Add to ORKGWe develop a graph generative adversarial network to generate sparse data sets like those produced at the CERN Large Hadron Collider (LHC). We demonstrate this approach by training on and generating sparse representations of MNIST handwritten digit images and jets of particles in proton-proton collisions like those at the LHC. We find the model successfully generates sparse MNIST digits and particle jet data. We quantify agreement between real and generated data with a graph-based Fr\'echet Inception distance, and the particle and jet feature-level 1-Wasserstein distance for the MNIST and jet datasets respectively
In this talk, I will present a Generative-Adversarial Network (GAN) based on convolutional neural ne...
We describe a multi-disciplinary project to use machine learning techniques based on neural networks...
In high energy physics, one of the most important processes for collider data analysis is the compar...
We develop a graph generative adversarial network to generate sparse data sets like those produced a...
Collisions at the CERN Large Hadron Collider (LHC) produce showers of particles that are detected by...
https://arxiv.org/abs/1701.05927 We provide a bridge between generative modeling in the Machine Lea...
Using generative adversarial networks (GANs), we investigate the possibility of creating large amoun...
Accurate and fast simulation of particle physics processes is crucial for the high-energy physics co...
In high energy physics, one of the most important processes for collider data analysis is the compar...
We explore the use of graph networks to deal with irregular-geometry detectors in the context of par...
We introduce a generative model to simulate radiation patterns within a jet using the Lund jet plane...
We investigate how a Generative Adversarial Network could be used to generate a list of particle fou...
Initial studies have suggested generative adversarial networks (GANs) have promise as fast simulatio...
LHCb is one of the major experiments operating at the Large Hadron Collider at CERN. The richness of...
Cosmological data is comprised of dark matter and ordinary matter forming halos, filaments, sheets a...
In this talk, I will present a Generative-Adversarial Network (GAN) based on convolutional neural ne...
We describe a multi-disciplinary project to use machine learning techniques based on neural networks...
In high energy physics, one of the most important processes for collider data analysis is the compar...
We develop a graph generative adversarial network to generate sparse data sets like those produced a...
Collisions at the CERN Large Hadron Collider (LHC) produce showers of particles that are detected by...
https://arxiv.org/abs/1701.05927 We provide a bridge between generative modeling in the Machine Lea...
Using generative adversarial networks (GANs), we investigate the possibility of creating large amoun...
Accurate and fast simulation of particle physics processes is crucial for the high-energy physics co...
In high energy physics, one of the most important processes for collider data analysis is the compar...
We explore the use of graph networks to deal with irregular-geometry detectors in the context of par...
We introduce a generative model to simulate radiation patterns within a jet using the Lund jet plane...
We investigate how a Generative Adversarial Network could be used to generate a list of particle fou...
Initial studies have suggested generative adversarial networks (GANs) have promise as fast simulatio...
LHCb is one of the major experiments operating at the Large Hadron Collider at CERN. The richness of...
Cosmological data is comprised of dark matter and ordinary matter forming halos, filaments, sheets a...
In this talk, I will present a Generative-Adversarial Network (GAN) based on convolutional neural ne...
We describe a multi-disciplinary project to use machine learning techniques based on neural networks...
In high energy physics, one of the most important processes for collider data analysis is the compar...
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