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Abstract: Neural ... DAG into a dual graph and then into a bipartite graph. This transformation makes it possible to accurately capture the topological order using multi-bigraph matching. In addition, ...

Colab Notebooks covering deep learning tools for biomolecular structure prediction and design ...

Abstract: Graph Neural Networks (GNNs) have been proven to be useful for learning graph-based knowledge ... To alleviate such phenomena, we propose a novel and general GNN framework, dubbed MC-GNN, ...

Graph Neural Networks GNNs have become a powerful tool for analyzing graph-structured data ... The experimental results show that XAI-DROP consistently surpasses random and XAI-based strategies across ...

Learn More A new neural-network architecture developed by researchers at Google might solve one of the great challenges for large language models (LLMs): extending their memory at inference time ...

Making Artificial Intelligence systems robustly perceive humans remains one of the most intricate challenges in computer ...

It is hard to compare this number to efficient deep neural networks, but this number can serve as a starting point for comparing SNN architecture. The actual extraction of computational efficiency ...