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Author  |
Zhengying Liu; Adrien Pavao; Zhen Xu; Sergio Escalera; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Sebastien Treguer | ||||
| Title | How far are we from true AutoML: reflection from winning solutions and results of AutoDL challenge | Type | Conference Article | ||
| Year | 2020 | Publication | 7th ICML Workshop on Automated Machine Learning | Abbreviated Journal | |
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| Abstract | Following the completion of the AutoDL challenge (the final challenge in the ChaLearn
AutoDL challenge series 2019), we investigate winning solutions and challenge results to answer an important motivational question: how far are we from achieving true AutoML? On one hand, the winning solutions achieve good (accurate and fast) classification performance on unseen datasets. On the other hand, all winning solutions still contain a considerable amount of hard-coded knowledge on the domain (or modality) such as image, video, text, speech and tabular. This form of ad-hoc meta-learning could be replaced by more automated forms of meta-learning in the future. Organizing a meta-learning challenge could help forging AutoML solutions that generalize to new unseen domains (e.g. new types of sensor data) as well as gaining insights on the AutoML problem from a more fundamental point of view. The datasets of the AutoDL challenge are a resource that can be used for further benchmarks and the code of the winners has been outsourced, which is a big step towards “democratizing” Deep Learning. |
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| Address | Virtual; July 2020 | ||||
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| Area | Expedition | Conference | ICML | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ LPX2020 | Serial | 3502 | ||
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