Making CNNs Interpretable by Building Dynamic Sequential Decision Forests with Top-down Hierarchy Learning — mentioned by nickpsecurity

Sat, 15 Mar 2025 12:53:16 GMT

That assumes it can even be done. It's worth looking into. There have been some projects in those areas. Mixing probabilistic logic with deep learning: https://arxiv.org/abs/1808.08485 https://github.com/ML-KULeuven/deepproblog Combining decision trees with neural nets for interpretability: https://arxiv.org/abs/2011.07553 https://arxiv.org/pdf/2106.02824v1 https://arxiv.org/pdf/1806.06988 https://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-... It looks like model transfer from uninterpretable, pretrained models to interpretable models is the best strategy to keep using. That also…

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Making CNNs Interpretable by Building Dynamic Sequential Decision Forests with Top-down Hierarchy Learning · HackerPapers discussions

Making CNNs Interpretable by Building Dynamic Sequential Decision Forests with Top-down Hierarchy Learning — mentioned by nickpsecurity