{"total":108,"rows":[{"id":"2107.07075","title":"Deep Learning on a Data Diet: Finding Important Examples Early in Training","created":"2021-07-15","cited_by_count":533,"area":null},{"id":"2109.03764","title":"Active Learning by Acquiring Contrastive Examples","created":"2021-09-08","cited_by_count":156,"area":null},{"id":"2203.13450","title":"A Comparative Survey of Deep Active Learning","created":"2022-03-25","cited_by_count":89,"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":"2306.05175","title":"Large-scale Dataset Pruning with Dynamic Uncertainty","created":"2023-06-08","cited_by_count":63,"area":null},{"id":"2107.02331","title":"Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering","created":"2021-07-06","cited_by_count":62,"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":"2311.00288","title":"Active Instruction Tuning: Improving Cross-Task Generalization by Training on Prompt Sensitive Tasks","created":"2023-11-01","cited_by_count":55,"area":null},{"id":"2410.15326","title":"A Survey of Uncertainty Estimation in LLMs: Theory Meets Practice","created":"2024-10-20","cited_by_count":55,"area":null},{"id":"2305.14264","title":"Active Learning Principles for In-Context Learning with Large Language Models","created":"2023-05-23","cited_by_count":54,"area":null},{"id":"2111.05193","title":"A Survey on Green Deep Learning","created":"2021-11-08","cited_by_count":52,"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":"2101.08133","title":"Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates","created":"2021-01-20","cited_by_count":39,"area":null},{"id":"2106.02968","title":"Low Budget Active Learning via Wasserstein Distance: An Integer Programming Approach","created":"2021-06-05","cited_by_count":34,"area":null},{"id":"2108.05570","title":"LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentation","created":"2021-08-12","cited_by_count":33,"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":"2111.01004","title":"Improving Contrastive Learning on Imbalanced Seed Data via Open-World Sampling","created":"2021-11-01","cited_by_count":30,"area":null},{"id":"2108.00219","title":"Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization","created":"2021-07-31","cited_by_count":28,"area":null},{"id":"2109.04282","title":"Cartography Active Learning","created":"2021-09-09","cited_by_count":26,"area":null},{"id":"2107.07114","title":"Uncertainty-Aware Reliable Text Classification","created":"2021-07-15","cited_by_count":23,"area":null},{"id":"2107.13098","title":"A Tale Of Two Long Tails","created":"2021-07-27","cited_by_count":19,"area":null},{"id":"2208.05379","title":"Multi-task Active Learning for Pre-trained Transformer-based Models","created":"2022-08-10","cited_by_count":16,"area":null},{"id":"2203.09735","title":"PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning","created":"2022-03-18","cited_by_count":14,"area":null},{"id":"2311.09825","title":"Human Still Wins over LLM: An Empirical Study of Active Learning on Domain-Specific Annotation Tasks","created":"2023-11-16","cited_by_count":13,"area":null},{"id":"2205.03598","title":"Towards Computationally Feasible Deep Active Learning","created":"2022-05-07","cited_by_count":11,"area":null},{"id":"2210.03822","title":"Is margin all you need? An extensive empirical study of active learning on tabular data","created":"2022-10-07","cited_by_count":10,"area":null},{"id":"2305.12710","title":"Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture","created":"2023-05-22","cited_by_count":10,"area":null},{"id":"2104.13936","title":"Diversity-Aware Batch Active Learning for Dependency Parsing","created":"2021-04-28","cited_by_count":9,"area":null},{"id":"2112.07522","title":"LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework","created":"2021-12-14","cited_by_count":9,"area":null},{"id":"2305.13342","title":"On the Limitations of Simulating Active Learning","created":"2023-05-21","cited_by_count":9,"area":null},{"id":"2202.08316","title":"FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction","created":"2022-02-16","cited_by_count":8,"area":null},{"id":"2302.12948","title":"Agile Modeling: From Concept to Classifier in Minutes","created":"2023-02-25","cited_by_count":8,"area":null},{"id":"2211.14819","title":"Deep Active Learning for Computer Vision: Past and Future","created":"2022-11-27","cited_by_count":7,"area":null},{"id":"2301.03252","title":"Active Learning for Abstractive Text Summarization","created":"2023-01-09","cited_by_count":7,"area":null},{"id":"2103.06490","title":"Active$^2$ Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation","created":"2021-03-11","cited_by_count":6,"area":null},{"id":"2109.12662","title":"Improving Question Answering Performance Using Knowledge Distillation and Active Learning","created":"2021-09-26","cited_by_count":6,"area":"distill"},{"id":"2110.10906","title":"Single-Modal Entropy