arXiv:2504.01250v2 Announce Type: replace Abstract: This paper presents the Robust Recurrent Deep Network (R2DN), a scalable parameterization of robust recurrent neural networks for machine learning and data-driven control. We construct R2DNs as the feedback interconnection of a linear time-invariant system and a 1-Lipschitz deep feedforward network, and directly parameterize the weights so that our models are stable (contracting) and robust to small input perturbations (Lipschitz) by design. Our parameterization uses a structure similar to the previously-proposed recurrent equilibrium network
The paper presents a new, scalable parameterization for robust recurrent neural networks, building on previous work and continuously advancing the field of AI foundational models.
This development improves the stability and robustness of recurrent neural networks, which is critical for their reliable application in machine learning and data-driven control, enabling more trustworthy AI systems.
The R2DN parameterization offers a design-based approach to ensure stability and robustness for recurrent neural networks, potentially simplifying the development and deployment of reliable AI applications.
- · AI researchers
- · Machine learning engineers
- · Control systems developers
Improved stability in AI models leads to more reliable real-world applications.
Increased adoption of recurrent neural networks in safety-critical systems due to enhanced robustness.
Accelerated development of autonomous AI agents capable of operating in dynamic and unpredictable environments.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG