6 citations · 6 across the 8 of their papers we have counts for
9 papers
SpectraKAN: Conditioning Spectral Operators
Chun-Wun Cheng, Carola-Bibiane Schönlieb, Angelica I. Aviles-Rivero
Spectral neural operators, particularly Fourier Neural Operators (FNO), are a powerful framework for learning solution operators of partial differential equations (PDEs) due to the…
Product Interaction: An Algebraic Formalism for Deep Learning Architectures
Haonan Dong, Chun-Wun Cheng, Angelica I. Aviles-Rivero
In this paper, we introduce product interactions, an algebraic formalism in which neural network layers are constructed from compositions of a multiplication operator defined over…
LEGATO: Good Identity Unlearning Is Continuous
Qiang Chen, Chun-Wun Cheng, Xiu Su +5
Machine unlearning has become a crucial role in enabling generative models trained on large datasets to remove sensitive, private, or copyright-protected data. However, existing ma…
Training-Free Dual Hyperbolic Adapters for Better Cross-Modal Reasoning
Yi Zhang, Chun-Wun Cheng, Junyi He +5
Recent research in Vision-Language Models (VLMs) has significantly advanced our capabilities in cross-modal reasoning. However, existing methods suffer from performance degradation…
DNA-Prior: Unsupervised Denoise Anything via Dual-Domain Prior
Yanqi Cheng, Chun-Wun Cheng, Jim Denholm +5
Medical imaging pipelines critically rely on robust denoising to stabilise downstream tasks such as segmentation and reconstruction. However, many existing denoisers depend on larg…
PDE Solvers Should Be Local: Fast, Stable Rollouts with Learned Local Stencils
Chun-Wun Cheng, Bin Dong, Carola-Bibiane Schönlieb +1
Neural operator models for solving partial differential equations (PDEs) often rely on global mixing mechanisms-such as spectral convolutions or attention-which tend to oversmooth…