Home  | News

15.08.2025

Tiny logo
Teaser image to MCML at IJCAI 2025

MCML at IJCAI 2025

One Accepted Paper (1 Workshop)

34th International Joint Conference on Artificial Intelligence, Montreal, Canada, Aug 16-22, 2025

We are happy to announce that MCML researchers have contributed a total of 1 paper to IJCAI 2025: 1 Workshop paper. Congrats to our researchers!

Workshops (1 paper)

J. BlakeM. Schubert
Aerial Coverage Path Planning in Nuclear Emergencies A Training and Evaluation Environment.
Demonstration Track @IJCAI 2025 - Demonstration Track at the 34th International Joint Conference on Artificial Intelligence. Montreal, Canada, Aug 16-22, 2025. DOI

#research #top-tier-work #schubert
Subscribe to RSS News feed

Related

Link to COSMOS – Teaching Vision-Language Models to Look Beyond the Obvious

19.02.2026

COSMOS – Teaching Vision-Language Models to Look Beyond the Obvious

Presented at CVPR 2025, COSMOS shows how smarter training helps VLMs learn from details and context, improving AI understanding without larger models.

Read more
Link to Needle in a Haystack: Finding Exact Moments in Long Videos

05.02.2026

Needle in a Haystack: Finding Exact Moments in Long Videos

ECCV 2024 research introduces RGNet, an AI model that finds exact moments in long videos using unified retrieval and grounding.

Read more
Link to Benjamin Busam Leads Design of Bavarian Earth Observation Satellite Network “CuBy”

04.02.2026

Benjamin Busam Leads Design of Bavarian Earth Observation Satellite Network “CuBy”

Benjamin Busam leads the scientific design of the “CuBy” satellite network, delivering AI-ready Earth observation data for Bavaria.

Read more
Link to Cracks in the foundations of cosmology

30.01.2026

Cracks in the Foundations of Cosmology

Daniel Grün examines cosmological tensions that challenge the Standard Model and may point toward new physics.

Read more
Link to How Machines Can Discover Hidden Rules Without Supervision

29.01.2026

How Machines Can Discover Hidden Rules Without Supervision

ICLR 2025 research shows how self-supervised learning uncovers hidden system dynamics from unlabeled, high-dimensional data.

Read more
Back to Top