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» Learning for stochastic dynamic programming
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MMAS
2011
Springer
14 years 9 months ago
Scalable Bayesian Reduced-Order Models for Simulating High-Dimensional Multiscale Dynamical Systems
While existing mathematical descriptions can accurately account for phenomena at microscopic scales (e.g. molecular dynamics), these are often high-dimensional, stochastic and thei...
Phaedon-Stelios Koutsourelakis, Elias Bilionis
112
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ATAL
2005
Springer
15 years 8 months ago
Rapid on-line temporal sequence prediction by an adaptive agent
Robust sequence prediction is an essential component of an intelligent agent acting in a dynamic world. We consider the case of near-future event prediction by an online learning ...
Steven Jensen, Daniel Boley, Maria L. Gini, Paul R...
112
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GECCO
2007
Springer
200views Optimization» more  GECCO 2007»
15 years 8 months ago
Adaptive genetic programming for option pricing
Genetic Programming (GP) is an automated computational programming methodology, inspired by the workings of natural evolution techniques. It has been applied to solve complex prob...
Zheng Yin, Anthony Brabazon, Conall O'Sullivan
151
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PDPTA
2007
15 years 4 months ago
Python-based Distributed Programming with Trickle
Abstract Trickle is a an extension to the Python programming language that provides explicit but simple mechanisms to write distributed scripts and programs. Trickle links together...
Gregory Benson, Alexey Fedosov
184
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Publication
226views
17 years 20 days ago
A Gentle Introduction to Multi-stage Programming
Multi-stage programming (MSP) is a paradigm for developing generic software that does not pay a runtime penalty for this generality. This is achieved through concise, carefully-des...
Walid Taha