Author Correction: Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider.

[BibT_eX]

[DOI]

Ekaterina Govorkova

Ema Puljak

Nat. Mach. Intell., 2022

Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider.

[BibT_eX]

[DOI]

Nat. Mach. Intell., 2022

Particle-based fast jet simulation at the LHC with variational autoencoders.

[BibT_eX]

[DOI]

Mary Touranakou

Nadezda Chernyavskaya

Mach. Learn. Sci. Technol., 2022

Lightweight jet reconstruction and identification as an object detection task.

[BibT_eX]

[DOI]

Mach. Learn. Sci. Technol., 2022

Source-agnostic gravitational-wave detection with recurrent autoencoders.

[BibT_eX]

[DOI]

Eric A. Moreno

Bartlomiej Borzyszkowski

Maurizio Pierini

Jean-Roch Vlimant

Maria Spiropulu

Mach. Learn. Sci. Technol., 2022

Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows.

[BibT_eX]

[DOI]

Pratik Jawahar

Thea Aarrestad

Nadezda Chernyavskaya

Frontiers Big Data, 2022

Applications and Techniques for Fast Machine Learning in Science.

[BibT_eX]

[DOI]

Frontiers Big Data, 2022

On the Evaluation of Generative Models in High Energy Physics.

[BibT_eX]

[DOI]

Raghav Kansal

Anni Li

Javier M. Duarte

Nadezda Chernyavskaya

Maurizio Pierini

Breno Orzari

Thiago Tomei

CoRR, 2022

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

[BibT_eX]

[DOI]

CoRR, 2022

Learning new physics efficiently with nonparametric methods.

[BibT_eX]

[DOI]

CoRR, 2022

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

[BibT_eX]

[DOI]

Shah Rukh Qasim

Nadezda Chernyavskaya

CoRR, 2022

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

[BibT_eX]

[DOI]

Mary Touranakou

Nadezda Chernyavskaya

CoRR, 2022

Machine Learning for Particle Flow Reconstruction at CMS.

[BibT_eX]

[DOI]

CoRR, 2022

Optimizing Graph Neural Networks for Jet Tagging in Particle Physics on FPGAs.

[BibT_eX]

[DOI]

Proceedings of the 32nd International Conference on Field-Programmable Logic and Applications, 2022

2021

Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors.

[BibT_eX]

[DOI]

Claudionor José Nunes Coelho Jr.

Thea Klaeboe Aarrestad

Nat. Mach. Intell., 2021

Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml.

[BibT_eX]

[DOI]

Giuseppe Di Guglielmo

Mach. Learn. Sci. Technol., 2021

Fast convolutional neural networks on FPGAs with hls4ml.

[BibT_eX]

[DOI]

Mach. Learn. Sci. Technol., 2021

Analysis-Specific Fast Simulation at the LHC with Deep Learning.

[BibT_eX]

[DOI]

Comput. Softw. Big Sci., 2021

Autoencoders for Semivisible Jet Detection.

[BibT_eX]

[DOI]

CoRR, 2021

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

[BibT_eX]

[DOI]

CoRR, 2021

Explaining machine-learned particle-flow reconstruction.

[BibT_eX]

[DOI]

CoRR, 2021

Applications and Techniques for Fast Machine Learning in Science.

[BibT_eX]

[DOI]

Allison McCarn Deiana

Nhan Tran

Joshua Agar

Michaela Blott

Giuseppe Di Guglielmo

Tomás E. Müller-Bravo

Seyedramin Rasoulinezhad

Maria Domenica Galati

Mohammed Attia Mohammed

Subramanian Ramamoorthy

CoRR, 2021

Particle Cloud Generation with Message Passing Generative Adversarial Networks.

[BibT_eX]

[DOI]

CoRR, 2021

A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC.

[BibT_eX]

[DOI]

Giuseppe Di Guglielmo

Farah Fahim

Christian Herwig

Manuel Blanco Valentin

CoRR, 2021

hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices.

[BibT_eX]

[DOI]

CoRR, 2021

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

[BibT_eX]

[DOI]

CoRR, 2021

Fast convolutional neural networks on FPGAs with hls4ml.

[BibT_eX]

[DOI]

Christoffer Petersson

Hampus Linander

Yutaro Iiyama

Giuseppe Di Guglielmo

CoRR, 2021

Particle Cloud Generation with Message Passing Generative Adversarial Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Accelerating Recurrent Neural Networks for Gravitational Wave Experiments.

[BibT_eX]

[DOI]

Bartlomiej Borzyszkowski

Proceedings of the 32nd IEEE International Conference on Application-specific Systems, 2021

2020

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

[BibT_eX]

[DOI]

Frontiers Big Data, 2020

Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs.

[BibT_eX]

[DOI]

CoRR, 2020

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

[BibT_eX]

[DOI]

CoRR, 2020

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

[BibT_eX]

[DOI]

CoRR, 2020

Ultra Low-latency, Low-area Inference Accelerators using Heterogeneous Deep Quantization with QKeras and hls4ml.

[BibT_eX]

[DOI]

Claudionor José Nunes Coelho Jr.

CoRR, 2020

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

[BibT_eX]

[DOI]

CoRR, 2020

Fast inference of Boosted Decision Trees in FPGAs for particle physics.

[BibT_eX]

[DOI]

Sioni Summers

Giuseppe Di Guglielmo

CoRR, 2020

2019

Detector Monitoring with Artificial Neural Networks at the CMS Experiment at the CERN Large Hadron Collider.

[BibT_eX]

[DOI]

Comput. Softw. Big Sci., December, 2019

Topology Classification with Deep Learning to Improve Real-Time Event Selection at the LHC.

[BibT_eX]

[DOI]

Comput. Softw. Big Sci., December, 2019

FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing.

[BibT_eX]

[DOI]

Comput. Softw. Big Sci., December, 2019

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

[BibT_eX]

[DOI]

CoRR, 2019

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

[BibT_eX]

[DOI]

CoRR, 2019

LHC analysis-specific datasets with Generative Adversarial Networks.

[BibT_eX]

[DOI]

CoRR, 2019

Anomaly Detection with Conditional Variational Autoencoders.

[BibT_eX]

[DOI]

Proceedings of the 18th IEEE International Conference On Machine Learning And Applications, 2019

Fast Inference of Deep Neural Networks for Real-time Particle Physics Applications.

[BibT_eX]

[DOI]

Proceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2019

2018

Variational Autoencoders for New Physics Mining at the Large Hadron Collider.

[BibT_eX]

[DOI]

CoRR, 2018

Pileup mitigation at the Large Hadron Collider with Graph Neural Networks.

[BibT_eX]

[DOI]

Jesus Arjona Martinez

CoRR, 2018

Fast inference of deep neural networks in FPGAs for particle physics.

[BibT_eX]

[DOI]

CoRR, 2018

2017

Deep learning for inferring cause of data anomalies.

[BibT_eX]

[DOI]

CoRR, 2017

Maurizio Pierini

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