| Approximating functions with multi-features by deep convolutional neural networks T Mao, Z Shi, DX Zhou Analysis and Applications 21 (01), 93-125, 2023 | 30 | 2023 |
| Theory of deep convolutional neural networks III: Approximating radial functions T Mao, Z Shi, DX Zhou Neural Networks 144, 778-790, 2021 | 29 | 2021 |
| Commutativity of normal compact operators via projective spectrum T Mao, Y Qiao, P Wang Proceedings of the American Mathematical Society 146 (3), 1165-1172, 2018 | 12 | 2018 |
| Rates of approximation by ReLU shallow neural networks T Mao, DX Zhou Journal of Complexity 79, 101784, 2023 | 10 | 2023 |
| Approximation of functions from Korobov spaces by deep convolutional neural networks T Mao, DX Zhou Advances in Computational Mathematics 48 (6), 84, 2022 | 9 | 2022 |
| Encoding of data sets and algorithms K Doctor, T Mao, H Mhaskar Applied Numerical Mathematics 200, 209-235, 2024 | 1 | 2024 |
| Expressivity and Approximation Properties of Deep Neural Networks with ReLU Activation J He, T Mao, J Xu arXiv preprint arXiv:2312.16483, 2023 | 1 | 2023 |
| Approximation of functions from Korobov spaces by shallow neural networks Y Liu, T Mao, DX Zhou Information Sciences, 120573, 2024 | | 2024 |
| Learning Korobov Functions by Correntropy and Convolutional Neural Networks Z Fang, T Mao, J Fan Neural Computation 36 (4), 718-743, 2024 | | 2024 |
| Tractability of approximation by general shallow networks H Mhaskar, T Mao arXiv preprint arXiv:2308.03230, 2023 | | 2023 |
