Publications

Are graph convolutional networks with random weights feasible? ★[link]ESI Highly Cited Paper 🏆
C. Huang, M. Li*, F. Cao, H. Fujita, Z. Li, X. Wu
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 3, pp. 2751-2768, 2023.

Multi-view graph convolutional networks with attention mechanism ★ [link][code]
K. Yao, J. Liang, J. Liang, M. Li, F. Cao
Artificial Intelligence, vol. 307, 103708, 2022.

HAQJSK: Hierarchical-aligned quantum Jensen-Shannon kernels for graph classification ★ [arxiv] [link]
L. Bai, L. Cui, Y. Wang, M. Li*, J. Li, P. Yu, E. R. Hancock
IEEE Transactions on Knowledge and Data Engineering, 2024, DOI: 10.1109/TKDE.2024.3389966.

Collaborative knowledge graph fusion by exploiting the open corpus ★ [link]
Y. Wang, Y. Wan, L. Bai, L. Cui, Z. Xu, M. Li, P. Yu, E. Hancock
IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 2, pp. 475-489, 2024.

Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms and Applications ★ [link]
M. Li, A. Micheli, Y. G. Wang, S. Pan, P. Lio, G. Gnecco, M. Sanguineti
IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 4, pp. 4367-4372, 2024.

Permutaion equivariant graph framelets for heterophilous graph learning ★ [link] [code]
J. Li, R. Zheng, H. Feng, M. Li*, X. Zhuang*
IEEE Transactions on Neural Networks and Learning Systems, 2024, DOI: 10.1109/TNNLS.2024.3370918.

Flow2GNN: Flexible two-way flow message passing for enhancing GNNs beyond homophily ★ [link]
C. Huang, Y. Wang, Y. Jiang, M. Li, X. Huang, S. Wang, S. Pan, C. Zhou
IEEE Transactions on Cybernetics, 2024, DOI: 10.1109/TCYB.2024.3412149.

EduCross: Dual adversarial bipartite hypergraph learning for cross-modal retrieval in multimodal educational slides ★ [link]
M. Li, S. Zhou, Y. Chen, C. Huang, Y. Jiang
Information Fusion, vol. 109, 102428, 2024.

Multimodal graph learning based on 3D Haar semi-tight framelet for student engagement prediction ★ [link]
M. Li, X. Zhuang, L. Bai, W. Ding
Information Fusion, vol. 105, 102224, 2024.

EduGraph: Learning path-based hypergraph neural networks for MOOC course recommendation ★ [link]
M. Li, Z. Li*, C. Huang*, Y. Jiang, X. Wu
IEEE Transactions on Big Data, 2024.

A simple yet effective framelet-based graph neural network for directed graphs★ [link] [code]
C. Zou, A. Han, L. Lin, M. Li, J. Gao
IEEE Transactions on Artificial Intelligence, vol. 5, no. 4, pp. 1647-1657, 2024.

GoLoG: Global-to-local decoupling graph network with joint optimization for hyperspectral image classification ★ [link]
B. Yang, H. Ye, M. Li*, F. Cao, S. Pan
IEEE Transactions on Geoscience and Remote Sensing, vol. 61, 5528014, 2023.

Path integral based convolution and pooling for graph neural networks ★ [link][code][PyG Implementation]
Z. Ma, J. Xuan, Y. G. Wang, M. Li, P. Lio
NeurIPS, 2020, pp. 16421-16433.

How universal polynomial bases enhance spectral graph neural networks: Heterophily, over-smoothing, and over-squashing ★ [link] [code]
K. Huang*, Y. G. Wang, M. Li*, P. Lio
ICML, 2024.

QBMK: Quantum-based matching kernels for un-attributed graphs ★ [link]
L. Bai, L. Cui, M. Li, Y. Wang, E. Hancock
ICML, 2024. (Spotlight Paper: 3.5% acceptance rate)

How powerful are shallow neural networks with bandlimited random weights? ★ [link]
M. Li, S. Sonoda, F. Cao, Y. G. Wang, J. Liang
ICML, 2023, pp. 19960-19981.

Haar graph pooling ★ [link][code]
Y. G. Wang, M. Li*, Z. Ma, G. Montufar, X. Zhuang, Y. Fan
ICML, 2020, pp. 9952-9962.

