# Hyper-parameter optimization of Deep Convolutional Networks for object recognition

arXiv: https://arxiv.org/abs/1501.07645 · HTML: https://mlsanity.com/paper/1501.07645/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2015.

## Built on by (most cited first, 1 of 1)

- [Genealogical Population-Based Training for Hyperparameter Optimization](https://mlsanity.com/paper/2109.14925/) (2021)

## Abstract (arXiv)

Recently sequential model based optimization (SMBO) has emerged as a promising hyper-parameter optimization strategy in machine learning. In this work, we investigate SMBO to identify architecture hyper-parameters of deep convolution networks (DCNs) object recognition. We propose a simple SMBO strategy that starts from a set of random initial DCN architectures to generate new architectures, which on training perform well on a given dataset. Using the proposed SMBO strategy we are able to identify a number of DCN architectures that produce results that are comparable to state-of-the-art results on object recognition benchmarks.