MonolithNet: Training monolithic deep neural networks via a partitioned training strategy
Bibliographic record
Abstract
In this study, we explore the training of monolithic deep neural net-works in an effective manner. One of the biggest challenges withtraining such networks to the desired level of accuracy is the dif-ficulty in converging to a good solution using iterative optimizationmethods such as stochastic gradient descent due to the enormousnumber of parameters that need to be learned. To achieve this,we introduce a partitioned training strategy, where proxy layersare connected to different partitions of a deep neural network toenable isolated training of a much smaller number of parametersto convergence. To illustrate the efficacy of this training strategy,we introduce MonolithNet, a massive residual deep neural networkconsisting of 437 million parameters. The trained MonolithNet wasable to achieve a top-1 accuracy of 97% on the CIFAR10 imageclassification dataset, which demonstrates the feasibility of the pro-posed training strategy for training monolithic deep neural networksto high accuracies.
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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 | 0.001 | 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 | 0.001 |
| 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 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".