{"total":62,"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":"2102.02887","title":"Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training","created":"2021-02-04","cited_by_count":110,"area":null},{"id":"2106.10404","title":"Sparse Training via Boosting Pruning Plasticity with Neuroregeneration","created":"2021-06-19","cited_by_count":84,"area":null},{"id":"2306.13812","title":"Maintaining Plasticity in Deep Continual Learning","created":"2023-06-23","cited_by_count":43,"area":null},{"id":"2206.09117","title":"NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks","created":"2022-06-18","cited_by_count":39,"area":null},{"id":"2206.01299","title":"Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees","created":"2022-06-02","cited_by_count":30,"area":null},{"id":"2304.02840","title":"NTK-SAP: Improving neural network pruning by aligning training dynamics","created":"2023-04-06","cited_by_count":30,"area":null},{"id":"2101.09048","title":"Selfish Sparse RNN Training","created":"2021-01-22","cited_by_count":26,"area":null},{"id":"2306.16788","title":"Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging","created":"2023-06-29","cited_by_count":20,"area":null},{"id":"2102.01732","title":"Truly Sparse Neural Networks at Scale","created":"2021-02-02","cited_by_count":16,"area":null},{"id":"2205.11921","title":"Compression-aware Training of Neural Networks using Frank-Wolfe","created":"2022-05-24","cited_by_count":16,"area":null},{"id":"2502.02496","title":"Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries","created":"2025-02-04","cited_by_count":14,"area":null},{"id":"2103.01636","title":"Sparse Training Theory for Scalable and Efficient Agents","created":"2021-03-02","cited_by_count":13,"area":null},{"id":"2209.14624","title":"Is Complexity Required for Neural Network Pruning? A Case Study on Global Magnitude Pruning","created":"2022-09-29","cited_by_count":13,"area":null},{"id":"2302.06548","title":"Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning","created":"2023-02-13","cited_by_count":13,"area":null},{"id":"2111.00843","title":"How I Learned to Stop Worrying and Love Retraining","created":"2021-11-01","cited_by_count":12,"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":"2310.01365","title":"Elephant Neural Networks: Born to Be a Continual Learner","created":"2023-10-02","cited_by_count":10,"area":null},{"id":"2402.01242","title":"Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness","created":"2024-02-02","cited_by_count":10,"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":"2302.14670","title":"Balanced Training for Sparse GANs","created":"2023-02-28","cited_by_count":8,"area":null},{"id":"2401.09949","title":"SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression","created":"2024-01-18","cited_by_count":7,"area":null},{"id":"2403.02914","title":"DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal Forecasting","created":"2024-03-05","cited_by_count":7,"area":null},{"id":"2405.02347","title":"COPAL: Continual Pruning in Large Language Generative Models","created":"2024-05-02","cited_by_count":6,"area":null},{"id":"2407.05510","title":"SCATTER: Algorithm-Circuit Co-Sparse Photonic Accelerator with Thermal-Tolerant, Power-Efficient In-situ Light Redistribution","created":"2024-07-07","cited_by_count":6,"area":null},{"id":"2105.12686","title":"Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities","created":"2021-05-26","cited_by_count":5,"area":null},{"id":"2306.05056","title":"Magnitude Attention-based Dynamic Pruning","created":"2023-06-08","cited_by_count":5,"area":null},{"id":"2207.11005","title":"AdaptCL: Adaptive Continual Learning for Tackling Heterogeneity in Sequential Datasets","created":"2022-07-22","cited_by_count":4,"area":null},{"id":"2311.12526","title":"Neural Network Pruning by Gradient Descent","created":"2023-11-21","cited_by_count":4,"area":null},{"id":"2105.12920","title":"Search Spaces for Neural Model Training","created":"2021-05-27","cited_by_count":3,"area":null},{"id":"2304.06305","title":"Boosting Convolutional Neural Networks with Middle Spectrum Grouped Convolution","created":"2023-04-13","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":"2311.16883","title":"Compressing