| Safe learning for control using control lyapunov functions and control barrier functions: A review A Anand, K Seel, V Gjærum, A Håkansson, H Robinson, A Saad Procedia Computer Science 192, 3987-3997, 2021 | 21 | 2021 |
| Advancing ocean observation with an ai-driven mobile robotic explorer A Saad, A Stahl, A Våge, E Davies, T Nordam, N Aberle, M Ludvigsen, ... Oceanography 33 (3), 50-59, 2020 | 18 | 2020 |
| Constraint reasoning with uncertain data using cdf-intervals A Saad, C Gervet, S Abdennadher International Conference on Integration of Artificial Intelligence (AI) and …, 2010 | 14 | 2010 |
| Recent advances in visual sensing and machine learning techniques for in-situ plankton-taxa classification A Saad, E Davies, A Stahl Authorea Preprints, 2022 | 9 | 2022 |
| Robust deep unsupervised learning framework to discover unseen plankton species E Salvesen, A Saad, A Stahl Fourteenth International Conference on Machine Vision (ICMV 2021) 12084, 241-250, 2022 | 9 | 2022 |
| The p-box cdf-intervals: A reliable constraint reasoning with quantifiable information A Saad, T Fruehwirth, C Gervet Theory and Practice of Logic Programming 14 (4-5), 461-475, 2014 | 9 | 2014 |
| Robust reasoning for autonomous cyber-physical systems in dynamic environments A Håkansson, A Saad, A Anand, V Gjærum, H Robinson, K Seel Procedia Computer Science 192, 3966-3978, 2021 | 7 | 2021 |
| Automatic in-situ instance and semantic segmentation of planktonic organisms using Mask R-CNN S Bergum, A Saad, A Stahl Global Oceans 2020: Singapore–US Gulf Coast, 1-8, 2020 | 7 | 2020 |
| Robust methods of unsupervised clustering to discover new planktonic species in-situ E Salvesen, A Saad, A Stahl Global Oceans 2020: Singapore–US Gulf Coast, 1-9, 2020 | 7 | 2020 |
| Leveraging similarity metrics to in-situ discover planktonic interspecies variations or mutations AL Teigen, A Saad, A Stahl Global Oceans 2020: Singapore–US Gulf Coast, 1-8, 2020 | 6 | 2020 |
| An instance segmentation framework for in-situ plankton taxa assessment A Saad, S Bergrum, A Stahl Thirteenth International Conference on Machine Vision 11605, 294-303, 2021 | 5 | 2021 |
| A combined informative and representative active learning approach for plankton taxa labeling ML Haug, A Saad, A Stahl Thirteenth International Conference on Digital Image Processing (ICDIP 2021 …, 2021 | 4 | 2021 |
| Few-shot open world learner AL Teigen, A Saad, A Stahl, R Mester IFAC-PapersOnLine 54 (16), 444-449, 2021 | 4 | 2021 |
| CIRAL: a hybrid active learning framework for plankon taxa labeling ML Haug, A Saad, A Stahl IFAC-PapersOnLine 54 (16), 450-457, 2021 | 3 | 2021 |
| CDF-Intervals Revisited A Saad, C Gervet, T Fruehwirth The Eleventh International Workshop on Constraint Modelling and …, 2012 | 3 | 2012 |
| The k-ary n-cube Network and its Dual: a Comparative Study M Mudawwar, A Saad Proceedings of the 13th IASTED International Conference on Parallel and …, 2001 | 3 | 2001 |
| Vision-based Real-time Zooplankton Detection and Classification using Faster R-CNN S Ansari, A Saad, A Stahl, M Rajachandran Authorea Preprints, 2022 | 2 | 2022 |
| Ramarl: Robustness analysis with multi-agent reinforcement learning-robust reasoning in autonomous cyber-physical systems A Saad, A Håkansson Procedia Computer Science 207, 3662-3671, 2022 | 2 | 2022 |
| MOG: a background extraction approach for data augmentation of time-series images in deep learning segmentation JN Borgersen, A Saad, A Stahl Fourteenth International Conference on Machine Vision (ICMV 2021) 12084, 360-368, 2022 | 1 | 2022 |
| Towards a balanced-labeled-dataset of planktons for a better in-situ taxa identification OS Kiese NTNU, 2020 | 1 | 2020 |
Annette StahlProfessor, Dept. Engineering Cybernetics, Norwegian University of Science and TechnologyVerified email at ntnu.no
