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Creators/Authors contains: "Joshi, Ameya"

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  1. ; ; ; (, IEEE Conference on Secure and Trustworthy Machine Learning)
    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; ; (, Engineering with Computers)
    Full Text Available 
  3. ; ; ; ; ; (, IEEE Transactions on Biometrics, Behavior, and Identity Science)
    Full Text Available 
  4. ; ; ; ; (, International Joint Conference on Biometrics)
    Full Text Available 
  5. ; ; ; (, Transactions on machine learning research)
    Accepted Manuscript Full Text Available
  6. ; ; ; ; (, Journal of Big Data Analytics in Transportation)
    Full Text Available 
  7. ; ; (, 2021 IEEE Data Science and Learning Workshop (DSLW))
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  8. ; ; ; ; ; ; ; (, IEEE)
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  9. ; ; ; ; ; ; ; ; (, Nature Computational Science)
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  10. ; ; ; ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    null (Ed.)
    Generative Adversarial Networks (GANs), while widely successful in modeling complex data distributions, have not yet been sufficiently leveraged in scientific computing and design. Reasons for this include the lack of flexibility of GANs to represent discrete-valued image data, as well as the lack of control over physical properties of generated samples. We propose a new conditional generative modeling approach (InvNet) that efficiently enables modeling discrete-valued images, while allowing control over their parameterized geometric and statistical properties. We evaluate our approach on several synthetic and real world problems: navigating manifolds of geometric shapes with desired sizes; generation of binary two-phase materials; and the (challenging) problem of generating multi-orientation polycrystalline microstructures. 
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