Coded Deep Learning: Framework and Preliminary Results
Bibliographic record
Abstract
Deep learning (DL) often achieves success at the cost of large model sizes and high computational complexity, making training and inference challenging in resource-limited environments. To address this, we introduce coded deep learning (CDL), a framework that integrates information-theoretic coding concepts into DL to compress model weights and activations, reduce computational complexity, and enable efficient model/data parallelism. Specifically, CDL: (i) introduces a probabilistic quantization method for model weights and activations, including a differentiable variant for gradient computation; (ii) executes both forward and backward passes on quantized values, significantly reducing floating-point operations and training complexity; (iii) enforces entropy constraints on weights and activations, ensuring compressibility throughout training and lowering communication costs in distributed settings; and (iv) produces a quantized model by default, reducing post-training inference and storage complexity. Extensive experiments demonstrate that CDL outperforms state-of-the-art DNN compression methods.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".