A LOCAL-PATCH BASED MULTI-STAGE ARTIFICIAL-NEURAL-NETWORK TRAINING PROCEDURE AND ITS APPLICATION TO MATERIAL CHARACTERIZATION
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A local-patch based multi-stage artificial-neural-network (ANN) training procedure is proposed in this paper, to improve the accuracy of an ANN trained by a backpropagation (BP) algorithm and, at the same time, to reduce the overall training time. In the proposed procedure a conventional one-stage training procedure is split into multiple stages: an initial training stage and subsequent re-training stage(s). In the initial stage the training data are so selected that the trained ANN has adequate ability of generalization, that is, if provided with a set of new input, the ANN can predict the right region where the output is located, but the accuracy of the solution is not necessarily high. In the following re-training stage(s), local patches of training data, either selected from an existing data pool or generated by numerical methods such as finite element method, are used to re-train the ANN to improve the accuracy. Several factors that may have significant effects on the proposed procedure were investigated based on function approximation. As an example of application, the procedure was then used to train an ANN with finite element data to characterize material parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 it