Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
2,000 | 2,817 | Bayesian Sets
Zoubin Ghahramani? and Katherine A. Heller
Gatsby Computational Neuroscience Unit
University College London
London WC1N 3AR, U.K.
{zoubin,heller}@gatsby.ucl.ac.uk
Abstract
Inspired by ?Google? Sets?, we consider the problem of retrieving items
from a concept or cluster, given a query consisting of a few... | 2817 |@word covariance:1 mammal:1 detective:1 mention:1 score:27 document:2 animated:2 current:1 com:2 discretization:1 comparing:1 yet:2 written:4 romance:3 realistic:1 informative:1 assuage:1 remove:1 website:2 item:27 marine:1 caveat:1 provides:3 along:2 beta:1 retrieving:3 consists:2 wild:1 behavioral:1 frequently:... |
2,001 | 2,818 | A Domain Decomposition Method for
Fast Manifold Learning
Hongyuan Zha
Department of Computer Science
Pennsylvania State University
University Park, PA 16802
zha@cse.psu.edu
Zhenyue Zhang
Department of Mathematics
Zhejiang University, Yuquan Campus,
Hangzhou, 310027, P. R. China
zyzhang@zju.edu.cn
Abstract
We propose... | 2818 |@word version:1 briefly:1 norm:1 seems:1 glue:7 open:1 a02:1 decomposition:15 mention:1 carry:1 reduction:4 initial:2 liu:1 contains:2 necessity:1 ati:1 recovered:4 comparing:1 si:2 readily:1 numerical:5 partition:9 plot:2 fund:1 leaf:1 xk:3 parametrization:2 smith:1 math:1 cse:1 successive:9 simpler:1 zhang:3 t0... |
2,002 | 2,819 | A Bayesian Spatial Scan Statistic
Daniel B. Neill
Andrew W. Moore
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
{neill,awm}@cs.cmu.edu
Gregory F. Cooper
Center for Biomedical Informatics
University of Pittsburgh
Pittsburgh, PA 15213
gfc@cbmi.pitt.edu
Abstract
We propose a new Bayesian me... | 2819 |@word proportion:5 stronger:1 gfc:1 anthrax:6 shading:1 harder:1 moment:1 series:1 score:11 tist:1 daniel:2 past:6 outperforms:2 comparing:1 si:15 yet:1 must:12 tot:9 realistic:1 informative:2 shape:3 biosurveillance:1 interpretable:1 discovering:1 denison:1 indicative:2 record:1 detecting:5 location:8 attack:3 f... |
2,003 | 282 | 474
Mel and Koch
Sigma-Pi Learning:
On Radial Basis Functions and Cortical
Associative Learning
Christof Koch
Bartlett W. Mel
Computation and Neural Systems Program
Caltech, 216-76
Pasadena, CA 91125
ABSTRACT
The goal in this work has been to identify the neuronal elements
of the cortical column that are most likel... | 282 |@word briefly:1 maz:1 hippocampus:1 mimick:1 open:1 decomposition:1 phy:1 disparity:1 hereafter:1 tuned:1 lapedes:2 current:1 activation:2 must:2 john:1 mesh:1 realistic:1 plasticity:2 girosi:3 motor:1 rinzel:1 v:1 hewes:1 math:1 contribute:1 location:1 along:1 ouput:1 consists:1 pathway:4 poised:1 presumed:1 spin... |
2,004 | 2,820 | Group and Topic Discovery
from Relations and Their Attributes
Xuerui Wang, Natasha Mohanty, Andrew McCallum
Department of Computer Science
University of Massachusetts
Amherst, MA 01003
{xuerui,nmohanty,mccallum}@cs.umass.edu
Abstract
We present a probabilistic generative model of entity relationships and
their attribu... | 2820 |@word middle:1 suitably:1 cyprus:1 git:1 pg:1 yea:1 uma:1 united:3 rart:2 com:2 comparing:2 protection:1 si:1 assigning:1 john:1 indonesia:2 treating:1 malaysia:1 alone:2 generative:2 discovering:3 fewer:1 intelligence:3 accordingly:1 mccallum:3 beginning:1 chile:2 coleman:1 core:1 record:4 liberal:1 org:1 mathem... |
2,005 | 2,821 | Generalization to Unseen Cases
Teemu Roos
Helsinki Institute for Information Technology
P.O.Box 68, 00014 Univ. of Helsinki, Finland
?
Peter Grunwald
CWI, P.O.Box 94079, 1090 GB,
Amsterdam, The Netherlands
teemu.roos@cs.helsinki.fi
pdg@cwi.nl
Petri Myllym?aki
Helsinki Institute for Information Technology
P.O.Box 68... | 2821 |@word repository:3 version:1 stronger:5 duda:1 nd:1 tedious:1 existing:5 current:1 com:1 surprising:1 yet:4 must:2 realistic:1 n0:1 alone:1 implying:1 selected:1 item:2 xk:1 short:1 tirri:2 psfrag:1 prove:1 ex2:1 manner:1 expected:1 behavior:1 themselves:1 frequently:1 growing:1 discretized:1 approval:1 little:2 ... |
2,006 | 2,822 | Bayesian Surprise Attracts Human Attention
Laurent Itti
Department of Computer Science
University of Southern California
Los Angeles, California 90089-2520, USA
itti@usc.edu
Pierre Baldi
Department of Computer Science
University of California, Irvine
Irvine, California 92697-3425, USA
pfbaldi@ics.uci.edu
Abstract
Th... | 2822 |@word cox:1 compression:1 hippocampus:1 stronger:2 open:1 instruction:1 d2:4 sensed:1 pick:1 carry:4 moment:1 vigorously:1 contains:1 score:13 document:1 subjective:3 outperforms:1 bradley:1 current:2 comparing:2 rowan:1 surprising:24 savage:1 jaynes:1 yet:4 assigning:1 attracted:4 must:1 readily:1 evans:1 iscan:... |
2,007 | 2,823 | Extracting Dynamical Structure Embedded in
Neural Activity
Byron M. Yu1 , Afsheen Afshar1,2 , Gopal Santhanam1 ,
Stephen I. Ryu1,3 , Krishna V. Shenoy1,4
1
Department of Electrical Engineering, 2 School of Medicine, 3 Department of
Neurosurgery, 4 Neurosciences Program, Stanford University, Stanford, CA 94305
{byronyu,... | 2823 |@word trial:49 steen:1 instruction:2 simulation:2 seek:1 rhesus:1 simplifying:1 covariance:4 p0:3 gradual:2 reduction:1 initial:2 configuration:1 tuned:1 reaction:2 current:2 ka:1 nt:1 activation:2 yet:1 must:3 numerical:1 underly:1 motor:18 drop:1 designed:1 update:1 cue:14 rts:4 ith:2 smith:1 filtered:1 straddl... |
2,008 | 2,824 | A Criterion for the Convergence of Learning
with Spike Timing Dependent Plasticity
Robert Legenstein and Wolfgang Maass
Institute for Theoretical Computer Science
Technische Universitaet Graz
A-8010 Graz, Austria
{legi,maass}@igi.tugraz.at
Abstract
We investigate under what conditions a neuron can learn by experiment... | 2824 |@word trial:9 proceeded:1 version:1 open:1 simulation:4 pulse:2 simplifying:1 minus:1 solid:3 initial:9 substitution:1 efficacy:3 current:9 si:2 reminiscent:2 realistic:5 plasticity:8 enables:1 update:2 alone:1 half:6 short:2 psth:1 simpler:1 mathematical:2 constructed:3 differential:1 pairing:1 manner:1 behavior... |
2,009 | 2,825 | Fast Online Policy Gradient Learning
with SMD Gain Vector Adaptation
Nicol N. Schraudolph Jin Yu Douglas Aberdeen
Statistical Machine Learning, National ICT Australia, Canberra
{nic.schraudolph,douglas.aberdeen}@nicta.com.au
Abstract
Reinforcement learning by direct policy gradient estimation is attractive
in theory b... | 2825 |@word mild:1 version:1 inversion:1 jacob:1 commute:1 arti:4 solid:1 harder:1 reduction:1 tuned:1 past:1 outperforms:3 current:2 com:1 assigning:1 must:3 john:1 realistic:1 additive:1 plot:2 update:9 stationary:1 greedy:1 fewer:1 intelligence:3 warmuth:1 cult:2 scotland:1 record:1 firstly:1 along:1 direct:2 become... |
2,010 | 2,826 | Temporal Abstraction
in Temporal-difference Networks
Richard S. Sutton, Eddie J. Rafols, Anna Koop
Department of Computing Science
University of Alberta
Edmonton, AB, Canada T6G 2E8
{sutton,erafols,anna}@cs.ualberta.ca
Abstract
We present a generalization of temporal-difference networks to include temporally abstract... | 2826 |@word trial:1 middle:1 version:1 briefly:1 open:4 termination:11 sensed:1 ytn:1 accommodate:1 moment:1 initial:1 series:1 o2:1 current:2 must:3 written:1 john:1 subsequent:1 happen:3 enables:1 designed:1 update:2 intelligence:5 selected:2 ith:1 short:1 record:1 colored:2 provides:1 node:41 location:2 successive:1... |
2,011 | 2,827 | Learning vehicular dynamics, with application
to modeling helicopters
Pieter Abbeel
Computer Science Dept.
Stanford University
Stanford, CA 94305
Varun Ganapathi
Computer Science Dept.
Stanford University
Stanford, CA 94305
Andrew Y. Ng
Computer Science Dept.
Stanford University
Stanford, CA 94305
Abstract
We consi... | 2827 |@word aircraft:1 d2:3 pieter:1 simulation:18 thereby:1 solid:1 blade:4 carry:2 initial:1 cyclic:1 contains:1 series:1 longitudinal:2 outperforms:3 current:4 discretization:1 ka:1 yet:1 must:2 thrust:4 plot:3 update:6 greedy:3 parameterization:8 core:1 short:1 gx:2 height:1 rc:1 along:1 c2:3 symposium:1 expected:3... |
2,012 | 2,828 | Asymptotics of Gaussian Regularized
Least-Squares
Ross A. Lippert
M.I.T., Department of Mathematics
77 Massachusetts Avenue
Cambridge, MA 02139-4307
lippert@math.mit.edu
Ryan M. Rifkin
Honda Research Institute USA, Inc.
