Rourke?
seano@cs.ucsd.edu
Gal Chechik?
gal@stanford.edu
Robin Friedman?
rcfriedm@ucsd.edu
Eleazar Eskin?
eeskin@cs.ucsd.edu
Abstract
Amino acid profiles, which capture position-specific mutation probabilities, are a richer encoding of biologic... | 2612 |@word illustrating:1 version:1 compression:3 stronger:1 nd:2 km:1 gish:1 decomposition:1 concise:1 tr:1 klk:1 substitution:3 series:1 score:10 outperforms:2 existing:1 discretization:11 comparing:1 nt:2 assigning:1 must:2 fn:2 distant:1 informative:1 remove:1 update:2 aps:1 v:1 intelligence:1 fewer:3 nq:1 tolle:1... |
1,776 | 2,613 | Boosting on manifolds: adaptive regularization
of base classifiers
Bal?azs K?egl and Ligen Wang
Department of Computer Science and Operations Research, University of Montreal
CP 6128 succ. Centre-Ville, Montr?eal, Canada H3C 3J7
{kegl|wanglige}@iro.umontreal.ca
Abstract
In this paper we propose to combine two powerful... | 2613 |@word norm:1 recursively:1 ld:1 reduction:2 contains:1 must:3 numerical:1 gv:1 greedy:1 intelligence:1 guess:2 warmuth:1 detecting:1 boosting:13 traverse:1 dn:4 along:2 constructed:1 combine:2 expected:1 indeed:1 behavior:1 decreasing:1 actual:1 becomes:1 distri:1 provided:1 bounded:1 underlying:1 estimating:1 sp... |
1,777 | 2,614 | Mass meta-analysis in Talairach space
Finn ?
Arup Nielsen
Neurobiology Research Unit, Rigshospitalet
Copenhagen, Denmark
and
Informatics and Mathematical Modelling, Technical University of Denmark,
Lyngby, Denmark
fn@imm.dtu.dk
Abstract
We provide a method for mass meta-analysis in a neuroinformatics
database contain... | 2614 |@word cingulate:8 heuristically:1 jacob:1 contains:3 score:2 series:1 reaction:1 anterior:3 surprising:1 activation:4 fn:1 analytic:2 motor:3 atlas:6 plot:1 resampling:5 intelligence:1 selected:3 metabolism:1 record:1 coarse:1 node:1 location:15 org:1 accessed:1 mathematical:1 constructed:1 become:1 descendant:1 ... |
1,778 | 2,615 | Kernels for Multi?task Learning
Charles A. Micchelli
Department of Mathematics and Statistics
State University of New York,
The University at Albany
1400 Washington Avenue, Albany, NY, 12222, USA
Massimiliano Pontil
Department of Computer Sciences
University College London
Gower Street, London WC1E 6BT, England, UK
A... | 2615 |@word multitask:1 kgk:1 polynomial:3 norm:5 seems:1 confirms:1 reduction:1 rkhs:9 written:1 fn:1 numerical:2 intelligence:1 dover:1 provides:2 herbrich:1 c2:4 consists:2 prove:2 introduce:1 indeed:2 tomaso:1 andrea:1 multi:12 increasing:1 provided:8 begin:1 moreover:4 bounded:3 what:1 minimizes:1 transformation:1... |
1,779 | 2,616 | The Variational Ising Classifier (VIC) algorithm
for coherently contaminated data
Oliver Williams
Dept. of Engineering
University of Cambridge
Andrew Blake
Microsoft Research Ltd.
