| Score-CAM: Score-weighted visual explanations for convolutional neural networks H Wang, Z Wang, M Du, F Yang, Z Zhang, S Ding, P Mardziel, X Hu Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 1504* | 2020 |
| Explainability for large language models: A survey H Zhao, H Chen, F Yang, N Liu, H Deng, H Cai, S Wang, D Yin, M Du ACM Transactions on Intelligent Systems and Technology, 2024 | 425 | 2024 |
| Fairness in deep learning: A computational perspective M Du, F Yang, N Zou, X Hu IEEE Intelligent Systems 36 (4), 25-34, 2020 | 293 | 2020 |
| Data-centric artificial intelligence: A survey D Zha, ZP Bhat, KH Lai, F Yang, Z Jiang, S Zhong, X Hu ACM Computing Surveys, 2023 | 254 | 2023 |
| An embarrassingly simple approach for trojan attack in deep neural networks R Tang, M Du, N Liu, F Yang, X Hu Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 217 | 2020 |
| XFake: Explainable fake news detector with visualizations F Yang, SK Pentyala, S Mohseni, M Du, H Yuan, R Linder, ED Ragan, S Ji, ... The World Wide Web Conference, 3600-3604, 2019 | 158 | 2019 |
| Data-centric ai: Perspectives and challenges D Zha, ZP Bhat, KH Lai, F Yang, X Hu Proceedings of the 2023 SIAM International Conference on Data Mining (SDM …, 2023 | 113 | 2023 |
| Evaluating explanation without ground truth in interpretable machine learning F Yang, M Du, X Hu arXiv preprint arXiv:1907.06831, 2019 | 109* | 2019 |
| Generalized demographic parity for group fairness Z Jiang, X Han, C Fan, F Yang, A Mostafavi, X Hu International Conference on Learning Representations, 2022 | 73 | 2022 |
| On attribution of recurrent neural network predictions via additive decomposition M Du, N Liu, F Yang, S Ji, X Hu The world wide web conference, 383-393, 2019 | 67 | 2019 |
| Machine learning explanations to prevent overtrust in fake news detection S Mohseni, F Yang, S Pentyala, M Du, Y Liu, N Lupfer, X Hu, S Ji, ... Proceedings of the international AAAI conference on web and social media 15 …, 2021 | 65 | 2021 |
| EXACT: Scalable graph neural networks training via extreme activation compression Z Liu, K Zhou, F Yang, L Li, R Chen, X Hu International Conference on Learning Representations, 2021 | 63 | 2021 |
| Learning credible deep neural networks with rationale regularization M Du, N Liu, F Yang, X Hu 2019 IEEE International Conference on Data Mining (ICDM), 150-159, 2019 | 58 | 2019 |
| Usable XAI: 10 strategies towards exploiting explainability in the LLM era X Wu, H Zhao, Y Zhu, Y Shi, F Yang, T Liu, X Zhai, W Yao, J Li, M Du, ... arXiv preprint arXiv:2403.08946, 2024 | 40* | 2024 |
| Efficient xai techniques: A taxonomic survey YN Chuang, G Wang, F Yang, Z Liu, X Cai, M Du, X Hu arXiv preprint arXiv:2302.03225, 2023 | 39* | 2023 |
| Towards interpretation of recommender systems with sorted explanation paths F Yang, N Liu, S Wang, X Hu 2018 IEEE International Conference on Data Mining (ICDM), 667-676, 2018 | 39 | 2018 |
| Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers? M Jin, Q Yu, J Huang, Q Zeng, Z Wang, W Hua, H Zhao, K Mei, Y Meng, ... Proceedings of the 31st International Conference on Computational …, 2025 | 37* | 2025 |
| Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Insertion R Tang, Q Feng, N Liu, F Yang, X Hu ACM SIGKDD Explorations, 2023 | 37* | 2023 |
| Model-based counterfactual synthesizer for interpretation F Yang, SS Alva, J Chen, X Hu Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 37 | 2021 |
| Trust evolution over time in explainable AI for fake news detection S Mohseni, F Yang, S Pentyala, M Du, Y Liu, N Lupfer, X Hu, S Ji, ... Fair & Responsible AI Workshop at CHI 2020, 2020 | 36* | 2020 |
Mengnan DuAssistant Professor, New Jersey Institute of TechnologyE-mailová adresa ověřena na: njit.edu