7 papers
End-to-End Learning of Safe Optimal Feedback Control in High Dimensions with Control Barrier Function Layers
Xingjian Li, Kelvin Kan, Deepanshu Verma +3
We consider the problem of learning high-dimensional semi-global feedback controllers under hard safety constraints enforced by control barrier functions (CBFs). Incorporating CBFs…
Dimension-Free Convergence of Discrete Diffusion Models: Adjoint Equations Induce the Right Space
Kelvin Kan, Xingjian Li, Benjamin J. Zhang +3
Discrete diffusion has become a leading framework for generative modeling in various applications including language, vision, and biology. Existing convergence theory, however, exh…
Zero-Shot Transferable Solution Method for Parametric Optimal Control Problems
Xingjian Li, Kelvin Kan, Deepanshu Verma +3
This paper presents a transferable solution method for optimal control problems with varying objectives using function encoder (FE) policies. Traditional optimization-based approac…
Dynamical Implicit Neural Representations
Yesom Park, Kelvin Kan, Thomas Flynn +4
Implicit Neural Representations (INRs) provide a powerful continuous framework for modeling complex visual and geometric signals, but spectral bias remains a fundamental challenge,…
Optimal Control for Transformer Architectures: Enhancing Generalization, Robustness and Efficiency
Kelvin Kan, Xingjian Li, Benjamin J. Zhang +3
We study Transformers through the perspective of optimal control theory, using tools from continuous-time formulations to derive actionable insights into training and architecture…
Stability of Transformers under Layer Normalization
Kelvin Kan, Xingjian Li, Benjamin J. Zhang +4
Despite their widespread use, training deep Transformers can be unstable. Layer normalization, a standard component, improves training stability, but its placement has often been a…