{"total":566,"rows":[{"id":"2103.00123","title":"GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training","created":"2021-02-27","cited_by_count":274,"area":null},{"id":"2407.21772","title":"ShieldGemma: Generative AI Content Moderation Based on Gemma","created":"2024-07-31","cited_by_count":243,"area":null},{"id":"2109.03764","title":"Active Learning by Acquiring Contrastive Examples","created":"2021-09-08","cited_by_count":156,"area":null},{"id":"2202.02794","title":"Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets","created":"2022-02-06","cited_by_count":133,"area":null},{"id":"2107.14263","title":"Batch Active Learning at Scale","created":"2021-07-29","cited_by_count":127,"area":null},{"id":"2210.15809","title":"Coverage-centric Coreset Selection for High Pruning Rates","created":"2022-10-28","cited_by_count":117,"area":null},{"id":"2203.13450","title":"A Comparative Survey of Deep Active Learning","created":"2022-03-25","cited_by_count":89,"area":null},{"id":"2106.09675","title":"Gone Fishing: Neural Active Learning with Fisher Embeddings","created":"2021-06-17","cited_by_count":88,"area":null},{"id":"2107.00717","title":"SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios","created":"2021-07-01","cited_by_count":84,"area":null},{"id":"2406.11011","title":"Data Shapley in One Training Run","created":"2024-06-16","cited_by_count":81,"area":null},{"id":"2310.19596","title":"LLMaAA: Making Large Language Models as Active Annotators","created":"2023-10-30","cited_by_count":79,"area":null},{"id":"2209.00099","title":"Efficient Methods for Natural Language Processing: A Survey","created":"2022-08-31","cited_by_count":78,"area":null},{"id":"2101.11665","title":"On Statistical Bias In Active Learning: How and When To Fix It","created":"2021-01-27","cited_by_count":75,"area":null},{"id":"2106.07760","title":"RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning","created":"2021-06-14","cited_by_count":74,"area":null},{"id":"2310.07931","title":"D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data Pruning","created":"2023-10-11","cited_by_count":69,"area":null},{"id":"2310.14664","title":"Data Pruning via Moving-one-Sample-out","created":"2023-10-23","cited_by_count":67,"area":null},{"id":"2205.11320","title":"Active Learning Through a Covering Lens","created":"2022-05-23","cited_by_count":66,"area":null},{"id":"2311.17874","title":"FisherRF: Active View Selection and Uncertainty Quantification for Radiance Fields using Fisher Information","created":"2023-11-29","cited_by_count":64,"area":null},{"id":"2203.07034","title":"Active Learning by Feature Mixing","created":"2022-03-14","cited_by_count":63,"area":null},{"id":"2304.08151","title":"Prediction-Oriented Bayesian Active Learning","created":"2023-04-17","cited_by_count":62,"area":null},{"id":"2111.12940","title":"Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation","created":"2021-11-25","cited_by_count":57,"area":null},{"id":"2112.01406","title":"Active Learning for Domain Adaptation: An Energy-Based Approach","created":"2021-12-02","cited_by_count":53,"area":null},{"id":"2204.07965","title":"Entropy-based Active Learning for Object Detection with Progressive Diversity Constraint","created":"2022-04-17","cited_by_count":53,"area":null},{"id":"2107.11769","title":"ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation","created":"2021-07-25","cited_by_count":52,"area":null},{"id":"2103.00128","title":"PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Subset Selection","created":"2021-02-27","cited_by_count":51,"area":null},{"id":"2403.03208","title":"Active Statistical Inference","created":"2024-03-05","cited_by_count":50,"area":null},{"id":"2405.15613","title":"Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach","created":"2024-05-24","cited_by_count":50,"area":null},{"id":"2203.09410","title":"A Framework and Benchmark for Deep Batch Active Learning for Regression","created":"2022-03-17","cited_by_count":49,"area":null},{"id":"2510.01135","title":"Prompt Curriculum Learning for Efficient LLM Post-Training","created":"2025-10-01","cited_by_count":45,"area":null},{"id":"2108.11458","title":"Reducing Label Effort: Self-Supervised meets Active Learning","created":"2021-08-25","cited_by_count":42,"area":null},{"id":"2210.10109","title":"A Survey of Active Learning for Natural Language Processing","created":"2022-10-18","cited_by_count":40,"area":null},{"id":"2202.00132","title":"Submodularity In Machine Learning and Artificial Intelligence","created":"2022-01-31","cited_by_count":38,"area":null},{"id":"2106.15324","title":"Effective Evaluation of Deep Active Learning on Image Classification Tasks","created":"2021-06-16","cited_by_count":37,"area":null},{"id":"2106.12059","title":"Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning","created":"2021-06-22","cited_by_count":36,"area":null},{"id":"2310.14230","title":"A