Citing Articles For: Machine learning amplitudes for faster event generation

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Amplitude surrogates for multi-jet processes

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Novel implementation of the matrix element method at next-to-leading order for the measurement of the Higgs self-coupling λ3H

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Learning the simplicity of scattering amplitudes

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Detection of quantum information masking via machine learning

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Discovery prospects for the light charged Higgs boson decay to an off-shell top quark and a bottom quark at future high-energy colliders

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Kicking it off(-shell) with direct diffusion

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Variance reduction via simultaneous importance sampling and control variates techniques using vegas

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Event generators for high-energy physics experiments

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High-precision regressors for particle physics

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Generating variable length full events from partons

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Loop amplitudes from precision networks

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MadNIS - Neural multi-channel importance sampling

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Generative networks for precision enthusiasts

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Phys. Rev. D 108 055024 (2023)

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Articles Citing

Machine learning amplitudes for faster event generation

FASTColor – Full-color amplitude surrogate toolkit for QCD

Extrapolating jet radiation with autoregressive transformers

BitHEP — The limits of low-precision ML in HEP

Amplitude uncertainties everywhere all at once

Forecasting generative amplification

Amplitude surrogates for multi-jet processes

How to trust learned loop amplitudes

Novel implementation of the matrix element method at next-to-leading order for the measurement of the Higgs self-coupling λ3H

LeStrat-Net: Lebesgue style stratification for Monte Carlo simulations powered by machine learning

Jet diffusion versus JetGPT – Modern networks for the LHC

Accurate surrogate amplitudes with calibrated uncertainties

Differentiable MadNIS-Lite

Learning the simplicity of scattering amplitudes

Detection of quantum information masking via machine learning

Discovery prospects for the light charged Higgs boson decay to an off-shell top quark and a bottom quark at future high-energy colliders

Kicking it off(-shell) with direct diffusion

Variance reduction via simultaneous importance sampling and control variates techniques using vegas

Event generators for high-energy physics experiments

High-precision regressors for particle physics

Generating variable length full events from partons

Loop amplitudes from precision networks

MadNIS - Neural multi-channel importance sampling

Generative networks for precision enthusiasts

Machine learning and LHC event generation

Exploring lepton flavor violation phenomena of the Z and Higgs bosons at electron-proton colliders

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