6 citations · 12 across the 14 of their papers we have counts for
7 papers · 1 filter
Similarity Pairing with Energy Mover's Distance for Self-Supervised Pre-Training at the LHC
Ho Fung Tsoi, Dylan Rankin
Many self-supervised methods for training foundation models at the Large Hadron Collider (LHC) rely on data augmentations to encourage the model to embed events into a representati…
Self-Supervised Learning for Robust Resonance Mass Regression in Cascade Decays
Ho Fung Tsoi, Alex Yang, Luis Felipe Gutierrez Zagazeta +2
Reconstructing the mass of a heavy resonance from its decay products with missing energy is one of the central tasks that directly determine the sensitivity in new physics searches…
Machine Can Automatically Discover Parametric Functions to Model HEP Data
Ho Fung Tsoi, Dylan Rankin, Cecile Caillol +5
In HEP data analyses, finding an adequate function to model binned data has largely relied on a manual process: guess a functional form by intuition, fit, examine, then repeat unti…
Chopping and distilling variational autoencoders for real-time anomaly detection in high energy physics
Max Cohen, Rajat Gupta, Sterre Hoogendoorn +3
Anomaly detection (AD) has recently emerged as an exciting alternative to conventional search strategies in high energy physics using artificial intelligence (AI) and machine learn…
Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision
Thea Klaeboe Aarrestad, Alaa Abdelhamid, Haider Abidi +457
Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape co…
Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)
Julia Gonski, Jennifer Ott, Jenni Ott +129
The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environmen…
jBOT: Semantic Jet Representation Clustering Emerges from Self-Distillation
Ho Fung Tsoi, Dylan Rankin
Self-supervised learning, in the context of foundation model training, is a powerful pre-training method for learning feature representations without labels, which often capture ge…