9.4k citations · 16.5k across the 107 of their papers we have counts for
10 papers · 1 filter
Learning to Design RNA
Frederic Runge, Danny Stoll, Stefan Falkner +1
Designing RNA molecules has garnered recent interest in medicine, synthetic biology, biotechnology and bioinformatics since many functional RNA molecules were shown to be involved…
Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search
Arber Zela, Aaron Klein, Stefan Falkner +1
While existing work on neural architecture search (NAS) tunes hyperparameters in a separate post-processing step, we demonstrate that architectural choices and other hyperparameter…
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
Stefan Falkner, Aaron Klein, Frank Hutter
Modern deep learning methods are very sensitive to many hyperparameters, and, due to the long training times of state-of-the-art models, vanilla Bayesian hyperparameter optimizatio…
Training Generative Reversible Networks
Robin Tibor Schirrmeister, Patryk Chrabąszcz, Frank Hutter +1
Generative models with an encoding component such as autoencoders currently receive great interest. However, training of autoencoders is typically complicated by the need to train…
Maximizing acquisition functions for Bayesian optimization
James T. Wilson, Frank Hutter, Marc Peter Deisenroth
Bayesian optimization is a sample-efficient approach to global optimization that relies on theoretically motivated value heuristics (acquisition functions) to guide its search proc…
Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution
Thomas Elsken, Jan Hendrik Metzen, Frank Hutter
Neural Architecture Search aims at automatically finding neural architectures that are competitive with architectures designed by human experts. While recent approaches have achiev…