By Dr Yu Guang Wang

Preprints

Ma Y; Yi K; Lio P; Jin S; Wang YG, 2025, How Particle System Theory Enhances Hypergraph Message Passing, http://arxiv.org/abs/2505.18505v1

Zhou Y; Ge Y; Dong X; Wang Y, 2024, How Out-of-Distribution Detection Learning Theory Enhances Transformer: Learnability and Reliability, http://arxiv.org/abs/2406.12915v5

Shen Y; Chen Z; Mamalakis M; He L; Xia H; Li T; Su Y; He J; Wang YG, 2024, A Fine-tuning Dataset and Benchmark for Large Language Models for Protein Understanding, http://arxiv.org/abs/2406.05540v2

Yi K; Zhou B; Shen Y; Liò P; Wang YG, 2023, Graph Denoising Diffusion for Inverse Protein Folding, http://arxiv.org/abs/2306.16819v2

Liu X; Zhou B; Zhang C; Wang YG, 2023, Framelet Message Passing, http://arxiv.org/abs/2302.14806v1

Shen Y; Zhou B; Xiong X; Gao R; Wang YG, 2022, How GNNs Facilitate CNNs in Mining Geometric Information from Large-Scale Medical Images, http://arxiv.org/abs/2206.07599v1

Wang Y; Yi K; Liu X; Wang YG; Jin S, 2022, ACMP: Allen-Cahn Message Passing for Graph Neural Networks with Particle Phase Transition, http://arxiv.org/abs/2206.05437v3

Chen H; Wang YG; Xiong H, 2022, Lower and Upper Bounds for Numbers of Linear Regions of Graph Convolutional Networks, http://arxiv.org/abs/2206.00228v1

Zhou B; Jiang Y; Wang YG; Liang J; Gao J; Pan S; Zhang X, 2022, Robust Graph Representation Learning for Local Corruption Recovery, http://arxiv.org/abs/2202.04936v4

Zhou B; Liu X; Liu Y; Huang Y; Liò P; Wang Y, 2021, Spectral Transform Forms Scalable Transformer, http://arxiv.org/abs/2111.07602v1

Zhou B; Li R; Zheng X; Wang YG; Gao J, 2021, Graph Denoising with Framelet Regularizer, http://arxiv.org/abs/2111.03264v1

Bodnar C; Frasca F; Wang YG; Otter N; Montúfar G; Liò P; Bronstein M, 2021, Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks

Zheng X; Zhou B; Gao J; Wang YG; Lio P; Li M; Montufar G, 2021, How Framelets Enhance Graph Neural Networks

Zheng X; Zhou B; Wang YG; Zhuang X, 2020, Decimated Framelet System on Graphs and Fast G-Framelet Transforms, http://arxiv.org/abs/2012.06922v2

Sourisseau M; Wang YG; Womersley RS; Wu H-T; Yu W-H, 2020, Improve Concentration of Frequency and Time (Conceft) by Novel Complex Spherical Designs, http://dx.doi.org/10.1101/2020.11.23.394007

Montúfar G; Wang YG, 2020, Distributed Learning via Filtered Hyperinterpolation on Manifolds, http://arxiv.org/abs/2007.09392v1

Ma Z; Xuan J; Wang YG; Li M; Lio P, 2020, Path Integral Based Convolution and Pooling for Graph Neural Networks, http://dx.doi.org/10.1088/1742-5468/ac3ae4

Yi K; Guo Y; Fan Y; Hamann J; Wang YG, 2020, CosmoVAE: Variational Autoencoder for CMB Image Inpainting, http://arxiv.org/abs/2001.11651v1

Hallett N; Yi K; Dick J; Hodge C; Sutton G; Wang YG; You J, 2020, Deep Learning Based Unsupervised and Semi-supervised Classification for Keratoconus, http://arxiv.org/abs/2001.11653v1

Lin S-B; Wang YG; Zhou D-X, 2019, Distributed filtered hyperinterpolation for noisy data on the sphere, http://arxiv.org/abs/1910.02434v1

Gia QTL; Li M; Wang YG, 2019, FaVeST: Fast Vector Spherical Harmonic Transforms, http://arxiv.org/abs/1908.00041v3

Li M; Broadbridge P; Olenko A; Wang YG, 2019, Fast Tensor Needlet Transforms for Tangent Vector Fields on the Sphere, http://arxiv.org/abs/1907.13339v1

Wang YG; Womersley RS; Wu H-T; Yu W-H, 2019, Numerical computation of triangular complex spherical designs with small mesh ratio, http://arxiv.org/abs/1907.13493v3

Li M; Ma Z; Wang YG; Zhuang X, 2019, Fast Haar Transforms for Graph Neural Networks, http://arxiv.org/abs/1907.04786v3

Ma Z; Li M; Wang Y, 2019, PAN: Path Integral Based Convolution for Deep Graph Neural Networks, http://arxiv.org/abs/1904.10996v1

Anh VV; Broadbridge P; Olenko A; Wang YG, 2017, On Approximation for Fractional Stochastic Partial Differential Equations on the Sphere, http://dx.doi.org/10.1007/s00477-018-1517-1

Wang YG; Zhu H, 2017, Analysis of Framelet Transforms on a Simplex, http://arxiv.org/abs/1701.01595v3

Brauchart JS; Reznikov AB; Saff EB; Sloan IH; Wang YG; Womersley RS, 2016, Random Point Sets on the Sphere—Hole Radii, Covering, and Separation, http://dx.doi.org/10.1080/10586458.2016.1226209

Wang YG; Zhuang X, 2016, Tight framelets and fast framelet filter bank transforms on manifolds, http://dx.doi.org/10.1016/j.acha.2018.02.001

Gia QTL; Sloan IH; Wang YG; Womerlsey RS, 2015, Needlet approximation for isotropic random fields on the sphere, http://arxiv.org/abs/1512.07790v2

Wang YG; Sloan IH; Womersley RS, 2015, Riemann localisation on the sphere, http://arxiv.org/abs/1510.06834v2

Wang Y; Cao F; Yuan Y, 2014, A study on effectiveness of extreme learning machine, http://dx.doi.org/10.1016/j.neucom.2010.11.030

Wang Y; Cao F, 2014, Approximation by boolean sums of Jackson operators on the sphere, http://arxiv.org/abs/1409.3923v1

Wang Y; Cao F, 2014, The Direct and Converse Inequalities for Jackson-Type Operators on Spherical Cap, http://dx.doi.org/10.1155/2009/205298

Brauchart JS; Dick J; Saff EB; Sloan IH; Wang YG; Womersley RS, 2014, Covering of spheres by spherical caps and worst-case error for equal weight cubature in Sobolev spaces, http://dx.doi.org/10.1016/j.jmaa.2015.05.079

Wang Y; Cao F, 2011, Approximation by Semigroups of Spherical Operators, http://dx.doi.org/10.1007/s11464-014-0361-y

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