Home  | News

15.10.2020

Teaser image to Invited presentation at 1st CIKM 2020 Workshop on Combining Symbolic and Sub-symbolic Methods and their Applications (CSSA-CIKM 2020)

Invited Presentation at 1st CIKM 2020 Workshop on Combining Symbolic and Sub-Symbolic Methods and Their Applications (CSSA-CIKM 2020)

Learning With Temporal Knowledge Graphs

MCML PI Volker Tresp, Yunpu Ma and Zhen Han review recently developed learning-based algorithms for temporal knowledge graphs completion and forecasting. Knowledge graphs, also known as episodic or time-dependent knowledge graphs are large-scale event databases that describe temporally evolving multi-relational data.

#research #tresp
Subscribe to RSS News feed

Related

Link to "See, Don’t Assume": Revealing and Reducing Gender Bias in AI

18.12.2025

"See, Don’t Assume": Revealing and Reducing Gender Bias in AI

ICLR 2025 research led by Zeynep Akata’s team reveals and reduces gender bias in popular vision-language AI models.

Link to Fabian Theis Featured in Handelsblatt on the Future of AI in Precision Medicine

16.12.2025

Fabian Theis Featured in Handelsblatt on the Future of AI in Precision Medicine

MCML PI Fabian Theis discusses AI-driven precision medicine and its growing impact on individualized healthcare and biomedical research.

Link to Gitta Kutyniok Featured in VDI Nachrichten on AI Ethics

16.12.2025

Gitta Kutyniok Featured in VDI Nachrichten on AI Ethics

Gitta Kutyniok discusses measurable criteria for ethical AI, promoting safe and responsible autonomous decision-making.

Link to Hinrich Schütze Featured in WirtschaftsWoche on Innovative AI Approaches

16.12.2025

Hinrich Schütze Featured in WirtschaftsWoche on Innovative AI Approaches

Hinrich Schütze discusses Giotto.ai’s efficient AI models, highlighting memory separation and context-aware decoding to improve robustness.

Link to Xiaoxiang Zhu Featured in Focus Online on Global 3D Building Atlas

16.12.2025

Xiaoxiang Zhu Featured in Focus Online on Global 3D Building Atlas

Xiaoxiang Zhu maps 2.75B buildings in 3D, revealing global urbanization, housing, and social inequalities using AI.

Back to Top