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 ...

Alzheimer's Disease (AD), a leading neurodegenerative disorder, presents significant global health challenges. Advances in graph neural networks (GNNs) offer promising tools for analyzing multimodal ...

This is a 3-page paper, along with (if relevant) the source code of your project, including instructions on how to run it. You may use your midterm project as a foundation for your final project, ...

Proceedings of The Eighth Annual Conference on Machine Learning and Systems Graph neural networks (GNNs), an emerging class of machine learning models for graphs, have gained popularity for their ...

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 ...

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 ...

The most common manifestation of neurological disorders in children is the occurrence of epileptic seizures. In this study, we propose a multi-branch graph convolutional network (MGCNA) framework with ...

Abstract: Activation functions are pivotal in neural networks, determining the output of each neuron. Traditionally, functions like sigmoid and ReLU have been static and deterministic. However, the ...