CMU Advanced NLP 2021 - Latent Variable Models
Explore latent variable models in advanced natural language processing through this comprehensive lecture from CMU's CS 11-711 course. Delve into the distinctions between generative and discriminative models, as well as deterministic and random variables. Gain insights into Variational Autoencoders (VAEs) and their applications in NLP, including techniques for handling discrete latent variables. Examine the trade-offs between learning features and learning structure. Cover topics such as evidence lower bound, procedural training, KL divergence annealing, and aggressive inference network learning. Understand sampling methods and reparameterization for discrete latent variables, equipping you with advanced knowledge for implementing sophisticated NLP models.