Javier M. Duarte

Orcid: 0000-0002-5076-7096

Affiliations:
  • University of California San Diego, La Jolla, CA, USA


According to our database1, Javier M. Duarte authored at least 71 papers between 2018 and 2024.

Collaborative distances:

Timeline

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Bibliography

2024
Tailor: Altering Skip Connections for Resource-Efficient Inference.
ACM Trans. Reconfigurable Technol. Syst., March, 2024

Corrigendum: Applications and techniques for fast machine learning in science.
Frontiers Big Data, 2024

Architectural Implications of Neural Network Inference for High Data-Rate, Low-Latency Scientific Applications.
CoRR, 2024

Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics.
CoRR, 2024

Sets are all you need: Ultrafast jet classification on FPGAs for HL-LHC.
CoRR, 2024


2023
Differentiable Earth mover's distance for data compression at the high-luminosity LHC.
Mach. Learn. Sci. Technol., December, 2023

LHC hadronic jet generation using convolutional variational autoencoders with normalizing flows.
Mach. Learn. Sci. Technol., December, 2023

FAIR AI models in high energy physics.
Mach. Learn. Sci. Technol., December, 2023

Snowmass 2021 Computational Frontier CompF4 Topical Group Report Storage and Processing Resource Access.
Comput. Softw. Big Sci., December, 2023

JetNet: A Python package for accessing open datasets and benchmarking machine learning methods in high energy physics.
J. Open Source Softw., November, 2023

Neural Architecture Codesign for Fast Bragg Peak Analysis.
CoRR, 2023

Induced Generative Adversarial Particle Transformers.
CoRR, 2023

Scalable neural network models and terascale datasets for particle-flow reconstruction.
CoRR, 2023

End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs.
CoRR, 2023

Progress towards an improved particle flow algorithm at CMS with machine learning.
CoRR, 2023

Tailor: Altering Skip Connections for Resource-Efficient Inference.
CoRR, 2023

Voyager - An Innovative Computational Resource for Artificial Intelligence & Machine Learning Applications in Science and Engineering.
Proceedings of the Practice and Experience in Advanced Research Computing, 2023

Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAs.
Proceedings of the 33rd International Conference on Field-Programmable Logic and Applications, 2023

Adapting Skip Connections for Resource-Efficient FPGA Inference.
Proceedings of the 2023 ACM/SIGDA International Symposium on Field Programmable Gate Arrays, 2023

2022
Author Correction: Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider.
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.
Nat. Mach. Intell., 2022

Particle-based fast jet simulation at the LHC with variational autoencoders.
Mach. Learn. Sci. Technol., 2022

Editorial: Efficient AI in particle physics and astrophysics.
Frontiers Artif. Intell., 2022

Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows.
Frontiers Big Data, 2022

Graph Neural Networks for Charged Particle Tracking on FPGAs.
Frontiers Big Data, 2022

Applications and Techniques for Fast Machine Learning in Science.
Frontiers Big Data, 2022

Lorentz Group Equivariant Autoencoders.
CoRR, 2022

On the Evaluation of Generative Models in High Energy Physics.
CoRR, 2022

Do graph neural networks learn traditional jet substructure?
CoRR, 2022

FAIR for AI: An interdisciplinary, international, inclusive, and diverse community building perspective.
CoRR, 2022

Snowmass 2021 Computational Frontier CompF4 Topical Group Report: Storage and Processing Resource Access.
CoRR, 2022

Data Science and Machine Learning in Education.
CoRR, 2022

FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning.
CoRR, 2022

Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark.
CoRR, 2022

QONNX: Representing Arbitrary-Precision Quantized Neural Networks.
CoRR, 2022

Physics Community Needs, Tools, and Resources for Machine Learning.
CoRR, 2022

Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges.
CoRR, 2022

Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders.
CoRR, 2022

Machine Learning for Particle Flow Reconstruction at CMS.
CoRR, 2022

FastStamp: Accelerating Neural Steganography and Digital Watermarking of Images on FPGAs.
Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, 2022

2021
Charged particle tracking with quantum annealing optimization.
Quantum Mach. Intell., 2021

Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml.
Mach. Learn. Sci. Technol., 2021

GPU coprocessors as a service for deep learning inference in high energy physics.
Mach. Learn. Sci. Technol., 2021

Fast convolutional neural networks on FPGAs with hls4ml.
Mach. Learn. Sci. Technol., 2021

Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural Network Inference.
Frontiers Artif. Intell., 2021

Charged Particle Tracking via Edge-Classifying Interaction Networks.
Comput. Softw. Big Sci., 2021

Graph Neural Networks for Charged Particle Tracking on FPGAs.
CoRR, 2021

Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance.
CoRR, 2021

Explaining machine-learned particle-flow reconstruction.
CoRR, 2021

Applications and Techniques for Fast Machine Learning in Science.
CoRR, 2021

A FAIR and AI-ready Higgs Boson Decay Dataset.
CoRR, 2021

Particle Cloud Generation with Message Passing Generative Adversarial Networks.
CoRR, 2021

MLPerf Tiny Benchmark.
CoRR, 2021

A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC.
CoRR, 2021

hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices.
CoRR, 2021

MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks.
CoRR, 2021

Fast convolutional neural networks on FPGAs with hls4ml.
CoRR, 2021

Particle Cloud Generation with Message Passing Generative Adversarial Networks.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021


2020
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics.
Frontiers Big Data, 2020

Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs.
CoRR, 2020

Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics.
CoRR, 2020

GPU coprocessors as a service for deep learning inference in high energy physics.
CoRR, 2020

Fast inference of Boosted Decision Trees in FPGAs for particle physics.
CoRR, 2020

FPGAs-as-a-Service Toolkit (FaaST).
Proceedings of the 2020 IEEE/ACM International Workshop on Heterogeneous High-performance Reconfigurable Computing, 2020

2019
FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing.
Comput. Softw. Big Sci., December, 2019

Charged particle tracking with quantum annealing-inspired optimization.
CoRR, 2019

Fast Inference of Deep Neural Networks for Real-time Particle Physics Applications.
Proceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2019

2018
Machine Learning in High Energy Physics Community White Paper.
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CoRR, 2018

Fast inference of deep neural networks in FPGAs for particle physics.
CoRR, 2018


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