Learning 3 - Latent Variable Models (11/18/2021)
Content:
- Generative vs. Discriminative, Deterministic vs. Random Variables
- Variational Autoencoders
- Handling Discrete Latent Variables
- Examples of Variational Autoencoders in NLP
- Learning Features vs. Learning Structure
- Required Reading: Tutorial on Variational Auto-encoders (Doersch 2016)
- Reference: Variational Auto-encoders (Kingma and Welling 2014)
- Reference: Generating Sentences from a Continuous Space (Bowman et al. 2016)
- Reference: Problems w/ Optimizing Latent Variables (Chen et al. 2017)
- Reference: Convoluton Decoders for VAE (Yang et al. 2017)
- Reference: Concrete Distribution (Maddison et al. 2017)
- Reference: Gumbel-Softmax (Jang et al. 2017)
- Reference: Variational Inference for Text Processing (Miao et al. 2016)
- Reference: Controllable Text Generation w/ VAE (Hu et al. 2017)
- Reference: Multi-space Variational Encoder-Decoders (Zhou and Neubig 2017)
- Reference: Recurrent Latent Variable Models (Chung et al. 2015)
- Reference: Language as a Latent Variable (Miao and Blunsom 2016)
- Reference: Emergence of Language in Multi-agent Games (Havrylov and Titov 2017)
- Reference: Natural Language Does Not Emerge Naturally (Kottur et al. 2017)
- Reference: Lagging Inference Networks (He et al., 2019)
- Reference: Unsupervised Recurrent Neural Network Grammars (Kim et al., 2019)
- Reference: StructVAE for Semi-supervised Semantic Parsing (Yin et al., 2018)
Slides: Latent Variable Slides
Sample Code: Latent Variable Code Examples