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07/13/2023
Deep Network Approximation: Beyond ReLU to Diverse Activation Functions
This paper explores the expressive power of deep neural networks for a d...
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06/29/2023
Why Shallow Networks Struggle with Approximating and Learning High Frequency: A Numerical Study
In this work, a comprehensive numerical study involving analysis and exp...
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01/29/2023
On Enhancing Expressive Power via Compositions of Single Fixed-Size ReLU Network
This paper studies the expressive power of deep neural networks from the...
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05/19/2022
Neural Network Architecture Beyond Width and Depth
This paper proposes a new neural network architecture by introducing an ...
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11/15/2021
ReLU Network Approximation in Terms of Intrinsic Parameters
This paper studies the approximation error of ReLU networks in terms of ...
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07/06/2021
Deep Network Approximation: Achieving Arbitrary Accuracy with Fixed Number of Neurons
This paper develops simple feed-forward neural networks that achieve the...
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02/28/2021
Optimal Approximation Rate of ReLU Networks in terms of Width and Depth
This paper concentrates on the approximation power of deep feed-forward ...
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10/25/2020
Neural Network Approximation: Three Hidden Layers Are Enough
A three-hidden-layer neural network with super approximation power is in...
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06/22/2020
Deep Network Approximation with Discrepancy Being Reciprocal of Width to Power of Depth
A new network with super approximation power is introduced. This network...
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01/09/2020
Deep Network Approximation for Smooth Functions
This paper establishes optimal approximation error characterization of d...
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06/13/2019
Deep Network Approximation Characterized by Number of Neurons
This paper quantitatively characterizes the approximation power of deep ...
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02/26/2019