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Lately, post-training quantization methods have gained considerable attention, as they are simple to use, and require only a small unlabeled calibration set.
Enhancements of the simple method for predicting incompressible fluid flows
Van Doormaal, J. P. and Raithby, G. D · 1984
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Binaryconnect: Training deep neural networks with binary weights during propagations
Courbariaux, M., Bengio, Y., and David, J.-P · 2015
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Han, S., Mao, H., and Dally, W. J · 2015
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Fixed point quantization of deep convolutional networks
Lin, D., Talathi, S., and Annapureddy, S · 2016
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Squad: 100,000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Rastegari, M., Ordonez, V., Redmon, J., and Farhadi, A · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Wu, Y., Schuster, M., Chen, Z., et al · 2016
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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Zhou, S., Wu, Y., Ni, Z., et al · 2016
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Quantized neural networks: Training neural networks with low precision weights and activations
Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., and Bengio, Y · 2017
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Incremental network quantization: Towards lossless cnns with low-precision weights
Zhou, A., Yao, A., Guo, Y., Xu, L., and Chen, Y · 2017
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Aciq: Analytical clipping for integer quantization of neural networks
Banner, R., Nahshan, Y., Hoffer, E., and Soudry, D · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
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Quantizing deep convolutional networks for efficient inference: A whitepaper
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Qkd: Quantization-aware knowledge distillation
Kim, J., Bhalgat, Y., Lee, J., Patel, C., and Kwak, N · 2019
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Same, same but different-recovering neural network quantization error through weight factorization
Meller, E., Finkelstein, A., Almog, U., and Grobman, M · 2019
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Data-free quantization through weight equalization and bias correction
Nagel, M., Baalen, M. v., Blankevoort, T., and Welling, M · 2019
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Hybrid 8-bit floating point (hfp8) training and inference for deep neural networks
Sun, X., Choi, J., Chen, C.-Y., et al · 2019
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Improving neural network quantization without retraining using outlier channel splitting
Zhao, R., Hu, Y., Dotzel, J., De Sa, C., and Zhang, Z · 2019
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Low-bit quantization of neural networks for efficient inference
Choukroun, Y., Kravchik, E., Yang, F., and Kisilev, P · 2019
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Fighting quantization bias with bias
Finkelstein, A., Almog, U., and Grobman, M · 2019
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The knowledge within: Methods for data-free model compression
Haroush, M., Hubara, I., Hoffer, E., and Soudry, D · 2019
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Knapsack pruning with inner distillation
Aflalo, Y., Noy, A., Lin, M., Friedman, I., and Zelnik, L · 2020
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Zeroq: A novel zero shot quantization framework
Cai, Y., Yao, Z., Dong, Z., et al · 2020
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Up or down? adaptive rounding for post-training quantization
Nagel, M., Amjad, R. A., van Baalen, M., Louizos, C., and Blankevoort, T · 2020
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