- Guest Editors
- Rama Chellappa, Sergios Theodoridis, and Andre van Schaik
- Publication Date
- 2021
Advances in Machine Learning and Deep Neural Networks
Guest Editors:
- Rama Chellappa, University of Maryland, College Park, U.S.A.
- Sergios Theodoridis, National and Kapodistrian University of Athens, Greece and Chinese University of Hong Kong, Shenzhen, China
- Andre van Schaik, International Centre for Neuromorphic Systems, Western Sydney University
Description
The papers in this special issue focus on key areas of high current research interest for machine learning that range from theory and applications, to hardware implementations. Theoretical developments related to causal inference in the context of deep networks, adversarial learning, generative adversarial networks, graph deep networks, spline deep networks and the merging of tropical geometry with deep neural networks will be included. Applications such as anomaly detection, computer vision, computational media intelligence/multimodal machine learning, and wireless communications will be considered. On the hardware front, two papers will be dedicated to brain inspired algorithms on non-Von Neumann machines and on neuromorphic computing respectively.