[23] M. Goldstein and S. Uchida. A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PloS one, 11(4):e0152173, 2016.
[24] Y. Gorishniy, I. Rubachev, V. Khrulkov, and A. Babenko. Revisiting deep learning models for tabular data. Advances in Neural Information Processing Systems, 34:18932-18943, 2021.
[25] L. Grinsztajn, E. Oyallon, and G. Varoquaux. Why do tree-based models still outperform deep learning on typical tabular data? In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2022.
[26] S. Han, X. Hu, H. Huang, M. Jiang, and Y. Zhao. ADBench: Anomaly detection benchmark. Advances in Neural Information Processing Systems (NeurIPS), 35:32142-32159, 2022.
[27] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016.
[28] P. Horton and K. Nakai. A probabilistic classification system for predicting the cellular localization sites of proteins. In Ismb, volume 4, pages 109-115, 1996.
[29] M. Jiang, C. Hou, A. Zheng, X. Hu, S. Han, H. Huang, X. He, P. S. Yu, and Y. Zhao. Weakly supervised anomaly detection: A survey. arXiv preprint arXiv:2302.04549, 2023.
[30] H. S. Jomaa, L. Schmidt-Thieme, and J. Grabocka. Dataset2vec: Learning dataset meta-features. Data Mining and Knowledge Discovery, 35:964–985, 2021.
[31] T. Kerssies. Neural architecture search for visual anomaly segmentation. arXiv preprint arXiv:2304.08975, 2023.
[32] D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
[33] M. Kudo, J. Toyama, and M. Shimbo. Multidimensional curve classification using passing-through regions. Pattern Recognition Letters, 20(11-13):1103-1111, 1999.
[34] K.-H. Lai, D. Zha, G. Wang, J. Xu, Y. Zhao, D. Kumar, Y. Chen, P. Zumkhawaka, M. Wan, D. Martinez, et al. Tods: An automated time series outlier detection system. In Proceedings of the aaai conference on artificial intelligence, volume 35, pages 16060-16062, 2021.
[35] K.-H. Lai, D. Zha, J. Xu, Y. Zhao, G. Wang, and X. Hu. Revisiting time series outlier detection: Definitions and benchmarks. In Neural Information Processing Systems (NeurIPS), 2021.
[36] A. Lazarevic, L. Ertoz, V. Kumar, A. Ozgur, and J. Srivastava. A comparative study of anomaly detection schemes in network intrusion detection. In SDM, pages 25-36. SIAM, 2003.
[37] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.
[38] C. Leibig, V. Allken, M. S. Ayhan, P. Berens, and S. Wahl. Leveraging uncertainty information from deep neural networks for disease detection. Scientific reports, 7(1):1-14, 2017.
[39] C. Li, X. Li, L. Feng, and J. Ouyang. Who is your right mixup partner in positive and unlabeled learning. In International Conference on Learning Representations, 2022.
[40] Y. Li, Z. Chen, D. Zha, K. Zhou, H. Jin, H. Chen, and X. Hu. Autood: Neural architecture search for outlier detection. In 2021 IEEE 37th International Conference on Data Engineering (ICDE), pages 2117-2122. IEEE, 2021.
[41] Y. Li, D. Zha, P. Venugopal, N. Zou, and X. Hu. Pyodds: An end-to-end outlier detection system with automated machine learning. In Companion Proceedings of the Web Conference 2020, pages 153-157, 2020.
[42] Z. Li, Y. Zhao, N. Botta, C. Ionescu, and X. Hu. Copod: copula-based outlier detection. In 2020 IEEE international conference on data mining (ICDM), pages 1118-1123. IEEE, 2020.
[43] Z. Li, Y. Zhao, X. Hu, N. Botta, C. Ionescu, and G. Chen. Ecod: Unsupervised outlier detection using empirical cumulative distribution functions. IEEE Transactions on Knowledge and Data Engineering, 2022.
[44] H.-J. Liao, C.-H. R. Lin, Y.-C. Lin, and K.-Y. Tung. Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications, 36(1):16-24, 2013.