% Key papers for Neural tangent kernel analysis (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2018_neural_57434,
  title = {{Neural Tangent Kernel: Convergence and Generalization in Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11819},
}

@inproceedings{iclr2018_on_28720,
  title = {{On the Information Bottleneck Theory of Deep Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/63},
}

@inproceedings{iclr2020_neural_29687,
  title = {{Neural Tangents: Fast and Easy Infinite Neural Networks in Python}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=SklD9yrFPS},
}

@inproceedings{icml2019_overparameterized_37637,
  title = {{Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3700},
}

@inproceedings{neurips2019_regularization_58899,
  title = {{Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14579},
}

@inproceedings{neurips2020_when_61103,
  title = {{When Do Neural Networks Outperform Kernel Methods?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17448},
}

@inproceedings{iclr2019_understanding_29074,
  title = {{Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/671},
}

@inproceedings{neurips2019_on_58733,
  title = {{On the Inductive Bias of Neural Tangent Kernels}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14236},
}

@inproceedings{neurips2020_on_60494,
  title = {{On the linearity of large non-linear models: when and why the tangent kernel is constant}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17948},
}

@inproceedings{aaai2021_deep_377,
  title = {{Deep Frequency Principle Towards Understanding Why Deeper Learning Is Faster}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/10541-deep-frequency-principle-towards-understanding-why-deeper-learning-is-faster/},
}

@inproceedings{neurips2018_the_57699,
  title = {{The Spectrum of the Fisher Information Matrix of a Single-Hidden-Layer Neural Network}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11528},
}

@inproceedings{icml2020_dynamics_38139,
  title = {{Dynamics of Deep Neural Networks and Neural Tangent Hierarchy}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5976},
}

@inproceedings{iclr2020_provable_29844,
  title = {{Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=rkgqN1SYvr},
}

@inproceedings{icml2020_the_38845,
  title = {{The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6681},
}

@inproceedings{icml2020_frequency_38227,
  title = {{Frequency Bias in Neural Networks for Input of Non-Uniform Density}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6225},
}

@inproceedings{neurips2020_a_59252,
  title = {{A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18564},
}

@inproceedings{icml2018_the_37032,
  title = {{The Dynamics of Learning: A Random Matrix Approach}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2023},
}

@inproceedings{iclr2020_the_29481,
  title = {{The Local Elasticity of Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=HJxMYANtPH},
}

@inproceedings{cvpr2022_projective_19626,
  title = {{Projective Manifold Gradient Layer for Deep Rotation Regression}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{cvpr2021_orthogonal_17551,
  title = {{Orthogonal Over-Parameterized Training}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{neurips2024_how_70748,
  title = {{How does PDE order affect the convergence of PINNs?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96377},
}

@inproceedings{icml2024_sliding_43653,
  title = {{Sliding Down the Stairs: How Correlated Latent Variables Accelerate Learning with Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34799},
}

@inproceedings{icml2024_asymptotics_43880,
  title = {{Asymptotics of feature learning in two-layer networks after one gradient-step}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34572},
}

@inproceedings{icml2024_a_44482,
  title = {{A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33970},
}

@inproceedings{neurips2024_on_73835,
  title = {{On the Impacts of the Random Initialization in the Neural Tangent Kernel Theory}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93673},
}

@inproceedings{neurips2025_better_75490,
  title = {{Better NTK Conditioning: A Free Lunch from (ReLU) Nonlinear Activation in Wide Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=8dTBydCMSr},
}

@inproceedings{neurips2024_scaling_71992,
  title = {{Scaling Laws in Linear Regression: Compute, Parameters, and Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95321},
}

@inproceedings{neurips2024_a_73615,
  title = {{A generalized neural tangent kernel for surrogate gradient learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93872},
}

@inproceedings{neurips2025_neural_75915,
  title = {{Neural Collapse under Gradient Flow on Shallow ReLU Networks for Orthogonally Separable Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=DbJ2f80JF8},
}

@inproceedings{iclr2025_local_82969,
  title = {{Local Loss Optimization in the Infinite Width: Stable Parameterization of Predictive Coding Networks and Target Propagation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28834},
}

@inproceedings{icml2024_how_43401,
  title = {{How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35051},
}

@inproceedings{icml2024_on_43709,
  title = {{On the Weight Dynamics of Deep Normalized Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34743},
}

@inproceedings{icml2024_sign_45210,
  title = {{Sign Gradient Descent-based Neuronal Dynamics: ANN-to-SNN Conversion Beyond ReLU Network}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33242},
}

@inproceedings{icml2025_emergence_45993,
  title = {{Emergence in non-neural models: grokking modular arithmetic via average gradient outer product}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46553},
}

@inproceedings{icml2025_neural_47770,
  title = {{Neural Collapse Beyond the Unconstrained Features Model: Landscape, Dynamics, and Generalization in the Mean-Field Regime}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44830},
}

@inproceedings{icml2025_provable_47783,
  title = {{Provable Benefits of Unsupervised Pre-training and Transfer Learning via Single-Index Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44817},
}

@inproceedings{icml2026_a_51955,
  title = {{A Fourier perspective on the learning dynamics of neural networks: from sample complexities to mechanistic insights}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=RQECNEUcbJ},
}

@inproceedings{icml2026_rethinking_52206,
  title = {{Rethinking Neural Network Learning Rates: A Stackelberg Perspective}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=TgohCXoblV},
}

@inproceedings{icml2026_dichotomy_55204,
  title = {{Dichotomy of Feature Learning and Unlearning: Fast-Slow Analysis on Neural Networks with Stochastic Gradient Descent}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=wg02GN1ygj},
}

@inproceedings{neurips2024_how_72620,
  title = {{How does Gradient Descent Learn Features --- A Local Analysis for Regularized Two-Layer Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94759},
}