Kernel Logistic Regression Approximation of an Understandable ReLU Neural Network

Published: 24 Apr 2023, Last Modified: 15 Jun 2023ICML 2023 PosterEveryoneRevisions
Abstract: This paper proposes an understandable neural network whose score function is modeled as an additive sum of univariate spline functions. It extends usual understandable models like generative additive models, spline-based models, and neural additive models. It is shown that this neural network can be approximated by a logistic regression whose inputs are obtained with a non-linear preprocessing of input data. This preprocessing depends on the neural network initialization but this paper establishes that it can be replaced by a non random kernel-based preprocessing that no longer depends on the initialization. Hence, the convergence of the training process is guaranteed and the solution is unique for a given training dataset.
Submission Number: 4934
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