| On the Euclidean distance of images L Wang, Y Zhang, J Feng IEEE transactions on pattern analysis and machine intelligence 27 (8), 1334-1339, 2005 | 633 | 2005 |
| Dual learning for machine translation D He, Y Xia, T Qin, L Wang, N Yu, TY Liu, WY Ma Advances in neural information processing systems 29, 820-828, 2016 | 615 | 2016 |
| Gradient descent finds global minima of deep neural networks S Du, J Lee, H Li, L Wang, X Zhai International Conference on Machine Learning, 1675-1685, 2019 | 519 | 2019 |
| The expressive power of neural networks: A view from the width Z Lu, H Pu, F Wang, Z Hu, L Wang Proceedings of the 31st International Conference on Neural Information …, 2017 | 475 | 2017 |
| A theoretical analysis of NDCG ranking measures Y Wang, L Wang, Y Li, D He, W Chen, TY Liu Proceedings of the 26th annual conference on learning theory (COLT 2013) 8, 6, 2013 | 364* | 2013 |
| Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks J Ding, A Li, Z Hu, L Wang International Conference on Medical Image Computing and Computer-Assisted …, 2017 | 224 | 2017 |
| Reppoints: Point set representation for object detection Z Yang, S Liu, H Hu, L Wang, S Lin Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 193 | 2019 |
| Learning to navigate for fine-grained classification Z Yang, T Luo, D Wang, Z Hu, J Gao, L Wang Proceedings of the European Conference on Computer Vision (ECCV), 420-435, 2018 | 189 | 2018 |
| The equivalence of two-dimensional PCA to line-based PCA L Wang, X Wang, X Zhang, J Feng Pattern Recognition Letters 26 (1), 57-60, 2005 | 144 | 2005 |
| Subspace distance analysis with application to adaptive bayesian algorithm for face recognition L Wang, X Wang, J Feng Pattern recognition 39 (3), 456-464, 2006 | 119 | 2006 |
| On image matrix based feature extraction algorithms L Wang, X Wang, J Feng IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 36 …, 2006 | 110 | 2006 |
| Frage: Frequency-agnostic word representation C Gong, D He, X Tan, T Qin, L Wang, TY Liu arXiv preprint arXiv:1809.06858, 2018 | 92 | 2018 |
| Learning from weak and noisy labels for semantic segmentation Z Lu, Z Fu, T Xiang, P Han, L Wang, X Gao IEEE transactions on pattern analysis and machine intelligence 39 (3), 486-500, 2016 | 90 | 2016 |
| On the depth of deep neural networks: A theoretical view S Sun, W Chen, L Wang, X Liu, TY Liu Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 89 | 2016 |
| On layer normalization in the transformer architecture R Xiong, Y Yang, D He, K Zheng, S Zheng, C Xing, H Zhang, Y Lan, ... International Conference on Machine Learning, 10524-10533, 2020 | 71 | 2020 |
| Cross-lingual knowledge graph alignment via graph matching neural network K Xu, L Wang, M Yu, Y Feng, Y Song, Z Wang, D Yu arXiv preprint arXiv:1905.11605, 2019 | 70 | 2019 |
| Generalization bounds of sgld for non-convex learning: Two theoretical viewpoints W Mou, L Wang, X Zhai, K Zheng Conference on Learning Theory, 605-638, 2018 | 68 | 2018 |
| Efficient private ERM for smooth objectives J Zhang, K Zheng, W Mou, L Wang arXiv preprint arXiv:1703.09947, 2017 | 68 | 2017 |
| Improving the generalization of adversarial training with domain adaptation C Song, K He, L Wang, JE Hopcroft arXiv preprint arXiv:1810.00740, 2018 | 66 | 2018 |
| Adversarially robust generalization just requires more unlabeled data R Zhai, T Cai, D He, C Dan, K He, J Hopcroft, L Wang arXiv preprint arXiv:1906.00555, 2019 | 61 | 2019 |
Di HeMicrosoft ResearchVerified email at microsoft.com
