Hamiltonian Monte Carlo
PulseAugur coverage of Hamiltonian Monte Carlo — every cluster mentioning Hamiltonian Monte Carlo across labs, papers, and developer communities, ranked by signal.
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New Bayesian inference framework uses transport maps for improved posterior sampling
Researchers have developed a new framework for source-space generalized Bayesian inference that combines efficient few-step prior transports with guarantees for posterior stability. This method represents the prior usin…
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Author withdraws research paper on advanced physics-informed neural networks
A research paper titled "Multi-Fidelity Physics-Informed Neural Networks with Bayesian Uncertainty Quantification and Adaptive Residual Learning for Efficient Solution of Parametric Partial Differential Equations" has b…
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New HMC algorithms detailed in arXiv papers
Two new papers explore advanced Hamiltonian Monte Carlo (HMC) algorithms for statistical modeling. The first paper details the Microcanonical Hamiltonian Monte Carlo algorithm, demonstrating its connection to thermodyna…
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Quantum algorithms promise speedups for sampling and optimization
Researchers have developed new quantum algorithms that offer speedups for sampling from complex probability distributions and for non-convex optimization tasks. These algorithms enhance classical methods like Langevin M…
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Hamiltonian Monte Carlo explained from a probabilistic perspective
This article delves into Hamiltonian Monte Carlo (HMC), a sophisticated algorithm that powers modern Bayesian inference and statistical machine learning frameworks like PyMC. While originating from physics, HMC is a var…
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New LazyHMC method enables Hamiltonian Monte Carlo for infinite-dimensional probabilistic programs
Researchers have developed LazyHMC, a new formulation of Hamiltonian Monte Carlo (HMC) designed for probabilistic programs that utilize lazy evaluation and operate in infinite-dimensional parameter spaces. This method a…
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New method uses neural networks to enhance Hamiltonian Monte Carlo for inference
Researchers have introduced Neural Surrogate HMC, a novel method that integrates neural likelihood estimation with Hamiltonian Monte Carlo for simulation-based inference. This approach leverages neural networks to appro…
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New Ray Tracing Sampler offers Bayesian sampling for neural networks
Researchers have developed a new family of Markov Chain Monte Carlo (MCMC) sampling methods called the Ray Tracing Sampler, inspired by light ray paths. This method offers significantly higher resilience to gradient hea…
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New Bayesian Framework Integrates Dimension Reduction for Gaussian Process Models
Researchers have developed a new Bayesian framework designed to address the challenges of Gaussian Process (GP) modeling with high-dimensional inputs. This novel approach integrates dimensionality reduction directly int…
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New NHMC method improves Boltzmann sampling accuracy
Researchers have developed a novel method called Neural Non-Equilibrium Hamiltonian Monte Carlo (NHMC) for more accurate sampling from Boltzmann densities. This approach trains a sampler to generate stochastic Hamiltoni…
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New HMC algorithms tackle bias and accelerate sampling times · 7 sources tracked
Researchers have developed new methods to address bias and improve efficiency in Hamiltonian Monte Carlo (HMC) algorithms. One study extends the concept of bias delocalization to unadjusted HMC and underdamped Langevin …
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New LL-HMC method makes uncertainty estimation in deep neural networks more feasible
Researchers have developed a new method called Last Layer Hamiltonian Monte Carlo (LL-HMC) to make uncertainty estimation in deep neural networks more computationally feasible. Traditional Hamiltonian Monte Carlo (HMC) …
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Diffusion models accelerate Schwinger model sampling in physics research
Researchers have explored a novel diffusion-based method for accelerating the sampling of the Schwinger model, a problem in lattice quantum field theory. They developed a U(1)-equivariant score-based generative model to…
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AI models enhance cosmological inference and uncertainty analysis
Two new arXiv papers explore the application of neural networks in cosmology. The first paper introduces a neural marking scheme to extract more cosmological information than traditional methods, significantly tightenin…
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AI diffusion models successfully sample SU(N) lattice gauge theories
Researchers have developed a diffusion model capable of sampling SU(N) lattice gauge theories, a significant advancement for computational physics. This implicit score matching framework was successfully applied to SU(3…
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New TSMC method optimizes trajectories and policies with differentiable dynamics
Researchers have introduced Tempered Sequential Monte Carlo (TSMC), a novel sampling-based framework for optimizing trajectories and policies within systems that have differentiable dynamics. This approach reframes cont…