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0. Referencehttps://proceedings.mlr.press/v9/glorot10a.html Understanding the difficulty of training deep feedforward neural networksWhereas before 2006 it appears that deep multi-layer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with experimental resul...proceedings.mlr.press1. Introduction- 본 논문은 Xavier Initialization..
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0. Referencehttps://arxiv.org/abs/1906.02629 When Does Label Smoothing Help?The generalization and learning speed of a multi-class neural network can often be significantly improved by using soft targets that are a weighted average of the hard targets and the uniform distribution over labels. Smoothing the labels in this way prevearxiv.org1. Introduction- Classification,Speech recognition, Machi..
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0. Referencehttps://arxiv.org/abs/1410.3916 Memory NetworksWe describe a new class of learning models called memory networks. Memory networks reason with inference components combined with a long-term memory component; they learn how to use these jointly. The long-term memory can be read and written to, with the garxiv.org1. Introduction- 대부분의 머신러닝 모델들은 long-term component를 읽고 잘 사용하지 못한다고 한다.- 예..
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0. Reference https://arxiv.org/abs/1409.3215 Sequence to Sequence Learning with Neural NetworksDeep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paparxiv.org1. Introduction- DNN은 음성 인식, Object De..
