Deep learning from a control vantage point
This article offers an overview of recent developments in deep learning from a control vantage point. It reviews several classes of models including feedforward and residual neural networks, transformers, large-language models, and selective state space models while explaining how control theory plays an important role in understanding their training, properties, or architecture design. The article explains how residual neural networks can be interpreted as the discretization of a continuous-time control system thereby enabling the use of control theoretic techniques for its analysis. To illustrate the power of this bridge between control and deep learning, two technical results are discu...
| Authors | Álvaro Rodríguez Abella, João Pedro Silvestre, Bahman Gharesifard et al. |
|---|---|
| Primary category | eess.SY |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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