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MBP: Matrix Backpropagation Package

MBP (Matrix Back Propagation) is an efficient implementation of the back-propagation algorithm for current-generation workstations. The algorithm includes a per-epoch adaptive technique for gradient descent. All the computations are done through matrix multiplications and make use of highly optimized C code. The goal is to reach almost peak-performances on RISCs with superscalar capabilities and fast caches. On some machines (and with large networks) a 30-40x speed-up can be measured respect to conventional implementations.
Origin: []
   as the files MBPv1.1.tar.Z (unix version) and    (DOS version)

Version: 1.1 (23-NOV-93) Requires: C, UNIX CD-ROM: Prime Time Freeware for AI, Issue 1-1 Author(s): Davide Anguita or DIBE University of Genova Via all'Opera Pia 11a 16145 Genova, ITALY Tel: +39-10-3532192 Fax: +39-10-3532175 Keywords: Authors!Anguita, Backpropagation, Gradient Descent, MBP, Machine Learning!Neural Networks, Matrix Backpropagation Package, Matrix Multiplication, Neural Networks, Univ. of Genova References: The documentation is included in the distribution as the postscript file D.Anguita, G.Parodi, R.Zunino - An efficient implementation of BP on RISC- based workstations. Neurocomputing, in press. D.Anguita, G.Parodi, R.Zunino - Speed improvement of the BP on current generation workstations. WCNN '93, Portland. D.Anguita, G.Parodi, R.Zunino - YPROP: yet another accelerating technique for the bp. ICANN '93, Amsterdam.
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