Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
1,800 | 2,635 | Heuristics for Ordering Cue Search in
Decision Making
Peter M. Todd
Anja Dieckmann
Center for Adaptive Behavior and Cognition
MPI for Human Development
Lentzeallee 94, 14195 Berlin, Germany
ptodd@mpib-berlin.mpg.de
dieckmann@mpib-berlin.mpg.de
Abstract
Simple lexicographic decision heuristics that consider cues one a... | 2635 |@word trial:5 proportion:5 seems:1 simulation:3 dieckmann:2 mammal:1 profit:1 minus:5 initial:1 efficacy:1 interestingly:1 ati:1 past:1 current:5 surprising:1 si:4 yet:2 must:1 subsequent:1 analytic:1 zacks:2 update:1 v:3 discrimination:18 cue:142 implying:2 selected:2 fewer:1 intelligence:1 beginning:1 short:2 r... |
1,801 | 2,636 | Analysis of a greedy active learning strategy
Sanjoy Dasgupta?
University of California, San Diego
dasgupta@cs.ucsd.edu
Abstract
We abstract out the core search problem of active learning schemes, to
better understand the extent to which adaptive labeling can improve sample complexity. We give various upper and lower... | 2636 |@word illustrating:1 version:11 seems:1 twelfth:1 pick:9 asks:1 accommodate:1 harder:1 whittled:1 reduction:1 existing:2 current:4 si:4 lang:1 intriguing:1 must:7 subsequent:1 benign:1 treating:1 greedy:22 fewer:2 device:1 leaf:9 website:1 intelligence:1 mccallum:2 core:1 short:1 node:2 revisited:1 hyperplanes:1 ... |
1,802 | 2,637 | Online Bounds for Bayesian Algorithms
Sham M. Kakade
Computer and Information Science Department
University of Pennsylvania
Andrew Y. Ng
Computer Science Department
Stanford University
Abstract
We present a competitive analysis of Bayesian learning algorithms in the
online learning setting and show that many simple B... | 2637 |@word norm:1 nd:1 covariance:2 p0:7 simplifying:1 boundedness:1 current:1 comparing:2 written:1 must:2 warmuth:8 xk:1 realism:1 completeness:1 provides:4 simpler:1 along:2 s2t:5 prove:2 consists:1 specialize:1 expected:3 indeed:1 behavior:1 examine:1 provided:2 maximizes:1 mass:2 interpreted:1 minimizes:1 q2:9 ev... |
1,803 | 2,638 | Synergistic Face Detection and Pose Estimation
with Energy-Based Models
Margarita Osadchy
NEC Labs America
Princeton NJ 08540
rita@osadchy.net
Matthew L. Miller
NEC Labs America
Princeton NJ 08540
mlm@nec-labs.com
Yann Le Cun
The Courant Institute
New York University
yann@cs.nyu.edu
Abstract
We describe a novel met... | 2638 |@word multitask:1 version:1 advantageous:1 seems:1 mitsubishi:1 brightness:1 profit:1 initial:2 configuration:3 score:1 shum:1 ours:1 document:1 current:1 com:1 comparing:1 must:6 tilted:4 confirming:1 designed:5 update:6 half:3 fewer:1 selected:1 plane:10 parameterizations:2 location:3 arctan:1 zhang:2 mathemati... |
1,804 | 2,639 | A Probabilistic Model for Online Document
Clustering with Application to Novelty Detection
Jian Zhang?
?School of Computer Science
Cargenie Mellon University
Pittsburgh, PA 15213
jian.zhang@cs.cmu.edu
Zoubin Ghahramani??
? Gatsby Computational Neuroscience Unit
University College London
London WC1N 3AR, UK
zoubin@gat... | 2639 |@word version:1 norm:1 nd:1 c0:1 minus:1 contains:3 score:7 document:47 existing:3 current:6 nt:2 stemmed:1 assigning:3 dx:1 written:1 happen:1 informative:1 update:2 generative:1 item:1 xk:3 beginning:1 ith:2 smith:1 blei:2 detecting:1 lavrenko:1 zhang:3 stopwords:1 alert:1 c2:1 initiative:1 consists:1 combine:2... |
1,805 | 264 | 298
Okamoto, Kawato, Ioui aod Miyake
Model Based Image Compression and
Adaptive Data Representation by
Interacting Filter Banks
SeiMiyake
Toshiaki Okamoto, Mitsuo Kawato, Toshio Ioui
ATR Auditory and
Visual Perception Research Laboratories
Sanpeidani, Inuidani. Seika-cho. Soraku-gun
Kyoto 619-02. Japan
NHK Science... | 264 |@word compression:16 sanpeidani:1 regularization:1 tokyo:1 laboratory:2 filter:20 simulation:1 stochastic:6 vc:4 centered:1 human:3 receptive:1 during:1 self:1 uniquely:1 usual:1 gradient:1 excitation:1 atr:1 simulated:1 vd:1 sci:1 configuration:7 foveal:1 decoder:1 gun:1 tuned:1 summation:1 mathematically:2 koch:... |
1,806 | 2,640 | A Three Tiered Approach for Articulated Object
Action Modeling and Recognition
Le Lu
Gregory D. Hager
Department of Computer Science
Johns Hopkins University
Baltimore, MD 21218
lelu/hager@cs.jhu.edu
Laurent Younes
Center of Imaging Science
Johns Hopkins University
Baltimore, MD 21218
younes@cis.jhu.edu
Abstract
Vis... | 2640 |@word determinant:1 eliminating:1 bigram:3 proportion:1 duda:1 hu:1 zelnik:1 rgb:1 decomposition:2 covariance:1 tr:1 accommodate:1 hager:2 reduction:4 moment:4 initial:4 series:2 exclusively:1 seriously:1 bhattacharyya:2 past:1 current:1 segmentaion:1 michal:1 scatter:3 john:2 numerical:1 subsequent:1 blur:1 info... |
1,807 | 2,641 | Incremental Learning for Visual Tracking
?
Jongwoo Lim? David Ross? Ruei-Sung Lin? Ming-Hsuan Yang?
University of Illinois ? University of Toronto ? Honda Research Institute
jlim1@uiuc.edu dross@cs.toronto.edu rlin1@uiuc.edu myang@honda-ri.com
Abstract
Most existing tracking algorithms construct a representation of ... | 2641 |@word norm:7 covariance:3 decomposition:3 brightness:1 thereby:7 hager:2 contains:4 series:1 ours:1 existing:4 current:4 com:1 scatter:2 john:1 additive:1 numerical:1 eigentracking:2 shape:1 enables:1 treating:1 update:23 isard:2 fewer:1 intelligence:1 pdw:2 provides:3 honda:2 toronto:2 location:4 constructed:3 d... |
1,808 | 2,642 | Adaptive Discriminative Generative Model
and Its Applications
?
Ruei-Sung Lin? David Ross? Jongwoo Lim? Ming-Hsuan Yang?
University of Illinois ? University of Toronto ? Honda Research Institute
rlin1@uiuc.edu dross@cs.toronto.edu jlim1@uiuc.edu myang@honda-ri.com
Abstract
This paper presents an adaptive discriminat... | 2642 |@word gradual:1 covariance:1 decomposition:3 paid:1 thereby:4 tr:1 ytn:1 initial:1 liu:1 contains:2 series:1 existing:2 current:4 com:1 john:1 additive:1 eigentracking:1 wx:1 shape:1 update:19 stationary:1 generative:25 selected:2 isard:1 accordingly:2 beginning:1 toronto:2 honda:2 location:11 simpler:1 height:1 ... |
1,809 | 2,643 | Hierarchical Bayesian Inference in
Networks of Spiking Neurons
Rajesh P. N. Rao
Department of Computer Science and Engineering
University of Washington, Seattle, WA 98195
rao@cs.washington.edu
Abstract
There is growing evidence from psychophysical and neurophysiological
studies that the brain utilizes Bayesian princi... | 2643 |@word determinant:1 middle:1 version:1 nd:1 open:3 brightness:1 attended:1 accommodate:1 initial:2 att:6 denoting:1 ording:1 reynolds:1 past:5 current:6 comparing:1 nt:5 realistic:1 shape:1 update:1 discrimination:1 stationary:1 generative:1 intelligence:1 xk:1 wolfram:1 node:1 location:4 become:1 doubly:1 combin... |
1,810 | 2,644 | A Temporal Kernel-Based Model for Tracking
Hand-Movements from Neural Activities
Lavi Shpigelman12 Koby Crammer1 Rony Paz23 Eilon Vaadia23 Yoram Singer1
1
School of computer Science and Engineering
2
Interdisciplinary Center for Neural Computation
3
Dept. of Physiology, Hadassah Medical School
The Hebrew University Jer... | 2644 |@word neurophysiology:1 trial:19 version:1 briefly:1 norm:1 open:2 rhesus:1 eng:1 mention:1 recursively:1 initial:2 contains:1 series:5 score:6 liquid:1 tuned:2 outperforms:1 current:2 comparing:1 ka:1 scatter:1 yet:1 additive:1 partition:1 eichhorn:1 motor:8 designed:1 plot:2 drop:1 generative:2 half:1 devising:... |
1,811 | 2,645 | Confidence Intervals for the Area under the
ROC Curve
Corinna Cortes
Google Research
1440 Broadway
New York, NY 10018
corinna@google.com
Mehryar Mohri
Courant Institute, NYU
719 Broadway
New York, NY 10003
mohri@cs.nyu.edu
Abstract
In many applications, good ranking is a highly desirable performance for
a classifier.... | 2645 |@word repository:2 version:1 briefly:2 polynomial:1 flach:1 km:3 crucially:1 salcedo:1 q1:2 thereby:1 fortuitous:1 moment:1 series:1 score:5 document:2 existing:3 com:1 z2:1 comparing:1 assigning:1 dx:1 must:1 readily:1 ronald:1 plot:2 v:2 selected:2 classier:1 provides:4 math:1 boosting:1 mathematical:1 ik:11 in... |
1,812 | 2,646 | Face Detection ? Efficient and Rank Deficient
Wolf Kienzle, G?okhan Bak?r, Matthias Franz and Bernhard Scho? lkopf
Max-Planck-Institute for Biological Cybernetics
Spemannstr. 38, D-72076 T?ubingen, Germany
{kienzle, gb, mof, bs}@tuebingen.mpg.de
Abstract
This paper proposes a method for computing fast approximations t... | 2646 |@word middle:1 version:1 briefly:1 polynomial:2 norm:4 seems:1 r:2 decomposition:4 euclidian:3 tr:1 solid:2 contains:1 exclusively:1 score:7 rkhs:2 interestingly:1 bootstrapped:1 past:1 existing:4 current:2 si:8 written:2 must:1 subsequent:2 girosi:2 kyb:1 x240:1 drop:1 plot:5 update:2 v:1 half:1 intelligence:1 a... |
1,813 | 2,647 | Non-Local Manifold Tangent Learning
Yoshua Bengio and Martin Monperrus
Dept. IRO, Universit?e de Montr?eal
P.O. Box 6128, Downtown Branch, Montreal, H3C 3J7, Qc, Canada
{bengioy,monperrm}@iro.umontreal.ca
Abstract
We claim and present arguments to the effect that a large class of manifold learning algorithms that are... | 2647 |@word illustrating:1 middle:1 propagate:1 decomposition:1 covariance:6 tr:1 reduction:3 egt:1 existing:1 recovered:2 yet:1 must:1 written:1 shape:2 analytic:7 designed:1 selected:1 plane:30 xk:4 characterization:1 provides:1 toronto:1 successive:1 gx:1 along:1 constructed:2 scholkopf:2 consists:1 combine:1 fittin... |
1,814 | 2,648 | Semi-Markov Conditional Random Fields for
Information Extraction
Sunita Sarawagi
Indian Institute of Technology
Bombay, India
sunita@iitb.ac.in
William W. Cohen
Center for Automated Learning & Discovery
Carnegie Mellon University
wcohen@cs.cmu.edu
Abstract
We describe semi-Markov conditional random fields (semi-CRFs)... | 2648 |@word version:10 polynomial:2 nd:1 recursively:1 configuration:1 contains:3 liu:1 document:2 current:1 comparing:1 si:7 must:2 parsing:1 written:2 partition:1 rote:1 drop:1 v:1 generative:1 intelligence:3 mccallum:3 beginning:1 short:1 record:1 completeness:1 node:1 location:2 banff:1 lexicon:1 five:7 unbounded:1... |
1,815 | 2,649 | Optimal information decoding from neuronal
populations with specific stimulus selectivity
Marcelo A. Montemurro
The University of Manchester
Faculty of Life Sciences
Moffat Building
PO Box 88, Manchester M60 1QD, UK
m.montemurro@manchester.ac.uk
Stefano Panzeri ?
