{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/151405"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/151405","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Software Library for Generative Model Applications","abstract":"The generation of data by machine learning models is a powerful concept that has impacted the field of Artificial Intelligence in the past few years. In this thesis, we focus on building a software library to facilitate the workflow, evaluation, and analysis of generative models. Our work is primarily aimed at helping a specialty chemicals company use a state of the art molecule generation model for their specific applications. We reference the body of work containing the model as DEG, short for Data-Efficient Graph Grammar Learning for Molecular Generation [16]. 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