A study on resistive-type truncated CVNS Distributed Neural Networks
Why this work is in the frame
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Bibliographic record
Abstract
Distributed Neural Networks (DNNs) are generally providing self-scaling property together with higher noise immunity for resistive-type neural networks. Continuous Valued Number System (CVNS) is a potential candidate to build the DNNs; however, implementation of a CVNS digit in its complete form needs a high resolution environment which is not practical. Truncation methods are applied to CVNS digits to make them adaptable to the low resolution environments. However, truncated CVNS operations may decrease the accuracy and immunity to noise compared to the complete CVNS operations. In this work, a truncated CVNS DNN is proposed, and studies over Noise to Signal Ratio (NSR) and accuracy are provided. Studies show that the accuracy is acceptable, and the NSR is still less than the NSR of conventional DNNs.
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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 | 0.001 | 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