Sparse tensors and discrete-time nonlinear filtering
Why this work is in the frame
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Bibliographic record
Abstract
In many applications it is desired that discrete-discrete filtering problem can be solved in a reliable and computationally efficient manner. In particular, the signal and measurement models often include nonlinearity and/or non-Gaussian characteristics. In this paper, it is pointed out that this can be done efficiently by noting two key observations. Firstly, the bulk of the computations associated with the ldquopredictionrdquo step can be done off-line. The second key point is that the transition probability tensor and the conditional probability density are effectively sparse and so can be efficiently stored and manipulated using sparse tensors. These ideas are crucial for efficiently solving the higher dimensional filtering problems. The resulting technique, termed sparse grid filtering, is demonstrated by some examples, where it is shown that it works very well.
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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 | 0.000 |
| 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