% Key papers for Adapter-based parameter-efficient tuning (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2022_few_66120,
  title = {{Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54470},
}

@inproceedings{iclr2021_on_30622,
  title = {{On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2558},
}

@inproceedings{icml2022_model_40838,
  title = {{Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16989},
}

@inproceedings{iclr2022_fine_31333,
  title = {{Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/5945},
}

@inproceedings{aaai2023_on_4286,
  title = {{On the Effectiveness of Parameter-Efficient Fine-Tuning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26505},
}

@inproceedings{icml2016_scalable_35974,
  title = {{Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/luketina16.html},
}

@inproceedings{neurips2021_compacter_62680,
  title = {{Compacter: Efficient Low-Rank Hypercomplex Adapter Layers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27655},
}

@inproceedings{neurips2022_lst_65742,
  title = {{LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/52927},
}

@inproceedings{iclr2023_adaptive_33111,
  title = {{Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11863},
}

@inproceedings{neurips2023_fine_68373,
  title = {{Fine-Tuning Language Models with Just Forward Passes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71437},
}

@inproceedings{neurips2022_lift_66180,
  title = {{LIFT: Language-Interfaced Fine-Tuning for Non-language Machine Learning Tasks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54500},
}

@inproceedings{cvpr2024_gradient_23640,
  title = {{Gradient-based Parameter Selection for Efficient Fine-Tuning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29636},
}

@inproceedings{aaai2024_sam_7199,
  title = {{SAM-PARSER: Fine-Tuning SAM Efficiently by Parameter Space Reconstruction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28250},
}

@inproceedings{neurips2020_stochastic_60878,
  title = {{Stochastic Normalization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17053},
}

@inproceedings{iclr2021_better_30263,
  title = {{Better Fine-Tuning by Reducing Representational Collapse}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3061},
}

@inproceedings{aaai2024_adaptergnn_5124,
  title = {{AdapterGNN: Parameter-Efficient Fine-Tuning Improves Generalization in GNNs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29264},
}

@inproceedings{cvpr2023_trainable_22403,
  title = {{Trainable Projected Gradient Method for Robust Fine-Tuning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/22267},
}

@inproceedings{aaai2021_isobn_813,
  title = {{IsoBN: Fine-Tuning BERT with Isotropic Batch Normalization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/14621-isobn-fine-tuning-bert-with-isotropic-batch-normalization/},
}

@inproceedings{cvpr2023_adapting_20346,
  title = {{Adapting Shortcut With Normalizing Flow: An Efficient Tuning Framework for Visual Recognition}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/22667},
}

@inproceedings{cvpr2024_unipt_25132,
  title = {{UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and Memory}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30555},
}

@inproceedings{cvpr2024_parameter_24436,
  title = {{Parameter Efficient Fine-tuning via Cross Block Orchestration for Segment Anything Model}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30697},
}

@inproceedings{cvpr2025_lessons_26772,
  title = {{Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognition}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34302},
}

@inproceedings{aaai2025_from_9247,
  title = {{From PEFT to DEFT: Parameter Efficient Finetuning for Reducing Activation Density in Transformers}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34227},
}

@inproceedings{cvpr2025_meta_26946,
  title = {{Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/32721},
}

@inproceedings{icml2024_rosa_43925,
  title = {{RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34527},
}

@inproceedings{cvpr2025_coeff_25735,
  title = {{Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large Models}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34230},
}

@inproceedings{aaai2025_pat_10234,
  title = {{PAT: Pruning-Aware Tuning for Large Language Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34649},
}

@inproceedings{cvpr2025_sensitivity_27641,
  title = {{Sensitivity-Aware Efficient Fine-Tuning via Compact Dynamic-Rank Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33380},
}

@inproceedings{iclr2024_how_34679,
  title = {{How to Fine-Tune Vision Models with SGD}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18395},
}

@inproceedings{cvpr2025_faster_26238,
  title = {{Faster Parameter-Efficient Tuning with Token Redundancy Reduction}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34177},
}

@inproceedings{neurips2024_svft_74286,
  title = {{SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93272},
}

@inproceedings{aaai2025_practical_10336,
  title = {{Practical Offloading for Fine-Tuning LLM on Commodity GPU via Learned Sparse Projectors}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34531},
}

@inproceedings{cvpr2025_loki_26804,
  title = {{LoKi: Low-dimensional KAN for Efficient Fine-tuning Image Models}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33430},
}

@inproceedings{neurips2024_spectral_72413,
  title = {{Spectral Adapter: Fine-Tuning in Spectral Space}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94943},
}

@inproceedings{aaai2025_tte_10920,
  title = {{TTE: Two Tokens Are Enough to Improve Parameter-Efficient Tuning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34226},
}

@inproceedings{cvpr2024_sheared_24814,
  title = {{Sheared Backpropagation for Fine-tuning Foundation Models}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29815},
}