The University of Manchester
Faculty of Life Sciences
... | 2649 |@word trial:3 determinant:2 faculty:2 crucially:1 jacob:1 configuration:1 contains:1 tuned:3 interestingly:1 current:1 si:1 must:2 john:1 motor:1 nervous:1 inspection:2 short:1 hypersphere:1 provides:1 coarse:2 location:1 preference:26 simpler:1 zhang:1 fitting:2 dan:1 shapley:1 inside:2 introduce:1 montemurro:2 ... |
1,816 | 265 | 542
Kassebaum, Thnorio and Schaefers
The Cocktail Party Problem:
Speech/Data Signal Separation Comparison
between Backpropagation and SONN
John Kassebaum
jak@ec.ecn.purdue.edu
Manoel Fernando Tenorio
tenorio@ee.ecn.purdue.edu
Christoph Schaefers
Parallel Distributed Structures Laboratory
School of Electrical Engi... | 265 |@word trial:1 version:3 polynomial:4 nd:7 open:1 additively:1 tried:1 covariance:1 excited:1 jacob:1 tr:1 initial:1 configuration:1 series:1 selecting:1 interestingly:1 franklin:2 kondo:2 activation:2 yet:1 readily:1 john:1 ikeda:2 realistic:1 fram:2 designed:3 plot:1 provides:2 node:22 simpler:2 direct:1 behavior... |
1,817 | 2,650 | Newscast EM
Wojtek Kowalczyk
Department of Computer Science
Vrije Universiteit Amsterdam
The Netherlands
wojtek@cs.vu.nl
Nikos Vlassis
Informatics Institute
University of Amsterdam
The Netherlands
vlassis@science.uva.nl
Abstract
We propose a gossip-based distributed algorithm for Gaussian mixture
learning, Newscast E... | 2650 |@word repository:1 steen:3 covariance:3 reduction:5 contains:1 outperforms:1 current:3 si:35 must:1 written:2 chicago:1 mstep:1 drop:3 plot:2 update:7 half:1 greedy:1 device:1 intelligence:1 beginning:1 affair:1 kbytes:1 pointer:1 node:99 banff:1 zhang:1 direct:1 contacted:1 combine:2 symp:3 manner:2 indeed:1 rou... |
1,818 | 2,651 | Multiple Relational Embedding
Roland Memisevic
Department of Computer Science
University of Toronto
roland@cs.toronto.edu
Geoffrey Hinton
Department of Computer Science
University of Toronto
hinton@cs.toronto.edu
Abstract
We describe a way of using multiple different types of similarity relationship to learn a low-d... | 2651 |@word momma:1 seems:1 cleanly:1 decomposition:1 pick:1 brightness:1 accommodate:2 reduction:7 contains:2 daniel:1 rightmost:1 bie:1 reminiscent:1 john:1 class1:1 plot:11 ith:2 colored:1 provides:1 toronto:4 successive:1 five:2 rc:10 along:1 constructed:4 qij:2 pairwise:1 freeman:1 encouraging:1 supplementing:1 ps... |
1,819 | 2,652 | Conditional Random Fields for Object
Recognition
Ariadna Quattoni Michael Collins Trevor Darrell
MIT Computer Science and Artificial Intelligence Laboratory
Cambridge, MA 02139
{ariadna, mcollins, trevor}@csail.mit.edu
Abstract
We present a discriminative part-based approach for the recognition of
object classes from... | 2652 |@word seems:2 carry:1 contains:1 prefix:1 contextual:1 surprising:1 must:1 written:1 partition:3 shape:4 v:1 alone:1 intelligence:1 generative:6 discrimination:1 plane:1 mccallum:2 ith:1 location:10 five:1 tagger:1 direct:1 consists:1 freitag:1 combine:2 manner:1 tagging:1 uiuc:2 multi:3 globally:1 increasing:1 p... |
1,820 | 2,653 | A Topographic Support Vector Machine:
Classification Using Local Label Configurations
Johannes Mohr
Clinic for Psychiatry and Psychotherapy
Charit?e Medical School
and
Bernstein Center for Computational Neuroscience Berlin
10117 Berlin, Germany
Klaus Obermayer
Department of Electrical Engineering and Computer Science
... | 2653 |@word version:1 inversion:6 polynomial:1 nd:1 calculus:1 queensland:2 configuration:18 contains:1 yet:2 written:1 shape:1 update:3 resampling:1 nervous:1 mccallum:1 haykin:1 location:1 mathematical:1 direct:1 become:1 consists:2 introduce:1 pairwise:1 multi:2 company:1 increasing:3 becomes:3 underlying:1 notation... |
1,821 | 2,654 | Surface Reconstruction using
Learned Shape Models
Jan Erik Solem
School of Technology and Society
Malm?o University, Sweden
jes@ts.mah.se
Fredrik Kahl
RSISE, Australian National University
ACT 0200, Australia
fredrik@maths.lth.se
Abstract
We consider the problem of geometrical surface reconstruction from one
or sever... | 2654 |@word deformed:1 middle:1 nd:1 open:3 grey:8 initial:3 kahl:2 current:1 yet:1 dx:1 written:2 attracted:1 must:1 visible:2 shape:25 cue:3 parametrization:1 math:1 location:1 mathematical:1 along:3 symposium:1 fitting:9 inside:1 manner:2 introduce:2 frequently:1 dist:2 multi:3 mechanic:1 inspired:1 automatically:5 ... |
1,822 | 2,655 | Maximum-Margin Matrix Factorization
Nathan Srebro
Dept. of Computer Science
University of Toronto
Toronto, ON, CANADA
nati@cs.toronto.edu
Jason D. M. Rennie
Tommi S. Jaakkola
Computer Science and Artificial Intelligence Lab
Massachusetts Institute of Technology
Cambridge, MA, USA
jrennie,tommi@csail.mit.edu
Abstract... | 2655 |@word version:1 norm:65 yi0:5 plsa:1 seek:1 decomposition:4 tr:11 harder:1 recovered:2 written:3 hofmann:2 enables:1 discrimination:5 alone:1 intelligence:1 selected:2 item:9 characterization:1 toronto:4 hyperplanes:2 five:2 unbounded:1 prove:2 fitting:3 combine:1 introduce:2 x0:1 expected:2 sdp:16 inspired:1 rel... |
1,823 | 2,656 | Outlier Detection with One-class Kernel Fisher
Discriminants
Volker Roth
ETH Zurich, Institute of Computational Science
Hirschengraben 84, CH-8092 Zurich
vroth@inf.ethz.ch
Abstract
The problem of detecting ?atypical objects? or ?outliers? is one of the
classical topics in (robust) statistics. Recently, it has been pro... | 2656 |@word middle:1 inversion:1 seems:3 proportion:1 nd:3 duda:1 hu:1 covariance:2 decomposition:2 solid:2 carry:1 contains:6 att:1 selecting:5 denoting:4 com:1 scatter:2 dx:1 must:1 numerical:3 plot:12 update:1 v:1 selected:2 vanishing:2 detecting:7 provides:4 characterization:2 org:1 constructed:1 fitting:5 underfit... |
1,824 | 2,657 | Class-size Independent Generalization Analsysis
of Some Discriminative Multi-Category
Classification Methods
Tong Zhang
IBM T.J. Watson Research Center
Yorktown Heights, NY 10598
tzhang@watson.ibm.com
Abstract
We consider the problem of deriving class-size independent generalization bounds for some regularized discrim... | 2657 |@word pw:2 pick:1 tr:1 com:1 parsing:1 ronald:1 limp:2 selected:1 xk:21 completeness:1 provides:1 zhang:4 daphne:1 height:1 become:1 consists:2 expected:5 behavior:4 p1:1 nonseparable:1 multi:18 relying:1 decreasing:1 increasing:1 becomes:1 bounded:3 notation:2 underlying:1 moreover:4 what:2 pto:2 nj:1 guarantee:... |
1,825 | 2,658 | Generative Affine Localisation and Tracking
John Winn
Andrew Blake
Microsoft Research Cambridge
Roger Needham Building
7 J. J. Thomson Avenue
Cambridge CB3 0FB, U.K
http://research.microsoft.com/mlp
Abstract
We present an extension to the Jojic and Frey (2001) layered sprite model
which allows for layers to undergo af... | 2658 |@word h:1 middle:1 r:4 harder:2 moment:2 contains:2 initialisation:1 current:2 com:1 si:5 john:1 informative:2 shape:6 update:6 stationary:1 generative:15 cue:3 alone:1 half:2 nebojsa:1 greedy:2 plane:1 xk:3 ith:4 node:9 location:3 firstly:1 simpler:3 org:2 along:2 constructed:2 direct:1 beta:3 consists:3 combine... |
1,826 | 2,659 | Learning syntactic patterns for automatic
hypernym discovery
Rion Snow
Daniel Jurafsky
Andrew Y. Ng
Computer Science Department
Stanford University
Stanford, CA 94305
Linguistics Department
Stanford University
Stanford, CA 94305
Computer Science Department
Stanford University
Stanford, CA 94305
rion@cs.stanford.e... | 2659 |@word version:1 tedious:1 tried:1 pold:3 hyponym:21 dramatic:1 initial:1 contains:2 fragment:1 score:15 series:1 daniel:1 karger:1 charniak:1 past:2 existing:1 stemmed:1 conjunct:1 tackling:1 parsing:1 john:1 happen:1 shakespeare:6 entrance:1 interannotator:5 hofmann:1 remove:1 designed:1 plot:2 sponsored:1 v:1 d... |
1,827 | 266 | 290
Viola
Neurally Inspired Plasticity in Oculomotor
Processes
Paul A. Viola
Artificial Intelligence Laboratory
M"assachusetts Institute of Technology
Cambridge, MA 02139
ABSTRACT
We have constructed a two axis camera positioning system which
