| Learning generative state space models for active inference O Çatal, S Wauthier, C De Boom, T Verbelen, B Dhoedt Frontiers in Computational Neuroscience 14, 574372, 2020 | 41 | 2020 |
| Deep active inference for autonomous robot navigation O Çatal, S Wauthier, T Verbelen, C De Boom, B Dhoedt arXiv preprint arXiv:2003.03220, 2020 | 10 | 2020 |
| Sleep: Model Reduction in Deep Active Inference ST Wauthier, O Çatal, C De Boom, T Verbelen, B Dhoedt Active Inference: First International Workshop, IWAI 2020, Co-located with …, 2020 | 8 | 2020 |
| Learning generative models for active inference using tensor networks ST Wauthier, B Vanhecke, T Verbelen, B Dhoedt International Workshop on Active Inference, 285-297, 2022 | 5 | 2022 |
| A learning gap between neuroscience and reinforcement learning ST Wauthier, P Mazzaglia, O Catal, C De Boom, T Verbelen, B Dhoedt arXiv preprint arXiv:2104.10995, 2021 | 5 | 2021 |
| Dynamic Narrowing of VAE Bottlenecks Using GECO and L0 Regularization C De Boom, S Wauthier, T Verbelen, B Dhoedt 2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021 | 3 | 2021 |
| Description of coherent elastic neutrino-nucleus scattering cross sections ST WAUTHIER KU Leuven, Ghent University, 2017 | 1 | 2017 |
| Model Reduction Through Progressive Latent Space Pruning in Deep Active Inference ST Wauthier, C De Boom, O Çatal, T Verbelen, B Dhoedt Front. Neurorobot. 16 (795846), 2022 | | 2022 |
| Tensor networks for active inference with discrete observation spaces S Wauthier, B Vanhecke, T Verbelen, B Dhoedt Machine Learning and the Physical Sciences workshop, part of NeurIPS2022 …, 2022 | | 2022 |
| Co-activation patterns for the study of brain connectivity in multiple sclerosis patients ST WAUTHIER Ghent University, 2018 | | 2018 |
Tim VerbelenVERSES Research Lab在 verses.io 的电子邮件经过验证
