Maurizio Pierini

research

∙ 06/22/2023

Triggering Dark Showers with Conditional Dual Auto-Encoders

Auto-encoders (AEs) have the potential to be effective and generic tools...

0 Luca Anzalone, et al. ∙

research

∙ 05/23/2023

Goodness of fit by Neyman-Pearson testing

The Neyman-Pearson strategy for hypothesis testing can be employed for g...

0 Gaia Grosso, et al. ∙

research

∙ 05/06/2023

Symbolic Regression on FPGAs for Fast Machine Learning Inference

The high-energy physics community is investigating the feasibility of de...

0 Ho Fung Tsoi, et al. ∙

research

∙ 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. ∙

research

∙ 02/15/2023

Towards Optimal Compression: Joint Pruning and Quantization

Compression of deep neural networks has become a necessary stage for opt...

0 Ben Zandonati, et al. ∙

research

∙ 01/25/2023

Unravelling physics beyond the standard model with classical and quantum anomaly detection

Much hope for finding new physics phenomena at microscopic scale relies ...

0 Julian Schuhmacher, et al. ∙

research

∙ 01/25/2023

Quantum anomaly detection in the latent space of proton collision events at the LHC

We propose a new strategy for anomaly detection at the LHC based on unsu...

0 Kinga Anna Woźniak, et al. ∙

research

∙ 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. ∙

research

∙ 10/16/2022

FIT: A Metric for Model Sensitivity

Model compression is vital to the deployment of deep learning on edge de...

0 Ben Zandonati, et al. ∙

research

∙ 09/28/2022

LL-GNN: Low Latency Graph Neural Networks on FPGAs for Particle Detectors

This work proposes a novel reconfigurable architecture for low latency G...

0 Zhiqiang Que, et al. ∙

research

∙ 05/16/2022

Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml

In this paper, we investigate how field programmable gate arrays can ser...

0 Nicolò Ghielmetti, et al. ∙

research

∙ 04/05/2022

Learning new physics efficiently with nonparametric methods

We present a machine learning approach for model-independent new physics...

0 Marco Letizia, et al. ∙

research

∙ 04/04/2022

End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks

We present an end-to-end reconstruction algorithm to build particle cand...

0 Shah Rukh Qasim, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 02/09/2022

Lightweight Jet Reconstruction and Identification as an Object Detection Task

We apply object detection techniques based on deep convolutional blocks ...

0 Adrian Alan Pol, et al. ∙

research

∙ 12/06/2021

Autoencoders for Semivisible Jet Detection

The production of dark matter particles from confining dark sectors may ...

0 Florencia Canelli, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 07/27/2021

Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders

We present an application of anomaly detection techniques based on deep ...

0 Eric A. Moreno, et al. ∙

research

∙ 06/26/2021

Accelerating Recurrent Neural Networks for Gravitational Wave Experiments

This paper presents novel reconfigurable architectures for reducing the ...

0 Zhiqiang Que, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 10/12/2020

Anomaly Detection With Conditional Variational Autoencoders

Exploiting the rapid advances in probabilistic inference, in particular ...

0 Adrian Alan Pol, et al. ∙

research

∙ 10/05/2020

Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning

We present a fast simulation application based on a Deep Neural Network,...

0 Cheng Chen, et al. ∙

research

∙ 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. ∙

research

∙ 05/04/2020

Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark

We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to ...

0 Oliver Knapp, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 12/14/2019

Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics

Using detailed simulations of calorimeter showers as training data, we i...

0 Dawit Belayneh, et al. ∙

research

∙ 02/21/2019

Learning representations of irregular particle-detector geometry with distance-weighted graph networks

We explore the use of graph networks to deal with irregular-geometry det...

0 Shah Rukh Qasim, et al. ∙

research

∙ 01/16/2019

LHC analysis-specific datasets with Generative Adversarial Networks

Using generative adversarial networks (GANs), we investigate the possibi...

0 Bobak Hashemi, et al. ∙

research

∙ 11/26/2018

Variational Autoencoders for New Physics Mining at the Large Hadron Collider

Using variational autoencoders trained on known physics processes, we de...

0 Olmo Cerri, et al. ∙

research

∙ 07/27/2018

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 ...

0 Adrian Alan Pol, et al. ∙

research

∙ 06/29/2018

Topology classification with deep learning to improve real-time event selection at the LHC

We show how event topology classification based on deep learning could b...

0 Thong Q. Nguyen, et al. ∙

research

∙ 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. ∙

Maurizio Pierini

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