Published in IJCNN, 2019
CNN, Medical Imaging
Recommended citation: Fangjun Li, et al. Multi-scale stepwise training strategy of convolutional neural networks for diabetic retinopathy severity assessment. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1-5). IEEE. https://ieeexplore.ieee.org/document/8851904
Published in BMC bioinformatics, 2020
Feature Selection, Machine Learning, Gene Data
Recommended citation: Fangjun Li, et al. An improved clear cell renal cell carcinoma stage prediction model based on gene sets.BMC bioinformatics, 21, 1-15. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03543-0
Published in P-VLAM, 2022
Diffusion Model, Image Manipulation
Recommended citation: Fangjun Li, et al. Exploring the GLIDE model for Human Action-effect Prediction. P-VLAM (2022): 1. https://aclanthology.org/2022.pvlam-1.pdf#page=9
Published in ACS, 2022
Ontology Knowledge, LLMs, Prompt Engineering
Recommended citation: Fangjun Li, et al. Ontology Knowledge-enhanced In-Context Learning for Action-Effect Prediction. In Advances in Cognitive Systems (ACS-2022) https://advancesincognitivesystems.github.io/acs2022/data/acs22_paper-7652.pdf
Published in AAAI Technical Track on Natural Language Processing II, 2024
Spatial Reaoning, LLMs, Prompt Engineering
Recommended citation: Fangjun Li, et al. Advancing Spatial Reasoning in Large Language Models: An In-Depth Evaluation and Enhancement Using the StepGame Benchmark. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 17, pp. 18500-18507) https://ojs.aaai.org/index.php/AAAI/article/view/29811
This work builds a real-world simulation benchmark for evaluating spatial reasoning abilities of language models.
An In-Depth Evaluation and Enhancement Using the StepGame Benchmark 
We proposed task-relevant ontology knowledge integration for in-context learning with generative pre-trained language models (PLMs). Specifically, we developed an ontology-to-text transformation to bridge the gap between symbolic knowledge and text. We further introduce unseen knowledge learning via PLMs to infer knowledge for concepts that do not have definitions in the knowledge base. 
Published:
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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