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possible thereafter. The aim of this project is to advance the development of multi-trait Bayesian linear regression models that enable the sharing of genomic information across traits and biological layers
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Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https
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confidence set (credibility set if prediction is Bayesian) for a multivariate estimate with statistical coverage guarantees. This PhD project aims to develop new CP methods for knowledge graphs (KGs), which
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-dimensional data, survival and event history analysis, model selection and criticism, graphical modelling, non-parametric methods, machine learning, hierarchical Bayesian modelling, and time- and space
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candidate, you will: Develop and apply Bayesian Network machine learning methods to analyze the dynamics of G-protein coupled receptors to uncover allosteric regulation that enables design of allosteric
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appropriate conditions, it provides a confidence set (credibility set if prediction is Bayesian) for a multivariate estimate with statistical coverage guarantees. This PhD project aims to develop new CP methods
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Dalhousie University | Halifax Mid Harbour Nova Scotia Provincial Government, Nova Scotia | Canada | about 19 hours ago
, and systems engineering. The advancement and application of techniques such as Systems-Theoretic Process Analysis (STPA), structured expert elicitation, and Bayesian Networks (BNs) are foreseen, and
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, non-parametric methods, machine learning, hierarchical Bayesian modelling, and time- and space-modelling. The group emphasizes general methodological development, often motivated by real-world
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an extraordinarily rich, technology-focused network. This includes DARPA program managers and research performers from leading universities, commercial firms, and non-profit R&D organizations. For early-career
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predictions. To mitigate these effects, advanced ML techniques such as Bayesian deep learning, probabilistic models, and uncertainty quantification methods can be applied to enhance model robustness