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Early detection of Parkinson's disease using a multi area graph convolutional network. [PDF]
Sci RepHuo H +6 more
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Spatiotemporal Deep Learning for Bridge Response Forecasting [PDF]
Journal of Structural Engineering, 2021 AbstractAccurate prediction/forecasting of the future response of civil infrastructure plays an essential role in health monitoring and safety assessment.
Ruiyang Zhang +3 more
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A deep spatiotemporal graph learning architecture for brain connectivity analysis
2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020In recent years, the conceptualisation of the brain as a "connectome" as summary measures derived from graph theory analyses, has become increasingly popular. Still, such approaches are inherently limited by the need to condense and simplify temporal fMRI dynamics and architecture into a purely spatial representation.
Azevedo T. +3 more
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Learning deep spatiotemporal features for video captioning
Pattern Recognition Letters, 2018Abstract In this paper, we propose a novel automatic video captioning system which translates videos to sentences, utilizing a deep neural network that is composed of three building parts of convolutional and recurrent structure. That is, the first subnetwork operates as feature extractor of single frames.
Eleftherios Daskalakis +2 more
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A Spatiotemporal Deep Learning Approach for Unsupervised Anomaly Detection in Cloud Systems
IEEE Transactions on Neural Networks and Learning Systems, 2023Anomaly detection is a critical task for maintaining the performance of a cloud system. Using data-driven methods to address this issue is the mainstream in recent years. However, due to the lack of labeled data for training in practice, it is necessary to enable an anomaly detection model trained on contaminated data in an unsupervised way.
Zilong He +7 more
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Deep Spatiotemporal Feature Learning with Application to Image Classification
2010 Ninth International Conference on Machine Learning and Applications, 2010Deep machine learning is an emerging framework for dealing with complex high-dimensionality data in a hierarchical fashion which draws some inspiration from biological sources. Despite the notable progress made in the field, there remains a need for an architecture that can represent temporal information with the same ease that spatial information is ...
Derek C. Rose +2 more
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Spatiotemporal Deep-Learning Networks for Shared-Parking Demand Prediction
Journal of Transportation Engineering, Part A: Systems, 2021AbstractOne fundamental issue in managing a shared-parking system is predicting shared-parking demand.
Yonghong Liu, Chunyu Liu, Xia Luo
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Context-Aware Deep Representation Learning for Geo-Spatiotemporal Analysis
2020 IEEE International Conference on Data Mining (ICDM), 2020The emergence of remote sensing technologies coupled with local monitoring workstations enables us the unprecedented ability to monitor the environment in large scale. Information mining from multi-channel geo-spatiotemporal data however poses great challenges to many computational sustainability applications.
Shuiwang Ji +5 more
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