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Artificial intelligence of things ( AIoT ) data acquisition based on graph neural networks: A systematical review
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/ail2.61openalex-snapshot
DARPA 's explainable AI ( XAI ) program: A retrospective
inferredcites →https://doi.org/10.1002/cpe.7429openalex-snapshot
LDP‐Fed+: A robust and privacy‐preserving federated learning based classification framework enabled by local differential privacy
inferredcites →https://doi.org/10.1007/978-3-030-04167-0_33openalex-snapshot
Structured Sequence Modeling with Graph Convolutional Recurrent Networks
inferredcites →https://doi.org/10.1016/j.aiopen.2021.01.001openalex-snapshot
Graph neural networks: A review of methods and applications
inferredcites →https://doi.org/10.1016/j.eswa.2022.117921openalex-snapshot
Graph neural network for traffic forecasting: A survey
inferredcites →https://doi.org/10.1016/j.inffus.2022.07.011openalex-snapshot
QAPP: A quality-aware and privacy-preserving medical image release scheme
inferredcites →https://doi.org/10.1016/j.jnca.2012.12.028openalex-snapshot
Opportunistic IoT: Exploring the harmonious interaction between human and the internet of things
inferredcites →https://doi.org/10.1016/j.media.2021.102062openalex-snapshot
A survey on active learning and human-in-the-loop deep learning for medical image analysis
inferredcites →https://doi.org/10.1016/j.patcog.2019.107000openalex-snapshot
Dynamic graph convolutional networks
inferredcites →https://doi.org/10.1016/j.tics.2019.02.006openalex-snapshot
Reinforcement Learning, Fast and Slow
inferredcites →https://doi.org/10.1017/s026988891300043xopenalex-snapshot
One-class classification: taxonomy of study and review of techniques
inferredcites →https://doi.org/10.1093/nsr/nwx106openalex-snapshot
A brief introduction to weakly supervised learning
inferredcites →https://doi.org/10.1109/cvpr.2015.7298640openalex-snapshot
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2018.00356openalex-snapshot
Adversarially Learned One-Class Classifier for Novelty Detection
inferredcites →https://doi.org/10.1109/cvpr.2019.00018openalex-snapshot
Learning Loss for Active Learning
inferredcites →https://doi.org/10.1109/cvpr.2019.00133openalex-snapshot
Graph Convolutional Label Noise Cleaner: Train a Plug-And-Play Action Classifier for Anomaly Detection
inferredcites →https://doi.org/10.1109/cvpr.2019.01103openalex-snapshot
Explainability Methods for Graph Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01419openalex-snapshot
Old Is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01443openalex-snapshot
Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction
inferredcites →https://doi.org/10.1109/iccv.2019.00156openalex-snapshot
S4L: Self-Supervised Semi-Supervised Learning
inferredcites →https://doi.org/10.1109/iccv.2019.00607openalex-snapshot
Variational Adversarial Active Learning
inferredcites →https://doi.org/10.1109/iccv.2019.00936openalex-snapshot
DeepGCNs: Can GCNs Go As Deep As CNNs?
