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JCNS
2010
90views more  JCNS 2010»
15 years 1 months ago
Fast Kalman filtering on quasilinear dendritic trees
Optimal filtering of noisy voltage signals on dendritic trees is a key problem in computational cellular neuroscience. However, the state variable in this problem -- the vector of...
Liam Paninski
JMLR
2010
137views more  JMLR 2010»
15 years 1 months ago
Importance Sampling for Continuous Time Bayesian Networks
A continuous time Bayesian network (CTBN) uses a structured representation to describe a dynamic system with a finite number of states which evolves in continuous time. Exact infe...
Yu Fan, Jing Xu, Christian R. Shelton
JMLR
2010
103views more  JMLR 2010»
15 years 1 months ago
Learning Nonlinear Dynamic Models from Non-sequenced Data
Virtually all methods of learning dynamic systems from data start from the same basic assumption: the learning algorithm will be given a sequence of data generated from the dynami...
Tzu-Kuo Huang, Le Song, Jeff Schneider
IWSOS
2011
Springer
14 years 9 months ago
Evolving Self-organizing Cellular Automata Based on Neural Network Genotypes
Abstract This paper depicts and evaluates an evolutionary design process for generating a complex self-organizing multicellular system based on Cellular Automata (CA). We extend th...
Wilfried Elmenreich, István Fehérv&a...
ML
2012
ACM
413views Machine Learning» more  ML 2012»
14 years 2 months ago
Gradient-based boosting for statistical relational learning: The relational dependency network case
Dependency networks approximate a joint probability distribution over multiple random variables as a product of conditional distributions. Relational Dependency Networks (RDNs) are...
Sriraam Natarajan, Tushar Khot, Kristian Kersting,...