A study on effectiveness of extreme learning machine. - Yuguang Wang 001; Feilong Cao; Yubo Yuan 001 (Neurocomputing, 2011)

Optimization approximation solution for regression problem based on extreme learning machine. - Yubo Yuan 001; Yuguang Wang 001; Feilong Cao (Neurocomputing, 2011)

A modified extreme learning machine with sigmoidal activation functions. - Zhixiang Chen 004; Hou-Ying Zhu; Yuguang Wang 001 (Neural Comput. Appl., 2013)

An iterative learning algorithm for feedforward neural networks with random weights. - Feilong Cao; Dianhui Wang 001; Hou-Ying Zhu; Yuguang Wang 001 (Inf. Sci., 2016)

Localized Tight Frames and Fast Framelet Transforms on the Simplex. - Yuguang Wang 001; Hou-Ying Zhu (CoRR, 2017)

Needlet approximation for isotropic random fields on the sphere. - Quoc Thong Le Gia; Ian H. Sloan; Yuguang Wang 001; Robert S. Womersley (J. Approx. Theory, 2017)

Random Point Sets on the Sphere - Hole Radii, Covering, and Separation. - Johann S. Brauchart; Alexander B. Reznikov; Edward B. Saff; Ian H. Sloan; Yuguang Wang 001; Robert S. Womersley (Exp. Math., 2018)

PAN: Path Integral Based Convolution for Deep Graph Neural Networks. - Zheng Ma; Ming Li 065; Yu Guang Wang 001 (CoRR, 2019)

Fast Tensor Needlet Transforms for Tangent Vector Fields on the Sphere. - Ming Li 065; Philip Broadbridge; Andriy Olenko; Yu Guang Wang 001 (CoRR, 2019)

Optimization-based quasi-uniform spherical t-design and generalized multitaper for complex physiological time series. - Matt Sourisseau; Yu Guang Wang 001; Hau-Tieng Wu; Wei-Hsuan Yu (CoRR, 2019)

FaVeST: Fast Vector Spherical Harmonic Transforms. - Quoc Thong Le Gia; Ming Li 065; Yu Guang Wang 001 (CoRR, 2019)

HaarPooling: Graph Pooling with Compressive Haar Basis. - Yu Guang Wang 001; Ming Li 065; Zheng Ma; Guido Montúfar; Xiaosheng Zhuang; Yanan Fan (CoRR, 2019)

Haar Graph Pooling. - Yuguang Wang 001; Ming Li 065; Zheng Ma; Guido Montúfar; Xiaosheng Zhuang; Yanan Fan (ICML, 2020)

Deep Learning Based Unsupervised and Semi-supervised Classification for Keratoconus. - Nicole Hallett; Kai Yi; Josef Dick; Christopher Hodge; Gerard Sutton; Yu Guang Wang 001; Jingjing You (IJCNN, 2020)

Cosmo VAE: Variational Autoencoder for CMB Image Inpainting. - Kai Yi; Yi Guo; Yanan Fan; Jan Hamann; Yu Guang Wang 001 (IJCNN, 2020)

Path Integral Based Convolution and Pooling for Graph Neural Networks. - Zheng Ma; Junyu Xuan; Yu Guang Wang 001; Ming Li 065; Pietro Liò (NeurIPS, 2020)

CosmoVAE: Variational Autoencoder for CMB Image Inpainting. - Kai Yi; Yi Guo; Yanan Fan; Jan Hamann; Yuguang Wang 001 (CoRR, 2020)

Graph Neural Networks with Haar Transform-Based Convolution and Pooling: A Complete Guide. - Xuebin Zheng; Bingxin Zhou; Ming Li 065; Yu Guang Wang 001; Junbin Gao (CoRR, 2020)

On the Approximation Lower Bound for Neural Nets with Random Weights. - Sho Sonoda; Ming Li 065; Feilong Cao; Changqin Huang; Yu Guang Wang 001 (CoRR, 2020)

Can neural networks learn persistent homology features? - Guido Montúfar; Nina Otter; Yuguang Wang 001 (CoRR, 2020)

Fast Haar Transforms for Graph Neural Networks. - Ming Li 065; Zheng Ma; Yu Guang Wang 001; Xiaosheng Zhuang (Neural Networks, 2020)

Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks. - Cristian Bodnar; Fabrizio Frasca; Yuguang Wang 001; Nina Otter; Guido F. Montúfar; Pietro Lió; Michael M. Bronstein (ICML, 2021)

How Framelets Enhance Graph Neural Networks. - Xuebin Zheng; Bingxin Zhou; Junbin Gao; Yuguang Wang 001; Pietro Lió; Ming Li 065; Guido Montúfar (ICML, 2021)

