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Aleatoric uncertainty captures the inherent randomness of the data, such as measurement noise.
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What uncertainties do we need in bayesian deep learning for computer vision?
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Why cold posteriors? on the suboptimal generalization of optimal bayes estimates
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Cyclical stochastic gradient MCMC for bayesian deep learning
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A Bayesian neural network predicts the dissolution of compact planetary systems
M. Cranmer, D. Tamayo, H. Rein, P. Battaglia, S. Hadden, P. J. Armitage, S. Ho, and D. N. Spergel · 2021
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Laplace redux-effortless bayesian deep learning
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Bayesian neural network priors revisited
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Scalable marginal likelihood estimation for model selection in deep learning
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Data augmentation in bayesian neural networks and the cold posterior effect
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Disentangling the roles of curation, data-augmentation and the prior in the cold posterior effect
L. Noci, K. Roth, G. Bachmann, S. Nowozin, and T. Hofmann · 2021
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Understanding deep learning (still) requires rethinking generalization
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals · 2021
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Probabilistic Machine Learning: Advanced Topics
K. P. Murphy · 2023
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