Bayesian model selection for deep exponential families
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
In their article "Deep Exponential Families" , Ranganath, Tang, Charlin and Blei (2014) introduce deep exponential families (DEFs), a special type of hierarchical models under which the layers of latent variables are linked through their canonical parameters. The goal of this thesis is to provide an ecient model selection technique for DEFs. The focus has been set on multinomial-like datasets generated from a Poisson DEFs which are analogous to classication problems. By using Markov Chain Monte Carlo sampling, we are able to look at Bayesian and frequentists predictive measures to achieve model selection. Finally, to assess the need for more complex systems, counts generated from a mixture of multinomials are studied under both the regular topic model approach and the DEFs modeling.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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