Martin Trapp

PhD

    20192022

    Research activity per year

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    Artistic and research interests

    Probabilistic Machine Learning, Probabilistic Circuits, Probabilistic Programming, Bayesian nonparametrics

    Artistic and research interests

    My research interests focus on probabilistic machine learning approaches in which the exploitation of structural properties (e.g., symmetries or conditional independencies) enables (exact and) efficient computations. Moreover, I am intrigued by prior specification and approximate inference in Bayesian deep learning, and approximate inference in Bayesian nonparametrics (Gaussian processes and priors on random partitions).

    In addition to my research, I’m part of the open-source project Turing.jl, which aims to provide a robust general-purpose probabilistic programming language for all branches of science. 

    Education/Academic qualification

    PhD, Graz University of Technology

    Nov 2015Aug 2020

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    • Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regression

      Yu, Z., Zhu, M., Trapp, M., Skryagin, A. & Kersting, K., 2021, Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence. p. 2008-2018 11 p. (Proceedings of Machine Learning Research ; vol. 161).

      Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

      Open Access
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    • Periodic Activation Functions Induce Stationarity

      Meronen, L., Trapp, M. & Solin, A., 2021, Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021). 13 p. (Advances in Neural Information Processing Systems).

      Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

      Open Access
      File
      6 Downloads (Pure)