Transformer-based models to fast-simulate the LHCb ECAL detector
Transformer
The Transformer architecture is freely inspired by Vaswani et al. [arXiv:1706.03762] and Dosovitskiy et al. [arXiv:2010.11929].
Discriminator
The Discriminator is implemented through the Deep Sets model proposed by Zaheer et al. [arXiv:1703.06114] and its architecture is freely inspired by what developed by the ATLAS Collaboration for flavor tagging [ATL-PHYS-PUB-2020-014].
Credits
Transformer implementation freely inspired by the TensorFlow tutorial Neural machine translation with a Transformer and Keras.
1 maintainer
Release files for calotron 0.0.12
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| calotron-0.0.12.tar.gz | 41.2 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| calotron-0.0.12-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 109.3 kB
Release files / calotron-0.0.12.tar.gz
| Download URL | calotron-0.0.12.tar.gz |
|---|---|
| Size | 41.2 kB |
| Tags | Source |
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twine/4.0.2 CPython/3.9.16
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Release files / calotron-0.0.12-py3-none-any.whl
| Download URL | calotron-0.0.12-py3-none-any.whl |
|---|---|
| Size | 68.2 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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twine/4.0.2 CPython/3.9.16
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