Abstract: Self-supervised learning (SSL ... applicable to different time-series data domains, such as novel sensor modalities in clinical data. We demonstrate the effectiveness of PFML through complex ...

This is the official repository of Rank Supervised Contrastive Learning for Time Series Classification, which was published in ICDM 2024 https://arxiv.org/abs/2401. ...

A team of researchers from the Indian Institute of Technology Kharagpur, India have proposed TinyTNAS, a cutting-edge hardware-aware multi-objective Neural Architecture Search tool specially designed ...

aiming to uncover patterns in unlabeled data for richer information. Contrastive self-supervised learning, particularly, has gained attention for time series classification. However, it introduces ...

Abstract. Deep neural networks, including transformers and convolutional neural networks, have significantly improved multivariate time series classification (MTSC). However, these methods often rely ...

For accurate classification, initializing a value to k is the most critical step in the kNN classifier. All the algorithms are implemented in python ... supervised learning classifiers. In real-time, ...

Among the many time series learning tasks of great importance, we here focus on semi-supervised learning based on a graph representation of the data. Two main aspects are studied in this paper. Namely ...