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NAS-Bench-101: Towards Reproducible Neural Architecture Search
Chris Ying · Aaron Klein · Eric Christiansen · Esteban Real · Kevin Murphy · Frank Hutter

Wed Jun 12 02:20 PM -- 02:25 PM (PDT) @ Hall A

Recent advances in neural architecture search (NAS) demand tremendous computational resources. This makes it difficult to reproduce experiments and imposes a barrier to entry to researchers without access to large scale computation. We aim to ameliorate these problems by introducing NAS-Bench-101, the first public architecture dataset for NAS research. To build it, we carefully constructed a compact---yet expressive---search space, exploiting graph isomorphisms to identify 423K unique architectures. Utilizing machine-years of computation, we trained them all with public code, and compiled the results into a large table. This allows researchers to evaluate the quality of a proposed model in milliseconds using various precomputed metrics. NAS-Bench-101 presents a unique opportunity to study the entire NAS loss landscape from a data-driven perspective, which we illustrate with our analysis. We also demonstrate the dataset's application to benchmarking by comparing a range of popular architecture optimization algorithms on it.

Author Information

Chris Ying (Ambient.ai)
Aaron Klein (University of Freiburg)
Eric Christiansen (Google)
Esteban Real (Google Inc.)
Kevin Murphy (Google Brain)
Frank Hutter (University of Freiburg and Bosch Center for Artificial Intelligence)
Frank Hutter

Frank Hutter is a Full Professor for Machine Learning at the Computer Science Department of the University of Freiburg (Germany), where he has been a faculty member since 2013. Before that, he was at the University of British Columbia (UBC) for eight years, for his PhD and postdoc. Frank's main research interests lie in machine learning, artificial intelligence and automated algorithm design. For his 2009 PhD thesis on algorithm configuration, he received the CAIAC doctoral dissertation award for the best thesis in AI in Canada that year, and with his coauthors, he received several best paper awards and prizes in international competitions on automated machine learning, SAT solving, and AI planning. Since 2016 he holds an ERC Starting Grant for a project on automating deep learning based on Bayesian optimization, Bayesian neural networks, and deep reinforcement learning.

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