Developing a novel recurrent neural network architecture with fewer parameters and good learning performance
Bibliographic record
Abstract
Recurrent neural networks (RNNs) are among the most promising of the many artificial intelligence techniques now under development, showing great potential for memory, interaction, and linguistic understanding. Among the more sophisticated RNNs are long short-term memory (LSTM) and gated recurrent units (GRUs), which emulate animal brain behavior; these methods yield superior memory and learning speed because of the excellent core structure of their architectures. In this study, we attempted to make further improvements in core structure and develop a novel, compact architecture with a high learning speed. We stochastically generated 30000 RNN architectures, evaluated their performance, and selected the one most capable of memorizing long contexts with relatively few parameters. This RNN, YamRNN, had fewer parameters than LSTM and GRU by a factor of two-thirds or better and reduced the time required to achieve the same learning performance on a sequence classification task as LSTM and GRU by 80% at maximum. This novel RNN architecture is expected to be useful for addressing problems such as predictions and analyses on contextual data and also suggests that there is room for the development of better architectures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".