【DL輪読会】AUTOGT: AUTOMATED GRAPH TRANSFORMER ARCHITECTURE SEARCHDeep Learning JP
![0から理解するニューラルネットアーキテクチャサーチ(NAS)](https://cdn-ak-scissors.b.st-hatena.com/image/square/f676547e6e8a56c97d293c8784985b3396013980/height=288;version=1;width=512/https%3A%2F%2Fcdn.slidesharecdn.com%2Fss_thumbnails%2F20210529nassuganuma-210529022656-thumbnail.jpg%3Fwidth%3D640%26height%3D640%26fit%3Dbounds)
Maintained by Difan Deng and Marius Lindauer. The following list considers papers related to neural architecture search. It is by no means complete. If you miss a paper on the list, please let us know. Please note that although NAS methods steadily improve, the quality of empirical evaluations in this field are still lagging behind compared to other areas in machine learning, AI and optimization.
Convolutional neural networks have gained a remarkable success in computer vision. However, most usable network architectures are hand-crafted and usually require expertise and elaborate design. In this paper, we provide a block-wise network generation pipeline called BlockQNN which automatically builds high-performance networks using the Q-Learning paradigm with epsilon-greedy exploration strateg
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