How framelets enhance graph neural networks ★[link] [code]
X. Zheng, B. Zhou, J. Gao, Y. G. Wang, P. Lio, M. Li, G. Montufar
ICML, 2021, pp. 12761-12771. (Spotlight Paper)

Stability and generalization of ℓp-regularized stochastic learning for GCN ★ [link]
S. Liu, L. Wei, S. Lv, M. Li
IJCAI, 2023, pp. 5685-5693.

BLoG: Bootstrapped graph representation learning with local and global regularization for recommendation ★[link]
M. Li, L. Zhang, L. Cui, L. Bai, Z. Li, X. Wu
Pattern Recognition, vol. 144, 109874, 2023.

QBER: Quantum-based entropic representations for un-attributed graphs ★ [link]
L. Cui, M. Li, L. Bai, Y. Wang, J. Li, Y. Wang, Z. Li, Y. Chen, E. Hancock
Pattern Recognition, vol. 145, 109877, 2024.

AG-Meta: Adaptive graph meta learning via representation consistency over local subgraphs ★ [link]
Y. Wang, C. Huang, M. Li, Q. Huang, X. Wu, J. Wu
Pattern Reocognition, vol. 151, 110387, 2024.

Fast Haar transforms for graph neural networks ★ [link]
M. Li, Z. Ma, Y. G. Wang, X. Zhuang
Neural Networks, vol. 128, pp. 188-198, 2020.

Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms and Applications ★ [link]
M. Li, A. Micheli, Y. G. Wang, S. Pan, P. Lio, G. Gnecco, M. Sanguineti
IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 4, pp. 4367-4372, 2024.

EduCross: Dual adversarial bipartite hypergraph learning for cross-modal retrieval in multimodal educational slides ★ [link]
M. Li, S. Zhou, Y. Chen, C. Huang, Y. Jiang
Information Fusion, vol. 109, 102428, 2024.

Multimodal graph learning based on 3D Haar semi-tight framelet for student engagement prediction ★ [link]
M. Li, X. Zhuang, L. Bai, W. Ding
Information Fusion, vol. 105, 102224, 2024.

HAQJSK: Hierarchical-aligned quantum Jensen-Shannon kernels for graph classification ★ [arxiv] [link]
L. Bai, L. Cui, Y. Wang, M. Li*, J. Li, P. Yu, E. R. Hancock
IEEE Transactions on Knowledge and Data Engineering, 2024, DOI: 10.1109/TKDE.2024.3389966.

Collaborative knowledge graph fusion by exploiting the open corpus ★ [link]
Y. Wang, Y. Wan, L. Bai, L. Cui, Z. Xu, M. Li, P. Yu, E. Hancock
IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 2, pp. 475-489, 2024.

XKT: Towards explainable knowledge tracing with multiple knowledge concept annotations [link]
C. Huang, Q. Huang, X. Huang, H. Wang, M. Li, K. Lin, Y. Chang
IEEE Transactions on Knowledge and Data Engineering, 2024, DOI: 10.1109/TKDE.2024.3418098.

Flow2GNN: Flexible two-way flow message passing for enhancing GNNs beyond homophily ★ [link]
C. Huang, Y. Wang, Y. Jiang, M. Li, X. Huang, S. Wang, S. Pan, C. Zhou
IEEE Transactions on Cybernetics, 2024, DOI: 10.1109/TCYB.2024.3412149.

How universal polynomial bases enhance spectral graph neural networks: Heterophily, over-smoothing, and over-squashing ★ [link] [code]
K. Huang*, Y. G. Wang, M. Li*, P. Lio
ICML, 2024.

QBMK: Quantum-based matching kernels for un-attributed graphs ★ [link]
L. Bai, L. Cui, M. Li, Y. Wang, E. Hancock
ICML, 2024. (Spotlight Paper: 3.5% acceptance rate)

Permutaion equivariant graph framelets for heterophilous graph learning ★ [link] [code]
J. Li, R. Zheng, H. Feng, M. Li*, X. Zhuang*
IEEE Transactions on Neural Networks and Learning Systems, 2024, DOI: 10.1109/TNNLS.2024.3370918.

Towards graph self-supervised learning with contrastive adjusted zooming ★ [link]
Y. Zheng, M. Jin, S. Pan, Y. F. Li, H. Peng, M. Li, Z. Li
IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 7, pp. 8882-8896, 2024.

EduGraph: Learning path-based hypergraph neural networks for MOOC course recommendation ★ [link]
M. Li, Z. Li*, C. Huang*, Y. Jiang, X. Wu
IEEE Transactions on Big Data, 2024.

