Nov 15, 2021 · From an approximation perspective, this paper shows that the number of parameters that need to be learned can be significantly smaller than ...
Abstract. One of the arguments to explain the success of deep learning is the powerful approximation ca- pacity of deep neural networks. Such capacity is.
For f ∈ ¿Lip and p ∈ [1,∞), ∃ ϕ realized by a ReLU network with n + 2 intrinsic parameters s.t.. ϕ. − f. Lp([0,1]d). ≤ 5. √ d2. −n. Page 9. 9/14.
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It is shown that the number of parameters that need to be learned can be significantly smaller than people typically expect and training a small part of ...
Nov 15, 2021 · Abstract. This paper studies the approximation error of ReLU networks in terms of the number of intrinsic parameters (i.e., those depending ...
Jun 14, 2022 · Abstract. One of the arguments to explain the success of deep learning is the powerful approximation ca- pacity of deep neural networks.
PDF | This paper studies the approximation error of ReLU networks in terms of the number of intrinsic parameters (i.e., those depending on the target.
We study the approximation of two-layer compositions f ( x ) = g ( ϕ ( x ) ) via deep networks with ReLU activation, where ϕ is a geometrically intuitive, ...
Deep neural network approximation via function compositions. S Zhang. PhD ... Deep network approximation in terms of intrinsic parameters. Z Shen, H Yang ...
This paper establishes the (nearly) optimal approximation error characterization of deep rectified linear unit (ReLU) networks for smooth functions in terms of ...