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Found 10 papers, 6 papers with code
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.
Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework
Traditional federated classification methods, even those designed for non-IID clients, assume that each client annotates its local data with respect to the same universal class set.
B2RL: An open-source Dataset for Building Batch Reinforcement Learning
Batch reinforcement learning (BRL) is an emerging research area in the RL community.
Critical Risk Indicators (CRIs) for the electric power grid: A survey and discussion of interconnected effects
We also provide relevant critical risk indicators (CRIs) across diverse domains that may influence electric power grid risks, including climate, ecology, hydrology, finance, space weather, and agriculture.
Applications
Sensei: Self-Supervised Sensor Name Segmentation
A sensor name, typically an alphanumeric string, encodes the key context (e. g., function and location) of a sensor needed for deploying smart building applications.
SeNsER: Learning Cross-Building Sensor Metadata Tagger
Sensor metadata tagging, akin to the named entity recognition task, provides key contextual information (e. g., measurement type and location) about sensors for running smart building applications.
Sensor-Type Classification in Buildings
One important aspect of normalization is to differentiate sensors by the typeof phenomena being observed.