A simple yet effective framelet-based graph neural network for directed graphs★ [link] [code]
C. Zou, A. Han, L. Lin, M. Li, J. Gao
IEEE Transactions on Artificial Intelligence, vol. 5, no. 4, pp. 1647-1657, 2024.

MM-tracker: Visual tracking with a multi-task model integrating detection and differentiating feature extraction
Z. Wang, M. Li, Z. Li, X. Zhang, M. Li*, Z. Li, W. Ding, X. Wu
IEEE Transactions on Emerging Topics in Computational Intelligence, 2024, accept.

Framelet based dual hypergraph neural networks for student engagement prediction ★ [poster] [Best Short Paper Award]
M. Li*, J. Shi
AI4ED-AAAI, 2024. Best Short Paper Award 🏆

QBER: Quantum-based entropic representations for un-attributed graphs ★ [link]
L. Cui, M. Li, L. Bai, Y. Wang, J. Li, Y. Wang, Z. Li, Y. Chen, E. Hancock
Pattern Recognition, vol. 145, 109877, 2024.

AG-Meta: Adaptive graph meta learning via representation consistency over local subgraphs ★ [link]
Y. Wang, C. Huang, M. Li, Q. Huang, X. Wu, J. Wu
Pattern Reocognition, vol. 151, 110387, 2024.

PointTransform networks for automatic depression level prediction via facial keypoints [link]
M. Niu, M. Li*, C. Fu
Knowledge-Based Systems, vol. 297, 111951, 2024.

Incorporation of peer-feedback into the pedagogical use of spherical video-based virtual reality in writing education [link]
Y. Chen, M. Li*, M. Cukurova, M. Jong
British Journal of Educational Technology, vol. 55, no.2, pp. 519-540, 2024.

Unleashing imagination: An effective pedagogical approach to integrate into spherical video-based virtual reality to improve students' creative writing [link]
Y. Chen, M. Li*, M. Cukurova*
Education and Information Technologies, vol. 29, pp. 6499-6523, 2024.

A systematic review of research on immersive technology-enhanced writing education: The current state and a research agenda [link]
Y. Chen, M. Li*, C. Huang, M. Cukurova, Q. Ma
IEEE Transactions on Learning Technologies, vol. 17, pp. 919-938, 2024.

Framelet-based dual hypergraph neural networks for student performance prediction [link]
Y. Yang, J. Shi, M. Li*, H. Fujita
International Journal of Machine Learning and Cybernetics, 2024, DOI: 10.1007/s13042-024-02124-4.

Robust graph neural networks with Dirichlet regularization and residual connection [link][code]
K. Yao, Z. Du, M. Li, F. Cao, J. Liang
International Journal of Machine Learning and Cybernetics, 2024, DOI: 10.1007/s13042-024-02117-3.

A joint parcellation and boundary network with multi-rate-shared dilated graph attention for cortical surface parcellation [link]
S. Liu, H. Ye, B. Yang, M. Li, F. Cao
Medical & Biological Engineering & Computing, vol. 62, pp. 537-549, 2024.

Real-time E-bike route planning with battery range prediction [Link] [Demo Video]
Z. Li, G. Ren, Y. Gu, S. Zhou, X. Liu, J. Huang, M. Li*
ACM International Conference on Web Search and Data Mining (WSDM), 2024, pp. 1070–1073.

Bridging smoothness and approximation: Theoretical insights into over-smoothing in graph neural networks [link]
G. Yang, J. Li, M. Li, H. Feng, D. X. Zhou
arXiv preprint, 2024.

HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning [link]
Z. Xu, L. Bai, L. Cui, M. Li, Y. Wang, E. R. Hancock
arXiv preprint, 2024.

ENADPool: The Edge-Node Attention-based Differentiable Pooling for graph neural networks [link]
Z. Zhao, L. Bai, L. Cui, M. Li, Y. Wang, L. Xu, E. Hancock
arXiv preprint, 2024.

ConsistencyDet: Robust object detector with denoising paradigm of consistency models [link] [code]
L. Jiang, Z. Wang, C. Wang, M. Li, J. Leng, X. Wu
submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.

A unified framework for exploratory learning-aided community detection under topological uncertainty ★ [link]
Y. Hou, C. Tran, M. Li, W. Y. Shin
submitted to IEEE Transactions on Evolutionary Computation, 2024.