based Active Learning for Visual Question Answering","created":"2021-10-21","cited_by_count":6,"area":null},{"id":"2210.14465","title":"Eeny, meeny, miny, moe. How to choose data for morphological inflection","created":"2022-10-26","cited_by_count":6,"area":"moe"},{"id":"2302.06976","title":"Investigating Multi-source Active Learning for Natural Language Inference","created":"2023-02-14","cited_by_count":6,"area":null},{"id":"2109.06321","title":"Mitigating Sampling Bias and Improving Robustness in Active Learning","created":"2021-09-13","cited_by_count":5,"area":null},{"id":"2110.04480","title":"Bayesian Active Summarization","created":"2021-10-09","cited_by_count":5,"area":null},{"id":"2204.00707","title":"Efficient Argument Structure Extraction with Transfer Learning and Active Learning","created":"2022-04-01","cited_by_count":5,"area":null},{"id":"2311.00906","title":"Re-weighting Tokens: A Simple and Effective Active Learning Strategy for Named Entity Recognition","created":"2023-11-02","cited_by_count":5,"area":null},{"id":"2403.01165","title":"STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Models","created":"2024-03-02","cited_by_count":5,"area":null},{"id":"2404.01588","title":"Hallucination Diversity-Aware Active Learning for Text Summarization","created":"2024-04-02","cited_by_count":5,"area":null},{"id":"2104.09576","title":"LaTeX-Numeric: Language-agnostic Text attribute eXtraction for E-commerce Numeric Attributes","created":"2021-04-19","cited_by_count":4,"area":null},{"id":"2304.13032","title":"A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction","created":"2023-04-06","cited_by_count":4,"area":null},{"id":"2204.01016","title":"On Efficiently Acquiring Annotations for Multilingual Models","created":"2022-04-03","cited_by_count":3,"area":null},{"id":"2206.13391","title":"Deep reinforced active learning for multi-class image classification","created":"2022-06-20","cited_by_count":3,"area":null},{"id":"2305.15040","title":"Active Learning for Natural Language Generation","created":"2023-05-24","cited_by_count":3,"area":null},{"id":"2310.17044","title":"Learning to Rank for Active Learning via Multi-Task Bilevel Optimization","created":"2023-10-25","cited_by_count":3,"area":null},{"id":"2312.01835","title":"Few Clicks Suffice: Active Test-Time Adaptation for Semantic Segmentation","created":"2023-12-04","cited_by_count":3,"area":null},{"id":"2403.12166","title":"The Power of Few: Accelerating and Enhancing Data Reweighting with Coreset Selection","created":"2024-03-18","cited_by_count":3,"area":null},{"id":"2406.13274","title":"In-Context Learning on a Budget: A Case Study in Token Classification","created":"2024-06-19","cited_by_count":3,"area":null},{"id":"2407.13989","title":"Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models","created":"2024-07-19","cited_by_count":3,"area":null},{"id":"2503.00867","title":"DUAL: Diversity and Uncertainty Active Learning for Text Summarization","created":"2025-03-02","cited_by_count":3,"area":null},{"id":"2109.06873","title":"Robust Contrastive Active Learning with Feature-guided Query Strategies","created":"2021-09-13","cited_by_count":2,"area":null},{"id":"2112.03073","title":"Active Learning for Event Extraction with Memory-based Loss Prediction Model","created":"2021-11-26","cited_by_count":2,"area":null},{"id":"2112.13737","title":"Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing","created":"2021-12-27","cited_by_count":2,"area":null},{"id":"2201.11885","title":"Boosting Entity Mention Detection for Targetted Twitter Streams with Global Contextual Embeddings","created":"2022-01-28","cited_by_count":2,"area":null},{"id":"2305.12634","title":"Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training","created":"2023-05-22","cited_by_count":2,"area":null},{"id":"2307.14380","title":"Robust Assignment of Labels for Active Learning with Sparse and Noisy Annotations","created":"2023-07-25","cited_by_count":2,"area":null},{"id":"2310.08944","title":"A Confidence-based Acquisition Model for Self-supervised Active Learning and Label Correction","created":"2023-10-13","cited_by_count":2,"area":null},{"id":"2310.10655","title":"Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification","created":"2023-09-05","cited_by_count":2,"area":null},{"id":"2402.11973","title":"Bayesian Active Learning for Censored Regression","created":"2024-02-19","cited_by_count":2,"area":null},{"id":"2407.03895","title":"Scoping Review of Active Learning Strategies and their Evaluation Environments for Entity Recognition Tasks","created":"2024-07-04","cited_by_count":2,"area":null},{"id":"2407.15383","title":"Is user feedback always informative? Retrieval Latent Defending for Semi-Supervised Domain Adaptation without Source Data","created":"2024-07-22","cited_by_count":2,"area":null},{"id":"2409.05199","title":"Interactive Machine Teaching by Labeling Rules and