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Antoine Wehenkel
Antoine Wehenkel
Phd Student (FNRS), University of Liège
Bestätigte E-Mail-Adresse bei student.ulg.ac.be - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Unconstrained Monotonic Neural Networks
A Wehenkel, G Louppe
Neural Information Processing Systems 2019 33, 2019
992019
Introducing neuromodulation in deep neural networks to learn adaptive behaviours
N Vecoven, D Ernst, A Wehenkel, G Drion
PloS one 15 (1), e0227922, 2020
312020
Graphical normalizing flows
A Wehenkel, G Louppe
International Conference on Artificial Intelligence and Statistics 2021, 37--45, 2020
172020
Parameter estimation of three-phase untransposed short transmission lines from synchrophasor measurements
A Wehenkel, A Mukhopadhyay, JY Le Boudec, M Paolone
IEEE Transactions on Instrumentation and Measurement 69 (9), 6143-6154, 2020
162020
Averting a crisis in simulation-based inference
J Hermans, A Delaunoy, F Rozet, A Wehenkel, G Louppe
arXiv preprint arXiv:2110.06581, 2021
132021
Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization
A Delaunoy, A Wehenkel, T Hinderer, S Nissanke, C Weniger, ...
Machine Learning and the Physical Sciences Workshop at NeurIPS2020, 2020
122020
A deep generative model for probabilistic energy forecasting in power systems: normalizing flows
J Dumas, A Wehenkel, D Lanaspeze, B Cornélusse, A Sutera
Applied Energy 305, 117871, 2022
112022
An app-based algorithmic approach for harvesting local and renewable energy using electric vehicles
A Dubois*, A Wehenkel*, R Fonteneau, F Olivier, D Ernst
Proceedings of the 9th International Conference on Agents and Artificial …, 2017
92017
You say Normalizing Flows I see Bayesian Networks
A Wehenkel, G Louppe
INNF+ Workshop @ ICML2020, 2020
82020
Recurrent machines for likelihood-free inference
A Pesah*, A Wehenkel*, G Louppe
MetaLearn Workshop @ NeurIPS2018, 2018
72018
Diffusion priors in variational autoencoders
A Wehenkel, G Louppe
INNF+ Workshop @ ICML2021, 2021
52021
Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference
M Vandegar, M Kagan, A Wehenkel, G Louppe
AISTATS2021, 2020
52020
A probabilistic forecast-driven strategy for a risk-aware participation in the capacity firming market
J Dumas, C Cointe, A Wehenkel, A Sutera, X Fettweis, B Cornélusse
IEEE Transactions on Sustainable Energy 13 (2), 1234-1243, 2021
42021
Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks
T Théate, A Wehenkel, A Bolland, G Louppe, D Ernst
arXiv preprint arXiv:2106.03228, 2021
12021
A probabilistic forecast-driven strategy for a risk-aware participation in the capacity firming market: extended version
J Dumas, C Cointe, A Wehenkel, A Sutera, X Fettweis, B Cornélusse
arXiv preprint arXiv:2105.13801, 2021
12021
Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference. 11 2020
M Vandegar, M Kagan, A Wehenkel, G Louppe
arXiv preprint arXiv:2011.05836, 0
1
Robust Hybrid Learning With Expert Augmentation
A Wehenkel, J Behrmann, H Hsu, G Sapiro, G Louppe, JH Jacobsen
arXiv preprint arXiv:2202.03881, 2022
2022
Cellular neuromodulation in artificial networks
N Vecoven, D Ernst, A Wehenkel, G Drion
NeurIPS 2019 Workshop Neuro AI, 2019
2019
SPECIAL SECTION ON ADVANCES IN RENEWABLE ENERGY FORECASTING: PREDICTABILITY, BUSINESS MODELS AND APPLICATIONS IN THE POWER INDUSTRY
RJ Bessa, P Pinson, G Kariniotakis, D Srinivasan, C Smith, N Amjady, ...
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