Nettsider med emneord «Ensemble learning»

The recent summer drought events in Europe and their associated devastating wildfires highlight the importance of understanding and predicting such extreme events and their impact.

We use quantum mechanical theory (density functional theory) and develop statistical methods such as Monte Carlo techniques, molecular dynamics, thermodynamic integration, genetic algorithms in conjunction with machine learning to understand more about deep earth processes and core-mantle
interactions.

BigInsight produces innovative solutions for key data-driven challenges facing our consortium of private, public and research partners, by developing original statistical and machine learning methodologies.

We develop and apply methods based on machine learning for chemistry and materials science. At the method level, our focus is on data (datasets computed with quantum mechanics methods), representations (graphs based on electronic structure theory), and models (graph neural networks and boosted trees).

Bayesian methods have recently regained a significant amount of attention in the machine community due to the development of scalable approximate Bayesian inference techniques. There are several advantages of using Bayesian approach: Parameter and prediction uncertainty become easily available, facilitating rigid statistical analysis. Furthermore, prior knowledge can be incorporated.