145 Tremont Street
Boston, MA 02111
rrifkin@honda-ri.com
Abstract
We consider regularized least-s... | 2828 |@word mild:1 version:1 achievable:1 polynomial:24 norm:1 nd:1 suitably:1 seems:1 tr:1 series:2 rkhs:2 bc:9 fgt:3 recovered:1 com:1 must:1 plot:1 drop:1 progressively:1 intelligence:1 yi1:1 ith:1 provides:1 math:1 honda:2 successive:4 si1:1 mathematical:1 along:2 prove:2 fitting:1 x0:17 behavior:5 colspan:2 freque... |
2,013 | 2,829 | Two view learning: SVM-2K, Theory and
Practice
Jason D.R. Farquhar
jdrf99r@ecs.soton.ac.uk
David R. Hardoon
drh@ecs.soton.ac.uk
Hongying Meng
hongying@cs.york.ac.uk
John Shawe-Taylor
jst@ecs.soton.ac.uk
Sandor Szedmak
ss03v@ecs.soton.ac.uk
School of Electronics and Computer Science,
University of Southampton, South... | 2829 |@word version:1 seems:2 norm:6 gjb:1 tr:7 reduction:1 moment:1 electronics:1 contains:2 document:2 ka:7 must:1 john:3 realistic:1 subsequent:1 confirming:1 xrce:1 half:2 item:1 org:1 along:1 combine:2 introductory:1 indeed:1 expected:1 frequently:3 examine:1 voc:4 encouraging:2 considering:2 hardoon:2 provided:3 ... |
2,014 | 283 | 340
Carter, Rudolph and Nucci
Operational Fault Tolerance
of CMAC Networks
Michael J. Carter
Franklin J. Rudolph
Adam J. Nucci
Intelligent Structures Group
Department of Electrical and Computer Engineering
University of New Hampshire
Durham, NH 03824-3591
ABSTRACT
The performance sensitivity of Albus' CMAC network... | 283 |@word proceeded:1 faculty:1 open:2 simulation:1 propagate:1 outlook:1 franklin:1 current:1 yet:1 attracted:1 readily:1 must:1 distant:1 benign:2 motor:2 designed:1 update:1 hash:3 v:1 selected:2 device:2 location:10 successive:1 symposium:1 incorrect:1 consists:1 prove:1 manner:1 mask:1 indeed:2 behavior:1 decreas... |
2,015 | 2,830 | Saliency Based on Information Maximization
Neil D.B. Bruce and John K. Tsotsos
Department of Computer Science and Centre for Vision Research
York University, Toronto, ON, M2N 5X8
{neil,tsotsos}@cs . yorku. c a
Abstract
A model of bottom-up overt attention is proposed based on the principle
of maximizing information s... | 2830 |@word instrumental:1 instruction:1 simulation:1 rgb:3 decomposition:1 solid:1 accommodate:1 configuration:1 efficacy:3 selecting:2 existing:3 current:1 contextual:1 comparing:1 john:1 chicago:2 informative:1 drop:2 mounting:1 selected:2 device:1 item:3 coughlan:1 provides:2 contribute:1 toronto:1 location:3 yarbu... |
2,016 | 2,831 | Faster Rates in Regression via Active Learning
Rui Castro
Rice University
Houston, TX 77005
rcastro@rice.edu
Rebecca Willett
University of Wisconsin
Madison, WI 53706
willett@cae.wisc.edu
Robert Nowak
University of Wisconsin
Madison, WI 53706
nowak@engr.wisc.edu
Abstract
This paper presents a rigorous statistical an... | 2831 |@word illustrating:1 polynomial:1 achievable:1 norm:1 seems:1 bf:3 d2:1 propagate:1 arti:1 reduction:1 initial:2 series:1 fragment:10 selecting:1 contains:3 precluding:1 outperforms:1 existing:1 past:1 must:1 partition:9 happen:1 lengthen:1 pertinent:1 n0:1 half:1 selected:2 leaf:8 intelligence:1 xk:1 ith:1 coars... |
2,017 | 2,832 | Layered Dynamic Textures
Antoni B. Chan
Nuno Vasconcelos
Department of Electrical and Computer Engineering
University of California, San Diego
abchan@ucsd.edu, nuno@ece.ucsd.edu
Abstract
A dynamic texture is a video model that treats a video as a sample from
a spatio-temporal stochastic process, specifically a linear... | 2832 |@word middle:2 briefly:1 stronger:4 confirms:1 bvt:2 tr:1 initial:3 configuration:2 contains:1 efficacy:3 series:2 existing:1 current:4 surprising:1 assigning:2 must:2 realistic:1 partition:1 enables:3 update:2 cue:1 leaf:1 generative:6 intelligence:2 accordingly:1 regressive:1 iterates:1 location:1 simpler:1 fav... |
2,018 | 2,833 | Walk-Sum Interpretation and Analysis of
Gaussian Belief Propagation
Jason K. Johnson, Dmitry M. Malioutov and Alan S. Willsky
Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Cambridge, MA 02139
{jasonj,dmm,willsky}@mit.edu
Abstract
This paper presents a new framework ba... | 2833 |@word eliminating:2 covariance:3 thereby:1 tr:1 recursively:1 series:9 remove:1 plot:4 v:4 prohibitive:1 leaf:2 fa9550:1 walksummable:8 provides:1 node:55 along:1 become:1 consists:1 prove:1 redefine:1 x0:2 pairwise:6 indeed:1 behavior:1 freeman:2 automatically:1 increasing:3 becomes:3 begin:2 notation:1 suffice:... |
2,019 | 2,834 | Cyclic Equilibria in Markov Games
Martin Zinkevich and Amy Greenwald
Department of Computer Science
Brown University
Providence, RI 02912
{maz,amy}@cs.brown.edu
Michael L. Littman
Department of Computer Science
Rutgers, The State University of NJ
Piscataway, NJ 08854?8019
mlittman@cs.rutgers.edu
Abstract
Although va... | 2834 |@word briefly:1 maz:1 polynomial:1 middle:1 open:2 hu:2 tat:1 q1:3 cyclic:38 selecting:1 united:1 past:1 existing:4 current:2 surprising:1 plot:1 update:7 stationary:38 half:1 core:1 along:1 constructed:1 prove:2 shapley:2 interscience:1 theoretically:1 ingenuity:2 behavior:1 nor:1 planning:2 multi:1 terminal:1 b... |
2,020 | 2,835 | Fast Gaussian Process Regression
using KD-Trees
Yirong Shen
Electrical Engineering Dept.
Stanford University
Stanford, CA 94305
Andrew Y. Ng
Computer Science Dept.
Stanford University
Stanford, CA 94305
Matthias Seeger
Computer Science Div.
UC Berkeley
Berkeley, CA 94720
Abstract
The computation required for Gaussi... | 2835 |@word version:1 middle:1 polynomial:1 briefly:1 nd:16 humidity:4 twelfth:1 d2:2 crucially:1 covariance:3 incurs:1 tr:1 recursively:2 contains:3 selecting:1 freitas:1 current:1 beygelzimer:1 lang:1 written:2 john:1 ronald:1 partition:2 informative:1 isotropic:13 beginning:1 farther:1 record:1 manfred:1 provides:1 ... |
2,021 | 2,836 | Optimizing spatio-temporal filters for improving
Brain-Computer Interfacing
Guido Dornhege1, Benjamin Blankertz1 , Matthias Krauledat1,3 ,
Florian Losch2 , Gabriel Curio2 and Klaus-Robert M?ller1,3
1
Fraunhofer FIRST.IDA, Kekul?str. 7, 12 489 Berlin, Germany
2 Campus Benjamin Franklin, Charit? University Medicine Berl... | 2836 |@word blankertz1:1 trial:22 nervenkr:2 stronger:2 norm:1 nd:1 open:1 accounting:1 covariance:1 eng:8 decomposition:1 analoguous:1 contains:1 exclusively:1 denoting:1 franklin:1 outperforms:3 past:1 current:1 ida:1 si:7 visible:2 chicago:1 motor:11 plot:5 discrimination:5 v:7 half:2 selected:1 device:3 pacemaker:1... |
2,022 | 2,837 | An Application of Markov Random Fields to
Range Sensing
James Diebel and Sebastian Thrun
Stanford AI Lab
Stanford University, Stanford, CA 94305
Abstract
This paper describes a highly successful application of MRFs to the problem of generating high-resolution range images. A new generation of
range sensors combines t... | 2837 |@word rgb:1 brightness:1 shot:1 carry:1 configuration:1 contains:1 shum:1 existing:1 recovered:1 yet:2 mesh:7 visible:1 numerical:1 partition:1 shape:3 enables:1 visibility:1 designed:1 update:1 alone:1 cue:1 device:3 provides:4 coarse:1 node:8 height:1 along:3 constructed:1 become:1 supply:1 combine:1 falsely:1 ... |
2,023 | 2,838 | Pattern Recognition from One Example by
Chopping
Franc?ois Fleuret
CVLAB/LCN ? EPFL
Lausanne, Switzerland
francois.fleuret@epfl.ch
Gilles Blanchard?
