3.8k citations · 4.1k across the 5 of their papers we have counts for
5 papers
Towards Benchmarking Foundation Models for Tabular Data With Text
Martin Mráz, Breenda Das, Anshul Gupta +2
Foundation models for tabular data are rapidly evolving, with increasing interest in extending them to support additional modalities such as free-text features. However, existing b…
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Marius Lindauer, Katharina Eggensperger, Matthias Feurer +6
Algorithm parameters, in particular hyperparameters of machine learning algorithms, can substantially impact their performance. To support users in determining well-performing hype…
DEHB: Evolutionary Hyperband for Scalable, Robust and Efficient Hyperparameter Optimization
Noor Awad, Neeratyoy Mallik, Frank Hutter
Modern machine learning algorithms crucially rely on several design decisions to achieve strong performance, making the problem of Hyperparameter Optimization (HPO) more important…
Deep learning with convolutional neural networks for EEG decoding and visualization
Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer +6
PLEASE READ AND CITE THE REVISED VERSION at Human Brain Mapping: http://onlinelibrary.wiley.com/doi/10.1002/hbm.23730/full Code available here: https://github.com/robintibor/braind…
CMA-ES for Hyperparameter Optimization of Deep Neural Networks
Ilya Loshchilov, Frank Hutter
Hyperparameters of deep neural networks are often optimized by grid search, random search or Bayesian optimization. As an alternative, we propose to use the Covariance Matrix Adapt…