Atılım Güneş Baydin

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Barak A. PearlmutterDept of Computer Science, Maynooth UniversityVerified email at pearlmutter.net
Yarin GalAssociate Professor, University of OxfordVerified email at cs.ox.ac.uk
Frank WoodUniversity of British ColumbiaVerified email at cs.ubc.ca
Philip TorrProfessor, University of OxfordVerified email at eng.ox.ac.uk
Jeffrey Mark SiskindPurdue UniversityVerified email at purdue.edu
Kyle CranmerUniversity of Wisconsin-MadisonVerified email at wisc.edu
Adam D CobbSRI InternationalVerified email at sri.com
Bradley Gram-HansenAI Research Scientist, Dataminr | DPhil, Oxford UniversityVerified email at dataminr.com
Andreas MunkPhD in Computer Science, University of British ColumbiaVerified email at ammunk.com
Alexey RadulSoftware Engineer, GoogleVerified email at google.com
Giacomo AcciariniDoctoral Researcher at European Space Agency, Advanced Concepts TeamVerified email at ext.esa.int
Tuan Anh LeResearch Scientist, Google DeepMindVerified email at google.com
Frank SoboczenskiAssistant Professor, University of York & Affiliate King's College LondonVerified email at york.ac.uk
Rob ZinkovUniversity of OxfordVerified email at robots.ox.ac.uk
Lukas HeinrichTechnical University of MunichVerified email at tum.de
Shawn Domagal-GoldmanResearch Space Scientist at NASA Goddard Space Flight CenterVerified email at nasa.gov
Giada ArneyNASA Goddard Space Flight CenterVerified email at nasa.gov
Daniel AngerhausenETH Zürich, BMSIS - former: CSH Bern, NASA-GSFC, RPIVerified email at phys.ethz.ch
Dr C. P. BridgesReader at Surrey Space Centre, University of SurreyVerified email at surrey.ac.uk
David Martínez-RubioCarlos III UniversityVerified email at ing.uc3m.es

Atılım Güneş Baydin

University of Oxford

Verified email at robots.ox.ac.uk - Homepage

Machine Learning Probabilistic Programming Simulation-based Inference Physical Sciences


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Automatic differentiation in machine learning: a survey AG Baydin, BA Pearlmutter, AA Radul, JM Siskind Journal of Machine Learning Research 18, 1-43, 2018	3889	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	276	2018
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	178	2021
Inference compilation and universal probabilistic programming TA Le, AG Baydin, F Wood 20th International Conference on Artificial Intelligence and Statistics …, 2017	170	2017
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	140	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	124	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	117	2022
Managing extreme AI risks amid rapid progress Y Bengio, G Hinton, A Yao, D Song, P Abbeel, T Darrell, YN Harari, ... Science 384 (6698), 842-845, 2024	116	2024
Alpha MAML: Adaptive Model-Agnostic Meta-Learning HS Behl, AG Baydin, PHS Torr 6th ICML Workshop on Automated Machine Learning, Thirty-Sixth International …, 2019	88	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	79	2023
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	79	2021
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	78	2019
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	67	2019
Gradients without backpropagation AG Baydin, BA Pearlmutter, D Syme, F Wood, P Torr arXiv preprint arXiv:2202.08587, 2022	65	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	57	2020
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	54	2023
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	50	2019
KL Guided Domain Adaptation T Nguyen, T Tran, Y Gal, P Torr, AG Baydin Tenth International Conference on Learning Representations (ICLR), 2022	42	2022
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	39	2020

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