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1,300 | 2,184 | Temporal Coherence, Natural Image Sequences,
and the Visual Cortex
Jarmo Hurri and Aapo Hyv?rinen
Neural Networks Research Centre
Helsinki University of Technology
P.O.Box 9800, 02015 HUT, Finland
{jarmo.hurri,aapo.hyvarinen}@hut.fi
Abstract
We show that two important properties of the primary visual cortex
emerge whe... | 2184 |@word version:1 wiesel:1 heuristically:1 hyv:6 simulation:2 bn:1 covariance:1 thereby:1 minus:1 moment:1 reduction:2 initial:2 kurt:1 current:1 must:1 john:1 wx:1 plot:1 generative:17 selected:2 reciprocal:1 short:1 consists:2 dan:1 manner:1 rding:1 inter:3 little:1 actual:1 window:1 laurenz:1 estimating:2 underl... |
1,301 | 2,185 | A Statistical Mechanics Approach to
Approximate Analytical Bootstrap Averages
D?orthe Malzahn
Manfred Opper
Informatics and Mathematical Modelling, Technical University of Denmark,
R.-Petersens-Plads Building 321, DK-2800 Lyngby, Denmark
Neural Computing Research Group, School of Engineering and A... | 2185 |@word trial:1 polynomial:2 retraining:3 simulation:8 covariance:3 outlook:1 moment:1 initial:1 series:1 contains:1 united:1 denoting:1 bootstrapped:2 existing:2 must:3 attracted:1 partition:3 resampling:4 guess:1 hamiltonian:1 manfred:1 contribute:1 simpler:2 mathematical:1 constructed:1 qualitative:1 specialize:... |
1,302 | 2,186 | Bayesian Estimation of Time-Frequency
Coefficients for Audio Signal Enhancement
Patrick J. Wolfe
Department of Engineering
University of Cambridge
Cambridge CB2 1PZ, UK
pjw47@eng.cam.ac.uk
Simon J. Godsill
Department of Engineering
University of Cambridge
Cambridge CB2 1PZ, UK
sjg@eng.cam.ac.uk
Abstract
The Bayesian... | 2186 |@word timefrequency:2 version:3 inversion:2 norm:1 briefly:1 open:1 grey:1 r:1 simulation:1 eng:3 decomposition:1 ality:1 rayner:1 carry:1 reduction:7 series:2 initialisation:4 periodically:1 additive:2 plot:4 v:1 stationary:1 generative:1 accordingly:1 plane:1 short:11 provides:2 completeness:1 math:1 attack:1 m... |
1,303 | 2,187 | Robust Novelty Detection with
Single-Class MPM
Gert R.G. Lanckriet
EECS, V.C. Berkeley
gert@eecs.berkeley. edu
Laurent EI Ghaoui
EECS, V.C. Berkeley
elghaoui@eecs.berkeley.edu
Michael I. Jordan
Computer Science and
Statistics, V.C. Berkeley
jordan@cs. berkeley. edu
Abstract
In this paper we consider the problem of ... | 2187 |@word version:2 norm:5 simulation:1 seek:1 covariance:20 simplifying:1 tr:1 tuned:1 envision:1 bhattacharyya:1 current:1 comparing:2 z2:2 must:2 readily:1 fn:18 partition:2 shape:1 treating:1 half:3 mpm:22 mln:1 accordingly:1 core:1 provides:1 characterization:1 lx:5 along:1 x1l:1 direct:1 viable:1 scholkopf:5 in... |
1,304 | 2,188 | Selectivity and Metaplasticity in a Unified
Calcium-Dependent Model
Luk Chong Yeung
Physics Department and
Institute for Brain & Neural Systems
Brown University
Providence, RI 02912
yeung@physics.brown.edu
Brian S. Blais
Department of Science & Technology
Bryant College
Smithfield, RI 02917
Institute for Brain & Neur... | 2188 |@word luk:1 hippocampus:2 sabatini:1 open:1 simulation:4 excited:1 solid:2 initial:2 efficacy:1 current:5 neurophys:1 activation:1 yet:1 must:1 fn:1 physiol:1 realistic:3 interspike:1 shape:1 plasticity:22 fund:1 n0:2 aps:1 alone:1 half:4 selected:1 preference:1 pairing:3 persistent:1 pathway:1 manner:2 spine:1 r... |
1,305 | 2,189 | Optoelectronic Implementation of a
FitzHugh-Nagumo Neural Model
Alexandre R.S. Romariz , Kelvin Wagner
Optoelectronic Computing Systems Center
University of Colorado, Boulder, CO, USA 80309-0425
romariz@colorado.edu
Abstract
An optoelectronic implementation of a spiking neuron model based on
the FitzHugh-Nagumo equat... | 2189 |@word illustrating:2 polynomial:2 seems:1 open:1 pulse:26 simulation:6 solid:1 electronics:5 responsivity:1 optically:1 liquid:1 tuned:1 renewed:1 longitudinal:1 current:8 activation:1 yet:1 guez:1 readily:1 fn:2 interrupted:1 v:1 device:2 p7:1 short:2 provides:1 ire:1 successive:1 zhang:1 height:1 differential:1... |
1,306 | 219 | A Systematic Study or the Input/Output Properties
A Systematic Study of the Input/Output Properties
of a 2 Compartment Model Neuron
With Active Membranes
Paul Rhodes
University of California, San Diego
ABSTRACT
The input/output properties of a 2 compartment model neuron are systematically
explored. Taken from the wo... | 219 |@word trial:5 unaltered:1 version:2 proportion:1 seems:1 open:1 simulation:2 pulse:6 simplifying:1 fonn:1 reduction:2 substitution:1 contains:1 efficacy:11 current:33 surprising:1 activation:13 yet:1 must:1 bd:3 realistic:6 subsequent:1 blur:1 plasticity:6 shape:7 hyperpolarizing:1 plot:1 v:1 nervous:1 iso:1 locat... |
1,307 | 2,190 | Spike Timing-Dependent Plasticity
in the Address Domain
R. Jacob Vogelstein1 , Francesco Tenore2 , Ralf Philipp2 , Miriam S. Adlerstein2 ,
David H. Goldberg2 and Gert Cauwenberghs2
1
Department of Biomedical Engineering
2
Department of Electrical and Computer Engineering
Johns Hopkins University, Baltimore, MD 21218
{j... | 2190 |@word trial:1 advantageous:1 scroll:1 pulse:1 jacob:1 covariance:1 initial:1 liu:1 contains:1 current:1 nt:1 activation:1 must:2 readily:1 john:1 realistic:1 plasticity:14 enables:1 designed:1 succeeding:1 update:3 aps:2 realism:1 short:2 infrastructure:3 detecting:1 provides:1 location:8 mathematical:1 transceiv... |
1,308 | 2,191 | Regularized Greedy Importance Sampling
Finnegan Southey Dale Schuurmans Ali Ghodsi
School of Computer Science
University of Waterloo
fdjsouth,dale,aghodsib @cs.uwaterloo.ca
Abstract
Greedy importance sampling is an unbiased estimation technique that reduces the variance of standard importance sampling by explicitly... | 2191 |@word version:2 seems:1 simulation:2 crucially:1 decomposition:2 dramatic:1 ipm:4 recursively:1 reduction:5 initial:2 configuration:6 contains:1 uncovered:1 series:2 warmer:1 assigning:2 must:4 realize:1 realistic:1 designed:1 drop:1 greedy:18 leaf:1 intelligence:2 core:1 provides:2 unbounded:1 along:1 direct:4 p... |
1,309 | 2,192 | Value-Directed Compression of POMDPs
Pascal Poupart
Craig Boutilier
Departement of Computer Science
University of Toronto
Toronto, ON, M5S 3H5
ppoupart@cs.toronto.edu
Department of Computer Science
University of Toronto
Toronto, ON, M5S 3H5
cebly@cs.toronto.edu
Abstract
We examine the problem of generating state-s... | 2192 |@word illustrating:1 briefly:1 manageable:1 compression:62 norm:4 polynomial:1 version:2 achievable:1 open:1 hu:1 additively:1 thereby:1 solid:2 recursively:1 carry:1 initial:1 substitution:1 contains:6 series:1 selecting:1 current:5 si:1 must:5 additive:9 subsequent:1 wx:1 realistic:1 intelligence:2 fewer:1 gree... |
1,310 | 2,193 | Hyperkernels
Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson
Research School of Information Sciences and Engineering
The Australian National University
Canberra, 0200 ACT, Australia
Cheng.Ong, Alex.Smola, Bob.Williamson @anu.edu.au
Abstract
We consider the problem of choosing a kernel suitable for estimati... | 2193 |@word determinant:1 repository:1 polynomial:1 norm:7 open:1 crucially:1 decomposition:1 pset:1 pick:1 elisseeff:1 tr:1 outlook:1 carry:1 series:4 contains:1 exclusively:1 tuned:3 bc:2 rkhs:13 existing:1 current:2 com:1 yet:3 written:3 treating:1 v:1 guess:1 parameterization:2 parameterizations:1 boosting:4 consul... |
1,311 | 2,194 | Informed Projections
David Cohn
Carnegie Mellon University
Pittsburgh, PA 15213
cohn+@cs.cmu.edu
Abstract
Low rank approximation techniques are widespread in pattern recognition research ? they include Latent Semantic Analysis (LSA), Probabilistic LSA, Principal Components Analysus (PCA), the Generative Aspect Model,... | 2194 |@word trial:1 briefly:1 version:1 plsa:1 covariance:1 mention:1 reduction:1 contains:1 document:30 interestingly:1 subjective:1 existing:1 outperforms:1 current:3 err:1 si:20 assigning:1 crawling:1 must:3 informative:1 hofmann:4 plot:2 aside:1 v:2 generative:4 selected:2 guess:1 intelligence:2 mccallum:4 beginnin... |
1,312 | 2,195 | A Minimal Intervention Principle for
Coordinated Movement
Emanuel Todorov
Department of Cognitive Science
University of California, San Diego
todorov@cogsci.ucsd.edu
Michael I. Jordan
