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Variational inference
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Classroom Contents
Neural Nets for NLP - Models with Latent Random Variables
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- 1 Introduction
- 2 Discriminative vs generative
- 3 Observed vs latent variables
- 4 Quiz
- 5 Latent Variable Models
- 6 Types of latent random variables
- 7 Example
- 8 Loss Function
- 9 Variational inference
- 10 Reconstruction loss and kl regularizer
- 11 Regularized auto encoder
- 12 Regularized autoencoder
- 13 Learning the VAE
- 14 Reparameterization Trick
- 15 General
- 16 Language
- 17 VAE
- 18 Reparameterization
- 19 Motivation
- 20 Consistency
- 21 Semantic Similarity
- 22 Solutions
- 23 Free Bits
- 24 Weaken Decoder
- 25 Aggressive Inference Network
- 26 Handling Discrete latent variables
- 27 Discrete latent variables
- 28 Sampling discrete variables
- 29 Gumball softmax
- 30 Application examples
- 31 Discrete random variables
- 32 Tree structured latent variables
- 33 Discussion question
