Bibliographic record
Abstract
Introduces smart neural nets that learn fast with ease by regular backpropagation. This is achieved by avoiding the use of the sigmoid non-linear function driven conventional or Socratic neurons, and choosing the neurons of the hidden layers and the output layer appropriately. To develop the smart neural nets, the authors introduce what they call 'the smart neurons' and 'the intelligent neurons' that have the underpinning of 'fuzzy thinking' or 'deBono thinking'. The intelligent neurons are obtained by introducing the non-emotional innovation feedback into the smart neurons. The intelligent neurons asymptotically become the same as the smart neurons. The smart neural nets are constructed by using the smart neurons and intelligent neurons. The smart neurons alone are employed to form the hidden layer (or layers) of the smart neural net. The output layer of the smart neural net is constructed by using the intelligent neurons alone. The authors compare the performance of the smart neural nets against that of the conventional neural nets toward the regular innovation backpropagation learning. Unlike the conventional neural nets, the smart neural nets seem to learn fast and smoothly by the regular innovation backpropagation learning. Further, the sigmoid non-linear function driven conventional or Socratic neurons are not essential to build feedforward neural nets. In fact, much more efficient and fast learning neural nets can be built by avoiding the conventional or Socratic neurons.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".