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A Robust In-Context Model for Conservation Laws: Injecting Context into Flux Neural Operators via Recurrent Vision Transformers
Taeyoung Kim, Joon-Hyuk Ko
We propose an architecture that augments the Flux Neural Operator (Flux NO), which combines the classical finite volume method (FVM) with neural operators, with ViT-based context i…
Loop Corrections in Random Feature Models: Training Error and Generalization Gap
Taeyoung Kim
We study fixed-design random feature ridge regression beyond the mean-kernel approximation. The expectation is taken over the frozen-feature ensemble, conditional on the training s…
Why Rectified Power Unit Networks Fail and How to Improve It: An Effective Field Theory Perspective
Taeyoung Kim, Myungjoo Kang
The Rectified Power Unit (RePU) activation function, a differentiable generalization of the Rectified Linear Unit (ReLU), has shown promise in constructing neural networks due to i…
Bounding the Rademacher Complexity of Fourier neural operators
Taeyoung Kim, Myungjoo Kang
A Fourier neural operator (FNO) is one of the physics-inspired machine learning methods. In particular, it is a neural operator. In recent times, several types of neural operators…