| Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design N Ståhl, G Falkman, A Karlsson, G Mathiason, J Bostrom Journal of chemical information and modeling 59 (7), 3166-3176, 2019 | 196 | 2019 |
| Evaluation of uncertainty quantification in deep learning N Ståhl, G Falkman, A Karlsson, G Mathiason International Conference on Information Processing and Management of …, 2020 | 48 | 2020 |
| Machine learning: a concise overview D Duarte, N Ståhl Data Science in Practice, 27-58, 2019 | 38 | 2019 |
| Using Machine Learning for Robust Target Prediction in a Basic Oxygen Furnace System J Bae, Y Li, N Ståhl, G Mathiason, N Kojola Metallurgical and materials transactions. B, process metallurgy and …, 2020 | 37 | 2020 |
| Using recurrent neural networks with attention for detecting problematic slab shapes in steel rolling N Ståhl, G Mathiason, G Falkman, A Karlsson Applied Mathematical Modelling 70, 365-377, 2019 | 37 | 2019 |
| Identifying wetland areas in historical maps using deep convolutional neural networks N Ståhl, L Weimann Ecological Informatics 68, 101557, 2022 | 24 | 2022 |
| Deep convolutional neural networks for the prediction of molecular properties: challenges and opportunities connected to the data N Ståhl, G Falkman, A Karlsson, G Mathiason, J Boström Journal of Integrative Bioinformatics 16 (1), 20180065, 2019 | 17 | 2019 |
| A self-organizing ensemble of deep neural networks for the classification of data from complex processes N Ståhl, G Falkman, G Mathiason, A Karlsson Information Processing and Management of Uncertainty in Knowledge-Based …, 2018 | 5 | 2018 |
| Using reinforcement learning for generating polynomial models to explain complex data N Ståhl, G Mathiason, D Alcacoas SN Computer Science 2 (2), 103, 2021 | 3 | 2021 |
| Understanding Robust Target Prediction in Basic Oxygen Furnace J Bae, G Mathiason, Y Li, N Kojola, N Ståhl 2021 The 2nd International Conference on Industrial Engineering and …, 2021 | 1 | 2021 |
| Integrating domain knowledge into deep learning: Increasing model performance through human expertise N Ståhl Högskolan i Skövde, 2021 | 1 | 2021 |
| The Effect of Sexual Selection on Cline Patterns in Biological Traits N Ståhl | 1 | 2016 |
| Well-Calibrated Rule Extractors U Johansson, T Löfström, N Ståhl Conformal and Probabilistic Prediction with Applications, 72-91, 2022 | | 2022 |
| Utilising Data from Multiple Production Lines for Predictive Deep Learning Models N Ståhl, G Mathiason, J Bae Distributed Computing and Artificial Intelligence, Volume 1: 18th …, 2022 | | 2022 |
| Improving the Use of Deep Convolutional Neural Networks for the Prediction of Molecular Properties N Ståhl, G Falkman, A Karlsson, G Mathiason, J Boström Practical Applications of Computational Biology and Bioinformatics, 12th …, 2019 | | 2019 |
| Complex data analysis J Bae, A Karlsson, J Mellin, N Ståhl, V Torra Data Science in Practice, 157-169, 2019 | | 2019 |
| Challenges and opportunities of analysing complex data using deep learning N Ståhl | | 2017 |
| Formalisering av algoritmer och matematiska bevis En formalisering av Toom-Cook algoritmen i Coq med SSReflect J Andersson, Å Lideström, D Oom, A Sjöberg, N Ståhl | | 2014 |
Gunnar MathiasonUniversity of SkövdeVerified email at his.se
