arXiv Statistics ML
Langevin-Informed Transfer Learning: Replacing Target Samples by Black-Box Feedback
Read the original on arXiv Statistics ML →The Flow has not summarised this story yet — read it at arXiv Statistics ML.
Related stories
arXiv Machine Learning
Jun 4
Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials
arXiv:2606. 04100v1 Announce Type: new Abstract: Machine learning interatomic potentials (MLIPs) enable efficient and accurate atomistic simulations but depend critically on the quality and diversity of the training data.
arXiv Machine Learning
Sep 17
Machine learning kinetics from molecular dynamics data
The article reviews modern machine learning techniques for estimating the committor and related kinetic statistics from molecular dynamics simulations. It emphasizes self‑supervised methods that solve the underlying dynamical equations instead of relying on labeled data, and unifies various approaches—generator‑based PDEs, variational principles, Markov state models, dynamical Galerkin approximation, and neural networks—under a common operator framework. The review also discusses practical guidance for handling non‑Markovian effects, sampling strategies, and outlines future research directions such as connections to reinforcement learning and generative modeling.
arXiv AI
Jul 1
Unsupervised Thermodynamics of Molecular Diffusion Models: Action-Operator Semantics and Auditable Free-Energy Readout
arXiv:2606. 30687v1 Announce Type: cross Abstract: Diffusion models are increasingly utilized for modeling molecular structures and conformational ensembles, yet the thermodynamic meaning of their learned representations and scores remains elusive.
arXiv Machine Learning
Jun 4
Gradient estimators for parameter inference in discrete stochastic kinetic models
arXiv:2604. 02121v2 Announce Type: replace-cross Abstract: Stochastic kinetic models are ubiquitous in physics, yet inferring their parameters from experimental data remains challenging.
arXiv Machine Learning
Jul 20
Neural Non-Equilibrium Hamiltonian Monte Carlo for Corrected Boltzmann Sampling
arXiv:2607. 15682v1 Announce Type: new Abstract: Sampling from an unnormalized Boltzmann density requires proposals that move probability mass globally while retaining enough path-probability information for statistical correction.
arXiv Machine Learning
Aug 11
Leveraging generative models to assist Monte Carlo sampling
arXiv:2608. 07648v1 Announce Type: cross Abstract: Sampling high-dimensional probability distributions is a central task in scientific computing, with applications ranging from Bayesian inference to statistical physics and molecular simulation.