Neural Computation
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Learning the Long-Term Structure of the Blues
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Learning to Forget: Continual Prediction with LSTM
Neural Computation
LSTM recurrent networks learn simple context-free and context-sensitive languages
IEEE Transactions on Neural Networks
Gradient calculations for dynamic recurrent neural networks: a survey
IEEE Transactions on Neural Networks
Neural Networks - 2005 Special issue: IJCNN 2005
2005 Special Issue: Learning protein secondary structure from sequential and relational data
Neural Networks - Special issue on neural networks and kernel methods for structured domains
ICML '06 Proceedings of the 23rd international conference on Machine learning
Training Recurrent Networks by Evolino
Neural Computation
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Coordinating with the Future: The Anticipatory Nature of Representation
Minds and Machines
Anticipations, Brains, Individual and Social Behavior: An Introduction to Anticipatory Systems
Anticipatory Behavior in Adaptive Learning Systems
REINFORCEMENT LEARNING FOR POMDP USING STATE CLASSIFICATION
Applied Artificial Intelligence
IEEE Transactions on Neural Networks
Evolving Memory Cell Structures for Sequence Learning
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
A novel approach for distributed application scheduling based on prediction of communication events
Future Generation Computer Systems
Multi-dimensional recurrent neural networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
An application of recurrent neural networks to discriminative keyword spotting
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
3d gesture recognition applying long short-term memory and contextual knowledge in a CAVE
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
Action classification in soccer videos with long short-term memory recurrent neural networks
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Bidirectional LSTM networks for improved phoneme classification and recognition
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Sequential deep learning for human action recognition
HBU'11 Proceedings of the Second international conference on Human Behavior Unterstanding
Text recognition in videos using a recurrent connectionist approach
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
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The temporal distance between events conveys information essential for numerous sequential tasks such as motor control and rhythm detection. While Hidden Markov Models tend to ignore this information, recurrent neural networks (RNNs) can in principle learn to make use of it. We focus on Long Short-Term Memory (LSTM) because it has been shown to outperform other RNNs on tasks involving long time lags. We find that LSTM augmented by "peephole connections" from its internal cells to its multiplicative gates can learn the fine distinction between sequences of spikes spaced either 50 or 49 time steps apart without the help of any short training exemplars. Without external resets or teacher forcing, our LSTM variant also learns to generate stable streams of precisely timed spikes and other highly nonlinear periodic patterns. This makes LSTM a promising approach for tasks that require the accurate measurement or generation of time intervals.