Conditional Restricted Boltzmann Machines for Structured Output\n Prediction
Why this work is in the frame
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Bibliographic record
Abstract
Conditional Restricted Boltzmann Machines (CRBMs) are rich probabilistic\nmodels that have recently been applied to a wide range of problems, including\ncollaborative filtering, classification, and modeling motion capture data.\nWhile much progress has been made in training non-conditional RBMs, these\nalgorithms are not applicable to conditional models and there has been almost\nno work on training and generating predictions from conditional RBMs for\nstructured output problems. We first argue that standard Contrastive\nDivergence-based learning may not be suitable for training CRBMs. We then\nidentify two distinct types of structured output prediction problems and\npropose an improved learning algorithm for each. The first problem type is one\nwhere the output space has arbitrary structure but the set of likely output\nconfigurations is relatively small, such as in multi-label classification. The\nsecond problem is one where the output space is arbitrarily structured but\nwhere the output space variability is much greater, such as in image denoising\nor pixel labeling. We show that the new learning algorithms can work much\nbetter than Contrastive Divergence on both types of problems.\n
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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