Fraunhofer FIRST
Berlin, Germany
blanchar@first.fhg.de
Abstract
We investigate the learning of the appearance of an object from a single
image of it. Instead of using a... | 2838 |@word version:1 chopping:19 shot:1 contains:3 selecting:1 comparing:2 nt:2 si:4 yet:1 assigning:1 realistic:1 visible:1 informative:1 s21:1 remove:1 designed:2 progressively:1 v:1 half:4 intelligence:1 guess:1 accordingly:1 provides:1 location:5 si1:8 c2:22 direct:3 become:1 consists:3 combine:2 introduce:1 excel... |
2,024 | 2,839 | Improved Risk Tail Bounds
for On-Line Algorithms *
Nicolo Cesa-Bianchi
DSI, Universita di Milano
via Comelico 39
20135 Milano, Italy
cesa-bianchi@dsi.unimi.it
Claudio Gentile
DICOM, Universita dell'Insubria
via Mazzini 5
21100 Varese, Italy
gentile@dsi.unimi.it
Abstract
We prove the strongest known bound for the ris... | 2839 |@word trial:3 nd:1 tedious:1 open:2 pick:1 contains:1 selecting:1 current:1 must:1 numerical:1 selected:3 short:1 lr:1 provides:1 zhang:3 dell:1 along:1 dicom:1 prove:4 introduce:5 expected:2 indeed:1 little:1 increasing:1 notation:3 bounded:4 underlying:2 argmin:3 substantially:1 certainty:1 nf:11 sip:1 control:... |
2,025 | 284 | 248
MalkofT
A Neural Network for Real-Time Signal Processing
Donald B. Malkoff
General Electric / Advanced Technology Laboratories
Moorestown Corporate Center
Building 145-2, Route 38
Moorestown, NJ 08057
ABSTRACT
This paper describes a neural network algorithm that (1) performs
temporal pattern matching in real-ti... | 284 |@word version:5 suitably:1 heuristically:1 initial:2 series:3 current:1 must:1 subsequent:1 realistic:1 numerical:3 enables:1 intelligence:2 item:1 regressive:1 infrastructure:1 characterization:1 node:30 contribute:2 become:1 symposium:1 consists:1 behavior:1 multi:1 hague:2 automatically:1 actual:1 window:5 tota... |
2,026 | 2,840 | Active Bidirectional Coupling in a Cochlear Chip
Bo Wen and Kwabena Boahen
Department of Bioengineering
University of Pennsylvania
Philadelphia, PA 19104
{wenbo,boahen}@seas.upenn.edu
Abstract
We present a novel cochlear model implemented in analog very large
scale integration (VLSI) technology that emulates nonlinea... | 2840 |@word version:3 compression:2 simulation:1 contraction:1 pressure:5 solid:1 tuned:1 longitudinal:5 existing:1 current:17 comparing:1 realize:2 tilted:1 realistic:1 partition:2 plot:1 half:5 device:1 tone:4 reciprocal:1 short:1 provides:1 detecting:1 location:3 sits:2 five:1 height:1 mathematical:5 along:1 nodal:2... |
2,027 | 2,841 | Predicting EMG Data from M1 Neurons
with Variational Bayesian Least Squares
Jo-Anne Ting1 , Aaron D?Souza1
Kenji Yamamoto3 , Toshinori Yoshioka2 , Donna Ho?man3
Shinji Kakei4 , Lauren Sergio6 , John Kalaska5
Mitsuo Kawato2 , Peter Strick3 , Stefan Schaal1,2
1
Comp. Science & Neuroscience, U.of S. California, Los Ange... | 2841 |@word neurophysiology:1 collinearity:2 version:3 inversion:5 seems:1 nd:1 arti:2 eld:1 shot:2 reduction:2 series:2 longitudinal:1 subjective:1 current:2 anne:1 surprising:1 activation:1 bd:2 john:1 numerical:2 additive:2 midway:1 motor:4 drop:2 interpretable:1 update:8 fund:1 device:3 manipulandum:2 wessberg:1 sm... |
2,028 | 2,842 | How fast to work: Response vigor, motivation
and tonic dopamine
1
Yael Niv1,2 Nathaniel D. Daw2 Peter Dayan2
ICNC, Hebrew University, Jerusalem 2 Gatsby Computational Neuroscience Unit, UCL
yaelniv@alice.nc.huji.ac.il {daw,dayan}@gatsby.ucl.ac.uk
Abstract
Reinforcement learning models have long promised to unify comp... | 2842 |@word middle:1 seems:2 replicate:1 nd:2 instrumental:1 open:4 proportionality:1 simulation:2 crucially:1 pressure:1 incurs:3 thereby:4 minus:1 solid:4 accommodate:1 harder:3 vigorously:6 rearing:1 subjective:2 existing:4 current:1 neurophys:1 must:1 john:1 realistic:1 enables:1 overriding:1 selected:4 manipulandu... |
2,029 | 2,843 | Transfer learning for text classification
Chuong B. Do
Computer Science Department
Stanford University
Stanford, CA 94305
Andrew Y. Ng
Computer Science Department
Stanford University
Stanford, CA 94305
Abstract
Linear text classification algorithms work by computing an inner product between a test document vector an... | 2843 |@word multitask:1 trial:1 version:2 proportion:3 open:1 heuristically:2 pieter:1 xtest:10 pick:2 contains:1 score:5 uma:1 selecting:1 karger:3 tuned:1 document:26 fa8750:1 outperforms:4 existing:2 horvitz:1 com:1 assigning:3 tackling:1 must:2 numerical:4 happen:1 kdd:1 designed:2 v:4 generative:1 fewer:2 selected... |
2,030 | 2,844 | A PAC-Bayes approach to the Set
Covering Machine
Fran?
cois Laviolette, Mario Marchand
IFT-GLO, Universit?e Laval
Sainte-Foy (QC) Canada, G1K-7P4
given name.surname@ift.ulaval.ca
Mohak Shah
SITE, University of Ottawa
Ottawa, Ont. Canada,K1N-6N5
mshah@site.uottawa.ca
Abstract
We design a new learning algorithm for th... | 2844 |@word middle:1 version:5 compression:11 km:1 r:12 current:1 si:12 must:1 john:2 remove:3 eab:1 greedy:8 half:1 intelligence:1 provides:2 location:2 simpler:1 consists:3 introduce:2 deteriorate:1 sacrifice:2 indeed:1 examine:1 decreasing:1 ont:1 equipped:1 haberman:1 totally:2 provided:1 moreover:2 notation:1 gqi:... |
2,031 | 2,845 | Response Analysis of Neuronal Population with
Synaptic Depression
Wentao Huang
Institute of Intelligent Information
Processing, Xidian University,
Xi'an 710071, China
wthuang@mail.xidian.edu.cn
Licheng Jiao
Institute of Intelligent Information
Processing, Xidian University,
Xi'an 710071, China
lchjiao@mail.xidian.edu... | 2845 |@word especially:1 normalized:1 signi:3 come:1 regehr:1 evolution:4 m1k:5 dua:1 thick:1 q0:10 d2:3 spike:1 simulation:2 stochastic:1 deal:2 dependence:3 silberberg:1 transient:2 q1:1 suprathreshold:1 eld:1 sin:1 minus:1 solid:2 sci:1 djv:3 m:1 f1:1 generalized:1 mail:4 presynaptic:1 dmk:2 neocortical:2 longitudin... |
2,032 | 2,846 | Benchmarking Non-Parametric Statistical Tests
Mikaela Keller?
IDIAP Research Institute
1920 Martigny
Switzerland
mkeller@idiap.ch
Samy Bengio
IDIAP Research Institute
1920 Martigny
Switzerland
bengio@idiap.ch
Siew Yeung Wong
IDIAP Research Institute
1920 Martigny
Switzerland
sywong@idiap.ch
Abstract
Although non-pa... | 2846 |@word seems:1 replicate:1 proportion:27 tried:1 bn:2 initial:2 contains:1 selecting:1 tuned:1 document:15 comparing:11 surprising:1 assigning:1 written:1 must:1 stemming:1 enables:1 aside:3 v:12 ecir:1 davison:1 tomorrow:1 inside:1 excellence:1 expected:2 indeed:2 behavior:7 multi:1 decomposed:1 actual:1 notation... |
2,033 | 2,847 | Off-Road Obstacle Avoidance through
End-to-End Learning
Yann LeCun
Courant Institute of Mathematical Sciences
New York University,
New York, NY 10004, USA
http://yann.lecun.com
Jan Ben
Net-Scale Technologies
Morganville, NJ 07751, USA
Eric Cosatto
NEC Laboratories,
Princeton, NJ 08540
Urs Muller
Net-Scale Technologi... | 2847 |@word middle:1 agc:2 seems:2 crucially:1 brightness:1 solid:1 contains:4 exclusively:1 disparity:3 document:1 past:1 current:3 com:2 comparing:1 activation:1 reminiscent:1 realistic:1 informative:1 drop:1 plot:1 grass:1 cue:1 half:2 intelligence:2 plane:6 short:1 record:2 quantized:1 location:3 successive:3 simpl... |
2,034 | 2,848 | Nearest Neighbor Based Feature Selection for
Regression and its Application to Neural Activity
Amir Navot12
Lavi Shpigelman12 Naftali Tishby12 Eilon Vaadia23
School of computer Science and Engineering
2
Interdisciplinary Center for Neural Computation
3
Dept. of Physiology, Hadassah Medical School
The Hebrew Universit... | 2848 |@word trial:5 version:4 eliminating:1 norm:3 elisseeff:1 pick:1 infogain:8 harder:1 wrapper:3 score:11 selecting:3 genetic:1 tuned:3 outperforms:1 current:2 comparing:2 yet:2 assigning:1 john:1 informative:2 motor:7 plot:2 v:3 alone:2 half:1 selected:8 greedy:1 intelligence:2 amir:1 accordingly:1 device:1 direct:... |
2,035 | 2,849 | Phase Synchrony Rate for the Recognition of
Motor Imagery in Brain-Computer Interface
Le Song
Nation ICT Australia
School of Information Technologies
The University of Sydney
NSW 2006, Australia
lesong@it.usyd.edu.au
Evian Gordon
Brain Resource Company
Scientific Chair, Brain Dynamics Center
Westmead Hospitial
