References
|
- Carvalho, J., Zhang, M., Geyer, R., Cotr … - Carvalho, J., Zhang, M., Geyer, R., Cotrini, C., & Buhmann, J. M. (2024). Invariant anomaly detection under distribution shifts: a causal perspective. Advances in Neural Information Processing Systems, 36.
- Wang, Z., & Veitch, V. (2022). A unified causal view of domain invariant representation learning.
-Ikonomovska, E., Gama, J., & Džeroski, S. (2015). Online tree-based ensembles and option trees for regression on evolving data streams. Neurocomputing, 150, 458-470.
- Muallem, A., Shetty, S., Pan, J. W., Zhao, J., & Biswal, B. (2017). Hoeffding tree algorithms for anomaly detection in streaming datasets: A survey. Journal of Information Security, 8(4).
- Fan, Y., Nowaczyk, S., & Antonelo, E. A. (2016, July). Predicting air compressor failures with echo state networks. In PHM Society European Conference (Vol. 3, No. 1).
- Gallicchio, C. (2024). Euler state networks: Non-dissipative reservoir computing. Neurocomputing, 579, 127411.
- Foumani, N. M., Tan, C. W., Webb, G. I., Rezatofighi, H., & Salehi, M. (2024). Series2vec: similarity-based self-supervised representation learning for time series classification. Data Mining and Knowledge Discovery, 1-25.
- Baevski, A., Hsu, W. N., Xu, Q., Babu, A., Gu, J., & Auli, M. (2022, June). Data2vec: A general framework for self-supervised learning in speech, vision and language. In International Conference on Machine Learning (pp. 1298-1312). PMLR.on Machine Learning (pp. 1298-1312). PMLR.
|