Uncertainty in recurrent neural network with dropout
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
Recurrent Neural Network is a powerful tool for processing temporal data. However, assessing prediction uncertainty from recurrent models has proven challenging. This thesis attempts to evaluate the validity of uncertainty from recurrent models using dropout. Traditional neural network focuses on optimising data likelihood; in order to obtain model and predictive uncertainty, we need to, instead, optimise model posterior. Model posterior is usually intractable, thus we employ various dropout based approach, in the form of variational Bayesian Monte Carlo, to estimate the learning objective. This technique is applied to existing recurrent neural network benchmarks MIMIC-III. The thesis shows that Monte Carlo dropout applied to recurrent neural network can give comparable performance to the current state of the art methods, and meaningful uncertainty of predictions.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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