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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.06051 |
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| _version_ | 1866910641076305920 |
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| author | Hashemi, Vahid Křetínský, Jan Rieder, Sabine Schön, Torsten Vorhoff, Jan |
| author_facet | Hashemi, Vahid Křetínský, Jan Rieder, Sabine Schön, Torsten Vorhoff, Jan |
| contents | Since neural networks can make wrong predictions even with high confidence, monitoring their behavior at runtime is important, especially in safety-critical domains like autonomous driving. In this paper, we combine ideas from previous monitoring approaches based on observing the activation values of hidden neurons. In particular, we combine the Gaussian-based approach, which observes whether the current value of each monitored neuron is similar to typical values observed during training, and the Outside-the-Box monitor, which creates clusters of the acceptable activation values, and, thus, considers the correlations of the neurons' values. Our experiments evaluate the achieved improvement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_06051 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Gaussian-Based and Outside-the-Box Runtime Monitoring Join Forces Hashemi, Vahid Křetínský, Jan Rieder, Sabine Schön, Torsten Vorhoff, Jan Machine Learning Since neural networks can make wrong predictions even with high confidence, monitoring their behavior at runtime is important, especially in safety-critical domains like autonomous driving. In this paper, we combine ideas from previous monitoring approaches based on observing the activation values of hidden neurons. In particular, we combine the Gaussian-based approach, which observes whether the current value of each monitored neuron is similar to typical values observed during training, and the Outside-the-Box monitor, which creates clusters of the acceptable activation values, and, thus, considers the correlations of the neurons' values. Our experiments evaluate the achieved improvement. |
| title | Gaussian-Based and Outside-the-Box Runtime Monitoring Join Forces |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.06051 |