Fisherman learning algorithm of the SOM realized in the CMOS technology
Why this work is in the frame
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Bibliographic record
Abstract
This study presents an idea of transistor level realization of the sherman learning algorithm of Self-Organizing Maps (SOMs) which is described in (4). The realization of this algorithm in hardware calls for a solution of several specic problems not present in software implementa- tion. The main problem is related to an iterative nature of the adaptation process of the neighboring neurons positioned at particular rings surround- ing the winning neuron. This makes the circuit structure of the SOM very complex. To come up with a feasible realization, we introduce some mod- ications to the original sherman algorithm. Detailed simulations of the software model of the SOM show that these modications do not have the negative impact on the learning process, and helps bring signicant reduc- tion of the circuit complexity. In consequence, a fully parallel adaptation of all neurons is possible, which makes the SOM very fast.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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