Graphormer Explained in 3 Minutes! | How Transformers Finally Learned Graphs скачать в хорошем качестве

Graphormer Explained in 3 Minutes! | How Transformers Finally Learned Graphs

Transformers dominate NLP and computer vision… but why did they struggle with graphs? 🤔 In this video, we break down Graphormer, a powerful idea showing that Transformers don’t fail on graphs because of architecture limitations, but because they lack structural understanding. Instead of redesigning attention, Graphormer introduces structural encodings that teach Transformers how graphs work: 🔹 Centrality Encoding → node importance 🔹 Spatial Encoding → shortest-path distance 🔹 Edge Encoding → relationship-aware attention By injecting graph structure directly into self-attention, Graphormer bridges the gap between Graph Neural Networks (GNNs) and Transformers, showing that message passing can emerge naturally from attention itself. #ai #deeplearning #GraphNeuralNetworks #Transformers #Graphormer #machinelearning #neuralnetworks #representationlearning #attentionmechanism #airesearch #computerscience #mlexplained