{"total":17,"rows":[{"id":"2308.05737","title":"Follow Anything: Open-set detection, tracking, and following in real-time","created":"2023-08-10","cited_by_count":29,"area":null},{"id":"2209.08554","title":"Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions","created":"2022-09-18","cited_by_count":20,"area":null},{"id":"2303.05151","title":"Provable Data Subset Selection For Efficient Neural Network Training","created":"2023-03-09","cited_by_count":14,"area":null},{"id":"2307.08086","title":"Dataset Distillation Meets Provable Subset Selection","created":"2023-07-16","cited_by_count":7,"area":null},{"id":"2404.13990","title":"QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models -- Extended Version","created":"2024-04-22","cited_by_count":7,"area":null},{"id":"2203.03009","title":"Coresets for Data Discretization and Sine Wave Fitting","created":"2022-03-06","cited_by_count":6,"area":null},{"id":"2504.16277","title":"DataS^3: Dataset Subset Selection for Specialization","created":"2025-04-22","cited_by_count":6,"area":null},{"id":"2209.11064","title":"Deep Learning on Home Drone: Searching for the Optimal Architecture","created":"2022-09-21","cited_by_count":5,"area":null},{"id":"2205.07427","title":"Exploring the Learning Difficulty of Data Theory and Measure","created":"2022-05-16","cited_by_count":4,"area":null},{"id":"2305.14113","title":"On the Size and Approximation Error of Distilled Sets","created":"2023-05-23","cited_by_count":3,"area":null},{"id":"2504.14572","title":"Data Selection for ERMs","created":"2025-04-20","cited_by_count":3,"area":null},{"id":"2507.15381","title":"To Label or Not to Label: PALM -- A Predictive Model for Evaluating Sample Efficiency in Active Learning Models","created":"2025-07-21","cited_by_count":2,"area":null},{"id":"2605.30337","title":"Efficient Test-Time Finetuning of LLMs via Convex Reconstruction and Gradient Caching","created":"2026-05-28","cited_by_count":2,"area":null},{"id":"2607.02721","title":"Provable Pruning for Efficient 3D Gaussian Splatting via Coresets","created":"2026-07-02","cited_by_count":2,"area":null},{"id":"2510.20800","title":"Compress to Impress: Efficient LLM Adaptation Using a Single Gradient Step on 100 Samples","created":"2025-10-23","cited_by_count":1,"area":null},{"id":"2607.00144","title":"A Mechanism-Driven Theory of Phase Transitions in Active Learning","created":"2026-06-30","cited_by_count":1,"area":null},{"id":"2606.02142","title":"TimeBlocks: Foundational and Continual Time-Series Blockbase -- Extended Version","created":"2026-06-01","cited_by_count":0,"area":null}],"next_cursor":null}