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'''<span style="font-family:微软雅黑; font-size:medium">Selected Paper</span>'''
<p style="text-align:center"><span style="font-family:Times New Roman,Times,serif"><span style="font-size:larger">2025</span></span></p>
#<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">Jie Zhang, Yirong Yao, Wei He, Yiqun Niu, Chongjun Wang. Regret Optimization Experience Replay in Off-policy Reinforcement Learning.</span></span><span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;"> In '''''Proceedings of ICASSP 2025''''', Accepted</span></span>
#<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">QIang Qiang He, Shu Wei Qian, Jie Zhang, Chongjun Wang. Inference Retrieval-Augmented Multi-Modal Chain-of-Thoughts Reasoning for Language Models.</span></span> <span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">In '''''Proceedings of ICASSP 2025''''', Accepted</span></span>
#<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">Haoyang Chen, Yi Xin, etc. </span></span>KNOWLEDGE IS POWERFUL: ART KNOWLEDGE-DRIVEN FRAMEWORK FOR PAINTING STYLE CLASSIFICATION INTEGRATING MULTIMODAL KNOWLEDGE<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">.</span></span> <span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">In '''''Proceedings of ICASSP 2025''''', Accepted</span></span>
#<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">Baoming Zhang, MingCai Chen, Jianqing Song, Shuangjie Li, Jie Zhang, Chongjun Wang. Normalize then Propagate: Efficient Homophilous Regularization for Few-shot Semi-Supervised Node Classification. In '''''Proceedings of AAAI 2025''''', Accepted</span></span>
#<span style="font-size:medium;"><span style="font-family:Times New Roman,Times,serif;">MingCai Chen, Yuntao Du., Wenyu Jiang, Baoming Zhang, Shuai Feng, Yi Xin, Chongjun Wang. Robust Logit Adjustment for Learning with Long-Tailed Noisy Data</span></span><span style="font-size:medium;"><span style="font-family:Times New Roman,Times,serif;">. </span></span><span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">In '''''Proceedings of AAAI 2025''''', Accepted</span></span>
