Home
Variational Inference: Foundations and Modern Methods (NIPS 2016 tutorial)
Steven Van Vaerenbergh
Jan 18, 2018
30,005 views
Dave Blei: "Black Box Variational Inference"
Ian Goodfellow: Generative Adversarial Networks (NIPS 2016 tutorial)
MLSS 2019 David Blei: Variational Inference: Foundations and Innovations (Part 1)
"Normalizing Flows" by Didrik Nielsen
Variational Inference: Foundations and Innovations
On the geometry of Stein variational gradient descent and related ensemble sampling methods
Variational Inference Lecture I|Probabilistic Modelling|Machine Learning
Stanford CS330 I Variational Inference and Generative Models l 2022 I Lecture 11
Fast Quantification of Uncertainty and Robustness with Variational Bayes
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions
Yee Whye Teh: On Bayesian Deep Learning and Deep Bayesian Learning (NIPS 2017 Keynote)
[DeepBayes2019]: Day 1, Lecture 3. Variational inference
Nonparametric Bayesian Methods: Models, Algorithms, and Applications I
David Blei Variational Inference Foundations and Innovations Part 2
Andrew Duncan – On the Geometry of Stein Variational Gradient Descent
Variational Autoencoder - Model, ELBO, loss function and maths explained easily!
Shocking SECRET Behind OpenAI o1 Model - Bans Anyone Who Dares Ask THIS!
2021 3.1 Variational inference, VAE's and normalizing flows - Rianne van den Berg
ICLR 2021 Keynote - "Geometric Deep Learning: The Erlangen Programme of ML" - M Bronstein
Tamara Broderick: Variational Bayes and Beyond: Bayesian Inference for Big Data (ICML 2018 tutorial)