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Found 3 papers, 3 papers with code
Endowing Protein Language Models with Structural Knowledge
Drawing from recent advances in graph transformers, our approach refines the self-attention mechanisms of pretrained language transformers by integrating structural information with structure extractor modules.
Fisher Information Embedding for Node and Graph Learning
Attention-based graph neural networks (GNNs), such as graph attention networks (GATs), have become popular neural architectures for processing graph-structured data and learning node embeddings.
k-Center Clustering with Outliers in Sliding Windows
We provide efficient algorithms for this important variant in the streaming model under the sliding window setting, where, at each time step, the dataset to be clustered is the window $W$ of the most recent data items.
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