6 citations · 8 across the 18 of their papers we have counts for
19 papers
Truecell reproduces R Seurat's single-cell analysis outputs natively in the Python ecosystem
Nuzla Ismail, Shanika Amarasoma, Michelle Galeas-Pena +1
Single-cell RNA-sequencing analyses predominantly use either Seurat (R) or Scanpy (Python), with the choice often driven by programming language preference. However, by default, th…
CATO: Charted Attention for Neural PDE Operators
Chun-Wun Cheng, Sifan Wang, Carola-Bibiane Schönlieb +1
Neural operators have emerged as powerful data-driven solvers for PDEs, offering substantial acceleration over classical numerical methods. However, existing transformer-based oper…
ProSMA-UNet: Decoder Conditioning for Proximal-Sparse Skip Feature Selection
Chun-Wun Cheng, Yanqi Cheng, Peiyuan Jing +4
Medical image segmentation commonly relies on U-shaped encoder-decoder architectures such as U-Net, where skip connections preserve fine spatial detail by injecting high-resolution…
3D Wavelet-Based Structural Priors for Controlled Diffusion in Whole-Body Low-Dose PET Denoising
Peiyuan Jing, Yue Yang, Chun-Wun Cheng +8
Low-dose Positron Emission Tomography (PET) imaging reduces patient radiation exposure but suffers from increased noise that degrades image quality and diagnostic reliability. Alth…
MAP-Diff: Multi-Anchor Guided Diffusion for Progressive 3D Whole-Body Low-Dose PET Denoising
Peiyuan Jing, Chun-Wun Cheng, Liutao Yang +7
Low-dose Positron Emission Tomography (PET) reduces radiation exposure but suffers from severe noise and quantitative degradation. Diffusion-based denoising models achieve strong f…
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
Modern neural architectures are largely developed as distinct empirical constructions, despite recurring principles across convolutional, attention, state-space, and equivariant mo…
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…
NeuTSFlow: Modeling Continuous Functions Behind Time Series Forecasting
Huibo Xu, Likang Wu, Xianquan Wang +4
Time series forecasting is a fundamental task with broad applications, yet conventional methods often treat data as discrete sequences, overlooking their origin as noisy samples of…
Implicit U-KAN2.0: Dynamic, Efficient and Interpretable Medical Image Segmentation
Chun-Wun Cheng, Yining Zhao, Yanqi Cheng +3
Image segmentation is a fundamental task in both image analysis and medical applications. State-of-the-art methods predominantly rely on encoder-decoder architectures with a U-shap…
Cross-Modal Few-Shot Learning with Second-Order Neural Ordinary Differential Equations
Yi Zhang, Chun-Wun Cheng, Junyi He +4
We introduce SONO, a novel method leveraging Second-Order Neural Ordinary Differential Equations (Second-Order NODEs) to enhance cross-modal few-shot learning. By employing a simpl…
Where Do We Stand with Implicit Neural Representations? A Technical and Performance Survey
Amer Essakine, Yanqi Cheng, Chun-Wun Cheng +5
Implicit Neural Representations (INRs) have emerged as a paradigm in knowledge representation, offering exceptional flexibility and performance across a diverse range of applicatio…
Mamba Neural Operator: Who Wins? Transformers vs. State-Space Models for PDEs
Chun-Wun Cheng, Jiahao Huang, Yi Zhang +3
Partial differential equations (PDEs) are widely used to model complex physical systems, but solving them efficiently remains a significant challenge. Recently, Transformers have e…
NODE-Adapter: Neural Ordinary Differential Equations for Better Vision-Language Reasoning
Yi Zhang, Chun-Wun Cheng, Ke Yu +3
In this paper, we consider the problem of prototype-based vision-language reasoning problem. We observe that existing methods encounter three major challenges: 1) escalating resour…
Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis
Angelica I. Aviles-Rivero, Chun-Wun Cheng, Zhongying Deng +2
Early detection of Alzheimer's disease's precursor stages is imperative for significantly enhancing patient outcomes and quality of life. This challenge is tackled through a semi-s…
The Missing U for Efficient Diffusion Models
Sergio Calvo-Ordonez, Chun-Wun Cheng, Jiahao Huang +4
Diffusion Probabilistic Models stand as a critical tool in generative modelling, enabling the generation of complex data distributions. This family of generative models yields reco…