
SourceAgnostic GravitationalWave Detection with Recurrent Autoencoders
We present an application of anomaly detection techniques based on deep ...
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Accelerating Recurrent Neural Networks for Gravitational Wave Experiments
This paper presents novel reconfigurable architectures for reducing the ...
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Particle Cloud Generation with Message Passing Generative Adversarial Networks
In high energy physics (HEP), jets are collections of correlated particl...
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MLPF: Efficient machinelearned particleflow reconstruction using graph neural networks
In generalpurpose particle detectors, the particle flow algorithm may b...
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Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics
We develop a graph generative adversarial network to generate sparse dat...
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Anomaly Detection With Conditional Variational Autoencoders
Exploiting the rapid advances in probabilistic inference, in particular ...
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Data Augmentation at the LHC through Analysisspecific Fast Simulation with Deep Learning
We present a fast simulation application based on a Deep Neural Network,...
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DistanceWeighted Graph Neural Networks on FPGAs for RealTime Particle Reconstruction in High Energy Physics
Graph neural networks have been shown to achieve excellent performance f...
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Adversarially Learned Anomaly Detection on CMS Open Data: rediscovering the top quark
We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to ...
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Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML
We present the implementation of binary and ternary neural networks in t...
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Fast inference of Boosted Decision Trees in FPGAs for particle physics
We describe the implementation of Boosted Decision Trees in the hls4ml l...
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Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
Using detailed simulations of calorimeter showers as training data, we i...
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Learning representations of irregular particledetector geometry with distanceweighted graph networks
We explore the use of graph networks to deal with irregulargeometry det...
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LHC analysisspecific datasets with Generative Adversarial Networks
Using generative adversarial networks (GANs), we investigate the possibi...
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Variational Autoencoders for New Physics Mining at the Large Hadron Collider
Using variational autoencoders trained on known physics processes, we de...
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Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collider
Reliable data quality monitoring is a key asset in delivering collision ...
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Topology classification with deep learning to improve realtime event selection at the LHC
We show how event topology classification based on deep learning could b...
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Fast inference of deep neural networks in FPGAs for particle physics
Recent results at the Large Hadron Collider (LHC) have pointed to enhanc...
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Maurizio Pierini
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