The **Adaptively Sparse Transformer** is a type of [Transformer](https://paperswithcode.com/method/transformer).

**Graphic Mutual Information**, or **GMI**, measures the correlation between input graphs and high-level hidden representations. GMI generalizes the idea of conventional mutual information computations from vector space to the graph domain where measuring mutual information from two aspects of node features and topological structure is indispensable. GMI exhibits several benefits: First, it is invariant to the isomorphic transformation of input graphs---an inevitable constraint in many existing graph representation learning algorithms; Besides, it can be efficiently estimated and maximized by current mutual information estimation methods such as MINE.

Graph Representation Learning via Graphical Mutual Information Maximization

Adaptively Sparse Transformer

Adaptively Sparse Transformers

We are introducing a universal handwritten image augmentation method that is language-agnostic. This groundbreaking technique can be applied to handwritten images in any language worldwide, marking it as the first of its kind. There are four methods for handwritten images which are ThickOCR, ThinOCR, Elongate OCR, Line Erase OCR.

Source	Adaptively Sparse Transformers
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com