Deep learning does not replace Bayesian modeling: Comparing research use via citation counting
Bibliographic record
Abstract
Abstract One could be excused for assuming that deep learning had or will soon usurp all credible work in reasoning, artificial intelligence, and statistics, but like most “meme” class broad generalizations the concept does not hold up to scrutiny. Memes do not generally matter since the experts will always know better; but in the case of Bayesian software like Stan and PyMC3, even their developers and advocates bemoan the apparent dominance of deep learning as manifested in popular culture, breathtaking performance, and most problematically from funding agency peer review that impacts our ability to further advance the field. The facts, however, do not support the assumed dominance of deep learning in science upon closer examination. This letter simply makes the argument by the crudest of possible metrics, citation count, that once the discipline of Computer Science is subtracted, Bayesian software accounts for nearly a third of research citations. Stan and PyMC3 dominate some fields, PyTorch, Keras, and TensorFlow dominate others with lot of variations in between. Bayesian and deep‐learning approaches are related but very different technologies in goals, implementation, and applicability with little actual overlap‐‐so this is not a surprise. For example, deep learning cannot bring the explainability of applied math/statistics and Bayesian methods do not scale to deep‐learning data sets. While deep‐learning behemoths like Facebook and Google use and support Bayesian efforts, the Bayesian packages scientists actually use are academic/volunteer efforts punching far above their weight class, and they need financial support. It would behoove funders to fully understand the impact and role of Bayesian methods in resource allocation.
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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 | 0.063 | 0.447 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.045 | 0.075 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".