| Fedml: A research library and benchmark for federated machine learning C He, S Li, J So, X Zeng, M Zhang, H Wang, X Wang, P Vepakomma, ... arXiv preprint arXiv:2007.13518, 2020 | 346* | 2020 |
| A sufficient condition for convergences of adam and rmsprop F Zou*, L Shen*, Z Jie, W Zhang, W Liu IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 11127 …, 2019 | 319 | 2019 |
| A Unified Analysis of AdaGrad with Weighted Aggregation and Momentum Acceleration L Shen, C Chen, F Zou, Z Jie, J Sun, W Liu IEEE Transactions on Neural Networks and Learning Systems, 2023 | 70* | 2023 |
| Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning L Zhang, L Shen, L Ding, D Tao, L Duan IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 | 55 | 2022 |
| Sparse Training via Boosting Pruning Plasticity with Neuroregeneration S Liu, T Chen, X Chen, Z Atashgahi, L Yin, H Kou, L Shen, M Pechenizkiy, ... NeurIPS, 2021 | 53 | 2021 |
| Stochastic Client Selection for Federated Learning with Volatile Clients T Huang, W Lin, L Shen, K Li, AY Zomaya IEEE Internet of Things Journal, 2022 | 30 | 2022 |
| Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks Z Guo, M Liu, Z Yuan, L Shen, W Liu, T Yang ICML, 2020 | 28 | 2020 |
| The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training S Liu, T Chen, X Chen, L Shen, DC Mocanu, Z Wang, M Pechenizkiy The Tenth International Conference on Learning Representations (ICLR), 2022 | 26 | 2022 |
| Fedcv: a federated learning framework for diverse computer vision tasks C He, AD Shah, Z Tang, DFAN Sivashunmugam, K Bhogaraju, M Shimpi, ... arXiv preprint arXiv:2111.11066, 2021 | 24 | 2021 |
| Robust Unlearnable Examples: Protecting Data Privacy Against Adversarial Learning S Fu, F He, Y Liu, L Shen, D Tao The Tenth International Conference on Learning Representations (ICLR), 2022 | 23* | 2022 |
| Enhanced Balanced Min Cut X Chen, W Hong, F Nie, JZ Huang, L Shen International Journal of Computer Vision 128 (7), 2020 | 22 | 2020 |
| DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training R Dai, L Shen, F He, X Tian, D Tao The 39th International Conference on Machine Learning (ICML), 2022 | 21 | 2022 |
| AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks Y Tian, L Shen, L Shen, G Su, Z Li, W Liu IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 | 19 | 2021 |
| Improving Sharpness-Aware Minimization with Fisher Mask for Better Generalization on Language Models Q Zhong, L Ding, L Shen, P Mi, J Liu, B Du, D Tao Proceedings of the 2022 Conference on Empirical Methods in Natural Language …, 2022 | 18 | 2022 |
| Attacking adversarial attacks as a defense B Wu, H Pan, L Shen, J Gu, S Zhao, Z Li, D Cai, X He, W Liu arXiv preprint arXiv:2106.04938, 2021 | 17 | 2021 |
| Quantized Adam with Error Feedback C Chen, L Shen, H Huang, W Liu ACM Transactions on Intelligent Systems and Technology 12 (5), 1–26, 2021 | 16 | 2021 |
| DAG-GAN: Causal structure learning with generative adversarial nets Y Gao, L Shen, ST Xia IEEE International Conference on Acoustics, Speech and Signal Processing …, 2021 | 16 | 2021 |
| Discrete Trust-aware Matrix Factorization for Fast Recommendation G Guo, E Yang, L Shen, X Yang, X He Proceedings of the Twenty-Eighth International Joint Conference on …, 2019 | 15 | 2019 |
| Differentiable Neural Architecture Search for Extremely Lightweight Image Super-Resolution H Huang, L Shen, C He, W Dong, W Liu IEEE Transactions on Circuits and Systems for Video Technology, 2022 | 14* | 2022 |
| Make Sharpness-Aware Minimization Stronger: A Sparsified Perturbation Approach P Mi, L Shen, T Ren, Y Zhou, X Sun, R Ji, D Tao The Thirty-Sixth Annual Conference on Neural Information Processing Systems …, 2022 | 14 | 2022 |
Dacheng TaoThe University of SydneyVerified email at sydney.edu.au
