TY - JOUR SP - 73 VL - 113 JF - Machine Learning A1 - Lopes, Vasco A1 - Carlucci, Fabio Maria A1 - Esperanca, Pedro M A1 - Singh, Marco A1 - Yang, Antoine A1 - Gabillon, Victor A1 - Xu, Hang A1 - Chen, Zewei A1 - Wang, Jun PB - Springer Verlag Y1 - 2024/01// ID - discovery10194864 UR - http://dx.doi.org/10.1007/s10994-023-06379-w N2 - The Neural Architecture Search (NAS) problem is typically formulated as a graph search problem where the goal is to learn the optimal operations over edges in order to maximize a graph-level global objective. Due to the large architecture parameter space, efficiency is a key bottleneck preventing NAS from its practical use. In this work, we address the issue by framing NAS as a multi-agent problem where agents control a subset of the network and coordinate to reach optimal architectures. We provide two distinct lightweight implementations, with reduced memory requirements (1/8th of state-of-the-art), and performances above those of much more computationally expensive methods. Theoretically, we demonstrate vanishing regrets of the form O(T) , with T being the total number of rounds. Finally, we perform experiments on CIFAR-10 and ImageNet, and aware that random search and random sampling are (often ignored) effective baselines, we conducted additional experiments on 3 alternative datasets, with complexity constraints, and 2 network configurations, and achieve competitive results in comparison with the baselines and other methods. N1 - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions. KW - AutoML KW - Computer Science KW - Computer Science KW - Artificial Intelligence KW - Computer vision KW - Multi arm bandits KW - Neural architecture search KW - Object recognition KW - Science & Technology KW - Technology AV - public TI - Manas: multi-agent neural architecture search SN - 0885-6125 EP - 96 IS - 1 ER -