ARL Mentor: Dr. Robert Jensen (https://www.linkedin.com/in/robert-jensen-phd-adhesives/)
1. [In progress] Yin, B., et al., Generative Self-Supervised Learning for Ordinal Regression on Long-tailed Data.
2. [IEEE ICMLA 2023] Yin, B., et al., AlloyGAN: Domain-Promptable Generative Adversarial Network for Generating Aluminum Alloy Microstructures. In 22nd IEEE International Conference on Machine Learning and Applications, 2023. (https://deepalloy.com/)
3. [IEEE ICMLA 2023] Yin, B., et al., DeepSC-Edge: Scientific Corrosion Segmentation with Edge-Guided and Class-Balanced Losses. In 22nd IEEE International Conference on Machine Learning and Applications, 2023.
4. [ACM CIKM 2023] Yin, B., et al., MOSS: AI Platform for Discovery of Corrosion-Resistant Materials. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023.
5. [IEEE ICMLA 2022] Josselyn, N., Yin, B., et al., Transferring Indoor Corrosion Image Assessment Models to Outdoor Images via Domain Adaptation, IEEE Intl. Conference on Machine Learning and Applications, 2022.
6. [Issued Patent 2022] Xu F., Yin, B., et al., Automatic discriminant analysis of graphic problems based on tracked points, CN Patent CN201811352610.2 A., issued at 2022.
7. [BMVC 2021] Yin, B., et al., Corrosion image data set for automating scientific assessment of materials. In British Machine Vision Conference (BMVC), 2021.
8. [ACL 2020] Sen, C., Hartvigsen, T., Yin, B., et al., Human attention maps for text classification: Do humans and neural networks focus on the same words?., the Annual Meeting of the Association for Computational Linguistics, 2020.
9. [IEEE Big Data 2020] Yin, B., et al., A. Corrosion assessment: Data mining for quantifying associations between indoor accelerated and outdoor natural tests., IEEE Intl. Conference on Big Data, 2020.
10. [ACM L@S 2017] Yin, B., et al., Observing Personalizations in Learning: Identifying Heterogeneous Treatment Effects Using Causal Trees. In the Fourth, ACM Conference on Learning @ Scale, MIT, MA, et al., Causal Forest vs. Naïve Causal Forest in Detecting Personalization: An Empirical Study in ASSISTments., Intl. Conference on Educational Data Mining, 2017.