Dear Colleagues,

In the recent decade, machine learning has emerged as an indispensable tool for incredible number of applications such as computer vision, medicine, fintech, autonomous systems, speech recognition, traffic management, social media, and many others. Machine learning models provide state-of-the-art and robust accuracy in various applications. The increasing deployment of machine learning algorithms introduces major computational challenges due to the explosive growth in model size and complexity. These challenges have been further emphasized due to the diverse hosting platforms from edge devices and cloud systems to high-performance computing. Given that each platform introduces different computational and cost constraints, the need for computational optimizations that are fine-tuned to the application and platform is crucial. This Special Issue looks for novel developments of computational optimizations for algorithms in the domain of machine learning algorithms such as:

Supervised, unsupervised, reinforcement, and hybrid machine learning classes.
Various types of machine learning algorithms: deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks, etc.
Application-specific machine learning models.
Machine learning optimization methods such as pruning, deep compression, and others.
Prof. Dr. Freddy Gabbay
Guest Editor