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Found 4 papers, 2 papers with code
On the Importance of Regularisation & Auxiliary Information in OOD Detection
Neural networks are often utilised in critical domain applications (e. g. self-driving cars, financial markets, and aerospace engineering), even though they exhibit overconfident predictions for ambiguous inputs.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+1
Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings
Deep neural networks have been successful in diverse discriminative classification tasks, although, they are poorly calibrated often assigning high probability to misclassified predictions.
On the Validity of Bayesian Neural Networks for Uncertainty Estimation
Deep neural networks (DNN) are versatile parametric models utilised successfully in a diverse number of tasks and domains.
A Categorisation of Post-hoc Explanations for Predictive Models
The ubiquity of machine learning based predictive models in modern society naturally leads people to ask how trustworthy those models are?
