A Novel Deep Learning ANN Supported on Langevin-Neelakanta Machine

In the contexts of deep learning (DL) considered in artificial intelligence (AI) efforts, relevant machine learning (ML) algorithms adopted refer to using a class of deep artificial neural network (ANN) that supports a learning process exercised with an enormous set of input data (labeled and/or unlabeled) so to predict at the output details on accurate features of labeled data present in the input data set.
 In the present study, a deep ANN is proposed thereof conceived with certain novel considerations: The proposed deep architecture consists of a large number of consequently placed structures of paired-layers. Each layer hosts identical number of neuronal units for computation and the neuronal units are massively interconnected across the entire network. Further, each paired-layer is independently subjected to unsupervised learning (USL). Hence, commencing from the input layer-pair, the excitatory (input) data supplied flows across the interconnected neurons of paired layers, terminating eventually at the final pair of layers, where the output is recovered. That is, the converged neuronal states at any given pair is iteratively passed on to the next pair and so on. The USL