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.

We study problem-dependent rates, i.e., generalization errors that scale tightly with the variance or the effectiveloss at the "besthypothesis."

Although BERT -style encoder models are heavily used in NLP research, many researchers do not pretrain their own BERTs from scratch due to the high cost of training. In the past half-decade since BERT first rose to prominence, many advances have been made with other transformer architectures and training configurations that have yet to be systematically incorporated into BERT.

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

Visual Decathlon [53] gathers 10 diversedatasets.

Asaresult,wefindoutthatprevious baselines for ultra-low bit precision quantization are significantly under-trained. Based on our study,we propose asimple yet effectivecompression pipeline for extreme compression, named XTC.