Bibliographic record
Abstract
Particle flow filters, introduced in a series of papers by Daum and Huang, are an attractive alternative to particle filters for filtering tasks in high-dimensional spaces or with very informative measurements. Many variants of particle flow filters have been developed, but all require approximations in multiple stages of the implementation, which leads to particles deviating from the true posterior distribution. To preserve the statistical consistency of the filtering algorithm, some recent papers embed the particle flow techniques within a particle filter, using them to generate a proposal distribution. In recent work, we developed such a particle flow particle filter, modifying the flow mechanism to ensure that the implemented, approximate flow was an invertible mapping. This property allows efficient computation of the importance weights. In this paper, we strive to reduce the computational overhead of the particle flow particle filter by incorporating clustering of the particles. Results from a multi-target acoustic tracking simulation demonstrate that we can significantly reduce the computational cost of particle flow particle filters with a relative small sacrifice in tracking accuracy.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".