110 papers with code • 0 benchmarks • 5 datasets
Automated Machine Learning (AutoML) is a general concept which covers diverse techniques for automated model learning including automatic data preprocessing, architecture search, and model selection. Source: Evaluating recommender systems for AI-driven data science (1905.09205)
However, this progress has largely focused on the architecture of neural networks, where it has relied on sophisticated expert-designed layers as building blocks---or similarly restrictive search spaces.
By pretraining on the same ImageNet21k, our EfficientNetV2 achieves 87. 3% top-1 accuracy on ImageNet ILSVRC2012, outperforming the recent ViT by 2. 0% accuracy while training 5x-11x faster using the same computing resources.
Ranked #2 on Image Classification on Stanford Cars
In this paper, we systematically study the impact of different kernel sizes, and observe that combining the benefits of multiple kernel sizes can lead to better accuracy and efficiency.
Ranked #287 on Image Classification on ImageNet
With the demand for machine learning increasing, so does the demand for tools which make it easier to use.
In this chapter, we present a genetic programming-based AutoML system called TPOT that optimizes a series of feature preprocessors and machine learning models with the goal of maximizing classification accuracy on a supervised classification problem.
As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts.
In this paper, we propose a novel framework enabling Bayesian optimization to guide the network morphism for efficient neural architecture search.