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Found 2 papers, 2 papers with code
Distributionally Robust Graph-based Recommendation System
DR-GNN addresses two core challenges: 1) To enable DRO to cater to graph data intertwined with GNN, we reinterpret GNN as a graph smoothing regularizer, thereby facilitating the nuanced application of DRO; 2) Given the typically sparse nature of recommendation data, which might impede robust optimization, we introduce slight perturbations in the training distribution to expand its support.
MS-Former: Memory-Supported Transformer for Weakly Supervised Change Detection with Patch-Level Annotations
How to capture the semantic variations associated with the changed and unchanged regions from the patch-level annotations to obtain promising change results is the critical challenge for the weakly supervised change detection task.
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