4 citations · 5 across the 8 of their papers we have counts for
4 papers · 1 filter
Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml
Katya Govorkova, Julian Garcia Pardinas, Vladimir Loncar +5
This paper presents an end-to-end demonstration of a viable, ultra-fast, radiation-hard machine learning (ML) application on FPGAs, which could be used in future high-energy physic…
It's not a FAD: first results in using Flows for unsupervised Anomaly Detection at 40 MHz at the Large Hadron Collider
Francesco Vaselli, Chang Sun, Thea Aarrestad +7
We present the first implementation of a Continuous Normalizing Flow (CNF) model for unsupervised anomaly detection within the realistic, high-rate environment of the Large Hadron…
Anomaly preserving contrastive neural embeddings for end-to-end model-independent searches at the LHC
Kyle Metzger, Lana Xu, Mia Sodini +4
Anomaly detection - identifying deviations from Standard Model predictions - is a key challenge at the Large Hadron Collider due to the size and complexity of its datasets. This is…
Jet Single Shot Detection
Adrian Alan Pol, Thea Aarrestad, Katya Govorkova +8
We apply object detection techniques based on Convolutional Neural Networks to jet reconstruction and identification at the CERN Large Hadron Collider. In particular, we focus on C…