% Key papers for Sparse training from scratch (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2020_group_15624,
  title = {{Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{iclr2018_deep_28434,
  title = {{Deep Rewiring: Training very sparse deep networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/291},
}

@inproceedings{iclr2017_dsd_28289,
  title = {{DSD: Dense-Sparse-Dense Training for Deep Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=HyoST_9xl},
}

@inproceedings{cvpr2021_dynamic_16923,
  title = {{Dynamic Slimmable Network}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{iclr2017_exploring_28242,
  title = {{Exploring Sparsity in Recurrent Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=BylSPv9gx},
}

@inproceedings{aaai2020_learning_103114,
  title = {{Learning Sparse Sharing Architectures for Multiple Tasks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6424},
}

@inproceedings{neurips2017_structured_56697,
  title = {{Structured Bayesian Pruning via Log-Normal Multiplicative Noise}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9445},
}

@inproceedings{icml2019_parameter_37644,
  title = {{Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3692},
}

@inproceedings{icml2017_combined_36099,
  title = {{Combined Group and Exclusive Sparsity for Deep Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/474},
}

@inproceedings{icml2020_soft_38768,
  title = {{Soft Threshold Weight Reparameterization for Learnable Sparsity}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5772},
}

@inproceedings{aaai2019_balanced_101797,
  title = {{Balanced Sparsity for Efficient DNN Inference on GPU}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4512},
}

@inproceedings{iclr2021_learning_30191,
  title = {{Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3174},
}

@inproceedings{cvpr2018_nestednet_13417,
  title = {{NestedNet: Learning Nested Sparse Structures in Deep Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Kim_NestedNet_Learning_Nested_CVPR_2018_paper.html},
}

@inproceedings{neurips2021_mest_62363,
  title = {{MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26988},
}

@inproceedings{iclr2022_the_31342,
  title = {{The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6925},
}

@inproceedings{cvpr2023_structured_22272,
  title = {{Structured Sparsity Learning for Efficient Video Super-Resolution}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/22025},
}

@inproceedings{iclr2020_dynamic_29644,
  title = {{Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=SJlbGJrtDB},
}

@inproceedings{cvpr2022_disparse_18661,
  title = {{DiSparse: Disentangled Sparsification for Multitask Model Compression}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{aaai2022_prune_2597,
  title = {{Prune and Tune Ensembles: Low-Cost Ensemble Learning with Sparse Independent Subnetworks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/08638-prune-and-tune-ensembles-low-cost-ensemble-learning-with-sparse-independent-subnetworks/},
}

@inproceedings{icml2021_do_39265,
  title = {{Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10199},
}

@inproceedings{icml2024_tinytrain_44218,
  title = {{TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34234},
}

@inproceedings{aaai2024_fast_5974,
  title = {{Fast and Controllable Post-training Sparsity: Learning Optimal Sparsity Allocation with Global Constraint in Minutes}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29108},
}

@inproceedings{iclr2024_repeated_34148,
  title = {{Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18927},
}

@inproceedings{iclr2024_dynamic_35098,
  title = {{Dynamic Sparse Training with Structured Sparsity}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17975},
}

@inproceedings{iclr2024_adaptive_34393,
  title = {{Adaptive Sharpness-Aware Pruning for Robust Sparse Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18682},
}

@inproceedings{iclr2024_sparse_35640,
  title = {{Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17433},
}

@inproceedings{icml2024_spade_45359,
  title = {{SPADE: Sparsity-Guided Debugging for Deep Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33092},
}

@inproceedings{icml2025_dynamic_47846,
  title = {{Dynamic Sparse Training of Diagonally Sparse Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44756},
}

@inproceedings{neurips2024_s_70763,
  title = {{S-STE: Continuous Pruning Function for Efficient 2:4 Sparse Pre-training}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96363},
}

@inproceedings{neurips2024_blast_73788,
  title = {{BLAST: Block-Level Adaptive Structured Matrices for Efficient Deep Neural Network Inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93718},
}

@inproceedings{cvpr2024_unipts_25133,
  title = {{UniPTS: A Unified Framework for Proficient Post-Training Sparsity}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29868},
}

@inproceedings{iclr2024_scaling_34999,
  title = {{Scaling Laws for Sparsely-Connected Foundation Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18075},
}

@inproceedings{icml2024_sparse_44615,
  title = {{Sparse-IFT: Sparse Iso-FLOP Transformations for Maximizing Training Efficiency}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33837},
}

@inproceedings{icml2025_safe_45885,
  title = {{SAFE: Finding Sparse and Flat Minima to Improve Pruning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46658},
}

@inproceedings{icml2025_a_47404,
  title = {{A Rescaling-Invariant Lipschitz Bound Based on Path-Metrics for Modern ReLU Network Parameterizations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45188},
}

@inproceedings{icml2025_network_48457,
  title = {{Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44160},
}

@inproceedings{icml2026_slidesparse_50862,
  title = {{SlideSparse: Fast and Flexible (2N-2):2N Structured Sparsity}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=GQ17DjsBcq},
}

@inproceedings{icml2026_partial_54109,
  title = {{Partial Fusion of Neural Networks: Efficient Tradeoffs Between Ensembles and Weight Aggregation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=lvRLG6C0zZ},
}

@inproceedings{neurips2024_sparse_71945,
  title = {{Sparse maximal update parameterization: A holistic approach to sparse training dynamics}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95363},
}

@inproceedings{neurips2024_navigating_72143,
  title = {{Navigating Extremes: Dynamic Sparsity in Large Output Spaces}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95193},
}