AEGK: Aligned Entropic Graph Kernels through continuous-time quantum walks [link]
L. Cui, L. Bai, M. Li, P. Ren, Y. Wang, Y. Philip, L. Zhang, E. R. Hancock
submitted to IEEE Transactions on Knowledge and Data Engineering, 2024.

Correlation information enhanced graph anomaly detection via hypergraph transformation [link]
C. Huang, C. Gao, M. Li, Y. Jiang, X. Huang, Z. Chen
submitted to IEEE Transactions on Knowledge and Data Engineering, 2024.

Adversarial key instance attack in multi-instance learning: Interpreting vulnerability and security
Y. Zhang, Z. Zhou, M. Li
submitted to IEEE Transactions on Information Forensics & Security, 2024.

On learning label noise robust networks via regularization: A topological view
C. Zhou, H. Meng, M. Li, Z. Zhou
submitted to IEEE Transactions on Neural Networks and Learning Systems, 2024.

Examining the Fourier spectrum of speech signal from a time-frequency perspective for automatic depression level prediction
M. Niu, J. Tao, Y. He, S. Zhang, M. Li
submitted to IEEE Transactions on Affective Computing, 2024.

Opinion dynamics informed neural networks for few-labeled graphs
W. Ye, J. Yang, X. Wei, R. Fan, M. Li, A. Singh
submitted to IEEE Transactions on Network Science and Engineering, 2024.

AutoSGRL: Automated framework construction for self-supervised graph representation learning
Y. Xie, Y. Chang, M. Gong, M. Li, A. K. Qin, X. Zhang
submitted to IEEE Transactions on Computational Social Systems, 2024.

Multi-topology contrastive graph representation learning
Y. Xie, J. Jia, C. Wen, D. Li, M. Li*
submitted to SCIENCE CHINA Information Sciences, 2024.

A scale-unified spatial learning network with boundary contrastive loss for cortical surface parcellation
H. Ye, S. Liu, M. Li, H. Zhu, F. Cao
submitted to Medical & Biological Engineering & Computing, 2024.

A feature reuse framework with texture-adaptive aggregation for reference-based super-resolution [link]
X. Mei, Y. Yang, M. Li*, C. Huang, K. Zhang, F. Zhang
submitted to Knowledge-Based Systems, 2023.

Small object detection super-resolution: A novel three-stage architecture for improved object detection and texture restoration in educational settings
X. Mei, K. Zhang, C. Huang, X. Chen, M. Li*, Z. Li, W. Ding, X. Wu
submitted to IEEE Transactions on Emerging Topics in Computational Intelligence, 2023.

Fast tensor needlet transforms for tangent vector fields on the sphere ✠[link]
M. Li, J. Chen, P. Broadbridge, A. Olenko, Y. G. Wang
submitted to Applied and Computational Harmonic Analysis, 2023.

Are graph convolutional networks with random weights feasible? ★[link]ESI Highly Cited Paper 🏆
C. Huang, M. Li*, F. Cao, H. Fujita, Z. Li, X. Wu
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 3, pp. 2751-2768, 2023.

From motivational experience to creative writing: A motivational AR-based learning approach to promoting Chinese writing performance and positive writing behaviours [link]
M. Li, Y. Chen, C. Huang, G. Hwang, M. Cukurova
Computers and Education, vol. 202, 104844, 2023.

How powerful are shallow neural networks with bandlimited random weights? ★ [link]
M. Li, S. Sonoda, F. Cao, Y. G. Wang, J. Liang
ICML, 2023, pp. 19960-19981.

Multiple pedestrian tracking with graph attention map on urban road scene ★[link]
Z. Wang, Z. Li, J. Leng, M. Li*, L. Bai
IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 8, pp. 8567-8579, 2023.

GoLoG: Global-to-local decoupling graph network with joint optimization for hyperspectral image classification ★ [link]
B. Yang, H. Ye, M. Li*, F. Cao, S. Pan
IEEE Transactions on Geoscience and Remote Sensing, vol. 61, 5528014, 2023.

Stability and generalization of ℓp-regularized stochastic learning for GCN ★ [link]
S. Lv, S. Liu, L. Wei, M. Li
IJCAI, 2023, pp. 5685-5693.

BLoG: Bootstrapped graph representation learning with local and global regularization for recommendation ★ [link]
M. Li, L. Zhang, L. Cui, L. Bai, Z. Li, X. Wu
Pattern Recognition, vol. 144, 109874, 2023.

