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NECO
2007
115views more  NECO 2007»
15 years 4 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...
137
Voted
ECML
2006
Springer
15 years 8 months ago
Efficient Non-linear Control Through Neuroevolution
Abstract. Many complex control problems are not amenable to traditional controller design. Not only is it difficult to model real systems, but often it is unclear what kind of beha...
Faustino J. Gomez, Jürgen Schmidhuber, Risto ...
ISCAS
2005
IEEE
154views Hardware» more  ISCAS 2005»
15 years 10 months ago
Back propagation learning of neural networks with chaotically-selected affordable neurons
— Cell assembly is one of explanations of information processing in the brain, in which an information is represented by a firing space pattern of a group of plural neurons. On ...
Yoko Uwate, Yoshifumi Nishio
147
Voted
TASLP
2010
157views more  TASLP 2010»
14 years 11 months ago
HMM-Based Reconstruction of Unreliable Spectrographic Data for Noise Robust Speech Recognition
This paper presents a framework for efficient HMM-based estimation of unreliable spectrographic speech data. It discusses the role of Hidden Markov Models (HMMs) during minimum mea...
Bengt J. Borgstrom, Abeer Alwan
JAIR
2006
120views more  JAIR 2006»
15 years 4 months ago
FluCaP: A Heuristic Search Planner for First-Order MDPs
We present a heuristic search algorithm for solving first-order Markov Decision Processes (FOMDPs). Our approach combines first-order state abstraction that avoids evaluating stat...
Steffen Hölldobler, Eldar Karabaev, Olga Skvo...