Near-Optimal Resampling in Particle Filters Using the Ising Energy Model
Why this work is in the frame
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Bibliographic record
Abstract
Resampling increasing the variance of the tracking algorithm in Particle Filtering (PF). Instead of utilizing resampling procedures that rely on asymptotic convergence properties, we show that intelligently selecting and replicating a set of samples can better represent the posterior approximation and improve the overall performance of the PF. To this end, we formulate the resampling procedure as an integer program that minimizes an upper bound on the Kullback-Leibler divergence (KLD) between the resampled distribution and the posterior approximation. We then transform the problem into an Ising energy minimization problem, which we are able to efficiently solve. Applying our novel paradigm to a challenging sequential importance resampling (SIR) simulation shows faster convergence over the number of resampled particles and a 35% improvement in the median KLD for a fixed number of particles.
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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