# Learning on tree architectures outperforms a convolutional feedforward network

arXiv: https://arxiv.org/abs/2211.11378 · HTML: https://mlsanity.com/paper/2211.11378/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 21 Nov 2022.

## Builds on (most cited first, 7 of 7)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [ResNet strikes back: An improved training procedure in timm](https://mlsanity.com/paper/2110.00476/) (2021)
- [Depthwise Separable Convolutions for Neural Machine Translation](https://mlsanity.com/paper/1706.03059/) (2017)
- [Brain experiments imply adaptation mechanisms which outperform common AI learning algorithms](https://mlsanity.com/paper/2005.04106/) (2020)

## Built on by (most cited first, 4 of 4)

- [Unified CNNs and transformers underlying learning mechanism reveals multi-head attention modus vivendi](https://mlsanity.com/paper/2501.12900/) (2025)
- [Enhancing the accuracies by performing pooling decisions adjacent to the output layer](https://mlsanity.com/paper/2303.05800/) (2023)
- [Role of Delay in Brain Dynamics](https://mlsanity.com/paper/2410.11384/) (2024)
- [Perforated Backpropagation: A Neuroscience Inspired Extension to Artificial Neural Networks](https://mlsanity.com/paper/2501.18018/) (2025)

## Abstract (arXiv)

Advanced deep learning architectures consist of tens of fully connected and convolutional hidden layers, currently extended to hundreds, are far from their biological realization. Their implausible biological dynamics relies on changing a weight in a non-local manner, as the number of routes between an output unit and a weight is typically large, using the backpropagation technique. Here, a 3-layer tree architecture inspired by experimental-based dendritic tree adaptations is developed and applied to the offline and online learning of the CIFAR-10 database. The proposed architecture outperforms the achievable success rates of the 5-layer convolutional LeNet. Moreover, the highly pruned tree backpropagation approach of the proposed architecture, where a single route connects an output unit and a weight, represents an efficient dendritic deep learning.