Introducing AdaNet: Fast and Flexible AutoML with Learning Guarantees

Posted by Charles Weill, Software Engineer, Google AI, NYCEnsemble learning, the art of combining different machine learning (ML) model predictions, is widely used with neural networks to achieve state-of-the-art performance, benefitting from a rich history and theoretical guarantees to enable success at challenges such as the Netflix Prize and various Kaggle competitions. However, they aren’t used much in practice due to long training times, and the ML model candidate selection requires its own domain expertise. But as computational power and specialized deep learning hardware such as TPUs become more readily available, machine learning models will grow larger and ensembles will become more prominent. Now, imagine a tool that automatically searches over neural architectures, and learns to combine the best ones into a high-quality model. Today, we’re excited to share AdaNet, a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet builds on our recent reinforcement learning


Original URL: http://feedproxy.google.com/~r/feedsapi/BwPx/~3/SGdlpChU7xA/introducing-adanet-fast-and-flexible.html

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