Researchers proposed a Position Encoding-Based Deformable Spatial Aggregation Module (PEBDSAM) that addresses over-smoothing, over-compression, fixed receptive fields, and topological noise in graph n…
Transformer-based models BERT and graph attention model GAT are transforming puzzle game design by outperforming traditional CNNs in predicting player behavior, tested in Candy Crush Saga. These gener…
Researchers introduced GAT, a purely transformer-based Generative Adversarial Network trained in a compact Variational Autoencoder latent space, achieving state-of-the-art single-step class-conditiona…
Fraud rings evade traditional machine learning models like XGBoost because they analyze individual rows rather than relationships between entities. Graph Neural Networks (GNNs) detect coordinated frau…