A new technique for restricted Boltzmann machine learning
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Bibliographic record
Abstract
Over the last decade, deep belief neural networks have been a hot topic in machine learning. Such networks can perform a deep hierarchical representation of input data. The first layer can extract low-level features, the second layer can extract high-level features and so on. In general, deep belief neural network represents many-layered perceptron and permits to overcome some limitations of conventional multilayer perceptron due to deep architecture. In this work we propose a new training technique called Reconstruction Error-Based Approach (REBA) for deep belief neural network based on restricted Boltzmann machine. In contrast to classical Hinton's training approach, which is based on a linear training rule, the proposed technique is based on a nonlinear learning rule. We demonstrate the performance of REBA technique for the MNIST dataset visualization. The main contribution of this paper is a novel view on the training of a restricted Boltzmann machine.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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