# A Novel Method for improving accuracy in neural network by reinstating traditional back propagation technique

arXiv: https://arxiv.org/abs/2308.05059 · HTML: https://mlsanity.com/paper/2308.05059/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 9 Aug 2023.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Direct Feedback Alignment Provides Learning in Deep Neural Networks](https://mlsanity.com/paper/1609.01596/) (2016)
- [Activated Gradients for Deep Neural Networks](https://mlsanity.com/paper/2107.04228/) (2021)
- [An Optimized Ensemble Deep Learning Model For Brain Tumor Classification](https://mlsanity.com/paper/2305.12844/) (2023)
- [Optimization Methods in Deep Learning: A Comprehensive Overview](https://mlsanity.com/paper/2302.09566/) (2023)

## Abstract (arXiv)

Deep learning has revolutionized industries like computer vision, natural language processing, and speech recognition. However, back propagation, the main method for training deep neural networks, faces challenges like computational overhead and vanishing gradients. In this paper, we propose a novel instant parameter update methodology that eliminates the need for computing gradients at each layer. Our approach accelerates learning, avoids the vanishing gradient problem, and outperforms state-of-the-art methods on benchmark data sets. This research presents a promising direction for efficient and effective deep neural network training.