Tabular Transformer

Tabular transformers are neural network architectures designed to process tabular data, aiming to improve upon traditional methods like gradient boosted decision trees in tasks like fraud detection, imputation, and prediction. Current research focuses on enhancing their performance through techniques such as self-supervised learning, pre-training on large datasets, and parameter-efficient fine-tuning, often incorporating them into multimodal frameworks that leverage both structured and unstructured data. These advancements are significant because they offer the potential for improved accuracy and efficiency in various applications, including healthcare, e-commerce, and data extraction from document images, particularly in scenarios with limited labeled data or noisy information.