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14 May 2018 Joint proximity association template for neural networks (Conference Presentation)
James LaRue
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Proceedings Volume 10652, Disruptive Technologies in Information Sciences; 1065214 (2018) https://doi.org/10.1117/12.2326891
Event: SPIE Defense + Security, 2018, Orlando, FL, United States
Abstract
A technical solution is described for implementing a computer-executed system of association memory matrices to replace the proximal layers of a convolutional neural network (CNN). An example method includes configuring one Associative Memory Matrix (AMM) for each configured layer in the CNN. This one-to-one conversion method motivates the name to the product: the Joint Proximity Association Template (JPAT) for Neural Networks. The invention is a numerically stable soft-ware based implementation that (1) reduces the long training times, (2) reduces the execution time, and (3) produces bidirectional intra-layer connections and potentially, inter-layer connections as well. The method further includes, potentially, forming a single AMM, from the multiple AMMs corresponding to the multiple and proximal layers of the CNN, in anticipation of the well-known Universal Approximation Theorem.
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James LaRue "Joint proximity association template for neural networks (Conference Presentation)", Proc. SPIE 10652, Disruptive Technologies in Information Sciences, 1065214 (14 May 2018); https://doi.org/10.1117/12.2326891
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KEYWORDS
Neural networks
Computing systems
Content addressable memory
Convolutional neural networks
Matrices
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