Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
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1,200 | 2,094 | Kernel Feature Spaces and
Nonlinear Blind Source Separation
Stefan Harmeling1?, Andreas Ziehe1 , Motoaki Kawanabe1, Klaus-Robert M?ller1,2
1
Fraunhofer FIRST.IDA, Kekul?str. 7, 12489 Berlin, Germany
2
University of Potsdam, Department of Computer Science,
August-Bebel-Strasse 89, 14482 Potsdam, Germany
{harmeli,ziehe... | 2094 |@word kong:1 middle:2 polynomial:1 grier:1 underline:1 glue:1 hyv:1 tried:1 pick:1 outperforms:1 existing:1 ida:1 scatter:1 written:1 enables:2 plot:2 v:3 short:1 provides:1 simpler:2 mathematical:2 become:2 ica:3 roughly:1 moulines:1 unfolded:1 str:1 ller1:1 provided:1 moreover:4 bounded:1 panel:9 project:1 what... |
1,201 | 2,095 | Prodding the ROC Curve: Constrained
Optimization of Classifier Performance
Michael C. Mozer*+, Robert Dodier*, Michael D. Colagrosso*+,
C?sar Guerra-Salcedo*, Richard Wolniewicz*
* Advanced Technology Group + Department of Computer Science
Athene Software
University of Colorado
2060 Broadway
Campus Box 430
Boulder, CO ... | 2095 |@word trial:1 judgement:1 seems:2 retraining:2 grey:1 seek:1 subscriber:19 salcedo:1 pick:1 pressure:1 solid:6 initial:1 series:2 score:1 offering:2 genetic:8 interestingly:1 past:1 current:1 surprising:1 yet:1 must:4 subsequent:2 shape:2 asymptote:1 plot:2 discrimination:5 parameterization:1 reciprocal:1 record:... |
1,202 | 2,096 | Bayesian time series classification
Peter Sykacek
Department of Engineering Science
University of Oxford
Oxford, OX1 3PJ, UK
psyk@robots.ox.ac.uk
Stephen Roberts
Department of Engineering Science
University of Oxford
Oxford, OX1 3PJ, UK
sjrob@robots.ox.ac.uk
Abstract
This paper proposes an approach to classification... | 2096 |@word ruanaidh:1 determinant:1 version:1 polynomial:1 stronger:1 suitably:1 covariance:7 necessity:1 series:13 denoting:2 comparing:1 analysed:1 numerical:1 realistic:1 shape:1 motor:7 update:7 v:2 stationary:1 generative:2 intelligence:1 iso:1 smith:1 short:1 regressive:2 node:5 successive:3 direct:1 become:1 pa... |
1,203 | 2,097 | Active Learning
in the Drug Discovery Process
Manfred K. Warmuth , Gunnar
R?atsch , Michael
Mathieson ,
Jun Liao , Christian Lemmen
Computer
Science Dep., Univ. of Calif. at Santa Cruz
FHG
FIRST,
Kekul?estr. 7, Berlin, Germany
DuPont Pharmaceuticals,150 California St. San Francisco.
manfred,mathie... | 2097 |@word bounced:1 exploitation:2 middle:1 version:17 seems:1 cal90:2 tried:1 pick:9 asks:1 versatile:1 harder:1 initial:2 contains:1 score:1 selecting:6 current:3 yet:2 scatter:2 must:1 fs98:3 cruz:1 shape:1 christian:1 dupont:3 plot:9 interpretable:1 atlas:1 half:7 selected:16 warmuth:1 plane:6 manfred:2 hypersphe... |
1,204 | 2,098 | Transform-invariant image decomposition
with similarity templates
Chris Stauffer, Erik Miller, and Kinh Tieu
MIT Artificial Intelligence Lab
Massachusetts Institute of Technology
Cambridge, MA 02139
{stauffer,emiller,tieu}@ai.mit.edu
Abstract
Recent work has shown impressive transform-invariant modeling
and clusterin... | 2098 |@word version:1 briefly:1 seek:2 rgb:2 decomposition:8 covariance:1 brightness:1 tr:1 initial:1 contains:2 document:1 blank:1 comparing:2 si:5 must:1 written:1 additive:1 hofmann:1 enables:4 shape:1 treating:2 v:1 grass:1 intelligence:2 selected:1 ith:1 colored:1 provides:1 node:5 location:4 hsv:1 codebook:1 kinh... |
1,205 | 2,099 | A theory of neural integration in the
head-direction system
Richard H.R. Hahnloser , Xiaohui Xie and H. Sebastian Seung
Howard Hughes Medical Institute
Dept. of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
rhahnloser|xhxie|seung @mit.edu
Abstract
Integration in the head-... | 2099 |@word neurophysiology:1 pulse:1 linearized:1 simulation:4 reduction:1 tuned:1 current:1 comparing:1 anterior:2 activation:2 designed:2 stationary:4 half:1 contribute:1 preference:1 simpler:1 zhang:4 differential:3 become:2 persistent:3 g4:1 inter:5 indeed:1 behavior:1 brain:2 integrator:5 little:1 jm:1 becomes:1 ... |
1,206 | 21 | 674
PA'ITERN CLASS DEGENERACY IN AN UNRESTRICfED STORAGE
DENSITY MEMORY
Christopher L. Scofield, Douglas L. Reilly,
Charles Elbaum, Leon N. Cooper
Nestor, Inc., 1 Richmond Square, Providence, Rhode Island,
02906.
ABSTRACT
The study of distributed memory systems has produced a
number of models which work well in limite... | 21 |@word simulation:1 bachman:1 initial:2 contains:1 current:3 surprising:1 liapunov:3 xk:2 dembo:2 dissertation:1 math:1 lending:1 location:5 symposium:1 incorrect:1 consists:1 acti:1 unlearning:1 introduce:1 manner:2 acquired:1 rapid:1 proliferation:1 nonseparable:1 little:1 cm:1 elbaum:4 ghosh:1 charge:4 interactiv... |
1,207 | 210 | 100
Servan-Schreiber, Printz and Cohen
The Effect of Catecholamines on Performance:
From Unit to System Behavior
David Servan-Schreiber, Harry Printz and Jonathan D. Cohen
School of Computer Science and Department of Psychology
Carnegie Mellon University
Pittsburgh. PA 15213
ABSTRACT
At the level of individual neur... | 210 |@word illustrating:1 version:1 simulation:10 thereby:2 responsivity:8 contains:1 activation:9 nell:2 subsequent:1 additive:1 shape:2 motor:2 depict:1 discrimination:1 nervous:2 tone:3 inspection:1 provides:2 along:1 behavioral:9 expected:1 presumed:1 behavior:11 themselves:1 brain:4 terminal:1 ol:1 freeman:1 actua... |
1,208 | 2,100 | Efficiency versus Convergence of Boolean
Kernels for On-Line Learning Algorithms
Roni Khardon
Tufts University
Medford, MA 02155
roni@eecs.tufts.edu
Dan Roth
University of Illinois
Urbana, IL 61801
danr@cs.uiuc.edu
Rocco Servedio
Harvard University
Cambridge, MA 02138
rocco@deas.harvard.edu
Abstract
We study online... | 2100 |@word pw:3 polynomial:9 open:1 tr:1 reduction:1 initial:1 contains:6 denoting:1 conjunctive:3 must:7 additive:1 update:5 intelligence:1 warmuth:2 beginning:1 mathematical:2 constructed:1 become:3 symposium:1 prove:3 consists:1 dan:1 indeed:1 behavior:2 themselves:1 uiuc:2 beled:2 resolve:1 enumeration:1 preclude:... |
1,209 | 2,101 | An Efficient, Exact Algorithm for Solving
Tree-Structured Graphical Games
Michael L. Littman
AT&T Labs- Research
Florham Park, NJ 07932-0971
mlittman?research.att.com
Michael Kearns
Department of Computer & Information Science
University of Pennsylvania
Philadelphia, PA 19104-6389
mkearns?cis.upenn.edu
Satinder Singh... | 2101 |@word eliminating:1 polynomial:4 replicate:1 open:2 minus:1 solid:1 accommodate:1 initial:1 mkearns:1 att:1 contains:1 selecting:1 rightmost:2 com:1 assigning:1 must:4 gv:1 v:3 intelligence:1 leaf:5 accordingly:1 ith:1 math:1 contribute:1 daphne:1 along:1 constructed:1 consists:3 manner:1 pairwise:1 upenn:1 indee... |
1,210 | 2,102 | Products of Gaussians
Christopher K. I. Williams
Division of Informatics
University of Edinburgh
Edinburgh EH1 2QL, UK
c. k. i. williams@ed.ac.uk
http://anc.ed.ac.uk
Felix V. Agakov
System Engineering Research Group
Chair of Manufacturing Technology
Universitiit Erlangen-Niirnberg
91058 Erlangen, Germany
F.Agakov@lft... | 2102 |@word inversion:2 loading:1 open:2 contraction:5 covariance:40 decomposition:3 ld:4 reduction:2 comparing:2 si:7 written:3 must:2 numerical:1 visible:10 update:1 stationary:1 ith:1 num:1 detecting:1 node:1 symposium:1 fitting:1 indeed:1 isi:1 examine:2 lrmxm:3 wml:1 spherical:2 company:1 project:1 xx:1 matched:1 ... |
1,211 | 2,103 | ACh, Uncertainty, and Cortical Inference
Peter Dayan
Angela Yu
Gatsby Computational Neuroscience Unit
17 Queen Square, London, England, WC1N 3AR.
dayan@gatsby.ucl.ac.uk
feraina@gatsby.ucl.ac.uk
Abstract
Acetylcholine (ACh) has been implicated in a wide variety of
tasks involving attentional processes and plasticity. F... | 2103 |@word neurophysiology:1 trial:1 version:1 middle:1 hippocampus:5 proportionality:1 gfih:1 crucially:2 propagate:1 simulation:1 r:1 solid:1 offending:1 series:1 bc:1 past:2 existing:1 current:1 contextual:9 scatter:1 written:1 readily:1 visible:1 realistic:1 wx:1 plasticity:6 shape:1 gv:1 motor:1 plot:2 rpn:1 gene... |
1,212 | 2,104 | Fast Parameter Estimation
Using Green's Functions
K. Y. Michael Wong
Department of Physics
Hong Kong University
of Science and Technology
Clear Water Bay, Hong Kong
phkywong@ust.hk
FuIi Li
Department of Applied Physics
Xian Jiaotong University
Xian , China 710049
flli @xjtu. edu. en
Abstract
We propose a method for ... | 2104 |@word kong:3 inversion:4 tedious:2 simulation:7 paid:1 mention:2 solid:2 initial:2 series:1 comparing:2 activation:12 perturbative:3 ust:1 written:1 kleen:1 enables:1 vanishing:1 num:1 provides:5 math:1 node:10 contribute:1 simpler:1 direct:1 become:1 unlearning:1 introduce:1 pairwise:1 nor:1 gjk:2 inspired:1 cpu... |
1,213 | 2,105 | Algorithmic Luckiness
Ralf Herbrich
Microsoft Research Ltd.