Weisfeiler and Lehman Go Cellular: CW Networks. - Cristian Bodnar; Fabrizio Frasca; Nina Otter; Yuguang Wang 001; Pietro Liò; Guido F. Montúfar; Michael M. Bronstein (NeurIPS, 2021)

Anomaly Detection in Dynamic Graphs via Transformer. - Yixin Liu 001; Shirui Pan; Yu Guang Wang 001; Fei Xiong; Liang Wang 017; Vincent C. S. Lee (CoRR, 2021)

Graph Denoising with Framelet Regularizer. - Bingxin Zhou; Ruikun Li 001; Xuebin Zheng; Yu Guang Wang 001; Junbin Gao (CoRR, 2021)

Spectral Transform Forms Scalable Transformer. - Bingxin Zhou; Xinliang Liu; Yuehua Liu; Yunying Huang; Pietro Liò; Yuguang Wang 001 (CoRR, 2021)

Distributed Filtered Hyperinterpolation for Noisy Data on the Sphere. - Shaobo Lin; Yuguang Wang 001; Ding-Xuan Zhou (SIAM J. Numer. Anal., 2021)

Algorithm 1018: FaVeST - Fast Vector Spherical Harmonic Transforms. - Quoc Thong Le Gia; Ming Li 065; Yu Guang Wang 001 (ACM Trans. Math. Softw., 2021)

Oversquashing in GNNs through the lens of information contraction and graph expansion. - Pradeep Kr. Banerjee; Kedar Karhadkar; Yuguang Wang 001; Uri Alon 002; Guido Montúfar (Allerton, 2022)

Well-Conditioned Spectral Transforms for Dynamic Graph Representation. - Bingxin Zhou; Xinliang Liu; Yuehua Liu; Yunying Huang; Pietro Liò; Yuguang Wang 001 (LoG, 2022)

APPROXIMATE EQUIVARIANCE SO(3) NEEDLET CONVOLUTION. - Kai Yi; Jialin Chen; Yu Guang Wang 001; Bingxin Zhou; Pietro Liò; Yanan Fan; Jan Hamann (TAG-ML, 2022)

Graph Neural Network for Local Corruption Recovery. - Bingxin Zhou; Yuanhong Jiang; Yu Guang Wang 001; Jingwei Liang; Junbin Gao; Shirui Pan; Xiaoqun Zhang (CoRR, 2022)

ACMP: Allen-Cahn Message Passing for Graph Neural Networks with Particle Phase Transition. - Yuelin Wang; Kai Yi; Xinliang Liu; Yu Guang Wang 001; Shi Jin (CoRR, 2022)

Distributed Learning via Filtered Hyperinterpolation on Manifolds. - Guido Montúfar; Yu Guang Wang 001 (Found. Comput. Math., 2022)

Decimated Framelet System on Graphs and Fast G-Framelet Transforms. - Xuebin Zheng; Bingxin Zhou; Yu Guang Wang 001; Xiaosheng Zhuang (J. Mach. Learn. Res., 2022)

Embedding graphs on Grassmann manifold. - Bingxin Zhou; Xuebin Zheng; Yu Guang Wang 001; Ming Li 065; Junbin Gao (Neural Networks, 2022)

How GNNs Facilitate CNNs in Mining Geometric Information from Large-Scale Medical Images. - Yiqing Shen 003; Bingxin Zhou; Xinye Xiong; Ruitian Gao; Yu Guang Wang 001 (BIBM, 2023)

EqMotion: Equivariant Multi-Agent Motion Prediction with Invariant Interaction Reasoning. - Chenxin Xu; Robby T. Tan; Yuhong Tan; Siheng Chen; Yu Guang Wang 001; Xinchao Wang; Yanfeng Wang 001 (CVPR, 2023)

Adaptive Importance Sampling and Quasi-Monte Carlo Methods for 6G URLLC Systems. - Xiongwen Ke; Houying Zhu; Kai Yi; Gaoning He; Ganghua Yang; Yu Guang Wang 001 (ICC, 2023)

ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks. - Yuelin Wang; Kai Yi; Xinliang Liu; Yu Guang Wang 001; Shi Jin (ICLR, 2023)

How Powerful are Shallow Neural Networks with Bandlimited Random Weights? - Ming Li 065; Sho Sonoda; Feilong Cao; Yu Guang Wang 001; Jiye Liang (ICML, 2023)

Graph Denoising Diffusion for Inverse Protein Folding. - Kai Yi; Bingxin Zhou; Yiqing Shen 003; Pietro Lió; Yuguang Wang 001 (NeurIPS, 2023)

Robust Graph Representation Learning for Local Corruption Recovery. - Bingxin Zhou; Yuanhong Jiang; Yuguang Wang 001; Jingwei Liang; Junbin Gao; Shirui Pan; Xiaoqun Zhang (WWW, 2023)

