HTell detects backdoors by random probing of the model head, reporting 99.03% true positive rate and 2.11% false positive rate at 12.69 ms per model on a benchmark of over 6700 models.
A simple framework for contrastive learning of visual representations
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
IPCCF improves collaborative filtering by propagating intents across graph structures with contrastive alignment to provide direct supervision and reduce biases in disentanglement.
citing papers explorer
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Fast and Lightweight Backdoor Detection via Head Random Probing
HTell detects backdoors by random probing of the model head, reporting 99.03% true positive rate and 2.11% false positive rate at 12.69 ms per model on a benchmark of over 6700 models.
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Intent Propagation Contrastive Collaborative Filtering
IPCCF improves collaborative filtering by propagating intents across graph structures with contrastive alignment to provide direct supervision and reduce biases in disentanglement.