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Research Blog

Welcome to our research blog, where we proudly showcase the talents and achievements of our researchers, with a special focus on our junior members. Here, you’ll gain insight into their innovative work and the fresh ideas they bring to the ever-evolving fields of AI and machine learning.

Link to Who Spreads Hate?

 

30.04.2025

Who Spreads Hate?

MCML Research Insight – With Dominique Geissler, Abdurahman Maarouf, and Stefan Feuerriegel

Hate speech on social media isn’t just offensive - it’s dangerous. It spreads quickly, harms mental health, and can even contribute to real-world violence. While many studies have focused on identifying hate speech or profiling those who create it, a key piece of the puzzle remained missing: Who …

Link to How Certain Is AI? An Introduction to Bayesian Deep Learning

 

29.04.2025

How Certain Is AI? An Introduction to Bayesian Deep Learning

Researcher in Focus: Emanuel Sommer

MCML Junior Member Emanuel Sommer is a PhD-student at the Munich Uncertainty Quantification AI Lab at LMU Munich supervised by David Rügamer. His research focuses on Scalable and Reliable (Bayesian) Deep Learning.

Link to Text2Loc: A Smarter Way to Navigate With Words

 

10.04.2025

Text2Loc: A Smarter Way to Navigate With Words

MCML Research Insight - With Yan Xia, Zifeng Ding and Daniel Cremers

Imagine standing in an unfamiliar part of a city, no GPS in sight. All you can say is, “I’m west of a green building, near a black garage.” That might be vague to a machine, but Text2Loc understands you perfectly. With this powerful new system, AI can find your exact location in a 3D map - just from how you describe the world around you.

Link to CUPS: Teaching AI to Understand Scenes Without Human Labels

 

03.04.2025

CUPS: Teaching AI to Understand Scenes Without Human Labels

MCML Research Insight - With Christoph Reich, Nikita Araslanov, and Daniel Cremers

What matters now

Understanding the location and semantics of objects in a scene is a significant task, enabling robots to navigate through complex environments or facilitating autonomous driving. Recent AI models for understanding scenes from images require significant guidance from humans in the form of pixel-level annotations to achieve accurate …

Link to Beyond the Black Box: Choosing the Right Feature Importance Method

 

27.03.2025

Beyond the Black Box: Choosing the Right Feature Importance Method

MCML Research Insight - With Fiona Katharina Ewald, Ludwig Bothmann, Giuseppe Casalicchio and Bernd Bischl

Machine learning models make powerful predictions, but can we really trust them if we don’t understand how they work? Global feature importance methods help us discover which factors really matter - but choosing the wrong method can lead to misleading conclusions. To see why this is important, consider a real-world example from medicine.

Link to ReNO: A Smarter Way to Enhance AI-Generated Images

 

13.03.2025

ReNO: A Smarter Way to Enhance AI-Generated Images

MCML Research Insight - With Luca Eyring, Shyamgopal Karthik, Karsten Roth and Zeynep Akata

Despite their impressive capabilities, Text-to-Image (T2I) models frequently misinterpret detailed prompts, leading to errors in object positioning, attribute accuracy, and color fidelity. Traditional improvements rely on extensive dataset training, which is not only computationally expensive but also may not generalize well to unseen prompts. To …

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