Ising Dropout with Node Grouping for Training and Compression of Deep Neural Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dropout is a popular regularization method to reduce over-fitting while training deep neural networks and compress the inference model. In this paper, we propose Ising dropout with node grouping, which represents a deep multilayer perceptron (MLP) neural network as a graph with fixed grouped nodes and uses the Ising energy to drop group of nodes. This method is an extension to our proposed Ising dropout method, which had the limit of solving the Ising energy model for MLPs with limited graph order. The proposed fixed grouping method enables applying drop-out to deep MLPs with any order. Performance of this method is evaluated on handwritten digits (MNIST), Fashion-MNIST, Free Spoken Digit Dataset (FSDD), and Street View House Numbers (SVHN) datasets and compared with the standard dropout and standout methods. Preliminary results show that the proposed approach can keep the classification performance competitive to the original network while eliminating optimization of unnecessary network parameters in each training cycle. This method can compress the inference model significantly while maintaining the classification performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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