Structurally Flexible Neural Networks: An Ai Approach To Solve A Symmetric Dilemma For Optimizing Units And Shared Parameters

The article discusses the challenges of structural rigidity in deep neural networks and explores methods such as Indirect Encoding and Graph Neural Networks to overcome this issue. Researchers from IT University Copenhagen have introduced Structurally Flexible Neural Networks (SFNNs) which use connected gated recurrent units and linear layers to solve the Symmetry Dilemma and show superior performance compared to previous methods.