Summary of Sef: a Method For Computing Prediction Intervals by Shifting the Error Function in Neural Networks, By E. V. Aretos and D. G. Sotiropoulos
SEF: A Method for Computing Prediction Intervals by Shifting the Error Function in Neural Networks
by E. V. Aretos, D. G. Sotiropoulos
First submitted to arxiv on: 8 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 new method called SEF (Shifting the Error Function) for quantifying uncertainty in neural network predictions. The approach trains a single neural network three times to generate estimates along with upper and lower bounds for a given problem. This is achieved by calculating a parameter from the initial network’s estimates, which is then integrated into the loss functions of the other two networks. The method is evaluated using synthetic datasets, comparing successful PI generation between SEF, PI3NN, and PIVEN methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to measure uncertainty in neural network predictions. It trains one network three times to get estimates and bounds for a problem. This helps make sure the predictions are reliable before making decisions. The method is tested on some fake data sets to see how well it works compared to other methods. |
Keywords
» Artificial intelligence » Neural network
