{"total":87,"rows":[{"id":"2305.11627","title":"LLM-Pruner: On the Structural Pruning of Large Language Models","created":"2023-05-19","cited_by_count":650,"area":null},{"id":"2208.11580","title":"Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning","created":"2022-08-24","cited_by_count":322,"area":null},{"id":"2301.12900","title":"DepGraph: Towards Any Structural Pruning","created":"2023-01-30","cited_by_count":203,"area":null},{"id":"2308.06767","title":"A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations","created":"2023-08-13","cited_by_count":202,"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":"2104.03438","title":"Convolutional Neural Network Pruning with Structural Redundancy Reduction","created":"2021-04-08","cited_by_count":73,"area":null},{"id":"2107.03356","title":"M-FAC: Efficient Matrix-Free Approximations of Second-Order Information","created":"2021-07-07","cited_by_count":54,"area":null},{"id":"2111.13445","title":"How Well Do Sparse Imagenet Models Transfer?","created":"2021-11-26","cited_by_count":50,"area":null},{"id":"2408.09632","title":"MoDeGPT: Modular Decomposition for Large Language Model Compression","created":"2024-08-19","cited_by_count":48,"area":null},{"id":"2101.08940","title":"Hessian-Aware Pruning and Optimal Neural Implant","created":"2021-01-22","cited_by_count":45,"area":null},{"id":"2103.06460","title":"Recent Advances on Neural Network Pruning at Initialization","created":"2021-03-11","cited_by_count":41,"area":null},{"id":"2203.04466","title":"The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks","created":"2022-03-09","cited_by_count":41,"area":null},{"id":"2312.17244","title":"The LLM Surgeon","created":"2023-12-28","cited_by_count":38,"area":null},{"id":"2202.08132","title":"Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients","created":"2022-02-16","cited_by_count":34,"area":null},{"id":"2110.13041","title":"Applications and Techniques for Fast Machine Learning in Science","created":"2021-10-25","cited_by_count":32,"area":null},{"id":"2505.07004","title":"GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance","created":"2025-05-11","cited_by_count":32,"area":null},{"id":"2210.13738","title":"Pruning's Effect on Generalization Through the Lens of Training and Regularization","created":"2022-10-25","cited_by_count":31,"area":null},{"id":"2110.11395","title":"SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning","created":"2021-10-19","cited_by_count":30,"area":null},{"id":"2203.04248","title":"Dual Lottery Ticket Hypothesis","created":"2022-03-08","cited_by_count":27,"area":null},{"id":"2303.02141","title":"Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!","created":"2023-03-03","cited_by_count":27,"area":null},{"id":"2306.01334","title":"Federated Domain Generalization: A Survey","created":"2023-06-02","cited_by_count":25,"area":null},{"id":"2204.02738","title":"Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network","created":"2022-04-06","cited_by_count":21,"area":null},{"id":"2206.10451","title":"Winning the Lottery Ahead of Time: Efficient Early Network Pruning","created":"2022-06-21","cited_by_count":21,"area":null},{"id":"2210.09223","title":"CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models","created":"2022-10-14","cited_by_count":20,"area":null},{"id":"2102.07804","title":"Scaling Up Exact Neural Network Compression by ReLU Stability","created":"2021-02-15","cited_by_count":19,"area":null},{"id":"2301.05219","title":"Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Pruning","created":"2023-01-12","cited_by_count":16,"area":null},{"id":"2303.08595","title":"Automatic Attention Pruning: Improving and Automating Model Pruning using Attentions","created":"2023-03-14","cited_by_count":11,"area":null},{"id":"2411.18376","title":"Preserving Deep Representations In One-Shot Pruning: A Hessian-Free Second-Order Optimization Framework","created":"2024-11-27","cited_by_count":11,"area":null},{"id":"2406.01820","title":"Finding Lottery Tickets in Vision Models via Data-driven Spectral Foresight Pruning","created":"2024-06-03","cited_by_count":10,"area":null},{"id":"2107.14444","title":"Manipulating Identical Filter Redundancy for Efficient Pruning on Deep and Complicated