Applications of Machine Learning to Lattice Quantum Field Theory

no code implementations • 10 Feb 2022 • Denis Boyda, Salvatore Calì, Sam Foreman, Lena Funcke, Daniel C. Hackett, Yin Lin, Gert Aarts, Andrei Alexandru, Xiao-Yong Jin, Biagio Lucini, Phiala E. Shanahan

There is great potential to apply machine learning in the area of numerical lattice quantum field theory, but full exploitation of that potential will require new strategies.

BIG-bench Machine Learning

Representation Bias in Data: A Survey on Identification and Resolution Techniques

no code implementations • 22 Mar 2022 • Nima Shahbazi, Yin Lin, Abolfazl Asudeh, H. V. Jagadish

Data-driven algorithms are only as good as the data they work with, while data sets, especially social data, often fail to represent minorities adequately.

Decision Making Fairness

Neural-network preconditioners for solving the Dirac equation in lattice gauge theory

no code implementations • 4 Aug 2022 • Salvatore Calì, Daniel C. Hackett, Yin Lin, Phiala E. Shanahan, Brian Xiao

This work develops neural-network--based preconditioners to accelerate solution of the Wilson-Dirac normal equation in lattice quantum field theories.

Exploring Part-Informed Visual-Language Learning for Person Re-Identification

no code implementations • 4 Aug 2023 • Yin Lin, Cong Liu, Yehansen Chen, Jinshui Hu, Bing Yin, BaoCai Yin, Zengfu Wang

Recently, visual-language learning has shown great potential in enhancing visual-based person re-identification (ReID).

Human Parsing Person Re-Identification