3 citations · 3 across the 5 of their papers we have counts for
6 papers
RINO: Renormalization Group Distillation with No Labels
Zichun Hao, Raghav Kansal, Abhijith Gandrakota +6
Supervised machine learning in high-energy physics (HEP) requires training on labeled simulations of particle collisions, which are known to be unfaithful to the underlying physics…
Energy Score-Guided Neural Gaussian Mixture Model for Predictive Uncertainty Quantification
Yang Yang, Chunlin Ji, Haoyang Li +1
Quantifying predictive uncertainty is essential for real world machine learning applications, especially in scenarios requiring reliable and interpretable predictions. Many common…
Building Machine Learning Challenges for Anomaly Detection in Science
Elizabeth G. Campolongo, Yuan-Tang Chou, Ekaterina Govorkova +148
Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not…
Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture
Subash Katel, Haoyang Li, Zihan Zhao +3
In high energy physics, self-supervised learning (SSL) methods have the potential to aid in the creation of machine learning models without the need for labeled datasets for a vari…
Reconstruction of boosted and resolved multi-Higgs-boson events with symmetry-preserving attention networks
Haoyang Li, Marko Stamenkovic, Alexander Shmakov +12
The production of multiple Higgs bosons at the CERN LHC provides a direct way to measure the trilinear and quartic Higgs self-interaction strengths as well as potential access to b…
Large-Scale Pretraining and Finetuning for Efficient Jet Classification in Particle Physics
Zihan Zhao, Farouk Mokhtar, Raghav Kansal +2
This study introduces an innovative approach to analyzing unlabeled data in high-energy physics (HEP) through the application of self-supervised learning (SSL). Faced with the incr…