Noseong Park's  talk at DataSig Seminar

DataSig

Come join us for Noseong Park's engaging talk at the DataSig Seminar, where we'll dive into all things data-related!

Noseong Park's talk at DataSig SeminarThe title: Differential Equation-inspired Deep Learning for Node Classification and Spatiotemporal Forecasting Abstract:Scientific knowledge, written in the form of differential equations, plays a vital role in various deep learning fields. In this talk, I will present a graph neural network (GNN) design based on reaction-diffusion equations, which addresses the notorious oversmoothing problem of GNNs. Since the self-attention of Transformers can also be viewed as a special case of graph processing, I will present how we can enhance Transformers in a similar way. I will also introduce a spatiotemporal forecasting model based on neural controlled differential equations (NCDEs). NCDEs were designed to process irregular time series in a continuous manner and for spatiotemporal processing, it needs to be combined with a spatial processing module, i.e., GNN. I will show how this can be done.

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Noseong Park's talk at DataSig Seminar

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Noseong Park's talk at DataSig Seminar

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