CB3 OFB Cambridge
United Kingdom
rherb@microsoft?com
Robert C. Williamson
Australian National University
Canberra 0200
Australia
Bob. Williamson @anu.edu.au
Abstract
In contrast to standard statistical learning theory which studies
uniform bounds on the exp... | 2105 |@word exploitation:2 version:1 compression:7 elisseeff:2 tr:1 united:1 chervonenkis:2 com:2 z2:1 exy:1 must:1 written:1 john:1 cruz:1 half:2 warmuth:4 herbrich:3 firstly:2 descendant:1 shorthand:2 combine:1 expected:6 os:1 nor:1 multi:2 decreasing:1 rdh:1 actual:1 classifies:1 notation:3 bounded:1 agnostic:1 whil... |
1,214 | 2,106 | Unsupervised Learning of Human Motion
Models
Yang Song, Luis Goncalves, and Pietro Perona
California Institute of Technology, 136-93, Pasadena, CA 9112 5, USA
yangs,luis,perona @vision.caltech.edu
Abstract
This paper presents an unsupervised learning algorithm that can derive
the probabilistic dependence structure o... | 2106 |@word kong:1 polynomial:1 johansson:2 decomposition:3 covariance:6 solid:2 initial:1 configuration:4 contains:2 liu:2 existing:3 comparing:1 written:2 luis:2 john:1 depict:1 v:4 greedy:14 selected:2 discovering:1 prohibitive:1 guess:1 intelligence:1 short:3 node:1 location:1 constructed:2 differential:12 become:1... |
1,215 | 2,107 | Tree-based reparameterization for
approximate inference on loopy graphs
Martin J. Wainwright, Tommi Jaakkola, and Alan S. Will sky
Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Cambridge, MA 02139
mjwain@mit.edu
tommi@ai.mit.edu
willsky@mit.edu
Abstract
We develop a... | 2107 |@word version:2 open:1 seek:2 decomposition:1 mention:1 thereby:1 initial:2 cyclic:1 interestingly:1 must:2 partition:2 update:21 aside:1 intelligence:1 parameterization:2 provides:5 characterization:8 iterates:1 node:25 successive:2 lx:2 qualitative:1 consists:1 xtl:1 manner:5 pairwise:3 expected:1 indeed:2 rapi... |
1,216 | 2,108 | Semi-Supervised MarginBoost
F. d'Alche-Buc
LIP6,UMR CNRS 7606,
Universite P. et M. Curie
75252 Paris Cedex, France
Yves Grandvalet
Heudiasyc, UMR CNRS 6599,
Universite de Technologie de Compiegne,
BP 20.529, 60205 Compiegne cedex, France
florence. dAlche@lip6.fr
Yves. Grandvalet@hds.utc.fr
Christophe Ambroise
Heud... | 2108 |@word norm:2 proportion:1 calculus:1 covariance:1 pg:3 citeseer:1 carry:1 initial:1 document:1 riitsch:2 current:1 com:1 must:1 additive:1 realistic:2 designed:1 discrimination:4 alone:1 prohibitive:1 selected:1 mccallum:1 provides:3 boosting:20 lor:1 five:1 direct:1 differential:2 anyboost:1 consists:1 combine:1... |
1,217 | 2,109 | Categorization by Learning
and Combining Object Parts
Bernd Heisele Thomas Serre Massimiliano Pontil Thomas Vetter Tomaso Poggio
Center for Biological
and Computational Learning, M.I.T., Cambridge, MA, USA
Honda R&D Americas, Inc., Boston, MA, USA
Department of Information Engineering, University of Siena,... | 2109 |@word polynomial:2 tedious:1 open:2 reduction:1 configuration:2 selecting:1 interestingly:1 outperforms:1 john:1 shape:1 intelligence:2 selected:2 detecting:3 location:3 honda:1 direct:1 fps:1 edelman:1 combine:2 acquired:1 expected:3 tomaso:1 growing:1 multi:2 detects:1 automatically:6 window:3 matched:1 baker:1... |
1,218 | 211 | A Large-Scale Neural Network
A LARGE-SCALE NEURAL NETWORK
WHICH RECOGNIZES HANDWRITTEN
KANJI CHARACTERS
Yoshihiro Mori
Kazuki Joe
ATR Auditory and Visual Perception Research Laboratories
Sanpeidani Inuidani Seika-cho Soraku-gun Kyoto 619-02 Japan
ABSTRACT
We propose a new way to construct a large-scale neural network... | 211 |@word especially:1 build:1 chinese:2 hypercube:1 indicate:1 sanpeidani:1 retraining:3 direction:2 assigned:1 intend:1 correct:1 integrative:1 filter:1 simulation:1 owing:1 laboratory:1 satisfactory:1 i2:1 x5:1 assistance:1 strategy:10 defmed:1 diagonal:2 distance:11 harder:1 subnet:13 atr:1 capacity:1 generalizati... |
1,219 | 2,110 | A Neural Oscillator Model of Auditory
Selective Attention
Stuart N. Wrigley and Guy J. Brown
Department of Computer Science, University of Sheffield, Regent Court,
211 Portobello Street, Sheffield S1 4DP, UK.
s.wrigley@dcs.shef.ac.uk, g.brown@dcs.shef.ac.uk
Abstract
A model of auditory grouping is described in which a... | 2110 |@word timefrequency:1 simulation:2 excited:1 attended:4 current:4 lang:1 must:2 plot:5 half:1 selected:1 tone:24 accordingly:1 xk:3 sudden:2 preference:1 firstly:2 sigmoidal:1 height:1 harmonically:6 become:2 driver:1 consists:2 regent:1 sustained:2 fitting:1 autocorrelation:5 manner:1 introduce:1 inter:2 mask:2 ... |
1,220 | 2,111 | Computing Time Lower Bounds for
Recurrent Sigmoidal Neural Networks
Michael Schmitt
Lehrstuhl Mathematik und Informatik, Fakultat fUr Mathematik
Ruhr-Universitat Bochum, D- 44780 Bochum, Germany
mschmitt@lmi.ruhr-uni-bochum.de
Abstract
Recurrent neural networks of analog units are computers for realvalued functions. ... | 2111 |@word polynomial:1 ruhr:2 yih:1 initial:1 chervonenkis:3 orponen:4 interestingly:1 nt:9 assigning:1 universality:1 must:1 partition:1 implying:1 haykin:2 characterization:1 valdes:1 node:53 sigmoidal:19 lor:1 shatter:2 c2:1 consists:1 little:1 cardinality:1 bounded:4 moreover:5 deutsche:1 what:1 nj:1 every:16 hyp... |
1,221 | 2,112 | Activity Driven Adaptive Stochastic
Resonance
Gregor Wenning and Klaus Oberrnayer
Department of Electrical Engineering and Computer Science
Technical University of Berlin
Franklinstr. 28/29 , 10587 Berlin
{grewe , oby}@cs.tu-berlin.de
Abstract
Cortical neurons might be considered as threshold elements integrating in... | 2112 |@word nd:1 simulation:2 t_:1 solid:3 versatile:1 ld:1 contains:1 series:1 cort:1 current:12 wilkens:1 must:2 realistic:3 plot:2 update:1 v:2 stationary:1 device:2 ial:1 inam:1 height:1 dn:1 behavioral:1 introduce:2 rapid:1 roughly:1 ol:2 f3h:2 decreasing:2 actual:1 increasing:2 maximizes:4 every:1 ti:1 f3m:1 yn:1... |
1,222 | 2,113 | On the Generalization Ability
of On-line Learning Algorithms
Nicol`o Cesa-Bianchi
DTI, University of Milan
via Bramante 65
26013 Crema, Italy
cesa-bianchi@dti.unimi.it
Alex Conconi
DTI, University of Milan
via Bramante 65
26013 Crema, Italy
conconi@dti.unimi.it
Claudio Gentile
DSI, University of Milan
via Comelico 3... | 2113 |@word h:6 trial:5 determinant:1 version:2 norm:4 nd:1 gfih:1 boundedness:1 initial:1 denoting:1 ours:1 o2:4 current:2 yet:1 additive:1 informative:1 update:3 fewer:1 warmuth:8 beginning:2 short:2 provides:1 org:1 mathematical:2 along:1 direct:2 prove:3 expected:4 behavior:1 brain:1 actual:1 abound:1 begin:1 notat... |
1,223 | 2,114 | Analog Soft-Pattern-Matching Classifier
using Floating-Gate MOS Technology
Toshihiko YAMASAKI and Tadashi SHIBATA*
Department of Electronic Engineering, School of Engineering
*Department of Frontier Informatics, School of Frontier Science
The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
yamasaki@... | 2114 |@word trial:5 illustrating:1 proportion:1 nd:3 open:1 simulation:2 solid:3 reduction:4 series:1 score:10 contains:1 current:5 si:1 written:7 realize:1 visible:1 realistic:1 shape:1 designed:1 plot:1 discrimination:1 intelligence:2 device:1 detecting:1 quantizer:1 node:3 location:2 firstly:1 height:4 supply:1 cons... |
1,224 | 2,115 | 3 state neurons for contextual processing
Adam Kepecs* and Sridhar Raghavachari
Volen Center for Complex Systems
Brandeis University
Waltham MA 02454
{kepecs,sraghava}@brandeis.edu
Abstract
Neurons receive excitatory inputs via both fast AMPA and slow
NMDA type receptors. We find that neurons receiving input via
NMDA... | 2115 |@word h:1 hippocampus:1 seems:2 open:1 simulation:1 carry:5 moment:1 initial:1 tuned:2 interestingly:1 current:20 contextual:20 ka:1 activation:3 dx:1 john:2 physiol:1 numerical:1 aoo:1 enables:1 wanted:1 motor:1 opin:1 rinzel:2 v:2 implying:1 cue:1 alone:1 device:2 indicative:1 reciprocal:1 location:1 along:1 an... |
1,225 | 2,116 | Batch Value Function Approximation via
Support Vectors
Thomas G Dietterich
Department of Computet Science
Oregon State University
Corvallis, OR, 97331
tgd@cs.orst.edu
Xin W"ang
Department of Computer Science
Oregon State University
Corvallis, OR, 97331
wangxi@cs. orst. edu
Abstract
We present three ways of combining... | 2116 |@word norm:4 nd:1 seek:4 tr:1 series:1 contains:1 score:2 tuned:1 existing:1 written:1 must:3 plot:1 greedy:4 fewer:2 imitate:1 record:1 preference:1 zhang:2 five:1 mathematical:2 along:4 constructed:1 fitting:2 combine:1 introduce:2 expected:2 themselves:1 terminal:3 bellman:10 discounted:1 td:2 little:1 cpu:6 b... |
1,226 | 2,117 | Effective size of receptive fields of inferior
temporal visual cortex neurons in natural scenes
Thomas P. Trappenberg
Dalhousie University
Faculty of Computer Science
5060 University Avenue, Halifax B3H 1W5, Canada
tt@cs.dal.ca
Edmund T. Rolls and Simon M. Stringer
University of Oxford,
Centre for Computational Neuros... | 2117 |@word neurophysiology:1 version:1 faculty:1 stronger:1 simulation:5 lobe:1 solid:3 foveal:3 tuned:1 blank:22 anterior:1 activation:1 enables:4 drop:1 succeeding:1 half:2 selected:4 parameterization:2 provides:1 node:7 location:7 sigmoidal:1 become:1 pathway:1 expected:2 distractor:7 integrator:1 brain:9 compensat... |
1,227 | 2,118 | Cobot: A Social Reinforcement Learning Agent
Charles Lee Isbell, Jr.
