8 citations · 12 across the 4 of their papers we have counts for
4 papers
Elucidating Representation Degradation Problem in Diffusion Model Training
Zhipeng Yao, Dazhou Li, Zitong Zhang +6
Diffusion models have achieved remarkable success, yet their training remains inefficient due to a severe optimization bottleneck, which we term Representation Degradation. As nois…
PyG 2.0: Scalable Learning on Real World Graphs
Matthias Fey, Jinu Sunil, Akihiro Nitta +10
PyG (PyTorch Geometric) has evolved significantly since its initial release, establishing itself as a leading framework for Graph Neural Networks. In this paper, we present Pyg 2.0…
ContextGNN: Beyond Two-Tower Recommendation Systems
Yiwen Yuan, Zecheng Zhang, Xinwei He +10
Recommendation systems predominantly utilize two-tower architectures, which evaluate user-item rankings through the inner product of their respective embeddings. However, one key l…
RelBench: A Benchmark for Deep Learning on Relational Databases
Joshua Robinson, Rishabh Ranjan, Weihua Hu +9
We present RelBench, a public benchmark for solving predictive tasks over relational databases with graph neural networks. RelBench provides databases and tasks spanning diverse do…