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Collaborative Research Center SFB 876 - Providing Information by Resource-Constrained Data Analysis


The collaborative research center SFB876 brings together data mining and embedded systems. On the one hand, embedded systems can be further improved using machine learning. On the other hand, data mining algorithms can be realized in hardware, e.g. FPGAs, or run on GPGPUs. The restrictions of ubiquitous systems in computing power, memory, and energy demand new algorithms for known learning tasks. These resource bounded learning algorithms may also be applied on extremely large data bases on servers.
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  SFB 876 at the Stanford Graph Learning Workshop 2021

The Stanford Graph Learning Workshop on September 16, 2021 will feature two talks from SFB 876. Matthias Fey and Jan Eric Lenssen, from subprojects A6 and B2, will each give a talk about their work on Graph Neural Networks (GNNs). Matthias Fey will talk about his now widely known and used GNN software library PyG (PyTorch Geometric) and its new functionalities in the area of heterogeneous graphs. Jan Eric Lenssen gives an overview of applications of Graph Neural Networks in the areas of computer vision and computer graphics.

Registration to participate in the livestream is available at the following link:
https://www.eventbrite.com/e/stanford-graph-learning-workshop-tickets-167490286957

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