Analysis of Different Deep Learning Architectures to Learn Generalised Classifier Stacking on Riemannian and Grassmann Manifolds
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Bibliographic record
Abstract
This paper considers different deep learning architectures to learn patterns that are objects lying on the Riemannian and Grassmann manifolds. Among them, we considered cascades of classifier ensembles (CCEs), convolutional neural networks (CNNs), and deep neural forests (DNFs). All aforementioned architectures have linearized and nonlinearized versions. Patterns that are objects of Riemannian manifolds are classifier prediction pairwise matrices (CPPMs) while objects of the Grassmann manifolds are obtained using decision profiles (DPs). We also compared our architectures with CCEs that operate in the Euclidean geometry. As seen from the experimental results deep learning architectures based on CNNs provided the best results.
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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 | 0.001 | 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) | 0.014 | 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