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Found 20 papers, 5 papers with code
NETS: A Non-Equilibrium Transport Sampler
We propose an algorithm, termed the Non-Equilibrium Transport Sampler (NETS), to sample from unnormalized probability distributions.
Flow Map Matching
Generative models based on dynamical transport of measure, such as diffusion models, flow matching models, and stochastic interpolants, learn an ordinary or stochastic differential equation whose trajectories push initial conditions from a known base distribution onto the target.
Practical applications of machine-learned flows on gauge fields
Normalizing flows are machine-learned maps between different lattice theories which can be used as components in exact sampling and inference schemes.
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
We propose a framework for probabilistic forecasting of dynamical systems based on generative modeling.
SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
We present Scalable Interpolant Transformers (SiT), a family of generative models built on the backbone of Diffusion Transformers (DiT).
Learning to Sample Better
These lecture notes provide an introduction to recent advances in generative modeling methods based on the dynamical transportation of measures, by means of which samples from a simple base measure are mapped to samples from a target measure of interest.
Stochastic interpolants with data-dependent couplings
In this work, using the framework of stochastic interpolants, we formalize how to \textit{couple} the base and the target densities, whereby samples from the base are computed conditionally given samples from the target in a way that is different from (but does preclude) incorporating information about class labels or continuous embeddings.
Multimarginal generative modeling with stochastic interpolants
Given a set of $K$ probability densities, we consider the multimarginal generative modeling problem of learning a joint distribution that recovers these densities as marginals.
Normalizing flows for lattice gauge theory in arbitrary space-time dimension
Applications of normalizing flows to the sampling of field configurations in lattice gauge theory have so far been explored almost exclusively in two space-time dimensions.
Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
The time-dependent probability density function of the stochastic interpolant is shown to satisfy a first-order transport equation as well as a family of forward and backward Fokker-Planck equations with tunable diffusion coefficient.
Aspects of scaling and scalability for flow-based sampling of lattice QCD
Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological freezing.
Building Normalizing Flows with Stochastic Interpolants
A generative model based on a continuous-time normalizing flow between any pair of base and target probability densities is proposed.
Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
This work presents gauge-equivariant architectures for flow-based sampling in fermionic lattice field theories using pseudofermions as stochastic estimators for the fermionic determinant.
Flow-based sampling in the lattice Schwinger model at criticality
In this work, we provide a numerical demonstration of robust flow-based sampling in the Schwinger model at the critical value of the fermion mass.
Flow-based sampling for multimodal distributions in lattice field theory
Recent results have demonstrated that samplers constructed with flow-based generative models are a promising new approach for configuration generation in lattice field theory.
Flow-based sampling for fermionic lattice field theories
Algorithms based on normalizing flows are emerging as promising machine learning approaches to sampling complicated probability distributions in a way that can be made asymptotically exact.
Introduction to Normalizing Flows for Lattice Field Theory
This notebook tutorial demonstrates a method for sampling Boltzmann distributions of lattice field theories using a class of machine learning models known as normalizing flows.
Sampling using $SU(N)$ gauge equivariant flows
We develop a flow-based sampling algorithm for $SU(N)$ lattice gauge theories that is gauge-invariant by construction.
Equivariant flow-based sampling for lattice gauge theory
We define a class of machine-learned flow-based sampling algorithms for lattice gauge theories that are gauge-invariant by construction.
Normalizing Flows on Tori and Spheres
Normalizing flows are a powerful tool for building expressive distributions in high dimensions.
