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Post-training quantization (PTQ) has emerged as a practical approach to compress large neural networks, making them highly efficient for deployment.
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Wang, P., Chen, Q., He, X., Cheng, J.: Towards accurate post-training network quantization via bit-split and stitching. In: International Conference on Machine Learning. pp. 9847–9856. PMLR (2020)
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He, Y., Liu, L., Liu, J., Wu, W., Zhou, H., Zhuang, B.: Ptqd: Accurate post-training quantization for diffusion models. Advances in Neural Information Processing Systems 36
2024
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Wortsman, M., Dettmers, T., Zettlemoyer, L., Morcos, A., Farhadi, A., Schmidt, L.: Stable and low-precision training for large-scale vision-language models. Advances in Neural Information Processing Systems 36
2024
Closest in time.