is roughly analogous to a single human eye. This Artificial-Eye (Aeye) com... | 266 |@word trial:1 open:2 simplifying:1 initial:1 configuration:1 contains:1 unintended:1 current:1 comparing:1 si:1 yet:1 issuing:1 must:3 vor:11 plasticity:7 girosi:1 motor:17 succeeding:1 update:2 intelligence:3 cue:1 device:3 foreseeable:1 characterization:2 optokinetic:2 successive:1 constructed:2 corridor:1 incor... |
1,828 | 2,660 | An Information Maximization Model of
Eye Movements
Laura Walker Renninger, James Coughlan, Preeti Verghese
Smith-Kettlewell Eye Research Institute
{laura, coughlan, preeti}@ski.org
Jitendra Malik
University of California, Berkeley
malik@eecs.berkeley.edu
Abstract
We propose a sequential information maximization model... | 2660 |@word trial:2 middle:2 sri:1 proportion:1 disk:1 pressed:1 crowding:1 foveal:1 series:1 selecting:2 past:1 current:6 comparing:1 discretization:1 z2:1 surprising:1 yet:3 must:5 chicago:2 distant:2 informative:6 shape:12 treating:1 update:4 discrimination:3 alone:1 cue:1 xk:1 coughlan:2 smith:2 short:2 provides:2 ... |
1,829 | 2,661 | Expectation Consistent Free Energies for
Approximate Inference
Manfred Opper
ISIS
School of Electronics and
Computer Science
University of Southampton
SO17 1BJ, United Kingdom
mo@ecs.soton.ac.uk
Ole Winther
Informatics and
Mathematical Modelling
Technical University of Denmark
DK-2800 Lyngby, Denmark
owi@imm.dtu.dk
... | 2661 |@word trial:3 determinant:2 achievable:1 seems:3 norm:2 simulation:2 covariance:1 thereby:1 tr:3 outlook:1 ld:4 kappen:1 initial:1 carry:1 contains:4 moment:12 united:1 electronics:1 denoting:1 surprising:1 dx:1 john:1 partition:4 shape:1 update:1 stationary:2 short:1 manfred:1 node:13 gec:6 simpler:1 mathematica... |
1,830 | 2,662 | Methods Towards Invasive Human
Brain Computer Interfaces
Thomas Navin Lal1 , Thilo Hinterberger2 , Guido Widman3 ,
Michael Schr?oder4 , Jeremy Hill1 , Wolfgang Rosenstiel4 ,
Christian E. Elger3 , Bernhard Sch?olkopf1 and Niels Birbaumer2,5
Max-Planck-Institute for Biological Cybernetics, Tu? bingen, Germany
{navin,jez,... | 2662 |@word neurophysiology:3 trial:8 norm:1 nd:1 open:1 solid:1 imaginary:2 current:1 comparing:1 must:1 john:1 toro:1 christian:2 motor:13 enables:1 plot:1 olkopf1:1 drop:1 v:3 discrimination:1 cue:5 half:1 device:4 selected:1 beginning:1 short:1 haykin:1 location:3 five:1 along:1 c2:1 direct:2 viable:1 consists:1 fi... |
1,831 | 2,663 | Contextual models for object detection using
boosted random fields
Antonio Torralba
MIT, CSAIL
Cambridge, MA 02139
torralba@mit.edu
Kevin P. Murphy
UBC, CS
Vancouver, BC V6T 1Z4
murphyk@cs.ubc.edu
William T. Freeman
MIT, CSAIL
Cambridge, MA 02139
billf@mit.edu
Abstract
We seek to both detect and segment objects in i... | 2663 |@word middle:1 seek:1 git:4 dramatic:1 harder:2 reduction:2 fragment:5 bc:4 current:1 contextual:9 si:22 yet:1 additive:4 wx:1 informative:3 sponsored:1 update:11 alone:1 selected:1 fewer:1 mccallum:1 detecting:2 boosting:28 node:15 location:3 gx:1 provides:2 become:1 combine:3 introduce:1 pairwise:1 inter:1 mask... |
1,832 | 2,664 | Dynamic Bayesian Networks for
Brain-Computer Interfaces
Pradeep Shenoy
Department of Computer Science
University of Washington
Seattle, WA 98195
pshenoy@cs.washington.edu
Rajesh P. N. Rao
Department of Computer Science
University of Washington
Seattle, WA 98195
rao@cs.washington.edu
Abstract
We describe an approach ... | 2664 |@word trial:3 meinicke:1 open:1 pressed:1 cp2:1 contains:1 exclusively:2 series:1 bootstrapped:2 prefix:1 past:1 current:3 engg:2 discernible:1 motor:6 plot:1 alone:1 cue:2 device:1 filtered:1 provides:2 detecting:2 node:2 simpler:2 along:4 constructed:1 iverson:1 differential:1 manner:3 behavior:1 preparatory:2 ... |
1,833 | 2,665 | Hierarchical Eigensolver for Transition Matrices
in Spectral Methods
?
Chakra Chennubhotla? and Allan D. Jepson?
Department of Computational Biology, University of Pittsburgh
?
Department of Computer Science, University of Toronto
Abstract
We show how to build hierarchical, reduced-rank representation for large
stoc... | 2665 |@word version:2 decomposition:12 pg:1 invoking:2 pick:2 accommodate:1 recursively:1 ld:1 initial:1 selecting:1 pna:1 diagonalized:1 outperforms:1 comparing:2 must:1 numerical:1 plot:2 update:4 stationary:15 pursued:1 greedy:2 half:3 de1:1 selected:1 fewer:1 desktop:1 alone:1 beginning:1 ith:2 qjk:2 coarse:27 node... |
1,834 | 2,666 | An Investigation of Practical Approximate
Nearest Neighbor Algorithms
Ting Liu, Andrew W. Moore, Alexander Gray and Ke Yang
School of Computer Science
Carnegie-Mellon University
Pittsburgh, PA 15213 USA
{tingliu, awm, agray, yangke}@cs.cmu.edu
Abstract
This paper concerns approximate nearest neighbor searching algori... | 2666 |@word repository:1 version:5 briefly:1 middle:1 norm:4 duda:1 nd:2 disk:8 twelfth:1 vldb:3 q1:1 pick:2 liu:2 series:2 contains:2 ours:4 outperforms:3 readily:1 john:1 partition:4 kdd:1 shape:2 designed:2 hash:4 v:2 intelligence:2 leaf:3 selected:1 unacceptably:1 plane:6 desktop:1 core:1 lr:7 hypersphere:2 certifi... |
1,835 | 2,667 | Using Random Forests in the Structured
Language Model
Peng Xu and Frederick Jelinek
Center for Language and Speech Processing
Department of Electrical and Computer Engineering
The Johns Hopkins University
{xp,jelinek}@jhu.edu
Abstract
In this paper, we explore the use of Random Forests (RFs) in the structured languag... | 2667 |@word arabic:1 version:1 manageable:1 bigram:1 open:1 t_:1 asks:1 tr:1 recursively:1 carry:1 reduction:2 initial:1 series:2 contains:2 charniak:1 prefix:11 current:2 nt:1 must:1 parsing:2 john:1 shape:1 headword:4 intelligence:1 leaf:7 item:3 beginning:2 prepended:1 ith:1 short:1 rescoring:1 node:23 tagger:4 cons... |
1,836 | 2,668 | A Hidden Markov Model for de Novo Peptide
Sequencing
Bernd Fischer, Volker Roth, Joachim M. Buhmann
Institute of Computational Science
ETH Zurich
CH-8092 Zurich, Switzerland
bernd.fischer@inf.ethz.ch
Jonas Grossmann, Sacha Baginsky,
Wilhelm Gruissem
Institute of Plant Sciences
ETH Zurich
CH-8092 Zurich, Switzerland
F... | 2668 |@word torsten:1 version:1 middle:1 proportion:1 open:1 simplifying:1 eng:1 initial:1 configuration:1 contains:1 fragment:16 exclusively:1 score:4 terminus:4 prefix:13 outperforms:2 reaction:1 current:2 discretization:2 surprising:1 si:1 john:2 realize:1 visible:1 plot:1 generative:2 selected:2 device:1 short:1 co... |
1,837 | 2,669 | Maximal Margin Labeling for Multi-Topic Text
Categorization
Hideto Kazawa, Tomonori Izumitani, Hirotoshi Taira and Eisaku Maeda
NTT Communication Science Laboratories
Nippon Telegraph and Telephone Corporation
2-4 Hikaridai, Seikacho, Sorakugun, Kyoto 619-0237 Japan
{kazawa,izumi,taira,maeda}@cslab.kecl.ntt.co.jp
Abs... | 2669 |@word middle:2 polynomial:1 norm:1 km:2 decomposition:2 reduction:2 configuration:1 contains:1 hereafter:1 document:2 outperforms:1 existing:4 com:1 si:2 written:3 john:1 numerical:1 berthier:1 v:2 selected:1 directory:1 boosting:1 location:1 five:2 rc:2 along:1 combine:1 frequently:1 multi:22 kazumi:2 voc:2 mmls... |
1,838 | 267 | Recognizing Hand-Printed Letters and Digits
Recognizing Hand-Printed Letters and Digits
Gale L. Martin James A. Pittman
MCC, Austin, Texas 78759
ABSTRACT
We are developing a hand-printed character recognition system using a multilayered neural net trained through backpropagation. We report on results of
training nets... | 267 |@word eliminating:1 wiesel:3 thchnical:1 polynomial:2 duda:2 proportionality:1 tr:2 reduction:1 initial:3 contains:1 selecting:1 ap1:1 document:1 bitmap:2 current:1 contextual:1 comparing:1 written:1 john:1 thble:1 enables:3 update:1 fewer:2 device:1 discovering:4 manry:2 node:26 successive:1 simpler:1 consists:1 ... |
1,839 | 2,670 | A Second order Cone Programming
Formulation for Classifying Missing Data
Chiranjib Bhattacharyya
Department of Computer Science and Automation
Indian Institute of Science
Bangalore, 560 012, India
chiru@csa.iisc.ernet.in
Pannagadatta K. S.
