Enhancing Neural Architecture Search Using Transfer Learning and Dynamic Search Spaces for Global Horizontal Irradiance Prediction
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
The neural architecture search technique is used to automate the engineering of neural network models. Several studies have applied this approach, mainly in the fields of image processing and natural language processing. Its application generally requires very long computing times before converging on the optimal architecture. This study proposes a hybrid approach that combines transfer learning and dynamic search space adaptation (TL-DSS) to reduce the architecture search time. To validate this approach, Long Short-Term Memory (LSTM) models were designed using different evolutionary algorithms, including artificial bee colony (ABC), genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO), which were developed to predict trends in global horizontal irradiation data. The performance measures of this approach include the performance of the proposed models, as evaluated via RMSE over a 24-h prediction window of the solar irradiance data trend on one hand, and CPU search time on the other. The results show that, in addition to reducing the search time by up to 89.09% depending on the search algorithm, the proposed approach enables the creation of models that are up to 99% more accurate than the non-enhanced approach. This study demonstrates that it is possible to reduce the search time of a neural architecture while ensuring that models achieve good performance.
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 | 0.000 | 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 | 0.000 | 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