[Compall] [IEEE CAI] CFP: Workshop on Neural Architecture Search @ IEEE CAI [15 January, 2025]

[Saúl Zapotecas Martínez]

<We apologize for multiple postings. Please kindly disseminate this Call
for Papers to your colleagues and contacts>

Dear Colleagues,

On behalf of the  Workshop on the *IEEE CAI2025
<https://cai.ieee.org/2025/>* Workshop on Neural Architecture Search
<https://sites.google.com/view/ieee-cai-nas/> organizers, I would like to
send our best wishes to you and your family for a happy, healthy, and
prosperous New Year 2025!

We are inviting you to submit your research work to the *CAI2025
<https://cai.ieee.org/2025/>* Workshop on Neural Architecture Search
<https://sites.google.com/view/ieee-cai-nas/>.

We would appreciate it if you forwarded this CFP to the appropriate
communities.

=============

Call for Papers
-------------------------------
 Workshop on the *IEEE CAI2025* Workshop on Neural Architecture Search
-------------------------------
*IEEE Conference on Artificial Intelligence (CAI)*
<https://cai.ieee.org/2025/>
*May 5-7, 2025*
Venue - Santa Clara, California, USA
-------------------------------

*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.

*Topics of Interest*

The workshop will explore evolutionary NAS's latest advancements and
methodologies, focusing on its application across various domains such as
computer vision, natural language processing, and reinforcement learning.
It will cover theoretical foundations, search strategies, evaluation
techniques, and practical implementations, providing participants with a
comprehensive understanding of the NAS landscape. A broad range of topics
will be discussed, including but not limited to:

   1. Representation and Encoding of Neural Networks
   2. Development of Objective Functions
   3. Multi-Objective NAS
   4. Bilevel NAS
   5. Parallel and Distributed search algorithms for NAS
   6. Assessment Methodologies
   7. Interpretability and Explainability
   8. Theoretical Foundations of NAS
   9. Real-World Applications and Case Studies

*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.

Important Dates

   - *Paper Submission Deadline*: 15 January, 2025
   - *Paper Acceptance Notification*: 1 March, 2025
   - *Final Paper Submission Deadline*: 7 March, 2025

The submission procedure, deadlines, and paper format follow the same
guidelines as the IEEE CAI'2025 main conference. Submissions must be made
via the IEEE CAI'2025 online system.

Organizers

--- *Saúl Zapotecas-Martínez* (szapotecas en inaoep.mx)
Computer Science Department
Instituto Nacional de Astrofísica Óptica y Electrónica, Tonantzintla,
Puebla, Mexico

--- *Alejandro Rosales-Pérez* (alejandro.rosales en cimat.mx)
Centro de Investigación en Matemáticas (CIMAT) Monterrey Campus

--- *Efrén Mezura-Montes* (emezura en uv.mx)
Artificial Intelligence Research Institute
University of Veracruz, MEXICO

-- 
Dr. Saúl Zapotecas-Martínez
Computer Science Department,
National Institute of Astrophysics, Optics and Electronics,
Luis Enrique Erro No. 1, Tonantzintla, Puebla 72840, MEXICO

szapotecas en inaoe.mx
https://ccc.inaoep.mx/~szapotecas/

-- 
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