{"total":45,"rows":[{"id":"2408.01287","title":"Deep Learning based Visually Rich Document Content Understanding: A Survey","created":"2024-08-02","cited_by_count":21,"area":null},{"id":"2507.23771","title":"Consensus-Driven Active Model Selection","created":"2025-07-31","cited_by_count":8,"area":null},{"id":"2506.06630","title":"Active Test-time Vision-Language Navigation","created":"2025-06-07","cited_by_count":7,"area":null},{"id":"2505.16791","title":"Cohort-Based Active Modality Acquisition","created":"2025-05-22","cited_by_count":5,"area":null},{"id":"2507.15504","title":"Quantifying and Narrowing the Unknown: Interactive Text-to-Video Retrieval via Uncertainty Minimization","created":"2025-07-21","cited_by_count":5,"area":null},{"id":"2606.28725","title":"DriftGuard: Safety-Aware Multi-Monitor Detection and Selective Adaptation for Evolving Toxicity Moderation","created":"2026-06-27","cited_by_count":4,"area":null},{"id":"2411.15217","title":"LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training","created":"2024-11-20","cited_by_count":2,"area":null},{"id":"2507.17151","title":"PICore: Physics-Informed Unsupervised Coreset Selection for Data Efficient Neural Operator Training","created":"2025-07-23","cited_by_count":2,"area":null},{"id":"2511.19301","title":"IDEAL-M3D: Instance Diversity-Enriched Active Learning for Monocular 3D Detection","created":"2025-11-24","cited_by_count":2,"area":null},{"id":"2603.24480","title":"Positive-First Most Ambiguous: A Simple Active Learning Criterion for Interactive Retrieval of Rare Categories","created":"2026-03-25","cited_by_count":2,"area":null},{"id":"2604.11995","title":"Loss-Driven Bayesian Active Learning","created":"2026-04-13","cited_by_count":2,"area":null},{"id":"2605.10162","title":"Active-SAOOD: Active Sparsely Annotated Oriented Object Detection in Remote Sensing Images","created":"2026-05-11","cited_by_count":2,"area":null},{"id":"2507.06537","title":"A model-agnostic active learning approach for animal detection from camera traps","created":"2025-07-09","cited_by_count":1,"area":null},{"id":"2508.10836","title":"SoK: Data Minimization in Machine Learning","created":"2025-08-14","cited_by_count":1,"area":null},{"id":"2510.10163","title":"SSeg: Active Sparse Point-Label Augmentation for Semantic Segmentation","created":"2025-10-11","cited_by_count":1,"area":null},{"id":"2605.17575","title":"UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shifts","created":"2026-05-17","cited_by_count":1,"area":null},{"id":"2605.24395","title":"AvAtar: Learning to Align via Active Optimal Transport","created":"2026-05-23","cited_by_count":1,"area":null},{"id":"2410.20041","title":"Sparse Linear Bandits with Blocking Constraints","created":"2024-10-26","cited_by_count":0,"area":null},{"id":"2501.00170","title":"Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection","created":"2024-12-30","cited_by_count":0,"area":null},{"id":"2502.03292","title":"ALPET: Active Few-shot Learning for Citation Worthiness Detection in Low-Resource Wikipedia Languages","created":"2025-02-05","cited_by_count":0,"area":null},{"id":"2503.00860","title":"Hierarchical graph sampling based minibatch learning with chain preservation and variance reduction","created":"2025-03-02","cited_by_count":0,"area":null},{"id":"2504.16136","title":"Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement","created":"2025-04-21","cited_by_count":0,"area":null},{"id":"2506.04739","title":"Lifelong Evolution: Collaborative Learning between Large and Small Language Models for Continuous Emergent Fake News Detection","created":"2025-06-05","cited_by_count":0,"area":null},{"id":"2508.19019","title":"Metric Matters: A Formal Evaluation of Similarity Measures in Active Learning for Cyber Threat Intelligence","created":"2025-08-26","cited_by_count":0,"area":null},{"id":"2509.24181","title":"Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification","created":"2025-09-29","cited_by_count":0,"area":null},{"id":"2510.04939","title":"Unsupervised Active Learning via Natural Feature Progressive Framework","created":"2025-10-06","cited_by_count":0,"area":null},{"id":"2511.17345","title":"Label-Efficient Skeleton-based Recognition with Stable-Invertible Graph Convolutional Networks","created":"2025-11-21","cited_by_count":0,"area":null},{"id":"2511.19364","title":"Neural surrogates for designing gravitational wave detectors","created":"2025-11-24","cited_by_count":0,"area":null},{"id":"2511.21625","title":"Active Learning for GCN-based Action Recognition","created":"2025-11-26","cited_by_count":0,"area":null},{"id":"2602.00047","title":"Lightweight Edge Learning via Dataset Pruning","created":"2026-01-19","cited_by_count":0,"area":null},{"id":"2602.07226","title":"Fault-Tolerant Evaluation for Sample-Efficient Model Performance Estimators","created":"2026-02-06","cited_by_count":0,"area":null},{"id":"2602.08809","title":"Efficient Deep Learning for Biometrics: Overview, Challenges and Trends in Ear of Frugal AI","created":"2026-02-09","cited_by_count":0,"area":null},{"id":"2602.19578","title":"Goal-Oriented Influence-Maximizing Data Acquisition for Learning and Optimization","created":"2026-02-23","cited_by_count":0,"area":null},{"id":"2604.00256","title":"Informed Machine Learning with Knowledge Landmarks","created":"2026-03-31","cited_by_count":0,"area":null},{"id":"2604.02019","title":"Feature Weighting Improves Pool-Based Sequential Active Learning for Regression","created":"2026-04-02","cited_by_count":0,"area":null},{"id":"2604.20000","title":"RareSpot+: A Benchmark, Model, and Active Learning Framework for Small and Rare Wildlife in Aerial Imagery","created":"2026-04-21","cited_by_count":0,"area":null},{"id":"2605.05590","title":"Uncertainty-Guided Edge Learning for Deep Image Regression in Remote Sensing","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.08843","title":"M$^3$: Reframing Training Measures for Discretized Physical Simulations","created":"2026-05-09","cited_by_count":0,"area":null},{"id":"2605.21348","title":"Data-Efficient Neural Operator Training via Physics-Based Active Learning","created":"2026-05-20","cited_by_count":0,"area":null},{"id":"2606.17805","title":"QueryMarket: Cost-Aware Online Active Learning in Data Markets","created":"2026-06-16","cited_by_count":0,"area":null},{"id":"2607.01089","title":"Group-invariant Coresets for Data-efficient Active Learning","created":"2026-07-01","cited_by_count":0,"area":null},{"id":"2608.14121","title":"Overcoming Shortcut Learning in Graph Neural Networks through Active Explanation Guidance","created":"2026-08-14","cited_by_count":0,"area":null},{"id":"2609.02212","title":"FuDU: A Fuzzy Dual-dimensional Uncertainty Framework for Streaming Active Learning in Industrial Defect Detection","created":"2026-09-02","cited_by_count":0,"area":null},{"id":"2609.37687","title":"WISE-ATTA: When to Ask for Labels in Budgeted Active Test-Time Adaptation","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.03454","title":"Measure Less, Know More: Self-Supervised Test-Time Feature Acquisition","created":"2026-10-02","cited_by_count":0,"area":null}],"next_cursor":null}