Deep Reinforcement Learning for Multiparameter Optimization in *de novo* Drug DesignN Ståhl, G Falkman, A Karlsson, G Mathiason, J Bostrom Journal of chemical information and modeling 59 (7), 3166-3176, 2019 | 55 | 2019 |

Machine learning: a concise overview D Duarte, N Ståhl Data Science in Practice, 27-58, 2019 | 13 | 2019 |

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 | 10 | 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 | 7 | 2020 |

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 | 7 | 2020 |

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), 2019 | 5 | 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 International Conference on Information Processing and Management of …, 2018 | 4 | 2018 |

The Effect of Sexual Selection on Cline Patterns in Biological Traits N Ståhl | 1 | 2016 |

Utilising Data from Multiple Production Lines for Predictive Deep Learning Models N Ståhl, G Mathiason, J Bae International Symposium on Distributed Computing and Artificial Intelligence …, 2021 | | 2021 |

Identifying Wetland Areas in Historical Maps using Deep Convolutional Neural Networks N Ståhl, L Weimann arXiv preprint arXiv:2108.04107, 2021 | | 2021 |

Using Reinforcement Learning for Generating Polynomial Models to Explain Complex Data N Ståhl, G Mathiason, D Alcacoas SN Computer Science 2 (2), 1-11, 2021 | | 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 | | 2021 |

Complex data analysis J Bae, A Karlsson, J Mellin, N Ståhl, V Torra Data Science in Practice, 157-169, 2019 | | 2019 |

Improving the Use of Deep Convolutional Neural Networks for the Prediction of Molecular Properties N Ståhl, G Falkman, A Karlsson, G Mathiason, J Boström International Conference on Practical Applications of Computational Biology …, 2018 | | 2018 |

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 |