Tagged
Generative Models
Reparameterization Trick & Gumbel-Softmax: A Deep Dive
Make sense of the reparameterization trick and Gumbel-Softmax: why gradients can flow through sampling, how temperature trades bias for variance, and the practical pitfalls of training discrete latent variables …
PDE and Machine Learning (7): Diffusion Models and Score Matching
Diffusion models are PDE solvers in disguise. We derive the heat equation, Fokker-Planck, score matching, DDPM, and DDIM from a unified PDE perspective and visualise every step.
Variational Autoencoder (VAE): From Intuition to Implementation and Troubleshooting
Build a VAE from scratch in PyTorch. Covers the ELBO objective, reparameterization trick, posterior collapse fixes, beta-VAE, and a complete training pipeline.