Cambridge, UK
Roberto Cipolla
Dept. of Engineering
University of Cambridge
omcw2@cam.ac.uk
Abstract
There has been substantial progress... | 2616 |@word unaltered:1 confirms:1 thereby:1 harder:1 configuration:2 contains:2 tuned:1 mages:1 past:1 must:3 written:1 john:1 partition:2 girosi:1 designed:1 update:3 progressively:1 intelligence:3 xk:3 location:2 simpler:1 mathematical:1 mask:3 expected:1 indeed:1 rapid:2 detects:1 automatically:1 becomes:1 project:... |
1,780 | 2,617 | The Convergence of Contrastive Divergences
Alan Yuille
Department of Statistics
University of California at Los Angeles
Los Angeles, CA 90095
yuille@stat.ucla.edu
Abstract
This paper analyses the Contrastive Divergence algorithm for learning
statistical parameters. We relate the algorithm to the stochastic approximat... | 2617 |@word briefly:1 stronger:1 simulation:2 p0:46 contrastive:9 boundedness:1 phy:1 liu:1 initial:1 current:1 nt:7 must:1 written:1 plasticity:1 enables:2 update:14 intelligence:1 steepest:10 draft:1 math:1 unbounded:1 mathematical:2 prove:1 introduce:1 expected:13 brain:1 decreasing:2 provided:6 estimating:1 moreove... |
1,781 | 2,618 | Seeing through water
Alexei A. Efros?
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213, U.S.A.
Volkan Isler, Jianbo Shi and Mirk?o Visontai
Dept. of Computer and Information Science
University of Pennsylvania
Philadelphia, PA 19104
efros@cs.cmu.edu
{isleri,jshi,mirko}@cis.upenn.edu
Abstra... | 2618 |@word cox:2 version:1 middle:6 disk:7 d2:5 fifteen:1 carry:2 reduction:2 series:1 contains:2 recovered:1 current:1 dx:4 must:4 john:1 tilted:1 distant:2 shape:1 plot:3 stationary:3 nebojsa:1 selected:1 plane:8 short:1 volkan:1 node:4 location:5 height:2 c2:10 constructed:1 ik:2 consists:1 overhead:1 pairwise:2 ac... |
1,782 | 2,619 | Self-Tuning Spectral Clustering
Pietro Perona
Lihi Zelnik-Manor
Department of Electrical Engineering
Department of Electrical Engineering
California Institute of Technology
California Institute of Technology
Pasadena, CA 91125, USA
Pasadena, CA 91125, USA
perona@vision.caltech.edu
lihi@vision.caltech.edu
http://www.vi... | 2619 |@word briefly:1 eliminating:1 open:2 d2:3 zelnik:1 reduction:1 initial:1 contains:1 selecting:7 zij:10 current:1 si:17 written:1 malized:1 john:1 happen:1 informative:1 shape:1 drop:1 plot:1 update:1 pursued:1 discovering:1 fewer:1 selected:2 intelligence:1 plane:1 completeness:1 provides:3 iterates:1 rc:1 sympos... |
1,783 | 262 | 498
Barben, Toomarian and Gulati
Adjoint Operator Algorithms for Faster
Learning in Dynamical Neural Networks
Nikzad Toomarian
Jacob Barhen
Sandeep Gulati
Center for Space Microelectronics Technology
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, CA 91109
ABSTRACT
A methodology for faster... | 262 |@word illustrating:1 pw:1 eliminating:1 km:3 simulation:2 seek:1 jacob:1 contraction:1 eng:3 thereby:2 mention:1 initial:1 necessity:1 efficacy:2 selecting:1 liquid:1 denoting:1 past:1 activation:1 must:4 additive:1 numerical:2 partition:1 j1:1 enables:1 sponsored:1 selected:3 equi:1 sigmoidal:2 oak:2 mathematical... |
1,784 | 2,620 | Learning efficient auditory codes using spikes
predicts cochlear filters
Evan Smith1
Michael S. Lewicki2
evan@cnbc.cmu.edu lewicki@cnbc.cmu.edu
Departments of Psychology1 & Computer Science2
Center for the Neural Basis of Cognition
Carnegie Mellon University
Abstract
The representation of acoustic signals at the cochl... | 2620 |@word briefly:1 unaltered:1 gradual:2 pulse:1 thereby:1 minus:1 initial:3 imaginary:1 current:1 yet:1 scatter:1 must:1 attracted:1 periodically:1 additive:1 shape:3 remove:1 drop:2 plot:1 update:1 treating:1 v:1 stationary:2 leaf:1 selected:1 ith:1 smith:1 colored:2 filtered:1 provides:2 quantizer:1 quantized:8 s... |
1,785 | 2,621 | A Cost-Shaping LP for
Bellman Error Minimization with
Performance Guarantees
Daniela Pucci de Farias
Mechanical Engineering
Massachusetts Institute of Technology
Benjamin Van Roy
Management Science and Engineering
and Electrical Engineering
Stanford University
Abstract
We introduce a new algorithm based on linear pro... | 2621 |@word version:8 polynomial:1 norm:4 stronger:1 open:1 willing:1 gradual:1 carry:1 initial:2 contains:1 selecting:1 past:1 current:2 yet:1 must:2 written:1 remove:1 plot:1 greedy:1 selected:2 intelligence:1 steepest:1 iterates:1 unbounded:2 mathematical:1 along:1 schweitzer:1 direct:1 differential:6 ect:1 shorthan... |
1,786 | 2,622 | The power of feature clustering: An application
to object detection
Shai Avidan
Mitsibishi Electric Research Labs
201 Broadway
Cambridge, MA 02139
avidan@merl.com
Moshe Butman
Adyoron Intelligent Systems LTD.