CNN","created":"2021-07-30","cited_by_count":9,"area":null},{"id":"2206.02976","title":"Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm","created":"2022-06-07","cited_by_count":9,"area":null},{"id":"2305.14765","title":"Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference","created":"2023-05-24","cited_by_count":9,"area":null},{"id":"2312.05705","title":"Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC","created":"2023-12-09","cited_by_count":9,"area":null},{"id":"2504.19274","title":"TeleSparse: Practical Privacy-Preserving Verification of Deep Neural Networks","created":"2025-04-27","cited_by_count":9,"area":null},{"id":"2301.07966","title":"Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions","created":"2023-01-19","cited_by_count":8,"area":null},{"id":"2402.07839","title":"Towards Meta-Pruning via Optimal Transport","created":"2024-02-12","cited_by_count":8,"area":null},{"id":"2503.16709","title":"QuartDepth: Post-Training Quantization for Real-Time Depth Estimation on the Edge","created":"2025-03-20","cited_by_count":8,"area":null},{"id":"2402.02855","title":"Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation","created":"2024-02-05","cited_by_count":7,"area":null},{"id":"2412.00143","title":"Is Oracle Pruning the True Oracle?","created":"2024-11-28","cited_by_count":7,"area":null},{"id":"2507.01649","title":"GradMetaNet: An Equivariant Architecture for Learning on Gradients","created":"2025-07-02","cited_by_count":7,"area":null},{"id":"2306.06098","title":"Error Feedback Can Accurately Compress Preconditioners","created":"2023-06-09","cited_by_count":6,"area":null},{"id":"2602.04852","title":"On State Reduction in Linear Attention","created":"2026-02-04","cited_by_count":6,"area":null},{"id":"2411.00147","title":"Mutual Information Preserving Neural Network Pruning","created":"2024-10-31","cited_by_count":5,"area":null},{"id":"2504.21174","title":"Efficient LLMs with AMP: Attention Heads and MLP Pruning","created":"2025-04-29","cited_by_count":5,"area":null},{"id":"2304.13718","title":"Sparsified Model Zoo Twins: Investigating Populations of Sparsified Neural Network Models","created":"2023-04-26","cited_by_count":4,"area":null},{"id":"2412.02328","title":"Efficient Model Compression Techniques with FishLeg","created":"2024-12-03","cited_by_count":4,"area":null},{"id":"2510.17700","title":"Elastic ViTs from Pretrained Models without Retraining","created":"2025-10-20","cited_by_count":4,"area":null},{"id":"2201.10520","title":"Adaptive Activation-based Structured Pruning","created":"2022-01-21","cited_by_count":3,"area":null},{"id":"2210.16169","title":"LOFT: Finding Lottery Tickets through Filter-wise Training","created":"2022-10-28","cited_by_count":3,"area":null},{"id":"2306.07030","title":"Resource Efficient Neural Networks Using Hessian Based Pruning","created":"2023-06-12","cited_by_count":3,"area":null},{"id":"2502.17941","title":"Optimal Brain Apoptosis","created":"2025-02-25","cited_by_count":3,"area":null},{"id":"2505.18350","title":"How Many Parameters Does Your Task Really Need? Task Specific Pruning with LLM-Sieve","created":"2025-05-23","cited_by_count":3,"area":null},{"id":"2506.00495","title":"FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts","created":"2025-05-31","cited_by_count":3,"area":null},{"id":"2602.17385","title":"Dataless Weight Disentanglement in Task Arithmetic via Kronecker-Factored Approximate Curvature","created":"2026-02-19","cited_by_count":3,"area":null},{"id":"2105.05916","title":"Dynamical Isometry: The Missing Ingredient for Neural Network Pruning","created":"2021-05-12","cited_by_count":2,"area":null},{"id":"2110.03298","title":"End-to-End Supermask Pruning: Learning to Prune Image Captioning Models","created":"2021-10-07","cited_by_count":2,"area":null},{"id":"2212.00291","title":"The Effect of Data Dimensionality on Neural Network Prunability","created":"2022-12-01","cited_by_count":2,"area":null},{"id":"2410.19185","title":"Tailored-LLaMA: Optimizing Few-Shot Learning in Pruned LLaMA Models with Task-Specific Prompts","created":"2024-10-24","cited_by_count":2,"area":null},{"id":"2411.11079","title":"Electrostatic Force Regularization for Neural Structured Pruning","created":"2024-11-17","cited_by_count":2,"area":null},{"id":"2506.12041","title":"Meta Pruning via Graph Metanetworks : A Universal Meta Learning Framework for Network Pruning","created":"2025-05-24","cited_by_count":2,"area":null},{"id":"2204.01385","title":"Aligned