**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.

**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.

Learning to Simulate Complex Physics with Graph Networks

Single-Headed Attention

Single Headed Attention RNN: Stop Thinking With Your Head

An agreement of a group to follow a common purpose is manifested by its coalescence into a coordinated behavior. The process of initiating this behavior and the period of decision-making by the group members necessarily precedes the coordinated behavior. Given time series of group members’ behavior, the goal is to find these periods of decision-making and identify the initiating individual, if one exists.

Image Source:  [Amornbunchornvej et al.](https://arxiv.org/pdf/1603.01570v2.pdf)

Source	Single Headed Attention RNN: Stop Thinking With Your Head
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com