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Medi-CAT: Contrastive Adversarial Training for Medical Image Classification
This paper proposes a training strategy Medi-CAT to overcome the underfitting and overfitting phenomena in medical imaging datasets.
A Unique Training Strategy to Enhance Language Models Capabilities for Health Mention Detection from Social Media Content
Following the need for an optimal language model competent in extracting useful patterns from social media text, the key goal of this paper is to train language models in such a way that they learn to derive generalized patterns.
A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets
Moreover, our analysis shows that adding noise at earlier layers improves models' performance whereas adding noise at intermediate layers deteriorates models' performance.
Improving Health Mentioning Classification of Tweets using Contrastive Adversarial Training
The idea is to learn word representation by its surrounding words and utilize emojis in the text to help improve the classification results.
Understanding Information Spreading Mechanisms During COVID-19 Pandemic by Analyzing the Impact of Tweet Text and User Features for Retweet Prediction
These social media platforms enable users to share information with other users who can reshare this information, thus causing this information to spread.
