@InProceedings{ZhengyingLiu2020,
author="Zhengying Liu
and Zhen Xu
and Shangeth Rajaa
and Meysam Madadi
and Julio C. S. Jacques Junior
and Sergio Escalera
and Adrien Pavao
and Sebastien Treguer
and Wei-Wei Tu
and Isabelle Guyon",
title="Towards Automated Deep Learning: Analysis of the AutoDL challenge series 2019",
booktitle="Proceedings of Machine Learning Research",
year="2020",
volume="123",
pages="242--252",
abstract="We present the design and results of recent competitions in Automated Deep Learning (AutoDL). In the AutoDL challenge series 2019, we organized 5 machine learning challenges: AutoCV, AutoCV2, AutoNLP, AutoSpeech and AutoDL. The first 4 challenges concern each a specific application domain, such as computer vision, natural language processing and speech recognition. At the time of March 2020, the last challenge AutoDL is still on-going and we only present its design. Some highlights of this work include: (1) a benchmark suite of baseline AutoML solutions, with emphasis on domains for which Deep Learning methods have had prior success (image, video, text, speech, etc); (2) a novel any-time learning framework, which opens doors for further theoretical consideration; (3) a repository of around 100 datasets (from all above domains) over half of which are released as public datasets to enable research on meta-learning; (4) analyses revealing that winning solutions generalize to new unseen datasets, validating progress towards universal AutoML solution; (5) open-sourcing of the challenge platform, the starting kit, the dataset formatting toolkit, and all winning solutions (All information available at \{autodl.chalearn.org\}).",
optnote="HUPBA",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3500), last updated on Tue, 20 Sep 2022 15:24:36 +0200",
opturl="http://proceedings.mlr.press/v123/liu20a.html",
file=":http://refbase.cvc.uab.es/files/LXR2020.pdf:PDF"
}