Deep Network Theory.pdf

CBMM_Memo_100_v3.pdf
Preview of Deep Network Theory
🔗 Source: cbmm.mit.edu
📊 Size: 1.31 MB
📄 Pages: 10 pages
⬇️ Downloads: 118

Summary

Deep learning is successful in many applications, but its theoretical understanding lags behind. Recent advances address three key questions: representation power of deep networks, optimization of empirical risk, and generalization properties of gradient descent techniques. Deep networks can approximate continuous functions on bounded domains, and for compositional functions, deep convolutional networks can have a linear dependence on dimensionality. Stochastic gradient descent can find global minima with high probability, and minimizing a surrogate loss function under a unit norm constraint can imply maximization of the margin. Standard gradient descent contains an implicit L2 unit norm constraint, offering a solution to the puzzle about generalization performance of deep overparametrized ReLU networks.

Description

Deep learning is successful in many applications, but its theoretical understanding lags behind.

Technical Information

  • File Format: PDF
  • File Size: 1.31 MB
  • Pages: 10
  • Language: EN
  • Total Downloads: 118
  • Last Updated: 2 weeks ago

Document Overview

This PDF document about Deep Network Theory provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Deep Network Theory.

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