Autoencoder-Augmented Machine-Learning-Based Uncertainty Quantification for Electromagnetic Imaging
Bibliographic record
Abstract
Uncertainty quantification of machine learning (ML) predictions is of key importance for the wide-spread adoption of ML-enabled electromagnetic imaging. As ML inference is a predictive process, providing a best (most likely) guess, supplementing that prediction with quantitative uncertainty can help to avoid costly errors when interpreting the output of a network. In this work, we present a novel two-output-branch neural network architecture that combines the Monte-Carlo Dropout Bayesian Convolutional Neural Network (BCNN) with an autoencoder (AE) to solve the data-to-image inverse problem. The inclusion of the autoencoder branch complements the predicted uncertainty image from the BCNN with a reconstruction of the network input. The data reconstruction (AE) path provides the user with additional information on the quality of the reconstruction, as a failed data reconstruction may be indicative of an out-of-range input, warranting further investigation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".