# SSFN -- Self Size-estimating Feed-forward Network with Low Complexity, Limited Need for Human Intervention, and Consistent Behaviour across Trials

arXiv: https://arxiv.org/abs/1905.07111 · HTML: https://mlsanity.com/paper/1905.07111/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2019.

## Built on by (most cited first, 2 of 2)

- [Neural Greedy Pursuit for Feature Selection](https://mlsanity.com/paper/2207.09390/) (2022)
- [Use of Deterministic Transforms to Design Weight Matrices of a Neural Network](https://mlsanity.com/paper/2110.03515/) (2021)

## Abstract (arXiv)

We design a self size-estimating feed-forward network (SSFN) using a joint optimization approach for estimation of number of layers, number of nodes and learning of weight matrices. The learning algorithm has a low computational complexity, preferably within few minutes using a laptop. In addition the algorithm has a limited need for human intervention to tune parameters. SSFN grows from a small-size network to a large-size network, guaranteeing a monotonically non-increasing cost with addition of nodes and layers. The learning approach uses judicious a combination of `lossless flow property' of some activation functions, convex optimization and instance of random matrix. Consistent performance -- low variation across Monte-Carlo trials -- is found for inference performance (classification accuracy) and estimation of network size.