{"total":21,"rows":[{"id":"2309.06240","title":"Calibration in Machine Learning Uncertainty Quantification: beyond consistency to target adaptivity","created":"2023-09-12","cited_by_count":13,"area":null},{"id":"2506.06915","title":"Graph Neural Networks in Modern AI-aided Drug Discovery","created":"2025-06-07","cited_by_count":12,"area":null},{"id":"2306.10060","title":"MUBen: Benchmarking the Uncertainty of Molecular Representation Models","created":"2023-06-14","cited_by_count":11,"area":null},{"id":"2212.01574","title":"Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS","created":"2022-12-03","cited_by_count":10,"area":null},{"id":"2301.05983","title":"On the role of Model Uncertainties in Bayesian Optimization","created":"2023-01-14","cited_by_count":8,"area":null},{"id":"2307.10438","title":"Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search","created":"2023-07-19","cited_by_count":8,"area":null},{"id":"2305.11905","title":"Properties of the ENCE and other MAD-based calibration metrics","created":"2023-05-17","cited_by_count":7,"area":null},{"id":"2306.05180","title":"Stratification of uncertainties recalibrated by isotonic regression and its impact on calibration error statistics","created":"2023-06-08","cited_by_count":6,"area":null},{"id":"2109.04007","title":"MaterialsAtlas.org: A Materials Informatics Web App Platform for Materials Discovery and Survey of State-of-the-Art","created":"2021-09-09","cited_by_count":5,"area":null},{"id":"2303.07170","title":"Validation of uncertainty quantification metrics: a primer based on the consistency and adaptivity concepts","created":"2023-03-13","cited_by_count":5,"area":null},{"id":"2310.11978","title":"Can bin-wise scaling improve consistency and adaptivity of prediction uncertainty for machine learning regression ?","created":"2023-10-18","cited_by_count":4,"area":null},{"id":"2402.00853","title":"LTAU-FF: Loss Trajectory Analysis for Uncertainty in Atomistic Force Fields","created":"2024-02-01","cited_by_count":4,"area":null},{"id":"2309.06348","title":"Band-gap regression with architecture-optimized message-passing neural networks","created":"2023-09-12","cited_by_count":2,"area":null},{"id":"2408.09461","title":"Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approaches","created":"2024-08-18","cited_by_count":2,"area":null},{"id":"2605.22593","title":"Do Deep Ensembles Actually Capture Uncertainty in Graph Neural Networks?","created":"2026-05-21","cited_by_count":2,"area":null},{"id":"2402.10043","title":"Negative impact of heavy-tailed uncertainty and error distributions on the reliability of calibration statistics for machine learning regression tasks","created":"2024-02-15","cited_by_count":1,"area":null},{"id":"2504.02606","title":"Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification","created":"2025-04-03","cited_by_count":1,"area":null},{"id":"2312.04174","title":"Coherent energy and force uncertainty in deep learning force fields","created":"2023-12-07","cited_by_count":0,"area":null},{"id":"2401.01506","title":"AIRI: Predicting Retention Indices and their Uncertainties using Artificial Intelligence","created":"2024-01-03","cited_by_count":0,"area":null},{"id":"2403.00423","title":"Validation of ML-UQ calibration statistics using simulated reference values: a sensitivity analysis","created":"2024-03-01","cited_by_count":0,"area":null},{"id":"2603.01750","title":"Practical Deep Heteroskedastic Regression","created":"2026-03-02","cited_by_count":0,"area":null}],"next_cursor":null}