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Abstract: We survey learning algorithms for recurrent neural networks with hidden units, and put the various techniques into a common framework. We discuss fixedpoint learning algorithms, namely recurrent backpropagation and deterministic Boltzmann Machines, and non-fixedpoint algorithms, namely backpropagation through time, Elman's history cutoff, and Jordan's output feedback architecture. Forward propagation, an online technique that uses adjoint equations, and variations thereof, are also discussed.... (Update)
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BibTeX entry: (Update)
Pearlmutter, B. A. (1995). Gradient calculations for dynamic recurrent neural networks: A survey. http://citeseer.ist.psu.edu/pearlmutter95gradient.html More
@article{ pearlmutter95gradient,
author = "Barak A. Pearlmutter",
title = "Gradient Calculations for Dynamic Recurrent Neural Networks: A Survey",
journal = "IEEE Transactions on Neural Networks",
volume = "6",
number = "5",
month = "September",
pages = "1212--1228",
year = "1995",
url = "citeseer.ist.psu.edu/pearlmutter95gradient.html" }
Citations (may not include all citations):92 Cooperative computation of stereo disparity (context) - Marr, Poggio - 1976 ACM
31 The appeal of parallel distributed processing (context) - McClelland, Rumelhart et al. ACM
17 Cooperating processes for lowlevel vision: A survey (context) - Davis, Rosenfeld - 1981
10 Relaxation and its role in vision (context) - Hinton - 1977
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