**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.

**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.

Temporal Graph Networks for Deep Learning on Dynamic Graphs

WaveGlow

WaveGlow: A Flow-based Generative Network for Speech Synthesis

Unsupervised machine learning approach for identifying information threads by leveraging answers to 5W1H questions from documents, the temporal relationships between documents and hierarchical agglomerative clustering (HAC).

Source	WaveGlow: A Flow-based Generative Network for Speech Synthesis
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com