{"total":22,"rows":[{"id":"2204.09656","title":"A Fast Post-Training Pruning Framework for Transformers","created":"2022-03-29","cited_by_count":162,"area":null},{"id":"2303.00566","title":"Structured Pruning for Deep Convolutional Neural Networks: A survey","created":"2023-03-01","cited_by_count":122,"area":null},{"id":"2310.02556","title":"NOLA: Compressing LoRA using Linear Combination of Random Basis","created":"2023-10-04","cited_by_count":29,"area":null},{"id":"2308.07209","title":"Unified Data-Free Compression: Pruning and Quantization without Fine-Tuning","created":"2023-08-14","cited_by_count":23,"area":null},{"id":"2105.14797","title":"RED : Looking for Redundancies for Data-Free Structured Compression of Deep Neural Networks","created":"2021-05-31","cited_by_count":19,"area":null},{"id":"2110.01397","title":"RED++ : Data-Free Pruning of Deep Neural Networks via Input Splitting and Output Merging","created":"2021-09-30","cited_by_count":19,"area":null},{"id":"2308.03449","title":"Accurate Retraining-free Pruning for Pretrained Encoder-based Language Models","created":"2023-08-07","cited_by_count":11,"area":null},{"id":"2307.00498","title":"Data-Free Quantization via Mixed-Precision Compensation without Fine-Tuning","created":"2023-07-02","cited_by_count":9,"area":null},{"id":"2111.09635","title":"Automatic Neural Network Pruning that Efficiently Preserves the Model Accuracy","created":"2021-11-18","cited_by_count":4,"area":null},{"id":"2406.19301","title":"MCNC: Manifold-Constrained Reparameterization for Neural Compression","created":"2024-06-27","cited_by_count":4,"area":null},{"id":"2409.03945","title":"TropNNC: Structured Neural Network Compression Using Tropical Geometry","created":"2024-09-05","cited_by_count":3,"area":null},{"id":"2107.02927","title":"Image Complexity Guided Network Compression for Biomedical Image Segmentation","created":"2021-07-06","cited_by_count":2,"area":null},{"id":"2406.00061","title":"STAT: Shrinking Transformers After Training","created":"2024-05-29","cited_by_count":2,"area":null},{"id":"2502.08474","title":"Training-Free Restoration of Pruned Neural Networks","created":"2025-02-06","cited_by_count":2,"area":null},{"id":"2206.08464","title":"PRANC: Pseudo RAndom Networks for Compacting deep models","created":"2022-06-16","cited_by_count":1,"area":null},{"id":"2205.08199","title":"Sharp asymptotics on the compression of two-layer neural networks","created":"2022-05-17","cited_by_count":0,"area":null},{"id":"2205.08695","title":"Hyperparameter Optimization with Neural Network Pruning","created":"2022-05-18","cited_by_count":0,"area":null},{"id":"2403.02473","title":"When do Convolutional Neural Networks Stop Learning?","created":"2024-03-04","cited_by_count":0,"area":null},{"id":"2405.17506","title":"Subspace Node Pruning","created":"2024-05-26","cited_by_count":0,"area":null},{"id":"2412.07114","title":"TT-MPD: Test Time Model Pruning and Distillation","created":"2024-12-10","cited_by_count":0,"area":null},{"id":"2504.03118","title":"NuWa: Deriving Lightweight Class-Specific Vision Transformers for Edge Devices","created":"2025-04-04","cited_by_count":0,"area":null},{"id":"2606.14346","title":"Squeeze-Release: Iterative Pruning with Exact Structural Minimization","created":"2026-06-12","cited_by_count":0,"area":null}],"next_cursor":null}