Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4942068 | Artificial Intelligence | 2017 | 22 Pages |
Abstract
Discriminative systems that can deal with graphs in input are known, however, generative or constructive approaches that can sample graphs from empirical distributions are less developed. Here we propose a Metropolis-Hastings approach that uses a novel type of graph grammar to efficiently learn proposal distributions in a data driven fashion. We report experimental results in a de-novo molecular synthesis problem where we show that the distribution of the molecules generated by the sampling procedure is accurate enough to improve a predictor's performance in a classification task.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Fabrizio Costa,