{"total":16,"rows":[{"id":"2211.05244","title":"Deep Learning for Time Series Anomaly Detection: A Survey","created":"2022-11-09","cited_by_count":131,"area":null},{"id":"2202.06649","title":"On the Importance of Building High-quality Training Datasets for Neural Code Search","created":"2022-02-14","cited_by_count":30,"area":null},{"id":"2206.05136","title":"Fast Deep Autoencoder for Federated learning","created":"2022-06-10","cited_by_count":10,"area":null},{"id":"2101.10181","title":"Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey","created":"2021-01-25","cited_by_count":9,"area":null},{"id":"2105.06256","title":"Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design","created":"2021-05-10","cited_by_count":9,"area":null},{"id":"2104.00972","title":"Resource-aware Time Series Imaging Classification for Wireless Link Layer Anomalies","created":"2021-04-02","cited_by_count":8,"area":null},{"id":"2209.04184","title":"Anomaly Detection through Unsupervised Federated Learning","created":"2022-09-09","cited_by_count":8,"area":null},{"id":"2108.03872","title":"Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach","created":"2021-08-09","cited_by_count":3,"area":null},{"id":"2510.18998","title":"An Encode-then-Decompose Approach to Unsupervised Time Series Anomaly Detection on Contaminated Training Data--Extended Version","created":"2025-10-21","cited_by_count":3,"area":null},{"id":"2402.07281","title":"Benchmarking Anomaly Detection Algorithms: Deep Learning and Beyond","created":"2024-02-11","cited_by_count":1,"area":null},{"id":"2508.08326","title":"Weather-Driven Agricultural Decision-Making Using Digital Twins Under Imperfect Conditions","created":"2025-08-10","cited_by_count":1,"area":null},{"id":"2211.02847","title":"Degradation Prediction of Semiconductor Lasers using Conditional Variational Autoencoder","created":"2022-11-05","cited_by_count":0,"area":null},{"id":"2303.01021","title":"CADeSH: Collaborative Anomaly Detection for Smart Homes","created":"2023-03-02","cited_by_count":0,"area":null},{"id":"2303.08823","title":"Wireless Sensor Networks anomaly detection using Machine Learning: A Survey","created":"2023-03-15","cited_by_count":0,"area":null},{"id":"2303.13015","title":"Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networks","created":"2023-03-23","cited_by_count":0,"area":null},{"id":"2506.00420","title":"A New Spatiotemporal Correlation Anomaly Detection Method that Integrates Contrastive Learning and Few-Shot Learning in Wireless Sensor Networks","created":"2025-05-31","cited_by_count":0,"area":null}],"next_cursor":null}