Dr Meng Fang

Computer Science

+44 (0)151 795 8407
Work email Meng.Fang@liverpool.ac.uk
Personal Websitehttps://mengf1.github.io/

Publications

Filter the publication list

Filter list by type

Show all
Journal article
Conference paper
Chapter
Other

2024

Large Language Models Are Neurosymbolic Reasoners (Conference Paper)

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

DOI: 10.1609/aaai.v38i16.29754

2023

Discourse-Aware Graph Networks for Textual Logical Reasoning (Journal article)

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

DOI: 10.1109/TPAMI.2023.3280178

Prescribed Safety Performance Imitation Learning From a Single Expert Dataset (Journal article)

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

DOI: 10.1109/TPAMI.2023.3287908

Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost (Conference Paper)

Yin, L., Liu, S., Fang, M., Huang, T., Menkovski, V., & Pechenizkiy, M. (2023). Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost. In Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023 Vol. 37 (pp. 10945-10953).

Dual-Modality Co-Learning for Unveiling Deepfake in Spatio-Temporal Space (Conference Paper)

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

DOI: 10.1145/3591106.3592284

Dynamic Contrastive Distillation for Image-Text Retrieval (Journal article)

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

DOI: 10.1109/tmm.2023.3236837

Shared dynamics learning for large-scale traveling salesman problem (Journal article)

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

DOI: 10.1016/j.aei.2023.102005

Joint reasoning with knowledge subgraphs for Multiple Choice Question Answering (Journal article)

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

DOI: 10.1016/j.ipm.2023.103297

Deep Reinforcement Learning for Real-Time Assembly Planning in Robot-Based Prefabricated Construction (Journal article)

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

DOI: 10.1109/TASE.2023.3236805

A Survey for Efficient Open Domain Question Answering (Conference Paper)

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? (Conference Paper)

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 (Conference Paper)

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 (Conference Paper)

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.

Dynamic Truck&#x2013;UAV Collaboration and Integrated Route Planning for Resilient Urban Emergency Response (Journal article)

Long, Y., Xu, G., Zhao, J., Xie, B., & Fang, M. (2023). Dynamic Truck&#x2013;UAV Collaboration and Integrated Route Planning for Resilient Urban Emergency Response. IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT. doi:10.1109/TEM.2023.3299693

DOI: 10.1109/TEM.2023.3299693

Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach (Conference Paper)

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 (Conference Paper)

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 (Conference Paper)

Shi, Z., Xu, Y., Fang, M., & Chen, L. (2023). Self-imitation Learning for Action Generation in Text-based Games. In EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference (pp. 703-726).

2022

Learning Granularity-Unified Representations for Text-to-Image Person Re-identification (Conference Paper)

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

DOI: 10.1145/3503161.3548028

RETHINKING GOAL-CONDITIONED SUPERVISED LEARNING AND ITS CONNECTION TO OFFLINE RL (Conference Paper)

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 (Conference Paper)

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 (Conference Paper)

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.