AI has successfully been applied in many areas of science, advancing technologies like weather prediction and protein folding ...
The workflow of CHAMP unfolds in a three-stage hierarchical process designed to synergistically integrate fine-grained atomic details with coarse-grained functional semantics. Dual-view Graph Encoding ...
Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...
According to Andrew Ng (@AndrewYNg), DeepLearning.AI has launched the PyTorch for Deep Learning Professional Certificate taught by Laurence Moroney (@lmoroney). This three-course program covers core ...
Abstract: In this paper, we propose a robust end-to-end classification model, Graph-in-Graph Neural Network (GIGNet), for automatic modulation recognition (AMR). In GIGNet, multi-level graph neural ...
From a neuroscience perspective, artificial neural networks are regarded as abstract models of biological neurons, yet they rely on biologically implausible backpropagation for training. Energy-based ...
Imagine standing atop a mountain, gazing at the vast landscape below, trying to make sense of the world around you. For centuries, explorers relied on such vantage points to map their surroundings.
Abstract: Missing node attributes pose a common problem in real-world graphs, impacting the performance of graph neural networks’ representation learning. Existing GNNs often struggle to effectively ...
According to mathematical legend, Peter Sarnak and Noga Alon made a bet about optimal graphs in the late 1980s. They’ve now both been proved wrong. It started with a bet. In the late 1980s, at a ...
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