Feicheng Wang
The Expressive Power of Neural Networks: A View from the Width
Zhou Lu, Hongming Pu, Feicheng Wang, Zhiqiang Hu, Liwei Wang
The expressive power of neural networks is important for understanding deep learning. Most existing works consider this problem from the view of the depth of a network. In this paper, we study how width affects the expressiveness of neural networks. Classical results state that depth-bounded (e.g.
The Expressive Power of Neural Networks: A View from the Width
Zhou Lu, Hongming Pu, Feicheng Wang, Zhiqiang Hu, Liwei Wang
The expressive power of neural networks is important for understanding deep learning. Most existing works consider this problem from the view of the depth of a network. In this paper, we study how width affects the expressiveness of neural networks. Classical results state that depth-bounded (e.g.