4 citations · 5 across the 6 of their papers we have counts for
6 papers
Vector Linking via Cross-Model Local Isometric Consistency
Ziying Chen, Yang Cao, He Sun +2
We study Vector Linking: given two embedding clouds produced by different black-box encoders over partially overlapping datasets, recover cross-model object correspondences using o…
Robust Graph Condensation via Classification Complexity Mitigation
Jiayi Luo, Qingyun Sun, Beining Yang +5
Graph condensation (GC) has gained significant attention for its ability to synthesize smaller yet informative graphs. However, existing studies often overlook the robustness of GC…
Bi-Directional Multi-Scale Graph Dataset Condensation via Information Bottleneck
Xingcheng Fu, Yisen Gao, Beining Yang +4
Dataset condensation has significantly improved model training efficiency, but its application on devices with different computing power brings new requirements for different data…
GC-Bench: An Open and Unified Benchmark for Graph Condensation
Qingyun Sun, Ziying Chen, Beining Yang +6
Graph condensation (GC) has recently garnered considerable attention due to its ability to reduce large-scale graph datasets while preserving their essential properties. The core c…
Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching
Tianle Zhang, Yuchen Zhang, Kun Wang +7
Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have raised growing concerns. As one of the most promising…
Does Graph Distillation See Like Vision Dataset Counterpart?
Beining Yang, Kai Wang, Qingyun Sun +5
Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have attracted increasing concerns. Existing graph condens…