7 citations · 17 across the 10 of their papers we have counts for
3 papers · 1 filter
A General Framework for User-Guided Bayesian Optimization
Carl Hvarfner, Frank Hutter, Luigi Nardi
The optimization of expensive-to-evaluate black-box functions is prevalent in various scientific disciplines. Bayesian optimization is an automatic, general and sample-efficient me…
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…
Beyond Random Augmentations: Pretraining with Hard Views
Fabio Ferreira, Ivo Rapant, Jörg K. H. Franke +1
Self-Supervised Learning (SSL) methods typically rely on random image augmentations, or views, to make models invariant to different transformations. We hypothesize that the effica…