Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI.

I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful. Every data point, every observation, every piece of knowledge doesn’t exist in ...

A high-performance AI framework enhances anomaly detection in industrial systems using optimized Graph Deviation Networks and graph attention ...

What Are Convolutional Neural Networks? Neural networks are systems, or structures of neurons, that enable AI to better understand data, allowing it to solve complex problems. While there are numerous ...

Meiya Pico's patent focuses on classifying social text using Graph Convolutional Networks. GCN is a deep learning model ...

Researchers review AI-powered inverse lithography, showing how deep learning boosts chip patterning precision and efficiency while facing scaling challenges.

Therefore, we explored a model based on graph convolutional neural networks (GCNN) to perform survival prediction of cancer patients using WSIs. Methods: We utilized WSIs collected from The Cancer ...

Results: Our final model comprised of a graph convolutional neural network using deconvolution scores and genome-wide methylation density features, which achieved an accuracy of 69% in a held-out ...

Scientists have created a novel probabilistic model for 5-minutes ahead PV power forecasting. The method combines a convolutional neural network with bidirectional long short-term memory ...