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Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics
Generative models, and Generative Adversarial Networks (GAN) in particul...
03/29/2021 ∙ by Su Yeon Chang, et al. ∙
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Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case
Deep learning is finding its way into high energy physics by replacing t...
03/18/2021 ∙ by Florian Rehm, et al. ∙
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Pandemic Drugs at Pandemic Speed: Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers
The race to meet the challenges of the global pandemic has served as a r...
03/04/2021 ∙ by Agastya P. Bhati, et al. ∙
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Deep Learning strategies for ProtoDUNE raw data denoising
In this work we investigate different machine learning based strategies ...
03/02/2021 ∙ by Marco Rossi, et al. ∙
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Quantum Generative Adversarial Networks in a Continuous-Variable Architecture to Simulate High Energy Physics Detectors
Deep Neural Networks (DNNs) come into the limelight in High Energy Physi...
01/26/2021 ∙ by Su Yeon Chang, et al. ∙
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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...
12/14/2019 ∙ by Dawit Belayneh, et al. ∙
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Deploying AI Frameworks on Secure HPC Systems with Containers
The increasing interest in the usage of Artificial Intelligence techniqu...
05/24/2019 ∙ by David Brayford, et al. ∙
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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. ∙
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