[2603.25699] Neural Network Conversion of Machine Learning Pipelines

Computer Science > Machine Learning arXiv:2603.25699 (cs) [Submitted on 26 Mar 2026] Title:Neural Network Conversion of Machine Learning Pipelines Authors:Man-Ling Sung, Jan Silovsky, Man-Hung Siu, Herbert Gish, Chinnu Pittapally View a PDF of the paper titled Neural Network Conversion of Machine Learning Pipelines, by Man-Ling Sung and 3 other authors View PDF HTML (experimental) Abstract:Transfer learning and knowledge distillation has recently gained a lot of attention in the deep learning community. One transfer approach, the student-teacher learning, has been shown to successfully create ``small'' student neural networks that mimic the performance of a much bigger and more complex ``teacher'' networks. In this paper, we investigate an extension to this approach and transfer from a non-neural-based machine learning pipeline as teacher to a neural network (NN) student, which would allow for joint optimization of the various pipeline components and a single unified inference engine for multiple ML tasks. In particular, we explore replacing the random forest classifier by transfer learning to a student NN. We experimented with various NN topologies on 100 OpenML tasks in which random forest has been one of the best solutions. Our results show that for the majority of the tasks, the student NN can indeed mimic the teacher if one can select the right NN hyper-parameters. We also investigated the use of random forest for selecting the right NN hyper-parameters. Comments: Sub...