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06/22/2023
Triggering Dark Showers with Conditional Dual Auto-Encoders
Auto-encoders (AEs) have the potential to be effective and generic tools...
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02/15/2023
Towards Optimal Compression: Joint Pruning and Quantization
Compression of deep neural networks has become a necessary stage for opt...
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11/18/2022
On the Evaluation of Generative Models in High Energy Physics
There has been a recent explosion in research into machine-learning-base...
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09/28/2022
LL-GNN: Low Latency Graph Neural Networks on FPGAs for Particle Detectors
This work proposes a novel reconfigurable architecture for low latency G...
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06/22/2021
Particle Cloud Generation with Message Passing Generative Adversarial Networks
In high energy physics (HEP), jets are collections of correlated particl...
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01/21/2021
MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
In general-purpose particle detectors, the particle flow algorithm may b...
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10/12/2020
Anomaly Detection With Conditional Variational Autoencoders
Exploiting the rapid advances in probabilistic inference, in particular ...
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03/11/2020
Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML
We present the implementation of binary and ternary neural networks in t...
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04/16/2018
