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Generic Neural Architecture Search via Regression
[article]
2021
arXiv
pre-print
Most existing neural architecture search (NAS) algorithms are dedicated to and evaluated by the downstream tasks, e.g., image classification in computer vision. However, extensive experiments have shown that, prominent neural architectures, such as ResNet in computer vision and LSTM in natural language processing, are generally good at extracting patterns from the input data and perform well on different downstream tasks. In this paper, we attempt to answer two fundamental questions related to
arXiv:2108.01899v2
fatcat:im7t62hb4zhdhga6saihesmywm