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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.08240 |
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| _version_ | 1866909534082039808 |
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| author | Simpson, Lachlan Costanza, Federico Millar, Kyle Cheng, Adriel Lim, Cheng-Chew Chew, Hong Gunn |
| author_facet | Simpson, Lachlan Costanza, Federico Millar, Kyle Cheng, Adriel Lim, Cheng-Chew Chew, Hong Gunn |
| contents | Integrated gradients is prevalent within machine learning to address the black-box problem of neural networks. The explanations given by integrated gradients depend on a choice of base-point. The choice of base-point is not a priori obvious and can lead to drastically different explanations. There is a longstanding hypothesis that data lies on a low dimensional Riemannian manifold. The quality of explanations on a manifold can be measured by the extent to which an explanation for a point lies in its tangent space. In this work, we propose that the base-point should be chosen such that it maximises the tangential alignment of the explanation. We formalise the notion of tangential alignment and provide theoretical conditions under which a base-point choice will provide explanations lying in the tangent space. We demonstrate how to approximate the optimal base-point on several well-known image classification datasets. Furthermore, we compare the optimal base-point choice with common base-points and three gradient explainability models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08240 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tangentially Aligned Integrated Gradients for User-Friendly Explanations Simpson, Lachlan Costanza, Federico Millar, Kyle Cheng, Adriel Lim, Cheng-Chew Chew, Hong Gunn Machine Learning Differential Geometry Integrated gradients is prevalent within machine learning to address the black-box problem of neural networks. The explanations given by integrated gradients depend on a choice of base-point. The choice of base-point is not a priori obvious and can lead to drastically different explanations. There is a longstanding hypothesis that data lies on a low dimensional Riemannian manifold. The quality of explanations on a manifold can be measured by the extent to which an explanation for a point lies in its tangent space. In this work, we propose that the base-point should be chosen such that it maximises the tangential alignment of the explanation. We formalise the notion of tangential alignment and provide theoretical conditions under which a base-point choice will provide explanations lying in the tangent space. We demonstrate how to approximate the optimal base-point on several well-known image classification datasets. Furthermore, we compare the optimal base-point choice with common base-points and three gradient explainability models. |
| title | Tangentially Aligned Integrated Gradients for User-Friendly Explanations |
| topic | Machine Learning Differential Geometry |
| url | https://arxiv.org/abs/2503.08240 |