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NECO
2007
115views more  NECO 2007»
15 years 6 months ago
Training Recurrent Networks by Evolino
In recent years, gradient-based LSTM recurrent neural networks (RNNs) solved many previously RNN-unlearnable tasks. Sometimes, however, gradient information is of little use for t...
Jürgen Schmidhuber, Daan Wierstra, Matteo Gag...
181
Voted
ICRA
2010
IEEE
145views Robotics» more  ICRA 2010»
15 years 5 months ago
Reinforcement learning of motor skills in high dimensions: A path integral approach
— Reinforcement learning (RL) is one of the most general approaches to learning control. Its applicability to complex motor systems, however, has been largely impossible so far d...
Evangelos Theodorou, Jonas Buchli, Stefan Schaal
KBS
2008
98views more  KBS 2008»
15 years 5 months ago
Mixed feature selection based on granulation and approximation
Feature subset selection presents a common challenge for the applications where data with tens or hundreds of features are available. Existing feature selection algorithms are mai...
Qinghua Hu, Jinfu Liu, Daren Yu
JCC
2010
74views more  JCC 2010»
15 years 5 months ago
A first-order system least-squares finite element method for the Poisson-Boltzmann equation
The Poisson-Boltzmann equation is an important tool in modeling solvent in biomolecular systems. In this paper, we focus on numerical approximations to the electrostatic potential...
Stephen D. Bond, Jehanzeb Hameed Chaudhry, Eric C....
MCS
2010
Springer
15 years 5 months ago
Goal-oriented a posteriori error estimates for transport problems
Some aspects of goal-oriented a posteriori error estimation are addressed in the context of steady convection-diffusion equations. The difference between the exact and approxima...
Dmitri Kuzmin, Sergey Korotov