| Why ResNet works? Residuals generalize F He, T Liu, D Tao IEEE Transactions on Neural Networks and Learning Systems 31 (12), 5349-5362, 2020 | 270 | 2020 |
| Control batch size and learning rate to generalize well: Theoretical and empirical evidence F He, T Liu, D Tao Advances on Neural Information Processing Systems, 2019 | 233 | 2019 |
| Progressive Reconstruction of Visual Structure for Image Inpainting J Li, F He, L Zhang, B Du, D Tao Proceedings of the IEEE International Conference on Computer Vision, 5962-5971, 2019 | 203 | 2019 |
| Collect and Select: Semantic Alignment Metric Learning for Few-Shot Learning F Hao, F He, J Cheng, L Wang, J Cao, D Tao Proceedings of the IEEE International Conference on Computer Vision, 8460-8469, 2019 | 164 | 2019 |
| Fast Spatio-Temporal Residual Network for Video Super-Resolution S Li, F He, B Du, L Zhang, Y Xu, D Tao Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019 | 162 | 2019 |
| DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training R Dai, L Shen, F He, X Tian, D Tao International Conference on Machine Learning, 2022 | 130 | 2022 |
| Shortcut Learning of Large Language Models in Natural Language Understanding M Du, F He, N Zou, D Tao, X Hu Communications of the ACM 67 (1), 110-120, 2024 | 100 | 2024 |
| Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers W Wang, Y Cao, J Zhang, F He, ZJ Zha, Y Wen, D Tao Proceedings of the 29th ACM International Conference on Multimedia, 1730-1738, 2021 | 96 | 2021 |
| Robust unlearnable examples: Protecting data against adversarial learning S Fu, F He, Y Liu, L Shen, D Tao International Conference on Learning Representations, 2022 | 84 | 2022 |
| Instance-dependent pu learning by bayesian optimal relabeling F He, T Liu, GI Webb, D Tao arXiv preprint arXiv:1808.02180, 2018 | 66 | 2018 |
| Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition Y He, C Chen, J Zhang, J Liu, F He, C Wang, B Du AAAI Conference on Artificial Intelligence, 2022 | 63 | 2022 |
| Artificial neural variability for deep learning: On overfitting, noise memorization, and catastrophic forgetting Z Xie, F He, S Fu, I Sato, D Tao, M Sugiyama Neural Computation 33 (8), 2163-2192, 2021 | 59 | 2021 |
| Recent advances in deep learning theory F He, D Tao arXiv preprint arXiv:2012.10931, 2020 | 51 | 2020 |
| Bridged Transformer for Vision and Point Cloud 3D Object Detection Y Wang, TQ Ye, L Cao, W Huang, F Sun, F He, D Tao Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2022 | 48 | 2022 |
| Understanding Generalization in Recurrent Neural Networks Z Tu, F He, D Tao International Conference on Learning Representations, 2020 | 42 | 2020 |
| Global-local interplay in semantic alignment for few-shot learning F Hao, F He, J Cheng, D Tao IEEE Transactions on Circuits and Systems for Video Technology 32 (7), 4351-4363, 2021 | 40 | 2021 |
| Siamese Network with Interactive Transformer for Video Object Segmentation M Lan, J Zhang, F He, L Zhang AAAI Conference on Artificial Intelligence, 2022 | 35 | 2022 |
| Topology-aware Generalization of Decentralized SGD T Zhu, F He, L Zhang, Z Niu, M Song, D Tao International Conference on Machine Learning, 2022 | 33 | 2022 |
| Piecewise linear activations substantially shape the loss surfaces of neural networks F He, B Wang, D Tao International Conference of Learning Representations, 2020 | 32 | 2020 |
| Achieving Personalized Federated Learning with Sparse Local Models T Huang, S Liu, L Shen, F He, W Lin, D Tao arXiv preprint arXiv:2201.11380, 2022 | 30 | 2022 |