24.2 Hamid Eghbal-zadeh - JKU : Improving out of distribution performance with robust and disentangled representations - Part 2/2

24.2 Hamid Eghbal-zadeh - JKU : Improving out of distribution performance with robust and disentangled representations - Part 2/2

Author: Manuel Pasieka April 15, 2022 Duration: 39:44

This is the second part of my interview with Hamid Eghbal-zadeh, post-doc at the Johannes Kepler University at the Institute of Machine Learning.

In the interview, we are talking about his research on a series of different aspects of representation learning with deep neural networks in order to make them more robust and improve their out-of-distribution behavior.

In this second part, we are talking about disentangled representations and the benefit they bring to agents trained in contextualized reinforcement tasks, in order to operate in unseen contexts and environments.

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Hosted by Manuel Pasieka, the Austrian Artificial Intelligence Podcast offers a grounded, local perspective on a global phenomenon. Instead of abstract theorizing, each conversation focuses on the tangible impact and practical applications of AI within Austria's unique ecosystem. You'll hear from a diverse range of guests-researchers, entrepreneurs, policymakers, and creatives-who are actively shaping this landscape, discussing both the remarkable opportunities and the nuanced challenges specific to the region. The discussions delve into how these technologies are being integrated into Austrian industry, academia, and society, moving beyond hype to examine real-world implementation and ethical considerations. This podcast serves as an essential audio forum for anyone in Austria, or with an interest in the European tech scene, looking to understand how artificial intelligence is evolving right here. It’s about the people behind the algorithms and the local stories within a global revolution. For those engaged with the content, questions and suggestions are always welcome at the provided email address.
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