Summary of A Prediction Rigidity Formalism For Low-cost Uncertainties in Trained Neural Networks, by Filippo Bigi et al.
A prediction rigidity formalism for low-cost uncertainties in trained neural networks
by Filippo Bigi, Sanggyu Chong, Michele Ceriotti, Federico Grasselli
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 “prediction rigidities” method provides a framework for quantifying uncertainty in pre-trained regressors, which is crucial for many scientific and technological applications. By solving a constrained optimization problem, the approach obtains uncertainties for arbitrary pre-trained regressors, establishing a strong connection to Bayesian inference. The method can be applied to neural networks without modifying their architecture or training procedure, thanks to a last-layer approximation. The effectiveness of “prediction rigidities” is demonstrated on various regression tasks, from simple toy models to applications in chemistry and meteorology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to make predictions more reliable by understanding how uncertain they are. Right now, most prediction models can’t tell us how sure they are about their answers outside of the data they were trained on. This makes it hard to use these models for important decisions or applications. The new “prediction rigidities” method helps solve this problem by finding a way to calculate uncertainty without changing the original model or its training process. It works for simple and complex predictions, including ones used in chemistry and meteorology. |
Keywords
* Artificial intelligence * Bayesian inference * Optimization * Regression
