Robust training of microwave neural models
Bibliographic record
Abstract
Neural networks recently gained attention as a fast and flexible vehicle to microwave modeling and design. Neural network models can be developed by learning from microwave data, through a process called training. The trained models can be used during microwave design to provide instant answers to the task they learnt. This article addresses certain key challenges in developing RF/microwave neural models. An iterative multistage (IMS) approach including a macro-level process and a stage-level process is proposed. At the macro-level, the IMS decomposes the complicated original task into several simpler subtasks or stages and at the stage-level, the IMS utilizes a variety of neural network structures and effective training techniques, including several existing techniques and a new Huber quasi-Newton (HQN) technique. The proposed HQN allows for the IMS approach to model only smooth portion of the problem behavior in one of the training stages, ignoring sharp/sudden variations. The advantages of the proposed microwave-oriented modeling techniques are demonstrated through examples. © 2002 John Wiley & Sons, Inc. Int J RF and Microwave CAE 12: 109–124, 2002.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".