A Novel Cooperative Neural Learning Algorithm for Data Fusion
Why this work is in the frame
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Bibliographic record
Abstract
A novel cooperative neural learning (CNL) algorithm based on a new linearly constrained least absolute deviation (LCLAD) method for data fusion is proposed in this paper. The state model of the proposed CNL algorithm combines adaptively three recurrent modular neural networks and is sample for implementation using both software and hardware. Unlike the conventional LAD approach, the propose LCLAD method can obtain the optimal fusion solution. Compared with the minimum variance method and linearly constrained least square method, the proposed LCLAD method can minimize an augmented least absolute deviation energy of the linearly fused information and has the robustness performance in non-Gaussian noise environments. Illustrative examples of signal and image fusion show that the quality of the solution can be more enhanced by the proposed CNL algorithm.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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 it