Who cites «Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations»
- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision ApplicationsarXiv:1704.04861Andrew G. Howard, Menglong Zhu, Bo Chen, et al.2017 · 9,729 citations
- The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural NetworksarXiv:1802.08232Nicholas Carlini, Chang Liu, Ulfar Erlingsson, et al.2018 · 692 citations
- Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency AnalysisACM Computing Surveys 2019Tal Ben-Nun, Torsten Hoefler2018 · 275 citations
- TinyStories: How Small Can Language Models Be and Still Speak Coherent English?Ronen Eldan, Yuanzhi Li2023 · 213 citations
- EAGLE: Speculative Sampling Requires Rethinking Feature UncertaintyarXiv:2401.15077Yuhui Li, Fangyun Wei, Chao Zhang, et al.2024 · 208 citations
- Compressing Large-Scale Transformer-Based Models: A Case Study on BERTTransactions of the Association for Computational Linguistics (TACL)Prakhar Ganesh, Yao Chen, Xin Lou, et al.2021 · 190 citations
- 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
- EAGLE-2: Faster Inference of Language Models with Dynamic Draft TreesConference on Empirical Methods in Natural Language Processing (EMNLP)Yuhui Li, Fangyun Wei, Chao Zhang, et al.2024 · 133 citations
- RNNs can generate bounded hierarchical languages with optimal memoryConference on Empirical Methods in Natural Language Processing (EMNLP)John Hewitt, Michael Hahn, Surya Ganguli, et al.2020 · 65 citations
- L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Hyesung Jeon, Yulhwa Kim, Jae-Joon Kim2025 · 15 citations
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