| Towards deeper graph neural networks with differentiable group normalization K Zhou, X Huang, Y Li, D Zha, R Chen, X Hu NeurIPS, 2020 | 195 | 2020 |
| Towards generalizable deepfake detection with locality-aware autoencoder M Du, S Pentyala, Y Li, X Hu Proceedings of the 29th ACM International Conference on Information …, 2020 | 129* | 2020 |
| Specae: Spectral autoencoder for anomaly detection in attributed networks Y Li, X Huang, J Li, M Du, N Zou Proceedings of the 28th ACM international conference on information and …, 2019 | 112 | 2019 |
| Graph recurrent networks with attributed random walks X Huang, Q Song, Y Li, X Hu Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 106 | 2019 |
| Is a single vector enough? exploring node polysemy for network embedding N Liu, Q Tan, Y Li, H Yang, J Zhou, X Hu Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 96 | 2019 |
| Pyodds: An end-to-end outlier detection system with automated machine learning Y Li, D Zha, P Venugopal, N Zou, X Hu Companion Proceedings of the Web Conference 2020, 153-157, 2020 | 56 | 2020 |
| Mitigating gender bias in captioning systems R Tang, M Du, Y Li, Z Liu, N Zou, X Hu Proceedings of the Web Conference 2021, 633-645, 2021 | 55 | 2021 |
| Dual policy distillation KH Lai, D Zha, Y Li, X Hu IJCAI, 2020 | 52 | 2020 |
| Automated anomaly detection via curiosity-guided search and self-imitation learning Y Li, Z Chen, D Zha, K Zhou, H Jin, H Chen, X Hu IEEE Transactions on Neural Networks and Learning Systems 33 (6), 2365-2377, 2021 | 48* | 2021 |
| Towards learning disentangled representations for time series Y Li, Z Chen, D Zha, M Du, J Ni, D Zhang, H Chen, X Hu Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 28* | 2022 |
| Autood: Neural architecture search for outlier detection Y Li, Z Chen, D Zha, K Zhou, H Jin, H Chen, X Hu 2021 IEEE 37th International Conference on Data Engineering (ICDE), 2117-2122, 2021 | 27 | 2021 |
| Deep structured cross-modal anomaly detection Y Li, N Liu, J Li, M Du, X Hu 2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019 | 20* | 2019 |
| Automating the design of neural networks for anomaly detection Z Chen, H Chen, Y Li US Patent App. 17/170,254, 2021 | 9 | 2021 |
| Pyodds: An end-to-end outlier detection system Y Li, D Zha, N Zou, X Hu arXiv preprint arXiv:1910.02575, 2019 | 8 | 2019 |
| Xdeep: An interpretation tool for deep neural networks F Yang, Z Zhang, H Wang, Y Li, X Hu arXiv preprint arXiv:1911.01005, 2019 | 6 | 2019 |
| Value of exploration: Measurements, findings and algorithms Y Su, X Wang, EY Le, L Liu, Y Li, H Lu, B Lipshitz, S Badam, L Heldt, S Bi, ... arXiv preprint arXiv:2305.07764, 2023 | 1 | 2023 |
| RES: An Interpretable Replicability Estimation System for Research Publications Z Wang, Q Feng, M Chatterjee, X Zhao, Y Liu, Y Li, AK Singh, ... Proceedings of the AAAI Conference on Artificial Intelligence 36 (11), 13230 …, 2022 | 1 | 2022 |
| Multitask Ranking System for Immersive Feed and No More Clicks: A Case Study of Short-Form Video Recommendation Q Liu, Z Zhao, L Liu, Z Zhang, J Shan, Y Li, S Bi, L Hong, EH Chi Proceedings of the 32nd ACM International Conference on Information and …, 2023 | | 2023 |
| Anomaly Detection with Complex Data Structures Y Li Texas A&M University, 2021 | | 2021 |
Xia "Ben" HuAssociate Professor of Computer Science, Rice University在 rice.edu 的电子邮件经过验证
