Concepts2
📚TheoryAdvanced
Graph Neural Network Theory
Graph Neural Networks (GNNs) learn on graphs by repeatedly letting each node aggregate messages from its neighbors and update its representation.
#graph neural networks#message passing#weisfeiler-leman+12
📚TheoryIntermediate
Universal Approximation Theorem
The Universal Approximation Theorem (UAT) says a feedforward neural network with one hidden layer and a non-polynomial activation (like sigmoid or ReLU) can approximate any continuous function on a compact set as closely as we want.
#universal approximation theorem#cybenko#hornik+12