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cs.LG2025★ 1 cited
Probing Network Decisions: Capturing Uncertainties and Unveiling Vulnerabilities Without Label Information
Youngju Joung, Sehyun Lee, Jaesik Choi
To improve trust and transparency, it is crucial to be able to interpret the decisions of Deep Neural classifiers (DNNs). Instance-level examinations, such as attribution technique…
cs.LG2025
Implicit Contrastive Representation Learning with Guided Stop-gradient
Byeongchan Lee, Sehyun Lee
In self-supervised representation learning, Siamese networks are a natural architecture for learning transformation-invariance by bringing representations of positive pairs closer…
cs.LG2022★ 1 cited
Variational Neural Temporal Point Process
Deokjun Eom, Sehyun Lee, Jaesik Choi
A temporal point process is a stochastic process that predicts which type of events is likely to happen and when the event will occur given a history of a sequence of events. There…