[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …
uncertainties during both optimization and decision making processes. They have been …
Shifts: A dataset of real distributional shift across multiple large-scale tasks
A Malinin, N Band, G Chesnokov, Y Gal… - ar** methods for improving robustness
to distributional shift and uncertainty estimation. In contrast, only limited work has examined …
to distributional shift and uncertainty estimation. In contrast, only limited work has examined …
Shifts 2.0: Extending the dataset of real distributional shifts
Distributional shift, or the mismatch between training and deployment data, is a significant
obstacle to the usage of machine learning in high-stakes industrial applications, such as …
obstacle to the usage of machine learning in high-stakes industrial applications, such as …
Uncertainty quantification for competency assessment of autonomous agents
For safe and reliable deployment in the real world, autonomous agents must elicit
appropriate levels of trust from human users. One method to build trust is to have agents …
appropriate levels of trust from human users. One method to build trust is to have agents …
Deep Generative Models for Competency Awareness and Uncertainty Quantification
A Acharya - 2023 - search.proquest.com
As they become increasingly advanced, autonomous agents are slowly integrating in the
real world and working alongside human users. To establish appropriate levels of trust …
real world and working alongside human users. To establish appropriate levels of trust …