Recurrent neural network optimisation based on linearly constrained numerical methods
Why this work is in the frame
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Bibliographic record
Abstract
Time-series data analysis has grown even more crucial in many sectors as information technology and big data expand rapidly. This work proposes a recurrent neural network (RNN) optimisation model based on the linear constraint numerical method, namely, LSTM-LP optimiser, which combines the powerful time-series modelling capability of long short-term memory (LSTM) and the optimisation characteristics of linear programming (LP) optimisation features, and so effectively improves the training efficiency and stability of the model in resource-constrained environments. This helps to efficiently capture the temporal dependencies in time-series data and solve the noise and missing problems in the data. On two datasets, experimental results show the LSTM-LP optimiser beats the conventional model in several performance criteria. Future studies will investigate more effective optimisation techniques, increase the generalisation capacity of the model, and simplify the hyperparameter tweaking process to thus further promote the model in practical uses.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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