NSW 20... | 2849 |@word trial:18 proportion:1 elly:2 eng:6 nsw:2 electronics:1 series:2 contains:1 tuned:2 outperforms:1 current:1 com:1 discretization:1 anterior:1 dx:1 distant:1 motor:19 cue:1 fewer:1 half:1 selected:4 short:1 filtered:2 mental:3 provides:1 boosting:1 location:2 zhang:1 five:1 qualitative:1 combine:1 manner:1 in... |
2,036 | 285 | 76
Kammen, Koch and Holmes
Collective Oscillations in the
Visual Cortex
Daniel Kammen & Christof Koch
Philip J. H oImes
Computation and Neural Systems
Dept. of Theor. & Applied Mechanics
Caltech 216-76
Cornell University
Pasadena, CA 91125
Ithaca, NY 14853
ABSTRACT
The firing patterns of populations of cells in the... | 285 |@word trial:4 version:1 middle:2 wiesel:2 excited:9 initial:6 series:1 contains:1 daniel:1 terminus:1 tuned:1 neurophys:1 surprising:1 intriguing:1 must:3 readily:1 additive:1 realistic:2 wx:1 stationary:1 math:2 location:2 mathematical:1 along:2 differential:1 consists:1 prove:1 dragged:1 pathway:1 olfactory:1 ma... |
2,037 | 2,850 | Maximum Margin Semi-Supervised
Learning for Structured Variables
Y. Altun, D. McAllester
TTI at Chicago
Chicago, IL 60637
altun,mcallester@tti-c.org
M. Belkin
Department of Computer Science
University of Chicago
Chicago, IL 60637
misha@cs.uchicago.edu
Abstract
Many real-world classification problems involve the pred... | 2850 |@word h:4 polynomial:2 norm:2 p0:40 klk:1 reduction:2 liu:1 score:4 fragment:3 document:1 comparing:1 attracted:1 parsing:1 written:2 realize:1 john:1 chicago:4 additive:1 partition:1 hofmann:3 v:1 leaf:1 fewer:1 mccallum:1 boosting:1 node:5 org:1 zhang:1 scholkopf:1 prove:1 combine:1 inter:5 expected:1 examine:1... |
2,038 | 2,851 | Learning Minimum Volume Sets
Clayton Scott
Statistics Department
Rice University
Houston, TX 77005
cscott@rice.edu
Robert Nowak
Electrical and Computer Engineering
University of Wisconsin
Madison, WI 53706
nowak@engr.wisc.edu
Abstract
Given a probability measure P and a reference measure ?, one is
often interested i... | 2851 |@word version:1 middle:2 polynomial:1 seems:1 solid:1 minmax:1 pub:1 chervonenkis:1 ours:2 prefix:2 scovel:2 must:1 additive:1 zeger:1 numerical:1 partition:11 detecting:1 direct:1 walther:1 combine:1 recognizable:1 multimodality:1 introduce:3 indeed:1 frequently:1 inspired:2 spherical:1 automatically:1 gov:1 act... |
2,039 | 2,852 | Spiking Inputs to a Winner-take-all Network
Matthias Oster and Shih-Chii Liu
Institute of Neuroinformatics
University of Zurich and ETH Zurich
Winterthurerstrasse 190
CH-8057 Zurich, Switzerland
{mao,shih}@ini.phys.ethz.ch
Abstract
Recurrent networks that perform a winner-take-all computation have
been studied extens... | 2852 |@word grey:1 p0:2 electronics:1 liu:7 configuration:2 efficacy:7 initial:2 tuned:1 current:3 analysed:1 happen:1 remove:1 plot:2 discrimination:2 intelligence:1 selected:7 device:1 beginning:1 inter:2 rapid:1 behavior:1 becomes:1 xx:1 circuit:3 israel:1 substantially:1 finding:1 winterthurerstrasse:1 every:3 ti:1... |
2,040 | 2,853 | Separation of Music Signals by Harmonic
Structure Modeling
Yun-Gang Zhang
Department of Automation
Tsinghua University
Beijing 100084, China
zyg00@mails.tsinghua.edu.cn
Chang-Shui Zhang
Department of Automation
Tsinghua University
Beijing 100084, China
zcs@mail.tsinghua.edu.cn
Abstract
Separation of music signals is... | 2853 |@word version:1 eliminating:1 open:1 takuya:1 contains:1 accompaniment:1 document:1 subjective:3 existing:3 scatter:1 must:1 shape:2 remove:1 polyphonic:3 selected:2 accordingly:1 experiment3:2 detecting:2 beauchamp:2 firstly:2 zhang:4 five:1 become:2 symposium:1 consists:3 poli:1 swets:1 ica:3 roughly:1 multi:14... |
2,041 | 2,854 | Non-Gaussian Component Analysis: a
Semi-parametric Framework for Linear
Dimension Reduction
1,4
?
G. Blanchard1 , M. Sugiyama1,2 , M. Kawanabe1 , V. Spokoiny3 , K.-R. Muller
1
Fraunhofer FIRST.IDA, Kekul?estr. 7, 12489 Berlin, Germany
Dept. of CS, Tokyo Inst. of Tech., 2-12-1, O-okayama, Meguro-ku, Tokyo, 152-8552, Jap... | 2854 |@word mild:2 cox:2 version:1 stronger:1 norm:3 underline:1 nd:1 hyv:2 simulation:2 covariance:8 decomposition:3 reduction:9 contains:1 selecting:1 okayama:1 existing:1 current:1 ida:1 discretization:1 dx:4 must:1 numerical:4 realistic:3 informative:3 visible:2 plot:2 alone:3 generative:1 selected:1 weierstrass:2 ... |
2,042 | 2,855 | Modeling Neuronal Interactivity using Dynamic
Bayesian Networks
Lei Zhang?,?, Dimitris Samaras?, Nelly Alia-Klein?, Nora Volkow?, Rita Goldstein?
? Computer Science Department, SUNY at Stony Brook, Stony Brook, NY
? Medical Department, Brookhaven National Laboratory, Upton, NY
Abstract
Functional Magnetic Resonance I... | 2855 |@word mri:1 briefly:1 cingulate:2 sex:1 instruction:1 t1r:2 pressed:1 solid:1 initial:4 series:1 exclusively:2 score:7 bc:3 subjective:1 current:4 comparing:2 anterior:2 activation:5 si:1 stony:2 must:1 oxygenation:1 medial:1 v:2 generative:1 greedy:1 advancement:1 selected:8 ith:1 short:1 provides:7 contribute:2... |
2,043 | 2,856 | Computing the Solution Path for the
Regularized Support Vector Regression
Ji Zhu?
Department of Statistics
University of Michigan
Ann Arbor, MI 48109
jizhu@umich.edu
Lacey Gunter
Department of Statistics
University of Michigan
Ann Arbor, MI 48109
lgunter@umich.edu
Abstract
In this paper we derive an algorithm that co... | 2856 |@word mild:1 seems:1 simulation:6 arti:1 accommodate:1 reduction:1 initial:2 series:2 selecting:2 rkhs:2 bhattacharyya:1 past:2 written:2 must:5 numerical:4 additive:2 informative:1 girosi:1 plot:1 xk:2 along:2 become:2 shpigelman:1 examine:1 inspired:1 relying:1 automatically:1 kohlmorgen:1 elbow:14 linearity:1 ... |
2,044 | 2,857 | Sparse Gaussian Processes using Pseudo-inputs
Edward Snelson
Zoubin Ghahramani
Gatsby Computational Neuroscience Unit
University College London
17 Queen Square, London WC1N 3AR, UK
{snelson,zoubin}@gatsby.ucl.ac.uk
Abstract
We present a new Gaussian process (GP) regression model whose covariance is parameterized by... | 2857 |@word inversion:3 seems:1 tedious:1 km:10 tried:2 covariance:14 concise:1 nystr:1 harder:1 catastrophically:1 moment:1 initial:4 series:1 selecting:2 initialisation:2 rightmost:1 outperforms:1 bd:1 must:1 happen:1 informative:1 enables:1 cheap:3 remove:1 plot:5 designed:1 bart:5 stationary:4 greedy:3 prohibitive:... |
2,045 | 2,858 | Integrate-and-Fire models with adaptation are
good enough: predicting spike times under
random current injection
Renaud Jolivet?
Brain Mind Institute, EPFL
CH-1015 Lausanne, Switzerland
renaud.jolivet@epfl.ch
Alexander Rauch
MPI for Biological Cybernetics
D-72012 T?ubingen, Germany
alexander.rauch@tuebingen.mpg.de
?
... | 2858 |@word open:1 grey:1 integrative:1 simulation:1 solid:2 mainen:2 past:1 current:14 comparing:1 written:2 fn:1 physiol:2 numerical:1 realistic:1 plasticity:1 happen:1 interspike:2 shape:2 plot:3 drop:2 stationary:1 short:2 provides:1 firstly:1 simpler:1 mathematical:1 constructed:1 direct:1 hopf:1 consists:1 pathwa... |
2,046 | 2,859 | Learning in Silicon: Timing is Everything
John V. Arthur and Kwabena Boahen
Department of Bioengineering
University of Pennsylvania
Philadelphia, PA 19104
{jarthur, boahen}@seas.upenn.edu
Abstract
We describe a neuromorphic chip that uses binary synapses with spike
timing-dependent plasticity (STDP) to learn stimulat... | 2859 |@word trial:2 version:1 hippocampus:9 proportion:1 mehta:1 pulse:2 excited:2 thereby:3 solid:2 shot:1 initial:1 efficacy:5 disparity:1 exclusively:1 current:14 comparing:1 activation:2 john:1 recasting:1 plasticity:10 hypothesize:1 designed:3 implying:1 half:2 selected:2 device:2 fewer:3 sram:8 realizing:1 rensha... |
2,047 | 286 | Neural Network Visualization
NEURAL NETWORK VISUALIZATION
Jakub Wejchert
Gerald Tesauro
IB M Research
T.J. Watson Research
Center
Yorktown Heights
NY 10598
ABSTRACT
We have developed graphics to visualize static and dynamic information in layered neural network learning systems. Emphasis was
placed on creating new v... | 286 |@word grey:1 simulation:7 carry:1 initial:2 configuration:5 written:1 must:1 plot:1 designed:1 half:1 accordingly:1 plane:2 colored:1 node:10 height:1 introduce:2 expected:1 roughly:1 behavior:1 multi:2 simulator:3 decreasing:1 little:1 window:14 totally:2 project:1 emerging:1 developed:1 temporal:2 quantitative:1... |