Department of Electrical Engineering
Indian Institute of Science
Bangalore, 560... | 2670 |@word repository:2 polynomial:2 norm:1 seems:1 confirms:1 covariance:10 kent:1 bhattacharyya:1 outperforms:2 si:8 yet:1 half:2 accordingly:1 recompute:1 provides:1 herbrich:1 zhang:1 five:1 mathematical:2 along:1 direct:1 become:1 initiative:1 polyhedral:4 deteriorate:1 expected:1 sdp:1 decomposed:1 considering:1... |
1,840 | 2,671 | At the Edge of Chaos: Real-time Computations and
Self-Organized Criticality in Recurrent Neural Networks
Thomas Natschl?ager
Software Competence
Center Hagenberg
A-4232 Hagenberg, Austria
Thomas.Natschlaeger@scch.at
Nils Bertschinger
Max Planck Institute for
Mathematics in the Sciences
D-04103 Leipzig, Germany
bertsc... | 2671 |@word bf:2 open:1 simulation:1 crucially:1 thereby:1 solid:3 initial:9 series:10 contains:2 liquid:3 activation:2 must:2 subsequent:1 visible:1 numerical:3 analytic:1 leipzig:1 plot:5 designed:1 update:4 discovering:1 accordingly:3 provides:1 math:1 node:12 lsm:1 ohl:1 consists:1 combine:1 expected:2 behavior:2 m... |
1,841 | 2,672 | Exponential Family Harmoniums
with an Application to Information Retrieval
Max Welling & Michal Rosen-Zvi
Information and Computer Science
University of California
Irvine CA 92697-3425 USA
welling@ics.uci.edu
Geoffrey Hinton
Department of Computer Science
University of Toronto
Toronto, 290G M5S 3G4, Canada
hinton@cs.... | 2672 |@word version:1 norm:1 efh:25 dealer:1 covariance:4 contrastive:8 gjb:8 tr:1 contains:2 denoting:1 document:30 outperforms:1 comparing:2 michal:1 must:1 readily:1 partition:3 hofmann:1 shape:1 update:1 depict:1 v:3 generative:1 intelligence:2 parameterization:1 blei:1 provides:1 lending:1 toronto:5 honda:1 firstl... |
1,842 | 2,673 | Convergence and No-Regret in Multiagent
Learning
Michael Bowling
Department of Computing Science
University of Alberta
Edmonton, Alberta
Canada T6G 2E8
bowling@cs.ualberta.ca
Abstract
Learning in a multiagent system is a challenging problem due to two key
factors. First, if other agents are simultaneously learning th... | 2673 |@word exploitation:1 version:1 stronger:1 norm:1 rigged:1 hu:1 seek:2 exclusively:1 selecting:2 past:1 current:2 update:9 stationary:4 intelligence:3 selected:3 amir:1 short:3 along:2 symposium:2 prove:4 shapley:1 combine:1 eleventh:1 manner:1 introduce:1 theoretically:1 x0:2 ra:1 expected:11 behavior:1 roughly:1... |
1,843 | 2,674 | Maximising Sensitivity in a Spiking Network
Anthony J. Bell,
Redwood Neuroscience Institute
1010 El Camino Real, Suite 380
Menlo Park, CA 94025
tbell@rni.org
Lucas C. Parra
Biomedical Engineering Department
City College of New York
New York, NY 10033
parra@ccny.cuny.edu
Abstract
We use unsupervised probabilistic mac... | 2674 |@word nihat:1 determinant:1 version:2 dtk:8 seems:2 simulation:4 propagate:1 thereby:1 moment:1 initial:1 contains:1 score:4 past:2 current:2 comparing:1 scatter:1 intriguing:1 jkl:2 must:2 yet:2 written:1 wx:1 plasticity:4 hoping:1 update:1 guess:1 maximised:1 ith:1 unmixed:1 math:1 location:1 org:1 sigmoidal:1 ... |
1,844 | 2,675 | Probabilistic computation in spiking populations
Richard S. Zemel
Dept. of Comp. Sci.
Univ. of Toronto
Quentin J. M. Huys
Gatsby CNU
UCL
Rama Natarajan
Dept. of Comp. Sci.
Univ. of Toronto
Peter Dayan
Gatsby CNU
UCL
Abstract
As animals interact with their environments, they must constantly update
estimates about t... | 2675 |@word trial:3 middle:1 version:1 bptt:2 tedious:1 simulation:2 tried:1 covariance:6 thereby:1 initial:1 zurada:1 ording:2 past:1 current:3 discretization:1 activation:1 dx:2 must:3 readily:1 blur:3 interspike:1 motor:1 update:2 v:1 stationary:7 cue:6 intelligence:1 inspection:1 ith:1 feedfoward:1 provides:2 toron... |
1,845 | 2,676 | Using the Equivalent Kernel to Understand
Gaussian Process Regression
Peter Sollich
Dept of Mathematics
King?s College London
Strand, London WC2R 2LS, UK
peter.sollich@kcl.ac.uk
Christopher K. I. Williams
School of Informatics
University of Edinburgh
5 Forrest Hill, Edinburgh EH1 2QL, UK
c.k.i.williams@ed.ac.uk
Abst... | 2676 |@word briefly:1 version:1 inversion:2 norm:1 seems:1 c0:3 calculus:1 r:2 covariance:9 papoulis:1 initial:1 series:1 rkhs:1 interestingly:1 comparing:1 surprising:2 dx:10 must:1 written:1 additive:1 numerical:8 j1:2 girosi:1 analytic:1 cheap:1 plot:5 treating:1 stationary:2 isotropic:2 provides:1 location:5 become... |
1,846 | 2,677 | Exponentiated Gradient Algorithms for
Large-margin Structured Classification
Peter L. Bartlett
U.C.Berkeley
Michael Collins
MIT CSAIL
bartlett@stat.berkeley.edu
mcollins@csail.mit.edu
Ben Taskar
Stanford University
David McAllester
TTI at Chicago
btaskar@cs.stanford.edu
mcallester@tti-c.org
Abstract
We consider... | 2677 |@word version:3 polynomial:1 decomposition:1 initial:5 configuration:3 contains:1 series:1 selecting:1 current:1 assigning:1 yet:1 parsing:4 john:2 chicago:1 hofmann:2 update:15 discrimination:1 v:1 selected:1 warmuth:3 mccallum:1 beginning:1 node:3 location:1 org:1 become:1 ik:2 incorrect:1 prove:2 consists:2 in... |
1,847 | 2,678 | Probabilistic Inference of Alternative Splicing
Events in Microarray Data
Ofer Shai, Brendan J. Frey, and Quaid D. Morris
Dept. of Electrical & Computer Engineering
University of Toronto, Toronto, ON
Qun Pan, Christine Misquitta, and Benjamin J. Blencowe
Banting & Best Dept. of Medical Research
University of Toronto, ... | 2678 |@word pcc:4 stronger:1 proportion:2 covariance:3 mammal:1 carry:1 score:1 genetic:1 tine:1 current:3 aberrant:1 si:2 scatter:1 liva:1 must:1 deposited:1 informative:1 designed:3 interpretable:1 update:1 plot:2 v:1 alone:1 generative:2 selected:4 half:1 xxz:1 isotropic:1 xk:2 eukaryote:2 infrastructure:1 node:1 to... |
1,848 | 2,679 | Message Errors in Belief Propagation
Alexander T. Ihler, John W. Fisher III, and Alan S. Willsky
Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
ihler@mit.edu, fisher@csail.mit.edu, willsky@mit.edu
Abstract
Belief propagation (BP) is an increasingly popular method of pe... | 2679 |@word mild:1 trial:3 version:2 stronger:4 replicate:1 proportionality:1 km:1 willing:1 contraction:6 simplifying:1 eng:1 tr:2 solid:3 initial:1 contains:2 dx:2 john:1 realistic:1 additive:5 shape:1 enables:1 treating:1 update:3 implying:2 isard:1 parametrization:1 coughlan:1 coarse:1 provides:2 node:18 quantized:... |
1,849 | 268 | 372
Touretzky and Wheeler
A Computational Basis for Phonology
David S. Touretzky
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Deirdre W. Wheeler
Department of Linguistics
University of Pittsburgh
Pittsburgh, PA 15260
ABSTRACT
The phonological structure of human languages is intricate,... | 268 |@word simulation:1 past:1 yet:1 written:1 must:1 john:2 chicago:1 subsequent:1 v:2 alone:1 generative:2 nervous:1 draft:1 provides:1 height:4 unbounded:2 become:2 eleventh:1 introduce:2 inter:1 intricate:1 behavior:2 inspired:1 deirdre:1 actual:1 becomes:3 confused:1 underlying:4 bounded:4 sut:2 string:1 nj:1 berk... |
1,850 | 2,680 | New Criteria and a New Algorithm for Learning
in Multi-Agent Systems
Rob Powers
Computer Science Department
Stanford University
Stanford, CA 94305
powers@cs.stanford.edu
Yoav Shoham
Computer Science Department
Stanford University
Stanford, CA 94305
shoham@cs.stanford.edu
Abstract
We propose a new set of criteria for... | 2680 |@word trial:1 polynomial:1 achievable:1 stronger:2 nd:1 advantageous:1 tat:3 asks:1 score:1 daring:1 outperforms:3 existing:5 past:1 current:1 comparing:1 must:3 john:1 designed:1 drop:1 stationary:18 intelligence:3 selected:3 devising:1 beginning:1 vbr:2 provides:1 successive:1 five:1 mathematical:1 ect:2 shorth... |
1,851 | 2,681 | Proximity graphs for clustering and manifold
learning
? Carreira-Perpin?
