Shiying Li

Orcid: 0000-0001-6988-8229

Affiliations:
  • University of North Carolina at Chapel Hill, NC, USA
  • University of Virginia, Department of Biomedical Engineering, Charlottesville, VI, USA
  • Vanderbilt University, Nashville, TN, USA (PhD 2019)


According to our database1, Shiying Li authored at least 15 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
End-to-End Signal Classification in Signed Cumulative Distribution Transform Space.
IEEE Trans. Pattern Anal. Mach. Intell., September, 2024

Linear optimal transport subspaces for point set classification.
CoRR, 2024

2023
Invariance encoding in sliced-Wasserstein space for image classification with limited training data.
Pattern Recognit., May, 2023

Approximation properties of slice-matching operators.
CoRR, 2023

The Radon Signed Cumulative Distribution Transform and its applications in classification of Signed Images.
CoRR, 2023

Measure transfer via stochastic slicing and matching.
CoRR, 2023

2022
Geodesic Properties of a Generalized Wasserstein Embedding for Time Series Analysis.
CoRR, 2022

Local Sliced-Wasserstein Feature Sets for Illumination-invariant Face Recognition.
CoRR, 2022

Geodesic Properties of a Generalized Wasserstein Embedding for Time Series Aanalysis.
Proceedings of the Topological, 2022

Nearest Subspace Search in The Signed Cumulative Distribution Transform Space For 1d Signal Classification.
Proceedings of the IEEE International Conference on Acoustics, 2022

Learning Energy-Based Models with Adversarial Training.
Proceedings of the Computer Vision - ECCV 2022, 2022

2021
Radon Cumulative Distribution Transform Subspace Modeling for Image Classification.
J. Math. Imaging Vis., 2021

2020
Parametric Signal Estimation Using the Cumulative Distribution Transform.
IEEE Trans. Signal Process., 2020

Analyzing and Improving Generative Adversarial Training for Generative Modeling and Out-of-Distribution Detection.
CoRR, 2020

Partitioning signal classes using transport transforms for data analysis and machine learning.
CoRR, 2020


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