inferredcites →https://doi.org/10.1109/jiot.2019.2952146openalex-snapshot
Local Differential Privacy for Deep Learning
inferredcites →https://doi.org/10.1109/jiot.2020.3018691openalex-snapshot
Robust Learning-Enabled Intelligence for the Internet of Things: A Survey From the Perspectives of Noisy Data and Adversarial Examples
inferredcites →https://doi.org/10.1109/jiot.2020.3039359openalex-snapshot
Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things
inferredcites →https://doi.org/10.1109/jiot.2021.3094295openalex-snapshot
Graph Neural Networks for Anomaly Detection in Industrial Internet of Things
inferredcites →https://doi.org/10.1109/jsen.2017.2735539openalex-snapshot
Advances on Sensing Technologies for Smart Cities and Power Grids: A Review
inferredcites →https://doi.org/10.1109/jsen.2021.3049714openalex-snapshot
Social Sensing in IoT Applications: A Review
inferredcites →https://doi.org/10.1109/jsyst.2021.3109082openalex-snapshot
Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids
inferredcites →https://doi.org/10.1109/mcom.2018.1700906openalex-snapshot
The Role of Edge Computing in Internet of Things
inferredcites →https://doi.org/10.1109/mis.2009.36openalex-snapshot
The Unreasonable Effectiveness of Data
inferredcites →https://doi.org/10.1109/mnet.2018.1700263openalex-snapshot
A Robust Dynamic Edge Network Architecture for the Internet of Things
inferredcites →https://doi.org/10.1109/mnet.2019.1800344openalex-snapshot
Edge Intelligence in the Cognitive Internet of Things: Improving Sensitivity and Interactivity
inferredcites →https://doi.org/10.1109/msp.2012.2235192openalex-snapshot
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
inferredcites →https://doi.org/10.1109/tai.2021.3076974openalex-snapshot
Graph Convolutional Neural Network for Human Action Recognition: A Comprehensive Survey
inferredcites →https://doi.org/10.1109/tifs.2020.2988575openalex-snapshot
Federated Learning With Differential Privacy: Algorithms and Performance Analysis
inferredcites →https://doi.org/10.1109/tkde.2020.2981333openalex-snapshot
Deep Learning on Graphs: A Survey
inferredcites →https://doi.org/10.1109/tkde.2021.3090866openalex-snapshot
Self-supervised Learning: Generative or Contrastive
inferredcites →https://doi.org/10.1109/tkde.2021.3124599openalex-snapshot
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
inferredcites →https://doi.org/10.1109/tkde.2021.3131584openalex-snapshot
Self-Supervised Learning on Graphs: Contrastive, Generative, or Predictive
inferredcites →https://doi.org/10.1109/tkde.2022.3154391openalex-snapshot
Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing
inferredcites →https://doi.org/10.1109/tnn.2008.2005605openalex-snapshot
The Graph Neural Network Model
inferredcites →https://doi.org/10.1109/tnnls.2020.2978386openalex-snapshot
A Comprehensive Survey on Graph Neural Networks
inferredcites →https://doi.org/10.1109/tsc.2019.2953033openalex-snapshot
Blockchain Empowered Arbitrable Data Auditing Scheme for Network Storage as a Service
inferredcites →https://doi.org/10.1109/tse.2019.2962027openalex-snapshot
Machine Learning Testing: Survey, Landscapes and Horizons
inferredcites →https://doi.org/10.1145/1390156.1390303openalex-snapshot
Deep learning via semi-supervised embedding
inferredcites →https://doi.org/10.1145/3299887.3299891openalex-snapshot
Data Lifecycle Challenges in Production Machine Learning
inferredcites →https://doi.org/10.1145/3308558.3313488openalex-snapshot
Graph Neural Networks for Social Recommendation
inferredcites →https://doi.org/10.1145/3313831.3376177openalex-snapshot
Understanding and Visualizing Data Iteration in Machine Learning
inferredcites →https://doi.org/10.1145/3318464.3389772openalex-snapshot
Human-in-the-loop Outlier Detection
inferredcites →https://doi.org/10.1145/3411764.3445518openalex-snapshot
“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI
inferredcites →https://doi.org/10.1145/3444690openalex-snapshot
A Review on Outlier/Anomaly Detection in Time Series Data
inferredcites →https://doi.org/10.1145/3457607openalex-snapshot
A Survey on Bias and Fairness in Machine Learning
inferredcites →https://doi.org/10.1145/3477141openalex-snapshot
Computing Graph Neural Networks: A Survey from Algorithms to Accelerators
inferredcites →https://doi.org/10.1145/3510414openalex-snapshot
A Survey on Active Deep Learning: From Model Driven to Data Driven
inferredcites →https://doi.org/10.1145/3535101openalex-snapshot
Graph Neural Networks in Recommender Systems: A Survey
inferredcites →https://doi.org/10.1145/3565973openalex-snapshot