? an
Miguel A.
Richard S. Zemel
Dept. of Computer Science, University of Toronto
6 King?s College Road. Toronto, ON M5S 3H5, Canada
Email: {miguel,zemel}@cs.toronto.edu
Abstract
Many machine learning algorithms for clustering or dimensionality re... | 2681 |@word version:4 seems:1 seek:2 perpin:1 tried:1 paid:1 pick:1 solid:1 reduction:10 contains:3 nonlocally:1 daniel:1 wd:1 si:1 must:1 john:1 realize:1 mst:24 subsequent:2 partition:3 remove:1 plot:5 progressively:1 v:1 half:1 isotropic:1 vanishing:1 provides:1 detecting:1 toronto:3 location:3 along:1 constructed:3... |
1,852 | 2,682 | Optimal sub-graphical models
Mukund Narasimhan? and Jeff Bilmes?
Dept. of Electrical Engineering
University of Washington
Seattle, WA 98195
{mukundn,bilmes}@ee.washington.edu
Abstract
We investigate the problem of reducing the complexity of a graphical
model (G, PG ) by finding a subgraph H of G, chosen from a class ... | 2682 |@word polynomial:17 dtrees:2 memoize:1 seek:1 sepa:1 decomposition:19 cml:1 pg:18 pick:5 recursively:3 reduction:2 liu:1 contains:6 selecting:1 karger:1 bc:1 existing:1 must:16 happen:1 partition:2 v:1 greedy:4 fewer:4 leaf:3 intelligence:1 node:15 height:1 along:1 constructed:2 symposium:1 consists:2 inside:1 da... |
1,853 | 2,683 | Rate- and Phase-coded Autoassociative Memory
M?t? Lengyel
Peter Dayan
Gatsby Computational Neuroscience Unit, University College London
17 Queen Square, London WC1N 3AR, United Kingdom
{lmate,dayan}@gatsby.ucl.ac.uk
Abstract
Areas of the brain involved in various forms of memory exhibit patterns
of neural activity qui... | 2683 |@word h:1 version:1 compression:1 seems:1 hippocampus:4 simulation:3 crucially:1 covariance:4 thereby:1 reduction:1 united:1 tuned:2 current:1 jaynes:1 yet:1 dx:1 import:1 physiol:1 numerical:2 additive:2 distant:1 plasticity:12 shape:1 designed:1 update:5 characterization:1 provides:1 contribute:1 simpler:1 zhan... |
1,854 | 2,684 | Joint MRI Bias Removal Using Entropy
Minimization Across Images
Erik G. Learned-Miller
Department of Computer Science
University of Massachusetts, Amherst
Amherst, MA 01003
Parvez Ahammad
Division of Electrical Engineering
University of California, Berkeley
Berkeley, CA 94720
Abstract
The correction of bias in magne... | 2684 |@word middle:6 mri:2 eliminating:1 nd:1 seek:1 bn:1 brightness:9 reduction:1 initial:2 series:1 existing:1 current:1 comparing:1 must:4 written:1 evans:1 realistic:1 numerical:2 remove:5 update:1 infant:8 half:2 isotropic:1 beginning:1 record:1 provides:1 location:8 five:1 mathematical:1 along:1 become:1 ica:1 fr... |
1,855 | 2,685 | The Laplacian PDF Distance: A Cost
Function for Clustering in a Kernel
Feature Space
Robert Jenssen1?, Deniz Erdogmus2 , Jose Principe2 , Torbj?rn Eltoft1
1
2
Department of Physics, University of Troms?, Norway
Computational NeuroEngineering Laboratory, University of Florida, USA
Abstract
A new distance measure bet... | 2685 |@word middle:1 briefly:2 norm:2 covariance:1 nystr:1 contains:1 interestingly:1 diagonalized:1 assigning:2 dx:19 must:1 written:3 readily:1 realize:1 deniz:1 intelligence:1 selected:1 ith:2 detecting:1 provides:1 math:1 mathematical:1 dn:1 c2:5 along:1 consists:1 troms:1 n22:1 introduce:2 pairwise:1 torbj:1 expec... |
1,856 | 2,686 | Efficient Kernel Discriminant Analysis via QR
Decomposition
Tao Xiong
Department of ECE
University of Minnesota
txiong@ece.umn.edu
Jieping Ye
Department of CSE
University of Minnesota
jieping@cs.umn.edu
Vladimir Cherkassky
Department of ECE
University of Minnesota
cherkass@ece.umn.edu
Qi Li
Department of CIS
Univer... | 2686 |@word version:1 nd:1 km:12 decomposition:19 reduction:7 liu:1 contains:3 existing:1 scatter:9 readily:1 john:1 refines:1 gv:1 sponsored:1 discrimination:1 v:3 greedy:1 ith:4 cse:2 accessed:1 rc:1 dn:1 c2:1 mathematical:1 consists:1 idr:1 multi:1 decreasing:3 considering:1 moreover:1 maximizes:1 developed:1 findin... |
1,857 | 2,687 | Multi-agent Cooperation
in Diverse Population Games
K. Y. Michael Wong, S. W. Lim and Z. Gao
Hong Kong University of Science and Technology, Hong Kong, China.
{phkywong, swlim, zhuogao}@ust.hk
Abstract
We consider multi-agent systems whose agents compete for resources by
striving to be in the minority group. The agen... | 2687 |@word kong:3 version:1 nd:3 simulation:10 pick:1 thereby:1 minus:1 initial:7 luo:1 assigning:1 ust:1 written:1 cant:1 enables:1 displace:1 alone:3 stationary:1 fewer:3 accordingly:1 vanishing:2 provides:1 contribute:6 preference:23 accessed:1 zhang:1 height:1 burst:1 along:3 consists:3 overhead:1 market:1 behavio... |
1,858 | 2,688 | Modelling Uncertainty in the Game of Go
David H. Stern
Department of Physics
Cambridge University
dhs26@cam.ac.uk
Thore Graepel
Microsoft Research
Cambridge, U.K.
thoreg@microsoft.com
David J. C. MacKay
Department of Physics
Cambridge University
mackay@mrao.cam.ac.uk
Abstract
Go is an ancient oriental game whose co... | 2688 |@word version:1 eliminating:1 c0:2 simulation:1 thoreg:1 score:3 gagliardi:1 interestingly:1 current:3 com:1 comparing:4 surprising:1 analysed:1 si:17 readily:1 john:1 update:1 joy:1 s0n:3 mccallum:1 affair:1 record:1 normalising:1 node:17 location:1 herbrich:1 org:2 firstly:1 evaluator:1 five:1 become:2 qualitat... |
1,859 | 2,689 | Modeling Nonlinear Dependencies in
Natural Images using Mixture of
Laplacian Distribution
Hyun Jin Park and Te Won Lee
Institute for Neural Computation, UCSD
9500 Gilman Drive, La Jolla, CA 92093-0523
{hjinpark, tewon}@ucsd.edu
Abstract
Capturing dependencies in images in an unsupervised manner is
important for many i... | 2689 |@word middle:1 seems:1 nd:5 decomposition:1 outperforms:1 current:3 written:1 enables:2 analytic:2 treating:1 intelligence:1 generative:2 provides:5 location:1 simpler:1 become:1 viable:1 manner:3 introduce:1 ica:38 project:1 discover:1 underlying:2 maximizes:1 strela:1 finding:1 transformation:1 sky:1 um:1 parti... |
1,860 | 269 | Predicting Weather Using a Genetic Memory
Predicting Weather Using a Genetic Memory: a Combination of Kanerva's Sparse Distributed Memory with
Holland's Genetic Algorithms
David Rogers
Research Institute for Advanced Computer Science
MS 230-5, NASA Ames Research Center
Moffett Field, CA 94035
ABSTRACT
Kanerva's spar... | 269 |@word middle:1 advantageous:1 open:2 pressure:4 initial:2 contains:2 score:1 selecting:1 genetic:60 interestingly:1 past:1 si:1 yet:1 written:2 must:2 riacs:1 designed:5 v:1 intelligence:1 selected:12 fewer:1 coarse:1 node:4 ames:2 location:44 preference:1 mathematical:2 along:1 consists:1 tagging:1 dist:1 ol:1 in... |
1,861 | 2,690 | On-Chip Compensation of Device-Mismatch
Effects in Analog VLSI Neural Networks
Miguel Figueroa
Department of Electrical Engineering, Universidad de Concepci?on
Casilla 160-C, Correo 3, Concepci?on, Chile
mfigueroa@die.udec.cl
Seth Bridges and Chris Diorio
Computer Science & Engineering, University of Washington
Box 35... | 2690 |@word mild:1 version:2 eliminating:1 stronger:1 pulse:17 simulation:1 paid:1 harder:1 reduction:2 initial:1 bc:1 current:10 comparing:1 percep:1 enables:1 remove:4 plot:1 update:28 v:2 half:1 selected:2 device:11 floatinggate:2 chile:1 provides:1 differential:10 symposium:1 absorbs:1 symp:1 introduce:1 x0:3 sacri... |
1,862 | 2,691 | Hierarchical Distributed Representations for
Statistical Language Modeling
John Blitzer, Kilian Q. Weinberger, Lawrence K. Saul, and Fernando C. N. Pereira
Department of Computer and Information Science, University of Pennsylvania
Levine Hall, 3330 Walnut Street, Philadelphia, PA 19104
{blitzer,kilianw,lsaul,pereira}@... | 2691 |@word polynomial:1 bigram:20 seek:1 covariance:2 decomposition:1 jacob:1 fifteen:1 recursively:1 reduction:15 initial:1 contains:2 yet:1 must:2 parsing:2 john:1 eleven:1 enables:1 hofmann:2 plot:1 interpretable:1 update:2 greedy:1 fewer:2 leaf:14 generative:1 record:1 gure:1 matrix1:1 node:2 banff:2 monday:1 five... |
1,863 | 2,692 | Limits of Spectral Clustering
Ulrike von Luxburg and Olivier Bousquet