Instances","created":"2024-09-08","cited_by_count":2,"area":null},{"id":"2501.01118","title":"Pruning-based Data Selection and Network Fusion for Efficient Deep Learning","created":"2025-01-02","cited_by_count":2,"area":null},{"id":"2503.09701","title":"Reassessing Active Learning Adoption in Contemporary NLP: A Community Survey","created":"2025-03-12","cited_by_count":2,"area":null},{"id":"2604.12303","title":"Labeled TrustSet Guided: Batch Active Learning with Reinforcement Learning","created":"2026-04-14","cited_by_count":2,"area":"deeprl"},{"id":"2111.03837","title":"Focusing on Potential Named Entities During Active Label Acquisition","created":"2021-11-06","cited_by_count":1,"area":null},{"id":"2212.11109","title":"MAViC: Multimodal Active Learning for Video Captioning","created":"2022-12-11","cited_by_count":1,"area":null},{"id":"2305.02459","title":"Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge","created":"2023-05-03","cited_by_count":1,"area":null},{"id":"2311.06379","title":"DeMuX: Data-efficient Multilingual Learning","created":"2023-11-10","cited_by_count":1,"area":null},{"id":"2311.15946","title":"Leveraging deep active learning to identify low-resource mobility functioning information in public clinical notes","created":"2023-11-27","cited_by_count":1,"area":null},{"id":"2312.05407","title":"ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation","created":"2023-12-08","cited_by_count":1,"area":null},{"id":"2312.09595","title":"Density Matters: Improved Core-set for Active Domain Adaptive Segmentation","created":"2023-12-15","cited_by_count":1,"area":null},{"id":"2403.16463","title":"Few-shot Named Entity Recognition via Superposition Concept Discrimination","created":"2024-03-25","cited_by_count":1,"area":null},{"id":"2505.23451","title":"A Reverse Causal Framework to Mitigate Spurious Correlations for Debiasing Scene Graph Generation","created":"2025-05-29","cited_by_count":1,"area":null},{"id":"2101.08854","title":"Active Hybrid Classification","created":"2021-01-21","cited_by_count":0,"area":null},{"id":"2109.01636","title":"Empirical Study of Named Entity Recognition Performance Using Distribution-aware Word Embedding","created":"2021-09-03","cited_by_count":0,"area":null},{"id":"2109.04698","title":"Face-NMS: A Core-set Selection Approach for Efficient Face Recognition","created":"2021-09-10","cited_by_count":0,"area":null},{"id":"2109.14676","title":"Active Refinement for Multi-Label Learning: A Pseudo-Label Approach","created":"2021-09-29","cited_by_count":0,"area":null},{"id":"2110.02069","title":"OPAD: An Optimized Policy-based Active Learning Framework for Document Content Analysis","created":"2021-10-01","cited_by_count":0,"area":null},{"id":"2111.04224","title":"Automated Detection of GDPR Disclosure Requirements in Privacy Policies using Deep Active Learning","created":"2021-11-08","cited_by_count":0,"area":null},{"id":"2202.04251","title":"Improving greedy core-set configurations for active learning with uncertainty-scaled distances","created":"2022-02-09","cited_by_count":0,"area":null},{"id":"2204.11858","title":"Data Uncertainty without Prediction Models","created":"2022-04-25","cited_by_count":0,"area":null},{"id":"2211.04128","title":"Active Learning with Tabular Language Models","created":"2022-11-08","cited_by_count":0,"area":null},{"id":"2212.06364","title":"ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data","created":"2022-12-13","cited_by_count":0,"area":null},{"id":"2301.00688","title":"Active Learning for Neural Machine Translation","created":"2022-12-30","cited_by_count":0,"area":null},{"id":"2301.09908","title":"Cross-lingual German Biomedical Information Extraction: from Zero-shot to Human-in-the-Loop","created":"2023-01-24","cited_by_count":0,"area":null},{"id":"2305.04049","title":"Actively Discovering New Slots for Task-oriented Conversation","created":"2023-05-06","cited_by_count":0,"area":null},{"id":"2310.14513","title":"Turn-Level Active Learning for Dialogue State Tracking","created":"2023-10-23","cited_by_count":0,"area":null},{"id":"2311.02345","title":"Perturbation-based Active Learning for Question Answering","created":"2023-11-04","cited_by_count":0,"area":null},{"id":"2311.11981","title":"H-COAL: Human Correction of AI-Generated Labels for Biomedical Named Entity Recognition","created":"2023-11-20","cited_by_count":0,"area":null},{"id":"2312.01306","title":"On Significance of Subword tokenization for Low Resource and Efficient Named Entity Recognition: A case study in Marathi","created":"2023-12-03","cited_by_count":0,"area":null},{"id":"2401.08038","title":"Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active Learning","created":"2024-01-16","cited_by_count":0,"area":null},{"id":"2401.15721","title":"A Study of Acquisition Functions for Medical Imaging Deep Active Learning","created":"2024-01-28","cited_by_count":0,"area":null}],"next_cursor":"100"}