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

@inproceedings{icml2017_learning_36256,
  title = {{Learning Important Features Through Propagating Activation Differences}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/614},
}

@inproceedings{cvpr2019_progressive_14674,
  title = {{Progressive Image Deraining Networks: A Better and Simpler Baseline}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{neurips2016_residual_55963,
  title = {{Residual Networks Behave Like Ensembles of Relatively Shallow Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7167},
}

@inproceedings{cvpr2017_fully_12295,
  title = {{Fully-Adaptive Feature Sharing in Multi-Task Networks With Applications in Person Attribute Classification}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{iclr2018_towards_28630,
  title = {{Towards better understanding of gradient-based attribution methods for Deep Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/302},
}

@inproceedings{iclr2018_learning_28544,
  title = {{Learning how to explain neural networks: PatternNet and PatternAttribution}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/322},
}

@inproceedings{cvpr2016_analyzing_11399,
  title = {{Analyzing Classifiers: Fisher Vectors and Deep Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{neurips2016_learning_55841,
  title = {{Learning the Number of Neurons in Deep Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/6970},
}

@inproceedings{neurips2019_learning_58512,
  title = {{Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13732},
}

@inproceedings{neurips2019_intrinsic_58429,
  title = {{Intrinsic dimension of data representations in deep neural networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13728},
}

@inproceedings{iclr2017_highway_28326,
  title = {{Highway and Residual Networks learn Unrolled Iterative Estimation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=Skn9Shcxe},
}

@inproceedings{neurips2018_supervised_57667,
  title = {{Supervised autoencoders: Improving generalization performance with unsupervised regularizers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11038},
}

@inproceedings{neurips2016_swapout_56029,
  title = {{Swapout: Learning an ensemble of deep architectures}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7348},
}

@inproceedings{cvpr2016_efficient_11520,
  title = {{Efficient Training of Very Deep Neural Networks for Supervised Hashing}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{iclr2021_do_30192,
  title = {{Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2813},
}

@inproceedings{aaai2021_deep_400,
  title = {{Deep Unsupervised Image Hashing  by Maximizing Bit Entropy}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/02002-deep-unsupervised-image-hashing-by-maximizing-bit-entropy/},
}

@inproceedings{icml2019_shallow_37739,
  title = {{Shallow-Deep Networks: Understanding and Mitigating Network Overthinking}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3840},
}

@inproceedings{aaai2020_relative_101235,
  title = {{Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5632},
}

@inproceedings{iclr2018_skip_28551,
  title = {{Skip Connections Eliminate Singularities}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/73},
}

@inproceedings{neurips2018_processing_57533,
  title = {{Processing of missing data by neural networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11279},
}

@inproceedings{neurips2024_the_71966,
  title = {{The GAN is dead; long live the GAN! A Modern GAN Baseline}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95345},
}

@inproceedings{cvpr2024_in_23787,
  title = {{In Search of a Data Transformation That Accelerates Neural Field Training}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31844},
}

@inproceedings{iclr2024_zipit_34206,
  title = {{ZipIt! Merging Models from Different Tasks without Training}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18869},
}

@inproceedings{neurips2025_biggergait_76644,
  title = {{BiggerGait: Unlocking Gait Recognition with Layer-wise Representations from Large Vision Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=LeJwcMLIlJ},
}

@inproceedings{icml2024_asymptotics_44396,
  title = {{Asymptotics of Learning with Deep Structured (Random) Features}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34056},
}

@inproceedings{neurips2024_beyond_71809,
  title = {{Beyond Slow Signs in High-fidelity Model Extraction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95487},
}

@inproceedings{neurips2024_understanding_71881,
  title = {{Understanding Visual Feature Reliance through the Lens of Complexity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95422},
}

@inproceedings{cvpr2025_comprehensive_25754,
  title = {{Comprehensive Information Bottleneck for Unveiling Universal Attribution to Interpret Vision Transformers}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33921},
}

@inproceedings{cvpr2025_from_26324,
  title = {{From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34603},
}

@inproceedings{icml2024_how_43797,
  title = {{How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34655},
}

@inproceedings{icml2024_codebook_44542,
  title = {{Codebook Features: Sparse and Discrete Interpretability for Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33910},
}

@inproceedings{neurips2024_weight_73872,
  title = {{Weight decay induces low-rank attention layers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93637},
}

@inproceedings{iclr2024_grokking_33562,
  title = {{Grokking as a First Order Phase Transition in Two Layer Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19513},
}

@inproceedings{iclr2024_to_34545,
  title = {{To Grok or not to Grok: Disentangling Generalization and Memorization on Corrupted Algorithmic Datasets}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18529},
}

@inproceedings{iclr2024_information_34993,
  title = {{Information Bottleneck Analysis of Deep Neural Networks via Lossy Compression}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18081},
}

@inproceedings{icml2024_towards_43310,
  title = {{Towards Interpretable Deep Local Learning with Successive Gradient Reconciliation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35142},
}

@inproceedings{icml2024_layerwise_43591,
  title = {{Layerwise Change of Knowledge in Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34861},
}

@inproceedings{icml2024_initial_44513,
  title = {{Initial Guessing Bias: How Untrained Networks Favor Some Classes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33939},
}

@inproceedings{neurips2024_neural_70170,
  title = {{Neural collapse vs. low-rank bias: Is deep neural collapse really optimal?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96913},
}

@inproceedings{neurips2024_exploring_71070,
  title = {{Exploring and Exploiting the Asymmetric Valley of Deep Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96141},
}