Models Got Talent: Identifying High Performing Wearable Human Activity Recognition Models Without Training
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908664688803840 |
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| author | Goldman, Richard Komperla, Varun Ploetz, Thomas Haresamudram, Harish |
| author_facet | Goldman, Richard Komperla, Varun Ploetz, Thomas Haresamudram, Harish |
| contents | A promising alternative to the computationally expensive Neural Architecture Search (NAS) involves the development of Zero Cost Proxies (ZCPs), which correlate well with trained performance, but can be computed through a single forward/backward pass on a randomly sampled batch of data. In this paper, we investigate the effectiveness of ZCPs for HAR on six benchmark datasets, and demonstrate that they discover network architectures that obtain within 5% of performance attained by full-scale training involving 1500 randomly sampled architectures. This results in substantial computational savings as high-performing architectures can be discovered with minimal training. Our experiments not only introduce ZCPs to sensor-based HAR, but also demonstrate that they are robust to data noise, further showcasing their suitability for practical scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06157 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Models Got Talent: Identifying High Performing Wearable Human Activity Recognition Models Without Training Goldman, Richard Komperla, Varun Ploetz, Thomas Haresamudram, Harish Machine Learning Artificial Intelligence A promising alternative to the computationally expensive Neural Architecture Search (NAS) involves the development of Zero Cost Proxies (ZCPs), which correlate well with trained performance, but can be computed through a single forward/backward pass on a randomly sampled batch of data. In this paper, we investigate the effectiveness of ZCPs for HAR on six benchmark datasets, and demonstrate that they discover network architectures that obtain within 5% of performance attained by full-scale training involving 1500 randomly sampled architectures. This results in substantial computational savings as high-performing architectures can be discovered with minimal training. Our experiments not only introduce ZCPs to sensor-based HAR, but also demonstrate that they are robust to data noise, further showcasing their suitability for practical scenarios. |
| title | Models Got Talent: Identifying High Performing Wearable Human Activity Recognition Models Without Training |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2511.06157 |