An autoencoder is a specific type of neural network. The main disadvantage of using a neural ... Common use-cases include data visualization in a 2D graph (if the data is reduced to just two columns ...

GNN, a framework to train robust GNNs under noisy conditions. Soft-GNN mitigates label noise impact through dynamic ...

High-entropy alloys (HEAs) offer tunable compositions and surface structures, presenting significant potential for creating ...

At ARVO 2025, in Salt Lake City, Utah, Patipol Tiyajamorn, talked about his poster on using graph neural networks to identify ...

Additionally, the research team localized the ChebNet graph neural network for precipitation, maintaining its effectiveness while significantly reducing computational complexity by avoiding global ...