PT Unknown
AU Zhengying Liu
   Zhen Xu
   Shangeth Rajaa
   Meysam Madadi
   Julio C. S. Jacques Junior
   Sergio Escalera
   Adrien Pavao
   Sebastien Treguer
   Wei-Wei Tu
   Isabelle Guyon
TI Towards Automated Deep Learning: Analysis of the AutoDL challenge series 2019
BT Proceedings of Machine Learning Research
PY 2020
BP 242
EP 252
VL 123
AB 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}).
ER