<title>Adaptive structure feed-forward neural networks using polynomial activation functions</title>
Bibliographic record
Abstract
In cascade-correlation (CC) and constructive one-hidden- layer networks, structural level adaptation is achieved by incorporating new hidden units with identical activation functions one at a time into the active evolutionary net. Functional level adaptation has not received considerable attention, since selecting the activation functions will increase the search space considerably, and a systematic and a rigorous algorithm for accomplishing the search will be required as well. In this paper, we present a new strategy that is applicable to both the fixed structure as well as the constructive network trainings by using different activation functions having hierarchical degrees of nonlinearities, as the constructive learning of a one- hidden-layer feed-forward neural network (FNN) is progressing. Specifically, the orthonormal Hermite polynomials are used as the activation functions of the hidden units, which have certain interesting properties that are beneficial in network training. Simulation results for several noisy regression problems have revealed that our scheme can produce FNNs that generalize much better than one-hidden-layer constructive FNNs with identical sigmoidal activation functions, in particular as applied to rather complicated problems.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".