75 citations · 154 across the 9 of their papers we have counts for
5 papers · 1 filter
Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks
Steven Adriaensen, Herilalaina Rakotoarison, Samuel Müller +1
Learning curve extrapolation aims to predict model performance in later epochs of training, based on the performance in earlier epochs. In this work, we argue that, while the inher…
Towards Automated Design of Riboswitches
Frederic Runge, Jörg K. H. Franke, Frank Hutter
Experimental screening and selection pipelines for the discovery of novel riboswitches are expensive, time-consuming, and inefficient. Using computational methods to reduce the num…
Scalable Deep Learning for RNA Secondary Structure Prediction
Jörg K. H. Franke, Frederic Runge, Frank Hutter
The field of RNA secondary structure prediction has made significant progress with the adoption of deep learning techniques. In this work, we present the RNAformer, a lean deep lea…
MO-DEHB: Evolutionary-based Hyperband for Multi-Objective Optimization
Noor Awad, Ayushi Sharma, Philipp Muller +2
Hyperparameter optimization (HPO) is a powerful technique for automating the tuning of machine learning (ML) models. However, in many real-world applications, accuracy is only one…
Neural Architecture Search: Insights from 1000 Papers
Colin White, Mahmoud Safari, Rhea Sukthanker +5
In the past decade, advances in deep learning have resulted in breakthroughs in a variety of areas, including computer vision, natural language understanding, speech recognition, a…