Comparative analysis of stochastic optimization methods for image classification using convolutional neural networks
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a comparative analysis of several stochastic optimization methods, provides an overview of their advantages and disadvantages, and displays results of comparative experiments, which conducted with simple configurations of convolutional neural network architectures. To perform the experiments, models were trained on the publicly available Canadian Institute For Advanced Research 10 dataset using various stochastic optimization algorithms, and the methods were then examined in terms of convergence speed and the accuracy of the trained model. The results show that the performance of the optimization methods depends on the dataset and the network architecture configuration, good results across different architectures were demonstrated by Adaptive Moment Estimation with Weight Decay Regularization and Stochastic Gradient Descent methods.
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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 | 0.000 |
| 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