Aggregate Representation Measure for Predictive Model Reusability
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910449042194432 |
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| author | Sangarya, Vishwesh Bradford, Richard Kim, Jung-Eun |
| author_facet | Sangarya, Vishwesh Bradford, Richard Kim, Jung-Eun |
| contents | In this paper, we propose a predictive quantifier to estimate the retraining cost of a trained model in distribution shifts. The proposed Aggregated Representation Measure (ARM) quantifies the change in the model's representation from the old to new data distribution. It provides, before actually retraining the model, a single concise index of resources - epochs, energy, and carbon emissions - required for the retraining. This enables reuse of a model with a much lower cost than training a new model from scratch. The experimental results indicate that ARM reasonably predicts retraining costs for varying noise intensities and enables comparisons among multiple model architectures to determine the most cost-effective and sustainable option. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_09600 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Aggregate Representation Measure for Predictive Model Reusability Sangarya, Vishwesh Bradford, Richard Kim, Jung-Eun Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Computers and Society In this paper, we propose a predictive quantifier to estimate the retraining cost of a trained model in distribution shifts. The proposed Aggregated Representation Measure (ARM) quantifies the change in the model's representation from the old to new data distribution. It provides, before actually retraining the model, a single concise index of resources - epochs, energy, and carbon emissions - required for the retraining. This enables reuse of a model with a much lower cost than training a new model from scratch. The experimental results indicate that ARM reasonably predicts retraining costs for varying noise intensities and enables comparisons among multiple model architectures to determine the most cost-effective and sustainable option. |
| title | Aggregate Representation Measure for Predictive Model Reusability |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Computers and Society |
| url | https://arxiv.org/abs/2405.09600 |