Hamiltonian Monte Carlo
PulseAugur coverage of Hamiltonian Monte Carlo — every cluster mentioning Hamiltonian Monte Carlo across labs, papers, and developer communities, ranked by signal.
5 day(s) with sentiment data
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Notes explain Hamiltonian Monte Carlo from a probabilistic viewpoint
A set of notes has been created to explain Hamiltonian Monte Carlo (HMC) from a purely probabilistic perspective, avoiding the typical physics-based motivations. The notes detail the process by introducing an auxiliary …
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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…