Atılım Güneş Baydin
Atılım Güneş Baydin
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Zitiert von
Zitiert von
Automatic differentiation in machine learning: a survey
AG Baydin, BA Pearlmutter, AA Radul, JM Siskind
Journal of Machine Learning Research 18, 1-43, 2018
Inference compilation and universal probabilistic programming
TA Le, AG Baydin, F Wood
20th International Conference on Artificial Intelligence and Statistics …, 2017
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
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
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
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
DiffSharp: An AD Library for. NET Languages
AG Baydin, BA Pearlmutter, JM Siskind
7th International Conference on Algorithmic Differentiation, 2016
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
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
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
CBR with commonsense reasoning and structure mapping: An application to mediation
AG Baydin, RL de Mántaras, S Simoff, C Sierra
International Conference on Case-Based Reasoning, 378-392, 2011
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
Tricks from Deep Learning
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
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
Transflow learning: Repurposing flow models without retraining
A Gambardella, AG Baydin, PHS Torr
arXiv preprint arXiv:1911.13270, 2019
Bayesian Deep Learning for Exoplanet Atmospheric Retrieval
F Soboczenski, MD Himes, MD O'Beirne, S Zorzan, AG Baydin, AD Cobb, ...
Third workshop on Bayesian Deep Learning (NeurIPS 2018), Montréal, Canada, 2018
A semantic network-based evolutionary algorithm for computational creativity
AG Baydin, RL de Mántaras, S Ontanón
Evolutionary Intelligence 8 (1), 3-21, 2015
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