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Intro

Discriminative vs. Generative Models

Quiz: What Types of Variables?

What is Latent Random Variable Model

Why Latent Variable Models?

Deep Structured Latent Variable Models • Specify structure, but interpretable structure is often discrete e.g. POS tags, dependency parse trees

Examples of Deep Latent Variable Models

A probabilistic perspective on Variational Auto-Encoder

What is Our Loss Function?

Practice

Variational Inference • Variational inference approximates the true posterior poll with a family of distributions

Variational Inference • Variational inference approximates the true posterior polar with a family of distributions

Variational Auto-Encoders

Variational Autoencoders

Learning VAE

Problem! Sampling Breaks Backprop

Solution: Re-parameterization Trick

Difficulties in Training . Of the two components in the VAE objective, the KL divergence term is much easier to learn

Solution 3

Weaken the Decoder

Discrete Latent Variables?

Method 1: Enumeration

Solution 4

Description:

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.

Neural Nets for NLP - Models with Latent Random Variables

Graham Neubig

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#Computer Science #Artificial Intelligence #Neural Networks #Natural Language Processing (NLP) #Deep Learning #Variational Autoencoders #Generative AI #Generative Models

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