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» Inferring Elapsed Time from Stochastic Neural Processes
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NIPS
2007
13 years 6 months ago
Inferring Elapsed Time from Stochastic Neural Processes
Many perceptual processes and neural computations, such as speech recognition, motor control and learning, depend on the ability to measure and mark the passage of time. However, ...
Misha Ahrens, Maneesh Sahani
NIPS
2007
13 years 6 months ago
Measuring Neural Synchrony by Message Passing
A novel approach to measure the interdependence of two time series is proposed, referred to as “stochastic event synchrony” (SES); it quantifies the alignment of two point pr...
Justin Dauwels, François B. Vialatte, Tomas...
SC
1995
ACM
13 years 8 months ago
Distributing a Chemical Process Optimization Application Over a Gigabit Network
We evaluate the impact of a gigabit network on the implementation of a distributed chemical process optimization application. The optimization problem is formulated as a stochasti...
Robert L. Clay, Peter Steenkiste
CSDA
2008
122views more  CSDA 2008»
13 years 5 months ago
Bayesian inference for nonlinear multivariate diffusion models observed with error
Diffusion processes governed by stochastic differential equations (SDEs) are a well established tool for modelling continuous time data from a wide range of areas. Consequently, t...
Andrew Golightly, Darren J. Wilkinson
SCP
2000
119views more  SCP 2000»
13 years 4 months ago
Automated compositional Markov chain generation for a plain-old telephone system
Obtaining performance models, like Markov chains and queueing networks, for systems of significant complexity and magnitude is a difficult task that is usually tackled using human...
Holger Hermanns, Joost-Pieter Katoen