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Found 3 papers, 2 papers with code
Learning Gradient-based Mixup towards Flatter Minima for Domain Generalization
To address the distribution shifts between training and test data, domain generalization (DG) leverages multiple source domains to learn a model that generalizes well to unseen domains.
Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems
Recommender Systems (RSs) in real-world applications often deal with billions of user interactions daily.
Clustered Task-Aware Meta-Learning by Learning from Learning Paths
To enable effective learning of new tasks with only few samples, meta-learning acquires common knowledge from the existing tasks with a globally shared meta-learner.
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