---
title: "Who cites Matthieu Courbariaux — 327 papers of our library"
description: "327 papers of the Elamur Research library cite the works of Matthieu Courbariaux, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/matthieu-courbariaux/cited-by/"
---
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# Who cites Matthieu Courbariaux

392 citations of their works from 327 papers of our library (of the 149,439 whose bibliographies we read) · 28 with a page here, the most cited first · [← Matthieu Courbariaux](https://md.research.elamur.ai/free/en/authors/matthieu-courbariaux/)

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. **[XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks](https://md.research.elamur.ai/free/en/papers/rastegari-2016-xnor-net-imagenet-classification-using/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, et al.*2016 · 4,402 citations*
3. [**Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 arXiv:1602.02830**Matthieu Courbariaux, Itay Hubara, Daniel Soudry, et al.*2016 · 2,143 citations*](https://md.research.elamur.ai/free/en/papers/courbariaux-2016-binarized-neural-networks-training-deep/)
4. [**DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients arXiv:1606.06160**Shuchang Zhou, Yuxin Wu, Zekun Ni, et al.*2016 · 1,762 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2016-dorefa-net-training-low-bitwidth/)
5. [**Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations arXiv:1609.07061**Itay Hubara, Matthieu Courbariaux, Daniel Soudry, et al.*2016 · 1,610 citations*](https://md.research.elamur.ai/free/en/papers/hubara-2016-quantized-neural-networks-training-neural/)
6. **[AMC: AutoML for Model Compression and Acceleration on Mobile Devices](https://md.research.elamur.ai/free/en/papers/he-2018-amc-automl-model-compression-acceleration/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Yihui He; Ji Lin; Zhijian Liu; Hanrui Wang; Li-Jia Li;, Song Han*2018 · 1,444 citations*
7. [**TVM: An Automated End-to-End Optimizing Compiler for Deep Learning arXiv:1802.04799**Tianqi Chen, Thierry Moreau, Ziheng Jiang, et al.*2018 · 1,384 citations*](https://md.research.elamur.ai/free/en/papers/chen-2018-tvm-automated-end-end-optimizing/)
8. [**FastText.zip: Compressing text classification models arXiv:1612.03651**Armand Joulin, Edouard Grave, Piotr Bojanowski, et al.*2016 · 1,219 citations*](https://md.research.elamur.ai/free/en/papers/joulin-2016-fasttext-zip-compressing-text-classification/)
9. [**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/)
10. [**ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware arXiv:1812.00332**Han Cai, Ligeng Zhu, Song Han*2018 · 479 citations*](https://md.research.elamur.ai/free/en/papers/cai-2018-proxylessnas-direct-neural-architecture-search/)
11. [**Hierarchical Multiscale Recurrent Neural Networks arXiv:1609.01704**Junyoung Chung, Sungjin Ahn, Yoshua Bengio*2016 · 288 citations*](https://md.research.elamur.ai/free/en/papers/chung-2016-hierarchical-multiscale-recurrent-neural-networks/)
12. [**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/)
13. [**Once-for-All: Train One Network and Specialize it for Efficient Deployment arXiv:1908.09791**Han Cai, Chuang Gan, Tianzhe Wang, et al.*2019 · 252 citations*](https://md.research.elamur.ai/free/en/papers/cai-2019-once-all-train-one-network/)
14. [**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/)
15. **[TernaryBERT: Distillation-aware Ultra-low Bit BERT](https://md.research.elamur.ai/free/en/papers/zhang-2020-ternarybert-distillation-aware-ultra-low/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wei Zhang, Lu Hou, Yichun Yin, et al.*2020 · 213 citations*
16. [**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/)
17. **[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*
18. [**8-bit Optimizers via Block-wise Quantization arXiv:2110.02861**Tim Dettmers, Mike Lewis, Sam Shleifer, et al.*2021 · 181 citations*](https://md.research.elamur.ai/free/en/papers/dettmers-2021-8-bit-optimizers-block-wise/)
19. **[BinaryBERT: Pushing the Limit of BERT Quantization](https://md.research.elamur.ai/free/en/papers/bai-2021-binarybert-pushing-limit-bert-quantization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoli Bai, Wei Zhang, Lu Hou, et al.*2021 · 166 citations*
20. [**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/)
21. **[Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery](https://md.research.elamur.ai/free/en/papers/wen-2023-hard-prompts-made-easy-gradient/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxin Wen, Neel Jain, John Kirchenbauer, et al.*2023 · 141 citations*
22. **[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*
23. **[I-BERT: Integer-only BERT Quantization](https://md.research.elamur.ai/free/en/papers/kim-2021-i-bert-integer-only-bert/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Sehoon Kim, Amir Gholami, Zhewei Yao, et al.*2021 · 122 citations*
24. [**LLM Inference Unveiled: Survey and Roofline Model Insights arXiv 2024**Zhihang Yuan, Yuzhang Shang, Yang Zhou, et al.*2024 · 91 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2024-llm-inference-unveiled-survey-roofline/)
25. **[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*
26. **[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*
27. [**FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation arXiv:2407.07093**Liqun Ma, Mingjie Sun, Zhiqiang Shen*2024 · 7 citations*](https://md.research.elamur.ai/free/en/papers/ma-2024-fbi-llm-scaling-up-fully/)
28. **[GRASPrune: Global Gating for Budgeted Structured Pruning of Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-grasprune-global-gating-budgeted-structured/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziyang Wang, Jiangfeng Xiao, Chuan Xiao, et al.*2026*

299 more citing papers of our library have no page here yet.

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