| Electrical properties of cold-pressing welded NbTi persistent joints J Liu, J Cheng, F Zhou, Q Wang, K Chang, X Li Cryogenics 58, 62-67, 2013 | 30 | 2013 |
| Efficient Inference for Nonparametric Hawkes Processes Using Auxiliary Latent Variables F Zhou, Z Li, X Fan, Y Wang, A Sowmya, F Chen Journal of Machine Learning Research, 2020 | 27 | 2020 |
| Accelerated Linearized Laplace Approximation for Bayesian Deep Learning Z Deng, F Zhou, J Zhu Annual Conference on Neural Information Processing Systems, 2022 | 21 | 2022 |
| Efficient EM-variational inference for nonparametric Hawkes process F Zhou, S Luo, Z Li, X Fan, Y Wang, A Sowmya, F Chen Statistics and Computing 31 (4), 46, 2021 | 18 | 2021 |
| Efficient Inference for Dynamic Flexible Interactions of Neural Populations F Zhou, Q Kong, Z Deng, J Kan, Y Zhang, C Feng, J Zhu Journal of Machine Learning Research 23, 1-49, 2022 | 17* | 2022 |
| Efficient Inference of Flexible Interaction in Spiking-neuron Networks F Zhou, Y Zhang, J Zhu International Conference on Learning Representations 2021, 2021 | 17 | 2021 |
| Contact resistance properties of cold-pressing superconducting joints J Cheng, L Li, F Zhou, J Liu, C Cui, X Hu, Y Dai, L Yan, S Cheng, Y Li IEEE Transactions on Applied Superconductivity 25 (3), 1-4, 2014 | 11 | 2014 |
| Revisiting logistic-softmax likelihood in bayesian meta-learning for few-shot classification T Ke, H Cao, Z Ling, F Zhou Advances in Neural Information Processing Systems 36, 2024 | 10 | 2024 |
| Fair Representation Learning with Unreliable Labels Y Zhang, F Zhou, Z Li, Y Wang, F Chen International Conference on Artificial Intelligence and Statistics, 2023 | 10 | 2023 |
| Fast Multi-resolution Segmentation for Nonstationary Hawkes Process Using Cumulants F Zhou, Z Li, X Fan, Y Wang, A Sowmya, F Chen International Journal of Data Science and Analytics 10, 321–330, 2020 | 9 | 2020 |
| Numerical simulation of NbTi superconducting joint with cold-pressing welding technology F Zhou, J Cheng, J Liu, Y Dai, Q Wang, N Xiao, L Yan IEEE transactions on applied superconductivity 23 (6), 40-45, 2013 | 9 | 2013 |
| Continuous-Time Edge Modelling Using Non-Parametric Point Processes X Fan, B Li, F Zhou, S Sisson Thirty-fifth Conference on Neural Information Processing Systems, 2021, 2021 | 8 | 2021 |
| Integration-free training for spatio-temporal multimodal covariate deep kernel point processes Y Zhang, Q Kong, F Zhou Advances in Neural Information Processing Systems 36, 25031-25049, 2023 | 7 | 2023 |
| A refined MISD algorithm based on Gaussian process regression F Zhou, Z Li, X Fan, Y Wang, A Sowmya, F Chen Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia …, 2018 | 7 | 2018 |
| Development of electromagnetic forming NbTi superconducting joint J Cheng, Q Wang, F Zhou, L Li, W Sun, S Chen, Y Dai, Y Fang, L Yan IEEE Transactions on Applied Superconductivity 26 (7), 1-5, 2016 | 7 | 2016 |
| Numerical simulation of mold shape’s influence on NbTi cold-pressing superconducting joint F Zhou, J Cheng, Y Dai, Q Wang, L Yan Physica C: Superconductivity 498, 9-13, 2014 | 7 | 2014 |
| pFedV: Mitigating Feature Distribution Skewness via Personalized Federated Learning with Variational Distribution Constraints Y Mou, J Geng, F Zhou, O Beyan, C Rong, S Decker Pacific-Asia Conference on Knowledge Discovery and Data Mining, 283-294, 2023 | 6 | 2023 |
| Deep ensemble as a Gaussian process approximate posterior Z Deng, F Zhou, J Chen, G Wu, J Zhu arXiv preprint arXiv:2205.00163, 2022 | 6 | 2022 |
| Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures Z Ling, L Li, Z Feng, Y Zhang, F Zhou, RC Qiu, Z Liao International Conference on Machine Learning, 2024 | 5 | 2024 |
| Investigation of orthogonal experiment for fabrication of a soldering joint for a 4-T HTS coil F Zhou, J Cheng, Y Xu, S Song, Y Dai, Q Wang IEEE Transactions on Applied Superconductivity 24 (5), 1-5, 2014 | 5 | 2014 |
Yixuan ZhangSoutheast Universityseu.edu.cn의 이메일 확인됨