196 citations · 915 across the 18 of their papers we have counts for
19 papers
Deep Network Approximation in Terms of Intrinsic Parameters
Zuowei Shen, Haizhao Yang, Shijun Zhang
One of the arguments to explain the success of deep learning is the powerful approximation capacity of deep neural networks. Such capacity is generally accompanied by the explosive…
Deep Network Approximation: Achieving Arbitrary Accuracy with Fixed Number of Neurons
Zuowei Shen, Haizhao Yang, Shijun Zhang
This paper develops simple feed-forward neural networks that achieve the universal approximation property for all continuous functions with a fixed finite number of neurons. These…
Optimal Approximation Rate of ReLU Networks in terms of Width and Depth
Zuowei Shen, Haizhao Yang, Shijun Zhang
This paper concentrates on the approximation power of deep feed-forward neural networks in terms of width and depth. It is proved by construction that ReLU networks with width $\ma…
Neural Network Approximation: Three Hidden Layers Are Enough
Zuowei Shen, Haizhao Yang, Shijun Zhang
A three-hidden-layer neural network with super approximation power is introduced. This network is built with the floor function (), the exponential function ($2^x…
Deep Network with Approximation Error Being Reciprocal of Width to Power of Square Root of Depth
Zuowei Shen, Haizhao Yang, Shijun Zhang
A new network with super approximation power is introduced. This network is built with Floor () or ReLU () activation function in each neuron and hen…
Optimization in Machine Learning: A Distribution Space Approach
Yongqiang Cai, Qianxiao Li, Zuowei Shen
We present the viewpoint that optimization problems encountered in machine learning can often be interpreted as minimizing a convex functional over a function space, but with a non…