{"total":110,"rows":[{"id":"2106.04533","title":"Chasing Sparsity in Vision Transformers: An End-to-End Exploration","created":"2021-06-08","cited_by_count":183,"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":"2207.03620","title":"More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity","created":"2022-07-07","cited_by_count":142,"area":null},{"id":"2202.14026","title":"Robust Training under Label Noise by Over-parameterization","created":"2022-02-28","cited_by_count":93,"area":null},{"id":"2202.02643","title":"The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training","created":"2022-02-05","cited_by_count":85,"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":"2206.00187","title":"DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training","created":"2022-06-01","cited_by_count":74,"area":null},{"id":"2206.10369","title":"The State of Sparse Training in Deep Reinforcement Learning","created":"2022-06-17","cited_by_count":61,"area":null},{"id":"2310.08915","title":"Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs","created":"2023-10-13","cited_by_count":61,"area":null},{"id":"2112.09824","title":"Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better","created":"2021-12-18","cited_by_count":55,"area":null},{"id":"2402.15751","title":"Sparse MeZO: Less Parameters for Better Performance in Zeroth-Order LLM Fine-Tuning","created":"2024-02-24","cited_by_count":54,"area":null},{"id":"2106.04217","title":"Dynamic Sparse Training for Deep Reinforcement Learning","created":"2021-06-08","cited_by_count":49,"area":null},{"id":"2406.00600","title":"Kolmogorov-Arnold Network for Satellite Image Classification in Remote Sensing","created":"2024-06-02","cited_by_count":47,"area":null},{"id":"2106.14568","title":"Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity","created":"2021-06-28","cited_by_count":44,"area":null},{"id":"2306.03805","title":"The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter","created":"2023-06-06","cited_by_count":42,"area":null},{"id":"2202.09844","title":"Sparsity Winning Twice: Better Robust Generalization from More Efficient Training","created":"2022-02-20","cited_by_count":41,"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":"2209.14764","title":"Model Zoos: A Dataset of Diverse Populations of Neural Network Models","created":"2022-09-29","cited_by_count":39,"area":null},{"id":"2305.02299","title":"Dynamic Sparse Training with Structured Sparsity","created":"2023-05-03","cited_by_count":38,"area":null},{"id":"2201.11380","title":"Achieving Personalized Federated Learning with Sparse Local Models","created":"2022-01-27","cited_by_count":31,"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":"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":"2206.12755","title":"Training Your Sparse Neural Network Better with Any Mask","created":"2022-06-26","cited_by_count":26,"area":null},{"id":"2302.02596","title":"Ten Lessons We Have Learned in the New \"Sparseland\": A Short Handbook for Sparse Neural Network Researchers","created":"2023-02-06","cited_by_count":25,"area":null},{"id":"2602.10825","title":"Flow caching for autoregressive video generation","created":"2026-02-11","cited_by_count":25,"area":null},{"id":"2103.16547","title":"The Elastic Lottery Ticket Hypothesis","created":"2021-03-30","cited_by_count":23,"area":null},{"id":"2310.07999","title":"LEMON: Lossless model expansion","created":"2023-10-12","cited_by_count":23,"area":"lr"},{"id":"2212.09840","title":"Dynamic Sparse Network for Time Series Classification: Learning What to \"see''","created":"2022-12-19","cited_by_count":22,"area":null},{"id":"2504.09039","title":"Sculpting Memory: Multi-Concept Forgetting in Diffusion Models via Dynamic Mask and Concept-Aware Optimization","created":"2025-04-12","cited_by_count":19,"area":"diffusion"},{"id":"2207.08629","title":"Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks","created":"2022-07-18","cited_by_count":18,"area":null},{"id":"2209.04766","title":"Towards Sparsification of Graph Neural Networks","created":"2022-09-11","cited_by_count":18,"area":null},{"id":"2305.19454","title":"Dynamic Sparsity Is Channel-Level Sparsity Learner","created":"2023-05-30","cited_by_count":18,"area":null},{"id":"2306.12230","title":"Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training","created":"2023-06-21","cited_by_count":17,"area":null},{"id":"2203.07808","title":"Interspace Pruning: Using Adaptive Filter Representations to Improve Training of Sparse CNNs","created":"2022-03-15","cited_by_count":16,"area":null},{"id":"2312.04727","title":"E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation","created":"2023-12-07","cited_by_count":16,"area":null},{"id":"2405.15743","title":"Sparse maximal update parameterization: A