{"total":27,"rows":[{"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":"2205.09588","title":"How catastrophic can catastrophic forgetting be in linear regression?","created":"2022-05-19","cited_by_count":81,"area":null},{"id":"2310.14664","title":"Data Pruning via Moving-one-Sample-out","created":"2023-10-23","cited_by_count":67,"area":null},{"id":"2402.05011","title":"Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching","created":"2024-02-07","cited_by_count":28,"area":null},{"id":"2206.07258","title":"CLNode: Curriculum Learning for Node Classification","created":"2022-06-15","cited_by_count":24,"area":null},{"id":"2103.00429","title":"Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models","created":"2021-02-28","cited_by_count":22,"area":null},{"id":"2306.16921","title":"Provable Advantage of Curriculum Learning on Parity Targets with Mixed Inputs","created":"2023-06-29","cited_by_count":22,"area":null},{"id":"2304.12877","title":"Proximal Curriculum for Reinforcement Learning Agents","created":"2023-04-25","cited_by_count":20,"area":null},{"id":"2301.13833","title":"A Mathematical Model for Curriculum Learning for Parities","created":"2023-01-31","cited_by_count":19,"area":null},{"id":"2310.09518","title":"Instruction Tuning with Human Curriculum","created":"2023-10-14","cited_by_count":15,"area":null},{"id":"2409.18061","title":"Optimal Protocols for Continual Learning via Statistical Physics and Control Theory","created":"2024-09-26","cited_by_count":14,"area":null},{"id":"2105.05553","title":"Principal Components Bias in Over-parameterized Linear Models, and its Manifestation in Deep Neural Networks","created":"2021-05-12","cited_by_count":10,"area":null},{"id":"2111.07126","title":"On the Statistical Benefits of Curriculum Learning","created":"2021-11-13","cited_by_count":10,"area":null},{"id":"2406.01589","title":"Tilting the Odds at the Lottery: the Interplay of Overparameterisation and Curricula in Neural Networks","created":"2024-06-03","cited_by_count":9,"area":null},{"id":"2208.02301","title":"HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding","created":"2022-08-03","cited_by_count":6,"area":null},{"id":"2309.14091","title":"On the Benefit of Optimal Transport for Curriculum Reinforcement Learning","created":"2023-09-25","cited_by_count":6,"area":null},{"id":"2210.00583","title":"The Dynamic of Consensus in Deep Networks and the Identification of Noisy Labels","created":"2022-10-02","cited_by_count":3,"area":null},{"id":"2507.07907","title":"A statistical physics framework for optimal learning","created":"2025-07-10","cited_by_count":3,"area":null},{"id":"2108.11785","title":"A Hierarchical Assessment of Adversarial Severity","created":"2021-08-26","cited_by_count":1,"area":null},{"id":"2402.13366","title":"Statistical curriculum learning: An elimination algorithm achieving an oracle risk","created":"2024-02-20","cited_by_count":1,"area":null},{"id":"2405.03228","title":"TED: Accelerate Model Training by Internal Generalization","created":"2024-05-06","cited_by_count":1,"area":null},{"id":"2411.17567","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","created":"2024-11-26","cited_by_count":1,"area":null},{"id":"2507.12750","title":"Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning","created":"2025-07-17","cited_by_count":1,"area":null},{"id":"2211.14395","title":"Deep Learning Training Procedure Augmentations","created":"2022-11-25","cited_by_count":0,"area":null},{"id":"2302.02119","title":"Diversity Induced Environment Design via Self-Play","created":"2023-02-04","cited_by_count":0,"area":null},{"id":"2408.14840","title":"CL4KGE: A Curriculum Learning Method for Knowledge Graph Embedding","created":"2024-08-27","cited_by_count":0,"area":null},{"id":"2510.03431","title":"Application of a Virtual Imaging Framework for Investigating a Deep Learning-Based Reconstruction Method for 3D Quantitative Photoacoustic Computed Tomography","created":"2025-10-03","cited_by_count":0,"area":null}],"next_cursor":null}