| Automatic differentiation in machine learning: a survey AG Baydin, BA Pearlmutter, AA Radul, JM Siskind Journal of Machine Learning Research 18, 1-43, 2018 | 3441 | 2018 |
| Online Learning Rate Adaptation with Hypergradient Descent AG Baydin, R Cornish, DM Rubio, M Schmidt, F Wood Sixth International Conference on Learning Representations (ICLR), 2018 | 266 | 2018 |
| Inference compilation and universal probabilistic programming TA Le, AG Baydin, F Wood 20th International Conference on Artificial Intelligence and Statistics …, 2017 | 163 | 2017 |
| Towards global flood mapping onboard low cost satellites with machine learning G Mateo-Garcia, J Veitch-Michaelis, L Smith, SV Oprea, G Schumann, ... Scientific Reports 11 (1), 1-12, 2021 | 148 | 2021 |
| Using synthetic data to train neural networks is model-based reasoning TA Le, AG Baydin, R Zinkov, F Wood Neural Networks (IJCNN), 2017 International Joint Conference on, 3514-3521, 2017 | 136 | 2017 |
| Simulation intelligence: Towards a new generation of scientific methods A Lavin, D Krakauer, H Zenil, J Gottschlich, T Mattson, J Brehmer, ... arXiv preprint arXiv:2112.03235, 2021 | 107 | 2021 |
| Technology readiness levels for machine learning systems A Lavin, CM Gilligan-Lee, A Visnjic, S Ganju, D Newman, S Ganguly, ... Nature Communications 13 (1), 6039, 2022 | 99 | 2022 |
| Alpha MAML: Adaptive Model-Agnostic Meta-Learning HS Behl, AG Baydin, PHS Torr 6th ICML Workshop on Automated Machine Learning, Thirty-Sixth International …, 2019 | 84 | 2019 |
| An ensemble of Bayesian neural networks for exoplanetary atmospheric retrieval AD Cobb, MD Himes, F Soboczenski, S Zorzan, MD O’Beirne, AG Baydin, ... The Astronomical Journal 158 (1), 33, 2019 | 70 | 2019 |
| Domain invariant representation learning with domain density transformations AT Nguyen, T Tran, Y Gal, AG Baydin Advances in Neural Information Processing Systems 34, 5264-5275, 2021 | 66 | 2021 |
| Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale AG Baydin, L Shao, W Bhimji, L Heinrich, LF Meadows, J Liu, A Munk, ... Proceedings of the International Conference for High Performance Computing …, 2019 | 64 | 2019 |
| Managing AI risks in an era of rapid progress Y Bengio, G Hinton, A Yao, D Song, P Abbeel, YN Harari, YQ Zhang, ... arXiv preprint arXiv:2310.17688, 2023 | 56 | 2023 |
| Gradients without backpropagation AG Baydin, BA Pearlmutter, D Syme, F Wood, P Torr arXiv preprint arXiv:2202.08587, 2022 | 56 | 2022 |
| Black-Box Optimization with Local Generative Surrogates S Shirobokov, V Belavin, M Kagan, A Ustyuzhanin, AG Baydin Advances in Neural Information Processing Systems 34 (NeurIPS), 2020 | 49 | 2020 |
| Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model AG Baydin, L Heinrich, W Bhimji, B Gram-Hansen, G Louppe, L Shao, ... Advances in Neural Information Processing Systems 33 (NeurIPS), 2019 | 48 | 2019 |
| Toward the end-to-end optimization of particle physics instruments with differentiable programming T Dorigo, A Giammanco, P Vischia, M Aehle, M Bawaj, A Boldyrev, ... Reviews in Physics 10, 100085, 2023 | 44 | 2023 |
| DiffSharp: An AD Library for. NET Languages AG Baydin, BA Pearlmutter, JM Siskind 7th International Conference on Algorithmic Differentiation, 2016 | 40* | 2016 |
| AutoSimulate:(Quickly) Learning Synthetic Data Generation HS Behl, AG Baydin, R Gal, PHS Torr, V Vineet 16th European Conference on Computer Vision (ECCV), 2020 | 37 | 2020 |
| Automatic differentiation of algorithms for machine learning AG Baydin, BA Pearlmutter AutoML Workshop, International Conference on Machine Learning (ICML …, 2014 | 37 | 2014 |
| Introducing an explicit symplectic integration scheme for Riemannian manifold Hamiltonian Monte Carlo AD Cobb, AG Baydin, A Markham, SJ Roberts arXiv preprint arXiv:1910.06243, 2019 | 35 | 2019 |
Yarin GalAssociate Professor, University of OxfordVerified email at cs.ox.ac.uk
