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ELPIS lab
description
A highly-customizable Hugo research group theme powered by Wowchemy website builder.
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ELPIS lab
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Vincent Fortuin
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2026-02-19 11:58:33
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ELPIS lab ELPIS lab ELPIS lab People Publications Contact ELPIS lab Our lab for Efficient Learning and Probabilistic Inference for Science (ELPIS) at Helmholtz AI and TU Munich studies the interface between Bayesian inference and deep learning with the goals of improving robustness, data-efficiency, and uncertainty estimation in modern machine learning approaches. While deep learning often leads to impressive performance in many applications, it can be over-confident in its predictions and require large datasets to train. Especially in scientific applications, where training data is scarce and detailed prior knowledge is available, insights from Bayesian statistics can be used to drastically improve these models. Important research questions include how to effectively specify priors in deep Bayesian models, how to harness unlabeled data to learn re-usable representations, how to transfer knowledge between tasks using meta-learning, and how to guarantee generalization...
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