holistic approach to sparse training dynamics","created":"2024-05-24","cited_by_count":16,"area":null},{"id":"2211.14627","title":"Where to Pay Attention in Sparse Training for Feature Selection?","created":"2022-11-26","cited_by_count":15,"area":null},{"id":"2211.16667","title":"Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-off","created":"2022-11-30","cited_by_count":15,"area":null},{"id":"2107.02306","title":"Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity","created":"2021-07-05","cited_by_count":14,"area":null},{"id":"2405.18080","title":"HarmoDT: Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning","created":"2024-05-28","cited_by_count":14,"area":"offlinerl"},{"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":"2310.02277","title":"Junk DNA Hypothesis: Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs \"Difficult\" Downstream Tasks in LLMs","created":"2023-09-29","cited_by_count":13,"area":null},{"id":"2506.14562","title":"AlphaDecay: Module-wise Weight Decay for Heavy-Tailed Balancing in LLMs","created":"2025-06-17","cited_by_count":13,"area":null},{"id":"2108.06277","title":"Towards Structured Dynamic Sparse Pre-Training of BERT","created":"2021-08-13","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":"2302.13019","title":"A Unified Framework for Soft Threshold Pruning","created":"2023-02-25","cited_by_count":12,"area":null},{"id":"2310.16391","title":"Winning Prize Comes from Losing Tickets: Improve Invariant Learning by Exploring Variant Parameters for Out-of-Distribution Generalization","created":"2023-10-25","cited_by_count":12,"area":null},{"id":"2209.11204","title":"Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training","created":"2022-09-22","cited_by_count":11,"area":null},{"id":"2410.03030","title":"Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness","created":"2024-10-03","cited_by_count":11,"area":null},{"id":"2308.02060","title":"Accurate Neural Network Pruning Requires Rethinking Sparse Optimization","created":"2023-08-03","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":"2203.14328","title":"On the Neural Tangent Kernel Analysis of Randomly Pruned Neural Networks","created":"2022-03-27","cited_by_count":9,"area":null},{"id":"2212.01076","title":"Are Straight-Through gradients and Soft-Thresholding all you need for Sparse Training?","created":"2022-12-02","cited_by_count":9,"area":null},{"id":"2308.07163","title":"HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization","created":"2023-08-14","cited_by_count":9,"area":null},{"id":"2402.05164","title":"A Resource Model For Neural Scaling Law","created":"2024-02-07","cited_by_count":9,"area":null},{"id":"2208.05969","title":"Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment","created":"2022-08-11","cited_by_count":8,"area":null},{"id":"2302.14670","title":"Balanced Training for Sparse GANs","created":"2023-02-28","cited_by_count":8,"area":null},{"id":"2308.14831","title":"Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates","created":"2023-08-28","cited_by_count":8,"area":null},{"id":"2203.02770","title":"Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance","created":"2022-03-05","cited_by_count":7,"area":null},{"id":"2303.07200","title":"Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks","created":"2023-03-10","cited_by_count":7,"area":null},{"id":"2205.15322","title":"Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training","created":"2022-05-30","cited_by_count":6,"area":null},{"id":"2407.02253","title":"Parameter-Selective Continual Test-Time Adaptation","created":"2024-07-02","cited_by_count":6,"area":null},{"id":"2411.09126","title":"SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency","created":"2024-11-14","cited_by_count":6,"area":null},{"id":"2201.12826","title":"OptG: Optimizing Gradient-driven Criteria in Network Sparsity","created":"2022-01-30","cited_by_count":5,"area":null},{"id":"2301.03573","title":"Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction","created":"2023-01-09","cited_by_count":5,"area":null},{"id":"2205.00334","title":"Engineering flexible machine learning systems by traversing functionally-invariant paths","created":"2022-04-30","cited_by_count":4,"area":null},{"id":"2302.09369","title":"Calibrating the Rigged Lottery: Making All Tickets Reliable","created":"2023-02-18","cited_by_count":4,"area":null},{"id":"2401.00996","title":"Safety and Performance, Why Not Both? Bi-Objective Optimized Model Compression against Heterogeneous Attacks Toward AI Software Deployment","created":"2024-01-02","cited_by_count":4,"area":"safety"},{"id":"2401.12736","title":"$ShiftwiseConv:$ Small Convolutional Kernel with Large Kernel Effect","created":"2024-01-23","cited_by_count":4,"area":null},{"id":"2501.19107","title":"Brain