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Poster
Multi-objective Deep Data Generation with Correlated Property Control
Shiyu Wang · Xiaojie Guo · Xuanyang Lin · Bo Pan · Yuanqi Du · Yinkai Wang · Yanfang Ye · Ashley Petersen · Austin Leitgeb · Saleh Alkhalifa · Kevin Minbiole · William M. Wuest · Amarda Shehu · Liang Zhao

Wed Nov 30 09:00 AM -- 11:00 AM (PST) @ Hall J #202
in Poster Session 3 »

Developing deep generative models has been an emerging field due to the ability to model and generate complex data for various purposes, such as image synthesis and molecular design. However, the advance of deep generative models is limited by the challenges to generate objects that possess multiple desired properties because: 1) the existence of complex correlation among real-world properties is common but hard to identify; 2) controlling individual property enforces an implicit partially control of its correlated properties, which is difficult to model; 3) controlling multiple properties under variour manners simultaneously is hard and underexplored. We address these challenges by proposing a novel deep generative framework that recovers semantics and correlation of properties through disentangled latent vectors. The correlation is handled via an explainable mask pooling layer, and properties are precisely retained by the generated objects via the mutual dependence between latent vectors and properties. Our generative model preserves properties of interest while handles correlation and conflicts of properties under a multi-objective optimization framework. The experiments demonstrate our model's superior performance in generating objects with desired properties.

Keywords: Variational Autoencoders · Deep generative models · disentangled learning · controllable generation · correlated properties
[ Paper]  [ Poster]  [ OpenReview] 

Author Information

Shiyu Wang (Emory University)
Shiyu Wang

Shiyu is currently a PhD candidate in Biostatistics at the Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University. He is honored to be advised by Dr. Liang Zhao and Dr. Zhaohui (Steve) Qin, working on deep generative models and graph neural networks.

Xiaojie Guo (JD.COM Silicon Valley Research Center)
Xuanyang Lin (Emory University)
Bo Pan (Emory University)
Yuanqi Du (Cornell University)
Yinkai Wang (Tufts University)
Yanfang Ye (University of Notre Dame)
Ashley Petersen (Villanova University)
Austin Leitgeb
Saleh Alkhalifa (Recursiv LLC)
Kevin Minbiole (Villanova University)
William M. Wuest
Amarda Shehu (George Mason University)
Amarda Shehu

Dr. Amarda Shehu is a Professor in the Department of Computer Science in the College of Engineering and Computing at George Mason University, where she is also Associate Vice President of Research for the Institute of Digital InnovAtion. Shehu obtained her Ph.D. from Rice University in 2008, where she was also an NIH predoctoral fellow in the Nanobiology Program and was dually trained in AI and Molecular Biophysics. Shehu's research is at the intersection of AI/ML and scientific enquiry across disciplines. In particular, her laboratory has made significant contributions to uncovering the relationship between macromolecular sequence, structure, dynamics, and function. Shehu has published over 160 technical papers with postdoctoral, graduate, undergraduate, and high-school students. She is a 2022 Fellow of the American Institute for Medical and Biological Engineering (AIMBE) and has received several awards, including the 2022 Outstanding Faculty Award from the State Council of Higher Education for Virginia, the 2021 Beck Family Presidential Medal for Faculty Excellence in Research and Scholarship, the 2018 Mason University Teaching Excellence Award, the 2014 Mason Emerging Researcher/Scholar/Creator Award, the 2013 Mason OSCAR Undergraduate Mentor Excellence Award, and the 2012 National Science Foundation (NSF) CAREER Award. Her research is regularly supported by various NSF programs, the Department of Defense, as well as state and private research awards. Shehu is currently the chair of the steering committee of the ACM/IEEE Journal on Transactions in Bioinformatics and Computational Biology, where she is also an associate editor. Shehu served as an NSF Program Director in the Information and Intelligent Systems Division of the Computer and Information Science and Engineering Directorate during 2019-2022. She was also an Inaugural Founding Co-Director of George Mason University’s Transdisciplinary Center for Advancing Human-Machine Partnerships.

Liang Zhao (Emory University)

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