Exploring the Intersection of Information Theory and Machine Learning
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
This study addresses the need for a unified framework demonstrating Information Theory’s (IT) pervasive impact across diverse Machine Learning (ML) tasks. We investigate how IT principles-including entropy, Mutual Information (MI), cross-entropy, KL-divergence, and Information Gain (IG)-rigorously guide ML model design, optimization, and interpretability. Our approach combines theoretical elucidation with empirical validation on standard benchmarks. IT enhances feature selection; for instance, MI-ranked features in the breast cancer dataset improved classifier accuracy to 95.1% (top 20) and 93% (top 5), outperforming F-score selection. It also improves model training; cross-entropy loss in Neural Networks (NNs) for Iris classification led to faster convergence and high accuracy (0.98 training, 0.95 validation), surpassing MSE loss. For generative models, KL-divergence effectively structures Variational Auto-Encoder (VAE) latent spaces from Modified National Institute of Standards and Technology (MNIST) data, promoting compact, continuous representations ideal for generation. Finally, the Information Bottleneck (IB) principle, applied to Canadian Institute For Advanced Research (CIFAR-100), yielded competitive test accuracy (51% vs. 50% for baseline Convolutional Neural Network. (CNN)) and reduced training time (925.02s vs. 1015.75s), highlighting its efficacy in learning compressed, predictive representations. These findings collectively underscore its continued crucial role as a unifying paradigm for addressing fundamental challenges in the evolving ML ecosystem, providing solutions for feature selection, model robustness, and generalization
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 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