{"total":23,"rows":[{"id":"2307.03132","title":"T-MARS: Improving Visual Representations by Circumventing Text Feature Learning","created":"2023-07-06","cited_by_count":43,"area":null},{"id":"2201.10055","title":"Identifying a Training-Set Attack's Target Using Renormalized Influence Estimation","created":"2022-01-25","cited_by_count":32,"area":null},{"id":"2103.12711","title":"A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions","created":"2021-03-23","cited_by_count":17,"area":null},{"id":"2404.05779","title":"Data Readiness for AI: A 360-Degree Survey","created":"2024-04-08","cited_by_count":15,"area":null},{"id":"2312.07101","title":"Meta-survey on outlier and anomaly detection","created":"2023-12-12","cited_by_count":13,"area":null},{"id":"2201.05115","title":"Functional Anomaly Detection: a Benchmark Study","created":"2022-01-13","cited_by_count":10,"area":null},{"id":"2311.08168","title":"Time-Uniform Confidence Spheres for Means of Random Vectors","created":"2023-11-14","cited_by_count":6,"area":null},{"id":"2408.11561","title":"Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Control","created":"2024-08-21","cited_by_count":5,"area":null},{"id":"2106.07473","title":"Time Series Anomaly Detection with label-free Model Selection","created":"2021-06-11","cited_by_count":3,"area":null},{"id":"2211.13527","title":"Beyond Mahalanobis-Based Scores for Textual OOD Detection","created":"2022-11-24","cited_by_count":3,"area":null},{"id":"2412.00606","title":"Fairness at Every Intersection: Uncovering and Mitigating Intersectional Biases in Multimodal Clinical Predictions","created":"2024-11-30","cited_by_count":2,"area":null},{"id":"2312.14136","title":"Fast kernel half-space depth for data with non-convex supports","created":"2023-12-21","cited_by_count":1,"area":null},{"id":"2410.24178","title":"AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties","created":"2024-10-31","cited_by_count":1,"area":null},{"id":"2511.17113","title":"AutoGraphAD: Unsupervised network anomaly detection using Variational Graph Autoencoders","created":"2025-11-21","cited_by_count":1,"area":null},{"id":"2212.02939","title":"Quantification of geogrid lateral restraint using transparent sand and deep learning-based image segmentation","created":"2022-12-06","cited_by_count":0,"area":null},{"id":"2212.13520","title":"Challenges in anomaly and change point detection","created":"2022-12-27","cited_by_count":0,"area":null},{"id":"2303.02052","title":"Interruptions detection in video conferences","created":"2023-02-25","cited_by_count":0,"area":null},{"id":"2312.16139","title":"Abnormal component analysis","created":"2023-12-26","cited_by_count":0,"area":null},{"id":"2501.00107","title":"An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework","created":"2024-12-30","cited_by_count":0,"area":null},{"id":"2509.15419","title":"Deep learning and abstractive summarisation for radiological reports: an empirical study for adapting the PEGASUS models' family with scarce data","created":"2025-09-18","cited_by_count":0,"area":null},{"id":"2509.23113","title":"Exploring LLM-based Frameworks for Fault Diagnosis","created":"2025-09-27","cited_by_count":0,"area":null},{"id":"2602.12622","title":"Efficient Personalized Federated PCA with Manifold Optimization for IoT Anomaly Detection","created":"2026-02-13","cited_by_count":0,"area":null},{"id":"2610.13780","title":"Detection of extremely rare dead cells in fluorescence microscopy images","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}