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Found 6 papers, 5 papers with code
Understanding the differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks
While connections between these approaches exist, such models are commonly developed in isolation and there is a lack of theoretical understanding of the shared principles underpinning these architectures and their subtle differences, greatly influencing performance and scalability.
Linearly Controlled Language Generation with Performative Guarantees
The increasing prevalence of Large Language Models (LMs) in critical applications highlights the need for controlled language generation strategies that are not only computationally efficient but that also enjoy performance guarantees.
State Space Models as Foundation Models: A Control Theoretic Overview
In recent years, there has been a growing interest in integrating linear state-space models (SSM) in deep neural network architectures of foundation models.
Global Performance Guarantees for Localized Model Predictive Control
We also present an algorithm to determine the communication structure for a given system that will preserve performance while minimizing computational complexity.
Data-driven Distributed and Localized Model Predictive Control
By imposing locality constraints on the system response, we show that the amount of data needed for our synthesis problem is independent of the size of the global system.
Frontiers in Scalable Distributed Control: SLS, MPC, and Beyond
The System Level Synthesis (SLS) approach facilitates distributed control of large cyberphysical networks in an easy-to-understand, computationally scalable way.
