Posted on October 21, 2019 by Arber Zela, Frank Hutter
By Understanding and Robustifying Differentiable Architecture Search Optimizing in the search of neural network architectures was initially defined as a discrete problem which intrinsically required to train and evaluate thousands of networks. This of course required huge amount of computational power, which was only possible for few institutions. One-shot neural architecture search (NAS) democratized this […]
Posted on September 6, 2019 by Marius Lindauer, Frank Hutter
By Neural architecture search (NAS) is currently one of the hottest topics in automated machine learning (see AutoML book), with a seemingly exponential increase in the number of papers written on the subject, see the figure above. While many NAS methods are fascinating (please see our survey article for an overview of the main trends […]
Posted on August 30, 2019 by Arber Zela, Yassine Marrakchi, Frank Hutter
By 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. While standard neural networks may lead to […]
Posted on August 9, 2019 by Frank Hutter
By Much work in neural architecture search (NAS) is extremely compute hungry — so compute hungry that it hurts progress and scientific rigor in the community. When individual experiments require 800 GPUs for weeks nobody in academia can meaningfully join the community, and even in companies with huge compute resources nobody thinks of repeating their […]
Posted on July 26, 2018 by André Biedenkapp, Frank Hutter
By Machine learning has achieved many successes in a wide range of application areas, but more often than not, these strongly rely on choosing the correct values for many hyperparameters (see e.g. Snoek et al., 2012). For example, we all know of the awesome results deep learning can achieve, but when we set its learning […]
Posted on July 13, 2018 by André Biedenkapp, Katharina Eggensperger, Matthias Feurer, Frank Hutter
By 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.
