Suivre
Jie Ding
Titre
Citée par
Citée par
Année
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
E Diao, J Ding, V Tarokh
https://arxiv.org/pdf/2010.01264.pdf, 2020
6152020
Model Selection Techniques -- An Overview
J Ding, V Tarokh, Y Yang
IEEE Signal Processing Magazine, 2018
4112018
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
1822020
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
1002017
Perturbation analysis of orthogonal matching pursuit
J Ding, L Chen, Y Gu
IEEE Transactions on Signal processing 61 (2), 398-410, 2012
852012
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
772022
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
722022
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
652013
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
562020
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
502023
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
492019
SemiFL: Communication efficient semi-supervised federated learning with unlabeled clients
E Diao, J Ding, V Tarokh
arXiv preprint arXiv:2106.01432 3, 2021
472021
Information laundering for model privacy
X Wang, Y Xiang, J Gao, J Ding
arXiv preprint arXiv:2009.06112, 2020
382020
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
352023
Information criteria for model selection
J Zhang, Y Yang, J Ding
Wiley Interdisciplinary Reviews: Computational Statistics 15 (5), e1607, 2023
332023
Drasic: Distributed recurrent autoencoder for scalable image compression
E Diao, J Ding, V Tarokh
2020 Data Compression Conference (DCC), 3-12, 2020
252020
Restricted recurrent neural networks
E Diao, J Ding, V Tarokh
2019 IEEE international conference on big data (big data), 56-63, 2019
252019
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
242022
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
242017
Fednas: Federated deep learning via neural architecture search
C He, E Mushtaq, J Ding, S Avestimehr
222021
Le système ne peut pas réaliser cette opération maintenant. Veuillez réessayer plus tard.
Articles 1–20