Javier Duarte | DeepAI

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∙ 09/13/2023

Scalable neural network models and terascale datasets for particle-flow reconstruction

We study scalable machine learning models for full event reconstruction ...

0 Joosep Pata, et al. ∙

∙ 06/20/2023

Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAs

In-time particle trajectory reconstruction in the Large Hadron Collider ...

0 Shi-Yu Huang, et al. ∙

∙ 03/30/2023

Progress towards an improved particle flow algorithm at CMS with machine learning

The particle-flow (PF) algorithm, which infers particles based on tracks...

0 Farouk Mokhtar, et al. ∙

∙ 12/14/2022

Lorentz Group Equivariant Autoencoders

There has been significant work recently in developing machine learning ...

0 Zichun Hao, et al. ∙

∙ 12/09/2022

FAIR AI Models in High Energy Physics

The findable, accessible, interoperable, and reusable (FAIR) data princi...

0 Javier Duarte, et al. ∙

∙ 11/18/2022

On the Evaluation of Generative Models in High Energy Physics

There has been a recent explosion in research into machine-learning-base...

0 Raghav Kansal, et al. ∙

∙ 11/17/2022

Do graph neural networks learn traditional jet substructure?

At the CERN LHC, the task of jet tagging, whose goal is to infer the ori...

0 Farouk Mokhtar, et al. ∙

∙ 09/26/2022

FastStamp: Accelerating Neural Steganography and Digital Watermarking of Images on FPGAs

Steganography and digital watermarking are the tasks of hiding recoverab...

7 Shehzeen Hussain, et al. ∙

∙ 07/19/2022

Data Science and Machine Learning in Education

The growing role of data science (DS) and machine learning (ML) in high-...

0 Gabriele Benelli, et al. ∙

∙ 03/23/2022

Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges

Many physical systems can be best understood as sets of discrete data wi...

297 Savannah Thais, et al. ∙

∙ 03/01/2022

Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders

We study how to use Deep Variational Autoencoders for a fast simulation ...

0 Mary Touranakou, et al. ∙

∙ 03/01/2022

Machine Learning for Particle Flow Reconstruction at CMS

We provide details on the implementation of a machine-learning based par...

0 Joosep Pata, et al. ∙

∙ 11/24/2021

Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance

Autoencoders have useful applications in high energy physics in anomaly ...

0 Steven Tsan, et al. ∙

∙ 11/24/2021

Explaining machine-learned particle-flow reconstruction

The particle-flow (PF) algorithm is used in general-purpose particle det...

0 Farouk Mokhtar, et al. ∙

∙ 06/22/2021

Particle Cloud Generation with Message Passing Generative Adversarial Networks

In high energy physics (HEP), jets are collections of correlated particl...

0 Raghav Kansal, et al. ∙

∙ 01/21/2021

MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks

In general-purpose particle detectors, the particle flow algorithm may b...

41 Joosep Pata, et al. ∙

∙ 11/30/2020

Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics

We develop a graph generative adversarial network to generate sparse dat...

0 Raghav Kansal, et al. ∙

∙ 08/08/2020

Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics

Graph neural networks have been shown to achieve excellent performance f...

0 Yutaro Iiyama, et al. ∙

∙ 07/20/2020

GPU coprocessors as a service for deep learning inference in high energy physics

In the next decade, the demands for computing in large scientific experi...

0 Jeffrey Krupa, et al. ∙

∙ 03/11/2020

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...

0 Giuseppe Di Guglielmo, et al. ∙

∙ 02/05/2020

Fast inference of Boosted Decision Trees in FPGAs for particle physics

We describe the implementation of Boosted Decision Trees in the hls4ml l...

0 Sioni Summers, et al. ∙

∙ 07/08/2018

Machine Learning in High Energy Physics Community White Paper

Machine learning is an important research area in particle physics, begi...

0 Kim Albertsson, et al. ∙

∙ 04/16/2018

Fast inference of deep neural networks in FPGAs for particle physics

Recent results at the Large Hadron Collider (LHC) have pointed to enhanc...

0 Javier Duarte, et al. ∙

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