Cover Image

Utilising Gradient-Based Proposals Within Sequential Monte Carlo Samplers for Training of Partial Bayesian Neural Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Millard, Andrew, Murphy, Joshua, Maskell, Simon, Zhao, Zheng
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Incorporating the ChEES Criterion into Sequential Monte Carlo Samplers
by: Millard, Andrew, et al.
Published: (2025)

Investigating Batch Inference in a Sequential Monte Carlo Framework for Neural Networks
by: Millard, Andrew, et al.
Published: (2026)

Humble your Overconfident Networks: Unlearning Overfitting via Sequential Monte Carlo Tempered Deep Ensembles
by: Millard, Andrew, et al.
Published: (2025)

Hess-MC2: Sequential Monte Carlo Squared using Hessian Information and Second Order Proposals
by: Murphy, Joshua, et al.
Published: (2025)

Enhanced SMC$^2$: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals
by: Rosato, Conor, et al.
Published: (2024)

Diffusion Path Samplers via Sequential Monte Carlo
by: Young, James Matthew, et al.
Published: (2026)

The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler with a Near-Optimal L-Kernel
by: Devlin, Lee, et al.
Published: (2021)

Particle-Guided Diffusion Models for Partial Differential Equations
by: Millard, Andrew, et al.
Published: (2026)

Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization
by: Kim, Kyurae, et al.
Published: (2025)

Stochastic Gradient Piecewise Deterministic Monte Carlo Samplers
by: Fearnhead, Paul, et al.
Published: (2024)

Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo
by: Kelvinius, Filip Ekström, et al.
Published: (2025)

Towards Scalable Bayesian Optimization via Gradient-Informed Bayesian Neural Networks
by: Makrygiorgos, Georgios, et al.
Published: (2025)

Multilevel and Sequential Monte Carlo for Training-Free Diffusion Guidance
by: Gleich, Aidan, et al.
Published: (2026)

Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic Space
by: Chen, Alex, et al.
Published: (2025)

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone
by: Behroozi, Peter
Published: (2025)

Testing Spintronics Implemented Monte Carlo Dropout-Based Bayesian Neural Networks
by: Ahmed, Soyed Tuhin, et al.
Published: (2024)

Elements of Sequential Monte Carlo
by: Naesseth, Christian A., et al.
Published: (2019)

Scalable Model-Based Clustering with Sequential Monte Carlo
by: Trojan, Connie, et al.
Published: (2026)

Online Variational Sequential Monte Carlo
by: Mastrototaro, Alessandro, et al.
Published: (2023)

Training Neural Samplers with Reverse Diffusive KL Divergence
by: He, Jiajun, et al.
Published: (2024)

Bayesian Neural Networks with Monte Carlo Dropout for Probabilistic Electricity Price Forecasting
by: Das, Abhinav, et al.
Published: (2025)

Structured Partial Stochasticity in Bayesian Neural Networks
by: Rochussen, Tommy
Published: (2024)

Dirichlet-Based Monte Carlo Dropout for Uncertainty Estimation in Neural Networks
by: Hoblos, Rouaa, et al.
Published: (2026)

Entropy-based Training Methods for Scalable Neural Implicit Sampler
by: Luo, Weijian, et al.
Published: (2023)

BitLogic: Training Framework for Gradient-Based FPGA-Native Neural Networks
by: Bührer, Simon, et al.
Published: (2026)

Martingale Posterior Neural Networks for Fast Sequential Decision Making
by: Duran-Martin, Gerardo, et al.
Published: (2025)

The Within-Orbit Adaptive Leapfrog No-U-Turn Sampler
by: Bou-Rabee, Nawaf, et al.
Published: (2025)

Multi-Level Monte Carlo Training of Neural Operators
by: Rowbottom, James, et al.
Published: (2025)

On the convergence of dynamic implementations of Hamiltonian Monte Carlo and No U-Turn Samplers
by: Durmus, Alain, et al.
Published: (2023)

Neural Bayesian Sequential Routing
by: Huang, Yongchao
Published: (2026)

Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo
by: Lee, Cheuk Kit, et al.
Published: (2025)

Stacking Variational Bayesian Monte Carlo
by: Silvestrin, Francesco, et al.
Published: (2025)

Enhancing Bayesian Network Structural Learning with Monte Carlo Tree Search
by: Laborda, Jorge D., et al.
Published: (2025)

Bayesian Online Natural Gradient (BONG)
by: Jones, Matt, et al.
Published: (2024)

Modelling Gas-Phase Reaction Kinetics with Guided Particle Diffusion Sampling
by: Millard, Andrew, et al.
Published: (2026)

Accelerating Hamiltonian Monte Carlo for Bayesian Inference in Neural Networks and Neural Operators
by: Thiagarajan, Ponkrshnan, et al.
Published: (2025)

Partially Stochastic Infinitely Deep Bayesian Neural Networks
by: Calvo-Ordonez, Sergio, et al.
Published: (2024)

Twice Sequential Monte Carlo for Tree Search
by: Oren, Yaniv, et al.
Published: (2025)

SPO: Sequential Monte Carlo Policy Optimisation
by: Macfarlane, Matthew V, et al.
Published: (2024)

Functional Stochastic Gradient MCMC for Bayesian Neural Networks
by: Wu, Mengjing, et al.
Published: (2024)