Max Planck Institute for Biological Cybernetics
Spemannstr. 38, 72076 T?ubingen, Germany
{ulrike.luxburg,olivier.bousquet}@tuebingen.mpg.de
Mikhail Belkin
The University of Chicago, Department of Computer Science
1100 E 58th st., Chicago, USA
misha@... | 2692 |@word version:5 norm:4 contains:2 seriously:1 dpn:3 dx:2 mesh:1 numerical:1 chicago:2 partition:28 kyb:1 designed:1 intelligence:1 indefinitely:1 provides:1 draft:1 mathematical:2 dn:17 constructed:6 direct:2 symposium:1 prove:8 consists:2 inside:2 introduce:1 indeed:1 expected:2 behavior:2 mpg:2 decreasing:1 dec... |
1,864 | 2,693 | Semi-parametric exponential family PCA
Sajama
Alon Orlitsky
Department of Electrical and Computer Engineering
University of California at San Diego, La Jolla, CA 92093
sajama@ucsd.edu and alon@ece.ucsd.edu
Abstract
We present a semi-parametric latent variable model based technique for
density modelling, dimensionalit... | 2693 |@word h:2 version:3 norm:1 nd:2 c0:3 simulation:6 tried:2 covariance:1 p0:1 pick:2 reduction:9 series:2 document:8 past:1 current:1 comparing:2 written:2 john:1 enables:1 mstep:1 drop:3 plot:1 update:2 zik:1 v:3 stationary:1 fewer:1 generative:1 intelligence:1 plane:4 isotropic:1 sys:8 sudden:1 nearness:1 revisit... |
1,865 | 2,694 | Saliency-Driven Image Acuity Modulation on a
Reconfigurable Silicon Array of Spiking Neurons
R. Jacob Vogelstein1 , Udayan Mallik2 , Eugenio Culurciello3 ,
Gert Cauwenberghs2 and Ralph Etienne-Cummings2
1
Dept. of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
2
Dept. of Electrical & Computer Engineer... | 2694 |@word version:1 c0:2 pulse:1 jacob:1 brightness:1 solid:1 reduction:1 liu:1 contains:3 series:1 bc:2 john:2 plasticity:1 shape:1 designed:6 intelligence:1 device:6 sram:1 realism:1 infrastructure:2 provides:2 location:4 philipp:1 constructed:1 c2:2 become:1 m7:1 transceiver:3 fabricate:1 inside:1 inter:2 rapid:3 ... |
1,866 | 2,695 | Breaking SVM Complexity
with Cross-Training
G?okhan H. Bak?r
Max Planck Institute
for Biological Cybernetics,
T?ubingen, Germany
gb@tuebingen.mpg.de
L?eon Bottou
NEC Labs America
Princeton NJ, USA
leon@bottou.org
Jason Weston
NEC Labs America
Princeton NJ, USA
jasonw@nec-labs.com
Abstract
We propose to selectively r... | 2695 |@word middle:3 achievable:2 norm:1 proportion:1 retraining:1 seems:1 bn:1 simplifying:1 concise:1 dramatic:1 multiedit:10 reduction:2 initial:3 configuration:1 contains:2 selecting:1 pub:1 com:2 comparing:1 si:2 must:4 kyb:1 remove:2 discrimination:3 prohibitive:1 selected:6 provides:1 location:1 org:1 mathematic... |
1,867 | 2,696 | Identifying protein-protein interaction sites on a
genome-wide scale
Haidong Wang? Eran Segalo Asa Ben-Hur? Daphne Koller? Douglas L. Brutlag?
?
Computer Science Department, Stanford University, CA 94305
{haidong, koller}@cs.stanford.edu
o
Center for Studies in Physics and Biology, Rockefeller University, NY 10021
era... | 2696 |@word proportion:4 mehta:1 uncovers:1 reduction:1 initial:1 contains:5 fragment:1 score:6 denoting:2 prefix:1 outperforms:2 existing:1 current:4 must:3 intelligence:1 discovering:2 short:2 completeness:1 provides:4 iterates:1 location:2 daphne:1 viable:1 chakrabarti:1 pathway:1 inside:1 introduce:1 expected:1 ind... |
1,868 | 2,697 | Theory of Localized Synfire Chain:
Characteristic Propagation Speed of
Stable Spike Patterns
Kosuke Hamaguchi
RIKEN Brain Science Institute
Wako, Saitama 351-0198, JAPAN
hammer@brain.riken.jp
Masato Okada
Dept. of Complexity Science and
Engineering, University of Tokyo,
Kashiwa, Chiba, 277-8561, JAPAN
okada@brain.rik... | 2697 |@word trial:1 wiesel:1 r:6 propagate:3 simulation:6 pulse:4 solid:1 initial:4 series:1 efficacy:2 mainen:1 wako:1 current:7 anterior:1 activation:1 intriguing:1 must:1 physiol:1 numerical:4 realistic:1 shape:4 enables:1 plot:1 stationary:1 record:1 rc:5 constructed:2 differential:1 become:1 consists:2 pathway:1 t... |
1,869 | 2,698 | Sharing Clusters Among Related Groups:
Hierarchical Dirichlet Processes
Yee Whye Teh(1) , Michael I. Jordan(1,2), Matthew J. Beal(3) and David M. Blei(1)
(1)
(3)
Computer Science Div., (2) Dept. of Statistics
Dept. of Computer Science
University of California at Berkeley
University of Toronto
Berkeley CA 94720, USA
Tor... | 2698 |@word achievable:1 proportion:6 reused:1 open:3 confirms:1 covariance:1 ecole:1 document:27 comparing:1 must:1 subsequent:1 realistic:1 informative:1 partition:7 j1:3 pertinent:1 plot:1 v:18 implying:1 generative:2 discovering:1 website:1 item:1 blei:5 node:1 toronto:3 sits:2 unbounded:1 direct:1 beta:2 introduce... |
1,870 | 2,699 | Optimal Aggregation of Classifiers and Boosting
Maps in Functional Magnetic Resonance
Imaging
Vladimir Koltchinskii
Department of Mathematics and Statistics
University of New Mexico
Albuquerque, NM, 87131
Manel Mart??nez-Ram?on
Department of Electrical and Computer Engineering
University of New Mexico
Albuquerque, NM,... | 2699 |@word trial:3 cox:1 mri:3 version:5 retraining:1 lobe:1 tr:1 shot:1 recursively:1 initial:1 selecting:1 current:1 comparing:1 activation:12 additive:2 shape:2 motor:6 atlas:1 discrimination:1 intelligence:2 tone:1 warmuth:2 filtered:1 mental:2 provides:2 boosting:28 detecting:1 location:1 dn:12 direct:1 become:2 ... |
1,871 | 27 | 573
BIT - SERIAL NEURAL NETWORKS
Alan F. Murray, Anthony V . W. Smith and Zoe F. Butler.
Department of Electrical Engineering, University of Edinburgh,
The King's Buildings, Mayfield Road, Edinburgh,
Scotland, EH93JL.
ABSTRACT
A bit - serial VLSI neural network is described from an initial architecture for a
synapse a... | 27 |@word manageable:1 eliminating:1 inversion:1 chopping:1 pulse:13 simulation:4 tried:1 solid:2 disappointingly:2 carry:1 electronics:1 initial:1 series:1 seriously:1 ours:1 interestingly:1 activation:34 yet:1 must:3 readily:1 update:5 v:3 signalling:3 scotland:1 smith:4 math:1 node:2 preference:1 sigmoidal:4 simpler... |
1,872 | 270 | 660
Geiger and Girosi
Coupled Markov Random Fields and
Mean Field Theory
Davi Geigerl
Artificial Intelligence
Laboratory, MIT
545 Tech. Sq. # 792
Cambridge, MA 02139
and
Federico Girosi
Artificial Intelligence
Laboratory, MIT
545 Tech. Sq. # 788
Cambridge, MA 02139
ABSTRACT
In recent years many researchers have i... | 270 |@word configuration:3 contains:1 suppressing:1 unction:1 partition:2 girosi:7 update:2 davi:1 intelligence:5 ional:1 dimen:1 tomaso:1 bility:1 becomes:4 factorized:1 mass:1 weinshall:1 nj:1 assoc:1 positive:1 before:1 understood:1 local:1 limit:1 acad:2 pami:1 range:3 averaged:2 sq:2 road:1 applying:2 writing:1 eq... |
1,873 | 2,700 | Generalization Error Bounds for Collaborative
Prediction with Low-Rank Matrices
Nathan Srebro
Department of Computer Science
University of Toronto
Toronto, ON, Canada
nati@cs.toronto.edu
Noga Alon
School of Mathematical Sciences
Tel Aviv University
Ramat Aviv, Israel
nogaa@tau.ac.il
Tommi S. Jaakkola
Computer Science... | 2700 |@word polynomial:12 norm:2 km:1 jacob:1 invoking:2 initial:1 configuration:21 chervonenkis:1 jaz:1 si:1 rpi:1 must:2 written:1 john:1 hofmann:1 enables:1 v:1 implying:1 intelligence:1 item:6 xk:2 realizing:1 matrix1:1 toronto:3 preference:8 simpler:2 five:1 unbounded:5 mathematical:2 symposium:2 focs:1 prove:3 fi... |
1,874 | 2,701 | Algebraic Set Kernels with Application to
Inference Over Local Image Representations
Amnon Shashua and Tamir Hazan ?
Abstract
This paper presents a general family of algebraic positive definite similarity functions over spaces of matrices with varying column rank. The
columns can represent local regions in an image (... | 2701 |@word trial:2 determinant:8 middle:1 version:9 polynomial:3 kondor:1 nd:7 grey:1 simulation:1 decomposition:1 thereby:3 configuration:2 tuned:1 must:2 john:1 j1:5 enables:1 moreno:1 treating:1 intelligence:1 selected:1 cook:2 item:1 vanishing:1 provides:3 along:1 constructed:2 direct:1 symposium:1 consists:1 fitt... |
1,875 | 2,702 | Nonparametric Transforms of Graph Kernels
for Semi-Supervised Learning
Xiaojin Zhu?
?
Jaz Kandola?
