Multimodal foundation models serve numerous applications at the intersection of vision and
language. Still, despite being pretrained on extensive data, they become outdated over time …

To accommodate real-world dynamics artificial intelligence systems need to cope with
sequentially arriving content in an online manner. Beyond regular Continual Learning (CL) …

For models consisting of a classifier in some representation space, learning online from a
non-stationary data stream often necessitates changes in the representation. So, the …

Continual learning (CL) is a paradigm which addresses the issue of how to learn from
sequentially arriving tasks. The goal of this paper is to introduce a CL framework which can …

Abstract Medical Concept Normalization (MCN) is a crucial process for deep information
extraction and natural language processing tasks, which plays a vital role in biomedical …

In online continual learning (CL), models trained on changing distributions easily forget
previously learned knowledge and bias toward newly received tasks. To address this issue …

Federated Learning (FL) methods often struggle in highly statistically heterogeneous
settings. Indeed, non-IID data distributions cause client drift and biased local solutions …

Biological organisms in nature are required to continuously learn from and adapt to the
environment throughout their lifetime. This ongoing learning capacity serves as the …

Graph class incremental learning (GCIL) requires the model to classify emerging nodes of
new classes while remembering old classes. Existing methods are designed to preserve …