State initialization for recurrent neural network modeling of time-series data
Bibliographic record
Abstract
To use a Recurrent Neural Network (RNN) for time series modeling, it is essential to properly initialize the network, that is, to set the hidden neuron outputs properly at the initial time. Normally, an RNN is initialized with zero state values or at steady state. In the context of dynamic system identification, such initializations imply the system to be modelled is in steady state, i.e., capturing transient behaviour of the system is difficult if the network states are not properly initialized. If the network initial states are not calculable from the training data, then a method to infer them, both throughout the training and validation phases, is needed. In this paper, we use a feed forward neural network to initialize a structurally deep recurrent neural network in learning and multi-step prediction of the altitude of a real quadrotor vehicle. To the best of our knowledge, this is the first time a neural network has outperformed a physics based model for multi-step time series prediction from recorded quadrotor flight data.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".