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Towards Automated Deep Learning: Analysis of the AutoDL challenge series 2019

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 ongoing and we only present its design. 1 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 "anytime 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 1. Its results will be presented in future work together with detailed introduction of winning solutions of each challenge.
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Contributor : Marc Schoenauer <>
Submitted on : Thursday, March 4, 2021 - 5:03:34 PM
Last modification on : Wednesday, April 14, 2021 - 3:40:08 AM


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  • HAL Id : hal-03159795, version 1


Zhengying Liu, Zhen Xu, Shangeth Rajaa, Meysam Madadi, Julio Jacques, et al.. Towards Automated Deep Learning: Analysis of the AutoDL challenge series 2019. NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, Competition and Demonstration Track, Dec 2020, Virtual, United States. pp.242-252. ⟨hal-03159795⟩



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