QBER: Quantum-based entropic representations for un-attributed graphs ★ [link]
L. Cui, M. Li, L. Bai, Y. Wang, J. Li, Y. Wang, Z. Li, Y. Chen, E. Hancock
Pattern Recognition, vol. 145, 109877, 2024.

Revisiting graph neural networks from hybrid regularized graph signal reconstruction ★ [link]
J. Miao, F. Cao, H. Ye, M. Li, B. Yang
Neural Networks, vol. 157, pp. 444-459, 2023.

Triplet teaching graph contrastive networks with self-evolving adaptive augmentation ★ [link][code]
J. Miao, F. Cao, M. Li, B. Yang, H. Ye
Pattern Recognition, vol. 142, 109687, 2023.

A new deep graph attention approach with influence and preference relationship reconstruction for rate prediction recommendation ★ [link]
H. Ye, Y. Song, M. Li, F. Cao
Information Processing and Management, vol. 60, no. 5, 103439, 2023.

MATHNET: Haar-Like wavelet multiresolution analysis for graph representation and learning ★ [link]
X. Zheng, B. Zhou, M. Li*, Y. G. Wang*, and J. Gao
Knowledge-Based Systems, vol. 273, 110609, 2023.

A disentangled linguistic graph model for explainable aspect-based sentiment analysis ★[link]
X. Mei, Y. Zhou, C. Zhu, M. Wu, M. Li*, S. Pan
Knowledge-Based Systems, vol. 260, 110150, 2023.

TeFNA: Text-centered fusion network with crossmodal attention for multimodal sentiment analysis [link]
C. Huang, J. Zhang, X. Wu, Y. Wang, M. Li*, X. Huang
Knowledge-Based Systems, vol. 269, 110502, 2023.

Entangled Quantum Neural Network [link]
Q Meng, J Zhang, Z Li, M. Li*, L Cui
In: Pandey, R., Srivastava, N., Singh, N.K., Tyagi, K. (eds) Quantum Computing: A Shift from Bits to Qubits. Studies in Computational Intelligence, 245-262, 2023.

Multi-view graph convolutional networks with attention mechanism ★ [link][code]
K. Yao, J. Liang, J. Liang, M. Li, F. Cao
Artificial Intelligence, vol. 307, 103708, 2022.

Embedding graphs on Grassmann manifold ★ [link][code]
B. Zhou, X. Zheng, Y. G. Wang, M. Li, J. Gao
Neural Networks, vol. 152, pp. 322-331, 2022.

Deep multi-graph neural networks with attention fusion for recommendation ★[link]
Y. Song, H. Ye, M. Li, F. Cao
Expert Systems with Applications, vol. 191, 116240, 2022.

Cell graph neural networks enable the digital staging of tumor microenvironment and precise prediction of patient survival in gastric cancer ★ [link] [code]
Y. Wang, Y. G. Wang, C. Hu, M. Li, Y. Fan, N. Otter, et al.
npj Precison Oncology, vol. 6, 45, 2022.

Empowering IoT predictive maintenance solutions with AI: A distributed system for manufacturing plant-wide monitoring [link]
Y. Liu, W. Yu, T. Dillon, W. Rahayu, M. Li*
IEEE Transactions on Industrial Informatics, vol. 18, no. 2, pp. 1345-1354, 2022.

Promoting deep writing with immersive technologies: An SVVR‐supported Chinese composition writing approach for primary schools [link]
Y. Chen, M. Li*, C. Huang, Z. Han, G. Hwang, G. Yang
British Journal of Educational Technology, vol. 53, no. 6, pp. 2071-2091, 2022.

Deeper insights into neural nets with random weights [link]
M. Li, G. Gnecco and M. Sanguineti
in: Proceedings of the Australasian Joint Conference on Artificial Intelligence (AJCAI), 2022, pp. 129-140.

Feedforward neural network reconstructed from high-order quantum systems
J. Zhang, Z. Li, X. Wang, H. Peng, M. Li
in: Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2022.

Neural network model reconstructed from entangled quantum states
J. Zhang, Z. Li, J. X, M. Li
in: Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2022.

How framelets enhance graph neural networks ★[link] [code]
X. Zheng, B. Zhou, J. Gao, Y. G. Wang, P. Lio, M. Li, G. Montufar
ICML, 2021, pp. 12761-12771. (Spotlight Paper)

Effective multiple pedestrian tracking system in video surveillance with monocular stationary camera [link]
Z. Wang, M. Li*, Y. Lu, Y. Bao, Z. Li, J. Zhao
Expert Systems with Applications, vol. 178, 114992, 2021.

