Philip C. Harris

Orcid: 0000-0001-8189-3741

According to our database1, Philip C. Harris authored at least 39 papers between 2018 and 2024.

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Bibliography

2024
GWAK: gravitational-wave anomalous knowledge with recurrent autoencoders.
Mach. Learn. Sci. Technol., 2024

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

Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models.
CoRR, 2024

Ultra Fast Transformers on FPGAs for Particle Physics Experiments.
CoRR, 2024

SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning.
CoRR, 2024


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

Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing.
Comput. Softw. Big Sci., December, 2023

Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml.
Mach. Learn. Sci. Technol., June, 2023

Optimizing Likelihood-free Inference using Self-supervised Neural Symmetry Embeddings.
CoRR, 2023

Symbolic Regression on FPGAs for Fast Machine Learning Inference.
CoRR, 2023

Knowledge Distillation for Anomaly Detection.
Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

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

<i>AIgean</i>: An Open Framework for Deploying Machine Learning on Heterogeneous Clusters.
ACM Trans. Reconfigurable Technol. Syst., 2022

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

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

Neural Embedding: Learning the Embedding of the Manifold of Physics Data.
CoRR, 2022

Data Science and Machine Learning in Education.
CoRR, 2022

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

A Software Ecosystem for Deploying Deep Learning in Gravitational Wave Physics.
Proceedings of the FlexScience '22: Proceedings of the 12th Workshop on AI and Scientific Computing at Scale using Flexible Computing Infrastructures, 2022

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

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

A FAIR and AI-ready Higgs Boson Decay Dataset.
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

2020
GPU-Accelerated Machine Learning Inference as a Service for Computing in Neutrino Experiments.
Frontiers Big Data, 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

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

AIgean: An Open Framework for Machine Learning on Heterogeneous Clusters.
Proceedings of the 28th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, 2020

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

Response to NITRD, NCO, NSF Request for Information on "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan".
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
Fast inference of deep neural networks in FPGAs for particle physics.
CoRR, 2018


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