| Efficientnet: Rethinking model scaling for convolutional neural networks M Tan, Q Le International Conference on Machine Learning, 6105-6114, 2019 | 1743 | 2019 |
| Mnasnet: Platform-aware neural architecture search for mobile M Tan, B Chen, R Pang, V Vasudevan, M Sandler, A Howard, QV Le Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 887 | 2019 |
| Searching for mobilenetv3 A Howard, M Sandler, G Chu, LC Chen, B Chen, M Tan, W Wang, Y Zhu, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 617 | 2019 |
| Efficientdet: Scalable and efficient object detection M Tan, R Pang, QV Le Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 313 | 2020 |
| Adversarial examples improve image recognition C Xie, M Tan, B Gong, J Wang, AL Yuille, QV Le Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 86 | 2020 |
| MixConv: Mixed Depthwise Convolutional Kernels M Tan, QV Le Proceedings of the 30th British Machine Vision Conference (BMVC), 2019 | 69 | 2019 |
| Elasticflow: A complexity-effective approach for pipelining irregular loop nests M Tan, G Liu, R Zhao, S Dai, Z Zhang 2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 78-85, 2015 | 43 | 2015 |
| Architectural specialization for inter-iteration loop dependence patterns S Srinath, B Ilbeyi, M Tan, G Liu, Z Zhang, C Batten 2014 47th Annual IEEE/ACM International Symposium on Microarchitecture, 583-595, 2014 | 33 | 2014 |
| Multithreaded pipeline synthesis for data-parallel kernels M Tan, B Liu, S Dai, Z Zhang 2014 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 718-725, 2014 | 30 | 2014 |
| Flushing-enabled loop pipelining for high-level synthesis S Dai, M Tan, K Hao, Z Zhang 2014 51st ACM/EDAC/IEEE Design Automation Conference (DAC), 1-6, 2014 | 28 | 2014 |
| Evolutionary-Neural Hybrid Agents for Architecture Search K Maziarz, A Khorlin, Q de Laroussilhe, S Jastrzębski, M Tan, ... arXiv preprint arXiv:1811.09828, 2018 | 27* | 2018 |
| Bignas: Scaling up neural architecture search with big single-stage models J Yu, P Jin, H Liu, G Bender, PJ Kindermans, M Tan, T Huang, X Song, ... European Conference on Computer Vision, 702-717, 2020 | 26 | 2020 |
| Spinenet: Learning scale-permuted backbone for recognition and localization X Du, TY Lin, P Jin, G Ghiasi, M Tan, Y Cui, QV Le, X Song Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 26 | 2020 |
| Assemblenet: Searching for multi-stream neural connectivity in video architectures MS Ryoo, AJ Piergiovanni, M Tan, A Angelova arXiv preprint arXiv:1905.13209, 2019 | 25 | 2019 |
| Area-efficient pipelining for FPGA-targeted high-level synthesis R Zhao, M Tan, S Dai, Z Zhang Proceedings of the 52nd Annual Design Automation Conference, 1-6, 2015 | 19 | 2015 |
| Smooth adversarial training C Xie, M Tan, B Gong, A Yuille, QV Le arXiv preprint arXiv:2006.14536, 2020 | 18 | 2020 |
| CASA: Correlation-aware speculative adders G Liu, Y Tao, M Tan, Z Zhang 2014 IEEE/ACM International Symposium on Low Power Electronics and Design …, 2014 | 17 | 2014 |
| Search to distill: Pearls are everywhere but not the eyes Y Liu, X Jia, M Tan, R Vemulapalli, Y Zhu, B Green, X Wang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 15 | 2020 |
| Mixnet: Mixed depthwise convolutional kernels M Tan, QV Le arXiv preprint arXiv:1907.09595, 2019 | 14 | 2019 |
| Mapping-aware constrained scheduling for LUT-based FPGAs M Tan, S Dai, U Gupta, Z Zhang Proceedings of the 2015 ACM/SIGDA International Symposium on Field …, 2015 | 13 | 2015 |
Quoc V. LeResearch Scientist, Google BrainVerifierad e-postadress på stanford.edu
