19 citations · 34 across the 5 of their papers we have counts for
6 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…
Training OOD Detectors in their Natural Habitats
Julian Katz-Samuels, Julia Nakhleh, Robert Nowak +1
Out-of-distribution (OOD) detection is important for machine learning models deployed in the wild. Recent methods use auxiliary outlier data to regularize the model for improved OO…
Identifying Entangled Physics Relationships through Sparse Matrix Decomposition to Inform Plasma Fusion Design
M. Giselle Fernández-Godino, Michael J. Grosskopf, Julia B. Nakhleh +3
A sustainable burn platform through inertial confinement fusion (ICF) has been an ongoing challenge for over 50 years. Mitigating engineering limitations and improving the current…
Exploring Sensitivity of ICF Outputs to Design Parameters in Experiments Using Machine Learning
Julia B. Nakhleh, M. Giselle Fernández-Godino, Michael J. Grosskopf +3
Building a sustainable burn platform in inertial confinement fusion (ICF) requires an understanding of the complex coupling of physical processes and the effects that key experimen…