Iterative deblending with robust Fourier thresholding
Bibliographic record
Abstract
The present piece offers a generalized view of the classic Fourier thresholding, which could be derived in two ways. One of these corresponds to an `l2 −`l0 regularization subproblem with Fourier sensing matrices. In the case of non-Gaussian noise, such as blending noise, one can generalize it by adopting alternative robust misfit terms, and solve it using iterative hard thresholding algorithms. In this way, one imposes sparsity in the transform that describes the data and robustness on the data itself. In doing so, this paper also illustrates how iterative deblending can be optimized using robust projection operators. Such denoisers provide strong blending noise attenuation at the early stages of iterative deblending, thereby improving its convergence rates. The above could be illustrated using a numerically blended dataset. Presentation Date: Wednesday, October 14, 2020 Session Start Time: 9:20 AM Presentation Time: 10:35 AM Location: Poster Station 13 Presentation Type: Poster
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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".