{"total":146,"rows":[{"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":"2310.05175","title":"Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity","created":"2023-10-08","cited_by_count":165,"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":"2110.13981","title":"CHIP: CHannel Independence-based Pruning for Compact Neural Networks","created":"2021-10-26","cited_by_count":78,"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":"2205.05676","title":"Revisiting Random Channel Pruning for Neural Network Compression","created":"2022-05-11","cited_by_count":50,"area":null},{"id":"2101.08940","title":"Hessian-Aware Pruning and Optimal Neural Implant","created":"2021-01-22","cited_by_count":45,"area":null},{"id":"2103.05861","title":"Manifold Regularized Dynamic Network Pruning","created":"2021-03-10","cited_by_count":43,"area":null},{"id":"2205.13571","title":"Low-rank lottery tickets: finding efficient low-rank neural networks via matrix differential equations","created":"2022-05-26","cited_by_count":43,"area":null},{"id":"2105.11228","title":"Towards Compact CNNs via Collaborative Compression","created":"2021-05-24","cited_by_count":36,"area":null},{"id":"2203.15794","title":"CHEX: CHannel EXploration for CNN Model Compression","created":"2022-03-29","cited_by_count":36,"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":"2106.06091","title":"DECORE: Deep Compression with Reinforcement Learning","created":"2021-06-11","cited_by_count":29,"area":null},{"id":"2102.05258","title":"Locally Free Weight Sharing for Network Width Search","created":"2021-02-10","cited_by_count":27,"area":null},{"id":"2111.12485","title":"Understanding the Dynamics of DNNs Using Graph Modularity","created":"2021-11-24","cited_by_count":26,"area":null},{"id":"2102.08329","title":"An Information-Theoretic Justification for Model Pruning","created":"2021-02-16","cited_by_count":25,"area":null},{"id":"2105.03193","title":"Network Pruning That Matters: A Case Study on Retraining Variants","created":"2021-05-07","cited_by_count":25,"area":null},{"id":"2410.14720","title":"SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep Models","created":"2024-10-14","cited_by_count":25,"area":null},{"id":"2104.11883","title":"Carrying out CNN Channel Pruning in a White Box","created":"2021-04-24","cited_by_count":22,"area":null},{"id":"2203.15331","title":"CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters","created":"2022-03-29","cited_by_count":20,"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":"2108.08532","title":"An Information Theory-inspired Strategy for Automatic Network Pruning","created":"2021-08-19","cited_by_count":17,"area":null},{"id":"2101.07985","title":"Network Pruning using Adaptive Exemplar Filters","created":"2021-01-20","cited_by_count":16,"area":null},{"id":"2202.07190","title":"Pruning Networks with Cross-Layer Ranking & k-Reciprocal Nearest Filters","created":"2022-02-15","cited_by_count":16,"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":"2201.03297","title":"GhostNets on Heterogeneous Devices via Cheap Operations","created":"2022-01-10","cited_by_count":15,"area":null},{"id":"2304.14271","title":"A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services","created":"2023-04-13","cited_by_count":14,"area":null},{"id":"2502.16638","title":"Automatic Joint Structured Pruning and Quantization for Efficient Neural Network Training and Compression","created":"2025-02-23","cited_by_count":14,"area":null},{"id":"2201.00043","title":"Multi-Dimensional Model Compression of Vision Transformer","created":"2021-12-31","cited_by_count":13,"area":null},{"id":"2104.11629","title":"DeepSpectrumLite: A Power-Efficient Transfer Learning Framework for Embedded Speech and Audio Processing from Decentralised Data","created":"2021-04-23","cited_by_count":12,"area":null},{"id":"2204.12266","title":"Attentive Fine-Grained Structured Sparsity for Image Restoration","created":"2022-04-26","cited_by_count":12,"area":null},{"id":"2209.01542","title":"Recurrent Bilinear Optimization for Binary Neural Networks","created":"2022-09-04","cited_by_count":12,"area":null},{"id":"2203.15751","title":"A Passive Similarity based CNN Filter Pruning for Efficient Acoustic Scene Classification","created":"2022-03-29","cited_by_count":11,"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":"2401.10341","title":"ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks","created":"2024-01-18","cited_by_count":11,"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":"2303.11923","title":"Performance-aware Approximation of Global Channel Pruning