Thea Aarrestad

Orcid: 0000-0002-7671-243X

According to our database1, Thea Aarrestad authored at least 25 papers between 2019 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
HGQ: High Granularity Quantization for Real-time Neural Networks on FPGAs.
Proceedings of the 2026 ACM/SIGDA International Symposium on Field Programmable Gate Arrays, 2026

2025
hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware.
CoRR, December, 2025

Pushing the limits of real-time ML: Nanosecond inference for Physics Discovery at the Large Hadron Collider.
Proceedings of the 15th Conference on Innovative Data Systems Research, 2025

2024
Ultrafast jet classification at the HL-LHC.
Mach. Learn. Sci. Technol., 2024

Distilling particle knowledge for fast reconstruction at high-energy physics experiments.
Mach. Learn. Sci. Technol., 2024

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

Gradient-based Automatic Per-Weight Mixed Precision Quantization for Neural Networks On-Chip.
CoRR, 2024

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

2023
Machine Learning for Anomaly Detection in Particle Physics.
CoRR, 2023

Within-Camera Multilayer Perceptron DVS Denoising.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml.
Mach. Learn. Sci. Technol., December, 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

Lightweight jet reconstruction and identification as an object detection task.
Mach. Learn. Sci. Technol., 2022

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

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

2021
Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors.
Nat. Mach. Intell., 2021

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

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

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

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

Accelerating Recurrent Neural Networks for Gravitational Wave Experiments.
Proceedings of the 32nd IEEE International Conference on Application-specific Systems, 2021

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

Ultra Low-latency, Low-area Inference Accelerators using Heterogeneous Deep Quantization with QKeras and hls4ml.
CoRR, 2020

2019
A novel multidimensional search for diboson resonances in the boosted dijet final state and encoding jet substructure with machine learning.
PhD thesis, 2019


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