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

@inproceedings{cvpr2018_look_13344,
  title = {{Look at Boundary: A Boundary-Aware Face Alignment Algorithm}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Wu_Look_at_Boundary_CVPR_2018_paper.html},
}

@inproceedings{cvpr2018_hyperparameter_13196,
  title = {{Hyperparameter Optimization for Tracking With Continuous Deep Q-Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Dong_Hyperparameter_Optimization_for_CVPR_2018_paper.html},
}

@inproceedings{neurips2017_resurrecting_56626,
  title = {{Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9255},
}

@inproceedings{neurips2017_sobolev_56672,
  title = {{Sobolev Training for Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9207},
}

@inproceedings{neurips2017_deep_56238,
  title = {{Deep Lattice Networks and Partial Monotonic Functions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9083},
}

@inproceedings{neurips2018_are_56873,
  title = {{Are GANs Created Equal? A Large-Scale Study}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11092},
}

@inproceedings{neurips2018_dendritic_57024,
  title = {{Dendritic cortical microcircuits approximate the backpropagation algorithm}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11833},
}

@inproceedings{neurips2019_controlling_58044,
  title = {{Controlling Neural Level Sets}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13406},
}

@inproceedings{cvpr2017_deep_12164,
  title = {{Deep Mixture of Linear Inverse Regressions Applied to Head-Pose Estimation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{neurips2020_neural_60402,
  title = {{Neural Networks Fail to Learn Periodic Functions and How to Fix It}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17324},
}

@inproceedings{icml2018_an_36515,
  title = {{An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/1992},
}

@inproceedings{cvpr2017_learning_12427,
  title = {{Learning to Predict Stereo Reliability Enforcing Local Consistency of Confidence Maps}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{neurips2019_efficient_58200,
  title = {{Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14599},
}

@inproceedings{neurips2020_coupling_59617,
  title = {{Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/16960},
}

@inproceedings{cvpr2018_towards_13664,
  title = {{Towards a Mathematical Understanding of the Difficulty in Learning With Feedforward Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Shen_Towards_a_Mathematical_CVPR_2018_paper.html},
}

@inproceedings{neurips2020_woodfisher_61110,
  title = {{WoodFisher: Efficient Second-Order Approximation for Neural Network Compression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18052},
}

@inproceedings{aaai2017_gated_100312,
  title = {{Gated Neural Networks for Option Pricing: Rationality by Design}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10505},
}

@inproceedings{neurips2020_deep_59663,
  title = {{Deep Smoothing of the Implied Volatility Surface}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/19076},
}

@inproceedings{icml2020_learning_38387,
  title = {{Learning Reasoning Strategies in End-to-End Differentiable Proving}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6364},
}

@inproceedings{cvpr2017_riemannian_12587,
  title = {{Riemannian Nonlinear Mixed Effects Models: Analyzing Longitudinal Deformations in Neuroimaging}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{cvpr2024_circuit_22966,
  title = {{Circuit Design and Efficient Simulation of Quantum Inner Product and Empirical Studies of Its Effect on Near-Term Hybrid Quantum-Classic Machine Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31187},
}

@inproceedings{aaai2024_reglo_7051,
  title = {{REGLO: Provable Neural Network Repair for Global Robustness Properties}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29094},
}

@inproceedings{iclr2024_forward_34271,
  title = {{Forward Learning with Top-Down Feedback: Empirical and Analytical Characterization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18804},
}

@inproceedings{aaai2024_deep_5643,
  title = {{Deep Quantum Error Correction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/27756},
}

@inproceedings{aaai2024_enumerating_5869,
  title = {{Enumerating Safe Regions in Deep Neural Networks with Provable Probabilistic Guarantees}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30134},
}

@inproceedings{cvpr2025_rashomon_27378,
  title = {{Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/32669},
}

@inproceedings{aaai2024_ginn_6085,
  title = {{GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equations}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29396},
}

@inproceedings{aaai2024_stability_7403,
  title = {{Stability Analysis of Switched Linear Systems with Neural Lyapunov Functions}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30092},
}

@inproceedings{iclr2024_understanding_34956,
  title = {{Understanding Certified Training with Interval Bound Propagation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18118},
}

@inproceedings{iclr2024_improving_35102,
  title = {{Improving equilibrium propagation without weight symmetry through Jacobian homeostasis}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17971},
}

@inproceedings{neurips2024_rmlr_73644,
  title = {{RMLR: Extending Multinomial Logistic Regression into General Geometries}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93848},
}

@inproceedings{aaai2024_piecewise_6954,
  title = {{Piecewise Linear Transformation – Propagating Aleatoric Uncertainty in Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30029},
}

@inproceedings{aaai2025_explainable_9051,
  title = {{Explainable Neural Networks with Guarantee: A Sparse Estimation Approach}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33985},
}

@inproceedings{iclr2024_transformers_35420,
  title = {{Transformers can optimally learn regression mixture models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17653},
}

@inproceedings{icml2024_positive_44930,
  title = {{Positive Concave Deep Equilibrium Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33522},
}

@inproceedings{icml2024_smooth_45266,
  title = {{Smooth Min-Max Monotonic Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33186},
}

@inproceedings{icml2025_softmax_47347,
  title = {{Softmax is not Enough (for Sharp Size Generalisation)}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45245},
}

@inproceedings{neurips2024_a_74461,
  title = {{A provable control of sensitivity of neural networks through a direct parameterization of the overall bi-Lipschitzness}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93113},
}

@inproceedings{neurips2025_neural_79323,
  title = {{Neural Rule Lists: Learning Discretizations, Rules, and Order in One Go}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=oBf5eZSeBT},
}

@inproceedings{iclr2025_on_82261,
  title = {{On the Relation between Trainability and Dequantization of Variational Quantum Learning Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29529},
}