| Runge–Kutta type discrete circadian RNN for resolving tri-criteria optimization scheme of noises perturbed redundant robot manipulators Z Zhang, X Deng, M He, T Chen, J Liang IEEE Transactions on Systems, Man, and Cybernetics: Systems 52 (3), 1405-1416, 2020 | 8 | 2020 |
| Learning with asymmetric kernels: Least squares and feature interpretation M He, F He, L Shi, X Huang, JAK Suykens IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 | 4 | 2023 |
| Learning non-parametric kernel via matrix decomposition for logistic regression K Wang, F He, M He, X Huang Pattern Recognition Letters 171, 177-183, 2023 | 2 | 2023 |
| Taylor Discrete Circadian Rhythms Neural Network for Resolving Bicriteria Optimization Problem of Redundant Robot Manipulators Perturbed by Periodic Noises Z Zhang, S Chen, M He IEEE Transactions on Industrial Informatics 18 (9), 6015-6025, 2021 | 2 | 2021 |
| Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels F He, M He, L Shi, X Huang, JAK Suykens arXiv preprint arXiv:2310.05236, 2023 | 1 | 2023 |
| Data Imputation by Pursuing Better Classification: A Supervised Kernel-Based Method R Yang, F He, M He, K Wang, X Huang arXiv preprint arXiv:2405.07800, 2024 | | 2024 |
| Decentralized Kernel Ridge Regression Based on Data-dependent Random Feature R Yang, F He, M He, J Yang, X Huang arXiv preprint arXiv:2405.07791, 2024 | | 2024 |
| Diffusion Representation for Asymmetric Kernels via Magnetic Transform M He, FAN He, R Yang, X Huang Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| Data Imputation by Pursuing Better Classification: A Supervised Learning Approach R Yang, FAN He, M He, K Wang | | 2023 |
| Random Fourier Features for Asymmetric Kernels M He, F He, F Liu, X Huang arXiv preprint arXiv:2209.08461, 2022 | | 2022 |
| Global Search and Analysis for the Nonconvex Two-Level Penalty F He, M He, L Shi, X Huang IEEE Transactions on Neural Networks and Learning Systems, 2022 | | 2022 |