34 Habarzel St.
Tel-Aviv, Israel
mosheb@adyoron.com
Abstract
We give a fast rejection scheme that is based o... | 2622 |@word briefly:1 duda:1 seek:1 tried:1 covariance:2 decomposition:1 pick:1 reduction:1 contains:1 score:1 selecting:1 shum:1 past:2 com:2 comparing:2 must:3 takeo:1 shape:2 cheap:2 wanted:1 drop:1 v:1 greedy:4 half:2 selected:2 intelligence:3 slowing:1 plane:3 core:1 detecting:1 boosting:1 characterization:1 zhang... |
1,787 | 2,623 | Stable adaptive control with online learning
Andrew Y. Ng
Stanford University
Stanford, CA 94305, USA
H. Jin Kim
Seoul National University
Seoul, Korea
Abstract
Learning algorithms have enjoyed numerous successes in robotic control
tasks. In problems with time-varying dynamics, online learning methods
have also prove... | 2623 |@word aircraft:4 mild:1 illustrating:1 norm:4 seems:1 justice:1 nd:1 johansson:1 d2:3 simulation:1 pick:3 thereby:1 boundedness:1 xkn:1 initial:1 contains:1 series:1 daniel:1 lqr:1 franklin:1 current:2 must:3 written:2 lqg:5 plot:2 update:1 maxv:1 n0:4 stationary:9 half:1 selected:2 slowing:1 accepting:1 complete... |
1,788 | 2,624 | Modeling Conversational Dynamics as a
Mixed-Memory Markov Process
Tanzeem Choudhury
Intel Research
tanzeem.choudhury@intel.com
Sumit Basu
Microsoft Research
sumitb@microsoft.com
Abstract
In this work, we quantitatively investigate the ways in which a
given person influences the joint turn-taking behavior in a
conver... | 2624 |@word fjij:1 seems:1 covariance:1 reduction:1 initial:1 score:7 existing:1 current:1 com:2 comparing:1 si:1 wanted:1 remove:1 v:1 implying:2 half:1 alone:1 betweenness:7 device:3 tone:1 indicative:2 colored:1 detecting:2 provides:1 complication:1 location:3 node:1 five:3 along:2 ect:3 combine:3 behavioral:1 autoc... |
1,789 | 2,625 | On the Adaptive Properties of Decision Trees
Clayton Scott
Statistics Department
Rice University
Houston, TX 77005
cscott@rice.edu
Robert Nowak
Electrical and Computer Engineering
University of Wisconsin
Madison, WI 53706
nowak@engr.wisc.edu
Abstract
Decision trees are surprisingly adaptive in three important respec... | 2625 |@word trial:1 polynomial:2 nd:1 c0:6 bf:3 additively:1 bn:8 decomposition:2 boundedness:2 fragment:5 existing:2 current:1 scovel:1 assigning:1 must:1 subsequent:1 happen:1 partition:7 discrimination:2 leaf:12 selected:1 dissertation:1 boosting:1 node:13 contribute:1 along:1 constructed:4 c2:8 manner:1 notably:1 i... |
1,790 | 2,626 | Efficient Out-of-Sample Extension of
Dominant-Set Clusters
Massimiliano Pavan and Marcello Pelillo
Dipartimento di Informatica, Universit`a Ca? Foscari di Venezia
Via Torino 155, 30172 Venezia Mestre, Italy
{pavan,pelillo}@dsi.unive.it
Abstract
Dominant sets are a new graph-theoretic concept that has proven to
be rele... | 2626 |@word trial:1 determinant:1 middle:1 brightness:2 thereby:2 nystr:2 contains:1 document:1 current:1 scatter:1 intriguing:3 assigning:1 john:1 numerical:2 happen:1 edgeweighted:2 partition:6 ahj:2 hofmann:1 extrapolating:1 item:4 accordingly:1 plane:1 short:1 provides:3 characterization:2 node:4 math:1 successive:... |
