A machine learning-based state estimation approach for varying noise distributions
Why this work is in the frame
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Bibliographic record
Abstract
The field of estimation theory is concerned with providing a system with the ability to extract relevant information about the environment, resulting in more effective interaction with the system’s surroundings through more well-informed, robust control actions. However, environments often exhibit high degrees of nonlinearity and other unwanted effects, posing a significant problem to popular techniques like the Kalman filter (KF), which yields an optimal only under specific conditions. One of these conditions is that the system and measurement noises are Gaussian, zero-mean with known covariance, a condition often hard to satisfy in practical applications. This research aims to address this issue by proposing a machine learning-based estimation approach capable of dealing with a wider range of noise types without the need for a known covariance. Harnessing the generative capabilities of machine learning techniques, we will demonstrate that the resultant model will prove to be a robust estimation strategy. Experimental simulations are carried out comparing the proposed approach with other conventional approaches on different varieties of functions corrupted by noises of varying distribution types.
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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) | 0.000 | 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