**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.

**ProxylessNAS** directly learns neural network architectures on the target task and target hardware without any proxy task. Additional contributions include:

- Using a new path-level pruning perspective for [neural architecture search](https://paperswithcode.com/method/neural-architecture-search), showing a close connection between NAS and model compression. Memory consumption is saved by one order of magnitude by using path-level binarization.
- Using a novel gradient-based approach (latency regularization loss) for handling hardware objectives (e.g. latency). Given different hardware platforms: CPU/GPU/Mobile, ProxylessNAS enables hardware-aware neural network specialization that’s exactly optimized for the target hardware.

ProxylessNAS

ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

Graph Representation Learning via Graphical Mutual Information Maximization

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

Source	Graph Representation Learning via Graphical Mutual Information Maximization
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com