Universal approximation results for the temporal restricted Boltzmann machine and the recurrent temporal restricted Boltzmann machine
Why this work is in the frame
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Bibliographic record
Abstract
The Restricted Boltzmann Machine (RBM) has proved to be a powerful tool in machine \nlearning, both on its own and as the building block for Deep Belief Networks (multi-layer \ngenerative graphical models). The RBM and Deep Belief Network have been shown to be \nuniversal approximators for probability distributions on binary vectors. In this paper we \nprove several similar universal approximation results for two variations of the Restricted \nBoltzmann Machine with time dependence, the Temporal Restricted Boltzmann Machine \n(TRBM) and the Recurrent Temporal Restricted Boltzmann Machine (RTRBM). We show \nthat the TRBM is a universal approximator for Markov chains and generalize the theorem \nto sequences with longer time dependence. We then prove that the RTRBM is a universal \napproximator for stochastic processes with nite time dependence. We conclude with a \ndiscussion on e ciency and how the constructions developed could explain some previous \nexperimental results.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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