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

Learning time-series representations when only unlabeled data or few labeled samples are
available can be a challenging task. Recently, contrastive self-supervised learning has …

Self-supervised learning establishes a new paradigm of learning representations with much
fewer or even no label annotations. Recently there has been remarkable progress on large …

This paper proposes a novel contrastive learning framework, called FOCAL, for extracting
comprehensive features from multimodal time-series sensing signals through self …

Existing inertial measurement unit (IMU) based human activity recognition (HAR)
approaches still face a major challenge when adopted across users in practice. The severe …

Long short-term memory faces challenges in information mining and parameter selection
due to inherent uncertainty and randomness. In this study, we propose a novel hybrid model …

WiFi channel state information (CSI)-based activity recognition has sparked numerous
studies due to its widespread availability and privacy protection. However, when applied in …

This paper presents ssLOTR (self-supervised learning on the rings), a system that shows the
feasibility of designing self-supervised learning based techniques for 3D finger motion …

Self-supervised learning (SSL) has emerged as a promising alternative to purely supervised
learning, since it can learn from labeled and unlabeled data using a pre-train-then-fine-tune …

This paper presents a semi-supervised learning framework that is new in being designed for
automatic modulation classification (AMC). By carefully utilizing unlabeled signal data with a …