| Seq2Seq RNNs and ARIMA models for Cryptocurrency Prediction: A Comparative Study J Rebane, I Karlsson, S Denic, P Papapetrou KDD Data Science in Fintech Workshop, 2018 | 92 | 2018 |
| Explainable time series tweaking via irreversible and reversible temporal transformations I Karlsson, J Rebane, P Papapetrou, A Gionis 2018 IEEE International Conference on Data Mining (ICDM), 207-216, 2018 | 45 | 2018 |
| A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records F Bagattini, I Karlsson, J Rebane, P Papapetrou BMC medical informatics and decision making 19, 1-20, 2019 | 44 | 2019 |
| Locally and globally explainable time series tweaking I Karlsson, J Rebane, P Papapetrou, A Gionis Knowledge and Information Systems 62 (5), 1671-1700, 2020 | 26 | 2020 |
| Exploiting complex medical data with interpretable deep learning for adverse drug event prediction J Rebane, I Samsten, P Papapetrou Artificial Intelligence in Medicine 109, 101942, 2020 | 22 | 2020 |
| Separate Ca2+ sources are buffered by distinct Ca2+ handling systems in Aplysia neuroendocrine cells CJ Groten, JT Rebane, G Blohm, NS Magoski Journal of Neuroscience 33 (15), 6476-6491, 2013 | 22 | 2013 |
| Achieving a data-driven risk assessment methodology for ethical AI A Felländer, J Rebane, S Larsson, M Wiggberg, F Heintz Digital Society 1 (2), 13, 2022 | 19 | 2022 |
| An investigation of interpretable deep learning for adverse drug event prediction J Rebane, I Karlsson, P Papapetrou 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems …, 2019 | 14 | 2019 |
| Assessing the clinical validity of attention-based and SHAP temporal explanations for adverse drug event predictions J Rebane, I Samsten, P Pantelidis, P Papapetrou 2021 IEEE 34th International Symposium on Computer-Based Medical Systems …, 2021 | 11 | 2021 |
| Humans construct survey estimates on the fly from a compartmentalised representation of the navigated environment T Meilinger, A Henson, J Rebane, HH Bülthoff, HA Mallot Spatial Cognition XI: 11th International Conference, Spatial Cognition 2018 …, 2018 | 11 | 2018 |
| SMILE: a feature-based temporal abstraction framework for event-interval sequence classification J Rebane, I Karlsson, L Bornemann, P Papapetrou Data mining and knowledge discovery 35 (1), 372-399, 2021 | 10 | 2021 |
| Ca2+ removal by the plasma membrane Ca2+-ATPase influences the contribution of mitochondria to activity-dependent Ca2+ dynamics in Aplysia neuroendocrine cells CJ Groten, JT Rebane, HM Hodgson, AK Chauhan, G Blohm, NS Magoski Journal of Neurophysiology 115 (5), 2615-2634, 2016 | 8 | 2016 |
| Mining disproportional frequent arrangements of event intervals for investigating adverse drug events Z Lee, J Rebane, P Papapetrou 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems …, 2020 | 5 | 2020 |
| Learning from administrative health registries J Rebane, I Karlsson, L Asker, H Boström, P Papapetrou ECML-PKDD 1960, 2017 | 3 | 2017 |
| The acquisition of survey knowledge through navigation T Meilinger, J Rebane, A Henson, HH Buelthoff, HA Mallot Pabst Science, 2015 | 1 | 2015 |
| Learning from Complex Medical Data Sources J Rebane Department of Computer and Systems Sciences, Stockholm University, 2022 | | 2022 |
| Constraints on models of human survey estimation: evidence from a learning study T Meilinger, J Rebane, A Henson, HH Bülthoff, HA Mallot International Workshop on Models and Representations in Spatial Cognition, 2016 | | 2016 |