for Multitask CNNs","created":"2023-03-21","cited_by_count":9,"area":null},{"id":"2307.10981","title":"PATROL: Privacy-Oriented Pruning for Collaborative Inference Against Model Inversion Attacks","created":"2023-07-20","cited_by_count":9,"area":null},{"id":"2403.18708","title":"Dense Vision Transformer Compression with Few Samples","created":"2024-03-27","cited_by_count":9,"area":null},{"id":"2603.16952","title":"Embodied Foundation Models at the Edge: A Survey of Deployment Constraints and Mitigation Strategies","created":"2026-03-16","cited_by_count":9,"area":null},{"id":"2208.12816","title":"Complexity-Driven CNN Compression for Resource-constrained Edge AI","created":"2022-08-26","cited_by_count":8,"area":null},{"id":"2102.00160","title":"Deep Model Compression based on the Training History","created":"2021-01-30","cited_by_count":7,"area":null},{"id":"2202.10716","title":"HRel: Filter Pruning based on High Relevance between Activation Maps and Class Labels","created":"2022-02-22","cited_by_count":7,"area":null},{"id":"2403.02799","title":"DPPA: Pruning Method for Large Language Model to Model Merging","created":"2024-03-05","cited_by_count":7,"area":null},{"id":"2408.16772","title":"An Effective Information Theoretic Framework for Channel Pruning","created":"2024-08-14","cited_by_count":7,"area":null},{"id":"2101.04935","title":"Single-path Bit Sharing for Automatic Loss-aware Model Compression","created":"2021-01-13","cited_by_count":6,"area":null},{"id":"2103.10858","title":"Toward Compact Deep Neural Networks via Energy-Aware Pruning","created":"2021-03-19","cited_by_count":6,"area":null},{"id":"2103.11083","title":"Compacting Deep Neural Networks for Internet of Things: Methods and Applications","created":"2021-03-20","cited_by_count":6,"area":null},{"id":"2105.03679","title":"EZCrop: Energy-Zoned Channels for Robust Output Pruning","created":"2021-05-08","cited_by_count":6,"area":null},{"id":"2105.06052","title":"Model Pruning Based on Quantified Similarity of Feature Maps","created":"2021-05-13","cited_by_count":6,"area":null},{"id":"2110.10921","title":"CATRO: Channel Pruning via Class-Aware Trace Ratio Optimization","created":"2021-10-21","cited_by_count":6,"area":null},{"id":"2211.12714","title":"Developmental Plasticity-inspired Adaptive Pruning for Deep Spiking and Artificial Neural Networks","created":"2022-11-23","cited_by_count":6,"area":null},{"id":"2302.08185","title":"WHC: Weighted Hybrid Criterion for Filter Pruning on Convolutional Neural Networks","created":"2023-02-16","cited_by_count":6,"area":null},{"id":"2101.02338","title":"Max-Affine Spline Insights Into Deep Network Pruning","created":"2021-01-07","cited_by_count":5,"area":null},{"id":"2110.14283","title":"How Important is Importance Sampling for Deep Budgeted Training?","created":"2021-10-27","cited_by_count":5,"area":null},{"id":"2201.06776","title":"Pruning-aware Sparse Regularization for Network Pruning","created":"2022-01-18","cited_by_count":5,"area":null},{"id":"2203.02651","title":"Ensemble Knowledge Guided Sub-network Search and Fine-tuning for Filter Pruning","created":"2022-03-05","cited_by_count":5,"area":null},{"id":"2207.06613","title":"T-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXit","created":"2022-07-14","cited_by_count":5,"area":"inference"},{"id":"2303.09736","title":"Dynamic Structure Pruning for Compressing CNNs","created":"2023-03-17","cited_by_count":5,"area":null},{"id":"2304.02319","title":"Efficient CNNs via Passive Filter Pruning","created":"2023-04-05","cited_by_count":5,"area":null},{"id":"2306.04147","title":"CFDP: Common Frequency Domain Pruning","created":"2023-06-07","cited_by_count":5,"area":null},{"id":"2106.14126","title":"AdaptCL: Efficient Collaborative Learning with Dynamic and Adaptive Pruning","created":"2021-06-27","cited_by_count":4,"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":"2206.01627","title":"Pruning for Feature-Preserving Circuits in CNNs","created":"2022-06-03","cited_by_count":4,"area":null},{"id":"2207.02632","title":"Network Pruning via Feature Shift Minimization","created":"2022-07-06","cited_by_count":4,"area":null},{"id":"2302.07612","title":"Towards Optimal Compression: Joint Pruning and Quantization","created":"2023-02-15","cited_by_count":4,"area":null},{"id":"2308.06755","title":"Influence Function Based Second-Order Channel Pruning-Evaluating True Loss Changes For Pruning Is Possible Without Retraining","created":"2023-08-13","cited_by_count":4,"area":null},{"id":"2401.08505","title":"Harnessing Orthogonality to Train Low-Rank Neural