Framelet Message Passing. - Xinliang Liu; Bingxin Zhou; Chutian Zhang; Yuguang Wang 001 (CoRR, 2023)

Graph Representation Learning for Interactive Biomolecule Systems. - Xinye Xiong; Bingxin Zhou; Yuguang Wang 001 (CoRR, 2023)

Accurate and Definite Mutational Effect Prediction with Lightweight Equivariant Graph Neural Networks. - Bingxin Zhou; Outongyi Lv; Kai Yi; Xinye Xiong; Pan Tan; Liang Hong; Yu Guang Wang 001 (CoRR, 2023)

Multi-level Protein Representation Learning for Blind Mutational Effect Prediction. - Yang Tan 001; Bingxin Zhou; Yuanhong Jiang; Yu Guang Wang 001; Liang Hong (CoRR, 2023)

LLQL: Logistic Likelihood Q-Learning for Reinforcement Learning. - Outongyi Lv; Bingxin Zhou; Yu Guang Wang 001 (CoRR, 2023)

Dirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation. - Jialin Chen; Yuelin Wang; Cristian Bodnar; Rex Ying; Pietro Lio; Yuguang Wang 001 (CoRR, 2023)

Numerical computation of triangular complex spherical designs with small mesh ratio. - Yuguang Wang 001; Robert S. Womersley; Hau-Tieng Wu; Wei-Hsuan Yu (J. Comput. Appl. Math., 2023)

MathNet: Haar-like wavelet multiresolution analysis for graph representation learning. - Xuebin Zheng; Bingxin Zhou; Ming Li 065; Yu Guang Wang 001; Junbin Gao (Knowl. Based Syst., 2023)

Lower and upper bounds for numbers of linear regions of graph convolutional networks. - Hao Chen; Yu Guang Wang 001; Huan Xiong (Neural Networks, 2023)

Anomaly Detection in Dynamic Graphs via Transformer. - Yixin Liu 001; Shirui Pan; Yu Guang Wang 001; Fei Xiong; Liang Wang 017; Qingfeng Chen; Vincent Cheng-Siong Lee (IEEE Trans. Knowl. Data Eng., 2023)

ProteinEngine: Empower LLM with Domain Knowledge for Protein Engineering. - Yiqing Shen 003; Outongyi Lv; Houying Zhu; Yu Guang Wang 001 (AIME (1), 2024)

A Fine-tuning Dataset and Benchmark for Large Language Models for Protein Understanding. - Yiqing Shen 003; Zan Chen; Michail Mamalakis; Luhan He; Haiyang Xia; Tianbin Li; Yanzhou Su; Junjun He; Yu Guang Wang 001 (BIBM, 2024)

TourSynbio: A Multi-Modal Large Model and Agent Framework to Bridge Text and Protein Sequences for Protein Engineering. - Yiqing Shen 003; Zan Chen; Michail Mamalakis; Yungeng Liu; Tianbin Li; Yanzhou Su; Junjun He; Pietro Liò; Yu Guang Wang 001 (BIBM, 2024)

LaGDif: Latent Graph Diffusion Model for Efficient Protein Inverse Folding with Self-Ensemble. - Taoyu Wu; Yuguang Wang 001; Yiqing Shen 003 (BIBM, 2024)

How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing. - Keke Huang; Yu Guang Wang 001; Ming Li 065; Pietro Lio (ICML, 2024)

Improving Antibody Design with Force-Guided Sampling in Diffusion Models. - Paulina Kulyte; Francisco Vargas 001; Simon Valentin Mathis; Yu Guang Wang 001; José Miguel Hernández-Lobato; Pietro Liò (CoRR, 2024)

A Survey for Large Language Models in Biomedicine. - Chong Wang; Mengyao Li; Junjun He; Zhongruo Wang; Erfan Darzi; Zan Chen; Jin Ye 002; Tianbin Li; Yanzhou Su; Jing Ke; Kaili Qu; Shuxin Li; et al. (CoRR, 2024)

Layer-diverse Negative Sampling for Graph Neural Networks. - Wei Duan 003; Jie Lu 001; Yu Guang Wang 001; Junyu Xuan (Trans. Mach. Learn. Res., 2024)

Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms, and Applications. - Ming Li 065; Alessio Micheli; Yu Guang Wang 001; Shirui Pan; Pietro Lió; Giorgio Stefano Gnecco; Marcello Sanguineti (IEEE Trans. Neural Networks Learn. Syst., 2024)

Graph Denoising With Framelet Regularizers. - Bingxin Zhou; Ruikun Li 001; Xuebin Zheng; Yu Guang Wang 001; Junbin Gao (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

A multimodal deep learning framework for enzyme turnover prediction with missing modality. - Xin Sun 018; Yu Guang Wang 001; Yiqing Shen 003 (Comput. Biol. Medicine, 2025)