Rule extraction (RE) from recurrent neural networks (RNNs) refers to finding models of the
underlying RNN, typically in the form of finite state machines, that mimic the network to a …

Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial
intelligence (AI), including computer vision, natural language processing, and speech …

Canine mammary tumors (CMTs) have high incidences and mortality rates in dogs. They are
also considered excellent models for human breast cancer studies. Diagnoses of both …

Sparsity helps reduce the computational complexity of deep neural networks by skipping
zeros. Taking advantage of sparsity is listed as a high priority in next generation DNN …

Variational methods have been previously explored as a tractable approximation to
Bayesian inference for neural networks. However the approaches proposed so far have only …

Standard methods for simultaneously inducing the structure and weights of recurrent neural
networks limit every task to an assumed class of architectures. Such a simplification is …

Practitioners frequently observe that pruning improves model generalization. A long-
standing hypothesis based on bias-variance trade-off attributes this generalization …

Granularity of sparsity affects the prediction accuracy of Deep Neural Network models. In this
paper we quantitatively measure the accuracy-sparsity relationship with different grain sizes …

Genetic Algorithms (GA) as a tool for a search and optimizing methodology has now
reached a mature stage. It has found many useful applications in both the scientific and …

Abstract Prognostic and systems Health Management (PHM) is an integral part of a system.
It is used for solving reliability problems that often manifest due to complexities in design …