| Unconstrained Monotonic Neural Networks A Wehenkel, G Louppe Neural Information Processing Systems 2019 33, 2019 | 93 | 2019 |
| Introducing neuromodulation in deep neural networks to learn adaptive behaviours N Vecoven, D Ernst, A Wehenkel, G Drion PloS one 15 (1), e0227922, 2020 | 29 | 2020 |
| 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 | 15 | 2020 |
| Graphical normalizing flows A Wehenkel, G Louppe International Conference on Artificial Intelligence and Statistics 2021, 37--45, 2020 | 13 | 2020 |
| Averting a crisis in simulation-based inference J Hermans, A Delaunoy, F Rozet, A Wehenkel, G Louppe arXiv preprint arXiv:2110.06581, 2021 | 11 | 2021 |
| 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 | 9 | 2022 |
| 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 | 9 | 2020 |
| 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 | 9 | 2017 |
| You say Normalizing Flows I see Bayesian Networks A Wehenkel, G Louppe INNF+ Workshop @ ICML2020, 2020 | 7 | 2020 |
| Recurrent machines for likelihood-free inference A Pesah*, A Wehenkel*, G Louppe MetaLearn Workshop @ NeurIPS2018, 2018 | 7 | 2018 |
| Diffusion priors in variational autoencoders A Wehenkel, G Louppe INNF+ Workshop @ ICML2021, 2021 | 5 | 2021 |
| Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference M Vandegar, M Kagan, A Wehenkel, G Louppe AISTATS2021, 2020 | 5 | 2020 |
| 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 | 4 | 2021 |
| Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks T Théate, A Wehenkel, A Bolland, G Louppe, D Ernst arXiv preprint arXiv:2106.03228, 2021 | 1 | 2021 |
| 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 | 1 | 2021 |
| 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, ... | | |
Damien ErnstProf. ULiège, Invited Prof. Télécom Paris and Chief Scientific Officer at HaulogyVerified email at ulg.ac.be
