| Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications H Xu, W Chen, N Zhao, Z Li, J Bu, Z Li, Y Liu, Y Zhao, D Pei, Y Feng, ... Proceedings of the 2018 world wide web conference, 187-196, 2018 | 1006 | 2018 |
| Opprentice: Towards practical and automatic anomaly detection through machine learning D Liu, Y Zhao, H Xu, Y Sun, D Pei, J Luo, X Jing, M Feng Proceedings of the 2015 internet measurement conference, 211-224, 2015 | 308 | 2015 |
| Unsupervised detection of microservice trace anomalies through service-level deep bayesian networks P Liu, H Xu, Q Ouyang, R Jiao, Z Chen, S Zhang, J Yang, L Mo, J Zeng, ... 2020 IEEE 31st International Symposium on Software Reliability Engineering …, 2020 | 164 | 2020 |
| Unsupervised anomaly detection for intricate kpis via adversarial training of vae W Chen, H Xu, Z Li, D Pei, J Chen, H Qiao, Y Feng, Z Wang IEEE INFOCOM 2019-IEEE conference on computer communications, 1891-1899, 2019 | 106 | 2019 |
| Unsupervised anomaly detection on microservice traces through graph vae Z Xie, H Xu, W Chen, W Li, H Jiang, L Su, H Wang, D Pei Proceedings of the ACM Web Conference 2023, 2874-2884, 2023 | 22 | 2023 |
| On the necessity and effectiveness of learning the prior of variational auto-encoder H Xu, W Chen, J Lai, Z Li, Y Zhao, D Pei arXiv preprint arXiv:1905.13452, 2019 | 18 | 2019 |
| Unsupervised clustering through gaussian mixture variational autoencoder with non-reparameterized variational inference and std annealing Z Li, Y Zhao, H Xu, W Chen, S Xu, Y Li, D Pei 2020 International Joint Conference on Neural Networks (IJCNN), 1-8, 2020 | 12 | 2020 |
| Shallow vaes with realnvp prior can perform as well as deep hierarchical vaes H Xu, W Chen, J Lai, Z Li, Y Zhao, D Pei International Conference on Neural Information Processing, 650-659, 2020 | 4 | 2020 |
| VAEPP: variational autoencoder with a pull-back prior W Chen, W Liu, Z Cai, H Xu, D Pei International Conference on Neural Information Processing, 366-379, 2020 | 3 | 2020 |
