May 3, 2019 · We study the expressivity of deep neural networks. Measuring a network's complexity by its number of connections or by its number of neurons.

May 5, 2021 · We study the expressivity of deep neural networks. Measuring a network's complexity by its number of connections or by its number of neurons ...

May 30, 2024 · Approximation spaces are used as a theoretical framework for the expressivity and the complexity of the neural networks are measured by either ...

Jun 13, 2019 · Abstract. We study the expressivity of deep neural networks. Measuring a network's complexity by its number of connections or by its number ...

We study the expressivity of deep neural networks. Measuring a network's complexity by its number of connections or by its number of neurons, ...

We study the expressivity of deep neural networks. Measuring a network's complexity by its number of connections or by its number of neurons, ...

May 2, 2019 · We study the expressivity of deep neural networks. Measuring a network's complexity by its number of connections or by its number of neurons, we ...

It is established that allowing the networks to have certain types of “skip connections” does not change the resulting approximation spaces, ...

May 5, 2021 · We introduce a novel perspective on the study of expressivity of deep neural networks by introducing the associated approximation spaces and ...

Jul 19, 2022 · This survey provides an in-depth and explanatory review of the approximation properties of deep neural networks, with a focus on feed-forward ...