| Grand: Graph neural diffusion B Chamberlain, J Rowbottom, MI Gorinova, M Bronstein, S Webb, E Rossi International Conference on Machine Learning, 1407-1418, 2021 | 112 | 2021 |
| A statistical approach to assessing neural network robustness S Webb, T Rainforth, YW Teh, MP Kumar arXiv preprint arXiv:1811.07209, 2018 | 73 | 2018 |
| Distributed Bayesian learning with stochastic natural gradient expectation propagation and the posterior server L Hasenclever, S Webb, T Lienart, S Vollmer, B Lakshminarayanan, ... The Journal of Machine Learning Research 18 (1), 3744-3780, 2017 | 61 | 2017 |
| Faithful inversion of generative models for effective amortized inference S Webb, A Golinski, R Zinkov, T Rainforth, YW Teh, F Wood Advances in Neural Information Processing Systems 31, 2018 | 40 | 2018 |
| Statistically robust neural network classification B Wang, S Webb, T Rainforth Uncertainty in Artificial Intelligence, 1735-1745, 2021 | 17 | 2021 |
| Distributed Bayesian learning with stochastic natural-gradient expectation propagation and the posterior server YW Teh, L Hasenclever, T Lienart, S Vollmer, S Webb, ... arXiv preprint arXiv:1512.09327, 2015 | 14 | 2015 |
| Improving automated variational inference with normalizing flows S Webb, JP Chen, M Jankowiak, N Goodman ICML, Workshop on Automated Machine Learning, 2019 | 5 | 2019 |
| A tighter monte carlo objective with rényi α-divergence measures S Webb, YW Teh NIPS Workshop, 2016 | 4 | 2016 |
| Neural networks for inference, inference for neural networks SD Webb University of Oxford, 2018 | | 2018 |
| Inference, Sampling, and Learning in Copula Cumulative Distribution Networks SD Webb arXiv preprint arXiv:1310.4456, 2013 | | 2013 |
| Implementation of the sequence memoizer in a probabilistic programming language S Webb | | |
