Följ
Niclas Ståhl
Niclas Ståhl
Jönköping University
Verifierad e-postadress på ju.se
Titel
Citeras av
Citeras av
År
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
1622019
Evaluation of uncertainty quantification in deep learning
N Ståhl, G Falkman, A Karlsson, G Mathiason
International Conference on Information Processing and Management of …, 2020
352020
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
292019
Machine learning: a concise overview
D Duarte, N Ståhl
Data Science in Practice, 27-58, 2019
292019
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
262020
Identifying wetland areas in historical maps using deep convolutional neural networks
N Ståhl, L Weimann
Ecological Informatics 68, 101557, 2022
182022
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
132019
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
52018
Using reinforcement learning for generating polynomial models to explain complex data
N Ståhl, G Mathiason, D Alcacoas
SN Computer Science 2 (2), 103, 2021
22021
Integrating domain knowledge into deep learning: Increasing model performance through human expertise
N Ståhl
Högskolan i Skövde, 2021
12021
The Effect of Sexual Selection on Cline Patterns in Biological Traits
N Ståhl
12016
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
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
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
Systemet kan inte utföra åtgärden just nu. Försök igen senare.
Artiklar 1–18