AutoML.org

Freiburg-Hannover

NAS-Bench-101: Towards Reproducible Neural Architecture Search

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 […]

Read More

Automatic Reinforcement Learning for Molecular Design

By In reinforcement learning (RL), one of the major machine learning (ML) paradigms, an agent interacts with an environment. How well an RL agent can solve a problem, can be sensitive to choices such as the policy network architecture, the training hyperparameters, or the specific dynamics of the environment. A common strategy to deal with […]

Read More

BOHB: Robust and Efficient Hyperparameter Optimization at Scale

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 […]

Read More

We did it again: world champions in AutoML

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.

Read More