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09/03/2023
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics
Sampling from known probability distributions is a ubiquitous task in co...
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05/03/2023
Normalizing flows for lattice gauge theory in arbitrary space-time dimension
Applications of normalizing flows to the sampling of field configuration...
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11/14/2022
Aspects of scaling and scalability for flow-based sampling of lattice QCD
Recent applications of machine-learned normalizing flows to sampling in ...
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07/18/2022
Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
This work presents gauge-equivariant architectures for flow-based sampli...
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03/17/2022
Symmetry-Based Representations for Artificial and Biological General Intelligence
Biological intelligence is remarkable in its ability to produce complex ...
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02/23/2022
Flow-based sampling in the lattice Schwinger model at criticality
Recent results suggest that flow-based algorithms may provide efficient ...
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11/16/2021
Normalizing flows for atomic solids
We present a machine-learning approach, based on normalizing flows, for ...
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10/04/2021
Implicit Riemannian Concave Potential Maps
We are interested in the challenging problem of modelling densities on R...
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06/10/2021
Flow-based sampling for fermionic lattice field theories
Algorithms based on normalizing flows are emerging as promising machine ...
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01/20/2021
Introduction to Normalizing Flows for Lattice Field Theory
This notebook tutorial demonstrates a method for sampling Boltzmann dist...
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09/11/2020
Physically Embedded Planning Problems: New Challenges for Reinforcement Learning
Recent work in deep reinforcement learning (RL) has produced algorithms ...
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08/12/2020
Sampling using SU(N) gauge equivariant flows
We develop a flow-based sampling algorithm for SU(N) lattice gauge theor...
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06/23/2020
Disentangling by Subspace Diffusion
We present a novel nonparametric algorithm for symmetry-based disentangl...
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03/13/2020
Equivariant flow-based sampling for lattice gauge theory
We define a class of machine-learned flow-based sampling algorithms for ...
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02/12/2020
Targeted free energy estimation via learned mappings
Free energy perturbation (FEP) was proposed by Zwanzig more than six dec...
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02/06/2020
Normalizing Flows on Tori and Spheres
Normalizing flows are a powerful tool for building expressive distributi...
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09/30/2019
Hamiltonian Generative Networks
The Hamiltonian formalism plays a central role in classical and quantum ...
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09/30/2019
Equivariant Hamiltonian Flows
This paper introduces equivariant hamiltonian flows, a method for learni...
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09/27/2019
Automated curricula through setter-solver interactions
Reinforcement learning algorithms use correlations between policies and ...
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05/13/2019
Differentiable Game Mechanics
Deep learning is built on the foundational guarantee that gradient desce...
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01/11/2019
An investigation of model-free planning
The field of reinforcement learning (RL) is facing increasingly challeng...
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12/05/2018
Towards a Definition of Disentangled Representations
How can intelligent agents solve a diverse set of tasks in a data-effici...
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11/15/2018
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search
Learning policies on data synthesized by models can in principle quench ...
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02/15/2018
The Mechanics of n-Player Differentiable Games
The cornerstone underpinning deep learning is the guarantee that gradien...
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02/08/2018
Learning and Querying Fast Generative Models for Reinforcement Learning
A key challenge in model-based reinforcement learning (RL) is to synthes...
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07/19/2017
Imagination-Augmented Agents for Deep Reinforcement Learning
We introduce Imagination-Augmented Agents (I2As), a novel architecture f...
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07/19/2017
Learning model-based planning from scratch
Conventional wisdom holds that model-based planning is a powerful approa...
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04/07/2017
