Publications
2024
Large Language Models Are Neurosymbolic Reasoners
Fang, M., Deng, S., Zhang, Y., Shi, Z., Chen, L., Pechenizkiy, M., & Wang, J. (n.d.). Large Language Models Are Neurosymbolic Reasoners. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38 (pp. 17985-17993). Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/aaai.v38i16.29754
Dynamic Truck–UAV Collaboration and Integrated Route Planning for Resilient Urban Emergency Response
Long, Y., Xu, G., Zhao, J., Xie, B., & Fang, M. (2023). Dynamic Truck–UAV Collaboration and Integrated Route Planning for Resilient Urban Emergency Response. IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT. doi:10.1109/TEM.2023.3299693
2023
Discourse-Aware Graph Networks for Textual Logical Reasoning
Huang, Y., Liu, L., Xu, K., Fang, M., Lin, L., & Liang, X. (2023). Discourse-Aware Graph Networks for Textual Logical Reasoning. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 45(10), 11668-11688. doi:10.1109/TPAMI.2023.3280178
Prescribed Safety Performance Imitation Learning From a Single Expert Dataset
Cheng, Z., Shen, L., Zhu, M., Guo, J., Fang, M., Liu, L., . . . Tao, D. (2023). Prescribed Safety Performance Imitation Learning From a Single Expert Dataset. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 45(10), 12236-12249. doi:10.1109/TPAMI.2023.3287908
Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost
Yin, L., Liu, S., Fang, M., Huang, T., Menkovski, V., & Pechenizkiy, M. (n.d.). Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 37 (pp. 10945-10953). Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/aaai.v37i9.26297
Dual-Modality Co-Learning for Unveiling Deepfake in Spatio-Temporal Space
Guan, J., Zhou, H., Guo, Z., Hu, T., Deng, L., Quan, C., . . . Zhao, Y. (2023). Dual-Modality Co-Learning for Unveiling Deepfake in Spatio-Temporal Space. In PROCEEDINGS OF THE 2023 ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL, ICMR 2023 (pp. 85-94). doi:10.1145/3591106.3592284
Dynamic Contrastive Distillation for Image-Text Retrieval
Rao, J., Ding, L., Qi, S., Fang, M., Liu, Y., Shen, L., & Tao, D. (2023). Dynamic Contrastive Distillation for Image-Text Retrieval. IEEE Transactions on Multimedia, 1-13. doi:10.1109/tmm.2023.3236837
Shared dynamics learning for large-scale traveling salesman problem
Xu, Y., Fang, M., Chen, L., Du, Y., Xu, G., & Zhang, C. (2023). Shared dynamics learning for large-scale traveling salesman problem. ADVANCED ENGINEERING INFORMATICS, 56. doi:10.1016/j.aei.2023.102005
Joint reasoning with knowledge subgraphs for Multiple Choice Question Answering
Zhang, Q., Chen, S., Fang, M., & Chen, X. (2023). Joint reasoning with knowledge subgraphs for Multiple Choice Question Answering. INFORMATION PROCESSING & MANAGEMENT, 60(3). doi:10.1016/j.ipm.2023.103297
Deep Reinforcement Learning for Real-Time Assembly Planning in Robot-Based Prefabricated Construction
Zhu, A., Dai, T., Xu, G., Pauwels, P., de Vries, B., & Fang, M. (2023). Deep Reinforcement Learning for Real-Time Assembly Planning in Robot-Based Prefabricated Construction. IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING. doi:10.1109/TASE.2023.3236805
A Survey for Efficient Open Domain Question Answering
Zhang, Q., Chen, S., Xu, D., Cao, Q., Chen, X., Cohn, T., & Fang, M. (2023). A Survey for Efficient Open Domain Question Answering. In Proceedings of the Annual Meeting of the Association for Computational Linguistics Vol. 1 (pp. 14447-14465).
Are Large Kernels Better Teachers than Transformers for ConvNets?
Huang, T., Yin, L., Zhang, Z., Shen, L., Fang, M., Pechenizkiy, M., . . . Liu, S. (2023). Are Large Kernels Better Teachers than Transformers for ConvNets?. In Proceedings of Machine Learning Research Vol. 202 (pp. 14023-14038).
CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models
Zhao, J., Fang, M., Shi, Z., Li, Y., Chen, L., & Pechenizkiy, M. (2023). CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models. In Proceedings of the Annual Meeting of the Association for Computational Linguistics Vol. 1 (pp. 13538-13556).
Dynamic Sparsity Is Channel-Level Sparsity Learner
Yin, L., Li, G., Fang, M., Shen, L., Huang, T., Wang, Z., . . . Liu, S. (2023). Dynamic Sparsity Is Channel-Level Sparsity Learner. In Advances in Neural Information Processing Systems Vol. 36.
Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach
Zhang, Y., Du, Y., Huang, B., Wang, Z., Wang, J., Fang, M., & Pechenizkiy, M. (2023). Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach. In Advances in Neural Information Processing Systems Vol. 36.
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist
Ni'Mah, I., Fang, M., Menkovski, V., & Pechenizkiy, M. (2023). NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist. In Proceedings of the Annual Meeting of the Association for Computational Linguistics Vol. 1 (pp. 1240-1266).
Self-imitation Learning for Action Generation in Text-based Games
Shi, Z., Xu, Y., Fang, M., & Chen, L. (2023). Self-imitation Learning for Action Generation in Text-based Games. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (pp. 703-726). Association for Computational Linguistics. doi:10.18653/v1/2023.eacl-main.50
2022
Learning Granularity-Unified Representations for Text-to-Image Person Re-identification
Shao, Z., Zhang, X., Fang, M., Lin, Z., Wang, J., & Ding, C. (2022). Learning Granularity-Unified Representations for Text-to-Image Person Re-identification. In Proceedings of the 30th ACM International Conference on Multimedia. ACM. doi:10.1145/3503161.3548028
RETHINKING GOAL-CONDITIONED SUPERVISED LEARNING AND ITS CONNECTION TO OFFLINE RL
Yang, R., Lu, Y., Li, W., Sun, H., Fang, M., Du, Y., . . . Zhang, C. (2022). RETHINKING GOAL-CONDITIONED SUPERVISED LEARNING AND ITS CONNECTION TO OFFLINE RL. In ICLR 2022 - 10th International Conference on Learning Representations.
TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack
Cao, Y., Li, D., Fang, M., Zhou, T., Gao, J., Zhan, Y., & Tao, D. (2022). TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022 (pp. 11975-11992).
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets
Huang, T., Chen, T., Fang, M., Menkovski, V., Zhao, J., Yin, L., . . . Liu, S. (2022). You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets. In Proceedings of Machine Learning Research Vol. 198.