eprintid: 10194864
rev_number: 8
eprint_status: archive
userid: 699
dir: disk0/10/19/48/64
datestamp: 2024-07-19 14:59:46
lastmod: 2024-09-13 06:10:08
status_changed: 2024-07-19 14:59:46
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Lopes, Vasco
creators_name: Carlucci, Fabio Maria
creators_name: Esperanca, Pedro M
creators_name: Singh, Marco
creators_name: Yang, Antoine
creators_name: Gabillon, Victor
creators_name: Xu, Hang
creators_name: Chen, Zewei
creators_name: Wang, Jun
title: Manas: multi-agent neural architecture search
ispublished: pub
divisions: UCL
divisions: B04
divisions: F48
keywords: AutoML, Computer Science, Computer Science, Artificial Intelligence, Computer vision, Multi arm bandits, Neural architecture search, Object recognition, Science & Technology, Technology
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: 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.
date: 2024-01
date_type: published
publisher: Springer Verlag
official_url: http://dx.doi.org/10.1007/s10994-023-06379-w
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2090757
doi: 10.1007/s10994-023-06379-w
lyricists_name: Wang, Jun
lyricists_id: JWANG00
actors_name: Wang, Jun
actors_id: JWANG00
actors_role: owner
funding_acknowledgements: 2020.04588.BD [FCT - Fundacao para a Ciencia e Tecnologia]; [Huawei Technologies R amp;D (UK) Ltd]
full_text_status: public
publication: Machine Learning
volume: 113
number: 1
pagerange: 73-96
pages: 24
issn: 0885-6125
citation:        Lopes, Vasco;    Carlucci, Fabio Maria;    Esperanca, Pedro M;    Singh, Marco;    Yang, Antoine;    Gabillon, Victor;    Xu, Hang;         ... Wang, Jun; + view all <#>        Lopes, Vasco;  Carlucci, Fabio Maria;  Esperanca, Pedro M;  Singh, Marco;  Yang, Antoine;  Gabillon, Victor;  Xu, Hang;  Chen, Zewei;  Wang, Jun;   - view fewer <#>    (2024)    Manas: multi-agent neural architecture search.                   Machine Learning , 113  (1)   pp. 73-96.    10.1007/s10994-023-06379-w <https://doi.org/10.1007/s10994-023-06379-w>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10194864/1/1909.01051v4.pdf