3 papers
cs.HC2026
PhiPlot: A Web-Based Interactive EDA Environment for Atmospherically Relevant Molecules
Matias Loukojärvi, Ananth Mahadevan, Katsiaryna Haitsiukevich +1
Advances in computational chemistry have produced high-dimensional datasets on atmospherically relevant molecules. To aid exploration of such datasets, particularly for the study o…
cs.LG2025
Fast and Interpretable Machine Learning Modelling of Atmospheric Molecular Clusters
Lauri Seppäläinen, Jakub KubeÄka, 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
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…