comprehensive survey on deep active learning in medical image analysis","created":"2023-10-22","cited_by_count":35,"area":null},{"id":"2104.08320","title":"On the Importance of Effectively Adapting Pretrained Language Models for Active Learning","created":"2021-04-16","cited_by_count":32,"area":null},{"id":"2110.08611","title":"Deep Active Learning by Leveraging Training Dynamics","created":"2021-10-16","cited_by_count":32,"area":null},{"id":"2404.07662","title":"PINNACLE: PINN Adaptive ColLocation and Experimental points selection","created":"2024-04-11","cited_by_count":32,"area":null},{"id":"2403.04272","title":"Active Generalized Category Discovery","created":"2024-03-07","cited_by_count":31,"area":null},{"id":"2202.01402","title":"GALAXY: Graph-based Active Learning at the Extreme","created":"2022-02-03","cited_by_count":29,"area":null},{"id":"2302.08893","title":"Active learning for data streams: a survey","created":"2023-02-17","cited_by_count":29,"area":null},{"id":"2402.02318","title":"Diversity Measurement and Subset Selection for Instruction Tuning Datasets","created":"2024-02-04","cited_by_count":29,"area":null},{"id":"2111.12880","title":"Active Learning at the ImageNet Scale","created":"2021-11-25","cited_by_count":28,"area":null},{"id":"2208.00549","title":"Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities","created":"2022-08-01","cited_by_count":28,"area":null},{"id":"2211.11612","title":"Plug and Play Active Learning for Object Detection","created":"2022-11-21","cited_by_count":28,"area":null},{"id":"2312.02567","title":"Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts","created":"2023-12-05","cited_by_count":28,"area":null},{"id":"2104.08312","title":"Data Shapley Valuation for Efficient Batch Active Learning","created":"2021-04-16","cited_by_count":27,"area":null},{"id":"2112.00166","title":"TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices using Submodular Mutual Information","created":"2021-11-30","cited_by_count":27,"area":null},{"id":"2301.09249","title":"Exploring Active 3D Object Detection from a Generalization Perspective","created":"2023-01-23","cited_by_count":26,"area":null},{"id":"2301.10625","title":"Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment","created":"2023-01-25","cited_by_count":26,"area":null},{"id":"2312.00678","title":"The Efficiency Spectrum of Large Language Models: An Algorithmic Survey","created":"2023-12-01","cited_by_count":26,"area":null},{"id":"2206.12569","title":"Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels","created":"2022-06-25","cited_by_count":25,"area":null},{"id":"2210.07805","title":"Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning","created":"2022-10-13","cited_by_count":25,"area":null},{"id":"2202.04108","title":"A Lagrangian Duality Approach to Active Learning","created":"2022-02-08","cited_by_count":24,"area":null},{"id":"2302.13824","title":"Dirichlet-based Uncertainty Calibration for Active Domain Adaptation","created":"2023-02-27","cited_by_count":24,"area":null},{"id":"2205.04980","title":"ALLSH: Active Learning Guided by Local Sensitivity and Hardness","created":"2022-05-10","cited_by_count":23,"area":null},{"id":"2401.06692","title":"An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models","created":"2024-01-12","cited_by_count":23,"area":null},{"id":"2410.01432","title":"Adaptive teachers for amortized samplers","created":"2024-10-02","cited_by_count":23,"area":null},{"id":"2106.03243","title":"Neural Active Learning with Performance Guarantees","created":"2021-06-06","cited_by_count":22,"area":null},{"id":"2106.11719","title":"Test Distribution-Aware Active Learning: A Principled Approach Against Distribution Shift and Outliers","created":"2021-06-22","cited_by_count":22,"area":null},{"id":"2110.14171","title":"Diversity Enhanced Active Learning with Strictly Proper Scoring Rules","created":"2021-10-27","cited_by_count":22,"area":null},{"id":"2205.15704","title":"Mitigating Dataset Bias by Using Per-sample Gradient","created":"2022-05-31","cited_by_count":22,"area":null},{"id":"2309.16971","title":"Multi-Resolution Active Learning of Fourier Neural Operators","created":"2023-09-29","cited_by_count":22,"area":null},{"id":"2310.04987","title":"Data-centric Graph Learning: A Survey","created":"2023-10-08","cited_by_count":22,"area":null},{"id":"2408.01536","title":"Active Learning for Neural PDE Solvers","created":"2024-08-02","cited_by_count":22,"area":null},{"id":"2209.06285","title":"Warm Start Active Learning with Proxy Labels \\& Selection via Semi-Supervised Fine-Tuning","created":"2022-09-13","cited_by_count":21,"area":null},{"id":"2301.07670","title":"Active learning for medical image segmentation with stochastic