% Key papers for Equivariant and disentangled latent representations (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2018_modeling_13380,
  title = {{Modeling Facial Geometry Using Compositional VAEs}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Bagautdinov_Modeling_Facial_Geometry_CVPR_2018_paper.html},
}

@inproceedings{neurips2019_learning_58477,
  title = {{Learning Disentangled Representations for Recommendation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13697},
}

@inproceedings{iclr2019_learning_28919,
  title = {{Learning Mixed-Curvature Representations in Product Spaces}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/848},
}

@inproceedings{icml2018_learning_36782,
  title = {{Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2370},
}

@inproceedings{icml2018_magan_36835,
  title = {{MAGAN: Aligning Biological Manifolds}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2455},
}

@inproceedings{iclr2020_counterfactuals_29653,
  title = {{Counterfactuals uncover the modular structure of deep generative models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=SJxDDpEKvH},
}

@inproceedings{iclr2021_the_30654,
  title = {{The role of Disentanglement in Generalisation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3173},
}

@inproceedings{neurips2018_geometry_57174,
  title = {{Geometry Based Data Generation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11156},
}

@inproceedings{icml2018_oi_37086,
  title = {{oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2410},
}

@inproceedings{icml2021_morphvae_39630,
  title = {{MorphVAE: Generating Neural Morphologies from 3D-Walks using a Variational Autoencoder with Spherical Latent Space}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10059},
}

@inproceedings{iclr2022_do_31445,
  title = {{Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6326},
}

@inproceedings{iclr2023_relative_32620,
  title = {{Relative representations enable zero-shot latent space communication}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11612},
}

@inproceedings{icml2021_benchmarks_39091,
  title = {{Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9179},
}

@inproceedings{icml2021_commutative_39147,
  title = {{Commutative Lie Group VAE for Disentanglement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8905},
}

@inproceedings{neurips2021_topographic_61558,
  title = {{Topographic VAEs learn Equivariant Capsules}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27441},
}

@inproceedings{neurips2021_noether_62579,
  title = {{Noether Networks: meta-learning useful conserved quantities}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26348},
}

@inproceedings{aaai2025_isumap_9611,
  title = {{IsUMap: Manifold Learning and Data Visualization Leveraging Vietoris-Rips Filtrations}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33946},
}

@inproceedings{icml2023_latent_42149,
  title = {{Latent Traversals in Generative Models as Potential Flows}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24826},
}

@inproceedings{neurips2020_learning_60164,
  title = {{Learning Disentangled Representations and Group Structure of Dynamical Environments}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17058},
}

@inproceedings{iclr2023_learning_32879,
  title = {{Learning Probabilistic Topological Representations Using Discrete Morse Theory}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11179},
}

@inproceedings{neurips2024_a_72828,
  title = {{A Generative Model of Symmetry Transformations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94571},
}

@inproceedings{iclr2025_on_82504,
  title = {{On Conformal Isometry of Grid Cells: Learning Distance-Preserving Position Embedding}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29290},
}

@inproceedings{icml2024_a_43725,
  title = {{A Space Group Symmetry Informed Network for O(3) Equivariant Crystal Tensor Prediction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34727},
}

@inproceedings{icml2025_tracegrad_48796,
  title = {{TraceGrad: a Framework Learning Expressive SO(3)-equivariant Non-linear Representations for Electronic-Structure Hamiltonian Prediction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43826},
}

@inproceedings{aaai2026_geo2vec_106663,
  title = {{Geo2Vec: Shape- and Distance-Aware Neural Representation of Geospatial Entities}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/38970},
}

@inproceedings{aaai2025_riemannian_10563,
  title = {{Riemannian Geometric-based Meta Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34185},
}

@inproceedings{iclr2024_neural_34852,
  title = {{Neural Fourier Transform: A General Approach to Equivariant Representation Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18222},
}

@inproceedings{icml2024_equivariant_43443,
  title = {{Equivariant Frames and the Impossibility of Continuous Canonicalization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35009},
}

@inproceedings{icml2024_sequential_43700,
  title = {{Sequential Disentanglement by Extracting Static Information From A Single Sequence Element}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34752},
}

@inproceedings{icml2024_equivariance_45044,
  title = {{Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33408},
}

@inproceedings{neurips2024_enriching_74250,
  title = {{Enriching Disentanglement: From Logical Definitions to Quantitative Metrics}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93305},
}

@inproceedings{icml2024_tripod_43263,
  title = {{Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35189},
}

@inproceedings{icml2024_transport_45496,
  title = {{Transport of Algebraic Structure to Latent Embeddings}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32955},
}

@inproceedings{icml2024_variational_45774,
  title = {{Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32678},
}

@inproceedings{icml2025_symmetry_46874,
  title = {{Symmetry-Aware GFlowNets}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45704},
}

@inproceedings{icml2025_atlasd_47795,
  title = {{AtlasD: Automatic Local Symmetry Discovery}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44805},
}

@inproceedings{icml2026_discovering_49629,
  title = {{Discovering Symmetry Groups with Flow Matching}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=4FSkKnTnvI},
}

@inproceedings{icml2026_coordinated_51662,
  title = {{Coordinated Disentanglement with Iterative Mode Discovery Under Hidden Correlations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Ocx8Ccodt9},
}

@inproceedings{icml2026_a_51823,
  title = {{A Geometric Lens on Physics-Aligned Data Compression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=QEEwlgt8PI},
}