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ICML
2010
IEEE
13 years 9 months ago
Probabilistic Backward and Forward Reasoning in Stochastic Relational Worlds
Inference in graphical models has emerged as a promising technique for planning. A recent approach to decision-theoretic planning in relational domains uses forward inference in d...
Tobias Lang, Marc Toussaint
NN
2006
Springer
127views Neural Networks» more  NN 2006»
13 years 8 months ago
The asymptotic equipartition property in reinforcement learning and its relation to return maximization
We discuss an important property called the asymptotic equipartition property on empirical sequences in reinforcement learning. This states that the typical set of empirical seque...
Kazunori Iwata, Kazushi Ikeda, Hideaki Sakai
DAC
2008
ACM
14 years 9 months ago
Automatic package and board decoupling capacitor placement using genetic algorithms and M-FDM
In the design of complex power distribution networks (PDN) with multiple power islands, it is required that the PDN represents a low impedance as seen by the digital modules. This...
Krishna Bharath, Ege Engin, Madhavan Swaminathan
MCSS
2008
Springer
13 years 8 months ago
Maximal solution to algebraic Riccati equations linked to infinite Markov jump linear systems
In this paper we deal with a perturbed algebraic Riccati equation in an infinite dimensional Banach space. Besides the interest in its own right, this class of equations appears, ...
Jack Baczynski, Marcelo D. Fragoso
TNN
2008
138views more  TNN 2008»
13 years 8 months ago
A Fast and Scalable Recurrent Neural Network Based on Stochastic Meta Descent
This brief presents an efficient and scalable online learning algorithm for recurrent neural networks (RNNs). The approach is based on the real-time recurrent learning (RTRL) algor...
Zhenzhen Liu, Itamar Elhanany