neural architecture search tools

Neural Architecture Search (NAS) tools are specialized software applications that automate the process of designing artificial neural networks. These tools are particularly useful for optimizing network architecture in terms of accuracy, latency, memory, or a custom metric. Here are some prominent NAS tools:

1. **Vertex AI Neural Architecture Search**:
- **Description**: This tool, offered by Google Cloud, allows for the search of optimal neural architectures. It is based on a technique that has been used to generate state-of-the-art computer vision models.
- **Usage**: It is not a tool for hyperparameter tuning but specifically for architecture search. It is recommended for use when traditional machine learning methods have reached their limits and there is sufficient data and in-house expertise to modify architecture parameters.
- **Limitations**: It is not suitable for limited training data or highly imbalanced datasets. It is also not recommended when heavy augmentations are already part of the baseline training.
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2. **AutoML Tools**:
- **Description**: AutoML is a broader field that includes NAS. It encompasses tools that automate the entire machine learning workflow, including data preprocessing, model selection, and hyperparameter tuning.
- **Usage**: These tools can include NAS as part of their pipeline, allowing for the automation of neural network design.
- **Examples**: Google's AutoML, TensorFlow's AutoML, and H2O's AutoML are examples of such tools.
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3. **NAS Research Literature**:
- **Description**: The research community has developed various NAS methods, including gradient-based and multi-fidelity approaches.
- **Usage**: These methods can be implemented using research papers and repositories that provide insights and code for NAS.
- **Examples**: Methods by the University of Texas Austin, which have developed TE-NAS and As-ViT for designing NAS models and predicting their success and accuracy.
- **Source**:

In summary, NAS tools are powerful resources for optimizing neural network architecture, offering a systematic approach to designing models that can outperform hand-designed architectures. They are particularly useful when traditional machine learning methods have reached their limits and there is sufficient data and expertise to modify architecture parameters.