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Thesis/Theses examined by Tresp, Volker

Number of items: 17.

Sharifzadehgolpayegani, Sahand (2023): On the importance of symbol grounding and top-down processes in computer vision. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Frikha, Ahmed (2022): Deep knowledge transfer for generalization across tasks and domains under data scarcity: on intersections of anomaly detection, few-shot learning, continual learning, domain generalization and data-free learning. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Han, Zhen (2022): Relational learning on temporal knowledge graphs. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Gu, Jindong (2022): Explainability and robustness of deep visual classification models. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Wu, Zhiliang (2022): Representation learning for uncertainty-aware clinical decision support. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Berrendorf, Max (2022): Machine learning for managing structured and semi-structured data. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Hildebrandt, Marcel (2021): Reasoning on graph-structured data with deep-learning, path-based methods, and tensor factorization. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Ma, Yunpu (2020): Learning with relational knowledge in the context of cognition, quantum computing, and causality. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Zhao, Rui (2020): Deep reinforcement learning in robotics and dialog systems. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Huang, Yi (2020): Scalable statistical learning for relation prediction on structured data. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Baier, Stephan (2019): Learning representations for supervised information fusion using tensor decompositions and deep learning methods. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Esteban, Cristóbal (2018): Deep learning for precision medicine. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Yang, Yinchong (2018): Enhancing representation learning with tensor decompositions for knowledge graphs and high dimensional sequence modeling. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Boidol, Jonathan (2017): Monitoring data streams: Classification under uncertainty and entropy-based dependency-detection on streaming data. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Krompaß, Denis (2015): Exploiting prior knowledge and latent variable representations for the statistical modeling and probabilistic querying of large knowledge graphs. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Jiang, Xueyan (2014): Integrating prior knowledge into factorization approaches for relational learning. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Nickel, Maximilian (2013): Tensor factorization for relational learning. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

This list was generated on Wed Feb 28 21:53:32 2024 CET.