**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.

**Prioritized Sweeping** is a reinforcement learning technique for model-based algorithms that prioritizes updates according to a measure of urgency, and performs these updates first. A queue is maintained of every state-action pair whose estimated value would change nontrivially if updated, prioritized by the size of the change. When the top pair in the queue is updated, the effect on each of its predecessor pairs is computed. If the effect is greater than some threshold, then the pair is inserted in the queue with the new priority.

Source: Sutton and Barto, Reinforcement Learning, 2nd Edition

Prioritized Sweeping

Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition

**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.

Source	Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com