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Found 4 papers, 2 papers with code
Self-Labeling the Job Shop Scheduling Problem
Inspired by Semi- and Self-Supervised learning, we show that it is possible to easily train generative models by sampling multiple solutions and using the best one according to the problem objective as a pseudo-label.
Are Existing Out-Of-Distribution Techniques Suitable for Network Intrusion Detection?
Our findings suggest that existing detectors can identify a consistent portion of new malicious traffic, and that improved embedding spaces enhance detection.
Learning the Quality of Machine Permutations in Job Shop Scheduling
Finally, we empirically demonstrate the value of predicting the quality of machine permutations by enhancing the performance of a simple Tabu Search algorithm inspired by the works in the literature.
On the Evaluation of Sequential Machine Learning for Network Intrusion Detection
Recent advances in deep learning renewed the research interests in machine learning for Network Intrusion Detection Systems (NIDS).
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