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• W3128968934 abstract "The performance of algorithms for neural architecture search strongly depends on the parametrization of the search space. We use contrastive learning to identify networks across different initializations based on their data Jacobians, and automatically produce the first architecture embeddings independent from the parametrization of the search space. Using our contrastive embeddings, we show that traditional black-box optimization algorithms, without modification, can reach state-of-the-art performance in Neural Architecture Search. As our method provides a unified embedding space, we perform for the first time transfer learning between search spaces. Finally, we show the evolution of embeddings during training, motivating future studies into using embeddings at different training stages to gain a deeper understanding of the networks in a search space." @default.
• W3128968934 created "2021-02-15" @default.
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• W3128968934 date "2021-02-08" @default.
• W3128968934 modified "2023-09-27" @default.
• W3128968934 title "Contrastive Embeddings for Neural Architectures" @default.
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