Adaptive Chaotic Injection to Reduce Overfitting in Artificial Neural Networks
Bibliographic record
Abstract
Artificial neural networks (ANNs) have become an integral tool in various fields of research. ANNs are mathematical models which can be trained to perform various prediction tasks. The effectiveness of an ANN can be impacted by overfitting which occurs when the ANN overfits to the training data. As a result, the ANN does not generalize well to novel data. In our research, we assess the feasibility of using a chaotic strange attractor to generate sequences of values to inject into an ANN to reduce overfitting. An adaptive method was developed to scale and inject the values into the neurons throughout training. The chaotic injection (ChI) was tested on three benchmark datasets using different ANN models. The results were compared against the baseline ANN, dropout (DO), and Gaussian noise injection (GNI). The ChI improved the performance of the ANN and converged faster than DO and GNI.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".