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Registered APPARATUS AND METHOD FOR LINEARLY APPROXIMATING DEEP NEURAL NETWORK MODEL
- Inventors
- Chung Hoon, Lee Yunkeun, Sung Joo Lee, Park Jeon Gue
- Application No.
- 16121836 (2018.09.05)
- Publication No.
- 20190272309 (2019.09.05)
- Registration No.
- 10789332 (2020.09.29)
- Country
- UNITED STATES
- Project Code
- 17HS5700, Core technology development of the spontaneous speech dialogue processing for the language learning, Lee Yunkeun
- Abstract
- Provided are an apparatus and method for linearly approximating a deep neural network (DNN) model which is a non-linear function. In general, a DNN model shows good performance in generation or classification tasks. However, the DNN fundamentally has non-linear characteristics, and therefore it is difficult to interpret how a result from inputs given to a black box model has been derived. To solve this problem, linear approximation of a DNN is proposed. The method for linearly approximating a DNN model includes 1) converting a neuron constituting a DNN into a polynomial, and 2) classifying the obtained polynomial as a polynomial of input signals and a polynomial of weights.
- KSP Keywords
- Black Box, Black box model, Box model, Deep neural network(DNN), Linear approximation, Linear function, Network model, Nonlinear characteristics, neural network, neural network model, non-linear, nonlinear function