Adaptive Inference: Theoretical Limits and Unexplored Opportunities
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929236013481984 |
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| author | Hor, Soheil Qian, Ying Pilanci, Mert Arbabian, Amin |
| author_facet | Hor, Soheil Qian, Ying Pilanci, Mert Arbabian, Amin |
| contents | This paper introduces the first theoretical framework for quantifying the efficiency and performance gain opportunity size of adaptive inference algorithms. We provide new approximate and exact bounds for the achievable efficiency and performance gains, supported by empirical evidence demonstrating the potential for 10-100x efficiency improvements in both Computer Vision and Natural Language Processing tasks without incurring any performance penalties. Additionally, we offer insights on improving achievable efficiency gains through the optimal selection and design of adaptive inference state spaces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04359 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive Inference: Theoretical Limits and Unexplored Opportunities Hor, Soheil Qian, Ying Pilanci, Mert Arbabian, Amin Machine Learning This paper introduces the first theoretical framework for quantifying the efficiency and performance gain opportunity size of adaptive inference algorithms. We provide new approximate and exact bounds for the achievable efficiency and performance gains, supported by empirical evidence demonstrating the potential for 10-100x efficiency improvements in both Computer Vision and Natural Language Processing tasks without incurring any performance penalties. Additionally, we offer insights on improving achievable efficiency gains through the optimal selection and design of adaptive inference state spaces. |
| title | Adaptive Inference: Theoretical Limits and Unexplored Opportunities |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.04359 |