Cooperative Recurrent Neural Networks for the Constrained $L_{1}$ Estimator
Bibliographic record
Abstract
The constrained least absolute deviation (L1) estimator is an attractive alternative to both the unconstrained L1estimator and the least-square estimator. This paper introduces a constrained L1method and proposes two cooperative recurrent neural networks (CRNNs) for the constrained L1estimator. Unlike existing cooperative neural networks, the proposed two CRNNs have a novel weighting cooperation scheme to integrate individual neural network information automatically. As a special case, the proposed continuous-time CRNN includes the existing continuous- time neural network for unconstrained L1estimator. Compared with existing continuous-time neural networks for the constrained L1estimator, the proposed continuous-time CRNN has a lower model complexity and the finite-time convergence to the exact optimal solution without any additional condition. Furthermore, compared with existing numerical algorithms for the constrained L1estimator, in addition to a low computational complexity, the proposed two CRNNs are suitable for parallel implementation and can deal with the L1estimation problem with degeneracy. The proposed two CRNNs are applied to parameter estimation problems under non-Gaussian noise environments. Simulation results demonstrate that the proposed CRNNs are indeed effective in dealing with the L1estimation problem with nonunique solutions and in obtaining a better solution than relevant algorithms.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".