We aim to build a pre-trained Graph Neural Network (GNN) model on molecules without human annotations or prior knowledge. Although various attempts have been proposed to overcome limitations in ...
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 recent years, Graph Neural Networks (GNNs) have attracted substantial attention due to their powerful ability in modeling graph-structured data and broad applications across various ...
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 ...
If you’re like me, you’ve heard plenty of talk about entity SEO and knowledge graphs over the past year. But when it comes to implementation, it’s not always clear which components are worth the ...
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