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Explores the mathematics of deep learning, neural networks, and their applications in computer vision tasks, addressing challenges and the need for robustness.
Delves into the geometric insights of deep learning models, exploring their vulnerability to perturbations and the importance of robustness and interpretability.
Explores techniques for delineation, including Hough transform, gradient orientation, and shape detection, emphasizing the importance of combining graph-based techniques and machine learning.
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.
Introduces convolutional neural networks for image processing, covering basic components, architectures, and practical applications, including denoising and segmentation.