Search Results for author:
Found 21 papers, 7 papers with code
Applications of flow models to the generation of correlated lattice QCD ensembles
Machine-learned normalizing flows can be used in the context of lattice quantum field theory to generate statistically correlated ensembles of lattice gauge fields at different action parameters.
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics
This Perspective outlines the advances in ML-based sampling motivated by lattice quantum field theory, in particular for the theory of quantum chromodynamics.
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.
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.
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.
Symmetry-Based Representations for Artificial and Biological General Intelligence
In this review article we are going to argue that symmetry transformations are a fundamental principle that can guide our search for what makes a good representation.
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.
Normalizing flows for atomic solids
We present a machine-learning approach, based on normalizing flows, for modelling atomic solids.
Implicit Riemannian Concave Potential Maps
We are interested in the challenging problem of modelling densities on Riemannian manifolds with a known symmetry group using normalising flows.
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.
Physically Embedded Planning Problems: New Challenges for Reinforcement Learning
To encourage progress towards this goal we introduce a set of physically embedded planning problems and make them publicly available.
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.
Disentangling by Subspace Diffusion
We present a novel nonparametric algorithm for symmetry-based disentangling of data manifolds, the Geometric Manifold Component Estimator (GEOMANCER).
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.
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.
Normalizing Flows on Tori and Spheres
Normalizing flows are a powerful tool for building expressive distributions in high dimensions.
Equivariant Hamiltonian Flows
This paper introduces equivariant hamiltonian flows, a method for learning expressive densities that are invariant with respect to a known Lie-algebra of local symmetry transformations while providing an equivariant representation of the data.
Hamiltonian Generative Networks
The Hamiltonian formalism plays a central role in classical and quantum physics.
An investigation of model-free planning
The field of reinforcement learning (RL) is facing increasingly challenging domains with combinatorial complexity.
Imagination-Augmented Agents for Deep Reinforcement Learning
We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects.
Model-based Reinforcement Learning
reinforcement-learning
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