Siamese Neural Network-Based Supervised Slow Feature Extraction for Soft Sensor Application
Bibliographic record
Abstract
Slow feature analysis (SFA) is an unsupervised learning method that extracts the latent variables from a time series dataset based on the temporal slowness aspect. Neural networks, owing to their ability to model complex nonlinearities, can be used to extract slow features (SFs) from a dataset obtained from a complicated process. Siamese neural networks can be used for this purpose. Siamese neural networks have a provision of handling two samples at a time and this aspect helps in extracting SFs. For supervised learning applications, the extracted SFs should help predict the outputs. In this article, we present two approaches that extract SFs using Siamese neural networks. The output relevance aspect is brought into feature extraction as a regularization term in the objective function of the Siamese neural network. Such regularization improves the performance of the neural network model. The proposed approaches are implemented on three datasets to demonstrate their effectiveness.
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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 | 0.000 |
| 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 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".