Neural Networks Use Distance Metrics
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866916497073373184 |
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| author | Oursland, Alan |
| author_facet | Oursland, Alan |
| contents | We present empirical evidence that neural networks with ReLU and Absolute Value activations learn distance-based representations. We independently manipulate both distance and intensity properties of internal activations in trained models, finding that both architectures are highly sensitive to small distance-based perturbations while maintaining robust performance under large intensity-based perturbations. These findings challenge the prevailing intensity-based interpretation of neural network activations and offer new insights into their learning and decision-making processes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17932 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Networks Use Distance Metrics Oursland, Alan Machine Learning Artificial Intelligence We present empirical evidence that neural networks with ReLU and Absolute Value activations learn distance-based representations. We independently manipulate both distance and intensity properties of internal activations in trained models, finding that both architectures are highly sensitive to small distance-based perturbations while maintaining robust performance under large intensity-based perturbations. These findings challenge the prevailing intensity-based interpretation of neural network activations and offer new insights into their learning and decision-making processes. |
| title | Neural Networks Use Distance Metrics |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2411.17932 |