Recent advancements in self-supervised learning (SSL) made it possible to learn generalizable visual representations from unlabeled data. The performance of Deep Learning models fine-tuned on pretrained SSL is par with the state-of-the-art supervised (SL) representations. Irrespective progress SSL, its generalizability has not been studied extensively. In this article, we perform a deeper analy...

Computational models are powerful tools that can enhance the understanding of scientific phenomena. The enterprise of modeling is most productive when the reasons underlying the adequacy of a model, and possibly its superiority to other models, are understood. This chapter begins with an overview of the main criteria that must be considered in model evaluation and selection, in particular expla...

Abstract Abusive language detection has become an integral part of the research, as reflected in numerous publications and several shared tasks conducted recent years. It been shown that obtained models perform well on datasets which they were trained, but have difficulty generalizing to other datasets. This work also focuses model generalization, – contrast previous we use homogeneous for our ...

As in many other social sciences, second/additional language (Lx) researchers are often interested generalizing their findings beyond the samples they collect data from. However, very little is known about range of learner backgrounds and settings found Lx research. Moreover, few papers that have addressed demographics sampled research paint a disappointing picture). The current study examines ...