{"total":46,"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":"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":"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":"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":"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":"2106.04217","title":"Dynamic Sparse Training for Deep Reinforcement Learning","created":"2021-06-08","cited_by_count":49,"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":"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":"2201.11380","title":"Achieving Personalized Federated Learning with Sparse Local Models","created":"2022-01-27","cited_by_count":31,"area":null},{"id":"2101.09048","title":"Selfish Sparse RNN Training","created":"2021-01-22","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":"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":"2102.01732","title":"Truly Sparse Neural Networks at Scale","created":"2021-02-02","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":"2105.04319","title":"A Bregman Learning Framework for Sparse Neural Networks","created":"2021-05-10","cited_by_count":15,"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":"2403.15173","title":"LSK3DNet: Towards Effective and Efficient 3D Perception with Large Sparse Kernels","created":"2024-03-22","cited_by_count":15,"area":null},{"id":"2211.15335","title":"You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets","created":"2022-11-28","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":"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":"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":"2208.13092","title":"Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited Edge","created":"2022-08-27","cited_by_count":12,"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":"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":"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":"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":"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":"2106.02479","title":"Neural Architecture Search via Bregman Iterations","created":"2021-06-04","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":"2301.00335","title":"Pruning Before Training May Improve Generalization, Provably","created":"2023-01-01","cited_by_count":3,"area":null},{"id":"2107.12917","title":"Experiments on Properties of Hidden Structures of Sparse Neural Networks","created":"2021-07-27","cited_by_count":2,"area":null},{"id":"2406.17614","title":"MSRS: Training Multimodal Speech Recognition Models from Scratch with Sparse Mask Optimization","created":"2024-06-25","cited_by_count":2,"area":null},{"id":"2407.17437","title":"Nerva: a Truly Sparse Implementation of Neural Networks","created":"2024-07-24","cited_by_count":2,"area":null},{"id":"2210.14012","title":"Gradient-based Weight Density Balancing for Robust Dynamic Sparse Training","created":"2022-10-25","cited_by_count":1,"area":null},{"id":"2312.02671","title":"Learning a Sparse Representation of Barron Functions with the Inverse Scale Space Flow","created":"2023-12-05","cited_by_count":1,"area":null},{"id":"2403.13204","title":"Diversity-Aware Agnostic Ensemble of Sharpness Minimizers","created":"2024-03-19","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":"2412.06545","title":"On How Iterative Magnitude Pruning Discovers Local Receptive Fields in Fully Connected Neural Networks","created":"2024-12-09","cited_by_count":1,"area":null},{"id":"2105.08111","title":"Livewired Neural Networks: Making Neurons That Fire Together Wire Together","created":"2021-05-17","cited_by_count":0,"area":null},{"id":"2106.13585","title":"Learning Gradual Argumentation Frameworks using Genetic Algorithms","created":"2021-06-25","cited_by_count":0,"area":null},{"id":"2108.07908","title":"M-ar-K-Fast Independent Component Analysis","created":"2021-08-17","cited_by_count":0,"area":null},{"id":"2505.17909","title":"NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling","created":"2025-05-23","cited_by_count":0,"area":null},{"id":"2506.22255","title":"Projected Compression: Trainable Projection for Efficient Transformer Compression","created":"2025-06-27","cited_by_count":0,"area":null}],"next_cursor":null}