By Dr Yu Guang Wang

Journal articles

Chen ZX; Zhu HY; Wang YG, 2013, 'A modified extreme learning machine with sigmoidal activation functions', Neural Computing and Applications, 22, pp. 541 - 550, http://dx.doi.org/10.1007/s00521-012-0860-2

Wang Y; Cao F, 2011, 'Approximation by Boolean sums of Jackson operators on the sphere', Journal of Computational Analysis and Applications, 13, pp. 830 - 842

Wang Y; Cao F; Yuan Y, 2011, 'A study on effectiveness of extreme learning machine', Neurocomputing, 74, pp. 2483 - 2490, http://dx.doi.org/10.1016/j.neucom.2010.11.030

Yuan Y; Wang Y; Cao F, 2011, 'Optimization approximation solution for regression problem based on extreme learning machine', Neurocomputing, 74, pp. 2475 - 2482, http://dx.doi.org/10.1016/j.neucom.2010.12.037

Cao F; Wang Y, 2009, 'The direct and converse inequalities for jackson-type operators on spherical cap', Journal of Inequalities and Applications, 2009, pp. 205298, http://dx.doi.org/10.1155/2009/205298

Conference Papers

Zhou B; Jiang Y; Wang Y; Liang J; Gao J; Pan S; Zhang X, 2023, 'Robust Graph Representation Learning for Local Corruption Recovery', in ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023, pp. 438 - 448, http://dx.doi.org/10.1145/3543507.3583399

Shen Y; Zhou B; Xiong X; Gao R; Wang YG, 2023, 'How GNNs Facilitate CNNs in Mining Geometric Information from Large-Scale Medical Images', in Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, pp. 2227 - 2230, http://dx.doi.org/10.1109/BIBM58861.2023.10385379

Li M; Sonoda S; Cao F; Wang YG; Liang J, 2023, 'How Powerful are Shallow Neural Networks with Bandlimited Random Weights?', in Proceedings of Machine Learning Research, pp. 19360 - 19384

Banerjee PK; Karhadkar K; Wang YG; Alon U; Montufar G, 2022, 'Oversquashing in GNNs through the lens of information contraction and graph expansion', in 2022 58th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2022, http://dx.doi.org/10.1109/Allerton49937.2022.9929363

Zhou B; Liu X; Liu Y; Huang Y; Liò P; Wang YG, 2022, 'Well-conditioned Spectral Transforms for Dynamic Graph Representation', in Proceedings of Machine Learning Research

Zheng X; Zhou B; Gao J; Wang YG; Liò P; Li M; Montúfar G, 2021, 'How Framelets Enhance Graph Neural Networks', in Proceedings of Machine Learning Research, pp. 12761 - 12771

Bodnar C; Frasca F; Otter N; Wang YG; Liò P; Montúfar G; Bronstein M, 2021, 'Weisfeiler and Lehman Go Cellular: CW Networks', in Advances in Neural Information Processing Systems, pp. 2625 - 2640

Bodnar C; Frasca F; Wang YG; Otter N; Montúfar G; Liò P; Bronstein MM, 2021, 'Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks', in Proceedings of Machine Learning Research, pp. 1026 - 1037

Wang YG; Zhuang X, 2019, 'Tight framelets on graphs for multiscale data analysis', in Proceedings of SPIE - The International Society for Optical Engineering, http://dx.doi.org/10.1117/12.2528414

Working Papers

Yi K; Chen J; Zhou B; Lio P; Fan Y; Hamann J, 2022, Approximate Equivariance SO(3) Needlet Convolution, http://dx.doi.org, https://arxiv.org/abs/2206.10385

Wang YG; Li M; Ma Z; Montufar G; Zhuang X; Fan Y, 2020, Haar graph pooling, http://dx.doi.org

Hamann J; Gia QTL; Sloan IH; Wang YG; Womersley RS, 2019, A New Probe of Gaussianity and Isotropy applied to the CMB Maps, http://dx.doi.org, http://arxiv.org/abs/1911.11442v2

Preprints

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

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