Grassmann graph embedding ★ [link][code]
B. Zhou, X. Zheng, Y. G. Wang, M. Li, J. Gao
ICLR Workshop on Geometrical and Topological Representation Learning (GTRL), 2021.

Stochastic configuration network ensembles with selective base models [link]
C. Huang, M. Li*, D. Wang
Neural Networks, vol. 137, pp. 106-118, 2021.

Algorithm 1018: FaVeST–fast vector spherical harmonic transforms [link][code]
Q. T. Le Gia, M. Li*, Y. G. Wang
ACM Transactions on Mathematical Softwares, vol. 47, no. 4, pp. 1-24, 2021.

2-D stochastic configuration networks for image data analytics [link]
M. Li, D. Wang
IEEE Transactions on Cybernetics, vol. 51, no. 1, pp. 359-372, 2021.

Path integral based convolution and pooling for graph neural networks ★ [link][code][PyG Implementation]
Z. Ma, J. Xuan, Y. G. Wang, M. Li, P. Lio
NeurIPS, 2020, pp. 16421-16433.

Exercise recommendation based on knowledge concept prediction [link]
Z. Wu, M. Li, Y. Tang, Q. Liang
Knowledge-Based Systems, vol. 210, 106481, 2020.

rcosmo: R Package for Analysis of Spherical, HEALPix and Cosmological Data [link][package]
D. Fryer, M. Li, A. Olenko
The R Journal, vol. 12, no. 1, 206-225, 2020.

Haar graph pooling ★ [link][code]
Y. G. Wang, M. Li*, Z. Ma, G. Montufar, X. Zhuang, Y. Fan
ICML, 2020, pp.9952-9962.

Fast Haar transforms for graph neural networks ★ [link]
M. Li, Z. Ma, Y. G. Wang, X. Zhuang
Neural Networks, vol. 128, pp. 188-198, 2020.

PAN: Path integral based convolution for deep graph neural networks ★ [link]
Z. Ma, M. Li, Y. G. Wang
ICML Workshop on Learning and Reasoning with Graph-Structured Representation, 2019.

Improved randomized learning algorithms for imbalanced and noisy educational data classification [link]
M. Li, C. Huang, Q. Hu, J. Zhu, Y. Tang
Computing, pp. 1-15, 2019.

Robust stochastic configuration networks with maximum correntropy criterion for uncertain data regression [link]
M. Li, C. Huang, and D. Wang
Information Sciences, vol. 473, 73-86, 2019.

Deep stochastic configuration networks with universal approximation property [link]
D. Wang, M. Li
Proceedings of the International Joint Conference on Neural Networks (IJCNN), pp. 1-8, IEEE, 2018.

Stochastic configuration networks: Fundamentals and algorithms [link][codek][code]ESI Highly Cited Paper 🏆
D. Wang, M. Li
IEEE Transactions on Cybernetics, vol. 47, no.10, pp. 3466-3479, 2017.

Insights into randomized algorithms for neural networks: Practical issues and common pitfalls [link]
M. Li, D. Wang
Information Sciences, vol. 382-383, pp. 170-178, 2017.

Robust stochastic configuration networks with kernel density estimation for uncertain data regression [link]
D. Wang, M. Li
Information Sciences, vol. 412-413, pp. 210-222, 2017.

Spherical data fitting by multiscale moving least squares [link]
F. Cao, M. Li
Applied Mathematical Modelling, vol. 39, no. 12, pp. 3448-3458, 2015.

Scattered data quasi-interpolation on spheres [link]
Z. Chen, F. Cao, M. Li
Mathematical Methods in the Applied Sciences, vol. 38, no. 12, pp. 2527-2536, 2015.

Multiscale interpolation on the sphere: Convergence rate and inverse theorem [link]
M. Li, F. Cao
Applied Mathematics and Computation, vol. 263, pp. 134-150, 2015.

Approximation by diffuse functional of generalized moving least squares on the sphere [link]
F. Cao, Y. Zhang, M. Li
Acta Mathematica Sinica, vol. 57, no. 3, pp. 607-614, 2014.

Local uniform error estimates for spherical basis functions interpolation [link]
M. Li, F. Cao
Mathematical Methods in the Applied Sciences, vol. 37, no. 9, pp. 1364-1376, 2014.