Chaotic simulated annealing in multilayer feedforward networks
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Bibliographic record
Abstract
This paper presents a method of chaotic simulated annealing for avoiding and subsequently escaping from local minima in the training of multilayer feedforward neural networks. A modified form of the standard simulated annealing algorithm is implemented using both Gaussian random numbers and various types of strange chaotic attractors for perturbation of network weight parameters. Specifically, the attractor generated by the logistic equation, Henon's (1976) attractor Rossler's attractor, and the Lorenz attractor are used at different initial conditions and parametric variations for chaotic perturbations. The variance fractal dimension is used as a quantitative measure of the geometric properties of the strange chaotic attractors. It is shown that, for this application, chaotic simulated annealing using the logistic equation is up to 600 percent faster than conventional simulated annealing with Gaussian random numbers.
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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 | 0.001 |
| 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