network science modelling of sparse neural networks enables Transformers and LLMs to perform as fully connected","created":"2025-01-31","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":"2110.00684","title":"Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)","created":"2021-10-01","cited_by_count":3,"area":null},{"id":"2301.00335","title":"Pruning Before Training May Improve Generalization, Provably","created":"2023-01-01","cited_by_count":3,"area":null},{"id":"2410.07771","title":"Full-Rank No More: Low-Rank Weight Training for Modern Speech Recognition Models","created":"2024-10-10","cited_by_count":3,"area":null},{"id":"2411.03171","title":"Navigating Extremes: Dynamic Sparsity in Large Output Spaces","created":"2024-11-05","cited_by_count":3,"area":null},{"id":"2501.18980","title":"Symmetric Pruning of Large Language Models","created":"2025-01-31","cited_by_count":3,"area":null},{"id":"2601.19503","title":"GradPruner: Gradient-Guided Layer Pruning Enabling Efficient Fine-Tuning and Inference for LLMs","created":"2026-01-27","cited_by_count":3,"area":null},{"id":"2602.05887","title":"Escaping Local Minima Provably in Non-convex Matrix Sensing: A Deterministic Framework via Simulated Lifting","created":"2026-02-05","cited_by_count":3,"area":null},{"id":"2202.05226","title":"Deadwooding: Robust Global Pruning for Deep Neural Networks","created":"2022-02-10","cited_by_count":2,"area":null},{"id":"2211.08544","title":"Exploiting the Partly Scratch-off Lottery Ticket for Quantization-Aware Training","created":"2022-11-12","cited_by_count":2,"area":null},{"id":"2305.16886","title":"Understanding Sparse Neural Networks from their Topology via Multipartite Graph Representations","created":"2023-05-26","cited_by_count":2,"area":null},{"id":"2312.03044","title":"REST: Enhancing Group Robustness in DNNs through Reweighted Sparse Training","created":"2023-12-05","cited_by_count":2,"area":null},{"id":"2505.24037","title":"Leave it to the Specialist: Repair Sparse LLMs with Sparse Fine-Tuning via Sparsity Evolution","created":"2025-05-29","cited_by_count":2,"area":null},{"id":"2510.17515","title":"The Graphon Limit Hypothesis: Understanding Neural Network Pruning via Infinite Width Analysis","created":"2025-10-20","cited_by_count":2,"area":null},{"id":"2305.18382","title":"Adaptive Sparsity Level during Training for Efficient Time Series Forecasting with Transformers","created":"2023-05-28","cited_by_count":1,"area":null},{"id":"2306.13586","title":"NetBooster: Empowering Tiny Deep Learning By Standing on the Shoulders of Deep Giants","created":"2023-06-23","cited_by_count":1,"area":null},{"id":"2401.06898","title":"Always-Sparse Training by Growing Connections with Guided Stochastic Exploration","created":"2024-01-12","cited_by_count":1,"area":null},{"id":"2403.20047","title":"Embracing Unknown Step by Step: Towards Reliable Sparse Training in Real World","created":"2024-03-29","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":"2411.01146","title":"Task-Aware Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning","created":"2024-11-02","cited_by_count":1,"area":"offlinerl"},{"id":"2411.13545","title":"Pushing the Limits of Sparsity: A Bag of Tricks for Extreme Pruning","created":"2024-11-20","cited_by_count":1,"area":null},{"id":"2502.03658","title":"Advancing Weight and Channel Sparsification with Enhanced Saliency","created":"2025-02-05","cited_by_count":1,"area":null},{"id":"2504.21380","title":"Sparse-to-Sparse Training of Diffusion Models","created":"2025-04-30","cited_by_count":1,"area":"diffusion"},{"id":"2607.08164","title":"Continual Test-Time Adaptation in Computer Vision: Methods, Benchmarks, and Future Directions","created":"2026-07-09","cited_by_count":1,"area":null},{"id":"2205.14305","title":"Ensemble2: Anomaly Detection via EVT-Ensemble Framework for Seasonal KPIs in Communication Network","created":"2022-05-28","cited_by_count":0,"area":null},{"id":"2209.13378","title":"Neural Network Panning: Screening the Optimal Sparse Network Before Training","created":"2022-09-27","cited_by_count":0,"area":null},{"id":"2211.09945","title":"VeriCompress: A Tool to Streamline the Synthesis of Verified Robust Compressed Neural Networks from Scratch","created":"2022-11-17","cited_by_count":0,"area":null},{"id":"2401.05571","title":"QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits","created":"2024-01-10","cited_by_count":0,"area":null},{"id":"2410.05107","title":"Hyper-Representations: Learning from Populations of Neural Networks","created":"2024-10-07","cited_by_count":0,"area":null},{"id":"2411.07711","title":"OWLed: Outlier-weighed Layerwise Pruning for Efficient Autonomous Driving Framework","created":"2024-11-12","cited_by_count":0,"area":"driving"}],"next_cursor":"100"}