A new acceleration technique for the backpropagation algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An adaptive momentum algorithm which can update the momentum coefficient automatically in every iteration step is presented. The basic idea comes from the optimal gradient method. It is very difficult to obtain the optimal gradient vector by analytical methods, but it can be proven that the optimal gradient vectors in two successive iteration steps are orthogonal. Based on this property, one can use the Gram-Schmidt orthogonalization method to ensure the orthogonality of the successive gradient vectors. The result of this process is equivalent to adding a momentum term to the standard backpropagation algorithm. The momentum coefficient is updated automatically in every iteration. Numerical simulations show that the adaptive momentum algorithm can eliminate possible divergent oscillations during the initial training, and can also accelerate the learning process and result in a lower error when the final convergence is reached.< <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
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