Christian R. Shelton
AT&T Labs-Research
Stanford University
Michael Kearns
Satinder Singh
Peter Stone
University of Pennsylvania Syntek Capital AT&T Labs-Research
Abstract
We report on the use of reinforcement learning with Cobot, a software agent
re... | 2118 |@word middle:1 version:1 seems:1 replicate:1 twelfth:1 open:3 willing:1 simplifying:1 pavel:1 dramatic:1 minus:1 moment:2 initial:1 series:1 contains:2 selecting:1 document:2 current:7 yet:1 must:5 numerical:2 entertaining:2 eleven:1 christian:1 wanted:2 designed:1 update:1 stationary:2 intelligence:2 fewer:1 alo... |
1,228 | 2,119 | Probabilistic principles in unsupervised learning
of visual structure: human data and a model
Shimon Edelman, Benjamin P. Hiles
& Hwajin Yang
Department of Psychology
Cornell University, Ithaca, NY 14853
se37,bph7,hy56 @cornell.edu
Nathan Intrator
Institute for Brain and Neural Systems
Box 1843, Brown University
Pro... | 2119 |@word trial:11 version:2 holyoak:1 simulation:2 attended:1 solid:1 accommodate:1 configuration:1 fragment:24 tuned:1 subjective:1 current:2 yet:2 shape:12 infant:4 half:1 fried:1 smith:1 hinged:1 short:1 provides:2 coarse:1 location:3 successive:2 five:2 differential:1 persistent:1 edelman:4 suspicious:12 consist... |
1,229 | 212 | 710
Pineda
Time DependentAdaptive Neural Networks
Fernando J. Pineda
Center for Microelectronics Technology
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, CA 91109
ABSTRACT
A comparison of algorithms that minimize error functions to train the
trajectories of recurrent networks, reveals how co... | 212 |@word seems:1 scalably:1 simulation:2 r:5 covariance:1 hannonic:1 commute:1 tr:2 initial:4 disparity:2 activation:2 yet:8 dx:1 must:8 additive:1 numerical:1 enables:1 motor:2 update:4 v:1 pursued:1 leaf:1 yr:2 nervous:1 accordingly:1 inspection:1 beginning:1 dissertation:1 attack:1 five:1 mathematical:2 along:1 be... |
1,230 | 2,120 | Bayesian Predictive Profiles with
Applications to Retail Transaction Data
Igor V. Cadez
Information and Computer Science
University of California
Irvine, CA 92697-3425, U.S.A.
icadez@ics.uci.edu
Padhraic Smyth
Information and Computer Science
University of California
Irvine, CA 92697-3425, U.S.A.
smyth@ics.uci.edu
A... | 2120 |@word solid:1 contains:2 score:10 cadez:7 document:3 tuned:3 past:1 existing:1 scatter:1 subsequent:1 numerical:1 hofmann:1 plot:5 generative:3 selected:3 item:21 mccallum:2 yi1:1 ith:1 record:3 provides:1 location:1 dn:1 ik:10 consists:4 combine:1 fitting:1 manner:1 expected:2 indeed:1 roughly:2 market:2 behavio... |
1,231 | 2,121 | Analysis of Sparse Bayesian Learning
Anita C. Fanl
Michael E. Tipping
Microsoft Research
St George House, 1 Guildhall St
Cambridge CB2 3NH, U.K .
Abstract
The recent introduction of the 'relevance vector machine' has effectively demonstrated how sparsity may be obtained in generalised
linear models within a Bayesian ... | 2121 |@word determinant:1 briefly:2 confirms:1 simulation:1 covariance:1 decomposition:1 dramatic:1 tlo:1 interestingly:1 amp:2 si:19 written:1 must:3 fn:1 numerical:1 additive:1 plot:1 update:3 stationary:8 generative:1 intelligence:1 maximised:1 normalising:1 rc:2 become:1 scholkopf:1 s2t:1 qualitative:1 combine:3 in... |
1,232 | 2,122 | Reducing multiclass to binary by
coupling probability estimates
Bianca Zadrozny
Department of Computer Science and Engineering
University of California, San Diego
La Jolla, CA 92093-0114
zadrozny@cs.ucsd.edu
Abstract
This paper presents a method for obtaining class membership probability estimates for multiclass clas... | 2122 |@word dietterich:4 repository:2 c:1 eliminating:1 predicted:5 implies:1 differ:1 direction:1 true:2 assigned:3 bakiri:4 leibler:3 attribute:1 question:1 stochastic:1 decomposition:1 round:1 distance:10 require:2 mlrepository:1 assign:2 series:1 score:9 selecting:1 generalization:1 ay:6 evaluate:1 reason:1 mathema... |
1,233 | 2,123 | Asymptotic Universality for Learning
Curves of Support Vector Machines
M.Opperl
R. Urbanczik 2
1 Neural Computing Research Group
School of Engineering and Applied Science
Aston University, Birmingham B4 7ET, UK.
opperm@aston.ac.uk
2Institut Fur Theoretische Physik,
Universitiit Wurzburg Am Rubland, D-97074 Wurzburg... | 2123 |@word mild:2 version:1 achievable:1 polynomial:15 physik:2 simulation:7 commute:1 reduction:1 itp:1 chervonenkis:1 denoting:1 current:1 z2:1 nt:1 universality:1 intriguing:1 must:1 written:1 transcendental:2 realistic:3 partition:4 additive:5 numerical:1 analytic:2 asymptote:5 stationary:2 plane:3 vanishing:1 cha... |
1,234 | 2,124 | Circuits for VLSI Implementation of
Temporally-Asymmetric Hebbian
Learning
Adria Bofill
Alan F. Murray
DanlOn P. Thompson
Dept. of Electrical Engineering
The University of Edinburgh
Edinburgh , EH93JL , UK
adria. bofill@ee.ed.ac. uk
alan. murray @ee.ed.ac.uk
damon. thompson @ee.ed.ac. uk
Abstract
Experimental data ... | 2124 |@word pulse:15 tr:1 carry:2 initial:1 efficacy:1 tuned:2 current:7 comparing:1 activation:4 must:1 plasticity:1 shape:1 motor:1 plot:2 designed:1 aps:1 device:1 smith:1 short:3 schaik:1 contribute:1 zhang:1 mathematical:1 along:1 constructed:1 driver:1 supply:1 introduce:1 planning:1 inspired:1 continuousvalued:1... |
1,235 | 2,125 | The Concave-Convex Procedure (CCCP)
A. L. Yuille and Anand Rangarajan *
Smith-Kettlewell Eye Research Institute,
2318 Fillmore Street,
San Francisco, CA 94115, USA.
Tel. (415) 345-2144. Fax. (415) 345-8455.
