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- W4291270684 abstract "Neural architecture search (NAS) refers to the use of search heuristics to optimise the topology of deep neural networks. NAS algorithms have produced topologies that outperform human-designed ones. However, contrasting alternative NAS methods is difficult. To address this, several tabular NAS benchmarks have been proposed that exhaustively evaluate all architectures in a given search space. We conduct a thorough fitness landscape analysis of a popular tabular, cell-based NAS benchmark. Our results indicate that NAS landscapes are multi-modal, but have a relatively low number of local optima, from which it is not hard to escape. We confirm that reducing the noise in estimating performance reduces the number of local optima. We hypothesise that local-search based NAS methods are likely to be competitive, which we confirm by implementing a landscape-aware iterated local search algorithm that can outperform more elaborate evolutionary and reinforcement learning NAS methods." @default.
- W4291270684 created "2022-08-14" @default.
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- W4291270684 date "2022-01-01" @default.
- W4291270684 modified "2023-10-05" @default.
- W4291270684 title "Neural Architecture Search: A Visual Analysis" @default.
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- W4291270684 doi "https://doi.org/10.1007/978-3-031-14714-2_42" @default.
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