Atılım Güneş Baydin
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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
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
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
Alpha MAML: Adaptive Model-Agnostic Meta-Learning
HS Behl, AG Baydin, PHS Torr
6th ICML Workshop on Automated Machine Learning, Thirty-Sixth International …, 2019
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
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
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
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
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
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
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
Evolution of central pattern generators for the control of a five-link bipedal walking mechanism
AG Baydin
Paladyn, Journal of Behavioral Robotics 3 (1), 45-53, 2012
Automatic differentiation of algorithms for machine learning
AG Baydin, BA Pearlmutter
AutoML Workshop, International Conference on Machine Learning (ICML …, 2014
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
DiffSharp: An AD Library for. NET Languages
AG Baydin, BA Pearlmutter, JM Siskind
7th International Conference on Algorithmic Differentiation, 2016
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
Automated generation of cross-domain analogies via evolutionary computation
AG Baydin, R López de Mántaras, S Ontańón
International Conference on Computational Creativity (ICCC 2012), Dublin …, 2012
AutoSimulate:(Quickly) Learning Synthetic Data Generation
HS Behl, AG Baydin, R Gal, PHS Torr, V Vineet
16th European Conference on Computer Vision (ECCV), 2020
Tricks from Deep Learning
AG Baydin, BA Pearlmutter, JM Siskind
7th International Conference on Algorithmic Differentiation, 2016
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