batches","created":"2023-01-18","cited_by_count":21,"area":null},{"id":"2109.03079","title":"GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentation","created":"2021-09-07","cited_by_count":20,"area":null},{"id":"2209.08425","title":"Introspective Learning : A Two-Stage Approach for Inference in Neural Networks","created":"2022-09-17","cited_by_count":20,"area":null},{"id":"2210.12704","title":"Batch Multi-Fidelity Active Learning with Budget Constraints","created":"2022-10-23","cited_by_count":20,"area":null},{"id":"2211.08358","title":"MEAL: Stable and Active Learning for Few-Shot Prompting","created":"2022-11-15","cited_by_count":20,"area":null},{"id":"2301.12603","title":"Do We Really Need Graph Neural Networks for Traffic Forecasting?","created":"2023-01-30","cited_by_count":20,"area":null},{"id":"2303.02535","title":"Streaming Active Learning with Deep Neural Networks","created":"2023-03-05","cited_by_count":20,"area":null},{"id":"2306.03208","title":"NLU on Data Diets: Dynamic Data Subset Selection for NLP Classification Tasks","created":"2023-06-05","cited_by_count":20,"area":null},{"id":"2306.09910","title":"LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning","created":"2023-06-16","cited_by_count":20,"area":null},{"id":"2406.10670","title":"CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training","created":"2024-06-15","cited_by_count":20,"area":null},{"id":"2510.19178","title":"Imbalanced Gradients in RL Post-Training of Multi-Task LLMs","created":"2025-10-22","cited_by_count":20,"area":null},{"id":"2107.10314","title":"Small-Text: Active Learning for Text Classification in Python","created":"2021-07-21","cited_by_count":19,"area":null},{"id":"2109.08901","title":"S$^3$VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation","created":"2021-09-18","cited_by_count":19,"area":null},{"id":"2502.11767","title":"From Selection to Generation: A Survey of LLM-based Active Learning","created":"2025-02-17","cited_by_count":19,"area":null},{"id":"2110.11875","title":"GeneDisco: A Benchmark for Experimental Design in Drug Discovery","created":"2021-10-22","cited_by_count":18,"area":null},{"id":"2203.05738","title":"Learning Distinctive Margin toward Active Domain Adaptation","created":"2022-03-11","cited_by_count":18,"area":null},{"id":"2302.07317","title":"Algorithm Selection for Deep Active Learning with Imbalanced Datasets","created":"2023-02-14","cited_by_count":18,"area":null},{"id":"2306.11180","title":"Hyperbolic Active Learning for Semantic Segmentation under Domain Shift","created":"2023-06-19","cited_by_count":18,"area":null},{"id":"2103.03757","title":"Discrepancy-Based Active Learning for Domain Adaptation","created":"2021-03-05","cited_by_count":17,"area":null},{"id":"2207.12112","title":"Active Learning Strategies for Weakly-supervised Object Detection","created":"2022-07-25","cited_by_count":17,"area":null},{"id":"2306.03543","title":"How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget","created":"2023-06-06","cited_by_count":17,"area":null},{"id":"2402.03235","title":"ActiveAnno3D -- An Active Learning Framework for Multi-Modal 3D Object Detection","created":"2024-02-05","cited_by_count":17,"area":null},{"id":"2404.13895","title":"Optimal Design for Human Preference Elicitation","created":"2024-04-22","cited_by_count":17,"area":null},{"id":"2203.10730","title":"Semantic Segmentation with Active Semi-Supervised Learning","created":"2022-03-21","cited_by_count":16,"area":null},{"id":"2205.07708","title":"Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving","created":"2022-05-16","cited_by_count":16,"area":null},{"id":"2209.06995","title":"Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approach","created":"2022-09-15","cited_by_count":16,"area":null},{"id":"2211.13579","title":"Knowledge-Aware Federated Active Learning with Non-IID Data","created":"2022-11-24","cited_by_count":16,"area":null},{"id":"2407.12212","title":"Generalized Coverage for More Robust Low-Budget Active Learning","created":"2024-07-16","cited_by_count":16,"area":null},{"id":"2110.04543","title":"Class-Balanced Active Learning for Image Classification","created":"2021-10-09","cited_by_count":15,"area":null},{"id":"2110.12033","title":"A Simple Baseline for Low-Budget Active Learning","created":"2021-10-22","cited_by_count":15,"area":null},{"id":"2202.05448","title":"Fast Rates in Pool-Based Batch Active Learning","created":"2022-02-11","cited_by_count":15,"area":null},{"id":"2208.03644","title":"Label-Efficient Domain Generalization via Collaborative Exploration and Generalization","created":"2022-08-07","cited_by_count":15,"area":null},{"id":"2412.20644","title":"Uncertainty Herding: One Active Learning Method for All Label Budgets","created":"2024-12-30","cited_by_count":15,"area":null},{"id":"2506.01701","title":"Data Pruning by Information Maximization","created":"2025-06-02","cited_by_count":15,"area":null}],"next_cursor":"100"}