14 citations · 30 across the 6 of their papers we have counts for
6 papers · 1 filter
SuperJEPA: Pretraining with a Supervision-Augmented Joint-Embedding Predictive Architecture for High Energy Physics
Dylan Sheldon Rankin, Ho Fung Tsoi
Foundation models often obtain powerful results through very large unlabeled training datasets that dwarf available labeled datasets in size, but high energy physics already provid…
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…
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…
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…