Email yuille@ski.org
* Prof. Anand Rangarajan. Dept. of CISE, Univ. of Florida Room 301, CSE
Building Gainesvil... | 2125 |@word xof:1 seek:2 gainesville:1 decomposition:2 minus:3 liu:1 existing:5 j1:1 designed:1 update:7 smith:1 coughlan:3 hfj:2 math:1 node:2 iterates:1 cse:1 org:1 mathematical:2 direct:1 kettlewell:1 prove:3 doubly:1 tuy:2 introduce:3 becomes:2 distri:1 estimating:1 bounded:3 moreover:4 panel:5 interpreted:1 minimi... |
1,236 | 2,126 | Very loopy belief propagation for
unwrapping phase images
Brendan J . Freyl, Ralf Koetter2, Nemanja Petrovic 1 ,2
Probabilistic and Statistical Inference Group, University of Toronto
http://www.psi.toronto.edu
Electrical and Computer Engineering, University of Illinois at Urbana
1
2
Abstract
Since the discovery th... | 2126 |@word version:1 configuration:2 loeliger:2 interestingly:1 existing:1 assigning:1 must:4 john:1 numerical:1 koetter:6 plot:1 greedy:2 selected:3 device:3 guess:1 intelligence:1 short:2 farther:1 t2j:1 loworder:1 toronto:2 sits:1 allerton:1 height:2 along:3 incorrect:1 consists:1 introduce:1 roughly:1 freeman:6 un... |
1,237 | 2,127 | On the Concentration of Spectral
Properties
John Shawe-Taylor
Royal Holloway, University of London
N ella Cristianini
BIOwulf Technologies
john@cs.rhul.ac.uk
nello@support-vector. net
Jaz Kandola
Royal Holloway, University of London
jaz@cs.rhul.ac.uk
Abstract
We consider the problem of measuring the eigenvalues ... | 2127 |@word version:1 polynomial:1 norm:4 lodhi:1 decomposition:4 tr:1 jaz:2 written:2 readily:1 john:3 plot:1 intelligence:1 selected:1 short:1 authority:1 mcdiarmid:4 org:1 mathematical:1 become:2 symposium:1 introduce:1 indeed:3 frequently:1 ol:1 provided:3 estimating:1 xx:2 notation:1 project:1 underlying:1 eigensp... |
1,238 | 2,128 | Online Learning with Kernels
Jyrki Kivinen
Alex J. Smola
Robert C. Williamson
Research School of Information Sciences and Engineering
Australian National University
Canberra, ACT 0200
Abstract
We consider online learning in a Reproducing Kernel Hilbert Space. Our
method is computationally efficient and leads to simpl... | 2128 |@word version:1 norm:2 closure:1 pick:1 thereby:1 series:1 rkhs:1 comparing:1 yet:1 must:1 written:1 fn:1 additive:2 happen:1 cheap:1 treating:1 update:19 discrimination:1 device:1 parameterization:1 boosting:2 math:1 location:2 herbrich:1 firstly:1 alert:1 become:1 huber:5 roughly:1 themselves:1 decreasing:1 aut... |
1,239 | 2,129 | Approximate Dynamic Programming
via Linear Programming
Daniela P. de Farias
Department of Management Science and Engineering
Stanford University
Stanford, CA 94305
pucci @stanford.edu
Benjamin Van Roy
Department of Management Science and Engineering
Stanford University
Stanford, CA 94305
bvr@stanford. edu
Abstract
Th... | 2129 |@word version:1 polynomial:2 seems:2 norm:5 stronger:1 open:2 simulation:2 incurs:1 reentrant:1 contains:1 exclusively:1 staterelevance:1 current:5 dx:1 must:3 numerical:1 shape:1 civ:1 stationary:2 greedy:4 prohibitive:1 selected:3 prespecified:1 provides:1 preference:1 mathematical:1 schweitzer:2 become:2 short... |
1,240 | 213 | Connectionist Architectures for Multi-Speaker Phoneme Recognition
Connectionist Architectures/or Multi-Speaker
Phoneme Recognition
John B. Hampshire n and Alex Waibel
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213-3890
ABSTRACT
We present a number of Time-Delay Neural Network (TDNN) based
... | 213 |@word version:3 eliminating:1 glue:1 tried:1 pg:1 idl:2 fonn:1 ld:2 initial:1 series:1 exclusively:3 daniel:1 activation:2 lang:2 john:1 obsolete:1 dissertation:1 direct:2 replication:1 combine:1 burr:1 manner:1 roughly:1 multi:26 formants:1 superstructure:8 kamm:1 becomes:2 provided:1 what:1 substantially:1 diffe... |
1,241 | 2,130 | A Bayesian Model Predicts Human Parse
Preference and Reading Times in Sentence
Processing
Srini Narayanan
SRI International and ICSI Berkeley
snarayan@cs.berkeley.edu
Daniel Jurafsky
University of Colorado, Boulder
jurafsky@colorado.edu
Abstract
Narayanan and Jurafsky (1998) proposed that human language comprehension... | 2130 |@word version:1 sri:1 judgement:1 simulation:1 accounting:1 rayner:2 detective:9 reduction:1 initial:11 series:1 daniel:1 prefix:4 past:4 current:2 contextual:1 surprising:1 conjunctive:5 written:1 parsing:6 must:1 treating:2 drop:2 update:1 selected:1 leaf:1 beginning:1 node:5 complication:1 preference:10 forger... |
1,242 | 2,131 | Dynamic Time-Alignment Kernel in
Support Vector Machine
Hiroshi Shimodaira
School of Information Science,
Japan Advanced Institute of
Science and Technology
sim@jaist.ac.jp
Mitsuru Nakai
School of Information Science,
Japan Advanced Institute of
Science and Technology
mit@jaist.ac.jp
Ken-ichi Noma
School of Informati... | 2131 |@word covariance:1 eng:1 past:1 outperforms:1 contextual:1 noma:1 enables:1 n0:1 generative:5 website:1 xk:2 smith:1 org:1 direct:1 symposium:1 manner:1 little:1 classifies:1 notation:1 maximizes:1 what:1 minimizes:1 developed:2 guarantee:1 classifier:7 k2:3 schwartz:2 uk:1 omit:1 positive:1 local:1 treat:1 despi... |
1,243 | 2,132 | Duality, Geometry, and Support Vector
Regression
Jinbo Bi and Kristin P. Bennett
Department of Mathematical Sciences
Rensselaer Polytechnic Institute
Troy, NY 12180
bij2@rpi.edu, bennek@rpi.edu
Abstract
We develop an intuitive geometric framework for support vector
regression (SVR). By examining when -tubes exist, w... | 2132 |@word trial:1 version:1 middle:1 c0:1 simplifying:1 tuned:1 bhattacharyya:1 jinbo:1 rpi:2 must:1 written:1 alone:1 plane:38 sys:2 ith:1 mathematical:1 along:5 constructed:4 rc:32 become:4 prove:1 x0:24 examine:1 considering:1 becomes:2 what:1 minimizes:1 developed:1 finding:2 every:2 act:1 exactly:1 rm:2 classifi... |
1,244 | 2,133 | Covariance Kernels from Bayesian
Generative Models
Matthias Seeger
Institute for Adaptive and Neural Computation
University of Edinburgh
5 Forrest Hill, Edinburgh EH1 2QL
seeger@dai.ed.ac.uk
Abstract
We propose the framework of mutual information kernels for
learning covariance kernels, as used in Support Vector mach... | 2133 |@word middle:3 seems:1 covariance:19 thereby:1 mention:1 tr:1 moment:1 reduction:1 contains:3 score:2 outperforms:1 comparing:2 dx:1 ws1:2 cruz:1 shape:1 analytic:2 plot:5 discrimination:1 generative:4 transposition:1 herbrich:1 attack:1 simpler:2 height:1 consists:1 prove:1 fitting:2 combine:1 manner:1 inter:1 e... |
1,245 | 2,134 | Model-Free Least Squares Policy Iteration
Michail G. Lagoudakis
Department of Computer Science
Duke University
Durham, NC 27708
mgl@cs.duke.edu
Ronald Parr
Department of Computer Science
Duke University
Durham, NC 27708
parr@cs.duke.edu
Abstract
We propose a new approach to reinforcement learning which combines
leas... | 2134 |@word trial:5 repository:1 version:1 inversion:1 manageable:1 stronger:1 nd:1 reused:1 km:3 additively:1 heretofore:1 r:1 tried:1 decomposition:1 simulation:1 pick:1 incurs:1 initial:2 t7:1 ati:1 reran:1 current:1 must:1 ronald:1 belmont:1 remove:1 update:5 stationary:2 generative:1 discovering:1 intelligence:2 n... |
1,246 | 2,135 | Novel iteration schemes for the Cluster
Variation Method
Hilbert J. Kappen
Department of Biophysics
Nijmegen University
Nijmegen, the Netherlands
bert?mbfys.kun.nl
Wim Wiegerinck
Department of Biophysics
Nijmegen University
Nijmegen, the Netherlands
wimw?mbfys.kun.nl
Abstract
The Cluster Variation method is a class o... | 2135 |@word mild:1 briefly:1 nd:1 termination:1 decomposition:2 covariance:1 recursively:1 kappen:3 contains:1 surprising:1 must:2 written:1 suermondt:1 numerical:1 plot:2 reproducible:1 update:1 node:9 af3:1 sii:1 consists:1 introduce:1 pairwise:1 deteriorate:1 expected:1 mbfys:2 ol:1 freeman:1 minimizes:1 developed:1... |
1,247 | 2,136 | Generalization Performance of Some Learning
Problems in Hilbert Functional Spaces
Tong Zhang
IBM T.J. Watson Research Center
Yorktown Heights, NY 10598
tzhang@watson.ibm.com
Abstract
We investigate the generalization performance of some learning problems in Hilbert functional Spaces. We introduce a notion of converge... | 2136 |@word dietterich:1 concept:1 skip:1 implies:6 multiplier:1 norm:1 true:2 equality:1 already:1 quantity:1 correct:1 concentration:1 vc:2 elisseeff:1 self:1 gradient:1 inferior:1 tr:1 covering:2 require:1 percentile:2 yorktown:1 moment:1 mx:1 berlin:1 contains:1 generalization:8 nello:1 reason:1 cp:1 c_:5 current:1... |
1,248 | 2,137 | Relative Density Nets: A New Way to
Combine Backpropagation with HMM's
Andrew D. Brown
Department of Computer Science
University of Toronto
Toronto, Canada M5S 3G4
andy@cs.utoronto.ca
Geoffrey E. Hinton
Gatsby Unit, UCL
London, UK WCIN 3AR
hinton@gatsby.ucl.ac.uk
Abstract
Logistic units in the first hidden layer of ... | 2137 |@word version:1 advantageous:1 seems:1 simulation:1 covariance:1 initial:2 score:2 past:1 outperforms:1 current:1 comparing:3 soules:1 assigning:1 written:1 john:1 treating:1 update:1 discrimination:1 generative:3 fewer:1 intelligence:1 ith:1 filtered:1 node:1 toronto:3 oak:3 mathematical:1 constructed:1 shorthan... |
