1 citations · 1 across the 3 of their papers we have counts for
4 papers
Deep Neural Variation Spaces: A Unifying Perspective on Depth and Complexity
Julia Nakhleh, Robert D. Nowak
We develop a unified function space theory of deep fully connected neural networks. Functions in our spaces are defined recursively as -bounded linear combinations of activ…
Global Minimizers of -Regularized Objectives Yield the Sparsest ReLU Neural Networks
Julia Nakhleh, Robert D. Nowak
Overparameterized neural networks can interpolate a given dataset in many different ways, prompting the fundamental question: which among these solutions should we prefer, and what…
The Effects of Multi-Task Learning on ReLU Neural Network Functions
Julia Nakhleh, Joseph Shenouda, Robert D. Nowak
This paper studies the properties of solutions to multi-task shallow ReLU neural network learning problems, wherein the network is trained to fit a dataset with minimal sum of squa…
ReLUs Are Sufficient for Learning Implicit Neural Representations
Joseph Shenouda, Yamin Zhou, Robert D. Nowak
Motivated by the growing theoretical understanding of neural networks that employ the Rectified Linear Unit (ReLU) as their activation function, we revisit the use of ReLU activati…