Software Library for Generative Model Applications
Name
hernandez-piti3080-meng-eecs-2023-thesis.pdf
Description
Thesis PDF
Size
1.44 MB
Format
Adobe PDF
Checksum (MD5)
9328c4f6e878264902f7fb6748d24056
Author(s)
Hernandez, Carlos
Advisor(s)
Oliva, Aude
Date Issued
June 2023
Publisher
Massachusetts Institute of Technology
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]. DEG is capable of creating synthesizable molecules from small amounts of data, making it quite attractive for companies looking for practical methods to explore new molecules. As an overarching goal, we will design our library to incorporate other types of generative models and become a tool that the field can benefit from.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
In Copyright - Educational Use Permitted
Copyright retained by author(s)
Persistent DSpace Link
