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Explores generative models for trajectory forecasting in autonomous vehicles, including discriminative vs generative models, VAES, GANS, and case studies.
Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Explores the evolution of cloud computing to support IoT applications and the challenges faced by deep learning algorithms in processing data from smart sensors.
Delves into training and applications of Vision-Language-Action models, emphasizing large language models' role in robotic control and the transfer of web knowledge. Results from experiments and future research directions are highlighted.
