Derivation of momentum LMS algorithms by minimizing objective functions
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Bibliographic record
Abstract
The momentum least-mean-squares (m-LMS) algorithm is extensively used in neural network and signal processing applications, and is an arbitrary extension to the LMS algorithm. It is shown that several different versions of the m-LMS algorithm can be obtained by minimizing different objective functions. It appears that the minimization of weighted average square error function and the weighted accumulated square error function leads to two widely used m-LMS algorithms. The minimization of the weighted average square error function also provides two new versions of the m-LMS algorithm. These old and new versions of the m-LMS algorithm are applied to a parameter estimation problem. From the results, it is found that the new versions of the m-LMS algorithm provide smaller variance of the parameter estimates.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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Full frame distilled prediction
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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