Computer Science and Statistics
University of California, Berkeley
jordan@cs.berkeley.edu
Abstract
Behavioral goals are achieved relia... | 2195 |@word trial:6 exploitation:1 version:1 eliminating:1 achievable:1 advantageous:1 johansson:1 open:1 confirms:2 seek:1 simulation:2 r:1 covariance:3 recursively:1 moment:3 reduction:1 configuration:1 contains:2 schoner:1 initial:1 lqr:2 bc:1 realistic:1 happen:1 additive:1 pqd:1 shape:1 lqg:3 motor:26 reproducible... |
1,313 | 2,196 | Effective Dimension and Generalization of
Kernel Learning
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 function Spaces. We introduce a concept of scalesensitive effective data dim... | 2196 |@word prof:1 concept:3 version:1 implies:4 skip:2 norm:6 true:3 hence:2 equality:3 question:1 quantity:5 parametric:3 seek:1 decomposition:6 pick:1 self:5 covering:2 distance:1 boundedness:2 yorktown:1 won:1 chaining:2 berlin:1 generalization:7 stone:1 proposition:5 of9:1 elementary:1 complete:1 cauchy:1 assuming... |
1,314 | 2,197 | Learning to Classify Galaxy Shapes Using the
EM Algorithm
Sergey Kirshner
Information and Computer Science
University of California
Irvine, CA 92697-3425
skirshne@ics.uci.edu
Igor V. Cadez
Sparta Inc.,
23382 Mill Creek Drive #100,
Laguna Hills, CA 92653
igor cadez@sparta.com
Padhraic Smyth
Information and Computer S... | 2197 |@word nd:1 lobe:17 simplifying:1 eng:1 eld:1 tr:1 yaleu:1 cyclic:1 score:3 cadez:4 subjective:1 existing:1 com:1 surprising:1 assigning:1 scatter:1 subsequent:1 cant:3 shape:1 plot:3 mislabelled:1 update:1 ith:1 core:16 short:1 compo:1 detecting:1 dn:1 c2:2 symposium:1 consists:5 prev:2 manner:3 indeed:1 ra:1 rap... |
1,315 | 2,198 | Dyadic Classification Trees
via
Structural Risk Minimization
Clayton Scott and Robert Nowak
Department of Electrical and Computer Engineering
Rice University
Houston, TX 77005
cscott,nowak @rice.edu
Abstract
Classification trees are one of the most popular types of classifiers, with
ease of implementation and interp... | 2198 |@word version:1 briefly:1 stronger:1 recursively:1 initial:8 contains:2 fragment:1 selecting:1 chervonenkis:2 outperforms:1 current:1 must:2 belmont:1 additive:2 partition:9 zeger:2 dct:25 realistic:1 enables:1 discrimination:8 greedy:4 selected:1 leaf:2 half:2 provides:1 node:8 successive:1 mathematical:1 along:... |
1,316 | 2,199 | Information Regularization with Partially
Labeled Data
Tommi Jaakkola
MIT AI Lab
Cambridge, MA 02139
tommi@ai.mit.edu
Martin Szummer
MIT AI Lab & CBCL
Cambridge, MA 02139
szummer@ai.mit.edu
Abstract
Classification with partially labeled data requires using a large number
of unlabeled examples (or an estimated margina... | 2199 |@word mild:1 version:1 stronger:1 calculus:2 covariance:1 simplifying:1 tr:1 solid:2 carry:1 initial:1 configuration:2 contains:2 score:1 dx:11 must:7 written:1 tailoring:1 shape:3 remove:1 treating:1 joy:1 discrimination:2 half:1 denison:1 xk:13 dover:1 vanishing:2 provides:1 location:1 firstly:1 direct:1 differ... |
1,317 | 22 | 201
NEW HARDWARE FOR MASSIVE NEURAL NETWORKS
D. D. Coon and A. G. U. Perera
Applied Technology Laboratory
University of Pittsburgh
Pittsburgh, PA 15260.
ABSTRACT
Transient phenomena associated with forward biased silicon p + - n - n + structures at 4.2K show remarkable similarities with biological neurons. The device... | 22 |@word advantageous:1 cm2:1 pulse:23 simulation:1 solid:1 reduction:1 electronics:2 l__:1 amp:2 current:20 si:1 additive:1 realistic:1 drop:1 v:3 discrimination:1 pursued:1 pacemaker:1 device:25 plane:3 short:1 fabricating:1 math:1 node:3 contribute:1 simpler:1 height:2 constructed:1 cray:1 sustained:1 behavior:3 fr... |
1,318 | 220 | 18
Harris-Warrick
MECHANISMS FOR NEUROMODULATION
OF BIOLOGICAL NEURAL NETWORKS
Ronald M. Harris-Warrick
Section of Neurobiology and Behavior
Cornell University
Ithaca, NY 14853
ABSTRACT
The pyloric Central Pattern Generator of the crustacean stomatogastric
ganglion is a well-defined biological neural network. This ... | 220 |@word hyperpolarized:2 pulse:1 fonn:3 initial:1 contains:2 efficacy:3 uncovered:1 current:5 activation:1 must:1 john:2 physiol:6 underly:1 ronald:1 hyperpolarizing:3 plasticity:2 motor:25 pacemaker:1 selected:1 nervous:5 lr:1 compo:3 characterization:1 complication:1 simpler:1 cpg:6 burst:5 direct:1 consists:1 ind... |
1,319 | 2,200 | A Bilinear Model for Sparse Coding
David B. Grimes and Rajesh P. N. Rao
Department of Computer Science and Engineering
University of Washington
Seattle, WA 98195-2350, U.S.A.
grimes,rao @cs.washington.edu
Abstract
Recent algorithms for sparse coding and independent component analysis (ICA) have demonstrated how loc... | 2200 |@word norm:2 simulation:1 decomposition:2 thereby:2 vigorously:1 reduction:2 x81:2 plot:2 update:2 depict:1 generative:12 selected:2 discovering:1 plane:1 provides:3 location:9 along:1 ica:6 indeed:2 growing:2 brain:1 freeman:4 informational:1 little:1 encouraging:1 considering:1 begin:1 provided:1 moreover:1 und... |
1,320 | 2,201 | Binary Thning is Optimal for eural Rate
Coding with High Temporal Resolution
Matthias Bethge:David Rotermund, and Klaus Pawelzik
Institute of Theoretical Physics
University of Bremen
28334 Bremen
{mbethge,davrot,pawelzik}@physik.uni-bremen.de
Abstract
Here we derive optimal gain functions for minimum mean square reco... | 2201 |@word illustrating:1 middle:2 compression:1 seems:1 advantageous:3 physik:1 adrian:2 grey:1 seek:1 pulse:2 methodologically:1 thereby:1 solid:1 reduction:1 mmse:10 reaction:1 dx:6 fn:1 subsequent:2 numerical:3 physiol:1 shape:6 wanted:1 plot:1 drop:1 half:2 nervous:1 parameterization:3 accordingly:1 short:4 burst... |
1,321 | 2,202 | Kernel Design Using Boosting
Koby Crammer Joseph Keshet Yoram Singer
School of Computer Science & Engineering
The Hebrew University, Jerusalem 91904, Israel
{kobics,jkeshet,singer}@cs.huji.ac.il
Abstract
The focus of the paper is the problem of learning kernel operators from
empirical data. We cast the kernel design ... | 2202 |@word mild:1 version:5 middle:2 polynomial:1 norm:8 lodhi:1 decomposition:1 elisseeff:1 accommodate:2 initial:4 contains:1 score:13 denoting:1 current:3 comparing:3 jaz:1 yet:1 scatter:3 written:1 john:3 additive:1 informative:2 ma0:1 enables:1 designed:2 plot:14 v:4 classier:1 core:1 short:1 eskin:1 boosting:24 ... |
1,322 | 2,203 | Manifold Parzen Windows
Pascal Vincent and Yoshua Bengio
Dept. IRO, Universit? de Montr?al
C.P. 6128, Montreal, Qc, H3C 3J7, Canada
{vincentp,bengioy}@iro.umontreal.ca
http://www.iro.umontreal.ca/ vincentp
Abstract
The similarity between objects is a fundamental element of many learning algorithms. Most non-parametric... | 2203 |@word determinant:1 nd:2 covariance:23 decomposition:3 mention:3 tr:1 reduction:3 myles:1 contains:1 tuned:1 outperforms:1 surprising:1 yet:1 assigning:3 must:2 shape:4 analytic:1 discrimination:1 prohibitive:1 parameterization:1 plane:2 isotropic:3 short:1 toronto:1 kiel:2 mathematical:1 along:9 symposium:2 inde... |
1,323 | 2,204 | Learning to Take Concurrent Actions
Khashayar Rohanimanesh
Department of Computer Science
University of Massachusetts
Amherst, MA 01003
khash@cs.umass.edu
Sridhar Mahadevan
Department of Computer Science
University of Massachusetts
Amherst, MA 01003
mahadeva@cs.umass.edu
Abstract
We investigate a general semi-Markov... | 2204 |@word trial:8 middle:2 interleave:1 open:2 termination:51 calculus:2 uma:2 hereafter:1 selecting:1 current:3 comparing:2 yet:1 written:2 interrupted:2 chicago:1 drop:2 update:1 smdp:3 intelligence:4 selected:1 fewer:1 hallway:8 beginning:1 indefinitely:1 dn:6 along:1 symposium:1 retrieving:1 ray:1 inside:1 introd... |
1,324 | 2,205 | Spikernels:
Embedding Spiking Neurons
in Inner-Product Spaces
Lavi Shpigelman Yoram Singer Rony Paz Eilon Vaadia
School of computer Science and Engineering
Interdisciplinary Center for Neural Computation
Dept. of Physiology, Hadassah Medical School
The Hebrew University Jerusalem, 91904, Israel
{shpigi,sin... | 2205 |@word neurophysiology:3 trial:3 middle:2 mri:1 polynomial:1 seems:2 norm:2 lodhi:2 r:2 rhesus:1 eng:1 n8:1 carry:1 initial:1 series:2 score:3 liquid:1 daniel:2 tuned:2 prefix:2 outperforms:1 current:3 comparing:1 ka:1 activation:1 scatter:2 written:1 john:3 ronald:1 shape:2 motor:13 plot:3 half:1 generative:1 dev... |
