What DDPM stands for: Denoising Diffusion Probabilistic Model

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

AdaGrad adapts learning rates based on historical gradients, reducing for frequently updated features

What does LSTM stand for in the context of neural networks: Long Short-Term Memory

LSTM: A type of recurrent neural network capable of learning long-term dependencies

Why proximal gradient descent is needed for L1 optimization

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

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 IS (Inception Score) measures: diversity and quality of generated images

Inception Score quantifies image diversity and generated images' quality