The invention is an add-on implementation of a stabilized association memory matrix system to an existing convolutional neural network framework. This invention emulates the intra-action and the inter-action of the cognitive processes of the (logical) left-brain and (intuitive) right-brain. 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 intralayer and interlayer connections. The implementation of this joint processing architecture is designed to take an existing hierarchy of stepped based processes, add next to it a parallel hierarchy of associative memory processes, and then connect the two processes by another set of associative memory processes. Or, the stepped-based process may be replaced with additional associative memory processes to enhance the emulation of several bidirectional intralayer and interlayer cognitive process communication. In addition, the invention can be used as a neural network layer compression tool that takes in a multilayer perceptron, also known as a multilayer neural network, and outputs a single layer perceptron. The final construction can be visualized as two vertical rails connected with a set of horizontal rungs which motivates the name to this invention: J. Patrick's Ladder: A Machine Learning Enhancement Tool.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An Association Memory Matrix (AMM) system is built in parallel to an existing Convolutional Neural Network (CNN) system. The AMM system is the invention. The CNN is an example of a neural network that would benefit from the AMM implementation. The CNN starts the training process. Layer outputs are stored as vectors. The training is stopped. The vectors are used to build matrices for the AMM system. The AMM system can then work independently from the CNN system. 2. The method of claim 1, wherein intra-layer AMMs connect the two systems in claim 1. The AMM system can incorporate several AMMs using the basic AMM structure and intra-layer connections to better represent cognitive processing. We acknowledge there are several designs on paper in existence; we claim this invention to be the first numerically stable interlayer/intra-layer software implemented and successfully tested algorithm. 3. The method of claim 2, wherein the CNN training error is reduced with AMM intra-layer input. 4. The method of claim 1, wherein the AMM trains to a specified threshold faster than the CNN. 5. The method of claim 4 wherein the faster training time is scaled to a full order of magnitude. 6. The method of claim 1 wherein the AMM test execution time is faster than the CNN test execution time. This is due to the inherent compressing, or ‘zipping’ nature of the algorithm; it takes a several layer process and produces a single layer process. 7. The method of claim 6 wherein the faster test execution time is scaled to a full order of magnitude.