arXiv:2605.21765v1 Announce Type: new Abstract: The practical adoption of sampling-based inference (SAI) in Bayesian neural networks (BNNs) remains limited, partly due to persistent misconceptions about the feasibility and efficiency of sampling. This position paper argues that SAI has achieved computational parity with optimization-based methods and is at the verge of superseding such methods for effective and efficient inference in BNNs. This development should be in the interest of the whole community, promoting BNNs as a principled paradigm with its long-standing yet unfulfilled promise of
The paper suggests that recent advancements have brought sampling-based inference to computational parity with optimization methods, challenging longstanding assumptions about its practicality in Bayesian deep learning.
This development could significantly enhance the robustness and reliability of AI models, addressing critical limitations in current deep learning paradigms and enabling more principled approaches.
The perceived limitations of sampling-based inference in Bayesian neural networks are being reassessed, potentially shifting the focus towards more effective and efficient inference methods.
- · AI researchers and developers
- · Sectors requiring explainable AI
- · Bayesian Deep Learning community
- · Overly reliant optimization-based methods
- · Current BNN development paradigms
Increased research and adoption of sampling-based inference in Bayesian Neural Networks.
Improved uncertainty quantification and model reliability in AI applications.
Enhanced trust and broader deployment of AI systems in critical domains.
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Read at arXiv cs.LG