Poster
in
Workshop: The Synergy of Scientific and Machine Learning Modelling (SynS & ML) Workshop
Neural Polytopes
Koji Hashimoto · Tomoya Naito · Hisashi Naito
Keywords: [ linear activation ] [ discrete geometry ] [ polytopes ]
We find that simple neural networks with ReLU activation generate polytopes as an approximation of a unit sphere in various dimensions. The species of polytopes are regulated by the network architecture, such as the number of units and layers. For a variety of activation functions, generalization of polytopes is obtained, which we call neural polytopes. They are a smooth analogue of polytopes, exhibiting geometric duality. This finding initiates research of discrete geome- try via machine learning and also a visualization of trained networks.