the Backward Pass of Large-Scale Neural Architectures by Structured Activation Pruning","created":"2023-11-28","cited_by_count":3,"area":null},{"id":"2405.07527","title":"Train Faster, Perform Better: Modular Adaptive Training in Over-Parameterized Models","created":"2024-05-13","cited_by_count":3,"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":"2204.05274","title":"MIME: Adapting a Single Neural Network for Multi-task Inference with Memory-efficient Dynamic Pruning","created":"2022-04-11","cited_by_count":2,"area":null},{"id":"2304.06941","title":"AUTOSPARSE: Towards Automated Sparse Training of Deep Neural Networks","created":"2023-04-14","cited_by_count":2,"area":null},{"id":"2305.14852","title":"Sparse Weight Averaging with Multiple Particles for Iterative Magnitude Pruning","created":"2023-05-24","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":"2409.04473","title":"Learning in Order! A Sequential Strategy to Learn Invariant Features for Multimodal Sentiment Analysis","created":"2024-09-05","cited_by_count":2,"area":null},{"id":"2105.04218","title":"Exploiting Elasticity in Tensor Ranks for Compressing Neural Networks","created":"2021-05-10","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":"2209.08277","title":"MiNL: Micro-images based Neural Representation for Light Fields","created":"2022-09-17","cited_by_count":1,"area":null},{"id":"2403.04805","title":"Pruning neural network models for gene regulatory dynamics using data and domain knowledge","created":"2024-03-05","cited_by_count":1,"area":null},{"id":"2404.01306","title":"NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models","created":"2024-02-28","cited_by_count":1,"area":null},{"id":"2409.20043","title":"OPONeRF: One-Point-One NeRF for Robust Neural Rendering","created":"2024-09-30","cited_by_count":1,"area":null},{"id":"2506.07975","title":"Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum","created":"2025-06-09","cited_by_count":1,"area":null},{"id":"2510.14444","title":"A Free Lunch in LLM Compression: Revisiting Retraining after Pruning","created":"2025-10-16","cited_by_count":1,"area":null},{"id":"2603.13418","title":"GPrune-LLM: Generalization-Aware Structured Pruning for Large Language Models","created":"2026-03-12","cited_by_count":1,"area":null},{"id":"2208.14625","title":"Temporal Flow Mask Attention for Open-Set Long-Tailed Recognition of Wild Animals in Camera-Trap Images","created":"2022-08-31","cited_by_count":0,"area":null},{"id":"2303.11881","title":"Protective Self-Adaptive Pruning to Better Compress DNNs","created":"2023-03-21","cited_by_count":0,"area":null},{"id":"2404.13504","title":"IMO: Greedy Layer-Wise Sparse Representation Learning for Out-of-Distribution Text Classification with Pre-trained Models","created":"2024-04-21","cited_by_count":0,"area":null},{"id":"2504.05158","title":"Leveraging Label Potential for Enhanced Multimodal Emotion Recognition","created":"2025-04-07","cited_by_count":0,"area":null},{"id":"2504.05349","title":"Hyperflux: Pruning Reveals Importance","created":"2025-04-06","cited_by_count":0,"area":null},{"id":"2602.21064","title":"Motivation is Something You Need","created":"2026-02-24","cited_by_count":0,"area":null},{"id":"2603.26595","title":"PQuantML: A Tool for End-to-End Hardware-aware Model Compression","created":"2026-03-27","cited_by_count":0,"area":null},{"id":"2606.12278","title":"Finding Sparse Subnetworks in One Training Cycle via Progressive Magnitude-Based Pruning","created":"2026-06-10","cited_by_count":0,"area":null},{"id":"2606.18691","title":"Robust and Interpretable Adaptation of Equivariant Materials Foundation Models via Sparsity-promoting Fine-tuning","created":"2026-06-17","cited_by_count":0,"area":null},{"id":"2606.20156","title":"Modularity-Free Conflict-Averse Training for Generalized PINNs","created":"2026-06-18","cited_by_count":0,"area":null},{"id":"2609.06557","title":"Hidden in Plain Sight: The Overlooked Significance of Canonical Elements for Extreme LLM Sparsity","created":"2026-09-06","cited_by_count":0,"area":null},{"id":"2610.04093","title":"Beyond Masked Sparsity: SNACK Enables Truly Sparse Neural Networks on GPU","created":"2026-10-02","cited_by_count":0,"area":null}],"next_cursor":null}