Accurate and Fast Deep Evolutionary Networks Structured Representation Through Activating and Freezing Dense Networks
Bibliographic record
Abstract
Deep neural networks have been scaled up to thousands of layers with the intent to improve their accuracy. Unfortunately, after some point, doubling the number of layers leads to only minor improvements, while the training difficulties increase substantially. In this article, we present an approach for constructing high-accuracy deep evolutionary networks and train them by activating and freezing dense networks (AFNets). The activating and freezing strategy enables us to reduce the classification error of test and reduce the training time required for deeper dense networks. We activate the layers that are being trained and construct a freezing box to freeze the idle and pretrained network layers in order to minimize memory consumption. The training speed in the early stage is not fast enough because many layers are activated for training. As the epochs gradually increase, the training speed becomes faster and faster since fewer and fewer layers are activated. Our method improves the convergence to the optimal performance within a limited number of epochs. Comprehensive experiments on a variety of data sets show that the proposed model achieves better performance when compared to the other state-of-the-art network models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".