no code implementations • 31 May 2023 • Kedar Karhadkar, Michael Murray, Hanna Tseran, Guido Montúfar
We study the loss landscape of both shallow and deep, mildly overparameterized ReLU neural networks on a generic finite input dataset for the squared error loss.
1 code implementation • 17 Jan 2023 • Hanna Tseran, Guido Montúfar
We study the gradients of a maxout network with respect to inputs and parameters and obtain bounds for the moments depending on the architecture and the parameter distribution.
1 code implementation • NeurIPS 2021 • Hanna Tseran, Guido Montúfar
Learning with neural networks relies on the complexity of the representable functions, but more importantly, the particular assignment of typical parameters to functions of different complexity.
