Parallel Learning of Large Fuzzy Cognitive Maps
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fuzzy cognitive maps (FCMs) are a class of discrete-time artificial neural networks that are used to model dynamic systems. A recently introduced supervised learning method, which is based on real-coded genetic algorithm (RCGA), allows learning high-quality FCMs from historical data. The current bottleneck of this learning method is its scalability, which originates from large continuous search space (of quadratic size with respect to the size of the FCM) and computational complexity of genetic optimization. To this end, the goal of this paper is to explore parallel nature of genetic algorithms to alleviate the scalability problem. We use the global single-population master-slave parallelization method to speed up the FCMs learning method. We investigate the influence of different hardware architectures on the computational time of the learning method by executing a wide range of synthetic and real-life benchmarking tests. We analyze the quality of the proposed parallel learning method in application to both dense and sparse large FCMs, i.e. maps that consist of several dozens of concepts. The parallelization is shown to provide substantial speed-ups, allowing doubling the size of the FCM that can be learned by parallelization with 8 processors.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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