Adaptive Activation Functions for Predictive Modeling with Sparse Experimental Data

A pivotal aspect in the design of neural networks lies in selecting activation functions, crucial for introducing nonlinear structures that capture intricate input-output patterns. While the effectiveness of adaptive or trainable activation functions has been studied in domains with ample data, like image classification problems, significant gaps persist in understanding their influence on classification accuracy and predictive uncertainty in settings characterized by limited data availability. This research aims to address these gaps by investigating the use of two types of …

classification cs.lg cs.ne data design domains experimental functions image input-output lies modeling networks neural networks patterns pivotal predictive predictive modeling stat.ml understanding