Classic Papers
Foundational papers in machine learning and information theory. Covers landmark works that shaped modern deep learning …
Machine Learning
Notes on ML methods, neural network architectures, and foundational papers. Covers generative models, geometric deep learning, and classic works.
Generative Models
Notes on VAEs, diffusion models, GANs, and other generative approaches. Covers theory, architectures, and training …
Geometric Deep Learning
Notes on equivariant networks, graph neural networks, and symmetry in deep learning. Covers E(3)-equivariance and …