School of Computer Science
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213 USA
Zoubin Ghahramani??
?
John Lafferty?
Gatsby Computational Neuroscience Unit
University College London
17 Queen Square
... | 2702 |@word trial:5 repository:1 kondor:2 norm:1 seems:1 km:1 decomposition:1 p0:1 elisseeff:1 pick:1 tr:4 ld:1 series:1 score:3 pub:1 rkhs:1 document:1 current:1 jaz:1 must:2 john:1 realize:1 v:4 parameterization:1 node:6 preference:1 five:1 mathematical:1 constructed:3 combine:1 introduce:1 theoretically:1 expected:1... |
1,876 | 2,703 | The cerebellum chip:
an analog VLSI implementation of a
cerebellar model of classical conditioning
Constanze Hofst?tter, Manuel Gil, Kynan Eng,
Giacomo Indiveri, Matti Mintz, J?rg Kramer* and Paul F. M. J. Verschure
Institute of Neuroinformatics
University/ETH Zurich
CH-8057 Zurich, Switzerland
pfmjv@ini.phys.ethz.ch
... | 2703 |@word trial:6 cu:2 version:1 illustrating:1 extinction:3 pulse:3 simulation:2 eng:1 thereby:1 initial:3 denoting:1 tuned:1 current:2 manuel:1 activation:3 olive:3 plasticity:1 shape:1 motor:2 designed:1 alone:1 shut:1 signalling:1 beginning:2 short:3 firstly:2 five:2 dn:15 supply:1 symposium:1 pathway:4 behaviora... |
1,877 | 2,704 | VDCBPI: an Approximate Scalable Algorithm
for Large POMDPs
Pascal Poupart
Department of Computer Science
University of Toronto
Toronto, ON M5S 3H5
ppoupart@cs.toronto.edu
Craig Boutilier
Department of Computer Science
University of Toronto
Toronto, ON M5S 3H5
cebly@cs.toronto.edu
Abstract
Existing algorithms for dis... | 2704 |@word version:2 briefly:2 compression:24 norm:1 solid:1 carry:1 initial:3 cyclic:1 configuration:3 bc:1 past:1 existing:4 outperforms:1 current:4 si:1 must:3 numerical:2 subsequent:1 update:1 n0:15 meuleau:1 node:26 toronto:6 successive:2 zhang:1 c2:1 combine:2 expected:10 behavior:1 p1:2 nor:1 growing:1 planning... |
1,878 | 2,705 | A Generalized Bradley-Terry Model: From
Group Competition to Individual Skill
Tzu-Kuo Huang
Chih-Jen Lin
Department of Computer Science
National Taiwan University
Taipei 106, Taiwan
Ruby C. Weng
Department of Statistics
National Chenechi University
Taipei 116, Taiwan
Abstract
The Bradley-Terry model for paired compa... | 2705 |@word version:2 r:10 tried:1 solid:1 initial:2 existing:2 bradley:15 si:2 written:1 dashdot:1 remove:1 update:8 stationary:4 half:1 intelligence:1 ith:2 turnier:1 record:1 provides:1 along:1 prove:4 combine:1 boldfaced:1 introduce:2 pairwise:5 indeed:1 p1:9 multi:14 globally:1 considering:1 becomes:1 bounded:1 mo... |
1,879 | 2,706 | Making Latin Manuscripts Searchable using
gHMM?s
Jaety Edwards Yee Whye Teh David Forsyth Roger Bock Michael Maire
{jaety,ywteh,daf,bock,mmaire}@cs.berkeley.edu
Grace Vesom
Department of Computer Science
UC Berkeley
Berkeley, CA 94720
Abstract
We describe a method that can make a scanned, handwritten mediaeval
latin ... | 2706 |@word repository:1 version:6 illustrating:1 bigram:7 stronger:1 attainable:1 manmatha:3 contains:4 series:1 substitution:1 score:1 document:28 rath:2 current:1 com:2 written:1 shape:5 plot:1 implying:1 cue:1 fewer:1 selected:1 intelligence:2 beginning:1 short:2 zoological:1 node:3 philipp:1 five:2 height:2 along:... |
1,880 | 2,707 | Neural network computation by
in vitro transcriptional circuits
Jongmin Kim1 , John J. Hopfield3 , Erik Winfree2
Biology , CNS and Computer Science2 , California Institute of Technology.
Molecular Biology3 , Princeton University.
{jongmin,winfree}@dna.caltech.edu, hopfield@princeton.edu
1
Abstract
The structural simi... | 2707 |@word open:1 instruction:1 km:7 simulation:4 jacob:1 attainable:1 solid:2 carry:1 ld:7 moment:1 initial:3 contains:1 genetic:10 past:1 reaction:20 current:1 comparing:1 recovered:1 activation:5 yet:1 must:6 john:1 tot:40 realistic:1 confirming:1 shape:1 implying:1 devising:2 jongmin:2 slowing:1 beginning:1 ith:1 ... |
1,881 | 2,708 | Who?s in the Picture?
Tamara L. Berg, Alexander C. Berg, Jaety Edwards and D.A. Forsyth
Computer Science Division
U.C. Berkeley
Berkeley, CA 94720
millert@cs.berkeley.edu
Abstract
The context in which a name appears in a caption provides powerful cues
as to who is depicted in the associated image. We obtain 44,773 fac... | 2708 |@word middle:2 rising:1 seems:1 open:6 tried:2 covariance:1 nystr:2 manmatha:1 quo:1 united:2 pless:1 outperforms:1 dole:3 freitas:1 com:1 lang:2 john:2 fn:1 blur:2 treating:2 concert:1 update:1 v:3 alone:5 cue:9 half:2 generative:2 item:8 selected:1 indicative:1 intelligence:5 beginning:3 blei:1 nearness:1 provi... |
1,882 | 2,709 | Semigroup Kernels on Finite Sets
Marco Cuturi
Computational Biology Group
Ecole des Mines de Paris
35 rue Saint Honor?e
77300 Fontainebleau
marco.cuturi@ensmp.fr
Jean-Philippe Vert
Computational Biology Group
Ecole des Mines de Paris
35 rue Saint Honor?e
77300 Fontainebleau
jean-philippe.vert@ensmp.fr
Abstract
Compl... | 2709 |@word determinant:1 kondor:1 briefly:1 norm:1 calculus:1 d2:1 grey:1 covariance:7 decomposition:1 homomorphism:1 initial:1 score:1 ecole:2 rkhs:3 tuned:1 existing:1 comparing:1 attracted:1 fn:2 numerical:1 shape:1 kyb:1 designed:1 discrimination:1 generative:1 selected:2 guess:1 merger:2 characterization:2 succes... |
1,883 | 2,710 | Bayesian Regularization and Nonnegative
Deconvolution for Time Delay Estimation
Yuanqing Lin, Daniel D. Lee
GRASP Laboratory, Department of Electrical and System Engineering
University of Pennsylvania, Philadelphia, PA 19104
linyuanq, ddlee@seas.upenn.edu
Abstract
Bayesian Regularization and Nonnegative Deconvolution... | 2710 |@word version:1 norm:4 deconvolutions:1 heuristically:1 azimuthal:1 ajj:2 covariance:4 decomposition:1 tr:1 initial:3 contains:1 daniel:1 current:1 written:1 additive:2 partition:1 update:9 generative:2 along:1 direct:3 consists:1 fitting:2 introduce:1 upenn:1 rapid:1 automatically:2 estimating:10 matched:1 disco... |
1,884 | 2,711 | Similarity and discrimination in classical
conditioning: A latent variable account
Aaron C. Courville*1,3 , Nathaniel D. Daw4 and David S. Touretzky2,3
1
Robotics Institute, 2 Computer Science Department,
3
Center for the Neural Basis of Cognition,
Carnegie Mellon University, Pittsburgh, PA 15213
4
Gatsby Computationa... | 2711 |@word trial:21 version:1 stronger:1 seems:3 nd:1 extinction:1 hippocampus:1 additively:2 simulation:4 gradual:1 carry:1 configuration:2 contains:2 bc:12 suppressing:1 ours:1 activation:6 yet:2 profusion:1 written:1 readily:1 vor:1 realistic:1 refuted:1 update:1 progressively:1 discrimination:8 alone:4 generative:... |
1,885 | 2,712 | A harmonic excitation state-space approach to
blind separation of speech
Rasmus Kongsgaard Olsson and Lars Kai Hansen
Informatics and Mathematical Modelling
Technical University of Denmark, 2800 Lyngby, Denmark
rko,lkh@imm.dtu.dk
Abstract
We discuss an identification framework for noisy speech mixtures. A
block-based... | 2712 |@word norm:1 hu:1 confirms:1 covariance:4 q1:1 solid:1 moment:2 series:4 past:1 nt:3 si:6 dx:4 fn:2 realistic:2 additive:2 informative:1 v:1 stationary:10 generative:4 fni:1 colored:1 org:1 zhang:1 mathematical:1 along:3 constructed:1 weinstein:1 autocorrelation:3 manner:1 acquired:1 themselves:1 multi:1 moulines... |
1,886 | 2,713 | The Entire Regularization Path for the Support
Vector Machine
Trevor Hastie
Department of Statistics
Stanford University
Stanford, CA 94305, USA
hastie@stanford.edu
Saharon Rosset
IBM Watson Research Center
P.O. Box 218
Yorktown Heights, N.Y. 10598
srosset@us.ibm.com
Robert Tibshirani
Department of Statistics
Stanfor... | 2713 |@word norm:4 mee:1 termination:1 simulation:2 decomposition:1 dramatic:1 carry:1 initial:4 configuration:1 series:1 hereafter:1 ours:1 com:1 must:4 happen:1 update:2 website:1 ith:1 org:1 zhang:1 height:1 along:4 surprised:1 fitting:3 inside:3 manner:1 inspired:1 xti:1 becomes:2 discover:1 notation:1 finding:1 gu... |
1,887 | 2,714 | Harmonising Chorales by Probabilistic Inference
Moray Allan and Christopher K. I. Williams
School of Informatics, University of Edinburgh
Edinburgh EH1 2QL
moray.allan@ed.ac.uk, c.k.i.williams@ed.ac.uk
Abstract
We describe how we used a data set of chorale harmonisations composed
by Johann Sebastian Bach to train Hid... | 2714 |@word c0:2 closure:1 initial:1 pub:1 selecting:1 genetic:6 feulner:1 clash:1 current:1 assigning:1 written:2 additive:1 visible:8 predetermined:1 alone:1 generative:2 leaf:1 instantiate:1 mccallum:1 short:1 filtered:1 provides:1 preference:1 simpler:2 harmonically:1 direct:1 become:1 symposium:1 compose:3 allan:3... |
1,888 | 2,715 | Theories Of Access Consciousness
Michael D. Colagrosso
Department of Computer Science
Colorado School of Mines
Golden, CO 80401 USA
mcolagro@mines.edu
Michael C. Mozer
Institute of Cognitive Science
University of Colorado
Boulder, CO 80309 USA
mozer@colorado.edu
Abstract
Theories of access consciousness address how i... | 2715 |@word trial:3 illustrating:1 briefly:1 version:1 judgement:7 seems:1 stronger:1 grey:2 simulation:10 propagate:1 solid:4 necessity:1 initial:4 series:4 fragment:1 practiced:2 interestingly:1 past:4 reaction:1 subjective:1 current:1 surprising:1 activation:14 yet:2 must:7 skepticism:1 subsequent:1 visible:1 cottre... |
1,889 | 2,716 | Bayesian inference in spiking neurons
Sophie Deneve?
