Filters are fundamental in extracting information from data. For time series and image data
that reside on Euclidean domains, filters are the crux of many signal processing and …

Continual learning on graph data has recently attracted paramount attention for its aim to
resolve the catastrophic forgetting problem on existing tasks while adapting the sequentially …

Memory replay based techniques have shown great success for continual learning with
incrementally accumulated Euclidean data. Directly applying them to continually expanding …

Data processing tasks over graphs couple the data residing over the nodes with the
topology through graph signal processing tools. Graph filters are one such prominent tool …

Graph filters are a staple tool for processing signals over graphs in a multitude of
downstream tasks. However, they are commonly designed for graphs with a fixed number of …

Memory replay based techniques have shown great success for continual learning with
incrementally accumulated Euclidean data. Directly applying them to continually expanding …

Simplicial convolutional filters can process signals defined over levels of a simplicial
complex such as nodes, edges, triangles, and so on with applications in eg, flow prediction …