1,791 | 2,627 | Joint Probabilistic Curve Clustering and
Alignment
Scott Gaffney and Padhraic Smyth
School of Information and Computer Science
University of California, Irvine, CA 92697-3425
{sgaffney,smyth}@ics.uci.edu
Abstract
Clustering and prediction of sets of curves is an important problem in
many areas of science and engineer... | 2627 |@word briefly:1 version:1 polynomial:6 covariance:2 tmg:1 contains:1 series:2 existing:1 recovered:1 written:4 readily:1 must:1 numerical:1 informative:1 shape:2 treating:2 plot:4 atlas:1 generative:2 selected:1 intelligence:2 dissertation:1 provides:1 zhang:1 five:1 height:5 ik:15 incorrect:1 consists:1 introduc... |
1,792 | 2,628 | A direct formulation for sparse PCA
using semidefinite programming
Alexandre d?Aspremont
EECS Dept.
U.C. Berkeley
Berkeley, CA 94720
alexandre.daspremont@m4x.org
Michael I. Jordan
EECS and Statistics Depts.
U.C. Berkeley
Berkeley, CA 94720
jordan@cs.berkeley.edu
Laurent El Ghaoui
SAC Capital
540 Madison Avenue
New Y... | 2628 |@word illustrating:1 polynomial:1 norm:3 loading:16 simulation:1 decomposition:9 covariance:5 tr:15 carry:1 reduction:2 initial:1 series:1 dspca:9 existing:1 current:2 com:1 comparing:1 ka:2 written:1 must:1 subsequent:1 numerical:2 additive:1 happen:1 drop:1 fewer:2 core:1 record:1 org:1 direct:2 become:2 viable... |
1,793 | 2,629 | A feature selection algorithm based on the global
minimization of a generalization error bound
Dori Peleg
Department of Electrical Engineering
Technion
Haifa, Israel
dorip@tx.technion.ac.il
Ron Meir
Department of Electrical Engineering
Technion
Haifa, Israel
rmeir@tx.technion.ac.il
Abstract
A novel linear feature sel... | 2629 |@word repository:1 polynomial:2 advantageous:1 norm:5 elisseeff:1 moment:1 contains:1 series:1 must:4 numerical:3 informative:1 predetermined:1 fund:1 selected:7 accordingly:1 provides:1 ron:1 nnp:1 zhang:1 five:1 mathematical:1 consists:3 pnp:1 tomaso:1 nor:1 sdp:1 globally:1 overwhelming:1 solver:1 cardinality:... |
1,794 | 263 | Designing Application-Specific Neural Networks
Designing Application-Specific
Neural Networks
Using the Genetic Algorithm
Steven A. Harp, Tariq Samad, Aloke Guha
Honeywell SSDC
1000 Boone Avenue North
Golden Valley, MN 55427
ABSTRACT
We present a general and systematic method for neural network
design based on the g... | 263 |@word cu:1 version:4 norm:1 nd:1 termination:1 thereby:3 initial:9 configuration:1 score:2 selecting:1 genetic:28 existing:1 current:4 yet:4 must:4 tot:1 realistic:1 subsequent:1 predetermined:2 analytic:1 aps:2 update:2 selected:1 leaf:1 become:1 supply:1 surprised:1 consists:1 behavioral:1 psf:2 expected:1 intri... |
1,795 | 2,630 | Fast Rates to Bayes for Kernel Methods
Ingo Steinwart? and Clint Scovel
Modeling, Algorithms and Informatics Group, CCS-3
Los Alamos National Laboratory
{ingo,jcs}@lanl.gov
Abstract
We establish learning rates to the Bayes risk for support vector machines
