% Key papers for Theory of depth and width (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2017_fractalnet_28297,
  title = {{FractalNet: Ultra-Deep Neural Networks without Residuals}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=S1VaB4cex},
}

@inproceedings{cvpr2024_rewrite_24659,
  title = {{Rewrite the Stars}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30691},
}

@inproceedings{iclr2025_kan_82001,
  title = {{KAN: Kolmogorov–Arnold Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29784},
}

@inproceedings{icml2017_input_36232,
  title = {{Input Convex Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/835},
}

@inproceedings{cvpr2016_sparsifying_11883,
  title = {{Sparsifying Neural Network Connections for Face Recognition}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{aaai2016_deep_105115,
  title = {{Deep Learning with S-Shaped Rectified Linear Activation Units}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10287},
}

@inproceedings{icml2018_beyond_36546,
  title = {{Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/1935},
}

@inproceedings{icml2018_on_36892,
  title = {{On the Power of Over-parametrization in Neural Networks with Quadratic Activation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/1923},
}

@inproceedings{neurips2018_resnet_57575,
  title = {{ResNet with one-neuron hidden layers is a Universal Approximator}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11598},
}

@inproceedings{aaai2018_orthogonal_102501,
  title = {{Orthogonal Weight Normalization: Solution to Optimization Over Multiple Dependent Stiefel Manifolds in Deep Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11768},
}

@inproceedings{icml2019_training_37817,
  title = {{Training Neural Networks with Local Error Signals}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3575},
}

@inproceedings{cvpr2020_low_15849,
  title = {{Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{iclr2018_the_28638,
  title = {{The power of deeper networks for expressing natural functions}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/39},
}

@inproceedings{neurips2016_maximal_55864,
  title = {{Maximal Sparsity with Deep Networks?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/6986},
}

@inproceedings{neurips2019_beyond_57953,
  title = {{Beyond the Single Neuron Convex Barrier for Neural Network Certification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14415},
}

@inproceedings{neurips2019_weight_59194,
  title = {{Weight Agnostic Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14530},
}

@inproceedings{neurips2017_mean_56472,
  title = {{Mean Field Residual Networks: On the Edge of Chaos}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9071},
}

@inproceedings{neurips2019_latent_58462,
  title = {{Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13836},
}

@inproceedings{neurips2020_evolving_59811,
  title = {{Evolving Normalization-Activation Layers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17920},
}

@inproceedings{cvpr2020_p_16003,
  title = {{P-nets: Deep Polynomial Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2024_neural_24283,
  title = {{Neural Redshift: Random Networks are not Random Functions}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29922},
}

@inproceedings{aaai2024_layer_6430,
  title = {{Layer Collaboration in the Forward-Forward Algorithm}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29324},
}

@inproceedings{neurips2024_addressing_71511,
  title = {{Addressing Spectral Bias of Deep Neural Networks by Multi-Grade Deep Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95752},
}

@inproceedings{iclr2024_mgno_33747,
  title = {{MgNO: Efficient Parameterization of Linear Operators via Multigrid}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19328},
}

@inproceedings{iclr2024_depthwise_34178,
  title = {{Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18897},
}

@inproceedings{iclr2025_on_84119,
  title = {{On the expressiveness and spectral bias of KANs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/27709},
}

@inproceedings{aaai2025_universality_11164,
  title = {{Universality of Real Minimal Complexity Reservoir}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33826},
}

@inproceedings{icml2024_rotational_44211,
  title = {{Rotational Equilibrium: How Weight Decay Balances Learning Across Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34241},
}

@inproceedings{neurips2024_theoretical_73253,
  title = {{Theoretical Characterisation of the Gauss Newton Conditioning in Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94185},
}

@inproceedings{aaai2025_formal_9221,
  title = {{Formal Synthesis of Barrier Certificates Using Fourier Kolmogorov-Arnold Network}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32101},
}

@inproceedings{icml2024_understanding_43312,
  title = {{Understanding MLP-Mixer as a wide and sparse MLP}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35140},
}

@inproceedings{icml2025_advancing_47296,
  title = {{Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45293},
}

@inproceedings{neurips2024_variation_70852,
  title = {{Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network Compression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/98328},
}

@inproceedings{neurips2024_implicit_73885,
  title = {{Implicit Regularization of Sharpness-Aware Minimization for Scale-Invariant Problems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93626},
}

@inproceedings{neurips2024_guiding_74613,
  title = {{Guiding Neural Collapse: Optimising Towards the Nearest Simplex Equiangular Tight Frame}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/92977},
}

@inproceedings{neurips2025_hankel_77710,
  title = {{Hankel Singular Value Regularization for Highly Compressible State Space Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=WkztaHpjt1},
}

@inproceedings{aaai2025_improving_9531,
  title = {{Improving Generalization of Deep Neural Networks by Optimum Shifting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33181},
}

@inproceedings{cvpr2025_do_25982,
  title = {{Do We Always Need the Simplicity Bias? Looking for Optimal Inductive Biases in the Wild}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33631},
}