{"total":62,"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":"2102.04010","title":"Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch","created":"2021-02-08","cited_by_count":179,"area":null},{"id":"2102.06790","title":"A Unified Lottery Ticket Hypothesis for Graph Neural Networks","created":"2021-02-12","cited_by_count":118,"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":"2204.00595","title":"Monarch: Expressive Structured Matrices for Efficient and Accurate Training","created":"2022-04-01","cited_by_count":106,"area":null},{"id":"2102.10472","title":"Learning Neural Network Subspaces","created":"2021-02-20","cited_by_count":82,"area":null},{"id":"2106.03517","title":"Top-KAST: Top-K Always Sparse Training","created":"2021-06-07","cited_by_count":79,"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":"2110.00296","title":"Powerpropagation: A sparsity inducing weight reparameterisation","created":"2021-10-01","cited_by_count":49,"area":null},{"id":"2104.11832","title":"Playing Lottery Tickets with Vision and Language","created":"2021-04-23","cited_by_count":45,"area":null},{"id":"2106.03225","title":"Efficient Lottery Ticket Finding: Less Data is More","created":"2021-06-06","cited_by_count":44,"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":"2511.13653","title":"Weight-sparse transformers have interpretable circuits","created":"2025-11-17","cited_by_count":44,"area":"mechinterp"},{"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":"2106.00134","title":"GANs Can Play Lottery Tickets Too","created":"2021-05-31","cited_by_count":32,"area":null},{"id":"2303.03428","title":"Towards provably efficient quantum algorithms for large-scale machine-learning models","created":"2023-03-06","cited_by_count":32,"area":null},{"id":"2209.13569","title":"Exploring Low Rank Training of Deep Neural Networks","created":"2022-09-27","cited_by_count":30,"area":null},{"id":"2110.14068","title":"Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks","created":"2021-10-26","cited_by_count":29,"area":null},{"id":"2303.10464","title":"SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Models","created":"2023-03-18","cited_by_count":28,"area":null},{"id":"2206.08684","title":"Sparse Double Descent: Where Network Pruning Aggravates Overfitting","created":"2022-06-17","cited_by_count":26,"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":"2103.00397","title":"Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspective","created":"2021-02-28","cited_by_count":25,"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":"2102.01670","title":"Keep the Gradients Flowing: Using Gradient Flow to Study Sparse Network Optimization","created":"2021-02-02","cited_by_count":22,"area":null},{"id":"2402.04744","title":"Progressive Gradient Flow for Robust N:M Sparsity Training in Transformers","created":"2024-02-07","cited_by_count":18,"area":null},{"id":"2407.05694","title":"On the Limitations of Compute Thresholds as a Governance Strategy","created":"2024-07-08","cited_by_count":18,"area":null},{"id":"2305.18383","title":"A Three-regime Model of Network Pruning","created":"2023-05-28","cited_by_count":15,"area":null},{"id":"2207.09455","title":"To update or not to update? Neurons at equilibrium in deep models","created":"2022-07-19","cited_by_count":13,"area":null},{"id":"2209.07617","title":"Training Recipe for N:M Structured Sparsity with Decaying Pruning Mask","created":"2022-09-15","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":"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":"2308.02060","title":"Accurate Neural Network Pruning Requires Rethinking Sparse Optimization","created":"2023-08-03","cited_by_count":10,"area":null},{"id":"2402.01089","title":"No Free Prune: Information-Theoretic Barriers to Pruning at Initialization","created":"2024-02-02","cited_by_count":8,"area":null},{"id":"2107.03453","title":"$S^3$: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks","created":"2021-07-07","cited_by_count":7,"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":"2503.01437","title":"Eau De $Q$-Network: Adaptive Distillation of Neural Networks in Deep Reinforcement Learning","created":"2025-03-03","cited_by_count":6,"area":"deeprl"},{"id":"2107.09532","title":"Estimation of a regression function on a manifold by fully connected deep neural networks","created":"2021-07-20","cited_by_count":5,"area":null},{"id":"2206.04762","title":"Data-Efficient Double-Win Lottery Tickets from Robust Pre-training","created":"2022-06-09","cited_by_count":5,"area":null},{"id":"2411.02139","title":"Theoretical characterisation of the Gauss-Newton conditioning in Neural Networks","created":"2024-11-04","cited_by_count":5,"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":"2410.12119","title":"Scaling Laws for Post Training Quantized Large Language Models","created":"2024-10-15","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":"2410.14570","title":"Understanding the Difficulty of Low-Precision Post-Training Quantization for LLMs","created":"2024-10-18","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":"2201.08652","title":"A phase transition for finding needles in nonlinear haystacks with LASSO artificial neural networks","created":"2022-01-21","cited_by_count":2,"area":null},{"id":"2303.14409","title":"Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression","created":"2023-03-25","cited_by_count":2,"area":null},{"id":"2304.11237","title":"Effective Neural Network $L_0$ Regularization With BinMask","created":"2023-04-21","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":"2407.08560","title":"Causal inference through multi-stage learning and doubly robust deep neural networks","created":"2024-07-11","cited_by_count":2,"area":null},{"id":"2203.13802","title":"Playing Lottery Tickets in Style Transfer Models","created":"2022-03-25","cited_by_count":1,"area":null},{"id":"2302.11244","title":"Considering Layerwise Importance in the Lottery Ticket Hypothesis","created":"2023-02-22","cited_by_count":1,"area":null},{"id":"2304.07613","title":"STen: Productive and Efficient Sparsity in PyTorch","created":"2023-04-15","cited_by_count":1,"area":null},{"id":"2305.10964","title":"Learning Activation Functions for Sparse Neural Networks","created":"2023-05-18","cited_by_count":1,"area":null},{"id":"2311.03609","title":"Testing RadiX-Nets: Advances in Viable Sparse Topologies","created":"2023-11-06","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":"2409.19391","title":"Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training","created":"2024-09-28","cited_by_count":1,"area":null},{"id":"2511.03983","title":"TwIST: Rigging the Lottery in Transformers with Independent Subnetwork Training","created":"2025-11-06","cited_by_count":1,"area":null},{"id":"2403.00952","title":"MediSwift: Efficient Sparse Pre-trained Biomedical Language Models","created":"2024-03-01","cited_by_count":0,"area":null},{"id":"2410.24187","title":"Chasing Better Deep Image Priors between Over- and Under-parameterization","created":"2024-10-31","cited_by_count":0,"area":null},{"id":"2601.21561","title":"SAL: Selective Adaptive Learning for Backpropagation-Free Training with Sparsification","created":"2026-01-29","cited_by_count":0,"area":null}],"next_cursor":null}