Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Løvland, Kristian"'
Deep sequence models are receiving significant interest in current machine learning research. By representing probability distributions that are fit to data using maximum likelihood estimation, such models can model data on general observation spaces
Externí odkaz:
http://arxiv.org/abs/2409.04120
In many industrial processes, an apparent lack of data limits the development of data-driven soft sensors. There are, however, often opportunities to learn stronger models by being more data-efficient. To achieve this, one can leverage knowledge abou
Externí odkaz:
http://arxiv.org/abs/2407.13310
Recent literature has explored various ways to improve soft sensors by utilizing learning algorithms with transferability. A performance gain is generally attained when knowledge is transferred among strongly related soft sensor learning tasks. A par
Externí odkaz:
http://arxiv.org/abs/2309.15828
Steady-state models which have been learned from historical operational data may be unfit for model-based optimization unless correlations in the training data which are introduced by control are accounted for. Using recent results from work on struc
Externí odkaz:
http://arxiv.org/abs/2211.05613