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Atılım Güneş Baydin
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Zitiert von
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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
16902018
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
1572018
Inference compilation and universal probabilistic programming
TA Le, AG Baydin, F Wood
20th International Conference on Artificial Intelligence and Statistics …, 2017
1262017
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
992017
Alpha MAML: Adaptive Model-Agnostic Meta-Learning
HS Behl, AG Baydin, PHS Torr
6th ICML Workshop on Automated Machine Learning, Thirty-Sixth International …, 2019
472019
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
452019
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
442019
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
352019
Automatic differentiation of algorithms for machine learning
AG Baydin, BA Pearlmutter
AutoML Workshop, International Conference on Machine Learning (ICML …, 2014
302014
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
272012
DiffSharp: An AD Library for. NET Languages
AG Baydin, BA Pearlmutter, JM Siskind
7th International Conference on Algorithmic Differentiation, 2016
25*2016
Technology readiness levels for machine learning systems
A Lavin, CM Gilligan-Lee, A Visnjic, S Ganju, D Newman, S Ganguly, ...
arXiv preprint arXiv:2101.03989, 2021
232021
Automated generation of cross-domain analogies via evolutionary computation
AG Baydin, R López de Mántaras, S Ontañón
International Conference on Computational Creativity (ICCC 2012), Dublin …, 2012
222012
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
212021
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
192020
End-to-end Training of Differentiable Pipelines Across Machine Learning Frameworks
M Milutinovic, AG Baydin, R Zinkov, W Harvey, D Song, F Wood, W Shen
NIPS 2017 Autodiff Workshop: The Future of Gradient-Based Machine Learning …, 2017
172017
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
142019
Simulation intelligence: Towards a new generation of scientific methods
A Lavin, H Zenil, B Paige, D Krakauer, J Gottschlich, T Mattson, ...
arXiv preprint arXiv:2112.03235, 2021
132021
AutoSimulate:(Quickly) Learning Synthetic Data Generation
HS Behl, AG Baydin, R Gal, PHS Torr, V Vineet
16th European Conference on Computer Vision (ECCV), 2020
132020
Tricks from Deep Learning
AG Baydin, BA Pearlmutter, JM Siskind
7th International Conference on Algorithmic Differentiation, 2016
132016
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