Deep learning (DL) is transforming whole industries as complicated decision-making processes are being automated by deep neural networks (DNNs) trained on real-world data. Driven in part by a rapidly expanding literature on DNN approximation theory showing that DNNs can approximate a rich variety of functions, these tools are increasingly being considered for problems in scientific computing. Yet, unlike more traditional algorithms in this field, relatively little is known about DNNs in relation to the principles of numerical analysis, namely, stability, accuracy, computational efficiency, and sample complexity. In this paper we first introduce a computational framework for examining DNNs in practice, and then use it to study their empirical performance with regard to these issues. We examine the performance of DNNs of different widths and depths on a variety of test functions in various dimensions, including smooth and piecewise smooth functions. We also compare DL against best-in-class methods for smooth function approximation based on compressed sensing. Our main conclusion from these experiments is that there is a crucial gap between the approximation theory of DNNs and their practical performance, with trained DNNs performing relatively poorly on functions for which there are strong approximation results (e.g., smooth functions) yet performing well in comparison to best-in-class methods for other functions. To analyze this gap further, we then provide some theoretical insights. We establish a practical existence theorem, which asserts the existence of a DNN architecture and training procedure that offers the same performance as compressed sensing. This result establishes a key theoretical benchmark. It demonstrates that the gap can be closed, albeit via a DNN approximation strategy which is guaranteed to perform as well as, but no better than, current best-in-class schemes. Nevertheless, it demonstrates the promise of practical DNN approximation by highlighting the potential for developing better schemes through the careful design of DNN architectures and training strategies.


  1. neural networks
  2. deep learning
  3. function approximation
  4. compressed sensing
  5. numerical analysis

MSC codes

  1. 41A25
  2. 41A46
  3. 42C05
  4. 65D05
  5. 65D15
  6. 65Y20
  7. 94A20

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Supplementary Material

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Index of Supplementary Materials

Title of paper: The gap between theory and practice in function approximation with deep neural networks

Authors: Ben Adcock and Nick Dexter

File: MLFA_supplement.pdf

Type: PDF

Contents: Additional information on the testing setup for the numerical experiments in this work, as well as additional numerical experiments relevant to the discussions, more details about truncation parameters and lower set-motivated recovery strategies in compressed sensing, and proofs of the exponential convergence of best s-term polynomial approximations for holomorphic functions, the convergence of compressed sensing on the same functions, and the proof of the main result on the approximation of such functions with deep neural networks.


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Information & Authors


Published In

cover image SIAM Journal on Mathematics of Data Science
SIAM Journal on Mathematics of Data Science
Pages: 624 - 655
ISSN (online): 2577-0187


Submitted: 17 January 2020
Accepted: 11 January 2021
Published online: 6 May 2021


  1. neural networks
  2. deep learning
  3. function approximation
  4. compressed sensing
  5. numerical analysis

MSC codes

  1. 41A25
  2. 41A46
  3. 42C05
  4. 65D05
  5. 65D15
  6. 65Y20
  7. 94A20



Funding Information

Natural Sciences and Engineering Research Council of Canada https://doi.org/10.13039/501100000038 : R611675

Funding Information

Pacific Institute for the Mathematical Sciences https://doi.org/10.13039/100009059

Funding Information

Simon Fraser University https://doi.org/10.13039/501100004326

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