{"total":21,"rows":[{"id":"2403.03853","title":"ShortGPT: Layers in Large Language Models are More Redundant Than You Expect","created":"2024-03-06","cited_by_count":383,"area":null},{"id":"2406.10594","title":"BlockPruner: Fine-grained Pruning for Large Language Models","created":"2024-06-15","cited_by_count":36,"area":null},{"id":"2409.12903","title":"Scaling Smart: Accelerating Large Language Model Pre-training with Small Model Initialization","created":"2024-09-19","cited_by_count":29,"area":null},{"id":"2407.16286","title":"A deeper look at depth pruning of LLMs","created":"2024-07-23","cited_by_count":23,"area":null},{"id":"2410.14649","title":"EvoPress: Accurate Dynamic Model Compression via Evolutionary Search","created":"2024-10-18","cited_by_count":20,"area":null},{"id":"2408.07221","title":"A Review of Pseudo-Labeling for Computer Vision","created":"2024-08-13","cited_by_count":16,"area":null},{"id":"2501.17771","title":"2SSP: A Two-Stage Framework for Structured Pruning of LLMs","created":"2025-01-29","cited_by_count":12,"area":null},{"id":"2405.02330","title":"Adaptive Semantic Token Selection for AI-native Goal-oriented Communications","created":"2024-04-25","cited_by_count":10,"area":null},{"id":"2408.08670","title":"Adaptive Layer Selection for Efficient Vision Transformer Fine-Tuning","created":"2024-08-16","cited_by_count":10,"area":null},{"id":"2404.16897","title":"Exploring Learngene via Stage-wise Weight Sharing for Initializing Variable-sized Models","created":"2024-04-25","cited_by_count":9,"area":null},{"id":"2405.18380","title":"Outlier-weighed Layerwise Sampling for LLM Fine-tuning","created":"2024-05-28","cited_by_count":8,"area":null},{"id":"2410.09982","title":"Self-Data Distillation for Recovering Quality in Pruned Large Language Models","created":"2024-10-13","cited_by_count":8,"area":null},{"id":"2505.19985","title":"Structured Initialization for Vision Transformers","created":"2025-05-26","cited_by_count":6,"area":null},{"id":"2502.12325","title":"From Dense to Dynamic: Token-Difficulty Driven MoEfication of Pre-Trained LLMs","created":"2025-02-17","cited_by_count":5,"area":null},{"id":"2404.02684","title":"Cross-Architecture Transfer Learning for Linear-Cost Inference Transformers","created":"2024-04-03","cited_by_count":3,"area":null},{"id":"2505.12216","title":"One-for-All Pruning: A Universal Model for Customized Compression of Large Language Models","created":"2025-05-18","cited_by_count":2,"area":null},{"id":"2602.08858","title":"FlattenGPT: Depth Compression for Transformer with Layer Flattening","created":"2026-02-09","cited_by_count":2,"area":null},{"id":"2608.00418","title":"Making Transformers Logical","created":"2026-08-01","cited_by_count":1,"area":null},{"id":"2412.11494","title":"FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing","created":"2024-12-16","cited_by_count":0,"area":null},{"id":"2506.05641","title":"Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones","created":"2025-06-06","cited_by_count":0,"area":null},{"id":"2608.02829","title":"Wiring Beats Blending: Structure-Aware Compensation for Transformer Downscaling","created":"2026-08-03","cited_by_count":0,"area":null}],"next_cursor":null}