1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2025
Fast and Interpretable Machine Learning Modelling of Atmospheric Molecular Clusters
Lauri Seppäläinen, Jakub Kubečka, Jonas Elm +1
Understanding how atmospheric molecular clusters form and grow is key to resolving one of the biggest uncertainties in climate modelling: the formation of new aerosol particles. Wh…
cs.LG2025★ 1 cited
GRADSTOP: Early Stopping of Gradient Descent via Posterior Sampling
Arash Jamshidi, Lauri Seppäläinen, Katsiaryna Haitsiukevich +3
Machine learning models are often learned by minimising a loss function on the training data using a gradient descent algorithm. These models often suffer from overfitting, leading…