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Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent

arXiv 2405.07619 ↗First posted 13 May 2024stat.ML cs.LGcited by 0

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Abstract (arXiv)

Image classification based on over-parametrized convolutional neural networks with a global average-pooling layer is considered. The weights of the network are learned by gradient descent. A bound on the rate of convergence of the difference between the misclassification risk of the newly introduced convolutional neural network estimate and the minimal possible value is derived.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 36 references that are in the map. First posted on arXiv 13 May 2024.

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18 of 36 references.

References · 18

  1. 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,621
  2. 2018A Convergence Theory for Deep Learning via Over-Parameterizationcited by 787
  3. 2019Benign Overfitting in Linear Regressioncited by 549
  4. 2021Deep learning: a statistical viewpointcited by 240
  5. 2016Deep Learning without Poor Local Minimacited by 234
  6. 2018A Convergence Analysis of Gradient Descent for Deep Linear Neural Networkscited by 184
  7. 2018On the Power of Over-parametrization in Neural Networks with Quadratic Activationcited by 102
  8. 2018Does data interpolation contradict statistical optimality?cited by 95
  9. 2017Stochastic Particle Gradient Descent for Infinite Ensemblescited by 52
  10. 2017Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minimacited by 40
  11. 2019Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizescited by 29
  12. 2018Adding One Neuron Can Eliminate All Bad Local Minimacited by 21
6 more
  1. 2020On the Neural Tangent Kernel of Deep Networks with Orthogonal Initializationcited by 21
  2. 2022Generalization Error Bounds for Deep Neural Networks Trained by SGDcited by 14
  3. 2020On the rate of convergence of image classifiers based on convolutional neural networkscited by 7
  4. 2021The Rate of Convergence of Variation-Constrained Deep Neural Networkscited by 4
  5. 2022Analysis of convolutional neural network image classifiers in a rotationally symmetric modelcited by 4
  6. 2021Analysis of convolutional neural network image classifiers in a hierarchical max-pooling model with additional local poolingcited by 2

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