**Temporal Graph Network**, or **TGN**, is a framework for deep learning on dynamic graphs represented as sequences of timed events. The memory (state) of the model at time $t$ consists of a vector $\mathbf{s}_i(t)$ for each node $i$ the model has seen so far. The memory of a node is updated after an event (e.g. interaction with another node or node-wise change), and its purpose is to represent the node's history in a compressed format. Thanks to this specific module, TGNs have the capability to memorize long term dependencies for each node in the graph. When a new node is encountered, its memory is initialized as the zero vector, and it is then updated for each event involving the node, even after the model has finished training.

**BLOOMZ** is a Multitask prompted finetuning (MTF) variant of BLOOM.

BLOOMZ

Crosslingual Generalization through Multitask Finetuning

Temporal Graph Networks for Deep Learning on Dynamic Graphs

**WaveGlow** is a flow-based generative model that generates audio by sampling from a distribution. Specifically samples are taken from a zero mean spherical Gaussian with the same number of dimensions as our desired output, and those samples are put through a series of layers that transforms the simple distribution to one which has the desired distribution.

Source	Temporal Graph Networks for Deep Learning on Dynamic Graphs
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com