2,048 | 2,860 | Generalization error bounds for classifiers
trained with interdependent data
Nicolas Usunier, Massih-Reza Amini, Patrick Gallinari
Department of Computer Science, University of Paris VI
8, rue du Capitaine Scott, 75015 Paris France
{usunier, amini, gallinari}@poleia.lip6.fr
Abstract
In this paper we propose a general... | 2860 |@word middle:1 version:2 relevancy:1 decomposition:3 document:1 janson:1 v:1 selected:1 rudin:1 xk:1 provides:1 boosting:2 herbrich:1 preference:1 mcdiarmid:5 zhang:1 dn:1 direct:2 prove:3 excellence:1 indeed:1 expected:2 considering:2 provided:1 notation:7 moreover:3 bounded:3 kind:2 minimizes:2 finding:1 guaran... |
2,049 | 2,861 | Kernels for gene regulatory regions
Jean-Philippe Vert
Geostatistics Center
Ecole des Mines de Paris - ParisTech
Jean-Philippe.Vert@ensmp.fr
Robert Thurman
Division of Medical Genetics
University of Washington
rthurman@u.washington.edu
William Stafford Noble
Department of Genome Sciences
University of Washington
nob... | 2861 |@word proportion:1 norm:1 mers:15 tried:1 accounting:1 recapitulate:1 pressure:1 moment:1 series:2 score:5 united:1 ecole:1 denoting:1 tuned:2 comparing:1 si:17 john:1 hypothesize:1 designed:1 gist:2 progressively:1 v:1 selected:2 parameterization:1 agglomerating:1 ith:1 short:7 chiang:1 eskin:1 provides:1 parame... |
2,050 | 2,862 | A Matching Pursuit Approach to
Sparse Gaussian Process Regression
S. Sathiya Keerthi
Yahoo! Research Labs
210 S. DeLacey Avenue
Pasadena, CA 91105
selvarak@yahoo-inc.com
Wei Chu
Gatsby Computational Neuroscience Unit
University College London
London, WC1N 3AR, UK
chuwei@gatsby.ucl.ac.uk
Abstract
In this paper we pro... | 2862 |@word trial:1 middle:1 manageable:1 nd:2 crucially:1 tried:1 covariance:5 nystr:2 delgado:1 initial:1 contains:1 score:9 interestingly:2 outperforms:1 current:5 com:1 chu:1 attracted:1 written:1 numerical:2 partition:2 informative:1 cheap:1 kyb:1 plot:5 greedy:6 prohibitive:1 selected:8 beginning:1 short:1 provid... |
2,051 | 2,863 | From Lasso regression to Feature vector
machine
1
Fan Li1 , Yiming Yang1 and Eric P. Xing1,2
LTI and CALD, School of Computer Science, Carnegie Mellon University,
Pittsburgh, PA USA 15213
{hustlf,yiming,epxing}@cs.cmu.edu
2
Abstract
Lasso regression tends to assign zero weights to most irrelevant or redundant featur... | 2863 |@word version:1 polynomial:1 compression:1 norm:3 tried:1 harder:1 tuned:1 existing:1 written:1 must:1 john:1 interpretable:1 v:2 selected:3 plane:11 xk:1 ith:1 inside:2 introduce:6 indeed:1 encouraging:3 little:1 begin:1 estimating:1 linearity:3 moreover:1 xx:5 what:1 pursue:1 developed:3 finding:1 transformatio... |
2,052 | 2,864 | Principles of real-time computing with feedback
applied to cortical microcircuit models
Wolfgang Maass, Prashant Joshi
Institute for Theoretical Computer Science
Technische Universitaet Graz
A-8010 Graz, Austria
maass,joshi@igi.tugraz.at
Eduardo D. Sontag
Department of Mathematics
Rutgers, The State University of New ... | 2864 |@word trial:6 suitably:1 open:1 pulse:1 simulation:3 pipa:1 thereby:2 shading:1 carry:2 series:1 orponen:1 existing:1 current:9 surprising:1 activation:3 written:1 subsequent:2 additive:2 realistic:3 plasticity:2 confirming:1 enables:2 fund:1 cue:2 fewer:1 short:1 filtered:1 provides:2 mathematical:2 burst:2 cons... |
2,053 | 2,865 | Fast Krylov Methods for N-Body Learning
Yang Wang
School of Computing Science
Simon Fraser University
ywang12@cs.sfu.ca
Nando de Freitas
Department of Computer Science
University of British Columbia
nando@cs.ubc.ca
Dustin Lang
Department of Computer Science
University of Toronto
dalang@cs.ubc.ca
Maryam Mahdaviani
D... | 2865 |@word kondor:1 polynomial:1 norm:2 simulation:1 covariance:2 decomposition:3 mention:1 tr:1 recursively:1 reduction:6 fgt:2 freitas:3 wd:1 lang:2 written:1 numerical:4 partition:1 klaas:1 plot:1 xk:1 provides:1 toronto:1 successive:1 attack:3 scholkopf:1 fitting:1 inside:1 introduce:1 rapid:1 behavior:1 multi:1 c... |
2,054 | 2,866 | Conditional Visual Tracking in Kernel Space
Cristian Sminchisescu1,2,3 Atul Kanujia3 Zhiguo Li3 Dimitris Metaxas3
1
TTI-C, 1497 East 50th Street, Chicago, IL, 60637, USA
2
University of Toronto, Department of Computer Science, Canada
3
Rutgers University, Department of Computer Science, USA
crismin@cs.toronto.edu, {ka... | 2866 |@word middle:1 version:1 polynomial:1 proportion:1 nd:1 triggs:2 azimuthal:1 atul:1 covariance:2 jacob:1 elisseeff:1 recursively:2 reduction:5 initial:1 efficacy:1 outperforms:2 current:1 distant:2 chicago:1 informative:1 shape:5 alone:2 generative:5 selected:2 greedy:1 isard:1 mccallum:1 filtered:4 toronto:2 suc... |
2,055 | 2,867 | A Theoretical Analysis of Robust Coding over
Noisy Overcomplete Channels
Eizaburo Doi1 , Doru C. Balcan2 , & Michael S. Lewicki1,2
1
Center for the Neural Basis of Cognition,
2
Computer Science Department,
Carnegie Mellon University, Pittsburgh, PA 15213
{edoi,dbalcan,lewicki}@cnbc.cmu.edu
Abstract
Biological sensory... | 2867 |@word cu:2 compression:1 norm:2 open:1 covariance:3 decomposition:1 dramatic:2 tr:4 reduction:3 configuration:5 z2:1 yet:2 must:1 additive:1 numerical:2 wx:1 plot:1 implying:1 accordingly:2 plane:2 isotropic:5 parametrization:1 smith:1 provides:2 along:14 c2:9 replication:2 kak22:1 prove:4 manner:1 cnbc:1 ica:7 b... |
2,056 | 2,868 | Bayesian model learning in
human visual perception
Gerg?o Orb?an
Collegium Budapest
Institute for Advanced Study
2 Szenth?aroms?ag utca, Budapest,
1014 Hungary
ogergo@colbud.hu
Richard N. Aslin
Department of Brain and Cognitive
Sciences, Center for Visual Science
University of Rochester
Rochester, New York 14627, USA
... | 2868 |@word trial:7 version:1 sharpens:1 open:1 instruction:1 hu:1 seek:3 crucially:1 simulation:3 zolt:1 united:1 ours:2 ording:1 activation:3 yet:1 subsequent:1 wx:3 shape:29 alone:2 generative:17 fewer:1 pursued:1 intelligence:1 accordingly:1 dover:1 core:1 provides:1 preference:3 sigmoidal:1 simpler:3 mathematical:... |
2,057 | 2,869 | Beyond Gaussian Processes: On the
Distributions of Infinite Networks
Ricky Der
Department of Mathematics
University of Pennsylvania
Philadelphia, PA 19104
rickyder@math.upenn.edu
Daniel Lee
Department of Electrical Engineering
University of Pennsylvania
Philadelphia, PA 19104
ddlee@seas.upenn.edu
Abstract
A general a... | 2869 |@word version:2 clts:1 suitably:2 closure:2 r:2 covariance:4 pg:1 moment:1 subordinating:1 selecting:1 daniel:1 si:1 yet:1 scatter:1 must:2 readily:1 john:1 fn:13 subsequent:1 thrust:1 plot:1 leaf:1 xk:1 filtered:1 sudden:1 math:1 parameterizations:1 evy:5 mathematical:1 c2:1 constructed:1 become:1 consists:2 fit... |
2,058 | 287 | 52
Grajski and Merzenich
Neural Network Simulation
of
Somatosensory Representational Plasticity
Kamil A. Grajski
Ford Aerospace
San Jose, CA 95161-9041
kamil@wd11.fac.ford.com
Michael M. Merzenich
Coleman Laboratories
UC San Francisco
San Francisco, CA 94143
ABSTRACT
The brain represents the skin surface as a topo... | 287 |@word middle:1 eliminating:2 hyperpolarized:1 disk:2 additively:1 simulation:12 mammal:1 shading:1 reduction:1 initial:1 contains:1 tuned:1 ka:1 com:1 neurophys:2 activation:3 john:1 subsequent:1 distant:1 plasticity:16 fund:1 alone:3 tenn:1 fewer:1 patterning:2 stationary:1 coleman:2 compo:1 node:3 location:4 con... |
2,059 | 2,870 | Mixture Modeling by Affinity Propagation
Brendan J. Frey and Delbert Dueck
University of Toronto
Software and demonstrations available at www.psi.toronto.edu
Abstract
Clustering is a fundamental problem in machine learning and has been
approached in many ways. Two general and quite different approaches
include iterat... | 2870 |@word trial:2 proportion:1 seek:1 tried:3 propagate:2 decomposition:1 p0:6 tr:1 recursively:4 initial:1 configuration:1 score:4 selecting:1 loeliger:1 denoting:1 document:1 interestingly:1 current:2 ka:1 si:10 assigning:1 written:1 realistic:1 additive:3 enables:1 plot:1 update:17 greedy:10 leaf:1 selected:3 spec... |
2,060 | 2,871 | Value Function Approximation with Diffusion
Wavelets and Laplacian Eigenfunctions
Sridhar Mahadevan
Department of Computer Science
University of Massachusetts
Amherst, MA 01003
mahadeva@cs.umass.edu
Mauro Maggioni
