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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
About me
Posts
Building an Academic Website
If you’re an academic, you need a website. Obviously I agree with this since you’re reading this on my website, but if you don’t have one, you should get one. Most universities these days provide a free option, usually powered by WordPress (both WashU and UNC use WordPress for their respective offerings). While these sites are quick to set up and come with the prestige of a .edu URL, they have several drawbacks that have been extensively written on.
publications
Electron correlations engineer catalytic activity of pyrochlore iridates for acidic water oxidation
C. Shang, C. Cao, D. Yu, Y. Yan, Y. Lin, H. Li, T. Zheng, X. Yan, W. Yu, S. Zhou, J. Zeng, Adv. Mater. 2019, 31, 1805104.
A gaussian process-bayesian bernoulli mixture model for multi-label active learning
Shi, W.*, Yu, D.* and Yu, Q., 2021. A gaussian process-bayesian bernoulli mixture model for multi-label active learning. Advances in Neural Information Processing Systems, 34, pp.27542-27554.
Discover-Then-Rank Unlabeled Support Vectors in the Dual Space for Multi-Class Active Learning
Yu, D., Shi, W. and Yu, Q., 2023, July. Discover-Then-Rank Unlabeled Support Vectors in the Dual Space for Multi-Class Active Learning. In International Conference on Machine Learning (pp. 40321-40338). PMLR.
STARS: spatial-temporal active re-sampling for label-efficient learning from noisy annotations
Yu, D., Shi, W. and Yu, Q., 2023, June. STARS: spatial-temporal active re-sampling for label-efficient learning from noisy annotations. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 9, pp. 10980-10988).
Actively testing your model while it learns: realizing label-efficient learning in practice
Yu, D., Shi, W. and Yu, Q., 2024. Actively testing your model while it learns: realizing label-efficient learning in practice. Advances in Neural Information Processing Systems, 36.
BOSS: Diversity-Difficulty Balanced One-Shot Subset Selection for Data-Efficient Deep Learning
Acharya, A., Yu, D., Yu, Q. and Liu, X., BOSS: Diversity-Difficulty Balanced One-Shot Subset Selection for Data-Efficient Deep Learning. In ICML 2024.
Deep energy-pressure regression for a thermodynamically consistent EOS model
Yu, D., Pandey, D.S., Hinz, J., Mihaylov, D., Karasiev, V.V., Hu, S.X. and Yu, Q., 2024. Deep energy-pressure regression for a thermodynamically consistent EOS model. Machine Learning: Science and Technology, 5(1), p.015031.
The development of thermodynamically consistent and physics-informed equation-of-state model through machine learning
Hinz, J., Yu, D., Pandey, D.S., Sapkota, H., Yu, Q., Mihaylov, D.I., Karasiev, V.V. and Hu, S.X., 2024. The development of thermodynamically consistent and physics-informed equation-of-state model through machine learning. APL Machine Learning, 2(2).
Evidential Mixture Machines: Deciphering Multi-Label Correlations for Active Learning Sensitivity
Yu, D., Li, M., Shi, W. and Yu, Q., Evidential Mixture Machines: Deciphering Multi-Label Correlations for Active Learning Sensitivity. In The Thirty-eighth Annual Conference on Neural Information Processing Systems..
