Language This workshop will be given in Swedish, but future versions of the workshop will be given in English. Beskrivning ENCCS med RISE erbjuder en halvdags-utbildning i AI som verktyg för förändring. Tanken är att du som deltagare skall få en förståelse för vad AI är, vilka sorters problem som kan lösas med teknikerna och […]
Language This workshop will be given in Swedish, but future versions of the workshop will be given in English. Beskrivning ENCCS med RISE erbjuder en halvdags-utbildning i AI som verktyg för förändring. Tanken är att du som deltagare skall få en förståelse för vad AI är, vilka sorters problem som kan lösas med teknikerna och […]
In recent years, Graph Neural Networks (GNNs) and Transformers have led to numerous breakthrough achievements in a variety of fields such as Natural Language Processing (NLP), chemistry, and physics. By doing away with the need for fixed-size inputs, these architectures significantly extend the scope of problems in which deep learning can be applied.
This workshop will take you from the representation of graphs and finite sets as inputs for neural networks to the implementation of full GNNs for a variety of tasks. You will learn about the central concepts used in GNNs in a hands-on setting using Jupyter Notebooks and a series of coding exercises. While the workshop will use problems from the field of chemistry as an example for applications, the skills you learn can be transferred to any domain where finite set or graph-based representations of data are appropriate. From GNNs, we will make the leap to Transformer architectures, and explain the conceptual ties between the two.
ENCCS with RISE offer a half-day course in A.I. as a tool for change. Participants will get an understanding of what artificial intelligence is, which problems can be solved with those techniques, and how they can be used within an organization.
We will give an overview on what processes one goes through when they utilize A.I. to solve problems. We are also going to illuminate the limitations of those techniques and give examples of when they are appropriate.
In recent years, Graph Neural Networks (GNNs) and Transformers have led to numerous breakthrough achievements in a variety of fields such as Natural Language Processing (NLP), chemistry, and physics. By doing away with the need for fixed-size inputs, these architectures significantly extend the scope of problems in which deep learning can be applied.
This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 951732. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and its associated countries .
