This Friday 09-04-2021, 5.30pm CEST, for the ContinualAI Reading Group, Federico Errica (University of Pisa) will present the paper:
Abstract: In this work, we study the phenomenon of catastrophic forgetting in the graph representation learning scenario. The primary objective of the analysis is to understand whether classical continual learning techniques for flat and sequential data have a tangible impact on performances when applied to graph data. To do so, we experiment with a structure-agnostic model and a deep graph network in a robust and controlled environment on three different datasets. The benchmark is complemented by an investigation on the effect of structure-preserving regularization techniques on catastrophic forgetting. We find that replay is the most effective strategy in so far, which also benefits the most from the use of regularization. Our findings suggest interesting future research at the intersection of the continual and graph representation learning fields. Finally, we provide researchers with a flexible software framework to reproduce our results and carry out further experiments.
The event will be moderated by: Vincenzo Lomonaco
Eventbrite event: Eventbrite Link
Miscrosoft Teams: click here to join
Slides: Continual AI - Catastrophic DGNs.pdf - Google Drive
YouTube recording: https://youtu.be/nDhnQDcvVQE
Looking forward to seeing you all there!