Jul 12, 2020
The field of tropical algebra is closely linked with the domain of neural networks with piecewise linear activations, since their output can be described via tropical polynomials in the max-plus semiring. In this work, we attempt to make use of methods stemming from a form of approximate division of such polynomials, which relies on the approximation of their Newton Polytopes, in order to minimize networks trained for multiclass classification problems. We make theoretical contributions in this domain, by proposing and analyzing methods which seek to reduce the size of such networks. In addition, we make experimental evaluations on the MNIST and Fashion-MNIST datasets, with our results demonstrating a significant reduction in network size, while retaining adequate performance.
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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