@misc{bdcd8f5d67d94ebeb497694c59973009,
title = "Compelling ReLU Networks to Exhibit Exponentially Many Linear Regions at Initialization and During Training",
abstract = "Deep ReLU networks have exponential expressive potential but behave identically to their shallow counterparts under random initialization. This paper introduces a novel training paradigm that constrains weights into mathematically optimized patterns. This enables the resulting network to make exponential use of its depth.",
keywords = "activation regions, linear regions, network initialization, ReLU activation",
author = "Max Milkert and David Hyde and Forrest Laine",
year = "2025",
doi = "10.2172/3028436",
language = "American English",
series = "Presented at the International Conference on Machine Learning (ICML), 13-19 July 2025, Vancouver, Canada",
publisher = "National Laboratory of the Rockies (NLR)",
address = "United States",
type = "Other",
}