Performing Complex Associations Using a Feature-Extracting Bidirectional Associative Memory
Bibliographic record
Abstract
Learning in Bidirectional Associative Memory (BAM) is typically based on Hebbian-type learning. Since Kosko’s paper on BAM in late 80s many improvements have been proposed. However, none of the proposed modifications allowed BAM to perform complex associative tasks that combine many-to-one with one-to-many associations. Even though BAMs are often deemed more plausible biologically, if they are not able to solve such mappings they will have difficulties establishing themselves as good models of cognition. This paper presents a BAM that can perform complex associations using only covariance matrices. It will be shown that this network can be trained to learn both the 2 and 3-bit parity problem and that the performance is increase by using a committee machine. The conditions that provide optimal learning performance within this latter network framework are explored along with some of its dynamical properties. The model is able to perform the nonlinear separation tasks while maintaining its properties.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".