Data-efficient training of autoencoders for mildly non-linear problems
Bibliographic record
Abstract
Principal Component Analysis (PCA) provides reliable dimensionality reduction (DR) when data possesses linear properties even for small datasets. However, faced with data that exhibits non-linear behaviour, PCA cannot perform optimally as compared to non-linear DR methods such as AutoEncoders. By contrast, AutoEncoders typically require much larger datasets for training than PCA. This data requirement is a critical impediment in applications where samples are scarce and expensive to come by. One such area is nanophotonics component design where generating a single data point might involve running optimization methods that use computationally demanding solvers. We propose Guided AutoEncoders (G-AE), standard AutoEncoders initialized using a numerically stable procedure to replicate PCA behaviour before training. Our results show this approach yields a marked reduction in the data size requirements for training the network along with gains in capturing non-linearity during dimensionality reduction and thus performing better than PCA alone.
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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 | 0.001 | 0.001 |
| 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 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".