A Cooperative Recurrent Neural Network Algorithm for Parameter Estimation of Autoregressive Signals
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Bibliographic record
Abstract
A cooperative recurrent neural network (CRNN) algorithm for parameter estimation of autoregressive (AR) signals is proposed in this paper. The proposed CRNN algorithm is based on a generalized least absolute deviation (GLAD) method, which generalizes significantly the conventional least absolute deviation method. Compared with second-order and high-order statistic algorithms, the proposed CRNN algorithm can obtain robustly an optimal AR parameter estimation without requiring measurement Gaussian noise. Unlike existing cooperative neural network algorithms, the proposed CRNN algorithm has a global convergence and a novel weighting cooperation scheme to integrate single neural network output automatically. Simulation results shows that the more accurate estimates can be attained by the proposed CRNN algorithm in the presence of non-Gaussian colored noise.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it