--- title: "Who cites Quantized Neural Networks: — 123 citing papers" description: "123 papers of the Elamur Research library cite «Quantized Neural Networks: Training Neural Networks with Low Precision Weights and…" canonical: "https://research.elamur.ai/free/en/papers/hubara-2016-quantized-neural-networks-training-neural/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/hubara-2016-quantized-neural-networks-training-neural/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Quantized Neural Networks: Training Neural…](https://md.research.elamur.ai/free/en/papers/hubara-2016-quantized-neural-networks-training-neural/) / cited by # Who cites «Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations» 123 papers of our library cite it (of the 149,439 whose bibliographies we read) · 10 with a page here, the most cited first · [← back to «Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations»](https://md.research.elamur.ai/free/en/papers/hubara-2016-quantized-neural-networks-training-neural/) 1. [**MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications arXiv:1704.04861**Andrew G. Howard, Menglong Zhu, Bo Chen, et al.*2017 · 9,729 citations*](https://md.research.elamur.ai/free/en/papers/howard-2017-mobilenets-efficient-convolutional-neural-networks/) 2. [**The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks arXiv:1802.08232**Nicholas Carlini, Chang Liu, Ulfar Erlingsson, et al.*2018 · 692 citations*](https://md.research.elamur.ai/free/en/papers/carlini-2018-secret-sharer-evaluating-testing-unintended/) 3. [**Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis ACM Computing Surveys 2019**Tal Ben-Nun, Torsten Hoefler*2018 · 275 citations*](https://md.research.elamur.ai/free/en/papers/nun-2018-demystifying-parallel-distributed-deep-learning/) 4. [**TinyStories: How Small Can Language Models Be and Still Speak Coherent English?**Ronen Eldan, Yuanzhi Li*2023 · 213 citations*](https://md.research.elamur.ai/free/en/papers/eldan-2023-tinystories-small-language-models-still/) 5. [**EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty arXiv:2401.15077**Yuhui Li, Fangyun Wei, Chao Zhang, et al.*2024 · 208 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-eagle-speculative-sampling-requires-rethinking/) 6. **[Compressing Large-Scale Transformer-Based Models: A Case Study on BERT](https://md.research.elamur.ai/free/en/papers/ganesh-2021-compressing-large-scale-transformer-based/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Prakhar Ganesh, Yao Chen, Xin Lou, et al.*2021 · 190 citations* 7. [**Efficient Processing of Deep Neural Networks: A Tutorial and Survey Proceedings of the IEEE 2017**Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, et al.*2017 · 146 citations*](https://md.research.elamur.ai/free/en/papers/sze-2017-efficient-processing-deep-neural-networks/) 8. **[EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees](https://md.research.elamur.ai/free/en/papers/li-2024-eagle-2-faster-inference-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yuhui Li, Fangyun Wei, Chao Zhang, et al.*2024 · 133 citations* 9. **[RNNs can generate bounded hierarchical languages with optimal memory](https://md.research.elamur.ai/free/en/papers/hewitt-2020-rnns-can-generate-bounded-hierarchical/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**John Hewitt, Michael Hahn, Surya Ganguli, et al.*2020 · 65 citations* 10. **[L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models](https://md.research.elamur.ai/free/en/papers/jeon-2025-l4q-parameter-efficient-quantization-aware/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hyesung Jeon, Yulhwa Kim, Jae-Joon Kim*2025 · 15 citations* 113 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/hubara-2016-quantized-neural-networks-training-neural/) — Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations