3 papers
cs.LG2026
Unveiling Hidden Convexity in Deep Learning: a Sparse Signal Processing Perspective
Emi Zeger, Mert Pilanci
Deep neural networks (DNNs), particularly those using Rectified Linear Unit (ReLU) activation functions, have achieved remarkable success across diverse machine learning tasks, inc…
cs.LG2024
Black Boxes and Looking Glasses: Multilevel Symmetries, Reflection Planes, and Convex Optimization in Deep Networks
Emi Zeger, Mert Pilanci
We show that training deep neural networks (DNNs) with absolute value activation and arbitrary input dimension can be formulated as equivalent convex Lasso problems with novel feat…
cs.LG2024
A Library of Mirrors: Deep Neural Nets in Low Dimensions are Convex Lasso Models with Reflection Features
Emi Zeger, Yifei Wang, Aaron Mishkin +3
We prove that training neural networks on 1-D data is equivalent to solving convex Lasso problems with discrete, explicitly defined dictionary matrices. We consider neural networks…