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Amanda Olmin

PhD student

My research lies in the intersection between the fields of deep learning and probabilistic modelling. The aim is to develop deep learning models that can reason about uncertainties, such as those emerging from noise inherent in most data.



Amanda Olmin, Jakob Lindqvist, Lennart Svensson, Fredrik Lindsten (2023) Active Learning with Weak Supervision for Gaussian Processes Neural Information Processing 29th International Conference, ICONIP 2022, Virtual Event, November 22–26, 2022, Proceedings, Part V, p. 195-204 Continue to DOI


Amanda Olmin, Fredrik Lindsten (2022) Robustness and Reliability When Training With Noisy Labels Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022, p. 922-942
Amanda Olmin (2022) On Uncertainty Quantification in Neural Networks: Ensemble Distillation and Weak Supervision


Hariprasath Govindarajan, Peter Lindskog, Dennis Lundstrom, Amanda Olmin, Jacob Roll, Fredrik Lindsten (2021) Self-Supervised Representation Learning for Content Based Image Retrieval of Complex Scenes IEEE Intelligent Vehicles Symposium, Proceedings, p. 249-256 Continue to DOI


Jakob Lindqvist, Amanda Olmin, Fredrik Lindsten, Lennart Svensson (2020) A General Framework for Ensemble Distribution Distillation 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), p. 1-6 Continue to DOI



Colleagues at STIMA