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Uncertainty representation and quantification are paramount in machine learning and constitute an important prerequisite for safety-critical applications.
DropConnect is effective in modeling uncertainty of Bayesian networks
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Transmission of information
Hartley, R. (1928) · 1928
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Probability Theory
Rényi, A. (1970) · 1970
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Elicitation of personal probabilities and expectations
Savage, L. J. (1971) · 1971
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On indeterminate probabilities
Levi, I. (1974) · 1974
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Version spaces: A candidate elimination approach to rule learning
Mitchell, T. M. (1977) · 1977
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Foresight: It’s logical laws, it’s subjective sources
De Finetti, B. (1980) · 1980
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The Enterprise of Knowledge
Levi, I. (1980) · 1980
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Entropy and specificity in a mathematical theory of evidence
Yager, R. (1983) · 1983
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On the uniqueness of possibilistic measure of uncertainty and information
Klir, G. and Mariano, M. (1987) · 1987
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Statistical Reasoning with Imprecise Probabilities
Walley, P. (1991) · 1991
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Representing partial ignorance
Dubois, D., Prade, H., and Smets, P. (1996) · 1996
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Aleatory and epistemic uncertainty in probability elicitation with an example from hazardous waste management
Hora, S. C. (1996) · 1996
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A non-specificity measure for convex sets of probability distributions
Abellan, J. and Moral, S. (2000) · 2000
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Strictly proper scoring rules, prediction, and estimation
Gneiting, T. and Raftery, A. (2005) · 2005
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Disaggregated total uncertainty measure for credal sets
Abellan, J., Klir, J., and Moral, S. (2006) · 2006
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Axiomatic characterizations of information measures
Csiszár, I. (2008) · 2008
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What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A. and Gal, Y. (2017) · 2017
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Decomposition of uncertainty in bayesian deep learning for efficient and risk-sensitive learning
Depeweg, S., Hernandez-Lobato, J.-M., Doshi-Velez, F., and Udluft, S. (2018) · 2018
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Understanding measures of uncertainty for adversarial example detection
Smith, L. and Gal, Y. (2018) · 2018
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Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods
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Pitfalls of epistemic uncertainty quantification through loss minimisation
Bengs, V., Hüllermeier, E., and Waegeman, W. (2022) · 2022
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Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures?
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Predictive uncertainty quantification via risk decompositions for strictly proper scoring rules
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