arXiv:2412.04177v2 Announce Type: replace Abstract: Recently, there has been an increasing interest in performing post-hoc uncertainty estimation about the predictions of pre-trained deep neural networks (DNNs). Given a pre-trained DNN via back-propagation, these methods enhance the original network by adding output confidence measures, such as error bars, without compromising its initial accuracy. In this context, we introduce a novel family of sparse variational Gaussian processes (GPs), where the posterior mean is fixed to any continuous function when using a universal kernel. Specifically,
The increasing complexity and deployment of deep neural networks necessitate robust methods for understanding prediction uncertainty, especially as AI systems are integrated into critical applications.
Improving post-hoc uncertainty estimation in deep learning makes AI systems more reliable and trustworthy, which is crucial for their broader adoption and for mitigating risks in sensitive domains.
This research introduces a novel method that allows pre-trained deep neural networks to provide better calibrated confidence measures without retraining, enhancing their practical utility and interpretability.
- · AI developers
- · Industries deploying AI (e.g., healthcare, finance)
- · AI safety researchers
- · Machine learning researchers
- · Systems highly sensitive to opaque AI decisions (without uncertainty measures)
More reliable and deployable AI systems with quantifiable uncertainty.
Increased trust and adoption of AI in high-stakes environments due to improved transparency on prediction confidence.
Accelerated development of AI agentic systems that can assess and communicate their own limitations, leading to safer and more autonomous operations.
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Read at arXiv cs.LG