Self-supervised learning has demonstrated state-of-the-art performance on various anomaly detection tasks. Learning … for multivariate statistical in-process control (MSPC) based on self-supervised learning. Our motivation for SSMSPC is to … leverage the potential of unsupervised representation learning by incorporating self-supervised learning into the general …

supervised learning for insurance fraud detection has long been a research focus, unsupervised learning has rarely been studied … learning, especially isolation forests, can successfully detect insurance fraud. Supervised learning also performs strongly …, despite few labeled fraud cases. Interestingly, unsupervised and supervised learning detect new fraudulent claims based on …

suggests that the considered technique of self- supervised learning is well suited for high-performance applications involving … techniques. Our model combines a suitable relaxation of the objective function to a continuous solution space with a self-supervised … learning method for neural networks, which does not require any labeled training data. Thereby, we avoid the pre-computation of …

algorithms: (1) a supervised learning algorithm to predict winds, (2) an unsupervised learning algorithm to forecast areas of …

. Often these datasets create bias in the supervised learning models trained for the prediction task. One of the most …