Neural Nets for NLP - Models with Latent Random Variables
Explore models with latent random variables in this comprehensive lecture from CMU's Neural Networks for NLP course. Delve into the distinctions between generative and discriminative models, as well as deterministic and random variables. Examine Variational Autoencoders (VAEs) in depth, including their structure, learning process, and challenges in training. Learn techniques for handling discrete latent variables and discover practical applications of VAEs in natural language processing. Gain insights into deep structured latent variable models and their importance in specifying interpretable structures like POS tags and dependency parse trees. Understand the probabilistic perspective on VAEs, explore variational inference methods, and study solutions to common training difficulties such as the re-parameterization trick and weakening the decoder.