# Additive regularization schedule for neural architecture search

arXiv: https://arxiv.org/abs/2406.12992 · HTML: https://mlsanity.com/paper/2406.12992/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [Stable Architectures for Deep Neural Networks](https://mlsanity.com/paper/1705.03341/) (2017)
- [Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activations](https://mlsanity.com/paper/1708.02691/) (2017)
- [Deep learning in business analytics and operations research: Models, applications and managerial implications](https://mlsanity.com/paper/1806.10897/) (2018)
- [Learning Credible Models](https://mlsanity.com/paper/1711.03190/) (2017)
- [Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image Segmentation](https://mlsanity.com/paper/1907.11587/) (2019)

## Abstract (arXiv)

Neural network structures have a critical impact on the accuracy and stability of forecasting. Neural architecture search procedures help design an optimal neural network according to some loss function, which represents a set of quality criteria. This paper investigates the problem of neural network structure optimization. It proposes a way to construct a loss function, which contains a set of additive elements. Each element is called the regularizer. It corresponds to some part of the neural network structure and represents a criterion to optimize. The optimization procedure changes the structure in iterations. To optimize various parts of the structure, the procedure changes the set of regularizers according to some schedule. The authors propose a way to construct the additive regularization schedule. By comparing regularized models with non-regularized ones for a collection of datasets the computational experiments show that the proposed method finds efficient neural network structure and delivers accurate networks of low complexity.