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Published in Bachelor thesis, University of Liverpool, 2021
Bachelor thesis on developing a teaching app to demonstrate Hebb’s rule in machine learning.
Recommended citation: Li, J. (2021). A Teaching App to Demonstrate Hebb's Learning Rule. Bachelor thesis, University of Liverpool. https://github.com/jimmylihui/jimmylihui.github.io/blob/master/files/app%20to%20illustrate%20hebb%20rule.pdf
Published in Master thesis, University College London, 2022
Master thesis on using PCA and machine learning for globular cluster membership determination.
Recommended citation: Li, J. (2022). Principal Component Analysis Machine Learning to Determine Membership of Globular Cluster M56. Master thesis, University College London. https://github.com/jimmylihui/jimmylihui.github.io/blob/master/files/Principal%20component%20analysis%20machine%20learning%20to%20determine%20membership%20of%20globular%20cluster%20M56.pdf
Published in 2024, 2024
Multi-scale and cross-channel gated transformer for multivariate long-term time-series forecasting.
Recommended citation: Li, J., Zhou, Z., & Yeung, D.Y. (2024). MGTST: Multi-scale and Cross-channel Gated Transformer for Multivariate Long-term Time-series Forecasting. https://scholar.google.com/citations?view_op=view_citation&hl=en&user=CGnz6VQAAAAJ&citation_for_view=CGnz6VQAAAAJ:u5HHmVD_uO8C
Published in arXiv preprint arXiv:2406.01627, 2024
A benchmarking suite for systematic evaluation of genomic foundation models.
Recommended citation: Liu, Z., Li, J., Li, S., Zang, Z., Tan, C., Huang, Y., Bai, Y., & Li, S.Z. (2024). Genbench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models. arXiv preprint arXiv:2406.01627. https://arxiv.org/abs/2406.01627
Published in arXiv preprint, 2025
Survey on safety mechanisms, training paradigms, and emerging challenges in LLM alignment.
Recommended citation: Lu, H., Fang, L., Zhang, R., et al. (2025). Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges. arXiv preprint arXiv:2507.19672. https://arxiv.org/abs/2507.19672
Published in NeurIPS 2025 Workshop on Learning from Time Series for Health, 2025
Instruction-tuned LLMs for robust J-peak detection in BCG/cardiomechanical signals.
Recommended citation: Li, J., Zhang, Y., Zeng, Z., Chen, J., Zhang, X., Lu, J., Song, W.Z., & Dou, F. (2025). Peak-R1: Instruction-Tuned Large Language Models for Robust J-Peak Detection in Cardiomechanical Signals. NeurIPS 2025 Workshop on Learning from Time Series for Health. https://scholar.google.com/citations?view_op=view_citation&hl=en&user=CGnz6VQAAAAJ&citation_for_view=CGnz6VQAAAAJ:9yKSN-GCB0IC
Published:
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Published:
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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