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Activity Number: 254 - Contributed Poster Presentations: Section on Statistical Learning and Data Science
Type: Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #330649
Title: A Generalization of Convolutional Neural Networks to Graph-Structured Data
Author(s): Yotam Hechtlinger* and Purvasha Chakravarti and Jining Qin
Companies: Carnegie Mellon Univ and Carnegie Mellon University and Carnegie Mellon University
Keywords: deep learning; convolutional neural networks ; graph; convolution; statistical learning

We introduces a generalization of Convolutional Neural Networks (CNNs) from low-dimensional grid data, such as images, to graph-structured data. We propose a novel spatial convolution utilizing a random walk to uncover the relations within the input, analogous to the way the standard convolution uses the spatial neighborhood of a pixel on the grid. The convolution has an intuitive interpretation, is efficient and scalable and can also be used on data with varying graph structure. Furthermore, this generalization can be applied to many standard regression or classification problems, by learning the the underlying graph. We empirically demonstrate the performance of the proposed CNN on MNIST, and challenge the state-of-the-art on Merck molecular activity data set.

Authors who are presenting talks have a * after their name.

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