Scope

Neural Architecture Search (NAS) is a powerful machine learning technique that automates the design of neural network architectures instead of manually defining the network's structure, such as layers, number of neurons, and activation functions. NAS can discover innovative architectures that surpass those designed manually, often resulting in more accurate, efficient, or straightforward models. NAS  involves searching through a predefined space of potential architectures to find the most effective one for a specific task, such as image classification or natural language processing. NAS aims to optimize aspects like accuracy, efficiency, and model size, often using techniques such as:

• Reinforcement Learning: Agents explore different architectures and learn to improve designs based on performance feedback.

• Evolutionary Algorithms: Inspired by natural selection, these algorithms evolve architectures over successive generations.

• Gradient-Based Methods: Utilize differentiable architecture representations to optimize structures using gradient descent.

The goal of NAS is to reduce the time and expertise required to design high-performing neural networks, making the process more efficient and accessible.

Submissions

We invite submissions of the following types of papers: regular research papers (up to 6 pages) and short position papers (up to 2 pages). Accepted workshop papers are published in the conference proceedings in the workshop section, available online via IEEE, and indexed by IEEE Xplore.

Organizers

Saúl Zapotecas-Martínez

Computer Science Department

Instituto Nacional de Astrofísica Óptica y Electrónica, Tonantzintla, Puebla, Mexico 

szapotecas [at] inaoep [dot] mx

Alejandro Rosales-Pérez
Centro de Investigación en Matemáticas (CIMAT)

Monterrey Campus

alejandro.rosales [at] cimat [dot] mx 

Efrén Mezura-Montes

Artificial Intelligence Research Institute

University of Veracruz, MEXICO

emezura [at] uv [dot] mx