Kloft, Marius

Deep Semi-Supervised Anomaly Detection

Ruff, L., Vandermeulen, R. A., Görnitz, N., Binder, A., Müller, E., Müller, K.-R., & Kloft, M. (2019) · arXiv

Anomaly Detection Techniques and ApplicationsAdversarial Robustness in Machine Learning

Cited by 388

“Predicting MOOC dropout over weeks using machine learning methods,”

Kloft, M., Stiehler, F., Zheng, Z., & Pinkwart, N. (2014) · Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs

Online Learning and AnalyticsOnline and Blended Learning

Cited by 325

Explainable Deep One-Class Classification

Liznerski, P., Ruff, L., Vandermeulen, R. A., Franks, B. J., Kloft, M., & Müller, K.-R. (2020) · arXiv

Anomaly Detection Techniques and ApplicationsExplainable Artificial Intelligence (XAI)

Cited by 185

Detecting Anomalies within Time Series using Local Neural Transformations

Schneider, T., Qiu, C., Kloft, M., Latif, D. A., Staab, S., Mandt, S., & Rudolph, M. (2022) · arXiv

Anomaly Detection Techniques and ApplicationsTime Series Analysis and Forecasting

Cited by 8