Search Results for author:
Found 8 papers, 4 papers with code
HERTA: A High-Efficiency and Rigorous Training Algorithm for Unfolded Graph Neural Networks
In this paper, we propose HERTA: a High-Efficiency and Rigorous Training Algorithm for Unfolded GNNs that accelerates the whole training process, achieving a nearly-linear time worst-case training guarantee.
Are Neurons Actually Collapsed? On the Fine-Grained Structure in Neural Representations
This appears to suggest that the last-layer representations are completely determined by the labels, and do not depend on the intrinsic structure of input distribution.
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks
Moreover, the complexity of this trade-off is compounded in the heterogeneous graph case due to the disparate heterophily relationships between nodes of different types.
Transformers from an Optimization Perspective
Deep learning models such as the Transformer are often constructed by heuristics and experience.
Implicit vs Unfolded Graph Neural Networks
It has been observed that graph neural networks (GNN) sometimes struggle to maintain a healthy balance between the efficient modeling long-range dependencies across nodes while avoiding unintended consequences such oversmoothed node representations or sensitivity to spurious edges.
Why Propagate Alone? Parallel Use of Labels and Features on Graphs
In this regard, it has recently been proposed to use a randomly-selected portion of the training labels as GNN inputs, concatenated with the original node features for making predictions on the remaining labels.
Graph Neural Networks Inspired by Classical Iterative Algorithms
Despite the recent success of graph neural networks (GNN), common architectures often exhibit significant limitations, including sensitivity to oversmoothing, long-range dependencies, and spurious edges, e. g., as can occur as a result of graph heterophily or adversarial attacks.
Relation of the Relations: A New Paradigm of the Relation Extraction Problem
In natural language, often multiple entities appear in the same text.
