Sep 30, 2021 · In this work, we investigate the problem of approximating the two central components of the Transformer -- multi-head self-attention and point-wise feed- ...

Redesigning the Transformer Architecture with. Insights from Multi-particle Dynamical Systems. Subhabrata Dutta. Jadavpur University. India subha0009@gmail.com.

Nov 9, 2021 · This paper develops an analogy between Transformer networks and dynamical systems of multiple interacting particles.

Jun 10, 2024 · The Transformer and its variants have been proven to be efficient sequence learners in many different domains.

The Transformer and its variants have been proven to be efficient sequence learners in many different domains. Despite their staggering success, ...

This work investigates the problem of approximating the two central components of the Transformer -- multi-head self-attention and point-wise feed-forward ...

Redesigning the Transformer Architecture with Insights from Multi-particle Dynamical Systems. Blind Submission by Conference • Redesigning the Transformer ...

0 About · Redesigning the Transformer Architecture with Insights from Multi-particle Dynamical Systems. 11:54. Redesigning the Transformer Architecture with ...

Implementation of Redesigning the Transformer Architecture with Insights from Multi-particle Dynamical Systems, accepted as spotlight paper in NeurIPS 2021.

Explore all code implementations available for Redesigning the Transformer Architecture with Insights from Multi-particle Dynamical Systems.