Composing Graphical Models with Neural Networks for Structured Representations and Fast Inference

0:41:36

Composing Graphical Models with Neural Networks for Structured Representations and Fast Inference

0:03:07

Composing graphical models with neural networks

0:37:32

Composing Graphical Models With Neural Networks, w/ David Duvenaud - #96

1:47:02

MIA: Matt Johnson, Composing graphical models with neural networks; Scott Linderman

0:16:45

Learning Discrete Graphical Models with Neural Networks

0:15:30

How to Read & Make Graphical Models?

0:07:07

Compositional Neural Scene Representations for Shading Inference

1:12:43

[MISS 2016] Max Welling - Deep Learning, Graphical Models and Bayesian Estimation

0:20:35

NIPS: Spotlight Session 9 - Graphical Models Spotlights, Model Selection

0:03:08

Probabilistic Graphical Models For Causal Relationships

0:38:49

Noisy natural gradient as variational inference - Roger Grosse

0:01:43

KDD 2023 - Universal and Generalizable Structure Learning for Graph Neural Networks

1:38:43

Lecture 9, Advanced Inference in Graphical Models

0:00:17

Predicting Stability of Towers with Graph Neural Network - 2D - First Model

0:39:24

Deep Recurrent Inverse Modeling - Max Welling

1:33:36

Lecture 4, Advanced Inference in Graphical Models

0:58:49

CAIDA Talk - Dec 6, 2019 - David Duvenaud

0:51:11

Tips & Tricks for Fast Neural Net Inference in Production / Дмитрий Коробченко (NVIDIA)

0:45:16

Yee Whye Teh: On Bayesian Deep Learning and Deep Bayesian Learning (NIPS 2017 Keynote)

0:46:48

Scaling Up Bayesian Inference for Big and Complex Data

0:54:38

[PURDUE MLSS] Graphical Models for the Internet by Alexander Smola (Part 7/8)

0:54:43

MIA Special Seminar: David Duvenaud, It's time to talk about irregularly-sampled time series

0:46:52

[MISS 2016] Carsten Rother - Graphical Models in BioImedical imaging

1:05:29

Variational Inference: Foundations and Innovations