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Found 8 papers, 3 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.
Targeted collapse regularized autoencoder for anomaly detection: black hole at the center
In practice, however, it is observed that autoencoders can generalize beyond the normal class and achieve a small reconstruction error on some of the anomalous samples.
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.
B2RL: An open-source Dataset for Building Batch Reinforcement Learning
Batch reinforcement learning (BRL) is an emerging research area in the RL community.
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.
Scaling Configuration of Energy Harvesting Sensors with Reinforcement Learning
We show that it is possible to reduce the number of RL policies by using a single policy for nodes that share similar lighting conditions.
Local Binary Pattern Networks
In this paper, we tackle the problem us- ing a strategy different from the existing literature by proposing local binary pattern networks or LBPNet, that is able to learn and perform binary operations in an end-to-end fashion.
