**Automatic Structured Variational Inference (ASVI)** is a fully automated method for constructing structured variational families, inspired by the closed-form update in conjugate Bayesian models. These convex-update families incorporate the forward pass of the input probabilistic program and can therefore capture complex statistical dependencies. Convex-update families have the same space and time complexity as the input probabilistic program and are therefore tractable for a very large family of models including both continuous and discrete variables.

**G3D** is a unified spatial-temporal graph convolutional operator that directly models cross-spacetime joint dependencies. It leverages dense cross-spacetime edges as skip connections for direct information propagation across the 3D spatial-temporal graph.

Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition

ASVI

Automatic structured variational inference

DASPP is a deeper version of the [ASPP](https://paperswithcode.com/method/aspp) module (the latter from [DeepLabv3](https://paperswithcode.com/method/deeplabv3)) that adds standard 3 × 3 [convolution](https://paperswithcode.com/method/convolution) after 3 × 3 dilated convolutions to refine the features and also fusing the input and the output of the DASPP module via short [residual connection](https://paperswithcode.com/method/residual-connection). Also, the number of convolution filters of ASPP is reduced from 255 to 96 to gain computational performance.

Source	Automatic structured variational inference
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com