<div dir="ltr"><div><font face="arial, sans-serif"><We apologize for multiple postings. Please kindly disseminate this Call for Papers to your colleagues and contacts><br><br></font><div><span style="color:rgb(51,51,51)"><font face="arial, sans-serif">Dear Colleagues,</font></span></div><div><font color="#333333" face="arial, sans-serif"><br></font><div style="padding-top:0px;border-top:0px;color:rgb(51,51,51)"><div style="padding-top:0px;border-top:0px">On behalf of the <font face="arial, sans-serif" style="background-color:initial"> </font><span style="background-color:initial">Workshop on</span><span style="background-color:initial"> </span><font face="arial, sans-serif">the <b><a href="https://cai.ieee.org/2025/" rel="noreferrer" target="_blank" style="color:rgb(0,105,166)">IEEE CAI2025</a></b> </font><a href="https://sites.google.com/view/ieee-cai-nas/" style="background-color:initial">Workshop on Neural Architecture Search</a> organizers, I would like to send our best wishes to you and your family for a happy, healthy, and prosperous New Year 2025!</div><div style="padding-top:0px;border-top:0px"><font face="arial, sans-serif"><br></font></div><div><font face="arial, sans-serif">We are inviting you to submit your research work to the <b><a href="https://cai.ieee.org/2025/" rel="noreferrer" target="_blank" style="color:rgb(0,105,166)">CAI2025</a></b></font> <a href="https://sites.google.com/view/ieee-cai-nas/">Workshop on Neural Architecture Search</a>.<font color="#0000ff" style="font-family:arial,sans-serif"></font></div><div><font face="arial, sans-serif"><br></font></div><div><font face="arial, sans-serif">We would appreciate it if you forwarded this CFP to the appropriate communities. </font></div><div><font face="arial, sans-serif"><br></font></div><div><font face="arial, sans-serif">=============</font></div><div><font face="arial, sans-serif"><br></font></div><div><div><font face="arial, sans-serif">Call for Papers</font></div></div></div></div></div><div><div style="color:rgb(51,51,51)"><font face="arial, sans-serif">------------------------------- </font></div><div style="color:rgb(51,51,51)"><font face="arial, sans-serif" style="background-color:initial"> </font><span style="background-color:initial">Workshop on</span><span style="background-color:initial"> </span><font face="arial, sans-serif">the <b>IEEE CAI2025</b> </font>Workshop on Neural Architecture Search</div><div style="color:rgb(51,51,51)"><span style="font-family:arial,sans-serif">------------------------------</span><span style="font-family:arial,sans-serif">- </span></div><div style="color:rgb(51,51,51)"><a href="https://cai.ieee.org/2025/" rel="noreferrer" target="_blank" style="color:rgb(0,105,166)"><b><font face="arial, sans-serif">IEEE Conference on Artificial Intelligence (CAI)</font></b></a></div><div style="color:rgb(51,51,51)"><span style="color:rgb(34,34,34)"><b>May 5-7, 2025</b></span></div><div style="color:rgb(51,51,51)"><font face="arial, sans-serif">Venue - </font><span style="color:rgb(34,34,34)">Santa Clara, California, USA</span></div><div style="color:rgb(51,51,51)"><font face="arial, sans-serif">------------------------------- </font></div></div><div><br></div><div><b style=""><font color="#ff00ff">Scope</font></b><br><br>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:<br><br><b>Reinforcement Learning</b>: Agents explore different architectures and learn to improve designs based on performance feedback.<br><br><b>Evolutionary Algorithms</b>: Inspired by natural selection, these algorithms evolve architectures over successive generations.<br><br><b>Gradient-Based Methods</b>: Utilize differentiable architecture representations to optimize structures using gradient descent.<br><br>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.<br><br><b><font color="#ff00ff">Topics of Interest</font></b><br><br>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:<br><ol><li>Representation and Encoding of Neural Networks</li><li>Development of Objective Functions</li><li>Multi-Objective NAS</li><li>Bilevel NAS</li><li>Parallel and Distributed search algorithms for NAS</li><li>Assessment Methodologies</li><li>Interpretability and Explainability</li><li>Theoretical Foundations of NAS</li><li>Real-World Applications and Case Studies</li></ol><b><font color="#ff00ff">Submissions</font></b><br><br>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.</div><div><br></div><div><span style="color:rgb(255,0,255);font-weight:700">Important Dates</span></div><div><ul><li><b>Paper Submission Deadline</b>: 15 January, 2025</li><li><b>Paper Acceptance Notification</b>: 1 March, 2025</li><li><b>Final Paper Submission Deadline</b>: 7 March, 2025</li></ul></div>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.<div><br></div><div><span style="color:rgb(255,0,255);font-weight:700">Organizers</span></div><div><br></div><div>--- <b>Saúl Zapotecas-Martínez</b> (<a href="mailto:szapotecas@inaoep.mx">szapotecas@inaoep.mx</a>)</div><div>Computer Science Department<br>Instituto Nacional de Astrofísica Óptica y Electrónica, Tonantzintla, Puebla, Mexico<br><br>--- <b>Alejandro Rosales-Pérez</b> (<a href="mailto:alejandro.rosales@cimat.mx">alejandro.rosales@cimat.mx</a>)</div><div>Centro de Investigación en Matemáticas (CIMAT) Monterrey Campus<br><br>--- <b>Efrén Mezura-Montes</b> (<a href="mailto:emezura@uv.mx">emezura@uv.mx</a>)<br>Artificial Intelligence Research Institute<br>University of Veracruz, MEXICO<br><br></div><span class="gmail_signature_prefix">-- </span><br><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div>Dr. Saúl Zapotecas-Martínez<br>Computer Science Department,<br>National Institute of Astrophysics, Optics and Electronics,<br>Luis Enrique Erro No. 1, Tonantzintla, Puebla 72840, MEXICO<br><br><a href="mailto:szapotecas@inaoe.mx" target="_blank">szapotecas@inaoe.mx</a></div><div><a href="https://ccc.inaoep.mx/~szapotecas/" target="_blank">https://ccc.inaoep.mx/~szapotecas/</a><br></div></div></div></div>
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