{"total":25,"rows":[{"id":"2201.08531","title":"Black-box Prompt Learning for Pre-trained Language Models","created":"2022-01-21","cited_by_count":70,"area":null},{"id":"2301.09819","title":"Model Agnostic Sample Reweighting for Out-of-Distribution Learning","created":"2023-01-24","cited_by_count":44,"area":null},{"id":"2301.09880","title":"Probabilistic Bilevel Coreset Selection","created":"2023-01-24","cited_by_count":27,"area":null},{"id":"2210.02412","title":"Why Random Pruning Is All We Need to Start Sparse","created":"2022-10-05","cited_by_count":26,"area":null},{"id":"2311.17431","title":"Grounding Foundation Models through Federated Transfer Learning: A General Framework","created":"2023-11-29","cited_by_count":24,"area":null},{"id":"2408.09966","title":"Mask in the Mirror: Implicit Sparsification","created":"2024-08-19","cited_by_count":14,"area":null},{"id":"2302.04852","title":"SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks","created":"2023-02-09","cited_by_count":13,"area":null},{"id":"2205.08099","title":"Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Survey","created":"2022-05-17","cited_by_count":12,"area":null},{"id":"2401.10341","title":"ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks","created":"2024-01-18","cited_by_count":11,"area":null},{"id":"2406.00431","title":"SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational Overhead","created":"2024-06-01","cited_by_count":9,"area":null},{"id":"2506.16500","title":"SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity","created":"2025-06-19","cited_by_count":9,"area":null},{"id":"2304.05511","title":"Training Large Language Models Efficiently with Sparsity and Dataflow","created":"2023-04-11","cited_by_count":7,"area":null},{"id":"2402.15978","title":"Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood","created":"2024-02-25","cited_by_count":7,"area":null},{"id":"2301.03573","title":"Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction","created":"2023-01-09","cited_by_count":5,"area":null},{"id":"2211.05554","title":"Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization","created":"2022-11-10","cited_by_count":3,"area":null},{"id":"2407.15464","title":"The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning","created":"2024-07-22","cited_by_count":3,"area":null},{"id":"2202.06488","title":"Finding Dynamics Preserving Adversarial Winning Tickets","created":"2022-02-14","cited_by_count":1,"area":null},{"id":"2210.14012","title":"Gradient-based Weight Density Balancing for Robust Dynamic Sparse Training","created":"2022-10-25","cited_by_count":1,"area":null},{"id":"2211.04655","title":"Perturbed Iterate SGD for Lipschitz Continuous Loss Functions with Numerical Error and Adaptive Step Sizes","created":"2022-11-09","cited_by_count":1,"area":null},{"id":"2402.11729","title":"Prospector Heads: Generalized Feature Attribution for Large Models & Data","created":"2024-02-18","cited_by_count":1,"area":null},{"id":"2502.03658","title":"Advancing Weight and Channel Sparsification with Enhanced Saliency","created":"2025-02-05","cited_by_count":1,"area":null},{"id":"2505.18107","title":"Accelerating Learned Image Compression Through Modeling Neural Training Dynamics","created":"2025-05-23","cited_by_count":1,"area":null},{"id":"2408.16289","title":"Convolutional Neural Network Compression Based on Low-Rank Decomposition","created":"2024-08-29","cited_by_count":0,"area":null},{"id":"2501.05633","title":"Regularized Top-$k$: A Bayesian Framework for Gradient Sparsification","created":"2025-01-10","cited_by_count":0,"area":null},{"id":"2507.22527","title":"FGFP: A Fractional Gaussian Filter and Pruning for Deep Neural Networks Compression","created":"2025-07-30","cited_by_count":0,"area":null}],"next_cursor":null}