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This raises the question of whether we can find an effective proxy search space (PS) that is only a small subset of GS to dramatically improve RandomNAS’s search efficiency while at the same time keeping a good correlation for the top-performing architectures.
We extract an optimal subset of architectural parameters for the BERT architecture from Devlin et al. (2018) by applying recent breakthroughs in algorithms for neural architecture search.
The obtained results indicate the high potential of the proposed framework and the superiority of the new Markov kernel.
By doing so, our network for search at each update satisfies the sparsity constraint and is efficient to train.
Pretrained contextualized embeddings are powerful word representations for structured prediction tasks.
Ranked #1 on Chunking on Penn Treebank
Automatic search of Quantized Neural Networks has attracted a lot of attention.
Despite the remarkable successes of Convolutional Neural Networks (CNNs) in computer vision, it is time-consuming and error-prone to manually design a CNN.
In this paper, we present a fast NPU-aware NAS methodology, called S3NAS, to find a CNN architecture with higher accuracy than the existing ones under a given latency constraint.
To this end, we pose questions that future differentiable methods for neural wiring discovery need to confront, hoping to evoke a discussion and rethinking on how much bias has been enforced implicitly in existing NAS methods.