9.4k citations · 16.5k across the 107 of their papers we have counts for
119 papers
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…
PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning
Neeratyoy Mallik, Edward Bergman, Carl Hvarfner +5
Hyperparameters of Deep Learning (DL) pipelines are crucial for their downstream performance. While a large number of methods for Hyperparameter Optimization (HPO) have been develo…
PED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspaces
Shuhei Watanabe, Archit Bansal, Frank Hutter
The recent rise in popularity of Hyperparameter Optimization (HPO) for deep learning has highlighted the role that good hyperparameter (HP) space design can play in training strong…
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…
PFNs4BO: In-Context Learning for Bayesian Optimization
Samuel Müller, Matthias Feurer, Noah Hollmann +1
In this paper, we use Prior-data Fitted Networks (PFNs) as a flexible surrogate for Bayesian Optimization (BO). PFNs are neural processes that are trained to approximate the poster…