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Uncertainty Quantification
Trinity: Trust, Resilience and Interpretability of Machine Learning Models
Jan 1, 2021
Toward Safe Decision-Making via Uncertainty Quantification in Machine Learning
Jan 1, 2021
Computational Intelligence in Uncertainty Quantification for Learning Controland Differential Games
Jan 1, 2021
Ursabench: Comprehensive benchmarking of approximate bayesian inference methods for deep neural networks
Jan 1, 2020
On uncertainty and robustness in large-scale intelligent data fusion systems
Jan 1, 2020
Generalized bayesian posterior expectation distillation for deep neural networks
Jan 1, 2020
Better call surrogates: A hybrid evolutionary algorithm for hyperparameter optimization
Jan 1, 2020
Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification
Jan 1, 2020
Adversarial Distillation of Bayesian Neural Networks
Jan 1, 2020
Are Graph Neural Networks Miscalibrated?
May 1, 2019
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