Graph Neural Networks in IoT: A Survey
inferredcites →https://doi.org/10.1201/9781315155678openalex-snapshot
Big Data and Computational Intelligence in Networking
inferredcites →https://doi.org/10.1609/aaai.v32i1.11604openalex-snapshot
Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning
inferredcites →https://doi.org/10.1609/aaai.v32i1.12328openalex-snapshot
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
inferredcites →https://doi.org/10.1609/aaai.v33i01.33013614openalex-snapshot
Towards Reliable Learning for High Stakes Applications
inferredcites →https://doi.org/10.1609/aaai.v33i01.33019837openalex-snapshot
Machine Learning with Crowdsourcing: A Brief Summary of the Past Research and Future Directions
inferredcites →https://doi.org/10.1609/aaai.v34i04.5984openalex-snapshot
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
inferredcites →https://doi.org/10.1609/aaai.v35i5.16523openalex-snapshot
Graph Neural Network-Based Anomaly Detection in Multivariate Time Series
inferredcites →https://doi.org/10.1609/aimag.v40i2.2850openalex-snapshot
DARPA's Explainable Artificial Intelligence Program
inferredcites →https://doi.org/10.23915/distill.00032openalex-snapshot
Understanding Convolutions on Graphs
inferredcites →https://doi.org/10.23915/distill.00033openalex-snapshot
A Gentle Introduction to Graph Neural Networks
inferredcites →https://doi.org/10.24963/ijcai.2018/362openalex-snapshot
Adversarially Regularized Graph Autoencoder for Graph Embedding
inferredcites →https://doi.org/10.24963/ijcai.2019/550openalex-snapshot
Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective
inferredcites →https://doi.org/10.48550/arxiv.1704.01212openalex-snapshot
Neural Message Passing for Quantum Chemistry
inferredcites →https://doi.org/10.48550/arxiv.1709.05584openalex-snapshot
Representation Learning on Graphs: Methods and Applications
inferredcites →https://doi.org/10.48550/arxiv.1804.06872openalex-snapshot
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
inferredcites →https://doi.org/10.48550/arxiv.1806.03536openalex-snapshot
Representation Learning on Graphs with Jumping Knowledge Networks
inferredcites →https://doi.org/10.48550/arxiv.1902.07153openalex-snapshot
Simplifying Graph Convolutional Networks
inferredcites →https://doi.org/10.48550/arxiv.1903.03894openalex-snapshot
GNNExplainer: Generating Explanations for Graph Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.2005.03675openalex-snapshot
Machine Learning on Graphs: A Model and Comprehensive Taxonomy
inferredcites →https://doi.org/10.5281/zenodo.15206559openalex-snapshot
Formal Semantics for Kolmogorov-Arnold Network Representations of Operational Games
inferredcites →https://doi.org/10.5281/zenodo.20695702openalex-snapshot
RhizoDB: A Bounded Activation-Flow Architecture for Graph-Based Memory Systems
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Locational False Data Injection Attack Detection in Smart Grid Using Recursive Variational Graph Autoencoder
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STCA-LLM: Spatial-Temporal Cross-Attention Large Language Model for Wind Speed Forecasting
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A Federated Gnn-Based Solution for Privacy-Aware Internet of Things (Iot) Intrusion Detection
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One-Class Anomaly Detection for Industrial Applications: A Comparative Survey and Experimental Study
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Introducing Security Mechanisms in OpenFog-Compliant Smart Buildings
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Pressure-Guided LSTM Modeling for Fermentation Quantification Prediction
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Integrating Artificial Intelligence and IoT for Smarter Collaboration
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Achieving Balance Between Innovation and Security in the Cloud With Artificial Intelligence of Things
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Enhancing Usability and Control in Artificial Intelligence of Things Environments (AIoT) Through Semantic Web Control Models
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Customer Centricity Orientation in Artificial Intelligence of Things (AIoT)
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Design of a hazard prediction system with intelligent multimodal fusion based on artificial intelligence & internet of things technology: taking a crib as an example
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