| HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients E Diao, J Ding, V Tarokh https://arxiv.org/pdf/2010.01264.pdf, 2020 | 284 | 2020 |
| Model Selection Techniques -- An Overview J Ding, V Tarokh, Y Yang IEEE Signal Processing Magazine, 2018 | 267 | 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 | 99 | 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 | 80 | 2017 |
| Perturbation analysis of orthogonal matching pursuit J Ding, L Chen, Y Gu IEEE Transactions on Signal Processing 61 (2), 398-410, 2012 | 79 | 2012 |
| Perturbation analysis of orthogonal matching pursuit J Ding, L Chen, Y Gu IEEE Transactions on Signal Processing 61 (2), 398-410, 2012 | 79 | 2012 |
| Fednas: Federated deep learning via neural architecture search C He, E Mushtaq, J Ding, S Avestimehr | 72 | 2021 |
| 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 | 41 | 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 | 41 | 2019 |
| SemiFL: Communication efficient semi-supervised federated learning with unlabeled clients E Diao, J Ding, V Tarokh NeurIPS 2022, arXiv preprint arXiv:2106.01432, 2021 | 34 | 2021 |
| 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 | 30 | 2022 |
| Information laundering for model privacy X Wang, Y Xiang, J Gao, J Ding arXiv preprint arXiv:2009.06112, 2020 | 23 | 2020 |
| 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 | 21 | 2017 |
| Restricted recurrent neural networks E Diao, J Ding, V Tarokh 2019 IEEE international conference on big data (big data), 56-63, 2019 | 15 | 2019 |
| Slants: Sequential adaptive nonlinear modeling of time series Q Han, J Ding, EM Airoldi, V Tarokh IEEE Transactions on Signal Processing 65 (19), 4994-5005, 2017 | 15 | 2017 |
| Complementary lattice arrays for coded aperture imaging J Ding, M Noshad, V Tarokh Journal of the Optical Society of America A 33 (5), 863-881, 2016 | 15 | 2016 |
| Drasic: Distributed recurrent autoencoder for scalable image compression E Diao, J Ding, V Tarokh 2020 Data Compression Conference (DCC), 3-12, 2020 | 14 | 2020 |
| Estimation of the evolutionary spectra with application to stationarity test Y Xiang, J Ding, V Tarokh IEEE Transactions on Signal Processing 67 (5), 1353-1365, 2019 | 13 | 2019 |
| Robustness of orthogonal matching pursuit for multiple measurement vectors in noisy scenario J Ding, L Chen, Y Gu 2012 IEEE International Conference on Acoustics, Speech and Signal …, 2012 | 13 | 2012 |
| Gradient information for representation and modeling J Ding, R Calderbank, V Tarokh Advances in Neural Information Processing Systems 32, 2019 | 12 | 2019 |
Enmao DiaoPhD Student, Duke UniversityVahvistettu sähköpostiosoite verkkotunnuksessa duke.edu
