{"total":40,"rows":[{"id":"2206.14486","title":"Beyond neural scaling laws: beating power law scaling via data pruning","created":"2022-06-29","cited_by_count":528,"area":null},{"id":"2308.12284","title":"D4: Improving LLM Pretraining via Document De-Duplication and Diversification","created":"2023-08-23","cited_by_count":150,"area":null},{"id":"2207.13887","title":"Adaptive Second Order Coresets for Data-efficient Machine Learning","created":"2022-07-28","cited_by_count":60,"area":null},{"id":"2306.01244","title":"Towards Sustainable Learning: Coresets for Data-efficient Deep Learning","created":"2023-06-02","cited_by_count":44,"area":null},{"id":"2210.06640","title":"Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities","created":"2022-10-13","cited_by_count":38,"area":null},{"id":"2505.17799","title":"A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advances","created":"2025-05-23","cited_by_count":28,"area":null},{"id":"2201.07459","title":"PT4AL: Using Self-Supervised Pretext Tasks for Active Learning","created":"2022-01-19","cited_by_count":27,"area":null},{"id":"2107.03176","title":"On Training Instance Selection for Few-Shot Neural Text Generation","created":"2021-07-07","cited_by_count":26,"area":null},{"id":"2401.01867","title":"Dataset Difficulty and the Role of Inductive Bias","created":"2024-01-03","cited_by_count":12,"area":null},{"id":"2309.17342","title":"Towards Free Data Selection with General-Purpose Models","created":"2023-09-29","cited_by_count":10,"area":null},{"id":"2301.13287","title":"MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning","created":"2023-01-30","cited_by_count":9,"area":null},{"id":"2312.10602","title":"A Weighted K-Center Algorithm for Data Subset Selection","created":"2023-12-17","cited_by_count":9,"area":null},{"id":"2203.09091","title":"deepNIR: Datasets for generating synthetic NIR images and improved fruit detection system using deep learning techniques","created":"2022-03-17","cited_by_count":7,"area":null},{"id":"2207.00694","title":"Efficient Adversarial Training With Data Pruning","created":"2022-07-01","cited_by_count":7,"area":null},{"id":"2504.06962","title":"Efficient Self-Supervised Learning for Earth Observation via Dynamic Dataset Curation","created":"2025-04-09","cited_by_count":7,"area":null},{"id":"2310.16499","title":"Data Optimization in Deep Learning: A Survey","created":"2023-10-25","cited_by_count":6,"area":null},{"id":"2310.16776","title":"DEFT: Data Efficient Fine-Tuning for Pre-Trained Language Models via Unsupervised Core-Set Selection","created":"2023-10-25","cited_by_count":6,"area":null},{"id":"2104.12835","title":"Less is more: Selecting informative and diverse subsets with balancing constraints","created":"2021-04-26","cited_by_count":5,"area":null},{"id":"2210.11717","title":"A Survey of Dataset Refinement for Problems in Computer Vision Datasets","created":"2022-10-21","cited_by_count":5,"area":null},{"id":"2411.10685","title":"From Prototypes to General Distributions: An Efficient Curriculum for Masked Image Modeling","created":"2024-11-16","cited_by_count":4,"area":null},{"id":"2106.04525","title":"A critical look at the current train/test split in machine learning","created":"2021-06-08","cited_by_count":3,"area":null},{"id":"2306.14113","title":"Exploring Data Redundancy in Real-world Image Classification through Data Selection","created":"2023-06-25","cited_by_count":3,"area":null},{"id":"2408.03340","title":"An Empirical Comparison of Video Frame Sampling Methods for Multi-Modal RAG Retrieval","created":"2024-07-22","cited_by_count":3,"area":null},{"id":"2507.04529","title":"A Data-Driven Novelty Score for Diverse In-Vehicle Data Recording","created":"2025-07-06","cited_by_count":3,"area":null},{"id":"2108.13055","title":"Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions","created":"2021-08-30","cited_by_count":2,"area":null},{"id":"2202.02661","title":"LiDAR dataset distillation within bayesian active learning framework: Understanding the effect of data augmentation","created":"2022-02-06","cited_by_count":2,"area":null},{"id":"2207.14386","title":"Efficient NLP Model Finetuning via Multistage Data Filtering","created":"2022-07-28","cited_by_count":2,"area":null},{"id":"2305.06677","title":"INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Language Models","created":"2023-05-11","cited_by_count":2,"area":null},{"id":"2308.08224","title":"How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning","created":"2023-08-16","cited_by_count":2,"area":null},{"id":"2605.15675","title":"Interaction-Aware Influence Functions for Group Attribution","created":"2026-05-15","cited_by_count":2,"area":null},{"id":"2404.04887","title":"A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images","created":"2024-04-07","cited_by_count":1,"area":null},{"id":"2112.12021","title":"Community Detection in Medical Image Datasets: Using Wavelets and Spectral Methods","created":"2021-12-22","cited_by_count":0,"area":null},{"id":"2201.10448","title":"How Low Can We Go? Pixel Annotation for Semantic Segmentation","created":"2022-01-25","cited_by_count":0,"area":null},{"id":"2302.10679","title":"Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset","created":"2023-02-21","cited_by_count":0,"area":null},{"id":"2309.09773","title":"Semantically Redundant Training Data Removal and Deep Model Classification Performance: A Study with Chest X-rays","created":"2023-09-18","cited_by_count":0,"area":null},{"id":"2311.07864","title":"Probing clustering in neural network representations","created":"2023-11-14","cited_by_count":0,"area":null},{"id":"2402.18742","title":"Comparing Importance Sampling Based Methods for Mitigating the Effect of Class Imbalance","created":"2024-02-28","cited_by_count":0,"area":null},{"id":"2408.16204","title":"Revisit Micro-batch Clipping: Adaptive Data Pruning via Gradient Manipulation","created":"2024-08-29","cited_by_count":0,"area":null},{"id":"2601.10067","title":"Efficient Content-based Recommendation Model Training via Noise-aware Coreset Selection","created":"2026-01-15","cited_by_count":0,"area":null},{"id":"2603.29474","title":"From Big Data to Fast Data: Towards High-Quality Datasets for Machine Learning Applications from Closed-Loop Data Collection","created":"2026-03-31","cited_by_count":0,"area":null}],"next_cursor":null}