Delves into the geometric insights of deep learning models, exploring their vulnerability to perturbations and the importance of robustness and interpretability.
Explores the evolution of vision theories and visual feedback systems, including bottom-up and top-down approaches, and iterative error feedback for human pose estimation.
Explores the influence of computational linguistics on deep learning architectures, covering grammar formalisms, connectionism, variable binding, and future directions.
