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Controlling the Latent Space of GANs through Reinforcement Learning: A Case Study on Task-based Image-to-Image Translation
Generative Adversarial Networks (GAN) have emerged as a formidable AI tool to generate realistic outputs based on training datasets.
CaRT: Certified Safety and Robust Tracking in Learning-based Motion Planning for Multi-Agent Systems
First, in a nominal setting, the analytical form of our CaRT safety filter formally ensures safe maneuvers of nonlinear multi-agent systems, optimally with minimal deviation from the learning-based policy.
Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings
Most existing sensor-based monitoring frameworks presume that a large available labeled dataset is processed to train accurate detection models.
Objective Prediction of Tomorrow's Affect Using Multi-Modal Physiological Data and Personal Chronicles: A Study of Monitoring College Student Well-being in 2020
Results showed that our model was able to predict next-day affect with accuracy comparable to state of the art methods.
Multi-Agent Motion Planning using Deep Learning for Space Applications
State-of-the-art motion planners cannot scale to a large number of systems.
