Self-supervised learning (SSL) has been incorporated into many state-of-the-art models in
various domains, where SSL defines pretext tasks based on unlabeled datasets to learn …

Abstract Representation learning of pathology whole-slide images (WSIs) has been has
primarily relied on weak supervision with Multiple Instance Learning (MIL). However the …

A well-trained deep neural network on balanced datasets usually exhibits the Neural
Collapse (NC) phenomenon, which is an informative indicator of the model achieving good …

Tabular data is prevalent across various domains in machine learning. Although Deep
Neural Network (DNN)-based methods have shown promising performance comparable to …

The growing success of deep learning in various domains has prompted investigations into
its application to tabular data, where deep models have shown promising results compared …

This research introduces the Boosted Ensemble deep Multi-Layer Layer Perceptron
(EdMLP) architecture with multiple output layers, a novel enhancement for the traditional …

Tabular data is one of the most common data sources in machine learning. Although a wide
range of classical methods demonstrate practical utilities in this field, deep learning methods …

Self-supervision is often used for pre-training to foster performance on a downstream task by
constructing meaningful representations of samples. Self-supervised learning (SSL) …

A neural network (NN) with the widely-used ReLU activation has been shown to partition the
sample space into many convex polytopes for prediction. However, the parametric way a NN …

Large language models (LLMs) are primarily designed to understand unstructured text.
When directly applied to structured formats such as tabular data, they may struggle to …