Self-supervised learning (SSL) has recently achieved impressive performance on various
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …

Time-Series Mining (TSM) is an important research area since it shows great potential in
practical applications. Deep learning models that rely on massive labeled data have been …

Time series forecasting holds significant importance in many real-world dynamic systems
and has been extensively studied. Unlike natural language process (NLP) and computer …

Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …

Self-Supervised Learning (SSL) is a new paradigm for learning discriminative
representations without labeled data, and has reached comparable or even state-of-the-art …

Most supervised geographic mapping methods with very-high-resolution (VHR) images are
designed for a specific task, leading to high label-dependency and inadequate task …

Learning semantic-rich representations from raw unlabeled time series data is critical for
downstream tasks such as classification and forecasting. Contrastive learning has recently …

Multimodal learning, which aims to understand and analyze information from multiple
modalities, has achieved substantial progress in the supervised regime in recent years …

Label-efficient time series representation learning, which aims to learn effective
representations with limited labeled data, is crucial for deploying deep learning models in …

Unsupervised representation learning approaches aim to learn discriminative feature
representations from unlabeled data, without the requirement of annotating every sample …