| Neural ode processes A Norcliffe, C Bodnar, B Day, J Moss, P Liò arXiv preprint arXiv:2103.12413, 2021 | 64 | 2021 |
| Gauche: A library for Gaussian processes in chemistry RR Griffiths, L Klarner, H Moss, A Ravuri, S Truong, Y Du, S Stanton, ... Advances in Neural Information Processing Systems 36, 2024 | 19 | 2024 |
| Approximate latent force model inference JD Moss, FL Opolka, B Dumitrascu, P Lió arXiv preprint arXiv:2109.11851, 2021 | 5 | 2021 |
| Meta-learning using privileged information for dynamics B Day, A Norcliffe, J Moss, P Liò arXiv preprint arXiv:2104.14290, 2021 | 4 | 2021 |
| Modular Neural Ordinary Differential Equations M Zhu, P Lio, J Moss arXiv preprint arXiv:2109.07359, 2021 | 3 | 2021 |
| GAUCHE: A library for Gaussian processes and Bayesian optimisation in chemistry RR Griffiths, L Klarner, A Ravuri, S Truong, B Rankovic, Y Du, A Jamasb, ... ICML 2022 Workshop on Adaptive Experimental Design and Active Learning in …, 2022 | 2 | 2022 |
| Neural ODE Processes: A Short Summary ALI Norcliffe, C Bodnar, B Day, J Moss, P Lio The Symbiosis of Deep Learning and Differential Equations, 2021 | 1 | 2021 |
| Gene regulatory network inference with latent force models J Moss, P Lió arXiv preprint arXiv:2010.02555, 2020 | 1 | 2020 |
| Meta-Learning Nonlinear Dynamical Systems with Deep Kernels J Moss, F Opolka, J England, P Lio | | 2023 |
| Meta-Learning Deep Kernels for Latent Force Inference J Moss, F Opolka, J England, P Lió 1st Workshop on the Synergy of Scientific and Machine Learning Modeling …, 2023 | | 2023 |
| Introduction to Probabilistic Machine Learning J Moss | | 2020 |
| Pseudotime Diffusion JD Moss, JL England, I Petach, P Lió | | |
