We propose a system for determining properties of mathematical functions given an image of their graph representation. We demonstrate our approach for two dimensional graphs(curves of single variable functions) and three-dimensional graphs (surfaces of two variable functions), studying the properties of convexity and symmetry. Our method uses a Convolutional Neural Network which classi\ufb01es functions according to these properties, without using any hand-crafted features. We propose algorithms for randomly constructing functions with convexity or symmetry properties, and use the images generated by these algorithms to train our network. Our system achieves a high accuracy on this task, even for functions where humans \ufb01nd it dif\ufb01cult to determine the function\u2019s properties from its image.<\/p>\n","protected":false},"excerpt":{"rendered":"
We propose a system for determining properties of mathematical functions given an image of their graph representation. We demonstrate our approach for two dimensional graphs(curves of single variable functions) and three-dimensional graphs (surfaces of two variable functions), studying the properties of convexity and symmetry. 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