The Helsinki Probabilistic Machine Learning Lab encompasses seven research groups at the Department of Computer Science of the University of Helsinki, all specializing in probabilistic machine ...

More information: Vasilis Michalakopoulos et al, A machine learning-based framework for clustering residential electricity load profiles to enhance demand response programs, Applied Energy (2024 ...

We use probabilistic machine learning to look at the calibration problem in a probabilistic framework based on Gaussian processes. This immediately gives a way of encoding prior beliefs about the ...

The Machine and Human Intelligence research group led by Principal Investigator Luigi Acerbi is looking for a postdoctoral researcher eager to work on new machine learning methods for robust, ...

Probabilistic Machine Learning We develop probabilistic modelling techniques to produce predictions for ML challenges that require uncertainty quantification. In particular we look at using grey-box ...

Cluster analysis, a commonly used machine-learning technique, uses these basic features to not only categorize materials and ...

The second part of the module introduces and provides training in further topics of probabilistic machine learning such as Graphical models, mixtures and cluster analysis, Variational approximation, ...

Notably, the 2024 Nobel Prize in Physics was awarded to John Hopfield and Geoffrey Hinton for their groundbreaking work in machine learning. Probabilistic computers have traditionally been limited ...

A new study introduced a machine learning-powered clustering model that incorporates both basic features and target properties, successfully grouping over 1,000 inorganic materials.