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A Byproduct of a Differentiable Neural Network—Data Weighting from an Implicit Form to an Explicit Form

Abstract : Data weighting is important for data preservation and data mining. This paper presents a data weighting—neural network data weighting which obtains data weighting through transforming the implicit weighting of neural network to explicit weighting. This method includes two phases: in the first phase, choose a differentiable neural network whose transfer function is differentiable, and train the neural network on the ground of training samples; in the second phase, input the training samples as test samples into the network, calculate partial derivatives of the outputs with respect to inputs based on the differential characteristics of neural network, and statistical partial derivatives with respect to each input data item are used to calculate the weight of the data item. In this way, implicit weights stored in the neural network are converted to explicit weights. Experiments show that the method is more accurate than art-of-state methods. Furthermore, the method can be used in more fields, where the differentiable neural network can be used. The types of data can be discrete, continuous, or labeled, and the number of output data items is unlimited.
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Submitted on : Tuesday, July 30, 2019 - 5:01:34 PM
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Tongfeng Sun. A Byproduct of a Differentiable Neural Network—Data Weighting from an Implicit Form to an Explicit Form. 10th International Conference on Intelligent Information Processing (IIP), Oct 2018, Nanning, China. pp.140-149, ⟨10.1007/978-3-030-00828-4_15⟩. ⟨hal-02197787⟩

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