Summary of Function Space Diversity For Uncertainty Prediction Via Repulsive Last-layer Ensembles, by Sophie Steger et al.
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles
by Sophie Steger, Christian Knoll, Bernhard Klein, Holger Fröning, Franz Pernkopf
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes a Bayesian inference approach to function space optimization, which addresses the challenges of overparameterization in neural networks. The authors discuss particle optimization and present practical modifications that improve uncertainty estimation and make it applicable for large and pretrained networks. Specifically, they show that input samples with diverse predictions are detrimental to model performance, but label-destroying data augmentation or unlabeled out-of-distribution data can improve prediction diversity and uncertainty estimates. They also propose a single multi-headed network as a minimal increase in parameters and computation compared to full deep ensembles. This approach allows seamless integration with pretrained networks for uncertainty-aware fine-tuning at minimal additional cost. The paper achieves competitive results in disentangling aleatoric and epistemic uncertainty, detecting out-of-domain data, and providing calibrated uncertainty estimates under distribution shifts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how computers can be more certain about what they know or don’t know. It talks about a way to make AI models better at figuring things out and making smart choices. The authors share some new ideas for how to improve these models, like using special kinds of data to help them learn. They also show that their approach works well on big datasets and can even help computers understand when they’re dealing with something outside of what they know. This research is important because it could lead to better AI systems that are more reliable and trustworthy. |
Keywords
» Artificial intelligence » Bayesian inference » Data augmentation » Fine tuning » Optimization