Gatsby Computational Neuroscience Unit
University College London
London, UK WC1N 3AR
sdeneve@gatsby.ucl.ac.uk
Abstract
We propose a new interpretation of spiking neurons as Bayesian integrators accumulating evidence over time about events in the external world
or th... | 2716 |@word trial:10 version:1 compression:1 seems:1 hippocampus:1 seek:1 linearized:2 propagate:1 thereby:1 initial:1 paw:1 efficacy:1 interestingly:1 past:3 current:2 happen:1 informative:4 plasticity:2 motor:3 plot:2 update:1 cue:5 generative:7 nervous:1 realism:1 short:1 sudden:1 provides:1 node:2 ron:10 lx:2 direc... |
1,890 | 2,717 | Binet-Cauchy Kernels
S.V.N. Vishwanathan, Alexander J. Smola
National ICT Australia, Machine Learning Program, Canberra, ACT 0200, Australia
{SVN.Vishwanathan, Alex.Smola}@nicta.com.au
Abstract
We propose a family of kernels based on the Binet-Cauchy theorem and its extension to Fredholm operators. This includes as s... | 2717 |@word determinant:17 kondor:2 version:3 flach:1 tedious:1 decomposition:3 pick:2 tr:17 incarnation:1 reduction:1 initial:4 contains:1 series:3 united:1 ka:5 com:1 yet:2 written:2 depict:3 v:4 selected:1 nq:1 warmuth:2 xk:1 short:2 mathematical:1 ik:1 chiuso:1 transducer:1 laub:1 artner:1 behavioral:2 introduce:1 ... |
1,891 | 2,718 | Semi-supervised Learning on Directed Graphs
Dengyong Zhou? , Bernhard Sch?olkopf? , and Thomas Hofmann??
?
Max Planck Institute for Biological Cybernetics
72076 Tuebingen, Germany
{dengyong.zhou, bernhard.schoelkopf}@tuebingen.mpg.de
?
Department of Computer Science, Brown University
Providence, RI 02912 USA
th@cs.brow... | 2718 |@word kondor:1 faculty:1 seems:2 norm:1 glue:1 confirms:1 citeseer:1 contains:1 score:2 series:1 document:1 comparing:1 yet:2 hofmann:1 treating:1 depict:1 v:1 intelligence:1 fewer:1 accordingly:2 mccallum:1 detecting:1 authority:21 node:19 mathematical:1 constructed:2 direct:1 chakrabarti:1 consists:4 freitag:1 ... |
1,892 | 2,719 | On Semi-Supervised Classification
Balaji Krishnapuram, David Williams, Ya Xue, Alex Hartemink, Lawrence Carin
Duke University, USA
M?ario A. T. Figueiredo
Instituto de Telecomunicac?o? es, Instituto Superior T?ecnico, Portugal
Abstract
A graph-based prior is proposed for parametric semi-supervised classification. The... | 2719 |@word trial:1 briefly:1 inversion:2 seek:1 minus:1 solid:3 configuration:1 interestingly:1 existing:1 current:2 additive:1 informative:1 treating:1 plot:1 update:1 designed:1 drop:1 alone:3 half:1 selected:3 v:1 intelligence:1 accordingly:1 beginning:1 smith:1 accepting:1 math:1 node:2 banff:1 unbounded:1 manner:... |
1,893 | 272 | 36
Bialek, Rieke, van Steveninck and Warland
Reading a Neural Code
William Bialek, Fred Rieke, R. R. de Ruyter van Steveninck 1 and
David Warland
Department of Physics, and
Department of Molecular and Cell Biology
University of California at Berkeley
Berkeley, California 94720
ABSTRACT
Traditional methods of studyin... | 272 |@word version:1 briefly:1 adrian:1 integrative:1 solid:1 carry:1 moment:2 initial:1 inefficiency:1 existing:1 reaction:2 comparing:1 surprising:1 must:7 fn:1 physiol:1 realistic:1 subsequent:1 fund:1 v:1 half:1 nervous:2 short:3 record:1 accepting:1 compo:1 caveat:1 characterization:2 provides:2 simpler:2 construc... |
1,894 | 2,720 | Trait selection for assessing beef meat quality
using non-linear SVM
J.J. del Coz, G. F. Bay?on, J. D??ez,
O. Luaces, A. Bahamonde
Artificial Intelligence Center
University of Oviedo at Gij?on
juanjo@aic.uniovi.es
?
Carlos Sanudo
Facultad de Veterinaria
University of Zaragoza
csanudo@posta.unizar.es
Abstract
In this... | 2720 |@word version:1 polynomial:8 seems:1 relevancy:1 reduction:2 score:2 att:3 subjective:2 past:1 comparing:1 adj:6 si:3 must:5 cruz:1 ministerio:1 kdd:1 remove:3 reproducible:1 designed:1 v:1 intelligence:4 selected:4 discovering:1 device:1 xk:3 cubist:3 toronto:1 preference:39 herbrich:2 banff:1 five:1 constructed... |
1,895 | 2,721 | Multiple Alignment of Continuous Time Series
Jennifer Listgarten? , Radford M. Neal? , Sam T. Roweis? and Andrew Emili?
Department of Computer Science, ? Banting and Best Department of Medical Research
and Program in Proteomics and Bioinformatics
University of Toronto, Toronto, Ontario, M5S 3G4
{jenn,radford,roweis}@c... | 2721 |@word middle:1 version:2 achievable:1 replicate:9 d2:1 crucially:1 uncovers:1 q1:1 pick:1 minus:1 xkn:1 initial:1 series:49 fragment:1 contains:1 liquid:2 current:1 z2:1 must:2 additive:1 analytic:1 drop:1 update:8 aside:1 poritz:1 generative:3 xk:5 short:1 provides:2 toronto:4 location:1 five:2 along:1 ik:6 cons... |
1,896 | 2,722 | Learning, Regularization and Ill-Posed Inverse
Problems
Lorenzo Rosasco
DISI, Universit`a di Genova
Genova, I
rosasco@disi.unige.it
Ernesto De Vito
Dipartimento di Matematica
Universit`a di Modena
and INFN, Sezione di Genova
Genova, I
devito@unimo.it
Andrea Caponnetto
DISI, Universit`a di Genova
Genova, I
caponnetto@d... | 2722 |@word exploitation:1 version:1 briefly:3 norm:3 yi0:1 open:1 closure:2 decomposition:1 attainable:1 series:1 interestingly:1 ka:8 discretization:4 sergei:1 john:3 girosi:1 selected:1 rudin:1 item:1 provides:1 math:4 theodoros:1 mcdiarmid:2 mathematical:1 direct:2 prove:1 interscience:1 firb:1 excellence:1 expecte... |
1,897 | 2,723 | Resolving Perceptual Aliasing In The Presence
Of Noisy Sensors?
Ronen I. Brafman & Guy Shani
Department of Computer Science
Ben-Gurion University
Beer-Sheva 84105, Israel
{brafman, shanigu}@cs.bgu.ac.il
Abstract
Agents learning to act in a partially observable domain may need to
overcome the problem of perceptual alia... | 2723 |@word version:1 briefly:1 smirnov:1 sensed:1 configuration:2 rightmost:1 past:2 existing:2 current:7 comparing:1 yet:1 must:1 realize:1 gurion:1 designed:2 update:2 greedy:1 leaf:23 mccallum:12 short:4 dissertation:1 meuleau:2 utile:9 provides:1 node:22 location:10 constructed:1 become:1 supply:2 predecessor:1 de... |
1,898 | 2,724 | Kernel Methods for Implicit Surface Modeling
?
Bernhard Sch?olkopf? , Joachim Giesen+? & Simon Spalinger+
Max Planck Institute for Biological Cybernetics, 72076 Tu? bingen, Germany
bernhard.schoelkopf@tuebingen.mpg.de
+
Department of Computer Science, ETH Zu? rich, Switzerland
giesen@inf.ethz.ch,spsimon@inf.ethz.ch
... | 2724 |@word repository:1 briefly:1 version:1 middle:3 norm:2 seems:1 tried:1 decomposition:2 outlook:1 solid:2 contains:1 rkhs:5 john:1 mesh:4 visible:1 happen:1 evans:1 shape:2 analytic:1 christian:1 drop:1 half:1 device:3 mccallum:1 colored:1 contribute:1 club:2 hyperplanes:4 unbounded:1 differential:1 become:2 short... |
1,899 | 2,725 | A Machine Learning Approach
to Conjoint Analysis
Olivier Chapelle, Za??d Harchaoui
Max Planck Institute for Biological Cybernetics
Spemannstr. 38 - 72076 T?ubingen - Germany
{olivier.chapelle,zaid.harchaoui}@tuebingen.mpg.de
Abstract
Choice-based conjoint analysis builds models of consumer preferences
over products w... | 2725 |@word trial:1 polynomial:1 seems:4 logit:1 simulation:1 covariance:7 pick:1 nystr:1 contains:1 series:1 selecting:2 mag:2 past:1 com:1 yet:1 chu:1 numerical:1 realistic:2 informative:2 zaid:1 designed:1 intelligence:2 xk:2 isotropic:1 preference:4 herbrich:1 consists:1 polyhedral:1 huber:1 expected:1 indeed:3 rou... |
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