% Key papers for Differentiable and symbolic reasoning (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2018_a_56820,
  title = {{A Unified View of Piecewise Linear Neural Network Verification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11470},
}

@inproceedings{iclr2017_making_28231,
  title = {{Making Neural Programming Architectures Generalize via Recursion}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=BkbY4psgg},
}

@inproceedings{neurips2020_ai_59303,
  title = {{AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18689},
}

@inproceedings{iclr2021_deep_30608,
  title = {{Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2578},
}

@inproceedings{iclr2019_neural_28753,
  title = {{Neural Logic Machines}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/816},
}

@inproceedings{iclr2019_learning_28782,
  title = {{Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/750},
}

@inproceedings{neurips2018_interpreting_57248,
  title = {{Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11478},
}

@inproceedings{aaai2022_tractable_2872,
  title = {{Tractable Explanations for d-DNNF Classifiers}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/05719-tractable-explanations-for-d-dnnf-classifiers/},
}

@inproceedings{aaai2021_differentiable_426,
  title = {{Differentiable Inductive Logic Programming for Structured Examples}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/05034-differentiable-inductive-logic-programming-for-structured-examples/},
}

@inproceedings{aaai2019_induction_101362,
  title = {{Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4163},
}

@inproceedings{neurips2018_neural_57428,
  title = {{Neural Guided Constraint Logic Programming for Program Synthesis}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11187},
}

@inproceedings{neurips2021_symbolic_63371,
  title = {{Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28513},
}

@inproceedings{aaai2021_teaching_1473,
  title = {{Teaching the Old Dog New Tricks: Supervised Learning with Constraints}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/03742-teaching-the-old-dog-new-tricks-supervised-learning-with-constraints/},
}

@inproceedings{neurips2022_a_63779,
  title = {{A Unified Framework for Deep Symbolic Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/52836},
}

@inproceedings{aaai2020_a_101914,
  title = {{A Constraint-Based Approach to Learning and Explanation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5774},
}

@inproceedings{neurips2021_training_62821,
  title = {{Training Neural Networks is ER-complete}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28857},
}

@inproceedings{iclr2018_combining_28546,
  title = {{Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/110},
}

@inproceedings{icml2020_it_38349,
  title = {{It’s Not What Machines Can Learn, It’s What We Cannot Teach}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6434},
}

@inproceedings{aaai2024_neural_6762,
  title = {{Neural Closure Certificates}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30141},
}

@inproceedings{neurips2023_training_67558,
  title = {{Training Fully Connected Neural Networks is \$existsmathbbR\$-Complete}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72181},
}

@inproceedings{aaai2024_deepsade_5655,
  title = {{DeepSaDe: Learning Neural Networks That Guarantee Domain Constraint Satisfaction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29109},
}

@inproceedings{aaai2024_what_7807,
  title = {{What Do Hebbian Learners Learn? Reduction Axioms for Iterated Hebbian Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29409},
}

@inproceedings{neurips2024_learning_72758,
  title = {{Learning to compute Gröbner bases}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94636},
}

@inproceedings{neurips2024_neural_73083,
  title = {{Neural Model Checking}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94345},
}

@inproceedings{aaai2024_generalisation_6109,
  title = {{Generalisation through Negation and Predicate Invention}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28915},
}

@inproceedings{aaai2025_dimension_8749,
  title = {{Dimension Reduction for Symbolic Regression}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33947},
}

@inproceedings{neurips2024_provably_72259,
  title = {{Provably Safe Neural Network Controllers via Differential Dynamic Logic}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95085},
}

@inproceedings{neurips2024_seev_73820,
  title = {{SEEV: Synthesis with Efficient Exact Verification for ReLU Neural Barrier Functions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93688},
}

@inproceedings{aaai2024_exponent_5925,
  title = {{Exponent Relaxation of Polynomial Zonotopes and Its Applications in Formal Neural Network Verification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30125},
}

@inproceedings{icml2025_validating_48666,
  title = {{Validating Mechanistic Interpretations: An Axiomatic Approach}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43956},
}

@inproceedings{cvpr2025_learning_26767,
  title = {{Learning-enabled Polynomial Lyapunov Function Synthesis via High-Accuracy Counterexample-Guided Framework}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33333},
}

@inproceedings{iclr2024_learning_34952,
  title = {{Learning Polynomial Problems with \$SL(2, mathbbR)\$-Equivariance}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18122},
}

@inproceedings{icml2025_primal_48243,
  title = {{Primal-Dual Neural Algorithmic Reasoning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44369},
}

@inproceedings{icml2026_caffnet_49378,
  title = {{CAffNet: Hard Constraint-Affine Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=20hdQQQrA4},
}

@inproceedings{icml2026_which_50902,
  title = {{Which Algorithms Can Graph Neural Networks Learn?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=GnmuZIlvxw},
}

@inproceedings{neurips2025_provable_74835,
  title = {{Provable Gradient Editing of Deep Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=1ffIkWo0yq},
}

@inproceedings{neurips2025_tropical_74994,
  title = {{Tropical Attention: Neural Algorithmic Reasoning for Combinatorial Algorithms}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=3CbwwCpsSk},
}

@inproceedings{neurips2025_a_76135,
  title = {{A learnability analysis on neuro-symbolic learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=FrdX7K4Gli},
}

@inproceedings{neurips2025_ctsketch_79198,
  title = {{CTSketch: Compositional Tensor Sketching for Scalable Neurosymbolic Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=mor7s1NGBV},
}