1,325 | 2,206 | Exact MAP Estimates by (Hyper)tree Agreement
Martin J. Wainwright,
Department of EECS,
UC Berkeley,
Berkeley, CA 94720
martinw@eecs.berkeley.edu
Tommi S. Jaakkola and Alan S. Willsky,
Department of EECS,
Massachusetts Institute of Technology,
Cambridge, MA, 02139
tommi,willsky @mit.edu
Abstract
We describe a metho... | 2206 |@word h:3 version:1 suitably:1 open:1 ayy:1 configuration:36 karger:1 interestingly:1 current:1 must:11 written:1 belmont:1 additive:1 happen:1 wx:1 subsequent:1 partition:2 koetter:2 designed:1 update:17 intelligence:1 accordingly:1 pointer:1 node:12 allerton:1 along:2 prove:4 consists:1 shorthand:1 manner:4 int... |
1,326 | 2,207 | Convergence Properties of some
Spike-Triggered Analysis Techniques
Liam Paninski
Center for Neural Science
New York University
New York, NY 10003
liam@cns. nyu. edu
http://www.cns.nyu.edu/rvliam
Abstract
vVe analyze the convergence properties of three spike-triggered data
analysis techniques. All of our results are o... | 2207 |@word version:4 seems:4 stronger:1 suitably:1 open:1 km:1 covariance:7 mention:1 harder:1 moment:4 necessity:2 series:1 denoting:1 tuned:2 current:1 nt:1 surprising:1 written:1 numerical:1 motor:4 designed:1 half:1 device:1 plane:2 lr:2 filtered:1 draft:2 mathematical:1 along:3 differential:1 supply:1 ik:1 calcul... |
1,327 | 2,208 | Convergent Combinations of
Reinforcement Learning with Linear
Function Approximation
Ralf Schoknecht
ILKD
University of Karlsruhe, Germany
ralf. schoknecht@ilkd. uni-karlsruhe. de
Artur Merke
Lehrstuhl Informatik 1
University of Dortmund, Germany
arturo merke@udo.edu
Abstract
Convergence for iterative reinforcement ... | 2208 |@word pw:3 polynomial:1 inversion:2 achievable:1 norm:3 bf:1 iki:1 tr:1 carry:1 initial:7 irnxn:1 contains:1 denoting:2 com:1 analysed:1 si:12 dx:3 written:2 belmont:1 fn:2 numerical:1 update:15 xif:1 intelligence:1 accordingly:1 xk:4 revisited:1 idi:1 ipi:2 constructed:1 direct:1 ik:1 manner:1 expected:1 nor:1 m... |
1,328 | 2,209 | Kernel-based Extraction of Slow Features:
Complex Cells Learn Disparity and Translation Invariance from Natural Images
Alistair Bray and Dominique Martinez*
CORTEX Group, LORIA-INRIA, Nancy, France
bray@loria.fr, dmartine@loria.jr
Abstract
In Slow Feature Analysis (SFA [1]), it has been demonstrated that
high-order i... | 2209 |@word version:3 polynomial:2 stronger:1 proportion:1 grey:1 confirms:1 dominique:1 simulation:13 covariance:4 necessity:1 series:2 disparity:12 current:2 activation:1 yet:2 must:4 written:2 realistic:1 shape:2 progressively:1 alone:1 greedy:2 half:3 selected:1 accordingly:1 maximised:1 iso:1 short:5 provides:4 su... |
1,329 | 221 | 92
Cowan and Friedman
Development and Regeneration of Eye-Brain
Maps: A Computational Model
J.D. Cowan and A.E. Friedman
Department of Mathematics. Committee on
Neurobiology. and Brain Research Institute.
The University of Chicago. 5734 S. Univ. Ave.?
Chicago. Illinois 60637
ABSTRACT
We outline a computational mode... | 221 |@word middle:1 wiesel:1 replicate:1 simulation:6 lobe:1 innervating:1 initial:1 fragment:2 genetic:1 existing:1 current:1 nt:3 physiol:1 subsequent:1 chicago:3 plasticity:8 occludes:1 occlude:3 half:11 ith:5 compo:3 provides:3 coarse:1 mathematical:1 along:2 differential:1 edelman:3 pathway:2 combine:1 rostral:2 p... |
1,330 | 2,210 | Multiple Cause Vector Quantization
David A. Ross and Richard S. Zemel
Department of Computer Science
University of Toronto
{dross,zemel}@cs.toronto.edu
Abstract
We propose a model that can learn parts-based representations of highdimensional data. Our key assumption is that the dimensions of the data
can be separated... | 2210 |@word proceeded:1 version:2 sex:1 d2:1 decomposition:5 blade:1 initial:1 contains:3 selecting:1 document:10 freitas:1 yet:1 bd:1 readily:1 must:1 hofmann:2 shape:11 remove:1 designed:1 depict:2 update:3 generative:6 selected:5 leaf:1 item:2 fewer:1 intelligence:1 blei:1 quantizer:2 codebook:1 toronto:5 location:1... |
1,331 | 2,211 | Topographic Map Formation by Silicon
Growth Cones
Brian Taba and Kwabena Boahen
Department of Bioengineering
University of Pennsylvania
Philadelphia, PA 19104
{blaba, kwabena}@neuroengineering.upenn.edu
Abstract
We describe a self-configuring neuromorphic chip that uses a
model of activity-dependent axon remodeling t... | 2211 |@word open:1 termination:1 pulse:1 solid:1 initial:10 coactive:1 current:8 com:1 activation:1 must:1 readily:1 subsequent:2 plasticity:3 unchanging:1 arrayed:1 disables:1 remove:1 plot:1 drop:1 update:2 asymptote:1 shape:1 cue:2 obsolete:2 selected:1 plane:6 trapping:1 beginning:1 disassembly:1 reciprocal:1 core:... |
1,332 | 2,212 | Fast Transformation-Invariant Factor Analysis
Anitha Kannan
Nebojsa Jojic
Brendan Frey
University of Toronto, Toronto, Canada
anitha, frey @psi.utoronto.ca
Microsoft Research, Redmond, WA, USA
jojic@microsoft.com
Abstract
Dimensionality reduction techniques such as principal component analysis and fact... | 2212 |@word mild:1 illustrating:1 version:1 determinant:2 loading:2 nd:1 linearized:4 covariance:3 brightness:1 tr:1 tmg:8 reduction:3 contains:6 current:1 com:1 periodically:1 enables:2 mstep:1 treating:1 update:3 nebojsa:1 generative:5 plane:3 isotropic:2 iterates:1 math:1 toronto:3 zhang:2 mathematical:1 become:1 ex... |
1,333 | 2,213 | Nonparametric Representation of Policies and
Value Functions: A Trajectory-Based Approach
Christopher G. Atkeson
Robotics Institute and HCII
Carnegie Mellon University
Pittsburgh, PA 15213, USA
cga@cmu.edu
Jun Morimoto
ATR Human Information Science Laboratories, Dept. 3
Keihanna Science City
Kyoto 619-0288, Japan
xmo... | 2213 |@word middle:2 version:1 retraining:1 simulation:2 linearized:1 covariance:5 pick:1 accommodate:1 initial:6 cyclic:1 series:5 minmax:1 practiced:1 lqr:2 past:2 neuneier:1 surprising:2 yet:1 must:4 thrust:1 motor:1 designed:1 update:8 intelligence:1 selected:1 parameterization:1 beginning:1 supplying:1 mental:1 pr... |
1,334 | 2,214 | Maximum Likelihood and the Information
Bottleneck
Noam Slonim Yair Weiss
School of Computer Science & Engineering,
Hebrew University, Jerusalem 91904, Israel
noamm,yweiss @cs.huji.ac.il
Abstract
The information bottleneck (IB) method is an information-theoretic formulation
for clustering problems. Given a joint dist... | 2214 |@word mild:1 compression:1 simulation:3 score:1 denoting:1 document:5 interestingly:1 comparing:3 lang:1 yet:1 must:2 john:1 partition:3 informative:1 hofmann:2 designed:1 update:1 generative:5 noamm:1 short:2 provides:1 allerton:1 mathematical:1 direct:5 become:1 prove:2 introduce:1 theoretically:1 indeed:2 roug... |
1,335 | 2,215 | Going Metric: Denoising Pairwise Data
Volker Roth
Informatik III, University of Bonn
Roemerstr 164, 53117 Bonn, Germany
roth?cs.uni-bonn.de
Julian Laub
Fraunhofer FIRST.IDA
Kekulestr. 7, 12489 Berlin, Germany
jlaub?first.fhg.de
Joachim M. Buhmann
Informatik III, University of Bonn
Roemerstr 164, 53117 Bonn, Germany
... | 2215 |@word illustrating:1 middle:2 briefly:1 eliminating:1 advantageous:2 cox:2 duda:1 open:1 gish:1 decomposition:1 euclidian:6 tr:1 harder:1 reduction:7 score:10 tuned:1 interestingly:1 existing:2 recovered:1 ida:2 si:2 yet:1 john:1 additive:3 subsequent:1 partition:1 j1:1 hofmann:1 standalone:1 resampling:1 generat... |
1,336 | 2,216 | Information Diffusion Kernels
John Lafferty
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213 USA
lafferty@cs.cmu.edu
Guy Lebanon
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213 USA
lebanon@cs.cmu.edu
Abstract
A new family of kernels for statistical learning is intr... | 2216 |@word faculty:6 schoen:1 kondor:2 polynomial:1 norm:2 covariance:1 attainable:1 solid:1 initial:1 contains:1 score:1 series:1 document:4 ours:1 outperforms:1 ka:1 tackling:1 yet:2 must:1 john:2 plot:1 v:5 generative:2 half:1 plane:1 beginning:1 five:1 trinomial:2 mathematical:3 differential:4 become:1 symposium:1... |
1,337 | 2,217 | Learning to Detect Natural Image Boundaries
Using Brightness and Texture
David R. Martin Charless C. Fowlkes Jitendra Malik
