| HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients E Diao, J Ding, V Tarokh https://arxiv.org/pdf/2010.01264.pdf, 2020 | 617 | 2020 |
| Model Selection Techniques -- An Overview J Ding, V Tarokh, Y Yang IEEE Signal Processing Magazine, 2018 | 412 | 2018 |
| Speech emotion recognition with dual-sequence LSTM architecture J Wang, M Xue, R Culhane, E Diao, J Ding, V Tarokh ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 183 | 2020 |
| Bridging AIC and BIC: a new criterion for autoregression J Ding, V Tarokh, Y Yang IEEE Transactions on Information Theory 64 (6), 4024-4043, 2017 | 99 | 2017 |
| Perturbation analysis of orthogonal matching pursuit J Ding, L Chen, Y Gu IEEE Transactions on Signal processing 61 (2), 398-410, 2012 | 78 | 2012 |
| Semifl: Semi-supervised federated learning for unlabeled clients with alternate training E Diao, J Ding, V Tarokh Advances in Neural Information Processing Systems 35, 17871-17884, 2022 | 75 | 2022 |
| Federated learning challenges and opportunities: An outlook J Ding, E Tramel, AK Sahu, S Wu, S Avestimehr, T Zhang ICASSP 2022-2022 IEEE international conference on acoustics, speech and …, 2022 | 73 | 2022 |
| Foaming behavior of microcellular foam polypropylene/modified nano calcium carbonate composites J Ding, J Shangguan, W Ma, Q Zhong Journal of Applied Polymer Science 128 (6), 3639-3651, 2013 | 65 | 2013 |
| Assisted Learning: A Framework for Multi-Organization Learning X Xian, X Wang, J Ding, R Ghanadan NeurIPS 2020 (spotlight), arXiv preprint arXiv:2004.00566, 2020 | 54 | 2020 |
| Bayesian model comparison with the Hyvärinen score: Computation and consistency S Shao, PE Jacob, J Ding, V Tarokh Journal of the American Statistical Association, 2019 | 51 | 2019 |
| Explainable multi-task learning for multi-modality biological data analysis X Tang, J Zhang, Y He, X Zhang, Z Lin, S Partarrieu, EB Hanna, Z Ren, ... Nature communications 14 (1), 2546, 2023 | 50 | 2023 |
| SemiFL: Communication efficient semi-supervised federated learning with unlabeled clients E Diao, J Ding, V Tarokh arXiv preprint arXiv:2106.01432 3, 2021 | 47 | 2021 |
| Information laundering for model privacy X Wang, Y Xiang, J Gao, J Ding arXiv preprint arXiv:2009.06112, 2020 | 37 | 2020 |
| Pruning deep neural networks from a sparsity perspective E Diao, G Wang, J Zhan, Y Yang, J Ding, V Tarokh arXiv preprint arXiv:2302.05601, 2023 | 36 | 2023 |
| Information criteria for model selection J Zhang, Y Yang, J Ding Wiley Interdisciplinary Reviews: Computational Statistics 15 (5), e1607, 2023 | 35 | 2023 |
| Drasic: Distributed recurrent autoencoder for scalable image compression E Diao, J Ding, V Tarokh 2020 Data Compression Conference (DCC), 3-12, 2020 | 26 | 2020 |
| Self-Aware Personalized Federated Learning H Chen, J Ding, E Tramel, S Wu, AK Sahu, S Avestimehr, T Zhang Conference on Neural Information Processing Systems (NeurIPS), 2022 | 25 | 2022 |
| Restricted recurrent neural networks E Diao, J Ding, V Tarokh 2019 IEEE international conference on big data (big data), 56-63, 2019 | 25 | 2019 |
| Multiple change point analysis: Fast implementation and strong consistency J Ding, Y Xiang, L Shen, V Tarokh IEEE Transactions on Signal Processing 65 (17), 4495-4510, 2017 | 24 | 2017 |
| Fednas: Federated deep learning via neural architecture search C He, E Mushtaq, J Ding, S Avestimehr | 23 | 2021 |
Enmao DiaoPh.D.Verificeret mail på colai.io
