Regularization Strategies for Neural Inverse Problems: A Comparative Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This work explores preliminary approaches to regularization in neuralinverse problems, where extension to neural operator architecturespresents unique theoretical and computational challenges [1, 3]. Wepropose an exploratory framework for hierarchical regularization thatleverages multi-scale structure, building upon classical Tikhonov theorywhile addressing deep learning model requirements [5, 2]. Preliminaryexperiments on small-scale problems suggest potential advantagesin multi-scale synthetic datasets, though computational overhead remainssubstantial and comprehensive validation against state-of-theartmethods is essential [9, 4]. These ideas may motivate future researchin regularization approaches for neural inverse problems whileidentifying critical validation requirements for practical deployment.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 it