Computer Science Division, EECS, U.C. Berkeley, Berkeley, CA 94720
dmartin,fowlkes,malik @cs.berkeley.edu
Abstract
The goal of this work is to accurately detect and localize boundaries in
natu... | 2217 |@word cylindrical:1 version:1 nd:3 disk:2 jacob:3 brightness:11 shading:1 moment:1 shiota:1 fragment:1 shum:1 tuned:1 outperforms:4 existing:3 comparing:2 shape:1 hofmann:1 plot:2 x160:1 discrimination:2 v:1 cue:9 half:6 pursued:1 greedy:1 intelligence:1 coughlan:1 lr:2 straddling:1 provides:7 boosting:3 detectin... |
1,338 | 2,218 | How to Combine Color and Shape
Information for 3D Object Recognition:
Kernels do the Thick
B. Caputo
Smith-Kettlewell Eye Research Institute,
2318 Fillmore Street,
94115 San Francisco, California, USA
caputo@ski.org
Gy. Dorko
Department of Computer Science,
Chair for Pattern Recognition,
University of Erlangen-Nurembe... | 2218 |@word h:5 middle:1 nd:3 heuristically:2 tr:1 configuration:3 contains:1 series:1 o2:1 existing:1 si:2 tackling:1 must:5 written:1 realize:1 partition:1 j1:1 shape:36 alone:2 cue:1 selected:2 smith:1 compo:1 provides:2 org:3 zhang:1 along:1 become:1 kettlewell:1 scholkopf:1 descendant:10 consists:1 prove:1 ijcv:2 ... |
1,339 | 2,219 | Timing and Partial Observability in the
Dopamine System
1
Nathaniel D. Daw1,3 , Aaron C. Courville2,3 , and David S. Touretzky1,3
Computer Science Department, 2 Robotics Institute, 3 Center for the Neural Basis of Cognition
Carnegie Mellon University, Pittsburgh, PA 15213
{daw,aaronc,dst}@cs.cmu.edu
Abstract
Accordi... | 2219 |@word trial:2 version:1 middle:1 eliminating:1 instrumental:1 stronger:1 seems:1 nd:2 simulation:3 r:2 excited:1 uphold:1 accommodate:1 moment:1 series:6 ours:2 elaborating:1 past:2 current:3 discretization:2 neurophys:1 surprising:1 lang:1 dx:1 written:1 must:2 subsequent:1 partition:1 update:5 v:1 cue:3 device:... |
1,340 | 222 | 186
Bourlard and Morgan
A Continuous Speech Recognition System
Embedding MLP into HMM
Herve Bourlard
Nelson Morgan
Philips Research Laboratory
Av. van Becelaere 2. Box 8
B-1170 Brussels. Belgium
IntI. Compo Sc. Institute
1947 Center Street. Suite 600
Berkeley. CA 94704. USA
ABSTRACT
We are developing a phoneme b... | 222 |@word nd:1 simplifying:2 noll:2 necessity:1 substitution:1 contains:1 score:1 series:1 initial:2 contextual:7 lang:1 must:1 moo:1 fn:2 realistic:1 entrance:2 applica:1 thble:1 remove:1 designed:1 discrimination:2 v:1 intelligence:1 scotland:1 compo:1 pointer:1 lr:1 quantized:2 simpler:2 along:1 consists:2 manner:1... |
1,341 | 2,220 | Stable Fixed Points of Loopy Belief
Propagation Are Minima of the Bethe
Free Energy
Tom Heskes
SNN, University of Nijmegen
Geert Grooteplein 21, 6252 EZ, Nijmegen, The Netherlands
Abstract
We extend recent work on the connection between loopy belief propagation
and the Bethe free energy. Constrained minimization of t... | 2220 |@word version:6 seems:2 tedious:1 proportionality:1 open:1 grooteplein:1 simulation:1 minus:1 initial:3 substitution:1 contains:2 cyclic:1 loeliger:1 yet:1 must:1 written:1 update:21 stationary:1 hamiltonian:1 lr:1 recompute:1 node:5 manner:1 introduce:3 uphill:1 indeed:2 behavior:1 ry:1 freeman:1 snn:1 actual:1 ... |
1,342 | 2,221 | Boosted Dyadic Kernel Discriminants
Baback Moghaddam
Mitsubishi Electric Research Laboratory
201 Broadway
Cambridge MA 02139 USA
baback@merl.com
Gregory Shakhnarovich
MIT AI Laboratory
200 Technology Square
Cambridge MA 02139 USA
gregory@ai.mit.edu
Abstract
We introduce a novel learning algorithm for binary classifi... | 2221 |@word trial:4 repository:4 version:1 norm:1 suitably:1 mitsubishi:1 covariance:2 solid:1 harder:1 reduction:1 selecting:1 ours:1 com:1 must:3 readily:1 designed:1 greedy:1 selected:3 assurance:1 accordingly:1 xk:1 provides:2 boosting:10 location:1 hyperplanes:2 sigmoidal:1 simpler:2 dn:1 constructed:1 direct:1 co... |
1,343 | 2,222 | Knowledge-Based Support Vector
Machine Classifiers
Glenn M. Fung, Olvi L. Mangasarian and Jude W. Shavlik
Computer Sciences Department, University of Wisconsin
Madison, WI 53706
gfung, olvi, shavlik@cs.wisc.edu
Abstract
Prior knowledge in the form of multiple polyhedral sets, each belonging to one of two categories, ... | 2222 |@word briefly:2 norm:10 prognostic:1 nd:1 open:1 minus:1 accommodate:1 contains:1 pub:2 outperforms:1 bradley:1 must:1 readily:3 john:1 refines:1 numerical:6 midway:1 girosi:1 depict:1 farkas:2 half:2 intelligence:3 plane:25 reciprocal:1 short:1 math:3 node:2 location:2 mulier:1 simpler:2 diagnosing:1 five:1 math... |
1,344 | 2,223 | Extracting Relevant Structures with
Side Information
Gal Chechik and Naftali Tishby
ggal,tishby @cs.huji.ac.il
School of Computer Science and Engineering and
The Interdisciplinary Center for Neural Computation
The Hebrew University of Jerusalem, 91904, Israel
Abstract
The problem of extracting the relevant aspects o... | 2223 |@word illustrating:1 agf:1 achievable:1 compression:2 stronger:3 nd:1 seek:2 solid:1 reduction:2 electronics:1 initial:1 contains:3 score:2 selecting:1 genetic:1 document:21 interestingly:1 outperforms:1 current:3 analysed:1 lang:1 informative:2 remove:1 designed:2 plot:1 v:1 greedy:2 half:2 amir:1 mccallum:1 sys... |
1,345 | 2,224 | A Probabilistic Approach to Single Channel
Blind Signal Separation
Gil-Jin Jang
Spoken Language Laboratory
KAIST, Daejon 305-701, South Korea
jangbal@bawi.org
http://speech.kaist.ac.kr/?jangbal
Te-Won Lee
Institute for Neural Computation
University of California, San Diego
La Jolla, CA 92093, U.S.A.
tewon@inc.ucsd.ed... | 2224 |@word kong:1 cleanly:1 decomposition:2 rayner:1 initial:1 current:5 recovered:4 z2:2 subsequent:1 periodically:1 additive:1 update:1 stationary:1 generative:6 half:2 website:1 selected:1 provides:2 location:1 org:1 constructed:1 fitting:1 autocorrelation:1 introduce:1 manner:3 presumed:1 ica:17 frequently:1 actua... |
1,346 | 2,225 | Visual Development Aids the Acquisition of
Motion Velocity Sensitivities
Robert A. Jacobs
Department of Brain and Cognitive Sciences
University of Rochester
Rochester, NY 14627
robbie@bcs.rochester.edu
Melissa Dominguez
Department of Computer Science
University of Rochester
Rochester, NY 14627
melissad@cs.rochester.e... | 2225 |@word version:17 nd:12 suitably:3 simulation:8 jacob:5 fifteen:2 thereby:1 solid:6 moment:1 initial:2 disparity:9 tuned:17 rightmost:1 surprising:1 nowlan:1 activation:1 numerical:1 subsequent:1 informative:3 shape:2 hypothesize:1 designed:3 v:4 stationary:3 discrimination:1 intelligence:1 item:9 short:1 filtered... |
1,347 | 2,226 | A Differential Semantics for Jointree
Algorithms
James D. P ark and Adnan Darwiche
Computer Science Department
Univ ersity of California, Los Angeles, CA 90095
{jd,darwiche}@cs.ucla.edu
Abstract
A new approach to inference in belief networks has been recently
proposed, which is based on an algebraic representation of... | 2226 |@word version:1 polynomial:17 jointree:47 adnan:1 hu:1 dramatic:1 contains:5 selecting:1 bc:1 yet:2 must:5 rote:1 leaf:1 selected:1 math:1 contribute:2 node:40 constructed:1 direct:1 differential:5 consists:1 hugin:10 darwiche:9 planning:1 multi:21 retriev:1 echnical:2 bounded:1 moreover:4 circuit:61 inward:8 fin... |
1,348 | 2,227 | Adaptive Classification by Variational Kalman
Filtering
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
We propose in this paper a pr... | 2227 |@word trial:3 repository:2 simulation:3 eng:2 initial:1 series:1 pub:2 tuned:1 freitas:2 recovered:1 must:1 john:1 additive:1 designed:1 update:5 v:2 stationary:11 half:3 leaf:1 isotropic:2 parametrization:2 data2:1 scotland:1 math:2 location:2 windowed:1 mathematical:1 combine:3 forgetting:1 expected:1 brain:1 a... |
1,349 | 2,228 | Learning in Zero-Sum Team Markov Games
Using Factored Value Functions
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 present a new method for learning good s... | 2228 |@word eliminating:1 contains:1 o2:14 current:1 yet:1 router:2 must:3 dechter:1 ronald:6 update:4 intelligence:2 fewer:1 discovering:1 beginning:1 mgl:1 complication:1 daphne:2 inside:2 introduce:3 manner:3 hardness:1 expected:3 blowup:2 behavior:1 planning:2 multi:1 ol:1 discounted:2 globally:1 little:1 enumerati... |
1,350 | 2,229 | Morton-Style Factorial Coding of Color in
