2024/07/04 by Siyang Liu, Hao-Run Cai, Liu, Si-Yang +5 · 3 citations
Computer Science · #FOS: Computer and information sciences #Intelligent Tutoring Systems and Adaptive Learning #Machine Learning (cs.LG) #Natural Language Processing Techniques #Semantic Web and Ontologies
paper · pdf · doi:10.48550/arxiv.2407.04057
openalex publication_date 2024/07/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 on tabular data are becoming promising alternatives due to their flexibility and ability to capture complex interactions within the data. Considering that deep tabular methods have diverse design philosophies, including the ways they handle features, design learning objectives, and construct model architectures, we introduce a versatile deep-learning toolbox called TALENT (Tabular Analytics and LEarNing Toolbox) to utilize, analyze, and compare tabular methods. TALENT encompasses an extensive collection of more than 20 deep tabular prediction methods, associated with various encoding and normalization modules, and provides a unified interface that is easily integrable with new methods as they emerge. In this paper, we present the design and functionality of the toolbox, illustrate its practical application through several case studies, and investigate the performance of various methods fairly based on our toolbox. Code is available at https://github.com/qile2000/LAMDA-TALENT.