In the recent decade, deep learning, a subset of artificial intelligence and machine learning,
has been used to identify patterns in big healthcare datasets for disease phenoty**, event …

The existence of class imbalance in a dataset can greatly bias the classifier towards majority
classification. This discrepancy can pose a serious problem for deep learning models, which …

We study the application of large language models to zero-shot and few-shot classification
of tabular data. We prompt the large language model with a serialization of the tabular data …

Tabular data is one of the most commonly used types of data in machine learning. Despite
recent advances in neural nets (NNs) for tabular data, there is still an active discussion on …

Heterogeneous tabular data are the most commonly used form of data and are essential for
numerous critical and computationally demanding applications. On homogeneous datasets …

A key element in solving real-life data science problems is selecting the types of models to
use. Tree ensemble models (such as XGBoost) are usually recommended for classification …

The existing literature on deep learning for tabular data proposes a wide range of novel
architectures and reports competitive results on various datasets. However, the proposed …

Fine-tuning pretrained language models (LMs) without making any architectural changes
has become a norm for learning various language downstream tasks. However, for non …

Recently, Transformer-like deep architectures have shown strong performance on tabular
data problems. Unlike traditional models, eg, MLP, these architectures map scalar values of …

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 …