Primary Visual Cortex
Javier R. Movellan
Institute for Neural Computation
University of California San Diego
La Jolla, CA 92093-0515
movellan@inc.ucsd.edu
Thomas Wachtler
Sloan Center for Theoretical Neurobiology
The Salk Institute
La Jolla, CA 92037, USA
thomas@salk.edu
Tho... | 2229 |@word trial:4 version:1 briefly:1 proportion:1 open:2 additively:1 rhesus:1 current:1 additive:1 subsequent:1 cue:2 selected:3 plane:2 colored:2 provides:3 direct:1 become:6 fixation:1 fitting:2 inside:1 introduce:2 indeed:1 expected:2 ica:2 behavior:1 brain:6 chi:4 krauskopf:2 audiovisual:1 decomposed:1 actual:3... |
1,351 | 223 | Effects of Firing Synchrony on Signal Propagation in Layered Networks
Effects of Firing Synchrony on Signal
Propagation in Layered Networks
G. T. Kenyon,l E. E. Fetz,2 R. D. Puffl
1 Department
of Physics FM-15, 2Department of Physiology and Biophysics SJ-40
University of Washington, Seattle, Wa. 98195
ABSTRACT
Spik... | 223 |@word simulation:7 propagate:1 tr:2 solid:4 initial:7 activation:1 must:1 physiol:1 subsequent:3 asymptote:1 designed:1 succeeding:1 v:1 ith:1 record:2 successive:2 simpler:1 mathematical:1 differential:1 gustafsson:1 combine:1 manner:1 examine:1 brain:1 td:1 little:1 project:1 underlying:1 circuit:1 mass:1 tic:1 ... |
1,352 | 2,230 | Transductive and Inductive Methods for
Approximate Gaussian Process Regression
1
Anton Schwaighofer1 2
TU Graz, Institute for Theoretical Computer Science
Inffeldgasse 16b, 8010 Graz, Austria
http://www.igi.tugraz.at/aschwaig
Volker Tresp2
Siemens Corporate Technology CT IC4
Otto-Hahn-Ring 6, 81739 Munich, Germany
ht... | 2230 |@word repository:1 briefly:1 inversion:2 seems:1 open:1 covariance:6 decomposition:1 nystr:9 reduction:1 contains:2 series:2 selecting:1 rkhs:1 comparing:1 yet:6 written:2 must:1 additive:2 nb2:4 confirming:1 greedy:7 selected:5 indicative:1 location:1 toronto:1 org:1 direct:1 consists:1 overhead:1 introduce:2 fa... |
1,353 | 2,231 | An Information Theoretic Approach to the
Functional Classification of Neurons
Elad Schneidman,1,2 William Bialek,1 and Michael J. Berry II2
1
Department of Physics and 2 Department of Molecular Biology
Princeton University, Princeton NJ 08544, USA
{elads,wbialek,berry}@princeton.edu
Abstract
A population of neurons ty... | 2231 |@word compression:1 seems:1 nd:3 open:1 cleanly:1 systeme:1 reduction:1 series:1 contains:1 rightmost:1 comparing:1 surprising:1 assigning:1 must:2 physiol:2 informative:2 shape:2 plot:1 discrimination:1 alone:1 greedy:3 selected:1 half:2 signalling:1 merger:7 record:2 provides:5 characterization:1 allerton:1 hei... |
1,354 | 2,232 | Support Vector Machines for
Multi ple-Instance Learning
Stuart Andrews, Ioannis Tsochantaridis and Thomas Hofmann
Department of Computer Science, Brown University, Providence, RI 02912
{stu,it,th}@cs.brown.edu
Abstract
This paper presents two new formulations of multiple-instance
learning as a maximum margin problem.... | 2232 |@word version:2 polynomial:1 seems:3 flach:1 heuristically:1 ratan:1 initial:2 configuration:1 contains:1 efficacy:1 document:7 outperforms:1 yet:3 written:1 mesh:2 shape:1 minmin:1 hofmann:1 designed:1 sponsored:1 update:8 pursued:1 selected:2 compo:1 ional:1 hyperplanes:1 simpler:1 zhang:1 scholkopf:1 consists:... |
1,355 | 2,233 | Circuit Model of Short-Term Synaptic Dynamics
Shih-Chii Liu, Malte Boegershausen, and Pascal Suter
Institute of Neuroinformatics
University of Zurich and ETH Zurich
Winterthurerstrasse 190
CH-8057 Zurich, Switzerland
shih@ini.phys.ethz.ch
Abstract
We describe a model of short-term synaptic depression that is derived
... | 2233 |@word trial:1 middle:2 pulse:1 simulation:5 solid:1 initial:1 liu:8 tuned:2 current:15 recovered:1 subsequent:1 plasticity:2 plot:1 update:3 device:1 short:11 schaik:3 infrastructure:1 node:1 simpler:1 along:3 c2:1 m7:1 differential:3 vpre:2 os:1 frequently:1 multi:1 terminal:1 pawelzik:1 vertebrate:1 baker:1 cir... |
1,356 | 2,234 | Learning with Multiple Labels
Rong Jin*
*School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213, USA
rong@es.emu.edu
Zoubin Ghahramanit*
tGatsby Computational Neuroscience Unit
University College London
London WCIN 3AR, UK
zoubin@gatsby.ucl.ae.uk
Abstract
In this paper, we study a special kind of... | 2234 |@word tried:1 accommodate:1 contains:1 series:1 esj:1 outperforms:1 ixj:2 si:8 yet:1 realistic:3 v:2 generative:1 leaf:2 selected:4 intelligence:2 mccallum:1 iterates:1 node:4 five:3 fitting:1 combine:1 presumed:1 isi:2 nor:1 distractor:8 multi:1 becomes:1 confused:1 estimating:1 moreover:1 kind:1 argmin:4 interp... |
1,357 | 2,235 | The Decision List Machine
Marina Sokolova
SITE, University of Ottawa
Ottawa, Ont. Canada,K1N-6N5
sokolova@site.uottawa.ca
Nathalie Japkowicz
SITE, University of Ottawa
Ottawa, Ont. Canada,K1N-6N5
nat@site.uottawa.ca
Mario Marchand
SITE, University of Ottawa
Ottawa, Ont. Canada,K1N-6N5
marchand@site.uottawa.ca
John Sha... | 2235 |@word repository:1 compression:15 seems:1 seek:1 bn:10 mention:1 accommodate:1 reduction:1 initial:2 contains:2 chervonenkis:1 err:1 current:1 si:1 must:1 john:3 cruz:1 ronald:1 partition:1 remove:6 greedy:8 warmuth:5 manfred:1 provides:2 simpler:1 constructed:4 symposium:1 consists:2 inside:3 introduce:1 x0:10 e... |
1,358 | 2,236 | Field-Programmable Learning Arrays
Seth Bridges, Miguel Figueroa, David Hsu, and Chris Diorio
Department of Computer Science and Engineering
University of Washington
114 Sieg Hall, Box 352350
Seattle, WA 98195-2350
seth,miguel,hsud,diorio @cs.washington.edu
Abstract
This paper introduces the Field-Programmable Lear... | 2236 |@word version:2 weq:1 pulse:1 simulation:3 seek:1 decomposition:1 solid:3 configuration:11 existing:1 current:17 comparing:1 must:1 realize:1 distant:2 enables:5 remove:1 plot:1 update:2 half:2 device:3 sram:3 core:6 provides:3 sieg:1 differential:7 symposium:1 viable:1 compose:2 combine:1 burr:1 manner:1 inter:8... |
1,359 | 2,237 | Reconstructing Stimulus-Driven Neural
Networks from Spike Times
Duane Q. Nykamp
UCLA Mathematics Department
Los Angeles, CA 90095
nykamp@math.ucla.edu
Abstract
We present a method to distinguish direct connections between two neurons from common input originating from other, unmeasured neurons.
The distinction is com... | 2237 |@word neurophysiology:1 simulation:9 simplifying:1 covariance:3 attainable:1 dramatic:1 minus:1 contains:2 amjad:1 rpi:2 must:2 written:2 realistic:2 j1:2 remove:1 alone:1 record:1 math:1 direct:23 become:1 viable:2 prove:1 manner:1 theoretically:2 expected:2 behavior:1 p1:3 ry:1 brain:2 perkel:1 becomes:2 xx:2 p... |
1,360 | 2,238 | Source Separation with a Sensor Array Using
Graphical Models and Subband Filtering
Hagai Attias
Microsoft Research
Redmond, WA 98052
hagaia@microsoft.com
Abstract
Source separation is an important problem at the intersection of several
fields, including machine learning, signal processing, and speech technology. Here... | 2238 |@word version:1 brandstein:1 proportion:1 seems:2 advantageous:1 seek:1 propagate:1 pressure:2 solid:1 harder:1 configuration:1 selecting:1 existing:2 xnj:1 imaginary:1 com:1 yet:2 dx:1 attracted:1 must:2 griebel:1 additive:1 shape:1 update:4 short:1 footing:2 filtered:1 coarse:1 node:2 windowed:2 blackwellized:1... |
1,361 | 2,239 | Artefactual Structure from Least Squares
Multidimensional Scaling
Nicholas P. Hughes
Department of Engineering Science
University of Oxford
Oxford, 0X1 3PJ, UK
nph@robots.ox.ac.uk
David Lowe
Neural Computing Research Group
Aston University
Birmingham, B4 7ET, UK
d.lowe@aston.ac.uk
Abstract
We consider the problem of... | 2239 |@word cox:2 sammon:4 seek:3 covariance:3 kent:1 tr:9 moment:1 reduction:3 configuration:18 series:2 disparity:8 initial:3 interestingly:1 informative:1 kdd:1 shape:1 analytic:1 plot:1 stationary:5 isotropic:11 normalising:2 provides:1 introduce:1 pairwise:2 inter:11 expected:2 indeed:1 examine:1 brain:2 globally:... |
1,362 | 224 | 2
Simmons
Acoustic-Imaging Computations by Echolocating Bats:
Unification of Diversely-Represented Stimulus
Features into Whole Images.
James A. Simmons
Department of Psychology
and Section of Neurobiology,
Division of Biology and Medicine
Brown University, Providence, RI 02912.
