Ising-Dropout: A Regularization Method for Training and Compression of\n Deep Neural Networks
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Bibliographic record
Abstract
Overfitting is a major problem in training machine learning models,\nspecifically deep neural networks. This problem may be caused by imbalanced\ndatasets and initialization of the model parameters, which conforms the model\ntoo closely to the training data and negatively affects the generalization\nperformance of the model for unseen data. The original dropout is a\nregularization technique to drop hidden units randomly during training. In this\npaper, we propose an adaptive technique to wisely drop the visible and hidden\nunits in a deep neural network using Ising energy of the network. The\npreliminary results show that the proposed approach can keep the classification\nperformance competitive to the original network while eliminating optimization\nof unnecessary network parameters in each training cycle. The dropout state of\nunits can also be applied to the trained (inference) model. This technique\ncould compress the network in terms of number of parameters up to 41.18% and\n55.86% for the classification task on the MNIST and Fashion-MNIST datasets,\nrespectively.\n
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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 | 0.000 |
| 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 it