Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Connectionist architectures for multi-speaker phoneme recognition
Advances in neural information processing systems 2
The “moving targets” training algorithm
Advances in neural information processing systems 2
Adjoint-functions and temporal learning algorithms in neural networks
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Neural computing: an introduction
Neural computing: an introduction
A practical Bayesian framework for backpropagation networks
Neural Computation
TD-Gammon, a self-teaching backgammon program, achieves master-level play
Neural Computation
Machine learning, neural and statistical classification
Learning to Predict by the Methods of Temporal Differences
Machine Learning
The "Moving Targets" Training Algorithm
Proceedings of the EURASIP Workshop 1990 on Neural Networks
Temporal credit assignment in reinforcement learning
Temporal credit assignment in reinforcement learning
Sequence Learning - Paradigms, Algorithms, and Applications
Diffusion of context and credit information in Markovian models
Journal of Artificial Intelligence Research
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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This review attempts to provide an insightful perspective on the role of time within neural network models and the use of neural networks for problems involving time. The most commonly used neural network models are defined and explained giving mention to important technical issues but avoiding great detail. The relationship between recurrent and feedforward networks is emphasised, along with the distinctions in their practical and theoretical abilities. Some practical examples are discussed to illustrate the major issues concerning the application of neural networks to data with various types of temporal structure, and finally some highlights of current research on the more difficult types of problems are presented.