Conveners
Overview Talks: Physics
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Overview Talks: Machine Learning and Computer Science
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Overview Talks: Overview Talks
- Jens Buss (Lamarr Institute, TU Dortmund University)
Overview Talks
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20 mins Talks + 5 mins Q&A
Introductions to Astroparticle physics.
In particular, Gamma-ray Astronomy and Neutrino Astronomy with a glace of Simulation topics.
20 mins Talks + 5 mins Q&A
Hybrid Machine Learning is all about inferring structured representations from empirical data, where by representations I mean transformed views of the data that make it more interpretable, or more usable for modelling and prediction. In this talk I will discuss how one can use neural networks to infer representations that satisfy partial differential equations, which one assumes model the...
This presentation will give a brief overview on the various dimensions comprising Trustworthy AI in general, namely Fairness, Privacy, Autonomy, Transparency and Reliability. A specific focus is given to the last two as they are typically more concerned with the inner workings of the AI system as such. When considering AI as tool for academic, that is scientific, use understanding limitations...
Human-centered Systems are designed to interact with humans and deliver explainable and comprehensible results.
At the Lamarr Institute, we are developing human-centered approaches for bridging the gap between ML methods and human minds. On the one hand, human-centered systems adapt to human goals, concepts, values, and ways of thinking. On the other hand, these systems take advantage of...
Embodied Artificial Intelligence (AI) refers to AI that is embedded in physical systems, such as robots, and can interact with the surroundings. Embodied agents thus learn from experience in order to improve their behavior, comparable to how human learning is based on exploration and interaction with the environment. This talk will give a brief overview of the interdisciplinary field of...
The scientific area of Resource-Aware Machine Learning tries to "make the most out of a bad situation," i.e., match existing solutions' performance while only using a fraction of the resources or surpassing existing solutions' performance. To do so, we try to bridge the gap between the mathematical concepts of Machine Learning, their expression in software, and their execution in hardware. We...
