arXiv:2507.08905v2 Announce Type: replace-cross Abstract: We explore the use of Hamiltonian Monte Carlo (HMC) sampling as a probabilistic last layer approach for deep neural networks (DNNs). While HMC is widely regarded as a gold standard for uncertainty estimation, the computational demands limit its application to large-scale datasets and large DNN architectures. Although the predictions from the sampled DNN parameters can be parallelized, the computational cost still scales linearly with the number of samples (similar to an ensemble). Last layer HMC (LL-HMC) reduces the required computation
The paper explores a timely method to make uncertainty estimation in deep neural networks more computationally feasible, addressing a key limitation for wider adoption.
Improved probabilistic modeling in AI systems enhances safety, reliability, and trustworthiness, crucial for critical applications and regulatory environments.
This research provides a pathway for wider application of more robust uncertainty quantification methods in deep learning, potentially making AI predictions more explainable and reliable.
- · AI safety researchers
- · High-stakes AI applications
- · Cloud computing providers
- · AI developers
- · AI systems without robust uncertainty quantification
Last layer HMC reduces the computational cost of robust uncertainty estimation in deep neural networks.
This improved efficiency could accelerate the adoption of probabilistic AI systems in fields requiring high-confidence predictions.
More reliable AI models might reduce deployment risks and foster greater public trust in autonomous decision-making systems.
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Read at arXiv cs.AI