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arXiv Machine Learning

Differentiable Systematic Resampling for Variational Sequential Monte Carlo

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.

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arXiv Statistics ML
Oct 23

Automatic Differentiation for Inference on Partially Observed Markov Process Models

The paper introduces a new class of algorithms that embed existing automatic differentiation (AD) particle filter methods, enabling a bias‑variance tradeoff and reducing mean squared error in likelihood estimation for partially observed nonlinear stochastic dynamical systems. Unlike prior approaches, these algorithms only require a differentiable simulator of the latent dynamics, not explicit transition probabilities. Numerical experiments demonstrate that a hybrid method—using AD to refine a coarse solution from iterated filtering—outperforms current state‑of‑the‑art techniques on a challenging scientific benchmark.

By Kevin Tan, Aaron J. Abkemeier, Kunyang He, Giles Hooker, Edward L. Ionides
benchmarkssafety
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arXiv Machine Learning
Sep 2

Efficient Adaptation of ROMs for Unsteady Flows Using Data Assimilation

The paper presents a lightweight retraining strategy for a parameterized Reduced Order Model (ROM) that achieves full‑model accuracy using only a fraction of the computational effort and sparse observations. The ROM architecture combines a Variational Autoencoder for dimensionality reduction with a transformer network that evolves latent states while accounting for the Reynolds number as an external control variable. By leveraging the probabilistic VAE, the method generates trajectory ensembles and uncertainty estimates, and adapts to out‑of‑sample parameters through sparse data assimilation with an ensemble Kalman filter, focusing retraining on the autoencoder to correct latent manifold distortions.

By Isma\"el Zighed, Andrea N\'ovoa, Luca Magri, Taraneh Sayadi
llmsfine-tuning
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arXiv Machine Learning
Sep 24

PR-Smoother: Simulator-Preserving Non-Gaussian Smoothing for Data Assimilation

PR‑Smoother is an amortized smoothing method that preserves the explicit use of a prescribed simulator in both the evidence lower bound and the variational family. It learns only future‑conditioned corrections to the simulator’s rollout, yielding a non‑Gaussian smoothing distribution that can jointly infer state, parameters, and sensor bias from observations alone. The approach recovers the exact smoother in deterministic and linear‑Gaussian limits and has been shown to capture multimodal posteriors in Lorenz‑96 and scale to 16,384‑dimensional Kolmogorov flow.

By Yuta Tarumi
multimodalsafety
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Hugging Face Trending Papers
Jul 29

Equilibrium Training of Energy-Based Models with Parallel Trajectory Tempering

Energy-Based Models (EBMs) provide an interpretable framework for generative modeling of scientific data, but poor Markov Chain Monte Carlo mixing often limits their reliability. We introduce a training algorithm based on Parallel Trajectory Tempering (PTT), which exploits the continuity of the optimization path to maintain equilibrium sampling throughout learning.

multimodalbenchmarks
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arXiv Statistics ML
Sep 28

Learning to Replace MCMC in Split-Gibbs Diffusion Posterior Sampling via Deep Unfolding

The paper introduces a learning-based approach to replace the MCMC step in split-Gibbs diffusion posterior sampling. By reformulating both Gibbs updates as Gaussian denoising problems, the method uses ODE diffusion for the prior step with a pretrained denoiser and a lightweight deep-unfolded network for the likelihood step. Experiments on nonlinear phase retrieval show that this alternative reduces likelihood-update cost while maintaining effectiveness compared to MCMC-based split Gibbs.

By Yi Zhang, Rui Guo, Mengchu Xu, Zhaofeng Liu, Yonina C. Eldar
diffusion
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