| Size-resolved online chemical analysis of nanoaerosol particles: a thermal desorption differential mobility analyzer coupled to a chemical ionization time-of-flight mass … AC Wagner, A Bergen, S Brilke, C Fuchs, M Ernst, J Hoker, M Heinritzi, ... Atmospheric Measurement Techniques 11 (10), 5489-5506, 2018 | 18 | 2018 |
| Recurrent connections aid occluded object recognition by discounting occluders MR Ernst, J Triesch, T Burwick Artificial Neural Networks and Machine Learning–ICANN 2019: Image Processing …, 2019 | 12 | 2019 |
| Contrastive Learning Through Time F Schneider, X Xu, MR Ernst, Z Yu, J Triesch SVRHM 2021 Workshop@ NeurIPS, 2021 | 11 | 2021 |
| Time to augment self-supervised visual representation learning A Aubret, MR Ernst, C Teulière, J Triesch Eleventh International Conference on Learning Representations (ICLR), 2023 …, 2023 | 8 | 2023 |
| MIMo: A Multi-Modal Infant Model for Studying Cognitive Development in Humans and AIs D Mattern, FM López, MR Ernst, A Aubret, J Triesch 2022 IEEE International Conference on Development and Learning (ICDL), 23-29, 2022 | 5 | 2022 |
| Recurrent processing improves occluded object recognition and gives rise to perceptual hysteresis MR Ernst, T Burwick, J Triesch Journal of Vision 21 (13), 6-6, 2021 | 4 | 2021 |
| Recurrent Feedback Improves Recognition of Partially Occluded Objects MR Ernst, J Triesch, T Burwick 28th European Symposium on Artificial Neural Networks, Computational …, 2020 | 3 | 2020 |
| MIMo: A Multi-Modal Infant Model for Studying Cognitive Development D Mattern, P Schumacher, FM López, MC Raabe, MR Ernst, A Aubret, ... IEEE Transactions on Cognitive and Developmental Systems, 2024 | 2 | 2024 |
| Recurrent Connectivity Aids Recognition of Partly Occluded Objects MR Ernst, J Triesch, T Burwick arXiv preprint arXiv:1909.06175, 2019 | 1 | 2019 |
| Self-Supervised Learning of Color Constancy MR Ernst, FM López, A Aubret, RW Fleming, J Triesch arXiv preprint arXiv:2404.08127, 2024 | | 2024 |
Jochen TrieschFrankfurt Institute for Advanced StudiesVerified email at fias.uni-frankfurt.de
