Summary of Unified Uncertainties: Combining Input, Data and Model Uncertainty Into a Single Formulation, by Matias Valdenegro-toro and Ivo Pascal De Jong and Marco Zullich
Unified Uncertainties: Combining Input, Data and Model Uncertainty into a Single Formulation
by Matias Valdenegro-Toro, Ivo Pascal de Jong, Marco Zullich
First submitted to arxiv on: 26 Jun 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 The paper proposes a method to propagate uncertainty in inputs through neural networks, which can estimate input, data, and model uncertainty simultaneously. This approach shows more stable decision boundaries under input noise compared to Monte Carlo sampling. The authors also discuss how input uncertainty affects output uncertainty, highlighting the importance of considering input uncertainty in machine learning models for reliable predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making sure that machine learning models are not just correct but also trustworthy by accounting for the uncertainty in their inputs. This is important because even small mistakes in the data used to train a model can affect its performance and make it unreliable. The researchers developed a new way to do this, which involves using a special type of neural network that can deal with uncertainty at multiple levels. They tested their approach and found that it works well, even when there is a lot of noise in the input data. This could be useful in situations where you know how much uncertainty is present in your data. |
Keywords
» Artificial intelligence » Machine learning » Neural network
