Automatic Differentiation of Tensor Expressions

Automatic Differentiation of Tensor Expressions

  • Date: Jun 19, 2019
  • Time: 11:30 AM (Local Time Germany)
  • Speaker: Dr. Sören Laue
  • Fakultät für Mathematik und Informatik, Universität Jena, Germany
  • Location: Max Planck Institute of Quantum Optics
  • Room: Herbert Walther Lecture Hall
Automatic differentiation is a powerful tool that allows to compute derivatives not only of mathematical expressions but also of functions that are given as a computer program.

While it has been used successfully in many areas it has recently gained considerable attention in the area of machine learning, especially in deep learning. In this talk, I will provide an introduction into the concept of automatic differentiation, explain the different algorithms for computing derivatives of computer programs, and provide examples. I will then focus on computing derivatives of problems involving not only scalars, but also vectors, matrices, and higher-order tensors. Finally, I will highlight the application of automatic differentiation to tensor networks.

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