Bibliographic record
Abstract
In Approximation Theory, the fundamental problem is to reconstruct a signal A ∈ Rn from linear measurements 〈A, ψi 〉 with respect to a dictionary Ψ for Rn. Recently, there has been tremendous excitement about the novel direction of Compressed Sensing [10] where the reconstruction can be done with very few — Õ(k)—linear measurements over a modified dictionary Ψ ′ if the information of the signal is concentrated in k coefficients over an orthonormal basis Ψ. These results have reconstruction error on any given signal that is optimal with respect to a broad class of signals. In a series of papers and meetings over the past year, a theory of Compressed Sensing has been developed by mathematicians. We develop an algorithmic perspective for the Compressed Sensing problem, showing that Compressed Sensing results resonate with prior work in Group Testing, Learning theory and Streaming algorithms. Our main contributions are new algorithms that present the most general results for Compressed Sensing with 1 + ɛ approximation on every signal, faster The dictionary Ψ denotes an orthonormal basis for Rn, i.e. Ψ is a set of n real-valued vectors
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".