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Found 9 papers, 5 papers with code
Target Score Matching
Denoising Score Matching estimates the score of a noised version of a target distribution by minimizing a regression loss and is widely used to train the popular class of Denoising Diffusion Models.
Estimating Gibbs free energies via isobaric-isothermal flows
We present a machine-learning model based on normalizing flows that is trained to sample from the isobaric-isothermal ensemble.
GraphCast: Learning skillful medium-range global weather forecasting
Global medium-range weather forecasting is critical to decision-making across many social and economic domains.
MultiScale MeshGraphNets
In recent years, there has been a growing interest in using machine learning to overcome the high cost of numerical simulation, with some learned models achieving impressive speed-ups over classical solvers whilst maintaining accuracy.
Normalizing flows for atomic solids
We present a machine-learning approach, based on normalizing flows, for modelling atomic solids.
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision
Using SyMetric, we identify a set of architectural choices that significantly improve the performance of a previously proposed model for inferring latent dynamics from pixels, the Hamiltonian Generative Network (HGN).
Which priors matter? Benchmarking models for learning latent dynamics
Learning dynamics is at the heart of many important applications of machine learning (ML), such as robotics and autonomous driving.
Targeted free energy estimation via learned mappings
Here, we cast Targeted FEP as a machine learning problem in which the mapping is parameterized as a neural network that is optimized so as to increase overlap.
Infinitely Deep Infinite-Width Networks
Infinite-width neural networks have been extensively used to study the theoretical properties underlying the extraordinary empirical success of standard, finite-width neural networks.
