Atılım Güneş Baydin
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Geciteerd door
Automatic differentiation in machine learning: a survey
AG Baydin, BA Pearlmutter, AA Radul, JM Siskind
Journal of Machine Learning Research 18, 1-43, 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
Inference compilation and universal probabilistic programming
TA Le, AG Baydin, F Wood
20th International Conference on Artificial Intelligence and Statistics …, 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
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
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
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
Alpha MAML: Adaptive Model-Agnostic Meta-Learning
HS Behl, AG Baydin, PHS Torr
6th ICML Workshop on Automated Machine Learning, Thirty-Sixth International …, 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
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
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
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
Gradients without backpropagation
AG Baydin, BA Pearlmutter, D Syme, F Wood, P Torr
arXiv preprint arXiv:2202.08587, 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
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
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
DiffSharp: An AD Library for. NET Languages
AG Baydin, BA Pearlmutter, JM Siskind
7th International Conference on Algorithmic Differentiation, 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
Automatic differentiation of algorithms for machine learning
AG Baydin, BA Pearlmutter
AutoML Workshop, International Conference on Machine Learning (ICML …, 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
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