# Mathematical Challenges in Deep Learning

Source: [Mathematical Challenges in Deep Learning](<https://www.sota2.com/research/paper/68fa5a3e1e209131ce9be299>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T16:23:40.204Z

Authors: Vahid Partovi Nia, Guojun Zhang, Ivan Kobyzev, Michael R. Metel, Xinlin Li, Ke Sun, Sobhan Hemati, Masoud Asgharian, Linglong Kong, Wulong Liu, Boxing Chen

Published: 2023-03-24T20:12:27.000Z

## Abstract

Deep models are dominating the artificial intelligence (AI) industry since the ImageNet challenge in 2012. The size of deep models is increasing ever since, which brings new challenges to this field with applications in cell phones, personal computers, autonomous cars, and wireless base stations. Here we list a set of problems, ranging from training, inference, generalization bound, and optimization with some formalism to communicate these challenges with mathematicians, statisticians, and theoretical computer scientists. This is a subjective view of the research questions in deep learning that benefits the tech industry in long run.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2303.15464>)

[PDF](<https://arxiv.org/pdf/2303.15464.pdf>)