We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| encode_tokens | Ran | this paper's copy was not recorded; identical code first harvested from heidelberg-hepml/lorentz-gatr pointer only · get_code("2dc511eb7ce3614f") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We show that the Lorentz-Equivariant Geometric Algebra Transformer (L-GATr) yields state-of-the-art performance for a wide range of machine learning tasks at the Large Hadron Collider. L-GATr represents data in a geometric algebra over space-time and is equivariant under Lorentz transformations. The underlying architecture is a versatile and scalable transformer, which is able to break symmetries if needed. We demonstrate the power of L-GATr for amplitude regression and jet classification, and then benchmark it as the first Lorentz-equivariant generative network. For all three LHC tasks, we find significant improvements over previous architectures.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2411.00446")
get_code_for_paper("2411.00446")
have("2411.00446")
Connect an agent — have() is free.