| Efficientnet: Rethinking model scaling for convolutional neural networks M Tan, Q Le International conference on machine learning, 6105-6114, 2019 | 11569 | 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 | 3878 | 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 | 3249 | 2020 |
| 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 | 2483 | 2019 |
| Efficientnetv2: Smaller models and faster training M Tan, QV Le International Conference on Machine Learning, 2021 | 802 | 2021 |
| Coatnet: Marrying convolution and attention for all data sizes Z Dai, H Liu, QV Le, M Tan Advances in Neural Information Processing Systems 34, 3965-3977, 2021 | 477 | 2021 |
| 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 | 384 | 2020 |
| MixConv: Mixed Depthwise Convolutional Kernels M Tan, QV Le Proceedings of the 30th British Machine Vision Conference (BMVC), 2019 | 296 | 2019 |
| Efficientnet: Rethinking model scaling for convolutional neural networks. arXiv 2019 M Tan, QV Le arXiv preprint arXiv:1905.11946, 2020 | 246 | 2020 |
| 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, ... Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 190 | 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 | 169 | 2020 |
| Nyströmformer: A nyström-based algorithm for approximating self-attention Y Xiong, Z Zeng, R Chakraborty, M Tan, G Fung, Y Li, V Singh Proceedings of the AAAI Conference on Artificial Intelligence 35 (16), 14138 …, 2021 | 164 | 2021 |
| Smooth adversarial training C Xie, M Tan, B Gong, A Yuille, QV Le arXiv preprint arXiv:2006.14536, 2020 | 134 | 2020 |
| Efficientnet: rethinking model scaling for convolutional neural networks (2019) M Tan, QV Le arXiv preprint arXiv:1905.11946, 1905 | 106 | 1905 |
| Movinets: Mobile video networks for efficient video recognition D Kondratyuk, L Yuan, Y Li, L Zhang, M Tan, M Brown, B Gong Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 101 | 2021 |
| Mobiledets: Searching for object detection architectures for mobile accelerators Y Xiong, H Liu, S Gupta, B Akin, G Bender, Y Wang, PJ Kindermans, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 93 | 2021 |
| Assemblenet: Searching for multi-stream neural connectivity in video architectures MS Ryoo, AJ Piergiovanni, M Tan, A Angelova International Conference on Learning Representations (ICLR), 2020 | 88 | 2020 |
| Combined scaling for zero-shot transfer learning H Pham, Z Dai, G Ghiasi, H Liu, AW Yu, MT Luong, M Tan, QV Le arXiv preprint arXiv:2111.10050, 2021 | 70* | 2021 |
| Shape-texture debiased neural network training Y Li, Q Yu, M Tan, J Mei, P Tang, W Shen, A Yuille, C Xie International Conference on Learning Representations (ICLR), 2020 | 69 | 2020 |
| DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection Y Li, AW Yu, T Meng, B Caine, J Ngiam, D Peng, J Shen, B Wu, Y Lu, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 62 | 2022 |
Quoc V. LeResearch Scientist, Google BrainPotvrđena adresa e-pošte na stanford.edu
