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An Improved Analysis Of TrainingOver-paramet Erized Deep Neural Networks.pdf

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Preview of An Improved Analysis of TrainingOver-paramet erized Deep Neural Networks
🔗 Source: papers.nips.cc
📊 Size: 491 KB
👤 Author: Difan Zou, Quanquan Gu
⬇️ Downloads: 83

Summary

The authors provide a milder over-parameterization condition and faster global convergence rates than previous work. They achieve this through two innovative proof techniques: a tighter gradient lower bound and a sharper characterization of the trajectory length. The results show that with Gaussian random initialization, gradient descent can achieve ε training loss within a certain number of iterations, and stochastic gradient descent can achieve ε expected training loss within a certain number of iterations. The over-parameterization condition is milder by a factor of eΩ(n16φ−4) and the iteration complexity is better by a factor of eO(n4φ−1) compared to the state-of-the-art result. The authors also specialize their results to two-layer ReLU networks, which outperforms the best-known results.

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Description

The authors provide a milder over-parameterization condition and faster global convergence rates than previous work.

Technical Information

  • File Format: PDF
  • File Size: 491 KB
  • Pages: 10
  • Language: EN
  • Author: Difan Zou, Quanquan Gu
  • Total Downloads: 83
  • Last Updated: 13 hours ago

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This PDF document about An Improved Analysis of TrainingOver-paramet erized Deep Neural Networks provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into An Improved Analysis of TrainingOver-paramet erized Deep Neural Networks.

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