A Spline Theory of Deep Learning

Abstract

Deep learning has significantly advanced our ability to address a wide range of difficult machine learning and signal processing problems. Today’s machine learning landscape is dominated by deep (neural) networks (DNs), which are compositions of a large number of simple parameterized linear and nonlinear operators. An all-too-familiar story is that of plugging a deep network into an application as a black box, learning its parameter values using copious training data, and then significantly improving performance over classical task-specific approaches.

Document Details

Document Type
DoD Grant Award
Publication Date
Sep 19, 2018
Source ID
FA95501810478

Entities

People

  • Richard G. Baraniuk

Organizations

  • Air Force Office of Scientific Research
  • Rice University
  • United States Air Force

Tags

Fields of Study

  • Computer science

Readers

  • Computational Fluid Dynamics (CFD)
  • Educational Psychology
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks