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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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03/07/2023
AI for Science: An Emerging Agenda
This report documents the programme and the outcomes of Dagstuhl Seminar...
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03/03/2023
Configurable calorimeter simulation for AI applications
A configurable calorimeter simulation for AI (COCOA) applications is pre...
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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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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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12/23/2021
The Quantum Trellis: A classical algorithm for sampling the parton shower with interference effects
Simulations of high-energy particle collisions, such as those used at th...
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12/06/2021
Simulation Intelligence: Towards a New Generation of Scientific Methods
The original "Seven Motifs" set forth a roadmap of essential methods for...
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10/13/2021
A neural simulation-based inference approach for characterizing the Galactic Center γ-ray excess
The nature of the Fermi gamma-ray Galactic Center Excess (GCE) has remai...
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07/01/2021
Flow-based sampling for multimodal distributions in lattice field theory
Recent results have demonstrated that samplers constructed with flow-bas...
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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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04/14/2021
Exact and Approximate Hierarchical Clustering Using A*
Hierarchical clustering is a critical task in numerous domains. Many app...
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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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11/16/2020
Hierarchical clustering in particle physics through reinforcement learning
Particle physics experiments often require the reconstruction of decay p...
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10/20/2020
Semi-parametric γ-ray modeling with Gaussian processes and variational inference
Mismodeling the uncertain, diffuse emission of Galactic origin can serio...
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10/13/2020
Simulation-based inference methods for particle physics
Our predictions for particle physics processes are realized in a chain o...
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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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06/19/2020
Discovering Symbolic Models from Deep Learning with Inductive Biases
We develop a general approach to distill symbolic representations of a l...
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03/31/2020
Flows for simultaneous manifold learning and density estimation
We introduce manifold-modeling flows (MFMFs), a new class of generative ...
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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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02/26/2020
Compact Representation of Uncertainty in Hierarchical Clustering
Hierarchical clustering is a fundamental task often used to discover mea...
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02/20/2020
Set2Graph: Learning Graphs From Sets
Many problems in machine learning (ML) can be cast as learning functions...
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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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11/04/2019
The frontier of simulation-based inference
Many domains of science have developed complex simulations to describe p...
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09/27/2019
Hamiltonian Graph Networks with ODE Integrators
We introduce an approach for imposing physically informed inductive bias...
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09/04/2019
Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
The subtle and unique imprint of dark matter substructure on extended ar...
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07/24/2019
MadMiner: Machine learning-based inference for particle physics
The legacy measurements of the LHC will require analyzing high-dimension...
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07/08/2019
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Probabilistic programming languages (PPLs) are receiving widespread atte...
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06/04/2019
Effective LHC measurements with matrix elements and machine learning
One major challenge for the legacy measurements at the LHC is that the l...
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04/11/2019
Inferring the quantum density matrix with machine learning
We introduce two methods for estimating the density matrix for a quantum...
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08/02/2018
Likelihood-free inference with an improved cross-entropy estimator
We extend recent work (Brehmer, et. al., 2018) that use neural networks ...
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07/20/2018
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
We present a novel framework that enables efficient probabilistic infere...
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07/08/2018
Machine Learning in High Energy Physics Community White Paper
Machine learning is an important research area in particle physics, begi...
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06/04/2018
Backdrop: Stochastic Backpropagation
We introduce backdrop, a flexible and simple-to-implement method, intuit...
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05/30/2018
Mining gold from implicit models to improve likelihood-free inference
Simulators often provide the best description of real-world phenomena; h...
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04/30/2018
A Guide to Constraining Effective Field Theories with Machine Learning
We develop, discuss, and compare several inference techniques to constra...
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04/30/2018
Constraining Effective Field Theories with Machine Learning
We present powerful new analysis techniques to constrain effective field...
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12/21/2017
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
We consider the problem of Bayesian inference in the family of probabili...
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07/22/2017
Adversarial Variational Optimization of Non-Differentiable Simulators
Complex computer simulators are increasingly used across fields of scien...
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02/02/2017
QCD-Aware Recursive Neural Networks for Jet Physics
Recent progress in applying machine learning for jet physics has been bu...
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11/03/2016
Learning to Pivot with Adversarial Networks
Several techniques for domain adaptation have been proposed to account f...
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06/06/2015