KDD 2023 - Deep Pipeline Embeddings for AutoML
Sebastian Pineda Arango, University of Freiburg
How can one effectively search for Machine Learning and Deep Learning Pipelines? Typically, pipelines contain numerous conditional hyperparameters and correlated features. Moreover, they often result in large search spaces. We propose learning an embedding function that enables a more efficient search. This function is implemented using a neural network, which can be meta-learned or designed based on knowledge of the pipeline structure. We demonstrate that this approach outperforms the state-of-the-art method. Additionally, it can be easily adapted to new components added to the pipeline.
Смотрите видео KDD 2023 - Deep Pipeline Embeddings for AutoML онлайн, длительностью часов минут секунд в хорошем качестве, которое загружено на канал Association for Computing Machinery (ACM) 12 Июль 2023. Делитесь ссылкой на видео в социальных сетях, чтобы ваши подписчики и друзья так же посмотрели это видео. Данный видеоклип посмотрели 118 раз и оно понравилось 5 посетителям.
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