Suren Sritharan

TL;DR: This paper carefully design and propose supervised, unsupervised, and reinforcement learning models to support rate maximization objective under user mobility, and studies the effects of practical systems such as latency and reliability on the rate maximizations with deep learning models.

TL;DR: This review presents a comprehensive analysis of the use of AI techniques for spatio-temporal modeling and forecasting and impact modeling on diverse populations as it relates to COVID-19 and lists potential paths of research for which AI based techniques can be used for greater impact in tackling the pandemic.

TL;DR: This paper presents a novel deep learning pipeline that can learn from both paired and unpaired datasets, and analyses the functionality and the performance of different components, hidden layers, and the entire pipeline.

TL;DR: In this paper, a novel deep learning pipeline that can learn from both paired and unpaired datasets is presented, where CNNs and GANs are optimized to minimize standard loss and adversarial loss, respectively.

TL;DR: In this paper, the authors presented a novel algorithm for the detection of sleep apnea with video processing, which is non-contact, accurate and lightweight enough to run on a single board computer.