Summary of Uncertainty Quantification For Deep Learning, by Peter Jan Van Leeuwen and J. Christine Chiu and C. Kevin Yang
Uncertainty Quantification for Deep Learning
by Peter Jan van Leeuwen, J. Christine Chiu, C. Kevin Yang
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 A novel approach to deep learning uncertainty quantification is presented, accounting for four sources of uncertainty: new input data, training and testing data, weight vectors, and the neural network itself. The method leverages Bayes’ theorem and conditional probability densities to systematically quantify each source of uncertainty. A practical way to combine these errors is also introduced. To illustrate the technique’s effectiveness, it is applied to cloud autoconversion rate prediction using aircraft-measured data from the Azores and a two-moment bin model. The results show that uncertainty in training and testing data dominates, followed by input data, neural network, and weight vector uncertainties. This methodology has practical implications for machine learning applications, particularly when incorporating uncertainty into training data makes models less sensitive to out-of-distribution inputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to measure how sure we are about the predictions made by deep learning models is introduced. This method takes into account four different sources of uncertainty: what’s new in the input data, how well the model was trained and tested, the weights used by the model, and how good the model is at making predictions. The approach uses special formulas to calculate each source of uncertainty and combines them all together. To show how this works, it’s applied to predicting cloud conditions using data from aircraft measurements in the Azores. This new method can be useful for machine learning because it helps us understand when our models might not work well with certain types of data. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Neural network » Probability
