🔗 Who cites «XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks»

206 papers of our library cite it (of the 149,439 whose bibliographies we read) · 18 with a page here, the most cited first · ← back to «XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks»

1. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision ApplicationsarXiv:1704.04861Andrew G. Howard, Menglong Zhu, Bo Chen, et al.2017 · 9,729 citations
2. DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth GradientsarXiv:1606.06160Shuchang Zhou, Yuxin Wu, Zekun Ni, et al.2016 · 1,762 citations
3. 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
4. AMC: AutoML for Model Compression and Acceleration on Mobile DevicesLecture notes in computer scienceYihui He; Ji Lin; Zhijian Liu; Hanrui Wang; Li-Jia Li;, Song Han2018 · 1,444 citations
5. TVM: An Automated End-to-End Optimizing Compiler for Deep LearningarXiv:1802.04799Tianqi Chen, Thierry Moreau, Ziheng Jiang, et al.2018 · 1,384 citations
6. Green AIarXiv:1907.10597Roy Schwartz, Jesse Dodge, Noah A. Smith, et al.2019 · 389 citations
7. A Survey on Model Compression for Large Language ModelsTransactions of the Association for Computational Linguistics (TACL)Xunyu Zhu, Jian Li, Yong Liu, et al.2024 · 381 citations
8. Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency AnalysisACM Computing Surveys 2019Tal Ben-Nun, Torsten Hoefler2018 · 275 citations
9. TernaryBERT: Distillation-aware Ultra-low Bit BERTConference on Empirical Methods in Natural Language Processing (EMNLP)Wei Zhang, Lu Hou, Yichun Yin, et al.2020 · 213 citations
10. 8-bit Optimizers via Block-wise QuantizationarXiv:2110.02861Tim Dettmers, Mike Lewis, Sam Shleifer, et al.2021 · 181 citations
11. 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
12. 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
13. Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and DiscoveryConference on Neural Information Processing Systems (NeurIPS)Yuxin Wen, Neel Jain, John Kirchenbauer, et al.2023 · 141 citations
14. The case for 4-bit precision: k-bit Inference Scaling LawsarXiv:2212.09720Tim Dettmers, Luke Zettlemoyer2022 · 123 citations
15. I-BERT: Integer-only BERT QuantizationInternational Conference on Machine Learning (ICML)Sehoon Kim, Amir Gholami, Zhewei Yao, et al.2021 · 122 citations
16. LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language ModelsInternational Conference on Learning Representations (ICLR)Gunho Park, Baeseong Park, Minsub Kim, et al.2022 · 103 citations
17. FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive DistillationarXiv:2407.07093Liqun Ma, Mingjie Sun, Zhiqiang Shen2024 · 7 citations
18. pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware TrainingFindings of the Association for Computational LinguisticsWenzheng Zhang, Bingzheng Liu2026

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