Bayesian-Assisted Multilayer Neural Network Structure Adaptation Method for Microwave Design
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
Automated model generation (AMG) is a systematic and efficient method to develop an artificial neural network (ANN) model for microwave components. By incorporating the Bayesian theory into AMG, the most compact multilayer perceptron (MLP) model with the highest accuracy can be developed. However, the existing Bayesian-based AMG method is only suitable for single-hidden-layer MLP. In this letter, we propose a novel Bayesian-assisted AMG method for neural networks with multiple hidden layers. A systematic algorithm is proposed to determine the optimal number of hidden layers and the number of hidden neurons in each hidden layer. Using the proposed method, a compact and accurate MLP model with a multi-hidden layer structure can be systematically developed. The proposed method can achieve a higher model accuracy than the previous Bayesian-based method with almost the same number of model network parameters. Two microwave filters are used to demonstrate the advantages of the proposed method.
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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 | 0.001 |
| 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