| HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients E Diao, J Ding, V Tarokh 2021 International Conference on Learning Representations (ICLR), 2021 | 411 | 2021 |
| Speech Emotion Recognition with Dual-Sequence LSTM Architecture J Wang, M Xue, R Culhane, E Diao, J Ding, V Tarokh 2020 IEEE International Conference on Acoustics, Speech and Signal …, 2020 | 134 | 2020 |
| SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training E Diao, J Ding, V Tarokh 2022 Advances in Neural Information Processing Systems (NeurIPS), 2022 | 74* | 2022 |
| Restricted Recurrent Neural Networks E Diao, J Ding, V Tarokh 2019 IEEE International Conference on Big Data (Big Data), 56-63, 2019 | 19 | 2019 |
| DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression E Diao, J Ding, V Tarokh 2020 Data Compression Conference (DCC), 3-12, 2020 | 18 | 2020 |
| GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations E Diao, J Ding, V Tarokh 2022 Advances in Neural Information Processing Systems (NeurIPS), 2022 | 15* | 2022 |
| Pruning Deep Neural Networks from a Sparsity Perspective E Diao, G Wang, J Zhang, Y Yang, J Ding, V Tarokh 2023 International Conference on Learning Representations (ICLR), 2023 | 13 | 2023 |
| Dimension Reduced Turbulent Flow Data from Deep Vector Quantizers M Momenifar, E Diao, V Tarokh, AD Bragg Journal of Turbulence 23 (4-5), 232-264, 2022 | 12 | 2022 |
| Decentralized Multi-Target Cross-Domain Recommendation for Multi-Organization Collaborations E Diao, V Tarokh, J Ding arXiv preprint arXiv:2110.13340, 2021 | 7* | 2021 |
| On Statistical Efficiency in Learning J Ding, E Diao, J Zhou, V Tarokh IEEE Transactions on Information Theory 67 (4), 2488-2506, 2020 | 7 | 2020 |
| HeteroFL: Computation and communication efficient federated learning for heterogeneous clients. arXiv 2020 E Diao, J Ding, V Tarokh arXiv preprint arXiv:2010.01264, 0 | 6 | |
| Personalized federated recommender systems with private and partially federated autoencoders Q Le, E Diao, X Wang, A Anwar, V Tarokh, J Ding 2022 56th Asilomar Conference on Signals, Systems, and Computers, 1157-1163, 2022 | 5 | 2022 |
| Score-Based Hypothesis Testing for Unnormalized Models S Wu, E Diao, K Elkhalil, J Ding, V Tarokh IEEE Access 10, 71936-71950, 2022 | 5 | 2022 |
| Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning Models M Momenifar, E Diao, V Tarokh, AD Bragg 2022 Workshop on AI to Accelerate Science and Engineering (AAAI), 2021 | 5 | 2021 |
| Multimodal Controller for Generative Models E Diao, J Ding, V Tarokh 2022 Computer Vision and Machine Intelligence (CVMI), 2020 | 5 | 2020 |
| A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow M Momenifar, E Diao, V Tarokh, AD Bragg 2022 Data Compression Conference (DCC), 2022 | 4 | 2022 |
| Robust Quickest Change Detection for Unnormalized Models S Wu, E Diao, J Ding, T Banerjee, V Tarokh 2023 Uncertainty in Artificial Intelligence, 2314-2323, 2023 | 3 | 2023 |
| Quickest Change Detection for Unnormalized Statistical Models S Wu, E Diao, T Banerjee, J Ding, V Tarokh arXiv preprint arXiv:2302.00250, 2023 | 3 | 2023 |
| Score-based Quickest Change Detection for Unnormalized Models S Wu, E Diao, T Banerjee, J Ding, V Tarokh 2023 International Conference on Artificial Intelligence and Statistics …, 2023 | 2 | 2023 |
| A Penalized Method for the Predictive Limit of Learning J Ding, E Diao, J Zhou, V Tarokh 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 2 | 2018 |
Jie DingAssociate Professor, University of Minnesota Twin CitiesVerified email at umn.edu