1,249 | 2,138 | Scaling of Probability-Based Optimization
Algorithms
J. L. Shapiro
Department of Computer Science University of Manchester
Manchester, M13 9PL U.K. jls@cs.man.ac.uk
Abstract
Population-based Incremental Learning is shown require very sensitive scaling of its learning rate. The learning rate must scale with
the system... | 2138 |@word trial:2 simulation:8 pbil:26 initial:2 selecting:1 genetic:5 yet:1 must:15 benign:1 remove:1 update:1 generative:1 guess:1 ith:1 stahel:1 accepting:1 lx:8 along:1 consists:1 expected:5 behavior:1 decomposed:1 decreasing:1 increasing:1 becomes:2 easiest:1 evolved:1 string:7 proposing:1 finding:3 scaled:3 uk:... |
1,250 | 2,139 | Stability-Based Model Selection
Tilman Lange, Mikio L. Braun, Volker Roth, Joachim M. Buhmann
(lange,braunm,roth,jb)@cs.uni-bonn.de
Institute of Computer Science, Dept. III,
University of Bonn
R?omerstra?e 164, 53117 Bonn, Germany
Abstract
Model selection is linked to model assessment, which is the problem of
compari... | 2139 |@word nd:3 elisseeff:1 attainable:1 myeloid:1 euclidian:1 ld:3 affymetrix:1 outperforms:1 current:1 comparing:1 recovered:1 must:3 readily:1 hofmann:1 enables:1 resampling:5 v:1 half:1 selected:1 lr:1 provides:1 ron:1 replication:2 learing:1 consists:4 walther:2 paragraph:1 inside:1 manner:1 introduce:1 pairwise:... |
1,251 | 214 | A Neural Network to Detect Homologies in Proteins
A Neural Network to Detect
Homologies in Proteins
Yoshua Bengio
Yannick Pouliot
School of Computer Science
McGill University
Montreal, Canada H3A 2A7
Department of Biology
McGill University
Montreal Neurological Institute
Samy Bengio
Patrick Agin
Departement dln... | 214 |@word trial:1 cu:1 version:1 advantageous:1 nd:5 bf:1 cha:5 hu:2 r:3 cla:1 substitution:1 score:10 heur:1 terminus:2 current:1 virus:2 nt:2 surprising:1 si:1 srd:1 designed:6 recept:1 discrimination:1 capitalizes:1 smith:2 tertiary:1 detecting:2 location:1 ohl:1 c6:1 lor:1 h4:1 beta:11 become:2 consists:1 inter:3 ... |
1,252 | 2,140 | Learning Attractor Landscapes for
Learning Motor Primitives
Auke Jan Ijspeert1,3?, Jun Nakanishi2 , and Stefan Schaal1,2
University of Southern California, Los Angeles, CA 90089-2520, USA
2
ATR Human Information Science Laboratories, Kyoto 619-0288, Japan
3
EPFL, Swiss Federal Institute of Technology, Lausanne, Switze... | 2140 |@word simulation:1 boundedness:1 ivaldi:1 initial:3 existing:1 current:3 comparing:2 distant:1 shape:4 motor:1 wanted:1 designed:1 aside:1 intelligence:1 parameterization:4 sarcos:1 location:1 five:2 differential:8 become:2 symposium:1 replication:1 qualitative:2 fitting:4 x0:2 acquired:1 expected:1 rapid:1 behav... |
1,253 | 2,141 | Global Versus Local Methods
in Nonlinear Dimensionality Reduction
Vin de Silva
Department of Mathematics,
Stanford University,
Stanford. CA 94305
silva@math.stanford.edu
Joshua B. Tenenbaum
Department of Brain and Cognitive Sciences,
Massachusetts Institute of Technology,
Cambridge. MA 02139
jbt@ai.mit.edu
Abstract
... | 2141 |@word cox:2 version:4 polynomial:1 norm:1 disk:2 seek:1 pick:1 reduction:8 inefficiency:1 configuration:4 poser:1 existing:1 recovered:2 trustworthy:1 surprising:1 written:1 cottrell:1 realistic:1 remove:1 designed:2 alone:1 generative:2 plane:1 math:1 location:1 organising:1 along:4 become:1 prove:1 combine:2 in... |
1,254 | 2,142 | Dopamine Induced Bistability Enhances
Signal Processing in Spiny Neurons
Aaron J. Gruber l ,2, Sara A. Solla2,3, and James C. Houk 2,l
Departments of Biomedical Engineeringl, Physiology2, and Physics and Astronomy3
Northwestern University, Chicago, IL 60201
{ a-gruberl, solla, j-houk }@northwestern.edu
Abstract
Singl... | 2142 |@word trial:10 seems:1 hyperpolarized:6 cm2:3 d2:1 grey:1 gradual:1 t_:1 solid:7 harder:1 initial:1 current:37 surprising:1 activation:12 yet:1 chicago:1 hyperpolarizing:5 subsequent:1 informative:1 shape:1 plasticity:1 motor:2 v:1 alone:1 cue:1 stationary:4 indicative:1 short:2 provides:5 characterization:1 node... |
1,255 | 2,143 | A Convergent Form of Approximate Policy
Iteration
Theodore J. Perkins
Department of Computer Science
University of Massachusetts Amherst
Amherst, MA 01003
perkins@cs.umass.edu
Doina Precup
School of Computer Science
McGill University
Montreal, Quebec, Canada H3A 2A7
dprecup@cs.mcgill.ca
Abstract
We study a new, model... | 2143 |@word exploitation:1 briefly:1 version:5 norm:1 twelfth:1 open:1 dekker:1 km:1 contraction:4 initial:5 uma:1 current:1 comparing:1 yet:1 readily:2 john:1 numerical:1 enables:1 update:7 stationary:5 greedy:7 fewer:1 guess:1 intelligence:1 constructed:1 become:1 prove:1 dprecup:1 expected:2 behavior:8 frequently:1 ... |
1,256 | 2,144 | Generalized 2 Linear 2 Models
Geoffrey J. Gordon
ggordon@es.emu.edu
Abstract
We introduce the Generalized 2 Linear 2 Model, a statistical estimator which combines features of nonlinear regression and factor analysis. A (GL)2M approximately decomposes a rectangular matrix
X into a simpler representation j(g(A)h(B)). H... | 2144 |@word middle:1 compression:3 nd:1 open:1 seek:1 git:1 decomposition:5 simplifying:1 u11:1 pick:2 pressure:1 tr:1 reduction:1 initial:1 contains:4 zij:1 daniel:1 current:1 must:7 written:1 takeo:1 j1:1 shape:1 drop:2 update:4 half:1 guess:1 warmuth:1 ith:1 vanishing:1 lr:2 manfred:1 provides:1 simpler:1 along:4 be... |
1,257 | 2,145 | Learning Sparse Multiscale Image
Representations
Phil Sallee
Department of Computer Science and
Center for Neuroscience, UC Davis
1544 Newton Ct.
Davis, CA 95616
sallee@cs.ucdavis.edu
Bruno A. Olshausen
Department of Psychology and
Center for Neuroscience, UC Davis
1544 Newton Ct.
Davis, CA 95616
baolshausen@ucdavis.... | 2145 |@word kolaczyk:1 compression:1 advantageous:1 solid:1 reduction:1 tapering:1 selecting:2 united:1 current:2 com:1 si:35 activation:1 additive:2 update:3 generative:1 selected:1 antoniadis:1 plane:1 sys:1 filtered:2 provides:1 sigmoidal:1 consists:1 coifman:1 wiener2:4 themselves:1 multi:1 lena:1 freeman:3 resolve... |
1,258 | 2,146 | Conditional Models on the Ranking Poset
Guy Lebanon
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
John Lafferty
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
lebanon@cs.cmu.edu
lafferty@cs.cmu.edu
Abstract
A distance-based conditional model on the ranking p... | 2146 |@word mild:1 version:2 commute:1 contains:2 score:3 series:1 o2:19 existing:2 assigning:1 john:1 partition:6 generative:3 selected:1 intelligence:1 item:20 lr:2 transposition:3 characterization:1 boosting:5 node:1 location:1 preference:1 detecting:1 cosets:3 psfrag:2 consists:2 prove:1 manner:2 indeed:1 examine:1... |
1,259 | 2,147 | Efficient Learning Equilibrium *
Ronen I. Brafman
Computer Science Department
Ben-Gurion University
Beer-Sheva, Israel
email: brafman@cs.bgu.ac.il
Moshe Tennenholtz
Computer Science Department
Stanford University
Stanford, CA 94305
e-mail: moshe@robotics.stanford.edu
Abstract
We introduce efficient learning equilibr... | 2147 |@word briefly:1 faculty:1 polynomial:8 hu:1 minus:1 shot:3 initial:1 contains:1 punishes:1 past:1 yet:1 attracted:1 must:1 gurion:2 update:2 intelligence:2 selected:7 ith:1 scie:1 mathematical:1 become:2 prove:3 consists:1 combine:1 shapley:1 introduce:1 indeed:1 expected:10 behavior:7 themselves:4 multi:7 ol:1 a... |
1,260 | 2,148 | Coulomb Classifiers: Generalizing
Support Vector Machines via an Analogy
to Electrostatic Systems
Sepp Hochreiter? , Michael C. Mozer? , and Klaus Obermayer?
?
Department of Electrical Engineering and Computer Science
Technische Universit?at Berlin, 10587 Berlin, Germany
?
Department of Computer Science
University of ... | 2148 |@word repository:3 briefly:1 cu:1 polynomial:3 simulation:2 minus:1 solid:1 shading:2 tr:1 contains:1 existing:6 ka:1 repelling:1 dx:1 attracted:2 must:5 hofmann:1 hochreit:1 treating:1 drop:1 intelligence:1 fewer:1 ith:1 dover:1 provides:2 coarse:1 location:4 herbrich:1 five:1 along:3 constructed:2 replication:1... |
1,261 | 2,149 | Identity Uncertainty and Citation Matching
Hanna Pasula, Bhaskara Marthi, Brian Milch, Stuart Russell, Ilya Shpitser
Computer Science Division, University Of California
387 Soda Hall, Berkeley, CA 94720-1776
pasula, marthi, milch, russell, ilyas@cs.berkeley.edu
Abstract
Identity uncertainty is a pervasive problem in ... | 2149 |@word cox:1 version:1 bigram:6 twelfth:1 grey:1 seitz:1 tried:1 citeseer:9 pick:1 maes:2 carry:1 initial:1 cyclic:2 contains:5 fragment:1 charniak:1 denoting:4 bibtex:1 outperforms:1 existing:1 current:2 com:1 recovered:1 assigning:1 must:5 parsing:1 written:1 kdd:2 cheap:1 remove:1 designed:2 stationary:2 genera... |
1,262 | 215 | Pulse-Firing Neural Chips for Hundreds of Neurons
PULSE-FIRING NEURAL CIDPS
FOR HUNDREDS OF NEURONS
Michael Brownlow
Lionel Tarassenko
Dept. Eng. Science
Univ. of Oxford
Oxford OX1 3PJ
Alan F. Murray
Dept. Electrical Eng.
