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portfolio

publications

Uncertainty Regularized Evidential Regression

Published in AAAI Conference on Artificial Intelligence (AAAI 2024, oral), 2024

We propose an uncertainty‑regularized evidential regression model that fixes the zero‑gradient issue in evidential learning and improves reliability for medical prediction tasks. :contentReference[oaicite:4]{index=4}

Recommended citation: Ye, K.*, Chen, T., Wei, H., & Zhan, L. (2024). Uncertainty Regularized Evidential Regression. AAAI 2024 (oral).
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BPEN: Brain Posterior Evidential Network for Trustworthy Brain Imaging Analysis

Published in Neural Networks, 2024, 2024

We develop BPEN, an evidential deep learning model that produces calibrated posterior estimates for brain imaging tasks, improving trustworthiness in neurodegenerative disease assessment.

Recommended citation: Ye, K.* et al. (2024). BPEN: Brain Posterior Evidential Network for Trustworthy Brain Imaging Analysis. Neural Networks, 2024.
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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.