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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2503.15748 |
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| _version_ | 1866917963182899200 |
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| author | Jin, Lisa Ma, Jianhao Liu, Zechun Gromov, Andrey Defazio, Aaron Xiao, Lin |
| author_facet | Jin, Lisa Ma, Jianhao Liu, Zechun Gromov, Andrey Defazio, Aaron Xiao, Lin |
| contents | We develop a principled method for quantization-aware training (QAT) of large-scale machine learning models. Specifically, we show that convex, piecewise-affine regularization (PAR) can effectively induce the model parameters to cluster towards discrete values. We minimize PAR-regularized loss functions using an aggregate proximal stochastic gradient method (AProx) and prove that it has last-iterate convergence. Our approach provides an interpretation of the straight-through estimator (STE), a widely used heuristic for QAT, as the asymptotic form of PARQ. We conduct experiments to demonstrate that PARQ obtains competitive performance on convolution- and transformer-based vision tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15748 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PARQ: Piecewise-Affine Regularized Quantization Jin, Lisa Ma, Jianhao Liu, Zechun Gromov, Andrey Defazio, Aaron Xiao, Lin Machine Learning Optimization and Control We develop a principled method for quantization-aware training (QAT) of large-scale machine learning models. Specifically, we show that convex, piecewise-affine regularization (PAR) can effectively induce the model parameters to cluster towards discrete values. We minimize PAR-regularized loss functions using an aggregate proximal stochastic gradient method (AProx) and prove that it has last-iterate convergence. Our approach provides an interpretation of the straight-through estimator (STE), a widely used heuristic for QAT, as the asymptotic form of PARQ. We conduct experiments to demonstrate that PARQ obtains competitive performance on convolution- and transformer-based vision tasks. |
| title | PARQ: Piecewise-Affine Regularized Quantization |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2503.15748 |