Conditional Restricted Boltzmann Machines for Structured Output\n Prediction
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
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".