OT-Transformer: A Continuous-time Transformer Architecture with Optimal Transport Regularization
This work addresses the need for more stable and generalizable transformer architectures, which is incremental as it builds on existing transformer methods with a novel regularization approach.
The paper tackled the problem of improving transformer models by proposing a continuous-time formulation with optimal transport regularization, resulting in enhanced training stability and generalization, and experimental results showing performance improvements over discrete counterparts and relevant models.
Transformers have achieved state-of-the-art performance in numerous tasks. In this paper, we propose a continuous-time formulation of transformers. Specifically, we consider a dynamical system whose governing equation is parametrized by transformer blocks. We leverage optimal transport theory to regularize the training problem, which enhances stability in training and improves generalization of the resulting model. Moreover, we demonstrate in theory that this regularization is necessary as it promotes uniqueness and regularity of solutions. Our model is flexible in that almost any existing transformer architectures can be adopted to construct the dynamical system with only slight modifications to the existing code. We perform extensive numerical experiments on tasks motivated by natural language processing, image classification, and point cloud classification. Our experimental results show that the proposed method improves the performance of its discrete counterpart and outperforms relevant comparing models.