Program in Applied Mathematics
Department of Mathematics
Yale University
New Haven, CT 06511
mauro.maggi... | 2871 |@word trial:3 middle:2 compression:2 polynomial:7 norm:7 nd:1 open:1 r:1 decomposition:1 rj0:1 nystr:2 tr:2 initial:3 contains:1 uma:1 rpi:15 written:1 belmont:1 partition:1 progressively:1 intelligence:2 beginning:1 ith:2 characterization:1 coarse:1 node:1 wxy:1 compressible:1 mathematical:1 along:1 direct:3 inc... |
2,061 | 2,872 | Efficient Estimation of OOMs
Herbert Jaeger, Mingjie Zhao, Andreas Kolling
International University Bremen
Bremen, Germany
h.jaeger|m.zhao|a.kolling@iu-bremen.de
Abstract
A standard method to obtain stochastic models for symbolic time series
is to train state-emitting hidden Markov models (SE-HMMs) with the
Baum-Welch... | 2872 |@word faculty:2 version:6 suitably:1 c0:5 termination:1 heuristically:1 additively:1 simulation:1 crucially:1 incurs:1 solid:1 initial:6 series:2 recovered:2 current:2 surprising:1 must:1 numerical:3 cheap:1 gv:3 drop:2 update:3 stationary:3 indicative:3 short:2 haykin:1 pointer:1 coarse:1 math:2 node:2 mathemati... |
2,062 | 2,873 | Modeling Memory Transfer and Savings in
Cerebellar Motor Learning
Naoki Masuda
RIKEN Brain Science Institute
Wako, Saitama 351-0198, Japan
masuda@brain.riken.jp
Shun-ichi Amari
RIKEN Brain Science Institute
Wako, Saitama 351-0198, Japan
amari@brain.riken.jp
Abstract
There is a long-standing controversy on the site o... | 2873 |@word private:1 longterm:1 seems:1 simulation:5 gradual:1 lobe:1 solid:2 carry:1 initial:1 necessity:1 wako:2 katoh:1 must:2 olive:1 written:1 vor:14 physiol:1 subsequent:1 numerical:11 wx:2 plasticity:10 periodically:1 enables:1 motor:16 depict:1 progressively:1 boyden:1 accordingly:1 plane:2 short:9 provides:1 ... |
2,063 | 2,874 | Noise and the two-thirds power law
Uri Maoz1,2,3 , Elon Portugaly3 , Tamar Flash2 and Yair Weiss3,1
1
Interdisciplinary Center for Neural Computation, The Hebrew University of Jerusalem, Edmond
Safra Campus, Givat Ram Jerusalem 91904, Israel; 2 Department of Computer Science and Applied
Mathematics, The Weizmann Ins... | 2874 |@word neurophysiology:2 seems:1 proportion:1 simulation:9 covariance:1 lacquaniti:1 series:7 denoting:2 elliptical:1 comparing:1 yet:2 must:3 conforming:1 shape:2 analytic:1 motor:18 remove:1 plot:3 alone:2 half:1 cue:1 device:4 eshkol:1 plane:4 colored:1 filtered:2 traverse:1 five:1 along:4 direct:1 become:1 dif... |
2,064 | 2,875 | Top-Down Control of Visual Attention:
A Rational Account
Michael C. Mozer
Dept. of Comp. Science &
Institute of Cog. Science
University of Colorado
Boulder, CO 80309 USA
Michael Shettel
Dept. of Comp. Science &
Institute of Cog. Science
University of Colorado
Boulder, CO 80309 USA
Shaun Vecera
Dept. of Psychology
Uni... | 2875 |@word trial:55 manageable:1 stronger:2 instruction:1 simulation:11 brightness:1 initial:1 configuration:3 contains:2 practiced:1 tuned:1 past:6 reaction:6 current:7 surprising:1 activation:5 must:1 slanted:1 subsequent:2 shape:1 asymptote:2 designed:1 update:2 discrimination:1 stationary:1 cue:4 discovering:1 sel... |
2,065 | 2,876 | Measuring Shared Information and Coordinated
Activity in Neuronal Networks
Kristina Lisa Klinkner
Cosma Rohilla Shalizi
Marcelo F. Camperi
Statistics Department
University of Michigan
Ann Arbor, MI 48109
kshalizi@umich.edu
Statistics Department
Carnegie Mellon University
Pittsburgh, PA 15213
cshalizi@stat.cmu.edu
... | 2876 |@word mild:1 version:1 briefly:1 seems:1 nd:1 haslinger:1 seek:1 simulation:3 covariance:2 pick:1 klk:1 accommodate:1 recursively:1 disappointingly:1 liu:5 series:11 configuration:1 contains:1 past:1 existing:1 current:7 comparing:1 ka:1 activation:1 must:3 subsequent:1 visible:1 informative:1 designed:1 concert:... |
2,066 | 2,877 | TD(0) Leads to Better Policies than
Approximate Value Iteration
Benjamin Van Roy
Management Science and Engineering and Electrical Engineering
Stanford University
Stanford, CA 94305
bvr@stanford.edu
Abstract
We consider approximate value iteration with a parameterized approximator in which the state space is partitio... | 2877 |@word version:5 manageable:1 norm:6 stronger:1 twelfth:1 open:4 simulation:1 contraction:3 dramatic:1 accommodate:2 carry:1 current:1 intriguing:1 must:2 john:1 belmont:1 happen:1 partition:19 stationary:1 greedy:14 selected:4 coarse:1 mathematical:1 differential:2 become:3 introduce:1 x0:1 expected:2 discounted:... |
2,067 | 2,878 | An Approximate Inference Approach for the
PCA Reconstruction Error
Manfred Opper
Electronics and Computer Science
University of Southampton
Southampton, SO17 1BJ
mo@ecs.soton.ac.uk
Abstract
The problem of computing a resample estimate for the reconstruction
error in PCA is reformulated as an inference problem with the... | 2878 |@word determinant:2 eliminating:1 c0:1 simulation:6 covariance:4 p0:5 thereby:1 tr:9 outlook:1 carry:2 moment:2 electronics:1 series:1 imaginary:1 si:4 perturbative:4 dx:13 must:2 written:1 numerical:2 happen:1 visible:1 partition:8 additive:1 analytic:2 enables:1 subsequent:1 n0:4 resampling:26 stationary:3 plan... |
2,068 | 2,879 | Dynamic Social Network Analysis using Latent
Space Models
Purnamrita Sarkar, Andrew W. Moore
Center for Automated Learning and Discovery
Carnegie Mellon University
Pittsburgh, PA 15213
(psarkar,awm)@cs.cmu.edu
Abstract
This paper explores two aspects of social network modeling. First,
we generalize a successful stati... | 2879 |@word version:4 norm:1 accounting:1 decomposition:1 citeseer:1 pick:1 euclidian:2 harder:1 initial:1 configuration:1 score:11 current:3 comparing:1 montaner:1 lang:1 must:2 john:1 refines:1 plot:2 treating:1 update:3 v:1 intelligence:1 guess:1 ith:2 colored:1 math:1 node:2 location:7 toronto:1 firstly:1 five:1 al... |
2,069 | 288 | 160
Tang
Analytic Solutions to the Formation of
Feature-Analysing Cells of a Three-Layer
Feedforward Visual Information
Processing Neural Net
D.S. Tang
Microelectronics and Computer Technology Corporation
3500 West Balcones Center Drive
Austin, TX 78759-6509
email: tang@mcc.com
ABSTRACT
Analytic solutions to the in... | 288 |@word briefly:1 multiplier:1 evolution:5 analytically:2 direction:2 hence:1 spatially:1 symmetric:7 receptive:10 simulation:2 propagate:1 consecutively:1 stochastic:2 white:1 exhibit:1 self:1 violates:1 tr:2 distance:1 lateral:1 sci:1 configuration:2 evident:1 theoretic:1 gexp:1 summation:1 complete:1 insert:1 cp:... |
2,070 | 2,880 | Fusion of Similarity Data in Clustering
Tilman Lange and Joachim M. Buhmann
(langet,jbuhmann)@inf.ethz.ch
Institute of Computational Science, Dept. of Computer Sience,
ETH Zurich, Switzerland
Abstract
Fusing multiple information sources can yield significant benefits to successfully accomplish learning tasks. Many st... | 2880 |@word norm:1 yi0:1 confirms:1 seek:1 decomposition:1 nystr:3 boundedness:1 initial:1 liu:1 uncovered:1 score:1 selecting:3 exclusively:1 daniel:1 document:1 past:1 current:1 recovered:1 jaynes:1 realize:1 partition:2 informative:1 hofmann:1 enables:2 plot:2 update:1 zik:1 alone:1 selected:1 iterates:1 boosting:2 ... |
2,071 | 2,881 | Large-Scale Multiclass Transduction
Thomas G?artner
Fraunhofer AIS.KD, 53754 Sankt Augustin, Thomas.Gaertner@ais.fraunhofer.de
Quoc V. Le, Simon Burton, Alex J. Smola, Vishy Vishwanathan
Statistical Machine Learning Program, NICTA and ANU, Canberra, ACT
{Quoc.Le, Simon.Burton, Alex.Smola, SVN.Vishwanathan}@nicta.com.a... | 2881 |@word kondor:1 version:1 polynomial:1 norm:2 nd:1 tried:1 covariance:2 tr:11 carry:1 reduction:1 initial:1 contains:1 series:1 ours:1 document:2 outperforms:1 existing:1 com:1 yet:1 john:1 numerical:1 partition:1 cheap:2 remove:1 update:2 intelligence:1 parameterization:2 warmuth:1 core:1 mathematical:2 direct:1 ... |
2,072 | 2,882 | Infinite Latent Feature Models
and the Indian Buffet Process
Thomas L. Griffiths
Cognitive and Linguistic Sciences
Brown University, Providence RI
tom griffiths@brown.edu
Zoubin Ghahramani
Gatsby Computational Neuroscience Unit
University College London, London
zoubin@gatsby.ucl.ac.uk
Abstract
