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Found 7 papers, 5 papers with code
Tabular Data: Is Attention All You Need?
In this paper, we introduce a large-scale empirical study comparing neural networks against gradient-boosted decision trees on tabular data, but also transformer-based architectures against traditional multi-layer perceptrons (MLP) with residual connections.
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How
With the ever-increasing number of pretrained models, machine learning practitioners are continuously faced with which pretrained model to use, and how to finetune it for a new dataset.
Breaking the Paradox of Explainable Deep Learning
Through extensive experiments, we demonstrate that our explainable deep networks are as accurate as state-of-the-art classifiers on tabular data.
Supervising the Multi-Fidelity Race of Hyperparameter Configurations
Multi-fidelity (gray-box) hyperparameter optimization techniques (HPO) have recently emerged as a promising direction for tuning Deep Learning methods.
Well-tuned Simple Nets Excel on Tabular Datasets
Tabular datasets are the last "unconquered castle" for deep learning, with traditional ML methods like Gradient-Boosted Decision Trees still performing strongly even against recent specialized neural architectures.
Regularization Cocktails
The regularization of prediction models is arguably the most crucial ingredient that allows Machine Learning solutions to generalize well on unseen data.
OpenML-Python: an extensible Python API for OpenML
It also provides functionality to conduct machine learning experiments, upload the results to OpenML, and reproduce results which are stored on OpenML.
