
Hierarchical clustering in particle physics through reinforcement learning
Particle physics experiments often require the reconstruction of decay p...
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Semiparametric γray modeling with Gaussian processes and variational inference
Mismodeling the uncertain, diffuse emission of Galactic origin can serio...
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Simulationbased inference methods for particle physics
Our predictions for particle physics processes are realized in a chain o...
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Sampling using SU(N) gauge equivariant flows
We develop a flowbased sampling algorithm for SU(N) lattice gauge theor...
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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...
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Flows for simultaneous manifold learning and density estimation
We introduce manifoldmodeling flows (MFMFs), a new class of generative ...
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Equivariant flowbased sampling for lattice gauge theory
We define a class of machinelearned flowbased sampling algorithms for ...
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Compact Representation of Uncertainty in Hierarchical Clustering
Hierarchical clustering is a fundamental task often used to discover mea...
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Set2Graph: Learning Graphs From Sets
Many problems in machine learning (ML) can be cast as learning functions...
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Normalizing Flows on Tori and Spheres
Normalizing flows are a powerful tool for building expressive distributi...
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The frontier of simulationbased inference
Many domains of science have developed complex simulations to describe p...
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Hamiltonian Graph Networks with ODE Integrators
We introduce an approach for imposing physically informed inductive bias...
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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...
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MadMiner: Machine learningbased inference for particle physics
The legacy measurements of the LHC will require analyzing highdimension...
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Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Probabilistic programming languages (PPLs) are receiving widespread atte...
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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...
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Inferring the quantum density matrix with machine learning
We introduce two methods for estimating the density matrix for a quantum...
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Likelihoodfree inference with an improved crossentropy estimator
We extend recent work (Brehmer, et. al., 2018) that use neural networks ...
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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...
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Machine Learning in High Energy Physics Community White Paper
Machine learning is an important research area in particle physics, begi...
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Backdrop: Stochastic Backpropagation
We introduce backdrop, a flexible and simpletoimplement method, intuit...
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Mining gold from implicit models to improve likelihoodfree inference
Simulators often provide the best description of realworld phenomena; h...
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A Guide to Constraining Effective Field Theories with Machine Learning
We develop, discuss, and compare several inference techniques to constra...
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Constraining Effective Field Theories with Machine Learning
We present powerful new analysis techniques to constrain effective field...
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Improvements to Inference Compilation for Probabilistic Programming in LargeScale Scientific Simulators
We consider the problem of Bayesian inference in the family of probabili...
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Adversarial Variational Optimization of NonDifferentiable Simulators
Complex computer simulators are increasingly used across fields of scien...
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QCDAware Recursive Neural Networks for Jet Physics
Recent progress in applying machine learning for jet physics has been bu...
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Learning to Pivot with Adversarial Networks
Several techniques for domain adaptation have been proposed to account f...
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Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
In many fields of science, generalized likelihood ratio tests are establ...
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Kyle Cranmer
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