Zenkai -- Framework For Exploring Beyond Backpropagation
Greg Short
Zenkai is an open-source framework designed to give researchers more control
and flexibility over building and training deep learning machines. It does this
by dividing the deep learning machine into layers of semi-autonomous learning
machines with their own target and learning algorithm. This is to allow
researchers greater exploration such as the use of non-differentiable layers or
learning algorithms beyond those based on error backpropagation.
  Backpropagation Rumelhart et al. [1986] has powered deep learning to become
one of the most exciting fields of the 21st century. As a result, a large
number of software tools have been developed to support efficient
implementation and training of neural networks through the use of backpropa-
gation. While these have been critical to the success of deep learning,
building frameworks around backpropagation can make it challenging to implement
solutions that do not adhere to it. Zenkai aims to make it easier to get around
these limitations and help researchers more easily explore new frontiers in
deep learning that do not strictly adhere to the backpropagation framework.