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pytorch implementation of "Distilling a Neural Network Into a Soft Decision Tree"
| Date | Stars |
|---|---|
| 2026-07-31 | 306 |
| 2026-08-06 | 306 |
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# Soft-Decision-Tree Soft-Decision-Tree is the pytorch implementation of [Distilling a Neural Network Into a Soft Decision Tree](https://arxiv.org/pdf/1711.09784.pdf), paper recently published on Arxiv about adopting decision tree algorithm into neural network. "If we could take the knowledge acquired by the neural net and express the same knowledge in a model that relies on hierarchical decisions instead, explaining a particular decision would be much easier." <img src="./img/figure2.png" width="768"> ## Requirements - python - [pytorch](https://pytorch.org/) ## Result I achieved 92.95% of test dataset accuracy on MNISTafter 40 epoches, without exploring enough of hyper-parameters (The paper achieved 94.45%). Higher accuracy might be achievable with searching hyper-parameters, or training longer epoches (if you can, please let me know :) ) ## Usage $ python main.py
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:66baab01fbedcc1e, llm:description: 'pytorch implementation of "Distilling a Neural Network Into a Soft Decision Tree"'
matched fp:66baab01fbedcc1e, llm:description: 'pytorch implementation of "Distilling a Neural Network Into a Soft Decision Tree"'
matched fp:66baab01fbedcc1e, llm:description: 'pytorch implementation of "Distilling a Neural Network Into a Soft Decision Tree"'