3 papers
cs.LG2024
Long Range Propagation on Continuous-Time Dynamic Graphs
Alessio Gravina, Giulio Lovisotto, Claudio Gallicchio +2
Learning Continuous-Time Dynamic Graphs (C-TDGs) requires accurately modeling spatio-temporal information on streams of irregularly sampled events. While many methods have been pro…
cs.LG2024
Learning-Based Link Anomaly Detection in Continuous-Time Dynamic Graphs
Tim Poštuvan, Claas Grohnfeldt, Michele Russo +1
Anomaly detection in continuous-time dynamic graphs is an emerging field yet under-explored in the context of learning algorithms. In this paper, we pioneer structured analyses of…
cs.CV2023
OpenIncrement: A Unified Framework for Open Set Recognition and Deep Class-Incremental Learning
Jiawen Xu, Claas Grohnfeldt, Odej Kao
In most works on deep incremental learning research, it is assumed that novel samples are pre-identified for neural network retraining. However, practical deep classifiers often mi…