PUICL is a transformer pretrained on synthetic PU data from structural causal models that solves positive-unlabeled classification via in-context learning without gradient updates or fitting.
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Multi-task autoencoders with outlier detection and federated SVDD loss filter noisy samples in non-IID federated learning, yielding accuracy gains up to 7% on CIFAR-10.
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In-Context Positive-Unlabeled Learning
PUICL is a transformer pretrained on synthetic PU data from structural causal models that solves positive-unlabeled classification via in-context learning without gradient updates or fitting.
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Sample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID Data
Multi-task autoencoders with outlier detection and federated SVDD loss filter noisy samples in non-IID federated learning, yielding accuracy gains up to 7% on CIFAR-10.