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cs.LG2021★ 1 cited
Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness
Glenn Dawson, Robi Polikar
Most studies on learning from noisy labels rely on unrealistic models of i.i.d. label noise, such as class-conditional transition matrices. More recent work on instance-dependent n…
cs.LG2021
OpinionRank: Extracting Ground Truth Labels from Unreliable Expert Opinions with Graph-Based Spectral Ranking
Glenn Dawson, Robi Polikar
As larger and more comprehensive datasets become standard in contemporary machine learning, it becomes increasingly more difficult to obtain reliable, trustworthy label information…
cs.LG2020
Targeted Forgetting and False Memory Formation in Continual Learners through Adversarial Backdoor Attacks
Muhammad Umer, Glenn Dawson, Robi Polikar
Artificial neural networks are well-known to be susceptible to catastrophic forgetting when continually learning from sequences of tasks. Various continual (or "incremental") learn…