no code implementations • 5 Apr 2024 • Mahesh Lorik Yadav, Harish Guruprasad Ramaswamy, Chandrashekar Lakshminarayanan
Unlike deep linear networks, the DLGN is capable of learning non-linear features (which are then linearly combined), and unlike ReLU networks these features are ultimately simple -- each feature is effectively an indicator function for a region compactly described as an intersection of (number of layers) half-spaces in the input space.
