variational inference

0:14:31

Variational Methods: How to Derive Inference for New Models (with Xanda Schofield)

0:20:46

[PLDI24] Probabilistic Programming with Programmable Variational Inference

1:13:45

Approximate Inference methods: EM, MAP and Variational Inference

0:43:16

Andrew Curtis - Variational Inference

1:54:14

#90, Demystifying MCMC & Variational Inference, with Charles Margossian

0:01:00

Episode 90 Demystifying MCMC & Variational Inference, with Charles Margossian

0:23:55

Variational Inference tutorial series Part 1 (Basic Information Theory )

0:00:59

Scalable Uncertainty for Computer Vision With Functional Variational Inference

0:04:43

The equivalence between Stein variational gradient descent and black-box variational inference

0:02:34

Variational Inference in Mixed Probabilistic Submodular Models

0:35:53

A Generalization Bound for Online Variational Inference

0:15:57

6.2 Sylvester Normalizing Flow For Variational Inference

0:02:59

Variational Inference in Mixed Probabilistic Submodular Models

0:35:33

Variational Autoencoder - VISUALLY EXPLAINED!

0:01:33

Enhanced Variational Inference for Bayesian Deep Learning using Inverse Autoregressive Flow

0:16:26

Black Box Variational Interference -- Rajesh Ranganath

1:54:44

08L – Self-supervised learning and variational inference

0:03:22

Discretely Relaxing Continuous Variables for tractable Variational Inference (NeurIPS 2018)

0:38:49

Noisy natural gradient as variational inference - Roger Grosse

0:37:58

Black-Box Variational Inference for Probabilistic Programs

0:54:07

Debdeep Pati - Seminar - ' Statistical and Algorithmic Foundations of Variational Inference'

0:20:50

Towards Verified Stochastic Variational Inference for Probabilistic Programs

0:13:04

Team 6. robust accurate stochastic optimization for variational inference

0:31:28

Emtiyaz Khan: Fast yet Simple Natural-Gradient Descent for Variational Inference