Ensemble methods are considered the state‐of‐the art solution for many machine learning
challenges. Such methods improve the predictive performance of a single model by training …

With the transition of facial expression recognition (FER) from laboratory-controlled to
challenging in-the-wild conditions and the recent success of deep learning techniques in …

While deep learning has enabled tremendous progress on text and image datasets, its
superiority on tabular data is not clear. We contribute extensive benchmarks of standard and …

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 …

Abstract In standard Convolutional Neural Networks (CNNs), the receptive fields of artificial
neurons in each layer are designed to share the same size. It is well-known in the …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

We present a simple, highly modularized network architecture for image classification. Our
network is constructed by repeating a building block that aggregates a set of transformations …

Deep domain adaptation has emerged as a new learning technique to address the lack of
massive amounts of labeled data. Compared to conventional methods, which learn shared …

We propose a novel high-performance and interpretable canonical deep tabular data
learning architecture, TabNet. TabNet uses sequential attention to choose which features to …