{"total":47,"rows":[{"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":"2205.09588","title":"How catastrophic can catastrophic forgetting be in linear regression?","created":"2022-05-19","cited_by_count":81,"area":null},{"id":"2309.08520","title":"Scaling Laws for Sparsely-Connected Foundation Models","created":"2023-09-15","cited_by_count":43,"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":"2201.13096","title":"SPDY: Accurate Pruning with Speedup Guarantees","created":"2022-01-31","cited_by_count":36,"area":null},{"id":"2205.15043","title":"RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch","created":"2022-05-30","cited_by_count":30,"area":null},{"id":"2211.10445","title":"Building a Subspace of Policies for Scalable Continual Learning","created":"2022-11-18","cited_by_count":28,"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":"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":"2205.08957","title":"Meta-Learning Sparse Compression Networks","created":"2022-05-18","cited_by_count":22,"area":null},{"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":"2212.11110","title":"Lifelong Reinforcement Learning with Modulating Masks","created":"2022-12-21","cited_by_count":22,"area":null},{"id":"2301.09479","title":"Modality-Agnostic Variational Compression of Implicit Neural Representations","created":"2023-01-23","cited_by_count":22,"area":null},{"id":"2203.06514","title":"Sparsity and Heterogeneous Dropout for Continual Learning in the Null Space of Neural Activations","created":"2022-03-12","cited_by_count":19,"area":null},{"id":"2305.19454","title":"Dynamic Sparsity Is Channel-Level Sparsity Learner","created":"2023-05-30","cited_by_count":18,"area":null},{"id":"2507.12224","title":"Optimizers Qualitatively Alter Solutions And We Should Leverage This","created":"2025-07-16","cited_by_count":17,"area":null},{"id":"2408.09966","title":"Mask in the Mirror: Implicit Sparsification","created":"2024-08-19","cited_by_count":14,"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":"2302.04852","title":"SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks","created":"2023-02-09","cited_by_count":13,"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":"2303.11934","title":"Sparse Distributed Memory is a Continual Learner","created":"2023-03-20","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":"2308.07163","title":"HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization","created":"2023-08-14","cited_by_count":9,"area":null},{"id":"2205.14107","title":"Spartan: Differentiable Sparsity via Regularized Transportation","created":"2022-05-27","cited_by_count":8,"area":null},{"id":"2301.12540","title":"Implicit Regularization for Group Sparsity","created":"2023-01-29","cited_by_count":8,"area":null},{"id":"2505.24275","title":"GradPower: Powering Gradients for Faster Language Model Pre-Training","created":"2025-05-30","cited_by_count":8,"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":"2302.09369","title":"Calibrating the Rigged Lottery: Making All Tickets Reliable","created":"2023-02-18","cited_by_count":4,"area":null},{"id":"2303.13957","title":"Factorizers for Distributed Sparse Block Codes","created":"2023-03-24","cited_by_count":4,"area":null},{"id":"2305.19394","title":"Synaptic Weight Distributions Depend on the Geometry of Plasticity","created":"2023-05-30","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":"2304.06941","title":"AUTOSPARSE: Towards Automated Sparse Training of Deep Neural Networks","created":"2023-04-14","cited_by_count":2,"area":null},{"id":"2311.17493","title":"Towards Higher Ranks via Adversarial Weight Pruning","created":"2023-11-29","cited_by_count":2,"area":null},{"id":"2207.04874","title":"Hebbian Continual Representation Learning","created":"2022-06-28","cited_by_count":1,"area":null},{"id":"2306.16993","title":"Weight Compander: A Simple Weight Reparameterization for Regularization","created":"2023-06-29","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":"2502.11439","title":"An Efficient Sparse Fine-Tuning with Low Quantization Error via Neural Network Pruning","created":"2025-02-17","cited_by_count":1,"area":null},{"id":"2506.05702","title":"Action-Adaptive Continual Learning: Enabling Policy Generalization under Dynamic Action Spaces","created":"2025-06-06","cited_by_count":1,"area":null},{"id":"2209.12839","title":"Efficient Multi-Prize Lottery Tickets: Enhanced Accuracy, Training, and Inference Speed","created":"2022-09-26","cited_by_count":0,"area":null},{"id":"2308.16596","title":"The Quest of Finding the Antidote to Sparse Double Descent","created":"2023-08-31","cited_by_count":0,"area":null},{"id":"2310.01165","title":"Towards guarantees for parameter isolation in continual learning","created":"2023-10-02","cited_by_count":0,"area":null},{"id":"2402.17902","title":"SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization","created":"2024-02-27","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":"2605.25134","title":"Theoretical Analysis of Sparse Optimization with Reparameterization, Weight Decay, and Adaptive Learning Rate","created":"2026-05-24","cited_by_count":0,"area":null}],"next_cursor":null}