Explainable Learning of Long-term Dependencies through Machine Learning
Bibliographic record
Abstract
Machine learning-based models have yielded remarkable results in a wide range of applications, revolutionizing industries over the last few decades. However, a variety of challenges from the technical point of view, such as the drastic increase in model size and complexity, have become a barrier for their portability and human interpretation. This work focuses on enhancing specific machine learning models used in the time-series forecasting domain. The study begins by demonstrating the effectiveness of a simple and interpretable-by-design machine learning model in handling a real-world time-series industry-related problem. This model incorporates new data while dynamically forgetting previous information, thus promoting continuous learning and adaptability laying the groundwork for practical applications within industries where real-time interpretable adaptation is crucial. Then, the well-established LSTM Neural Network, an advanced but less interpretable model able to learn long and more complex time dependencies, is modified to generate a model, named E-LSTM, with extended temporal connectivity to better capture long-term dependencies. Experimental results demonstrate improved performance with no significant increase in model size across various datasets, showcasing the potential to have balance between performance and model size. Finally, a new LSTM architecture built upon the E-LSTM’s increased temporal connectivity while embedded with interpretability is proposed, called Generalized Interpretable LSTM (GI-LSTM). This architecture is designed to offer a more holistic interpretation of its learned long-term dependencies, providing semi-local interpretability by offering insights into the detected relevance across time-series data. Furthermore, the GI-LSTM outperforms alternative models, generally produces smaller models, and shows that performance does not necessarily come at the cost of interpretability.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".