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Conference Paper
Empirical Study of Rectifier and Dropout in Feedforward Neural Networks
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- Authors
- Sungpil Woo, Sunhwan Lim, Daeyoung Kim
- Issue Date
- 2024-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2024, pp.205-206
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- In this paper, we analyze the unique sparsity properties of dropout, rectified activation functions, and other activation functions through simple experiments. We explore how the distinctive membership functions of each contribute to these sparsity characteristics.
- KSP Keywords
- Activation function, Feedforward neural network(FNN), Membership Functions, Sparsity properties, empirical study, neural network(NN)