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Found 4 papers, 1 papers with code
Unknown Domain Inconsistency Minimization for Domain Generalization
In particular, by aligning the loss landscape acquired in the source domain to the loss landscape of perturbed domains, we expect to achieve generalization grounded on these flat minima for the unknown domains.
SAAL: Sharpness-Aware Active Learning
Specifically, our proposed method, Sharpness-Aware Active Learning (SAAL), constructs its acquisition function by selecting unlabeled instances whose perturbed loss becomes maximum.
LADA: Look-Ahead Data Acquisition via Augmentation for Active Learning
Active learning effectively collects data instances for training deep learning models when the labeled dataset is limited and the annotation cost is high.
Sequential Recommendation with Relation-Aware Kernelized Self-Attention
This work merges the self-attention of the Transformer and the sequential recommendation by adding a probabilistic model of the recommendation task specifics.
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