Evaluating Machine Learning Models for Molecular Property Prediction: Performance and Robustness on Out-of-Distribution Data
Sign Up to like & getrecommendations! Published in 2025 at "Journal of Chemical Information and Modeling"
DOI: 10.1021/acs.jcim.5c00475
Abstract: Today, machine learning models are employed extensively to predict the physicochemical and biological properties of molecules. Their performance is typically evaluated on in-distribution (ID) data, i.e., data originating from the same distribution as the training… read more here.
Keywords: ood data; performance; distribution; machine learning ... See more keywords
MOOD: Leveraging Out-of-Distribution Data to Enhance Imbalanced Semi-Supervised Learning
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2025.3573963
Abstract: The imbalanced semi-supervised learning (SSL) has emerged as a critical research area due to the prevalence of class imbalanced and partially labeled data in real-world scenarios. As the requirement for data volume increases, naturally collected… read more here.
Keywords: ood data; distribution; ood; supervised learning ... See more keywords
Detecting Out-of-Distribution Data in Wireless Communications Applications of Deep Learning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Wireless Communications"
DOI: 10.1109/twc.2021.3112663
Abstract: Deep learning-based classification algorithms offer no performance guarantees when deployed on testing data not generated by the same process as the training data. Such out-of-distribution (OOD) data often cause classification errors that are hard to… read more here.
Keywords: ood data; wireless communications; communications applications; deep learning ... See more keywords
A Novel Method for Medical Predictive Models in Small Data Using Out-of-Distribution Data and Transfer Learning
Sign Up to like & getrecommendations! Published in 2024 at "Mathematics"
DOI: 10.3390/math12020237
Abstract: Applying deep learning to medical research with limited data is challenging. This study focuses on addressing this difficulty through a case study, predicting acute respiratory failure (ARF) in patients with acute pesticide poisoning. Commonly, out-of-distribution… read more here.
Keywords: ood data; transfer learning; distribution; limited data ... See more keywords