| A scalable laplace approximation for neural networks H Ritter, A Botev, D Barber 6th international conference on learning representations, ICLR 2018 …, 2018 | 400 | 2018 |
| Online structured laplace approximations for overcoming catastrophic forgetting H Ritter, A Botev, D Barber Advances in Neural Information Processing Systems 31, 2018 | 288 | 2018 |
| Practical Gauss-Newton optimisation for deep learning A Botev, H Ritter, D Barber International Conference on Machine Learning, 557-565, 2017 | 218 | 2017 |
| Addressing catastrophic forgetting in few-shot problems P Yap, H Ritter, D Barber International Conference on Machine Learning, 11909-11919, 2021 | 21 | 2021 |
| Sparse uncertainty representation in deep learning with inducing weights H Ritter, M Kukla, C Zhang, Y Li Advances in Neural Information Processing Systems 34, 6515-6528, 2021 | 16 | 2021 |
| TyXe: Pyro-based Bayesian neural nets for Pytorch H Ritter, T Karaletsos Proceedings of Machine Learning and Systems 4, 398-413, 2022 | 9 | 2022 |
| Training set cleansing of backdoor poisoning by self-supervised representation learning H Wang, S Karami, O Dia, H Ritter, E Emamjomeh-Zadeh, J Chen, ... ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 5 | 2023 |
| Black-box coreset variational inference D Manousakas, H Ritter, T Karaletsos Advances in Neural Information Processing Systems 35, 34175-34187, 2022 | 3 | 2022 |
| Gaussian mean field regularizes by limiting learned information J Kunze, L Kirsch, H Ritter, D Barber Entropy 21 (8), 758, 2019 | 3 | 2019 |
| Scalable approximate inference methods for Bayesian deep learning JH Ritter UCL (University College London), 2023 | | 2023 |
| Noisy Information Bottlenecks for Generalization J Kunze, L Kirsch, H Ritter, D Barber | | 2018 |
David BarberDepartment of Computer Science, University College LondonVerified email at ucl.ac.uk
