Deep Learning Optimization.pdf

03_775-788_00920_Bpast.No_.66-6_31.12.18_K2.pdf
Preview of Deep Learning Optimization
🔗 Source: cbmm.mit.edu
📊 Size: 5.43 MB
📄 Pages: 13 pages
⬇️ Downloads: 92

Summary

The landscape of empirical risk of overparametrized deep convolutional neural networks (DCNNs) is characterized by a large number of global minimizers with zero empirical error, which are degenerate, unlike local minima. Theoretical analysis using Bezout theorem and experimental results on CIFAR-10 dataset support this claim. A simple model of the landscape is proposed, consisting of high-dimensional basins with flat global minima, rugged but regular overall landscape, and no local minima. Stochastic gradient descent (SGD) is shown to concentrate on large volume, "flat" minima, selecting degenerate minimizers with high probability.

Description

The landscape of empirical risk of overparametrized deep convolutional neural networks (DCNNs) is characterized by a large number of global minimizers with...

Technical Information

  • File Format: PDF
  • File Size: 5.43 MB
  • Pages: 13
  • Language: EN
  • Total Downloads: 92
  • Last Updated: 2 hours ago

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