# MEESO: A Multi-objective End-to-End Self-Optimized Approach for Automatically Building Deep Learning Models

arXiv: https://arxiv.org/abs/2211.10921 · HTML: https://mlsanity.com/paper/2211.10921/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 49 references that are in the map.
- First posted on arXiv 20 Nov 2022.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [Large-Scale Evolution of Image Classifiers](https://mlsanity.com/paper/1703.01041/) (2017)
- [BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search](https://mlsanity.com/paper/1910.11858/) (2019)
- [Auto-Keras: An Efficient Neural Architecture Search System](https://mlsanity.com/paper/1806.10282/) (2018)
- [Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/1802.09941/) (2018)
- [Searching for Efficient Multi-Scale Architectures for Dense Image Prediction](https://mlsanity.com/paper/1809.04184/) (2018)
- [When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks](https://mlsanity.com/paper/1911.10695/) (2019)
- [NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search](https://mlsanity.com/paper/2007.10396/) (2020)
- [A Genetic Programming Approach to Designing Convolutional Neural Network Architectures](https://mlsanity.com/paper/1704.00764/) (2017)
- [Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques and Tools](https://mlsanity.com/paper/1903.11314/) (2019)
- [BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits](https://mlsanity.com/paper/1405.7430/) (2014)
- [Evolving Robust Neural Architectures to Defend from Adversarial Attacks](https://mlsanity.com/paper/1906.11667/) (2019)

## Abstract (arXiv)

Deep learning has been widely used in various applications from different fields such as computer vision, natural language processing, etc. However, the training models are often manually developed via many costly experiments. This manual work usually requires substantial computing resources, time, and experience. To simplify the use of deep learning and alleviate human effort, automated deep learning has emerged as a potential tool that releases the burden for both users and researchers. Generally, an automatic approach should support the diversity of model selection and the evaluation should allow users to decide upon their demands. To that end, we propose a multi-objective end-to-end self-optimized approach for constructing deep learning models automatically. Experimental results on well-known datasets such as MNIST, Fashion, and Cifar10 show that our algorithm can discover various competitive models compared with the state-of-the-art approach. In addition, our approach also introduces multi-objective trade-off solutions for both accuracy and uncertainty metrics for users to make better decisions.