/explore
|
You are here 
|
iclr-blogposts.github.io | ||
| | | | |
www.depthfirstlearning.com
[AI summary] The user has provided a detailed and complex set of questions and reading materials related to normalizing flows, variational inference, and generative models. The content covers topics such as the use of normalizing flows to enhance variational posteriors, the inference gap, and the implementation of models like NICE and RealNVP. The user is likely seeking guidance on how to approach these questions, possibly for academic or research purposes. |
|
| | | | | ||
| | | | |
www.lesswrong.com
This blog post is largely based on an EMNLP paper with Ethan Perez and Chris Buckley. It also benefited from discussions with and comments from Hady... |
|
| | | | | ||
| | | | |
lilianweng.github.io
[Updated on 2021-09-19: Highly recommend this blog post on score-based generative modeling by Yang Song (author of several key papers in the references)]. [Updated on 2022-08-27: Added classifier-free guidance, GLIDE, unCLIP and Imagen. [Updated on 2022-08-31: Added latent diffusion model. [Updated on 2024-04-13: Added progressive distillation, consistency models, and the Model Architecture section. |
|
| | | | | ||
| | | | |
sander.ai
Perspectives on diffusion, or how diffusion models are autoencoders, deep latent variable models, score function predictors, reverse SDE solvers, flow-based models, RNNs, and autoregressive models, all at once! |
|
| | | |||
