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Found 4 papers, 1 papers with code
Implementing Active Learning in Cybersecurity: Detecting Anomalies in Redacted Emails
In this paper we present research results concerning the application of Active Learning to anomaly detection in redacted emails, comparing the utility of different methods for implementing active learning in this context.
DICE: A Simple Sparsification Method for Out-of-distribution Detection
Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying machine learning models in the real world.
Contrastive Label Disambiguation for Partial Label Learning
Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity.
Towards Unknown-aware Deep Q-Learning
Furthermore, we provide theoretical guarantees that our method can improve OOD uncertainty estimation while ensuring the convergence performance of the in-distribution environment.
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