13 citations · 44 across the 9 of their papers we have counts for
8 papers · 1 filter
HERTA: A High-Efficiency and Rigorous Training Algorithm for Unfolded Graph Neural Networks
Yongyi Yang, Jiaming Yang, Wei Hu +1
As a variant of Graph Neural Networks (GNNs), Unfolded GNNs offer enhanced interpretability and flexibility over traditional designs. Nevertheless, they still suffer from scalabili…
Going Beyond Linear Mode Connectivity: The Layerwise Linear Feature Connectivity
Zhanpeng Zhou, Yongyi Yang, Xiaojiang Yang +2
Recent work has revealed many intriguing empirical phenomena in neural network training, despite the poorly understood and highly complex loss landscapes and training dynamics. One…
Are Neurons Actually Collapsed? On the Fine-Grained Structure in Neural Representations
Yongyi Yang, Jacob Steinhardt, Wei Hu
Recent work has observed an intriguing ''Neural Collapse'' phenomenon in well-trained neural networks, where the last-layer representations of training samples with the same label…
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks
Hongjoon Ahn, Yongyi Yang, Quan Gan +2
Heterogeneous graph neural networks (GNNs) achieve strong performance on node classification tasks in a semi-supervised learning setting. However, as in the simpler homogeneous GNN…
Transformers from an Optimization Perspective
Yongyi Yang, Zengfeng Huang, David Wipf
Deep learning models such as the Transformer are often constructed by heuristics and experience. To provide a complementary foundation, in this work we study the following problem:…
Implicit vs Unfolded Graph Neural Networks
Yongyi Yang, Tang Liu, Yangkun Wang +2
It has been observed that message-passing graph neural networks (GNN) sometimes struggle to maintain a healthy balance between the efficient/scalable modeling of long-range depende…
Why Propagate Alone? Parallel Use of Labels and Features on Graphs
Yangkun Wang, Jiarui Jin, Weinan Zhang +7
Graph neural networks (GNNs) and label propagation represent two interrelated modeling strategies designed to exploit graph structure in tasks such as node property prediction. The…
Graph Neural Networks Inspired by Classical Iterative Algorithms
Yongyi Yang, Tang Liu, Yangkun Wang +6
Despite the recent success of graph neural networks (GNN), common architectures often exhibit significant limitations, including sensitivity to oversmoothing, long-range dependenci…