(SVMs) with hinge loss. In particular, for SVMs with Gaussian ... | 2630 |@word kong:1 version:3 achievable:1 seems:2 stronger:2 polynomial:1 norm:2 p0:4 euclidian:3 contains:3 rkhs:16 scovel:2 dx:1 must:1 benign:1 enables:1 zhang:1 c2:1 consists:1 introduce:3 x0:3 indeed:3 roughly:2 nor:1 gov:2 considering:1 increasing:1 becomes:2 begin:1 estimating:1 bounded:1 notation:1 moreover:1 w... |
1,796 | 2,631 | Real-Time Pitch Determination of One or More
Voices by Nonnegative Matrix Factorization
Fei Sha and Lawrence K. Saul
Dept. of Computer and Information Science
University of Pennsylvania, Philadelphia, PA 19104
{feisha,lsaul}@cis.upenn.edu
Abstract
An auditory ?scene?, composed of overlapping acoustic sources, can be
... | 2631 |@word middle:1 briefly:1 stronger:1 seems:1 nd:1 verona:1 rapt:7 imaginary:1 recovered:1 reminiscent:2 must:4 distant:1 wx:2 analytic:1 plot:1 update:3 polyphonic:3 stationary:2 cue:3 half:3 pursued:1 tone:1 plane:1 postprocess:1 ith:1 short:1 provides:3 windowed:1 along:1 autocorrelation:1 upenn:1 discretized:3 ... |
1,797 | 2,632 | Distributed Information Regularization on
Graphs
Adrian Corduneanu
CSAIL MIT
Cambridge, MA 02139
adrianc@csail.mit.edu
Tommi Jaakkola
CSAIL MIT
Cambridge, MA 02139
tommi@csail.mit.edu
Abstract
We provide a principle for semi-supervised learning based on optimizing
the rate of communicating labels for unlabeled point... | 2632 |@word c0:1 adrian:1 seek:2 moment:1 score:3 document:20 nt:1 assigning:1 yet:1 must:2 written:1 stemming:1 update:6 larization:1 selected:2 website:1 plane:1 mccallum:1 provides:1 contribute:1 simpler:1 qij:3 consists:2 combine:3 introduce:1 manner:1 inspired:1 decreasing:1 provided:2 xx:5 underlying:1 notation:1... |
1,798 | 2,633 | Approximately Efficient Online Mechanism
Design
David C. Parkes
DEAS, Maxwell-Dworkin
Harvard University
parkes@eecs.harvard.edu
Satinder Singh
Comp. Science and Engin.
University of Michigan
baveja@umich.edu
Dimah Yanovsky
Harvard College
yanovsky@fas.harvard.edu
Abstract
Online mechanism design (OMD) addresses th... | 2633 |@word private:4 polynomial:2 nd:1 series:1 selecting:1 current:4 comparing:1 si:1 yet:2 must:3 john:1 remove:2 drop:1 v:5 generative:2 leaf:2 intelligence:1 parameterization:1 parkes:6 pdvcg:2 provides:3 contribute:1 node:10 ron:1 become:2 supply:1 indeed:1 expected:24 themselves:2 nor:1 planning:1 discounted:1 d... |
1,799 | 2,634 | Nearly Tight Bounds for the Continuum-Armed
Bandit Problem
Robert Kleinberg?
Abstract
In the multi-armed bandit problem, an online algorithm must choose
from a set of strategies in a sequence of n trials so as to minimize the
total cost of the chosen strategies. While nearly tight upper and lower
bounds are known in t... | 2634 |@word trial:8 exploitation:1 middle:1 polynomial:3 norm:3 open:3 rigged:1 d2:1 seek:1 boundedness:1 moment:1 initial:1 inefficiency:1 series:1 score:1 ours:1 current:1 must:5 john:1 subsequent:1 partition:1 shape:1 update:3 stationary:1 greedy:7 fewer:1 warmuth:1 isotropic:3 beginning:1 earson:1 math:1 contribute... |
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