Momentum Transformer

Momentum Transformer research explores enhancing the efficiency and accuracy of transformer networks by incorporating momentum-based optimization techniques. Current research focuses on applying momentum to improve linear attention mechanisms, addressing challenges in decentralized optimization and data heterogeneity, and developing adaptive momentum strategies for various applications. This work aims to bridge the performance gap between computationally expensive standard transformers and their faster, but often less accurate, linear counterparts, impacting fields ranging from machine learning and financial modeling to robotics and computer vision. The resulting improvements in training speed and accuracy have significant implications for large-scale applications.