9 citations · 13 across the 5 of their papers we have counts for
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cs.LG2026
Hierarchical rank-evolving representation for physics-informed neural networks
Ruoyang Su, Xi-Le Zhao, Kun Li +1
Recently, tensor-based physics-informed neural networks (T-PINNs) have received increasing attention. However, existing T-PINNs still face a fundamental challenge: they mainly rely…
cs.LG2023★ 3 cited
Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation
Li'ang Li, Yifei Duan, Guanghua Ji +1
The study of universal approximation properties (UAP) for neural networks (NN) has a long history. When the network width is unlimited, only a single hidden layer is sufficient for…
cs.LG2019
Thick-Net: Parallel Network Structure for Sequential Modeling
Yu-Xuan Li, Jin-Yuan Liu, Liang Li +1
Recurrent neural networks have been widely used in sequence learning tasks. In previous studies, the performance of the model has always been improved by either wider or deeper str…