| R-GAP: Recursive Gradient Attack on Privacy J Zhu, M Blaschko International Conference on Learning Representations (ICLR), 2020 | 113 | 2020 |
| Learning path tracking for real car-like mobile robots from simulation D Kamran, J Zhu, M Lauer 2019 European Conference on Mobile Robots (ECMR), 1-6, 2019 | 24 | 2019 |
| Confidence-aware personalized federated learning via variational expectation maximization J Zhu, X Ma, MB Blaschko Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 10 | 2023 |
| Improving Differentially Private SGD via Randomly Sparsified Gradients J Zhu, MB Blaschko Transactions on Machine Learning Research, 2023 | 8* | 2023 |
| Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning J Zhu, R Yao, MB Blaschko International Conference on Machine Learning (ICML), 2023 | 5 | 2023 |
| Localization in Aerial Imagery with Grid Maps using LocGAN H Hu, J Zhu, S Wirges, M Lauer 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2860-2865, 2019 | 3 | 2019 |
| Tackling Personalized Federated Learning with Label Concept Drift via Hierarchical Bayesian Modeling X Ma, J Zhu, MB Blaschko Workshop on Federated Learning: Recent Advances and New Challenges (in …, 0 | 2* | |
| Implicit Neural Representations for Robust Joint Sparse-View CT Reconstruction J Shi, J Zhu, DM Pelt, KJ Batenburg, MB Blaschko arXiv preprint arXiv:2405.02509, 2024 | | 2024 |
| Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better E Liu, J Zhu, Z Lin, X Ning, MB Blaschko, S Yekhanin, S Yan, G Dai, ... arXiv preprint arXiv:2404.02241, 2024 | | 2024 |
| Rescaling Intermediate Features Makes Trained Consistency Models Perform Better J Zhu, Z Lin, E Liu, X Ning, MB Blaschko The Second Tiny Papers Track at ICLR 2024, 0 | | |
Matthew BlaschkoKU LeuvenPatvirtintas el. paštas esat.kuleuven.be
