15.03.2021

Teaser image to MCML at ECIR 2021

MCML at ECIR 2021

43rd European Conference on Information Retrieval (ECIR 2021). March 28 – April 1, 2021, Virtual

We are happy to announce that MCML researchers are represented at ECIR 2021.

We are happy to announce that MCML researchers are represented with 3 papers at ECIR 2021:

M. Berrendorf, E. Faerman and V. Tresp.
Active Learning for Entity Alignment.
DOI. GitHub.
M. Berrendorf, L. Wacker and E. Faerman.
A Critical Assessment of State-of-the-Art in Entity Alignment.
DOI. GitHub.
M. Fromm, M. Berrendorf, S. Obermeier, T. Seidl and E. Faerman.
Diversity Aware Relevance Learning for Argument Search.
DOI. GitHub.

15.03.2021


Subscribe to RSS News feed

Related

Link to When Clinical Expertise Meets AI Innovation – with Michael Ingrisch

25.06.2025

When Clinical Expertise Meets AI Innovation – With Michael Ingrisch

The new research film features Michael Ingrisch, who shows how AI and clinical expertise can solve real challenges in radiology together.

Link to Autonomous Driving: From Infinite Possibilities to Safe Decisions— with Matthias Althoff

23.06.2025

Autonomous Driving: From Infinite Possibilities to Safe Decisions— With Matthias Althoff

The new research film features Matthias Althoff explaining how his team verifies autonomous vehicle safety using EDGAR and rigorous testing.

Link to ERC Advanced Grant for Massimo Fornasier

20.06.2025

ERC Advanced Grant for Massimo Fornasier

Massimo Fornasier was awarded ERC Advanced Grant to develop advanced algorithms for solving complex nonconvex optimization problems.

Link to ERC Advanced Grant for Albrecht Schmidt

18.06.2025

ERC Advanced Grant for Albrecht Schmidt

Albrecht Schmidt receives ERC Advanced Grant for research on personalized generative AI to support memory, planning, and creativity.

Link to Better Data, Smarter AI: Why Quality Matters – with Frauke Kreuter

11.06.2025

Better Data, Smarter AI: Why Quality Matters – With Frauke Kreuter

In our new research film, Frauke Kreuter explains how data quality shapes fair, reliable, and socially responsible AI systems.