Summary of Variational Bayesian Last Layers, by James Harrison et al.
Variational Bayesian Last Layers
by James Harrison, John Willes, Jasper Snoek
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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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 This paper introduces a novel deterministic variational approach to train Bayesian last-layer neural networks, offering a sampling-free, single-pass model and loss that enhances uncertainty estimation. The proposed Variational Bayesian Last Layer (VBLL) can be trained and evaluated with nearly quadratic complexity in the last layer width, making it computationally efficient for standard architectures. Experimental results demonstrate improved predictive accuracy, calibration, and out-of-distribution detection over baselines in both regression and classification tasks. Furthermore, combining VBLL layers with variational Bayesian feature learning yields a lower-variance collapsed variational inference method for Bayesian neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to make Bayesian last-layer neural networks work better. It does this by introducing a new approach that doesn’t need sampling and can be used on top of regular neural networks. The new method, called VBLL, helps predict things more accurately and makes it easier to detect when something is outside the normal range. The authors tested VBLL on different tasks and found that it works better than other methods in both predicting numbers and classifying things. They also showed that combining VBLL with another technique can make Bayesian neural networks even better. |
Keywords
» Artificial intelligence » Classification » Inference » Regression