We define a probability... | 2882 |@word version:1 proportion:1 covariance:3 pick:1 tr:1 contains:1 cellphone:1 ecole:1 kmk:1 current:1 recovered:1 fn:1 partition:8 plot:2 zik:14 generative:1 pursued:1 ith:6 provides:1 location:1 unbounded:3 along:1 c2:1 direct:1 beta:2 ik:2 welldefined:1 nor:1 multi:1 metaphor:1 cardinality:1 provided:2 bounded:1... |
2,073 | 2,883 | Online Discovery and Learning
of Predictive State Representations
Peter McCracken
Department of Computing Science
University of Alberta
Edmonton, Alberta
Canada, T6G 2E8
peterm@cs.ualberta.ca
Michael Bowling
Department of Computing Science
University of Alberta
Edmonton, Alberta
Canada, T6G 2E8
bowling@cs.ualberta.ca... | 2883 |@word trial:4 repository:1 version:2 open:1 initial:2 necessity:1 contains:1 series:1 selecting:1 prefix:1 past:1 existing:1 o2:4 current:15 must:5 happen:2 wiewiora:1 plot:2 succeeding:1 update:5 stationary:2 intelligence:1 discovering:3 selected:9 parameterization:1 oldest:1 beginning:1 core:42 constructed:1 di... |
2,074 | 2,884 | Selecting Landmark Points for Sparse Manifold
Learning
J. G. Silva
ISEL/ISR
R. Conselheiro Emidio Navarro
1950.062 Lisbon, Portugal
jgs@isel.ipl.pt
J. S. Marques
IST/ISR
Av. Rovisco Pais
1949-001 Lisbon, Portugal
jsm@isr.ist.utl.pt
J. M. Lemos
INESC-ID/IST
R. Alves Redol, 9
1000-029 Lisbon, Portugal
jlml@inesc-id.pt... | 2884 |@word mild:1 version:1 briefly:1 middle:1 norm:4 open:2 accounting:1 covariance:3 harder:1 reduction:5 necessity:1 contains:1 selecting:5 existing:2 yet:1 scatter:1 must:4 readily:2 numerical:1 designed:1 plot:2 generative:1 prohibitive:1 selected:1 parameterization:6 isotropic:1 huo:1 diffeomorphically:1 provide... |
2,075 | 2,885 | Neural mechanisms of contrast dependent
receptive field size in V1
Jim Wielaard and Paul Sajda
Department of Biomedical Engineering
Columbia University
New York, NY 10027
(djw21, ps629)@columbia.edu
Abstract
Based on a large scale spiking neuron model of the input layers 4C? and ? of
macaque, we identify neural mecha... | 2885 |@word trial:1 seems:3 additively:1 r:37 simulation:3 p0:1 extrastriate:1 configuration:5 foveal:1 rog:4 current:2 blank:1 comparing:1 si:1 scatter:1 yet:1 extraclassical:1 numerical:1 realistic:6 eleven:1 designed:1 leaf:1 indicative:1 isotropic:3 cavanaugh:2 short:2 provides:1 location:1 constructed:2 profound:1... |
2,076 | 2,886 | Efficient estimation of hidden state dynamics
from spike trains
M?arton G. Dan?oczy
Inst. for Theoretical Biology
Humboldt University, Berlin
Invalidenstr. 43
10115 Berlin, Germany
m.danoczy@biologie.hu-berlin.de
Richard H. R. Hahnloser
Inst. for Neuroinformatics
UNIZH / ETHZ
Winterthurerstrasse 190
8057 Zurich, Swit... | 2886 |@word middle:1 compression:1 hippocampus:1 seems:1 smirnov:1 unif:1 hu:1 bn:5 covariance:1 contains:1 series:1 denoting:1 past:2 current:3 ka:1 analysed:1 numerical:1 happen:1 interspike:2 motor:1 plot:1 generative:2 selected:1 nervous:1 accordingly:1 inspection:1 haykin:1 burst:1 ik:1 dan:1 fitting:2 inside:1 ra... |
2,077 | 2,887 | Context as Filtering
Daichi Mochihashi
ATR, Spoken Language Communication
Research Laboratories
Hikaridai 2-2-2, Keihanna Science City
Kyoto, Japan
daichi.mochihashi@atr.jp
Yuji Matsumoto
Graduate School of Information Science
Nara Institute of Science and Technology
Takayama 8916-5, Ikoma City
Nara, Japan
matsu@is.n... | 2887 |@word trial:1 briefly:1 compression:1 d2:1 tr:1 recursively:1 takuya:1 reduction:2 contains:1 selecting:2 daniel:1 document:15 freitas:1 current:2 contextual:5 nt:2 wd:1 com:1 must:2 written:1 distant:1 subsequent:1 hofmann:2 zacks:1 designed:1 plot:2 update:6 stationary:1 generative:6 selected:2 accordingly:1 mc... |
2,078 | 2,888 | Large-scale biophysical parameter estimation in
single neurons via constrained linear regression
Misha B. Ahrens? , Quentin J.M. Huys? , Liam Paninski
Gatsby Computational Neuroscience Unit
University College London
{ahrens, qhuys, liam}@gatsby.ucl.ac.uk
Abstract
Our understanding of the input-output function of sing... | 2888 |@word middle:2 version:1 open:2 proportionality:1 grey:1 cm2:3 decomposition:3 covariance:2 dramatic:1 thereby:2 solid:1 harder:1 carry:1 moment:3 initial:1 contains:1 mainen:1 denoting:1 current:13 nt:1 written:1 must:1 physiol:1 distant:1 visible:1 realistic:1 numerical:1 v:1 selected:1 realism:1 supplying:1 ca... |
2,079 | 2,889 | AER Building Blocks for Multi-Layer Multi-Chip
Neuromorphic Vision Systems
R. Serrano-Gotarredona1, M. Oster2, P. Lichtsteiner2, A. Linares-Barranco4, R. PazVicente4, F. G?mez-Rodr?guez4, H. Kolle Riis3, T. Delbr?ck2, S. C. Liu2, S. Zahnd2,
A. M. Whatley2, R. Douglas2, P. H?fliger3, G. Jimenez-Moreno4, A. Civit4, T.
Se... | 2889 |@word loading:1 pulse:3 brightness:2 solid:1 reduction:1 electronics:3 configuration:1 contains:1 efficacy:1 series:1 jimenez:1 liu:2 tuned:1 suppressing:1 current:3 yet:2 must:1 interrupted:1 subsequent:1 shape:2 enables:1 moreno:1 designed:1 sponsored:1 alone:1 half:6 merger:2 core:2 ck2:1 infrastructure:1 dete... |
2,080 | 289 | 316
Atkeson
Using Local Models to Control Movement
Christopher G. Atkeson
Department of Brain and Cognitive Sciences
and the Artificial Intelligence Laboratory
Massachusetts Institute of Technology
NE43-771, 545 Technology Square
Cambridge, MA 02139
cga@ai.mit.edu
ABSTRACT
This paper explores the use of a model neur... | 289 |@word cox:1 faculty:1 version:2 polynomial:4 seems:1 dekker:1 calculus:1 simulation:1 reduction:1 series:3 att:1 bhattacharyya:2 current:1 com:1 activation:1 yet:2 numerical:1 motor:4 mandell:1 fund:1 alone:1 intelligence:2 selected:5 half:2 smith:2 institution:1 mathematical:1 along:1 symposium:1 consists:2 fitti... |
2,081 | 2,890 | A General and Efficient Multiple Kernel
Learning Algorithm
S?oren Sonnenburg?
Fraunhofer FIRST
Kekul?estr. 7
12489 Berlin
Germany
sonne@first.fhg.de
Gunnar R?atsch
Friedrich Miescher Lab
Max Planck Society
Spemannstr. 39
T?ubingen, Germany
Christin Sch?afer
Fraunhofer FIRST
Kekul?estr. 7
12489 Berlin
Germany
raetsch... | 2890 |@word illustrating:1 version:1 momma:1 norm:4 grey:3 tr:2 harder:1 existing:2 current:1 jinbo:1 yet:1 bie:1 written:1 kdd:2 shape:2 designed:1 drop:1 interpretable:2 discrimination:1 v:1 selected:1 website:1 warmuth:1 provides:2 boosting:6 simpler:1 zhang:1 five:2 direct:1 fitting:1 inside:1 excellence:1 indeed:1... |
2,082 | 2,891 | Statistical Convergence of Kernel CCA
Kenji Fukumizu
Institute of Statistical Mathematics
Tokyo 106-8569 Japan
fukumizu@ism.ac.jp
Francis R. Bach
Centre de Morphologie Mathematique
Ecole des Mines de Paris, France
francis.bach@mines.org
Arthur Gretton
Max Planck Institute for Biological Cybernetics
72076 T?
ubingen,... | 2891 |@word h:4 inversion:1 seems:1 norm:22 stronger:4 bn:14 covariance:20 decomposition:1 reduction:1 series:1 ecole:1 rkhs:11 ka:1 exy:1 yet:2 fn:1 short:1 provides:2 herbrich:1 org:1 mathematical:2 direct:3 prove:3 interscience:1 introduce:1 mpg:1 increasing:1 xx:59 bounded:9 notation:1 moreover:2 baker:1 eigenspace... |
2,083 | 2,892 | Logic and MRF Circuitry for Labeling
Occluding and Thinline Visual Contours
Eric Saund
Palo Alto Research Center
3333 Coyote Hill Rd.
Palo Alto, CA 94304
saund@parc.com
Abstract
This paper presents representation and logic for labeling contrast edges
and ridges in visual scenes in terms of both surface occlusion (bor... | 2892 |@word version:1 termination:5 closure:1 seek:1 propagate:1 paid:1 pressure:1 tr:1 solid:4 initial:1 configuration:5 series:1 liu:2 contains:3 com:1 must:1 distant:1 visible:15 occludes:1 shape:1 visibility:1 praeger:1 device:1 node:29 preference:1 five:1 mathematical:1 constructed:1 become:1 symposium:1 pairing:2... |
2,084 | 2,893 | Analyzing Auditory Neurons by Learning
Distance Functions
Inna Weiner1
Tomer Hertz1,2
Israel Nelken2,3
Daphna Weinshall1,2
1
School of Computer Science and Engineering,
The Center for Neural Computation, 3 Department of Neurobiology,
The Hebrew University of Jerusalem, Jerusalem, Israel, 91904
weinerin,tomboy,daphn... | 2893 |@word briefly:1 version:4 stronger:2 reduction:1 initial:2 score:2 interestingly:2 current:2 surprising:2 distant:2 partition:5 plot:2 update:1 v:1 selected:2 tone:1 xk:1 iso:1 characterization:10 provides:1 boosting:3 psth:2 simpler:1 anesthesia:1 burst:1 constructed:1 direct:1 consists:1 combine:2 fitting:1 pat... |
2,085 | 2,894 | Visual Encoding with Jittering Eyes
Michele Rucci?