Univ. of Edinburgh
Mayfield Road
Edinburgh EH9 3JL
Alister Hamilton
II Song Han(l)
H. Martin R... | 215 |@word cox:3 pulse:46 eng:3 solid:1 electronics:2 initial:1 contains:1 current:8 si:2 yet:2 remove:2 aside:1 fewer:1 device:2 unacceptably:1 signalling:1 accordingly:1 smith:2 authority:2 node:2 firstly:1 rc:1 direct:1 supply:2 symposium:1 prove:1 mayfield:1 inter:1 secret:1 expected:1 integrator:3 terminal:2 becom... |
1,263 | 2,150 | Bayesian Monte Carlo
Carl Edward Rasmussen and Zoubin Ghahramani
Gatsby Computational Neuroscience Unit
University College London
17 Queen Square, London WC1N 3AR, England
edward,zoubin@gatsby.ucl.ac.uk
http://www.gatsby.ucl.ac.uk
Abstract
We investigate Bayesian alternatives to classical Monte Carlo methods
for eval... | 2150 |@word middle:3 achievable:2 polynomial:1 seems:1 calculus:1 seek:1 covariance:12 dramatic:1 minus:2 solid:2 reduction:1 series:1 outperforms:2 numerical:3 happen:1 partition:1 informative:1 shape:1 designed:1 stationary:1 smith:2 normalising:1 location:1 firstly:1 hermite:2 prove:1 fitting:1 multimodality:1 theor... |
1,264 | 2,151 | Discriminative Binaural Sound Localization
Ehud Ben-Reuven and Yoram Singer
School of Computer Science & Engineering
The Hebrew University, Jerusalem 91904, Israel
udi@benreuven.com, singer@cs.huji.ac.il
Abstract
Time difference of arrival (TDOA) is commonly used to estimate the azimuth of a source in a microphone arr... | 2151 |@word proceeded:1 version:4 achievable:1 polynomial:2 duda:1 azimuthal:1 propagate:1 seek:1 covariance:2 decomposition:1 reduction:1 score:2 denoting:1 outperforms:1 err:2 current:1 com:2 comparing:1 written:1 remove:1 treating:2 localise:1 designed:1 update:8 stationary:4 plane:1 short:5 record:1 mental:1 ire:1 ... |
1,265 | 2,152 | Interpreting Neural Response Variability as
Monte Carlo Sampling of the Posterior
Patrik O. Hoyer and Aapo Hyv?arinen
Neural Networks Research Centre
Helsinki University of Technology
P.O. Box 9800, FIN-02015 HUT, Finland
http://www.cis.hut.fi/phoyer/
patrik.hoyer@hut.fi
Abstract
The responses of cortical sensory neu... | 2152 |@word neurophysiology:1 trial:8 proportion:1 seems:4 suitably:1 hyv:2 simulation:2 covariance:2 reduction:1 exclusively:1 tuned:1 denoting:1 past:1 subjective:1 current:4 contextual:1 surprising:2 culprit:1 yet:1 must:4 additive:1 plot:2 update:1 half:1 generative:1 selected:1 intelligence:1 isotropic:1 indefinit... |
1,266 | 2,153 | Prediction and Semantic Association
Thomas L. Griffiths & Mark Steyvers
Department of Psychology
Stanford University, Stanford, CA 94305-2130
{gruffydd,msteyver}@psych.stanford.edu
Abstract
We explore the consequences of viewing semantic association as
the result of attempting to predict the concepts likely to arise i... | 2153 |@word version:1 norm:8 seems:3 nonsensical:1 simulation:1 decomposition:2 paid:1 pick:1 initial:1 plentiful:1 contains:2 document:27 existing:2 current:2 contextual:1 comparing:2 z2:3 nt:1 written:2 hofmann:1 wanted:1 remove:2 designed:1 joy:1 alone:1 generative:9 cue:4 mcevoy:2 ith:2 blei:1 provides:2 location:1... |
1,267 | 2,154 | Margin-Based Algorithms
for Information Filtering
Nicol`o Cesa-Bianchi
DTI, University of Milan
via Bramante 65
26013 Crema, Italy
cesa-bianchi@dti.unimi.it
Alex Conconi
DTI, University of Milan
via Bramante 65
26013 Crema, Italy
conconi@dti.unimi.it
Claudio Gentile
CRII, Universit`a dell?Insubria
Via Ravasi, 2
2110... | 2154 |@word trial:6 determinant:1 version:3 judgement:6 thereby:1 minus:1 tr:1 document:13 ours:1 bc:2 current:1 must:1 bd:3 happen:1 remove:2 drop:1 plot:2 update:6 selected:1 warmuth:3 parametrization:1 dell:1 stopwords:1 along:1 focs:1 prove:5 introduce:2 indeed:1 expected:7 behavior:2 inspired:3 actual:1 project:1 ... |
1,268 | 2,155 | Independent Components Analysis
through Product Density Estimation
'frevor Hastie and Rob Tibshirani
Department of Statistics
Stanford University
Stanford, CA, 94305
{ hastie, tibs } @stat.stanford. edu
Abstract
We present a simple direct approach for solving the ICA problem,
using density estimation and maximum like... | 2155 |@word determinant:1 version:5 norm:1 seems:1 suitably:1 simulation:5 covariance:2 kent:2 decomposition:1 pick:1 moment:4 selecting:1 rkhs:1 si:6 negentropy:5 tilted:4 additive:2 plot:3 interpretable:1 update:2 designed:1 fewer:1 record:1 lr:3 detecting:1 simpler:1 five:1 direct:2 fitting:3 combine:1 pairwise:1 in... |
1,269 | 2,156 | Adaptive Scaling for Feature Selection in SVMs
Yves Grandvalet
Heudiasyc, UMR CNRS 6599,
Universit?e de Technologie de Compi`egne,
Compi`egne, France
Yves.Grandvalet@utc.fr
St?ephane Canu
PSI
INSA de Rouen,
St Etienne du Rouvray, France
Stephane.Canu@insa-rouen.fr
Abstract
This paper introduces an algorithm for the ... | 2156 |@word trial:3 illustrating:1 version:7 norm:3 smirnov:1 retraining:1 open:1 tried:1 initial:1 series:3 score:1 selecting:2 tuned:2 bradley:1 comparing:1 assigning:1 yet:1 visible:1 interpretable:1 update:7 discrimination:1 selected:4 egne:2 provides:2 five:1 become:1 consists:5 fitting:1 pairwise:1 mask:3 notably... |
1,270 | 2,157 | Learning to Perceive Transparency from
the Statistics of Natural Scenes
Anat Levin
Assaf Zomet
Yair Weiss
School of Computer Science and Engineering
The Hebrew University of Jerusalem
91904 Jerusalem, Israel
{alevin,zomet,yweiss}@cs.huji.ac.il
Abstract
Certain simple images are known to trigger a percept of transparen... | 2157 |@word semitransparent:2 compression:1 tried:2 decomposition:31 contains:2 discretization:3 surprising:1 yet:1 koetter:1 nian:1 plot:1 update:1 half:1 node:1 location:6 preference:1 simpler:1 surprised:1 qualitative:5 assaf:1 x0:3 indeed:2 freeman:2 automatically:1 little:2 window:1 becomes:1 israel:1 finding:2 qu... |
1,271 | 2,158 | ynamic
Causal Learning
Thomas L. Griffiths
David Danks
Institute for Human & Machine Cognition
Department of Psychology
University of West Florida
Stanford University
Stanford, CA 94305-2130
Pensacola, FL 32501
ddanks@ai.uwf.edu
gruffydd@psych.stanford.edu
Joshua B. Tenenbaum
Department of Brain & Cognitive Sciences
... | 2158 |@word trial:6 version:2 proportion:1 seems:2 holyoak:1 gradual:2 simulation:1 simplifying:1 initial:7 score:2 past:1 current:3 comparing:1 must:1 readily:1 confirming:1 asymptote:7 discrimination:1 generative:12 cue:1 device:1 parameterization:5 short:2 characterization:1 parameterizations:2 five:1 mathematical:1... |
1,272 | 2,159 | Rational Kernels
Corinna Cortes Patrick Haffner Mehryar Mohri
AT&T Labs ? Research
180 Park Avenue, Florham Park, NJ 07932, USA
corinna, haffner, mohri @research.att.com
Abstract
We introduce a general family of kernels based on weighted transducers or rational relations, rational kernels, that can be used for analy... | 2159 |@word cu:1 briefly:2 bigram:3 polynomial:1 lodhi:1 closure:1 tr:1 reduction:1 initial:4 substitution:2 series:2 att:2 existing:1 com:2 john:2 cruz:1 applica:1 christian:1 aside:1 boosting:1 readability:1 herbrich:1 denis:1 simpler:1 constructed:1 ucsc:1 become:1 scholkopf:1 transducer:49 consists:1 combine:2 insi... |
1,273 | 216 | Rule Representations in a Connectionist Chunker
Rule Representations in a Connectionist Chunker
David S. Touretzky
Gillette Elvgren
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
m
ABSTRACT
We present two connectionist architectures for chunking of symbolic
rewrite rules. One uses backpro... | 216 |@word version:2 retraining:1 bf:1 initial:4 contains:2 selecting:1 activation:4 assigning:1 si:3 must:4 bd:1 enables:1 motor:1 update:1 hts:1 cue:1 selected:1 intelligence:2 fbe:1 contribute:2 become:2 consists:1 combine:1 eleventh:2 behavioral:1 introduce:2 manner:1 acquired:1 indeed:1 behavior:4 integrator:1 det... |
1,274 | 2,160 | On the Dirichlet Prior and Bayesian
Regularization
Harald Steck
Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139
harald@ai.mit.edu
Tommi S. Jaakkola
Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139
tommi@ai.mit.edu
Abstract
A com... | 2160 |@word briefly:2 polynomial:4 stronger:1 sex:1 steck:2 diametrically:1 crucially:1 accounting:1 configuration:12 series:2 score:6 selecting:1 recovered:1 ka:1 surprising:3 written:1 must:1 ixil:2 noninformative:2 v:2 intelligence:3 leaf:1 vanishing:4 provides:1 along:3 mla:1 become:2 ik:1 manner:2 introduce:1 inde... |
1,275 | 2,161 | Analysis of Information in Speech based
on MANOVA
Sachin s. Kajarekarl and Hynek Hermansky l ,2
1 Department of Electrical and Computer Engineering
