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

@inproceedings{neurips2017_the_56715,
  title = {{The Expressive Power of Neural Networks: A View from the Width}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9394},
}

@inproceedings{neurips2018_efficient_57085,
  title = {{Efficient Neural Network Robustness Certification with General Activation Functions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11484},
}

@inproceedings{icml2018_spurious_36997,
  title = {{Spurious Local Minima are Common in Two-Layer ReLU Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2033},
}

@inproceedings{iclr2018_understanding_28403,
  title = {{Understanding Deep Neural Networks with Rectified Linear Units}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/155},
}

@inproceedings{neurips2019_deep_58106,
  title = {{Deep ReLU Networks Have Surprisingly Few Activation Patterns}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13275},
}

@inproceedings{icml2019_complexity_37235,
  title = {{Complexity of Linear Regions in Deep Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3567},
}

@inproceedings{neurips2020_the_60967,
  title = {{The phase diagram of approximation rates for deep neural networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17855},
}

@inproceedings{iclr2021_minimum_30250,
  title = {{Minimum Width for Universal Approximation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2920},
}

@inproceedings{icml2018_bounding_36556,
  title = {{Bounding and Counting Linear Regions of Deep Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2449},
}

@inproceedings{aaai2020_empirical_102154,
  title = {{Empirical Bounds on Linear Regions of Deep Rectifier Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6016},
}

@inproceedings{iclr2019_on_29035,
  title = {{On the Universal Approximability and Complexity Bounds of Quantized ReLU Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/699},
}

@inproceedings{iclr2020_a_29421,
  title = {{A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=H1lNPxHKDH},
}

@inproceedings{iclr2020_depth_29276,
  title = {{Depth-Width Trade-offs for ReLU Networks via Sharkovsky's Theorem}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=BJe55gBtvH},
}

@inproceedings{neurips2021_towards_61179,
  title = {{Towards Lower Bounds on the Depth of ReLU Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28531},
}

@inproceedings{icml2020_better_37972,
  title = {{Better depth-width trade-offs for neural networks through the lens of dynamical systems}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6651},
}

@inproceedings{neurips2018_on_57462,
  title = {{On Neuronal Capacity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11742},
}

@inproceedings{iclr2022_deep_31480,
  title = {{Deep ReLU Networks Preserve Expected Length}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6660},
}

@inproceedings{aaai2024_deepbern_5651,
  title = {{DeepBern-Nets: Taming the Complexity of Certifying Neural Networks Using Bernstein Polynomial Activations and Precise Bound Propagation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30117},
}

@inproceedings{neurips2020_neural_60405,
  title = {{Neural Networks with Small Weights and Depth-Separation Barriers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17952},
}

@inproceedings{iclr2023_achieve_32999,
  title = {{Achieve the Minimum Width of Neural Networks for Universal Approximation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/12225},
}

@inproceedings{iclr2024_minimum_34831,
  title = {{Minimum width for universal approximation using ReLU networks on compact domain}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18243},
}

@inproceedings{neurips2025_minimum_77263,
  title = {{Minimum Width for Deep, Narrow MLP: A Diffeomorphism Approach}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=S2fswKCN9T},
}

@inproceedings{neurips2025_depth_77765,
  title = {{Depth-Bounds for Neural Networks via the Braid Arrangement}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=XO9fhSZkBh},
}

@inproceedings{icml2024_on_44518,
  title = {{On dimensionality of feature vectors in MPNNs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33934},
}

@inproceedings{icml2025_on_48266,
  title = {{On the Local Complexity of Linear Regions in Deep ReLU Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44346},
}

@inproceedings{icml2026_why_54264,
  title = {{Why ReLU? A Bit-Model Dichotomy for Deep Network Training}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=nMS1YTjHMH},
}

@inproceedings{neurips2025_the_76834,
  title = {{The Computational Complexity of Counting Linear Regions in ReLU Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=NQ8YNyX2uy},
}

@inproceedings{iclr2025_on_83843,
  title = {{On the Expressiveness of Rational ReLU Neural Networks With Bounded Depth}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/27977},
}

@inproceedings{iclr2026_characterizing_86769,
  title = {{Characterizing the Discrete Geometry of ReLU Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=TgLW2DiRDG},
}

@inproceedings{iclr2024_optimal_33596,
  title = {{OPTIMAL ROBUST MEMORIZATION WITH RELU NEURAL NETWORKS}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19479},
}

@inproceedings{icml2024_relu_45353,
  title = {{ReLU Network with Width \$d+mathcalO(1)\$ Can Achieve Optimal Approximation Rate}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33098},
}

@inproceedings{icml2024_the_45452,
  title = {{The Effect of Weight Precision on the Neuron Count in Deep ReLU Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32999},
}

@inproceedings{icml2025_minimum_46444,
  title = {{Minimum Width for Universal Approximation using Squashable Activation Functions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46122},
}

@inproceedings{icml2026_on_50028,
  title = {{On Minimum Depth and Width of Floating-Point Neural Networks for Representing Floating-Point Functions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=8IxY2DUiBi},
}

@inproceedings{iclr2026_parameterized_89362,
  title = {{Parameterized Hardness of Zonotope Containment and Neural Network Verification}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=y8N45EEW05},
}

@inproceedings{neurips2026_intrinsic_92527,
  title = {{Intrinsic Information Theoretic Analysis of ReLU Nets}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/155566},
}

@inproceedings{neurips2026_tropical_92943,
  title = {{Tropical Boundary Complexity of Deep ReLU Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/156044},
}

@inproceedings{neurips2026_comparing_94912,
  title = {{Comparing Linear Regions in ReLU-Type Networks: Theory and Monte Carlo Methods}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/153164},
}