We lifted 13 functions out of this paper's own repositories and ran 9 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 |
|---|---|---|
| abogatskiy/pelican | canonical | 9 of 13 |
| Function | Status | Where it lives |
|---|---|---|
| batch_stack | Ran | abogatskiy/pelican/src/dataloaders/collate.py code served (permissive licence) · get_code("24e331d34e433ea7") |
| batch_stack_general | Ran | abogatskiy/pelican/src/dataloaders/collate.py code served (permissive licence) · get_code("10a3df87d5f15661") |
| dot4 | Ran | abogatskiy/pelican/src/layers/generic_layers.py code served (permissive licence) · get_code("694761f75d833a17") |
| drop_zeros | Ran | abogatskiy/pelican/src/dataloaders/collate.py code served (permissive licence) · get_code("722a984c73411427") |
| expand_var_list | Ran | abogatskiy/pelican/src/models/pelican_classifier.py code served (permissive licence) · get_code("d9fc2929e852c9fa") |
| get_activation_fn | Ran | abogatskiy/pelican/src/layers/generic_layers.py code served (permissive licence) · get_code("c3b4e5ac5f9963cc") |
| silu | Ran | abogatskiy/pelican/src/layers/generic_layers.py code served (permissive licence) · get_code("f1b30721af3f5563") |
| suggest_params | Ran | abogatskiy/pelican/optuna_pelican_classifier.py code served (permissive licence) · get_code("87aa69a0754cb533") |
| suggest_params | Ran | abogatskiy/pelican/optuna_pelican_cov.py code served (permissive licence) · get_code("acd7554fd7e413e6") |
| add_pid_jc | Not yet run | abogatskiy/pelican/src/models/pelican_classifier.py code served (permissive licence) · get_code("04cd63c8bd04eecf") |
| initialize_datasets | Not yet run | abogatskiy/pelican/src/dataloaders/utils.py code served (permissive licence) · get_code("fda94d00076b72e0") |
| onehot | Not yet run | abogatskiy/pelican/src/models/pelican_cov.py code served (permissive licence) · get_code("aac3499a7873a4b0") |
| qg_onehot | Not yet run | abogatskiy/pelican/src/models/pelican_classifier.py code served (permissive licence) · get_code("4e1231b1e3c1a574") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Many current approaches to machine learning in particle physics use generic architectures that require large numbers of parameters and disregard underlying physics principles, limiting their applicability as scientific modeling tools. In this work, we present a machine learning architecture that uses a set of inputs maximally reduced with respect to the full 6-dimensional Lorentz symmetry, and is fully permutation-equivariant throughout. We study the application of this network architecture to the standard task of top quark tagging and show that the resulting network outperforms all existing competitors despite much lower model complexity. In addition, we present a Lorentz-covariant variant of the same network applied to a 4-momentum regression task.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2211.00454")
get_code_for_paper("2211.00454")
have("2211.00454")
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