Department of Cognitive and Neural Systems
Boston University
Boston, MA 02215
rucci@cns.bu.edu
Abstract
Under natural viewing conditions, small movements of the eye and body
prevent the maintenance of a steady direction of gaze. It is known that
stimuli tend to fade ... | 2894 |@word briefly:2 eliminating:1 stronger:1 replicate:1 proportionality:1 r:6 simulation:1 rhesus:1 decorrelate:3 extrastriate:1 substitution:1 series:1 exclusively:1 tuned:2 envision:1 current:1 kowler:1 activation:1 yet:1 refresh:1 physiol:1 visible:1 enables:1 designed:1 discrimination:1 stationary:4 provides:1 c... |
2,086 | 2,895 | Using ?epitomes? to model genetic diversity:
Rational design of HIV vaccine cocktails
Nebojsa Jojic, Vladimir Jojic, Brendan Frey, Chris Meek and David Heckerman
Microsoft Research
Abstract
We introduce a new model of genetic diversity which summarizes a large
input dataset into an epitome, a short sequence or a smal... | 2895 |@word version:2 mers:5 essay:1 contains:1 fragment:16 genetic:5 denoting:1 cleared:1 emn:4 reaction:4 clash:1 com:2 virus:10 surprising:1 yet:1 exposing:1 john:1 partition:2 shape:1 plot:1 update:2 nebojsa:1 generative:5 selected:2 short:6 provides:1 location:1 attack:3 five:1 phylogenetic:1 height:1 constructed:... |
2,087 | 2,896 | Generalization in Clustering with Unobserved
Features
Eyal Krupka and Naftali Tishby
School of Computer Science and Engineering,
Interdisciplinary Center for Neural Computation
The Hebrew University Jerusalem, 91904, Israel
{eyalkr,tishby}@cs.huji.ac.il
Abstract
We argue that when objects are characterized by many att... | 2896 |@word mild:1 achievable:2 q1:1 contains:1 selecting:1 document:1 yet:2 assigning:1 partition:4 shape:1 enables:2 alone:2 greedy:2 selected:8 amir:2 toronto:1 preference:2 allerton:1 mathematical:1 prove:3 interscience:1 introduce:1 theoretically:1 expected:13 examine:1 increasing:1 estimating:1 moreover:1 bounded... |
2,088 | 2,897 | Unbiased Estimator of Shape Parameter for
Spiking Irregularities under Changing
Environments
Keiji Miura
Kyoto University
JST PRESTO
Masato Okada
University of Tokyo
JST PRESTO
RIKEN BSI
Shun-ichi Amari
RIKEN BSI
Abstract
We considered a gamma distribution of interspike intervals as a statistical model for neuronal ... | 2897 |@word cox:1 crucially:1 series:1 score:1 attainability:1 written:3 ikeda:7 john:1 interspike:6 shape:6 motor:2 funahashi:1 provides:1 math:1 mathematical:1 beta:1 differential:1 inter:2 behavior:1 increasing:1 becomes:1 provided:1 estimating:38 baker:2 miyazaki:1 kind:2 monkey:2 developed:1 every:1 ti:19 um:2 wha... |
2,089 | 2,898 | Fast Information Value
for Graphical Models
Andrew W. Moore
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
awm@cs.cmu.edu
Brigham S. Anderson
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
brigham@cmu.edu
Abstract
Calculations that quantify the dependencies betw... | 2898 |@word cu:10 pw:1 polynomial:4 seems:1 nd:2 termination:2 decomposition:1 recursively:1 reduction:1 initial:1 selecting:2 hereafter:2 bc:2 trinary:1 current:4 must:4 written:1 realistic:1 informative:1 prohibitive:1 leaf:2 short:1 node:49 constructed:1 qualitative:1 prove:1 introduce:1 pairwise:1 ra:11 expected:5 ... |
2,090 | 2,899 | Tensor Subspace Analysis
Xiaofei He1
Deng Cai2
Partha Niyogi1
Department of Computer Science, University of Chicago
{xiaofei, niyogi}@cs.uchicago.edu
2
Department of Computer Science, University of Illinois at Urbana-Champaign
dengcai2@uiuc.edu
1
Abstract
Previous work has demonstrated that the image variations of ma... | 2899 |@word norm:1 open:1 hu:1 decomposition:1 lpp:18 incurs:1 tr:27 reduction:13 contains:1 series:1 outperforms:1 written:1 chicago:1 laplacianfaces:13 plot:2 tsa:40 v:3 discrimination:1 intelligence:3 selected:2 plane:1 short:1 provides:1 zhang:2 five:1 differential:1 consists:1 introduce:1 manner:2 expected:1 uiuc:... |
2,091 | 29 | 524
BASINS OF ATTRACTION FOR
ELECTRONIC NEURAL NETWORKS
C. M. Marcus
R. M. Westervelt
Division of Applied Sciences and Department of Physics
Harvard University, Cambridge, MA 02138
ABSTRACT
We have studied the basins of attraction for fixed point and
oscillatory attractors in an electronic analog neural network. Basin... | 29 |@word open:3 propagate:1 pg:4 dramatic:1 solid:1 carry:1 reduction:2 initial:17 configuration:23 pub:1 must:2 numerical:1 visible:1 periodically:2 shape:8 designed:1 plot:1 mackey:1 discovering:1 device:2 leaf:1 liapunov:1 fewer:2 plane:1 math:3 location:3 five:2 rc:9 constructed:2 become:1 qualitative:1 consists:1... |
2,092 | 290 | 232
Sejnowski, Yuhas, Goldstein and Jenkins
Combining Visual and Acoustic Speech Signals
with a Neural Network Improves Intelligibility
T .J. Sejnowski
The Salk Institute
and
Department of Biology
The University of
California at San Diego
San Diego, CA 92037
B.P. Yuhas
M.H. Goldstein, Jr.
Department of Electrical
a... | 290 |@word bining:1 covariance:1 barney:2 configuration:2 series:2 exclusively:1 current:1 comparing:1 com:1 si:2 must:1 john:4 visible:3 shape:2 remove:1 alone:3 selected:2 steepest:1 short:6 provides:1 codebook:1 accessed:1 five:2 along:3 constructed:2 prove:1 yuhas:7 pathway:1 combine:3 formants:1 hague:1 audiovisua... |
2,093 | 2,900 | Nonparametric inference of prior probabilities
from Bayes-optimal behavior
Liam Paninski?
Department of Statistics, Columbia University
liam@stat.columbia.edu; http://www.stat.columbia.edu/?liam
Abstract
We discuss a method for obtaining a subject?s a priori beliefs from
his/her behavior in a psychophysics context, un... | 2900 |@word trial:17 version:1 briefly:1 seems:2 suitably:1 open:2 simulation:1 seek:1 score:1 past:1 savage:1 numerical:1 visibility:1 v:1 discrimination:1 half:1 aside:1 short:1 provides:1 simpler:1 become:1 qualitative:1 behavioral:1 expected:2 behavior:5 examine:1 frequently:1 chap:2 actual:2 increasing:1 becomes:1... |
2,094 | 2,901 | Is Early Vision Optimized for Extracting
Higher-order Dependencies?
Yan Karklin
yan+@cs.cmu.edu
Michael S. Lewicki?
lewicki@cnbc.cmu.edu
Computer Science Department &
Center for the Neural Basis of Cognition
Carnegie Mellon University
Abstract
Linear implementations of the efficient coding hypothesis, such as indepe... | 2901 |@word neurophysiology:2 middle:2 hyv:1 thereby:1 solid:1 valois:1 tuned:1 scatter:2 must:1 additive:1 unchanging:1 shape:8 plot:3 stationary:3 generative:3 filtered:1 provides:1 characterization:1 along:1 fitting:1 cnbc:1 expected:1 ica:11 behavior:1 examine:3 multi:4 inspired:1 automatically:1 provided:1 underly... |
2,095 | 2,902 | Size Regularized Cut for Data Clustering
Yixin Chen
Department of CS
Univ. of New Orleans
yixin@cs.uno.edu
Ya Zhang
Department of EECS
Uinv. of Kansas
yazhang@ittc.ku.edu
Xiang Ji
NEC-Labs America, Inc.
xji@sv.nec-labs.com
Abstract
We present a novel spectral clustering method that enables users to incorporate prio... | 2902 |@word version:2 polynomial:3 km:2 recursively:1 reduction:1 initial:1 contains:2 score:3 karger:1 document:9 outperforms:1 steiner:1 com:1 comparing:1 v21:5 si:6 must:2 written:1 john:1 additive:1 partition:32 numerical:1 enables:3 cue:1 selected:3 intelligence:3 kyk:1 provides:4 node:3 location:1 zhang:1 along:4... |
2,096 | 2,903 | Assessing Approximations for
Gaussian Process Classification
Malte Kuss and Carl Edward Rasmussen
Max Planck Institute for Biological Cybernetics
Spemannstra?e 38, 72076 T?ubingen, Germany
{kuss,carl}@tuebingen.mpg.de
Abstract
Gaussian processes are attractive models for probabilistic classification
but unfortunately ... | 2903 |@word inversion:1 seems:2 logit:1 sex:1 covariance:12 dramatic:1 moment:2 exclusively:1 seriously:1 past:1 kmk:4 comparing:2 surprising:1 yet:1 attracted:1 slanted:1 must:1 realistic:1 informative:1 shape:1 analytic:3 v:4 half:3 selected:1 prohibitive:1 intelligence:1 maximised:1 affair:1 smith:1 provides:3 locat... |
2,097 | 2,904 | Hierarchical Linear/Constant Time SLAM
Using Particle Filters for Dense Maps
Austin I. Eliazar
Ronald Parr
Department of Computer Science
Duke University
Durham, NC 27708
{eliazar,parr}@cs.duke.edu
Abstract
We present an improvement to the DP-SLAM algorithm for simultaneous localization and mapping (SLAM) that mainta... | 2904 |@word version:4 manageable:1 seek:1 gradual:1 reduction:1 cyclic:2 contains:4 series:2 existing:1 freitas:1 current:9 discretization:3 must:2 john:1 ronald:1 enables:1 update:14 resampling:4 intelligence:1 leaf:2 fewer:2 menendez:1 fastslam:2 smith:1 core:2 wolfram:2 pointer:10 provides:2 coarse:4 node:30 direct:... |
2,098 | 2,905 | Divergences, surrogate loss functions and
experimental design
XuanLong Nguyen
University of California
Berkeley, CA 94720
xuanlong@cs.berkeley.edu
Martin J. Wainwright
University of California
Berkeley, CA 94720
wainwrig@eecs.berkeley.edu
Michael I. Jordan
University of California
Berkeley, CA 94720
jordan@cs.berkel... | 2905 |@word mild:1 version:3 d2:1 semicontinuous:2 q1:12 series:1 hereafter:1 bhattacharyya:1 wainwrig:1 current:1 comparing:1 must:5 written:1 realize:3 discrimination:4 accordingly:1 short:1 provides:4 characterization:1 quantizer:5 math:1 zhang:1 dn:13 c2:1 direct:1 prove:3 hellinger:8 introduce:2 indeed:3 behavior:... |
2,099 | 2,906 | Correlated Topic Models
David M. Blei
Department of Computer Science
Princeton University
John D. Lafferty
School of Computer Science
Carnegie Mellon University
Abstract
Topic models, such as latent Dirichlet allocation (LDA), can be useful
tools for the statistical analysis of document collections and other discret... | 2906 |@word blindness:1 manageable:1 proportion:11 hippocampus:1 nd:1 sex:1 simulation:1 lobe:1 covariance:7 profit:1 tr:1 carry:1 phosphorylation:1 contains:1 series:1 denoting:1 document:33 genetic:1 africa:1 atlantic:1 past:1 wd:1 comparing:1 activation:1 romance:1 john:1 grain:1 realistic:2 analytic:2 motor:1 plot:... |
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