Jennifer Ngadiuba

Orcid: 0000-0002-0055-2935

According to our database1, Jennifer Ngadiuba authored at least 30 papers between 2018 and 2024.

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

Timeline

Legend:

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Online presence:

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Bibliography

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

Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning.
CoRR, 2024


2023
Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder.
CoRR, 2023

Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation.
CoRR, 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

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

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

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

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

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

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

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
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

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

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

2019
FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing.
Comput. Softw. Big Sci., December, 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
Fast inference of deep neural networks in FPGAs for particle physics.
CoRR, 2018


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