🔗 Who cites «DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients»

140 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · ← back to «DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients»

1. Quantized Neural Networks: Training Neural Networks with Low Precision Weights and ActivationsarXiv:1609.07061Itay Hubara, Matthieu Courbariaux, Daniel Soudry, et al.2016 · 1,610 citations
2. QSGD: Communication-Efficient SGD via Gradient Quantization and EncodingarXiv:1610.02132Dan Alistarh, Demjan Grubic, Jerry Li, et al.2016 · 962 citations
3. Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency AnalysisACM Computing Surveys 2019Tal Ben-Nun, Torsten Hoefler2018 · 275 citations
4. BinaryBERT: Pushing the Limit of BERT QuantizationAnnual Meeting of the Association for Computational Linguistics (ACL)Haoli Bai, Wei Zhang, Lu Hou, et al.2021 · 166 citations
5. OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language ModelsProceedings of the AAAI Conference on Artificial IntelligenceChanghun Lee, Jungyu Jin, Taesu Kim, et al.2023 · 149 citations
6. Efficient Processing of Deep Neural Networks: A Tutorial and SurveyProceedings of the IEEE 2017Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, et al.2017 · 146 citations
7. I-BERT: Integer-only BERT QuantizationInternational Conference on Machine Learning (ICML)Sehoon Kim, Amir Gholami, Zhewei Yao, et al.2021 · 122 citations
8. FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive DistillationarXiv:2407.07093Liqun Ma, Mingjie Sun, Zhiqiang Shen2024 · 7 citations

132 more papers of our library cite it; they have no page here yet.

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