A NAS Benchmark for Diverse Tasks
(NeurIPS 2022 Datasets and Benchmarks Track)
Neural architecture search (NAS) benchmarks and methods prioritize performance on well-studied tasks, e.g.,
image classification on CIFAR and ImageNet. To mitigate this bias, NAS-Bench-360 is a benchmark suite for
evaluating state-of-the-art NAS methods on a diverse set of tasks. The selection spans different application
domains, dataset sizes, problem dimensionalities, and learning objectives. Please contact Renbo Tu
(renbo.tu@mail.utoronto.ca) or Nicholas Roberts (nick11roberts@cs.wisc.edu) if you have questions or wish to
contribute to the benchmark.