Zheng Zhao, Jennifer Andersson
We lifted 5 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| zgbkdlm/diffres | canonical | 0 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| energy | Not yet run | zgbkdlm/diffres/demos/two_rings.py pointer only (licence: MPL-2.0) · get_code("9e457c09d2d09e8e") |
| kf_pred | Not yet run | zgbkdlm/diffres/diffres/gaussian_filters.py pointer only (licence: MPL-2.0) · get_code("1531bcaadf796cbc") |
| logpdf_likelihood | Not yet run | zgbkdlm/diffres/demos/two_rings.py pointer only (licence: MPL-2.0) · get_code("efa11d55cd5871f8") |
| logpdf_prior | Not yet run | zgbkdlm/diffres/demos/two_rings.py pointer only (licence: MPL-2.0) · get_code("28bf86a3292f9089") |
| nnx_load | Not yet run | zgbkdlm/diffres/diffres/nns.py pointer only (licence: MPL-2.0) · get_code("b03551eda9bbdcef") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper is concerned with differentiable resampling in the context of sequential Monte Carlo (e.g., particle filtering). Drawing on reparametrisation, we propose a new resampling method that is informative and instantly differentiable, based on a training-free diffusion model surrogate. We theoretically prove that our diffusion resampling method provides a consistent resampling distribution, and we show empirically that it outperforms the state-of-the-art differentiable resampling methods on multiple filtering and parameter estimation benchmarks. Finally, we show that it achieves competitive end-to-end performance when used in learning a complex dynamics-decoder model with high-dimensional image observations.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2512.10401")
get_code_for_paper("2512.10401")
have("2512.10401")
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