arXiv:2602.01279v2 Announce Type: replace Abstract: Bayesian Last Layers (BLLs) provide a convenient and computationally efficient way to estimate uncertainty in neural networks. However, they underestimate epistemic uncertainty because they apply a Bayesian treatment only to the final layer, ignoring uncertainty induced by earlier layers. We propose a method that improves BLLs by leveraging a projection of Neural Tangent Kernel (NTK) features onto the space spanned by the last-layer features. This enables posterior inference that accounts for variability of the full network while retaining th
This research addresses a known limitation in Bayesian Neural Networks, aiming for more reliable uncertainty estimates, which is crucial as AI models are integrated into more sensitive applications.
Improved uncertainty quantification in AI models enhances their trustworthiness and safety, accelerating adoption in critical decision-making processes across various sectors.
By more accurately estimating epistemic uncertainty, this method allows BLLs to provide more robust predictions, making BNNs more viable for real-world deployment.
- · AI safety researchers
- · Deep learning practitioners
- · Sectors requiring high AI reliability (e.g., healthcare, autonomous driving)
- · AI models with poor uncertainty quantification
Increased confidence in the outputs of AI systems using these improved Bayesian last layers.
Faster integration of AI into regulated or high-stakes environments due to better reliability.
Potential for new AI applications where robust uncertainty estimation is a prerequisite for deployment.
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Read at arXiv cs.LG