Blogs on training data » Lamarr Institute

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The FRIES Trust Score: How AI Trustworthiness Can Be Quantified

11. June 2025

How can the trustworthiness of AI models be measured objectively and systematically? The FRIES Trust Score offers a structured approach to enhance transparency and comparability in models and datasets....

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Basics

Conscious use of Large Language Models: How to avoid biases?

17. July 2024

Bias in training data of large language models is a problem. Learn to recognize and minimize the effects of unconscious biases to achieve fairer and more diverse results. ...

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Basics

Ethical Use of Training Data: Ensuring Fairness and Data Protection in AI

03. July 2024

Discover how ethical use of training data minimizes bias, ensures data protection, and fosters trust in Artificial Intelligence....

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Basics

Automatic classification of a publication network using a Graph Attention Network

19. May 2023

Social networks are omnipresent these days. But how can information be extracted from them with the help of neural networks? To answer this question, we present the Graph Attention Network....

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Research

Sum-Product Networks – A new deep architecture for Machine Learning

12. July 2022

Clear semantics with reference to training data, learning of structure and parameters, efficient inference and versatile applicability: this is what Sum-Product Networks combine - a new type of model architecture for Machine Learning....

00 Blog Becker - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Basics

I forgot to remember to forget: Forgetting as a new requirement for AI

29. September 2021

The right to be forgotten came into force with the GDPR. In Machine Learning, targeted forgetting not only opens up aspects of data security and privacy, but also potential for the applicability and further development of learning models....

Basics

Optimization in Machine Learning

13. January 2021

Optimization is a key component of Machine Learning that allows models to be trained based on data. It usually works in the background but is especially important for highly complex learning problems and difficult data....