b'Kyle Cranmer'

Introduction to Normalizing Flows for Lattice Field Theory

This notebook tutorial demonstrates a method for sampling Boltzmann dist...
01/20/2021 ∙ by Michael S. Albergo, et al. ∙ 22 ∙ share

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Hierarchical clustering in particle physics through reinforcement learning

Particle physics experiments often require the reconstruction of decay p...
11/16/2020 ∙ by Johann Brehmer, et al. ∙ 0 ∙ share

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

Mismodeling the uncertain, diffuse emission of Galactic origin can serio...
10/20/2020 ∙ by Siddharth Mishra-Sharma, et al. ∙ 0 ∙ share

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Simulation-based inference methods for particle physics

Our predictions for particle physics processes are realized in a chain o...
10/13/2020 ∙ by Johann Brehmer, et al. ∙ 13 ∙ share

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Sampling using SU(N) gauge equivariant flows

We develop a flow-based sampling algorithm for SU(N) lattice gauge theor...
08/12/2020 ∙ by Denis Boyda, et al. ∙ 11 ∙ share

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Discovering Symbolic Models from Deep Learning with Inductive Biases

We develop a general approach to distill symbolic representations of a l...
06/19/2020 ∙ by Miles Cranmer, et al. ∙ 12 ∙ share

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Flows for simultaneous manifold learning and density estimation

We introduce manifold-modeling flows (MFMFs), a new class of generative ...
03/31/2020 ∙ by Johann Brehmer, et al. ∙ 12 ∙ share

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Equivariant flow-based sampling for lattice gauge theory

We define a class of machine-learned flow-based sampling algorithms for ...
03/13/2020 ∙ by Gurtej Kanwar, et al. ∙ 0 ∙ share

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Compact Representation of Uncertainty in Hierarchical Clustering

Hierarchical clustering is a fundamental task often used to discover mea...
02/26/2020 ∙ by Craig S. Greenberg, et al. ∙ 7 ∙ share

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Set2Graph: Learning Graphs From Sets

Many problems in machine learning (ML) can be cast as learning functions...
02/20/2020 ∙ by Hadar Serviansky, et al. ∙ 28 ∙ share

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Normalizing Flows on Tori and Spheres

Normalizing flows are a powerful tool for building expressive distributi...
02/06/2020 ∙ by Danilo Jimenez Rezende, et al. ∙ 5 ∙ share

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The frontier of simulation-based inference

Many domains of science have developed complex simulations to describe p...
11/04/2019 ∙ by Kyle Cranmer, et al. ∙ 91 ∙ share

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Hamiltonian Graph Networks with ODE Integrators

We introduce an approach for imposing physically informed inductive bias...
09/27/2019 ∙ by Alvaro Sanchez-Gonzalez, et al. ∙ 0 ∙ share

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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...
09/04/2019 ∙ by Johann Brehmer, et al. ∙ 24 ∙ share

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MadMiner: Machine learning-based inference for particle physics

The legacy measurements of the LHC will require analyzing high-dimension...
07/24/2019 ∙ by Johann Brehmer, et al. ∙ 0 ∙ share

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Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale

Probabilistic programming languages (PPLs) are receiving widespread atte...
07/08/2019 ∙ by Atılım Güneş Baydin, et al. ∙ 2 ∙ share

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Effective LHC measurements with matrix elements and machine learning

One major challenge for the legacy measurements at the LHC is that the l...
06/04/2019 ∙ by Johann Brehmer, et al. ∙ 2 ∙ share

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Inferring the quantum density matrix with machine learning

We introduce two methods for estimating the density matrix for a quantum...
04/11/2019 ∙ by Kyle Cranmer, et al. ∙ 0 ∙ share

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Likelihood-free inference with an improved cross-entropy estimator

We extend recent work (Brehmer, et. al., 2018) that use neural networks ...
08/02/2018 ∙ by Markus Stoye, et al. ∙ 0 ∙ share

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Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

We present a novel framework that enables efficient probabilistic infere...
07/20/2018 ∙ by Atilim Gunes Baydin, et al. ∙ 4 ∙ share

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Machine Learning in High Energy Physics Community White Paper

Machine learning is an important research area in particle physics, begi...
07/08/2018 ∙ by Kim Albertsson, et al. ∙ 0 ∙ share

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Backdrop: Stochastic Backpropagation

We introduce backdrop, a flexible and simple-to-implement method, intuit...
06/04/2018 ∙ by Siavash Golkar, et al. ∙ 0 ∙ share

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Mining gold from implicit models to improve likelihood-free inference

Simulators often provide the best description of real-world phenomena; h...
05/30/2018 ∙ by Johann Brehmer, et al. ∙ 0 ∙ share

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A Guide to Constraining Effective Field Theories with Machine Learning

We develop, discuss, and compare several inference techniques to constra...
04/30/2018 ∙ by Johann Brehmer, et al. ∙ 0 ∙ share

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Constraining Effective Field Theories with Machine Learning

We present powerful new analysis techniques to constrain effective field...
04/30/2018 ∙ by Johann Brehmer, et al. ∙ 0 ∙ share

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Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators

We consider the problem of Bayesian inference in the family of probabili...
12/21/2017 ∙ by Mario Lezcano Casado, et al. ∙ 0 ∙ share

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Adversarial Variational Optimization of Non-Differentiable Simulators

Complex computer simulators are increasingly used across fields of scien...
07/22/2017 ∙ by Gilles Louppe, et al. ∙ 0 ∙ share

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QCD-Aware Recursive Neural Networks for Jet Physics

Recent progress in applying machine learning for jet physics has been bu...
02/02/2017 ∙ by Gilles Louppe, et al. ∙ 0 ∙ share

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Learning to Pivot with Adversarial Networks

Several techniques for domain adaptation have been proposed to account f...
11/03/2016 ∙ by Gilles Louppe, et al. ∙ 0 ∙ share

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Approximating Likelihood Ratios with Calibrated Discriminative Classifiers

In many fields of science, generalized likelihood ratio tests are establ...
06/06/2015 ∙ by Kyle Cranmer, et al. ∙ 0 ∙ share

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Kyle Cranmer

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