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科研进展

添加325字节, 2024年2月20日 (星期二)
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= '''<span style="color:#800000"><span style="font-size:smaller">研究成果</span></span>''' =
'''<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">2024</span></span></p>
#<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">Liu Z., Xia C., He W., Wang C. Trustworthiness and Self-awareness in Large Language Models: An Exploration through the Think-Solve-Verify Framework. In&nbsp;'''''Proceedings of'''''<i>'''Coling'''</i>'''''&nbsp;2024''''',&nbsp;Accepted</span></span> #<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">Liu Z., etc.&nbsp;'''Are Transformer-based Models more Robust than CNN-based Models?'''.&nbsp;'''Neural Networks''',&nbsp;Accepted</span></span>
#<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">Li&nbsp;S., etc. SEEKING SIMILARITIES WHILE REMOVING DIFFERENCES: GRAPH NEURAL NETWORKS BASED ON NODE CORRELATION.&nbsp;In&nbsp;'''''Proceedings of ICASSP 2024''''',&nbsp;Accepted</span></span>
#<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">Liu Z., etc. Understanding Data Augmentation from a Robustness Perspective.&nbsp;In&nbsp;'''''Proceedings of ICASSP 2024''''',&nbsp;Accepted</span></span>
#<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">Liu Z., etc. From Game Theory to Visual Recognition: Advancing DNN Robustness.&nbsp;In&nbsp;'''''Proceedings of ICASSP 2024''''',&nbsp;Accepted</span></span>
#<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">Jiang W., etc. EDOS: Diverse Outlier Sampling for Out-of-Distribution Detection.&nbsp;In&nbsp;'''''Proceedings of ICLR 2024''''',&nbsp;Accepted​​​​​​​Accepted</span></span>
#<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">CASE: Exploiting Intra-class Compactness and Inter-class Separability of Feature Embeddings for Out-of-Distribution Detection.&nbsp;In&nbsp;'''''Proceedings of AAAI 2024''''',&nbsp;Accepted</span></span>
#<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants.&nbsp;In&nbsp;'''''Proceedings of AAAI 2024''''',&nbsp;Accepted</span></span>
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