Inthiswork,wepropose S3GCwhich uses contrastive learning along with Graph Neural Networks and node features to learn clusterable features. We empirically demonstrate that S3GC is able to learn the correct cluster structure evenwhen graph information ornode features are individually not informative enough to learn correct clusters.

DeepICLR (Poster), 2(3):4, [54] Yang Wang, inrealnetworks:22nd International Distributed, pages 25-34.

Wefurther propose attention sharing strategies to facilitate parameter sharing and specialization in multilingual and multi-domain sequencemodeling.

It is widely acknowledged that large language models (LLMs) encode a vast reservoirofknowledge after being trained onmass data.