PSIJ Transfer Function Response Prediction via NARNET and KBNNs
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
In this paper, nonlinear autoregressive neural network is combined with knowledge-based neural network in order to develop an efficient method that further expands the bandwidth of the jitter transfer function. In the proposed hybrid approach, a knowledge-based neural network is developed using the training data from two types of models: fast-to-evaluate analytical model for jitter transfer function and computationally expensive circuit simulator generated accurate jitter transfer function response. Knowledge-based neural network can efficiently produce relatively accurate prediction of the jitter profile within the desired frequency range, using which a large number of data points is generated. In the next step, nonlinear autoregressive neural network is trained using data obtained from knowledge-based neural network. Proposed nonlinear autoregressive neural network ensures reasonable accuracy even beyond the frequency range of the original accurate data that is used in developing the knowledge-based neural network. A case study with 32nm CMOS technology is presented to demonstrate the validity of the proposed approach compared to a circuit simulator.
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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 | 0.000 |
| 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