Networks","created":"2024-01-16","cited_by_count":4,"area":null},{"id":"2402.17862","title":"REPrune: Channel Pruning via Kernel Representative Selection","created":"2024-02-27","cited_by_count":4,"area":null},{"id":"2506.11449","title":"Dynamic Sparse Training of Diagonally Sparse Networks","created":"2025-06-13","cited_by_count":4,"area":null},{"id":"2507.04792","title":"Model Compression using Progressive Channel Pruning","created":"2025-07-07","cited_by_count":4,"area":null},{"id":"2107.05787","title":"Data-Driven Low-Rank Neural Network Compression","created":"2021-07-13","cited_by_count":3,"area":null},{"id":"2109.09310","title":"Learning Versatile Convolution Filters for Efficient Visual Recognition","created":"2021-09-20","cited_by_count":3,"area":null},{"id":"2201.10520","title":"Adaptive Activation-based Structured Pruning","created":"2022-01-21","cited_by_count":3,"area":null},{"id":"2203.11700","title":"Exploring Linear Feature Disentanglement For Neural Networks","created":"2022-03-22","cited_by_count":3,"area":null},{"id":"2204.05566","title":"Compact Model Training by Low-Rank Projection with Energy Transfer","created":"2022-04-12","cited_by_count":3,"area":null},{"id":"2508.05521","title":"Optimal Brain Connection: Towards Efficient Structural Pruning","created":"2025-08-07","cited_by_count":3,"area":null},{"id":"2607.06915","title":"Smart Scissor: Coupling Spatial Redundancy Reduction and CNN Compression for Embedded Hardware","created":"2026-07-08","cited_by_count":3,"area":null},{"id":"2101.06407","title":"ACP: Automatic Channel Pruning via Clustering and Swarm Intelligence Optimization for CNN","created":"2021-01-16","cited_by_count":2,"area":null},{"id":"2102.02804","title":"A Deeper Look into Convolutions via Eigenvalue-based Pruning","created":"2021-02-04","cited_by_count":2,"area":null},{"id":"2106.10486","title":"CompConv: A Compact Convolution Module for Efficient Feature Learning","created":"2021-06-19","cited_by_count":2,"area":null},{"id":"2209.07475","title":"Neural Networks Reduction via Lumping","created":"2022-09-15","cited_by_count":2,"area":null},{"id":"2303.03645","title":"Filter Pruning based on Information Capacity and Independence","created":"2023-03-07","cited_by_count":2,"area":null},{"id":"2307.00198","title":"Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter Sampler","created":"2023-07-01","cited_by_count":2,"area":null},{"id":"2308.13957","title":"Differentiable Weight Masks for Domain Transfer","created":"2023-08-26","cited_by_count":2,"area":null},{"id":"2310.06344","title":"Filter Pruning For CNN With Enhanced Linear Representation Redundancy","created":"2023-10-10","cited_by_count":2,"area":null},{"id":"2404.08010","title":"Differentiable Search for Finding Optimal Quantization Strategy","created":"2024-04-10","cited_by_count":2,"area":null},{"id":"2405.06038","title":"From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks","created":"2024-05-09","cited_by_count":2,"area":null},{"id":"2407.15875","title":"Shapley Pruning for Neural Network Compression","created":"2024-07-19","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":"2509.13662","title":"Deep Lookup Network","created":"2025-09-17","cited_by_count":2,"area":null},{"id":"2107.05328","title":"Structured Directional Pruning via Perturbation Orthogonal Projection","created":"2021-07-12","cited_by_count":1,"area":null},{"id":"2108.13728","title":"Pruning with Compensation: Efficient Channel Pruning for Deep Convolutional Neural Networks","created":"2021-08-31","cited_by_count":1,"area":null},{"id":"2110.10842","title":"SMOF: Squeezing More Out of Filters Yields Hardware-Friendly CNN Pruning","created":"2021-10-21","cited_by_count":1,"area":null},{"id":"2110.12477","title":"Exploring Gradient Flow Based Saliency for DNN Model Compression","created":"2021-10-24","cited_by_count":1,"area":null},{"id":"2110.15192","title":"RGP: Neural Network Pruning through Its Regular Graph Structure","created":"2021-10-28","cited_by_count":1,"area":null},{"id":"2111.02399","title":"Learning Pruned Structure and Weights Simultaneously from Scratch: an Attention based Approach","created":"2021-11-01","cited_by_count":1,"area":null},{"id":"2112.07282","title":"SNF: Filter Pruning via Searching the Proper Number of Filters","created":"2021-12-14","cited_by_count":1,"area":null},{"id":"2112.10898","title":"Load-balanced Gather-scatter Patterns for Sparse Deep Neural Networks","created":"2021-12-20","cited_by_count":1,"area":null},{"id":"2204.07412","title":"End-to-End Sensitivity-Based Filter Pruning","created":"2022-04-15","cited_by_count":1,"area":null}],"next_cursor":"100"}