Combining Statistical Depth and Fermat Distance for Uncertainty Quantification
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929719691182080 |
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| author | Nguyen, Hai-Vy Gamboa, Fabrice Chhaibi, Reda Zhang, Sixin Gratton, Serge Giaccone, Thierry |
| author_facet | Nguyen, Hai-Vy Gamboa, Fabrice Chhaibi, Reda Zhang, Sixin Gratton, Serge Giaccone, Thierry |
| contents | We measure the Out-of-domain uncertainty in the prediction of Neural Networks using a statistical notion called ``Lens Depth'' (LD) combined with Fermat Distance, which is able to capture precisely the ``depth'' of a point with respect to a distribution in feature space, without any assumption about the form of distribution. Our method has no trainable parameter. The method is applicable to any classification model as it is applied directly in feature space at test time and does not intervene in training process. As such, it does not impact the performance of the original model. The proposed method gives excellent qualitative result on toy datasets and can give competitive or better uncertainty estimation on standard deep learning datasets compared to strong baseline methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_08476 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Combining Statistical Depth and Fermat Distance for Uncertainty Quantification Nguyen, Hai-Vy Gamboa, Fabrice Chhaibi, Reda Zhang, Sixin Gratton, Serge Giaccone, Thierry Machine Learning Artificial Intelligence Probability Applications We measure the Out-of-domain uncertainty in the prediction of Neural Networks using a statistical notion called ``Lens Depth'' (LD) combined with Fermat Distance, which is able to capture precisely the ``depth'' of a point with respect to a distribution in feature space, without any assumption about the form of distribution. Our method has no trainable parameter. The method is applicable to any classification model as it is applied directly in feature space at test time and does not intervene in training process. As such, it does not impact the performance of the original model. The proposed method gives excellent qualitative result on toy datasets and can give competitive or better uncertainty estimation on standard deep learning datasets compared to strong baseline methods. |
| title | Combining Statistical Depth and Fermat Distance for Uncertainty Quantification |
| topic | Machine Learning Artificial Intelligence Probability Applications |
| url | https://arxiv.org/abs/2404.08476 |