b'Kyle Cranmer'

research

∙ 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...

0 Kyle Cranmer, et al. ∙

research

∙ 05/03/2023

Normalizing flows for lattice gauge theory in arbitrary space-time dimension

Applications of normalizing flows to the sampling of field configuration...

0 Ryan Abbott, et al. ∙

research

∙ 03/07/2023

AI for Science: An Emerging Agenda

This report documents the programme and the outcomes of Dagstuhl Seminar...

0 Philipp Berens, et al. ∙

research

∙ 03/03/2023

Configurable calorimeter simulation for AI applications

A configurable calorimeter simulation for AI (COCOA) applications is pre...

0 Francesco Armando Di Bello, et al. ∙

research

∙ 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 ...

0 Ryan Abbott, et al. ∙

research

∙ 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...

0 Ryan Abbott, et al. ∙

research

∙ 02/23/2022

Flow-based sampling in the lattice Schwinger model at criticality

Recent results suggest that flow-based algorithms may provide efficient ...

0 Michael S. Albergo, et al. ∙

research

∙ 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...

0 Sebastian Macaluso, et al. ∙

research

∙ 12/06/2021

Simulation Intelligence: Towards a New Generation of Scientific Methods

The original "Seven Motifs" set forth a roadmap of essential methods for...

50 Alexander Lavin, et al. ∙

research

∙ 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...

0 Siddharth Mishra-Sharma, et al. ∙

research

∙ 07/01/2021

Flow-based sampling for multimodal distributions in lattice field theory

Recent results have demonstrated that samplers constructed with flow-bas...

0 Daniel C. Hackett, et al. ∙

research

∙ 06/10/2021

Flow-based sampling for fermionic lattice field theories

Algorithms based on normalizing flows are emerging as promising machine ...

0 Michael S. Albergo, et al. ∙

research

∙ 04/14/2021

Exact and Approximate Hierarchical Clustering Using A*

Hierarchical clustering is a critical task in numerous domains. Many app...

7 Craig S. Greenberg, et al. ∙

research

∙ 01/20/2021

Introduction to Normalizing Flows for Lattice Field Theory

This notebook tutorial demonstrates a method for sampling Boltzmann dist...

22 Michael S. Albergo, et al. ∙

research

∙ 11/16/2020

Hierarchical clustering in particle physics through reinforcement learning

Particle physics experiments often require the reconstruction of decay p...

0 Johann Brehmer, et al. ∙

research

∙ 10/20/2020

Semi-parametric γ-ray modeling with Gaussian processes and variational inference

Mismodeling the uncertain, diffuse emission of Galactic origin can serio...

0 Siddharth Mishra-Sharma, et al. ∙

research

∙ 10/13/2020

Simulation-based inference methods for particle physics

Our predictions for particle physics processes are realized in a chain o...

13 Johann Brehmer, et al. ∙

research

∙ 08/12/2020

Sampling using SU(N) gauge equivariant flows

We develop a flow-based sampling algorithm for SU(N) lattice gauge theor...

11 Denis Boyda, et al. ∙

research

∙ 06/19/2020

Discovering Symbolic Models from Deep Learning with Inductive Biases

We develop a general approach to distill symbolic representations of a l...

12 Miles Cranmer, et al. ∙

research

∙ 03/31/2020

Flows for simultaneous manifold learning and density estimation

We introduce manifold-modeling flows (MFMFs), a new class of generative ...

12 Johann Brehmer, et al. ∙

research

∙ 03/13/2020

Equivariant flow-based sampling for lattice gauge theory

We define a class of machine-learned flow-based sampling algorithms for ...

0 Gurtej Kanwar, et al. ∙

research

∙ 02/26/2020

Compact Representation of Uncertainty in Hierarchical Clustering

Hierarchical clustering is a fundamental task often used to discover mea...

7 Craig S. Greenberg, et al. ∙

research

∙ 02/20/2020

Set2Graph: Learning Graphs From Sets

Many problems in machine learning (ML) can be cast as learning functions...

28 Hadar Serviansky, et al. ∙

research

∙ 02/06/2020

Normalizing Flows on Tori and Spheres

Normalizing flows are a powerful tool for building expressive distributi...

5 Danilo Jimenez Rezende, et al. ∙

research

∙ 11/04/2019

The frontier of simulation-based inference

Many domains of science have developed complex simulations to describe p...

91 Kyle Cranmer, et al. ∙

research

∙ 09/27/2019

Hamiltonian Graph Networks with ODE Integrators

We introduce an approach for imposing physically informed inductive bias...

0 Alvaro Sanchez-Gonzalez, et al. ∙

research

∙ 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...

24 Johann Brehmer, et al. ∙

research

∙ 07/24/2019

MadMiner: Machine learning-based inference for particle physics

The legacy measurements of the LHC will require analyzing high-dimension...

0 Johann Brehmer, et al. ∙

research

∙ 07/08/2019

Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale

Probabilistic programming languages (PPLs) are receiving widespread atte...

2 Atılım Güneş Baydin, et al. ∙

research

∙ 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...

2 Johann Brehmer, et al. ∙

research

∙ 04/11/2019

Inferring the quantum density matrix with machine learning

We introduce two methods for estimating the density matrix for a quantum...

0 Kyle Cranmer, et al. ∙

research

∙ 08/02/2018

Likelihood-free inference with an improved cross-entropy estimator

We extend recent work (Brehmer, et. al., 2018) that use neural networks ...

0 Markus Stoye, et al. ∙

research

∙ 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...

4 Atilim Gunes Baydin, et al. ∙

research

∙ 07/08/2018

Machine Learning in High Energy Physics Community White Paper

Machine learning is an important research area in particle physics, begi...

0 Kim Albertsson, et al. ∙

research

∙ 06/04/2018

Backdrop: Stochastic Backpropagation

We introduce backdrop, a flexible and simple-to-implement method, intuit...

0 Siavash Golkar, et al. ∙

research

∙ 05/30/2018

Mining gold from implicit models to improve likelihood-free inference

Simulators often provide the best description of real-world phenomena; h...

0 Johann Brehmer, et al. ∙

research

∙ 04/30/2018

A Guide to Constraining Effective Field Theories with Machine Learning

We develop, discuss, and compare several inference techniques to constra...

0 Johann Brehmer, et al. ∙

research

∙ 04/30/2018

Constraining Effective Field Theories with Machine Learning

We present powerful new analysis techniques to constrain effective field...

0 Johann Brehmer, et al. ∙

research

∙ 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...

0 Mario Lezcano Casado, et al. ∙

research

∙ 07/22/2017

Adversarial Variational Optimization of Non-Differentiable Simulators

Complex computer simulators are increasingly used across fields of scien...

0 Gilles Louppe, et al. ∙

research

∙ 02/02/2017

QCD-Aware Recursive Neural Networks for Jet Physics

Recent progress in applying machine learning for jet physics has been bu...

0 Gilles Louppe, et al. ∙

research

∙ 11/03/2016

Learning to Pivot with Adversarial Networks

Several techniques for domain adaptation have been proposed to account f...

0 Gilles Louppe, et al. ∙

research

∙ 06/06/2015

Approximating Likelihood Ratios with Calibrated Discriminative Classifiers

In many fields of science, generalized likelihood ratio tests are establ...

0 Kyle Cranmer, et al. ∙

Kyle Cranmer

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