% Key papers for Infinite-width networks as Gaussian processes (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2018_deep_28400,
  title = {{Deep Neural Networks as Gaussian Processes}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/91},
}

@inproceedings{aaai2017_structured_104402,
  title = {{Structured Inference Networks for Nonlinear State Space Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10779},
}

@inproceedings{iclr2019_bayesian_28741,
  title = {{Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/703},
}

@inproceedings{iclr2019_functional_29192,
  title = {{FUNCTIONAL VARIATIONAL BAYESIAN NEURAL NETWORKS}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1035},
}

@inproceedings{iclr2018_gaussian_28478,
  title = {{Gaussian Process Behaviour in Wide Deep Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/161},
}

@inproceedings{icml2017_random_36355,
  title = {{Random Feature Expansions for Deep Gaussian Processes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/576},
}

@inproceedings{icml2018_conditional_36588,
  title = {{Conditional Neural Processes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2253},
}

@inproceedings{icml2018_differentiable_36635,
  title = {{Differentiable Compositional Kernel Learning for Gaussian Processes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2378},
}

@inproceedings{neurips2020_bayesian_59439,
  title = {{Bayesian Deep Ensembles via the Neural Tangent Kernel}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17124},
}

@inproceedings{neurips2020_meta_60304,
  title = {{Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18552},
}

@inproceedings{neurips2019_reliable_58905,
  title = {{Reliable training and estimation of variance networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13746},
}

@inproceedings{icml2022_transformer_41289,
  title = {{Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17523},
}

@inproceedings{neurips2019_approximate_57903,
  title = {{Approximate Inference Turns Deep Networks into Gaussian Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13490},
}

@inproceedings{aaai2024_gaussian_6104,
  title = {{Gaussian Process Neural Additive Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29628},
}

@inproceedings{neurips2022_invariance_65716,
  title = {{Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53206},
}

@inproceedings{neurips2020_stationary_60865,
  title = {{Stationary Activations for Uncertainty Calibration in Deep Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17699},
}

@inproceedings{icml2021_the_40018,
  title = {{The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10761},
}

@inproceedings{iclr2022_practical_30842,
  title = {{Practical Conditional Neural Process Via Tractable Dependent Predictions}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6738},
}

@inproceedings{neurips2021_the_62478,
  title = {{The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26428},
}

@inproceedings{neurips2019_deep_58105,
  title = {{Deep Random Splines for Point Process Intensity Estimation of Neural Population Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14274},
}

@inproceedings{icml2024_an_43597,
  title = {{An Infinite-Width Analysis on the Jacobian-Regularised Training of a Neural Network}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34855},
}

@inproceedings{neurips2024_on_71622,
  title = {{On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95652},
}

@inproceedings{iclr2024_beyond_33491,
  title = {{Beyond IID weights: sparse and low-rank deep Neural Networks are also Gaussian Processes}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19584},
}

@inproceedings{icml2024_critical_45737,
  title = {{Critical feature learning in deep neural networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32715},
}

@inproceedings{icml2025_r_48679,
  title = {{Rényi Neural Processes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43943},
}

@inproceedings{icml2026_proeval_53149,
  title = {{ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=cYYVYK1Dgg},
}

@inproceedings{icml2026_random_54336,
  title = {{Random Process Flow Matching: Generative Implicit Representations of Multivariate Random Fields}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=nzYTAVwZeJ},
}

@inproceedings{neurips2025_infinite_75373,
  title = {{Infinite Neural Operators: Gaussian processes on functions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=7M9JGaoAeC},
}

@inproceedings{neurips2025_zero_79454,
  title = {{Zero-Shot Performance Prediction for Probabilistic Scaling Laws}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=paiyYD81Wr},
}

@inproceedings{iclr2025_when_81273,
  title = {{When narrower is better: the narrow width limit of Bayesian parallel branching neural networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30498},
}

@inproceedings{iclr2025_residual_81672,
  title = {{Residual Deep Gaussian Processes on Manifolds}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30105},
}

@inproceedings{iclr2025_a_82960,
  title = {{A Unified Theory of Quantum Neural Network Loss Landscapes}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28843},
}

@inproceedings{iclr2025_deep_83868,
  title = {{Deep Kernel Posterior Learning under Infinite Variance Prior Weights}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/27952},
}

@inproceedings{neurips2026_continuous_90143,
  title = {{Continuous-depth Deep Gaussian Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/150043},
}