TOWARDS A NOVEL APPROACH TO TRANSFORMERS

The field of deep learning has witnessed remarkable advancements propelled by transformer models. However, the inherent website randomness in their training process often introduces unpredictability and hinders their robustness. This paper presents "Det: Towards Robust and Efficient Deterministic Transformers," a novel framework aimed at mitigating