**Graph Network-Based Simulators** is a type of graph neural network that represents the state of a physical system with particles, expressed as nodes in a graph, and computes dynamics via learned message-passing.

**KungFu** is a distributed ML library for TensorFlow that is designed to enable adaptive training. KungFu allows users to express high-level Adaptation Policies (APs) that describe how to change hyper- and system parameters during training. APs take real-time monitored metrics (e.g. signal-to-noise ratios and noise scale) as input and trigger control actions (e.g. cluster rescaling or synchronisation strategy updates). For execution, APs are translated into monitoring and control operators, which are embedded in the dataflow graph. APs exploit an efficient asynchronous collective communication layer, which ensures concurrency and consistency
of monitoring and adaptation operations.

KungFu

Learning to Simulate Complex Physics with Graph Networks

**Single-Headed Attention** is a single-headed attention module used in the [SHA-RNN](https://paperswithcode.com/method/sha-rnn) language model. The principle design reasons for single-headedness were simplicity (avoiding running out of memory) and scepticism about the benefits of using multiple heads.

Source	Learning to Simulate Complex Physics with Graph Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com