A Survey of Ensemble Methods for Mitigating Memristive Neural Network Non-idealities
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Bibliographic record
Abstract
In this work, ensemble methods are presented and tested as universal ways to improve the performance of Mem-ristive Deep Neural Networks (MDNNs) with non-idealities. The Generalized Ensemble Method and Weighted Voting ensemble methods improve the accuracy of classification on the MNIST dataset by 6.5% and 6.6% respectively, thus showing that they are more effective than basic Ensemble Averaging which has been investigated before, as well as other methods such as Voting. Different weighting schemes for Weighted Voting were tested, and we present Algorithm 1 and 2, which are the theoretically and experimentally optimal weighting schemes respectively. Our work serves as a guideline for choosing ensemble methods for MDNNs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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