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Neural Architecture Performance Prediction Using Graph Neural Networks.

, , , and . GCPR, volume 12544 of Lecture Notes in Computer Science, page 188-201. Springer, (2020)

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An Evaluation of Zero-Cost Proxies - From Neural Architecture Performance Prediction to Model Robustness., , and . DAGM, volume 14264 of Lecture Notes in Computer Science, page 624-638. Springer, (2023)Surprisingly Strong Performance Prediction with Neural Graph Features., , , , , , and . CoRR, (2024)Topology Learning for Prediction, Generation, and Robustness in Neural Architecture Search.. University of Mannheim, Germany, (2023)Neural Architecture Performance Prediction Using Graph Neural Networks., , , and . GCPR, volume 12544 of Lecture Notes in Computer Science, page 188-201. Springer, (2020)A Benders Decomposition Approach to Correlation Clustering., , , and . MLHPC/AI4S@SC, page 9-16. IEEE, (2020)Are Vision Language Models Texture or Shape Biased and Can We Steer Them?, , , , , , , and . CoRR, (2024)A Variational-Sequential Graph Autoencoder for Neural Architecture Performance Prediction., , , and . CoRR, (2019)Smooth Variational Graph Embeddings for Efficient Neural Architecture Search., , , , and . IJCNN, page 1-8. IEEE, (2021)Learning Where to Look - Generative NAS is Surprisingly Efficient., , and . ECCV (23), volume 13683 of Lecture Notes in Computer Science, page 257-273. Springer, (2022)Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks., , , , , and . ICLR, OpenReview.net, (2022)