1 citations · 1 across the 3 of their papers we have counts for
4 papers
Interpretable Meta-Learning for Multi-Objective Chemical Search
Antonio Varagnolo, Yulia Pimonova, Michael G. Taylor +2
Navigating the vast space of synthetically accessible molecules demands surrogate models that are interpretable and capable of handling multiple competing objectives at the same ti…
Meta-Learning Linear Models for Molecular Property Prediction
Yulia Pimonova, Michael G. Taylor, Alice Allen +2
Chemists in search of structure-property relationships face great challenges due to limited high quality, concordant datasets. Machine learning (ML) has significantly advanced pred…
Effects of Structural Allocation of Geometric Task Diversity in Linear Meta-Learning Models
Saptati Datta, Nicolas W. Hengartner, Yulia Pimonova +2
Meta-learning aims to leverage information across related tasks to improve prediction on unlabeled data for new tasks when only a small number of labeled observations are available…
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials
Sakib Matin, Emily Shinkle, Yulia Pimonova +5
The quality of machine learning interatomic potentials (MLIPs) strongly depends on the quantity of training data as well as the quantum chemistry (QC) level of theory used. Dataset…