Search Results for author: Kai-Feng Chen

Found 4 papers, 2 papers with code

Jet Discrimination with Quantum Complete Graph Neural Network

no code implementations8 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.

Quantum Machine Learning

Flow-based sampling for multimodal distributions in lattice field theory

no code implementations1 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.

Deep Learning Jet Substructure from Two-Particle Correlation

1 code implementation5 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

Graph Convolutional Neural Networks for Web-Scale Recommender Systems

5 code implementations6 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.

Recommendation Systems

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