OGI School of Science and Engineering at OHSU
Beaverton, OR
2International Computer Science Institute
Berkeley, CA
{ sachin,hynek} @asp.ogi.edu
Abstract
We propose analysi... | 2161 |@word determinant:6 timefrequency:1 underline:1 relevancy:1 covariance:13 decomposition:1 carry:2 reduction:1 contains:1 past:3 current:10 comparing:4 john:1 vuuren:2 along:1 consists:2 decomposed:1 actual:2 considering:2 lowest:2 interpreted:1 proposing:1 spoken:1 temporal:26 berkeley:1 every:2 y3:1 yn:1 overest... |
1,276 | 2,162 | Real-Time Monitoring of Complex Industrial
Processes with Particle Filters
Rub?en Morales-Men?endez
Dept. of Mechatronics and Automation
ITESM campus Monterrey
Monterrey, NL M?exico
rmm@itesm.mx
Nando de Freitas and David Poole
Dept. of Computer Science
University of British Columbia
Vancouver, BC, V6T 1Z4, Canada
na... | 2162 |@word version:1 dekker:1 open:2 simulation:3 propagate:1 covariance:1 gertler:1 initial:1 selecting:1 bc:1 past:3 freitas:5 existing:1 yet:1 numerical:1 enables:1 wanted:1 update:1 resampling:1 v:3 indicative:1 mathematical:1 consists:1 overhead:1 introduce:1 expected:1 valve:1 pf:14 considering:2 campus:1 notati... |
1,277 | 2,163 | One-Class LP Classifier for Dissimilarity
Representations
1
El?zbieta P?ekalska1 , David M.J.Tax2 and Robert P.W. Duin1
Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, The Netherlands
2
Fraunhofer Institute FIRST.IDA, Kekul?str.7, D-12489 Berlin, Germany
ela@ph.tn.tudelft.nl,davidt@first.fraunhofer.de
A... | 2163 |@word dubuisson:1 norm:2 seems:1 open:2 jacob:1 thereby:2 contains:6 ida:1 shape:2 plot:5 selected:1 plane:1 xk:1 accepting:1 hypersphere:1 provides:1 org:1 simpler:1 five:1 unbounded:3 mathematical:2 constructed:1 become:2 above1:1 symposium:1 consists:2 inside:3 expected:2 behavior:3 p1:1 themselves:1 globally:... |
1,278 | 2,164 | Distance Metric Learning, with Application
to Clustering with Side-Information
Eric P. Xing, Andrew Y. Ng, Michael I. Jordan and Stuart Russell
University of California, Berkeley
Berkeley, CA 94720
epxing,ang,jordan,russell @cs.berkeley.edu
Abstract
Many algorithms rely critically on being given a good metric over ... | 2164 |@word trial:2 repository:1 briefly:1 cox:2 norm:1 seems:1 pick:1 harder:1 reduction:1 contains:1 score:2 document:3 interestingly:1 outperforms:1 surprising:1 written:1 reminiscent:1 john:1 enables:1 wanted:1 plot:2 update:1 v:1 alone:1 generative:1 provides:1 allerton:1 vxw:1 scholkopf:1 expected:1 roughly:1 exa... |
1,279 | 2,165 | Charting a Manifold
Matthew Brand
Mitsubishi Electric Research Labs
201 Broadway, Cambridge MA 02139 USA
www.merl.com/people/brand/
Abstract
We construct a nonlinear mapping from a high-dimensional sample space
to a low-dimensional vector space, effectively recovering a Cartesian
coordinate system for the manifold fr... | 2165 |@word compression:1 norm:1 seems:1 seek:1 mitsubishi:1 covariance:7 decomposition:1 datagenerating:1 thereby:1 accommodate:1 reduction:6 configuration:1 contains:2 recovered:3 com:1 assigning:1 realize:1 cottrell:2 numerical:1 partition:1 shape:2 remove:1 drop:1 plot:1 designed:1 implying:1 fewer:2 merger:3 isotr... |
1,280 | 2,166 | Adapting Codes and Embeddings for Polychotomies
Gunnar R?atsch, Alexander J. Smola
RSISE, CSL, Machine Learning Group
The Australian National University
Canberra, 0200 ACT, Australia
Gunnar.Raetsch, Alex.Smola @anu.edu.au
Sebastian Mika
Fraunhofer FIRST
Kekulestr. 7
12489 Berlin, Germany
mika@first.fhg.de
Abstract... | 2166 |@word repository:1 version:1 achievable:1 norm:3 mb1:1 polynomial:1 dekel:1 twelfth:1 grey:1 gfih:1 simplifying:1 tr:1 initial:1 contains:1 att:1 selecting:1 chervonenkis:1 ours:1 existing:1 err:1 comparing:1 com:1 assigning:4 yet:2 must:3 readily:1 drop:1 v:2 discrimination:1 intelligence:1 fewer:1 leaf:1 select... |
1,281 | 2,167 | A Model for Learning Variance Components of
Natural Images
Michael S. Lewicki?
lewicki@cnbc.cmu.edu
Yan Karklin
yan+@cs.cmu.edu
Computer Science Department &
Center for the Neural Basis of Cognition
Carnegie Mellon University
Abstract
We present a hierarchical Bayesian model for learning efficient codes of
higher-or... | 2167 |@word middle:1 simulation:1 contains:2 tuned:1 current:1 scatter:1 yet:1 realistic:1 shape:2 plot:1 half:2 inspection:1 beginning:1 ith:1 coarse:2 provides:3 location:7 fitting:1 cnbc:1 ica:8 themselves:2 multi:1 becomes:1 discover:1 linearity:3 underlying:1 maximizes:1 what:1 kind:2 extremum:1 transformation:3 s... |
1,282 | 2,168 | Rate Distortion Function in the Spin Glass State:
a Toy Model
Tatsuto Murayama and Masato Okada
Laboratory for Mathematical Neuroscience
RIKEN Brain Science Institute
Saitama, 351-0198, JAPAN
{murayama,okada}@brain.riken.go.jp
Abstract
We applied statistical mechanics to an inverse problem of linear mapping
to invest... | 2168 |@word version:2 briefly:2 achievable:2 compression:12 proportion:1 seems:1 closure:1 simulation:1 tr:2 solid:1 initial:1 hereafter:2 selecting:1 current:2 paramagnetic:3 si:4 dx:3 written:2 must:2 realize:1 numerical:3 additive:2 j1:2 partition:2 selected:2 hamiltonian:2 record:2 provides:2 ire:1 firstly:1 si1:2 ... |
1,283 | 2,169 | Feature Selection by Maximum Marginal
Diversity
Nuno Vasconcelos
Department of Electrical and Computer Engineering
University of California, San Diego
nuno@media.mit.edu
Abstract
We address the question of feature selection in the context of visual
recognition. It is shown that, besides efficient from a computational... | 2169 |@word cpe:7 illustrating:1 middle:3 achievable:1 seek:1 covariance:1 decomposition:2 initial:1 contains:1 score:3 selecting:1 past:1 outperforms:1 current:3 yet:1 dct:2 additive:1 distant:1 visible:1 subsequent:1 enables:1 plot:5 discrimination:2 alone:1 v:1 intelligence:2 haykin:1 characterization:3 coarse:1 loc... |
1,284 | 217 | VLSI Implementation of a High-Capacity Neural Network
VLSI Implementation of a High-Capacity
Neural Network Associative Memory
Tzi-Dar Chiueh 1 and Rodney M. Goodman
Department of Electrical Engineering (116-81)
California Institute of Technology
Pasadena, CA 91125, USA
ABSTRACT
In this paper we describe the VLSI de... | 217 |@word effect:4 trial:2 implemented:2 divider:1 multiplier:1 build:1 evolution:5 hence:2 direction:2 read:1 open:2 flattens:1 occurs:1 simulation:2 illustrated:1 sgn:2 said:1 pick:1 implementing:1 shot:1 simulated:4 carry:1 capacity:11 m:1 decoder:1 series:1 really:1 degrade:1 complete:1 summation:3 reason:1 perfor... |
1,285 | 2,170 | Retinal Processing Emulation in a
Programmable 2-Layer Analog Array
Processor CMOS Chip
R. Carmona, F. Jim?
enez-Garrido, R. Dom??nguez-Castro,
S. Espejo, A. Rodr??guez-V?
azquez
Instituto de Microelectr?
onica de Sevilla-CNM-CSIC
Avda. Reina Mercedes s/n 41012 Sevilla (SPAIN)
rcarmona@imse.cnm.es
Abstract
A bio-insp... | 2170 |@word cnn:14 version:2 advantageous:1 nd:2 instruction:1 solid:1 electronics:1 configuration:1 contains:1 outperforms:2 current:40 comparing:1 guez:3 must:3 written:1 john:1 realize:1 mesh:1 ota:1 designed:7 device:1 yno:1 plane:2 core:6 chua:1 propagative:1 node:8 differential:2 consists:2 pathway:1 overhead:2 i... |
1,286 | 2,171 | Reinforcement Learning to Play an Optimal
Nash Equilibrium in Team Markov Games
Xiaofeng Wang
ECE Department
Carnegie Mellon University
Pittsburgh, PA 15213
xiaofeng@andrew.cmu.edu
Tuomas Sandholm
CS Department
Carnegie Mellon University
Pittsburgh, PA 15213
sandholm@cs.cmu.edu
Abstract
Multiagent learning is a key ... | 2171 |@word exploitation:1 open:1 hu:1 accommodate:1 moment:1 initial:5 contains:2 existing:2 current:1 john:2 enables:2 update:1 v:1 stationary:11 greedy:3 half:1 selected:1 record:1 prove:8 inside:2 introduce:1 expected:9 behavior:1 nor:1 planning:1 multi:4 chi:1 terminal:9 discounted:4 decreasing:1 decomposed:1 actu... |
1,287 | 2,172 | VIBES: A Variational Inference
Engine for Bayesian Networks
Christopher M. Bishop
Microsoft Research
Cambridge, CB3 0FB, U.K.
research.microsoft.com/?cmbishop
David Spiegelhalter
MRC Biostatistics Unit
Cambridge, U.K.
david.spiegelhalter@mrc-bsu.cam.ac.uk
John Winn
Department of Physics
University of Cambridge, U.K.... | 2172 |@word illustrating:1 version:1 briefly:1 termination:2 seek:1 decomposition:1 covariance:1 pick:3 shot:2 moment:1 phy:1 ridden:1 current:2 com:1 tackling:1 yet:1 must:6 written:1 assigning:1 john:2 readily:1 visible:1 plot:1 update:11 intelligence:1 parameterization:1 xk:3 footing:1 provides:1 parameterizations:1... |
1,288 | 2,173 | Incremental Gaussian Processes
?
