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Spatiotemporal Deep Learning for Bridge Response Forecasting
Journal of Structural Engineering, 2021AbstractAccurate 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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Regional air quality forecasting using spatiotemporal deep learning
Journal of Cleaner Production, 2021Abstract Accelerated urbanization and industrialization have led to poor air quality, which threatens human health with various lung ailments. Monitoring, modeling, and forecasting air quality would be a prudent way to promote awareness and defend human beings from the adversities of air pollution.
S Abirami, P Chitra
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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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DBlink: dynamic localization microscopy in super spatiotemporal resolution via deep learning
Nature Methods, 2022AbstractSingle molecule localization microscopy (SMLM) has revolutionized biological imaging, improving the spatial resolution of traditional microscopes by an order of magnitude. However, SMLM techniques depend on accumulation of many localizations over thousands of recorded frames to yield a single super-resolved image, which is time consuming. Hence,
Alon Saguy +5 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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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.
Hanzi Mao +5 more
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Spatiotemporal prediction with deep learning on graphs
2019Spatiotemporal data is ubiquitous in our daily life, ranging from climate science, transportation, social media, to various dynamical systems. The data is usually collected from a set of correlated objects over time, where objects can be sensors, locations, regions, particles, users, etc.
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Spatiotemporal-Aware Region Recommendation with Deep Metric Learning
2019Personalized points of interests (POI) recommendation is an important basis for location-based services. A typical application scenario is to recommend a region with reliable POIs to a user when he/she travels to an unfamiliar area without any background knowledge.
Hengpeng Xu +4 more
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Deep Predictive Coding for Multimodal Spatiotemporal Representation Learning
LatinX in AI at Neural Information Processing Systems Conference 2019, 2019Common sense reasoning relates to the capacity of learning representations that disentangle hidden factors behind spatiotemporal sensory data. In this work, we hypothesize that the predictive coding theory of perception and learning from neuroscience literature may be a promising candidate for implementing such common-sense inductive biases.
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