Training Transformers for Tabular Data: An Optimal Transport Approach to Self-Attention

This research explores a novel perspective on training Transformers for tabular data using optimal transport theory to improve self-attention mechanisms. The paper likely offers insights into how to efficiently train Transformers for structured data, potentially leading to better performance and generalization.

• •Focuses on optimizing Transformer training for tabular data.
• •Utilizes optimal transport theory for self-attention.
• •Suggests potential improvements in performance and generalization.