Structured and Efficient Variational Deep Learning with Matrix Gaussian\n Posteriors
Bibliographic record
Abstract
We introduce a variational Bayesian neural network where the parameters are\ngoverned via a probability distribution on random matrices. Specifically, we\nemploy a matrix variate Gaussian \\cite{gupta1999matrix} parameter posterior\ndistribution where we explicitly model the covariance among the input and\noutput dimensions of each layer. Furthermore, with approximate covariance\nmatrices we can achieve a more efficient way to represent those correlations\nthat is also cheaper than fully factorized parameter posteriors. We further\nshow that with the "local reprarametrization trick"\n\\cite{kingma2015variational} on this posterior distribution we arrive at a\nGaussian Process \\cite{rasmussen2006gaussian} interpretation of the hidden\nunits in each layer and we, similarly with \\cite{gal2015dropout}, provide\nconnections with deep Gaussian processes. We continue in taking advantage of\nthis duality and incorporate "pseudo-data" \\cite{snelson2005sparse} in our\nmodel, which in turn allows for more efficient sampling while maintaining the\nproperties of the original model. The validity of the proposed approach is\nverified through extensive experiments.\n
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".