activity
20172024
most citedFederated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence

104 citations · 611 across the 55 of their papers we have counts for

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Showing 2022Show all

14 papers · 1 filter

cs.LG2022★ 7 cited

Detecting Change Intervals with Isolation Distributional Kernel

Yang Cao, Ye Zhu, Kai Ming Ting +4

Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis. Although many unsupervised Change-Point Detection (CPD) methods have…

cs.LG2022

SeqLink: A Robust Neural-ODE Architecture for Modelling Partially Observed Time Series

Futoon M. Abushaqra, Hao Xue, Yongli Ren +1

Ordinary Differential Equations (ODE) based models have become popular as foundation models for solving many time series problems. Combining neural ODEs with traditional RNN models…

cs.LG2022

Integrated Convolutional and Recurrent Neural Networks for Health Risk Prediction using Patient Journey Data with Many Missing Values

Yuxi Liu, Shaowen Qin, Antonio Jimeno Yepes +3

Predicting the health risks of patients using Electronic Health Records (EHR) has attracted considerable attention in recent years, especially with the development of deep learning…

cs.CY2022

Analysing Donors' Behaviour in Non-profit Organisations for Disaster Resilience: The 2019--2020 Australian Bushfires Case Study

Dilini Rajapaksha, Kacper Sokol, Jeffrey Chan +3

With the advancement and proliferation of technology, non-profit organisations have embraced social media platforms to improve their operational capabilities through brand advocacy…

cs.LG2022★ 1 cited

Leveraging Language Foundation Models for Human Mobility Forecasting

Hao Xue, Bhanu Prakash Voutharoja, Flora D. Salim

In this paper, we propose a novel pipeline that leverages language foundation models for temporal sequential pattern mining, such as for human mobility forecasting tasks. For examp…

cs.HC2022

Imagining Future Digital Assistants at Work: A Study of Task Management Needs

Yonchanok Khaokaew, Indigo Holcombe-James, Mohammad Saiedur Rahaman +11

Digital Assistants (DAs) can support workers in the workplace and beyond. However, target user needs are not fully understood, and the functions that workers would ideally want a D…