Shepard Interpolation Neural Networks with K-Means: A Shallow Learning Method for Time Series Classification
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
Deep neural network architectures have redefined benchmark machine learning challenges, from classification to anomaly detection, and have become popular in the time series domain. However, deep learning techniques fall short in time series classification (TSC) because the explainability of deep learning is still abstract, and the training requires vast amounts of data, which utilizes computational power. These obstacles are not the case with Shepard Interpolation Neural Networks (SINN), a shallow learning architecture approach for deep learning tasks. Based on a statistical interpolation technique rather than a biological brain, SINN require little data to achieve high accuracy in its training. Additionally, its explainability can be equated to feature mapping onto hyper surfaces in the feature space. Our proposed algorithm outperforms the other state-of-the-art algorithms on the popular UCR time series classification benchmark data set and outperforms LSTMs on data sets which have significantly smaller training data than testing.
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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 | 0.001 |
| 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