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Reliable uncertainty quantification (UQ) in machine learning (ML) regression tasks is becoming the focus of many studies in materials and chemical science.
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High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach
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Accurate uncertainties for deep learning using calibrated regression
V. Kuleshov, N. Fenner, and S. Ermon · 2018
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Uncertainty Estimates and Multi-hypotheses Networks for Optical Flow
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L. McInnes, J. Healy, and J. Melville · 2018
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F. Musil, M. J. Willatt, M. A. Langovoy, and M. Ceriotti · 2019
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J. P. Janet, C. Duan, T. Yang, A. Nandy, and H. J. Kulik · 2019
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Uncertainty Quantification Using Neural Networks for Molecular Property Prediction
L. Hirschfeld, K. Swanson, K. Yang, R. Barzilay, and C. W. Coley · 2020
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K. Tran, W. Neiswanger, J. Yoon, Q. Zhang, E. Xing, and Z. W. Ulissi · 2020
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G. Scalia, C. A. Grambow, B. Pernici, Y.-P. Li, and W. H. Green · 2020
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Beyond pinball loss: Quantile methods for calibrated uncertainty quantification
Y. Chung, W. Neiswanger, I. Char, and J. Schneider · 2020
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S. Zhao, T. Ma, and S. Ermon · 2020
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Well-calibrated regression uncertainty in medical imaging with deep learning
M.-H. Laves, S. Ihler, J. F. Fast, L. A. Kahrs, and T. Ortmaier · 2020
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Evaluating and Calibrating Uncertainty Prediction in Regression Tasks
D. Levi, L. Gispan, N. Giladi, and E. Fetaya · 2020
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Metrics for Benchmarking and Uncertainty Quantification: Quality, Applicability, and Best Practices for Machine Learning in Chemistry
Evaluating and Calibrating Uncertainty Prediction in Regression Tasks
D. Levi, L. Gispan, N. Giladi, and E. Fetaya · 2022
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Calibrated uncertainty for molecular property prediction using ensembles of message passing neural networks
J. Busk, P. B. Jørgensen, A. Bhowmik, M. N. Schmidt, O. Winther, and T. Vegge · 2022
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Molecule-specific uncertainty quantification in quantum chemical studies
M. Reiher · 2022
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Uncertainty quantification for predictions of atomistic neural networks
L. I. Vazquez-Salazar, E. D. Boittier, and M. Meuwly · 2022
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The long road to calibrated prediction uncertainty in computational chemistry
P. Pernot · 2022
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G. Vishwakarma, A. Sonpal, and J. Hachmann · 2021
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A review of uncertainty quantification in deep learning: Techniques, applications and challenges
M. Abdar, F. Pourpanah, S. Hussain, D. Rezazadegan, L. Liu, M. Ghavamzadeh, P. Fieguth, X. Cao, A. Khosravi, U. R. Acharya, V. Makarenkov, and S. Nahavandi · 2021
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A Survey of Uncertainty in Deep Neural Networks
J. Gawlikowski, C. R. N. Tassi, M. Ali, J. Lee, M. Humt, J. Feng, A. Kruspe, R. Triebel, P. Jung, R. Roscher, M. Shahzad, W. Yang, R. Bamler, and X. X. Zhu · 2021
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Pairwise difference regression: A machine learning meta-algorithm for improved prediction and uncertainty quantification in chemical search
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CRUDE: Calibrating Regression Uncertainty Distributions Empirically
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Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
E. Hüllermeier and W. Waegeman · 2021
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A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
A. N. Angelopoulos and S. Bates · 2021
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Calibration after bootstrap for accurate uncertainty quantification in regression models
G. Palmer, S. Du, A. Politowicz, J. P. Emory, X. Yang, A. Gautam, G. Gupta, Z. Li, R. Jacobs, and D. Morgan · 2022
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Confidence curves for UQ validation: probabilistic reference vs. oracle
P. Pernot · 2022
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Clarifying Trust of Materials Property Predictions using Neural Networks with Distribution-Specific Uncertainty Quantification
C. Gruich, V. Madhavan, Y. Wang, and B. Goldsmith · 2023
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Characterizing Uncertainty in Machine Learning for Chemistry
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Machine learning guided optimal composition selection of niobium alloys for high temperature applications
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Reliable neural networks for regression uncertainty estimation
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Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets
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Codes and data for the reproduction of the results of the present paper , 2023
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Errviewlib-v1.7.3 , 2023
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