Niclas Ståhl
Niclas Ståhl
Jönköping University
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Cited by
Cited by
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
Machine learning: a concise overview
D Duarte, N Ståhl
Data Science in Practice, 27-58, 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
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
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
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
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
Identifying wetland areas in historical maps using deep convolutional neural networks
N Ståhl, L Weimann
Ecological Informatics 68, 101557, 2022
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
Integrating domain knowledge into deep learning: Increasing model performance through human expertise
N Ståhl
Högskolan i Skövde, 2021
The Effect of Sexual Selection on Cline Patterns in Biological Traits
N Ståhl
Well-Calibrated Rule Extractors
U Johansson, T Löfström, N Ståhl
Conformal and Probabilistic Prediction with Applications, 72-91, 2022
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
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
Complex data analysis
J Bae, A Karlsson, J Mellin, N Ståhl, V Torra
Data Science in Practice, 157-169, 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
Challenges and opportunities of analysing complex data using deep learning
N Ståhl
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
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