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Google’s model search automatically optimizes and identifies AI models

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Google today announced the release of ModelSearch, an open source platform designed to enable researchers to develop machine learning models efficiently and automatically. Instead of focusing on a specific domain, Google says that model search is domain-independent, so you can find a model architecture that fits your dataset and problems while minimizing coding time and computational resources. Stated.

The success of AI models often depends on how well they work for different workloads. However, it can be very difficult to design a model that can be generalized well. In recent years, the AutoML algorithm has emerged, allowing researchers to find the right model without having to experiment manually. However, these algorithms are often computationally intensive and require thousands of models for training.

Built on Google’s TensorFlow machine learning framework and run on a single machine or multiple machines, model search consists of multiple trainers, search algorithms, transfer learning algorithms, and a database that stores evaluated models. Will be done. Model search runs AI model training and evaluation experiments adaptively and asynchronously, with all trainers conducting each experiment individually and sharing the knowledge gained from the experiments. At the beginning of every cycle, the search algorithm searches all completed attempts and decides what to try next. It then modifies one of the best architectures found up to that point and assigns the resulting model to the trainer.

To further improve efficiency and accuracy, model search employs transfer learning during the experiment. For example, use knowledge distillation and weight sharing to bootstrap some of the model’s variables from a previously trained model. This allows for faster training, which in turn gives you the opportunity to discover more, seemingly better architectures.

After performing a model search, users can compare many models found during the search. In addition, you can create your own search space to customize the architectural elements of your model.

Above: An example of model evolution over many experiments. Each color represents a different type of architectural component.

Image credit: Google

According to Google, in internal experiments, model search improved the production model with minimal iterations, especially in the areas of keyword spotting and language identification. We were also able to find a suitable architecture for image classification on the thoroughly researched CIFAR-10 open source imaging dataset.

We hope that the model search code will provide researchers with a flexible and domain-agnostic framework for discovering machine learning models. Google research engineer Hanna Mazzawi and research scientist Xavi Gonzalvo wrote in a blog post. Building on previous knowledge of a particular domain, this framework provides state-of-the-art performance for well-studied problems when provided with a search space consisting of standard building blocks. We believe it is powerful enough to build a model.

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