# Zenkai -- Framework For Exploring Beyond Backpropagation

arXiv: https://arxiv.org/abs/2311.09663 · HTML: https://mlsanity.com/paper/2311.09663/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 16 Nov 2023.

## Builds on (most cited first, 11 of 11)

- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [Pyro: Deep Universal Probabilistic Programming](https://mlsanity.com/paper/1810.09538/) (2018)
- [Direct Feedback Alignment Provides Learning in Deep Neural Networks](https://mlsanity.com/paper/1609.01596/) (2016)
- [How Auto-Encoders Could Provide Credit Assignment in Deep Networks via Target Propagation](https://mlsanity.com/paper/1407.7906/) (2014)
- [Theano: A Python framework for fast computation of mathematical expressions](https://mlsanity.com/paper/1605.02688/) (2016)
- [How Important is Weight Symmetry in Backpropagation?](https://mlsanity.com/paper/1510.05067/) (2015)
- [A Theoretical Framework for Target Propagation](https://mlsanity.com/paper/2006.14331/) (2020)
- [Estimating or Propagating Gradients Through Stochastic Neurons](https://mlsanity.com/paper/1305.2982/) (2013)
- [Feedback alignment in deep convolutional networks](https://mlsanity.com/paper/1812.06488/) (2018)
- [Chainer: A Deep Learning Framework for Accelerating the Research Cycle](https://mlsanity.com/paper/1908.00213/) (2019)
- [Target Propagation via Regularized Inversion](https://mlsanity.com/paper/2112.01453/) (2021)

## Abstract (arXiv)

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.