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UAI
1996
14 years 11 months ago
Bayesian Learning of Loglinear Models for Neural Connectivity
This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
Kathryn B. Laskey, Laura Martignon
NIPS
2003
14 years 11 months ago
Minimising Contrastive Divergence in Noisy, Mixed-mode VLSI Neurons
This paper presents VLSI circuits with continuous-valued probabilistic behaviour realized by injecting noise into each computing unit(neuron). Interconnecting the noisy neurons fo...
Hsin Chen, Patrice Fleury, Alan F. Murray
CORR
2010
Springer
127views Education» more  CORR 2010»
14 years 9 months ago
Statistical and Computational Tradeoffs in Stochastic Composite Likelihood
Maximum likelihood estimators are often of limited practical use due to the intensive computation they require. We propose a family of alternative estimators that maximize a stoch...
Joshua Dillon, Guy Lebanon
FOCI
2007
IEEE
15 years 4 months ago
Almost All Learning Machines are Singular
— A learning machine is called singular if its Fisher information matrix is singular. Almost all learning machines used in information processing are singular, for example, layer...
Sumio Watanabe
62
Voted
SC
2000
ACM
15 years 2 months ago
Parallel Algorithms for Radiation Transport on Unstructured Grids
The method of discrete ordinates is commonly used to solve the Boltzmann radiation transport equation for applications ranging from simulations of fires to weapons effects. The ...
Steve Plimpton, Bruce Hendrickson, Shawn Burns, Wi...