Untangling tradeoffs between recurrence and self-attention in neural networks
This work addresses scalability and optimization challenges in deep learning for sequential tasks, offering incremental improvements with theoretical guarantees.
The paper tackles the problem of vanishing gradients in recurrent networks by analyzing how self-attention affects gradient propagation, proving bounds to mitigate this issue, and proposes a relevancy screening mechanism to balance attention and recurrence for scalable performance.
Attention and self-attention mechanisms, are now central to state-of-the-art deep learning on sequential tasks. However, most recent progress hinges on heuristic approaches with limited understanding of attention's role in model optimization and computation, and rely on considerable memory and computational resources that scale poorly. In this work, we present a formal analysis of how self-attention affects gradient propagation in recurrent networks, and prove that it mitigates the problem of vanishing gradients when trying to capture long-term dependencies by establishing concrete bounds for gradient norms. Building on these results, we propose a relevancy screening mechanism, inspired by the cognitive process of memory consolidation, that allows for a scalable use of sparse self-attention with recurrence. While providing guarantees to avoid vanishing gradients, we use simple numerical experiments to demonstrate the tradeoffs in performance and computational resources by efficiently balancing attention and recurrence. Based on our results, we propose a concrete direction of research to improve scalability of attentive networks.