Backpropagation: theory, architectures, and applications

TL;DR: This chapter discusses Backpropagation and Unsupervised Learning in Linear Networks, a model for Spatial Coherence as an Internal Teacher for a Neural Network, and Gradient Descent Learning Algorithms for Recurrent Networks and Their Computational Complexity.

Abstract: Contents: D.E. Rumelhart, R. Durbin, R. Golden, Y. Chauvin, Backpropagation: The Basic Theory. A. Waibel, T. Hanazawa, G. Hinton, K. Shikano, K.J. Lang, Phoneme Recognition Using Time-Delay Neural Networks. C. Schley, Y. Chauvin, V. Henkle, Automated Aircraft Flare and Touchdown Control Using Neural Networks. F.J. Pineda, Recurrent Backpropagation Networks. M.C. Mozer, A Focused Backpropagation Algorithm for Temporal Pattern Recognition. D.H. Nguyen, B. Widrow, Nonlinear Control with Neural Networks. M.I. Jordan, D.E. Rumelhart, Forward Models: Supervised Learning with a Distal Teacher. S.J. Hanson, Backpropagation: Some Comments and Variations. A. Cleeremans, D. Servan-Schreiber, J.L. McClelland, Graded State Machines: The Representation of Temporal Contingencies in Feedback Networks. S. Becker, G.E. Hinton, Spatial Coherence as an Internal Teacher for a Neural Network. J.R. Bachrach, M.C. Mozer, Connectionist Modeling and Control of Finite State Systems Given Partial State Information. P. Baldi, Y. Chauvin, K. Hornik, Backpropagation and Unsupervised Learning in Linear Networks. R.J. Williams, D. Zipser, Gradient-Based Learning Algorithms for Recurrent Networks and Their Computational Complexity. P. Baldi, Y. Chauvin, When Neural Networks Play Sherlock Homes. P. Baldi, Gradient Descent Learning Algorithms: A Unified Perspective.