Summary of Sampling From Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics, by Daniel Paulin et al.
Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics
by Daniel Paulin, Peter A. Whalley, Neil K. Chada, Benedict Leimkuhler
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Numerical Analysis (math.NA); Probability (math.PR); Computation (stat.CO); Methodology (stat.ME)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed scalable kinetic Langevin dynamics algorithm efficiently samples large parameter spaces in big data and AI applications. By combining a symmetric forward/backward sweep over minibatches with a symmetric discretization of Langevin dynamics, the scheme achieves excellent control of sampling bias as a function of stepsize. The Symmetric Minibatch Splitting-UBU (SMS-UBU) integrator demonstrates bias O(h^2 d^(1/2)) in dimension d>0 with stepsize h>0. The algorithm is applied to explore local modes of the posterior distribution of Bayesian neural networks and evaluate calibration performance on three datasets: Fashion-MNIST, Celeb-A, and chest X-ray. Results show that BNNs sampled with SMS-UBU can offer significantly better calibration performance compared to standard methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to sample large spaces in big data and AI applications. It’s like a special tool that helps find the best settings for complex models. The algorithm is designed to work well even when there are many variables to consider. The researchers tested this approach on three different datasets and found that it can make predictions more accurate. |
