A Hierarchical Nonparametric Bayesian Model Based on Scaled Dirichlet Distribution
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
Data clustering is one of the principle unsupervised learning methods in various domains of science. In this paper, we propose a new clustering approach based on hierarchical Dirichlet processes of scaled Dirichlet distribution. Our motivation is flexibility of this distribution which provides a good potential to fit non-Gaussian data. Also, some issues such as the high costs of labeling medical data and sensitivity in this domain encouraged us to construct an unsupervised learning algorithm. We applied batch and online variational inference to learn our model as both methods can estimate model parameters and complexity, simultaneously. To demonstrate the capability of our proposed model, we tested our framework on two applications related to Internet of Health Things (IoHT) and computer-assisted diagnosis (CAD). Our proposed model demonstrates comparable results to similar alternatives.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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