ABSTRACT
The echolocating bat, Eptes... | 224 |@word seems:1 pulse:1 orf:1 hannonic:1 mammal:1 carry:1 initial:3 series:1 tuned:1 comparing:1 must:3 grain:1 physiol:3 numerical:1 shape:4 discrimination:6 stationary:1 nervous:1 reciprocal:1 short:1 farther:2 compo:2 filtered:1 provides:1 location:4 along:7 constructed:1 consists:2 behavioral:1 expected:1 indeed... |
1,363 | 2,240 | Fast Sparse Gaussian Process Methods:
The Informative Vector Machine
Neil Lawrence
University of Sheffield
211 Portobello Street
Sheffield, S1 4DP
neil@dcs.shef.ac.uk
Matthias Seeger
University of Edinburgh
5 Forrest Hill
Edinburgh, EH1 2QL
seeger@dai.ed.ac.uk
Ralf Herbrich
Microsoft Research Ltd
7 J J Thomson Avenue... | 2240 |@word version:2 compression:1 open:1 heuristically:1 d2:3 covariance:8 evaluating:1 pick:1 nystr:1 solid:1 reduction:1 moment:3 substitution:1 contains:2 score:9 att:1 initial:3 bitmap:1 current:2 com:2 comparing:3 yet:2 written:1 must:1 john:1 numerical:1 informative:4 remove:2 plot:1 update:7 discrimination:1 g... |
1,364 | 2,241 | Approximate Linear Programming for
Average-Cost Dynamic Programming
Daniela Pucci de Farias
IBM Almaden Research Center
650 Harry Road, San Jose, CA 95120
pucci@mit.edu
Benjamin Van Roy
Department of Management Science and Engineering
Stanford University
Stanford, CA 94305
bvr@stanford.edu
Abstract
This paper extends... | 2241 |@word exploitation:1 version:4 manageable:1 polynomial:1 norm:3 advantageous:1 c0:1 open:1 crite:1 incurs:1 initial:1 selecting:2 staterelevance:2 current:3 comparing:1 surprising:1 yet:1 must:1 shape:2 drop:1 stationary:7 greedy:10 selected:1 guess:2 intelligence:1 accordingly:1 prespecified:1 provides:1 contrib... |
1,365 | 2,242 | Forward-Decoding Kernel-Based
Phone Sequence Recognition
Shantanu Chakrabartty and Gert Cauwenberghs
Center for Language and Speech Processing
Department of Electrical and Computer Engineering
Johns Hopkins University, Baltimore MD 21218
{shantanu,gert}@jhu.edu
Abstract
Forward decoding kernel machines (FDKM) combine... | 2242 |@word polynomial:1 norm:1 proportion:1 thereby:1 recursively:1 initial:2 substitution:1 series:1 current:1 contextual:1 si:1 yet:1 reminiscent:1 import:1 john:1 subsequent:1 speakerindependent:1 girosi:1 moreno:1 discrimination:3 generative:1 fewer:1 intelligence:2 prohibitive:2 steepest:2 core:1 provides:4 contr... |
1,366 | 2,243 | A Digital Antennal Lobe for Pattern
Equalization: Analysis and Design
Alex Holub, Gilles Laurent and Pietro Perona
Computation and Neural Systems, California Institute of Technology
holub@caltech.edu, laurentg@caltech.edu, perona@caltech.edu
Abstract
Re-mapping patterns in order to equalize their distribution may
gre... | 2243 |@word auu:1 open:1 calculus:1 simulation:5 lobe:9 decorrelate:1 initial:6 neeman:1 current:1 activation:1 yet:1 must:2 dive:1 analytic:1 designed:2 plot:1 update:3 half:2 patterning:1 nervous:1 characterization:1 location:2 mathematical:1 olfactory:6 introduce:1 acquired:1 alm:1 expected:1 indeed:1 presumed:1 beh... |
1,367 | 2,244 | Parametric Mixture Models for
Multi-Labeled Text
Naonori Ueda
Kazumi Saito
NTT Communication Science Laboratories
2-4 Hikaridai, Seikacho, Kyoto 619-0237 Japan
{ueda,saito}@cslab.kecl.ntt.co.jp
Abstract
We propose probabilistic generative models, called parametric mixture models (PMMs), for multiclass, multi-labeled t... | 2244 |@word trial:2 version:3 proportion:1 seems:1 d2:1 tried:1 initial:1 tuned:1 document:14 current:1 com:2 assigning:1 john:1 fn:2 wanted:1 update:6 discrimination:2 generative:6 prohibitive:1 greedy:1 mccallum:1 ith:2 steepest:1 blei:2 simpler:2 five:4 dn:2 constructed:1 along:1 become:1 symposium:1 consists:2 mann... |
1,368 | 2,245 | Learning a Forward Model of a Reflex
Bernd Porr and Florentin W?org?otter
Computational Neuroscience
Psychology
University of Stirling
FK9 4LR Stirling, UK
bp1,faw1 @cn.stir.ac.uk
Abstract
We develop a systems theoretical treatment of a behavioural system that
interacts with its environment in a closed loop situati... | 2245 |@word trial:1 eliminating:1 open:1 simulation:1 pulse:1 thereby:1 carry:2 initial:2 series:1 daniel:1 reaction:4 imaginary:1 existing:1 comparing:1 current:2 must:4 john:2 realize:1 subsequent:1 unchanging:2 motor:1 drop:1 pursued:1 obsolete:2 indicative:1 accordingly:2 isotropic:4 iso:5 lr:1 filtered:12 org:1 co... |
1,369 | 2,246 | Expected and Unexpected Uncertainty:
ACh and NE in the Neocortex
Angela Yu
Peter Dayan
Gatsby Computational Neuroscience Unit
17 Queen Square, London WC1N 3AR, United Kingdom.
feraina@gatsby.ucl.ac.uk
dayan@gatsby.ucl.ac.uk
Abstract
Inference and adaptation in noisy and changing, rich sensory environments are rife wit... | 2246 |@word neurophysiology:1 noradrenergic:2 trial:3 version:2 hippocampus:2 stronger:1 nd:1 extinction:1 open:1 hsieh:1 dramatic:3 thereby:1 serie:1 initial:2 series:1 score:1 united:1 interestingly:2 existing:2 current:2 contextual:5 activation:1 scatter:1 yet:1 must:1 realistic:1 subsequent:1 plasticity:3 gv:1 asym... |
1,370 | 2,247 | Linear Combinations of Optic Flow Vectors for
Estimating Self-Motion ?a Real-World Test of a
Neural Model
Matthias O. Franz
MPI f?ur biologische Kybernetik
Spemannstr. 38
D-72076 T?ubingen, Germany
mof@tuebingen.mpg.de
Javaan S. Chahl
Center of Visual Sciences, RSBS
Australian National University
Canberra, ACT, Austra... | 2247 |@word neurophysiology:1 trial:1 achievable:1 stronger:1 open:1 grey:1 covariance:6 tr:3 contains:1 series:1 tuned:3 current:6 written:2 additive:2 happen:1 wx:4 hofmann:1 hoping:1 v:1 filtered:1 nearness:6 location:4 height:1 mathematical:1 along:6 direct:1 corridor:2 consists:1 manner:1 inter:1 indeed:1 mpg:1 ex... |
1,371 | 2,248 | Concentration Inequalities for the Missing Mass
and for Histogram Rule Error
David McAllester
Toyota Technological Institute at Chicago
mcallester@tti-c.org
Luis Ortiz
University of Pennsylvania
leo@cis.upenn.edu
Abstract
This paper gives distribution-free concentration inequalities for the missing mass and the erro... | 2248 |@word version:1 bigram:1 stronger:1 bf:1 open:3 tr:1 multicommodity:1 moment:4 selecting:1 ka:1 written:2 luis:2 chicago:2 intelligence:1 item:4 hamiltonian:1 desh:1 bvu:1 clarified:1 org:1 mcdiarmid:1 simpler:1 symposium:1 prove:11 upenn:1 frequently:1 mechanic:2 nor:1 decreasing:4 increasing:4 estimating:1 unde... |
1,372 | 2,249 | Combining Dimensions and Features in
Similarity-Based Representations
Daniel J. Navarro
Department of Psychology
Ohio State University
navarro.20@osu.edu
Michael D. Lee
Department of Psychology
University of Adelaide
michael.lee@psychology.adelaide.edu.au
Abstract
This paper develops a new representational model of ... | 2249 |@word cox:4 version:1 seems:1 nd:2 attended:1 minus:1 daniel:1 denoting:1 current:6 ka:2 recovered:1 marquardt:2 must:2 fn:2 numerical:2 additive:9 partition:1 shape:1 analytic:1 interpretable:1 update:1 fewer:2 cult:1 ith:8 mental:1 parameterizations:1 location:3 mathematical:1 along:2 ect:2 combine:2 fitting:2 ... |
1,373 | 225 | 574
Nowlan
Maximum Likelihood Competitive Learning
Steven J. Nowlan 1
Department of Computer Science
University of Toronto
Toronto, Canada
M5S lA4
ABSTRACT
One popular class of unsupervised algorithms are competitive algorithms. In the traditional view of competition, only one competitor,
the winner, adapts for any ... | 225 |@word version:6 selforganization:1 proportion:2 duda:3 dekker:1 simulation:4 tried:1 covariance:2 independant:1 tr:1 barney:2 yaleu:1 reduction:2 configuration:1 series:1 contains:1 initial:1 current:7 comparing:3 nowlan:10 activation:2 assigning:1 must:1 john:2 girosi:2 update:3 discrimination:2 selected:3 coarse... |
1,374 | 2,250 | Mean-Field Approach to a Probabilistic Model
in Information Retrieval
Bin Wu, K. Y. Michael Wong
Department of Physics
Hong Kong University of Science and Technology
Clear Water Bay, Hong Kong
phwbd@ust.hk phkywong@ust.hk
David Bodoff
Department of ISMT
Hong Kong University of Science and Technology
Clear Water Bay, Ho... | 2250 |@word kong:5 repository:1 version:2 inversion:1 tedious:3 relevancy:8 decomposition:1 pick:1 carry:1 document:58 systemwide:2 outperforms:1 imaginary:1 comparing:1 ust:3 written:1 cottrell:1 numerical:1 subsequent:2 partition:1 shape:1 enables:1 hofmann:1 hypothesize:2 record:1 hypersphere:3 provides:1 location:2... |
