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김미선(MiSun Kim),문재영(Jaeyoung Moon),한동일(Dongil Han) 대한전자공학회 2018 대한전자공학회 학술대회 Vol.2018 No.6
Nowadays, most researchers in machine learning field will agree that the deep neural networ ks(DNNs) provide the best performance in pattern recognition, computer vision, natural language processing and so on. But nobody knows exactly how DNNs work inside. So many DNNs architectures are built relying on mathematical heuristic and prior knowledge. We can make some new models by ourselves depending on the platforms, the type of data and applications. If we can experiment all possible cases, then we will find the best approximate solution. Unfortunately, it is hard to do like that because experimenting every possible case costs too much resources. In this paper, we suggest the method to find optimal neural networks model among various models by using Monte-Carlo Tree Search [1].