2021 3.1 Variational inference, VAE's and normalizing flows - Rianne van den Berg

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2021 3.2 Generative Adversarial Networks - Tatjana Chavdarova
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2021 3.2 Generative Adversarial Networks - Tatjana Chavdarova

What are Normalizing Flows?
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What are Normalizing Flows?

2021 1.1 Introduction to Machine Learning - Christopher Bishop
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2021 1.1 Introduction to Machine Learning - Christopher Bishop

Stanford CS330 I Variational Inference and Generative Models l 2022 I Lecture 11
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Stanford CS330 I Variational Inference and Generative Models l 2022 I Lecture 11

Variational Inference | Evidence Lower Bound (ELBO) | Intuition & Visualization
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Variational Inference | Evidence Lower Bound (ELBO) | Intuition & Visualization

Understanding Variational Autoencoders (VAEs)
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Understanding Variational Autoencoders (VAEs)

[M2L 2025] 3.2 Reinforcement Learning for LLMs - Jessica Hamrick
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[M2L 2025] 3.2 Reinforcement Learning for LLMs - Jessica Hamrick

How I Understand Flow Matching
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How I Understand Flow Matching

Tutorial on Denoising Diffusion-based Generative Modeling: Foundations and Applications
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Tutorial on Denoising Diffusion-based Generative Modeling: Foundations and Applications

"Normalizing Flows" by Didrik Nielsen
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"Normalizing Flows" by Didrik Nielsen

[M2L 2025] 1.2 Modularity and Compositionality for Collaborative, Efficient ... - Ivan Vulić
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[M2L 2025] 1.2 Modularity and Compositionality for Collaborative, Efficient ... - Ivan Vulić

Mathing the Variational AutoEncoder: Deriving the ELBO Loss
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Mathing the Variational AutoEncoder: Deriving the ELBO Loss

Tamara Broderick: Variational Bayes and Beyond: Bayesian Inference for Big Data (ICML 2018 tutorial)
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Tamara Broderick: Variational Bayes and Beyond: Bayesian Inference for Big Data (ICML 2018 tutorial)

CS480/680 Lecture 23: Normalizing flows (Priyank Jaini)
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CS480/680 Lecture 23: Normalizing flows (Priyank Jaini)

Diffusion and Score-Based Generative Models
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Diffusion and Score-Based Generative Models

Flow Matching for Generative Modeling (Paper Explained)
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Flow Matching for Generative Modeling (Paper Explained)

2022.10 Variational autoencoders and Diffusion Models - Tim Salimans
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2022.10 Variational autoencoders and Diffusion Models - Tim Salimans

Normalizing Flows and Invertible Neural Networks in Computer Vision (CVPR 2021 Tutorial)
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Normalizing Flows and Invertible Neural Networks in Computer Vision (CVPR 2021 Tutorial)

Bayesian modeling without the math: An introduction to PyMC3
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Bayesian modeling without the math: An introduction to PyMC3

Scaling Up Bayesian Inference for Big and Complex Data
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Scaling Up Bayesian Inference for Big and Complex Data