1,375 | 2,251 | Replay, Repair and Consolidation
Szabolcs K?ali
Institute of Experimental Medicine
Hungarian Academy of Sciences
Budapest 1450, Hungary
kali@koki.hu
Peter Dayan
Gatsby Computational Neuroscience Unit
University College London
17 Queen Square, London WC1N 3AR, U.K.
dayan@gatsby.ucl.ac.uk
Abstract
A standard view of me... | 2251 |@word repository:2 version:2 briefly:1 hippocampus:36 seems:2 anterograde:2 extinction:1 c0:1 hu:1 simulation:3 covariance:3 contrastive:1 solid:1 shot:1 initial:3 contains:1 efficacy:2 existing:4 current:1 blank:1 activation:3 yet:2 buckingham:1 must:1 distant:3 subsequent:1 happen:1 plasticity:11 visible:3 opin... |
1,376 | 2,252 | Categorization Under Complexity: A Unified
MDL Account of Human Learning of Regular
and Irregular Categories
Jacob Feldman*
Department of Psychology
Center for Cognitive Science
Rutgers University
Piscataway, NJ 08854
jacob@ruccs.rutgers.edu
David Fass
Department of Psychology
Center for Cognitive Science
Rutgers Uni... | 2252 |@word determinant:1 briefly:2 seems:2 nd:1 simulation:1 jacob:3 mention:1 contains:1 subjective:4 must:1 plot:1 v:1 pursued:1 selected:2 rnxn:1 ofmathematical:2 location:1 instructs:1 five:1 height:1 mathematical:1 along:1 ect:1 viable:1 consists:1 lx2:1 manipulability:1 manner:1 theoretically:1 indeed:2 behavior... |
1,377 | 2,253 | Automatic Acquisition and Efficient
Representation of Syntactic Structures
Zach Solan, Eytan Ruppin, David Horn
Faculty of Exact Sciences
Tel Aviv University
Tel Aviv, Israel 69978
{rsolan,ruppin,horn}@post.tau.ac.il
Shimon Edelman
Department of Psychology
Cornell University
Ithaca, NY 14853, USA
se37@cornell.edu
Ab... | 2253 |@word cu:2 briefly:2 faculty:1 compression:1 stronger:1 c0:2 gradual:1 solan:1 concise:2 solid:1 recursively:5 initial:4 contains:2 selecting:1 prefix:1 existing:3 current:1 com:1 comparing:1 activation:7 yet:4 must:2 exposing:1 cindy:7 enables:2 asymptote:1 designed:2 progressively:1 childes:4 v:2 generative:3 l... |
1,378 | 2,254 | Hidden Markov Model of Cortical Synaptic
Plasticity: Derivation of the Learning Rule
Michael Eisele
W. M. Keck Center
for Integrative Neuroscience
San Francisco, CA 94143-0444
eisele@phy.ucsf.edu
Kenneth D. Miller
W. M. Keck Center
for Integrative Neuroscience
San Francisco, CA 94143-0444
ken@phy.ucsf.edu
Abstract
C... | 2254 |@word stronger:1 km:1 integrative:2 simulation:1 paulsen:1 thereby:2 recursively:1 initial:1 phy:2 past:13 current:1 yet:1 written:2 numerical:1 distant:1 plasticity:18 opin:1 update:7 alone:2 selected:1 accordingly:1 short:1 revisited:1 firstly:1 mathematical:1 dan:2 combine:2 introduce:1 expected:1 rapid:1 litt... |
1,379 | 2,255 | The Effect of Singularities in a Learning
Machine when the True Parameters Do
Not Lie on Such Singularities
Sumio Watanabe
Precision and Intelligence Laboratory
Tokyo Institute of Technology
4259 Nagatsuta, Midori-ku, Yokohama, 226-8503 Japan
E-mail: swatanab@pi.titech.ac.jp
Shun-ichi Amari
Laboratory for Mathematical... | 2255 |@word effect:7 true:26 norm:1 hence:2 direction:1 tokyo:1 laboratory:2 parametric:2 cos2:1 stochastic:2 bn:1 covariance:2 kb:5 eg:11 sin:4 shun:1 education:1 distance:5 hx:3 coincides:1 criterion:2 generalization:22 mail:2 singularity:37 longitudinal:1 wako:1 secondly:1 reason:1 clarify:4 ka:2 comparing:2 nt:1 ho... |
1,380 | 2,256 | Improving Transfer Rates in Brain Computer
Interfacing: A Case Study
Peter Meinicke, Matthias Kaper, Florian Hoppe, Manfred Heumann and Helge Ritter
University of Bielefeld
Bielefeld, Germany
{pmeinick, mkaper, fhoppe, helge} @techfak.uni-bielefeld.de
Abstract
In this paper we present results of a study on brain comp... | 2256 |@word neurophysiology:2 trial:15 version:1 briefly:1 meinicke:2 instruction:1 tried:2 attainable:1 attended:1 thereby:2 initial:4 series:4 contains:1 score:7 o2:1 outperforms:1 current:1 realize:2 tetraplegic:1 subsequent:1 realistic:1 motor:1 designed:1 discrimination:1 alone:1 selected:5 device:4 slowing:1 reco... |
1,381 | 2,257 | Cluster Kernels for
Semi-Supervised Learning
Olivier Chapelle, Jason Weston, Bernhard Scholkopf
Max Planck Institute for Biological Cybernetics, 72076 Tiibingen, Germany
{first. last} @tuebingen.mpg.de
Abstract
We propose a framework to incorporate unlabeled data in kernel
classifier, based on the idea that two point... | 2257 |@word trial:1 middle:1 version:2 polynomial:6 covariance:1 document:1 outperforms:1 existing:1 ka:1 surprising:1 ij1:3 readily:1 happen:1 shape:1 analytic:1 designed:1 discrimination:1 generative:6 selected:3 intelligence:1 mccallum:1 argm:1 lr:1 klx:1 scholkopf:3 consists:1 indeed:1 behavior:3 mpg:1 automaticall... |
1,382 | 2,258 | Critical Lines in Symmetry of Mixture Models
and its Application to Component Splitting
Kenji Fukumizu
Institute of Statistical
Mathematics
Tokyo 106-8569 Japan
fukumizu@ism.ac.jp
Shotaro Akaho
AIST
Tsukuba 305-8568 Japan
s.akaho@aist.go.jp
Shun-ichi Amari
RIKEN
Wako 351-0198 Japan
amari@brain.riken.go.jp
Abstract
... | 2258 |@word trial:2 compression:2 loading:2 r:2 covariance:6 decomposition:2 tr:1 moment:1 initial:1 series:1 hereafter:2 selecting:1 document:1 bc:1 wako:1 must:1 realize:1 additive:1 shape:1 cheap:1 unidentifiability:1 plane:2 parametrization:3 blei:1 toronto:1 along:2 direct:2 consists:1 fitting:1 manner:1 brain:1 u... |
1,383 | 2,259 | How the Poverty of the Stimulus
Solves the Poverty of the Stimulus
WilleIll ZuideIlla
Language Evolution and Computation Research Unit
and Institute for Cell, Animal and Population Biology
University of Edinburgh
40 George Square, Edinburgh EH8 9LL, United Kingdom
jelle@ling.ed.ac.uk
Abstract
Language acquisition is ... | 2259 |@word version:2 compression:8 open:1 pieter:1 simulation:5 solan:1 pressure:1 initial:4 substitution:1 contains:1 united:1 bc:1 interestingly:1 existing:1 current:1 comparing:1 cad:1 surprising:1 lang:2 must:2 john:2 subsequent:2 shape:1 designed:1 fund:1 v:1 infant:3 alone:1 fewer:1 generative:1 smith:1 short:1 ... |
1,384 | 226 | 676
Baum
The Perceptron Algorithm Is Fast tor
Non-Malicious Distributions
Erice B. Baum
NEC Research Institute
4 Independence Way
Princeton, NJ 08540
Abstract: Within the context of Valiant's protocol for learning, the Perceptron
algorithm is shown to learn an arbitrary half-space in time O(r;;) if D, the probabili... | 226 |@word trial:2 polynomial:15 seems:2 rno:3 seek:1 innermost:1 pick:2 initial:1 contains:2 chervonenkis:4 current:1 z2:1 nt:1 yet:3 must:4 readily:3 tot:1 update:20 half:14 warmuth:1 plane:1 hamiltonian:1 draft:1 ron:4 hyperplanes:2 five:1 along:1 c2:4 become:1 initiative:1 prove:1 symp:1 inside:6 expected:1 indeed:... |
1,385 | 2,260 | Handling Missing Data with Variational
Bayesian Learning of ICA
Kwokleung Chan, Te-Won Lee and Terrence Sejnowski
The Salk Institute, Computational Neurobiology Laboratory,
10010 N. Torrey Pines Road,
La Jolla,, CA 92037, USA
{kwchan,tewon,terry}@salk.edu
Abstract
Missing data is common in real-world datasets and is ... | 2260 |@word h:1 manageable:1 polynomial:5 nd:1 covariance:1 solid:4 reduction:3 recovered:4 nt:1 yet:1 subsequent:1 plot:2 update:1 generative:5 short:1 direct:1 beta:1 symposium:1 fitting:1 inside:1 introduce:2 expected:1 ica:30 xz:3 brain:1 discounted:1 automatically:1 ont:6 encouraging:1 little:1 xx:3 notation:1 ske... |
1,386 | 2,261 | Margin Analysis of the LVQ Algorithm
Koby Crammer
kobics@cs.huji.ac.il
Ran Gilad-Bachrach
ranb@cs.huji.ac.il
Amir Navot
anavot@cs.huji.ac.il
Naftali Tishby
tishby@cs.huji.ac.il
School of Computer Science and Engineering and
Interdisciplinary Center for Neural Computation
The Hebrew University, Jerusalem, Israel
Ab... | 2261 |@word version:8 norm:1 seems:1 that2:1 seek:1 dramatic:1 initial:1 contains:1 selecting:1 current:1 comparing:1 buckingham:2 attracted:1 designed:1 update:12 v:1 discrimination:1 amir:1 accordingly:1 xk:2 lr:1 iterates:1 provides:1 boosting:3 constructed:1 direct:1 become:2 symposium:1 surprised:1 incorrect:4 con... |
1,387 | 2,262 | ?Name That Song!?: A Probabilistic Approach
to Querying on Music and Text
Eric Brochu
Department of Computer Science
University of British Columbia
Vancouver, BC, Canada
ebrochu@cs.ubc.ca
Nando de Freitas
Department of Computer Science
University of British Columbia
Vancouver, BC, Canada
nando@cs.ubc.ca
Abstract
We ... | 2262 |@word bigram:1 instrumental:1 open:1 initial:2 plentiful:1 series:2 score:18 contains:2 bc:2 document:23 freitas:3 existing:1 contextual:2 fn:1 numerical:1 hofmann:1 polyphonic:2 alone:2 intelligence:2 selected:4 guess:1 website:1 item:1 summarisation:1 beginning:2 short:1 blei:1 iterates:1 lexicon:1 saturday:1 s... |
1,388 | 2,263 | Nash Propagation for Loopy Graphical Games
Luis E. Ortiz
Michael Kearns
Department of Computer and Information Science
University of Pennsylvania
leortiz,mkearns @cis.upenn.edu
Abstract
We introduce NashProp, an iterative and local message-passing algorithm for computing Nash equilibria in multi-player games repres... | 2263 |@word trial:2 briefly:1 manageable:1 polynomial:3 stronger:1 seems:2 pick:1 reduction:2 mkearns:1 series:2 interestingly:1 discretization:5 chordal:15 yet:2 assigning:1 must:9 luis:1 drop:1 plot:3 alone:1 intelligence:2 provides:3 node:14 preference:4 incorrect:1 consists:1 introduce:3 upenn:1 expected:5 behavior... |
1,389 | 2,264 | Annealing and the Rate Distortion Problem
Albert E. Parker
Department of Mathematical Sciences
Montana State University
Bozeman, MT 59771
parker@math.montana.edu
Tom?as? Gedeon
Department of Mathematical Sciences
Montana State University
gedeon@math.montana.edu
Alexander G. Dimitrov
Center for Computational Biology
... | 2264 |@word determinant:1 mri:1 compression:3 norm:1 hu:2 q1:1 initial:5 series:1 jaynes:1 intriguing:1 must:1 john:3 numerical:10 happen:1 stationary:1 guess:5 quantizer:6 math:2 location:1 allerton:1 mathematical:2 along:1 constructed:1 symposium:1 ik:3 introduce:3 behavior:1 p1:3 equivariant:1 cpu:1 becomes:1 begin:... |
