3 citations · 3 across the 8 of their papers we have counts for
9 papers
Core-KAN: Continuous Vision Kernels with Kolmogorov-Arnold Networks
Lan Guo, Mengling Li, Haoran Li +4
Conventional convolutional kernels are typically defined on fixed discrete grids, limiting their ability to accommodate heterogeneous local structures. Existing adaptive operators…
HYDRA: Hyperbolic Dynamic Representation Architecture for Kolmogorov-Arnold Networks
Zhao Su, Yuxin Xia, Haoran Li +4
Kolmogorov-Arnold Networks (KANs) enhance nonlinear function approximation by replacing scalar weights with learnable univariate functions. However, assigning an independent functi…
FedHPro: Federated Hyper-Prototype Learning via Gradient Matching
Huan Wang, Jun Shen, Haoran Li +6
Federated Learning (FL) enables collaborative training of distributed clients while protecting privacy. To enhance generalization capability in FL, prototype-based FL is in the spo…
Learning to Fuse and Reconstruct Multi-View Graphs for Diabetic Retinopathy Grading
Haoran Li, Yuxin Lin, Huan Wang +9
Diabetic retinopathy (DR) is one of the leading causes of vision loss worldwide, making early and accurate DR grading critical for timely intervention. Recent clinical practices le…
FedDifRC: Unlocking the Potential of Text-to-Image Diffusion Models in Heterogeneous Federated Learning
Huan Wang, Haoran Li, Huaming Chen +3
Federated learning aims at training models collaboratively across participants while protecting privacy. However, one major challenge for this paradigm is the data heterogeneity is…
FedSC: Federated Learning with Semantic-Aware Collaboration
Huan Wang, Haoran Li, Huaming Chen +3
Federated learning (FL) aims to train models collaboratively across clients without sharing data for privacy-preserving. However, one major challenge is the data heterogeneity issu…
FedSKC: Federated Learning with Non-IID Data via Structural Knowledge Collaboration
Huan Wang, Haoran Li, Huaming Chen +5
With the advancement of edge computing, federated learning (FL) displays a bright promise as a privacy-preserving collaborative learning paradigm. However, one major challenge for…
Vox-UDA: Voxel-wise Unsupervised Domain Adaptation for Cryo-Electron Subtomogram Segmentation with Denoised Pseudo Labeling
Haoran Li, Xingjian Li, Jiahua Shi +6
Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology facilitating the study of macromolecular structures at near-atomic resolution. Recent volumetric segmentation approach…
FDNet: Frequency Domain Denoising Network For Cell Segmentation in Astrocytes Derived From Induced Pluripotent Stem Cells
Haoran Li, Jiahua Shi, Huaming Chen +5
Artificially generated induced pluripotent stem cells (iPSCs) from somatic cells play an important role for disease modeling and drug screening of neurodegenerative diseases. Astro…