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Institute of Explainable Machine Learning

The Institute of Explainable Machine Learning (EML) is dedicated to advancing the understanding of AI models, for the development of more reliable and transparent systems.

The Institute of Explainable Machine Learning (EML) is dedicated to advancing the understanding of AI models, for the development of more reliable and transparent systems.

About our Research

Our research directions include:

  • multi-modality
  • explainability
  • zero-shot learning

For more information, check our recent publications.

Our Team

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Postdoctoral Researchers
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PhD Researchers
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Collaborating Researchers
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Collaborating Faculty

EML Publications

Machine Learning Algorithms
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IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2025

Context-Aware Multimodal Pretraining

Karsten Roth, Zeynep Akata, Dima Damen, Ivana Balazevic*, Olivier J. Henaff*

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IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2025

COSMOS: Cross-Modality Self-Distillation for Vision Language Pre-training

Sanghwan Kim, Rui Xiao, Mariana-Iuliana Georgescu, Stephan Alaniz, Zeynep Akata

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Transfer und Netzwerke
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IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2025

FLAIR: VLM with Fine-grained Language-informed Image Representations

Rui Xiao, Sanghwan Kim, Mariana-Iuliana Georgescu, Zeynep Akata, Stephan Alaniz

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All Publications

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