66 search results - page 3 / 14 » Learning Precise Timing with LSTM Recurrent Networks |
ESANN
2006
14 years 11 months ago
2006
Abstract. We introduce a new class of recurrent, truly sequential SVM-like devices with internal adaptive states, trained by a novel method called EVOlution of systems with KErnel-...
ESANN
2008
14 years 11 months ago
2008
Frequently, sequences of state transitions are triggered by specific signals. Learning these triggered sequences with recurrent neural networks implies storing them as different at...
ICONIP
2007
14 years 11 months ago
2007
One of the authors has proposed a simple learning algorithm for recurrent neural networks, which requires computational cost and memory capacity in practical order O(n2 )[1]. The a...
ICANN
2001
Springer
15 years 1 months ago
2001
Springer
This paper studies the use of discrete-time recurrent neural networks for predicting the next symbol in a sequence. The focus is on online prediction, a task much harder than the c...
ESANN
2000
14 years 10 months ago
2000
: Recurrent neural networks possess interesting universal approximation capabilities, making them good candidates for time series modeling. Unfortunately, long term dependencies ar...