What AdaGrad does: divides learning rate by sqrt of sum of squared gradients

Why proximal gradient descent is needed for L1 optimization

Proximal gradient descent handles non-differentiable L1 regularization, enabling sparse solutions

How does batch normalization contribute to training deep neural networks: by normalizing input features within each batch to have zero mean and unit variance to accelerate convergence and improve generalization?

Batch normalization stabilizes and accelerates deep learning training by normalizing input features

What denoising score matching does: learns to denoise, which equals learning the score

Denoising score matching learns to remove noise, enhancing signal representation and interpretation

What score matching does: learns the gradient of the log-density without normalizing

Score matching approximates log-density gradients for variational inference without normalization

What the compute-optimal training ratio is: roughly 20 tokens per parameter

Optimal training ratio: Approximately 20 tokens/parameter

What DDPM stands for: Denoising Diffusion Probabilistic Model

DDPM: Denoising Diffusion Probabilistic Model for generative tasks