Stack Attention (wip)
For following a line of research that augments attention with a differentiable stack, beginning with DuSell et al. at ETH Zurich
Citations
@misc{dusell2024stackattentionimprovingability,
title = {Stack Attention: Improving the Ability of Transformers to Model Hierarchical Patterns},
author = {Brian DuSell and David Chiang},
year = {2024},
eprint = {2310.01749},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2310.01749},
}
@misc{zhang2025stacktranslargelanguagemodel,
title = {StackTrans: From Large Language Model to Large Pushdown Automata Model},
author = {Kechi Zhang and Ge Li and Jia Li and Huangzhao Zhang and Yihong Dong and Jia Li and Jingjing Xu and Zhi Jin},
year = {2025},
eprint = {2507.15343},
archivePrefix = {arXiv},
primaryClass = {cs.SE},
url = {https://arxiv.org/abs/2507.15343},
}
Release files for stack-attention-pytorch 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| stack_attention_pytorch-0.0.1.tar.gz | 4.2 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| stack_attention_pytorch-0.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 7.8 kB
Release files / stack_attention_pytorch-0.0.1.tar.gz
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Release files / stack_attention_pytorch-0.0.1-py3-none-any.whl
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| Tags | Python 3 |
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