arXiv research

Compressed Monte Carlo improves efficiency in Bayesian inference.

Machine learning uncovers hidden correlations in granular material behavior.

Novel method reduces costly model evaluations in inference problems.

Jointly estimates flow fields and particle properties from Lagrangian data.

BCAE-2D compresses 3D data from a time projection chamber at high speed.

Automated tool reduces FPGA inference latency to 5 μs for deep neural networks.

Magnetic particle imaging (MPI) data is commonly reconstructed using a system matrix acquired in a time-consuming calibration measurement. The calibration approach has the important advantage over model-based reconstruction that it takes the complex particle physics as well as system imperfections into account. This be…

New method estimates rate-distortion function using optimal transport.

Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques have become very popular in signal processing over the last years. Importance Sampling (IS) is a well-known Monte Carlo technique that approximates integrals involving a posterior distribution by means of weighted samples. In th…

Optimal weights improve particle-based approximations of discrete distributions.

Interacting particle methods are increasingly used to sample from complex and high-dimensional distributions. These stochastic particle integration techniques can be interpreted as an universal acceptance-rejection sequential particle sampler equipped with adaptive and interacting recycling mechanisms. Practically, the…

A method for optimal Bayesian filtering using progressive particle flow and optimal transport maps.

New method accelerates energetic variational inference using particle dynamics.

Online convex optimization is a sequential prediction framework with the goal to track and adapt to the environment through evaluating proper convex loss functions. We study efficient particle filtering methods from the perspective of such a framework. We formulate an efficient particle filtering methods for the non-st…

We introduce a new sequential Monte Carlo algorithm we call the particle cascade. The particle cascade is an asynchronous, anytime alternative to traditional particle filtering algorithms. It uses no barrier synchronizations which leads to improved particle throughput and memory efficiency. It is an anytime algorithm i…

New method learns latent energy models using particle algorithms.

New method optimizes multiple objectives using particle dynamics and gradient flow.

Cryo-electron microscopy (cryo-EM) is an emerging experimental method to characterize the structure of large biomolecular assemblies. Single particle cryo-EM records 2D images (so-called micrographs) of projections of the three-dimensional particle, which need to be processed to obtain the three-dimensional reconstruct…

Framework expands particle filtering to estimate states beyond prior boundaries.

New algorithm trains latent diffusion models using interacting particles.

MPM-ParVI uses particle sampling for variational inference.

A method makes particle filters differentiable without altering their forward pass.

Estimates log-likelihood of interacting particle systems using virtual particles.

CPS solves inverse problems using forward passes and constrained particle seeking.

New method for online learning in interacting particle systems.

Unified approach unites GANs and diffusion models using particle methods.

Improves SVGD for high-dimensional Bayesian inference by reducing variance collapse.

New particle-based method improves semi-supervised learning robustness to label noise.

Paper studies particle method for LSV model calibration, proving convergence and error bounds.

A new EVI framework improves ParVI methods by maintaining variational structure and reducing KL-divergence.

Paper introduces a new adaptive gradient method with gradient compression for distributed training.

Electrostatics method samples complex distributions deterministically.

State space models (SSMs) provide a flexible framework for modeling complex time series via a latent stochastic process. Inference for nonlinear, non-Gaussian SSMs is often tackled with particle methods that do not scale well to long time series. The challenge is two-fold: not only do computations scale linearly with t…

New taxonomy and evaluation of neural network compression methods.

Cryo-electron microscopy (cryoEM) is an increasingly popular method for protein structure determination. However, identifying a sufficient number of particles for analysis (often >100,000) can take months of manual effort. Current computational approaches are limited by high false positive rates and require significant…

New methods combine MALA and mGRAD for scalable Bayesian inference in high-dimensional state-space models.

This paper studies when particle filtering is efficient for planning in partially observed systems.

Study of free particle's geometry and its perturbations using complex projective structures.

We consider the combined use of resampling and partial rejection control in sequential Monte Carlo methods, also known as particle filters. While the variance reducing properties of rejection control are known, there has not been (to the best of our knowledge) any work on unbiased estimation of the marginal likelihood …

GPU-accelerated particle methods outperform neural samplers in LFT benchmarks.

Paper compares semi-supervised training for differentiable particle filters.

Proposes a new linearity-based neural network compression method.

A new method, REC, compresses images by encoding their latent representations efficiently.

Bayesian inference for neural networks improves uncertainty quantification.

GER learns particle dynamics from unpaired snapshots using physics-informed GANs.

Particle Markov chain Monte Carlo techniques rank among current state-of-the-art methods for probabilistic program inference. A drawback of these techniques is that they rely on importance resampling, which results in degenerate particle trajectories and a low effective sample size for variables sampled early in a prog…

Proposes a method to train neural networks directly on compressed text data.

Proposes PGPS for efficient Bayesian inference.