Nonlinear Slow Feature Analysis for Oscillating Characteristics Under Deep Encoder-Decoder Framework
Why this work is in the frame
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Bibliographic record
Abstract
Slow feature analysis aims to linearly transform measured data into uncorrelated signals that vary from slow to fast. While earlier extensions successfully extracted slow features from nonlinear sequential data, they lacked a modeling preference for nonstationary and oscillating features due to constraints on the prior distribution. To address this limitation, a semisupervised encoder-decoder architecture is proposed in this article, integrating a statistical preference for such characteristics. This regularization is achieved by introducing a first-order autoregressive Gaussian prior within a regular variational auto-encoder framework, as opposed to the standard Gaussian distribution. The evidence lower bound associated with the proposed model is derived using the variational Bayesian inference, and the model parameters are estimated iteratively. The effectiveness of the proposed approach is evaluated on both simulated and real industrial processes.
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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 | 0.001 |
| 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