TY  - CONF
AU  - Zhengying Liu
AU  - Zhen Xu
AU  - Shangeth Rajaa
AU  - Meysam Madadi
AU  - Julio C. S. Jacques Junior
AU  - Sergio Escalera
AU  - Adrien Pavao
AU  - Sebastien Treguer
AU  - Wei-Wei Tu
AU  - Isabelle Guyon
A2  - NEURIPS
PY  - 2020//
TI  - Towards Automated Deep Learning: Analysis of the AutoDL challenge series 2019
BT  - Proceedings of Machine Learning Research
SP  - 242
EP  - 252
VL  - 123
N2  - 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}).
UR  - http://proceedings.mlr.press/v123/liu20a.html
L1  - http://refbase.cvc.uab.es/files/LXR2020.pdf
N1  - HUPBA
ID  - Zhengying Liu2020
ER  -