Joaquin Quinonero-Candela
Informatics and Mathematical Modelling
Technical University of Denmark
DK-2800 Lyngby, Denmark
jqc@imm.dtu.dk
Ole Winther
Informatics and Mathematical Modelling
Technical University of Denmark
DK-2800 Lyngby, Denmark
owi@imm.dtu.dk
Abstract
In this paper, we ... | 2173 |@word inversion:2 seems:1 simulation:2 covariance:6 thereby:1 nystr:1 initial:2 contains:4 series:7 tuned:1 document:1 current:1 loglik:1 written:2 readily:2 numerical:1 partition:1 realistic:1 happen:1 treating:1 update:6 mackey:7 v:2 intelligence:1 selected:3 greedy:1 xk:1 nnsp:1 ith:1 manfred:2 contribute:1 to... |
1,289 | 2,174 | Application of Variational Bayesian Approach to
Speech Recognition
Shinji Watanabe, Yasuhiro Minami, Atsushi Nakamura and Naonori Ueda
NTT Communication Science Laboratories, NTT Corporation
2-4, Hikaridai, Seika-cho, Soraku-gun, Kyoto, Japan
{watanabe,minami,ats,ueda}@cslab.kecl.ntt.co.jp
Abstract
In this paper, we ... | 2174 |@word briefly:1 retraining:2 covariance:3 score:2 selecting:2 hereafter:1 od:2 must:1 partition:1 enables:1 designed:1 update:1 leaf:2 fewer:1 provides:1 node:10 constructed:2 consists:2 fitting:2 roughly:1 seika:1 inappropriate:1 window:1 estimating:3 moreover:2 maximizes:1 tying:1 kind:1 spoken:2 st0:2 corporat... |
1,290 | 2,175 | The RA Scanner: Prediction of Rheumatoid
Joint Inflammation Based on Laser Imaging
1
Anton Schwaighofer1 2
TU Graz, Institute for Theoretical Computer Science
Inffeldgasse 16b, 8010 Graz, Austria
http://www.igi.tugraz.at/aschwaig
Volker Tresp, Peter Mayer
Siemens Corporate Technology, Department of Neural Computatio... | 2175 |@word mri:5 proportion:1 seems:2 nd:1 willing:1 covariance:3 outlook:1 moment:1 initial:1 liu:2 contains:1 tuned:1 outperforms:1 subjective:1 worsening:1 yet:5 numerical:1 shape:1 wanted:1 plot:1 half:1 selected:1 device:1 intelligence:1 provides:1 contribute:1 toronto:1 org:1 along:1 crossval:3 persistent:1 acqu... |
1,291 | 2,176 | Automatic Derivation of Statistical Algorithms:
The EM Family and Beyond
Alexander G. Gray
Carnegie Mellon University
agray@cs.cmu.edu
Bernd Fischer and Johann Schumann
RIACS / NASA Ames
fisch,schumann @email.arc.nasa.gov
Wray Buntine
Helsinki Institute for IT
buntine@hiit.fi
Abstract
Machine learning has reached... | 2176 |@word repository:2 version:3 achievable:1 polynomial:1 proportion:1 seek:1 decomposition:11 simplifying:1 covariance:1 concise:1 recursively:3 functor:2 necessity:1 substitution:1 contains:2 fragment:4 loc:3 initial:6 loeliger:1 document:1 reinvented:1 existing:2 current:1 yet:1 written:2 must:2 readily:1 riacs:1... |
1,292 | 2,177 | Learning Sparse Topographic Representations
with Products of Student-t Distributions
Max Welling and Geoffrey Hinton
Department of Computer Science
University of Toronto
10 King?s College Road
Toronto, M5S 3G5 Canada
welling,hinton @cs.toronto.edu
Simon Osindero
Gatsby Unit
University College London
17 Queen Square
... | 2177 |@word determinant:1 norm:7 seems:2 heuristically:1 covariance:3 contrastive:3 interestingly:1 partition:3 shape:1 analytic:1 remove:3 update:5 generative:3 leaf:1 intelligence:1 inspection:1 isotropic:2 filtered:2 provides:1 contribute:1 toronto:3 location:6 node:4 fitting:4 wiener2:1 indeed:1 ica:6 rapid:1 behav... |
1,293 | 2,178 | Neural Decoding of Cursor Motion Using a Kalman Filter
W. Wu
M. J. Black
Y. Gao
M. Serruya
A. Shaikhouni
E. Bienenstock
J. P. Donoghue
Division of Applied Mathematics, Dept. of Computer Science,
Dept. of Neuroscience, Division of Biology and Medicine,
Brown University, Providence, RI 02912
weiw... | 2178 |@word neurophysiology:3 trial:3 briefly:1 norm:1 propagate:1 carolina:1 simplifying:1 covariance:4 tr:1 solid:4 initial:2 contains:1 nordhausen:1 current:3 must:2 john:1 realistic:1 motor:13 plot:2 designed:1 update:4 generative:2 device:5 manipulandum:4 wessberg:1 plane:1 oldest:1 beginning:1 scotland:1 short:1 ... |
1,294 | 2,179 | Mismatch String Kernels for SVM Protein
Classification
Christina Leslie
Department of Computer Science
Columbia University
cleslie@cs.columbia.edu
Eleazar Eskin
Department of Computer Science
Columbia University
eeskin@cs.columbia.edu
Jason Weston
Max-Planck Institute
Tuebingen, Germany
weston@tuebingen.mpg.de
Willi... | 2179 |@word version:1 norm:2 lodhi:1 mers:7 willing:1 additively:1 gish:1 substitution:1 series:1 score:23 selecting:2 prefix:5 existing:1 current:5 comparing:1 john:1 cruz:1 designed:2 plot:13 update:2 generative:5 leaf:5 pointer:3 eskin:2 detecting:1 node:12 traverse:1 simpler:1 zhang:2 phylogenetic:1 along:1 ucsc:1 ... |
1,295 | 218 | 750
Koch, Bair, Harris, Horiuchi, Hsu and Luo
Real- Time Computer Vision and Robotics
Using Analog VLSI Circuits
Christof Koch
Wyeth Bair
John G. Harris
Timothy Horiuchi
Andrew Hsu
Jin Luo
Computation and Neural Systems Program
Caltech 216-76
Pasadena, CA 91125
ABSTRACT
The long-term goal of our laboratory is the d... | 218 |@word middle:3 version:2 rising:1 simulation:1 current:6 recovered:1 luo:4 follower:1 john:1 mesh:1 additive:2 girosi:1 shape:2 designed:2 drop:1 mounting:1 stationary:3 device:3 accordingly:1 plane:1 detecting:2 node:5 location:4 along:1 resistive:16 expected:1 behavior:1 embody:1 terminal:2 increasing:1 becomes:... |
1,296 | 2,180 | Automatic Alignment of Local Representations
Yee Whye Teh and Sam Roweis
Department of Computer Science, University of Toronto
ywteh,roweis @cs.toronto.edu
Abstract
We present an automatic alignment procedure which maps the disparate
internal representations learned by several local dimensionality reduction
experts ... | 2180 |@word norm:1 loading:2 nd:3 disk:1 r:1 crucially:1 seek:1 covariance:2 pressure:1 thereby:1 tr:1 reduction:12 ours:2 rightmost:1 must:2 additive:1 informative:1 extrapolating:1 v:1 pursued:1 fewer:1 guess:2 generative:1 retroactively:2 toronto:3 along:1 constructed:1 become:1 advocate:1 fitting:2 nor:2 globally:1... |
1,297 | 2,181 | Approximate Inference and
Protein-Folding
Chen Yanover and Yair Weiss
School of Computer Science and Engineering
The Hebrew University of J erusalem
91904 Jerusalem, Israel
{cheny,yweiss} @cs.huji.ac.it
Abstract
Side-chain prediction is an important subtask in the protein-folding
problem. We show that finding a minim... | 2181 |@word version:1 seems:1 open:1 seek:2 tried:1 bn:1 soare:1 reduction:1 configuration:15 contains:2 series:1 denoting:1 existing:1 clash:2 comparing:2 yet:2 assigning:1 joaquim:1 realistic:1 numerical:1 koetter:1 wanted:1 update:3 bart:1 intelligence:1 selected:1 dunbrack:3 ith:1 node:12 org:1 simpler:1 ray:1 acqu... |
1,298 | 2,182 | Recovering Articulated Model Topology from Observed
Rigid Motion
Leonid Taycher, John W. Fisher III, and Trevor Darrell
Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA, 02139
{lodrion, fisher, trevor}@ai.mit.edu
Abstract
Accurate representation of articulated motion is a challen... | 2182 |@word decomposition:2 covariance:1 necessity:1 configuration:1 liu:1 daniel:1 brien:1 recovered:7 si:1 must:2 luis:1 written:1 john:3 ronald:1 takeo:1 academia:1 v:1 intelligence:1 selected:1 parameterization:3 plane:5 node:10 location:2 mtj:1 zhang:1 mathematical:1 c2:9 direct:1 brostow:1 yuan:1 fitting:1 pairwi... |
1,299 | 2,183 | Half-Lives of EigenFlows for Spectral Clustering
Chakra Chennubhotla & Allan D. Jepson
Department of Computer Science, University of Toronto, Canada M5S 3H5
chakra,jepson @cs.toronto.edu
Abstract
Using a Markov chain perspective of spectral clustering we present an
algorithm to automatically find the number of stab... | 2183 |@word stronger:1 grey:1 seek:1 linearized:1 tried:1 decomposition:1 simplifying:1 pg:1 brightness:1 pick:2 initial:6 fragment:3 selecting:1 bc:1 current:5 must:5 numerical:1 partition:3 remove:1 plot:2 update:1 stationary:5 half:10 generative:1 fewer:1 selected:2 intelligence:1 filtered:1 provides:4 node:2 toront... |
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