# Towards Searching Efficient and Accurate Neural Network Architectures in Binary Classification Problems

arXiv: https://arxiv.org/abs/2101.06511 · HTML: https://mlsanity.com/paper/2101.06511/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 16 Jan 2021.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [SMASH: One-Shot Model Architecture Search through HyperNetworks](https://mlsanity.com/paper/1708.05344/) (2017)
- [End-to-End Attention-based Large Vocabulary Speech Recognition](https://mlsanity.com/paper/1508.04395/) (2015)
- [Recovery Guarantees for One-hidden-layer Neural Networks](https://mlsanity.com/paper/1706.03175/) (2017)
- [Learning One-hidden-layer ReLU Networks via Gradient Descent](https://mlsanity.com/paper/1806.07808/) (2018)
- [Convolutional Neural Fabrics](https://mlsanity.com/paper/1606.02492/) (2016)
- [Learning Non-overlapping Convolutional Neural Networks with Multiple Kernels](https://mlsanity.com/paper/1711.03440/) (2017)
- [Adversarial Attacks on Convolutional Neural Networks in Facial Recognition Domain](https://mlsanity.com/paper/2001.11137/) (2020)
- [Adversarial Attacks against Neural Networks in Audio Domain: Exploiting Principal Components](https://mlsanity.com/paper/2007.07001/) (2020)
- [Machine learning applications to DNA subsequence and restriction site analysis](https://mlsanity.com/paper/2011.03544/) (2020)

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

- [Case study on quantum convolutional neural network scalability](https://mlsanity.com/paper/2207.07160/) (2022)
- [Adaptive Neural Networks Using Residual Fitting](https://mlsanity.com/paper/2301.05744/) (2023)

## Abstract (arXiv)

In recent years, deep neural networks have had great success in machine learning and pattern recognition. Architecture size for a neural network contributes significantly to the success of any neural network. In this study, we optimize the selection process by investigating different search algorithms to find a neural network architecture size that yields the highest accuracy. We apply binary search on a very well-defined binary classification network search space and compare the results to those of linear search. We also propose how to relax some of the assumptions regarding the dataset so that our solution can be generalized to any binary classification problem. We report a 100-fold running time improvement over the naive linear search when we apply the binary search method to our datasets in order to find the best architecture candidate. By finding the optimal architecture size for any binary classification problem quickly, we hope that our research contributes to discovering intelligent algorithms for optimizing architecture size selection in machine learning.