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Post-training quantization (PTQ) has been gaining popularity for the deployment of deep neural networks on resource-limited devices since unlike quantization-aware training, neither a full training dataset nor end-to-end training is required at all.
Building a large annotated corpus of English: The Penn Treebank
Marcus, M. P., Santorini, B., and Marcinkiewicz, M. A · 2004
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Y., Leonard, N., and Courville, A · 2013
Earlier work this paper cites.
Learning both weights and connections for efficient neural network
Han, S., Pool, J., Tran, J., and Dally, W · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
Earlier work this paper cites.
Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., and Bengio, Y · 2016
Earlier work this paper cites.
Han, S., Mao, H., and Dally, W. J · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Pointer sentinel mixture models, 2016
Merity, S., Xiong, C., Bradbury, J., and Socher, R · 2016
Earlier work this paper cites.
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P · 2016
Earlier work this paper cites.
The WebNLG challenge: Generating text from RDF data
Gardent, C., Shimorina, A., Narayan, S., and Perez-Beltrachini, L · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
Think you have solved question answering? try arc, the ai2 reasoning challenge
Clark, P., Cowhey, I., Etzioni, O., Khot, T., Sabharwal, A., Schoenick, C., and Tafjord, O · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Earlier work this paper cites.
Can a suit of armor conduct electricity? a new dataset for open book question answering
Mihaylov, T., Clark, P., Khot, T., and Sabharwal, A · 2018
Earlier work this paper cites.
Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2018
Earlier work this paper cites.
Glue: A multi-task benchmark and analysis platform for natural language understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. R · 2018
Earlier work this paper cites.
BoolQ: Exploring the surprising difficulty of natural yes/no questions
Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., and Toutanova, K · 2019
Earlier work this paper cites.
Parameter-efficient transfer learning for nlp
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., and Gelly, S · 2019
Earlier work this paper cites.
Jain, S. R., Gural, A., Wu, M., and Dick, C. H · 2019
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Learning to quantize deep networks by optimizing quantization intervals with task loss
Jung, S., Son, C., Lee, S., Son, J., Han, J.-J., Kwak, Y., Ju Hwang, S., and Choi, C · 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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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
Cited alongside, same era.
Q8bert: Quantized 8bit bert
Zafrir, O., Boudoukh, G., Izsak, P., and Wasserblat, M · 2019
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GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow, March 2021
Black, S., Gao, L., Wang, P., Leahy, C., and Biderman, S · 2021
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Understanding and overcoming the challenges of efficient transformer quantization
Bondarenko, Y., Nagel, M., and Blankevoort, T · 2021
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A framework for few-shot language model evaluation, September 2021
Gao, L., Tow, J., Biderman, S., Black, S., DiPofi, A., Foster, C., Golding, L., Hsu, J., McDonell, K., Muennighoff, N., Phang, J., Reynolds, L., Tang, E., Thite, A., Wang, B., Wang, K., and Zou, A · 2021
Later among the works it cites.
Accurate post training quantization with small calibration sets
Hubara, I., Nahshan, Y., Hanani, Y., Banner, R., and Soudry, D · 2021
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Performance evaluation of int8 quantized inference on mobile gpus
Kim, S., Park, G., and Yi, Y · 2021
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Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., and Choi, Y · 2019
Cited alongside, same era.
Improving neural network quantization without retraining using outlier channel splitting
Zhao, R., Hu, Y., Dotzel, J., De Sa, C., and Zhang, Z · 2019
Cited alongside, same era.
Piqa: Reasoning about physical commonsense in natural language
Bisk, Y., Zellers, R., Gao, J., Choi, Y., et al · 2020
Cited alongside, same era.
Learned step size quantization
Esser, S. K., McKinstry, J. L., Bablani, D., Appuswamy, R., and Modha, D. S · 2020
Cited alongside, same era.
Autoq: Automated kernel-wise neural network quantization
Lou, Q., Guo, F., Kim, M., Liu, L., and Jiang., L · 2020
Cited alongside, same era.
Up or down? Adaptive rounding for post-training quantization
Nagel, M., Amjad, R. A., Van Baalen, M., Louizos, C., and Blankevoort, T · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2020
Cited alongside, same era.
Cluster-promoting quantization with bit-drop for minimizing network quantization loss
Lee, J. H., Yun, J., Hwang, S. J., and Yang, E · 2021
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BRECQ: Pushing the limit of post-training quantization by block reconstruction
Li, Y., Gong, R., Tan, X., Yang, Y., Hu, P., Zhang, Q., Yu, F., Wang, W., and Gu, S · 2021
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A white paper on neural network quantization
Nagel, M., Fournarakis, M., Amjad, R. A., Bondarenko, Y., van Baalen, M., and Blankevoort, T · 2021
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Loss aware post-training quantization
Nahshan, Y., Chmiel, B., Baskin, C., Zheltonozhskii, E., Banner, R., Bronstein, A. M., and Mendelson, A · 2021
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Winogrande: An adversarial winograd schema challenge at scale
Sakaguchi, K., Bras, R. L., Bhagavatula, C., and Choi, Y · 2021
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Llm. int8 (): 8-bit matrix multiplication for transformers at scale
Dettmers, T., Lewis, M., Belkada, Y., and Zettlemoyer, L · 2022
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LoRA: Low-rank adaptation of large language models
Hu, E. J., yelong shen, Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2022
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P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks
Liu, X., Ji, K., Fu, Y., Tam, W., Du, Z., Yang, Z., and Tang, J · 2022
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QDrop: Randomly dropping quantization for extremely low-bit post-training quantization
Wei, X., Gong, R., Li, Y., Liu, X., and Yu, F · 2022
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Smoothquant: Accurate and efficient post-training quantization for large language models
Xiao, G., Lin, J., Seznec, M., Demouth, J., and Han, S · 2022
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Zeroquant: Efficient and affordable post-training quantization for large-scale transformers
Yao, Z., Aminabadi, R. Y., Zhang, M., Wu, X., Li, C., and He, Y · 2022
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Opt: Open pre-trained transformer language models
Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X. V., et al · 2022
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Llama: Open and efficient foundation language models, 2023
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., and Lample, G · 2023
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