[Akira Imakura] Development of a nonlinear nonnegative matrix factorization-based algorithm for deep neural networks

Researcher

Akira Imakura

Akira Imakura

University of Tsukuba
Faculty of Engineering, Information and Systems
Assistant Professor

Outline

The backpropagation, based on the stochastic gradient descent method, is the most successful and the de-facto standard algorithm for computing deep neural networks (DNNs) in several applications such as image recognition. In this research project, we propose and develop a novel algorithm for DNNs based on a nonlinear nonnegative matrix factorization, which is compatible with parallel computing.

Program

  • CREST
  • PRESTO
  • ACT-I
  • ERATO
  • ACT-C
  • ACCEL
  • ALCA
  • RISTEX
Finish programs
  • Pamphlet
  • ProjectDB
  • GlobalActivity
  • Diversity-EN
  • OS_Policy-EN
  • Question-E