AutoDispNet: Improving Disparity Estimation with AutoML

By Arber Zela, Yassine Marrakchi and Frank Hutter Compared to the state of computer vision 20 years ago, deep learning has enabled more generic methodologies that can be applied to various tasks by automatically extracting meaningful features from the data. However, in practice those methodologies are not as generic as it looks at first glance. […]

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LEMONADE: Efficient Multi-objective Neural Architecture Search with Network Morphisms

Most work on neural architecture search (NAS, see our recent survey) solely optimizes for one criterion: high performance (measured in terms of accuracy). This often results in large and complex network architectures that cannot be used in real-world applications with several other important criteria including memory requirement, energy consumption and latency. The other problem in […]

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PoSH Auto-sklearn

We did it again: world champions in AutoML

By André Biedenkapp, Katharina Eggensperger, Matthias Feurer, Frank Hutter Our ML Freiburg lab is the world champion in automatic machine learning (AutoML) again! After winning the first international AutoML challenge (2015-2016), we also just won the second international AutoML challenge (2017-2018). Our system PoSH-Auto-sklearn outperformed all other 41 participating AutoML systems. What is AutoML and […]

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