He has made substantial contributions to early work in modern Bayesian deep learning - quantifying uncertainty in deep learning - and developed machine learning and artificial intelligence tools that can inform their users when the tools are ‘guessing at random’. Yarin Gal is Associate Professor of Machine Learning at the University of Oxford Computer Science department, UK, and leads the Oxford Applied and Theoretical Machine Learning (OATML) group. On the statistical formalism of uncertainty quantification. Scientific multi-agent reinforcement learning for wall-models of turbulent flows. Korali: efficient and scalable software framework for Bayesian uncertainty quantification and stochastic optimization. Improving deterministic uncertainty estimation in deep learning for classification and regression. Van Amersfoort, J., Smith, L., Jesson, A., Key, O. 34th International Conference on Neural Information Processing Systems (NIPS’20) 7498–7512 (Curran Associates, 2020). Simple and principled uncertainty estimation with deterministic deep learning via distance awareness. 37th International Conference on Machine Learning Vol 119 9690–9700 (PMLR, 2020). Uncertainty estimation using a single deep deterministic neural network. Bayesian Learning for Neural Networks (Springer, 1996). 33rd International Conference on International Conference on Machine Learning Vol 48 1050–1059 (PMLR, 2016). Dropout as a Bayesian approximation: representing model uncertainty in deep learning. Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. A review of uncertainty quantification in deep learning: techniques, applications and challenges. The frontier of simulation-based inference.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |