Uncertainty Quantification in Machine Learning Solutions for Near-Field Microwave Imaging
Bibliographic record
Abstract
Three machine-learning-based uncertainty quantification methods for microwave imaging are reviewed and compared. Measurement data from a model of a 2-D near-field microwave imaging chamber is passed to a data-to-image neural network to produce a reconstruction of a target. First, we consider two methods that create an uncertainty map of the reconstructed target. We demonstrate that uncertainty maps correspond well with the true error when the test example is well represented by the targets in the training set, but the maps do not produce a meaningful representation of the true error when the test target lies out-of-range of the training data (e.g. the target is of drastically different shape or physical properties). In the third method, two autoencoder-based approaches (autoencoding the data versus autoencoding the image) are reviewed to demonstrate their ability to detect whether a given test example is in-range for a given model. The value of combining uncertainty maps with autoencoder-based approaches is discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".