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07/31/2022
Adaptive Temperature Scaling for Robust Calibration of Deep Neural Networks
In this paper, we study the post-hoc calibration of modern neural networ...
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05/30/2022
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification
This work introduces the Efficient Transformed Gaussian Process (ETGP), ...
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11/03/2020
Transforming Gaussian Processes With Normalizing Flows
Gaussian Processes (GPs) can be used as flexible, non-parametric functio...
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03/22/2020
Improving Calibration in Mixup-trained Deep Neural Networks through Confidence-Based Loss Functions
Deep Neural Networks (DNN) represent the state of the art in many tasks....
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09/18/2019
Bayesian Strategies for Likelihood Ratio Computation in Forensic Voice Comparison with Automatic Systems
This paper explores several strategies for Forensic Voice Comparison (FV...
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08/23/2019
Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks
Deep Neural Networks (DNNs) have achieved state-of-the-art accuracy perf...
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03/21/2019