arXiv:2602.06500v2 Announce Type: replace Abstract: Scaling inference methods such as Markov chain Monte Carlo to high-dimensional models remains a central challenge in Bayesian deep learning. A promising recent proposal, microcanonical Langevin Monte Carlo, has shown state-of-the-art performance across a wide range of problems. However, its reliance on full-dataset gradients makes it prohibitively expensive for large-scale problems. This paper addresses a fundamental question: Can microcanonical dynamics effectively leverage mini-batch gradient noise? We provide the first systematic study of
The paper addresses a core challenge in scaling Bayesian deep learning, a field constantly seeking more efficient methods for high-dimensional models.
Improving the efficiency of inference methods like microcanonical Langevin Monte Carlo allows for broader application of advanced AI techniques to large-scale, real-world problems.
The potential to use mini-batch gradients with microcanonical Langevin dynamics could make these powerful AI models more practical for large datasets, lowering computational barriers.
- · AI researchers
- · Big data analytics
- · Cloud computing providers
- · Deep learning practitioners
- · Inefficient MCMC methods
- · Hardware limited users
This research provides a more scalable pathway for Bayesian deep learning applications, potentially reducing compute requirements per model.
Improved efficiency could democratize access to advanced AI modeling for organizations with smaller computational budgets.
Wider adoption of more robust uncertainty-aware AI models could lead to more reliable and trustworthy AI systems across various industries.
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Read at arXiv cs.LG