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@@ -93,6 +93,8 @@ Currently, the library supports the following HDC/VSA models:
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- [Binary Spatter Codes (BSC)](https://torchhd.readthedocs.io/en/stable/generated/torchhd.BSCTensor.html)
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- [Holographic Reduced Representations (HRR)](https://torchhd.readthedocs.io/en/stable/generated/torchhd.HRRTensor.html)
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- [Fourier Holographic Reduced Representations (FHRR)](https://torchhd.readthedocs.io/en/stable/generated/torchhd.FHRRTensor.html)
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- [Binary Sparse Block Codes (B-SBC)](https://torchhd.readthedocs.io/en/stable/generated/torchhd.BSBCTensor.html)
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- [Vector-Derived Transformation Binding (VTB)](https://torchhd.readthedocs.io/en/stable/generated/torchhd.VTBTensor.html)
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We welcome anyone to help with contributing more models to the library!
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## Cite
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Consider citing [our paper](https://arxiv.org/abs/2205.09208) if you use Torchhd in your work:
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Consider citing [our paper](https://jmlr.org/papers/v24/23-0300.html) published in the Journal of Machine Learning Research (JMLR) if you use Torchhd in your work:
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```
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@article{heddes2022torchhd,
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title={Torchhd: An Open-Source Python Library to Support Hyperdimensional Computing Research},
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author={Heddes, Mike and Nunes, Igor and Vergés, Pere and Desai, Dheyay and Givargis, Tony and Nicolau, Alexandru},
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journal={arXiv preprint arXiv:2205.09208},
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year={2022}
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@article{JMLR:v24:23-0300,
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author = {Mike Heddes and Igor Nunes and Pere Vergés and Denis Kleyko and Danny Abraham and Tony Givargis and Alexandru Nicolau and Alexander Veidenbaum},
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title = {Torchhd: An Open Source Python Library to Support Research on Hyperdimensional Computing and Vector Symbolic Architectures},
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journal = {Journal of Machine Learning Research},
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year = {2023},
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volume = {24},
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number = {255},
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pages = {1--10},
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url = {http://jmlr.org/papers/v24/23-0300.html}
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}
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```

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