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arXiv Machine Learning By Fredrik Cumlin, Saikat Chatterjee

Differentiable Systematic Resampling for Variational Sequential Monte Carlo

Read the original on arXiv Machine Learning →

The paper introduces Differentiable Systematic Resampling (DSR), a temperature-controlled relaxation of systematic resampling that maintains the CDF-ordered, banded structure while allowing full gradient flow. DSR converges to exact systematic resampling as temperature approaches zero and the authors prove a pointwise exponential convergence rate for the induced bias. Compared to optimal-transport-based differentiable resampling, DSR avoids iterative solvers and has substantially lower computational overhead, achieving comparable or superior filtering and dynamics learning performance on stochastic dynamical systems and real-world handwriting data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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