Explainability Methods for Deep Learning Neural Networks

Deep learning neural networks, exemplified by models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Generative Adversarial Networks (GANs), have achieved remarkable success in various domains, such as computer vision and natural language processing. Their effectiveness extends to real-world applications, spanning healthcare, economics and autonomous vehicles, where they excel in elucidating and leveraging complex data and information.

While achieving notable successes, the intricate architectures and inherent opacity of deep learning neural networks frequently pose challenges to complete mastery, limiting their deployment in critical applications, especially within interdisciplinary domains. In addressing this issue and broadening the research scope, we are extending the focus of our collection to encompass the successful applications of deep learning neural networks. This expansion seeks to highlight their efficacy in extracting valuable insights from complex data and information, aiming to enhance understanding and utilization in diverse contexts.

We invite researchers and experts in the field of deep learning to contribute their original research papers to this collection. The primary objective of this collection is to showcase cutting-edge research on explainability techniques and applications of deep learning neural networks, encompassing a spectrum of neural architectures and ensuring broad applicability across different domains. This collection aims to advance the understanding and practical implementation of deep learning neural networks, promoting utility and accessibility for a wider audience.