no code implementations • 8 Mar 2024 • Yi-An Chen, Kai-Feng Chen
Machine learning, particularly deep neural networks, has been widely utilized in high energy physics and has shown remarkable results in various applications.
no code implementations • 1 Jul 2021 • Daniel C. Hackett, Chung-Chun Hsieh, Michael S. Albergo, Denis Boyda, Jiunn-Wei Chen, Kai-Feng Chen, Kyle Cranmer, Gurtej Kanwar, Phiala E. Shanahan
Recent results have demonstrated that samplers constructed with flow-based generative models are a promising new approach for configuration generation in lattice field theory.
1 code implementation • 5 Nov 2019 • Kai-Feng Chen, Yang-Ting Chien
Deciphering the complex information contained in jets produced in collider events requires a physical organization of the jet data.
High Energy Physics - Phenomenology High Energy Physics - Experiment Nuclear Experiment
5 code implementations • 6 Jun 2018 • Rex Ying, Ruining He, Kai-Feng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec
We develop a data-efficient Graph Convolutional Network (GCN) algorithm PinSage, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i. e., items) that incorporate both graph structure as well as node feature information.