Acceleration of back propagation through initial weight pre-training with delta rule
Why this work is in the frame
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Bibliographic record
Abstract
A training strategy for backpropagation (BP) neural networks, named delta pre-training (DPT), is proposed. The core of the training strategy is based on pre-training the initial weights for BP networks using the delta rule, instead of using random values. After pre-training, the normal BP training procedure is carried out to complete network training. With the DPT, the convergence rate for training BP networks can be significantly improved. Since the DPT deals only with initial weight settings, most variations of the standard BP algorithm can be combined with the DPT so as to further speed up convergence. With regards to on-chip learning in VLSI implementations, only a little additional circuitry is required for the pre-training phase with the DPT. Simulation results using the proposed training method show its superiority over previous methods.< <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 | 0.001 |
| 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