Mathematics-Inspired Models: A Green and Interpretable Learning Paradigm for Multimedia Computing
Bibliographic record
Abstract
The advances of machine learning (ML), and AI in general, have attracted unprecedented attention in intelligent multimedia computing and many other fields. However, due to the concern for sustainability and black-box nature of ML models, especially deep neural networks (DNNs), green and interpretable learnings have been extensively studied in recent years, despite suspicions on effectiveness, subjectivity of interpretability, and complexity. To address these concerns and suspicions, this article starts with a survey on recent discoveries in green learning and interpretable learning and then presents mathematics-inspired (M-I) learning models. We will demonstrate that the M-I models are green in nature with numerous interpretable properties. Finally, we present several examples in multi-view information computing on both static image-based and dynamic video-based tasks to demonstrate that the M-I methodology promises a plausible and sustainable path for natural evolution of ML, which is worth further investment in.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".