| Black-box variational inference for stochastic differential equations T Ryder, A Golightly, AS McGough, D Prangle International Conference on Machine Learning, 4423-4432, 2018 | 60 | 2018 |
| iflow: Numerically invertible flows for efficient lossless compression via a uniform coder S Zhang, N Kang, T Ryder, Z Li Advances in Neural Information Processing Systems 34, 5822-5833, 2021 | 11 | 2021 |
| Black-box inference for non-linear latent force models W Ward, T Ryder, D Prangle, M Alvarez International Conference on Artificial Intelligence and Statistics, 3088-3098, 2020 | 11 | 2020 |
| The Neural Moving Average Model for Scalable Variational Inference of State Space Models T Ryder, D Prangle, A Golightly, I Matthews 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021), 2021 | 7* | 2021 |
| Black-box autoregressive density estimation for state-space models T Ryder, A Golighty, AS McGough, D Prangle NeurIPS Workshop on Bayesian Deep Learning, 2018 | 6 | 2018 |
| Split Hierarchical Variational Compression T Ryder, C Zhang, N Kang, S Zhang 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 | 3 | 2022 |
| A framework for variational inference and data assimilation of soil biogeochemical models using state space approximations and normalizing flows HW Xie, D Sujono, T Ryder, EB Sudderth, S Allison Authorea Preprints, 2022 | 1 | 2022 |
| Variational inference for stochastic processes T Ryder Newcastle University, 2021 | 1 | 2021 |
| Image encoding and decoding, video encoding and decoding: methods, systems and training methods C Besenbruch, A Cherganski, C Finlay, A Lytchier, J Rayner, T Ryder, ... US Patent 11,606,560, 2023 | | 2023 |
