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Found 6 papers, 3 papers with code
Downstream Task Guided Masking Learning in Masked Autoencoders Using Multi-Level Optimization
Masked Autoencoder (MAE) is a notable method for self-supervised pretraining in visual representation learning.
Large Language Models for Time Series: A Survey
Large Language Models (LLMs) have seen significant use in domains such as natural language processing and computer vision.
Physics-Informed Data Denoising for Real-Life Sensing Systems
By obviating the need for ground truth clean data, our method offers a practical denoising solution for real-world applications.
PrimeNet: Pre-Training for Irregular Multivariate Time Series
In this work, we propose PrimeNet to learn a self-supervised representation for irregular multivariate time series.
Towards Diverse and Coherent Augmentation for Time-Series Forecasting
We note that augmentation designed for forecasting requires diversity as well as coherence with the original temporal dynamics.
Unleashing the Power of Shared Label Structures for Human Activity Recognition
In this paper, we propose SHARE, a HAR framework that takes into account shared structures of label names for different activities.
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