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Neural Information ProcessingDeep Networks

Neuronale Informationsverarbeitung

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Deep Networks

Deep neural networks are very successful in many application areas. Nevertheless, it is unclear so far why they are so successful. Especially the success of overparameterized neural networks contradicts the findings of statistical learning theory. With the help of the analysis of representations, we try to reveal new insights. We are interested in the following questions.

  • Are (visual) tasks "related" and can we quantify the "closeness" of tasks?
  • What are the contributions of data set (input statistics) vs. task demand (input-output statistics)?
  • How can we efficiently mine these relationships?
  • Does the concept of an intermediate-level representation help?
  • Are there universal representations for (visual) data, which allow for an efficient solution for many "everyday" tasks?

Currently, we meet every Thursday at 2 pm to discuss these issues and gain new insights. If you are interested, don't hesitate to get in touch with .

Selected Publications:

An Interactive Introduction to Model-Agnostic Meta-Learning
Citation key Mueller20210
Author Müller, L. and Ploner, M. and Goerttler, T. and Obermayer, K.
Year 2021
Journal Workshop on Visualization for AI Explainability at IEEE VIS
Abstract In this article, we give an interactive introduction to model-agnostic meta-learning (MAML), a well-establish method in the area of meta-learning. Meta-learning is a research field that attempts to equip conventional machine learning architectures with the power to gain meta-knowledge about a range of tasks to solve problems like the one above on a human level of accuracy.
Bibtex Type of Publication Selected:structured selected:publications selected:main selected:quantify
Link to publication Link to original publication Download Bibtex entry

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