1,390 | 2,265 | Branching Law for Axons
Dmitri B. Chklovskii and Armen Stepanyants
Cold Spring Harbor Laboratory
1 Bungtown Rd.
Cold Spring Harbor, NY 11724
mitya@cshl. edu stepanya@cshl.edu
Abstract
What determines the caliber of axonal branches? We pursue the
hypothesis that the axonal caliber has evolved to minimize signal
propag... | 2265 |@word cylindrical:1 squid:1 d2:2 simulation:1 ld:2 contains:1 reaction:1 comparing:1 si:1 physiol:2 motor:1 plot:1 v:1 nervous:1 adal:1 along:9 multi:1 terminal:1 actual:1 increasing:1 vertebrate:1 cherniak:1 circuit:1 mass:2 what:2 evolved:1 viscous:3 pursue:1 minimizes:3 giant:1 attenuation:1 thicker:1 um:2 t1:... |
1,391 | 2,266 | FloatBoost Learning for Classification
Stan Z. Li
Microsoft Research Asia
Beijing, China
ZhenQiu Zhang
Institute of Automation
CAS, Beijing, China
Heung-Yeung Shum
Microsoft Research Asia
Beijing, China
HongJiang Zhang
Microsoft Research Asia
Beijing, China
Abstract
AdaBoost [3] minimizes an upper error bound w... | 2266 |@word version:2 stronger:2 norm:1 shum:2 imposter:1 past:1 current:1 com:1 yet:2 additive:2 numerical:1 girosi:1 remove:2 drop:3 designed:1 update:2 v:1 newest:1 half:1 fewer:6 selected:4 greedy:1 intelligence:2 provides:1 boosting:12 zhang:4 constructed:1 consists:1 eleventh:1 rapid:1 multi:2 floatboost:27 littl... |
1,392 | 2,267 | Data-Dependent Bounds for Bayesian
Mixture Methods
Ron Meir
Department of Electrical Engineering
Technion, Haifa 32000, Israel
rmeir@ee.technion.ac.il
Tong Zhang
IBM T.J. Watson Research Center
Yorktown Heights, NY 10598, USA
tzhang@watson.ibm.com
Abstract
We consider Bayesian mixture approaches, where a predictor is... | 2267 |@word polynomial:1 norm:5 twelfth:1 p0:8 paid:1 pick:1 chervonenkis:1 denoting:1 current:1 com:1 subsequent:1 statis:1 fund:1 implying:1 provides:2 boosting:3 ron:1 herbrich:2 zhang:3 height:1 along:1 constructed:3 direct:2 become:1 eleventh:1 introduce:1 expected:1 behavior:1 themselves:1 cardinality:1 provided:... |
1,393 | 2,268 | Shape Recipes: Scene Representations that Refer
to the Image
William T. Freeman and Antonio Torralba
Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139
{wtf, torralba}@ai.mit.edu
Abstract
The goal of low-level vision is to estimate an underlying scene, given
an observed image... | 2268 |@word illustrating:1 version:1 compression:1 nd:1 linearized:1 decomposition:2 solid:2 shading:10 initial:1 configuration:1 series:3 contains:1 disparity:1 bitmap:1 comparing:1 visible:3 unchanging:1 shape:165 plot:6 alone:1 intelligence:3 half:4 cue:1 leaf:1 sys:1 filtered:1 provides:1 location:2 centerline:1 zh... |
1,394 | 2,269 | Ranking with Large Margin Principle: Two
Approaches*
Amnon Shashua
School of CS&E
Hebrew University of Jerusalem
Jerusalem 91904, Israel
email: shashua@cs.huji.ac.il
Anat Levin
School of CS&E
Hebrew University of Jerusalem
Jerusalem 91904, Israel
email: alevin@cs.huji.ac.il
Abstract
We discuss the problem of ranking... | 2269 |@word norm:1 nd:3 seek:1 eng:1 carry:1 c1ass:1 contains:2 denoting:1 past:1 existing:1 recovered:1 bd:1 must:1 alone:2 half:1 selected:1 intelligence:1 item:4 vanishing:2 herbrich:1 hyperplanes:13 direct:2 become:1 symposium:1 ik:3 scholkopf:1 inside:1 introduce:3 indeed:1 multi:7 actual:1 becomes:2 provided:1 un... |
1,395 | 227 | Meiosis Networks
Meiosis Networks
1
Stephen Jose Hanson
Learning and Knowledge Acquisition Group
Siemens Research Center
Princeton, NJ 08540
ABSTRACT
A central problem in connectionist modelling is the control of
network and architectural resources during learning. In the present
approach, weights reflect a coarse ... | 227 |@word version:1 seems:1 pulse:1 dramatic:1 efficacy:1 existing:1 unction:1 activation:2 must:4 written:1 distant:1 entertaining:1 cheap:1 update:6 stationary:2 half:2 nervous:2 provides:2 coarse:2 node:11 parameterizations:1 simpler:1 nodal:1 burst:1 constructed:1 beta:2 consists:1 burr:1 introduce:1 degress:1 bra... |
1,396 | 2,270 | An Estimation-Theoretic Framework for
the Presentation of Multiple Stimuli
Christian W. Eurich?
Institute for Theoretical Neurophysics
University of Bremen
Otto-Hahn-Allee 1
D-28359 Bremen, Germany
eurich@physik.uni-bremen.de
Abstract
A framework is introduced for assessing the encoding accuracy and
the discriminatio... | 2270 |@word wiesel:1 physik:2 attended:9 solid:3 extrastriate:1 configuration:3 past:1 diagonalized:1 current:1 attracted:1 written:4 mst:2 physiol:1 christian:1 discrimination:4 ith:1 detecting:1 zhang:1 along:1 c2:3 alert:1 marley:1 inside:1 grieve:1 x0:1 expected:3 behavior:5 distractor:1 considering:1 increasing:1 ... |
1,397 | 2,271 | Dynamic Structure Super-Resolution
Amos J Storkey
Institute of Adaptive and Neural Computation
Division of Informatics and Institute of Astronomy
University of Edinburgh
5 Forrest Hill, Edinburgh UK
a.storkey@ed.ac.uk
Abstract
The problem of super-resolution involves generating feasible higher
resolution images, whic... | 2271 |@word trial:1 version:2 indiscriminate:1 proportionality:1 km:2 gradual:1 rgb:1 tr:1 shading:1 series:1 zij:5 denoting:1 subjective:1 tackling:1 must:1 written:1 realistic:1 visible:3 additive:2 shape:2 update:1 half:1 intelligence:2 website:2 provides:3 node:16 ames:1 simpler:1 qij:8 consists:2 manner:3 presumed... |
1,398 | 2,272 | Fast Kernels for String and Tree Matching
S. V. N. Vishwanathan
Dept. of Compo Sci. & Automation
Indian Institute of Science
Bangalore, 560012, India
vishy@csa . iisc . ernet . in
Alexander J. Smola
Machine Learning Group, RSISE
Australian National University
Canberra, ACT 0200, Australia
Alex . Smola@anu . edu . au
... | 2272 |@word compression:1 simplifying:1 incurs:1 thereby:1 contains:3 score:6 document:1 prefix:17 outperforms:1 current:1 comparing:1 yet:2 must:2 parsing:1 cruz:1 numerical:1 remove:2 plot:1 leaf:16 selected:1 amir:1 accordingly:1 compo:1 pointer:4 eskin:1 detecting:1 node:54 location:1 cse:1 herbrich:1 along:4 const... |
1,399 | 2,273 | Graph-Driven Features Extraction from
Microarray Data using Diffusion Kernels and
Kernel CCA
Jean-Philippe Vert
Ecole des Mines de Paris
Jean-Philippe.Vert@mines.org
Minoru Kanehisa
Bioinformatics Center, Kyoto University
kanehisa@kuicr.kyoto-u.ac.jp
Abstract
We present an algorithm to extract features from high-dime... | 2273 |@word briefly:2 version:2 polynomial:1 norm:8 seems:1 proportion:1 kondor:1 r:1 tried:1 decomposition:5 series:5 ecole:1 rkhs:9 bc:1 reaction:9 comparing:1 manuel:1 activation:2 girosi:1 enables:1 reproducible:4 v:5 provides:1 node:1 successive:6 org:1 mathematical:1 kasarskis:1 consists:2 pathway:8 indeed:2 expe... |
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