Weight Regularizers for Pruning Pretrained Neural Networks","created":"2022-04-04","cited_by_count":1,"area":null},{"id":"2206.00853","title":"Masked Bayesian Neural Networks : Computation and Optimality","created":"2022-06-02","cited_by_count":1,"area":null},{"id":"2406.03879","title":"Decay Pruning Method: Smooth Pruning With a Self-Rectifying Procedure","created":"2024-06-06","cited_by_count":1,"area":null},{"id":"2409.09196","title":"Are Sparse Neural Networks Better Hard Sample Learners?","created":"2024-09-13","cited_by_count":1,"area":null},{"id":"2501.13439","title":"One-Cycle Structured Pruning via Stability-Driven Subnetwork Search","created":"2025-01-23","cited_by_count":1,"area":null},{"id":"2508.00305","title":"Systematic Evaluation of Optimization Techniques for Long-Context Language Models","created":"2025-08-01","cited_by_count":1,"area":null},{"id":"2510.13832","title":"Entropy Meets Importance: A Unified Head Importance-Entropy Score for Stable and Efficient Transformer Pruning","created":"2025-10-10","cited_by_count":1,"area":null},{"id":"2603.29108","title":"Efficient Bilevel Optimization with KFAC-Based Hypergradients","created":"2026-03-31","cited_by_count":1,"area":null},{"id":"2606.30226","title":"Characterizing Optimizer-Dependent Training Dynamics Through Hessian Eigenvector Displacement and Localization","created":"2026-06-29","cited_by_count":1,"area":null},{"id":"2205.08695","title":"Hyperparameter Optimization with Neural Network Pruning","created":"2022-05-18","cited_by_count":0,"area":null},{"id":"2405.10802","title":"Reduced storage direct tensor ring decomposition for convolutional neural networks compression","created":"2024-05-17","cited_by_count":0,"area":null},{"id":"2406.06773","title":"Evaluating Zero-Shot Long-Context LLM Compression","created":"2024-06-10","cited_by_count":0,"area":null},{"id":"2406.12315","title":"A Comprehensive Study of Structural Pruning for Vision Models","created":"2024-06-18","cited_by_count":0,"area":null},{"id":"2408.14757","title":"Learning effective pruning at initialization from iterative pruning","created":"2024-08-27","cited_by_count":0,"area":null},{"id":"2409.03777","title":"A Greedy Hierarchical Approach to Whole-Network Filter-Pruning in CNNs","created":"2024-08-22","cited_by_count":0,"area":null},{"id":"2411.16901","title":"Deep Convolutional Neural Networks Structured Pruning via Gravity Regularization","created":"2024-11-25","cited_by_count":0,"area":null},{"id":"2506.22015","title":"Towards Universal & Efficient Model Compression via Exponential Torque Pruning","created":"2025-06-27","cited_by_count":0,"area":null},{"id":"2507.14170","title":"Catalyst: a Novel Regularizer for Structured Pruning with Auxiliary Extension of Parameter Space","created":"2025-07-10","cited_by_count":0,"area":null},{"id":"2507.14171","title":"IPPRO: Importance-based Pruning with PRojective Offset for Magnitude-indifferent Structural Pruning","created":"2025-07-10","cited_by_count":0,"area":null},{"id":"2508.02291","title":"FAIR-Pruner: A Flexible Framework for Automatic Layer-Wise Pruning via Tolerance of Difference","created":"2025-08-04","cited_by_count":0,"area":null},{"id":"2508.18192","title":"Unraveling the cognitive patterns of Large Language Models through module communities","created":"2025-08-25","cited_by_count":0,"area":null},{"id":"2510.08999","title":"SQS: Bayesian DNN Compression through Sparse Quantized Sub-distributions","created":"2025-10-10","cited_by_count":0,"area":null},{"id":"2511.06265","title":"CAMP-HiVe: Cyclic Pair Merging based Efficient DNN Pruning with Hessian-Vector Approximation for Resource-Constrained Systems","created":"2025-11-09","cited_by_count":0,"area":null},{"id":"2512.15469","title":"Metanetworks as Regulatory Operators: Learning to Edit for Requirement Compliance","created":"2025-12-17","cited_by_count":0,"area":null},{"id":"2606.00442","title":"Exploiting weight-space symmetries for approximating curvature","created":"2026-05-30","cited_by_count":0,"area":null},{"id":"2608.15901","title":"Layers Matter: Why Continual Learning Regularization Should Be Layer-Adaptive","created":"2026-08-16","cited_by_count":0,"area":null},{"id":"2609.33424","title":"A Light Bilevel Refinement Aligns Self-Supervised Representations for Stronger Task-Specific Learning","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}