--- title: "Who cites One weird trick for parallelizing — 126 citing papers" description: "126 papers of the Elamur Research library cite «One weird trick for parallelizing convolutional neural networks», found in their bibliographies…" canonical: "https://research.elamur.ai/free/en/papers/krizhevsky-2014-one-weird-trick-parallelizing-convolutional/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/krizhevsky-2014-one-weird-trick-parallelizing-convolutional/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [One weird trick for parallelizing convolutional…](https://md.research.elamur.ai/free/en/papers/krizhevsky-2014-one-weird-trick-parallelizing-convolutional/) / cited by # Who cites «One weird trick for parallelizing convolutional neural networks» 126 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 «One weird trick for parallelizing convolutional neural networks»](https://md.research.elamur.ai/free/en/papers/krizhevsky-2014-one-weird-trick-parallelizing-convolutional/) 1. [**Very Deep Convolutional Networks for Large-Scale Image Recognition arXiv:1409.1556**Karen Simonyan, Andrew Zisserman*2014 · 73,393 citations*](https://md.research.elamur.ai/free/en/papers/simonyan-2014-very-deep-convolutional-networks-large/) 2. [**TensorFlow: A system for large-scale machine learning arXiv:1605.08695**Martin Abadi, Paul Barham, Jianmin Chen, et al.*2016 · 12,711 citations*](https://md.research.elamur.ai/free/en/papers/abadi-2016-tensorflow-system-large-scale-machine/) 3. [**Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683**Colin Raffel, Noam Shazeer, Adam Roberts, et al.*2019 · 11,325 citations*](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/) 4. [**TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems arXiv:1603.04467**Martín Abadi, Ashish Agarwal, Paul Barham, et al.*2016 · 9,512 citations*](https://md.research.elamur.ai/free/en/papers/abadi-2016-tensorflow-large-scale-machine-learning/) 5. **[Memory Aware Synapses: Learning what (not) to forget](https://md.research.elamur.ai/free/en/papers/aljundi-2017-memory-aware-synapses-learning-forget/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, et al.*2017 · 1,608 citations* 6. [**Training Tips for the Transformer Model arXiv:1804.00247 · The Prague Bulletin of Mathematical Linguistics 110, April 2018, pp. 43-70**Martin Popel, Ondřej Bojar*2018 · 370 citations*](https://md.research.elamur.ai/free/en/papers/popel-2018-training-tips-transformer-model/) 7. [**Beyond Data and Model Parallelism for Deep Neural Networks SysML 2019**Zhihao Jia, Matei Zaharia, Alex Aiken*2018 · 280 citations*](https://md.research.elamur.ai/free/en/papers/jia-2018-beyond-data-model-parallelism-deep/) 8. [**Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning OSDI 2022**Lianmin Zheng, Zhuohan Li, Hao Zhang, et al.*2022 · 279 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2022-alpa-automating-inter-intra-operator/) 9. [**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/) 10. [**PipeDream: Fast and Efficient Pipeline Parallel DNN Training arXiv:1806.03377**Aaron Harlap, Deepak Narayanan, Amar Phanishayee, et al.*2018 · 162 citations*](https://md.research.elamur.ai/free/en/papers/harlap-2018-pipedream-fast-efficient-pipeline-parallel/) 11. [**Fast Algorithms for Convolutional Neural Networks CVPR 2016**Andrew Lavin, Scott Gray*2015 · 154 citations*](https://md.research.elamur.ai/free/en/papers/lavin-2015-fast-algorithms-convolutional-neural-networks/) 12. [**Efficient Large Scale Language Modeling with Mixtures of Experts arXiv:2112.10684**Mikel Artetxe, Shruti Bhosale, Naman Goyal, et al.*2021 · 113 citations*](https://md.research.elamur.ai/free/en/papers/artetxe-2021-efficient-large-scale-language-modeling/) 13. [**ImageNet Training in Minutes arXiv:1709.05011**Yang You, Zhao Zhang, Cho-Jui Hsieh, et al.*2017 · 112 citations*](https://md.research.elamur.ai/free/en/papers/you-2017-imagenet-training-minutes/) 14. [**DAPPLE: A Pipelined Data Parallel Approach for Training Large Models arXiv:2007.01045**Shiqing Fan, Yi Rong, Chen Meng, et al.*2020 · 107 citations*](https://md.research.elamur.ai/free/en/papers/fan-2020-dapple-pipelined-data-parallel-approach/) 15. [**vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design MICRO 2016**Minsoo Rhu, Natalia Gimelshein, Jason Clemons, et al.*2016 · 81 citations*](https://md.research.elamur.ai/free/en/papers/rhu-2016-vdnn-virtualized-deep-neural-networks/) 16. [**Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation arXiv:2307.15337**Xuefei Ning, Zinan Lin, Zixuan Zhou, et al.*2023 · 72 citations*](https://md.research.elamur.ai/free/en/papers/ning-2023-skeleton-thought-prompting-llms-efficient/) 17. [**Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent arXiv:2411.02265**Xingwu Sun, Yanfeng Chen, Yiqing Huang, et al.*2024 · 25 citations*](https://md.research.elamur.ai/free/en/papers/sun-2024-hunyuan-large-open-source/) 18. [**PanGu-π Pro:Rethinking Optimization and Architecture for Tiny Language Models arXiv:2402.02791**Yehui Tang, Kai Han, Fangcheng Liu, et al.*2024 · 8 citations*](https://md.research.elamur.ai/free/en/papers/tang-2024-pangu-pi-pro-rethinking/) 108 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/krizhevsky-2014-one-weird-trick-parallelizing-convolutional/) — One weird trick for parallelizing convolutional neural networks