11 citations · 19 across the 6 of their papers we have counts for
6 papers
Towards Diverse and Coherent Augmentation for Time-Series Forecasting
Xiyuan Zhang, Ranak Roy Chowdhury, Jingbo Shang +2
Time-series data augmentation mitigates the issue of insufficient training data for deep learning models. Yet, existing augmentation methods are mainly designed for classification,…
Unleashing the Power of Shared Label Structures for Human Activity Recognition
Xiyuan Zhang, Ranak Roy Chowdhury, Jiayun Zhang +3
Current human activity recognition (HAR) techniques regard activity labels as integer class IDs without explicitly modeling the semantics of class labels. We observe that different…
Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework
Jiayun Zhang, Xiyuan Zhang, Xinyang Zhang +3
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
Hsin-Yu Liu, Xiaohan Fu, Bharathan Balaji +2
Batch reinforcement learning (BRL) is an emerging research area in the RL community. It learns exclusively from static datasets (i.e. replay buffers) without interaction with the e…
Sensei: Self-Supervised Sensor Name Segmentation
Jiaman Wu, Dezhi Hong, Rajesh Gupta +1
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. Sensor names, h…
Sensor-Type Classification in Buildings
Dezhi Hong, Jorge Ortiz, Arka Bhattacharya +1
Many sensors/meters are deployed in commercial buildings to monitor and optimize their performance. However, because sensor metadata is inconsistent across buildings, software-base…