From the 1 of 5 linked papers with an AI index.
5 papers
Semi-Supervised Learning for Molecular Graphs via Ensemble Consensus
Rasmus Tirsgaard, Laurits Fredsgaard, Marisa Wodrich +2
The paper proposes a semi-supervised learning approach for molecular graph data that uses an ensemble consensus objective to improve prediction accuracy, robustness, and calibratio…
Equivariant Graph Neural Networks Improve Optical Spectra Prediction for Materials Screening
Kasper Helverskov Petersen, François R J Cornet, Martin Ovesen +3
Scalable prediction of optical spectra is a critical component of high-throughput materials screening for optoelectronic applications such as solar cells. Existing surrogate models…
Practical Deep Heteroskedastic Regression
Mikkel Jordahn, Jonas Vestergaard Jensen, James Harrison +2
Uncertainty quantification (UQ) in deep learning regression is of wide interest, as it supports critical applications including sequential decision making and risk-sensitive tasks.…
On Local Posterior Structure in Deep Ensembles
Mikkel Jordahn, Jonas Vestergaard Jensen, Mikkel N. Schmidt +1
Bayesian Neural Networks (BNNs) often improve model calibration and predictive uncertainty quantification compared to point estimators such as maximum-a-posteriori (MAP). Similarly…
Bayesian Optimization via Continual Variational Last Layer Training
Paul Brunzema, Mikkel Jordahn, John Willes +3
Gaussian Processes (GPs) are widely seen as the state-of-the-art surrogate models for Bayesian optimization (BO) due to their ability to model uncertainty and their performance on…