Incremental Communication for Adaptive Resonance Theory Networks
Bibliographic record
Abstract
We have proposed earlier the incremental internode communication method to reduce the communication cost as well as the time of the learning process in artificial neural networks (ANNs). In this paper, the limited precision incremental communication method is applied to a class of recurrent neural networks, the adaptive resonance theory 2 (ART2) networks. Simulation studies are carried out to examine the effects of the incremental communication method on the convergence behavior of ART2 networks. We have found that, 7-13-b precision is sufficient to obtain almost the same results as those with full (32-b) precision conventional communication. A theoretical error analysis is also carried out to analyze the effects of the limited precision incremental communication. The simulation and analytical results show that the limited precision errors are bounded and do not seriously degrade the convergence of ART2 networks. Therefore, the incremental communication can be incorporated in parallel and special-purpose very large scale integration (VLSI) implementations of the ART2 networks.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".