Pseudo labeling and consistency regularization approaches with confidence-based
thresholding have made great progress in semi-supervised learning (SSL). In this paper, we …

Instruction tuning large language models (LLMs) remains a challenging task, owing to the
complexity of hyperparameter selection and the difficulty involved in evaluating the tuned …

Abstract Machine learning research has long focused on models rather than datasets, and
prominent datasets are used for common ML tasks without regard to the breadth, difficulty …

The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the
limited labeled data and massive unlabeled data to improve the model's generalization …

Self-training methods have gained significant attention in recent years due to their
effectiveness in leveraging small labeled datasets and large unlabeled observations for …

Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

Spatio-temporal predictive learning is a learning paradigm that enables models to learn
spatial and temporal patterns by predicting future frames from given past frames in an …

Abstract Semi-Supervised Learning (SSL) has been an effective way to leverage abundant
unlabeled data with extremely scarce labeled data. However, most SSL methods are …

Semi-supervised learning (SSL) aims to leverage massive unlabeled data when labels are
expensive to obtain. Unfortunately, in many real-world applications, the collected unlabeled …

Systematic review is a crucial method that has been widely used. by scholars from different
research domains. However, screening for relevant scientific literature from paper …