A Robust RLS-DCD Adaptive Algorithm with Variable Regularization Parameter
Why this work is in the frame
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Bibliographic record
Abstract
In adaptive filtering applications, the recursive least-squares (RLS) algorithm represents a benchmark in terms of its fast convergence rate. Among the low-complexity versions of this algorithm, the solution based on the dichotomous coordinate descent (DCD) method, namely RLS-DCD, stands as one of the most practical choices. In order to improve the robustness of the RLS-DCD, a new regularization technique is presented in this paper. The resulting variable regularization parameter of the algorithm takes into account the presence of different types of perturbations, like the external noise and the model uncertainties. Thus, the proposed variable-regularized RLS-DCD algorithm owns improved robustness in noisy conditions and challenging scenarios. Simulation results obtained in the framework of echo cancellation support its performance features.
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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 | 0.000 |
| 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 it