% Key papers for Joint pruning and quantization (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2017_on_12504,
  title = {{On Compressing Deep Models by Low Rank and Sparse Decomposition}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{cvpr2018_clip_12882,
  title = {{CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Tung_CLIP-Q_Deep_Network_CVPR_2018_paper.html},
}

@inproceedings{neurips2016_dynamic_55697,
  title = {{Dynamic Network Surgery for Efficient DNNs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7374},
}

@inproceedings{neurips2017_compression_56204,
  title = {{Compression-aware Training of Deep Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8880},
}

@inproceedings{icml2018_compressing_36586,
  title = {{Compressing Neural Networks using the Variational Information Bottleneck}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2411},
}

@inproceedings{iclr2020_dynamic_29618,
  title = {{Dynamic Model Pruning with Feedback}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=SJem8lSFwB},
}

@inproceedings{cvpr2021_towards_18016,
  title = {{Towards Compact CNNs via Collaborative Compression}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{iclr2017_soft_28260,
  title = {{Soft Weight-Sharing for Neural Network Compression}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=HJGwcKclx},
}

@inproceedings{aaai2018_compressed_105760,
  title = {{Compressed Sensing MRI Using a Recursive Dilated Network}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11869},
}

@inproceedings{cvpr2020_automatic_15172,
  title = {{Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-Based Approach}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{aaai2021_opq_1076,
  title = {{OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/07780-opq-compressing-deep-neural-networks-with-one-shot-pruning-quantization/},
}

@inproceedings{cvpr2019_cascaded_13927,
  title = {{Cascaded Projection: End-To-End Network Compression and Acceleration}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{icml2018_weightless_37083,
  title = {{Weightless: Lossy weight encoding for deep neural network compression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2218},
}

@inproceedings{iclr2018_learning_28725,
  title = {{LEARNING TO SHARE: SIMULTANEOUS PARAMETER TYING AND SPARSIFICATION IN DEEP LEARNING}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/27},
}

@inproceedings{neurips2021_aligned_63157,
  title = {{Aligned Structured Sparsity Learning for Efficient Image Super-Resolution}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27395},
}

@inproceedings{aaai2020_harmonious_102276,
  title = {{Harmonious Coexistence of Structured Weight Pruning and Ternarization for Deep Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6138},
}

@inproceedings{aaai2022_batude_1765,
  title = {{BATUDE: Budget-Aware Neural Network Compression Based on Tucker Decomposition}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/08874-batude-budget-aware-neural-network-compression-based-on-tucker-decomposition/},
}

@inproceedings{aaai2021_provable_1208,
  title = {{Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/06974-provable-benefits-of-overparameterization-in-model-compression-from-double-descent-to-pruning-neural-networks/},
}

@inproceedings{iclr2021_learnable_30723,
  title = {{Learnable Embedding sizes for Recommender Systems}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3372},
}

@inproceedings{iclr2022_language_31778,
  title = {{Language model compression with weighted low-rank factorization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6157},
}

@inproceedings{aaai2024_data_5594,
  title = {{Data Shunt: Collaboration of Small and Large Models for Lower Costs and Better Performance}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29003},
}

@inproceedings{neurips2024_slimsam_72741,
  title = {{SlimSAM: 0.1\% Data Makes Segment Anything Slim}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94649},
}

@inproceedings{cvpr2025_temporal_27835,
  title = {{Temporal Action Detection Model Compression by Progressive Block Drop}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/32910},
}

@inproceedings{aaai2025_genomics_9333,
  title = {{Genomics Data Lossless Compression with (S, K)-Mer Encoding and Deep Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33371},
}

@inproceedings{cvpr2025_surgeon_27558,
  title = {{SURGEON: Memory-Adaptive Fully Test-Time Adaptation via Dynamic Activation Sparsity}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34154},
}

@inproceedings{cvpr2025_scale_27575,
  title = {{Scale Efficient Training for Large Datasets}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33785},
}

@inproceedings{aaai2024_block_5306,
  title = {{Block-Level Goal Recognition Design}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29979},
}

@inproceedings{icml2024_maestro_43574,
  title = {{Maestro: Uncovering Low-Rank Structures via Trainable Decomposition}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34878},
}

@inproceedings{neurips2024_espace_71399,
  title = {{ESPACE: Dimensionality Reduction of Activations for Model Compression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95852},
}

@inproceedings{cvpr2025_learning_26739,
  title = {{Learning Compatible Multi-Prize Subnetworks for Asymmetric Retrieval}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33238},
}

@inproceedings{iclr2024_going_33621,
  title = {{Going Beyond Neural Network Feature Similarity: The Network Feature Complexity and Its Interpretation Using Category Theory}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19454},
}

@inproceedings{iclr2024_in_35672,
  title = {{In defense of parameter sharing for model-compression}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17401},
}

@inproceedings{icml2024_bws_45371,
  title = {{BWS: Best Window Selection Based on Sample Scores for Data Pruning across Broad Ranges}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33080},
}

@inproceedings{icml2025_eigenspectrum_46260,
  title = {{Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46300},
}

@inproceedings{icml2025_compressing_46324,
  title = {{Compressing tree ensembles through Level-wise Optimization and Pruning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46237},
}

@inproceedings{icml2026_advancing_51093,
  title = {{Advancing SVD-based LLM Compression via Layer-Wise Error Model Search}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=IjIgNPFuCt},
}

@inproceedings{icml2026_zero_54838,
  title = {{Zero Sum SVD: Balancing Loss Sensitivity for Low Rank LLM Compression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=tAdsDOkRBw},
}

@inproceedings{icml2026_rethinking_55084,
  title = {{Rethinking Parameter Sharing as Graph Coloring for Structured Compression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=vNCE03zff8},
}

@inproceedings{neurips2025_breaking_79249,
  title = {{Breaking the Compression Ceiling: Data-Free Pipeline for Ultra-Efficient Delta Compression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=nPYtkZu65y},
}

@inproceedings{iclr2026_cut_85908,
  title = {{Cut Less, Fold More: Model Compression through the Lens of Projection Geometry}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=JV9CEtKLQF},
}